PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. DATE DUE DATE DUE DATE DUE MSU Is An Atfirmetlve Action/Equal Opportunity lndltution fl DECISION SUPPORT FOR LIVESTOCK PRODUCTION: INTEGRATION OF INFORMATION MANAGEMENT, SYSTEMS MODELING, AND COMPUTER SIMULATION TECHNIQUES By James Walter Lloyd A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics ABSTRACT DECISION SUPPORT FOR LIVESTOCK PRODUCTION: INTEGRATION OF INFORMATION MANAGEMENT, SYSTEMS MODELING, AND COMPUTER SIMULATION TECHNIQUES By James Walter Lloyd Recent advances in the understanding of livestock biology have compounded the difficulty of choice between alternative technologies for livestock producers. To facilitate this choice toward better achievement of decision maker objectives, a decision support system (DSS) was developed through integration of information management, systems modeling, and computer simulation techniques. In conjunction with the Swine Health Information System, the economics of health management in growing and finishing pigs was used as a framework for DSS development. Data on physical production, animal health, and financial production were accumulated in a single database. Techniques were developed to obtain health data from market hog slaughter without disrupting the ordinary marketing patterns of participating producers. Based on these data, necessary sample sizes to achieve statistical confidence were estimated. Depending on herd size and desired confidence level, recommendations for the participating producers ranged from 8 to 53 slaughter health checks per year. Further, an analytical model was formulated through a systems approach, emphasizing the generation of producer-specific, stochastic, technological expectations. During simulations using this model, changes in subclinical disease prevalence, feed additive use, and available floor space per pig served to modify current production (as determined from physical production data) Financial production data were subsequently included, thus allowing thorough evaluation of technological alternatives. Application to producer-specific health management situations was demonstrated by considering the financial effects of altering specific disease rates over distinct time periods. Recommendations for future DSS developments centered on expanded data collection to enable rigorous external validation. As with everything else I do, this work is wholeheartedly dedicated to Penny, Kelby, Janelle, Brandon, and Danton. iii ACKNOWLEDGMENTS Completion of this dissertation would not have been possible without thoughtful input and/or continual support from a broad group of people. At this point, I wish to express my gratitude. First, my graduate committee deserves recognition for offering both challenging intellectual development and unending personal support. Special thanks go to Steve Harsh (dissertation advisor) and John Kaneene for efforts which were Often "above and beyond the call.” Notwithstanding, the significant contributions of Gerald Schwab (major professor), Brad Thacker, Andy Thulin, and Jack Judy were also very much appreciated. All graduate students should be lucky enough to have such a committee. Next, key participants in the Swine Health Information Management System (SHIMS) project warrant mention. The financial and cooperative support of United States Department of Agriculture, Food Safety and Inspection Service; United States Department of Agriculture, Economic Research Service; Michigan Agricultural Experiment Station; and Michigan Cooperative Extension Service were essential. Achievement of the initial grant would not have been possible without Eileen vanRavenswaay. Further. the success of the project was only achieved through the willing cooperation of participating producers, marketing agents, and slaughter plants. The critical importance Of this cooperation cannot be over-emphasized and gratitude is extended to the entire SHIMS group. SHIMS computer programming for the database and associated reports was provided by Margaret Beaver. Also, technical assistance in preparation of this document was received from Eileen Salmond and MaryEllen Shea. Ladies, your contributions were highly commendable. iv Finally, family and friends generously offered enduring support. Friends provided listening ears and sounding boards. Both parents and parents-in-law showed enormous blind faith in where the "kid" was headed. And my wife and children have been a perpetual source of strength, while quietly tolerating the accompanying adversity. Without question, this mission could not have been accomplished in the absence of such unselfish patronage. Thank you all. TABLE OF CONTENTS Page LIST OF TABLES x LIST OF FIGURES xiv LIST OF FOOTNOTES xv CHAPTER 1: Background and general purpose 1 Introduction 1 Problem statement 3 Research goal 4 Research Objectives 4 Research benefits 5 Summary 6 CHAPTER 2: Review of the literature 7 Introduction 7 Conceptual development 7 Information management system characteristics 10 Data collection 11 Data processing 12 Information management systems for livestock 13 Analytical model characteristics 16 Analytical modeling techniques for livestock 20 Traditional production economics 20 Expected utility 23 Other variations of production economics 26 Linear programming and variations 28 vi I.-. Goal programming 33 Other decision models 34 Other production models 37 Computerized simulation models 40 Summary 46 CHAPTER 3: Information management system 47 Introduction 47 Materials and methods 47 Swine health information management system 47 On-farm data collection 51 Off-farm data collection 53 Computerized database 61 Results and discussion 72 Producer attitudes 72 Diseases at slaughter 74 Costs of the system 90 Benefits of the system 93 Recommendations for the future 94 Summary 96 CHAPTER 4: The analytical model 97 Introduction 97 The systems approach 98 Conceptual model overview 99 Systems modeling and computer simulation techniques 105 General features 105 Discrete delays 106 Distributed delay 106 Triangular probability density functions 111 vii Autocorrelation Table look-up function Delay modification Alpha-beta tracker Optimization Income over variable costs Parameter estimation Initial DELAY Disease effects Feed additive effects Space effects Feed efficiency Alpha-beta tracker Discussion System definition Techniques employed Data Validation Summary CHAPTER 5: Results of decision support system application Introduction Information management system Data quantity Data quality The analytical model Parameter estimation Simulation output and evaluation Summary viii 111 120 121 129 132 134 134 135 140 142 145 147 148 148 149 150 150 151 152 152 152 153 155 159 159 164 188 CHAPTER 6: Summary and recommendations 190 Introduction 190 Information management system 190 Data collection 191 Data processing 192 Analytical model 194 Resolution 195 Realism 19S Generality and precision 196 Applicability 197 Conclusions and recommendations 199 Summary 204 APPENDIX A 206 APPENDIX B 235 APPENDIX C 237 APPENDIX D 280 APPENDIX E 288 APPENDIX F 291 BIBLIOGRAPHY 321 ix 2.1 2.4 3.1 3.3 3.4 3.5 3.6 3.7 38 3.9 310 3.11 LIST OF TABLES Summary of resolution attributes for computerized simulation models in broilers, sheep and beef cattle production Summary of resolution attributes for computerized simulation models in dairy cattle and swine production Summary of realism and applicability attributes for computerized simulation models in broiler, sheep and beef cattle production Summary of realism and applicability attributes for computerized simulation models in dairy cattle production SHIMS participation in Michigan, 1986 to 1988 SHIMS disease classification at slaughter, Michigan, 1985 to 1988 Participating SHIMS slaughter facilities, 1985 to 1988 Stage of processing and possible concurrence for SHIMS slaughter health check observations, 1985 to 1988 SHIMS slaughter health check personnel requirements, 1985 to 1988 SHIMS farm management report calculations, 1986 to 1988 SHIMS pilot herd slaughter health check summary, 1986 to 1988 Disease prevalence rate variances for SHIMS pilot producers, April 1986 through March 1988 (disease rates expressed as proportions) SHIMS sample size calculations for number of herds, April 1986 through March 1988 (disease rates expressed as proportions; a = 0.05) SHIMS sample size calculations for number of slaughter health checks per farm per year, April 1986 through March 1988 (disease rates expressed as proportions; a = 0.05) SHIMS sample size calculations for number of individual pig health evaluations per shipment to slaughter, April 1986 through March 1988 (disease rates expressed as proportions; a = 0.05) Page. 42 43 45 50 55 57 59 63 75 81 3.13 3.14 4.1 42 4.3 4.4 45 4.6 4.7 48 4.9 4.10 5.1 5.2 5.3 Results of linear regression analyses evaluating potential trend, season, and producer effects on disease prevalence rates for SHIMS pilot producers, April 1986 through March 1988 Results of logit regression analyses evaluating potential trend, season, and producer effects on disease prevalence rates for SHIMS pilot producers, April 1986 through March 1988 Variable costs of SHIMS operation, April 1986 through March 1988 Production factors that affect hog performance in the physical production model, Michigan State University SHIMS project, 1985 through 1988 Hypothetical distribution of individual hog growth rates Production factor groups for initial autocorrelation of performance effects Variable series projected with the alpha-beta tracker Proportional effects of SHIMS disease categories on finisher average daily gain (negative values indicate a depression of growth rate) Proportional effects of SHIMS disease categories on grower average daily gain (negative values indicate a depression of growth rate) Proportional effects of SHIMS disease categories on nursery average daily gain (negative values indicate depression of growth rate) Proportional effects of SHIMS feed additive categoried on finisher average daily gain (positive values indicate enhancement of growth rate) Proportional effects of SHIMS feed additive categories on grower average daily gain (positive values indicate enhancement of growth rate) Proportional effects Of SHIMS feed additive categories on nursery average daily gain (positive values indicate enhancement of growth rate) Data received from SHIMS producers, spring 1986 through winter 1988 Estimated length of finisher phase for SHIMS producers, spring 1986 through winter 1988 Estimated total length of post weaning period for SHIMS producers, spring 1986 through winter 1988 xi «8%? 103 109 119 130 137 138 139 141 141 141 154 160 160 5.4 55 5.6 5.7 58 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 Estimated feed use per pig-day for SHIMS producers, spring 1986 through winter 1988 Summary of beta coefficients for SHIMS pilot producer alpha-beta trackers, spring 1986 through winter 1988 Sample output from SHIMS computer simulation model Simulation projections based on unmodified DELAY for SHIMS producers, summer 1986 through winter 1988 (RNIALYSIS 1) Accuracy of simulation projections based on unmodified DELAY for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 1) Simulation projections based on unmodified DELAY for SHIMS producers, summer 1986 through winter 1988 (ANAL IS 2) Accuracy of simulation projections based on unmodified DELAY for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 2) Monte Carlo projections of total hogs marketed based on modification of DELAY using observed disease rates (DIS +1) and pig deaths (DTH 9for SHIMS producers, summer 19862 through winter 1988 (R ALYSIS 3) Monte Carlo mean DELAY projections based on modification of DELAYq using observed disease rates (DIS +2!) and pig deaths (DTH for SHIMS producers, summer 1 rough winter 1988 ANAQYSIS 3) Accuracy of Monte Carlo projections based on modification of DELAYg using observed disease rates (DIS fl): and pig deaths (DTH for SHIMS producers, summer 1 rough winter 1988 (ANAQPSIS 3) Monte Carlo projections of total hogs marketed based on modification of DELAY using disease rates (DIS +1) and pig deaths (DTH +1) as pro; cted with the alpha-beta gacker for SHIMS produ ers, summer 1986 through winter 1988 (ANALYSIS 4) Monte Carlo mean DELAY projections based on modification Of DELAY using disease rates (DIS 1) and pig deaths (DTHQ +1) as projec d with the alpha—beta uglier for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 4) Accuracy of Monte Carlo projections based on modification of DELAY using disease rates (DIS 1) and pig deaths (DTHQH) as projec%d with the alpha-beta trg-ker for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 4) Accuracy of alpha-beta tracker data projections for SHIMS producers, summer 1986 through winter 1988 xii E 161 163 165 171 172 173 174 175 176 177 178 179 180 181 5.19 520 5.21 522 523 Monte Carlo pro'ections of total hogs marketed based on modification of ELAY using disease rates (DIS +1) and pig deaths (DTH ) of zeronor SHIMS producers, sugmer 1986 through winte 1988 (ANALYSIS 5) Monte Carlo mean DELAY projections based on modification of DELAY using disease rates (DIS 1) and pig deaths (DTH +1 of zero r SHIMS producers, sum er 1986 through winter 1 (ANALYSIS 5) Projected income over variable costs (IOVC) with and without diseases (DIS 1) and pig deaths (DTH ) for SHIMS producers, summer 1986 rough winter 1988 (ANABYSIS 5) Monte Carlo projections of total hogs marketed based on modification of DELAY using specifically modified pneumonia and atrophic rhinitis prev lence rates for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 6) Monte Carlo mean DELAY projections based on modification Of DELAY using specifically modified pneumonia and atrophic rhinitis p evalence rates for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 6) Projected income over variable costs (IOVC) with observed diseases (DIS 1) and pig deaths (DTH + ) compared to specifically m dified pneumonia and atr phic rhinitis prevalence rates for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 6) xiii 184 186 187 187 188 3.1 4.1 42 43 4.4 45 4.6 4.7 48 4.9 4.10 LIST OF FIGURES The agricultural production decision making process SHIMS organizational scheme Schematic representation of the physical production model, Michigan State University SHIMS project Schematic representation of the financial production model, Michigan State University SHIMS project The Erlang family of probability density functions (from Manetsch and Park, 1982) Triangular probability density function Autocorrelation function Combined proportional production effects Schematic representation of DELAY modification, Michigan State University SHIMS project Effects of available floor space per pig on growth rate- finisher phase Effects of available floor space per pig on growth rate— grower phase Effects of available floor space per pig on growth rate— nursery phase xiv \O 51 100 101 108 112 118 126 128 144 144 145 LIST OF FOOTNOTES aDairyComp, Valley Agricultural Software, Tulare, California. bCowSearch, Vermont Computer Software, Inc, Vergennes, Vermont. cPigChamp, Department of Lar e Animal Clinical Sciences, College of Veterinary Medicine, University of innesota, St. Paul, Minnesota. dCash Flow Planner (CFP), Cooperative Extension Service, Michigan State University, East Lansing, Michigan. eFinPaek, Department of Agricultural and Applied Economics, Minnesota Extension Service, University of Minnesota, St. Paul, Minnesota. fFAHRMX, Department of Large Animal Clinical Sciences, College of Veterinary Medicine, Michigan State University, East Lansing, Michigan. gNAHMS, USDA/APHIA, Ft. Collins, Colorado. hSwineGraphics, Webster City, Iowa. 'TelFarm, Department of Agricultural Economics, Cooperative Extension Service, Michigan State University, East Lansing, Michigan. jNOMADZ, D&B Computing Services, Wilton, Connecticut. kdBASE III Plus, Ashton-Tate, Torrance, California. lMicroTSP, Quantitative Microsoftware, Inc, Irvine, California. mMicrosoft QuickBASIC, Microsoft Corporation, Redmond, Washington. nM-OPTSIM, Department of Electrical Engineering and Systems Science. Michigan State University, East Lansing, Michigan. 0PlotIT, Scott P. Eisensmith, Department of Crop and Soil Sciences, Michigan State University, East Lansing, Michigan. XV CHAPTER 1 BACKGROUND AND GENERAL PURPOSE INTRODUCTION In his book ' f ' a to uct'on and esource se Earl O. Heady (1952) stated that the role of the production economist in the area of farm management ". . . is that of facilitating choice in production patterns and resource use so that the ends or objectives of farmers and consumers can be attained." Generally, this role has been approached through: (1) assisting resolution of the choices farm managers face between production alternatives (Offer prescriptions) toward the achievement of individual decision maker objectives and (2) attempting to predict the behavior of farm managers faced with particular circumstances toward optimal policy considerations. The first approach holds the individual farmer’s well being as the primary focus, while the second is concerned mainly with economy wide issues, including consumers’ interests. Though the ultimate targets of these two approaches differ. a solid understanding of the decision making process of the farm firm is paramount to either type of analysis. T.W. Schultz (1939) aptly described farm decision making as a two-step procedure. The first step involves formulation of price and technological expectations. These expectations, in turn, form the basis for the second step, development of a production plan. This framework can be used to categorize the work of agricultural economists in the area of farm management: marketing research and formulation of marketing strategies provide the basis for work in price expectations; production analysis supports formation of technological expectations (TES), and decision analysis, along with the entire body of decision theory, offers assistance in formulating production plans. Though this categorization may appear to be nicely discrete, much of the work done in 1.91- -- _ a__' ‘fl'f... l v- "q. n-I 2 farm management may involve two, or even all three, of these areas. For instance, production economics generally involves aspects of both production and decision analysis. One analytical approach used by many agricultural economists is that of first gathering background information and then constructing a mathematical model to represent the problem of concern. The relative complexity of this exercise varies across a spectrum from pure intuition to extremely sophisticated, computerized processes. However, regardless of the complexity involved, collection of data and processing it into useful information constitutes an information management system And since the entire procedure is used to assist the decision making of managers, the result is often referred to as a decision support system. Admittedly, these terms are somewhat loosely defined at this point, but will be further developed in Chapter 2. Because agricultural production faces continual uncertainty from biological. environmental, and economic sources, the difficulty in conducting economic analyses for farm management is substantial. This is especially true of livestock production. Effective research in recent years has markedly improved the understanding of livestock biology in areas such as nutrition, genetics, and health management (among others). Considerable growth in the number of technological options available to livestock producers has followed. Since many of these options are new and unfamiliar to individual producers, uncertainty has also been effectively increased. This situation occurs across livestock species, places growing importance on the formation of TES. and poses a special challenge for the agricultural economist. Returning to the two-step paradigm of Schultz (1939), it is apparent that if uncertainty affects the formation of both price and technological expectations, the ensuing production plans are also influenced. Accordingly, uncertainty has been the nemesis of agricultural economists interested in farm management. Whereas the farm management economist is usually judged by the production plan’s success in achieving decision maker objectives, it is quite Obvious from Schultz’s analysis that the quality of 3 the plan is directly limited by the quality of the underlying expectations. Often, however, the production aspect of this vital step is not sufficiently emphasized. Agricultural economists have traditionally paid much attention (in a relative sense) to decision analysis and marketing strategies, but have tended to base their TEs on assumption or on the work of biological and physical scientists. Several authors have discussed the comparative lack of emphasis on technology (Heady, 1952; Schultz, 1939; Nix, 1979, Johnson, 1957; and Johnson, 1963), and some important points they make are: 1. relatively little incentive exists for subject matter and problem solving research since disciplinary acclaim is achieved mainly through basic (theoretical) work; 2. the interdisciplinary nature of investigations into the formation of TEs leads to administrative inconvenience and poses further problems if mutual intellectual respect between investigators is inadequate; and 3. many of the modeling techniques applied to the area of TEs are inadequate in their representation of agricultural production, since assumptions of linear, deterministic, static, and fully understood processes are generally inappropriate. PROBLEM STATEMENT The problem that has arisen from these circumstances is that, despite the marked importance of TES, structured methods of TE formation have not been incorporated into rigorous economic analyses for livestock production. The agricultural economist’s skills in data collection, mathematics, abstract modeling, problem solving, and understanding decision maker objectives have not been fully utilized in the area of technological elucidation. Since TES are critical to virtually all work in the area of farm management, an increased emphasis on interdisciplinary research of this type is warranted 4 An area of agricultural production where this problem is especially prominent is animal health management. While the capability of veterinarians to advise in the management of livestock health toward prespecified, health—related goals is quite well developed, the ability to effectively assess the associated economic ramifications is weak to non-existent. Generally, this results from not including expertise from both veterinarians and economists into single, rigorous economic analyses. Consequently, TEs associated with alternative health management practices are frequently very poorly developed. 89592319131911 The goal of this research is to construct a prototype decision support system for livestock production using growing pigs (weaning to market) as an analytical framework. Computerized information management and mathematical modeling techniques will be employed to form the TEs on which production plans will be based. While many aspects of market hog production will be addressed, special emphasis will be placed on the economics of animal health management. In that regard, the relative adequacy Of the existing market hog disease knowledge base will be assessed Though Schultz (1939) has noted the critical importance of both prices and technology, resource limitations preclude explicit inclusion of methods for price expectation formation. Thus, it will be extremely important to maintain flexibility to accommodate varied sources of price information. Research Objectives Initially, information management techniques applicable to livestock production will be reviewed to consider their usefulness in supporting the formation of accurate TEs. In addition, the various approaches which have been used to model livestock production and/or formulate production plans will be reviewed for their general strengths and weaknesses. These reviews will be presented in Chapter 2 Once these reviews are complete, the capabilities of the agricultural economist will be combined with input from the biological sciences in order to build both an 5 information management system and a production model for growing pigs which form TEs and production plans. The information management system will include techniques for data collectiOn, computerized processing, and output generation, while the modeling process will utilize systems analysis and computerized simulation. Special emphasis will be placed on the economic importance of animal health and its management. Though this work only addresses growing pigs from weaning to market, both the approach and the principles employed should be applicable to other livestock enterprises. Presentation of these aspects of the research will occur in Chapters 3, 4. 5, and 6. Development of the information management system and preliminary data collection results will appear in Chapter 3. In Chapter 4, the analytical model will be presented, while initial application results for the composite decision support system will be presented and discussed in Chapter 5. Finally, Chapter 6 will summarize the results and put forth recommendations for building on the progress this research attains. Research Benefits Immediate benefits of this research will include: 1. identification of useful techniques and potential limiting factors for building information management systems to support TEs and production plans in livestock production; 2. identification of useful modeling techniques for formation of T138 and production plans in the production of livestock; 3. identification of critical economic factors in livestock management, especially those concerning animal health; and 4. identification of areas where further technological research is needed to adequately understand the economic aspects of the biological processes involved. Ultimately, the knowledge gained from this research will make decision maker Objectives more obtainable in livestock production. Further application should include: 6 a starting point for further research (to include possible revisions of the production model and the potential incorporation of price expectations), a teaching tool to convey modeling techniques to future researchers, and to teach management techniques to future livestock producers and/or advisors; support of direct consulting with livestock producers regarding management techniques; individual use by livestock producers in support of their own decisions; and use by policy makers to evaluate the potential ramifications of various regulatory options. SUMMARY Livestock producers face a widening array of production options. The uncertainty involved makes the pursuit Of decision maker Objectives an increasingly complex undertaking. Through a structured, interdisciplinary approach, this research seeks to make successful pursuit of decision maker objectives more achievable by designing a prototype decision support system which facilitates the formation of technological expectations and production plans. ,. \ \- l . .- — . . ... I -: -.. _ ... . .q‘ \_ . .u x I... ._~l « ‘. i l l N a CHAPTER 2 REVIEW OF THE LITERATURE INTRODUCTION Using the production of livestock as a framework, this chapter will review those general features that agricultural economists should consider important when choosing information management systems and mathematical models to support individual farm prescriptions. Accompanying these general reviews, the specific information management and mathematical modeling techniques commonly used to guide decisions in livestock production will be evaluated individually and examples will be cited. Strengths and weaknesses will be assessed in relation to the adequacy of the resulting production plan. While the integral role of price expectations is once again recognized, only production information and production and decision models will be considered in an attempt to maintain a manageable scope while providing an adequate background for the information management system and livestock production model to be formulated later. CONCEPTUAL DEVELOPMENT As presented in Chapter 1. T.W. Schultz (1939) described the decision making Process in managing agricultural production as having two steps: formation of expectations for prices and technology, and development of a production plan. But formation of expectations for agricultural production, regardless of the exact t(“:Chnique(s) involved, requires prior information on either the market or the production Process of interest. For this reason, Schultz’s first step really involves two phases: Obtaining information and the actual expectation formation. "I ‘0 or u. 8 However, deciding exactly what information is important and how it should be obtained is no trivial task. Just as the quality of the production plan is constrained by the expectation (as Schultz states), the quality of the expectation is very closely tied to the caliber of available information. As a result, the sufficiency of the information Obtained is ultimately determined by how well the production plan achieves the decision maker’s objectives. Therefore, to understand the relative importance of various information and its best utilization in forming expectations, it is necessary to actually use the information to derive expectations and to implement the ensuing production plan. For production information, this is an iterative and interactive process and involves management of production information, formation of technological expectations (or TEs) using a mathematical production model, and guidance for production planning with an analytical decision model. Figure 21 helps adapt the Schultz paradigm to the decision support system framework introduced in Chapter 1 Again, though similar processes are involved when prices are considered (as indicated in Figure 2.1), focus will be maintained on physical production. Initially, the decision maker must personally decide which production information is deemed necessary to appropriately form TEs and how it should be obtained. This beginning phase, construction of the information management system (01' IMS), may rest solely on prior experience of the decision maker or may utilize outside sources of information (referred to in Figure 21 as "Experts"). The resulting IMS may be simple or complex, depending on the characteristics of the situation. Once this body of information is obtained, the decision maker takes the next step in the process-use of a mathematical model to form TEs. Again, the degree of SOphistication can range from a purely mental model (involving, for instance, only extrapolation from previous production levels) to one that is extremely complicated. Regardless of its exact structure, some sort of mathematical model must be employed t0 form TEs. Following their formation, the decision maker combines TEs with aVailable price information to design a production plan which is anticipated to best meet «a \ .11 3271': ., ‘- .‘hu '2'“.- A“ .. v. - l ‘.,. v.‘~ ‘1 n. no 1. «s \._ .I . 'o. u 'u ‘ . X's -“.‘-‘ _n. ." . -‘ u ' . ,‘ 'b .‘ . ‘u ., .V \ ‘- . \ l v. ‘ c '-. t u ‘r. . .3 I s "l .' . n l I .~ ofl ‘. l "PUT AND REVIEW "" 033mg: MAKER ’ ’ / melon / / j 1 'exesnrs“ R \ annulus m segregation /; / p. | \ \\ INFORlsaTION mow / / ' \ \ mess / / I \ \\ / / | \ \ tongues | magma TECHNOLOGICAL | Pelee execcrmous EXPECTATIONS W FORMATION or PRODUCTION PLAN Figure 21 The agricultural production decision making process his/her personal objectives. This process involves use of an analytical decision model, Which can also vary from simple "status quo" policies to rigorous optimizations. When the actual outcome of the production cycle is compared to original expectations (in light of decision maker objectives), input from the decision maker and/or "experts" is repeated. Input at this point starts the second iteration and serves to Snide potential changes for the other stages of the process. For example, it may be discovered from the first iteration that information regarding a certain specific area of the production process is quite unimportant to management of the eventual outcome. In this case, resources could be conserved by not obtaining such information in successive cycles. Alternatively, information regarding other aspects of production might prove insufficient for adequate support of the production plan. Obviously, the next iteration should seek to enhance the quality of information in these areas. Finally, critical review may well point to a problem in the formation of TES, rather than with the production information obtained. Such a finding would then warrant alterations in «’v .. . '. — m- _ _ —~,. .. u... ~,, n-. "o. I ‘— l.' - -. ..I "‘ u. I u '~ “.. '1. —- *I A.‘- C. 4 ‘0 ‘- ‘ s ‘e ‘wo . . ‘ K h )- ' \ 'v o\_ " . ‘- I I 4 .n .“ . w “\ '~ 1.. . . . ‘4. " l- e Q o , I 10 the analytical model employed. Once all changes have been implemented, the second iteration continues through the remaining phases to complete the cycle. While the main flow of information is sequential from one stage to the next, the decision maker can interact directly with any individual stage at virtually any time by either obtaining feedback or exerting influence and making changes. In effect, the perpetual process measures the value of production information to the decision maker. Information is only of value if it is found to hold a net positive effect on the amount of welfare the decision maker derives from the production process. In this regard, the process closely parallels the position taken by Davis (1974), who asserts that the inherent value of information is determined by the net change in outcome achieved if the information is employed in production. Through this iterative procedure, the producer is constructing a system which Obtains production information, and then uses it to support production decisions. Though, as discussed, the degree of sophistication, complexity, and sufficiency surely varies between producers, the perpetual process is that of building a decision support system (DSS). INFORMATION MANAGEMENT SYSTEM CHARACTERISTICS Harsh, et al. (1981) defined four types of information required by the farm manager: descriptive, diagnostic. predictive, and prescriptive. In this context, the IMS provides descriptive information by collecting and processing production data into useful Summary information which describes the critical aspects of the production process. Then the information is "fed" to the mathematical model portion of the DSS. Here the Production model, regardless of its exact nature, provides diagnosis and prediction. Diagnosis is accomplished through processes such as sensitivity analysis, comparative Statics, and shadow pricing whereby critical factors limiting production are discovered and evaluated. Pursuant to diagnosis, prediction occurs by appropriately altering input and production levels in successive solutions of the production model. Finally, \"t‘ nor-.0- I g... 5:2 r. .»- -,. H I. ”'0. ‘I . _ <9 . .. ‘0 ,H . ‘ ‘I -. ‘ I. 7“ II a . . .. -1. ~ ._~ 1 ,_' “"4 ‘0 v s‘. y I My ‘ l l‘ I‘ ~. I . - NI ‘1 . ‘3' n. 9. s, r e " \. u- ’< 1 l t. . A e a 1 \ .g o a 11 prescription is provided by using the decision model to evaluate these successive input/output combinations in light of given objectives. Because the analytical production and decision models (and their resulting diagnostic, predictive, and prescriptive information) will be discussed subsequently, only the management of descriptive production information will be reviewed at this point. This information management lies entirely within the ”Obtaining Information on Production" stage of Figure 21. Management of descriptive production information involves two major phases: data collection and processing/summarization. Though some important features warrant consideration in virtually all production situations, it is impossible to conceive a single list of criteria to be followed in every single case. Variability is introduced by differences in production process characteristics, decision maker objectives, decision maker capabilities, production and decision models to be employed, and available resources from farm to farm. However, appropriate attention must always be paid to the accuracy and timeliness of both data collection and processing. Accuracy and punctuality are critical to the success of an IMS. Not only must errors in data collection and processing be minimized, but time lags in collection and extended processing turnaround must also be avoided. To be successful, an IMS must provide the decision maker "good" information when it is needed. Dat l ci In spite of these differences, certain factors are critical to nearly all data Collection efforts. These need to be given appropriate attention to prevent potential problems with the accuracy and punctuality with which the data are collected. Leading this list is the amount and types of data to be collected. As discussed in the Conceptual Development section, it is often difficult to know precisely what information is important. Ultimately, the value of any data to be collected is determined by behavioral changes and the resulting net change in outcome achieved (Davis, 1974) However, when Collecting data one point should never be overlooked: each datum to be collected. 12 whether regarding production inputs or outputs, must have an intended purpose. Unused data not only wastes collection and processing resources, but also dilutes the attention given to truly useful data, thereby increasing the potential for inaccuracy and tardiness. On the other hand, if support for specific decisions is desired, the amount and types of data collected must be adequate for those purposes. Closely related to the "What to collect?" question is that of "How to collect?" If the desired data can only be obtained through expending a huge physical and/or intellectual effort, then problems may again ensue. Further, the immediate method of data recording must be considered. The technique should be both straightforward and easy to complete to prevent unnecessary errors and/or delays. A final consideration for data collection is the motivation(s) of the person(s) involved. While this certainly varies between individuals, adequate motivation is a necessary prerequisite to successful data collection. ta oc si Following these concerns for collection of the data are similar issues regarding its processing and/or summarization. Topping this list are questions surrounding the output Of the process. As discussed previously, it is usually difficult to know precisely What information is important short of at least one full iteration through the process described in Figure 21. However, an initial desired content must be defined before outputs for data processing are actually formulated. Further, the desired destination of the output must be known. Whether headed for a computerized file or a printed report, content is closely followed in importance by format. Regardless of content, if the output format is not readily usable. the effectiveness of the entire process is seriously damaged. After considering output content and destination/format, it is necessary to review data entry and storage issues. The amount of data to be handled, the relative complexity 0f the desired processing, and the available resources should guide the choice between ..._ at LA n is "I. ~ vs- .0. \. n .. l-u ’14. ‘u . .. \ I. < 3. 7'1. Tl L- l - b. . ‘V .v ‘7 -- .\I . .h. . ‘0 --.; u . ., . 'V i ‘ . ‘.I \ . e . . 'I ~- . w ‘4 n ‘t. r n ‘- V . I . I'. .. L . . . ‘ I 13 viable options. When evaluating data entry options, it is again important to remember the degree of physical and intellectual difficulty involved. As with data collection, the last consideration for data processing is the motivation(s) of the person(s) involved. The relative success achieved in this phase is often very closely tied to human factors for the people involved. Success cannot be attained if motivation is inadequate. Finally, it is important to recall the value of information at this point. All data collection and processing procedures must be reviewed in the context of Figure 21 and the position taken by Davis (1974) Individual facets of an IMS should only be maintained if they make a net positive contribution to the outcome of the production process. INFORMATION MANAGEMENT SYSTEMS FOR LIVESTOCK Using these characteristics as a base, selected information management systems (IMSs) useful in support of livestock production management decisions will now be reviewed. For the most part, two general approaches are used for livestock information management systems: individual farm and multi-farm. As the names imply, the individual farm method involves management of information from a single farm, while the multi-farm technique includes both information from one farm of special interest and information from a group of similar farms. Examples of individual farm systems which will be reviewed are DairyComp,a CowSearch,b PigChamp,c Cash Flow Planner (CFP),Cl and F inPacke while FAHRMX,f NAHMS.g SwineGraphics,h and TelFarmi are the multi-farm systems to be considered. Though managerial use of the individual farm IMSs listed provides substantial descriptive information on the production process, each has its particular strengths and limitations. To begin with, the amount of data collected with these systems is probably nowhere excessive, and may not actually be adequate to provide decision support in all areas of production. More specifically, DairyComp,a CowSearch,b and PigChampc all 14 focus on historical output levels and current status of physical production while largely ignoring both physical inputs and the financial aspects of production. On the other hand, CFPd and F inPacke (which can be used to support the production of almost any species of livestock) tend to focus on the history and current status of production’s financial management, giving only limited attention to inputs, outputs, and the current status of physical production. While the adequacy of the data may present some limitations for these particular systems, the other important aspects of data collection discussed above present no problems. Overall, techniques of data collection and recording are more than adequate. Further, user motivation is generally high as individuals use the systems in hopes of improving production’s performance. As a result, motivation is usually adequate to assure sufficient accuracy and punctuality in data collection. The data processing aspects of these systems are also favorable. As with data collection, user motivation is normally high. Also, data entry and storage are facilitated by being entirely on-farm and computerized. When combined, these factors have marked positive effects on the accuracy and punctuality of data processing. Finally, the outputs from these systems are generally satisfactory. Substantial flexibility exists, allowing the individual to select between alternative output options according to particular preferences for content and format. In summary, decisions requiring only historical physical output and current production status information for the individual farm are supported quite well by information management systems such as DairyComp,a CowSearch,b and PigChamp.C On the other hand, those decisions which require financial management information for the individual farm are well supported by systems such as FinPack.e However, support for formation of TES and production plans is somewhat restricted by these types of systems, since none possess comprehensive information on both physical production (inputs and outputs) and financial management. Further, while production’s "track record" is indeed very useful to the livestock manager, support for diagnosis and '0.“ "a -"" I - -... .- n...”- n. v-~.... _ ~. . -. ,_ .._ g‘ ‘5 ,4 _ I n"!.. '~ .IC 1 o n ‘_' h. . "-— . n.- - .. u. . ‘- Ii U l. .\ . ’\ . . ‘ a a. ‘v -. ..'. 5.. ' . I b ‘u 4', - - \. a ’. ‘I. 4 . p e‘ . I ~ 1 4 \ ' I 1 a” - 15 prescription (such as non-historical, ”What if?" questions) would require combination with information from other similar farms and/or a quantitative production model. Toward this end, multi-farm information management systems incorporate information from similar farms. Examples of these include the Food Animal Health Resource System (F AHRMXf) for dairy producers, SwineGraphicsh for hog producers, the National Animal Health Monitoring System (NAHMSg) for dairy and hog production, and TelFarmi for virtually all livestock species. These systems report information both for the particular farm of interest and parallel information from a group of comparable farms. However, once again data collection is not uniform across the various areas of production. FAHRMXf and NAHMSg tend to lean toward description of physical production’s historical output and current status (especially health status) and away from physical inputs and financial management. On the other hand, TelFarmi is much more complete in describing the financial aspects of management than in physical production. SwineGraphicsh begins to bridge the gap between financial and physical production. Generally speaking, SwineGraphicsh provides information on physical input quantities, input costs, physical output quantities, and output values. Also, the current status of production is included except for some aspects of livestock health. h SwineGraphics provides the most comprehensive body of production information of the systems reviewed. While data may be inadequate in some areas, the techniques of data collection and the driving motivation for SwineGraphicsh and TelFarmi are similar to those discussed for the individual farm systems and, as such, present no real problems. On the other hand. FAHRMXf was a pilot demonstration and NAHMSg originated as a research effort. On both projects, producers were specially selected for participation. As a result, data collection duties for- these systems were divided between producers, project managers, and research co-workers. Although the specific data collection techniques involved are adequate, the producers’ motivation for participation might cast a shadow on data accuracy. This point has been recognized by coordinators for both rho it .. . no I ...,. ‘5. .. F" 16 systems and considerable effort is expended by data collecting co-workers to assure that data quality is acceptable. As with the individual farm systems discussed, the output of these multi-farm systems has content and format that is quite useable. Further, these systems accomplish data processing centrally. While this may alleviate potential accuracy problems arising from user processing, the possibility of turnaround time lags is introduced. Finally, though motivation for processing is not directly tied to the user’s financial success, central processing is usually performed by professionals whose own financial renumeration is often correlated with the quality of their work. In summary, the adequacy of information provided by these multi-farm information management systems is generally improved over the individual farm systems discussed. Addition of information from similar farms lends assistance to diagnosis and prescription. However, formation of TEs and production plans remains difficult due to lack of comprehensive physical and financial data. ANALYTICAL MODEL CHARACTERISTICS Consensus among economists on the exact attributes which are important for inclusion in good analytical models of production will probably never be achieved. However, several general features provide key focal points for both model builders and reviewers. Four such model characteristics were proposed by VanDyne and Abramsky (1975) when they reviewed many quantitative models developed for agriculture. Their framework for review involved the following categories: 1) resolution, 2) realism. 3) generality, and 4) precision. Resolution and realism refer to how well the model represents reality. For a livestock production model to accurately depict reality, it should first include sufficient resolution to capture both the biology and economics involved. The direct effects of biological factors such as animal environment. sex, age, diseases, nutrition, and genetics need to be considered for possible relevance. Similarly, a livestock decision model .-.-on. ' a l yaw“ win- '1‘ I . .., . . 1 u. . I I "4. 1 mm. v . 4. I ‘ to n. ;. .,_ ' - .-- a. -e ‘l. x .. . "vl I! " D, L \ --._ .I. l W. r1 1 ‘g -8 4 "’V . ' ~ ' I l o. ., ‘3". '1 n . _. \ n.‘) 'n '| ._ ~ . 1 . . 17 should consider economic factors such as decision maker objectives, market conditions, and certain aspects of financial management. Attention here should include the decision maker’s attitudes toward risk, social status, standard of living, human-animal bonds, and growth of the firm while concurrently recognizing the importance of actual input and output prices, the time value of money, cash flows, labor and facility availability, asset liquidity, and financial leverage. Mere inclusion of those factors deemed relevant is not sufficient unless the model appropriately addresses the actual relationships that exist. For this reason, livestock models should allow for the realism of complex factor interactions, random (stochastic) variation, and dynamic progression. For example, interactions are known to exist between such factors as dietary energy and protein levels, animal environment and health management inputs, and animal concentrations and nutrition. Also, virtually all biological production processes in agriculture contain elements of random variation, both within and between farms. Similarly, the economic environment provides perpetual market stochasticity in the form of changing demand and supply conditions, which ultimately leads to variation in output marketability, input availability, and prices of both inputs and outputs. Finally, because biological and economic factors (such as relative animal health and factor prices) are never constant, it is critical to permit change in system parameters over time. To attain this dynamic realism, time-paths can be followed continuously or in discrete increments. If time is continuous, differential calculus is necessary, while difference equations are used to capture discrete changes (Chaing, 1974). If the discrete time intervals are very small (infinitesimal), the solution of the difference equations approaches that of the corresponding differential equations as a mathematical limit. As a practical matter, either approach can be used to model livestock production. However, if discrete intervals are employed, the time-step must be small enough to allow the difference equation solution to closely approximate the differential equation solution. 18 In contrast to resolution and realism, generality and precision refer to the performance of the model when applied to actual situations at various production levels. For instance, generality with livestock models at the farm level indicates that the model is broadly applicable across farms with similar production types, while precision implies an ability to represent events on one particular farm over time. At another system level, generality with animal-level livestock models indicates that the model is broadly applicable across similar animals in the same herd, while precision implies the ability to predict the events of one particular animal over time. Generality and precision often pose antagonistic goals, since an increase in generality often necessitates a loss of precision, and vice versa. The degree that each may be achieved depends in large part on the source of the data used to build and run the model. Use of primary data (collected by the model builder/user) might be expected to meet generality-precision goals more effectively than one using secondary data (collected by someone else), since primary data provide much greater familiarity with data strengths, weaknesses, and interpretations. Regardless of the ultimate focus, a certain amount of precision is necessary for a model to be generally applicable, since all groups are composed of discrete individuals. In addition to these four general characteristics forwarded by VanDyne and Abramsky (1975), models intended for use in livestock decision support should be reviewed for a fifth attribute: their applicability. For example, when attempting prescription for decision problems that occur in the production of livestock, the capacity for constrained optimization is often valuable. Then, once decision maker Objectives have been considered, costs can be minimized subject to output constraints, profit can be maximized subject to cost constraints, output can be maximized subject to technological constraints, etc. Also, model flexibility is important since similar problems often arise in non-identical situations. For instance, the asset replacement decision is very similar whether considering livestock or machinery, but the associated cash flows and asset lives could be expected to differ considerably. Finally, the capability for ..-:‘ ’pa \ . . -~'¢~-- as» e...- 'xt-Iu .0“... .. V .' F's-v * ‘-I\§. 5'3 19 sensitivity analysis is extremely useful. This allows the researcher to define critical areas of insufficient information and allows the decision maker to explore the possible consequences of not exactly following the prescription dictated by the model. When reviewing models for livestock production according to the five major characteristics suggested, the ultimate question becomes, "How good is good?" This question must be addressed within the context of each individual modeling situation by first asking, "How well does the model achieve the modeler’s (or user’s) objectives within the constraints on modeling resources?" Resource application in modeling efforts should not exceed that amount necessary (or available) to meet the modeler’s needs. Thus, while certain modeling situations may require a higher order of technical sophistication, the problems faced in other cases might be met with a more straightforward, simplistic approach. Care must also be taken, however, to avoid over-simplification, or the model may not be "good" enough in the areas discussed to meet the modeler’s Objectives. This position was echoed by Dent (1975) during a review of systems theory application in agriculture. Dent categorized agricultural production processes into four system levels. These are: Level 1: Biochemical and physical systems, Level 2: Plant and animal systems, Level 3: Farm business systems, and Level 4: National and international systems. Most of the models at Levels 1 and 2 are constructed by biologists, while economists tend to model at Levels 3 and 4. Dent suggested that an important reason for failure of models at Levels 3 and 4 is a lack of appreciation by the modeler(s) for the structure and/or function of the various underlying biological subsystems (or Levels 1 and 2). In the context of VanDyne and Abramsky (1975). the models’ failures indicate lack of generality and/or precision and result from a lack of realism and/or resolution. Or, in the mode of Schultz (1939), the production plan (decision model) might fail if 20 insufficient attention is paid to formation of technological expectations (production model). These attitudes were mirrored in Johnson and Zerby’s (1973) statement that ". . . there is little virtue in a simplicity that distorts reality." At the same time, Bywater and Baldwin (1984) argued that too much concern for detail can doom an otherwise healthy modeling effort. Their contention was that a model can easily become mired in details that are irrelevant for the immediate problem, and that often substantial aggregation and generalization is warranted in order to facilitate case of use and smooth model performance. In this case they seem to be joined by 1.1... Dillon (1979), who wrote: The mathematical solution for determining the path of a thrown baseball requires solving complex differential equations; however, those who can catch baseballs need not solve those equations, even though their catches imply that they do. Thus, while the modeler’s objectives are probably the most important underlying criteria for model construction or review, either inadequate or inordinate attention to the details Of the actual production and decision processes involved can easily limit the usefulness of the resulting models. ANALYTICAL MODELING TECHNIQUES FOR LIVESTOCK Various approaches have been taken to analytical modeling in livestock production. Those which have received substantial attention in the literature will be reviewed individually. Traditi nal ucti e no Over the years, a rather large, well-structured body of theory has evolved in the production area of microeconomics (Beattie and Taylor, 1985) However. integration of structured economic theory into the analytical efforts of applied farm management specialists has not strictly paralleled the development achieved by theoreticians. Moreover, the extent to which the neoclassical approach allows the farm management analyst to meet the theoretical objectives of prediction and/or prescription has been the f I .40 -.;.O l s‘h4'v» 1". '0 s n. "*6". “u.!. .- . u «D. u . .1 I . ‘Q ‘ . n. n I .l ‘\ § . I . . . s... 't N ._ I n 's n -\ t ‘ c a ‘. "v e .. I n s o ‘ u . . 21 focus of much debate (Schultz, 1939; Johnson, 1957; Johnson, 1963; Nix, 1979; and Heady, 1952) While a comprehensive, rigorous review of all of the debate’s constituent arguments will not be attempted, several perceived strengths and weaknesses will now be explored. This takes place in the course of reviewing applications of traditional microeconomics to the formulation of livestock production plans. Though it was both preceded and followed by numerous publications with a similar orientation, probably the single largest collection of work in the application of economic theory to agriculture was the book written by Heady and Dillon (1961). The basic approach, which is centered around formulation of a production function and corresponding isoquants, has been repeated more recently in the area of livestock production by such authors as Epplin and Heady (1982) to determine optimal rations, Russell and Young (1983) to evaluate technical efficiency, and Adeyemo (1986) (who formulates both production and cost functions) to review factor proportion issues. Relatively minor variations of the approach appear in Sonka, et al. (1976); Bhide, et al. (1980); and Brokken and Bywater (1982), who all estimated isoquants directly in an effort to define optimal ration formulations. Basically, this approach is both a production and decision model; it actually formulates a technological expectation along with recommending a production plan through formal optimization. Herein lies its primary strength. Following optimization. sensitivity analysis around the recommended production point can be performed via comparative statics. However, several important limitations must also be noted. First, the approach inherently assumes that production can be adequately described with a single, well- behaved, deterministic, statistically estimated production function which contains all information relevant to production and considers only the current time period as important. Unfortunately, livestock production is far too complex to be described with such a function. as changes (often abrupt) are known to occur in response to factors such as disease, weather changes, and estrus. Previous production levels are likewise 2 important. However, none of these factors were included in the above studies. If they were adequately captured, a dynamic production function with "corners" (or undifferentiable points) would have been necessary. Also, the very nature of biological processes brings stochasticity (random variation) to nearly all agricultural production, with or without random effects of the environment. Thus, the underlying single, well- behaved, deterministic, static, representative function assumption is violated in reality. As a result, the production model may lack resolution, realism, generality, and precision. Statistical estimation of production in the absence of a thorough understanding of the technology involved can lead to grossly inappropriate results (Bywater and Baldwin, 1984) For instance, estimation across a group of "seemingly similar" farms may well result in reasonable generality, but poor precision. On the other hand, estimation from a single farm or experimental trial (as in the examples cited) might yield adequate precision, but regardless which of these sources is utilized, the relationships determined from the data are virtually never generalizable to non-identical situations (Lloyd, et al., 1987a), even if they are based on a firm understanding of the process involved rather than a statistical "best fit." So generality is also absent, making the model quite impractical to use for individual farms, since (even if all other aspects of the approach were acceptable) it would be necessary to "re-fit" for each new farm to be analyzed. It might be argued that this is a problem of misspecification. However, a thorough technical understanding of livestock production would include recognition of inherent limitations in the current knowledge base. For example, it is not possible to say that "two hogs raised under identical environmental and nutritional conditions will perform precisely the same if they are of the same breed" because considerable variation exists even within individual breeds. Similarly, the effects of disease on production cannot be expected to be identical across farms and over time. Again, substantial variability exists (Lloyd, et al., 1987a). Thus, even if the "functions" are specified to the full limit of current knowledge, problems may arise from untempered statistical estimation. we. ___.... I 3-1 ‘0 a. I . .. . « o .. .. 7 u *— )l u. 1 - _\ 23 Finally, this analytical approach implies that the critical decision faced by producers is one of absolute production toward profit maximization (or cost minimization if the output level is fixed), is discretely faced in the current time period (static), and is based on perfect (deterministic) price information. But, in reality, livestock producers more often face a relative production question (adjustment), based on both the levels and rates of current and past production (dynamic), toward achieving personal objectives. These objectives may or may not be purely profit maximization, since factors such as firm growth, risk preference, and aspects of financial management are known to be important. In addition, input and output prices are virtually never certain, since their respective markets are inherently stochastic. Thus, the decision model also lacks resolution, realism, and precision. Most of these shortcomings were astutely anticipated by Schultz (1939) Together his perceptions of the attempt to formulate single, well-behaved static functions which were constant across farms and across time lead to the statement, "There are, however, Iam convinced, a number of reasons for believing that is a poor use of resources for agricultural economists to try to work up these constants." More recently other authors have also used the literature as a forum to air some of their concerns about the limitations of this traditional approach. For example, Upton (1979) discussed the realism and resolution problems of the production model when he said that the choice of functional form is arbitrary and mentions that farming is so Complex and dynamic that ”any attempt to represent such a system by a single equation is unlikely to be operationally meaningful." Also, he addressed the lack of generality problem with the production model when he discussed the difficulties associated with attempted aggregation. Conversely, Leibenstein (1979) was more concerned with the decision model and focused on how the maximization of profits to the exclusion of all other possible decision maker objectives is inappropriate. Ann!- I. I'm-1p t. "$“3‘ Hus.“ 0'. .3 *‘ 24 E I II .I. In trying to overcome some of these criticisms, theorists have made various attempts to broaden the approach. Most common among these is the expected utility framework, which was well presented with numerous applications by, among others, Robison and Barry (1986) Basically, the technique involves the addition of uncertainty to the production and/or decision model (in the form of stochastic technology and/or prices, respectively) and modification of the decision model objectives to maximization of expected utility. When broadly approached, expected utility theory involves specification of the decision maker utility function across the entire range of possible production outcomes and their respective probabilities of occurrence. The utility level of any given outcome is discerned by considering the resulting possible output levels, the corresponding output values, their probabilities of occurrence, and all other relevant aspects of such production. Once defined, this function then facilitates choice between production alternatives through allowing maximization of the utility which the decision maker expects to achieve. To identify a global maximum, this approach requires a utility function with the same mathematical properties discussed for the traditional production economics approach. Theoretically, expected utility may be attractive. However, comprehensive, mathematically tractable utility functions which consider output levels, values, probabilities of occurrence, and other relevant production aspects are difficult to accurately specify in most real world situations. Even if accurate specification is possible, it may not be a cost effective exercise if solely directed toward providing management assistance. Therefore, the broad approach described above is frequently forgone in favor of a procedure which considers only the potential profit levels and their respective probabilities of occurrence. In this special case, the utility level is determined by the mean and variance of the profit distribution (certainty equivalent profit) .‘r—v '— - .‘ca - I. “do. 25 Strengths of this approach include those mentioned for the traditional model: both technological expectations and production plan are addressed, the capacity for formal optimization is inherent, and sensitivity analysis around the recommended production point is again possible through comparative statics. Further, the addition of uncertainty to the production and/or decision model and the relaxation of exclusive profit maximization objectives for the decision model are admirable. However, significant weaknesses remain. Though uncertainty is now allowed in the production model, the violations of other assumptions have not been addressed. Specifically, the theory still assumes that a single, well-behaved, statistically estimated production function based entirely in the current time period contains all the relevant information to form technological expectations. As a result, the production model again lacks resolution, realism, generality, and precision if applied to the production of livestock. In turning to the decision model, more problems are encountered. While movement away from exclusive, current-period profit maximization has occurred, it is easily debatable whether or not exclusive, current-period maximization of expected utility (certainty equivalent profit) which is based only on the mean and variance of the profit distribution, represents an improvement. At least for livestock production, the assumption remains too restrictive, and as a result, the decision model again lacks resolution, realism, and precision. Finally, nothing has been done to alleviate the generality problems, since individual farm or experimental data are still the basis for model formulation. In addition, when more than one source of stochasticity is incorporated, the model becomes mathematically intractable and it becomes necessary to specify the individual decision maker’s risk preference in order to solve the decision model. Since the production of livestock nearly always entails a minimum of two sources of random variation (biology and price) and since it is extremely difficult to definitively specify risk attitudes of decision makers, the applicability of the approach is severely limited. 26 Again, the literature debating the merits of this approach is quite extensive, from which a few extractions will be made. Antle (1983a) recognized that a dynamic, stochastic production model is necessary, and wrote that the decision model has ". . . little relation to the decision problems farmers face." In suggesting direction for future work, he acknowledged the vital importance of technological expectations to production plans by appropriately recommending that ". . . measurement of production risk must precede analysis of production risk" and proposed formulation of . . dynamic, risk-neutral models as a first step." Similarly, Jolly (1983) noted the need for both a dynamic production model and a decision model that more closely depicts the actual management process in agriculture. In addition, he accurately perceived production as extremely site- and time-specific. He expressed that: . . . the gap between the rapidly growing economics literature on risk response and the farm management techniques and needs of producers and agricultural advisors is both alarming and discouraging. Like Antle (1983a), Jolly recognized the importance of technological expectations, since he suggested that in the future, ". . . greater attention must be paid to realistically modeling the technical and institutional relationships that determine the firm’s performance." Both Chambers (1983) and Holt (1983) concurred with the need for dynamic, stochastic production and decision models. Holt went further to join Johnson and Zerby (1973; Chapter X1) in suggesting that perhaps systems modeling and computerized simulation offer a viable alternative with relatively great potential. For the most part, nearly all the remaining production and decision model techniques developed and used in agricultural economics have been the result of the traditional model’s shortcomings. These approaches range from mild variations to those that are completely different. Again, each of the major categories will be reviewed (at least briefly) in light of the needs of livestock producers. 27 DIM" [IIUIEI'EO'S A stochastic production function model was applied to livestock by Anderson and Griffiths (1981). Strengths of their approach include provision of an expectation for production in the form of a probability distribution. This feature allows the decision maker not only to review the effects of particular inputs on the output level, but also to evaluate their effects on the amount of risk involved. However, serious limitations remain. First of all, the problems discussed above regarding the attempted comprehensive description of livestock production with a single, static function are present in this approach. Secondly, a decision model was not discussed and therefore the capacity for Optimization and sensitivity analysis is not apparent. Finally, the source of data for this exercise was across farms and over time. As a result, the site- and time-specific nature of livestock production bring a function with low predictive power, in spite of some complex mathematics. In summary, though stochasticity has been added to the production model and perhaps some useful insights result, the approach still lacks resolution, realism, precision. generality, and applicability. An approach which perhaps goes one step further is that of Antle (1983b), who incorporated both stochasticity and dynamics. The basic technique involved division of the relevant decision horizon into discrete stages, and then fitting a stochastic production function in each stage. These within-stage functions contained information from either previous stages, successive stages, or both. To achieve dynamics, then, the entire system of equations for the relevant decision horizon was solved simultaneously. The strengths of this attempt begin in similar fashion to those of the expected utility model: elements of both production and decision models are present, formal optimization is possible, sensitivity analysis around the recommended optimum should be possible, and random variation can be included. In addition, the simultaneous solution of the equation system allows the model to be dynamic. On initial inspection, this technique appears to have been successful in overcoming the most serious criticisms of more traditional efforts. However, significant ... e “U .- e' aux. I .. .2- .o-q .. .. o- '7‘] '- 1 A. A \.LI .1. J ._ .4 .. _ . ‘l ‘- . A. l. ‘ ‘ ‘.I '- ‘. .'\. .l u I " ._~ at H at . u \ ,5 .l.‘l .‘,.. 28 limitations remain because it is still assumed that production can be adequately described with a single, well-behaved, statistically estimated function (now within individual time periods) If the time periods are very small, the truly continuous nature of production can be effectively approximated. However, the single, statistical equation problem makes the accuracy of such a model questionable. Therefore, the resolution, realism, generality, and precision of the production model are again suspect. For the decision model the same small discrete periods vs. actually continuous issue applies. Further, even if dynamics are effectively achieved, the decision criterion is still limited to a single maximization. So, in spite of substantial progress, the decision model also remains limited. As an alternative to the single equation per discrete time period approach, Antle and Goodger (1984) proposed the use of several equations: one each to predict the first three moments of the output’s probability distribution. As with Anderson and Griffiths (1981), this approach is very insightful when evaluating the effects particular inputs may have on the probability distribution of output. Also, it might be possible to combine this approach with Antle’s (1983b) small, discrete period technique to approximate a dynamic system. However, the single, statistically estimated equation problem would remain, in addition to the problem of adequately representing the actual objectives of the decision maker and a potential lack of generality. Rather than pursuing small, discrete time periods to model the dynamics of agricultural production, Chavas, et al. (1985) have proposed a continuous time approach using differential equations to achieve technological expectations. Though their model was deterministic, the authors claim that stochasticity can be accommodated (at the expense of mathematical simplicity) This model should then be expected to perform better than those discussed previously, but the single, statistically estimated equation and the generality problems remain. Also, possible decision maker objectives are still somewhat limited. Nevertheless, the movement to a continuous time approach may represent significant progress when modeling physical production. However, it may not Iii. It... u... L... 29 be necessary for the decision model, since a decision horizon of one day (discrete time step) is quite consistent with most decisions made in livestock production. In summary, the theoretical work that has been done in attempts to make traditional production economics more realistic and usable for agricultural economists interested in farm management prescriptions has made important advances, but remains short of completion when the production of livestock is used as an analytical framework. The lingering areas of weakness are: 1. the complex process of livestock production cannot be captured in a single mathematical equation, even if that equation is stochastic and dynamic; 2. when using statistical techniques to arrive at a "best fit" equation to represent even a small facet of livestock production, it is imperative to incorporate familiarity with the underlying biological processes into the models; 3. once the relationships are estimated using data from one farm or from a controlled experiment, they cannot be expected to hold true for non- identical situations (either for different farms or for the same farm at different times); and 4. it is inappropriate to limit the decision maker’s objectives to a single optimization, whether cost minimization, profit maximization, or certainty equivalent profit maximization. Other analytical approaches will now be reviewed. 1' E . IV . i Outside the "mainstream" theoretical developments discussed thusfar, an analytical technique which has seen much use in agricultural economics is linear programming (LP) A good, basic presentation of the technique can be found in Anderson, et al. (1985) Generally speaking, LP contains an objective function to be maximized or minimized subject to constraints. The optimum solution (the production point that maximizes or minimizes the objective function) provides a recommended production plan. While it is not strictly necessary to specify an explicit mathematical production model, such a technological expectation is Often included either as part of the objective function or as part of the constraints. This is usually the case for LP models formulated to address livestock production. .4 .. I-A in. _.a “Vol ...I .. ‘g- 1 .u 30 Though LP provides both technological expectations (production model) and recommended production plans (decision model), the basic approach has several limitations. When considering the production model, all functional relationships contained in both the objective function and the constraints must be linear and deterministic, and it is assumed that these functions contain all the relevant information about production. Further, the relationships contained in the model are again typically determined from farm- or experiment-specific data and thus are not generalizable and the approach is generally static. These limitations present problems similar to those encountered when production economics assumes that a single, well-behaved, static, deterministic function estimated over a relatively narrow database can adequately represent production across similar farms. Considering the decision model, LP assumes that the objective function accurately represents the individual decision maker’s objectives. This presents limitations similar to those discussed for traditional production economics. As a result, the basic approach of LP as applied to livestock production generally lacks a certain degree of resolution, realism, generality, and precision in both the production and decision models. However, the capacities for formal optimization and rigorous sensitivity analysis make the approach attractive for its applicability. Various modifications of the LP technique have been aimed at one or more of the production and decision model limitations. Some examples follow. Traditionally, LP has been used to address a wide array of problems in agricultural economics. Probably the most common use of LP in the field has been to formulate least-cost rations for livestock production, considering only feed ingredients, their costs, and their nutrient contents. Pope and Heady (1983), having recognized the limitations of such a restricted problem formulation, broadened this approach to also include the availability and cost of labor and capital, while still maintaining a primary 31 focus on the nutritional program for feeding beef cattle. Their objective function maximized returns to labor and management over a single time period. In similar fashion, Klein, et al. (1986) have maximized a proxy for profit over a single time period for dairy cattle subject to constraints on nutrition, technology, labor, and capital. However, they have also recognized the fact that milk production is non- linear, and have accordingly separated the production process into several linear stages in an effort to approximate the curvilinear relationship that actually exists. Gutierrez-Aleman, et al. (1986a,b) have also taken technology, labor, and capital into account toward maximization of gross margin. But, in contrast to the single enterprise models, their approach reflects a recognition that management of livestock production frequently goes beyond strictly livestock considerations, and have thus included crop production and family factors in their whole-farm LP for small ruminant producers in Brazil. A similar whole "farm" approach was used by Thomas, et al. (1983) when they sought to maximize profits from reindeer herd management. This model also included such biological as factors for calf survival, calving percentage, and mortality rates. which all appear as functions of controllable management parameters. Reyes, et al. (1981) used comparable techniques when they maximized income- over-feed-cost (IOFC) for dairy cattle, since the technological realism of nutrition and seasonal variation were combined with attention to calving intervals and weight losses. They, like Klein, et al. (1986) have divided the production process into linear segments (a multistage approach) in an effort to approximate a curvilinear relationship. In addition, the importance of cash flow is recognized and is incorporated in the exercise. In a combination of some techniques previously discussed, Rozzi, et al. (1984) have formulated a whole-farm LP for a dairy which also has the option of raising beef and crops. This model considers land, labor, capital, and nutrition as constraints, and 32 adopts a multistage production approach in an attempt to circumvent potential nonlinearity problems. In contrast to the technique of formulating multiple production stages within a single time period as in Klein, et al. (1986), Reyes, et al. (1981), and Rozzi, et al. (1984) the method of Talpaz, et al. (1986) involved setting an extremely short time horizon (one day) for the entire linear model for feeding broilers, and then resetting model parameters and repeating problem solution. This procedure not only aspired to address nonlinearities, but also has partially overcome the static nature of LP through resetting parameters in each time period. Though not truly continuous, this segmentation of time provides a more dynamic framework which increases the realism in both the production and decision models. Janssen and Hassler (1981) combined this type of dynamic framework with multistaging for growing and finishing market hogs. Every two weeks the price expectations for inputs and outputs were reset and a new solution was Obtained. Again, the attempt was to minimize the effects of nonlinearities and to more adequately capture the dynamic nature of the system involved toward maximizing expected net revenue. Fawcett, et al. (19783,b) and Glen (1983) used similar procedures in considering growing pigs. However, a major difference with these works was their reliance on a biological description of the growth process. This interdisciplinary approach was used to maximize pig growth in Fawcett, et al. (1978a) and to minimize pig feeding costs in Fawcett, et al. (1978b) and Glen (1983) Though this increase of biological factors in the model offered more resolution to the formation of technological expectations, the models still lacked consideration of important factors such as disease and random variation. To this point, variations in the basic LP format have been discussed which attempt to overcome problems with nonlinearities and dynamic relationships that occur 33 in the reality of livestock production. Also, the incorporation of biologically formulated production models has attempted to address the assumption that a single mathematical function can capture the resolution necessary to model livestock production accurately. However, the fact that LP optimizes only a single objective function remains as a serious hurdle, since decision makers may hold several objectives concurrently in reality. For this purpose, goal programming has been developed. W Goal programming has been described by Romero and Rehman (1984) and involves incorporation of more than one decision maker goal into the modeling process. Basically, either the goals are simply prioritized and the problem is solved lexicographically, or a weighting scheme is applied according to relative importance of the various goals and the problem is solved using appropriate penalties. Rehman and Romero (1984, 1987) suggested using this technique for feeding livestock, while Sandiford (1986) proposes a use in fishery management. Finally, when the precision and generality of these various LP-based modeling procedures are reviewed, it can be seen that applications of the techniques are invariably founded on data of limited scope. This problem was also encountered with production economics. Because the data are collected either in an experimental setting or across a very limited number of commercial farms, model applications should be carefully limited to the confines of the environment in which the data were collected; generalization to non-identical situations must be avoided. In summary, linear programming suffers from many of the same limitations as traditional production economics if used only in its basic form. However, the technique is flexible and achieves some success in addressing theoretical criticisms through technique modification. As discussed previously, an analytical technique must possess several important qualities to be truly useful in formulating technological expectations and production plans in the livestock industry. It should contain the resolution of biological accuracy, financial management, and actual decision maker objectives. It e"{ M‘- ‘O4 0,. n .- N o. .‘s. .t .. i s 1.‘ 1 I! r I . 34 needs the realism of random variation and dynamic interactions. Precision is necessary for the technique to be useful on particular farms, and generality is required to be useful across farms. Optimization and sensitivity analysis help achieve applicability. Through the modifications discussed, LP makes considerable progress toward meeting these goals, but shortcomings remain. Other major approaches to decision and production modeling will be considered next. e ' ' el Because of the difficulties encountered when attempting to model agricultural production accurately, agricultural economists commonly use analytical models which are solely decision-oriented. In such cases, the formation of technological expectations (TES) occurs independently from the analytical decision process. Those techniques commonly used in this fashion include budgeting exercises (both total and partial), decision trees, and dynamic programming. Budgeting. Application of budgeting to agricultural production was discussed in Harsh, et al. (1981) Budgets of some form are used in the day-to—day management of virtually all livestock production. Budgeting can be considered a "passive" decision model, in that the process doesn’t actively recommend the best production plan, but merely compares alternatives. However, once the expected financial outcome of the production choices being considered is known, the individual decision maker involved can then choose the alternative which most completely achieves his/her objectives. In this manner, budgeting implicitly employs the decision maker’s utility function without requiring its explicit specification. The decision maker can effectively weigh the financial aspects of the budget with the associated non-financial aspects en route to maximizing his/her utility over the production options considered. When reviewed in this theoretical light, budgeting offers a technique which should be extremely attractive to economists interested in the welfare of producers. Strengths of budgeting include wide applicability, ease of use, and passive decision model. The implicit use of utility functions avoids misspecification errors. ocu‘ ; , . \ Ll. - .\.44O! A .- ~- 6. 7" ! 1 — I Q. ‘ ‘.' ,n 35 Either adjustment or absolute production questions can be addressed. Uncertainty can be included by repeating the process over the range of possible values for the uncertain variable(s), and then associating a probability of occurrence with the respective outcome. The realism, resolution, and precision of this approach are limited only by the caliber of the underlying price and technological expectations. Also, because each application mandates individual farm data, the technique can achieve excellent generality; it can be applied in virtually all farming situations. Though use of individual farm data provides certain strengths, the requirement for such data might also be viewed as the major source of limitation for budgeting. With only information from a single farm, the total input and output effects of proposed production changes can be troublesome to accurately project if such alternatives have not been previously employed on the farm being evaluated. As a result, accurate TEs can be difficult or even impossible to achieve for certain proposed changes in production. Thus, problems with generality and precision can occur. Finally, sensitivity analysis is possible with budgeting exercises, but is often time consuming. This is due to the fact that the sensitivity of outcome to various production factors can only be assessed by repeating virtually the entire budgeting exercise. In this regard, the assistance of a digital computer can be invaluable. Partial budgeting (Harsh, et al., 1981) can also expedite this process But caution must be exercised so that important changes in inputs and/or outputs are not inadvertently omitted. Decision Trees. Another decision model which is used in agricultural production is the decision tree. A review of this technique can be found in Anderson, et al. (1985) Examples of application to livestock production are provided by Fetrow, et a1. (1985) and Parsons, et al. (1986) The decision tree is more of an "active" decision model than budgeting, since a definite course of action is recommended based on its estimated monetary value (EMV) (Anderson, et al., 1985). i v' 3"?»- uns . . -.. -)s I. . .. u 1‘ ‘I 1‘ “, h L N. n ., ‘I ., v. . a .V' v \ .A 4 ‘fi {)1 . -\_ 36 Strengths of this technique include its relative ease of use and wide applicability. Again, adjustment or absolute production options can be considered. Also, the EMV incorporates a measure of the uncertainty involved. However, the decision tree holds several serious limitations. Underlying production model notwithstanding, the realism, resolution, and precision of this decision model are questionable. Though the EMV does incorporate uncertainty, its ability to accurately represent the decision maker’s utility function or personal objectives is arguable at best. Further, the need for individual farm data can lead to precision and generality problems as discussed previously with budgeting exercises. Finally, sensitivity analysis is again achieved only through iterative analysis and true global optimality is once more impossible due to the inherent limitations of comparing production alternatives. Dynamic Programming. The last decision model to be considered is dynamic programming (DP) This technique, mentioned briefly in the linear programming section, deals with sequential decision problems and is explained in detail by Bellman and Dreyfus (1962) and Bellman and Kalaba (1965) In its pure form, DP involves the charting of a time-path for decision maker action over discrete steps, and is an "active" decision model since a specific time-path is recommended as the best action(s) among available alternatives. The relative quality of DP as a decision model depends heavily on the attributes of the underlying calculations. Thus, the degree of realism, resolution, precision, and generality depend on these calculations, and how well they relate to the actual objectives Of the decision maker involved. However, the technique is quite flexible and applicable to a broad array of sequential decision problems. Examples of the application of DP to livestock production include the work by Glen (1983) cited above, which used linear programming to perform calculations over time in addressing problems associated with growing pigs. Also, Dijkhuizen, et a1. (1985, 1986) employed DP to address livestock replacement questions for dairy cattle and swine, 37 respectively. These authors use partial budgeting and net present value techniques to perform the underlying calculations. In summary, budgeting, decision trees, and dynamic programming are analytical decision models that require additional information on physical production if they are to be effectively used to support livestock production decisions. While each technique has individual attributes and limitations, the quality of the decision support provided is very closely tied to the underlying information quality. WW Having recognized the need for a better understanding of livestock production’s biological and physical aspects, one group of scientists has endeavored to construct mathematical models which accurately represent the relationships involved. This group is largely composed of animal scientists and agricultural engineers, with scattered input from statisticians, physiologists, economists, and veterinarians. For the most part, the resulting models are solely concerned with production, avoiding the analytical decision questions. Such modeling efforts exist for virtually all livestock species. In general, biological production models seek to overcome the criticism that other techniques attempt to describe the complex livestock production process with a single, well-behaved mathematical equation. Though stochasticity is not a common ingredient in these biologically-based models, most are calculated over a small enough time period (usually one day) to effectively achieve dynamics. Overall, three primary considerations that affect the relative realism, resolution, generality, precision, and applicability of these models are: 1. the trade-offs between deduction, empiricism, and statistics, as introduced earlier and discussed by Dent (1975), Bywater and Baldwin (1984), and Whittemore (1986), 2 the model’s biological focus (such as nutrition, reproduction, or growth/production), and 3. the model’s intended use. Major efforts will be reviewed according to the species being modeled. 38 Broilers. Reece and Lott (1983) used linear regression to build a statistical model of growth for broilers. The model’s limitations arise from predicting body weight solely as a function of age over a range of environmental temperatures. Also, the model depicted a discrete entity (or single animal) without regarding interactions with the remaining population. Consideration of factors such as nutrition, sex, health, and random variation were not included. Sheep. Orsini and Arnold (1986) presented a more deductive model of growth for grazing sheep. They included some age and nutritional considerations, but fail to address environment, disease, sex, flock, and random variation. Beef. A similar model for growth of feedlot beef cattle is found in Oltjen, et al. (1986) This model sought to explain growth as a function of body type and dietary metabolizable energy. However, again environment, disease, sex, herd, and random variation were not included. Loewer, et al. (1983) also used a basic deductive approach to model the growth of feedlot beef cattle. In contrast to Oltjen, et al. (1986), this model considered a broader array of nutritional factors (metabolizable energy, digestible protein, and percent dry matter) along with the effects of temperature, humidity, sex, and pregnancy. Still, the model failed to include disease, herd, and random variation. Dairy. Switching to dairy cattle, Koong, et al. (1982) constructed a deductive model for growth and milk production. This model included nutritional nitrogen and energy, giving much attention to metabolism and energy flows. Limitations arose as a result of exclusion of environment, disease, herd, and random variation. Hulme, et al. (1986) also considered dietary protein and energy in a more empirical model of dairy production, and sought as well to include the effects of genetic potential and pregnancy. However, once again disease, environment, herd, and random variation were not included. ’TI 39 Swine. The last class of production models to be reviewed are those for hogs. Quijandria and Robison (1971) have formulated a statistical model of hog growth, regressing weight on age and regressing backfat on both age and weight over a single growth phase between 119 and 154 days of age. The consideration of only a single growth phase prohibited dynamics. In addition, environment, sex, disease, herd, nutrition, and random variation were excluded. Close and Mount (1971) and Verstegen, et al. (1973) also used a statistical approach to estimate the relationship between growth and environmental temperature in hogs. In subsequent papers, Close and Mount (1975, 1978), Close, et al. (1978), and Close (1978) evaluated growth as a statistical function of environmental temperature and dietary metabolizable energy. Other factors were not considered. Finally, Irvin, et al. (1975) also used statistics to model hog growth in relation to dietary protein level and certain genetic factors. Teter, et al. (1073) used a deductive approach, but have also formulated hog growth as a function of environmental temperature and metabolizable energy intake. The deduction was broadened by Bruce and Clark (1979) to include the effects of air velocity, floor type, body weight, and group size along with metabolizable energy and environmental temperature. However, sex, disease, herd, and random variation were still lacking. Christianson, et al. (1982) considered virtually the same factors as Bruce and Clark (1979), but focused on estimating compensatory growth. Moughan (1985) concentrated on the deductive relationship of nutrition to hog growth. Though other factors were not included, their sensitivity analysis indicated that values used for the maximum rate of protein deposition and the energy required for maintenance were extremely critical to the outcome of such production models. Finally, Whittemore (1983) developed a model of hog growth based partly on deduction and partly on empiricism. Explanatory factors included dietary protein and digestible energy, environmental temperature, floor type, air movements, and hog body d-w_.__. a-Ivv 5" .0" -..a. .~-. 4... 40 type and sex. Though disease, herd, and random variation were excluded, this model is probably the most complete of all those reviewed in its representation of hog growth. In summary, the models reviewed whose sole focus is the actual livestock production process are quite diverse in their basic approach and in the explanatory factors included. As a result, the relative degree of realism, resolution, precision, generality and applicability that each achieves is highly dependent on the model’s intended use. 0 t ' ' ti l The final type of analytical model to be reviewed for livestock production is computerized simulation. Under its broadest definition, a computerized simulation model is nothing more than computerization of virtually any mathematical model to allow ease of implementation, analysis, and solution. The underlying model can be anything from a simple budget (purely decision) to an extremely complex biological production model (purely production) It can be static or dynamic, and can be deterministic or stochastic. In this regard, the computer should be viewed only as a tool to assist computation, and the caliber of the simulation’s performance should be solely attributed to the quality of the underlying mathematical model. So, once again, the attributes of a computerized simulation model are evaluated by reviewing the attributes and limitations of a given mathematical production or decision model in light of its intended use. However, because of the enormous computational power involved, these computerized simulation models theoretically hold a much greater potential for accurately modeling a complex production or decision process. Examples of many computerized simulations which have been constructed for livestock production can be found in the literature. In order to structure the process, the following specific categories were evaluated during review: Nutrition—Does the model consider quantity and/or quality of feed involved? Environment—Does the model contain environmental effects? 41 Reproduction—Is reproduction considered? Disease—Are factors included to account for common diseases? Time step—How frequently does recalculation occur? This is critical to the model’s relative ability to represent dynamic situations. Stochasticity—Does the model contain random variation? Aggregation—Does the model focus on the individual or the population? If concern is with discrete individuals, are dynamic interactions within the population considered? If concern is with the aggregate population, is the distribution of individual characteristics maintained? Sensitivity analysis—Is sensitivity analysis performed? Decision model-Is the decision model active or passive? Economics—Does the model contain explicit economics of any type? Within each of the mentioned categories considerable variation can occur, and the resulting realism, resolution, generality, precision, and applicability will change accordingly. However, the list provides a useful format for at least cursory evaluation of computerized simulation models for livestock production and provides a structure by which the important attributes can be reviewed. Though the current review will not be exhaustive, most major efforts will be included. For organizational purposes, review of the major features is summarized in Tables 2.1. 2.2, 2.3, and 2.4. Models concerned with broiler, sheep, and beef production have their resolution summarized in Table 21 and their realism and applicability are reviewed in Table 2.3. Evaluation of the resolution of models for dairy and swine production can be found in Table 2.2, while their realism and applicability are addressed in Table 2.4. Within the criteria of realism, resolution, precision, generality, and applicability provided, computerized simulation models Offer a valuable contribution both to the management of livestock production and to corresponding research (Lloyd, et al., 1987b) Prospective management changes can be easily evaluated at a relatively small marginal cost. This capability is useful for diagnosis, prediction, and prescription in farm .3-.. . 3- ‘ .A I P‘s ~\l“ n h‘_h 1,“... . F s. n '1. .j‘ s I '1' 42 Table 2.]. Summary of resolution attributes for computerized simulation models in broilers, sheep and beef cattle production Author(s) Nutrition Environment Reproduction Disease Broilers Aho & Timmons, - + NA _ 1985 Sheep Meek & Morris, 1981 + + NA + White, et al., 1983 + + + _ Blackburn & + - + _ Cartwright, 1987 Beef cattle Clarke, et al., 1982 + — + _ Levine & + _ + _ Hohenboken, 1981 Sere & Doppler, 1981 - — _ - Fox & Black, 1984 + + NA - Congleton, Jr. & - — + + Goodwill, 1980 Sanders & + .. + _. Cartwright, 1979 Halter & Dean, 1965 + + NA - Kahn & Spedding, + — + _ 1983. 1984; Kahn & Lehrer, 1984 Forster, et al., 1984 + - NA — Oltjen, et al., 1986 + — NA .. Johnson & Notter, — + + _ 1987 + = present; — = absent; NA = not applicable 43 Table 22 Summary of resolution attributes for computerized simulation models in dairy cattle and swine production Author(s) Nutrition Environment Reproduction Disease Dairy cattle Bywater & Dent, 1976; + — + _ Bywater, 1976 Oltenacu, et al., 1980 - — + _. Oltenacu, et al., 1981 - — + _ Lovering & McIsaac, + + _ .. 1981 Gartner & Herbert, — — + _ 1979, Gartner, 1981, 1982a,b Boneschanscher, - — + ._ et al., 1982 Forbes, 1983 + — _ _ Congleton, Jr, 1984; + — + + Congleton, Jr. & King, 1984 Arendonk, 1985 + — + _ Brockington, et al., + — + _ 1983, 1986 Swine Watt, et al., 1987 + + NA - Blackie & Dent, 1976 + ._ NA _ Jolly, et al., 1980 + — + _. Allen & Stewart, 1983 + - + - Tess, et al., 1983a,b,c + — + _ McPhee & Macbeth, 1984 + — + _ Macbeth & McPhee, 1986 Singh, 1986 + — + _ ¥ + = present; - = absent; NA = not applicable 44 Table 2.3. Summary of realism and applicability attributes for computerized simulation models in broiler, sheep and beef cattle production Time Sensi- Step Random Aggre— tivity Decision (days) variation gation analysis model Economics Broilers Aho & Timmons, 1985 ? — AE + P + Sheep Meek & Morris, 1981 7 + DE + P + White, et al., 1983 7 + DE + P + Blackburn & 15 - DE + P — Cartwright, 1987 Beef cattle Clarke, et al., 14 — DE + P + 1982 Levine & ? - DE + P — Hohenboken, 1981 Sere & Doppler, 1981 ? - AE + P + Fox & Black, 1984 1 - DE + P — Congleton, Jr. & 7 - AE + P - Goodwill, 1980 Sanders & 30 -- AE - P - Cartwright, 1979 Halter & Dean, 1965 ? — AE + P + Kahn & Spedding, 1-30 + DE + P — 1983, 1984; Kahn & Lehrer, 1984 Forster, et al., 1 -+- AE + P + 1984 Oltjen, et al., .01-100 + DE + P - 1986 Johnson & Notter, ? + DE + P - 1987 ? = unclear; — = absent; AB = aggregate entity + = present; P = passive; DE = discrete entity 45 Table 2.4. Summary of realism and applicability attributes for computerized simulation models in dairy cattle production Time Sensi- Step Random Aggre- tivity Decision (days) variation gation analysis model Economics Dairy cattle Bywater & Dent, 1976; 1 - DE + P - Bywater, 1976 Oltenacu, et al., 1980 1 + DE + P — Oltenacu, et al., 1981 1 + DE + P + Lovering & McIsaac, 365 — AE + P + 1981 Gartner & Herbert, 365 — DE + P + 1979; Gartner, 1981, 1982a,b Boneschanscher, 365 — AE + P + et a1, 1982 Forbes, 1983 .0007 — DE - P — Congleton, Jr, 1 + DE + P + 1984; Congleton, Jr. & King, 1984 Arendonk, 1985 ? + DE + P + Brockington, et al., 1 + DE + P + 1983, 1986 Swine Watt, et al, 1987 1 — DE + P + Blackie & Dent, 1976 ? — AE + P + Jolly, et al., 1980 14 — ? + A + Allen & Stewart, 1983 1 + DE + P — Tess, et al., 1983a,b,c 1 - DE + P + McPhee & Macbeth, ? - ? + P + 1984; Macbeth & McPhee, 1986 Singh, 1986 1 + DE + P + -- == absent; DE = discrete entity; + = present; P = passive AB = aggregate entity; ? = unclear; A = active .,.l; 4.1 .4... ‘|.. e u o. .5. -\ Q 46 management. Further, if models based on current knowledge of the production processes involved fail to predict accurately, effective sensitivity analysis can focus research attention on the areas where existing knowledge is weak. Because of the computational power involved, use of computers should promote the inclusion of increased realism, resolution, generality, precision, and applicability in production and decision models for livestock agriculture. When coupled with their potential for information management (data entry, storage, and processing and information output), computerized simulations should greatly enhance livestock decision support systems. SUMMARY Various information management and analytical modeling techniques have been applied to livestock production. Individual strengths and weaknesses have been discussed. In an attempt to expand upon these strengths and alleviate the weaknesses, a unique decision support system for growing and finishing hogs will be presented in the remaining chapters. The decision support system will emphasize livestock health management. CHAPTERS INFORMATION MANAGEMENT SYSTEM INTRODUCTION As presented in the literature review of Chapter 2, decision support for livestock production requires information on the specific farm of interest and a (set of) mathematical model(s) to analyze its technological aspects in light of decision maker Objectives. In an effort to provide such support, this research was undertaken to evaluate the individual usefulness and potential for integration of various information management, systems modeling, and computer simulation techniques when applied to livestock production. The goal is to develop a prototype decision support system for production of growing and finishing hogs with emphasis on animal health management. This chapter describes the portion of the resulting decision support system which corresponds to a more traditional information management system. Both design features and initial data collection results will be included. Successive chapters will discuss analytical model development and results from initial applications of the composite decision support system, respectively. MATERIALS AND METHODS Swine Health Information Magagement System The specific information management system (IMS) to be described was developed as part of the Swine Health Information Management System (SHIMS) SHIMS was a cooperative research project between the United States Department of Agriculture’s Economic Research Service (USDA-ERS) and Michigan State University (MSU) whose main goal was to design a prototype information management system for 47 48 swine production with emphasis on health management. The initial project was conducted from July 1, 1985 through June 30, 1988 and enjoyed the additional support (both cooperative and financial) of the USDA’s Food Safety and Inspection Service (FSIS), the Michigan Agricultural Experiment Station, the Michigan Cooperative Extension Service, and the Departments of Agricultural Economics, Animal Science, and Large Animal Clinical Sciences at MSU. Specific objectives were to: a. design a swine health information management system to monitor disease frequency and assess the effects of disease problems and treatments on swine growth and production costs, b. implement the system in cooperation with a sample of swine producers at their places of business, and c. monitor the implementation in order to allow assessment of the benefits and costs of the system. These objectives were to be achieved by collecting relevant data regarding the production, finance, and health of hogs commercially produced in Michigan. Initially, a pilot producer group was to be selected based on willingness to participate. To provide improved statistical capabilities, the pilot phase was to be followed by an expanded sample phase involving 75-80 herds selected by multistage random techniques. To facilitate pilot group selection, an informational meeting and presentation on SHIMS was held in Battle Creek, Michigan on February 26, 1986. Battle Creek was chosen because of its proximity to the primary hog-producing region of the state. Twelve hog producers were invited to this meeting with SHIMS personnel from MSU. Invitations were issued based on knowledge held by either MSU staff or extension agriculture agents regarding producer attitudes toward progressive production practices, university activities, and cooperation. Of those 12 producers, 6 were interested in Voluntary participation, and thus formed the pilot group. All six members of the SHIMS pilot group produced hogs in farrow-to-finish, total confinement systems. In addition to hogs, two participants had beef cow-calf vi 49 enterprises and all six raised feed crops. During the pilot phase, the smallest producer’s mean herd size was 125 breeding females, while the largest was 1365. All producers used crates for farrowing, and the number of crates per farm ranged from 24 to 296. Typical of a large proportion of the state’s market hog production, SHIMS pilot producers were located in the south-central and southwest regions of Michigan’s lower peninsula, including Branch, Jackson, Eaton, and Kalamazoo counties. Pilot participants were not compensated monetarily, but received the benefits of interaction with university personnel, periodic farm management reports, and quarterly market hog health evaluation at no charge. These aspects provided producer motivation for data collection. Though the broad array of support attained across academic disciplines and government agencies indicated wide acceptance of the project’s goals, it also provided administrative difficulties. Interagency and interdepartmental communication gaps coupled with a certain amount of "disciplinary territoriality" somewhat hindered the project’s progress. One direct result is the fact that an expanded sample (75-80 herds) was not attained during the period of the initial project as had been intended. However, most of the administrative hurdles were successfully overcome through concerted efforts toward maintaining adequate communication and promoting a healthy atmosphere of mutual intellectual respect. Without such personal contributions by virtually all project participants, the achievement of any level of success might well have been precluded. The first step in designing the IMS was to define the data to be collected. For this purpose, the advice and assistance Of a broad array of individuals was sought. This was accomplished through formation of an "expert" advisory group consisting of slaughter industry representatives, marketing agents, extension agriculture agents, producer group representatives, and government officials. A summary of the affiliations Of those people who have contributed to SHIMS in various capacities appears in Table 3.1. 50 Table 3.1. SHIMS participation in Michigan, 1986 to 1988 Affiliation Number of people University Direct participants Additional resource people Extension agriculture agents Slaughter industry Producer group representatives Federal government-USDA State government Marketing agents Pilot producers Practicing veterinarians Total % hawwmumnfim Only data thought to be important for monitoring the frequency of disease and in assessing its economic importance to production were considered for collection on SHIMS. Arriving at a precise definition of this body of data was extremely difficult due to two factors: (1) the problems being addressed were multidisciplinary in nature, and (2) the broad array of "experts" provided an equally broad array of implicit research objectives. The multidisciplinary nature of the problems necessitated the broad group of participants, but also resulted in a multiplicity of discipline-related sub-objectives all within the project’s broader scope. Though at any specific juncture the scope of the database may have seemed to be very discretely defined, in reality the development was (and continues to be) a dynamic, iterative process. Figure 3.1 provides an organizational scheme for the SHIMS project. Sources of data are included categorically at the top of the figure. In addition to elucidating the spectrum of data collection, Figure 3.1 also points out the destinations for the resulting information. Once again, it is crucial to note that not only did the IMS support a summary report and an analytical model, but it also provided basis for perpetual system re-evaluation. Feedback from producers and advisory group members has been invaluable in this regard. 51 Form ’ ‘ m I Herd Production Finaleial Visits . Records .. ‘ Records ’ Historical Slaughter Information Data Ancillary ENTET 0‘“ garnet" Forms \, ta PROCESS oars ‘/ usu“ sumac: luronumou Q... MM Investigators Group System Quarterly Fornt Evaluation Management Reports Quarterly Newsletter I“lenient State Uls'verslty “Prices and weather Figure 3.1. SHIMS organizational scheme 0 -far 0 ti Once the important data were defined, techniques were needed to facilitate both ease and accuracy in collection and processing. Development of the forms to be used involved several steps. First, several existing data collection systems for swine production were examined to discover features desirable for SHIMS Included in the review were PigCHAMPc, Swine Graphicsh, Telfarmi, and the Michigan Swine Record- Keeping Project (Schwab and Hogberg, 1983) Useful attributes were then combined and modified in an attempt to meet the specific needs of the SHIMS project. Finally, the resulting collection of modified forms was compared to the predetermined definition of the desired data. Where differences occurred, original forms were designed to fill the voids. The entire body of data collection forms appears in Appendix A. In addition to supporting the project’s objectives, specific concerns in their development were: (a) the completeness of the data to be collected, (b) the ease of data collection, (c) the format in which the data were to be collected, and (d) the ease of data processing. '1" HI t...” :‘I “9' lab .la . .so. . .1. ea— ". '- .-. “1 1‘... . .s._ .7, 1 . >- ‘.‘:v.’ ~'-. I ‘ p .. g. . ,It . uq ' ‘hb I... 0 H ‘s. "‘-< I . N. \‘ ._ . l, :1 we. .' . , ‘s .‘o. . .,~ , M g . 9. e. I .1 'I 'I I D 'I, p“ . \' ‘e ‘ 8 ‘4 . r 52 Once these forms were developed to the satisfaction of SHIMS personnel, they were combined with a producer consent form containing a guarantee of confidentiality and then submitted to the University Committee for Research Involving Human Subjects at MSU. Approval of the project and its data collection forms was achieved from the committee following initial submission; no changes were requested, nor was pretesting required. Data regarding general management practices, historical disease problems, current herd observations, nutrition, and facilities were obtained on an initial farm visit by MSU personnel. Since this required walking through all swine facilities, extreme caution was exercised to maintain the highest possible disease control standards. Including travel, interview, facility tour (with herd observation), and attending discussion, these visits generally consumed a total of 6 to 8 hours per farm. Following the farm visit, SHIMS producers initiated data collection in the areas of physical production, marketing, feed usage, death losses, and production expenses according to the respective data collection forms presented in Appendix A. For the pilot group, the exact techniques employed varied by producer according to individual record systems which may have already been in place. SHIMS forms were used where other systems were not employed, but flexibility was maintained to minimize double recording by SHIMS producers. As a result, the mechanics by which these data were supplied to MSU personnel varied between producers according to time schedules and data formats. The primary strength of SHIMS on-farm data collection centers on the comprehensive, well-structured approach to production. The system combines aspects of facility description, physical productivity, and economics in a single database, reflecting the multiple disciplines involved. However, some notable limitations exist regarding certain areas of excessive data, other data insufficiencies, and a few weaknesses in collection technique. Since the goals of the SHIMS project focus solely on the health and production of market hogs, rvw‘f 53 data regarding other aspects of swine production are probably excessive. While the importance of breeding herd and preweaning management is readily acknowledged, its immediate relevance to the health and productivity of growing/finishing pigs is limited. Maintenance of these extra data has definite potential benefits for producers and might prove useful to researchers at some future point, but the associated collection efforts tend to jeopardize the quality and quantity of the remaining data, which are more important to the immediate SHIMS goals. On the other hand, thorough economic evaluation of grow/finish hog health management should probably encompass several potentially relevant areas not included in the SHIMS database. These include the firm’s debt load and cash flow characteristics, clinical animal health observations (antemortem and on—farm), further subclinical observations (through subsampling and laboratory testing), and description of the micro environments of the growing pigs involved. Because of limitations in data collection resources and capabilities, these admittedly important factors have necessarily been excluded from SHIMS. Finally, technique flexibility is an integral part of a pilot data collection project, but each additional data format and submission routine represents considerable added entry/processing effort. Further, structured techniques for periodic update of descriptive data need to be developed. These would capture any potentially important changes in facilities and/or management. Off-farm Data Collection Sources of SHIMS data also existed outside the farm (see Figure 3.1) The first of these is "External Data" and refers to time series data sets of importance to hog production, but maintained independently from SHIMS. Included in this category are weather and price data. These data can be retrieved from the National Weather Service and the USDA if warranted by future analyses. The other major, off-farm data are obtained through postmortem health evaluations. Of primary concern are the so-called "production diseases," which are rarely al.-n. 1") win. I a. \I. Q "h' n e ‘1“... e.¢~ \- e I. , .g“‘ ' O l— 7-». 54 evident clinically, but are generally thought to have major effects on production efficiency. The presence or absence of these diseases by degree of severity is noted at slaughter and is then included in the database. Table 3.2 provides a description of the categories used for SHIMS. Development of the technical aspects of data collection at slaughter was a two- stage process. Initially, a slaughter health check (SHC) was conducted for the sole purpose of evaluating potential data collection procedures. After this was complete, the techniques required refinement before application to SHIMS pilot herds. Conducted on November 5, 1985, the initial SHC involved a group of 12 hog producers selected only because they were interested in the health status of their slaughter hogs, and not based on a special potential for eventual SHIMS pilot herd status. As a result of this "trial run," the importance of maintaining the regular marketing channels of potential SHIMS producers became apparent. Several producers in this first group (whose usual marketing patterns were disrupted by the slaughter check) were not content with the prices they received for their hogs, and as a result expressed hesitation about future participation. Thus, an integral part of the SHC technique modification was to maintain producer satisfaction with market price by following hogs through their routine marketing channels. This involved: 1. establishing communication with intermediate hog marketing agents (since only 15—20% of hogs produced in Michigan are marketed directly to slaughter), 2 gaining approval from a wider array of slaughter plants, some of which were not previously accustomed to SHCS, and 3. development of SHC evaluation techniques to accommodate successful data collection at high processing line speeds. High speed capabilities were necessary because a large proportion of the slaughter hogs 55 Table 3.2. SHIMS disease classification at slaughter, Michigan, 1985 to 1988 Category Score Description Percent of total Pneumonia lung volume involved None 0 0 Mild 1 1-9 Moderate 2 10-19 Severe 3 20+ Number of scars on Ascariasis diaphragmatic surface None 0 0 Mild 1 1-5 Moderate 2 6-10 Severe 3 11+ Mange Location of papules None 0 absent Mild 1 axillary/inguinal Moderate 2 diffuse ventral Severe 3 entire carcass Average size of ventral Atrophic Rhinitis nasal meatus in millimeters None 0 <5 Mild 1 6-7 Moderate 2 8—9 Severe 3 10+ Pleuritis Degree of pleuritis Absent 0 none Present 1 any Pericarditis Degree of pericarditis Absent 0 none Present 1 any Degree of nasal Nasal septum deviation septum deviation Absent 0 none Present 1 any 56 produced in Michigan are normally processed in high speed slaughter facilities (line speeds in excess of 1000 hogs per hour) The slaughter facilities involved in the pilot phase of the SHIMS project are descriptively listed in Table 3.3. As discussed, these plants were selected based the usual marketing patterns of the SHIMS pilot producer group. Though eventual cooperation was excellent in all plants, initial participation was offered with considerable hesitation in those plants unaccustomed to routine SHCs. Based on the volatility of the retail pork markets, resulting from recent chemical residue and bacterial contamination issues, and in light of the large financial losses which would be associated with virtually any disruption of routine processing procedures on high-speed lines, this initial hesitation was entirely understandable. Management personnel in these plants warrant commendation for being willing to take a chance and contribute to this research project in an attempt to benefit the entire industry. At the same time, any researchers potentially involved in similar efforts need to continually recognize and respect the risks that slaughter plant management assumes when their participation is offered. During the pilot phase, SHCs were completed four times per year for each SHIMS producer. For organizational purposes, calendar years were divided into four quarters: January to March, April to June, July to September, and October to December. Within each quarter, market hogs were selected based on both the normal marketing schedule of individual producers and administrative convenience for MSU personnel conducting the postmortem evaluations. Once a workable date was identified for a given producer, as many of the hogs marketed on that day were evaluated as was technically feasible. Total size of groups evaluated during the pilot phase ranged (approximately) from 60 to 400 hogs. In general, the technical limitations occurred in three areas: marketing coordination, hog identification, and actual postmortem evaluation. ,-_‘ ~¢ .9... 'I‘ 0“.. 57 Table 33. Participating SHIMS slaughter facilities, 1985 to 1988 Approximate Name Location Processing Speed (Piss Per hour) Utica Packing Company Utica, Michigan 1000+ Thornapple Valley, Inc. Detroit, Michigan 1000+ Frederick Division Dinner Bell Foods, Inc. Troy, Ohio 350+ Wilson Foods, Inc. Logansport, Indiana 350+ The usual SHC activity sequence follows. 1. The producer was contacted to establish acceptable market date and to request that hogs be tattooed. If hogs were not marketed directly to the slaughter plant, the marketing agent was contacted to: a. confirm the acceptability of date, b. ascertain location of slaughter (given producer and date), and c. arrange the time of arrival for hogs at slaughter plant. The slaughter plant was contacted to confirm acceptability of date and time with hog buyers and management. USDA—FSIS inspectors were contacted to confirm acceptability of date and time. MSU personnel traveled to plant and conducted SHC. SHC findings were summarized and the report was sent to: a. the producer, b. the producer’s veterinarian, c. the producer’s extension agent, and d. USDA-FSIS personnel. 58 When possible, hogs from two producers were combined on the same day to decrease total travel time required by MSU personnel. However, such combinations effectively increased the difficulty of coordination, since not only did producer market schedules need to mesh, but also the types/weights of hogs marketed needed to be similar to permit marketing at the same plant. Again, because pilot producers were not compensated for their participation, it was extremely important that routine marketing procedures be followed, so that neither a lower market price nor any other disruption of established market conditions could be associated with the SHCS of SHIMS. The excellent cooperation and support of marketing and slaughter plant personnel has been vital to coordination in this regard. The proportion of a producer’s hogs identifiable at slaughter directly affected the scope of SHC which was possible on any given occasion. Identification problems were found to hold serious potential for limiting data collection. For instance, sometimes as few as 20% of the desired hogs were definitively identifiable at slaughter. These problems were more common during the first two quarters of data collection, when maintenance of discrete hog groups by producer was essential due to absence of tattoos. To minimize such difficulties, the cooperation and support of marketing and slaughter plant personnel again provided invaluable contributions. From the third quarter of data collection until the end of the project, SHIMS market hogs leaving the farm for slaughter and subsequent health check received a back-slap tattoo to facilitate identification. Tattoo hammers were provided as part of a hog identification trial funded by the National Pork Producers Council (Thulin, et al, 1988) These tattoos eliminated large scale identification problems. However, grouping hogs still made data collection easier due to the high-speeds at which processing occurred. It is important to note at this point that the techniques available for identification coupled with the high-speed processing precluded individual hog identification. Therefore, use of disease rates (specific point prevalences), rather than individual hog disease conditions was mandatory. '9': . on. 1‘,” 1 unit at ,\ -‘ o . 1 ...,. ‘ A 59 The stage of processing where health status of the viscera, carcass, and head were evaluated at slaughter depended on individual plant characteristics. These are summarized along with the possibilities of concurrent observations in Table 3.4. Also, SHC personnel requirements varied from plant to plant. These are summarized in Table 35. Table 3.4 Stage of processing and possible concurrence for SHIMS slaughter health check observations, 1985 to 1988 Mam Utica Thornapple Dinner Wilson Observation Packing Valley Bell Foods Foods Site of evaluationa Viscera PF PF PF PF Carcass C or PF C or PF PF C or PF Head C C PF PF Posible concurrent observations Viscera and carcass yes yes yes yes Head and carcass yes yes no no Head and viscera no no no no 8C = cooler; PF = processing floor In all plants, the condition of the viscera was evaluated at the processing floor’s viscera tray following FSIS inspection. Pneumonia and ascariasis were the primary focus of this observation. However, other conditions normally noted by FSIS were also recorded at this point, including abscess, pleuritis, pericarditis, and peritonitis. Due to line speeds, two persons were often required at the viscera tray, one to observe pathology and one to record the observations. Even with two people, it often became necessary to skip some animals in order to keep up, though this occurred as infrequently as possible. Stopping the processing line to permit SHIMS data collection was never considered as an option and never occurred. Finally, comprehensive evaluation of the viscera usually required physical manipulation. To prevent cross-contamination of "clean" viscera, extreme care was always taken to avoid direct contact with those that had been condemned by F SIS inspectors. This also resulted in skipping of some animals, 60 Table 3.5. SHIMS personnel requirements per slaughter health check, 1985 to 1988 l Utica Thornapple Dinner Wilson Observation Packing Valley Bell Foods Foods Personnel required (people) Viscera 2 1 1 Carcass 1 1 1 1 Head 1 1 1 1 Time required (hours)a Viscera 025 0.25 0.75 0.75 Carcass 0.25 0.25 0.25 0.25 Head 1.50 1.50 1.50 1.50 Total personnel requirement (person-hours)b 200-275 200-225 225-300 225-300 :Does not include travel or in-plant organizational times Depends on presence or absence of concurrent observations since observation without manipulation was not always possible, and high speed processing provided insufficient time for hand washing between hogs. Evaluation of the carcass for mange took place either on the processing line (by a person other than the one or two working at the viscera tray) or in the cooler (where it was often accomplished by the same people that evaluated viscera) Here again, other conditions normally diagnosed by FSIS, such as arthritis and abscess, were also recorded. Observation of the head for atrophic rhinitis occurred in the cooler at some plants and on the processing floor at others. Before evaluation, transverse section of the snout was necessary at the level of the first check tooth. Slaughter plant management and employees provided assistance in this regard by either modifying the normal processing sequence of the head or by temporarily leaving a partial empty rail next to SHIMS hogs in the cooler, depending on individual plant variations. Such assistance was critical to successful Observation. As a result, the number Of hogs available for evaluation of atrophic rhinitis was determined either by the number of heads diverted from routine processing or by the extra cooler space available, respectively. 61 Recording of data at slaughter generally utilized the forms contained in Appendix B. Occasionally, however, SHIMS personnel shortages required the use of voice recording devices. While the background noise in the slaughter plants involved with SHIMS was substantial, attachment of a clip-on microphone to the shirt collar made it possible for a single person to achieve both successful observation and recording. Depending on the total number of hogs evaluated, the particular slaughter plant, and the number of SHIMS people involved, a typical SHC required 1.75 to 3.0 hours, excluding travel. The main strengths of SHIMS data collection at slaughter center on the techniques developed to reliably follow hogs through routine marketing channels. Especially in regions such as Michigan where only a relatively small proportion of hogs are marketed directly to slaughter, the capability to perform SHCS without disrupting normal market activities is critical to gaining industry acceptance. The willingness of virtually all market participants to cooperate is most notable. Further, the ability to observe and record the postmortem condition of hog health at the rate of over 1000 hogs per hour is important. Limitations of the methods used are also closely related to marketing channels. As mentioned, individual hog identification is not possible with the SHC system described. Though disease rates provide useful information, procurement of data on disease conditions of individual hogs would markedly increase the number of available individual observations, thereby greatly increasing the potential for meaningful scientific inference. Further, the locations of the slaughter plant array made the required travel time quite substantial, which might tend to diminish the practicality of routine, individual producer SHCS according to the scheme presented. Computarjaed Database Using the on- and off—farm data collection forms as templates, a computerized database was designed and programmed for data management. Since the amount of data to be collected for each farm was relatively large, and in anticipation of an eventual 62 SHIMS sample size of 75 to 80 farms, a mainframe database management system was selected. Computer software used was the NOMADZj database manager by Dunn and Bradstreet. Appendix C contains details of the file structures contained within the database. Data entry and processing were performed by MSU personnel. To provide the information obtained on current production status to producers, their veterinarians, and their extension agents, a useful format for output was needed Again, the advisory group provided valuable input on both the desired content and format for the periodic farm management reports. An example of the report appears in Appendix D and exposition of the calculations involved appears in Table 3.6. Because of its research focus, the IMS was initially developed on a mainframe computer to facilitate handling of a relatively large database by a group of researchers (most notably for between-farm comparisons and to allow statistical analysis) However, a parallel version of the IMS is being programmed in DBASE III+ (Ashton-Tate)k for use on IBM-compatible microcomputers. While development of this system, called the MSU Swine Record System, lags behind that on the mainframe, current intent is to generate a virtually identical system with equivalent ability for describing the current status of production and supporting a subsequent analytical model. The main strength of the database and farm management report is the assembly of biological, physical, and financial production information in a single location. However, since the SHIMS project was intended to focus on health management, many facets of these three areas of production were probably not included at the level of detail needed for adequate production management in its broadest sense. Further, the strict quarterly report format is not frequent enough for some production parameters or types of production, but may be too often for others. For example, a continuously farrowing operation running on a 14 day schedule might need monthly reports to effectively maintain current knowledge of production, while one- and two-litter pasture operations might be adequately served at 6—month or yearly intervals. Greater flexibility in this regard is being developed in the microcomputer version. 63 Table 3.6. SHIMS farm management report calculations, 1986 to 1988 Underlying Calculations Average inventory = o l'vto' f0 ases ve r'd total number of inventories taken over period Gilts saved = (ending total female inventory for period) — (beginning total female inventory for period) + (total cull sows sold during period) + ( total sow deaths during period) + (total bred gilts sold during period) + (total open gilts sold during period) - (total open gilts purchased during period) - (total bred gilts purchased during period) Total hogs leaving finisher = (total number of market hogs sold during period) + (total number of underweight market hogs sold during period) + (total number of gilts saved during period) + (total number of hogs consumed during period) Rolling quarterly averagea = same calculation as uart rl but over to four uarters total number of quarters included Reproductive Efficiency Measures Pounds marketable pork produced per quarter = (total ending inventory weight for quarter) — (total beginning inventory weight for quarter) + (total weight sold) + (total weight consumed during quarter) - (total weight purchased during quarter) per female = o marketable or roduced r uarter average total female inventory for quarter per litter = pounds markatable pork produced per quarter total number of litters farrowed during quarter Pigs produced per quarter = (total ending inventory for quarter) - (total beginning inventory for quarter) + (total number sold during quarter) + (total number consumed during quarter) - (total number purchased during quarter) per female = pigs pgpdugad per gaarter average total female inventory for quarter per litter = mg ploducad per quarter total number of litters farrowed during quarter a Only calculated if producer’s data exist for at least two quarters Table 36. continued Pigs born per quarter - total pigs born during quarter per female = ' rter average total female inventory for quarter per litter a ' o e uarter total number of litters farrowed during quarter Pigs born live per quarter = total live born during quarter per female = mg pom live pet; quarter average total female inventory for quarter per litter =- piga bo_r_n liva par quarter total number of litters farrowed during quarter Pigs weaned per quarter = total pigs weaned during quarter per female = Wow average total female inventory for quarter per litter = pigs weaned per quarter total number of litters farrowed during quarter Litters weaned per quarter = total litters weaned during quarter per female = We: average female inventory for quarter Female/boar ratio = avgxage tptal female inventory for quarter average total boar inventory for quarter 65 Table 3.6 continued Efficiency in Feed Usage Lactation total pounds fed = total pounds lactation ration(s) fed during quarter average pounds/femalelday = t ' a ' i rter (average lactation inventory for quarter) * (total number of days in quarter) Gestation total pounds fed = total pounds gestation ration(s) fed during quarter average pounds/female/day = to ou ds st i r i0 5 e ui uarter (average gestation inventory for quarter) * (total number of days in quarter) Starter total pounds fed = total pounds starter ration(s) fed during quarter average pounds/piglday = tntal pounds starter; ratipnis) fen during quarter (average preweaned inventory for quarter) * (total number of days in quarter) Nursery total pounds fed = total pounds nursery ration(s) fed during quarter average pounds/pig/day = tntal po onnga nngsgry gatioms) fed during quarter (average nursery inventory for quarter) * (total number of days in quarter) Grower total pounds fed = total pounds grower ration(s) fed during quarter average pounds/pig/day = total pounds glower ration(s) fed during quarter (average grower inventory for quarter) * (total number of days in quarter) Finisher total pounds fed = total pounds finisher ration(s) fed during quarter average pounds/pig/day = tptal pnu nd a finianax ratipnjs) fad during quarter (average finisher inventory for quarter) * (total number of days in quarter) Table 3.6. continued Efficiency in Facility Usage Litters farrowed/crate =- b 't r w d ' a ter total number of farrowing crates Pigs weaned/crate =- e o ' we d ' a ter total number of farrowing crates Nursery turnover = tngai nnmbai; pi pig§ weaned during quarter total nursery capacity Grower to finisher turnover = tgiai hogs leaving finisher during quarter (total grower capacity) + (total finisher capacity) Marketing Measures Market hogs sold number sold = total number market hogs sold during quarter average market weight =- to 1 ads t 0 sold dur'n uarter total number market hogs sold during quarter sales price per cwt = t 05$ 0 r e' ts o a 'e 0 sales durin uarter (total pounds market hogs sold during quarter) * 100 marketing costs per cwt = t t a k ti osts o et 0 5 es d r'n uarter (total pounds market hogs sold during quarter) * 100 Feeder pigs sold number sold = total number feeder pigs sold during quarter average market weight = tnial pnunds feede; pigs soid during quarter total number feeder pigs sold during quarter sales price per cwt = a s o e ' ts o feede ' sales durin uarter (total pounds feeder pigs sold during quarter) * 100 marketing costs per cwt = tota m r etin 0 ts r feede ' sales durin uarter (total pounds feeder pigs sold during quarter) * 100 67 Table 3.6. continued Underweight market hogs soldb number sold - total number underweight market hogs sold during quarter average market weight = ' u i rter total number underweight market hogs sold during quarter sales price per cwt =- o ' t u d rwe' ht market ho sales durin uarter (total pounds underweight market hogs sold during quarter) * 100 marketing costs per cwt = t a ti ost nd rwei arket ho sales durin uarter (total pounds underweight market hogs sold during quarter) * 100 Cull sows sold number sold = total number cull sows sold during quarter average market weight = t s cu sows sold du in uarter total number cull sows sold during quarter sales price per cwt = s w sales (1 ri ua ter (total pounds cull sows sold during quarter) * 100 marketing costs per cwt = ti o t ull s w sal s durin uarter (total pounds cull sows sold during quarter) * 100 Cull boars sold number sold = total number cull boars sold during quarter average market weight = tpiai mantis guil boais sold during guartei; total number cull boars sold during quarter sales price per cwt = t ss 0 r ei t o c l boar sales durin uarter (total pounds cull boars sold during quarter) * 100 marketing costs per cwt = ' 0st ul boa sal s du in uarter (total pounds cull boars sold during quarter) * 100 b Underweight was either as defined by producers or less than 200 pounds Table 3.6. continued Boars sold for replacement number sold - total number boars sold for replacement during quarter average market weight - o u ' u r er totan number boars sold for replacement during quarter sales price per cwt = to o cei s o or re e ent durin uarter (total pounds boars sold for replacement during quarter) * 100 marketing costs per cwt = t t e i osts for boars sol laceme dur'n uarter (total pounds boars sold for replacement during quarter) * 100 Open gilts sold for replacement number sold = total number open gilts sold for replacement during quarter average market weight = tota ds i ts sold for re lacement durin uarter total number open gilts sold for replacement during quarter sales price per cwt = t tal oss do 1 ecei ts re 0 ' ts o d for re lacement durin uarter (total pounds open gilts sold for replacement during quarter) * 100 marketing costs per cwt = t' 0 ts o o e ' is old 0 re lacement durin uarter (total pounds open gilts sold for replacement during quarter) * 100 Bred gilts sold for replacement number sold = total number bred gilts sold for replacement during quarter average market weight = ioiai ponnds piad gilts sold for replacement during quarter total number bred gilts sold for replacement during quarter sales price per cwt -= ta 0 lla ecei ts fro bred 'lts sold for r lacement durin uarter (total pounds bred gilts sold for replacement during quarter) * 100 marketing costs per cwt = t ' ' osts f0 bred i s sold for re lac ment durin uarter (total pounds bred gilts sold for replacement during quarter) * 100 e l . .J .11 69 Table 3.6. continued Cash Expenses Per th Pork Purchased feed expense = arter) (total pounds marketable pork produced during quarter) * 100 Repairs and maintenance expense = t re ' a ' t ce in rt r (total pounds marketable pork produced during quarter) * 100 Veterinary care and drugs expense == vete ' a se in uarter (total pounds marketable pork produced during quarter) * 100 Labor expense = (inial lapo: gappnse gazing quarter) (total pounds marketable pork produced during quarter) * 100 Supplies expense = s ' e e se i a ter) (total pounds marketable pork produced during quarter) * 100 Fuel expense = ex e ' rter) (total pounds marketable pork produced during quarter) * 100 Electricity expense = (iniai alactrjciiy gxpgnaa singing quarter) (total pounds marketable pork produced during quarter) * 100 Telephone expense = iiptal teiephong aapenae singing quarter) (total pounds marketable pork produced during quarter) * 100 Trucking expense = (iptal trunking aapensa during quarter) (total pounds marketable pork produced during quarter) * 100 Marketing expense = (total marketing expense during quarter) (total pounds marketable pork produced during quarter) * 100 Insurance expense = t t ' u anc ense du in uarter) (total pounds marketable pork produced during quarter) * 100 Interest expense = t ta ' tere t e nse ur'n uarter) (total pounds marketable pork produced during quarter) * 100 Taxes expense == (iota! taxes eapensa during quarter) (total pounds marketable pork produced during quarter) * 100 lb... ‘ in = 'v ‘6. 70 Table 3.6. continued Replacement females expense - Wm) (total pounds marketable pork produced during quarter) * 100 Replacement boars expense - ) rter (total pounds marketable pork produced during quarter) * 100 Other cash expenses == Wt) (total pounds marketable pork produced during quarter) * 100 Disease Rates Specific point prevalences (percent) = b i s t slau to b cate or * 1.00 total number of hogs evaluated at slaughter by category Pig Mortality Summary Stillborn percent a f st' 0 ° a * 00 (total pigs born during quarter) Mummies percent = be of m ies durin uarter * 100 (total pigs born during quarter) Preweaned pig deaths percentc = e o ew aned i eaths b reason durin uarter / 3) * 100 (average preweaned pig inventory for quarter) Prenursery weaned pig deaths percentc = o u e we ned i d at s b reason durin uarter / 3 * 100 (average prenursery weaned pig inventory for quarter) Nursery pig deaths percentc = (nnmbgr oi nngsery pig deaths Lay ieason duging quarter / 3L* 100 (average nursery pig inventory for quarter) Grower pig deaths percentc = (nnmbgz pi gxnwer pig geaihs by iaaaon gaping quarter / 3) * 100 (average grower pig inventory for quarter) Finisher pig deaths percentc = of ms i deaths b reason durin uarter / 3) * 100 (average finisher pig inventory for quarter) c Monthly deaths as percent of average inventory ill a) ,» . P" _.' 3" Table 3.6. continued Breeding Herd Removals Sow culls percentc =- * 1 (average total female inventory for quarter Sow deaths percentc = * (average total female inventory for quarter Boar culls percentc =- W (average boar inventory for quarter Boar deaths percentc = to o e t 'n u t * 100 (average boar inventory for quarter Sow cull summary percentd = t a sow culls b reaso u ' ua r * 100 (total sows culled during quarter) Sow death summary percente = sowd sb r s uri u te *100 (total sow deaths during quarter) Summary Table-Annual Measuresf Pigs born per sow per year = (rolling quarterly average for pigs born per sow per quarter) * 4 Pigs born live per sow per year = (rolling quarterly average for pigs born live per sow per quarter) * 4 Pigs weaned per sow per year = (rolling quarterly average for pigs weaned per sow per quarter) * 4 Pigs produced per sow per year = (rolling quarterly average for pigs produced per sow per quarter) * 4 Pounds marketable pork per sow per year = (rolling quarterly average for pounds marketable pork per sow per quarter) * 4 Litters weaned per sow per year = (rolling quarterly average for litters weaned per sow per quarter) * 4 c Monthly deaths as percent of average inventory Percent of total sow culls e Percent of total sow deaths Only calculated if producer’s data exist for at least two quarters 72 Table 3.6. continued Pigs produced per crate per year =- ' t v ' u te * 4 (total number of crates) Annual nursery turnover = (rolling quarterly average for nursery turnover) * 4 Annual grow-finish turnover = (rolling quarterly average for grow-finish turnover) * 4 Whole farm feed efficiency = t (total pounds marketable pork produced during year) RESULTS AND DISCUSSION Produgai; Atiiindga Slaughter Health Checks. Pilot producers responded very positively to SHIMS slaughter health checks and were generally eager to receive the resulting reports. The positive attitude even included an interest in broadening the scope Of SHC to encompass chemical residue evaluation, which somewhat surprised Michigan State University personnel. Though not included on the pilot phase due to potential problems with confidentiality, residue evaluation remains a serious candidate for inclusion in future studies. Producers also provided valuable assistance when making necessary SHC arrangements, including gaining initial access to particular slaughter plants. This assistance, however, was contingent on the maintenance of routine marketing channels. Hesitation to SHC was only encountered when introducing the tattoo technique, where concern was expressed with the disruption of usual hog sorting/loading sequences and the possibility of over-exciting the hogs involved. However, these concerns failed to materialize into any real problems, except for one producer, who talked his marketing cooperative into applying the tattoos when the hogs were unloaded, rather than doing it himself at sorting/loading. Hog identification was not appreciably affected. On- farm Data Collection. Unfortunately, SHIMS pilot producers were not nearly as enthusiastic when supplying their on-farm data as when arranging SHCS. As a result. IQ; Ida s.». ua "H 4 a o,“ e r ‘ 73 the data obtained was often incomplete and/or very late. Over the pilot project’s two- year course, only one producer provided all the on-farm data requested, and then some of it was as much as a year old when obtained Another producer provided data for the entire period, but was up to two years late and did not include any data on feed usage. One producer provided complete data for seven of eight quarters, but was up to one year late. Still another producer was religiously punctual, but only provided data from seven of eight quarters and these lacked feed usage. Finally, two producers failed to provide even one complete quarter of data. Within the set of on-farm data that was provided, wide variation existed in the format in which data were made available to MSU. Some of the SHIMS data collection forms were used unchanged, while some were modified by individual producers to meet their own perceived management needs. Also, some data were provided electronically. Theoretically, this approach is appealing, but in reality the formats employed again varied widely. These included producer-programmed spreadsheets, PigCHAMPc (both versions 1.2 and 2.1), and Telfarmi. Because of this substantial format variability, much revision and modification was required to achieve compatibility with the SHIMS database. These problems with on-farm data collection were severe enough to be fatal if encountered during a full-scale research project. However, as a pilot project, SHIMS is fortunate to have the opportunity to make substantial changes. Such changes are specifically discussed in Chapter 6, and are critical to the success of future efforts. Reports. As mentioned, SHIMS pilot producers were generally eager to receive their SHC reports. How this information was used is uncertain, since the project’s aim was merely to implement the information management system, and not to recommend management changes based on the information provided Judging from the positive producer responses, however, the content and format of the reports (see Appendix D) was quite acceptable. These responses were elicited informally during telephone conversations and other personal communications. In general, all six pilot producers 74 expressed marked interest in the prevalence of disease in their market hogs, especially the changes in prevalence over time. Other specific responses included concern with persistent disease levels over time, association of uncharacteristically high levels with environmental and/or weather factors, and association of decreasing prevalence with particular health management practices. Also, producers frequently posed the question, "Now that we know how much disease I have, how much is it costing me?" Responses such as these indicate an implicit satisfaction with content and format. Since problems existed in the on-farm data collection process, complete quarterly farm management reports were sporadic and producer response to these reports was therefore more difficult to assess. When provided, the reports were often very late or markedly incomplete. However, the historical calculation "Rolling Quarterly Average" (see Table 3.6 for computation) and the current SHIMS average were included partly as a result of producer requests. Also, producers suggested inclusion of information regarding the entire range of production values characteristic of other SHIMS producers. Thus, even though substantial management differences occur both over time and between farms, SHIMS pilot producers were definitely interested in having comparative production information at their disposal to complement information regarding their own current SIRIUS. Diseases at Slaughig: Data Summary. A summary of the pilot project SHCS appears in Table 3.7. A similar format to that presented was used when reports were provided following individual SHCS. Notice that the total number of hogs evaluated was dependent upon the disease category being considered. This was due to a combination of identification problems and high processing line speeds. As discussed, not all hogs were identifiable at slaughter, and some were identified but not evaluated because the high processing rate often precluded comprehensive pathology evaluation. Also, condemned viscera were often unobservable for pneumonia and/or ascariasis, but these hogs were readily evaluated for atrophic rhinitis and/or mange. Since the within-plant locations of 75 Table 3.7. SHIMS pilot herd slaughter health check summary, 1986 to 1988 Description Number of Pigs Percent Pneumonia None 1534 29.12 Mild 2707 51.39 Moderate 612 1162 Severe 415 7.88 Total 5268 Ascariasis None 4286 81.25 Mild 593 11.24 Moderate 198 3.75 Severe 198 3.75 Total 5275 Mange None 4671 85.50 Mild 667 1221 Moderate 112 205 Severe 13 024 Total 5463 Atrophic rhinitis None 1364 5852 Mild 667 28.61 Moderate 233 10.00 Severe 67 287 Total 2331 Pleuritis Absent 5139 9755 Present 129 245 Total 5268 Pericarditis Absent 5243 9953 Present 25 0.47 Total 5268 Nasal septum deviation Absent 1797 77.09 Present 534 2291 Total 2331 Tattoo quality Good 3045 89.64 Marginal 141 621 Illegible 211 6.21 Total 3397 76 evaluation varied by disease category, and since the evaluation techniques varied inherently with the type of disease being considered, numbers of observations were seldom identical across all disease categories for any given group of hogs. Because pleuritis and pericarditis were evaluated concurrently with pneumonia, their prevalence rates (found in the appropriate percent column) utilized the same denominator as the pneumonia rates. Similarly, the prevalence of nasal septum deviation used the same denominator as the atrophic rhinitis rates. Finally, note that evaluation of tattoo quality was also included in the report. In general, good quality tattoos were clear and easy to read However, because the nature of each group’s tattoo was known prior to SHC, a tattoo of marginal quality was legible, but might have been illegible had the character of the tattoo been truly unknown at the time of evaluation. Tattoos classified as illegible sometimes indicated very poor quality, but generally represented hogs that seemed to have been missed entirely during the tattoo application process. The reader is reminded that these slaughter health data were obtained exclusively through non-random, or convenience sampling techniques. For this reason, their use as a basis for generalization must be strictly avoided, since the degree to which the pilot herds were actually representative of the entire industry was not established Sample Size Calculations. Information on the occurrence of disease in slaughter hogs has been obtained While, as mentioned, these data are not sufficient for rigorous statistical analysis (due to limitations in sample size and selection technique), they are nonetheless useful for estimation of various sample sizes required for future investigations seeking inferential power to the entire population. To project these sample sizes, it was first necessary to estimate the population variance for the disease rates of interest based on the findings from this pilot project. In effect, the SHIMS project involved a three-stage sampling procedure. In the first stage, individual herds were selected from the entire hog-producing industry in Michigan. For the second stage, individual shipments of hogs to slaughter were selected from the total number of groups marketed by individual producers over the course of 77 the project. And finally, within shipments, a subsampling (third stage) of hogs occurred for individual health evaluations. Sampling at all three stages occurred without replacement. To estimate the population variance for each of the disease prevalence rates of interest, the approach of Farver (1987) was adopted However, because Farver presented techniques for two-stage sampling, the approach required slight modification. For two- stage sampling without replacement, Farver presented the following formula to estimate the variance of a disease prevalence rate: 2 2 2 Sb 1 11 Ml S WI v(p) = (H) -— + — 2 "'72 (1"fi)'_' n nN i=i M mi The variables were defined as: v(p) = the unbiased estimate of the variance of the sampling distribution of p; N = the total number of herds in the population: 11 = the total number of herds sampled; f = nlN, the proportion of herds in the population sampled; Ml the mean number of animals per herd in the population; Mi the total number of animals in the ith herd; m the total number Of animals subsampled from the ith sampled herd; in... u ll i mi/Mi’ the proportion of animals subsampled from the ith sampled herd; the estimate of the proportion of the animals in the ith herd that are positive for the presence of a particular microorganism, obtained by dividing the number of animals found to be positive in the subsample from the ith herd by mi; .39 II n p = 2 Mipi/M’ n, the estimate of the proportion of animals in the population i=1 that are positive for the presence of a particular microorganism; 1 n M-p. 52b ___ _ 2( l i n-1 i=1 M’ herd prevalence; and - p)2; the estimate of the among-herd variance in the s wi= mii l-pi), the estimate of the variance within the ith herd of the variable describing animal infection status. 78 Since, as mentioned, SHIMS involved three sampling stages, Farver’s approach was first applied to individual herds for discerning the within-herd variance of each disease prevalence rate. Sampling stages for this analysis were selected shipments of hogs to slaughter within herds and selected animals for health evaluation within shipments. For this purpose, individual herds were considered as distinct populations, and the following variable definitions were employed: v(p)II the unbiased estimate of the within-herd variance of the sampling distribution of a given disease prevalence rate, p; N = the total number of shipments of hogs to slaughter over the course of the pilot project for an individual herd population; n = the total number of shipments sampled from an individual herd population; f = n/N, the proportion of shipments from the herd population sampled; M’ = the mean number of animals per shipment for the herd population; Mi = the total number of animals in the ith shipment; mi = the total number of animals subsampled from the ith sampled shipment; fi = mi/Mi’ the proportion of animals subsampled from the ith sampled shipment; Pi = the estimate of the proportion of the animals in the ith shipment that are positive for the presence of a particular disease, obtained by dividing the number of animals found to be positive in the subsample from the ith shipment by mi; 11 p =.2 Mipi/M’ n, the estimate of the proportion of animals in the herd i=1 population that are positive for the presence of a particular disease; 5 b = — 2 (— - p) , the estimate of the among-shipment variance m the n-1 i=1 M’ shipment prevalence; and szwi= mipi(1-pit))/(m -l), the estimate of the variance within the ith shipment of the varia 1e escribing animal disease status. Thus, an estimate of the variance of each disease prevalence rate was Obtained from each of the six pilot producers. 79 Subsequently, Farver’s approach was again applied, but this time across herds, to estimate the overall (industry) variance of each disease prevalence rate. Sampling stages for this analysis were herds within the (Michigan) industry and shipments within herds. For this purpose, the Michigan hog industry was considered as the single population of interest, and the following variable definitions were employed: Viv) = the unbiased estimate of the industry-wide variance of the sampling distribution of a given disease prevalence rate, p; N =3 the total number of herds in the industry population; {1 = f = M’= 3 u p = .=1 Sb= S wi= the total number of herds sampled; n/N, the proportion of herds in the industry population sampled; the mean number of shipments to slaughter per herd in the industry population over the course of the pilot project; the total number of shipments to slaughter over the course of the pilot project from the ith herd; the total number of shipments subsampled from the ith sampled herd; mi/Mi, the proportion of shipments subsampled from the ith sampled herd; the estimate of the proportion of the animals in the ith herd that are positive for the presence of a particular disease, obtained by dividing the total number of animals found to be positive in all shipments from the ith herd by the total number of animals evaluated in all shipments from the ith herd; n 2 Mipi/M’ n, the estimate of the proportion of animals in the industry population that are positive for the presence of a particular disease; — 2 (— - p) ; the estimate of the among-herd variance in the n-l i=1 M’ herd prevalence; and the estimate of the variance within the ith herd of the variable describing animal disease status, obtained from first stage application of Farver’s variance formula. The outcome of these variance calculations appears in Table 38. While the limitations imposed by the methods of SHIMS sample selection are again acknowledged, these variances were used to estimate sample sizes necessary to achieve inferential 80 Table 38. Disease prevalence rate variances for SHIMS pilot producers, April 1986 through March 1988 (disease rates expressed as proportions) Disease Variance Mild pneumonia 0.018590 Moderate pneumonia 0.001658 Severe pneumonia 0.002730 Pleuritis 0.000850 Pericarditis 0.000005 Mild ascariasis 0.001539 Moderate ascariasis 0.000525 Severe ascariasis 0.000467 Mild mange 0.003080 Moderate mange 0.000329 Severe mange 0.000004 Mild atrophic rhinitis 0.006798 Moderate atrophic rhinitis 0.001061 Severe atrophic rhinitis 0.000124 Nasal septum deviation 0.002545 confidence. For this purpose, the following formula was employed (Cochran, 1977): t2‘tis2/d2 n = 1 +(1/N)h(t2*sz/d2) where n = the required sample size, t = the abscissa of the normal curve that cuts Off an area of a at the tails, s2 = an estimate of the variance of a given disease prevalence rate obtained from the SHIMS pilot project slaughter health checks, d = the acceptable level of absolute deviation of the estimated disease rate from the true disease rate (with the disease rates and the deviation, d, expressed as proportions), and N = the size of the reference population. Due to the nature of the ratio and to be as conservative as possible, the two disease conditions whose prevalence rates held the greatest variance were used for calculation purposes. Results of sample size estimations based on the pilot phase appear in Table 3.9, 3.10, and 3.11. 81 Table 3.9. SHIMS sam le size calculations for number of herds, April 1986 through March 1988 disease rates expressed as proportions; a = 0.05) Absolute Population Required Disease Variance (s2) Deviation ((1) Size (N)a Sample Size (n)b Mild 0.018590 0.02 800 151 pneumonia 0.03 800 75 0.04 800 44 Mild OIXB798 0.02 8(X) 63 atrophic 0.03 800 29 rhinitis 0.04 800 17 rEstimated total number of farrow-to—finish hog producers in Michigan with at least b fifty sows (source-Thulin, 1986 and United States Department of Commerce, 1984) Estimated number of herds required to achieve (1-a) level of confidence. The value of d chosen for these analyses was somewhat arbitrary. As presented above, (1 represents the amount of statistical accuracy that is theoretically acceptable when estimating disease prevalence rates from the general population of market hogs produced in Michigan. However, an issue of equal importance is the amount of accuracy that is acceptable on an economic basis. While a certain sampling procedure may be necessary to achieve acceptable statistical results, perhaps attaining that level of confidence costs more than the information is worth. Therefore, the production value of disease prevalence information must be considered before definitive guidelines are set for economically acceptable sampling procedures. In the absence of reliable knowledge regarding the value of this disease information to hog production, and in an attempt to determine such a value, sampling guidelines should probably follow procedures necessary to achieve acceptable statistical results, since these provide an upper limit for the amount Of accuracy required To have an adequate representation of the Michigan swine industry, the first sample size of interest is the number of individual farms necessarily included As such, illustrative sample size estimations appear in Table 3.9. For example, to achieve 95% confidence (a = 0.05) that observed prevalence rates of mild pneumonia (expressed as 82 Table 3.10. SHIMS sample size calculations for number of slaughter health checks per farm per year, April 1986 through March 1988 (disease rates expressed as proportions; a = 0.05) 2 Absolute Population Required b Disease Variance (s ) Deviation (d) Size (N )a Sample Size (n) Mild 0.018590 0.02 10 9 pneumonia 0.03 10 9 0.04 10 8 0.02 50 39 0.03 50 31 0.04 50 24 0.02 75 53 0.03 75 39 0.04 75 29 Mild 0.006798 0.02 10 9 atrophic 0.03 10 8 rhinitis 0.04 10 6 0.02 50 29 0.03 50 19 0.04 50 13 0.02 75 37 0.03 75 22 0.04 75 14 "rEstimated total number of shipments marketed annually for an individual producer. b For SHIMS pilot project, minimum = 10, maximum = 75, and median = 50. Estimated number of shipments required for slaughter health checks to achieve (l-a) level of confidence. use I" .\h 83 Table 3.11. SHIMS sample size calculations for number of individual pig health evaluations per shipment to slaughter, April 1986 through March 1988 (disease rates expressed as proportions; a = 0.05) Absolute Population Required Disease Variance (s2) Deviation ((1) Size (N)a Sample Size (n)b Mild 0.018590 0.02 100 65 pneumonia 0.03 100 45 0.04 100 32 0.02 240 105 0.03 240 61 0.04 240 39 Mild 0.006798 0.02 100 40 atrophic 0.03 100 23 rhinitis 0.04 100 15 0.02 240 53 0.03 240 27 0.04 240 16 afiEstimated total number of pigs in one shipment. For SHIMS pilot project, this number varied from about 100 to about 240. b Estimated number of individual pig health evaluations per shipment to achieve (l-a) level of confidence. proportions) fall within an absolute deviation of 0.03 from the true rates would require an estimated 75 herds. Other levels of confidence and the second highest variance have been evaluated for comparison purposes. Within these herds, then, it becomes important to know how many Of the market hog shipments leaving the farm for slaughter must be sampled. Obviously, this varies from farm to farm and depends not only on the variance and acceptable deviation, but also on the total number of shipments which occur from a particular farm over time. Again, representative calculations have been made and the results appear in Table 3.10. As with estimations of the farm level sample size, various levels of absolute deviation have been considered for both of the two largest disease prevalence rate variances. In addition, this evaluation included several potential population sizes. Of these, N = 10 84 represents the minimum number of market hog shipments in a year for the SHIMS pilot group, while N = 50 represents the median and N = 75 corresponds to the maximum. Finally, once a shipment of market hogs has been selected for slaughter health check, it is critical to know how many of the hogs in that shipment must be evaluated to provide confidence in the disease prevalence rates observed. For this reason, representative calculations have been made and are presented in Table 3.11. As with previous calculations, various absolute deviations have been considered for the two largest disease prevalence rate variances. Also, different population sizes are included in the evaluation. In this case, N = 100 approximates some of the smaller shipments of hogs evaluated during the SHIMS pilot project and N = 240 represents the larger shipments. Incidentally, N = 240 can safely be considered as an upper limit, since it also depicts the maximum capacity for one truckload of market hogs. These estimations provide useful sampling guidelines. It appears from these initial calculations that the proposed expansion to a sample size of 75-80 herds, which represents approximately 10% of the farrow-to-finish hog producers in Michigan with at least 50 sows (Thulin, 1986 and United States Department of Commerce, 1984), is statistically reasonable. Further, this evaluation suggests that quarterly SHCS may not be frequent enough to accurately assess the disease processes on all farms. It will be necessary to address this question on an individual-farm basis in the future. And finally, it has been shown that missed Observations at slaughter can easily be tolerated (within reason) This is reassuring in light of the high processing line speeds discussed earlier. As an ending note on sample sizes, the above equation can be solved mathematically to provide the amount of absolute deviation, d, which can be expected for given population sizes, sample sizes, disease variances, and statistical confidence levels. In an effort to document the fallacy of using these pilot data as a basis for generalization, this was performed for a sample of six Michigan farrow-to-finish hog producers from an industry containing 800 (commercial operations with at least 50 sows, from Thulin, 1986 and United States Department of Commerce, 1984) Using the largest 85 disease variance to provide conservatism, it was found that, even in absence of sampling bias, an absolute deviation up to i 0.111 (or i 11.1%) can be expected in industry disease rates estimated from these data. Given that many of these disease rates are expected to be around 0.10 (or 10%), this is unacceptable statistical precision. Following the earlier discussion regarding the amount of accuracy that is economical, this precision must also be viewed (at this point) as economically unacceptable. Comparative Analysis. Again, the SHIMS pilot phase disease data are inadequate for rigorous statistical analyses and accompanying population/industry generalization. However, in addition to estimating sample size requirements, certain relationships were evaluated (as a matter of interest) explicitly within the pilot producer group. It was felt that such evaluations might provide initial disease insights such as seasonality, differences between farms, and differences over time. Due to the limitations of the database, the disease classification scheme employed was modified slightly to facilitate cursory consideration of seasonality, farm differences, and time trend in disease prevalence rates. Because the insights being sought were admittedly preliminary, it was deemed acceptable to evaluate each class of diseases as a single entity. To accommodate this approach, a single measure of disease prevalence was sought for each of the four main disease classes: pneumonia, ascariasis, mange, and atrophic rhinitis. It has been reported that moderate and severe disease conditions are more important to the relative performance of production than is a mild condition (Straw, 1987) Therefore, a lone prevalence rate was constructed for each disease group by simply adding the appropriate moderate and severe prevalence rates. As a result, each SHC performed during the pilot SHIMS project yielded only four of these "combined" disease prevalence rates for use in the current analysis: pneumonia, ascariasis, mange, and atrophic rhinitis. To evaluate the relationships of interest, multiple linear regression was performed Initially, the following equation was used to evaluate simple linear relationships: DR- where DRi SEASZ SEAS3 SEAS4 FARM2 FARM3 FARM4 FARMS and FARM6 86 pa + (51 4- QTR) + (52 :- SEAsz) + (p3 4 SEAS3) + (84 4 SEAS4) + 0’5 .. FARM2) + (p6 .. FARM3) + (p7 .. FARM4) + (58 .. FARMS) + (pg . FARM6) "combined" disease prevalence rates for pneumonia, ascariasis, mange, and atrophic rhinitis; (i = 1 to 4, respectively); regression intercept term; linear regression coefficients (j = 1 to 9); time trend variable taking values of 1 to 8 corresponding to the calendar uarters from spring (April, May, and June) 1986 to winter (January, ebruary, and March) 1988; a discrete, (0,1) variable representing spring SHC (April, May, June); a discrete, (0,1) variable representing summer SHC (July, August, September); a discrete, (0,1) variable representing fall SHC (October, November, December); a discrete, (0,1) variable representing farm number 2; a discrete, (0,1) variable representing farm number 3; a discrete, (0,1) variable representing farm number 4; a discrete, (0,1) variable representing farm number 5; a discrete, (0,1) variable representing farm number 6. The QTR variable is intended to capture time trend in disease rates, while the SEAS and FARM variables were included to adjust for seasonal and farm effects, respectively. Regression analysis was performed using MicroTSPl, and results are found in Table 3.12 These analyses were undertaken with full recognition of the potential statistical problems associated with repeated observations on the same individuals (in this case, repeated SHCS on each of the six pilot farms) Since repeated disease prevalence rate observations for the same farm are certainly not independent, autocorrelation of the error term must be expected While the Durbin-Watson statistics did not indicate a definitive presence of autocorrelation, they generally also failed to indicate that such a relationship was definitively absent. In the spirit of initial screening, and recognizing 87 Table 3.12 Results of linear regression analyses evaluating potential trend, season, and producer effects on disease prevalence rates for SHIMS pilot producers, April 1986 through March 1988 Disease Variable Coefficient t-Statistic Pneumonia Intercept 0.04742 1.14071 QTR 0.01846 3.76857 R2 5 017924 SSN2 -0.00668 0.21296 Ad j.R = 0.7432 SSN 3 -0.03610 -1.22797 SSN 4 -0.01655 0.58791 FARM2 -0.05098 -1.50199 FARM3 0.03246 0.95632 FARM4 0.27138 7.99546 F ARMS -0.04178 -1.23095 FARM6 0.01843 0.54287 Ascariasis Intercept 0.17805 265399 2 QTR -0.00986 -1.24749 R 3 0.2708 SSN2 0.00186 0.03666 Ad j.R = 0.0981 SSN3 0.03767 0.79405 SSN4 -0.01414 0.31140 FAR M2 011461 -209228 FARM3 0.07495 4.36822 FARM4 -0.12491 -228030 FAR M5 -0.10554 -1.92679 FARM6 -0.00766 -0.13987 Mange Intercept 0.02153 0.64173 2 QTR 0.00423 -1.06922 R =2: 0.3695 SSN 2 0.01933 0.76358 Ad j.R = 0.2201 SSN3 -0.01107 -0.46667 SSN4 -0.00303 0.13328 FARM2 -0.00117 -0.04268 FARM3 0.08407 3.06926 F ARM4 0.01058 0.38627 FARMS -0.00013 -0.00490 FARM6 0.05798 2.11670 Atrophic Intercept -0.03176 —0.52510 rhinitis QTR 0.00633 088876 2 SSN2 0.08033 1.76023 R 3 0.3483 SSN3 0.06706 1.56823 Ad j.R = 0.1939 SSN4 0.06893 1.68369 FARM2 -0.00777 -0.15733 FARM3 0.12004 243105 F ARM4 0.09491 1.92205 FARMS 0.11677 236484 F ARM6 0.13082 264936 88 the efficiency problems (Pindyck and Rubinfeld, 1981) which accompany autocorrelation (artificial depression of estimated standard errors), an absence of statistical significance was deemed a more reliable finding than was its presence. One of the primary reasons for evaluating SHIMS disease rates over time was to help address the question, "Were the SHIMS pilot producers able to use the information that they were provided to effectively decrease their disease rates?" For example, the presence of a significant negative trend when regressing disease rates against time might have suggested the ability for the producers involved to use SHIMS disease information toward decreasing disease frequencies. Admittedly, definitive documentation of this ability would require both expanded, random sampling and inclusion of a control group of producers not receiving SHIMS information. On the other hand, even if expanded, random sampling occurred with a control group included, absence of a negative trend could not be conclusively interpreted as an inability to successfully use the SHIMS disease information. Such an interpretation would necessitate a prior assumption that the SHIMS producers perceive all the diseases addressed as undesirable at any positive prevalence rate. This assumption would be invalid, and therefore cannot be accepted Other questions of interest included, "Was there a difference in disease prevalence between seasons on the SHIMS project?" and, "Was there a difference in disease prevalence between SHIMS producers?" These were addressed (within database limitations) by evaluating the non-time independent variables for the regressions. Any significance associated with a SEAS or FARM variable would suggest a difference in the prevalence of disease within the pilot group between seasons (as defined) and/or producers, respectively. The regressions performed failed to Show a significant negative trend for any of the diseases considered However, pneumonia showed a significant positive trend To understand the relative importance of this finding, the limitations of the database and the statistical analysis need to be fully recognized. As mentioned previously, definitive 89 conclusions are not possible from this cursory analysis since the sample was small relative to the entire Michigan swine industry, it was selected purely by non-random (convenience) techniques, and it fails to contain a control group not receiving the SHIMS information. When subjectively reviewing the pilot phase of the SHIMS project, it was found that two of the six producers experienced substantial increases in respiratory problems toward the end of the project which were related to specific, known environmental and management factors independent of the SHIMS project. One of these producers experienced mechanical difficulties with ventilation and the other experienced an acute outbreak of swine influenza. Further, if the pneumonia regression is explored further, it can be seen that, relative to the significant differences between farms, the time trend variable contributes a small amount to the total variation in pneumonia prevalence rate. Thus, the potential economic importance of the positive trend in pneumonia is seriously discounted Finally, in light of the probable presence of autocorrelation, the coefficient of determination should be suspected of artificial inflation. The statistical significance achieved must be held in suspicion until more rigorous analysis can supply robust confirmation or rebuttal. Probably more interesting than the amount of trend in this data set is the lack of seasonality and the marked consistence of significant variation between farms. The absence of seasonality suggests either that season is not important or that environmental conditions need to be defined more precisely than is afforded by a single seasonal variable. Reported information (Lindqvist, 1974) indicates that environmental conditions are important to the frequency of disease in hog production. What these preliminary results signify is that either the seasons have been inappropriately delimited, or that specific environmental factors such as temperature and humidity cannot be adequately captured in a seasonal variable when dealing with confinement hog production systems. Finally, the consistent variability between farms for all disease classes helps build a 90 strong case for obtaining individual farm data when performing economic analyses of animal health management. For the last regression consideration, it was recognized that perhaps the strictly linear model might be inappropriate for predicting a proportion such as disease prevalence rates. As a result, corresponding analyses were performed using the logit (Pindyck and Rubinfeld, 1981) of disease prevalence as the dependent variable. Results of these are presented in Table 3.13. For the most part, the previous discussion also holds for the logit model. ts t Because the SHIMS was developed as a cooperative research effort, the fixed, start-up costs were considered sunk; only the variable costs associated with system operation were considered As such, Table 3.14 contains a summary of the estimated cost of obtaining the SHIMS information for a "hypothetically typical" SHIMS farm. Both first and subsequent year estimations are included Also, expansion to a total cost for 80 herds is presented, based first on four SHCS per year (as occurred during the pilot project) and then assuming eight SHCS per year. It was felt that eight SHCS per year represented a reasonable lower end requirement for "typical" Michigan hog producers. The analysis was included only to Show the effect of increasing the number of SHCS in a general sense, and not necessarily to accurately represent the characteristics of the Michigan swine industry. Explicit calculations can be found in Appendix E. It should be noted that certain overhead costs relating to advisory personnel at MSU have been omitted Also, a substantial economy of scale exists in the area of SHCS, since as the number of participating farms increases, the opportunity for coordination of data collection for more than one herd on the same day also increases. Savings on personnel and travel in this category may approach a maximum of 20%. Other less substantial economies may also exist in the areas of administration and report generation. The net effect of these economies, which were estimated subjectively based 91 Table 3.13. Results of logit regression analyses evaluating potential trend, season, and producer effects on disease prevalence rates for SHIMS pilot producers, April 1986 through March 1988 Disease Variable Coefficient t-Statistic Pneumonia Intercept 3.18336 230067 2 QTR 030062 -184352 R = 0.3744 SSN2 1.03264 0.98899 Ad j.R2 = 0.2263 SSN3 0.10441 0.10672 SSN4 0.02420 0.02584 FARM2 0.69906 0.61877 F ARM3 0.26269 0.23252 FARM4 -1.63420 -1.44651 F ARMS 247656 219211 FARM6 0.07198 0.06372 Ascariasis Intercept 034629 0.09089 2 QTR 0.42434 0.94506 R 3 0.4480 SSN2 4.63082 0.56723 Adj.R = 0.3172 SSN3 298835 1.10925 SSN4 5.16175 200119 FARM2 539169 1.73322 FARM3 0.81373 0.26158 FARM4 10.80003 3.47179 FARMS 682592 219426 FARM6 246199 0.79143 Mange Intercept 1202918 275330 2 QTR 0.52349 1.01670 R 3 0.3012 SSN2 0.99151 030074 Ad j.R = 0.1357 SSN3 235596 0.76260 SSN 4 0.62517 0.21136 FARM2 0.08618 0.02416 FARM3 844310 -236682 FAR M4 ~210635 0.59047 FARMS 0.01208 0.00339 FARM6 8.28596 ~232277 Atrophic Intercept 802062 263601 rhinitis QTR 0.21101 0.58844 2 SSN2 0.15008 0.06536 R 7 023$ SSN3 031566 0.14672 AdiR = 0.0578 SSN4 -1.80107 087434 FARM2 0.11697 0.04708 FARM3 -5.02381 -202217 FARM4 -294501 -1.18542 FARMS 4.75877 4.91549 FARM6 -5.04679 -203143 92 Table 3.14. Variable costs of SHIMS operation, April 1986 through March 1988 Source of Cost Single Farm (four SHC/year) Eighty Farms (four SHC/year) Eighty Farms (eight SHC/year) Initial visit personnel travel forms data entry3 data storage subtotal On-farm data collection producer time forms b data entry data storage subtotal Slaughter health checks personnel travel forms administrative data entry6 and report subtotal Quarterly report personnel-run computer time mailing subtotal FIRST YEAR TOTAL SUBSEQUENT YEAR TOTAL ($lfarm) 3 100.00 26.40 1.50 1.83 _Zfl 3 132.13 120 00 3.00 14.60 .329 $ 141.50 (Slféirm/year) ($lfarm/year) S 240 00 70.40 1.00 55.00 £525 3 431.68 ($lfarm/year) $ 4.00 12.84 .288 $ 19.72 3 725.03 3 59290 (S/farm 3 8000.00 2112.00 120.00 146.40 42% $10,570.40 (Slfarm/ year) $ 9600.00 240.00 1168.00 M 5 11320.00 ($lfarm/year) S 15,360.00 4505.60 80.00 3960.00 4700.1§ $ 28,605.76 ($lfarm/year) $ 320.00 1027.20 2X40 3 1577.60 3 $2,073.76 3 41503.36 ($lf arm) 3 8000.00 2112.00 120.00 146.40 1%00 $10,570.40 (Slfarm/year) 3 9600.00 240.00 1168.00 31200 5 11320.00 ($/farm/year) $ 30,720.00 9011.20 160.00 7920.00 m 3 $7,211.52 (Slfarm/year) $ 320.00 1027.20 211,40 3 1577.60 3 81,679.52 $ 70,109.12 3 b Represents approximately 1081 individual data elements per farm per initial visit Represents approximately 8640 individual data elements per farm per year Represents approximately 26 individual data elements per farm per SHC 93 on a thorough understanding of the data collection process, is to decrease the cost per farm (up to a maximum of about 10%) as the number of participating farms increases. W The full benefits of the SHIMS project cannot be readily estimated at this juncture due to the incomplete nature of the data and its subsequent analysis. However, several general areas of potential benefit can be discussed. Participating SHIMS producers should benefit directly. The information generated should assist these producers in choosing between alternative health management strategies and should help provide "bench marks" for evaluation of the success in health management. Thus, the decision support provided should decrease the uncertainty involved in hog production and thereby provide decision outcomes with higher value to the producers involved This higher value may result from increased profit potential, assistance in risk management, or both, and its equivalent dollar amount is the value that Davis (1974) would assign to the information SHIMS provides. Hog producers not participating with SHIMS stand to benefit indirectly because new knowledge will be generated by the research involved. Publication of the research results to disseminate this new knowledge should allow a broader range of producers to better manage the health of their market hogs and more effectively achieve their own management objectives. Consumers should also directly benefit from the SHIMS project through a safer food supply. A better informed producer industry should lead to more appropriate use of chemical feed additives. Consequently, the potential exists for both fewer residue and health problems in the hogs sent to slaughter. While precise estimation of the dollar value of this benefit will be difficult, it can possibly be approached through techniques such as contingency valuation. Indirectly, consumers will also benefit if hog production becomes more efficient across the producer industry. Obviously, such a development will not be immediately achieved, but will result in a decreased price of pork if it should occur. 94 As production becomes more efficient, the competitive position of the US. hog industry will be improved. Such a development will allow hogs produced in the US. to be more attractive in the world economy, thus providing additional demand to domestic production in the form of increased export opportunity. At the same time, the domestic market will provide less opportunity for competition from imported hogs. Finally, another very important, but perhaps somewhat intangible benefit has been the broad array of cooperation achieved on the SHIMS project. This cooperation includes: (1) the support of both the Cooperative Extension Service and the Agricultural Experiment Station across the academic disciplines of veterinary medicine, epidemiology, agricultural economics, and animal science; (2) the USDA agencies of Extension, ERS, and FSIS; and (3) the various levels of professional livestock production, spanning state and federal governments, the slaughter industry, marketing agents, academia, practicing veterinarians, extension agents, and livestock producers. The livestock industries currently face some complex problems that will be most effectively addressed through such broad, cooperative efforts. R o d t'ons fo ut r In light of developments achieved during the pilot phase of the SHIMS project, several recommendations can be made for future information management efforts. These occur in four main areas: sample selection, data collection at slaughter. on-farm data collection, and administrative support. First, if truly representative information on Michigan’s hog industry is desired, participating farms need to be selected according to appropriate random procedures, rather than convenience. Also, future attention needs to be given to sample selection techniques within farm and within shipment of market hogs. Assurance must be achieved that the market hog shipments selected for SHC provide unbiased information on the farm of origin. Care must be taken to avoid sorting bias. Further, selection techniques during SHC (such as skipping hogs due to processing speed, skipping hogs due to FSIS condemnation, and unidentifiable hogs) must not introduce bias. This area 95 warrants special attention, since condemnations often are due to disease conditions present. Missing these observations would almost surely bias the sample. Based on results of sample size calculations which appear in Table 3.8, the inclusion of 75-80 herds seems adequate to achieve both statistical and biological significance. Table 3.8 also indicates that it will be important to consider the normal marketing frequency on an individual farm basis. As can be seen, quarterly SHCs will not offer much confidence in the statistical accuracy of disease rates observed. In addition, it seems that, while the necessary number of hogs per shipment to be evaluated is directly dependent on the total group size of available hogs, a minimum of 50% for smaller groups (<100 hogs) and 25% for larger groups (>200 hogs) is reasonable. Not only do these calculations provide guidance for extended research, but they also carry an important message for veterinary practitioners who offer SHCs as a service. To reiterate: it appears that quarterly SHCS are generally not frequent enough to provide a 95% statistical confidence in the accuracy of the disease rates observed. Further, in lieu of desirable accuracy estimates based on economic analysis, a minimum of 40 to 60 hogs should be evaluated per SHC. When considering on-farm data collection, it will be extremely important in the future to request only the minimum amount of data necessary to achieve the goals of individual projects. As discussed previously, requests for excessive data collection, especially by producers, will ultimately detract from the quality of the important data in both timeliness and accuracy. Also, standardizing the format for data collection is extremely important. Numerous formats greatly increase both entry/processing time and the probability of entry/processing errors. To improve the quality of on-farm data, it may become necessary to explicitly compensate participating producers, or to more adequately and convincingly document the direct benefits received from participation. Finally, if the SHIMS project is to continue, increased support will be required from FSIS in the area of conducting SHCs. The hog market structure in Michigan mandates excessive travel for MSU personnel to provide the necessary SHCs for the 96 expanded sample size. Thus, the responsibility for this activity will need to be assumed by FSIS. Coupled with this increased support will be an increase in administrative duties to coordinate SHCS with FSIS However, this should not present a problem given the success achieved and support garnered during the pilot phase in this regard. SUMMARY An information management system to support evaluation of the economics of market hog production and health management has been described. Techniques involved, strengths, limitations, and recommendations for the future have been included While costs of the system have been specifically discussed, quantification of benefits is dependent on development of an analytical model, which will be described in the next chapter. CHAPTER 4 THE ANALYTICAL MODEL INTRODUCTION As discussed in Chapter 2, the descriptive information supplied by an information management system (IMS) provides a basis from which further information for diagnosis, prediction, and prescription can be attained. For this purpose, mathematical production models help form technological expectations for diagnosis and prediction, while decision models support prescription. In the current research, an IMS and an analytical model for livestock production were simultaneously developed toward their joint application as a decision support system. Chapter 3 described the IMS; this chapter presents the analytical model. Initially, the general approach of systems modeling will be explained. Then, a broad, conceptual overview of the current analytical model will be forwarded. Following this overview, the specific systems modeling concepts and computer simulation techniques which were employed for the current research will be presented. These aspects are necessarily presented together, since they are often virtually inseparable; many of the simulation techniques were developed specifically to implement the underlying systems modeling concepts. Subsequently, the methods employed for estimation of system parameters will be presented. This section will deal with the approaches involved both in projecting exogenous input levels and in defining functional relationships included in the model. These relationships are critical to model performance since they determine the rate of change for states of the system and input variables. 98 Finally, a preliminary discussion is included based solely on the individual properties of the modeling techniques involved. Further discussion is diverted to Chapter 5, where results of initial decision support system application will be presented, and Chapter 6, where conclusions and recommendations will be made. THE SYSTEMS APPROACH The analytical model for this research was constructed via the "systems approach." Basically, the systems approach has been defined by Manetsch and Park (1982) as a method of problem solving which seeks to efficiently satisfy an identified set of needs in light of trade-offs between those needs and resource constraints. More specifically, this approach . . . overtly seeks to include all factors which are important in arriving at a "good" solution to the given problem and . . . makes use of quantitative models and often computer simulation of those models to assist in making rational decisions. Further, they elaborate with the following illustration: N .vSYStemS AppI-Oachn > '2': Xi (read "includes but 1‘1 is greater than") = A methodology for planning/management = A multidisciplinary team = Organization = Mathematical modeling techni ues x5 = Disciplined non uantitative thinking x = Simulation techniques x7 = Optimization techniques x8 = Application of computers X x x3 x Along the same lines, Sutherland (1975) listed four basic characteristics inherent in the problem solving methodology of a true systems scientist: 1 The approach must be interdisciplinary, since real-world problems inevitably cross the artificial boundaries drawn by academic disciplinarians. 2. The capacity for both quantitative and qualitative analysis is essential. 99 3. ". . . the properties of the problem at band should determine the analytical approach we take, not any a priori methodological biases nor any convenient preferences we happen to hold independent of emerging problem realities." This means that such analyses should ". . . bring all the structure and order possible to the problems we face, without bringing so much that real complexities are sacrificed to analytical expedience." 4. There exists a holistic appreciation for the subjects involved, in which the basic units of analysis are "unreduced wholes." As can be seen, the systems methodology borders on a problem solving philosophy. It assures that the "needs" associated with a particular problem situation are adequately, but efficiently fulfilled. The characteristics of the problem dictate the analytical technique employed. In this regard, the approach can (and perhaps should) be applied to problems of virtually any magnitude (large or small) and of any relative complexity (from simple to extremely complicated). In the broadest sense, virtually any of the commonly used modeling techniques for agricultural production could be involved in a systems approach, given appropriate needs and resource constraints. At the same time, a priori selection of any particular analytical technique independent of the specific needs and constraints of a given situation violates the system scientist’s credo. CONCEPTUAL MODEL OVERVIEW The system being modeled was a confinement, continuous production hog growing enterprise, including nursery, grower, and finisher phases. The model’s goal was to predict income over variable costs and to evaluate the sensitivity of this prediction to alternative health management practices. For purposes of exposition, schematic representation of the model is divided into physical and financial aspects. These appear in Figures 4.1 and 42, respectively. To maintain clarity, references to specific model features will be indicated by use of italics with the exception of formulas and tables where normal script will be employed. 100 80?... mzumm 5.39.33 28m 5.9.3.2 .388 actuate...— uflom me... «you... «£39 2:65 .82.. .5323... 3.2.2.: 2:65 .235... .236 3:52 2: o. :3... 02.6... o 8.32:3... >38 82...... a a . 5.3563 .38.. 23.2.3 to. x .r 2.... .26 5.82.... .3 .m— E _.MW— w”; , 4 q 80: .2 auras: Exes; A a a .— 23. .3... a... any... a... 11.713 x.) . _fij .8..th on. we acts—.6832 2382.8 .3. 0.3»...— — 3.3.5»... I‘ Suck . no... { 95.2.52 .ereo3/j \ ’0’. + ) & 3.2.3,... 2.2.5 .3581 to... r 4 ....“....”..HB gag... ...... _Hsérfl 86.3.5 95mm b.3955 88m :meoaz .388 zero—.83 .2235. of .0 3.3.5856. otafiofim NV 2sz moim 20m .8: t moo... .0150: I J I mw>o T®+A1 A\ r n A! 8.5 a... .88... 94°02. _ B? :25 03:23 too... mug-cm h 8E a... \ w u .8 a... , 102 Figure 4.1 contains the backbone of the analytical model: physical production. Represented by the connected boxes labeled NURSERY, GROWER, and F1 N I SH ER, the main channel through the figure corresponds to flow of hogs through the respective production phases. Provided Initial Inventories, weaned pigs entered the nursery as inputs into the system and market hogs exited as outputs. Management controlled the amount of time spent in the NURSERY and GROWER through discrete Weaning Schedules (pig flows), while the variable time spent in the FINISHER phase (DELAY) was determined by the Marketing Schedules along with other factors in the system. Thus, total transit time for pigs in this system was variable. The individual production factors which affected growth rate. and consequently transit time, are explicitly listed in Table 4.1, and include changes in Feed Additive use, available Floor Space per pig (as determined by Current Inventories), and Disease Rates (specific point prevalences at slaughter). Ultimately, these changing factors could exert their influence in any or all of the three production phases. However, their net effect on total transit time, regardless of the phase being influenced, was to alter DELAY. This process is represented by the box labeled DELMOD, where either the Initial DELAY (historical DELAY obtained from the IMS) or the immediate past period DELAY was appropriately modified to determine the future DELAY. Disease Rates and Death Rates were projected using an alpha-beta tracker, which will be discussed later. These projection processes are represented by the boxes labeled afiT in Figure 4.1. Finally, the boxes containing fdt indicate numerical integration, where system flow rates were "gathered" over time. For example, market hogs exit the FINISHER phase according to a market rate expressed in hogs per day. Over time, a total MARKET HOGS SOLD can be calculated considering the marketing rate and the length of time (number of days) involved. Similar explanations are applicable for Death 103 Rates to calculate total numbers of deaths over time, for Current Inventories to discover the total number of PIG DAYS, and for Feeder Pig Sale Rates to provide total Feeder Pigs Sold. Table 4.1. Production factors that affect hog performance in the physical production model, Michigan State University SHIMS project, 1985 through 1988 Disease Rates Environment Mild pneumonia Available floor space per pig Moderate pneumonia Severe pneumonia Feed Additives Mild ascariasis Moderate ascariasis Antibiotics Severe ascariasis Sulfas Mild mange Carbadox Moderate mange Copper Severe mange Anthelmintics Mild atrophic rhinitis Moderate atrophic rhinitis Severe atrophic rhinitis Nasal septum deviation Pleuritis Pericarditis Figure 4.2 depicts combination of the financial aspects of production with the outcome of Figure 4.1. Again, aflT indicates projection with an alpha-beta tracker. Here, feed consumption (Pounds per Pig Day), Market Hog Price, NON-FEED CASH EXPENSES, and Market Hog Weight were the variables projected. Basically, physical production levels were multiplied (in boxes labelcd II) by appropriate market prices and variable expense levels to attain (through the summation, 2) an expected INCOME OVER VARIABLE COSTS. This projected financial outcome, then, was designed to provide information to the decision maker for consideration in light of his/her personal objectives. Initial values of both the state and rate variables in Figures 4.1 and 42 were obtained from the Swine Health Information Management System (SHIMS) database 104 described in Chapter 3. Because the SHIMS database was maintained on a mainframe computer and the current model utilized a microcomputer, data transfer was necessary. This was accomplished both electronically and manually. In addition to the SHIMS data, projected prices for feed, feed additives, and feeder pigs, as well as the current status of the weaning and marketing cycles had to be provided. As presented, the current status of production was ascertained from the database and used as a basis for change. In this sense, the method was a production adjustment approach, as opposed to an absolute production approach. The adjustment technique is theoretically appealing, since established producers following a continuous production scheme seldom ask, "How much should I produce?" in an absolute sense, but generally ask the same question in relation to current production levels. Further, gains in the model building process were achieved by using the adjustment approach. Much information about technology is implicitly included in the observation of production’s current level and rate, and this information enhances the accuracy of prediction. It seems logical that accurately predicting the absolute magnitude of production might be more difficult than establishing current levels and then accurately predicting marginal changes. Therefore, by using a known starting point, many absolute production parameters which are critical to prediction but extremely difficult (if not impossible) to accurately quantify become irrelevant to the model since they don’t change. Included in the category of unchanging variables are genetic potential, farm- specific environmental effects, and many nutritional effects. For example, if genetics don’t change, genetic potential is implicitly and adequately quantified by observing current production performance and using that observation as a basis for projection. As long as the herd’s genetics are not changing, their effect on production will be the same (within limits of normal random variation) in future periods as it was in past periods, and will therefore be accurately included if past production performance levels, appropriately adjusted for factors that do change, are used for projection. If farm- 105 specific housing characteristics don’t change (other than pig concentration), and if the rations being fed stay the same (other than amount consumed), the same line of reasoning can be used for certain environmental and nutritional parameters. Not only are direct effects captured in this fashion, but also any interactions that may be present, however complex. Such an adjustment approach effectively addresses many of the modeler’s toughest challenges. SYSTEMS MODELING AND COMPUTER SIMULATION TECHNIQUES W The programming language used for computerization was QuickBASICm (version 4.0 by Microsoft) Development and initial implementation occurred on a COMPAQ Portable II microcomputer equipped with a math co-processor. In addition, an executive simulation program (MOPTSIMn) provided an initial framework for several of the subroutines and functions used. However, extensive modifications were necessary in virtually all cases to meet the specific needs of this project. Programming code for the simulation appears in Appendix F. Before discussing specific features of the model, the step-size (DT) employed warrants mention. Whenever numerical (Euler) integration is used, a certain amount of error is introduced via discrete approximation of the continuous processes being modeled (Manetsch and Park, 19$). The magnitude of this error is directly related to the size of the time step (DT) employed, and can be evaluated by considering the model’s ability to conserve flow. For the current research, this evaluation entailed determining the percentage difference between (beginning inventory + pigs weaned) and (ending inventory + deaths + marketings). Initially, the model was run with ten iterations per day (DT = 0.1) With this step size, errors in conservation of flow due to numerical integrations were generally 5 0.1% over the course of simulating three months. When DT was increased to one iteration per day, errors were still 5 0.5% over a similar time frame. Though a larger 106 DT (for instance DT = 5 days) would have perhaps provided acceptable simulation results, DT = l was maintained since it is consistent with normal discrete biological, environmental, and managerial activities. Associated simulation run-time on the COMPAQ Portable II was approximately on minute per producer-quarter (simulation for one producer over a single annual quarter) Because Monte Carlo analysis involved 200 simulations per producer-quarter for each scenario evaluated, a COMPAQ 386/20 (equipped with a math co-processor) was employed for Monte Carlo analyses. Simulation run-time on this computer involved about 10 seconds per producer-quarter. is t In Figure 4.1, the boxes labeled NURSERY and GROWER refer to discrete time delays in the computer simulation model and correspond to the time pigs spend in the respective production phases on the farm. The length of these delays was determined by the weaning and pig flow schedules for the farm of interest. For purposes of simulation, it was assumed that on the day pigs were weaned and moved into the NURSERY, pigs were also moved from the NURSERY to the GROWER and from the GROWER to the FINISHER. With the beginning of simulation, the initial state of these discrete delays was determined by the current inventories and the current stage of the weaning schedule. During the respective delays, the NURSERY and GROWER experienced death losses at rates derived from the SHIMS database, and, as mentioned, an alpha-beta tracker. Inputs to the NURSERY delay were provided by weaned pigs entering the system. As discussed, pigs leaving the NURSERY provided input to the GROWER. Further, pigs left the GROWER according to the same weaning schedule. These delays were provided by the DCTDEL subroutine in Appendix F. Distributed Qelay In contrast to the discrete delays of the NURSERY and GROWER, the box labeled FINISHER in Figure 4.1 refers to a distributed, or continuous delay (Manetsch, 107 1976; Abkin and Wolf, 1976). This technique represented the dynamic pig population in the finish phase, received input from the GROWER, and, like the other phases, experienced losses according to the death rate. When the entire population was considered, the distributed delay caused the different amounts of time (or delays) that individual hogs spend in the FINISH ER phase to be distributed according to an Erlang probability density function (Manetsch and Park, 1982). The general form of this distribution can be represented as: a*kk* tk‘1*e-kat ( ) () f0) = (k - 11' 1 where E[t] = — a 1 and Var[t] = (k * a2) For the distributed delay, t represents time, while k and a are constants which specifically define the particular delay process being modeled. Because, as stated, the mean of the Erlang distribution is 1/a, the mean delay for a distributed delay process determines the value of the constant a. In Figure 4.1, the mean delay is represented by DELAY, and consequently 1 DELAY . Thus, the shape of the output distribution was determined by its mean (DELAY), and variance. As presented, the variance of the Erlang-distributed delay times is related to both the mean (Ila) and the parameter k. The effect of k on the shape of the Erlang distribution can be seen in Figure 4.3. As the figure illustrates, the distribution is exponential if k = 1, but as k increases, a Gaussian distribution is approximated. Thus, the value of a is determined by the mean delay time, and the value of k should be 108 selected specifically to match the output probability density function’s shape with reality (as closely as possible) for the delay process of interest. fir) ,_\k=25 I \ I \ I \ \ I \ I k:|O \ /l’—\\ \ \ k=| // \\\ \\- / / \‘ / / ‘/“/~ ‘~“\A\\\ k=2// I] =3 / I §“ ——‘:\‘E\~.~__ ’ / / / “3““:- /A/ —/1 1’! l 1 ‘—- 1- 1. Q 2.‘ 20 40 I000 Figure 4.3. The Erlang family of probability density functions (from Manetsch and Park, 1982) For current purposes, selection of an appropriate k value necessitated an understanding of hog growth patterns. In general, for any given group of hogs, some individuals will markedly out-perform the rest in terms of growth rate. Subsequently, the majority of the remaining hogs will experience a growth rate relatively close to the > mean for the group. Finally, the slowest-growing portion of the group may contain individuals whose growth rate is extremely slow. Generally speaking. the performance of this slow-growing portion lies much further. in a relative sense, from the entire group’s overall mean than does that of the fastest-growing portion. The resulting 109 distribution of growth rates is similar to a normal distribution, but is somewhat skewed to the right. For further clarification, the following example is offered. Suppose 100 hogs of identical weight and sex were placed on performance test where all hogs experienced exactly the same nutrition and environmental conditions. Further, suppose these hogs were fed precisely to an arbitrary target weight, and the mean number of days required to achieve that weight was 100. The distribution of individual hogs’ days-to-weight would be expected to follow the general pattern presented in Table 42. Note that absolute deviation from the mean of 100 days in this fabricated example is irrelevant The important feature is the general distribution characteristics of days-to-weight. Table 4.2. Hypothetical distribution of individual hog growth rates Number of days Number of individual to target weight hogs in group less than 80 4 81 to 85 7 86 to 90 11 91 to 95 15 96 to 100 16 101 to 105 15 106 to 110 12 111 to 115 8 116 to 120 5 121 to 125 3 126 to 130 1 over 130 3 Because the Erlang distributions with k parameters in the range of 30 to 50 most closely match the described growth characteristics of market hogs, a value of 40 was chosen as a feasible mid-range. The distributed delay, which also experienced death losses, was accomplished by the following subroutine, adapted from MOPTSIMn and programmed for QuickBASICm: 110 DEFINT I-N SUB DELTV (UDTV, YDTV, QTV(), TQTV, Dths, DTV, DT, KDTV) STATIC DDDl = DTV / KDTV BDDl = 1 / DDDl KDTVI =- KDTV - 1 FOR JJDD = 1 TO KDTVI QTV(IIDD) = QTV(JJDD) + DT * (QTV(JJDD + 1) / DDDl - QTV(JJDD) ... BDD1)—(Dths * QTV(JJDD) / TQTV) NEXT JJDD QTV(KDTV) = QTV(KDTV) + DT * (UDTV - QTV(KDTV) * BDDl) TQTV = 0 FOR IIDD = 1 TO KDTV TQTV = TQTV + QTV(IIDD) NEXT IIDD YDTV = QTV(l) / DDDl END SUB REM UDTV = input rate in pigs/day REM YDTV = output rate in pigs/day REM QTV() = intermediate storage array REM TQTV = total storage REM Dths = number of pig deaths per DT REM DTV = DELAY REM DT = time step REM KDTV = number of intermediate stages (dimension of QTV()) As discussed, output from the distributed delay is continuous. However, hogs are marketed in discrete groups. Therefore, it was necessary for the model to convert the continuous flow of hogs out of the distributed delay into a flow of discrete groups, appropriately varying in size according to the inherent growth rates. To accomplish this, hogs exiting the distributed delay continuously were gathered and held in a "pool" awaiting market. Though this programming technique was necessary for modeling purposes, it can be thought to correspond to those hogs that attain a marketable weight sometime between scheduled marketing days, but must be held on the farm for an additional period of time due to the fixed marketing schedule. As such, the pool in the model was emptied at discrete time intervals, which were determined by the marketing schedule. Also, because the pool was a legitimate part of the FINISHER phase, death losses occurred from the pool at the same rate as from the distributed delay. Since input to the pool was continuous and emptying occurred according to the normal marketing 111 interval, the mean amount of time hogs spent in the pool was one half of the marketing interval. Therefore, when combined with the mean time spent in the distributed delay, the expected total time in the FINISHER phase can be represented as follows: Marketing Interval E[Total FINISHER Time] = DELAY + 2 ob bi it De ' n to By their character, biological systems frequently exhibit substantial random variation. Considering the effects of various factors on hog production offers no exception. This is perhaps reflected in the fact that the literature often holds several different estimates of the "actual" performance effect of individual production factors. For current purposes, these various estimates formed the basis for the inclusion of stochasticity. Technically, the effect of each individual factor was hypothesized to form a triangular probability density function, as represented in Figure 4.4. Estimates were included for the lowest possible (A), most likely (B), and highest possible (C) production effect of each factor. During simulation, [0,1] random numbers were generated and then passed through inverse transformation (Manetsch and Park, 1982) based on these triangular probability distributions. The result was a unique, stochastically generated production effect each time the subroutine was called. Inverse transformation occurred in the TRIDIST subroutine of Appendix F. W Returning to the concept of an adjustment approach, it was deemed desirable to autocorrelate the random numbers generated from one period to the next. Thus, though ultimately stochastic, current production factor effects would be related to those of immediate past periods. Manetsch and Park (1982) described a technique useful in introducing exponential autocorrelation to a generated series of random numbers. The following equation defines the desired activity: 112 r(t) = (pm * r(t-DT» + «1 — mm . so» where r(t) =- the value of the autocorrelated, random variable to use in the current time period, DT = the time—step size in relation to the denominator of model rate variables, r(t-DT) = the value of the autocorrelated, random variable used in the immediate past time period, s(t) = an independent random number to be generated in the current time period, and fl = the desired autocorrelation coefficient. fi(a'i) A __2_ ._ ______ Ci'Ai : l I l O J a“! Ai Bi Ci Figure 4.4. Triangular probability density function As individual series of random numbers, r(t), r(t—DT), and s(t) each has an associated probability density function which can be described, at least in part, by respective means and variances. In the long run, the r(t) and r(t-DT) distributions are 113 nearly identical, differing only in the first and last numbers generated. Since this distribution is generally predetermined, based on the stochastic process being modeled, the characteristics of the s(t) distribution are dictated by the autocorrelation coefficient, p, the time step, DT, and the probability distribution, r(t). Only in rare instances would s(t) be expected to be identical to r(t) or to be distributed as [0,1] uniform. Thus, it is critical to fully understand the statistical ramifications of the above equation so that the autocorrelated, random variables used possess the desired statistical properties. Along these lines, Ross (1985) showed that, if X and Y are independent random variables and a and b are constants, then E[(a * X) + (b * Y)] = (a * E[XD + (b * MW) and Var[(a ... X) + (b .. Y)] = (a2 * Var[XD + (b2 .. Var[Y]). For current purposes, r(t) =(8*X)+(b*Y), r(t-DT) = X , s(t) = Y , fiDT = a , and (1 - MDT = b . Now, because E[r(t)] = (fiDT * E[r(t-DT)]) + (<1 - a)” * E[s(t)]). . E[r(t)] - (199T * E[r(t-DT)]) it follows that E[s(t)] = (l—mDT Also, since Var[r(t)] = (BZDT * Var[r(t-DT)]) + ((1 - fi)2DT * Var[s(t)]), Var[r(t)] - (fiZDT * Var[r(t—DT)]) it can be seen that Var[s(t)] = (l-fiFDT Finally, because r(t) and r(t-DT) are virtually identical, EMF-DD] = EMU] and Var[r(t-DT)] = Var[r(t)]. 114 Thus, it is possible to determine the necessary properties of s(t), based on knowledge of 13, DT, E[r(t)], and Var[r(t)]. In the current exercise, DT = 1 because rates were expressed in quantities per day and step size was one day. As such, 19 became a simple correlation coefficient. Also, to maintain desired characteristics of the triangular distributions, the distribution of r(t), and thus r(t—DT), was [0,1] uniform. For this reason, E[r(t)] = 05 and Var[r(t)] 0.08333 (or 1/12). Thus, the only remaining parameter to be defined was 5. Estimates of ,8 are non-existent in the literature. However, certain restrictions can be derived from other properties of the processes involved, which constrain the statistical properties of s(t). Since DT = 1 and E[r(t)] = E[r(t—DT)] = 05, E[s(t)] = 0.5 regardless of the fi value. This can be illustrated mathematically as follows: E[r(t)] - (I3DT * E[r(t-DT)]) because E[s(t)] = (1 — mm 0.5 - (,6 * 05) by substitution E[s(t)] .- (1 - fl) (1 - B) * 05 by simplification E[s(t)] = _— (1 - B) and E[s(t)] = 05 . Thus, the mean of s(t) was unaffected by [3. On the other hand, 13 is very important to the variance of s(t); theoretical restrictions which may exist on Var[s(t)] are therefore critical. Because of the definitive [0,1] endpoints desired on r(t), s(t) also required [0,1] endpoints. The maximum variance for a probability distribution with definitive [0,1] endpoints is provided by the binomial distribution, whose variance is 0.25. The minimum possible variance for s(t) was determined as follows: 11.5 Var[r(t)] 4132‘” * Var[r(t-DT)D because Var[s(t)] = (l—fiFDT 0-3% by substitution Var[s(t)] = 2 * 0.08333 (1 - 19) or Var[s(t)] = A * 0.08333 a-Bh where = 0-59. From this it can be seen that to minimize Var[s(t)] over the range 0 5 fl 3 1 is equivalent to minimizing A over the range 0 _<_ p g 1. Because dA >0, dfl A was minimized over this range where 19 = 0. By substituting for )3, a value of A = 1 was obtained. Therefore, the minimum variance obtainable for s(t) was given by Var[s(t)] = A * 0.08333 or Var[s(t)] = 0.08333 . Returning to the issue of a value for 19, then, useful restrictions have been identified. At minimum Var[s(t)], ,3 = 0 as mentioned. At maximum Var[s(t)], ,6 was calculated from the above equation(s) as follows: a-fih because Var[s(t)] = 2 * 0.08333 (1 - fl) a-fih by substitution 0.25 = 2 * 0.08333 (1 - fi) 3 a-ph so = 0-39 1 + ,8 or 3 = __ 1 - I3 and B = 0.5. 116 These values provided the feasible range as 0 5 fl 5. 05, corresponding to a range in Var[s(t)] of 0.08333 5 Var[s(t)] _<_ 0.25. Without guidance from the literature, the median value of Var[s(t)] = 0.1667 was selected for current modeling purposes. Following the above procedure for calculating fl (and remembering that both r(t) and r(t-DT) are distributed as [0,1] uniform), this value of Var[s(t)] dictated that p = 0.333. In practical terms, 3 = 0.333 assured that the proportional effect which individual production factors have on growth rate in the current period was related to the same factor’s effect in the immediate past period by a simple correlation of 0333. The remaining 0.667 of the proportional effect was determined by the probability distribution s(t). Considering the desired statistical properties of s(t), it remained to define a probability density function to meet the requirements. The necessary properties included: 1. s(t) distributed over [0,1], 2. E[s(t)] = 0.5, and 3. Var[s(t)] = 0.1667. Further, it was recognized that, by the definition of probability density function, II p... 1 f(s(t) * dt) 0 E[(s(t»21 - (E[s(t)])2 1 f(t2 * s(t) * dt) - 0.25 0 and Var[s(t)] = 0.1667 . Thus, a suitable function for s(t) was sought. As when choosing a function appropriate for combining multiple proportional production effects, mathematical tractability was considered in the search for s(t), but only after theoretical constructs were satisfied. The desired variance was greater than the value of 0.08333 which is characteristic of the uniform [0,1] distribution. Such a variance required that relatively more area exist under the curve near the definitive [0,1] 117 endpoints of s(t) than when near the mean of 0.5. Further, a smooth function was preferred which would be symmetrical while approximating an inverted Gauss distribution. It was recognized that, with relatively minor adjustment, a trigonometric function might provide the desired qualities. Therefore, by correcting for appropriate period and x-intercept, the following equation was used: ' s(t) = a*[cos(21rt)+1]b, 05:51. Given proper values for a and b, this function would meet current requirements. As'suggested by the aforementioned definition of a probability density function, simultaneous solution of 1 f(a * [cos(27rt) + 1]b * dt) = 1 0 1 and f(t2 .. a =1: [cos(21rt) + 11b .. dt) — 025 = 0.1667 0 was possible. Doing so provided the following values: a = 0.6974495 and b = 1.903711 . Solution was accomplished iteratively with the aid of a microcomputer. The resulting function is represented graphically in Figure 4.5. Finally, not only did it seem reasonable to correlate individual factor performance effects over time within production phases, but it also seemed logical to correlate these effects, at least initially, between phases for individual factors, and even between factors within similar factor groups. Therefore, the same modeling approach was used to introduce such initial autocorrelation. As a result, the effect that individual production factors had on performance was initially correlated between the NURSERY, GROWER, and FINISHER phases. Also, initial performance effects were correlated within groups of similar factors, resulting in relationships such as might be expected between the effects of mild, moderate, and severe pneumonia on a given farm. 118 s(t) 3.0-3 1.90371 1 s(t) = .6974495(cos(21rt.)+1) 2.5-: 2.0-: 1.5-{ 1.0{ 05-: Figure 45. Autocorrelation function All such production factor groups are listed in Table 4.3, where initial autocorrelations are indicated by arrows. For purposes of clarification, Table 4.3 indicates that for the pneumonia group, only the effect of mild pneumonia in the NURSERY had an independent, random initial variation for any given farm. The remaining pneumonia disease category effects across production phases were autocorrelated. As indicated, the effect of moderate pneumonia in the NURSERY was autocorrelated with the effect of mild pneumonia in the NURSERY. The effect of severe pneumonia in the NURSERY was then autocorrelated with the effect of moderate pneumonia in the NURSERY. Similarly, the effect of mild pneumonia in the GROWER was autocorrelated with the effect of mild pneumonia in the NURSERY. Then, the effect of moderate pneumonia in the GROWER was autocorrelated with the effect of 119 Table 4.3. Production factor groups for initial autocorrelation of performance effects GROUP 1-PNEUMONIA Nursery Grower Finisher Mild X - X - X t t 1 Moderate X X X l l 1 Severe X X X GROUP 2—ASCARIASIS Nursery Grower Finisher Mild X .. X _. X l l 1 Moderate X X X l l 1 Severe X X X GROUP 3—MANGE Nursery Grower Finisher Mild X~x-.x 1 l 1 Moderate X X X l l 1 Severe X X X GROUP 4—ATROPHIC RHINITIS Nursery Grower Finisher Mild X-oX-oX l l 1 Moderate X X X t t 1 Severe X X X l l l Septal X X X Deviation GROUP S—PLEURITIS, PERICARDITIS Nursery Grower Finisher Pleuritis X .. X - X l l l Pericarditis X X X GROUP 6—SPACE PER PIG Nursery Grower Finisher X-oX-oX 120 mild pneumonia in the GROWER. Thus, the pattern for initial autocorrelation of production effects is apparent. The arrows in Table 4.3 indicate the order in which correlation is introduced by the computer simulation model. However, this order is not mathematically critical to the underlying systems model; the arrows could all be reversed without changing the mathematical results. Thus, for example, the interpretation should be that the effects of moderate and mild pneumonia in the NURSERY are correlated, without concern for which is determined first by the computer. To summarize autocorrelation, the [0,1] random number which was passed to a triangular probability distribution for inverse transformation has been referred to as r(t). This number was independently generated only during the initial time step and only once for each production factor group. Remaining r(t) values within the first time step were autocorrelated across production phases for a given factor group, and across individual factors within groups for a given production phase. In subsequent time steps, all values of r(t) were autocorrelated with immediate past values for the same individual production factor. These autocorrelations were, in effect, simple correlations of the magnitude 0.333. The remaining 0.667 was determined independently from the probability density function which has been discussed and referred to as s(t) To obtain a value from s(t), a number was generated independently from a [0,1] uniform distribution and passed through s(t) for inverse transformation. When this value from s(t) was appropriately combined with the autocorrelated portion, the result was a value for r(t) which was both autocorrelated and distributed as [0,1] uniform. Thus, the desired properties of the triangular distribution were preserved. Table Look-up Function The trigonometric function developed for autocorrelation purposes nicely satisfied the desired statistical properties. However, one other property of the function was its relative difficulty for digital solution. Though the computer employed was certainly capable of such solution. the frequency with which the function was called for 121 inverse transformation (up to 63 times per time step) resulted in considerable lengthening of simulation run-times. For this reason, a table look-up function was introduced. The table look-up function can be thought to store ordinate values of a particular function in an array ordered according to the value of the abscissa. When the ordinate value corresponding to a given abscissa value is sought, the table look-up locates the array values between which the abscissa of interest lies and performs linear interpolation on the corresponding ordinate values. As might be expected, the accuracy of the table look-up function is directly related to the size of the abscissa increments stored in array. Precision can be increased by decreasing the increment size. Though limited sacrifice may be made on the side of accuracy, the speed with which digital computers can perform the relatively simple tasks involved greatly exceeds that required to do complex mathematics such as contained in the s(t) function defined previously. To restrict accuracy problems, the increment size employed in the current model was 0.01 over the desired abscissa range of [0,1]. The process was accomplished by the TABEX subroutine of Appendix F. E l I I [EC .0 Initially, DELAY entered the model as an observed parameter from past production (Initial DELAY from Figure 4.1). However, DELAY experienced modification, as represented by the box labeled DELMOD in Figure 4.1. This modification, as mentioned, resulted from changes in feed additive use, available floor space per pig, and disease rates observed at slaughter. Though some of these factors have their biological effect in the NURSERY or GROWER phases, their net impact is to change the amount of time pigs take to reach market weight. Because of the predetermined, management-controlled discrete delays in earlier phases, the only flexibility in total transit time for the model (and generally also in reality) is to change the time spent in the FINISHER phase. In the model this 122 was accomplished by changing DELAY. An example of how these effects are experienced in the actual system is provided by a hypothetical GROWER pig with atrophic rhinitis. Because this pig is diseased, it will be expected to grow more slowly than if it were disease-free. However, because the amount of time spent in the GROWER is fixed, the net effect is delivery of a smaller pig to the FINISHER phase. As a result, the amount of weight gain (and consequently the length of time) required in the FINISHER is increased. Explanation of the DELAY modification technique follows. As discussed, the model used an adjustment approach to represent production. For DELAY modification, this meant that feed additives might be introduced or withdrawn, available floor space per pig may expand or contract, and disease rates might escalate or decline. Again, these changes may occur in any of the three production phases, and resulting changes in DELAY can be increases or decreases. The magnitude of expected change corresponding to a known, discrete change in disease rates, feed additives, or space per pig were determined through survey of the literature. Usually, research in these areas has published an expected proportional (or percentage) effect on average daily gain (ADG), rather than transit time (or "days to market." Mathematically, this usual approach can be expressed as: ADG(t+DT) = ADG(t) * (1 + PrEffADG) , where ADG = average daily gain, t = current time period, DT = time step, and PrEffADG = expected proportional effect of change in production factor on growth rate over a time period of length DT. Verbally, this equation says that the new growth rate, ADG(t+DT), is equal to the current growth rate, ADG(t), multiplied by the quantity one plus the expected proportional change, or (1 + PrEffADG) 123 Assuming target weight for any particular production phase is relatively constant, the above format can be modified to address transit time instead of growth rate, since days-to-weight is the mathematical inverse of growth rate. For factors that affect the entire population, such as space per pig and feed additives, the following technique was used: 1 DTW(t+DT) = DTW(t) * (1 + PrEffADG) or DTW(t+DT) = DTW(t) * (1 + PrEffDTw) , where DTW = days to target weight, -PrEff (1 + PrEffADG) , and other variables are as defined above. For factors that only affect part of the herd, like disease rates, the following approach was used: 1 DTW(t+DT) = (DTW(t) * (1 - DDR» + (DTW(t) :- DDR * (1 + PrEffADG)) or DTW(t+DT) = DTW(t) * (1 + (DDR * PrEffD-rw» , where DDR = the change in disease rate from time (t) to time (t+DT), and other variables are as defined previously. Basically, this is a weighted average approach. The proportion of the herd not experiencing a change in disease status (1 — DDR) has no change in performance (DTW(t)). However, the proportion of the herd whose disease status changes undergoes an alteration of performance according to PrEffADG. If the entire herd experiences a change in disease status, then DDR would equal one. In this situation, these equations become equivalent to those in the preceding section which were developed for factors affecting the entire population, and the two adjustment techniques are seen to be consistent. As previously mentioned, all specific factors that affect production in this 124 model are listed in Table 4.1. Though only one list appears, each item can change performance in any of the NURSERY, GROWER, or FINISHER phases. For single factor changes considered discretely, this approach would seem to be adequate. However, when the simultaneous effects of numerous potential changes in production need to be evaluated, an acceptable approach for their concurrent consideration is not as definitive, and suggested techniques are not available in the literature. Theoretically, since the individual factor adjustment techniques discussed are mathematically consistent, it should be possible to combine the individual effects of a group of changing factors into a single, "total" proportional effect. Then, this combined effect would change days to target weight as: DTW(t+DT) .-_ DTW(t) * (1 + TotPrEffDTw) , where TotPrEffDTw represents the combined effect as discussed. If all factors contributing to TotPrEffDTw have equal relative importance, ie. an expected one percent production difference from changing disease rates is as important as a one percent change from space per pig, the following theoretical boundaries can be placed on TotPrEffDTw: 1. TotPrEffDTw > -1, since PrEffDTw g -1 would result in DTW(t+DT) g 0; 2. ITotPrEffDTw | s liglPrEffDTwO) | for production factors 1 to n; and 3. TotPrEffDTw monotonically increases (and thus has no maximum), but increases at a continually decreasing rate. The first of these conditions provides that when increasing performance and consequently decreasing the number of days required to reach a target weight (greater than the current weight), it is biologically impossible to have a negative value for days- to-weight. Increasing growth rate can substantially decrease the number of days 125 required to achieve a prescribed weight gain, but it is not possible to decrease the number of days-to-weight beyond zero. The second condition states that the combined proportional effect of n factors on production (TotPrEffDTw) is not greater in absolute magnitude than the simple linear sum of the n individual production effects. For example, if two different factors change simultaneously and each is expected to increase growth rate by five percent, the combined effect would not be expected to exceed ten percent. Finally, the first part of the third provision allows days-to-weight to increase infinitely, establishing no maximum for days-to-weight. Such a provision is theoretically sound, since even though individuals may grow very, very slowly, there can be no definitive, absolute upper limit established for the number of days required to attain a given target weight. The second part of the condition merely says that a negative proportional effect on production probably has a smaller relative effect on a hog that is already slow-growing than it has on a high-producing individual. Based on these properties, the following relationship was hypothesized and included in the model: {ex—1 forng y = ln (x+1) for x 2 0 where x = the linear sum of individual proportional production factor effects and y = the net proportional production effect resulting from simultaneous consideration of all individual production factor effects contained in x. This relationship is illustrated graphically in Figure 4.6. Though the choice was ultimately arbitrary, this function was first chosen because it satisfied the theoretical conditions discussed. In addition, the function y = f(x) is mathematically well-behaved, since it is both continuous (uninterrupted) and continuously differentiable (possessing a first derivative everywhere on the function). From evaluation of the figure, it can be seen that TotPrEffDTw, represented as the solid curve, is everywhere greater than negative one. Thus, the first conditions is 126 . satisfied. Also, the absolute value of TotPrEffD-rw is always less in absolute magnitude than the simple linear sum of the n individual production effects, represented by the broken-line diagonal, satisfying the second condition. Finally, as the simple linear sum of individual factor proportional effects increases, TotPrEffDTw also increased, but at a decreasing rate. TotPrEffDTw, therefore, has no maximum, and condition three is satisfied. 41 r F. 1 I r‘I‘I'l' -6 -5 -4 -3 -2 -1 H. N—i a... .J 0.. 05‘ x = linear sum of individual proportional production factor effects y = net combined proportional production effect Figure 4.6. Combined proportional production effects Thus, to summarize, DELAY was stochastically modified with the assistance of triangular probability distributions which were autocorrelated. The net proportional production effect resulting from combining the effects of several individual production factors was determined by the function presented in Figure 4.6. A schematic overview 127 of the DELAY modification process (contained in subroutine DELMOD of Appendix F) appears in Figure 4.7. For purposes of clarification, the following hypothetical example is offered. If pigs become crowded and simultaneously experience an increase in respiratory disease, the net effect of these concurrent changes would be captured as follows. The effect of decreasing available floor space per pig would be stochastically obtained from a triangular probability density function. This function is defined by the highest possible, most likely, and lowest possible effects expected for the given change in space per pig. However, the stochastic event would not be independently generated: it would be autocorrelated with the effects experienced as a result of changing available floor space per pig in the immediate past time period. Thus, the response of the pigs would be random, but would also be related to previous responses to changes in floor space. A similar process would generate an expected effect for the increasing respiratory disease. Again, the result would be obtained stochastically from a characteristic triangular probability density function, with the outcome autocorrelated with the production effect associated with changing pneumonia in past periods. Finally, the net production effect expected from experiencing simultaneous changes in these two production factors would be obtained through use of the function proposed for combining individual production effects. This would be accomplished by first adding the individual proportional effect obtained for changing floor space to that obtained for changing respiratory disease. The resulting sum would then be "passed through" the natural logarithm function presented above. Thus, the function’s value corresponding to the given sum would provide the net effect considered by the model to be exerted by these two simultaneously changing production factors. Due to the methods employed in defining this critical modification process, serious scrutiny should ensue when data permit. Obviously, theoretical appeal does not guarantee validity. 80.3%. 953m 3.33:5 85m 5.35.2 6038:608 >38 . 2. >53 129 New Simulation inherently involves projection. However, successful prediction of system performance and output often hinges on the availability of information regarding future values of certain underlying system variables (input levels and prices, output prices, disease rates, death rates, rate of feed consumption, etc.). Therefore, accurate simulation of system performance is frequently contingent upon the accuracy of previous forecasts for these system variables. However, completion of such forecasts is not a trivial task. The variables of interest are often difficult to observe reliably, which markedly complicates their prediction. In the absence of extensive sets of accurate data with which to formulate predictive statistical models for these system variables, certain projection techniques based on time series data are useful. Among these is the alpha-beta tracker (Manetsch, 1984). An alpha-beta tracker predicts an unknown based solely on its current observed value, its current perceived rate of change, and its previously predicted value. The equations for the alpha-beta tracker are: y(t) = yp(t) + (a * [v(t) - yp(t)l), (,3 * [u(t) - ypml) DT dy(t) = dy(t—DT) + yp(t+DT) = y(t) + (DT * dy(t)). where u(t) = measurement of the unknown from observation at time t, y(t) = the estimate of the unknown at time t, yp(t) = prediction of the unknown at time t based on observations through time (t-DT), dy(t) = estimated rate of change of the unknown at time t, yp(t+DT) = prediction of the unknown at time (t+DT) based on observations through time t, and (2,19 = design parameters which tailor the alpha-beta tracker’s performance to the particular system being modeled. 130 Notice that, in the first equation, the alpha-beta tracker "corrects" its own previous prediction based on an observation, u(t), assumed to contain measurement error. For the current model, those variables forecast using an alpha-beta tracker are listed in Table 4.4. For illustrative purposes, the variables are organized in Table 4.4 according to general type. However, each of the 38 variables contained in the table were individually projected with its own alpha-beta tracker. Use of the alpha-beta trackers in relation to the rest of the model appears in Figures 4.1 and 42. Table 4.4. Variable series projected with the alpha-beta tracker Disease Rates Non-feed Cash Expenses Mild pneumonia Repairs and maintenance Moderate pneumonia Veterinary care and drugs Severe pneumonia Labor Mild ascariasis Supplies Moderate ascariasis Fuel Severe ascariasis Electricity Mild mange Telephone Moderate mange Trucking Severe mange Marketing Mild atrophic rhinitis Insurance Moderate atrophic rhinitis Interest Severe atrophic rhinitis Taxes Nasal septum deviation Pleuritis Replacement females Replacement boars Pericarditis Other cash expenses Pig Deaths Feed Consumption Finisher Finisher Grower Grower Nursery Nursery Marketing Measures Market hog weight Market hog price gt"' As discussed in Chapter 2, the capacity for optimization is often desired for use with simulation because of its contribution to the model’s applicability. Also, optimization can be useful in defining system parameters, as will be discussed later. For this modeling exercise, the optimization technique employed was the COMPLEX routine 131 of Box (Kuester and Mize, 1973) Basically, COMPLEX involves a non-gradient search algorithm. The user-defined objective function is optimized over the variables of concern by starting with a "best guess" at the true optimum and then randomly generating an entire set of alternative "possible" optima. This set must contain at least one more possible optimum than the number of variables included in the search. Once generated, these points are treated as vertices of a multidimensional geometric figure (or "complex"), whose centroid is subsequently calculated. Corresponding values of the objective function are calculated for each of the vertices and for the centroid. Starting from the vertex with the worst objective function value and proceeding through the centroid, COMPLEX establishes a search direction. After moving a user- controlled distance in this direction, a new point is defined and evaluated for its corresponding objective function value. If this value is an improvement over that of the original worst vertex, the new point is substituted for the old worst vertex in the multidimensional figure. On the other hand, if the objective function value at the new point is not as good as that of the original worst vertex, COMPLEX moves back toward the centroid. The distance of this movement is also user-controlled and again, a "new" point is defined where the objective function value is evaluated and compared to that of the original worst vertex. This process continues either until a "better" point is discovered, or until a user- defined criterion for maximum number of unsuccessful improvement attempts is exceeded, whereby the search is terminated short of the optimum. Barring such premature termination, inferior vertices are progressively replaced. An acceptable neighborhood and value for the optimum is achieved when the objective function values of an acceptable number of "new" vertices are within a prescribed amount of absolute objective function value variation. These search parameters are also user-defined. COMPLEX is relatively efficient in moving to the neighborhood of the optimum. It is also capable of optimizing virtually any objective function, constrained or 132 unconstrained, and linear or non-linear. For these reasons, it provided a very good optimization routine for the current work. 0 v ' s Finally, it is important to consider the methods used for financial calculations. Because fixed costs, assets, and liabilities were not included in the database, financial analysis was restricted to estimation of Income Over Variable Costs, as presented in Figure 4.2. Though certain aspects of a thorough financial analysis are precluded as a result, most short-term health management alternatives do not entail major changes in fixed costs, assets, or liabilities. If particular situations should arise where these factors are indeed critical to the analysis, the current model will supply the needed information on the changing variable factors to a somewhat broader analysis conducted after the model projections have been obtained. In the current model, revenues are projected by combining the projections for hog marketings with those for market weights and market prices. Both feeder pigs and market hogs are included (see Figure 42), but the required information on numbers and prices for feeder pigs needs to be specifically entered (these are not projected). As a result, expected income was estimated as: TPRQ+1 = (PMHSQ+1 * PMHPQ+1* PMHWQ+1) + (PFPSQ +1 * PFPPQ +1) where TPRQH = total projected revenue, PMHSQ+1 = projected market hog sales (from physical production model), PMHPQH = projected market hog price (from alpha-beta tracker), PMHWQ +1 = projected market hog weight (from alpha-beta tracker), PFPSQH = projected feeder pig sales (as entered by user), and PFPPQH = projected feeder pig price (as entered by user). All variable costs were formulated on a "per pig-day" basis, where input rates were obtained from the physical production model (PIG DAYS from Figures 4.1 and 42) Non-feed, variable input prices (NON-FEED CASH EXPENSES from Figure 4.2) were 133 projected with the alpha-beta tracker, but feed prices required input by the user. The feed prices entered were the total costs per pound for each of the nursery, grower, or finisher rations. Since only one price per ration was considered, it became a "blend" price which combined purchased and raised feeds. This approach was taken to preclude the need for distinguishing between home-raised and purchased feeds. Doing so also avoided the need to explicitly address appropriate transfer pricing techniques for feeds (between crop and livestock enterprises) in the model. Thus, total expected variable costs were estimated as: (PNPDQH * PNFPPQ +1 * PNFCPDQH) + (PGPDQ +1 * TPVCQ+1 = PGFPPQ+1 * PGFCPDQ+1) + (PFPDQ+1 * PFFPPQ+1 * PFFCPDQ+1) + (PTPDQ +1 * E PNFVCPDQ+1) where TPVCQH = total projected variable costs, PNPDQH = projected nursery pig-days (from physical production model), PNFPPQH = projected nursery feed price per pound (as entered by user), PNFCPDQ +1 == projectid nursery feed consumption per pig—day (from alpha-beta tracker PGPDQH = projected grower pig-days (from physical production model), PGFPPQH = projected grower feed price per pound (as entered by user), PGFCPDQ +1 = projected grower feed consumption per pig—day (from alpha-beta tracker), PFPDQ+1 = projected finisher pig-days (from physical production model), PFFPPQ+1 = projected finisher feed price per pound (as entered by user), PFFCPDQH = projected finisher feed consumption per pig-day (from alpha- beta tracker), PTPDQH = projected total pig—days (from physical production model), PNFVCPDQ+1 = projected non-feed variable costs per pig-day (from alpha-beta tracker), and n = 15, the number of different non-feed expense categories (from Table 4.4). 134 Finally, the computation of INCOME OVER VARIABLE COSTS proceeded as follows: where PIOVCQ+1 = projected income over variable costs and other variables were as previously defined. PARAMETER ESTIMATION By its nature, systems modeling involves prediction of state changes for a given system over time. Initial state and system inputs provide the bases for change and system outputs are consequential. The rates of change for these state and input variables are referred to as system parameters. How successful the model is at projection is determined by the degree to which the model’s system parameter values represent actual rates of change for the system being modeled. Therefore, definition of system parameters is an extremely critical step in the modeling process. Various techniques are used to discover system parameters, depending on system characteristics and available modeling resources. These techniques range in degree of sophistication from simple use of expert opinion, to statistical estimation, to "reverse optimization" (which minimizes the difference between model projections and observed system performance). Those model parameters whose estimation was critical to the current work were DELAY, disease effects, feed additive effects, space effects, feed efficiency, and the alpha-beta tracker parameters. Ini i D Using only aggregate population data makes it difficult to accurately calculate a mean time spent in the production system (or days to market) for growing hogs. This is especially true if a relatively short time period is considered and if pigs entering and leaving the system are grouped. Thus, even though the SHIMS database specifically addresses physical production, the system parameter DELAY was difficult to estimate. 135 Because DELAY is expected to vary both across farms and over time, expert opinion and statistical estimation were not practical For these reasons, "reverse optimization" using COMPLEX was employed. Such optimization is referred to as "reverse" since observed system performance was used to estimate system parameters through optimization rather than optimizing projected system performance based on observed system parameters. In this case, optimization involved discovery of the mean DELAY which would minimize the sum of squared differences between observed and predicted values for ending inventory and total hogs marketed over individual 3-month periods. A modified version of the system model described earlier in this chapter was used for projecting pig- days. Changes to the model entailed removal of delay modifications and elimination of random variation while using observed values for input levels, specific days of input, and specific days of output. A unique search was conducted for each farm for every quarter. And each search involved a single variable: the mean DELAY for that particular three month period. The DELAY value resulting from optimization in any given quarter provided initial input for the DELMOD subroutine when the following quarter was simulated. W15 Numerous accounts of the effects of disease on hog production exist in the literature. Commonly included in these is consideration of the production diseases included in the SHIMS database. Nearly all of these report decreased growth rates associated with disease. Though authors generally agree that this is the type of production effect that disease imparts, considerable disagreement exists regarding the absolute magnitudes of those effects. When individual diseases are considered, it is found that very limited information is available on the relative production effects according to severity of affliction. 136 For example, Lindqvist (1974) reported that hogs with pneumonic lesions at slaughter exhibited lower average daily gain (ADG) over the nursery, grower, and finisher phases than hogs without lesions. The mean differences observed were decreases of 62% and 0.2% for hogs with severe and mild lesions, respectively. However, the maximum difference in ADG was observed to be 26% and 9.6% lower for hogs with severe and mild lesions, respectively. In hogs with pleuritic lesions, the mean difference recorded was a 5.0% depression associated with pleuritis, with the worst being 19.5% lower. Sever ascarid-related liver scarring was associated with a mean ADG depression of 4.3% across several groups, while the maximum depression was 13.9%. And mildly ascarid-scarred livers accompanied hogs with a mean decrease of 1.2% in ADG, while the maximum decrease encountered was 7.74%. Hale, et al. (1985) reported a decreased ADG in grower and finisher hogs of 92% associated with experimental infection with ascariasis. In contrast, Flesja, et al. (1984) concluded that evidence of ascariasis at slaughter could not be associated with any detectable performance difference. However, they did conclude that decreased ADGs in nursery, grower, and finisher pigs of 92%, 4.4%, and 1.6% were associated with presence of atrophic rhinitis, severe pneumonia, and moderate pneumonia at slaughter, respectively. Finally, Straw and Ralston (1987) also reported decreased ADG associated with disease present at slaughter. For nursery, grower, and finisher hogs, the suggested the decreases to be 3 to 26%, 7.6 to 12.5%, and 4.5% for pneumonia, mange, and ascariasis, respectively. With atrophic rhinitis, they documented decreased of 0 to 9% and 10% for grower/finisher and nursery pigs, respectively. Because of the described state of current knowledge, the characteristics of the SHIMS database, and the design objectives of the current modeling exercise, estimation of disease effect parameters presented some potential problems. To facilitate DELAY modification, the effect of disease on growth rate was of particular interest. However, 137 insufficient data existed in the pilot phase of the SHIMS project to allow statistical estimation of parameters. While such estimation should be considered as a high priority for future efforts, it was necessary to use a combination of the above published reports and expert opinion (Thacker, 1987) to provide the needed values for the current modeling exercise. Summaries of these appear by production phase in Tables 45, 4.6, and 4.7. Notice that parameter presentation is readily amenable to the format required for the triangular probability density functions discussed earlier. Also, recall that even though these diseases affect production in all three production phases, diagnosis occurs only at slaughter. I Table 45. Proportional effects of SHIMS disease categories on finisher average daily gain (negative values indicate a depression of growth rate) Low Mode High Severe pneumonia -0.044 -0.122 -026 Moderate pneumonia -0.021 —0.044 —0.126 Mild pneumonia 0.0 —0.021 —0.044 Severe ascariasis -0.043 -0.091 —0.139 Moderate ascariasis -0.024 -—0.05 —0.077 Mild ascariasis 0.0 —0.012 -0.05 Severe mange -0.076 —0.125 —0206 Moderate mange —0.046 -0.076 -0.125 Mild mange 0.0 —0.046 -0.076 Severe atrophic rhinitis -0.059 -0.09 -0.138 Moderate atrophic rhinitis —0.027 —0.059 —0.09 Mild atrophic rhinitis 0.0 -0.027 —0.059 Pleuritis ' 0.0 —0.05 —0.195 Pericarditis 0.0 —0.05 -0.195 Nasal septum deviation 0.0 -0.059 —0.138 Numbers in Tables 4.5, 4.6, and 4.7 correspond to expected individual pig proportional effects on average daily gain, or PrEffADG as discussed in the section on delay modification, associated with an increased frequency of disease. If disease rates decrease, the following transformation is necessary to maintain the format of proportional change from a known starting point: ADG(HD) = so ADG(LD) = where ADG(HD) = ADG(LD) = and PrEffADGODR) = 138 (1 + PrEffADGODR» * ADG(LD), 1 ADG(HD) .. (1 + PrEffADGODR» , average daily gain with "high" disease rate, average daily gain with "low" disease rate, the proportional effect on average daily gain of increasrng the disease rate (ie. the values in Tables 45, 4.6, and 4.7). Therefore, if the system is moving from "high" to "low" disease, the proportional effect of the change on average daily gain, now defined as PrEffADG(DDR), is obtained as follows: (1 + PrEff A13603139.» or PrEffADG(DDR) 1 (1+ PrEffADG(IDR)) 1 - 1 . Table 4.6. Proportional effects of SHIMS disease categories on grower average daily gain (negative values indicate a depression of growth rate) Low Mode High Severe pneumonia -0.044 —0.122 -026 Moderate pneumonia —0.021 -0.044 —0.126 Mild pneumonia 0.0 —0.016 -0.044 Severe ascariasis —0.043 -0.091 —0.139 Moderate ascariasis -0.024 —0.05 -0.077 Mild ascariasis 0.0 —0.012 —0.05 Severe mange -0.076 —0.125 ~0206 Moderate mange —0.046 -0.076 —0.125 Mild mange 0.0 —0.046 -0.076 Severe atrophic rhinitis —0.06 —0.092 —0.141 Moderate atrophic rhinitis —0.028 —0.06 -0.092 Mild atrophic rhinitis 0.0 -0.028 -0.06 Pleuritis 0.0 -0.05 --0.195 Pericarditis 0.0 —0.05 —0.195 Nasal septum deviation 0.0 -0.06 -0.141 139 Table 4.7. Proportional effects of SHIMS disease categories on nursery average daily gain (negative values indicate depression of growth rate) Low Mode High Severe pneumonia —0.044 -0.122 -026 Moderate pneumonia —0.021 —0.044 -0.126 Mild pneumonia 0.0 -0.016 —0.044 Severe ascariasis -0.043 -0.091 —0.139 Moderate ascariasis -0.024 —0.05 —0.077 Mild ascariasis 0.0 —0.012 —0.05 Severe mange —0.076 -0.125 -0206 Moderate mange -0.046 -0.076 -0.125 Mild mange 0.0 -0.046 —0.076 Severe atrophic rhinitis -0.03 -0.065 -0.10 Moderate atrophic rhinitis —0.03 —0.065 -0.10 Mild atrophic rhinitis 0.0 -0.03 —0.065 Pleuritis 0.0 -0.05 —0.195 Pericarditis 0.0 -0.05 —0.195 Nasal septum deviation 0.0 —0.065 -0.153 As a closing comment on disease effects, it is important to understand how the observations of disease rates obtained four times annually were adapted to the one day time—step framework of the analytical model. Basically, the approach was simple linear interpolation, where the daily changes in disease rates for the three month period being simulated were all equal proportions of the total. Specifically, they were calculated as: (DRPQ+1 - DROQ) DDR = NDAYS where DDR = daily change in disease rate, DRPQH = projected disease prevalence rate that will be observed at next quarter’s slaughter health check, DROQ = disease prevalence rate observed at the current quarter’s slaughter health check, and NDAYS = total number of days in the quarter being simulated. Thus, as suggested, all simulated days for a given quarter involve the same complement of disease rate changes. 140 e d ' 've ect Similar to the situation with disease effects, the literature contains numerous reports regarding some feed additive effects of interest for the current model, but scant information on others. These effects have generally been reported as a "growth promotion" effect, without regard for possible effects on disease or death rates. For example, Stahly, et al. (1980) reported an increase in ADG in nursery pigs of 17 to 22% associated with introduction of antibiotics into the feed, and an improvement of 22% corresponding to addition of copper. In contrast, Ribeiro de Lima, et al. (1981) contended the associated increases in ADG were only 3.4 to 42% for antibiotics and 4% for copper in the nursery. Powley, et al. (1981) documented increases in ADG with antibiotic feeding of 4% in the finisher, 4 to 14% in the grower, and 14% in the nursery. Biehl, et al. (1985) reported that increases in ADG of 102% and 7.6% were seen with use of antibiotics and carbadox, respectively, in the nursery. Similarly, an NCR- 89 (1984) committee documented an increase of 132% in ADG with use of antibiotics and sulfas in the nursery. In the grower and finisher, the increased ADG associated with antibiotic feeding was reported to be 1.5% by NCR-89 (1986) and no effect was observed by Moser, et al. (1985) Finally, Root and Mahan (1982) found that ADG was improved in the nursery by 10% and 3% associated with use of carbadox and copper, respectively. Similarly, Zimmerman, et al. (1982) documented increases of 0 to 25% and 0 to 5% for use of carbadox in the grower and finisher, respectively. They also found that feeding of pyrantel had no effect in the grower, but increased ADG by 31% in finisher pigs. Again, effect on growth rate was of primary interest, but the relative data shortage necessitated the selective blending of these published accounts with expert opinion (Thacker, 1987) into the desired lowest—most likely—highest format. Coefficients appear in Tables 48, 4.9 and 4.10 as they are contained in the model for increases in feed additives. Once more, these are individual pig effects, such as PrEffADG, and 141 Table 4.8 Proportional effects of SHIMS feed additive categories on finisher average daily gain (positive values indicate enhancement of growth rate) Low Mode High Antibiotics 0.0 +0.028 +0.056 Carbadox 0.0 +0.025 +0.05 Anthelmintics 0.0 +0.025 +0.05 Copper 0.0 +0.01 +0.02 Sulfa 0.0 +0028 +0.056 Table 4.9. Proportional effects of SHIMS feed additive categories on grower average daily gain (positive values indicate enhancement of growth rate) Low Mode High Antibiotics +0.015 +0.076 +0.14 Carbadox 0.0 +0.113 +0.25 Anthelmintics 0.0 +0.11 +031 Copper 0.0 +0.01 +0.02 Sulfa +0.015 +0.076 +0.14 Table 4.10. Proportional effects of SHIMS feed additive categories on nursery average daily gain (positive values indicate enhancement of growth rate) Low Mode High Antibiotics +0.034 +0.125 +022 Carbadox +0.024 +0.088 +0155 Anthelmintics 0.0 +0.08 +031 Copper +0.03 +0.097 +022 Sulfa +0.034 +0125 +022 142 transformation must occur to preserve the proportional-effect basis when feed additives are decreased. Finally, recall from previous discussion that feed additives are generally administered to the entire population, and as such the model implements their overall effects according to: Also, the effect that results only happens once, at the time of the management change (addition or removal of feed additives). However, the question arose regarding whether or not the alteration of performance was instantaneous. As with other aspects of the e current model. published reports in this regard were not found, and again expert opinion was incorporated (Thacker, 1987). Consequently, the model implemented the production effects of feed additive changes over a three day period. Because the basic modification was multiplicative, each of the three days involved the following adaptation of the above equation: DTW(t+DT) = DTW(t) * (1 + PrEfrDTW)V3. W As with disease and feed additives, published accounts of the effect of available floor space per pig on production vary considerably. For instance, Lindvall (1981) reported that decreasing available floor space in the nursery from 0.25 to 0.17 mZ/pig resulted in a decrease of 8% in ADG, but changing from 025 to 0.13 mZ/pig brought about a 20% ADG decrease. Also in the nursery, NCR-89 (1984) found an increase of 10.6% in ADG when available floor space per pig was increased from 0.14 to 0.23 m2 In the grower, Moser, et al. (1985) observed a decrease in ADG of 62% when floor space per pig was decreased from 037 to 028 m2. Also, they observed a decrease of 7% when decreasing per pig floor space from 0.74 to 0.56 m2. NCR-89 (1986) recorded a decrease of 5.7% in ADG when space per pig went from 0.46 to 032 m2 in the grower. while a decrease of 2.9% was observed in the finisher with a reduction in space per pig 143 from 0.74 to 056 m2. Finally, Randolph, et al. (1981) reported a diminished ADG of 7% 2 2 associated with space per pig of 0.33 m as compared to 0.60 m in the grower and finisher. Combining these mixed reports with the need for an adjustment approach resulted in the mathematical relationships defined for the FINISH ER, GROWER, and NURSERY which appear in Figures 48, 4.9, and 4.10, respectively. These relationships were defined using the relative space effects on average daily gain contained in the literature. For instance, Lindvall (1981) reported that a change in available NURSERY floor space from 025 to 0.17 mZ/pig was associated with a decrease of 8% in average daily gain. It is this 8% change that is important from Figure 4.10, and not the absolute percentage effects on growth rate that appear on the vertical axis. For fitting the curves, a non-linear regression technique (linearization) available with PlotIT° was used. Therefore, when reviewing Figures 48, 4.9, and 4.10, it is critical to remember the adjustment approach, so that only changes in growth rate which result from changes in available floor space are considered. Also, the proportional effects contained in the figures correspond to decreases in available floor space per pig. For example, Figure 4.10 indicates that the expected relative change in growth rate associated with decreasing the available NURSERY floor space from 0.3 to 025 m2/pig is: f(025) — f(03) = -0.11463080 - (—0.07850064) = -0.03613 , or about a 3.6% decrease in average daily gain. If, on the other hand, the floor space increased from 0.25 to 03 m2/pig, transformation of the above result yields: 1 PrEffADG = —— - 1 (1 - 0.03613) = 0.03748, or about a 3.7% improvement in average daily gain. 144 Y °~°1 Y - 3(1) . 3):?(3(2) - X) + 3(a) ,,..-——--"‘ fé . 3(1) - -—.870387800 Ax” / :3 3(2) - 454973400 /’ / ‘” 0 -.1- 3(3) - .01570526 / "" / 64; // 8 1- « . 1:5 0 g -.2- 0-o g t... 0.: 1 3 ° —.3- as 2 OJ 0:: -.4 f I ' l X ' I I I I I I I T I I ‘ I ' I I I f 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 A . . 2 . vallable floor space per pig (rn /p1g) Figure 48 Effects of available floor space per pig on growth rate—finisher phase Y 0.0+ a) O I; 0 //’ '9 __‘ o -.14 ;/ “a; // 9.. . 1:19 o p - 2- 9'0 O 1.. law 05 Y - 3(1) ‘ 3x3(3(2) 1 X) + 3(3) .2 -.34 3(1) - -1.25222600 .1 . 3 3(2) - -a.14930900 35’ 3(3) - -.01250566 "-‘ I I ' I r I ' I e I ' I X 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Available floor space per pig (mz/ pig) Figure 4.9. Effects of available floor space per pig on growth rate-grower phase 145 Y 0.0 _fl__,___.-.- «0) W I’I”—"’ o It ’I a /./ “a / _. m -.1- , o...» /‘ I: G x- .9. H / fin / / 0‘: -.2~ 7 3° / La ‘5‘“ 'I “a // Y = 3(1) 1 3x1=(3(2) - X) + 3(3) 0) .2 ° .—.3- 3(1) = -.763570300 3 ' 3(2) = -7.64102400 85’ 3(3) = —.000349353 --4 ' I I I I I ' I ' I I I X 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Available floor space per pig (ma/pig) Figure 4.10. Effects of available floor space per pig on growth rate—nursery phase To accommodate the triangular distribution format, a potential range of values needed identified. When the equations appearing in Figures 48, 4.9, and 4.10 were estimated, the maximum proportional residuals observed in any of the equations was - 0.115 (or -11.5%) in the nursery. Thus, this proportional variation (_-+_-_0.115 or 141.5%) was established on the percentage growth rate changes. Feed Efficiency Conspicuous in its relative absence from the discussion to this point is the efficiency of feed utilization. Certainly the literature has afforded ample attention to factors that affect feed consumption and its conversion efficiency, and expert opinions abound on the subject. However, problems arise when observations of adjustment starting points are sought for modeling purposes. First, accurate measurement of feed usage is often difficult due to variation in feed delivery systems. The most difficult of these to precisely track involves attempts to estimate quantities of feed used as they are removed from home-produced inventories. 146 In addition, the conversion efficiency of feed to pork is directly dependent on both the quantity and quality of feed actually consumed. Even if total feed usage were accurately observable, the proportion of that total which is consumed versus wasted would probably still be indeterminable. Further, hog rations very frequently contain commercially prepared feedstuffs whose exact nutritional values are unknown. Even though feed tags may be available, the amount of information they contain is variable, and is frequently inadequate. For example, dietary energy levels are critical to nutritional evaluation. However, minimum legal requirements for feed tags include only protein, fat, fiber, and occasionally certain minerals (Boyd, 1988) As such, information on the energy content of a commercially prepared feed might easily be lacking. Situations such as this often preclude the thorough nutritional evaluation which must precede analysis of feed conversion efficiency, and appropriate modeling. In lieu of automated data collection on feed usage/consumption and extensive nutritional evaluation of feeds being fed, an alternative approach needed developed for the current modeling exercise. As discussed in the previous chapter, the SHIMS database attempts to capture the total amount of feed used over individual three-month periods according to ration. When this is combined with the total number of pig-days for the same time period (as observed with the aid of COMPLEX in the process of DELAY optimization), 3 mean amount of feed used per pig-day can be calculated. Trends in feed usage per pig-day are projected with the alpha-beta tracker (see Table 4.4). Combination of such projections with simulation of changes in the resident hog population permits prediction of feed usage. Limited as such in calculation capabilities, the effects of disease, feed additives, and available floor space on overall efficiency of feed conversion are necessarily restricted in the current model. Alterations in the amount of feed required per pound of pork produced are accomplished by changing the total number of days required to achieve market weight (DELMOD) Biologically, this corresponds to an increase in the nutrition required for maintenance relative to that needed for growth. 147 AIMEE; The final parameters which were estimated were the a and p values for the alpha-beta trackers. As with previously discussed aspects of the current model, the values of a and )9 for the alpha-beta trackers needed to be determined based on characteristics of the system being modeled. Ideally, the parameters a and 5 should be discovered by an optimization routine focused on the modeler’s objectives (Manetsch, 1984). For current purposes, the objective was to predict system variable values as accurately as possible. Unique values for a and 13 were selected for each of the 38 system variable series being forecast (see Table 4.4) Thus, each series had its own alpha- beta tracker. To encourage accuracy, the sum of the squared differences between projected and observed variable values was minimized over the entire current range of each data series. As such, a and [3 were re-estimated for each data series following each quarterly observation using COMPLEX as the optimization routine. These updated a and )6 values were then used for projection into the following quarter. More directly, the sequence of activity for the alpha-beta tracker in each of the 38 data series was: I. predict variable value with alpha—beta tracker for the approaching time period (t+DT), 2. observe variable value once (t+DT) is attained, re-estimate a and fl over entire variable series through present using COMPLEX. and 4. repeat steps 1 through 3 for subsequent time periods. Initial values of yp(t) and dy(t-DT) were assumed to be zero. To enhance the quality of prediction with the alpha-beta tracker, minimum variance on y(t) and dy(t) was sought. According to Manetsch (1984), this minimum 148 variance condition is referred to as critical damping, and is achieved by employing the following identity: or = (2 * 1905) — ,3 Thus, the COMPLEX needed only solve for 3. DISCUSSION In evaluating the economic analysis provided by the systems model and its associated computerized simulation, it is critical to recall that, at present, this is exclusively a research model. As such, attention should center on the techniques and parameters employed. Though thorough discussion is premature at this point, some potentially critical issues related to these techniques and parameters will be forwarded. Bringing these areas to light will prevent them from being overlooked in later discussion, and should accommodate their consideration during the remainder of the exercise. System Definition The NURSERY. GROWER, and FINISHER phases of hog production provide a seemingly discrete and easily defined system for modeling purposes. Exclusion of other aspects of production from the model greatly facilitated the modeling process. However. consideration of hog production and health management from weaning to market may also require additional information from other production phases before thorough economic analysis can occur. For example, perhaps preweaning history has a critical impact on how disease affects production in a given herd. This history might include factors such as pig health management, pig nutrition, lactating sow nutrition, lactating sow health management, gestating sow nutrition, etc. Further, even though an adjustment approach is employed, it may become evident that environmental factors in addition to available floor space warrant consideration when predicting hog 149 performance. Because the systems approach is expressly iterative, factors such as these should be considered even when evaluating results from initial applications. WM Assuming an adequate system definition, the systems modeling and computer simulation techniques employed should also be continually scrutinized. For instance, even though the alpha-beta tracker can be successful in predicting state variables for some systems (Manetsch, 1984), it may be necessary to use more traditional epidemiological models to adequately predict disease and death rates. This might be especially true when the model is predicting changes in disease and death rates associated with particular management changes. Since the alpha-beta tracker relies heavily on time trends, the effects of abrupt management changes, which may well represent turning points in production, might be more adequately modeled with an epidemiologic model incorporating appropriate risk factors. Further, the economic effects of alternative health management strategies can presently be evaluated only if the corresponding disease and death rates are known; the model cannot be expected to predict these rates (or, for that matter, evaluate the associated economics) without an epidemiologic model. In similar fashion, the methods of parameter estimation deserve attention. Use of COMPLEX to estimate mean DELAY s is somewhat innovative. If successful, the approach offers a substantial contribution to the information procurement process for hog producers. However, its performance needs to be scrutinized to assure its applicability. Likewise, expert opinion and literature reports on the performance effects of changing production factors should be rigorously examined. These production modifications are critical to the model’s performance. Along with the review of alpha-beta trackers for prediction, the use of discrete delays, distributed delays, and triangular probability density functions to represent a dynamic hog population should be closely followed. For instance, the aggregate, (population) modeling techniques offer much in the way of depicting a true herd 150 management approach and also afford remarkable computational efficiency. However, alternative methods that consider the hog population as a group of discrete individuals, each possessing prescribed probabilities of disease, death, slow growth, and accelerated growth may, in fact, provide better overall system representation. Again, these areas provide examples of the type of perpetual reevaluation characteristic of the systems approach. Suggestions are made at this point to stimulate on-going critical review. Q31; Because this exercise involves both an analytical model and an information management system. perpetual review should also embrace the database and all of its various features. Specific mention here serves only as a reminder that a good analytical model might well go unrecognized for want of good data to drive it. In light of some of the preliminary results and sample size calculations presented in Chapter 3, this comment might be especially relevant for the current research. 2311193211 Solutions to the uncertainties posed above can only be achieved through model validation. This process should involve two steps. First, the model should be developed on a small group of farms. For the current research, this group consisted of the SHIMS pilot producers. This phase of validation involved mostly subjective comparison of model predictions to corresponding system observations. Results of this process involving the SHIMS producers will be discussed in Chapter 5. Once initial fine-tuning is complete, an expanded group of farms should be employed. The size of this group should ideally follow the sample size estimations made in Chapter 3, and selection should be according to appropriate random procedures to prevent bias. Sampling at slaughter, if continued, should also follow the guidelines discussed in Chapter 3. Provided successful expansion, predictions from the model can be statistically compared to subsequent observations to furnish a more rigorous, objective validation. Further discussion of validation is deferred to Chapter 6. 151 SUMMARY Chapter 4 has presented the analytical model portion of a decision support system for production and health management of growing/finishing hogs. Both mathematical modeling and computer simulation techniques have been addressed. Preliminary discussion has also occurred with the intent of stimulating perpetual critical evaluation. Chapter 5 considers the results of initial model applications. CHAPTER 5 RESULTS OF DECISION SUPPORT SYSTEM APPLICATION INTRODUCTION The two preceding chapters respectively described the development of an information management system and an analytical model focused on swine production and health management. Together, these tools were intended to provide health management decision support for growing and finishing pigs. This chapter presents the results obtained from initial joint application of the information management system and analytical model. A subsequent chapter will discuss these results in an assessment of the ultimate applicability of the techniques employed for on-farm decision support. INFORMATION MANAGEMENT SYSTEM As discussed in Chapter 3, the information management system designed for this research was implemented as a pilot project involving six commercial hog producers in Michigan from spring 1986 to winter 1988. Several notable strengths and weaknesses were also pointed out. Among the strengths was the structured combination of data on both physical and financial production in a single database. At the same time, the shortcomings arose primarily from the types and amounts of data collected on these different areas of production. While the system was not sufficiently broad to provide comprehensive coverage of all aspects of production, it often requested considerably more data than was strictly necessary to address the issue of health management in the growing and finishing pigs. As a result, both the quantity and quality of data provided 152 153 by pilot participants experienced serious dilution. Discussion of the dilution’s severity follows. 122mm An overview of the data actually received from the SHIMS pilot producers appears in Table 5.1. From this it can be seen that of the possible forty-eight "producer- quarters" (one producer’s data during one of the eight annual quarters covered by the project’s data collection), only fourteen were completely achieved. As such, these fourteen producer-quarters provided the only complete SHIMS farm management reports of the pilot phase, and also provided the only basis for thorough review of the corresponding analytical model. However, twelve of the remaining thirty-four producer- quarters lacked only data on feed usage. Though absence of feed usage information precluded complete financial analysis, the physical production aspects of both the SHIMS farm management report and the analytical research model were still feasible. Thus, a total of twenty-six producer-quarters were available for analysis of physical production. On evaluation of Table 5.1, the most immediately obvious feature is the lack of data for Producer 6. In short, even though most of the requested data were already being maintained by this producer, the perceived benefits of providing the data to MSU personnel were obviously less than the perceived cost, in terms of effort required. This may have been related to the willingness of MSU to continue providing slaughter health checks even without receiving the desired, on-farm data in hopes of eventual historical reconstruction. A similar situation occurred with Producer 1, from whom on-farm data were received only after project completion. As Table 5.1 reveals. historical reconstruction for Producer 1 was not entirely successful. These data collection problems occurred in spite of repeated inquires by SHIMS personnel at MSU. However, in attempting to maintain an atmosphere of cooperation, these inquiries were generally make through a positive approach, and were never pressuring. 154 Table 5.1. Data received from SHIMS producers, spring 1986 through winter 1988 Producer Data number Quarter Sp Su class 1986 1986 ,_. §~n ,_. §€ (D '0 g “a: §= H gm gs SHC PDAT CEXP FU SHC PDAT CEXP F U SHC PDAT CEXP FU SHC PDAT CEXP F U SHC PDAT CEXP FU SHC PDAT CEXP F U I ><><>< XXOX I><><>< ><><><>< ><>< I><><>< XXXX XXOX OOOX ><><><>< O>< O><><>< I I I X ><><><>< O><><>< I I I X ><><><>< O><><>< I><><>< ><><><>< ><>< II I>< ><><><>< O><><>< I><><>< ><><><>< XXOX I I I>< ><><><>< O><><>< I><><>< ><><><>< ><>< II I>< ><><><>< O><><>< I><><>< ><><><>< XXOX II I>< ><><><>< O><><>< I><><>< ><><><>< ><>< I>< O><><>< III>< SHC PDAT CEXP FU Slaughter health check data Data on pig inventories and flows Cash production expense data Feed usage data Complete data Incomplete data No data 155 Further examination of Table 5.1 reveals evidence that the SHIMS data class most difficult to achieve was feed usage. Only two of the six pilot producers were able (or willing) to provide the requested data on the quantity of feeds being fed. Producer 4 was able to provide amounts (physical quantities) of feed ingredients as they were purchased, but the rations in use were not sufficiently unique to allow adequate estimation of quantities fed. Producer 3 maintained batch information initially, but batch sizes were not judged to be sufficiently uniform (in the absence of individual batch-weights) to provide reliable information. Following feed usage data in lack of successful achievement was physical production data. As with feed use. physical production data were often provided in an incomplete fashion. The characteristics of "incompleteness" involved insufficient data related to either pig inventories or pig flows, or both. For example, Producer 1 routinely maintained sufficient data on marketings and inventories, but failed to maintain weaning data. Other individual producer-quarters selectively lacked complete data as exceptions, rather than as routine. Finally, the most success was achieved in collecting financial production data. These data were generally adapted from existing financial record systems, and were usually provided in those formats. As such, financial records probably required the least amount of extra producer effort. W! Though, as discussed, complete data were obtained for fourteen producer-quarters, and sufficient physical production data were procured for an additional twelve producer- quarters, several questions regarding the quality of the data received have surfaced. Primarily, these relate to frequent inconsistency between pig inventory and pig flow data, and to inherent data "lumpiness." During the course of the pilot project, quarterly pig inventory data were requested (as discussed in Chapter 3) from participating producers. The original intent of researchers was that these inventories be obtained through actual herd counts on or 156 near the recommended dates. In actuality, and despite repeated encouragements, real quarterly counts were performed only by Producer 4. Inventory data from remaining producers (if provided) were achieved through either linear interpolation between actual annual counts, or through "appropriately" adjusting occasional counted inventories in light of known pig flows (weanings, marketings, deaths, etc.) For some management purposes, these "inventory" methods may provide sufficient accuracy; however, as will be discussed later in this chapter, the data which result from such techniques are too inconsistent to form a solid systems modeling and computer simulation base. Another potential source of inconsistency in the SHIMS database was the inherent "lumpiness" of the data. Lumpiness arose from the non-continuous nature of most management processes immediately peripheral to actual physical production. These included feed procurement (which occurred in "batches"), marketings (which occurred according to a non-continuous, predetermined schedule), weanings (which also took place according to schedule), and the incurring of other various cash expenses (such as annual property taxes, biannual interest installments, and fuel procurement). If reliable information regarding the actual input/output rates involved were available, effective analysis would have been virtually unimpeded (though somewhat complicated) by lumpiness. However, since the lumpy data were the sole source of input/output rates for the SHIMS database, the corresponding analysis must be interpreted very carefully. For clarification, several examples follow. Several particular problems regarding input/output lumpiness have become evident in the SHIMS database. First, though absolute a priori information was not available on SHIMS input/output rates, at least the expected schedules for activities such as weaning and marketing were known. In this case, inconsistency has arisen when pre- determined schedules appear not to be followed The most prominent example of this was provided by Producer 3. Ordinarily, this producer followed a weekly marketing schedule, except (according to the database) during December, when virtually no hogs were marketed, and January, when a veritable flood of hogs was marketed during the 157 first week. Most likely, this "phenomenon" was related to income tax management strategies. Following this marked marketing increase, seemingly "ordinary" marketing ensued. In this case, comparison of expected (based on pig flow data) January 1 inventory with an actual count might have revealed lack of consistency. Comparing these data to model projections would also likely reveal substantial differences between observed and projected values. However, as has been suggested, these differences were related (at least in part) to circumstances distinctly apart from poor model performance. The second major source of data inconsistency resulted from aggregation of lumps into "super-lumps." This process occurred most commonly with Producer 4, who ordinarily weaned and marketed pigs at weekly intervals, but aggregated weanings data into sow-group summaries (usually involving weanings that occurred over periods of four weeks). Further, marketings were aggregated into monthly summaries. As a result, the mean amount of time that pigs required to reach market weight as calculated from the "super-lump" data may have been inconsistent with that calculated from actual data on weanings and marketings. Also, weanings for a given sow-group were recorded on the last day that weanings from the particular group occurred. If, by circumstance, the last weaning for a given sow-group occurred during the first week of a month, the recorded data artificially overestimated weanings for that month, while underestimating weanings during the previous month. As a result, pig-flow inventory expectations for the beginning of the month were often inconsistent with counted inventories. Again, model performance must be judged accordingly. Production involving such markedly uneven flows of inputs and outputs can be handled through several alternative approaches. First, as briefly discussed, additional data can be collected on the actual rates which occur by explicitly focusing on smaller time periods. Secondly, an extended history of production can be evaluated to "smooth" the rates of use over a longer period of time. Finally, the time horizon of the analysis can be extended. As implied with the previous suggestions, the detrimental effects of lumpiness are diminished as the total amount of time being considered lengthens. 158 As suggested, one of the major areas of inconsistency arising from the data problems discussed was between expected pig inventories (based on pig flow and beginning inventory calculations), and data actually obtained regarding observed inventories. When each of the twenty-six SHIMS producer-quarters with sufficient physical production data were evaluated for such a lack of consistence, the following calculations were employed: Expected Ending = Beginning Inventory - Total Exits + Total Entries Inventory Proportional IExpected Ending Inventory — Observed Inventoryl Data = Inconsistency Observed Inventory Over the twenty-six producer-quarters, it was found that the mean proportional data inconsistency in physical production, calculated as above, was 0.08385. These errors ranged from a minimum of 0.00 to a maximum of 0.35. However, at least one participant (Producer 5) was known to calculate the inventory "observations," (between annual counted "observations") using an approach similar to that presented above for "Expected Ending Inventory." Also, it is interesting to note that one of these "observations" for Producer 5 provided the minimum observed proportional data inconsistency (0.00) Therefore, it is difficult to know just how accurately the actual data inconsistency is captured by the calculated mean data inconsistency. The probable importance of these findings will be addressed in the following chapter. However, it should be recognized at this point that substantial doubt has been cast on the quality of the physical production data contained in the twenty-six SHIMS producer-quarters. Evaluation of the subsequent analytical model application should bear this fact in mind. 159 THE ANALYTICAL MODEL When considering initial application results from the analytical model described in Chapter 4, two general areas warrant attention: the estimation of model parameters and the actual implementation of the model for purposes of simulation. Since the relative success achieved with simulation was directly dependent upon the quality of coefficients (or parameters) employed by the underlying model, the results of parameter estimation will be discussed first. W From Chapter 4, recall that several groups of system parameters needed to be quantified. Of these, the proportional growth rate effects of subclinical diseases, available floor space, and presence of feed additives were obtained from the literature. On the other hand, the current growth rate (as represented by Initial DELAY in Figure 4.1), the current rate of feed utilization, and the alpha-beta tracker parameters were estimated from the database by producer-quarter. The results of these estimations follow. DELAY. As presented in Chapter 4, the mean amount of time pigs spent in the finisher phase was estimated with the assistance of the COMPLEX optimization routine by producer-quarter. Results then provided input for simulation in the form of Initial DELAY. For producers with sufficient physical production data to allow such estimation, a summary of the results appear in Table 5.2. When the prescribed lengths of nursery and grower phases were included, the total length of the post-weaning period could be estimated. A summary of these appears in Table 5.3. While the results presented in Tables 52 and 53 may seem reasonable, some important concerns warrant mention. First, when using COMPLEX for parameter estimation, it is quite possible that the system as defined might have contained insufficient information to produce a "well-behaved" surface for optimization. This condition might have corresponded to either a highly irregular or nearly flat surface. 160 Table 5.2. Estimated length of finisher phase for SHIMS producers, spring 1986 through winter 1988 Length of finisher phase (days) Producer Number Mean Low High 2 10752 83.73 115.35 3 116.40 98.76 141.36 4 80.65 68.17 105.92 5 91.47 8234 102.57 Both of these situations signify a weak functional relationship for optimization and both can make identification of an unique optimum very difficult. When estimating the DELAYS, however, this did not occur. Repeated solution of individual producer-quarters consistently produced virtually identical results. Table 53. Estimated total length of post weaning period for SHIMS producers, spring 1986 through winter 1988 Length of post weaning period (days) Producer Number Mean Low High 2 16552 146.73 178.35 3 207.40 189.76 232.36 4 164.65 152.17 189.92 5 167.47 158.34 178.57 Another optimization concern relates back to the earlier data quality discussion. Since the estimation process rested on pig flow and pig inventory data, the quality of these was critical. Thus, the preceding discussion regarding data inconsistencies is especially pertinent. Finally, though the values resulting from the estimation process seem reasonable, they must be viewed cautiously until the technique can be validated 161 through concurrent, on-farm observation of the DELAY experienced by pigs in corresponding systems. Feed Use. When the optimal DELAY was discovered for a given producer- quarter, the estimated number of pig-days associated with that optimum was also recorded by production phase. Thus, each DELAY also provided an estimate of the number of nursery, grower, and finisher pig-days. Combining these figures with data on the total amount of feed(s) by ration provided estimates, by phase, of the amount of feed used per pig per day during individual producer-quarters. Since, as presented, feed usage data were only obtained from fourteen producer-quarters, the scope of this estimation was limited. Results appear in Table 5.4, where it can readily be seen that lumpiness is a primary concern, and that fundamental data quality may also be suspect, especially in the spring quarter of 1986 for Producer 5. It is interesting to note that this was also the producer-quarter with the maximum proportional data inconsistency (035) related to pigs in the system. Table 5.4. Estimated feed use per pig-day for SHIMS producers, spring 1986 through winter 1988 Production Phase Producer Number Quarter Nursery Grower Finisher (pounds feed/pig-day) 2 Su 1986 3 1.858 . F 1986 1.841 1.627 4.700 W 1987 2.369 1582 5.708 Sp 1987 1.897 1.646 5577 Su 1987 1.790 2.377 5.170 F 1987 2101 2.439 7563 W 1988 1.110 1091 3285 5 Sp 1986 1.794 23.310 17.618 Su 1986 1.891 4.855 3513 F 1986 3.770 7.177 2.769 W 1987 4.068 13384 0300 Sp 1987 2.624 5262 2.904 Su 1987 3580 9.293 1.078 F 1987 2.940 10.435 0.769 162 Because of the method used to estimate feed use per pig-day, widely divergent results could be related to either problems with hog population or feed use data. Though a definitive explanation cannot be offered, it is felt that the remarkable results obtained for Producer 5 in spring 1986 were related to errors associated with initiation of data collection. Beyond this, the fluctuations seen with this producer can probably be attributed to a combination of lumpy feed use data and the calculation (as opposed to counting) of pig inventories. As discussed previously, lumpiness can be appropriately taken into consideration provided fundamental data quality is adequate. However, the degree of success that this approach achieved in accurately estimating feed use per pig-day hinged critically on not only the quality of feed use data, but also on the caliber of pig inventory and pig flow data. Further, the estimated number of pig—days rested on the suitability of the COMPLEX optimization routine as well. Thus, again, some of the results (though limited) seem feasible, but confidence cannot be established in the absence of model validation. Alpha-beta Tracker. Returning again to Chapter 4, recall that a projection technique (called the alpha-beta tracker) was used for projection of certain data series that were required for simulation. Estimation of the parameters (a and )6) involved the use of the COMPLEX optimization routine in an attempt to minimize the error of prediction. Unique parameter values were estimated for each of the thirty-nine data series, and for each producer-quarter. As presented in Chapter 4, critical damping required that a be a function of )3, and therefore only )9 values were estimated through optimization. Summarization of the )3 values for the thirty-nine data series and twenty- six producer-quarters appears in Table 55. Some of the variability present in the )8 values may have resulted from under- identification. Unlike the optimizations to discover DELAY, repeated 5 value estimation for a given data series and producer-quarter did not always yield the same output. This 163 Table 55. Summary of beta coefficients for SHIMS pilot producer alpha-beta trackers, spring 1986 through winter 1988 Standard Data series Mean deviation Disease prevalence rates Severe pneumonia 1.1318 0.4249 Moderate pneumonia 0.9735 0.1412 Mild pneumonia 0.9984 0.0053 Severe ascariasis 1.2091 0.6785 Moderate ascariasis 1.0934 0.6797 Mild ascariasis 0.9883 0.0902 Severe mange 1.6897 1.0805 Moderate mange 1.2111 0.6155 Mild mange 1.0255 02138 Severe atrophic rhinitis 1.1522 0.6707 Moderate atrophic rhinitis 1.0898 0.4700 Mild atrophic rhinitis 0.9974 0.0118 Pleuritis 0.9784 03056 Pericarditis 0.8920 03636 Nasal septum deviation 1.0443 02690 Non-feed cash expenses Purchased feed 1.0032 0.0088 Repair and maintenance 0.9555 0.1182 Veterinary care and drugs 0.9047 02007 Labor 0.9810 0.0606 Supplies 08626 0.1705 Fuel 08089 0.1950 Electricity 0.9509 0.1142 Telephone 08502 0.6799 Trucking 1.3343 0.9654 Marketing 1.0962 0.7131 Insurance 0.8099 0.4046 Interest 1.2884 0.7664 Taxes 13303 0.9438 Replacement females 1.9292 1.2134 Replacement boars 2.3262 1.2353 Other 08893 0.1536 Pig deaths Finisher 0.9922 0.0546 Grower 0.9978 0.0201 Nursery 0.9956 0.0205 Marketing measures Market hog weight 0.9988 0.0090 Market hog price 1.0017 0.0083 Feed use Finisher 0.9993 0.0053 Grower 0.9961 0.0054 Nursery 0.9977 0.0069 164 was noticed to be especially true when the producer-quarter involved was early in the project. Further, it is interesting to note that many of the mean I? values were very close to 1.0. Reviewing the alpha-beta tracker equations presented in Chapter 4 reveals that if fl = 1.0, the critical damping condition provides that a = 1.0. As a result, y(k) = u(k), which implies a lack of systematic observational error. When coupled with the apparent occurrence of multiple optima, this lack of systematic error suggests that the optimization surfaces involved may have indeed been highly irregular. As discussed previously, this would indicate the presence of a weak functional relationship. For the alpha-beta tracker, this would suggest a weak relationship between successive observations of the variables being projected. Simulation Output and Evaluation To facilitate evaluation of analytical model output, a format was needed which presented the results of projection in a thorough, clear, and concise manner. Toward that end, the report presented in Table 5.6 was developed. As is evident, the format developed addressed both physical and financial production, and contained information on both the projected expectations and their variations. Also, the sources of data used for simulation were included. Because the validity of the model had not been firmly established, these reports were not provided to SHIMS pilot producers. Before presenting the results attained from simulations performed with the analytical model, it is necessary to explain the various scenarios evaluated. As is the case in actual production, an unlimited number of simulation scenarios were possible. Because primary objectives for this research centered on evaluating the usefulness of this modeling approach in providing decision support for animal health management, the scenarios examined attempted to silhouette major modeling techniques and the effects of subclinical disease. Also in light of the objectives, all scenarios used actual observations for pig weanings and feeder pig sales to preclude projection errors arising from these sources. 165 Table 5.6 Sample output from SHIMS computer simulation model Producer: XXXXXX Number of runs: 200 SHIMS SIMULATION OUTPUT Physical and Financial Performance 1. Data Sources 9MP.“ Expense rates, market weights and market prices market Feed usage Disease and death rates DELAY Ending Inventories Finisher Grower Nursery Average DELAY (days) Ending DELAY (days) Weanings Death Losses Finisher Grower Nursery Marketings Market hogs Feeder pigs Feed consumption (tons) Finisher Grower Nursery Total Mean 2,485.00 706.00 940.00 103.78 102.26 2,639.00 34.00 1200 48.00 1978.00 100.00 837.00 71.00 87.00 Projected Quarter: 4/87 actual observation actual observation actual observation model projection Standard Deviation Low 55.00 2,386.00 I t 2.78 9892 452 93.02 * 3 55.00 1,881.00 800 821.00 * I 8.11) 97900 High 2,581.00 108.77 112.83 2,078.00 * 85200 1,010.!” 166 Table 5.6 (cont’d). Mean 9. Revenue Market hogs $288,026.13 (price = $43.65/cwt) (weight = 333.64 lbs/head) Feeder pigs 4,543.14 (price = $156.66/cwt) (weight = 29.00 lbs/head) Total $292,569.25 10. Feed Expense Finisher $92,06355 (price = $0.05/lb) Grower 9,93680 (price = $0.07 per pound) Nursery 18,176.10 (price = $0.10 per pound) Total $120,176.45 11. Non-feed Cash Expense Standard Deviation Low High $7,988.68 $273,981.56 $3m55853 37% $278,524.69 $307,101.66 Repair and maintenance $10,114.19 Veterinary care and drugs 1,884.63 Labor 21,484.79 Supplies 1,256.42 Fuel 2.19874 Electricity 7,601.35 Telephone 0.00 Trucking 125.64 Marketing 2,63848 Insurance 2,26156 Interest 5.08850 Taxes 691.03 Replacement females 0.00 Replacement boars 0.00 Other cash expense 11,621.89 Total 36696723 12. Income Over $105,42558 Variable Costs $917.70 $90,363.02 $93,768.24 $917.70 $11,845.91 $121,881.13 5 61.65 $ 9,999.94 $10,228.71 11.49 1.86334 1905.97 130.97 21.24211 21,728.07 7.66 1.24223 1270.65 13.40 2,173.90 2223.63 4634 7,515.48 7,687.42 0.00 0.00 0.00 0.77 12422 127.06 16.08 2,608.69 2.66836 13.79 2,236.01 2,287.17 31.02 5,031.03 5,146.12 4.21 683.23 698.86 0.00 0.00 0.00 0.00 0.00 0.00 7084 11,490.62 11.753.49 $40322 $66,210.78 $67,725.52 $8,051.58 $88,918.04 $122,353.04 167 The following conventions have been used to help maintain clarity in discussion: variable values from the base producer-quarter for simulation will be subscripted 0, while those from the projected producer-quarter will be subscripted Q-I-l. Thus, model inputs for pig weanings and feeder pig sales were observed values of PIGSWEANEDQ+1 and FPIGSOLDQH, respectively, rather than projected values. Also, to achieve simulation, 3 complete producer quarter of data was required for model initialization, thereby providing a basis for projection. As a result, the first complete quarter of data for each producer served only the initialization purpose, and could not be projected. Thus, only twenty-two of the twenty-six producer-quarters with complete physical production data were candidates for projection and subsequent model evaluation. Because the model was stochastic in its modifications of DELAY, a Monte Carlo approach was employed for all projections involving DELAY alteration; each Monte Carlo involved 200 runs under identical conditions (except for the random variation) As a consequence, results of these analyses were obtained in the form of probability distributions, where results for scenarios not involving DELAY modification were single point estimates. For purposes of subjective comparison. total hogs marketed and mean DELAY by producer-quarter have been chosen as the criteria for assessing simulation performance regarding physical production. To facilitate such assessment, appropriate observed values of total hogs marketed and mean DELAY are presented by producer- quarter in all presentations of simulation results. Marketing observations were obtained from the SHIMS database, while DELAY observations were obtained from COMPLEX optimization as discussed in Chapter 4. Though subjective comparisons can be insightful, techniques for objectively reviewing the accuracy of a model’s predictions provide a more consistent, reliable means of evaluation. Due to the questionable validity of DELAY observations using COMPLEX (at least at this point), and in an attempt to maintain as much objectivity 168 as possible, only projected vs. observed marketings were included in such evaluations for this model. Also, because herd size and methods of data collection varied considerably between individual SHIMS pilot producers, objective evaluation of the model’s accuracy was undertaken producer-by-producer; no attempt was made to compare the model’s predictive power between producers. For current purposes, two methods were used to evaluate the model’s predictive power: mean absolute prediction error and Theil’s “2 statistic (Leuthold, 1975) Mean absolute prediction error was chosen because, for evaluating a model predicting numbers of hogs sold, it provides a practical, intuitively appealing measure of performance. This is especially true when the mean absolute prediction error is compared to the mean number of hogs a given producer markets per quarter, thereby defining the mean proportional difference between predictions and observations. The method of calculation follows: 5: | P — A | MAE = Q Q N where MAE = mean absolute prediction error, P0 = model prediction of total hogs marketed for producer-quarter Q (mean prediction of stochastic), A0 = observation of total hogs marketed for producer-quarter Q, and N = total number of producer quarters involved in the evaluation. Recall that producer was held constant for all such calculations and that observations of total hogs marketed for individual producer-quarters are summarized in each table containing simulation results to facilitate comparisons. As discussed by Leuthold (1975), Theil’s “2 statistic provides a suitable approach to evaluating the predictive performance of a model relative to that of other models. Comparison to a naive, no-change model is implicit in the calculation, while explicit comparison to other model formulations is also possible. For comparison to the no- change model, Theil’s “2 statistic assumes the null hypothesis of no-change in system performance. The calculation involved is: 169 < 2100 - P04) - (AQ - A0012 >05 u = 2 ( 2 (AQ - 410.92 >05 where u2 = Theil’s u2 statistic, P0 = model prediction of total hogs marketed for producer—quarter Q (mean prediction if stochastic), P04 = model prediction of total hogs marketed for producer-quarter Q-l (mean prediction if stochastic), A0 = observation of total hogs marketed for producepquarter Q, A04 = observation of total hogs marketed for producer-quarter Q-1, and N = total number of producer-quarters involved in the evaluation. Again, recall that producer was always held constant, and observations of total hogs marketed by producer-quarter are summarized in each table of results. From this equation, it can be derived that a model which predicts perfectly would yield u2 = 0, while the naive model yields “2 = 1.0. Thus, values of 1.0 < “2 < co indicate prediction less accurate than the naive model, while 0 < “2 < 1.0 signify predictions better than assuming no change. Finally, if u2 = 05, for example, the error of prediction is 50% that of the no change modeL These evaluations were performed for all of the following simulation scenarios. Evaluation of Data Qnality ANALYSIS 1. To establish a baseline for model performance, the first simulation scenario involved projection using the observed mean DELAY 0+1 for the producer- quarter being simulated, and without imposing any DELAY modification. This simulation addressed the question, "If the future performance of the system is known, how well will the model predict?" While this scenario may seem trivial at first, it provided both a better understanding of existing problems with data quality and a basis for relative comparisons between scenarios. Projections involving DELAY modifications should not be expected to outdistance projections using "known" performance criteria. 170 Results of these simulations appear in Table 5.7, and evaluation of their accuracy is presented in Table 5.8. Because all variable values employed in the first scenario were system observations, inaccuracy in the predictions obtained must be attributed to problems with data quality. Though Theil’s u2 statistics indicate a substantial improvement over the naive model (as well it should), it is quite disconcerting to note that for Producer 3, the model failed to explain even half of the change between successive producer-quarters. However, this finding is not too surprising when considering the previous discussions on data quality. Recall that Producer 3 substantially altered ordinary marketing patterns for apparent tax management purposes. As discussed, this was expected to impart the appearance of a deleterious effect on model performance. ANALYSIS 2. The next scenario examined the ability of the model to predict if the current DELAY Q observation was used for projection without modification. In effect, the question was asked, "If current performance is known, but information regarding the upcoming quarter’s performance is completely lacking, how well does the model predict?" Results of these simulations provided a basis for evaluating the DELAY modification process. Outcomes are presented in Tables 5.9 and 5.10. With the exception of Producer 3, this scenario, as expected, failed to predict as accurately as that using all known parameters (ANALYSIS 1). For Producer 3, the improvement in accuracy was quite small, and should be attributed to the combined effects of data quality peculiarities (recall previous discussion) and chance. Prediction was least effective for Producer 5. This may have been related to the fact that this producer was known to calculate inventories, as previously discussed. Also, as the smallest producer in the pilot group, lumpiness in weanings and marketings was considered to be a very real problem, especially because projected marketing schedules were not rigorously followed. As with the tax management of Producer 3, this "irregular lumpiness" in marketings may have contributed to an appearance of questionable model 171 Table 5.7. Simulation projections based on unmodified DELAY 1 for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 1) Observed Projected Producer Total Hogs Total Hogs Number Quarter Marketed Marketedofl 2 Su 1986 2013 1997 F 1986 2032 2112 W 1987 2155 1956 Sp 1987 2117 2059 Su 1987 1873 2147 F 1987 2419 2359 W 1988 2284 2360 Mean 2128 2141 3 Su 1986 1222 1131 F 1986 801 1202 W 1987 1742 1428 Sp 1987 1084 1148 Su 1987 983 1024 F 1987 1006 1454 W 1988 1633 1266 Mean 1210 1236 4 F 1986 4833 4025 W 1987 4041 3828 Sp 1987 3994 4132 Su 1987 3607 3846 F 1987 4388 4547 W 1988 3728 3612 Mean 4098 3998 5 Su 1986 401 344 F 1986 434 505 W 1987 499 454 Sp 1987 331 364 Sn 1987 443 426 F 1987 443 381 Mean 425 412 172 Table 58. Accuracy of simulation projections based on unmodified DELAY +1 for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 1) Mean Producer Absolute Theil’s u Number Error Statistic 2 109.00 025085 3 24657 0.63845 4 27800 0.19209 5 4750 0.42753 performance. Considering the entire group of producers. accuracy of prediction was diminished, but the model still predicted considerably better than the naive model, as evidenced by Theil’s “2 statistics. Evaluatio 0 st odel ANALYSIS 3. Next, simulation was focused on the basic ability of the model to predict system performance with "perfect" information on disease rates and pig deaths. For this approach, the hypothetical question being addressed was, "If perfect information regarding future pig deaths and prevalence of subclinical disease at slaughter were possible, how well would the model predict system performance?" To facilitate the analysis, the desired perfect information was emulated by using actual observations for the simulated period regarding the diseases (DISQ +1) and deaths (DTHQH) of interest, and then allowing the model to start with DELAYQ and adjust accordingly. For evaluation of model performance, projected total hog marketings (THOGSMKTQH) and mean DELAYQ +1 were again compared to respective observations by individual producer-quarters. These appear in Tables 5.11, 5.12, and 5.13. Since this scenario involved DELAY modification, Monte Carlo analysis was performed as is reflected by the inclusion of both mean values and variability estimates in Tables 5.11 and 5.12. General evaluation of these results would intuitively indicate a reasonable amount of precision, but perhaps questionable accuracy. When compared to the mean 173 Table 5.9. Simulation projections based on unmodified DELAY for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 2) Observed Projected Producer Total Hogs Total Hogs Number Quarter Marketed MarketedQ+1 2 Su 1986 2013 1944 F 1986 2032 1893 W 1987 2155 2085 Sp 1987 2117 2029 Su 1987 1873 2087 F 1987 2419 2060 W 1988 2284 2195 Mean 2128 2042 3 Su 1986 1222 1393 F 1986 801 1110 W 1987 1742 1188 Sp 1987 1084 1426 Su 1987 983 1169 F 1987 1006 1008 W 1988 1633 1444 Mean 1210 1248 4 F 1986 4833 3121 W 1987 4041 3353 Sp 1987 3994 4222 Su 1987 3607 4398 F 1987 4388 4090 W 1988 3728 3855 Mean 4098 3840 5 Su 1986 401 249 F 1986 434 524 W 1987 499 378 Sp 1987 331 368 Sn 1987 443 476 F 1987 443 319 Mean 425 386 174 Table 5.10. Accuracy of simulation projections based on unmodified DELAY for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 2) Producer Mean Absolute Theil’s “2 Number Projection Error Statistic 2 146.86 030163 3 250.43 0.60963 4 64150 0.47850 5 92.83 083285 observed marketings, the mean absolute projection error is seen to range from 9% to 21% across producers. Further, when compared to the results of ANALYSIS 2, the modification of DELAY using known disease rates and pig deaths is seen to offer little in the way of improved predictive ability. In fact, predictions for three of the four producers were slightly less accurate than those of ANALYSIS 2. Potential reasons for the relatively poorer prediction for Producer 5 were discussed under ANALYSIS 2 Despite the decreased accuracy obtained, Theil’s u2 statistics still indicate that the model predicted substantially better than the naive, no-change model for all producers. This finding remains encouraging when considered in light of existing problems with data quality. Further discussion on the usefulness of disease and death information is deferred to Chapter 6. ANALYSIS 4. Though ANALYSIS 3 has cast some doubt on the contribution offered to simulation by disease and death information, it was deemed worthwhile to evaluate the model’s performance if only current and past disease and death information were used. Therefore. ANALYSIS 4 sought to evaluate the predictive power of the model when using alpha-beta tracker projected disease prevalence rates at slaughter and pig deaths. Using earlier notation, both DISQH and DTHQ+1 were used to modify DELAY Q, except that the values of DISQH and DTHQH were not observed for this analysis; they were projected with the alpha-beta tracker. Also following earlier format, the question being asked in this case was, ”How well does the model project if it relies 175 Table 5.11. Monte Carlo projections of total hogs marketed based on modification of DELAYQ using observed disease rates (DISQH) and pig deaths (DTH +1) for SHIMS producers, summer 1986 through winter 1%8 (ANALYSIS 3 Projected Total Observed Hogs MarketedQ+1 Producer Total Hogs Number Quarter Marketed Mean SD 2 Su 1986 2013 1865 55 F 1986 2032 1817 51 W 1%7 2155 2016 57 Sp 1987 2117 1891 55 Su 1%7 1873 1972 56 F 1987 2419 1978 55 W 1988 2284 2206 71 Mean 2128 1964 3 Su 1986 1222 1460 69 F 1986 801 1121 57 W 1987 1742 1211 60 Sp 1987 1084 1427 71 Su 1987 983 1153 57 F 1987 1006 941 45 W 1988 1633 1628 72 Mean 1210 1277 4 F 1986 4833 3129 48 W 1%7 4041 3283 38 p 1%7 3994 4254 56 Su 1%7 3607 4636 49 F 1987 4388 4170 63 W 1988 3728 3721 36 Mean 40% 3866 5 Su 1986 401 234 4 F 1986 434 539 13 W 1%7 499 373 12 Sp 1987 331 331 5 Su 1987 443 473 22 F 1987 443 315 13 Mean 425 378 SD = standard deviation 176 Table 5.12. Monte Carlo mean DELAY projections based on modification of DELAY using observed disease rates (DISQH) and pig deaths (DTH producers, summer 1986 through winter 1988 (ANALYSIS 3 1) for SHIMS Projected Mean Observed DELAYQH Producer Mean Number Quarter DELAY Mean SE 2 Su 1986 10886 11681 3.47 F 1986 97.78 11358 3.14 W 1987 103.98 101.18 2.75 Sp 1987 10250 111.75 322 Sn 1987 99.67 108.62 3.08 F 1987 8729 103.78 278 W 1988 8023 86.94 3.19 Mean 97.19 106.09 3 Su 1986 12624 96.80 5.11 F 1986 116.43 125.48 6.48 W 1987 96.66 114.75 5.68 Sp 1987 120.70 96.91 521 Su 1987 13786 12297 6.14 F 1987 9526 149.17 736 W 1988 10860 8832 434 Mean 11454 113.49 4 F 1986 7845 10227 153 W 1%7 64.67 80.45 1.24 Sp 1987 6733 6383 1.64 Su 1987 80.09 6328 1.05 F 1987 70.69 7864 125 W 1988 7636 73.40 0.84 Mean 72.93 76.% 5 Su 1986 7480 99.77 201 F 1986 8871 66.70 4.12 W 1987 7284 89.97 381 Sp 1987 7630 85.64 102 Sn 1%7 89.45 7687 3.75 F 1987 7861 9123 332 Mean H112 85.03 SE = standard error 177 Table 5.13. Accuracy of Monte Carlo projections based on modification of DELAY using observed disease rates (DISQ+1) and pig deaths (DTH +1) for SHIMS producers, summer 1%6 through winter 1988 (ANALYSIS 3% Producer Mean Absolute Theil’s u2 Number Projection Error Statistic 2 19229 031395 3 23886 058890 4 66183 051266 5 92.67 087485 on the alpha-beta tracker to predict disease rates and pig deaths?" Again, DELAY modification was stochastic, so Monte Carlo analysis was performed; results appear in Tables 5.14, 5.15, and 5.16. Before discussing the relative quality of the projections obtained with ANALYSIS 4, it will be helpful to assess the quality of data prediction provided by the alpha-beta tracker. To facilitate this evaluation, the mean absolute deviation and Theil’s u2 statistic were calculated for each data series projected with the alpha-beta tracker across all producer-quarters. Here, predictions were evaluated across producers, because data collection was felt to be more consistent for these series. Financial data, as mentioned earlier, was the class most likely to be successfully collected across producers. Further, data on disease rates were collected consistently, since they were all achieved by MSU personnel. Review of these predictions’ accuracy is presented in Table 5.17. In general, the alpha-beta tracker didn’t predict very well in this application. The absolute errors for disease prevalence rates ranged from about 0.015 (15%), which might be acceptable under certain circumstances, to over 026 (26%), which would virtually never be acceptable. Further, examination of Theil’s “2 statistics indicate that none of the disease prevalence rates were predicted better than would be provided with the naive model. Similar results, with a few exceptions, were obtained for the remaining data series projected. The apparent perfect prediction for replacement female and replacement boar expenses was related to the fact that none of the SHIMS producers 178 Table 5.14. Monte Carlo projections of total hogs marketed based on modification of DELAYQ using disease rates (DISQH) and pig deaths (DTHQH) as projected with the alpha-beta tracker for SHIMS producers, summer 1%6 through winter 1988 (ANALYSIS 4) Projected Total Observed Hogs MarketedQ+1 Producer Total Hogs Number Quarter Marketed Mean SD 2 Su 1986 2013 1808 50 F 1986 2032 1742 48 W 1987 2155 1997 55 Sp 1987 2117 1912 56 Su 1987 1873 1924 55 F 1987 2419 1936 52 W 1988 2284 2222 69 Mean 21% 1934 3 Su 1986 1222 1227 59 F 1986 801 1077 54 W 1987 1742 1170 57 Sp 1987 1084 1472 71 Su 1987 %3 1097 52 F 1987 1006 996 49 W 1988 1633 1455 72 Mean 1210 1213 4 F 1986 4833 2844 42 W 1987 4041 3196 37 Sp 1987 3994 4365 60 Su 1987 3607 4235 51 F 1987 4388 4233 58 W 1988 3728 3861 36 Mean 40% 3789 5 Su 1986 401 206 4 F 1986 434 533 11 W 1%7 499 367 12 Sp 1987 331 345 4 Su 1987 443 446 12 F 1%7 443 300 10 Mean 425 366 SD = standard deviation 179 Table 5.15. Monte Carlo mean DELAY projections based on modification of DELAYQ using disease rates (DISQH) and pig deaths (DTHQH) as projected with the alpha-beta tracker for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 4) Projected Mean Observed DELAYQH Producer Mean Number Quarter DELAY Mean SE 2 Su 1986 10886 12030 337 F 1986 97.78 11872 328 W 1987 103% 10262 272 Sp 1987 10250 109.93 320 Su 1987 99.67 11144 3.18 F 1987 8729 105.99 280 W 1988 8023 8606 3.04 Mean 97.19 10787 3 Su 1986 126.24 11552 6.00 F 1986 116.43 129.97 656 W 1%7 96.66 119.18 587 Sp 1987 120.70 94.10 4.91 Su 1987 13786 126.07 6.16 F 1987 95.26 14389 724 W 1988 10860 93.94 483 Mean 11454 11752 4 F 1986 7845 113.47 172 W 1987 64.67 80.67 127 Sp 1987 6733 6179 157 Sn 1987 80.09 7088 115 F 1987 70.69 76.99 114 W 1988 76.36 7102 081 Mean 7293 79.14 5 Su 1986 7480 11813 240 F 1%6 8871 67.11 4.18 W 1987 7284 9239 3.96 Sp 1987 7630 8335 0.96 Su 1987 89.45 81.44 422 F 1987 7861 95.75 3.11 Mean 8112 89.70 SE = standard error 180 Table 5.16. Accuracy of Monte Carlo projections based on modification of DELAY using disease rates (DISQH) and pig deaths (DTHQH) as projected with the alpha-beta tracker for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 4) Producer Mean Absolute Theil’s u2 Number Projection Error Statistic 2 207.71 031558 3 220.43 0.63900 4 687.67 0.47707 5 97.67 0.91526 reported any expenses of these type over the entire project. More than likely, the failure of the technique can be related to data features such as the lumpiness discussed previously. Also, suggestive evidence was presented earlier in this chapter which indicated a somewhat weak functional relationship between successive observations of these variables. As such, their prediction based solely on time series (as occurs with the alpha-beta tracker) cannot be expected to be overly successful. Again, further discussion is deferred to Chapter 6. In light of the lack of acceptable alpha-beta tracker performance, simulations using data generated as such must be judged accordingly. Therefore, when returning to Table 5.16, it is not surprising that ANALYSIS 4 did not predict as well, in general, as did ANALYSIS 3. The main features of this comparison which bear mention are: the model still predicted better than the assumption of no change, and even though ANALYSIS 4 employed erroneous values for diseases and deaths, the resulting predictions were not much worse (relatively) than those obtained using observed data values. ANALYSIS 5. The next scenario was designed to confirm that the simulated production and financial effects of disease and deaths were logically consistent. For this purpose, the question was posed, "How well does the model predict if all future disease rates and pig deaths are assumed to be zero, and how do these results compare to those 181 Table 5.17. Accuracy of alpha-beta tracker data projections for SHIMS producers, summer 1986 through winter 1988 Mean Absolute Theil’s u Data series Projection Error statistic Disease prevalence rates Severe pneumonia 0.099% 1.50495 Moderate pneumonia 0.06658 107781 Mild pneumonia 026509 185416 Severe ascariasis 0.04358 426044 Moderate ascariasis 0.03088 3.13665 Mild ascariasis 0.14974 223406 Severe mange 0.01563 257282 Moderate mange 0.03813 2.85471 Mild mange 0.11472 115947 Severe atrophic rhinitis 0.05667 199550 Moderate atrophic rhinitis 0.09330 157539 Mild atrophic rhinitis 0.19767 137230 Pleuritis 0.02061 1.85534 Pericarditis 0.01493 284161 Nasal septum deviation 0.16887 175063 Non-feed cash expenses Purchased feed 139.65982 3.74579 Repair and maintenance 805562 051958 Veterinary care and drugs 10.74076 453317 Labor 1890576 110040 Supplies 0.74190 0.78411 Fuel 0.75999 124105 Electricity 220451 054742 Telephone 022312 117656 Trucking 171265 080918 Marketing 084006 0.75166 Insurance 0.46450 112158 Interest 1258992 111823 Taxes 0.79011 177069 Replacement females 0.00000 0.00000 Replacement boars 0.00000 0.00000 Other 7.12753 5.91861 Pig deaths Finisher 2856425 101909 Grower 1338520 107151 Nursery 3952869 0.90890 Marketing measures Market hog weight 3830460 100215 Market hog price 2023995 130397 Feed use Finisher 4140473 0.41854 Grower 19.04404 057089 Nursery 7.06406 333092 182 obtained from simulations involving actual disease observations?" Using the established notation, again both DISQ+1 and DTHQ+1 were used to modify DELAY Q, but this time the values of DISQ+1 and DTHQH were assumed to be zero. Monte Carlo results were summarized in Tables 5.18 and 5.19. However, in this case, the output of ANALYSIS 3 (see Tables 5.11 and 5.12) were included for purposes of comparison. Recall that ANALYSIS 3 modified DELAY Q using observed values of disease and death data. Without exception, ANALYSIS 5 predicted an increase and the total number of hogs marketed and a decrease in mean DELAY when compared to ANALYSIS 3. This is exactly as would be anticipated if the presence of diseases and deaths have the expected deleterious impact on production. It is interesting to note that, while the variability in mean DELAY generally decreased with such decreasing disease rates and pig deaths, the variability in total hogs marketed actually increased somewhat. It is felt that this increase was mostly the result of the increased number of marketings, since the coefficients of variation were only mildly affected. As a matter of interest, the question may be posed, "In a financial sense, how much would it be worth to the producer if zero disease prevalence rates and pig deaths could be achieved?" Because only Producers 2 and 5 provided data regarding feed use, the ability to address this question was somewhat limited for the current SHIMS producers. However, the estimation procedure for income over variable costs (IOVC) as presented in Chapter 4 was applied to the results of ANALYSIS 5 for these two producers. Recall that non-feed, variable costs were formulated on a "per pig-day" basis, where input rates were obtained from the database. Product prices and market weights were also obtained from actual observations. For this analysis, the following feed prices were employed (prices represent a blend of purchased and home-raised feed prices): finisher = $0.055 per pound grower $0.07 per pound nursery - $0.105 per pound . 183 Table 5.18. Monte Carlo projections of total hogs marketed based on modification of DELAYQ using disease rates (DISQ+1) and pig deaths (DTH 1) of zero for SHIMS producers, summer 1%6 through winter 1988 (ANALYSIS 5) Projected Total Projected Total Hogs MarketedQ+ Hogs MarketedQ+ DIS ,DTH >0 DIS ,DTH =0 Producer 0+1 0+1 0+1 0+1 Number Quarter Mean SD Mean SD 2 Su 1986 1865 55 1957 60 F 1%6 1817 51 1891 53 W 1%7 2016 57 2100 57 Sp 1987 1891 55 2004 64 Su 1%7 1972 56 2083 63 F 1%7 1978 55 2063 61 W 1988 2206 71 2309 78 Mean 1%4 2058 3 Su 1986 1460 69 1682 72 F 1986 1121 57 1281 66 W 1987 1211 60 1359 71 Sp 1%7 1427 71 1638 73 Su 1987 1153 57 1313 68 F 1987 941 45 1099 56 W 1988 1628 72 1836 69 Mean 1277 1458 4 F 1986 3129 48 3596 58 W 1%7 3283 38 3637 41 Sp 1987 4254 56 4709 34 Su 1%7 4636 49 5102 57 F 1987 4170 63 4705 56 y W 1988 3721 36 4333 32 Mean 3866 4347 5 Su 1986 234 4 252 5 F 1%6 539 13 566 31 W 1987 373 12 391 13 Sp 1%7 331 5 365 2 Su 1%7 473 22 550 41 F 1%7 315 13 332 15 Mean 378 409 SD = standard deviation 184 Table 5.19. Monte Carlo mean DELAY projections based on modification of DELAY using disease rates (DISQ+1) and pig deaths (DTH 1) of zero for SHIMS producers, summer 1986 through winter 1988 (ANA YSIS 5) Projected Mean Projected Mean DELAYl-Cllfl DELAY 1 D18 ,DT >0 DIS ,DT =0 Producer 0+1 0+1 0+1 0+1 Number Quarter Mean SE Mean SE 2 Su 1986 116.81 3.47 111.59 3.41 F 1986 113.58 3.14 109.41 2.99 W 1%7 10118 275 9806 254 Sp 1%7 11175 322 10625 334 Sn 1%7 10862 3.08 103.63 3.08 F 1%7 103.78 2.78 100.35 283 W 1988 8694 3.19 8326 3.16 Mean 115.09 101.79 3 Su 1986 96.80 5.11 8620 4.45 F 1%6 125.48 6.48 11325 5.75 W 1987 114.75 568 105.97 5.42 Sp 1%7 96.91 521 8762 451 Sn 1987 12297 6.14 11024 5.70 F 1%7 149.17 736 130.14 6.70 W 1988 8832 4.34 7629 4.05 Mean 113.49 10139 4 F 1986 10227 153 91.13 139 W 1%7 80.45 124 7177 1.10 p 1%7 6383 164 5486 139 Su 1987 6328 105 56.71 0.94 F 1987 7864 1.25 70.23 108 W 1988 73.40 084 6337 0.69 Mean 76% 68.01 5 Su 1986 99.77 201 9246 193 F 1986 66.70 4.12 63.18 385 W 1%7 89.97 381 8466 357 Sp 1987 85.64 102 7752 103 Sn 1%7 76.87 3.75 70.62 336 F 1987 9123 332 87.05 3.10 Mean 85.03 79.25 SE = standard error 185 Since these prices were not included in the SHIMS database, they were (approximately) reconstructed for the simulated period with the assistance of the Mason Elevator Company, of Mason, Michigan. When the expected IOVC results of ANALYSIS 5 are compared to those of ANALYSIS 3 for Producers 2 and 5, the difference offers an answer to the question regarding the financial value of disease- and death-free production. Results of this comparison are presented in Table 520. As might be expected based on the projected improvements in physical production in the absence of disease, financial production also improved in the absence of disease. Without exception, projected IOVC was higher with ANALYSIS 5 than with ANALYSIS 3. However, it should be noted that the cost of disease eradication was not assessed in this analysis. The maximum amount the producers should be willing to pay for one quarter of these lower disease rates, provided accurate projections, is the difference between the IOVC of ANALYSIS 5 and that of ANALYSIS 3. Of course, this also assumes that disease eradication is technically feasible. Once more, further discussion will occur in Chapter 6. ti is'o 0 ANALYSIS 6. The final simulation scenario to be presented was designed to display the analytical model’s capability for evaluating very specific changes in production circumstances. To provide illustration, the hypothetical situation was constructed where a producer desired to assess the production and financial effects of making environmental changes to help alleviate respiratory disease. Following these changes, the producer expected all pneumonia prevalence rates at slaughter in the upcoming quarter to be 50% of the levels they would have achieved without the environmental changes. In addition, all atrophic rhinitis rates were expected to be at 90% of the no—change levels. No effects on death rates were assumed. The question is posed, "What physical and financial production performance can be expected if the outlined environmental changes are made?" 186 Table 520. Projected income over variable costs (IOVC) with and without diseases (DISQH) and pig deaths (DTHQH) for SHIMS producers, summer 1%6 through winter 1988 (ANALYSIS 5) Projected IOVC Producer Number Quarter Mean SD CV Mean SD CV 2 F 1986 $ 103,102 $ 5942 0.0576 $ 111,634 $ 6163 0.0552 W 1%7 94,396 6315 0.0669 103,137 6330 0.0614 Sp 1%7 112,814 6820 0.0605 127,288 8020 0.0630 Su 1987 91,519 6959 0.0760 105,161 7803 0.0742 F 1987 105,426 8052 0.0764 117,0% 8976 0.0767 W 1988 108,651 6687 0.0615 118,279 7409 0.0626 5 Su 1986 8930 460 0.0515 10,807 537 0.0497 F 1986 53,083 1871 0.0352 57,060 4410 0.0773 W 1987 29,008 1456 0.0502 31,122 1546 0.0497 Sp 1%7 8078 463 0.0573 10,997 194 0.0176 Su 1%7 74,465 4277 0.0574 88,974 7881 0.0886 F 1987 1116 1317 11801 2668 1520 05697 SD = standard deviation CV = coefficient of variation Based on previous data quantity and quality discussions associated with specific producers in the SHIMS pilot group, Producer 2 alone was chosen for this analysis. Feed prices used were the same as in ANALYSIS 5. Since the current model evaluates only on the basis of a three month time horizon, each quarter simulated for this scenario must be considered an independent attempt to answer the producer’s question; prolonged disease rate changes are not assumed. Results are presented in Tables 521, 522, and 5.23. For comparison purposes, both projections achieved with the actually observed disease rates and pig deaths and those attained using disease rates and pig deaths of zero have been included. As the results show, the model’s logical consistency remained unscathed; performance for ANALYSIS 6 fell between the observed disease scenario (ANALYSIS 3) and the not disease, no death scenario (ANALYSIS 5). As a matter of interest, the mean difference in IOVC for a three month period which was associated with making 187 Table 521 Monte Carlo projections of total hogs marketed based on modification of DELAYQ using specifically modified pneumonia and atrophic rhinitis prevalence rates for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 6) Projected Total Projected Total Hogs MarketedQ+1 Hogs Marketed DISQ+1 = Obs. PnQ+l = 05 * 3. 13111044 3 Obs. Ale = 0.9 * Obs. Producer Number Quarter Mean SD Mean SD 2 Su 1986 1865 55 1889 57 F 1986 1817 51 1836 48 W 1%7 2016 57 2039 58 Sp 1%7 1891 55 1917 57 Su 1987 1972 56 2005 60 F 1987 1978 55 2008 56 W 1988 2206 71 2239 68 Mean 1%4 19% SD - standard deviation Pn = pneumonia prevalence rates AR =- atrophic rhinitis prevalence rates Obs. = observed rates Table 522 Monte Carlo mean DELAY projections based on modification of DELAYQ using specifically modified pneumonia and atrophic rhinitis prevalence rates for SHIMS producers, summer 1986 through winter 1988 (ANALYSIS 6) Projected Mean Projected Mean DISQ+1 = Obs. PnQ+1 = 05 * Obs. DTHQ+1 = Obs. Ale = 0.9 * Obs. Producer Number Quarter Mean SE Mean SE 2 Su 1986 11681 3.47 11531 350 F 1986 11358 3.14 11241 292 W 1987 10118 275 100.08 272 Sp 1987 11175 322 110.22 324 Su 1%7 10862 3.08 106.84 3.16 F 1%7 103.78 278 10227 274 W 1988 86.94 3.19 8551 296 Mean “5.09 10466 SE = standard error Pn = pneumonia prevalence rates AR = atrophic rhinitis prevalence rates Obs. = observed rates 188 Table 523. Projected income over variable costs (IOVC) with observed diseases (DISQH) and pig deaths (DTHQH) compared to specifically modified pneumonia and atrophic rhinitis prevalence rates for SHIMS producers, summer 1%6 through winter 1%8 (ANALYSIS 6) Projected IOVC DISQ+1 = Obs. PnQ+1 = 0.5 * Obs. DTHQH = Obs. ARQ+1 = 0.9 * Obs. Producer Number Quarter Mean SD CV Mean SD CV 2 F 1%6 $ 103,102 $ 5942 0.0576 3 105,419 $ 5597 0.0531 W 1%7 94,396 6315 0.0669 97,199 6404 0.0659 Sp 1987 112,814 6820 0.0605 116,278 7096 0.0610 Su 1987 91,519 6959 0.0760 95.929 7428 0.0774 F 1987 105,426 8052 0.0764 110,275 8208 0.0744 W 1988 108,651 6687 0.0615 112,110 6410 0.0572 SD = standard deviation CV == coefficient of variation Pn = pneumonia prevalence rates AR = atrophic rhinitis prevalence rates Obs. = observed rates the proposed management changes was +33550. The cost to attain this would involve both the cost of SHIMS information (see Table 3.14) and the cost of any management change required to affect the improved disease rates. This example displays both the flexibility of the model and its potential usefulness for assessing the financial aspects of proposed management changes. These results, in turn, could be used to help managers achieve their objectives more effectively. Provided the model can be validated to provide confidence in the results, decision makers should find these capabilities beneficial when faced with choices between management alternatives. SUMMARY This chapter has presented the initial application results for a decision support system developed to address health management issues in market hog production. 189 Several strengths and limitations have surfaced. Further discussion of these in relation to potential on-farm applicability and to recommendations for future system development will occur in Chapter 6. CHAPTER 6 SUMMARY AND RECOMMENDATIONS INTRODUCTION In Chapter 1, the role of the production economist was described as one . . of facilitating choice in production patterns and resource use so that the ends or objectives of farmers and consumers can be attained" (Heady, 1952). Chapter 2 followed by reviewing historical approaches of production economists to this task. Then, Chapters 3, 4, and 5 described the methods used for this purpose by the SHIMS research project and the associated initial results. In short, much discussion regarding information management systems, analytical models, and decision support systems has ensued, both in general and in specific reference to the current project’s strengths and limitations. This chapter will summarize the current research in light of its stated goals and will focus discussion on the potential applicability of the decision support system to actual production situations. The structured approach for review presented in Chapter 2 will again be employed. Recommendations will then be made toward future enhancements. INFORMATION MANAGEMENT SYSTEM In Chapter 2, the discussion of information management systems involved two major considerations: data collection and data processing. These aspects of the SHIMS project will be reviewed individually. Both developments during the pilot phase and the potential for actual implementation to support real-time production decisions will be discussed. 190 191 Wan The first data collection question posed in Chapter 2 was, "What to collect?" As alluded to briefly in Chapter 3, the SHIMS accumulation of physical production, animal health, and financial production data in a single database is somewhat unique when information management systems for market hog production are considered. The pilot project was successful in integrating health data collected at slaughter with both physical input/output data and revenue/expense data collected on-farm. As such, the resulting database would provide a single, comprehensive basis for evaluating the economics of health management in market hog production for research purposes. Similarly, the database would provide adequate support for real-time decisions regarding choices between alternative health management strategies for these livestock. Though the scope of the pilot information management system was somewhat limited (because only growing/finishing pigs were considered), a very useful prototype was provided for future expansion. These expansions might include other diseases and/or phases involved in market hog production, or adaptation to non-porcine livestock species. The second database question posed in Chapter 2 was, "How to collect?" Most noteworthy when considering the SHIMS project in this regard was the method of slaughter health check developed. High speed techniques for pathology evaluation and observation recording were employed, making it possible to collect the desired animal health data at slaughter. While this approach is certainly not unique to SHIMS, the combination of these capabilities with marketing coordination allowed data collection to proceed without disrupting the usual marketing patterns of participating producers. This feature is especially critical if representative samples are to be achieved at slaughter and if the system is eventually intended to be employed in real-time decision support for commercial production of hogs. The major constraint in performing slaughter health checks was the limited availability of university human and capital 1' 88011 ICES. 192 Because of the large amount of travel required to follow Michigan hogs through normal marketing channels, provision of this data collection could easily become financially and physically prohibitive for a limited-resource research project. However, once the value of this data and its associated information has been more adequately documented, producers should theoretically be willing to pay for the service in pursuit of the accompanying decision support. Further, since some benefits may also accrue to consumers and non-participating producers (as discussed in Chapter 3), government or other groups (such as consumer organizations) may also become interested in providing long-term assistance. The final data collection issue posed in Chapter 2 was that of the motivation of people involved. Because of the SHIMS research interests, the motivation for slaughter health checks was surely adequate. However, pilot producers were motivated in their data collection only by the value individually ascribed to the combination of management information received and interaction with university personnel. As mentioned in Chapter 5, this motivation was apparently adequate for all but one of the pilot producers. Since a more convincing documentation of associated benefits is now possible using pilot project results and the analytical model developed, producer motivations should be improved for future endeavors. As discussed, eventual applications in real-time hog production will be similarly motivated by the perceived value of the resulting information. Data nrngessjng Following the lead of Chapter 2, the first data processing issue to be addressed for the current information management system will be output destination/format. Judging from positive comments received from producers, the content and format of both the slaughter health check and farm management reports were adequate. In general, as discussed in Chapter 3, producers were eager to receive reports containing information on their own production characteristics. Again, the farm management report successfully consolidated information on physical production (inputs, outputs, and 193 animal health) with financial production information (revenues and expenses). Not only was current information provided, but also historical performance was included to facilitate subjective evaluation of production trends. In addition, summary information from other producers was furnished, providing individuals a basis on which to assess relative performance (see Appendix D). 7 Finally, perhaps the most noteworthy output destination/format issue for the current information management system was the foundation it provided for the attending analytical model. As will be discussed in the following section, this unique feature afforded both generality and precision to the model. Further, such use of the information management system helped glean as much information from the database as was technically possible. The quality of diagnostic, prescriptive, and predictive information (Harsh, et al, 1981) was thereby greatly enhanced. The next concern of data processing involved the actual events of entry, processing, and storage. As presented in Chapter 2, SHIMS used a mainframe computer database manager. While research was facilitated as a result, the exclusive future use of a mainframe may hamper the applicability of the system for actual, on-farm problem solving. For this reason, the parallel development of a similar information management system for the microcomputer (as discussed in Chapter 3) is encouraging. Success in this development is critically hinged on ease-of-use, but when achieved the entire information management system will be much closer to actual problem solving situations. This should greatly enhance the usefulness of the system, since reports will be available virtually as soon as the data are entered. Also, potential data inconsistencies should be detected sooner and more reliably. Finally, the motivations of the people involved in entry/processing will also be enhanced by a completely on-farm system. Producers who use the system will have their financial well-being at least partially related to the caliber of the associated data entry/processing effort. This situation should provide more than adequate motivation. 194 ANALYTICAL MODEL As when considering the information management system, the framework for review established in Chapter 2 will be followed in reviewing the current analytical model. In that approach, the following attributes were addressed: resolution, realism, generality, precision, and applicability. At this point, it is critical to remember that the ultimate judgement of a particular model’s attributes is somewhat subjective. Further, as presented in Chapter 2, such assessments rest on system and problem characteristics, modeler’s objectives, and available modeling resources. With the current research, the modeler’s objectives were to use the modeling approach to build a decision support system for livestock health management. However, because the analytical model will require validation before its overall performance can be objectively evaluated, the existence of somewhat limited modeling resources has hindered thorough assessment. As a result, complete objective judgement of the relative adequacy of each of the five attributes listed is not possible at this point, and the following sections will review the attributes of the analytical model somewhat subjectively, without always judging their adequacy. Also, full compliance with data collection procedures will be assumed for purpose of model discussions. Finally, the potential usefulness of the model in actual problem solving situations will be discussed, since herein lies its contribution to the discipline of economies. In Chapter 2, a distinction was drawn between analytical production models and analytical decision models. To briefly summarize, production models provide technological expectations for use in decision models, where the production plan is formulated in light of decision maker objectives. As it currently stands, the analytical model associated with the SHIMS project contains both production and decision components. However, the model’s primary focus is provision of technological expectations. The corresponding decision model is largely passive, in that expected financial outcome is provided for prescribed production tactics, but an active, 195 recommended production plan is not generated. Both production and decision models will be considered in the following discussion. 3391110211 For the current production model, biological resolution was achieved through inclusion of such factors as specific disease conditions present at slaughter, available floor space by production phase, death rates, and rate of feed use. Further, assuming no abrupt changes, the effects of genetics, nutrition, and other environmental factors on production were captured implicitly through observation of current production performance from the database. For financial resolution, the model has appropriately included sales of both feeder pigs and market hogs. In addition, variable expenses were addressed according to fourteen separate classifications (see Appendix A—Production Expenses). As a result of these factors, the amount of resolution in the production model was quite substantial. When considering the decision model, the passive approach allows decision makers to pursue their own personal objectives, regardless of their exact nature, because specific production recommendations are not forwarded. Decision makers using the model must actively infuse preferences into the choice between various production alternatives based on the projected outcomes. This method avoids the imposition of any specified objectives which are assumed to be universally applicable, 3 problem often encountered in the literature review of Chapter 2 @1133. As discussed in Chapter 2, the virtues of resolution are often limited in the absence of realistic representation of the interrelationships present. Realism was attained in the current production model through an adjustment approach using various systems modeling and computer simulation techniques. Overall, the adjustment approach employed is intuitively appealing. Logically, it seems realistic to ascertain the current status of production for use as a basis to predict future system performance. Further, the physical production model was 196 stochastic and autocorrelated. Production was modified according to a "tempered" combination of the effects of disease conditions, feed additive use, and available floor space which followed a natural logarithm-based function. Not only did this modification contain autocorrelated stochasticity, but it was also dynamic. As discussed in Chapter 4, the time-step of one day closely approximated the continuous time situation, thereby allowing changes to occur on a continuous basis. Consequently, today’s performance was always stochastically modified according to changing system circumstances (diseases, deaths, available floor space, and feed additive use), while retaining a relationship with yesterday’s performance. Finally, the model represented the hog population as a single dynamic unit, rather than as a large group of dynamic individuals. Since this technique concurrently maintained a distribution of individual traits (such as growth and death rates), the usual approach of producers to managing groups of growing hogs was captured. As with the review of resolution, the amount of realism in the decision model was implicitly substantial due to its passive approach. In addition, the output from Monte Carlo analyses included both the mean projected outcomes and their variances. An illustration of the output produced from one such analysis was presented in Table 5.6. These features should allow future decision makers to evaluate projections based on criteria such as certainty equivalent income, first degree stochastic dominance, second degree stochastic dominance, etc. Further, trade-offs between multiple decision maker objectives can be evaluated through appropriate "What if?" scenario analysis. Again, the lack of specific recommendations avoids the possibility of imposing non- representative decision maker objectives. W203 Though generality and precision represent distinctly different concepts, they will be reviewed together for the current production model since both were achieved through a single mechanism: use of individual-farm data. This feature is perhaps one of the model’s greatest assets. In one sense, the approach was broadly generic, since it could 197 be applied to virtually any confinement, hog-growing system with an adequate record system. On the other hand, it became very specifically tailored to individual farms once data from that farm were entered. One of the most commonly expressed concerns in the literature review of Chapter 2 was one of statistically estimated functions lacking validity outside their particular experimental population/sample. Integration of the analytical model with an information management system as accomplished by the current research completely avoided this potential problem. For the current production model, precision and generality refer to how well performance was predicted for selected individual producers and for a group of producers, respectively. In spite of some questionable data quality, the current model was shown in Chapter 5 to consistently predict better than the naive, no-change model for all individuals providing data in the pilot group. Thus, the eventual precision and generality for real-time decision support should be very good. As with resolution and realism, the generality and precision of the decision model were determined by the absence of active recommendations. As such, the model should perform equally as well across a group of decision makers as it does for the individuals involved. Annlj'cabilify The final model attribute for review is applicability. The paramount concern in this regard must be related to eventual use of the analytical model for actual support of production decisions. For these purposes, the model’s applicability should be very good. Just as the potential for evaluating the importance of both the presence and severity of disease was exhibited by ANALYSIS 6 in Chapter 5, similar sensitivity analyses could be accomplished for available floor space and feed additive use. These analyses can be very specific. For example, the effects of changing a single disease in one phase of production can be accomplished as easily as evaluating the effects of decreasing the entire available floor space or removal of certain feed additives. Further, 1% the resulting outputs contain the expected outcomes and their variances for both physical production and the associated financial circumstances (income over variable costs). The resulting information should be extremely useful for producers. For example, if Producer 2 reviewed ANALYSIS 6 for fall quarter 1986, the results would be seen to indicate that, on average, up to $2317 could be spent to decrease pneumonia prevalence rates by 50% and atrophic rhinitis prevalence rates by 10% for one three month period (see Table 523). However, the output generated also indicates that when the variability in production in considered, the probability distributions of these two management options exhibit considerable overlap. In fact, because the difference between the means is only about one half of one standard deviation, the distributions are statistically indistinguishable. Thus, the decision maker is faced with a possibility of improved income over variable costs, but certainly without guarantees. The decision regarding whether or not to invest in the proposed management changes would depend critically on both the cost involved and the associated epidemiological probability of achieving the described decrease in disease rates. If, for instance, the effect could also be achieved through use of antibiotics in the feed, perhaps an added benefit may be achieved, making the alternative seem more desirable. Or, if the disease rates remained low for an extended time period, the analysis would change. As discussed, similar analyses could be readily performed for other diseases, available floor space. and/or the use of feed additives. Finally, the computer technology involved warrants explicit mention. Implementation of the model on a microcomputer greatly enhances applicability. The limits on "What if?" analyses are imposed only by the user’s interests and the availability of time. Computerized implementation expedites the mere calculation involved. Eventually, having such capacity entirely on-farm will make turn around time virtually non-existent while providing excellent accessibility for purposes of user interaction. With recent advances in microcomputer technology, the potential only stands to improve. 199 CONCLUSIONS AND RECOMMENDATIONS As stated in Chapter 1 the goal of this research was to construct a prototype decision support system for livestock production using growing pigs as an analytical framework, placing emphasis on animal health management. This has been accomplished. A multidisciplinary approach has been used to successfully integrate information management, systems modeling, and computer simulation techniques. The research has received the guidance of academia, industry, and government. From academia, both extension and research interests were involved across the disciplines of agricultural economics, operations research (including systems science), veterinary medicine, epidemiology, and animal science. Interaction with the hog producing industry has actively included slaughter, marketing, producer, and veterinary input. And involvement of both state and federal governments has been achieved through the Michigan Department of Agriculture; the United States Department of Agriculture (USDA), Economic Research Service; USDA. Food Safety and Inspection Service (FSIS); USDA, Extension; and (to a lesser extent) USDA, Animal and Plant Health Inspection Service. The resulting decision support system holds much potential, both for actual implementation in the future and as a prototype for development of similar systems to address other livestock production problems. However, to fully capitalize on the present accomplishments, validation of the system using an expanded sample of farms will be necessary. Though certain information regarding hog performance may be attainable from various ongoing and future experimental trials, the mission of the production economist should be to facilitate choice in actual production situations. Since experimental trials often (usually) involve control of certain aspects of production that may exhibit considerable variation during commercial production, the resulting information may be of limited value for current purposes. Specifically, environments are frequently controlled, as are disease conditions and nutritional variation, in an attempt to silhouette the aspects of production which interest the investigator(s) involved Nevertheless, environment, disease, and 200 nutrition vary simultaneously in the course of ordinary commercial production. This normal (albeit complicated) situation will provide a much more useful basis for validation of the current decision support system. Therefore, an expanded sample of commercial farms is recommended. This expansion should build on current findings through incorporation of the accompanying recommendations. For the information management system, these recommendations are summarized and enumerated below. 1. To maximize inferential capabilities the expanded sample of producers should be selected according to appropriate random procedures. and should include a sample of about 80 herds (as discussed in Chapter 3) Also according to sample size calculations from Chapter 3, frequency of slaughter health check should be tailored to individual farm sizes and marketing characteristics. In general, this will involve more slaughter health checks than the rate of four per year as occurred during the pilot project. According to herd size and desired confidence, recommendations for the pilot group ranged from 8 to 53 slaughter health checks per year. Related to the frequency of slaughter health checks and the increased sample size, it will be necessary to garner substantial extramural support for conducting future slaughter health checks. Such support could be financial, allowing hired slaughter health checks, or in the form of labor contributions. A good potential candidate as a source of potential labor support is USDA-FSIS based on their interest and active participation in the pilot project. On-farm data not immediately critical for evaluating the economics of health management for growing pigs should not be collected. These data include factors such as reproductive management, farrowing rates, and preweaned pig deaths. 10. 11. 12. 201 Explicit information on pig flows should be collected. If strict all-in—all- out management is followed, pig flow schedules will be adequate. If not, numbers of pigs being moved and dates of movement will be required. Along with the pig flow data mentioned, structured subsampling should occur involving individual pig identification. This will facilitate observation of average transit times, thereby allowing evaluation of COMPLEX optimization as a method to estimate DELAY. Where possible and practical, lumpiness of data should be minimized Though complete elimination of lumps in favor of flow rates cannot be expected, data aggregation into "super-lumps" should be strictly avoided. Pig inventory data need to be counted rather than calculated. Formats for provision of data should be limited to a select, standardized few. While the importance of flexibility is recognized, careful selection of those few standard formats which will be accepted should easily accommodate most producers. Methods of collecting feed use data should be reviewed in an effort to increase producer compliance. For this purpose, the advisory input of producers will be extremely important. Advice of producers will also be useful in determining appropriate methods to assure adequate producer motivation. Ultimately, the economic benefits which accrue to the producer by virtue of participation should become more evident, but strong documentation of these benefits will necessarily be delayed until after system validation. In the interim, potential motivation techniques such as payment for participation and/or periodic collection assistance through occasional farm visits should be considered. Methods of capturing substantial management changes need to be developed. These might involve periodic surveys and/or farm visits. 13. 14. 15. 202 Regarding the farm management reports, periodic summaries involving all participating producers should be considered. These should contain the production value ranges which interested the pilot producers. Producer requests and/or comments should be periodically elicited. This elicitation should be explicit, rather than a standing offer to receive comments, and focus should be maintained on data collection techniques and output content/format. To enhance system function, electronic communication between the database and analytical model should be developed. Because of the expanded sample size, this will probably require conversion of the analytical model to mainframe computer compatibility, thereby allowing the research aspects of system development to be completed. Once the sample size has been appropriately expanded and data collection (reflecting the recommended changes) has been implemented, system validation should proceed. Again, several specific recommendations can be made based on experiences to this point. As before, these will be enumerated. 1. Using the subsample data with pigs individually identified, the use of COMPLEX optimization for observation of system performance should be evaluated. This step is extremely critical, since the entire model rests on modifications made to the initial DELAY. If COMPLEX is found to be inappropriate, an alternative method of observing current system performance needs to be discerned to allow success with the remainder of the validation process. Certain specific modeling techniques need to be heavily scrutinized. Primary concern centers on the methods used to modify DELAY, including: a. the effects of changes in disease rates, available floor space, and feed additive use as obtained from the literature; 203 b. the use of logarithm functions to "temper" the combined effects of simultaneous change in several production factors; and c. the function used to achieve autocorrelation. If model predictions of system performance are found to be inadequate after the evaluations suggested under 1. and 2, priority areas for further modeling efforts include environment, nutrition (especially feed efficiency), and clinical diseases. If warranted, attempts might be made in these areas to increase the resolution and realism of the model. This, in turn, may require modifications of the database to include features such as subsampling. For example, pigs in the system at various phases may be subsampled for purposes of laboratory health evaluation. Similarly, hogs at slaughter may require subsampling and subsequent laboratory testing. Feeds may require subsampling to fully ascertain nutritional parameters. And environments may need subsampled to help define the micro-environments of growing pigs. If alpha-beta tracker performance for disease rate prediction is found to be inadequate on the expanded sample (as it was in the pilot sample), development of a more traditional epidemiological model should be considered. Relative expenditure of resources in this regard should be based on the results of economic sensitivity analysis to the diseases contained in the model. In addition to strong environmental considerations, heavy emphasis should be placed on discerning the relationships between feed additives and disease rates as well as disease rates and pig deaths. To enhance the ability of the model to support rigorous economic analyses, reliable sources of market information for future periods should be utilized. Independent marketing models should be strongly considered, and could conceivably be integrated electronically in the long run. 204 6. Pending the outcome of analyses such as suggested, further changes to the database and/or analytical model may be recommended. These potential avenues for change should virtually never be discounted entirely, since the modeling process is truly iterative, as was displayed in Figure 21 7. As a final research note, it will be extremely important to use the model for rigorous assessment of the economics associated with various management options. Of primary interest in this regard will be diseases and the use of feed additives. Such information will be very useful to: a. government for regulatory activities, b. academia for teaching and further research, and c. producers for management. Following the structured validation process, the decision support system should be ready for development of an appropriate user interface to facilitate implementation by individual producers. Also, complete conversion to the microcomputer may be desirable. SUMMARY The goal of this research was to construct a prototype decision support system for livestock production, focusing on animal health management. Through application of some innovative techniques, this has been successfully accomplished. The system developed has shown much promise by combining individual farm production data with an original analytical model. Modeling techniques from systems science have been adapted for current purposes, and computerized implementation has facilitated analysis of the complex relationships involved in production. Though the accomplishments have been substantial, full realization of the benefits associated with the current effort will only be achieved when the system is validated and trial implementation occurs. Whatever developments ensue, however, the pilot SHIMS project has provided an invaluable experience in multidisciplinary research. Because the problems associated 205 with ". . . facilitating choice in production patterns . . (Heady, 1952) seldom fall discretely within the boundaries of any single academic discipline, this experience has provided an important example of broadening the approach of the production economist. Fostering and expanding such efforts should yield substantial social benefits, since doing so will widen the economist’s repertoire. If more choices are adequately facilitated as a result, the well-being of society will ultimately be improved. APPENDICES APPENDIX A IXPTHSFUDIXIIX SHINE HEALTH INFORMATION MANAGEMENT SYSTEM INITIAL VISIT FORM GENERAL INFORMATION Date: Farm Code: Farm Name: Telephone: ( )- - Owner's Name: Address: Production Type (check all that apply) Farrow-to-finish Feeder pig producer Finish only Seed stock producer Other Livestock (respond appropriately) Average No. on Hand Dairy cattle Beef cattle Sheep Goats Horses Veal Poultry Crops raised (check all that apply and respond appropriately) Cash Crop Livestock Feed Acres Corn grain Corn silage Oats Barley Wheat Soybeans Hay 206 207 SHIMS General Information Page 2 Health Management Practices Veterinary service is used times/year. When used, the purpose is: (check all that apply) to investigate disease problems to treat sick pigs to do herd work veterinary service is contracted Traffic Control (check all that apply): shower in/out boots required coveralls required truck traffic control foot baths Incoming Stock Management (check all that apply): isolation from 0ther animals blood test before arrival blood test after arrival automatically treated for disease on arrival automatically vaccinated for disease on arrival live animals are not purchased BreedinggHerd Management Always Sometimes Pen mate 1 Hand mate Artificial insemination Breed on first heat post-weaning Pregnancy test Group farrowing 21 day heat cnecks D—‘r—‘Ht—‘D—‘H NNNNNNN uwwwwww Age of gilts at breeding months Height of gilts at breeding Genetics Source of Replacements: Purchased Raised Gilts Boars Abpr-F-b Never mmmummm SHIMS 208 Page 3 General Information Predominant Breeds Involved: (check all that apply) Maternal Sires Terminal Sires Hamp Duroc York Ch.Nhite Landrace Lg.Hhite Frequency of boar purchase times/year. Age of boars at purchase months Feed Height of boars at purchase Source of boars: (check all that apply) Breeding company Commercial producer(s) Purebred breeder(s) Handling; Feed Analysis Frequency: times/year Mixer type (check all that apply): Portable grinder-mixer with scales Portable grinder-mixer without scales Volumetric mixer Stationary vertical mixer Stationary horizontal mixer Pig Management Age at weaning Height at weaning Age at castration Age at tail docking Age at teeth clipping Provision of supplemental milk: (check one) Starting at day 1 post-farrowing Starting at day 7 post-farrowing Starting at day 14 post-farrowing Starting at day 21 post-farrowing Provision of creep feed: (Check one) Starting at day 1 post-farrowing Starting at day 7 post-farrowing Starting at day 14 post-farrowing Starting at day 21 post-farrowing Page 4 Ration Number Source : l. Purchased : 2. Home Mixed O. .0 w”. 209 RATION USAGE Form Amount : 1. Meal : 1. Limit Fed : Pig Start : 2. Pelleted : 2. Ad lib : Height Pig End Height 210 MASTER PREPARED FEEDS LIST Date: Farm Code: Page 5 Prepared Feed : : Percent : Percent Number (PF #) : Deseription : Crude Protein : Fat 211 HEALTH MANAGEMENT PRACTICES Date: Farm Code: DISEASE (see disease code list) PRODUCT (see product code list) ROUTE 1. water 2. feed 3. injectable DOSAGE mg/ton (feed products only PRODUCTION PHASE (use inventory codes from below) FREQUENCY l. as needed 2. continuous DURATION Number of treatment days (0 a continuous) PURPOSE 1. growth promotion 2. disease prevention: 3. disease treatment : + ----------------------- + 1. Gestating females 7. Preweaned pigs 2. Lactating females 8. Prenursery weaned 3. Open sows pigs 4. Cull sows 9. Nursery pigs 5. Open gilts 10. Grower pigs 6. Boars ll. Finisher pigs + ....................... + I. § OCDNOWkO-DN CD 40 41 42 212 SHIMS HEALTH MANAGEMENT PRODUCT LIST Vaccines Product Bordetella Broncfiiseptica Clostridium perfringens E. coli Erysipelas Haemophilus parasuis Haemophilus pleuropneumonia Leptospirosis Parvovirus Pasteurella multocida Drugs Product Amprolium Apramycin (Apralan) B. M. D. Carbadox Ciodrin Deccoquinate Dichlorvos (Atgard) Electrolytes Erythromycin Fenbendazole (Safeguard) Fenthion (Tigumon) Fenvalerate (Ectrin) Flavomycin Gentamycin (Gentocin) Hygromycin Iron Ivermectin L-S 50 Levamisole Lincomycin Lindane Malathion Neomycin Oxytocin Penicillin Benzathine (Flocillin) Pen/Strep Procaine Pen G Product Pseudorabies Rotavirus Salmonella Streptococcus suis 11 other Swine dysentery T.G.E. Product Permethrin lEctaoan, Permaban, Permectrin) Piperazine Probiotic Prolate Prostaglandin Pyrantel tartarate (Banminth) Rabon Ronnel Selenium Spectinomycin Sulfachlorpyridazine Sulfamerazine Sulfamethazine Sulfathiazole Tetracycline Chlortetracycline Oxytetracycline LA-ZOO Thiabendazole Tiamulin Triple sulfa Tylosin (Tylan) Virginiamycin Vitamin A Vitamin D Vitamin E 213 HISTORICAL DISEASE PROBLEMS Date: Farm Code: + -------------------- + PROBLEM CODES 1. - Not a problem 2. - Problem within the last year 3. a Problem over 1 year ago 4. - Both 2 and 3 + -------------------- + DISEASE PROBLEM CODE DISEASE PROBLEM CODE ABScessation Nutr. deficiency Anemia vit E/selenium Arthritis other Atrophic rhinitis Osteochondrosis Cannibalism P.S.S. Clost. perfringens Parvovirus Coccidiosis Pneumonia Deformed mycoplasma Diarrhea pasteurella preweaning Poor milking postweaning Prolapses Downer sows rectal Dystocia vaginal E. coli Pseudorabies Edema disease Rectal stricture Enteric torsions Rotavirus Erysipelas Round worms Greasy pig disease Salmonellosis Haemophilus Shaker pigs parasuis Streptococcus pleurooneumonia suis (II) Hemorrhagic bowel other Hernia Swine dysentery scrotal Swine influenza umbilical T.G.E. Lameness Terminal ileitis Leptospirosis Tetanus Lice Toxicities Mange Ulcers Mastitis Urinary infection Metritis Vaginal discharge Mycotoxicosis CODE mNOIU'hFUNI-I 214 SHIMS DISEASE CODE LIST DISEASE Abscessation Anemia Arthritis Atrophic rhinitis Cannibalism Clostridium perfringens Coccidiosis Deformed Diarrhea preweaning postweaning Downer sows Dystocia E. coli Edema disease Enteric torsions Erysipelas Greasy pig disease Haemophilus parasuis pleuropneumonia Hemorrhagic bowel Hernia scrotal umbilical Lameness Leptospirosis Lice Mange Mastitis Metritis Mycotoxicosis CODE DISEASE Nutritional deficiency vit E/selenium other Osteochondrosis P.S.S. Parvovirus Pneumonia mycoplasma pasteurella Poor milking Prolapses rectal vaginal Pseudorabies Rectal stricture Rotavirus Round worms Salmonellosis Shaker pigs Streptococcus suis (II) other Swine dysentery Swine influenza T.G.E. Terminal ileitis Tetanus Toxicities Ulcers Urinary infection Vaginal discharge 215 RATION FORMULATIONS Date: Farm Code: + .................................... + RATION CODES 1 8 Starter #1 7 - Grower #1 13 - Lactation #1 19 . Boar #1 2 8 Starter #2 8 - Grower #2 14 - Lactation #2 20 s Boar #2 3 - Starter #3 9 a Grower #3 15 2 Lactation #3 21 = Gilt #1 4 a Nursery #1 10 = Finisher #1 16 - Gestation #1 22 s Gilt #2 5 . Nursery #2 11 a Finisher #2 17 - Gestation #2 23 = Creep #1 6 a Nursery #3 12 a Finisher #3 18 a Gestation #3 24 a Creep #2 + ------------------------------------ + Ingredient Ration Codes or PF # ’Total 2 Height 216 FARRONING FACILITY FORM DATE: FARM CODE: FACILITY NO: Building Type (choose one) Enclosed . M O F . Cargill . Pasture . Other thNfi Ventilation Type (choose one) 1. NaturaT 2. Mechanical 3. Combination Sgpplemental Heat (check all that apply) 1. Space heater unit 2. Floor heat (hot water) 3. Hovers 4. Heat lamps 5. Heat pads Percent Solid Floor Number of Crates Crate Hidth (choose one) 4 1/2 feet 5 feet Other Solid Floor Type (check all that apply) 1. Dirt 2. Cement 3. Hood 4. None Slotted Floor Type (check all that apply) 1. Cement 2. Plastic-coated metal 3. Hire 4. T-bar 5. Other Manure Handling_(check all that apply) 1. Manual removal 2. Flush 3. Pit with plug 4 Pit w/o plug 5. Part of lagoon system 6. Scraper system Sow Feeder Type (check all that apply) 1. Trough w/hopper 2. Trough w/o hopper 3. Floor Creep Feeder Type (check all that MM) 1. Trough w/hopper 2. Trough w/o hopper 3. Floor No. Haterers/Pen or Crate Sows 1. Nipple 2. Cup All-in-all-out Management Pigs 217 BREEDING AND GESTATION FACILITY FORM DATE: FARM CODE: FACILITY NO: Building Type (choose one) 1. EnclBSed Manure Handling (check all that 2. M O F apply)" 3. Cargill 1. Manual removal 4. Pasture 2. Flush 5. Other 3. Pit with plug 4. Pit w/o plug Ventilation Type (choose one) 5. Part of lagoon system 1. NaturaT 6. Scraper system 2. Mechanical 3. Combination Feeder Typg (check all that apply) 1. Trough w/hopper Supplemental Heat (check all that Hidth/pen apply) 2. Trough w/o hopper 1. Space heater unit Hidth/pen 2. Floor heat 3. Floor (hot water) 3. Hovers No. Haterers/pen or crate 4. Heat lamps l. Nipple 5. Heat pads 2. Cup Percent Solid Floor Number of Crates All-in-all-out Management Solid Floor Type (check all that apply) 1. 2. 3. 4. DiFt Cement Hood None Slotted Floor Type (check all that apply) 1. 2. 3. 4. 5. Cement Plastic-coated metal Hire T-bar Other PIG FACILITY FORM DATE: FARM CODE: FACILITY NO: Facility Use (choose one) I. Hot nursery 2. Nursery 3. Grower 4. Finisher Building Type (choose one) 1. Enclosed 2. M 0 F 3. Cargill 4. Pasture 5. Other Ventilation Type (choose one) 1. Natural 2. Mechanical 3. Combination Supplemental Heat (check all that apply) 1. Space heater unit 2. Floor heat (hot water) 3. Hovers 4. Heat lamps 5. Heat pads Percent Solid Floor Solid Floor Type (check all that apply)— 1. Dirt 2. Cement 3. Hood 4. None Slotted Floor Type (check all that apply) 1. Cement 2. Plastic-coated metal 3. Hire (non-deck) 4. Hire decks 50 T-Daf‘ 6. Other 218 Manure Handling (check all that 6WD) mwar-i O 6. Feeder Manual removal Flush Pit with plug Pit w/o plug Part of lagoon Scraper syscem Type (respond to all that appfi)“ 1. 7. 8. Fenceline/Bain- bridge (no.) No. holes/feeder Hole width (in.) Round trough with water (no.) Diameter (ft.) Round trough w/o water (no.) Diameter (ft.) Straight trough w/hopper (no.) Length (ft.) Straight trough w/o hopper (no.) Length (ft.) Round feeder w/holes (no.) No. holes/feeder Hole width (in.) Drop system Other Total Haterers by Type I. 2. 3. 4. Nipple Cup Tank Other All-in-all-out Management 219' PEN SIZE FORM DATE: FARM CODE: Facility Number Pen Pen Number of Pens ’Hidth Length DATE: 220 BREEDING AND GESTATION OBSERVATIONS OBSERVED BY: FARM CODE: (+ - Adequate; Room temperature (0F) Moisture Gases Fan condition Inlet condition Heat source Cleanliness Haterers Feeder management Light Floor Body condition Hair coats Mange Thin sows Discharges Lameness Feet Downers Testicle size Prolapses Pig temperament Scours Shouts Sneezing Eyes Coughing Vices Other Sumary: - 8 Needs Attention) Comments DATE: 221 FARROHING OBSERVATIONS OBSERVED BY: FARM CODE: Room temperature (°F) Moisture Gases Fan condition Inlet condition Heat source Cleanliness Haterers Feeder management Sow appetite Creep management Creep feed management Floor Sow haircoat Underlines Sow temperament Discharges Feet Shoulder ulcers Pig haircoat Pig hydration Pig temperament Pig fill Estimated pig gain Scours Sneezing Coughing Arthritis Navel ill Teeth clipped Lacerations/abrasions Teat/vulva necrosis Anemia Other Summary: (+ . Adequate; - 2 Needs attention) Comments 222 GROHER OBSERVATIONS DATE: OBSERVED BY: FARM CODE: (+ - Adequate; - 2 Needs attention) + - Comments Room temperature (0F) Moisture Gases Fan condition Inlet condition Heat source Cleanliness Haterers Feeder management Sort Pen uniformity Average number pigs per pen Feed texture Fill Hydration Hair coat Skin condition Scours Sneezing Tearing Shouts Coughing Labored breathing Lameness Eyes Anemia Prolapses Vices Other Summary: DATE: 223 NURSERY OBSERVATIONS FARM CODE: Room temperature (0F) Moisture Gases Fan condition Inlet condition Heat source Cleanliness Haterers Feeder management Sort Pen uniformity Average number pigs per pen Feed texture Fill Hydration Hair coat Skin condition Scours Sneezing Tearing Shouts Coughing Labored breathing Lameness Eyes Anemia Prolapses Vices Other Sumary: OBSERVED BY: (+ - Adequate; - 8 Needs attention) em DATE: 224 FINISHER OBSERVATIONS FARM CODE: Room temperature (0F) Moisture Gases Fan condition Inlet condition Heat source Cleanliness Haterers Feeder management Sort Pen uniformity Average number pigs per pen Feed texture Fill Hydration Hair coat Skin condition Scours Sneezing Tearing Shouts Coughing Labored breathing Lameness Eyes Anemia Prolapses Vices Other Summar : OBSERVED BY: (+ a Adequate; - 2 Needs attention) M 225 Farm: SHIMS FARROWING RECORD DATE No. Litters No. Pigs Nfi-‘igs No. 'No. Figs No. Sows mm/ddlyy Farrowed Born Liveborn Mummies Weaned Weaned 226 Farm: SHIMS FARROWING ROOM MORTALITY No. Pre-Weaned Pig Deaths Date Reason mm/dd/yy Crushed Deformed Scours Starved Sudden Weak Other Dem 227 SHIMS POST-WEANING MORTALITY Key to Death Reason Codes Farm: Phase Codes I 12 = Pre-Nursery Weaned Pigs 2 13 -.- Nursery Pigs 3 14 = Grower Figs 4 15 : Finisher Pigs 5 l = Pneumonia = Scours = Lameness = Injury = Abscess 6 = Chronic Poor Doer 11 = Unknown Reasons 7 : Cannibalism 8 = Sudden Death 9 = Starvation 10 = Other Conditions or Reasons 4! I ' fi Date Phase No. Reason Average Comments mm/dd/yy Code Pigs Code Weight 228 Farm: SHIMS . BREEDING HERD REMOVALS Key to Removal Reason Codes 1 .-. Reproductive Problem 7 = Sudden Death 2 : Lameness/Injury/ Down 8 = Depopulate/Test and 3 = Old Age , Remove 4 = Thin/Unthrifty 9 = Poor performance of offspring 5 = Mastitis/ Poor Udder IO = Financial 6 = Abscess 11 = Other Conditions or Reasons 12 = Unknown Reasons 9 Jr Date Sex No. Type of Removal Comments mm/dd/yy Head Head Removal Reason 1 = female I = cull Code 2 - male 2 = death 229 Farm: SHIMS MARKETINCS Key to Swine Class Codes 1 = Feeder Pigs 6 = Boars for Replacement 2 = Market Hogs 7 = Open Gilts for Replacement = Underweight Market Hogs 8 = Bred Gilts for = Cull Sows Replacement 5 = Cull Boars 9 = Homeused WE Swine No. of ToTal Gross Dollar Marketing Costs Purchaser mm/dd/yy Class Pigs Weight Receipts (Incl. Commissions, fees, Code Mkt. Costs) Check off, etc. 5 S $ $ $ S S S $ $ S L S $ 5 $ $ $ S S S S L S S S S L S S S S S S $ » $ Farm: SHIMS ANIMAL PURCHASES Key to Swine Class Codes 1 = Feeder Pigs 7 = Open Gilts for Replacement 6 = Boars for Replacement 8 = Bred Gilts for Replacement Wan Swine No. of Figs Average Cross Dollar Seller mm/dd/yy Class Codes Weight Cost 6 S S $ S L MMMMMMMMMMMMMM Farm: 231 SHIMS INVENTORY DATE mmlddlyy HEAD WEIGHT PER HEA_I_)_ Gestating Females Lactating Females Open Sows Cull Sows Open Gilts Boers Pre-Weaned Pigs Pre-Nursery Weaned Pigs Nursery Pigs (less than 50 lbs) Grower Pigs (SO-125 lbs) Finisher Pigs (125 lbw-market weight) Month: PRODUCTION EXPENSES (Except Livestock Purchases) Farm Code: Date EXPENSE ITEM CODES 1. Purchased feed 2. Repairs and maintenance 3. Vet. and drugs 4. Labor 5. Supplies 6. Fuel 7. Electricity 8. 9. IO. 11. 12. 13. 14. Telephone Trucking Marketing Insurance Interest Taxes Other Expense Item Code Dollar Amount 233 PRODUCTION EXPENSES (Except Livestock Purchases) Month: Farm Code: EXPENSE ITEM CODES 1. Purchased feed 8. 2. Repairs and maintenance 9. 3. Vet. and drugs 10. 4. Labor ' 11. 5. Supplies 12. 6. Fuel 13. 7. Electricity 14. Telephone Trucking Marketing Insurance Interest Taxes Other Date Expenses Item Code Sub-Item Code Dollar Amount FEED USAGE Farm Code. + ------------------------------------ + RATION CODES 1 - Starter #1 7 . Grower #1 13 . Lactation #1 19 . Boar #1 2 8 Starter #2 8 - Grower #2 14 - Lactation #2 20 a Boar #2 3 - Starter #3 9 . Grower #3 15 - Lactation #3 21 - Gilt #1 4 - Nursery #1 10 - Finisher #1 16 - Gestation #1 22 - Gilt #2 5 a Nursery #2 11 - Finisher #2 17 . Gestation #2 23 = Creep #1 6 - Nursery #3 12 a Finisher #3 18 - Gestation #3 24 a Creep #2 + .................................... + Record each batch mixed or delivered. Indicate any discarded or returned feed. . RATION CODE : AMOUNT DELIVERED OR MIXED DATE : (One Code Per Line) : (Total Height) APPENDIX B APPENDIX B SLAUGHTER CHECK DATA DA’I'E: OWNER: NO. HOGS CHECKED: mm mvesnoxromsy LINE LINE LINE QBDEBLLIIiQSL—BWE 9.9M LUBE lam—“LEU- RM Q_BRDE JLQSLN .113...” RQQMM. 1. 36. 71. 2. 37. 72. 3. 38. 73. 4. 39. 74. 5. 40. 75. 6. 41 76. 7. 42. 77. 8. 43. 78. 9. 44. 79. 10. 45. 80. 11 46. 81. 12. 47. 82. 13. 48. 83. 14. 49. 84. 15. 50. 85. 16. 51. 86. 17. 52. 87. 18. 53. 88. 19. 54. 89. 20. 55. 90. 21. 56. 91. 22. 57. 92. 23. 58. 93. 24. 59. 94. 25. 60. 95. 26. 61. 96. 27. 62. 97 28. 63. 98. 29. 64. 99 30. 65. 100 31. 66. 32. 67. 33 68. 34. 69. 35. 70. 235 SLAUGHTER(XHKH[DATA DELHOGSCHECKED: 0 DATE: mvesnoxrorusy PLANTE 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 5203399131: 93110931 0000000000 0000000000 0000000000 0000000000 0000000000 (0) 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 (1) 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 (2) 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 (3) 0000000000 0000000000 0000000000 0000000000 0000000000 iflflfl1u.DEVUUflON TOTAL OUNT 1 2 3 4 5 6 7 8 9 10 TOTAL COQE! 1 2 3 4 5 6 7 8 9 m M 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 (0) 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 (1) 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 (2) 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 0000000000 (3) 0000000000 0000000000 0000000000 (IMMKENTS APPENDIX C APPENDIX C «a: Lease ..L: .L. .:.:. .52.: .18 .mA— c .254 L LL. .:.:. .. LL. 4...... "a 3:1..— .wm__.Lxuo.u. u._oL .oL:ooLu_LLLLL. u..a. u._o. ou-a4w: .Am. Avon—ASL. Lunwu. .au_:-. c:_aUI L §.¢u:_g=§uu 8w»— :wAL sup— 8w»— zuAL ZUA_ zuA_ Imp— tuAL xw—L In». In.— twp— Imu— Imu- Imp— 29A— 3w». IuA— twp— sh»— Imu— In»— gm»— lub— In». quL Ia»— In»— In»— lub— In». tun— nu»— lup— Ifip— Imu— 89»— SEA— Imp— INA— no... nan Loo-L u.uaouu .xmcg. ua—a1m: .3; 2.5.5.5.. 269...... L .3: 996.33.. .89.... £555.59: 2.33. 55:. L........... 2:92. 89.8.. 9 ..L. L. E: .LeuL......:._ - 58.96:... 2:92. . 8 3 8.9.... .3: . ...8 9 3.8 L. E: :38“: ...... 2:92. .3: .....L..9.E... 389.9. I.. .3: 996.33... 389.9. ......LLLL1LE9. .9... 5.9.. L.L..g. 2:92. 888 9 9381. EL. L.LflulaL .....LLEL 2:92. 8 9 2.83. L E: .L.: ......L... - 58.5.5.5. 2:92. 8 9 38... E: .L.... 9 3.8 L .3: L588 .39.. 2:99. .3; ......599. 389.9. -3 E: ..L96.....o.. 389.9. -....LLLI52: 35.5 5.9.. . L.L... 9:92. o .- .qu EL. . ....8. 2:92. .9. 9 3.8 u .3: .5885... 2:99. . .3: .3353! 98...... -L. .3: 996.33.. 3.8.. .3899. 4:35.352: LL38. 5.9.. 22.95.... I 33: .LLLSL 2:92. o 9 .35 .L E: .L.L ......L... - - .3333... 2:99. L 9 3.2-3.-.. .3: . 3.39.5... 9:92. 8 9 .3:-...... E: 2582...... 2:99. 8 9 39.-...... .3: 29.55.99 2:99. 8 9 9..-...-.. E: ...agaxsw... 2:99. .8 9 LIL-..L-L. E: .59: .952: 2:92. 8 9 9..... L... L. E: L.LL ...-...... - - ..§.L..L.L5.=.. 2:99. o 9 ..L... .L.. .L E: 2:85.... - I ......L..L.L5.=.. 9:99. o 9 ..L... L... L. E: 228...... - - .8339: 2:92. o 9 ...... .... .L E: .LLL ...-...... I - .359LL.L.LL5LL=.L 2:99. o 9 .L.... .... L. E: L.LL 8...... - - . ..382 ..L... 5...... 2:99. o 9 .L.... ......L E: L.L...LBLLL ..9:L .. 2.35.. 8 9 9.... 8.. L. 5: .LL. ...-...... - I 55.333. 9:92. o 9 .5. L .L E: .2375... - 5.9-.8358. 2:92. o 9 .918 L L. E: LLLL ...-...... I I 3.98358. 0 9 .39. ..L. E: :9: . 8o 9 51.8.... E: L59. 9.... 8 9 9..-9...... .3: 9.6.23.8. o 9 .65. .8. L. E: .LLL ...-...... I - ...-.-..L . 2.35.. o 9 .L.... .8. LL .3: .LLL ...-...... - - .59.: . 2:99. o 9 0.5 .8. L. .3: .2375... - - .L....uL . 9:99. o 9 .....o .8. L. E: .LLL8.:.:.. I - 98.38.. 2.35.. L 9 .8. .8. L. E: 2:85.... I I .88L98. 2:99. o 9 as. .8. L. E: -. 55 88.85.. 8......95356 3:. 2536) ....o..u..x.. ..ag \ausuauepupaa... ua.o¢ua ....o..u..:.. .xu.u.n.u.«¢ua. u..aagu...¢aa¢z. a..a..aa.a..=.. ca.a¢u= ..aa.»._..u¢.. oa.a¢us ..u.¢o. oa.a¢uz «Baas-t. 2:93.. .ua_u tu¢u.-u.lul 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 000¢ 0000¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00¢ 00 0¢ up¢o 0¢ 22.3..-.- 5: omaaa¢xnan xw.. 3.5.3...- .3: eugaabtm8ua¢32¢t. ux_o. ma.a.. o ......an.u>.. ...»...uuaun.u~muuwwwmw. “an“«wu MM».... w“ .Huuwwwuw.u "WW“ . ....u.. . >.. . ...—... . .... .- .... - .. u . - ..m..:.....o.. .....mz- ... .. .3. ... ...... ..a.............__... ...... .. ...... . 2.3...." . ii... I O - . ' | . II II ...au.....~.u..... ....... . .. ..auu....-. .... . ......a.-.->.. . .....a...->... . . . ...... ....... .... .4 .... . .... . ...:2...-.->.. . ..a.......>... ......u............. .3:... . ......M-.-... . ......i... . . I 9 I l c I .. ...:o..=.o. ......4. ......a....... .....4. ...... . ........o..->.. . .....wao...>... .5... ...... . “flaw...“ . flag...“ . . I a. 6 I l C I I. .a.....§.......__... .. .. a... . .... . ......u-.-... . .............. .... o o I ' 9 . . I- ..4>o¢....o mm... ......x . . .4 .......-. .... . .......H.4.o.. o...4.. ......o.. .. _u ... ...... . ...... ... a...4.. ... .. .. .....a.-. .... "......._....u.... u...... ...... .. .........>.. .... . . .~......... . .. ...-. .... ............w..:.. ......s ...... .. ......-..>.. .... . . ...4o. ....... ..4. .. .... . .... u .....o.......... ....4.. ...... .. .......).>.. .... . ...ou...... ......x - u .....o.."....:.. ....4.. ...... .4 ......-..>._ .... ..... ..4.....¢.. - ..ou..4. . .... u ......a.......:. ......x ...... .. .....a....>.. .... ...:o..:.o. ..oo..4. ......u....... ........ ...... .. ......a......=.. ......s ...... .. .....a.-.->.. .... . - u ...a. ..."....w.. ......x ...... .. ..a.......>.. .... - -. ...ox....o... . u ...a. ........=.. ....4.. ...... .. ..a....-.->.. .... . .... x. . ....a.-.. + ....4..... u ..4.. ...m....... ......x ...... .. ...:u.....>.. .... . ..aoo. nu.“4wuuum“wmon. m.w~..=.u ... a .... - . ....a ...”.w..:.. 0...... ...... .. .4.....-..>.. .... . . H . .... ...... .. .....o. .. ...... ....4oan....... ....4ux ...... .. ..o....>.. .... . ........o....=.o. 9...... ...... .. ..o...=.o... .... . .....o......:.. ....4.. ...... .. ..o.-..>.. .... . ....wa. ......a ...... .. .4wa-.. .... u ....u ......s..... a._.4.. ...... .. .....u.o...>.. .... ...—........:.. ....... ...... .. ....a.-.. .... u ....e ...o.....:.. u...... ...... .. .......o..->.. .... ......4... ......x ...... .. ....4..-.. .... u ...o. ..aum....ma. ......s ...... .4 .....au...>.. .... . mwmuuwwu. o...... ...... .4 ..aoum-.. .... u ...o. ..au”....=.. ......s ...... .. .....ao-.->.. .... .. . «...... ...... .4 ......m.... .... u ...o. ...o”.....:. ......x ...... .4 aa....o...>.. .... ...“..a... 9...... ... .. .4 ....:.u-.. .... u ...o. ...o..w..=.. ......z ...... .. .o....o..->.. .... ....49. u...... .... .. up....... .. .... . ...............w.. ....4.. ...... .. ....u.....>.. .... ....a....... o...... - . .......u4."....:.. ....4u. ...... .. .......-..>.. .... ..... ......m.... - ......4. .. .... . ..........u....... ......z ...... .. ......u-;.>.. .... ...:a..=.o....au.... ......u........ ........ ...... . ......p.uunmmmuun. wuunuwu ..m.«» m« .....WW4M.»u“ awn“ .- . .....4........ o...... ...H.. .. ...4....o.-.. .... ....ou...... ....... - ......uau..... o...4.. ... .. .. ...4......-.. .... .w... ......m.... - moaux... ... .... ......z:......=.. ....4.. ...... .. .....a.... .... ...:o..=.o. mono.... >.....»......... .o...>.. ...... ........>........ ....4.. ...... .. ......a.. .. .... ....o....... o...4w. ...... .4 ..o.....n.. .... ....ou..4.-.u. ... ....mu. - ......p.......:.. o...... ...... .. .4.......-.. .... . - ..o.. ..a..u.. ......»x. an: ... ...... ....... ....4u. .... .. ......... .. .... ..... a. ......o... ......... ......... - - ......u..... .....u. - .«u........op. «...... ... ... .. .... ... ... ...... ..... ..4......w. - ..oux... .. .... ......u...u.. ....4w. ..... .. ......-... .... ...:a..=.a. ..uo.... ....u»......... 9..... ....4. . ......3..... a...4.. ..... .. ......... .... . - ......u.o .o.. ......x .. .. .... ..-... .... ......u.......>.. ....... ... .. .4 .......>4... .... . ......u..=. ....4u. .. .. u.....:.... .... ...o.4......... ....... ... .4 .o....... .... ..o."»._..u... ....... .. .. o.»....u..-... .... ......u....=.. ......s ... .. .4 ..wm.=..a. ...“ . ..a.....n. ”u“u«wu u... .. ......>u ... .... ....au....... ....4.. .. .4 ...... ... .. 9.... . - ....... ....... .... .. .....u... .. .... . ..... ......wm.u. - ........ ... .... ....au...... ....... ... .u . ...:a..=.o. ...—...... ... ..oux... ......»u........ u...... ...... .... ......u.. - ......4. . w..:¢..:so.wu.uu.... a.............. ...... ...... ......a...... - - - .83....8..:......:.. 3...... . .4 34.32... ...: - - . . .....u.o...>.. . ..... ....o~...=o .. ....4u. .. .. ~.....r.. .... . n.2,»... . an»... ...... ...... ......es.........__... .. .. as .. .... . O . ' ' o o . .l .....a............o.. .....m.- ....... .4 ... . ... ...... . .........ao... ....... . .. n......-.. .... - - . . ......-..>.“ ..... ......nuam.muuunwu .... .. ......u. .. .... ¢ 368...... . 38.3.3... . n - o 31......2: . 23.15.... .8. 2.....25.E..... 3.9... . .. 28...-.. .... . .3. . .... . 5...... . .... ...... ......o... 2...... .... .4 5.8.... .- ...: g ”on... .. 00.: 000—323 120.333.2313 "0:. 58 no... .. no": 000:0:3 gins-giguu ...: ‘2‘11 "3.8-0:2: .3: 8:: .«w!* 5...-.. ...: ...-8 .... 3...-.. ...: ...—2...... ...: 5.2-Sofia ...: 2.5.8... .... ..4. .o ...: 38.3%.. ...: .awaux:uluovnw>uxu:wma_ Ban: mo :5! “Cue-«:3: ... . max—9&2 Ate-“:8... Away-mpg: 3:5. 2—208 «A—nOvlu— —8: .ug. Gl_§ «gnaw-0:8: 33330—73. claw: £23.35: .anigb. ut—SU. .Aw.°vnm:l: . 3. 03.3! .A—uavIu—_x: .Dhgnfg. Giza! Laney-«=8: .bgu‘uw: 03.3! .npueulu=l: ...—8&8. g—Q! “swag-up—l: . 0‘0. «vs—a! .a—uouln=l_._ . (wt. s-a! .a—n0vlu=8: .buaxn. 3.9! a; ...-s - nu": 08:0:3 on< om< o u< o u< ou< o u< 5.5-8 ...: 8;... ...: ....8-8 .... ...-8 ...: ....3-8 ...: .58.... ...: .58.... ...: 55...... .3: {Set-8 ...: 32:2... ...: 3.16.... ...: ....-8 ...: 8.4.... ...: 29...... ...: 28-....-8 ...: 8...... ...: .5... ...: ...-... ...: .8..-8 ...: :33... ...: «...-8...... ...: .25... ...: Sud-B ...: =2... ...: .....-8 ...: 85:23:51.0 «0:5 2:35.... ...... 2:3... fine-...... .3: t¢.. u... m . 8. 2.4. .-. 8.. 2...... . 2.... 2... . . ...... . ... ... u . 8. um. .-. a... u». . . .... um. . . .... . >.. a... u . 8. a... .-. S... 3.. . . .... ...... . . ...... ......23353... 2.34... 48:53.23... 2...... :8215233 2:9... .....u..... 2:9... ..........u........ 2...... .......n............... 2.34.. .....umu...>:....$8.. 2...... ......m...>..:3_.. .25... .........w>3.o.. ...n... ...—8.3:... 2:9... . 1.225.... 2...... ..4..o.s....u..>... 2:9... ......93..%.....8._ 9...... ......93...8.:.. 2...... .....95...6.. 2.3... ...8...«... .....u. git-...... ......u...:o. ...q... ......3...... ....wa . $.34... 2.34m. :58"...§u.88. 334.. ......w..8...>.. 9...... ...... m. 8:: .5. . ...... ...... .. .... .5. . ...... ...... .4 .... .5. . ...... ...... .. .2... .5. . ...... ...... .... .6. .- ...... .... .... ....- ...: .... ....-.x..- ...: .... .... .8.- ...: .... ... ...- ...: .... ...-.....- ...: .... 8......- ...: .... 3.....5- ...: .... ....-.EF ...: .... ...-....- ...: .... ...-.....- .... .... ......3... .... .... .... .....- ...: .... ...-...- ...: .... ...-u..- ...: .... 35-...- ...: .... .... .... ...: ... 2......- ...: ... ......- ...: .... ...-B...- .... .... 8.5...- ...: .... 32.....- ...: .... ...... .....- .... .... .89.... .... . m. :5.-. ...: .... m. 3.2-...... ...: .... .. .... . ...: ..wa.!....m8..... 3.2.2.5... 5...... ...... . .. .. 5...... .... ... 9.. .. .8885: .... ... .. .. 8...... ...: ...... u. 8.5... . ...: o .. .8033... ...: a... .. .... . ...: $38.5... 2...... .3; ......u! .895... ...: .ag—gguggleebwa-uuuux— 2.! ans! .. awning. 08.9! 08 .000; u. no": 08(2):... .. .. ......5... ...: 692.3323“. 3:. 242 2.331.383 u. 2.33.. . ......83... 2.33.. ...... 332323.... 2.33.. . .wbdc. an—ndw: . .8822? 2.33.. . .... 33.3.3: : 389.2. .. 3.. 2.8.3.9. .892. 8.8.2.332. .... 3.. 3.2.. 2283...... : 3...... 2.33.. o 2 3..... 8 3.. 2283...... : .38.... 2.33.. o 2 8... 8 3.. «3282......muxhw... o 2 2.83:8 3.. . a . 2283...... : 2.32. 2.33.. o 2 2.32 8 3.. 2283...... : . 3... 2:93.. ... 2 33 8 3.. 2283...... : 32...}... 2.33.. o 2 ma. 8 3.. 2283...... : 2.33.882: 2.33.. o 2 ...33 8 3.. .....88228muxfi... o 2 28:8 3.. . . . 2283...... : 3......3. 2.33.. o 2 3.83 8 3.. 2283...... : ......2... 2.33.. o 2 .3. 8 3.. 2283...... : .8232... 2.33.. o 2 «3.3 8 3.. 2283...... : .288. 2.33.. o 2 288 8 3.. 2283...... : .88....3. 2.33.. o 2 ...3 8 3.. 2283...... : .2822... 2.33.. o 2 ...... 8 3.. 2283...... : 8.228... 2.33.. o 2 3..... 8 3.. 2283...... : ...... 2.33.. o 2 .... 8 3.. 2283...... : .8328... 2.33.. o 2 3.. 8 3.. 2283...... : : .3232... 2.... 2.33.. o 2 3.. .... 8 3.. 2283...... : 2.5.3.. 2.33.. o 2 ...-... 8 3.. 2283...... : . 83. 2.33.. o 2 28 8 3.. 2283...... : : ...-33.8... 2:93.. o 2 2.. 8... 8 3.. 2283...... : .882. 2.33.. o 2 32.. 8 3.. 2283...... : 22.2.23... 2.33.. o 2 :3... 8 3.. 2283...... : 82.2.3... 2.33.. o 2 ...! 8 3.. 2283...... : 29.8.52... 2.33.. o 2 ...... 8 3.. 2283...... : 3.8.2.. 2.33.. o 2 2. 8 3.. 2283...... : .32... 2.33.. 8 2 32a 8 3.. 2283...... : 35.3.8. 2.33.. o 2 35.3.9. 8 3... 2283...... : : .3282. 2.33.. o 2 .3. .8. 8 3.. ~... .3: .. 2... 82.83.. 8......25..§.u ...: 000 at npc a< 090 m1 upa=)u*».Aw—3. 0330! ..A0 Cum—Ax: .0—80».A8p£0>8. 0:300: .A0» Cum—Ax: .aA—Ampwx. 08300.. .A0» Sump—8AA 3:305... 0:39. .A0. 3.02:: .3802. 0:20: .... ......x.. . A... 03300: .A0. Camp—8A.. .uAsdAcu».Owcua. 0.300: .A0» Sun—A8: .0082“: 0530:. .A0» :30:an ..AA!»>sonxo . b8—tg . >>\on\0 . bl—Cfi . :anxn « . A. ...-g .5 3 2 3!! .38. .331. 9..?! o... .o I... sauna” 32!. . 3..! so 393.80 3 33 0.5. can. 3 coo. as . . a... Aoau I A.... o. oo ...:..oo ... ...... .. ...u ago. . ...u u....:.. .3... .oo A...u.‘o. .. sou.» so 38 .38» “Agog. >...w> \o _ n SUI :1 w. 0‘ .gu .p—xwg wwdu \o :c\ .oauu .aoo.. opoo .3 .5528 8 :55. :2. .958. .8 88 E... 2.3.. Q -..--.. b ‘I — \o I... .Ifitii: ..3 ex 3.: I»: . 3:. 3 g .3 8.3 0.... 53:0 . u so 3.3 ..o .13 £3 .35 3 :8. cs _. .00 nus» .-. I QCOI ..l‘ m- I A. . 3.: A. no I... :3 .... A25 385 . :38! A. . SA! A. .z.. ..au.>.uc. A.. . ha... 03 no... I. an“: 823:3 863.335.13.545 30.: . 33 :- AS 2:! .. ..3 3.69.. 3.3.31. 02.4.8... 3 .A.... 39> on . n ~28 .. . pj-It u . 82:“ 2.3843 39.3435. 8:58.... 35¢! ‘28 . p2: .. 5.23.3 yuan 23:39.8 . . 3.: . . . 2:: \c 3:.- I..c&o ox A. ax.:u no...u .. o..ao..u .\ ...... ua¢¢. ua.a¢u= o u¢ .o¢> a cup. A. ...m..:.. I . .puw.x_. .»-¢a.¢a. ca.a«po- a an». .A."...«A.:_. I I .mz:»u...u..<.um.. oa_a¢ua o u< nu how. a sub. .A.”...m_.x_. I ..m.-<...oa:uua. ou.a. .ua¢<~o-¢. oa.o2u 22 2222220 w 0 I Sunny: 22 .8.80 2 22:22:32,2I 22 2222220 0 u. 20:02.2..2I2: 22.100 000 2 2222:w_x2>2| 22 222.520 2.2- 22: .20 2.0a Ila. 00. 000 00 22 22322 uronI III..- 32.500 0 I 22:82:52 00. 80 2 .2222: 202I22 2222220 2 0 I .022832I22 .8 80 2 3220:832I22 2222220 . “I I 22.50025 22 .00 000 0 3223.302»- 22 32520 302 I100 IE: 200.000 00 u< 322—o- :00 IIEu 5.2.63 9 I 222322>2Iua100 000 a 3220.232222qu wujuwo m o I 20.23.222.322 «00 000 m 32.2023222I92 202.63 . aII uxmofga 2.- 200.000 2 22.22.03.202 22 25020 . o I 22.02 .23.. Ila 200 30 00 m2 22302 .232 luau 222.620 0 I 222392>II22 00.000 2 220222322>2I3 $2330 . o I 20.233.22qu 200.000 0 222202322>2Iun 5.2820 . 0|. 2332202 22 200.000 0 3222232222 22 2.2320 . 0 I 322 .232 IE.- n.00.000 00 m2 22.2.02I .232 IIIJ 222230 . 0 I 2222022223 m2 . 00.000 2 3222282222., 22 22¢..qu . .0 I 38222252 "8.08 2 23.982282 22 2220220 .oI I 2302232 222 09.000 2 3223022202I 22 2.2.620 .o I 302 .2022 III..- ...00 000 00 «2 332-I 2822 Ito 32.620 .3 «2020 II n90— 0.0—3:3 50. 302—32—515— .0.: ...o I 22.5202I m2. 00.000 22 20 I .2332- 22. 28. 08 2 .0 n 2152221..»- 00. 000 «2 ..o I 322 I25 II22| ..00 00000 me I 2222222222: on 200. 000 .o I 09222o>2I I2: 200. 000 m oII 2.6222922 222 200.0002 .. o I 302 22220 It.- 200.000 00 3 22.22.52.2I I22 2222220 22.20.5202 I22 2222220 czgfl 22 2222220 .2 .322 .5 £5 2222220 2222222222>2I 22 2222220 M22532om2m 22 2222220 2 ‘UHO:<¢<< A<<<~l A ~‘ 22 h ..IIHIE '3“ HI 8 ‘ _I U “I a Azuxwgll 0‘ w¢¢29uo 3302 01 ‘5 union: 2.22: 2‘ $2922.22”...- .0 I 20:22.:I I22. 22:0I22. _.'-2 (no 0: m o.- 220233 22. n I 202.22% 22. 9223a u 2 I 228.23I u. .8. 80 2 2223.... :2 22222202 8. 80 2 .2: 2.. I22 2222220 .880 2 22:22 I22 2222220 8. 80 2 22:22 I22 222.220 .880 2 22.2.20233I22 2222220 8. 08 2 22:29:05.2 222.220 8.80.2 2:92.322 2222220. 8. 80 2 22.08.52 22 222.220 . .2 I :2- 2 2.2.2.. ..8 08. 8 2 222520 2. 2.2.2 222220 20 I 022...... I502 28.8.8 2 22252320. I55 222.220 .2 . “22-22” 2...... In?“ 2. “22.228222 5...... 22.22222 9N 0 I .0220. 22 2220M080? 2 22:08.5 22 2222220 2 0 I 288522 2 8.80.8 2 3:08.522 2222220 . 2 I 0.28.5.2 $8.80 8 2 2220208822 2222220 ..0 I :3. 2 . 8.80 8 2 22:.IeI22 2222220 . . I £25.22 .8 80.8 2 22:08. I22 2222220 0 I 2.: 22 . 8.80 80 0 2 222422.; 22 2222222 .22 02:23:22 «3 .. a I 2.2.522. 0 I Sigma. I II "at! I .0 I 222222.22. o .223. we I 2:02.200- ..20 52:32. a I 2202222002.. 0 2 (22222—2. . 0 I 822-202 . . 88.8 2 22:22.5: 2222220 .. 03 00 2 32.28.02 2220220. . 08 8 2 222822.502 222220 . 08 8 2 22.22.222.02 2222220 2 000.00 2 22202222202 2220220 . . 80 8 2 22:23.2 2222200 ..80 8 2 2222:8282 2222220 2 R. % M2 32:02:32 222220. 08.8 22 32.82282 2222220 2 3262:2222 22222202 00». 00 22 3282222202 2222020 \2 23:20:. 2\ . .o I 2.222222. 20000 22 8.22.2222? 2222020 .3225... I 222292.222 :2 2w: .2. I SKI :22 2— .50 2.2.: .2. v 52222.22 2. .00 2w..— . I 22:22:02 2— 29.2 .2 I 22.2.52 .2212! I [£22.22 ..00 2.2.: 2 I .222355 2. we I 5.2.0.3. 2539 summon ...-.2 22:22.22. 28 .2 . 22:22!!- I 222255 2.2.02 .2 .2 2 n 2 .2 0: ..8-2.220212222222022. 22:22 .00 22.: .2. I 22222.22 2. . 2:22 22.: .0. I 22.22222 ... .2 22 2022.222 . 21283222222223 222.25. 5.02.2 2.22223 2220 . . I.- .“i K In S — 2: g & Nn-OMONC: 0 222222.. b I .2. 2 8022220 .. 202: 8022023 280.222.223.52. 246 .- l .2 I .2 l a .2 I Q 3 . 23v...” 2: .numuuo 2: .nmufi 2 naugh— 2: .2”. 2.2 O I .2 . n . a . 222.2 a . 232.12 .... . 22.2 a . 23:2 .8 2 222:}: 22.52 .guuhmmfi “flaw“ .38 2 22.5.2 2252 .8 2 3.22.2 2252 mg a Manic}. “"an Mfi m“ magic... 2%“ £8 2 52:22 2252.“.822mmuvcufl wufiww“ 22.2.2223 .... “25 $2 a “22.. 22“ 28.8: 2 3252.2 2252 28.8: 2 52:22 2252 28.28 2 25:32 2252 £8.88 2 32.2.2 252: £8.88 2 32:22 2252 38.88 2 323.12 2252 u : .. h :82 n :92 u a: a . 512.“... “aghasipm a“ n :3. .u n 5...: m“ ” wag“ .6 I p . I u i u i . 3.3.2....Lflu222Hm2222uu212. unuaqau “nun“..S . .8: 2 32:32 2252 .8: 2 5:212 2252 “88 2 5:82 2252 “8: 2 5:92 2252 “:8 2 22:22 2252 “:8 2 5:32 2252 “8: 2 5:22 2252 “a a “$5.22 “HEW“ “88 2 mg 2252 . 5.... 5 $.32. "a. mafia £88: 2 3.3.22 22.52 «no.8: 2 a 2252 ”28.28 2 a 2 2252 “28.8. 2 fix. ..2 2252 5.2.. 2 255...... “22.2 8...... a 2:53 2.2“ 28.8: 2 3522 22.5»: 28.8». 2 25.22 2252 2. 5:82. .uiI 21 .2 a . 232 2 . 532 C 2 .o : m“ . 0323...“: 6:32.": 8:82.“: .: $2.5 nag o I a o I a n I ‘3 u I 2 I o I .o 3.... ... £2 3:. J32 M" u g m: . W232 2.“ . Mag 0 I g I I a u I a u I 2 I g u I 38 2 .2232 2252 . 2 32:22 2252 .28 2 3.832 2252 “8: 2 2:32 2252 .8: 2 5:82 2252 .8 2 5:32 2252 “8: 2 22:32 2252 “8: 2 22:32 2252 2 22:32 2252 f? M ‘ A N - i d g U U G .. 2 .8 2 . “8&2 28:22 2252 .8 2 25:32 2252 28.8. 2 a 32 2252 .868 2 .8: 32 2252 .2888 2 a 32 2252 “28.8: 2 a 2252 “28.8: 2 a 2252 “2888 2 5:32 2252 “28.28 2 .2132 2252 "28 :8 2 8232 2252 $8 8: 2 5:32 2252 "28.88 2 3232 2252 28.8: 2 3522 2252 .88: 2 2.232 2252 3.832 3.. £32 5 . 832 m: . :22 m: . :32 m: . :22 ..: . 5.3 .2 . 222.... . :32 u: . :22 “o . :22 “a . :32 .22.?23 ... :2 .2 . 32 "a . 8:2 ..o . “32 u: . n22 ... . .o a 3““ “Laugh-29mm“? .2 .. d2 we . Z2 .28 .8 2 c :a 2 2252 .8: 2 5522 2252 ":8 2 3:32 2252 “88 2 5:32 2252 .8 2 5:32 2252 8 2 5:32 2252 m8: 2 5:32 2252 "8: 2 5:32 2252 38 2 5:32 2252 . . 8: 2 5:22 2252 .8 2 5:52 2252 .8: 88 2 32 S2 2252 8 8o 2 82 $2 2252 .28 .8 2 2m. 22 2252 ..8 :8 2 a 2 2252 "28.28 2 t 2 2252 .2888 2 52:2 2252 “28.8: 2 25:32 2252 "28 :8 2 3532 2252 28.8: 2 .8232 2252 28.8: 2 2222 2252 80ng .. can: boo—3:3 gins—3.22.929 2.. I . £8.30 «2 3.2.39.2 main—mo 38.80 «2 3x228 uqfiuua .o ..ocflmunéfi Shmmaéou emu“ mm - £5.- “9 - 53 u . u . u . : .ncnnué-czuénc—nu 3 u 2.“. mo.- 222 3 . 8.2 a . 82 3 . 82 a . £2 . . .o I add.- 3 u .23.- SI: awed ..o 2 2h.- . ..o . flown 32:2 .8 2: 852548.22 2252 .o . 523 2:35.. “coo 2.132.222. __aouco¢|w25u a .o I .38. .53 m "a u :3 v.03: “onus-52.8 qua-H3333 m«8.2.8 2 3:3: .6. 2 2252 H98. 2 22:22 2252 $8 2 22:22 2252 .8 2 .8832 2252 .8 2 3222 2252 “8 2 .282 2252 m8 2 32:2 2252 “88 2 .882 2252 “8: 2 5:22 2252 .8 2 5:22 2252 .8 2 5:32 2252 M88 2 .ch2.. 2252 u2.8 2 22:32 2252 38.88 2 .3: :2 2252 $8.88 2 32 :2 2252 5.8.8 2 Emwfi- 2252 “28.88 2 :5 2 2252 “2% u my...“ “flaw“ “$8 a mum“ gm“ 2 H “28.88 2 .232 22.52 .2888 2 S222 2252 .888 2 .822 2252 28.88 .222 2252 2 . . .262: 9:50. 2\ . .o - 3.2-Elana .5980 3 32:. w¢§uuo .: . £85.82 .2888 2 5535.22 2252 .... . ...-fix. “8.8 2 afofiu} 2252 . 92 .2888 2 .35. a... 2252 ...-82.552 .2868 22 2:528:28.— 2252 53328: $8.88 22 5:225:92 2252 L \ I «auction .- 890. .o . c.5222 .888 2 5532:: 2 2252 o. 38 . 2.3.7.3 120...... . u: .\ : 2:5..— ..o - 51.-52‘ no . >2!!- ..9 - 0:35..- 22 23.553 2.262. .800 m2 23:225..- 2253 2 33:52 2252 ..8: 2 228952 2252 \c 2.33 til!- 1 ..o u 1.- ..o : Zoe 3 a 2:9- 5 u I: 3 u 392 .o n a: .. 2 3:22 2252 “88 2 .3:- 2252 2 .352 2252 “88 2 22...: 2252 . 2 22.2 2252 ..88 2 .222 2252 \c 85.2 ax no 2 5:3 .6 n 22:.- 3 u :0.- .a - 2... ..a n it: ..c - Na... 3 . 253 ..a n pl...- 3 2 F5.- ..c n E.- 28 8 2 32:2 2252 .88 2 .222 2252 . .2888 2 32:2 2252 .88 2 .222 2252 “8.88 2 32.:2 2252 88 2 .3522 2252 .888 2 2:222 2252 .88 2 3:22 2252 28.88 2 552 2252 .88 2 5:22 2252 . . 2. 2:22.31 . .ou'ou..onl55-2lue.au~flua : .on—‘uaén—séniouéuj .2888 2 32:2 2252 88: 2 222.2 22.52 28.88 2 35.222 2252 "88 2 2222.2 2252 «8.88 2 3:38.. 2252 88 2 4:22 2252 .2828 2 .352 2252 .88 2 ME: 2252 .2. 22 . . . . “2 . :2 u: . .5 .o . ~52 .: . ~12 .o u .32 .c a 12 .o . 52 .o .. S2 .2888 2 32.2 2252 .88 2 322 2252 38.8: 2 3.2.2 2252 £88 2 222-_- 2252 £8.88 2 2:32 22.52 ..88 2 5...: 2252 28.88 2 3.52 2252 .88: 2 .3:.- 2252 03 "can; .. can: coo—3:3 gigs—gar—Buu “0:5 2AF7 .0 0.300. «in!!! a. 30.6... 33 \0 ..5i. 2.:00. 2.7.5 0\ ... ......" ...... \. 0.8..0_:w.0u $20.... 0 .. aha... ....0u .... 0x 0 ...... 0 :00. .... M082”... 09.00.. \. 0830.30.00 00:09.0 0. n 00:00.... .33 x. “.0068... 9.3.00. 2...... 0\ .0030... 00.55 .0 18.80.3300 0000: 900* 0. a 00.600. .33 .. :88... 8....” ...... .. .00....0. ...... .. 05:33.00 8.090.. 3...?0. . 00...... .33 .. .. it... 2...... 2.... .. 30...! 00...... .0 0.3—00.3.0“. .0....00 .. . .0....00 .33 .0 “.300...- 8...0o 2...... 0. 200.5 09.01 x. 0530.39.00 10.... ... u 00.... 33 .0 ..0000533. 2.0... 2...... 0\ 8.02.. 00.5.. .0 3:05... . 0 0......006 .0 .3. 8...: 0s .0 0.8303300 83:02.. 0. . :02.- ...—«u . .....3002. 05:00. 2...... .. ........... 08.2.8... u ....... c. .0039 838 . ....c...u....u.uwl..»0.. 89......“ I . . 09.000 . 00.0!00 0 0.09.”. . 0.00.00 \ 00.0.5.0 0 c335 .023“...- . 0 00.0 00.... . 2...... .00.. 00 . 0 00.0 .030 . 05.1 "00.0.5.0 . 0 00.0 2.00.00: 8...! .0 330.5... .0320:- >..00...0...._. 00.20.. .800 ..o. .. ..Bu \ 8 00...» .. ... haul... 00.2.8... ...“ .0 8.0800 800...... 0. .3. «00.0H03Hi o 00.0 0 00.5% .8... 0 0 09.00:”..8 .— 30." .0» :1 o 00 0 00.. 0 aw... n . 0.2.00: :8. .— .00.0 000 in 0 00.0.50- . 00.0.5.0 an... ~ .0 038.031.»). .— 39 an... . ... 09.00.. :0; .— u .oa ....cm. .0. 0a. .880... ... . 0.80... .88. 3 0 00.0.53 3 0 00.09300 3 0 020...: 30.0.“...- . 0 00.0.0 .0 3‘833... .98 “0000.09.13. . 00.0.8.5 0 00.08.55 .8 8...... .68 8. I 3 0 00.0. .880... ..0 . 0...... .... 3.80.... ..0 . .38... .. .880 .8 0!. . 0 a. .— 388 538 50.0. .0 20.0.0. 0.. 3 0030...: >080 ..o. .353 0. 2 0 0.. 3.0 0 00.5.00 3.. . 09.000 3.~ . 0.00.00 » .. x a 39.... 8.2 .20.... 20.8. 3 u ..0 0......0. 30.39 So 0. 8.20.0... 0.2... 2063 3...... ...... . 8.28838...» .0 20.8.. 3 0 ch... 60.98 «0 3.8.5.. a 2393 3 0 53020.0. .388 00 3.2.5.8300 «00.88 «8.0000 .. «on: 08.3.30 80.28.513.210 .0... 3.. .89....“ .8888. 2 8...... u... 20.8. . .. .-. 80 .888 2 8...? 80 20.8.. . .. . 8 3...... .888 8 2 8.... 8...... 2.0.8.. ... . ......c... 0 .888 8 2 8.2.2.5... 0 20.8.. . .. . ...... ... 8.8.8 2 8:...“ ... 2.0.8.. ....- ....: £3... .88. 8 2 8...... 8.8.... 20.8.. .. . 28.-.... 0 8.8. 8 2 8...... £8... 2.0.8. .. ..................................888 ..8 0 I 0 0 . . ... . 0... 1.9.0 .888 a 8...... 1.19.0 20.8. .. fin... ...... ...... .. - .. | I . II . 0 . |. 0 I .|.* 3 0 0.0 0.3 3 0 30.1.3 3 0 .090“..- .u 0 30:2... .. 3 . 23:02... 3 0 000:0...0 3 0 30.0.5 3 0 000 0....- 3 0 000 0....- 3 0 .0.. 0.5 .3 0 .....o 0.5 n .0888 2 82.8.0.0. 20.8. .088. 2 883...... 2.0.8.. W088. 2 823...... 2.0.8. .0888 a 888...... 20.8.. .088. 2 8.8.5.... 2.0.8.. .088. 0 8.... 0...0 2.0.8.. 2.8.8. 2 8.2.3.0.... 2.0.8.. ..8 8 2 8.08.... 2.0.8.. .88 2 82.8.0.5 2.0.8.. .88 2 8.2.10... 20.8.. ..8 2 823...... 2.0.8.. .% 2 822.020 20.8.. .8. 2 8.8.5.... 20.8.. o 2 8.... 0.... 20.8.. 88 2 8.2. 0.5 20.8.. .. 53...: .0 288. .. 8.53:3... 2.0.8.. .. . 83...... .... ..8 0. 8.53:2... 2.0.8.. .. .. 8...... .0 "x88. .. 8.8.000... 2.0.8.. .. . ....Il ... ..8 .. 8.8.00.0 ... 20.8. .0 820...: I2... influx: 0s ... . ...... . .. ..8. .. . ..8... ... . £810.... . 2...... ... . .2“. w. . 9:2... ... . avg}... .03 “an... .00.... .0.... .09. ......m.8.mo.§mo.= Mispwofimwu... .. . n. 3... .. . 2...... ... .. .32.... .. . 28.... ... . 9...... ..8 2 8.25:... 20.8.. .8. 2 48.8.... 20.8. . .8. 2 82.60... 20.8. .8. 2 8.25.... 20.8.. 88 2 88.5.... 20.8.. .8. 2 8.25.... 20.8.. 88 2 88.5.... 208. m8. 2 8.2.3.... 20.8.. 88 2 8.25.... 20.8.. .8. 2 88.8.... 20.8.. 88 2 8.8%.... 20.8.. 88.8. 2 8.... ...... 2.0.8. 2 8... ...... 20.8.. ...888 2 8.5.8... 2.0.8.. 0“ 8.... 2.0.8.. .0888 2 8 .. 8...... 20.8.. 0 § Q 0 ...»... .noo. go. go. . 88.3.... 2.58.. 28.8. 2 8. . 82... 2.0.8.. 2 88...... 20.8.. 28.8. 2 8... . 8.98.... 20.8.. .0888 2 82:30.. 20.8.. E II 2 0J U U 6 §§§§ ... . ...... . .. £8.10 ... . :81. ... . .2010... .. 3...... ... . 3:3. m. . .29... ... . 3...}... 00. .00—al.03c ......a—zna ...... .8“...§mo...wqu.mo.:fiu0mo.z .. . .. ... . 3...... ... .. .30.... .. . 0.8.... .. .. ...—.0.. .8. 2 8.0.5.0.. 20.8. ..8 2 8.38.... 2.0.8. .8. 2 832.8... 20.8.. .8. 2 8.35.... 20.8.. 88 2 8.35.... 2.0.8.. .8. 2 8.38.... 20.8.. .88 2 83.8.... 20.8. .8. 2 8.35.... 20.8.. ..8 2 8.388. 20.8.. 88 2 8.38.... 20.8. ..8 2 83.5.... 20.8. .8888 2 8.... ...... 2.0.8.. #888 2 8.: ...... 20.8.. .0888 2 8.... 20.8. .0888 2 8... 20.8.. .0888 2 8... m»... 20.8.. 28.8. 2 83.8.... 20.8.. .088. 2 8. u «..8. 20.8. .0888 2 8.3.1.... 208.. .0888 2 8.38.... 20.8.. .0888 2 8.38.... 20.8. .0888 2 82...... 20.8.. n I 6 w u I u I i .. . =8... .. . 2.01.... mung“. .. ..Mfi .u . m5... .3 «0.0.. 3 3.: 08.3.5 55.....1/4—5/«0 .0... V 248 “a I 5.35-3.3“ 3 I 3..-«unzi- 3 I 5.99...- .6 I 33:33: . I 9.3-5. u .o I ...:- >9...- .o I nun—1:. .- .a I m-uuuzia . o I .conlclz. #5....- " o I 2:. >599...- u u p I u I .3333 I :3 >5 >33 u e I I... >5 >3..- . o I ... >5 >33 .- I... 3...... 2 2. ... ....» :I: I. .531... :- I 83...... >5 :1. 3.3. 533 .8 ans» w v a. m— n I \I _ I .3 3.1 ......m ..__:o :5 I 5.... rcuz: :2... :- o I 5.... [:12 32:5 55 >25 I .28.. c >c. :21 :I o u .23. : .6 3.3.... . 3 >35 I 309.269.: 9.21. :I o I 33.2.92. 32:; o >2. I I.Zo.&qcm>c. 9.23 :I o I 3.1.5.559: H.255 ..35 >5..- I c3103.: >5 n.0,... :- o u ...-010.369: H.258 .38 .5:- I .691: 2.. 2...... :I c I bonus“): H.255 .30 :5 I 2.9.031... >5 9.0.3 :- o u 3.93% :12. 8:15 .— 53 >3..- I mama I. )5. 9.0.3 :I m: I ..IOAxa‘Z. 3:55 ...... >2. I :35? u ...... :2... ..I an . .....cuao-.. 2. 33.5 .33 >55 I 33:355.: for! :I can I 33:35:)... 3.2.... .35 .5:- I .82. I312... ...-.1 :- 3» I 33595:): 3.2.6 ..33 >15 I lazu- uxn>c_ 9.21 :- onn I 50:..- ‘:->c H.255 ":3 >3..- I let: 3 >5 :23 3. En I .3:: a >c 32:3 .8 .3... . I ... : I \. 2...... ... 2... 3..: ...... 3:... 23:32.. ...... I. ua— _ax§ 53.33:— “ms—.3 \c I... I... ... I... .o .045: 3 — I I. ..3 I. “can 3....»— . :8 :3.» _ I13 :3 m “a. an ...Smw... :3... as.” \I — I Est—:3 3.. Ix ......wifi \I m < 32w- ~mx=fi 2:... «:0 I\ I ...-Sm meu \. :8... ... s 31 I\ . I :9:- zxiu 3:. 3:5. :3... m... \I .39.. ... h 8:. I\ .3 3.: .o. I 23...: : .3..— . I iguana: \. . I :3... 3...... ...... an I. .3:...“ 3..... > ..8: .o a I... I. . 2.1.... ...... . I :2.- 8.._.a a... 3:5. 33:. :3 ..8 ..w... .n. I €5.32»: .9. .I . I :9... 3...: ...... an I. .3:...“ as... x. :8... .o a $1 I. . 3......“ :3 . I :83. 3...: a... 3:!- 33... 3a .8 ..u... .... I 25....3 : 53:25:53... 3:. e3 =00; .. a": Ono—3:3 .....u \I .. I :2: 3.....“ 5:... «.... .3:!“ #2.. t :8... .o . In... I. . 35:... :6 .. I :33. 3.....“ 5... 3:5. :3... ea ..8 ..u... .... I ...}...flu: \. .. < :9... 3...... ...... ea .3:: #3... r :2... .o n a... I. . 31.... .3 . I :2... 3...... a... .....I: 33.: a... ..8 .3... 3. I 25.5%... P . < :3...- ~m.._.a ...... m5 3......“ .3... x. :89. 3 ~ 8:. I\ . 85...; :6 . I :2... 8...... 3... 3:5. as... «.... .2 ..u... .n. I Eximf: \. .. < :2: 3...... ...... «5 t :25 .o . I... I. . 5......» ....a . I .33. 3.....“ a»... 3:5. .33: m5 ..8 ..u... .~. I 2.5....»- = .3 mm P 2 :2: as...» ...—E an 35.3 was... . 85....» :5 an... _ I >35. ..L .... . \I tone. .0 s 32. I\ \I ton... .o a on... I\ m dunno“. \I :8: .o m 31 1 m Jhuuhflm \I :39. ‘o a 3!... «x M mafia-u: \I :8... .... n cal I\ m manna-”W \I tau. ... ~ 3!. I\ m gunmen. \. :8! ... . 31. I. . 39...; .....u ... =2: 2...; .3... 3:5. .33: an .8 3.: . . I ...-...:- ..a.. .. 051...: a: \I p I 31:; I\ ...!w \. ~ v 5:8... .A.-”nan \ o3 I.... x . SI... ....3 \I .... II: .... Sag-.33 one}: =— I\ 23.}: I a. S \I Iota: Inca-1. 23:3 «2:3 I\ ..8 .3..— . Jun .— . u . a: 2.. I. 3..... 28 \I rm I ..- .... 3833 38x33- »...ca Ix 333.53 I 33.53 .3:—3.335.383 I “and.“ .0.. .. .. \I I 3.3.31... 2...; I\ \I 2:. can ..I > In... I\ 3 . ..- I .3 .3..— u I ...— = u .— o a I ..- 33 £2.89. .3; .26... .33 \ C . am..— I .- .— ..!u Lain Sui mango-a 1 «an: Ono—3:3 I\ C \ C \ ..u I \ .— ...... 952'... 2.38. 2...... I\ xumémlgiszuu "0.: 249 #93. 28 . . 5...... = o 8393.- II :IuIE. v:- ._— . 88.38..— I. 33¢... 36.... 5...... I I 23.1.3.3- . IRE-.9... ...II al.-.53." .... do .8 .5... 8 p I 8. no I >.ou....EH:H>5u u o I 9.35 .. 2:.- .. o I 530.51ch..— ue I 5.9.0.8 : .IE: .. o I 3..—Joucnwchu .. o I Sincnz... “a I Into: ... >5: .. o u III-on .. >5u . a I .08 :55. I ..oIIIx..>5._.oIIIEEwuuquuE: «a I 5. >9.” ..a I :93 >95 .6 I 95: no.8. .a I {cu-50K. ..o I 5&9. .- .o I aoKI>ILMaIé n nun. >9. - .o u wcool>ohu .o I Inca-I" o I 5.3 : >5. 3%: u o I I! .5552...- ..aIIEE.>uK-uqu:>:—>I§ “a I :3 >5 3...: “on nus» a I ...- u— \I Q V dd v— I\ “gm \I ...-on u... IZIII. .3. 953.0..- .o uc- . I\ I .2; I u .5» 96.- I ...-Z55: « to... u...- I 33.98.: .. 9.3. u>= I Sgt-...!- “ .....mlgm I ...-3.35.1.- “ ao..&Iu>Iu I 23:33.3. . .33 we. I. I ...—..8... ...... m): I 33:358. . 335:.- I 233355 . ..IQoIEI- I 233-Cocoa . .3. 26.— I 3.3333: .. «Ion o>Iu I 3.3.-7:3 .3 wau I I .23 I I u ...-.5... m >5. I GEE-.5: “ .28.. .. 2.: I 6.3.28.3.- .. >..II.:: :55: I 23:35:- . 9.2.9.3895. I 3..-.55....- “ 528$?“ >5. I 2.3.32.ch ..ImaI: 2.: . ...-..8... ......SS .I. a... I 23:36:...- “ Ion—.30ICI53 I 3.3.8.233 . concoaoIcIE: I 33.83.09...- u'305 c 2.: I 333:3.- “ '0:qu .. E; I ...-28:3..- .8 ...... . Ins... .9. “Ito.- \ uu>5Iu>Iu I 52...”?!— .. IE...- \ Izod—>2- I Sang-n .. Ito.- \ cuaoIo>Ia I canola:- .. Ito.- \ aonwua>Iu I .3 I33 "It: \ :0 u).- I a. a>= ..8 an... w I 5&3 .— .9.» I I 31955 5.6—I93 I amt—Hg: ...-on :I>m. I Coon we. m3. I ...-Blueluz- ...!“ . >5 .Ifio. .2 Bone“ _II. 3?“:- . .5 I:I>: . .. I I.- I .5 I >0- “ “"39. : a." I ..8; ...-.- I .8.“ a): 08 "can: I. in": fizotg gins—gipauu ...: 2955.33. >5 o cavaIEImInt: ...-H95- I ...-ma...- 3:o:oaoI:I>5 o «:qu3. I 2.3?!- .. 3n..:u|:¢>5 o _.aolo...‘ I ..auIa>I.- “ 3..-coco ... >5 o 585:. I clone:- n>na I uu-hlugu I 95: .. I23- I:I>5 o and . u>oa I «no “...:qu I:I>:_ I a: .... I 5:3 I 5:0. I . a .252: .3. S. no I 25:79:.- ..a I 39513:- 5 I _::mn>u_- no I seamgau .c I 9.2.Io>c.— no I tying-u no I .3- «>2- 3 I 13.9..- ..o I :3 u>Iu no I coco 9.5— ..n I woo—Igo- ..c I wuuo .n>-.~ “a I Inn—cu.- .o I ...-ma «on :3. ..o I pt: “26>: huuqm >5- Iv 33.5...— I.Iu >5 >3...» IA 38. >5 8. 3..-AI. 3.2.. Gmdm .8— ? not-n b.3525 £895.. ....6 Scumu 0509.03 I\ I I I I I I .33 2.. . 33$...- .IE.>5 IIE.E_auI£>:. IIEE—a U..>5n.:>5.- .35 823.5. _.g I I . 3.8.8 I surf.- II ..oIgn... m>5-.ou..!8.n:>:= .. a I >..:..§.I:I>5- u a I 95:0 I:I>5a u o I Sikh—c.5- ua I 3.9.09 : 0......- .. a I 3..-...... .. .IEh- “ e I 109.1952..- .o I ...-u: .. .E: u n n It... : >5u .. o I .33 :I>5u uoIlE.>5-uoIlE>5auqua>5a 2.... 28 I.“ ..8 2 . 33.53 II 2... 2.. u... 3 . 35.58 I. :6 2.. 2.8.: .c I a we... .— o _- \I 83 53:25 59.50 E: I\ 2mE=Icl>5 I 5..I>I..a.- 9.18.5695 I 19390.3 ..>.II..I. : .5 I 9.336%... . 9.3.0.5595 I stay-I3...- I “ck-.IKIFE— I coca-KI: we. I ...-uanti- uioaosachzh. I aria-Izh- uiotomfi: >5 I woo—.533 mat-co ... 2..— " puma >33 “ Imam >5 I Cum-93550.3 ...Ioa : >5 I .35 : >5 3.5“. I... * >5. I I! >555...»- ..l£>5III..>5 >0}. u2>5Iu2.E.>i.xl ...... .8 .....38... :83 I. . J >55. .8 “Aw I I I ' I I I .93 .3...:l§23:€.8 I 35”“ an I >55: ”8 an... “in .3. pcaiauvoui I 38.9... . I >35...- .o.. an: aim upon-tsuna- I 3 u.- up I 235..- .8 ..u... ...-u >.I.. Iv 328338.833. .. am... .33 ...I.. Iv 318853.88}. .. In... 33 2.. Iv $263.38.... .. \I .38. >5 33.33 3286.32.53 3.5;...qu- 3..:£§8.8I8I ...... I. “on :9.— n..I!“- .— m.I8.. 28 ... I _- I 3 I I .835 .c o 2.335 II :1. .5 I.- .. . 3.}; II I»... >5 2%. “OI 5 £93.... .. In... 823:3 5.525.256 3:. 35.: . ....I.»I=u>c.: I figment... .55.. \ .835: I .35... ..cu: \ ....8..I m 2.: I .28. I..I>5: “Ito: \ :55 >.: I u>>5I>.: 3...: \ >........ :55: I >395: :55: ..8 IE.» — I Ito: .— .....u: \ ...}...nIcIE: I ...-...AICIE: 2!: ....u: \ c.2836 05.: I ie....nncIE: \: “If.“ 3.995 .I...m >352... w.....m 1 . 3...... : \ mama c >5: I ..3I..I>5: ...-on ..I>.I.. I ...... mo. 5.: I {03585: 3...: \ ... m .IE: I ......IQoIcIE: "It . >5 .Ifio. .3 >.:II .II. .35.: 3...: \ 32.35.55: I ...-....uIcIE: . ......guclz: o .5 >.: I .5 ...: ....u: \ ...-c.8255: I 3.9.1.555: . .28.. I..I>5 o .8. H>.: I .8. I>.: ....u: \ I35. ....55: I IS... I..I>5: .. >395: ... >5 9 ....t >.: I ...tI>.: J5: \ 12.3 .. >5: I Imam m >5: “.3:-KENS. o .....AI>.: I c...:I>.: . ...»: x ...on .I. D... we... I .cofieIErgi: I ...-....qu >5 o ......AI5I: I 30.39.: . 2...: \ It . >.I.. >w. I It .5552: 2.38:3. >5 0 ......IKIm 05. o ....I>.: I ....I>.: 5...: \ It .IE. .5. I 2555...: ....ucoaocht. o ...-5.: I ...-H5: .. ...u: \ ... >5 >>I : I ... 2:524: . .85....Ic55 . 2:95.: I ...6 >0: 3...: \ cw: >...n: I 5.5.5: . 3..—8955... o c085: I 599.: 3...: x .8. >93: I I... >..a: .. -..-“~55... o .u. 5.: I .9. ”>0: 3...: x 53.131: I 5.32:3: .I... I: >5 0 u I >.: I a >-: ....u: \ 1.53.1: I ain‘t: ...: >5 0 ..55 >.: I u..>5|>.: 3...: \ 9.3.59.3 I 33.5.3: .— o It: I Itu: 35.: . ... It... I .8 It... .8 8:9... ...... 8. ....u: \ ... >95: I a. 3...: I I m8. .3 .3... p I .5: = 3 I ...>5I>.: 3 I .....AI>.: no I $5.53 3 I 3..:5: I I32: ... I 9.35.: no I .....AHE: ... I ....5c: 3 I ...-5.: 35:28.53: I .....I:>.: no I :3. >.: no I .....o >.: an I w... >.: no I wooo5.: I I “anti—5.5:: I .....Iu>:: no I I20: 55 ...-on :3 on: 3 I pl: .3 >.: 35:50.: u>.: I 3..: o>.: 35:}..3. 9.: I ...... :>.: I I .31. >5: uv ...v >5 35:)...Algo: I 53.9.: .. ...u.:\....nnu>.: I aim->3 ...... >5 >93: II ...! >5 on: 33>. a! 53:: I I 25:)..853: I ......oogo: I I .....uv >5 I 395...: 35:21.92..— I ..:oln>:: 35.28953: I c0853: ..It . >5Io It : >5: I It .55: 3...:Duo. u>.: I a... 94 35:}:- :>.: I a... u>.: ..It >.I.. o It 55: I :55: I .20.:2...» 9.: I 33.3.5: I I .... 5.... . ... >5: I want: 35.1.8... 9...: I 33.2.9.0: 35:}..3. 9.: I 33:33:: ......c.mI..I>:. o 5.... :55: I ....5 : >5: 3....:>...ml¢>m~>. 2:33:93: . ucu.:\.....mtu>.: I 9.35.3.3: ......om I: >5 I ......III >5: I ....o. I..I>5: 3:31:03 am. a: I 3:...3: 35:21.0 m>.: I 3:21:58: .>......... :55 o >.0I..t :55: I ESEICHE: 35:23.53: I 33.8.3.6: 35:38.53: I ...-.3888: 3.3.565... 0 3333655: I ...-...AIc55: 3.8:)... :>.: I 3:3...3: ..cu:3.o: «3: I 2.3.-.23.: ...-......AI... 2.735.393... 5...: I 5.3.3.525: .3 u... a I .5: .. I I233 c >5 I vim: .. >5: I ..oAIcIE: x. to: 3 .II .o. I\ I..G.u 3:308 ..I>.I.. o 5.9.2... .I. 05: I ......KoIgIE: I I 3.55 >I:Io ..IS... :>.: I 52:53: ...o........I..I>5 o 3.23555: I 39.3.6555: 2.03 «w. >m: o ....3 ..8 MI: I ...onlaoaI:>.: "so-.3355... o 10:55:: I toil—.55: 3|: .o. >.: o 5.. .3 9.: I ... .3I:>I: .-.... IcI>5 9 3:0. I..I>...: I '30. 5.55: . .5 5:: 9 ....»IE‘: I ....ulgu: .-.: : >5 o I»... .. >5: I I: c >5: . .8. 5.: o .8. In>.: I .8. I95: \I .3... >5 . ...... 2.9.5.. ...—.a I\ .. ...... >.: o ...... 9.: I ...... 9..: .9: ....KI>.: . 5.59.: I c..AIu>.: 348 . 0:8 ......adi: o ....xfluz: I ....QHEI: . r I .: ......IKJI: . 39.55:. I .....Iu>.: I ....Ia>.: I I .8 3:05.: o ...:Igu: I ...:Ia>.: :5 q: 0 3.2.6: Iv 3.1 >5 ...—3 .: . 33...: II 34. >5 250.: u ......5: o .....uIE: I ....uIn>.: :aoo . . c.5055 o ......IIE: I c0353: 3 I T .. .u >.: o «a 9..: I :0. 9..: \I Twat-2.. «Sufi. .m. €633.52...—v mc...w.: I\ 2 I>.: o . I93: I .. I->I: .. ...3 ..I>.I.. o .03 m w... n. I ..oaIch..I>..n: I I I m3: . It . >55 It . >.I.. >m. I It .5552: ..Itu: \ ....3 we. 5.: I ...8235: . I... >.I.. I It 55 w. I 5.5.55.3: ..Ito: \ 2... .3 >.:II pl. .85.: .. ... >5IoI... >5 >.I I ... >5I>o.n: .. Ito: \ .5 5.: I .5 5.: Kieth. >5 I c ... >93: I 5’53} . Ito: \ .8. 5.: I .6. 5.: 3.18.: :55 o .6. >95: I .8. >33: . Ito: \ ...... >.: I ....t >.: ...}...AICIE. o 6.5.3.1.: I .....AIth: .Itu: \ ....AI>.: I CPR->5 ...-.....AncIt. o ......Hzt: I ....AH‘K: .Ito: \ ....AH>.: I 3..—Hg: .P...htl..I>5 o 3.1.50.3: I 550.9: ..Ito: \ ....5.: I .....I>.: "'20. :55 I .qu33: I .9. 5...: “Ito: \ ....I>I: I «.35.: «um.» I .. .... o .I 3...: I . >91: . : x 23.5.: I 33.5.: \I ...... >5 >93: . I :3 >5 >25... ...»... I\ n Ito: \ 35.: I 595.: ...... >5 I 2... >5 5...: ug\.3.>.:Iuo..>.: “9.: 8...... .. a... 82.2.8 5......53:3.u..:. 5...... .. a... 98.328 8.52:3..38 «.... 251 I \c n I ad : C\ moan .zauctctuounaai I Q35“: .Suunaxauhaguau I 43.. “32.-=3!!qu I 995:- ..o I $35.... ..a I $3.35 ..a I nan-.5:- IuCu. .. I “ucufiuxncgnr-I4Inoginc. _ Scot-x I 353:0...3-363 I \I as! on — I _ .3. I\ “in “9m “3m "III-IuouIioaouI .- :3: 23:36... I “sou-Iuowlifiou 3”“; In“ 3.: m” lino-SHE m“ “3..—Jon ifiouiuaun-ouinoa 3.: NI. 8303 cw... _ “8 5.: p I. 25.3 = . n8 ....ch u: .2 ...—.3533.- I 33!... Sada “Sm “IS-..8: ... o Ion-.555: I 3385:: ..8 3.....» .8: .2 233-.3:..- . Souls: 3 53mm “0.. .5: 8 p I 5 8‘ ..a I canto: “a I 3..-'3!- 3 I 3..-:53.- ..o I .39.; NI, m .5: : I\ I 3.: 2532.33 we» win- I {Influencin- 355)‘. .3 m>m3 I 30. 5323-: . «..8-i... u >55 I I! .55!- “ meat-i! 05.- I 5:5...— I. £03): >55II an 2. I- “ “no“?! u... no.3..- I 5:555. - . .53).. >5 >39. I a: >5 >913 ..8 3.: — I :5: = I I \I “8: 8 . I 3 .3» IP :3 3.33 an» >ma o ...-3 won m3.- .. .gIonIaoaIgana 3'. you >3 9 p'. non u>=u I ply. you 9:.- I I I ".96 “lib.- \ {can no. u: I CIBIS~I>5 “Ito. \ ‘0. a: >-.- I pl: .3 5.- 6a 3.: _. I 53...qu— xa «Imus 31.35 .Iouum >5:->5. warm I\ _ o ...-3 we. n3 I {Ionian-I:- _ o pl: .3 >3 I in. .8 >9- 2 o I13- I I29- I LS 1:32: 53qu: «a I laud “a I 2.3 you >3 .o I .0: 3. >5 :8 I I “88953 "I 3835 33 >5 >89: II 83 >5 a? Emer- auts. 533 I I ...-won. >5 I cum—WE..- mll . 25.. IE . >5..- I I! .55.. “I! >5 o I! 95.. I I£I>5Iu m2. >5 o a: >5!- I .1 >53 \I 5:. >5 . :3 ...-....3. ...—h .\ .93 WEST 28 IF 0 d I.“ :5 2 I 3938 I. 38.»... I: 3 . 3838 I. 38.2: 289.: 3¢.=a_g.._~:zuu .0. _m “2‘! anon-ac; II can: coo—3:3 - .o I _ \I >585»... ...-tam .3» .3 ~!o.5u!5wuoo ”x “IE3: .595». Inuit—I33..- uI... .IE. I a: >5 >91.- I ual>5 3.x..- x. ..33 >5 >93..- . I omen >5 39m. «Eh I\ “3:“. >5 I :3 2.. >93.- can “38.. 28 .— o 7 I _.- I I .3 3° ... I Si: II :3 >5 u:- = I 3.38: II 38. >5 253—. h I .o I .- ...auViIIES I 83...: >5 5!- agfiwfl.“ “.2. .5: 2 — I I 8. I I I I .9 Inca-m >= 5 3 I I! . Eh: we I I! >5.- uoII an >= 3 no I I! u5 w. I.- 3 I «1 >5 >39.- ..o I :3 >5 >0 5 no I p'. you g...— P :5: . I .5? 2...... 32:3“ . 1.3:... Era .\ #883: 33.32: .-.}...2- ... I _.o I E. 3.23.. I .3. u 833 u 533 I I a .25: a I «In-.3. 3 I I! . >5: 3 I I! >5.- 3 I u: >5..- xc Son 38:35 .3::- ucItao .3. to... i ..Oa lua- n- d‘ = \c Ql xi = I\ “am ....- 5... \ 3.3.—I3 I 3.05 - T93..- \ cog-g“.- I ...-9..». 3.5:: \ I25: I $3.55 35: x no... I...- I It... IS.- 35: x a... a. I no... In .35: \ 3..-=5.- I cos-Sou “use: \ 08.355 I ...-Luis I 3 .3. I\ 3: ..qu .3. I\ . Sam .93 “cos-Mucuncl o cabana“: I .9.- 5: :2: a I 2:33.98 3 “I...” .0» com o I: .- I II..- 3 5.: n I Int-SEX I. «no: no. :8. o .9355..- I ...—Emu 3.: ~ II .935 awn _ .8 an... _ II 3:3: : ..8 3.3m... 23 3. “Saving... I 83...: .9; “...-508:... I ...-.655 I .3:! u2. 3:: .8: c2 2:35-33 I 338: 3. 533 ..8 “33 2 p I Z 8.. no I 3..-.5.- 5 I .9395: ..o I ISIS—Sue .o I .3395:— ..8 3.: q I ..- : «finhuufi nun-“II“ u \I. I .5: : I\ “in \I 3 .5: o. \I 3;: :5: x I! D5- I 5K5.- 353 x 2. >5.- I a: >5: 55:25-53:25 "3: 033.: I. «an: non—3:3 252 who «.0.; I. an": 823:3 «.235. 58.3 I I .9: I I8. I 2:»... III. :8 :33 I 3.3.8.333 .8. I I ...»... III I :8 ..8. I .8 :8: . I 2.3.8.138 .8 3.: o I ...»... 2.3 I. II 30:09.. ..3 «c.8339. .33 it :3 35:33.. I\ I I I .93 $878.33 I .853. I first: IE...“ I I3... . “fizz o I newer-333 I noIISXIEIIaImnuIIIZSIIS I35 .333 I 9.. Sue IE.- I at :8 Ila «35.60.33 I QIIIIIomIHEa I IIIIIIIBHIS I35Iom.>.3 I sting: .3- I :uIaIl! 3- I35 .333 I too-gag“... I tonne“... u...- .8 5.: I I 8:. .223 I. :8: I 8:78.33 8.: I I .- I. .315... I 8... 8.33 3.: n I ..I I. ..8 8.: ~ I ..I I. \cnlud: 0‘ Iain .9: 35 I IIIII III... I IIIII II!- IICuwu I aIIququIEIu I IIIquw-uIItou 3:0: \ non-5.5%»...- I v:- 9.3.5:..- 35: I 3.5.—II... «3..- I :9. It «33 I8 aux» 0 av ecu: I— I II «ensue. :II 3 .2: III I I93 IIIIIZYSIIISSIIEIIS I 322.3535. I IIIIZSIIEII 239.353.... [.53. I a... Son It: I a... :8 It... ..II.I....I:IIIIII..95IIII.I: I I non-goImAIIIIa I v:- 9.3.5:... IIIII>I§5Ia2uIRIII£ .33: I ..uhfiIl-I I3.- I 5.3““.- IIIEEHsszcuISSHIE: I :3ng.- ..w..— o I 88925.3”... I. 23.3 03.853.938.23 I «.00 35a um..— I I 830533. I. 2.5; as?!» I fall! «3: I 5.55.! ~33 23.326 3.35 I :5! 333:3 I too-..IIaIuaua I too-guIRIIuauu .an 59:5 I38. I I I .8 58.5 :2. SI SIS-a Ila.- IoI:-o IE».- 3 I cots—Ila: «35 .o I two-5.. usu- IISSEIIEI I .88.... I .9... 31.53: II 33 I. as 3353 II 83 I 833 .8 .8... 8L I .5 .2 I I: I III Zoo II... ..o I IIIIIZXIIEII I: I ISIS: I83 3 I Itasca-III): 6.. am: I I >35..- I. .3:: n I ...- I. II 3.33.. .358 II II I fiIRIIIEISIa III-2.9.2.1 I3 ETI. II \c Ii—I‘ £00. .3. 0\ Igu II It .5: 3 p I ..I III II «flu I. 23.2.8: I 888...... I. II In: “in 29 8.8-I3I5I I 8.... II. I .3- IIIQII'EHISE I a... 3.5.—Ila ”a“! “Howl“... .8 3.: I I 88...... II I. EIiIa—izisluu "0. II I96 2.. 33.253255 I I: I 23.93:»..335 I 95:00»? I Iluflowfi 39 ans: I I 831....- 3 I. I I I I9.» 23883.3 3:65 I not I ILIo I35 23.95: ...-.35 I 9.. Icon 55 I .33 III} 8 3.: I I 888.... II I. 28.86.3585 I uhtIrltIufiu I gut-IIIEIIBI 23.125 n33..." I «IFS .3323 I van-goat..- I 3.2.9555- ..8 3.: 231.28: I .82.? I. .8 .5: 3 I I ... «2 III: I I I. I. II 88.: I I .... I. II .9: I I 20 833..- 3335 I In I ...-Eilpaagcs I QIIISunpfllfinuoquunnwkItu I8 5.: a I .885... II I. I I .3 8.884382. I III: I .III .3. 23.5.“...3-5 I IIIIS-opls I Izgwluca I8 3.: I I 888.... II I. «in 2a IquI 3!: I I II 23.5...Mnuzs mun-IIIIBHIEQV I “"3ng .8 In: I I 888.... II I. 2.. SI.»- >2» I ..uhlIllluou I sail-II 3 ..IIuuao>IIa3>Ic~IMI55~u3>25§hlIIuI I tun-go I)“ I8 aux: I v 5 I. 2.5 38.5 58.: I8 88.5 5.: SI . I I I 3..... he I 85.8 I II. III...- 3 I II... III-null.- 3 Ion”. ..Pplla 3 I ”0.. Ian .55 3 I :9. It a3- 3 I III-Isuzu... III-vol: II 33”.. 8853 II 88 I ...-I a; cadmium: III-III: II 83w... 86.53 II II... I 8‘ not-III 8.3. 538 II “8.3850881... u «8898.33 3:8 I... I»... S 8:8 II: I. .IIIoI ....» :I: II .382: III. .538 I .88.: I ...-I 53. 538 .8 3.: I I .- I. II ........ v3.5.3: :5:- lg 18353.3 ............ II zi§00§§§¥§0zzoz :_ has: I! I ..(t. a< §§§§§3§II3 who «00.; II no": Soto—>3 gig—235’s 3.: 253 . ....ouIIquISYImu I .OUIIBII-momu- I .onIIOAIIIIOqu . 39:32 $3.55 I 3.: I. .0...- I BoIIxIIImI- .0.. 3... o I 832.3 III. I I I I IA... .. 3295.. 5.8m» I 6.333 menu was I ...-....m- ..omu II..- . 2.8.3.333}: I .IuIIBoIIIoIm- I .SIIonuI-IoIou .. 29:33 5.)!» I .30- x ..8...- I u.oIIaI..omua .0.. .3... n I £892....“ .. I. .9. . .I¢.9IEII_:>!I.~ I ...-....oIIuquIwutu I ...—....mIIonuI-nx.“ .. :IoquJIIIfi-Yocu I a-uIlquII—oI: I qunao-QIIIoIIu .. 2.8-3.0 lug-II: I .8. so: Iuomuu I .8 zonal-no.5 .. 29:33 (3.3.: I 3..-I: .63.- I ....o-IxIaomuu ..8 3.: I I 838......“I: . .Ia.§II3>Im~ I ..u.n.oII..1.IMIEu I ......EmquaIiEa . CoouuofaiIS.>I:~ I unulgIIIoIoa I «Sulfa-9.?- .. 2.8.3.5 Eng-V: I .on .5 :0on I .3 x... unwri- .. 29:33 5.35 I 30- x .5.— I Eo- .. n3 ..8 3.: n I 383.30.."u: .. .335: ...—:2... I .....IEIIIiImIza . ...:IEIIIIIIHS .. ..IouIIfISIESIIIS I «35153.5.- . «Snail-no.0.- . c.8393 Gag-mu I .8 SI IImIm- I .oo ail-no.5.- . Caz—:0. 5:1." I v.0. : :8.- I 30. x a! .8 am... I. I Inouziuouw: . ....IIEII...>I.I.. I 2:32.885... I ......BmIIIIwIIle. .. 233-335355 I u-uIVIoIIIIoIo- I .IulvoIIIIIOIu- .. 3.83-qu lag-mu I .8 v8. swam?- I .8. not» mIoIo- .. 3.5.33 .3:-5 I Eo- .. to»: I Bo. ... vow..— .8 3.: . I 88.39“... .. in 935153.: I BoIIIhEu I 3..-III}; . C .33 I335 I Bo- ...u I 30. use .0.. ..w... 0 II 330......- l I. \I :3: 3 .. I .a I... I. .9; \I 23.33.... I Ivan-......- .. I. ......m... . :0 21:53:: I Bacall”... I VIE-cool}: .. C .38 13:... I $8323 I... I 3.538... w..- .8 3.: I I IBIS...“ III. I I I I IA... . 2295.. 3.3m. I ...-.38 :8 mac- I .385. :3 5.... .. CooquuIIlIs.>I:~ I 302.853.: I 392.358qu I 2.0.83..- Iav>Im~ I .8 Sun Imam: I .0... Sum mogu- . 39:33 13>...- I Eo- .. :3: I v.3 .. 5mg: ..8 .3... a I 383:...“ I I. I I I Kim I .3“; 13>}: I ...-.30- 3? MI..— I C...3m- Smo 5...- .. noouuosIclli-gocu I quIIEoIIIoI: I .IuIIEoI-IIqu I CEO-Io... tug-mu I .8 3.0 Imam: I .8 Sum unob- . 32:33 5.35 I n.0- 1 Zoo.- I 0.0. x Smo- .o.. ..u... I I 330:.)- I I. I I I IIBI I :28... lag-mu I ...-.30- mof was I 2.33m- ..om. ls..- . 283.....II33I... I IIuIIBII-SIu- I .IuIIoaIIIIoI: .. ...—853... lug-m: I .3 IR... Imam: I .8 IRE moo;- .. 32:33 18.55 I v...- ... .2..- I u.o. : Ian: ..8 3.: I I 898.3943: I 3235.. Iago—I: I ...-.30- monu ME.- I 3:05- .59. Ila I 3330....8 I335 I :9 I39 I85.- I .Iu ...-Au =95.- enouooos II 3.: 082330 gig—(83:35 ....I . ...ouIIquIII.>I.I.~ I .onIIBIIImqu- I .otIIBmImIoIQ- I 29:33 3.85 I u...- 3 I39- I 38 3 Lemon «8 gm... m I 338::- I I. I I I I -..... I :38... ISIS—I: I 3..-.33 no... MI...- I efivtfiu .88 II..- . 388.....3Il3.§.~ I «3:83.53...- I «Ions-unalts I 3.83qu .3:—I: I .3. :30 Imam.- I .8. ..on witn- . 39:33 13.35 I 39. a .83- . 33 .. acme.- .oa am... a I 33......“ I I. I I I I him I :25 until: I ...-.33 .IS W...- I 333m- .5 Is I CSIIIIIIIIJEIE I «85:58.:- I .3 QIIBIS . 2.83qu Irina I .3 I. Iceman I .ouIlImIquu . 39:33 I335 I 33 a .5.- I v.o¢ 1 PS.- .8 .3... n I Isa-.....mhuI. . 2:93.. 935.: I ...-....IIIIiIrIS I ......I.mII...IIl5 I 38332-153»...- I «82358.... I «cabin-I23 . 2.8.8... 3.8... I .8 .1 III...»- I .8 ...-5.8.8 I 39:33 I?!- I 3... 3 a! I 30- : a! «on .3... ~ I 338.39.“: . 22:5: 13.6% I ...-.28 nu: was I ...-....mIIuIIIIleI .. 3833.35.13.65 I 3050358.: I IBIVIIIIISID- . 2.8.8... 3.3.: I .8 III. Imam.- I .8 IIII m8... .. 29:33 I38:— I v.0. .. ‘00: I 39. a van: .8 3.: p I 882.39.“: I .Iartlllu.>!.n I ...oIIII-l I 3..-I55 I 2 .38 57!: I 3°- 25 I v.3 w!- . 0.. .3...— 9 II 882....- m I. .8 3.: 2.3.5....- I Ivan-.... I. I. .8 ..8: 8 p I 5 SI .8 am..— . I ... I. ...... Soil .35: .0.. 33...:- ..u58 39.5 go: 9‘ II. 3.1 ......o I... ...: co XIII I... £9... 3. II.>I:I.. I. .8833 II 38 I 1.: 388:. II 38 I 533 usu ...3 .8883- II 8qu- . III-v.53 II :8 I 8. 3.53 3.:- .33» .353... I... .358 I 882.. I ...II 33. .33“ .8 3.: RIhmhh II ........... 83:8 IIIIII S... 13.3.3.3 ........ I. wu— nB-Ql g. bugs: 5. .81 .. In": 823...... .aI.II...s>.__S/.u.I:I 254 .M 2c 30 «OD-5 3w... «E»...- \ 32 III: I 32 was .8... {5.3 x 3... .... ..Bo. ....- . .8 3.: . A 3c. 8.33 = . ..5: I 25-8.33 3.: I I .... = ......S... I 2... .333 3.: n I ... ___ ...... 3.: ~II ...- .. ... I 8... 8.33 \I Inc...)- 123 III — A 3‘5. Ix . ...—am .9: . mason-33 \ 3.53“": I .3533..- I. .2383 gag-mu PIES: o 3.9. .5 553.65 I .33 :1 [133.65. I .3533..- .ou-E... 3 ¢ 339.23..ch I. I I 3.9:»..- .I uuunuII cumulus up I .IIRII: PIE: :mn III...- 9. SmoIIEma I :3 III..- I :3 IE..- .zIIe .83.- I .83 II:- II. . 2: .88: II. * >5.- wBquua 2...... I I I I ......8 so... 2.... t 29.. up... III-3.35 I 3253955335 I .- :ooIIEIuvzcu o ..Iu_oalltu.=>ccu o Ewan“. 333.35 I .I zoom III-3.335 I all . >5 8.1.3.55 . All . 2:38.: I 232:: I I “a I 253:.- 353278. .8:- I ...-II .8:- swE — A new: I. I I I .93 2eL>.Efl§§uo—. 83.392833 III-3.3.: I ..8-Iv .8...— I I I “in 325 n I 53$ .83.- I 5.3 .82. . I I .2 8.3.3 53 8. .~ I .26... n v..- — I I: a new 3313:...— I Ivan-...» a El III—tau- II S... a II can}: “A .qu 3 ES: .9 I 5.3 .83.- ..oa 85*.- 3 w I he 8: . \. ._. I .5.- .. .\ I :3 .85: kaolin; lug-$.55 I 3523 WE.- “...qu \ “Earnings: I 318w «.3. Leo: \IGFIII: 183.55 I vuoIIuISIa 35: 233 ..IIIIEIS>I§ I 33 ITIIEI- LE: :38 I .133... I 38 Ci..- 35: \ .33 lIEISIIS I 33 WE.- UE: \ A30. nan-VIE: I 39- as: .8 3.: F I .5: = \I as: 3 p I .u .3. cs «an we:.gsuxofllgncosa:...iguIEIEIaYICu I Encoulmscuu chazgggiguuzu: 0 30:39.3 2.3:}: I van-ans”. ax: unlisixgo- 81 KIEwuZIABoI “illucnavzs I Bo- I‘ll—:3 5.2.2.253. I .1523... I £53.65 I 32 ..ISII- “31:95.3.3533 1...... 31...»...— . 32 we: 5.2523325... . .32 2.5.2:. . 3o. ....- .. =33. ..8. ..o. .\ inn-a u .InrtEII-gocu o valiant: I vole-Sulfa. u C .33 lug-5 o 1983:: I 3183:: .. an": boasts .8...:a_g.=b:/uuuo.: nno "III; .I an": Soto—No I I... 3.: o I IvquIm ...II : u :35 lug-cu o 30- :nzueu I 33 c ”in: I I I 3.: o I 88min...- I = u 3235: [2.6:— o 33 .. Zoo: I 33 : Sam..- I I I xw: s I imam...- I 2 u $235: 53.5 o 33 c .88.- I 33 : .8.qu 3.: I I 882...?" = “ :EBEEIISDES I 30II§III31I= I 30IIGIMEa“ .. 29:33 £3.65 o 23 I :3.- I Ho- 2 salu- ..on ..wa n I 88005.3 I m— I I I I I .93 n 3295: 539: o 33 :lIIIE: I So- uiIlE: u 39.—:3 lug-cu o Eon x at: I 30- : ti: “on :2... ~ I uncut—Jana: « 3: 565.55 I 3..-IE: I 3355- .. C .33 lug-cu o 303..»- I 33»...- . 8 3!: 0 II Ivonne-II la u— ..35 :3... hung us 30a... .5: 8; . ...-56.33: I 308...: I “II.- ougua Iv :3 I v...- 33985 II In... I 533 I I ..c I “US$833.— .o I 3.5322: 3 I 39- : .8qu- ..o I So. CISMA: “a I go- :Iupuo: I I I we I Bo- .5 5...: ..o I 33 a .5.- ..o I 30- 51 IE..- ..o I Bo- : “1:.— ..o I 30155- 5 I 38...: I .8 .5... 3 .I.I.. S. I I ..o I upo- amaouu 3 I go- : ~3an 3 I a 8.0.3 II..- .o I I 5mg .55“ .o I IIupuoIIEIu no I Eoluuna "a I iamu III...- . a I 3: .5 IE..- ..o I 33 I. .5..- .o I 30- 21 £3 .9 I Bonn-Imus: 6 I 3o. 3!: ..o I Eo- III.- .8 3.: — A >55... 3. .I 8.3.! 9..... .3:... .\ 3.: n I .... : I I I53 339:: a o ...-In .8.- I 53.. :3- I I no: -5395 an: 8; “~I 3.68.31:- p ImIInfiIIuIvumoqIIouInaiIi 3.3:.- II Sun A Fl 303.... :- I .38.... a 53w» .9 I 5.8. .8..- \I 33...! so: ...: I\ 3.3 \I v v xi = I\In.u.wzw u :0 Sling-en . 2.538ng I iguIWEu .. 3 .33 83:5 . “92:33 ...- I .8533 w:- uoo am..— 0 I Income...“ 5 : I I I I Iézm n 3295: ...-YEW: . 3533 53 m3...- I 233m. :3 .55 u Auaoouox..IIIl-.>I:~ I “noun—Sungla- I :uISuAI-IoLua . 2.8.3.3 13.3mm o .3 :3 Imam?- I 30 Sam much: u 303:8 1.3.55 I 30. ... :3- I 30. .. Imp— ..on .3:: u I 330:...” I. .— I I I I Kean m 39.35: (3.61% o 0.330- w‘o II}..- I 3335- 5m... IE.— .. AanouuoaLIIIlav>Ic~ I gob—0058....- I unclaiolquoguu .. 2.8-3.3 Ital: o .8 Sue Imam: I .8 Sum “no.3- .. Caz—33 (3.35 I go- .I :09- I 30- 2 Swan “8 3!: s I 8890:...“ I 2 I I I I . he; n 3395: lag-mu I :33...- m3. 115 I 233m. ..omg IE.- “ 2332...: 13.35 o .3 .60.. :95: I :u ..on. :95.- §.-=_g._=3uu "3: 255 .3 .0003 2.....33I I. 55.3.... o 2.3.3..- ..I £2.32: I Aymaratu. aw... n I ..- .— .98.... . 5.53... I 335...... I .3823. IE..>I:m . I 3...?- o .8 ...... A I .— 33....- o ...-.3:} I c- .- .o I AIR". \I .3 0.3-20, . I 3:...- u .. I.I—...... dzI ..SI II..- QIII I.I..ao 8 3365.”..3-1!‘ SSSIII .. ...-Iv I30: 5333 onII :8 3..—5.58 . I2: I>I.....I.- lac . >5. .5“... I\ .39? ..SI 8.3:. I .QIII ..aoI Ill-.55 . 2.332 I..8 5.35.. I 2.33..-1.85535: . § "8. IS— .21.: 8— 8' 8— 8 «an: “gala! l8l->g o Anadvl—On In: 18d~>l¢u o :81.— . z: :hflflu>2u I an!!! _ I>C—dv>!~wv I O I0 I O u .3 LEAH-2:... I .....u..30>c9.u.. I .38 a ..8. \ .a. I...» 38.3.. I 3......828 0. .8I ...... o c ...... .. .....II = .333... -.....I II... \ 32 I .I.8.. I ......I...8w>II 9. .8 ...... a I 2333 ..8 [.13 .— I Ago-II... I. 3..-x ...... .96 I 5.9-I 1 I509- \ .Iu HHS? 09.3. I 33.3.82.” on I ago-I ... I52.- \ 31.38.88: I 6.3.4.8038 II .85.... o I 38 .. -..8. = 2.36.9? ..ooI I... x 6.3 : ..8. I ......I...oom>II 0. .8 ...... a A 33331:? I.I... ._ .8. .93 I ABIII ..I I85..- \ .IuI I.I...I I38... I “...-23.5} o- I 6.8 ..I .3... x .8 .3. 4.9.... I ....angI 9. .8 mm... a AI 6.3 I: 39.-I .— ......6.III .3. II!- \ 3o- : .3... I 33.88.8- 0. .9. ...... a I ...-.39. .3. l5 .— .9. .93 I ABS-I. ..I I88 \ quI :33 I33... I 2.3.9.30...” u- . 3.0-I .. I588 \ .8. .33 I395: I 33.5.3838 u- .ooI am..— 9 I 6.... I .3..qu .— ...—.39” ..8... I8 \ .30- : .30.- I 335.38: 8 .8 3.: o I :33..- .Bu I5 .— u I 3.0-I348... \ .IuI I83 I83... I 33.883238“- . I 3.9-Isis: \ .8. I83 I33... I 23.11.6380. .8 an... o II ...SI: ..8-I.I: ...-.30II .88 II... x 33 I 33. I 33.9838- 0. .8 ...... o I ...-.23 ..8. II... .. ..8. . u I“??- 3 H8 \ .III I8 I32... I $3.853Inu- Iago-I .- l..- \ .3I .3 9.3-. I :3ch In .8 Ian... 6 A v.3 .. liI .. £53.98 I. I8 \ u...- a .5.- I 23.3: on .8 .....— a I 336.00 3. Jun“... “9...“... I «I.I-I I Ive...- \ .IuI .80? I333. I 23.83.33 u- . 3.0-I 3 VII: \ .8. ‘0: I130: I ......eouwgqu on .8 an... a A 6.0. a $00: .— xuaIu:_<5.=—:znu ...: 0.0—3:3 :9 “In: I. O A«I.0.:.I03>_U=£.mqu10I-.)IS. o 3.0. ImeUI:>I:~ o :5: . Z... I>0._Qu.>Ic.. I .- ~ I mm": 2.332- 3: I.I... \ 32 I.. I2... I 23.8.:gII 8 . 8. . ....III ..I u 8. I ......II ..I Kazan-I .8 am..— 6 A 6.36.3 3: June.— I.3 \ .IUI..1III¢..:. I £3.31... Io- ...I. \ .8. ..I 9..... I 3.8.3.8 I. .8 ...... a . 38 .. ...-I. = ...-I I.I..- \ 6.3 a .3 I 335.13% I.- ausp e A 2:30. .1 I5 .— x. .9530. 8:0...- 9...I.:u II I\ \I~a..,-:I\ . .93 .8. \. I.I. .3:...- .\ \II 260.52.0uI sac-...}: o 26......- I I “>9: I 30..I .30 45533:. wIIIE . >533:— 0>Im.._au .\ .. .I .32 I.I...SS. I 32- ...I. ..o I 0.3-...: .c I M3. 31 .o I 6.3 ....II. ..o I 33 I33!- .6 I 60.3.2.3 5.3.. ..o I ugIcIoI ...-u ... I 6.0. .30... .o I 6.3 :03: ..o I Eo- IIEI‘ .o I o...- ...-a 3:. I335. .. o.:.I 03> .. 0...: 03> . 3: 0.:— Uvn.>=..d \ 3:. Ion; . 3:. ..6.> . ...: 3:. I6.>_Mu\ . o.:.I 03> .. 3:. In.) I u a... 3:. ...—>5.- \ .. SEI 03> .. 2c. .06... I .. a... 0.5.9.0..33 \ . 3:. Ion: . 3c. .06; .. o... u 3:. I06.>_m. .. ...—..I . 8c. 8...... . .8- .5- .8 In... n I ... .— T .I I 0...: #33...- 3 {I .9... ...-.30. I....on m3:- I 336.3 I..mnI 5:. 3 x ..u II..an 30.5. I .III ......I 30.3 m.- \ .3 I23 Inmouu. I .8 :53 ~33. 00.36- x 3..- : I23.— I Bo. : .3...- su.6.o- Eco III... I 3333 ..moI l...- 6. \ .3 I..ooI :93.— I ..u I..ooI =93.- ..333 .3. IE.- I 2.36.08 ..omo II...- 3 x .313. 9.3 I .3 I3... 9.9- M. \ .oo .3. Imam: I .....I .38 I 0323 \ m.om I: Ito-u. I “soul a .63. x ...—.30- .II. vi...- I 2.36...- .I5 II... 6.36. \ ..oI II. 3.0.- I ..uI I.I PE. 0.3- I _ool .5 I38- 3... 3.23 m 6.... x .5.- I upo-I I: .5- . a..:- . 6.3m.— \ ...—.33. I . o.:.I It .. 2:. on 02.13.33 . c.5I 03> . 3c. .06; I on: .o.:.l . I. ‘0 .323 x 60.580 II!» I 6.523 mac. .3:. 06.33 \ 6835.89 ...: I 8.80 .... ova—3.8... 8.83592512- .0.: :8. ..I: a»... . 5.3352 . 23.....vo- u..-. .o. .».- \ 2...a¢.a. u. . ..u.. I . 0. .oa sax. . . ..m. .o. a. .. P 2.0.555: 33 .8533 5 33I55 5:I>:.I>o..n_u 5:I>:= 22.1 K \c 93.3.- I..2 .5 i I I so «bemoan-Ila: 22.-3 ex \- uuouo‘uhlI. 5189 a... 33 .3 a... >:. 3.1: ... .6.- u:_.... K . 29 5.33.335 I 2.538ng ...-5 o I I I a .38 53%."qu “was“??? . ...“...nuwunus . 2:385: o .8]. o ‘.I=GCO O nu.o-Iu:a~>o5 o 3395933235 I 3395255 a ...-53...... .92 .azu .3..:J333 . ...?!Jvfils . ....SSI._%.I5 “3:75.33 \ 335033. I55 n 32:33:: I55 “3:. 5.33 \ $353 3.. 55 m 3253 32:5:- ."nfiIo-wnnnwu- “ "u“...flfi“ - "u"... I55_ .o.¢..«u.,.ua . ....c.oa ...annsca I ....c.on-m._ .3:. .aa sma— . A cue. ou.>.ua .. 3:3- . 3:78.23 aw..— . a an 2 . >.uc:.. . o.c. .u.>.u¢ aux. n I a. .. .4: am..— ~Ia 3 2 .o u 3:. 5.33 \c 3.535 .55.- ...2 5.3.3.3 5 i I \o n I 3‘ —0\ “aw » 505:3 \ :33 8.555.- In. 2352-5- am... a A 9.33 2 u 32.555523. - 2.3.... 39.3- ..w.2 o a ......52J5 2 .9. «pucpIuoonga- \ :aoqu. I55a - .3:-55.52. “ 25p :2 o)...- x 55.. 3.I 55- a 73.32.. 5 .12 won m»:- \ Eon w) . 55¢ - ......aoqumSnEa ..I: .3 9pm: 5 Eon . .355- . 2.3.3: 53..- . .52 am. on." \ rancid: .. 2523853: :52 .3 9.3.x anon. on. a .52qu— u: . a A to. .3 ...-3 2 I \. «pm-ulaoquzmq 2....“ NEKIw: anon—Io.- «5.... “coal! ~33 558 55- 3o. 55- 5: >5 >92...- 5: z...- 2......— ..fi§.~3o 5.3.55 3.3.55 5-05.3.5 5:55.. 3..... i I 2 9:11 Its-3.235 . 2.553 553).: I 220- 55335 9 .5:I>:.I>o.&.3>5 I 25.56.3235 .. 39:23- I I I? Inga-goings 553 .5- 33 .33 ... 2... >85: 2. >53 .5... «...-355......- 55?5o 33...... .3n>:..>o.x.. 23.5... 2...... K I 23355.33)!» I 3553 «am-:65 o I 223 5.35 o 3.. >:. 35335 I 3.. 3.32.5 - Run. 3- o I . 3 0 59:38- 3 - omen—Io.“ .92 33.. \ .3un I55a - Send I55. 2 .5: \ 5552 I55- - 5033 I55: 2 .5: \ Eon 2.. 55¢ - EonIo> n«Ila:- « “:9: x :..on 3. 55a .. :..on no. 55- 3.. 3.2 o c .53 2 I \. .c».. on . . .a .o. n. I ...u .2 3.55. 5.2-:55: 5- :«235-35 ...... I I 2 322952555: I... o 5.33. I55: .. ...-..I: I55- » 30.35.32.353h132 o :sonIo... 5:3 - EonIo» 55. « 3.3.5.353 a... 3: o Eon 3. 55¢ - Son 3 55a . No 09.5.. 83. um. um ‘20 I\ I “an . 285528. .2335 o can: uk— 3 n ...-..I a. an 2 «558.2235 0 ...-..I... I: a 5552 I: 2 32.28.2235 0 :..on 5.. 3 - Eon 5.. .- xusiux-gazauu "0.: 63 no... I. an»: 0.33:3 . 335322235 o .....onIaoEqu - :uonI-oEIu- . :3 02.... Bug “on 3...... .55 32 I I I ..o a :30 2. 3 no a :33 SKI: no .. Son 2... «a no u :..on SKIS $221.23. a «.85. .2 m5 35.93..- .. 33:35 .2 5 33:3.- .. 38:35 .2 823 .3 .5: 8 r n we so. I I 3 .. :3". 2 55a 3 a 5023.255. 3 n .....on 22. 55- 3 I Eon go. 55- >. 33.53 ..5 3353.- guon ...-u .32 3.3 ... 80.: .\ ..«u.u .uu.uu «8 am..— . a 35..- 2 xus— n u no 2 \c on...)- .5::- 82.3 5333—3 .2 so 00...... no: .3. 1 .6.“& 2 $052.28. .2335 . 335m: . 55 _- 335x... 2 5:- .. sac-SuaEUSK-cu o 6.35552 I5:- u 23:333. I55 . 322.3... .2235 o 3.25353 55 - SSEonIoszg . E..on«a...&2a.>-5 o .3353 no. 55 H 2.253 3. I55 .....5I2235 c «25.55:.- n 3.5.-I55 2.2.8:. .2235 o 9.32. 5cm n 9.8... 55 I .0: :2: e v an 2 \o «.92.... 5. 33.35 2 5352 .2 «c. ox .92 I I I I5; 2 .5553 .2335 o ...-5w... .9... 5:.— u 3.53 «JIIEu a 250.30.223.55 0 33555.. I55 a ...-55in: I55 . ...-3.3.5.2235 o 2.2:..onI3JIEEu .. c.3532: 5:: « 3.352.321.55 o 3353 a... 5:.- Iu. 3352. no. I55 23.5.2333: o 3.5I55 - 325I55 2.2.3:. .2135 o 9.3... 55 a ...-3: was .8 aux» 223-£2- . 3352 .2 2 .8 use: 3 p u .a 8. xwxp o a an m. u :3 3..... 52.2 I “3 00.22 35 i2 3.- 3.5I55 .o a one»... 55 \c “333:! .2 5352 .2 an: ox \. .3...- :.:.o >5. .8....- »o: an 50. 32 ...co .... ..=. no ua...a .... ...c.. .o. :w.» ..n.. .\ .3553 3 382.332 859.3- ... 3823:. .2 :25 3.2.. 23.3 and .aau \. 55>- >...3..5c 5.... ox ...-65: ca 38235”... ...o.mu. .. ...v...ca .. no. mo..oaI. auxga .uu.ua ...uouu... .....a¢.uaao I .uouuc.. .. c... aux.UI s I.:c:I I Ignaz“ .35I I323 \ I.IIQII 2325 .8 ..I... . . S... .833 .. . :53 I S... I23.3 ..I... I I ..I .. .3233 . S... 8.33. ..I... I I 3 ._ .8 ..I... I I ..I : .....I .gI.c..:aI IIIIJII I.I:III I.IIIIII IIIIJII III.:I I vou.uuogua ..o I IIIIIII3uI .oa aux. I I an I. \I I.III. Ic.Ia I. .IzIIg: Ic.I.IIIu I. III: III..II¢II I\ \I IIII: guII .o. I\ .III \I I v II ”3% .III ..IIII :I.>Icu I III I I IIImaI zwz— I. II ca.III : L. am:— I— II co.III I: I. ..I-II :I.>IcI I III.:I I .quau an:— n. Iv co.II. : L. .3:. 2 II :03: I: I. ...-II :I.>I:I I zI.c..:- I gI.c.m:I amz— mp Iv co.uII : L. 3w:— o II co..I. I: I. ...III aav>I3I I so. I I IIImaa swap I II co..I. : L. ..I... I I. 8:8 I: .. ...-II 3.)): I I35- I CHI-..I aux. I II :I.II. I: II. aux. I II ¢I.II. I: I. ..I-II :I.>IcI I I.IIIII I I.I.maa awa— n Iv ca.II. : L. swap ' II co.II. I: I. .09 ans. I v I. I. \I I I II ..I I\ «can \I on IcuIa I. — I w. .o. I\ “can \I ..a.mIII.m.I I Inca-II. 3. I. I\ .III III :I.>IcI I IIIoaq I «Inca. aux. I. II co.II. : L. tux. I. II co.II. I: .. ..IIII :-.>IcI I III.:I I III.:I ans. n. II co.II. : L. awz— n. II cI.II. I: I. ...-II :I.>Icu I gI.c..:I I :I.c.~:a swap ~. Iv ca.II. : L. aux. a II co.III I: I. ..IIII :u.>IcI I so. I I Io.m:I swap 0 II co..I. I: L. aux. ~ II ca..I. I: .. ..I-II :-.>Icn I Igacsu I Ignaz- :ua— I II ca..I. I: L. :u:. I II ca.II. I: .. ..I-II :a.>I:I I I.Iuaaq I I.Iumaa swap n Iv co.II. : L. aux. — II co.III I: .. x u . ..I .....n. .p I IcuIaIcI I Icu.:IcI .II.c..:II I IaguaII I I35: I :33: I «Ian: I «3.3- I 5%.. .o I IaInIIII .8 ..I... .I c .3 3 I. “on swap ..I.IIII.3.¢ I Ivan-II. an I. annb— oaop\0—\~o tum.¢ma.9¢I.I.I.I Io. Ico.II.:I.II ......I.... I\ u o I qu.:n. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII ua_—:o¢¢:a :03.wm:0uum IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIOIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII . Ina—m- . .uI.II “I I ...-changaca.>Icu I .pvcuauIsacII .3..— n I ..I I. \I .I>ac.:I >IIIII: III II. IsaIIII .Iach Ix .nIu.:ca \ .x-vcao: III. Inc. I .aa.c.:III:ca ..I... I I 8843 .. . 3:88.... \ 33:3: IIu.mIg I 33.... I.Iodu . IIIIIIEJCI \ I.II... Ilse: I asawuu II.I..I .8 am..- 9 I III-.855 I. ..Ion Io. Io>II \ .30. III. Io>II I .aavopIIII Ina... aux. IZIon III II>II I. m ..I....I I55 I 3.5...- I.I..mIS .. ...-:88?- .........fi.................. .....u.. ......I._.... .. I I M :3... ...IS \ 3888.3 .. 3......qu....“ . I.I... ..I.&3.I 3 I 33:3. 3 .oc aux: I932: 2.- a. .....I .. IIII.§I\&3.QIIII I .II...>I IIIIIQI I.IIII. .35 I. . a v o «I .III I Ich M" n A“H.HQCIIIMIou u aIuwIII>II CI “ . 13:83? 3 I .388... 6.8.3 J. .I .au..g¢.095mu I .II..II>I Ic.onaq .I 23.8.. 3 I 23.88.03 ..I. I .3....B.I 3 I .II..%..:I I.I.“ :u IvanuosnImII anuoIanII . I... ..II.&3.3..&3.I I3 ...I .3...“qu ... I &£.I III .8 ..I... I I .mu... .....3- .3 I93 I 3.52% 3 I53 I 23...... . 3 mI.8. .8 IIIII I 23:83 III. I55 I ......ISILIuI ..8. III. I33 I SIEBI II . I55 I 23.8333 ..8. .8 I93 I .3..:3I I 3.55 I 3:35.. 3 .383. .. a... 2.2323 5.33.59.23.98... 2.338. on?!» o 32.3 a .32.: an... .— 2328 03>...» o ...-cos a {in am... 3 23:3 5.35 o 13...: u :2:- au... 9 2.3285835 o ...:- u 3.:- x.... a 23:0an3.35 0 93.3 n 033. am... 5 2:33 .535 o .3... a .3... am... M n u 009 Illlll I -Cddd‘d‘ddd. - 2.3.23 .33.: o 93- a 9:- an... «Ca—.3 3.9-5 o .30... n .3...» an... e Amazon .33.: o :3. o :3. ...... 2.3.8. 03.5...» ¢ ...-..8: - ...-no.3- 2.... 28:8 8.2.5 . 3...... .. 3...... ...... . .3 gm..— 23§33 - 33!. 2 .8 .5: 3 p a 3. .oo aw... w - ..- 2 .33 :59; 52.2 .8 3593 ...: 8. «a u .353. 3 u 3:- 3 a .353 no a .32.: no a ...-cue ... u 33:.- .c - ...:- 3 a 9...: .o u .3..- . o u 9.0- . o u .35.... u o u :3: .o u ...-an...- 3 .. ...-...: “as. a 3333 3 83m. 0:}: n. 33 . not-AI- 3.22.823 éééééééééi§ - .&&L&&&&&&&& a 33.38 3 83%. will... : :8 . 8. 8:33 3.2.. 538 2.32.... .... ...—2.8 .. 888.. u :1- 3.3. 533 .8 ...... e v .- = \. ............ can! too. .0 5.8.0.... .3. 23:30.3 ........... c. oo99¢.oo.¢¢¢¢+¢¢o¢¢.¢oo¢oooo¢oooo¢.o¢ooo.0....coo.ooooooooooo¢co¢ooooooooo ma_—:O¢o:u xex.moawaxu o¢¢o¢o¢¢¢¢¢¢oooo¢§¢¢ooooo¢o¢+.o¢oo¢+o¢to¢oo¢o....ooooooo.ooooooooooooooooo . ..8..- . .uu..a P . >55... : 5.: n .. ... :- .. I 3... r9... 32888.3: . .3... m... :2: am... a a :83. 33>...» 2 35: \ 5300.3: _- 2.3.30.3.- 25.....:3.>2~ o 2.8.3.335 o 2.35-3.33 + 3..-:33)...» 0 2:83:35 0 33.33.33 - c3333: 3 a 882.3: .a on .5: 3 . a 3 3. .x .93 .3 o ...—as..- . :33: o :33- . :33: .3 a 2.35.: x .33.... ¢ .333 .. 32:3 ..3 . .335... . 5.5.3: . 5.5.3.. . .35 a. .3 o .315.- \ 393:3. . 3.5:.- n so. .- .3 o 9.9!... . .35... o 2:53 a 935: o 9.35.: x :33 3 o ...-:33 a ...-:33 I .o o .3 guns .3. c. .9. .3. 3.35 o :33 - :omau am... 2 3 5:... :I2 I an... o. 2 5:... a 2 .3. 3.35 . .033 a “9.2.3 2!. a. 3 5:... :I2 I a...— n. a. .8.... a 2 2.- 3.35 o 5.5....- n ...—5.:- 3983. .. an“... 323:3 5.....2254236 3:. aw... u. 3 .8.... :I2 I an... o n. 5.... a 2 .3.- au.>oc~ o :9. n 39.3 an... o 3 .82... :I2 I am... 5 .3 53... a 2 2.!- 3.>oc~ o 955- .. 23.5- 3... o ..v .8.... :I2 I swap c an ca.uo. : .— 2»!- 3.>-cu o ...-:3 n :3: an... n 3 5.... :I2 an... i a. 5:... a 2 .3 3...: .83 8. ...-50.3....- n 8.3!... 3 VII 33.533 23 a u:- oaiua I. 8.9 a 833 .8 “co: 3 p u _u 8. «a o :83- . o u .333 3 .. 9353 no .. :33: «c I noos- .. o n :33 a a - 5.5.3 2 o a 3933 no .- 9.3-6 3 - 93:3 .8 am... — a >35... 2 an... n - ..- 2 .. valance“. V3. .38 .3. cot-.333 39.26 8...; .3:..- u. u a - 5.5.3: 2 o n 393:... o I .3. “ox-3:...- . 25. 953.65.52.23 . a £39,233 I an: A 2.... 33:5 2 “as-3:3 \ .393 9.3.3539. . n ”mug-39.9.- 3... a a .39. 3.35 2 ...-3:3 . 2.22:3.35}; . .. Su.o>a£§ I am... a a .93.. .5335 2 ...-3:3 . 2.: 968352.83. . _. G .3:-:3 I ..u... o A to: 3.35 . .c 33795 :23 .33.?- 30 a. u... ... £3.33 . so: 9332:3033 . a an... 38.3 "N... o . Coo. I 3.35 2 I I r u ..8: .28.. 9.5.33. 33.. 96.2.33. .3993 :83 .:o .25 :83. a: .... c. 3.53:...- \ 2 Soon 263.553.09.- . o «www.93- xu... o A 3.8 o .35 2 .8 ...... a A . 3... : .. “13.-.3 33:33... 2. .. .>.§5..<§€...3 . 153:3 an... . a 393.5 2 ...... ma... 2 :83§..:3&3-:§3§ . 25.2%“ .o . 53.5.. 235.3 .. 23.35.:- «8. a 23.832 2.35. . 25:39.33 $.33.- - ...-.8233- :83- .. 33.3383 .333 I 3382”“:- «can 2!. I \o I... .3. 39..»- HSSQI u... ox “an: 3:888:8- . 3.... 8.. £2: ...... a . t3. 8. = 235.33 . 3833 . 2:5... . 2333 o :33: o :33- .. 588.3: a . 588:8. 3 c :33 a :33. 3 o :33 .. :33— 3 a 5.5.: .. iii“. .u c :9. n .8.. a . .v a .35- n 935-. .e c :3 n ...-:3... no n «3913.. n :33: u e .. 5.5....- m o n 39.3 no u 9.3!..- .o n ...-:33 .8 am: n _- 3 2 so I... .3. 39.2.- 2358 a. 3.... .233 . :83 a .89.. n3 "co-m .. an": Ono—3:3 55.38.35.233.”- .0.: 259 ...-mu...u. 3..-no.3:- uuu-m noon: o “..8. men on. u>wn c.:. c 38:55:. o 3:.- cw... on. 35:153.:- .....u o 33.: I 33.: o 33.3 I 333. o :33» I 333: 33...: a 3335 . 33...; n 23.5 338.3 I 331.5 33.6.5 I 3..-3|..- . 23....- I 33...: 30:00; .. an“: 08:23: .>m. new: . . .....u. . .....na .uonuocqa-. . ....aaaa . ....Nsaa “3.- 9.3... an. 33.5...- . 2.3.-Bu .ucoc mcoein o “savceq I “saves“ . u>wo um. I o 2:..- I 2.3:- ..uacuu..-. . ..-.~I.- . ....~I.u .n_.u um. .-u ..«..u.- . .“u.wu.a ..coc um. . .u c.» n .a c a “9.3.3... . o 330....- I ...-.L. .331.-. 3...... . 3:9: .>m. :oca . . .... a u ......- ..8-.§. . 2:1... . 2:2... .3... 3...... . 2...... . 33.8. .20... 308:. w :3 a I 338: \. ..c..an. . ..a..ca.a.a .\ ....u . . ....o.aua- ans. . . ucaouaq .. aux. . . a. .— 3 o «.3034 a :30qu ...... 53... 52.2 .8 SS... .6... 8. no I «533 ? .... ... 3.... .....23 ..x \a 3 no... $33?- I... ... 8:3 .3. 33... 3.9.2.0 .0 me» ..x .96 Q I 3 I xmazh“ ..8 ...... .. .. ..- .. . .oau . .~ I .8 .n I >830..- .oo 2.3. n . a. .. so . 35.3 o 339... 3 v2.3 0.. 3.... can-...... 9...; a... ox \- >_..3..o..& .3. so .3:! 3 I. 3.9.8.. I...» ...: some... 2.— .\ “33.39.- Iv 3.3.-3|. a... 33.33: I. 3.3.-:0... 833 3% Saw a 33.30- Iv 3.3.-3H. n 38x33.- .. 3.3.3.. o 8‘ :3 I. 992.. 333 a x. ..c.. ..oc.. o. an .m so.» ..I.. .\ . 389...: :3 .2538 n 83...: . ...... 8...... 533 .oa aux. e v .u .. ".35 Sudan ... .o 2. 235...: 238.. L f 88 .5... .353... t \o .33 .353..- .3 33.... u... 98:23.3 .\ :¢9§:§:00:99:§2z§009099600¢09000OOOOOOOOOOOOOOOOOOOOOO ua— paaoa 58.33 ooo¢¢¢¢¢o+¢¢+oooo¢¢+¢¢o¢¢¢¢¢¢ooooc9909§oooooocooco.o¢ooooooooocoooooo¢oooo u sun—u. tum.¢.3_ou\.oauooau I a:¢.u¢u.¢£uooa .oopca.u>:_ o>oa\xcuoae I “adv-oaxouoa «co—ca.1>:_Io>ou\.ouc_oa. I Axuvuou.ouc_o- .3223... u».§..:2.o3 .. 33.8.32... .oo—c..:>m o>oa\u.¢saa. I anauuoux.quoa uo¢.oaaz>c_ o-qxxu:.uou. I ....uouxua..oa .oc.oa.3>c.lu>ou\o_ouou. I Agavaoao.uuou “co—...:>c.no>oa\uo.ooav I “aqueouoc.ooa moo—.3:»... 2.61.3.8. I 23.8.3.3 .cc—oauu>c. ¢>quaaoouu I nauvuouaaucu “833.5... 962.32.: . 2.3.8.8.... .8pcwuz)... 952:3: I 333:0).- uccpcaux>c. annex..oao.oav I agavuou..oco.oa .8....32. 25.3.3.3 _. 33.83:...- .8 ...... a A :5... 9.3 .. .....u - ...... “8..— 83.3 \ ......qu I .053- .m.... 0333 x :33 I ..I... move—I033: \ .353 I .3:—Ia ...“... .333 \ .52.: I .53.: 3...: 3.33 \ ...-63 I fanc- 32. .233 \ aunt-a I .33.. “8:78.23 \ 330- I :33 no..._13_33 \ 3.3. I 3.03 23... 6.33 x .3:: I .3..- .m.... .223 x 9.3 I 9.8 “3.; «3.33 x .333 I .333 ....3... 8.33 \ ~26.- I no).- uous 0533 x ...-9..- I ...-no...- .8... 3.33 . ..8...- ... 3...... . .8 am..— — A 3... I223 .— . 3:3.- I 35.5333 3.: a I an 2 . 3.35..- . 3... .333 .3..— n I 5 .- .8 an: ~ A an m— ? .3 33:09.0 .... .3:: no): ...-a...‘ 389: 2...... ox \. .22.... .6... .3. .\ ...... I \Invxd:cxluaau "..I__oo envoacn o .ocuooa I .osuoou swan e. I oaouuou .— ...op_oo o-.>o¢u . no... I xenon ans. n— I 03691.. .— ua.o_.ouuoa.>ac~ o .Iuc_oa I .ouc.o- :wx— u. I avenues .— u..I__ou ouv>oc~ . .nuc.ou I .auc.ou aux» pp I ouounuu ‘— ua.o__om oqv>I¢~ . a.-¢oa I a.osou aux. a. I ouoonoa .- ...I._ou ouv>Ic~ o sassy-u I sassy-u xwa. o I cadence .— ua.a..ounoa.>oc~ . a.o~o- I 0....“ awsp o I ououuou m. ...-__oUnouv>o:~ o oo_ooa I oo_oo- aux» s I 93001.: ‘— u..o_.ou ouv>qc~ o .03... - _o:.o- swap 0 I ououucu .— m...~_ou Ia.>qc~ . canoe I canon swap n I Incense ‘— 2323 .30.... o .8.-.3 I .33.. 3.: e I 33:: .— .AmIZou 03.5.: o .26- I .2..- aux— n I 33...: 3 2.3.3 .333 o ...-9.8 a ...-49.3 3.: u I 33:.- .— ...58 3.25 . 2...... .. 3...... ...... . .. 88 8 .. .8 ...... e v .. .. \I Q I an m- 0‘ . r .5: 3 . .. .- .... a . .9. - so 23.1.5...- _. 8.8-.... I m. K .93 “...—.3 230.5 o .053.- I .353. 3*» 3 I 83.... a. .2323 03.35 I .3qu I :35 nua— n— I 83:: m. 2.338. 03.65 9 .3:...» I .353 am..— ~— I 889 on 3 36 “on: I can: ago—3:3 55.335.122.13 «0. . m waxy-.55 2.5.5.5 . 25.5.5 ..5... .. . 8:: .5 .. .55.}... 5.” 25.8.5 . 35.8.5 ..5... 2 . 8.... .5 5. 325.5 . c.5555 . 2.5.85 ..5... o . ..8... .5 5. 58.5.5 . c.5585 . 25.85 ..5... 5 . 8.... .5 5. V2935 . c.5855 . $55.55 ..5... 5 . :8... .5 5. 32.5555 . 25.35 .. $5.55 ..5... 5 . 8.... .5 5. 55292.5 . 35.55 . 25.55 ..5... 5 . 822.5 5. 5.29.2.5 . 2.5.55 . 25.55 ..5... 5 . 8.....5 5. 58.5.5 . c.5555 . 25.55 ..5... 5 . 832.5 5. Maine-5 . $5.55 . 25.55 ..5... 5 . 8:: .5 5. .5295: 5 . 35.55 .55....5 ..5... 5 . c8... .55 5. 53.95: a. . .5533 5-855 _- 550.... .65- 5. .25.»... 5 .. ...82 5 .5 65.... 5.”: maxi... 5 . 2.5.5.55 . 2.5.5.35 ..5... 5. . 5.... .5 ...... .5255... 5 . 2.5.5.55 . 2.5.5.85 ..5... .5 . ..8... .5 5. 58.93... 5- . 35.8.55 . 25.8.55 55.: 2 . ..8... .5 5. ..295... 5 $5.55.. . $5.25.... ..5... o . c3... -5 5. 5.29:..5 . $5.55.. . 35.85.. ..5... 5 . 8...... .5 5. "8.93.5 . 35.58.. . 25355.. ..5... 5 . :3... .5 5. 58.9.3.5 . 25.51.. . 25.85.. ..5... 5 . ..8... .5 5. 5525.5 . .5515... .. .5555...” ..5... 5 . 8.... .5 5. 5.2.5.3-5 . 25.55.. . .5553 ..5... 5 .. 5.... .5 5. 32.5.2.5 . 255:2... _. 35.55.... ..5... 5 . 8...: .5 5. 5.29.3.5 . 25.55.. .. $5.55.. ..5... 5 .. 8:: .5 5. 5.29:. 5 . 2.55... 35555.55. .5... . _. 8...... .5 5. 55 .5 an . 35.3.5 :3- . ...-on :35 5. 5:3 .5 .8 ..5... 5 . .38.. 5 5. ..5... . .. .... 5 5. .8 :2: 2335843; I .88.: ac 5- .055 «Cu: 05 u n .u 35 .055 :9: m I u. u- “ 530 ..3003 hung I.oo a520,... zu05..- 8 35.5.5.5. o..- 335552. 055533 .5. n 55 555 .5 5.5. n ..5 . 55 . 535:5 .55.}: o. .555- a ...u.:o5 .v ...u .5 ua..oa-5 55:55 .55.55 .05 25:5 5 u 55 ..amw.5 5.5-}: ... 38W .5 ...u.:u5 .. ...u. a 585 no...545 55555 555.55 5.5uouu555- .55552555ao5 . .uou¢.5.5 :55: 55155 555.55 .05 55:5 5 . 55 5. .55555 555.55 .5 I {52‘ 5..............-.-. 5.5.6.8.. 5...... 95:53... ............... .5 5c 3:55.- «55:3 .53 .5 5. 5 . 55 u... 8:... 58:3 .35 5 . .5 :25. .8.... 2.. 5.... .5 5. .3:... 3555-5555 551 u...- 33053 8... 5.50.55.35.55 :22... .5 ooco...ooooooooooooooo.o.oooooooo.oooooooooococooooooo......ooooooocoooooo wa_—:oaaan xoa..<>oxuu oooooooooooooooooooooooooo¢59.059595955590595.95..........5ooo...ooo...o.o 55555.. 553.355 - .3 an... .5. 5 >358... 5. 3.. .85.. .- 55.55 825255.. 55.555.555.536 8:. 5.3 53.5 can: .....5 58. . 5855-5 ”555555555555 n 2555555 .85 . .85... ...-3555255555 .. 5555.555 .8. . 585555 .5552555355- .. 5555553 58 :5... o ... 535555 #3555355 5. 53.55.55 . 355-55 5 355055 n 355.5 ..I:- 5.5 .. 355-5 II..- . .95 .8. 55......3555255855 . 35.8.55 .8 2.55.53.52.38: .. 25.5.55 8" “5.2.3.55255525. . 25.5.55 ..8 . 5 25.25 . 25.5.: 5.23.55.53.85. . 25.5.55 5.5.2.5555555. . $555.5 .8 ..5... 5 . ...5....I..5.>25 5. . 35:25 . c.5555 . 35......555 59.5 8 ... . 2.53.55.55.35. . 35.585 8. . .5 53:35:55.3. . 35.5.55 ... . 2 5 .§§.55...5.....5. . .55....85 .8. . . ..5.§.5535.&5. . 25.55 am..— 0 A nauduiuaglpi m— . ......3 . 35.5.5 . 35.5335 c p o c p a o A a c u c A J a g 0 A J . 3..: 58. . 2.5....5555355555 . 25.5.55 8. . .... 552.55.25.35. .. 25.5255 ...... . 5.55.23.53.35. .. 255.255 .85 . 355.23.525.55. . 25.555 .oo 3.: o 5 2355555553353 5— 53.35; o 338-: o 33?; o 3:5..- . 33>:I3a .85 55.895.525.585. . 2.5.895 55.295.55.558 5“. . 355.855 2.5.5.3552: 5. . 25.855 5.255.553“: . 35.5.55 5.8955553553 cm. . .55....85 .5555n355525. 5. . 25.8.5 55.. o . 2.5.8.5333... 5. . .55.. 5 . c.5585 .. 35.8935 5....5 5 5 .55. .55 . 35:55 5 558.5 5 2.5.35 . .55....5 5 5 3.5.5.5 .. 2.5.5.5 A 8 c. v d O A J d if ...-.85 5 25.1.5 . 25.555 .558: 5 35:85 . 25:5 ...-.85 5 2.5.55 . 35.55 .5585 5 25.35 . 2.5.35 5 25.5.5.5 . £5.55 5 553.5 5 25.5.5 . 25.85 5 553.5 5 25.5 . 25.5.5 55.2.5 5 35.525 . 25.525 .585 5 255.25 . 35:25 552.5 5 2.5.55 . 35.555 .5585 5 35.2.5 . .55. 35.35 5 35...... . 25:85 555855 5 5 . 25.85 5.58.5 5 25.5"..5 .. .55. .515 5.58.5 5 25...... ...-3.5 5 ...5. 505.5 am..— 5 u an 5. 39.5 p a 5.335.- 55 55.33535333— 5.. .5 o. o. 5.... U . ‘ Ono-3:“:— \I .338 . I 33:. 8..: I. 8.95:“ 8 I 2:538 I 2:538 .3... = I :88. ”8 I. II 8...... ..8 .... I. .93 8.95:- 8- I 2:838 I 2:838 .3... 2 I :88. n8 .. 8.95:- 8. .8.:88 I 2::88 .3... o I :88. I8 I. I L93 .8928 I 2:58 I 8.8::- .3... I I :88: n8 .. ..8-.239 II... I 2:..8-38 I 2:..8-58 8293.8 I 2::38 I 2::38 .3... I I :88. H8 .. ...IIII I..: I 2:5... I..... I 2:8: .38 8.95: -8 I 2.888 I 2:88. .3... I I :88. I8 I. .:88II I..: I 2:8. I58 I 2:8. I..:- 8.95:- I8 I 2::88 I 2:88. .3... a I :88: I8 .. 82:... II... I 2:... I58 I 2:... I38 .8.95:-8 I 2:5:8 I 2:5:8 .3... I I :88. I8 I. .88....- I..: I 2:3... I..:I I 2:8. .3:. 8.95:8 I 2:88 I 2::38 .3... I I :8... I8 .. 9.838 I..: I 2:8 .88 I 2:.8 I38 8.93.8 I 2:88.. 2:88. .3... ~ I :88. I8 I. .8858 I.I. I 2:2: ..8 I 2:2: ...:I 8.95: 8 I 2::.:: 8..“ 2::58 .3... . I :88: I8 .. 8.39.3 I: I .335 I 335 .3. no I ...-on :35 I 3..-OI :33 .8 am..— — u an m— .8 :2: ~ I .26.: A» u— xuxp ~ I I: Au 5. 261 \I N I ad m— I\ mam Iazu \I .58 8 . I I.I .... I. .93 8.95:- I8 I 2:88 I 2:8.8 .3... .... I :88. I8 .. I. 3:388: I 88!: In: .. I. I93 8.95:- 8 I 2:58 I 2:5.8 .3... = I :88. I8 = .8885: I..... I 2:5: I38 I 2:5:- II..:I 8.959 8- I 2:88 I 2:88 .3... 3 I :88. I8 .. ...III II... I 2:8: -._..:I I 2:8: I38 8.95:- -8I 2:88 I 2:58 .3.... I :88. I8 I. :88.- I...I I 2:8. I58 I 2:8. -...:I 8.95:- I8 I 2:88 I 2:88 .3... a I :88. I8 .. 8...... I..: I 2:... I3:- I 2:... I38 8.95:- 8 I 2:88 I 2:88 .3... I I :88. I8 I. .858II II... I 2::8 I28 I 2:3. I58 8.95:- I8 I 2:88 I 2:88 .3... I I :88. I8 .. $8.38 I... I 2:.8- ...:I I 2:8. ...:I 8.959 8 I 2::8 I 2:88 .3... ... I :88. I8 .. 82.5.: ..8 I 2:95 ...:I I 2:2: ..8 8.95:- 8 I 2:58 I 2:58 .3... I I :88. I8 .. ..8-.3- :: I 33:. I 32:. 8.95:- I8 I 2:88 I 2:88 .3... n I :88. I8 .. .8 .3... ..::..........I I 83...: I.I .. 8.95:- 8 I 2:88 I 2:88 .3... ~ I :88. I8 : .8 .5: 8 . I .8 8. 8.959 8 I 2:58 I 2:58 .3... . I :88. 8 I. .8 .5..— ~ I Jun“ 3395: 5 I ...-on .83 a ...-on .83 .I ...:SSI I... . I 88.8 .... .:..I \I 2.88 I9 .8 83 ....oI..o.I I. I “.833 II".- I .93 ......o ...-..I. .38.... 8.95:- -:8I 2:838 I 2:838 .3... ~. I 888-8 I. ..8 «..8-to. .33 c2 3295.9 3 I 3353:- I 3353:.- ..u... 2 I con-35¢ 2 .... I 88:. 8.959 8II I2:::.:8 I 2:838 .3... 2 I SIIIII8 .. .93 8.95:- 8 I 2:8:8 I 2::88 .3... o I S88I8 .. .389...- I. 38.35 I... 8.95:- 8 I 2:88 I 2:8:8 .3... I I 88:8 .. 8838 II 88.88 I. 3.89.... 8...... 8:8 8.95:- I8 I 2::38 I 2::38 .3... I I 882-8 .. .8 .3... ~ I 8 x 323 .8.95:- 8 I 2::38 I 2:88 .3... I I SIIIII8 .. .93 .8.9:.- 8 I 2::38 I 2:8:8 .3... a I 882-8 .. .3393: I: 33:3... HI. 3293:5- I 33:38 I 2.35::- xw... I I sou-PC: .. 83.53 3 3.9.3... I. 84 3.3.83 3.2.. 333 239.55:- I 33:33 I 33:33 am..— n I .832 I5 .- ..88.I:I.I 8:588 I 83...: I. :1... .3...... .83. 8.9.2.8 I 2:88. I 8:88 .3... ~ I :88. I8 I. .8 .3... . I 8 I. 8.95: 8 I .8.:.:IuiI:2::.:8 .3... . I :88. I8 ___ .8. 8 I 5.8 ....8 I ....8- :58 \I 8..-5... :5 88...: I. \I :3 I. .8 .3... . I .268: 8 .. .93 .3... . I III 8 = I I ..8. .8.3........-.. 28.58 8.98882285858 8.9:: I 8.58:... I..: \I ~ I ..I : I. . .93 .8: \I 8...: 8 . II: 8 I. . .93 .I 2:39.328 I 83...... 8 8 II .93 :advsoaI IoEIIEav>Ic~>I28III 83.35" I axuvuoiuil In: .8 .3... o I 28:39 8. .5::.,I:II : I. .93 \I ~ I .269... an . .....II..._............,....,... . 2.....5 .. .....23- -.. . .89. . “......fi. .... .. .9... .. .. I I O I I 28:89 8.. 458.28 . 28.59.- 8.9.5::38 I 2:58.:8- :I 8.959 8 I 2:838 I 2:838 .3... ... I :88. I8 I. m. I 8 .3... ~ I 8 .. ..-8.95: -8 I 2::88 I 2::88 .3... o I :28. I8 I. .. I ..I .3... . I 8 .. ”8.95:- -8 I 2::88 I 2:89.. .3... I I 888-8 I. II ...-.5... :5 8.8:.8 I. .8....5- 8 I 285:8 I 2::38 .3... I I 888-8 .. .53.: .8.: .895:- 8 I 2::38 I 2:8:8 .3... I I 888-8 .. \I .3285! 98.. £32.... Ix .II...9I: I8 I 23:38 I 23:3:- xw... ... I 582-8 I. .:..8- ....8 I 2:...I8I .3..:8 8.95:- 8 I 2:58. I 2::.:8 .3... I I :88. I8 I. .....8 .88 I ...:.:.8- 8828 ”8.95:- 8 I 2::38 I 2::38 .3... n I :28. I8 .. ......8 .58 I 2:88 38:8 .8359 8 I 2::~:8 I 2::88 .3... ~ I :88. I8 .. .I 8 '88.: ..8. 8. I. .93 .I8.95: 8I 82:5 8.“ 2::38 .3... . I 88... I8 I. .8. 8 I :88 .88 I ...I8 .38 .8 .3... n I .88: 8 .. .3... u I 8.4983: 8.95:- 8 I 2:58 I 2:58 .3... ~. I 88.9 8 I. on“: ova—3:3 lamina—gazazuu «0.: \I . I 8 : I893 .93 8.959 8 I 2:838 I 2:83.... .3... 2 I :88. I8 .. cs 30.; II 00.3— oootozwa 55.853593513— «0.: 03 "Io-a I I 262 .3...-...”... . 3.32%... . 3:25.... ...... ~ . 83...... ._ .....95: a... . 3:25....” . ask—5.... ...... . . 8.... ...... .. “a .95... u . ...-«u .3::- . ...-on «.5... .8 ...... 2 . .3...-1.. ._ 3.3 225-..... . 3.32:... . 3:53.. ...... : . 5.3.-.... .. .....th 1.. . 3:83... . 3:85. 5.: 2 . 832-..... ._ BED-Ell..- . 6.3.5.8.: - :3: i 2:: o - :33....&.— 2 3.2.5.5... . 3:5,... . 3:51... ...... a . 828-..... ._ 3295..-}: . ...-En}.- - 33:58.- ..u... u - 5.3.-3a; .— .....»5...&. . 3:51.. . 3:53... ...... o . 8.5-... .. .....Ea-i. . 3.2.2... . 3.2.1.. ...: m . 823-..... .. JED-£11: o 3:53a- - 3:53.; .3..— . n can-«.-....- .— .....35: .... . 3:51.. . 3:5... ...... n . 822-..... .. segue-a... . ....c_1u.~...»fl.c.a. ...: . . :3... ...... .. ..nu. .- o 5.0.. 30:.- u ...-on 10.... .8 ...... 2 u 33...... ._ .8 ...... . . .. .. r N . .. : .\ ...... \. ...... o. . ....h 3. .\ ...... \. 2.35:3. .. 33...... . : 1 . 2w... .9. .....nena..3..2c:1.. 3:2...3... ...... : . 822...... .. 3.9-1-1.3.58... . 3:28.... ...: 2 . 8:35... .. 3.95.5... . 3:2 . ...... o . 528...... .. 3.3.2.1. . 3:25.... .. 3:28.... ...... o . Sat-.... : 3.93-... . 3:25.... . 3:25.... ...... . . 5.3.-.... .. 3.9-...... . 3:25.... . 3:28.... ...... . . 823-..... .. .3...-...)... . 3:25.... . 3:2...1... ...... m . 8:35... .. 3.5.1.. . 3:25.... . 3:25.... ...... . . 522-..... : 3.5.1.. . 3:25.... . 3:25.... ...... n . 5....-1. .. 3.5.x... . 3:25.... . 3:25.... ...: ~ . 8.5-1.. .. 3.3!: .... . 3:25.... . 3.25:... .....-— . .3.... .A. ._ .... a... . .....u c... . 5.3 c... .3 ...... 2 . 32.5.... ._ .az. . Con-ah}: : O I O I O I o I o I O I o I O I O - ...-3.10...- 55 - anti-:1- : .. - ...... 3.9-3-15.2325—1... .3:—:53..- zw... Z - com-«.-I... .— . 5.2.5:... ...... S . com... a... .— ...Sul- o 3323....- - 3:25....- zw... n - con-aha. .— 3335... . 3:25.... . 3:25.... ...... ~ . 823...... .. 3.9:. a... . 3:25....“ . Swazi. ...... . . 5.... ...... .. u. .35: a o ...-o... 9...:- u ...-on 3::- .8 ...... 2 . 821...... .. 3... tum.¢uz_3~:I.35.>3~ I m2... «59.1.: .8 ...... o A :8... 8.2:..2. . ..o n 2.... 2.3. h I \o 3.3:?" n 33:. .5 - “to—u 3.3.3 3 .v no. 283. .5... i 22...... $53.35 . 2:35... um. lug—:1: . - 2:353 33.55.35 o ..I-Jot... 2:33.: a vial-39.3 . ... . ...... 3.9... 3.5.3.7.... 2.5.3.1.... :9 "Ia-m .. on": Quota—3° 865.323.:315 .0.: 263 x. u 9.33:3!- .I. econ-...;- . 2E1 -\ 28:1.5-35323 o Can»... «and?!» n 2.0.... on: \- u .93-HIE? . n 339.5: . Eta ax "cox A ..lnfiauvuaS-cu M 25:32. .3 5:335 - 332:3: \0 2:3ch 3: IE- .5.53 3:35:- 5-35 3 .i u 2.300313: 5013.95: 1.5.3... 2: ox £293: . 1.53.:- x. 53.2: . .38.. 2...: .\ u..§vcaiunu.au§u~>2~ -25.:qu a): 5:333 - mic: . 5 I find .wcu 2318.- «o - E33.” no u in 03.- 3 n 53... on: u u a. to: u n .93... e on a - 5331 n :13... a . £85 a . .53....- a . 5:8. a . 2:03 .o .-.»!!- ..o n 53... :1.- 3 - ace: .3 ..o n 33... a1.- 3 n .33 x: .3 \. 23:33.3 3.520: .55..- l u I I . u 52 . 2.552. 5.355 \ 2:8 nee-:2: . 235-Sofi- .8— . 2:35... a 31!:- \ 22.8 £3.55-- $35.8.K- “ca .5... a A 25.52. an. 55:35 : I I I ...!u “8. . 2:... it: . 2.553 m3. .3:: I I :5.ch 3526c: - $3533.28.- 82 - 2:3ch 3: 5525-8. tot-:33 . 535311.38. «8 am..— 0 A 2:353 «>2 5:335 m. I I I . u “8F. .5: . 2.22:8 33:2: . ...-.....uvsafia ..IEI.I>:=\2§-uu .8.-:35 o .3:—3|: 3.33:: - ...-vcmzwuhogu em: "on: .3 2!: o A I! _. E: ‘— xc 353:: Us: I... 12.: ox \c 3. >3: 3 as. :2, .3. ux 3 - ca 5.: N I an .— xc ...-i! a.- ..3 on ...—.30 :a cx ..— n 5 .3:— — n .3 2 \...-.-...... 3:53 3‘ 23.—2:33 I... .\ 3... .22- \ c.3253: . 2335......- ..€.. \ 3:285. . 3:25.1- ..E: x 3:5 . . 2.5.53. $5: \ ...ESK. . 5251. ".5: x .3251. . .3253- ".53 \ :ESR. . .3951. ".5: \ .3:—£1. . 3.2.61. “.5: \ .3251: . =55...- “Eu: \ .3:—3&- . .3251. n.5: ‘ ...acqa. . £551. ..8: \ .3251. . .3251.- .ia: 5.8 s: . 5.3 5: ”.5: x ...-55.1. . ...—553.. ..S: \ 23:83. . 23:55.. 35: \ ...: . ...:éa. $5: \ :551. . :55:- 0.5: ~ .325): . ...:El. ".5: \ £3.53. . 33.5.5 “.5: x :32“)... . £3.51. ”.5: \ 5:5}: . 5:51. N.5: \ 3:23: . £3.51- 0.5: \ .523: . £3.51. ..5: \ :55}: . 5:5,...- 35: x ..8. 39.5 n =3 .3..:- mg: \ 3:25.}: . 2:25—1- Lcu= \ 3328.1. . 2338.1. 55.52.3536 3.: I. on“: 323:3 .3:— N u an m— ? on :31. ..8. 3. i “in \u—nxn:.\ ...—2w .3 anemi- . 2:553: . 355:5 3.: : . 832-2: 95: an . 2:28.}: . 35:21. 3.: 2 . 8.5.1: «2952-15- . 2.12:0}: . 2.32: a :2: o a .3:: :9- :_93.&. . 2:953: . 355:3 =2: SHEFK. . 2:252: . 232:3: 3.: 33.3.1; . 232:3: . 2:25;} 5.: .293}. . 2:2ch- . 35:3: 3.: .39...-1. . :zecvfi. . 2:25a- 3.: 3.9.5.2.. . 232:3: . 2325.1. 3.: 3.95:}. . 232:3“. . £325}: 3.: 36:: I: . 3:251... . 2:351: 25:- I 39.—95: 3a- . 53... c:- n ...-at ...: ..8 3.: e. . 325...: : .5 Batman . - Conn-..I}: 9.3: an.— o . com-unusa- mEBEEIJau . n u con-«..I:- uBE-lnzau . n - Sungla- muiil I: o u - Susanna.- mEBEEIa- . u - con-34x.- mSQwEIJQu . u - can-Elan,- muazélxa- . u - sou-obfu— uEBSCIJQ- . u u can ..I]..- uEQEEmzau . u n :3:in— AEBEE I: o - can-oLIuau A a 5.3 :0... .3... . 253.3 : I I .9; 9.5.}: . 2:55.}: . $3253: a... z . 5.3.53 : 9:: I; . 3:353: . 2328.3 3.: 2 . 8.5-}: = .29.: z: . 232:3.— . :32: a 3.: o . 82..-}: : 3.35m}: . 3:288. . 2:251- ..w... a . 8:253 33.2-2: . 2.5:}. . 3:25;: 3.: s . :8 a. nno "on... .. an“: Quota—>3 5¢.=x_;._:=/uu 264 I 232313 22: I 2.3.5.00 1025422353333. I 33......- 3 I 2323....— .2 3.: a I 53.33 8:833... 3 3 I 3333...: I I I I .qu : I 233%}: 3:8....IIBI.3II>I3:..3EIIm 33.5335: I $338.38. I 3 I 2.323;... £287.33 .3 ..I-31:33... .53 .3335: I 23:3 :3 I. 3 I 3323:... .I I c. 3.; ~ I ..I 3 2 I 5-3.: . I ..I I. 2:872:59}:23231-32:3 I 2333....» .2 3.: c I 2:8 .3 2333.: : “38.....2 III-5.3351335: I 2332...: I I I I any “on 3.: a I 2:2 9:33.: : ..nzoo—ISIE .om 9322353.. $353.65: I 233-FISH - .. u 3:87:53 .3 93:25:33 .83 [1.3.353 I 33:39 zo- a. #53. I 32.19::235.1335: I 23.:— u 3 I 5 3.: ~ I 3 .. 2 I 5.3.: _ I ..I : .232 I .ShIIIItzigi 35:: I 23¢ .....- 33 3.: a I :II. .3 gI332 : 3:3. I 2.3.3.233 35:: I :3 I 328— I acokfiuzuzaiviugocu: I 33.3}: I I IxIIISZMIISIu 35.-I.I unacc- no uco I\ #28. I ..IIKIIIIIEI3EII3IIS: . 2331- \ ....3I.IIA.I)I. 2:8 I: .13 I 9.83 23.-... XIII: main 1 .38. I .58....3...3i3..§: I 2.33:: 2:35.. z. 9.35: I :33 .5 .II-NRIQEI. I m 3..... R 9,: 3:8. I 3 I 33%....- \I u R on»... . I K I. Eta .\ #28. I 3 I 33...}: 2:32:35. I 3:32 :2: . .EI.I23>ISI R 9..: “n28. I v I 3:11.": .6 I X1930; .38. I 23.: 32.35.3335: . 333.1. 25237338: “”:8— I AcoiIgI-Jcifiiuias. I 332....— := . I: . . _ I: ...:qu 5.3.6.: I ”3.3:. .3 an: o I 23.59.4305 3. 2 A ~\ 3:33.35 3 I 3:73:33 3 I 2.3“. 39.93.55 I III—on:- \I “SKI?!- . I ..I-55.5.?! ETA I\ I 2 NA Aux-.0329: 3 I ZIIuIIIZaY-FI I 513:- I I .93 \I 3 5.03 at: 5.03 29.: £33 :3.“ . 5:“. c: no.3 95:. 2:3 3:8. I ._.IBIISIIIIE.3.3.4.335.3 I 2.333.: .23.... I: In :38 . c I In. 23.3 ...-.8. main L 328. I .233253.223....2335: I 2333.: 253335. . ... 2335: . c .1359: I 235.. In I»: 323' I 2.33 am. o».¢>235:33>3~: I 2.3 33 3:3: :3- \ 2.35: : >c~=>2m I 5103 3:8— I SIIoAIISI? {2.3.6.33333 I 33 a I 3.: n M A :32 35.35 : mnxaoo— I 3..-320:: 32:353-735. I 3..—Kg- 2333 3..-.— \ €2.95 : >5 ...-cm I 2:3.— unzs. I ..IIBIII.IIII-:.=3£fl3.§:3 I 333.3- I I 3.: n m 2 I.I. .335: : .38. I 2. 3.533.233.2233 . c.3343 28:32 .5 :5: 2...... c 2:33: I 2:8. 328. I S. .I.II>I-:23:mfl3>IS: I 2333- 3.: a I :33 .5 .1325 I. 328. I ....IEIISIIII-zitciflros3 I 2333. I I 328. I ..ISAIIIIIIII3:_.3=§3>IS3 I :33...— I \I I Saw: :3 . I 5:: .II. .5... 328— I g....IonIuouvgouzamuvcwfluv>2~: I 63332» .I 0:3: 56.- . I Samar:- . .00.: :3 b I Iona-...}! .cEa I\ ma: — I 3.33 «353-2235293353 I 33...: LcoxzunovI-IIEaKICS I Agog-..Iuiugns I Eon-Ila! mnks—IA—uuoabor 5:25.535 3.3.—33:5: I 333313-09 a “8025.00 3935.53. u :03: mayo-cm I 9:3... as 3.8—.233 .8 I..-3:533... 3% 5:35.33 I 23:3 :8 I. I I? I ...qu ...: 32...: .I. ...-3 ......a I: 3 I 3.- 3:— m I an m. u— I :3sz — I an m. nooks-«o. a: 52.33.: I 2:3 3..-30:3 I .90.... as «on 3.: o I 2.33 .3 95.-:23 I. I I I I I “an \I “3.3:: 333m- ”! I5 2330- ui In: a: .1 28. I 53.339.335.223 5 L .u I c.3333 . .32 .I. :3 . I 3.8.131. .... I\ :8— . 5353?.335.22.352.33...3 I c.3233 I I :2 5.3.2. I I I 23. I 2.35:: :IIEI..:._.3§33>233 I :3 Su- 2:322 .5 5:35: I 2.338 :1 3.335 I .38 .I. .II. I 2 cqummI-ESSIEI: I :3 I. I I I I I 2 ”13.2335: I 333%- I \I I 8.333 Ema .II...- =33mII38 II... I 2 5.3 a u I 33:33 5.8 III. 2332 ..qu I... .II. 3 E. II a II. I E: .5... I\ I 2 13:33:35: I 333%. I 2 ..3...33>I.=: . 3333. I I £3 I 2 33:83:35: I ...-KI“.- \I u 233-u: an. 93. . I Suzanna): ET I\ I 2 3.533.353 I 3..-Eme- 2:>a_§3>ac~ I Ago—3.3)}: I 3:33.335 I 33:31 can 9:..- I 2 .5. ___: a x I 333%- I 2 .5. 35333.3: I 2.345.... \I I I Enum. . I 2:8. . .....a II as o I 2 35.3 2:35:35 2 35.: «93:33:10 : 2:333:35 £33: I “can .3..— o A 330.... 30:33 2 I . ..35-85..:=3:3352335: I 2333- I . .3...-In 335.23.353.35: I 332:: \I I u 2- ImI. . I 2:8.» 2...... I3 I I .3...-II 33.332.385.338: I 23.. 3. 23.: 3.33:5...» .. 2.33.55 I :8: v. — I 2.3.5.1. 335.223.55.35: I 23 u I I 3.: e I 2...... I.I-=1: : .82 I 2.3...-853.32.23:33.65: I ...-KB- \I :93... In: I I .93: In: . ...-9qu . I 1:3.» 3...... I\ 28: I 2.35.853...qu23:23:53 I 23.5. 28:33.32. .3... I..:zzcznziza I E.3.I,I:3 I I I.I}. we... I ...-....IIIIIIIEIS:3:33..2.: I 3335 3.: a I 2:35... .2... 1.5.3.: : “.8. I 3.338-835:33:33:53 I 23%..- ..8 3.: e I East... 2:33.: : ..8. I ...-....IIIIIIIIS-z33:33.6ch I 3333 I 28— I Aa.3...-IP335u:33:32.65: I 6.333; \I a £3: I.I.- .II 3.2-..I: . ...-TIE I\ 28: I 2.353.. . .. : 33:23:33 I 2.3%.: 23:35-83:23 I 89.: I333: I 5:... In: omens-L I. on”: coo—3:3 5922—59251”. "3: muons-m .. on": Quote—ac .3...-3.34::an «0.: 265 2.3.8:..-8....33 ... .8 ....8. ... .. . 8.... u .. u u . 8...... .8 2:358 9.: 55.3.33 3 .8 28....3 8.. 5:33.33 3 .8 2.35.8 2.: 38.2.33 ... .8 18.28 ... .. . 8.... u 0.2!. .8 . 2.; ......- 3: J. 3..... .. 3..—.8. co . . 0...... . 3...... .8 253%.... 8.3.33 .8 .8 28:83.. 8.3.33 ... .8 2.3.2.3 8.3.33 3 .8 ..8... ... .. . 8.... . 8...... .8 25:88:85.3... ... .8 28:88-8.333 8 .8 2.3.8.3 8.3.33 3 .8 .3...... ... .. . 8.... - - . 8...... .8 25.58 83.55.... 33 3 .8 28.58 83.83333 3 .8 2.353 8. 5.8.3.33 3 .8 28...... .... .o . 8.... a 0...... ..o . :3. 3... .n. 3..... . .u ..I: co . . 3..... .3...... .8 25. 8.8188333 .. .8 28....3. 8.3.33 3 .8 2.3. 8.8.3.33 3 .8 ..8... ... .u . . .8 8.... .3...... .8 253.823.33.33 3 .8 28.333.88.333 3 .8 2.32.88.85.33 o. .8 .38.. ... .. . . .8 8.... .3...... .8 2538...... 8. ...33 3 .8 28.33.38.333 3 .8 2.3.98. 8.3.3:. o. .8 18.3.. ... .. . 8.... . 3...... .8 . 2.8.... .... ... 8.... o ...».8. ca . . 3..... . 8...... .8 25...... 8.3.33 3 .8 28...... 8.3.33 3 .8 2.33.... 8.3.33 .... .8 ..8... ... .u . 8..... . 3...... .8 25.38.88.333 3 .8 28.83.8533 3 .8 2.3.8.... 93.5.3; on .3 ...-.3. ..o. ... . 3...... ......3 .8 2..“ 8.3.33 3 .8 28...... 8.3.33 3 .8 .5 2.. 035.33 3 .3 2.8.3.. .... .o . 8.... . 3...... .8 . 88.8.. ...... 3.84.... .23. ... 8.... .....Su :0 o . 0:58 “.....d. s 00080000000001IOIIOIIIOIIIII00000. .U...’ .....l: .8 .858... 33.3.... 3238...... 8.... .....S. .... . . 8.... ......s. .8 . . 8.... .835. .8 25.333.33.33 .. .8 28...........3....3... 3 .8 28.33.8523... 3 .8 ...... ......c... 8.... 5...... .8 25.385.33.33 3 .8 28.88.333.33 3 .8 28.38.83.333 2. .8 ...... .28.... 8.... .....5. .8 25.333.32.33 .. .8 28.33.333.32. 3 .8 2.33.33.33.33 o. .8 .8.. 38...... 8.... .3...... .8 25.838.85.33 3 .8 28.33.383.33... 3 .8 2.3232333333 ... .8 .8.. 38.3.»... 8..... «a...50 co aacavzdgu-mgzgmz 3 .00 283.233.832.33 09 .0.. . 2:............. ....c... . ...... . ....oo. . 2....-.)...2......c.s...>.c... . ........x.. ....oc. . ..c..-.)...2......=.a...>.c... . ........x.. ....oo. . ..c..-.)...2......c.:...>.c... . .......¢a.. ....oo. . ..c..-............c.:...>.c... . ........2.. ....oo. . ..c..-............c.a...>.c... . ........:.. ....oc. . 2.3..--...x.......cna...>.c... . .......nz.. ....oo. . 2.c..-....x.......c~:.....c... . .......~a.. ....oo. . ..c.. ......2.....c.3...>.c... . w.......z.. .8 ...... o . 2.... 9.8.38... n .8.. .38. . .8..-.3..2.....c.3.3.2... .. 2.33.2.8 .332 . :3... 8822.338... ...-..8. .. 3.332.... ....oa. . .ao..-.>........3. ...-c... . .......o:.. ....oo. . 22°..-a,..\.......z.:...».c... . ........s.. ....oo. . .3...-.>..........c.a...>.c... . ........x.. ....oo. . .....-o...........c.x...>.:... . ........z.. ....oo. . 23o..-.>..........c.:.....c... . ........:.. ....oo. . .ao..-.>..........c.:...>.c... . ........a.. ....33. . .3...-............cna...>.c... . ........a.. ....ao. . .38..-.>..........c~x...>.:... . .......~a.. C .3... ......2...:......3..2... _. £33.38 .8 ...... a . ..8... 83...... .. o .. . . ....8.....2-98.2...3£28.88.823.323. .23...2.8.58223.38.1353....33 ...... 328.....2 98.28.28.832... . 2.32 58 «on: .. no.5. Quota—>3 5......3..=3.u no... n .8.. . .83. 53.2... «...... . . . 8.... ...5...8.~......3 \. .... 8.3.8.. 9.33.3: . n 3.4. 533 .3. 8.3.8.8 ...... c. .0o.0..o9.9.90.ooooooooo+¢o¢¢.ooooo.oooooooooooooooooooooooooooooooooooooo wa..:0¢oao xoz.nc¢:.sa ooooooooooooo¢¢¢¢¢¢o¢¢ooo¢oooooo0.0.0.000...ooooooooo......ooooooooooooooo 2.3... ...... .....3. co . . 3..... 3.. 8.2.9.. o. . u a. ..o. no u .o 2.9.. .... I.. .... . . ...... .3...... co 2............32.3... n. .8 2:35.... 32.33 3 .8 2.3.8.3325... 3 .8 126...... . . .8 ...... .....3. co . 33...... o. .038... ... p .8 ...... .....S. .... . . 3.... ......5. S 2......3...232.3_.. ... .8 253.38.232.33 3 .8 25.233.25.5329 ~n .o... 126......— >......:a .n. — .3 3..... I I.. ..8. co . . 3..... .3...... 8.25... 8.3.2.33 3 .8 2:38 .... 332...... .. .8 2.38 5.2.32.3... ... .8 . 3... x 8.8.. 3... .~. . .8 3.... I .. .....S. S . . ...... .3...... cm 2...... 83:32.33 3 .8 23... 5:52.33 3 .8 2.38 8.3.32.33 .... .8 ...-8.8.6.... .8.... ... . .8 3.... .....8 co . . 3.... 3...! co ...: ........................ . ...... 3.3!. S .33.. .....2. .. 533:... ...... .....5. co . . ...... .....5. co . . ...... .93 .3...... .... 2.3.8.2333. 3 .8 23.....5.3.....33 3 .8 2... 8.5.32.3... 3 .8 .8. 3. ...o.. .. .8 3.... 3:28 S 25. 23:32.33 3 .8 2:39,... 332...... ... .8 23.92.332.33 on .8 . 33...}... o ...2. .. .8 8 ... ......5. S 12...... .. . 3 ... ... .5. co . . a. .... .3...... .... 2:“..88.8r..2.33 3 .8 2:38.38 32...... 3 .8 2 .. .3...... 32.33 on .8 .89.... fix... . .8 3.... .3...... .... 2.... .883253 3 .8 2 . ...... 32...... 3 .8 2.33..-8.83.2.3... on .8 .23... .3. 3:22. .. .8 8 ... ......I... .3 .330... .m . .. .... .....5. co . . o. .... .3...... .8 25.33.353.333 3 .8 233......2332......3 3 8 2.. ..8-3532.33 ... .8 .8» 3. .3.... .. .8 ...... .....5. co 2.... 335.233... .o .8 2 . 38833.32. o. .3 23.93.253.333 on .8 .33.. .3. ...-3).. c .8 3..... u .....5. ..o ......2. .q . 3..... ......3. co . . ......- .....l.. 8 24.88.332.33 3 .8 25.8....33........... ... .8 . 332.31.32.33 on .8 .8. a... 33.. q .8 3.... .....l: 8 25.58.332.33 3 .8 2d..§.:832.3.. 3 .8 2.3838332323 on .8 ...-u... ...... 8:22. .. .8 ...... u ....5. co .3...... .n . ...... ... ..5. co . . ...... .3...... .... 2.38... .2.33 3 .8 2:38.... 32...... 3 .8 . 2.8.38. .2.33 a. .8 .8» 3. :3. . .8 8.... .....3. 5 2.... 38332.33 .. .8 2.... .88 32.33 3 .8 .8.-8. .. 3... 82.2.3 5......25...368.3 2.39.38.53.53... on .8 3.33.8.3... 8...... .. .8 ...... u ....I... .... .2233... .~ . ...... o....!. s o . ...L’ .....S. S 25.88.33.253 3 .8 23.038.33.23... 3 .8 2. 33.8.3323... ... .8 .8 3. .3.... .. .8 ...... ......S. S 2...: 8.8.3.253 . .8 22.... 38.3.2.3... 3 .8 2.39.83.32.33 on .8 . 82.-8...... 8.22. e .8 3.... .. ....5. cc .8339... .. . 0...... . 3.. .93 .....3. co . . ...... .3-8.o ..8. ......5. co . .308. ...: .3. ...) .8.. 3... .... . 8..... .8 ...... o - 8.2.8.... 2 «0...... .8 .....-....o-....!.......... 3..... . .....5. .... .3...... .3. ... 333:... 8.... .....5. 3 . ........ . 3 .8 . ......... . .. .8 .......-. ...“...wldx...“ .8822. .... .8 .8822. .. .8 28...... Roan-uh.“ .322? no .8 3.3.3:... 3 .8 .3883. 3 .8 3...... .3...... S 38...... 3 .8 .95.... 3 .8 3.... .....S. S 288532.33 . . . 2388.32.33 .... .8 2.88... 332.33 . 88.. 2.... 8.... .....5. .... . ............... .............'... . ...“, .....5. I. . ~ a... . ..8...- chg 3...... . ...... ...5...5.~......3 P ~38. 2.2.... .. ¢.ooooooooooooo.o9o...o.¢o¢¢¢¢o¢o¢¢¢o¢¢oo¢o¢¢+o¢ooocooooooooooooooooOOOoo. 3:83 82.3.3239 999999999999999 99 999999999999999999999999999999999999999999999999999999999 .22... I m....l.. co . . ...... .3...-... .... 2:32... 8:32.33 3 .8 23.28. 8:32.33 ... .8 2.32... 8:32.33 3 .8 .2... ..o.\..u...mnl..u.n... ... nan .l o . . 3.3!. .... 2.....28... 8.2.33 3 .8 2.3.9.8... 8.2.33 3 .8 2.3.8.2.. 8.2.33 3 .8 .338. .... .. . 8..... I I I I u ....5. .8 2:35.... ... 5:32.33 3 .8 2.3.8.. ... .3...-.2.33 3 .8 2.3.8.. ... 5.32.33 2. .8 ......3 ... .. . 8.... .3...... .... .85... ..8... ... 8.... “”53. co . . 3...... ......5. .... ......8..&J...33_.. .... .8 2.3.9.. 8.2.33 .. .8 2.3.2.2... 8.2.33 3 .8 ...-.... .... .. . ...... .3...... .... 2.....2...2&J3.33... ... .8 28.333.28.25... .. .8 2.3.9.8.... 8.2.33 3 .8 ...-.8. .... ... . ...... I u ....5. .8 2:35.... 8.33.2 33 S .8 2:352. 33.8.2.3... .. .8 2.3.8.. .... 5:32.33 a .8 13.3.. ..o. .. . 3.... . ....5. .... . 8...... 8.. ... ...... I 35.5. .8 . . 3..... .3...... .... 2.3.33... 932.33 .... .8 2.3.8»... 8.2.3... 3 .8 2.3.83... 3.2.33 3 .8 ..8... .... .. . 8.... .3...... .... ..c...8>....-8.2.3.a 3 .8 25.833.932.33 3 .8 3.. 8..... -. ...... 323:3 5.42.3323... 8:. 22(§7 . 2.3:. .....5. 8 . . 3..... «......3. co . . 8..... .3...... 8 28.8388383383 3 .8 I 28.....8.>. 8:383 3 .8 28.8.38... 8:383 on .8 .... .... ...8 9. .88.. .... a .8 ...... .3...... .a 28...: 838:383 .. .8 I 2888...... 8:383 ; .8 2.3.8.3... 8:383 8 .8 ...... .... 8... ...m. ... a .8 ...... ......3. .8 28.23.383.333»... 3 .8 I 2.8.338... 8:383 .m .8 2.3.3.38. 8:38... 8 .8 .23... 8..... 8.82. .... m .8 ...... .3...... 8 28.38I..3II8:383 3 .8 I I 25.0.8 ...... [38:38.3 3 .8 28.3.. ..8 55:38.. a .8 .38 .35.. ... a .8 ...... .3...... ..o . 38.8.. . .2 38 ...... 8.. ... ...... .3...... co . . 8...: ......l: .8 25.838.853.383 3 .8 I 2.3.8.8.... 8:383 3 .8 28.8.38». 8:383 on .8 ..8 .... ...8 9. .88.. .... a .8 ...... ...:5. 3 25.8.. 8.38.383 3 .8 I 25.8.8... 8:383 3 .8 2.38.8... 8:38.. 3 .8 ..8 .... 8... ...m. ... n .8 ...... .3...... .... 28......8....I8:383 3 .8 I 28.23.82,. 8:38... 3 .8 28......83. 8:383 3 .8 as“... .3.... 8&2... .... n .8 ...... .3...... 8 .5.38b.85.8:383 3 .8 I I 28.3.. ..8 55:38.3 3 .8 28.3.. .... 5:383 nm..8 .38 .83.. ... . .8 ...... .3...... .... . 3...... . ..8 38 ...3 c8. ... ...... “......s. 8 . . 8..... .3...... .8 25:88.3385383 3 .8 I 28.8.8.2. 8:383 3 .8 28.8.3.3. 8:383 on .8 ...... .... ...8 8.8.8.. .... a .8 ...... .3...... .a 25:88:88....83 3 .8 I 28.3.88... 8:383 3 .8 28.8.8.3. 8:383 on .8 ..8 ... 8.... ...m... ... ... .8 ...... .....3. 3 2c...a..oa..>.qu:<.am.o so .8 I 28.3.8.2. 8:383 3 .8 28:23.9. 8:383 on .8 .3...: ......m 8&2... .... ... .8 ...... .....IS 8 28.38I.B.II8:383 3 .8 I I 25.38 .38 55:38... 3 .8 28.3.. ...... 8:383 n» .8 .38 .85.. ... n .8 ...... 3...... I. . ...-18...... .3. Bow ..8. .o. 8..... .....3. co . . 8..... .....5. 3 258.88.385.82. 3 .8 .I 23:83..)- 3:33... pm .8 25.8.38). 8:38... on .8 ....u .... 3.3 8.3.3.. .P m .8 8..... 3...... I. 25.8.3..>.I8:<.8.a so .8 I 28.8.38... 8:383 2.. .8 23.8.38). 3:383 on .8 ...... .... 8..... 8.3 ... m .8 ...... .3...... .3 2.8.3.8....I8:383 3 .8 I 28:338.). 8:383 2.. .8 28.8.3.8. 8:383 3 .8 .3. ... .8.... 8...... .3. m .8 8..... .....8 .8 28......L.38IIE..3.83 3 .8 I I 28.38 .38 58:38.3 3 .8 25.38 ..8 55:33:. an .8 .33 845.. ... m .8 8..... .3...... ... . 38 8.8 ....u .... ...... .....S. S . . ...... .3...... 8 25:388...I8:383 3 .8 g .00.; II hm“: 0.0—3:3 55......4525133... I 28.8.8828 8:38... pm 8 2.3.8388). 3:38... on .8 ....u 8.. 8.3 9..-8.3.. .u. a .8 8..... .....3. I. 2.8.8.8).Iou:<.83 so 8 I 28.8388... 8:383 .n 8 2.3.8.8). 8:385 on .8 ...... ... 8..... ...mu .0. a .8 37... .338 so 2c...18.8>.Iou:<..8.a so .8 I 28:88.). 8:38... 3 8 2.3.2.388... 8:385 on .8 .23.... ......qx Some: .9 n .8 87... .....5. .8 25.38I88Il3u:383 .3 8 I I 28.38 .88 35:38... a. _8 2.3.38 .88 55:38.9 an .8 .33 .38.: ... a .8 3 .... 8...... I. . 3.3 .33 ....u ... 9. .... .....5. co . . 3 .... .3...... .a 25.832.38.383 3 .8 I 25.83.... 8:383 .... .8 28:25.... 8:383 on .8 ..8 ... 3.8 8:38.... .... ... .8 ...... .....S. .8 28.838.85.83 3 .8 I 28.8.3... 8:383 a .8 28.33.... 8:383 ...n .8 ..8 ... 8..... ...m. ... a .8 ...... .....5. 8 28.....8..I8:...83 3 .8 I 28:15.... 8:383 3 .8 28:25.... 8:383 an .8 .23... ...... 8...}. .... n .8 ...... .....3. .8 2ca.v.o.Il.Il3-.=.3.o 3 .8 I I 28.3.. ... .3...:383 8 .8 28.3.. 3 55:38.. an .8 .38 .. .. ... ... .8 ...... .3...... .... . 38 .8.. .3...... . .....u... .... ...... .3...... .8 . . 8..... “...—I.. .8 25:88:08Iou.=.33 so .8 1 25.300...)- 0d=<=u3 pm .00 28:88.... 8:383 on .8 ..8 ... 8.8 238.5: .3 a .8 ...... no.38 3 25.588.08Iou:<.33 3 .8 I 23.58030). 035.33 pa .00 2.3.8.8.... 8:383 on .8 ...... .... 8.... ......u .u. ... .8 3..... .3...... .8 25:80.2.qu32329 so .8 I 28.3... ... 8:383 3 .8 2838...». 8:383 on .8 .23.... .3...... . £2... .... n .8 ...... “3..!- 3 2c-.n.o.IV...IIEu.=.83 3 .8 I I 28.38 8.. 8:383 3 .8 28.38 8.. 55:38.. an .8 .38 .33.. ... ... .8 ...... .....5. .8 . v.3 an... 88.. .~. 3..... .....3. co . . 3.... ...:5. .8 25.8.1... 8:383 3 .8 25.8.1... 8:383 3 .8 28.8.1... 8:383 8 .8 ..8 .... ...8 2.88m. .... m .8 ...... .....8 .3 25.8.8>-qu:<.83 so .8 I 25.8.1... 8:383 3 .8 28:81.... 8:383 8 .8 ..8 ... 8..... ...m. ... a .8 ...... .3...... .a 25.....1....I8:...83 3 .8 I 3:31.... 8:383 .. .8 28:312.. 8:383 8 .8 .3...: 8.8.8.8.... .... a .8 3.... .3...... 3 25382158583 3 .8 I I ...-.38 .... 5...:383 .. .8 28.38 :1 55:38:. a .8 .38 .85.. ... a .8 ...... .3...... ..o . 38 .8.. 3...... ... ...... ...:5. ... ... ...... . a. .8 . ..... ..-.. 3 .8 ......-... 8 .8 .. . .8 ...... .....3. co ...-.3:. no .8 .8935. 8 .8 8...... 8 .8 883.... 8:82... . .8 ...... .....3. .3 3.2.3 . 3 .8 .3833? 3 .8 3:83.. 3 .8 ...... .3...... ... 28...... ... .8 ......8. 8 .8 ...... .3...... ... 2:838:38... . . . 3.88.8:383 8 .8 3.8.... 8:383 .... .8 . 889...... . .8 ...... “0...... 8 ...I. ........ ..IIIIIIII.I.II.III . 3.... 3.38 8 . .3 ...... .. 8.: 82323 8.3353338 ...: 268 \c .3 2.. .8 3..-1.8:. 8... ..I. .\ ... ...... S . .3... . .. .8 . ...... . on .8 28.8.8. 5...... o. .8 .859 . .8 ...... .....S. S ......a .583. .n .3 .....3 9......05 .n .3 .3.... .3...... .— .3 8.... .....5. S .88... 55...... N. .8 .885... . .8 ...... .....3. S . . ...... .....3. co . . .n .... . .8.... ..8... 8:83... 9...... . ...... ...... u7......5 \. ...... .83... .8 .283 .\ 3:33.339:...ozoooooooozozoo..........oo3.3:.........ozoooooo 2.23....“ .8... meat...» 90900‘QOOOOOOOOOOOOOOOOOOOOOOOOO‘OOOOOOOO00090060000OOOOOQ§OOOOOOOOOOOOO+O m 31.53 ...... .....S. 3 . . 8.... .8 .33... 8 p . . .8 ... ...... .3 8..... o. 3..... .o 8...... 1 2. . . . ......5. co .I.§.o......&.a m. .8 2.88853... 3 .3 35:888.»..5... an .8 .. .. 88-8.. ...8 . ...... .....3 co .......... ~o .3 12...... 3 .3 .......... ~n .3 8..... ......5. co .. ...... 2588.228. . 2.3.8.32... . 2.3.8.8... . 2.3.8.3... . 2.3.8.858 . 2.3.8.8.... . 2.3.8.33. . 23.0.33... 0 2.3.8. .... . 2.3.8.8... . 2.3.8.3... . 2...... 3. . 2.3.8.3.... o 2.. 38...... . 2.3.883... . 82.88. ..I-.3322... . .l... ...-o... . .8233... . 52.8.3... . ...-28.8.... . 3.88.32... . 33.8.33. . 5.3.8.3.... . ......8. 8.. o 3.888.... . 73.8.3... o ......8 8. . 73.8.3... . 33.8.2... . ... ......3... . ..88. 2.3.8.8.... . 2.3.8.838 . ...... 88.8 . 2.3.8.8.. . 3.3.8.858 . 2.2.8.32... . ..«..o......... . 2.3.8.3.... . ........P..... o ........o..... . 2.3.8.3... o ......8 ... . $3.833... . 33.8.3; . .. 88.73... . .3388. ... a 3.5.8. ... u .....8. ... . o ......8. ... . 36.3.8. .868 8 8.5.8. 8...... .8.... 8 ..8... 8...... .8.... 8 8.52:8. 5.... 8.8. .. 888.3. 8...... .....5. .... 25.8.8583582. 3 .8 28:35:83....5... on .8 2.....8...:8..5.5.. 8 .8 .882... .....o .2 . ...... "8...... co . . 8.... .....5. .8 25. 8.58855... 3 .8 28:853. 5...... 5 .8 2...: 8.2.85.5... 8 .8 ....3 .888. 8 .... . ...... .....5. co . . 8.... .....5. .... 25:88:85.5... ... .8 28:88.. 5...... 5 .8 2.3.8.8555... a .8 ....8. .88... 8 .2 . ...... .....5. co . . 8.... .3...... .a 2588.38.55... 3 .8 2555.855... 5 .8 2.3.8.3855... 8. .8 ..8: .~. . ...... .....3 co . . 8.... .....5. ... 258.88.85.53 8 .8 28.88:...8522. 5 .8 .3...... .. R... 823:... .8.-8.33.52. ...... 2388.88.35.33 .n .3 .....8... .3 . 8.... “8:8. 3 . . 8.... ......5. ..o 255.32.85.53 3 .8 25:33:85.5... 5 .8 2.3.8.8855... ... .8 .8532. .... . ...... .....S. 8 . . ...... ......5. ..o 25.31.835.53 ... .8 25........8..5.5.. on .8 2...... .8855... ... .8 . .3...... .o . ...... .....8. co . . 8.... ......5. ..o 25.32.338.55... ... .8 25.828.835.83 .... .8 2...:8..8..85...3 8 .8 .832... .. . ...... “Cd—5.53 co . . .uuha ......5. ..o 25:33:85.5... ... .8 25:83:85.5... a... .8 2.3.8.8855... ... .8 .828... .. . ...... “8:3. .8 . . 8.... .3...... ... 2583818....53 ... .8 28.382.85.53 3. .8 28.88.835.83 ... .8 3.3.8.... .. . ...... “8:!- co . . 8.... “.3...... ..o 2588.28.35.33 3 .3 2838.08.35.33 on .3 2.....8.3.....<...m.a .n .3 ...... .n . 8.... .....3 co . . 8.... ......5. ..o 25:89.8.»«2... 8 .8 28....9dww5g... on .8 2...... 8.5.5:... .8 ...: a J . ...... .....5. co . . ...... ......5. ..0 25.323.03.53... 3 .3 28.333.03.333 on .3 2.3.033335329 .n .3 . 3.... .n . 8.... “8...... co . . 8.... ......5. ..o 25.322855... N .8 2888.88.55... 5 .8 2.3.8.8855... ... .8 .. ... . ..8 82...... .~ . ...... .....5. co . . 8.... ......5. .a 25:3...8..85.8.. 3 .8 25:35....uw38. 5 .8 2.33.83.33.53... ... .3 33.5... n... ... u .. . 8.... ......3. co .33... 38-3: . 8.... .....5. .8 . . 8.... m8...... .8 ..c...8u........=...m.o 3 .3 33.838.035.32. on .3 28:38:83353 ... .8 .88... ..8. Banana...» .8 "an“ . s . . ...... s .. ........................ 0000.... o IIll...IIIIOOIOIOOIOIIOIIO . . 33...... co .0.-...... .3 .3 .3".o><. a. .3 .38.... .n .3 ....o. .3 .... 3.2.... ...... . .3 ...... ......5. ca .22.... 3 .3 .3.....3. o. .3 ......Su. 3 .3 8.... . .....5. co ......au. 3 .3 .o:~..o... o. .3 8..... .....S. 8 288385.53 . . . ...-.8855... 9. .3 .38.... 233...... .... .3 . 389...... p .3 8.... N.“‘!. s OIIIOOIODOOIOI'IIDIOIIIIIIIIIIIC O ...: .....3 ..o . v one. . pagan Sum—.58 «...—an . 0...... O Q 0 Q I ' . 5 o n . a 0 ~ I - o F . J. \. .... 32.3... 28.3... . v a...» . 35.—... 9...... 3... .\ z:9:339:33...39:33:00..33:39:33.:oiooztooozz: 3:8...“ 33.35:: 00.900066960900990996969900‘OOOOOOOOOOOO‘OOOOOOOOOOOOO¢§O‘OO000.909.00.009 no: .00... .. sun: ones—Ra 55.358.332.13 .0.: 269 3.3!. S 23333.33 2 .3 33333.33 2.. .3 33833.33 2 .3 3.3.... ~ .3 ...... 2.3.4.283... 3.. . .38. 2.3.3.2....32333 . .33. $43.3. 3.13.23. 3 . 3.3- .2. . .33- . .. .33. no a 333 . .. .33. 3...... .3. .. ...... 33.3 .3. .. 3.3. 3...... .03an co ..-..-.. an .00 .....-.. an .23 ........ e— .00 0...... .....5. .... 2.33.3333 3 .3 22.333533 3 .3 2.33.3333 3 .3 2.3 33.33 3 .3 23.33.33... 3 .3 2.333333 2 .3 ...... .... n .3 ...... .3...... ... .8.-333.33 3 .3 2.333333 3 .3 233.3533 3 .3 2.333333 3 .3 2.33.3333 3 .3 2.333333 2 .3 ......3... n .3 ...... .3...... .... 3.8.32.3... 3 .3 2.333333 3 .3 238.3333 3 .3 2.333.333 3 .3 2.33.3333 3 .3 2.383.333 2 .3 .33. n .3 3.... 3.3.5. ... ....1..-.......-.. ...... ......5. ... .2332... 8:3. 3.... 3.33. .8 . . 3..... «a. .l.. :2. 225.333.533 No .3 223.23.35.32. en .3 2.33.0.- ><.2.m.o nv .3 2.33..—:33... en .0.. 2 33335.23; - .3 22.33.35.322. r.— .o. ..>.2. . on. p .3 8..... ...:I... .8 . . 3..... .3...... ... 2.33..-333.33 3 .3 22.333.33.33 en .3 2.3.53.3»:3... ...— .3 .233. ~ .3 3..... 3.3!. .... 1...... 3 .3 ........ 3 .3 ...--... .. .3 ...... .....S. S 22.33.33.33 3 .3 22.333333 3 .3 22.33.3333 3 .3 22.333333 3 .3 2233.33.33 3 .3 2.333333 2 .3 .3...... n .3 ...... .3...... ... 23.333333 3 .3 22.33.3333 3 .3 2.333333... 3 .3 2.333333 3 .3 2.3333333 3 .3 2.33.3333 2 .3 .333... n .3 ...... ......5. ... 22.32.333.33 3 .3 2.3.3333... 3 .3 2.3.3.3333 .. .3 22.3.3333... 3 .3 2.3.3.3533 3 .3 223.2333... 2 .3 ...... n .3 ...... 3.3!. 3 22.33.3333 3 .3 2.333.333 on .3 22.32.33.333 3 .3 2.4.2.3353; en .3 :gvcsav><.&n—o NN .09 «Axavcgdvb£.3_“_“"§wuu&§. n .00 “nun" . .....---.. .....5. 5 3.23.3. 3.... ......5. co . . 3...... 3.2!. ... 23.3.3333 3 .3 223.35.33.33 3 .3 223.35.33.33 2 .3 .332. ... .3 ...... .22.... 8 ..-..... 3 .3 1...... 3 .3 ...-.... .2 .3 ...... ...:S. 8 2.333.333 3 .3 22.33.3333 3 .3 2.3.3333... 3 .3 2.333.533 3 .3 2231.33.33 3 .3 22.333333 2 .3 .82.... n .3 ...... .3...... 8 22.33.3333 3 .3 233332.33 3 .3 22.33.3333 3 .3 2.33.3.3... 3 .3 2331.33.33 3 .3 2.33.2.3... 2 .3 .3332. n .3 ...... .3...... ... 2.3.3.3333 m. .3 28:33:33 3 .3 2.3.3.3333 3 .3 2.3.1.3333 3 .3 23.-333.33 3 .3 2.3.2.5333... 2 .3 .2... n .3 ...... .3...... S 2.3.3.3333 3 .3 23.5.3333 38...... .. 3.: $2323 53:33:53.3... en .23 2.353.323; nv .00 2:3.»«53 en .3 :xdvsuc.»<.mm_a - .09 aaxav§~><.An—6 meN-UBu’eu... n .00 "flu...” . ....-.. ....§. :2. .3:... 3..... u. :5. .8 . . 3.... .....S. 8 22.333.33.33 3 .3 2 .3....5...=.3.2. 3 .3 22.32.333.333 2 .3 .332... ~ .3 ...... ......3. co ..-..... no .3 ........ nn ... ........ o. .o. ....a ...:S. 8 22.-33.33.33 m. .3 22.333333 3 .3 2.33.33.33 3 .3 23:33.33 3 .3 2.33.3333 3 .3 2.333333 2 .3 .3...... n .3 3..... ......S. 3 22.33.3333 3 .3 2233.33.33 3 .3 2.33.3333 .. .3 22.33.3333 3 .3 2.33..-:53... - .3 2333.35.33 mp .3 ...-Lona. n .3 3..... .....S. ... 228.22.33.33 3 .3 2.3—£33.33 3 .3 22.32.333.33 3 .3 22.33.33.33 3 .3 2.3....3333 3 .3 22.33.33.33 2 .3 .3... n .3 8.... ...:S. 5 2233.33333 3 .3 2.353.333 3 .3 235.3333 3 .3 2.353333 3 .3 23:53:52.2.— - .00 2.32:.»cuuflwlmu ....Mu .0262. n .00 "nun . ............. 3.2.5. 8 .22.. 3.2... 3..... .0. .5. co . . 3..... .....3. 8 25.3.35“... 3 .3 2.3 3:. .3 3 .3 2.3.3.3333 3 3 2 3 33.33 3 .3 22.3 .3333 3 .3 2 .3 33.33 2 .3 2.83:... 2 .3 3..... ...:S. 8 2.3.3.3: .3 3 .3 23.33... 3 3 .3 2.3.3.3333 n. .3 2 3. 33.33 3 .3 22.3. .3333 3 .3 2.3. 3.2.3... 2 .3 .....3... 2 .3 3..... a. 2!. co . . 3..... ......S. 8 28.233.33.33 3 .3 2 3.2.3.3333 3 .3 2.32.93.33.33 2 .3 3.3.... ~ .3 ...... ...:S. S .....:. 3 .3 ........ 3 .3 ........ .2 .3 3..... 3.2.5. 5 22.33.3333 3 .3 2.333333 3 .3 2.3.33 333 3 .3 22.33333... 3 .3 2233.33.33 3. .3 22.3 33.33 2 .3 .3...... n .3 ...... ...:S. .... 2.3333333 3 .3 28.3.3533 3 .3 22.3 .3333 3 .3 2.3 33.33 3 .3 2...-.~..&u3=.2.m_a - .00 :3.~.a==..am.o n— .00 .3338. n .00 3.... ...:S. 8 22.32.333.33 3 .3 28.233333 3 .3 22.328.33.33 3 .3 22.323.33.33 3 .3 22.323.33.33 3 .3 2.3.8333... 2 .3 ...... n .3 ...... .....5. S 2.33.3333 3 .3 2.333333 3 .3 22.33.33.33 3 .3 2.383.333 3 .3 2233.33.33 3 .3 233333.33 2 .3 . .8... n .3 ...... 3.3!. 8 2393...... 3..... “...‘g s ........ ......- ...:S. ... .33.... 33 3. 3 .3 .3...... 8.. c 33.... 3.. n . 3..... r .3...... 8 22...................... 3 .3 2.3.5.3333... t x. 3 .3 2.3.5.3333... 2 .3 . 382. 3..... 3..... .x no: "on... .. so»: 00¢—3:3 83.28—39.3236 3.: 270 on .oo .:.I.o.:n..»<..u.o on .o. ....o.. .— .ou I...) .....3 3 ......... 3 .3 ......... cm .3 ........ «n .3 I..... .8 ...... o . 538:5 : ...... o . 3....315 .. um... .... .~ . .c..c.. . .cuoc... .3...... S .3......anu33... 3 .3 33.33 :55... on .3 ......3 32...... on .3 ...:2. 2 .3 ...... ...:S. S ..-...... 3 .3 ..-...... a ... ........ 3 .3 .. ... I .8- .... o A 533 . 3 25.3.3... 5 u 2353:... .. afar... 35.2%.... 35.2%.... 35. .c:.-.. I. a..c:.ou. I. u.2::..ou I. ...cq.o.u I.nu I 95......33: 2.33..-... 5 n . 353:... 5. I.. ...... 35.2%.... 3 22%.... 35. 2:...- I. 3333. I. 36.3.3 I. .2.33m I..: I “.3...-.31.: 2.5.3.3 c: n 3.33323. 2... -23.... 35.23.... 35.233. 35. 3:...- I.ca2..u.3. 3..-2.3.9.. 6.32.333 3:: I ...-.3....- .o I 0313.1: ... I 3530...... ... I ...-.31.: . 33.08 .. 3:30.35 .53... .3.... .. 3533.5 .53.... .33 .. 5:22.. #3.... .... I u. o .32.: I .32.: .3...... .8 25...... 35.2.3... 3 .3 ......o . .-.3 ...... .8 ...... 8... . 25...... 35.2.5 2 ...... o . 2.3.... 35.0...“ 2 .... I 3 3.33.: I .33.: .....5. .... 25...... 35.3.... 3 .3 23.... 35.3.... on .3 2.3.... 35:33... 3 .3 ......o . .-.3 ...... .8 ...... a . 2.3.... 35.)... : ...... I a. o .33.: I .32.: .3...... .8 25...: 35.5.3... 3 .3 ...... . .-.3 ...... .00 am..— 86 A 2:33.. smug-en .. ...... o I 2.3.3.. 33......“ .. ...... I n. .2305: I .33.: .3...... ... 25.3.. 35.5.3... 3 .3 23.... 35:53... 8 .3 2.3.3.. 35.2.3... um .3 ...-o: . .I.3 I..... .8 ...... o . 2.3.3.. 33.33 .. 2.... I a. o .32.: I .32.: “03.5. .8 25...... 33:34.... 3 .3 .333 . .I.3 0...... .3 ..w... 8... . 23.3. 3536:. .. ...... a . 2.3.5. 35.0...“ 2 .9. I .. .I.3I¢.: I .33.: .....S. ..o 25...... 35.2.5... 3 .3 233. 35:33... on .3 ...-.uao I.c:.»<.am.o sn .0. .cuuoam . .I.ou I...) .2 ...... o a 2:...3 33.3.... .. 2.... I 3 o .38.: I .33.: 3.2!. 8 25.... 35.5.... 3 .3 .3:... . .-.3 ...... .8 ...... 8... . 25.... 35...... : ...... a . 2.3... 35...... : ...... I .. .I.c3c.: I .32.: .3...... .... 25.... 35.2.3... 3 .3 23... 35.3.... 8 .3 2.3... 35.3.... R .3 .3:... . .-.3 ...... .8 ...... o . 2.3... 35...... 2 3...... .. a... 3.3.3.. 5.3333236 8... I 2 o .33.: I .32.: .3...... ..o 23.3. 33.5.33 3 .3 .933. . .L.... I..... ..8 ...... S... . 25.3. 35...... 2 an... a I 2.33. 3:30....“ .. .9. I 3 3.38.: I .33.: ...:5. ..o 25.3. 35.2.3... 3 .3 :33. 35.2.3. 3 .3 2:.3. 35.5.3; sn .3 :39" . .L.... 0...... .8 .3..— o a 2:23. £23.66. 3 2.... I 2 o .3...... . .32... .3...... .8 25:... 35.2.3... 3 .3 .3...... . .-.3 ...... .3 ...... 8.. . 25:... 35.2... .. ...... a . 2.3.... 35.0%“ 2 I 3 ”.32.: I .32.: “......a... ..o 25:... 35.2.5... 3 .3 23.... 35.2.3... 3 .3 2.3.3 35.2.3... 2 .3 .32.... . .-.3 ...... .8 ...... o . 2.3.... 35...... .. 2.... - 2 . .3...... . .3...... .....5. ..o 25.2. 35.25.93 .3 .3...... . .-.3 ...... .8 ...... 3.. a 23...... £3.53 .. ...... o u 2.3:... 35.0%“ .. I 3 3.38.: I .32.: .3...... ..o 25.33 35.2.5... 3 .3 232. 35.2.3... ... .3 2.....3 35.2.3... 3 .3 2.2.33 . .I.3 ...... .8 ...... o . 2.3:... 35.2... .. 3 . 3...... . 3...... I .....I... S .22.... .... 53.3.... ...... .....I... 3 2.3.0.3 3:533 3 .3 2.33.3 3.5.3... on .3 $5.83!! 5.3.3.. 3 .3 .n .55... 3..... .3...... S 2353.. .. 3:33.. 3 .3 3353:... 3.35:. on .3 2 3333....- c:.»<.3.a sn .3 .2333». I..... M..’.!. s .OIIIOIIIO. 3 .8 .OIIIIOOOO'OOI. 3 .09 ..I.I..Il. : ‘00 .-0I..-..‘I..........'..... . .0“ ..‘i “0...... co .IN «...-3. 3 .2. .... 3..... . 3 .3 1.45.. . 3 .3 .3...... . .3 3.... .3...... 8 23355.22... . . . 2333.32.33 en .3 .33.... ....»333 ~. .3 . 38934.. p .3 I..... 32.3 .8 1.-.-.. E .3 ..-..-.......-......... en .3 .....-........-..---.... .3 I..... .3...-... 3 .o I .. as .3 .2335... 2.226.. 9... en .3 .23... 33.3.... «3..... p .3 I..... .oI.cuoc.: .~Ic: ...I: r .33. ...-3 . . ...: . 3%.... 2:5... 1 0.. 09000990000909. 0 O9099690000000OOOOOOQO§OOOOOOQOOOOOOOOOOOOOOOOO0090060. 3:83..» 83.39....“ 0909000099090966900.99.00¢+69099090990z9§§z¢059§000069000090099.9690... .3...... \. ...... .2333... ..u... .33. 3...... I v . ......x. a. .. ...... £3. :3. .33. .... :5... . n . ...... .. \I ...... .23.... .3...)... ... .3... . 3...... I. .I u. .3.... .>o..I> I. o... I ~ . .....l I. r r... .3.... ..3 3.3... 3.5 . . .....3 .363... 2...... a .8 .8... .. 3... 2.2328 3.3.33.5... ...: 271 u.... ...... o a 2:232:55 .9.. 2 o .52.: n .52.: .....S. .... 2532.55.83 3 .8 ..8... . 8.... 8 ...... 3... . 25.82.52,... = 5.: .. . 25.5.3.2...cm .9. 2 o .52.: a .52.: .....5. .8 253285.238... .8 25.8.8338... 8 .8 25...... 3.183 5 .8 .....3 . 8.... .co ...... o A 2:232:32. .9.. 2 . .52.: u .52.: .....S. ..o 253955.83 3 .8 5.888... . 8.... .8 5.: 3... . 25. 5.8... .. ...... o . 25.55.02“ .9. 2 o .52.: u .52.: 5...... .... 25.55.... 83 3 .8 2533.58.28... 8 .8 25. 5.5.83 5 .8 8.888. . 8.... ..8 ...... o . 2539.588. .9.. 2 o .52.: u .52.: .825. .... 2535.553 3 .8 .58.. Samoa... 8.... .8 ...... 3... . 25. 5.85 .. 5.: o _. 25.5..oflm 2 o .52.: u .52.: .....la .... 2439.5. :83 3 .8 25.53.8383 8 .8 5.5.0855... 3 .8 .58.. 883 . 8.... .8 ...... o . 25.58.82. .9.. 2 . .88... . .88... ......3 .... 253.5888... 3 .8 8:8.5 . 8.... .8 ...... 3... . 25. 35.2.5 .. 5.: o . 25.53582. .9.. 2 . .52.: n .52.: .....5. 8 25.55.58... 3 .8 253.33.85.83 .... .8 25.55.5383 .3 .8 38.8.5.8 . 8.... .8 ...... o . 25.55.85 .9.. 2 o .52.: u .52.: “8...... .8 253953583 3 .8 ..8. .8.. 8.8.“... . 8.... .8 ...... 3... . 25. 95.8... .. ...... .. . 25.55.)...“ .9. 2 o .52.: a .52.: 8:8... .... 25395.58... 3 .8 25.8.5553 8 .8 253955.53 3 .8 ..8. .8.. 8.8.... . 8.... .8 ...... .. . 25.5.585 .9.. 2 o .52.: a .52.: .....l: .5 25355.83 3 .8 .885 . 8.... .8 ...... 3... . 25.55.88 .. ...... o - 2:293:02“ .9. 2 o .52.: u .52.: .88.... .... 25395.5... .. 3 .8 25.588383 9.. .8 25.52.8553 5 .8 .888. . 8.... 2.. «can; .. 3.: boo—3:3 & tac.:a.g..._:/uu .0. .5 .8 ...... ... . 2535.8... .9.. 2 o .52. u n .52.: .3...... ..o 253358.383 3 .8 ...... c . 8.... .8 ...... 3... . 25 5.8.: .. ...... .. . 25315.3...“ 2 o .52.: a .52.: 3 .8 253155.83 8 .8 ......53 R .8 3.5.... . 8..... .8 ...... o . 25.53.82. .9.. 2 3 .52.: o .52.: 5:33. ..o 2c...3~l.u..<.3.a 3 .5 .533 . ...... .8 ...... 3... . 2535.8... .. 5.: .. . 25.52:...fl“ 2 o .52.: n .52.: ......5. .3 25.55.35...“ 3 .8 253958383 8 .8 2.5.5. 5.5.3... 3 .o... .533 . ...... .8 ...... a . 2.8.5.5.»...~ .9.. 2 o .52.: n .52.: .....S. .3 25.53.8383 3 .8 .888.- . 8.... .8 ...... 3.3 . 25 958.... .. 5.: o . 25.53:...flw 2 o .52.: u .52.: .....5. .3 253.55%... 3 .8 25395.58... 8 .8 25.8 ......8... 5 .8 .8288 . 8.... .8 ...... .. . 25.51.82. .9.. 2 o .52.: u .52.: “3..-3. .5 25.33.53... 2:... 8:25. ..o 25.533383 3 .8 .838“ . 8.... .8 ...... 3... A 25. 5.85 .. 5.: e . 25.5.38)...“ 3.. 2 o .52.: u .52.: .....5. ..o 2535.....8B.3 .8 2535.553 8 .8 2.5.5. .53»... R .5 .233... . 3..... .8 5.: o . 25.5.3388 .9.. 2 o .52.: .- .52.: ......5. 3 25.25.5383 3 .8 .8858 . 8.... .8 ...... 3... . 25 .58.... .. ...... .. .. 252.2534...“ 2 o .52.: u .52.:. 3 3 .8 25335.33... 8 .8 ......83 R .8 .8552... . 8.... .....a:. go ..c..3.:3...<. .....c. .— .8 ...... 3 . 25.5582. .. 3 ¢ .52.: n .52. .....5 co .3... «.25.. 52.52.25 ...... .....5. .8 ................... ...... .....S. 5 8...... ... 3.5.... 8.... .....IS 5 . . ...... .9.. .n o .52....“ u .52.: .3...-5 co 35.53.53.353 3 .3 «92.-.... 35...“... . 5.5.33.5... ... 3.. 8.... .- 2...: 32323 272 ..a. u— o .cuoc..: n .c.«c..- .3...... .... 25.25.132.33 3 .8 ...... . ...... .8 ...... 3.. . 25.25.3532. 2 ...... . . 2.325.830vfi .. o .cuoc..a a .cuoc..: ......s. a. ..........a.......... .. ... ..........:.......... .. ... 2.32.21.32.33 2 .8 ...... . ...... . .8 ...u. . . 2.325.832... 2 .... 5 .25.. . 2328...... 232 ...... 2328...... ...... ...... 2 . .52.: a .53.: .3...... .... 25.2.5.2.33 3 .8 .835... . 8.... .8 ...... 3.. . 2.325.532. .. ...... .. . 2.32.5532“ .... a. . .58.: u .52.: .....S. .... 25.25.332.33 3 .8 2.3252323... 8 .8 2.3253233? 2 .8 5.3).... . 8..... . .8 ...... . . 2.32.3532. .. .... .. .25 . . 232...... 232 .5 232.85 ...... ...... 2 o .52.: n .52.: .3...... ... 25.253.32.33 3 .8 .58. 88... . 3.... .8 ...... 8.. . 25.2.9532. .. ...... . . 2.325130%“ .. o .52.: n .52.: .3...... ... 25.25132 3.. 3 .8 2.325.832.33 8 .8 2.32 32.3.. 2 .8 ...... 88... . 8.... .8 a... o . 2.325.532. 2 .... .. :25 . . 5.255 232 .5 2325.... ...... .... 2 o .53.: a .52.: .3...... .... 25.25.53.333 3 .8 .8322...“ . ...... .8 ...... 3.. . .232 ..532. 3 ...... . . 2.325830%“ 3 o .52.: u .53.: .3...... ... 25.25132. 3 3 .8 2.3255353; 8 .8 2.325 .2.33 2 .8 3.2.2.5... . ...... .8 ......” . . 2.325.532. .. .... .. :25 . . 2325.5 232 .5 2322.5 ...... .... 2. . .53.: a .53.: ......l.. 1. 25.531.32.33 1 .3 Loan .2... 35.5 . 0...... .o. a... 8... . .....n...:...>.c. .. a... a . .......cos...>.c. ..au 3 . .53.: a .52.: ......5: a. .........a....<..... .. ... .........a.......... .. ... .......c.:.......... .2 ... ..oo. .o.. 8.2.... . a...) ... 2.. . . .......cos...>.c. .. .... .. ...... . . ........:.. ...... . ......coza. .2... ...... 2 o .53.: a .52.: 3.3!. .... 25.2.5.2.33 3 .8 .88.... . ...... .8 ...... 3.. . 2.328.532. .. ...... . . 2.32.2534...“ use .00.; .. 3.: coo—3:3 .— «x 5- C\ 5— ¢\ & 53.33-59.33; .0.: .— o .58.: a .53.: .3...... .... 2....25.5.2.33 3 .8 2.328.232.33 8 .8 2.32.83.32.33 2 .8 .88.... . 8..... .8 5.. o . 2.325.532. .. 2 .... .. 22.5 . . 5.28.5 232 5 2325.5 ...... .. ...... 3 o .52.. a a .52.: 8.3.5. .3 25.253.32.33 3 .8 ...... .. . 8..... .8 ...... .... . 25.2 ..532. .. ...... o . 2.32.....5..........“ 3 3 o .53.: a .53.: ...:S. .... 25.253.32.33 3 .8 2...25.5.2.33 8 .8 22.25.332.333 2 .8 .23.... . ...... . .8 ......“ o . 2.325.532. .. .. .... .. 8...... . . 2325.... 232 ...... 2325.5 ...... .. .... 3 o .52.: a .52.: ......5. ... 25.253.32.33 3 .8 .828.“ . 3..... ..8 ...... S... . 2....2 2.532. 2 ...... o . 2.325253%“ .. u. o .53.: a .52.: .3...... ... 2....2...5.2.3...u.3 .8 2.3.—5.5.2.3.. 8 .8 2.32... .2.33 2 .8 .82.... ...... .8 ...... . . 2.32 .32. .. .. .... .. .25 u . 2325.5 232 .5 2325.... ...... .. ...... 2 . .82... . .82... 8.2.5. ... 25.25.5253 3 .8 .938“ . ...... .8 ...... 3.. . 25.2 532. .. ...... o .. 2.325830%“ .. . 3 o .53.: u .52.: ......5. .... 2....2..~......2.3.. 3 .8 2.325.553... 8 .8 2.32553333 2 .8 .238. . ...... .8 a... . . 2.325.532. .. .. .... .. 22.5 .. . 2325.5 232 . 2325.5 ...... .. ...... 2 ¢ .52.: n .52.: .3...... .... 2.5.2.2....2233 3 .8 3.832 . ...... .oa ...... 3.: . 2.3m. 2.3.55 .— ...... o . 2.325130%“ .. 3 o .52.: n .53.: .3...... .... 2....2....5.2.3.. 3 .8 2.3258323... 8 .8 232.515.2333 2 .8 3.852. . ...... .8 ...... o . 2.325.532. 3 .. .... .. 22.... . . 232...... 2325.5 232...... ...... .. a... o .50.. : a .52.: ...—..I: 5 .3... .33... 0...... “0...!5 5 . . 0...... ...... a. o .52.: n .52.: .3...... .... 223.23.32.33 3 .8 .52.... . 8.... .8 ...... .... . 2 82.532. .. ...... a .. 2.2.2.5....“ .. 2 o .58.: a .52.: .3...... .... 25.5.5.2..3...u.3 .8 2.3.25.2.33 8 .8 2.3.... .2.33 2 .8 .584... . ...... Ono—3:3 55.33—32—3/8 ...: :0 "on... .- 3.: 273 ...... .. . 23.3....835flm 3 up . .cuoc..- - «cone..- .....5. .8 25.32.83.333 3 .8 23.382.83.33... ... .8 23.35.183.33... 3 .8 .58.... . 3.... .8 ...... a . 23.3....83..8. 3 .....u u— o «Coos—.4 n acouc..q .3...... .8 25.38.83.333 3 .8 1...... . ...... .8 ...... 3... . 25.38.8338. 3 .3... .. . 23.35.8308“ 3 .9. a. . .cu»:..a - .cuoc..u .....S. .8 25.38.8333... 3 .8 23.38.8333... ... .8 23.35.83.333 .3 .8 ..8... . 3.... .8 ...... .. . 23.3......83..8. 3 ...... up . .cuoc..a . 3:808... .3...... .8 25.388.83.33... 3 .8 .83.)...” . ...... . .8 ...... 3... . 25.3 383.8. 3 ...... o . 23.35.8338“ .... up o «cues... - .Cuoc_.d .3...... .8 25.388.83.33... 3 .8 23.380.833.33 .... .8 2.33.88.33.33 .n .8 .8328... . ...... .8 ...... o . 233.583.,8. 3 ...... .p o usuoc..a - ucuqc..u .3...... .8 25.35.83.333 3 .8 .....3 8.8.. . 3.... .8 .3... S... . 25.3 3...... 3 .3... o .. 23.358308“ 3 .... a. o .cuoc..- . .cuoc..u .3...... .... 25.35.83... 33 3 .8 23.38.83.333 ... .8 2.... 3.333 ..n .8 .58.. 8.8... . ...... .8 .3... .. . 23.3583..8. 3 ...... up o .cooc..u - .cuoc..a .3...... .8 25.38.83.333 3 .8 .8358.“ . ...... .8 ...... S... . 25.3 ..8-...... 3 .3... a . 2.335830%“ 3 up . .8808... a .Cuoc..a .3...... .8 25.38.833k33 3 .8 23.38.8333... ... .8 233... 3.333 .3 .8 33.3.8... . ...... 8 ...... .. . 23.3...83..8. 3 ...... u. . .Cuoc..a . .cuoc..a .3...... .8 25.38.83.333 3 .8 .88 .8.. 8.8:... . 3.... .8 ...... 3... . 25.3883..8. 3 ...... o . 23.3.5830...“ 3 .9. a. . .cuuc_.- - .cuuc..- ......5. .8 25.33.83.333 3 .8 23.38.832.33 ... .8 233583.333 .n .8 ...8 .8.. 8.8.... . ...... .8 ...... o . 23.35838... 3 ...... a. . .cuoc..a - «88.8..» .3...... .8 25.383.83.33... 3 .8 3.8.8 . ...... .8 ...... S... . 25.38.8338. 3 tu8.¢ma—¢x/4_—:/.u ...—5 & cue «co-s .. onusp Qua—\o—xuo ...... o . 23.35.83...mum 3 up o ucuoc..u - venue..u ......5. .... 25.38.83.333 3 .8 23.383333... .... .8 2335.83.33... .n .8 3.88.. . 8..... .8 ...... a . 23.35.8335 3 ...... up . .8908. a - .cuoc..a .3...... .8 25.38.383.33... 3 .8 ...... c . 8.... .8 ...... 8... . 25.3 183...... 3 .3... a . 23.3583fiuw 3 up o “cone—.a n .Cuuc..a .3...... .8 25.35.8333... 3 .8 ..3.........8......3... 8 .8 2335.83.33... .n .8 1.3.... . 3.... .8 ...... .. . 23.3.3.8...5 3 .93 u. o .8008... - .Cuoc..a .3...... .8 25.38.83.333 3 .8 .88....“ . 8.... .8 ...... 3... . 25.3 3.83.65 3 ...... a . 2.335833% 3 u. o «cone..u - .cuoc..- ......5. .8 25.38.83....3...m 3 .8 23.35.83.333 ... .8 2:35 3.333 .n .8 ...-8.... . ...... . .8 ...... e . 23:35:83.8. 3 .....u up o «cu-c..a a gauge..u .3...... .8 25.35.83.333 3 .8 .....8. . ...... .8 ...... 3... . 2.. 3.5.833... 3 ...... a . .3.....N83oflm 3 .— . ucuoc..a a «Coon..a .....5. .8 253883.33... 3 .8 23.38.83.333 ... .8 233583.333 3 .8 .....8. . 3.... .8 ...... a . 23.3583..8. 3 ...... up o ucuoc._a a ucuoc._a .3...... .8 25.38.183.33... 3 .8 3.8!...“ . .3... .8 .3... 3... . 25.3 383...... 3 .3... o .. 23.3...83oflw 3 up o 3390:... - .cuoc..u .....S. .8 25.38.8333... 3 .8 23.38.383.33... .... .8 233583.33... .n .8 3.58.. . .3.... .8 .3... a . 2.335.833... 3 .~ 0 sauce... I .3902..a .3:!- co .3... 80.3.5. 3..... .....nzo co . . 0...: ...... .- . «cu-c... - .Cuoc..q .3...... .8 25.382.83.33... 3 .8 .58.... . ...... .8 ...... 3... . 25.382838. 3 ...... 3 . 23.3....83omuw 3 .. . .88... .. .88... ......5. .8 25.282.83.33... 3 .8 23.38.83.333 .... .8 23.35.83.333 3 .8 .58.... . ...... .8 .. . o . 23.35.8328. 3 P ..8. 8. 5.8... . .5332}... .3... ....5 2335...... 8.8 3 n50 .00-8 .. owns. ooo—\o'\~o lus.uta_¢x’4.—:zuu "...; 274 as: ...-s .. cans. coo—\o—xuc no .- I..-«33‘ no a ...-.3... 3 I ...-33 88.8. .. 8.38... 35...... £8.88 3 ...-.8... 3...... .88 8 ...-8.2.8.3.“...88 2 o 3.8:... u .505: .3...... .... 25.33.133.83 ... .8 .58.... . ...... .8 3... .8... . 2.5.32.8...8. 3 3.: a .. 23.35.3309.“ 2 o 3.3:... u 3.92... .3...... .... 25.33.833.83 .8 2....3....&33.83 8 .8 2.33.... 33.83 3 .8 .58.... . 8.... .8 3.: a . 23.3.3.8...8. 3 .....u 2 o 332.: ... 3.93.: .....5. .... 25.38.833.83 8 .8 13.3 . 3..... .8 3.: 3... . 25.38.3532. 3 3.: .. . 2.338838% 3 .. 2 . .82.... .. .82.... .3...... .... 25.38.832.83 .... .8 23.38.833.83 8 .8 23.38.133.83 8 .8 ......o . ...... .8 3.: a . 23.38.8532. 3 33 2 . .52...- u .52..- ......I.. .... 25.381338... ... .8 .8323... . 3.... .8 3.: o . 25.3883..5 3 3.: o . 2.338353%“ 3 2 o «Si...- - .52..- ..:.I.. .... 25.38.13.3fi3 3 .8 23.385.3383 8 .8 2.3.3 «:53... 5n .3 Eat-33m . 3..... .8 3.: a . 2.338.532. 3 33 2 . .52....- n 3.03.... .3...... .5 25833.35... 3 .8 .....3 8...... . 8.... .8 3.: 3... A 25.3 3.5.. 3 3.: o . 2.338830%“ 3 2 o 3.90:...- .. «5!...- .....I.. .... 254.883... 3 ... .8 2.338.558... 8 .8 ...33 .3383 3 .8 .....3 888 . 8.... ..8 3.: o . 2.3351533. 3 ...... 2 o .52....- o 330...... .3...... .... 25.38.133.83 3 .8 ......3.E..... . 3.... .8 3.: 3... . 25.3 .838... 3 3.: o . 2....3583...2. 3 .83 2 o .50....- n 332.... .3...... .... 25.38832. 3 3 .8 2.3338338... 8 .8 2.33.: 33.83 3 .8 ......3.s.8 . 8.... .8 3.: .. . 23358.38. 3 33 2 o .52.: n 330...... .3...... .... 25.38.5338... ... .8 .88 .8. 38.5 . ...... .8 3... 8... . 25.38835. 3 3.: o . 2.335330%“ 3 2 o .59....- n 3.9!...- .....I.. .... 25.38.5358... 8 .8 23.38.833.38 8 .8 53.23-34.51.“- «0.: & 23.35.135.82. 3 .8 2.8.. ..8. 9.536 . 8..... .8 3.: .. . 2.335.532. 33 2 o 3.0-...: I 3.0!...- .0.2.5. ..o ..cu.m.amzaa.»<.3.o 3 .3 .3322 3..... .8 3.: 3... . 25.3.. ...-... 3 3.: a . 2.333830%“ 3 . .52... . .82... ......5. .... 25.388338... 3 .8 23.38.133.83 8 .8 23.383338... 3 .8 .88.... . 3.... .0.. an... o n 2339395335 33 2 o 3.8... .— o 3.0!...- 8...§. .5 25.38.833.83 3 .8 ...... c . 3.... .8 3.: 3... . 25.3 85.3... 3 3.: .. . 2.335.53fl“ 2 o «50...: a «5!...- .......5. .... 25.3.1558... .... .8 23.38.833.83 8 .8 2.335.558... 3 .8 .3...... . ...... .8 3.: ... . 2....3583»!. ...... 2 o «co-...: - .52....- .....aa. so ..c....anaa...<....a .. ... ....cauau . ....a .8 3.: S... . 25.3 3.5.5.. 3 3.: o .. 2.335330%“ 2 o .53.... .. 23...: .3...... .3 25.38.8338. 3 .8 23.388358... 8 .8 2.335 33...... 3 .8 ...-8..... . 3.... .8 3.: .. . 2.335835. 33 2 o 3.33.3 a .52....- .....5. ..o 25.38.133.83 3 .8 ....8 . 3.... .8 3.: 3... . 25.3 .35 3 3.: o . 2.335830%.“ 2 o 3.35: n “cone—5 ......5. .... 2.58.85.383 3 .8 2....3..~....33..83 8 .8 23.35.53.383 3 .8 .838 . 8.... .8 .5... o . 2.3.3..~83>2~ .....u 2 o 232...: o .32...- .o...§. ..o 2....3...z.3:..83 3 .8 8.5-8“ . 8.... .8 3.: 3... . 25.3 .138... 3 3.: a . 2.33.3830...“ .8.. 2 o 3.3:... a 330...: .3...... 3 25.33.333.83 3 .8 2.33.33.33.83 8 .8 2.33.31.33.33 3 .8 3.858.. . 3..... .8 3.: a . 2.33.38.32. & .~ o 3.8:... n 3.3....- uo...l.o .8 .3: .3...—c... 3..... .....Ii .3 . . 3..... 33 2 o «.3......- n .52.: .3...... .... 25............33.83 3 .8 .581... . ...... .8 3.: 3... . 25.3....532. 3 no so: .. on“: Ono—3:3 83.38—34.53 3. I 275 . ...... 3 o .3...... n .3......- .....l.. .... 25.88.2583 3 .8 £288.28... 8 .8 2232.32.83 3 .8 .58. 332.888. . 8.... .3 ...... o . 22332.8... 2 3... 2 o .32.. a .. .3...... 8.2!. .... 25.8.8283 3 .8 ..8... 3.83.. n . 3.... .8 ...... 3... . 2.... 38.88 2 .....— o - 2.3.533...ch .. .9. 2 o .32... .. .32... ..:.-5. 8 25.28.28... 3 .8 22.283583 8 .8 22.58.3583 8 .8 ..8... 2.8.8 a. . 8..... .3 ...... a A 2......3..>.¢. 2 . .o o .32...- . .32.... ..:...-8 .8 ...-....-......-.. . .3 ...... ..:...... .3 .2.33. ..3 3.. . .8 3.... .....3. .8 .:.:..... 3 .3 ..:...... . 3 .3 ..:... . ..n .3 8.... 3.38.. ..8 .8283... 3 .3 .8283... . a. .3 2.2.5.. . 4n .3 8..... ...:.... co .9.—.3 . we .09 .30.. . o. .00 0...... «..:... co . . 0:... 8.2!. S 23.888222... 3 .8 2883.32.83 ... .8 2.888322... 3. .8 ..:.... o. .8 8.... ...:.-a S ...:.... 3 .8..-..u.... a .8 35.“. 8 .8 ...... ..:.....8I33Iau o 33.39.39... . 313.3. 22.8w. 88 8 . 22...... .8. 8 . 33.8.. 22.58.. 88.8. . .2.38 38 Se. . 88.8. no I 333. .3068 .0 99.3.3. unique ... .. .333. 390.000 .- 3332... 2.393 m o n .3...... .0000 .n .3...... 5.4.3.. . o .39.”: n .32.... I I I I ..:.-5. 3 25.8... 88 8.2.83 3 .8 22.88 ..8 8.2.83 3 .8 3 8 22.28.. 88.8.3383 3 .8 .....8. u 8.... I I I I ..:.... co 2......8 83 8.2.83 3 .8.22...8 88 8.2.83 8 .8 3 8 22.38 38 84.2.83 8 .8 .....8 . 8.... ...:.—3. :0 .:.:..-..II. 0...... 8.2!. S .339... .2... 8..... ..:.-3. co . . 3...... 8.2.3 8 238882283 3 .8 2.8882283 ... .8 2.8.33.2.83 3. .8 ...:.... ... .8 8.... ...:..- co ...:.... «o .3 5-..“... ca .3 ..n...n. ..n .3 3..... 283.83.... . 23:38.-.. _. 3.88.. 22.38 ..8 8 . ...:....u 3m 8 . 33:3. 2.3.5.8. 3033. o «.30.... so... Sc. - .332... ... n 3...... .363 3 3:33.- 22.30 ... .. .9683. 38.3 8 338.2. .2.33 a o a .:o..o.. .0000 .0 .:o..o.a u...u.° .m o .39: .a u .39.... I I I I ...:.... 3 25.88 ..8 8.2.83 3 .8 22.83 ..8 8.2.83 8 .8 3 8 22.58.. 8:8..283 2 .8 .:.:... . 8..... I I I I .....l3 3 2......8 ..8 8.2.83 3 .8 22...... ..8 8.2.83 8 .8 3 8 22.38 .88 8.12.83 s. .8 ....8 . 3.... ...—.5. co .............. 8..... ...:.... S 82.6.... 3.. 8.... ..:.-s. .8 . . 3..... as n .32...- «ozu‘z co . . Cu...) 8.25. 8 ..:...... 3 .8 .:.:.....-... .. .8 ............ ... .8 .. . .8 8..... .388. .. 8.2 $23.)... 5.3225251u8... .....3 3 8.233... 3 .3 ...888 . 3 .8 .83... . 3 .8 .88.. . .8 8.... “..:... ..8 38.30 . 3 .00 .9.... . 3 .00 an...) ..:.-s. S 2.38.2.8: . . . 238822.83 o... .8 .88... 2.2.83 2 .8 . 289...... . .8 8..... ..:...... S ..:.... E .8 .. ... .8 ........................ p .3 8..... ..:...... S .2 .8... 2 .8 8.2.5.... 32.. 83...... .~ .8 :88. 2223.. ...:.... . .8 8.... .o I uCUOC..d 3... 3..—..:... 3 3.332.. 03.3: .0 n no .:.:.. 353.. o. N .mu 3...: .....u .....3 3 . . 3.... .0.. 733. o. . n .a .3. 3... .. a 33.5 .— 331...- . no _- 33. .8 am... an v .cuocuflu: .a o .3...... a .32.: ...:.... S 28.83222... 3 .8 3.888.283 on .3 2.33..-:33; on .3 ..:.... 3 .3 ...... “0.28.. 3 ..-:.... 3 .3 ...:.... on .8 ..:.-.. «n .3 3..... .8 ...... o . 5:8... 2 2.: o . 8.88... ......m2 “m o .32... u .32... ...:.... S 3.382.283 3 .8 3.382.283 ... .8 2.883.222. on .8 ..:.... ... .8 8.... 3.25. S ...:..-. 3 .8 ...:-.-. a. .8 ..:.-.. 3 .8 8..... . . a .... .6... gun—am . ...-.3... 2 . c. 2 .. .. c. 2 . 2..a328:...8.88.25.28b23...88.2833L2 ......318.28.288.28.«$8.28. ...—..8. 8...... ...:.... a... 8.238....328825.2... ...:....88..2..:....L.. .......2.182....28.82......mma...c..m...2i.. L533: ...:.... ”Hm. 3.28.8.3...88.3.28 25.28822...— .. ...........au..mm..882c..2am& ..:...”...8. 2 8.3.8.2.. ..:.... ..:.. ...... .... . 233%...5. ~ ...:..»th. 0 3.83.... 2... «..:..szauicui . ......a. a. ....... .1... ......aoxn........aoaa........a.xa........vozu......mmm833.. ....m...)¢........anza...... .a~:a.......va.aa.. ...... ..mmu....... “.3... ......aoxa........amsa........a.za......... ........ 8:... 2.....a.:a........a.za........a~;a........a.sa.. ......a..mmu....... .3... ......aoxa......n...:.......”...xa........a ......n. a)... 2..nm.a.:a......n.q.aa... ....a~sa......n.a.aa.. 2...... ......ao.“mu.....a :u.....a.amu.....a»xmu. ....aoza......amza......a ...... . ......a~ ...... . . .......c..:. .......c¢.a... ......co:¢........=.xa........c. ......n.coaa.. ......cmxa........c.xq........cnxa........c~aa......n.c.aa.. ......c..aa .......¢a. . ......:oza........c.:.........c. ........coa... ......cmxa........c.:a........c.xa........c~:a........c.aa.. ......c..aa .......co.:... 2225882228822225 2232812 2.....c.aa.....nm.c.aa..2...n.cnaa... ....c~aa......n.c.xa.. 2...: ..:...... 22. 22.8.... 22.5.... . 331.23.51.32. .....cnau..2..c ..:...:— n ...-.3... to "Co... .. anus, coo—\o—xg gins—53:51.03... I ...-.33 276 g .00.; I. an: 08:35 .9.. a. I 3.9!... a I ace-c..- .I...!I .8 2538.38: 3 .8 ..8: 2:88 I . I..... .8 3.: 3... I 253.833... .. ...... a I 2.358....flm 3 . 2 I .505: I «.33.; ...:.... .8 25333.38: 3 .8 2.33.93... 8: on .8 2.358.238: R .8 ..8... .238. I.. . 8..... .8 ...... o I 2.35.33... 3 .n I .50....- I .59.... 8.2!. 8 .:....:.......... I..... .3...... 8 .351... ..:.... ..8. I..... 3:338 co « . 3...... .3. 2 I .51....- I .52.: ...:.... .... 253283.38: 3 .8 .8.... . I..... .oo 3.:. no.9 I ..:..3~.noa.>IcI .. ...... o I 2.38.8309.“ 3 up I 2323‘ I ace-..:. ...:.... .... 253333.38: 3 .8 2.38.83.38: a .8 2.35.8338: R .8 88.5 . 8..... .8 ...... a I 2.35.833... 3 .9.. 2 I .52.: I 380...: ..:.!I .3 25323.38: 3 .8 .35.. . I..... .8 ...... 3... I 2:32.833... 3 ...... o I 2.35.8309.“ 3 up I .52.; I «50...:- .I.:.!I 8 253283.383 3 .8 233283.38: 8 .8 2335.83.38: .n .8 ...:.3 . 8.... .8 ...... o I 2.35.8335 3 Q .2 3 I «58.3 I .82... ...:.!I 8 25.8.8338: 3 .8 ..:....Ic: . I..... .8 ...... S... I 25.8.8338 3 ...... o I 2.38.830“ 3 3 I .58..- I «co-...; ...:.... 8 23873.38: 3 .8 ...-3:38:38: 3 .8 ...-38.83.38... 3 .8 3:82.. . I..... . .8 ...... a I . 8 I 2 3 .83 IS 3 ...... «a I “co-...: I .52.: ...:.... 8 25383.38: 3 .8 .2.33... ... . I..... .3:... co .3:-Eat... ..oo . 3..... .3 ..w... 3... I 2:: 3.3... a. ...... a I 2.388309.“ 3 an I .51....- I .52.: ...:.... .... 25333.38: 3 .8 2%.»883383 3 .8 23.83.38: 3 .8 . . .3 .o . I..... .3:...- co .3:-9.3.0.. .80.. . 3..... .8 ...... o I 2.388338 .. ...... .. I ..8... I I .82..- .I.:I.I 8 25383.38: 3 .8 .:I. . I..... .8 ...... 8.: I 25 .38 I. nu..— a I 283.8503)...» .- gig-(54:33 3.: .9.. .. I «88... I .82.: .3...... 3 22388338: 3 .8 2.3883 I 8: on .8 23.-3:33:33; sn .3 .8.. a . 3...: .8 ...... o I 2.35833... .9.. up I ugzd I 9.0!:- .I...!I ..o 253383.583 3 .8 ....II: can... . 8..... .8 ...... 3... I 2533828. 3 ...... o I 2.3...83oflw 3 I .51.... I .3.....- .I...I.I .8 25333.38: 3 .8 2333338: on .8 2.3..2833._8: .3 .8 .58.. 883 . I..... .8 ...... o I 2.35833... 3... u. I .58...- I .58.: ..:...... .3 25333.38: 3 .8 .882... . I..... ..8 ...... 3... I 2533335.. 3 ...... o I 2.38830“ 3 I .52.: I .58.: ..:...... .... 28383.38: 3 .8 23333.38: 8 .8 2.3883338: R .8 .882... . I..... .8 ...... o I 2.3.83.3... .9.. up I “600::- I 3.005: ..:.-I.. 8 2533.338: 3 .8 ..:...... 88...... . I..... .8 ...... S... I 2.3 .833... 3 ...... o I 2.38830...“ .9. 2. I ago-...; I .51....- .I...I.I 3 25383.38: 3 .8 23333.38: 8 .8 2.388338... 3 .8 ..8... 8.33:3. . I..... .8 ...... o I 2.3.83.3... Q .9.. 2 I .51.... I .58.: ...:.... .... 25383.38: 3 .8 1.3.852... . 8..... .8 ...... S... I 2... 338. 3 ...... o I 2.3.3830“ 3 I ..8-...: I «5!...- .I...!I .8 25333.38: 3 .8 2 333.38: a .8 23:83.38: 3 _8 . 5.552.... . I..... .8 ...... .. I 2.3.38.3... .9. 3 I .58.... I .58..- .I...!I ..o 253283.38: 3 .8 .8.. 3o . 3...... ..8 ...... 3... I 25323.3... 3 ...... o I 23:38.59.“ 3 I .52.: I .58.: ...:.... .6 25333.38: 3 .8 23323.38: 2.. .8 23583.33: 3 .8 .II. ...o . 8..... .8 ...... o I 23:33.3... 2.... 3 I .58.: I 3.8....- .I...!I .... 25323238... 3 .8 .8\>B.I=III¢III. . 8..... .8 ..I... S... I 25323.8 3 ...... a I 2.432832. ’3 .00.; I. 3.: 0033—39 it u— .8....:s.$.§6 3.: :r77' .00.; . an . .:uoc..a n “cu-c..- .6556 .8 253.8333... 3 .8 233.33... 33 8 .8 225-.3—nna.»<.3u_a sn .0» 2.30.3 o>..u:oo on . o.-.: .oo gnaw a A ...:.c—anuu>oc~ 3. .~ o ozone... u «cu-c..a .o: [3 ca .23.. 3..... .03.... co . . 0:53 ....... . .............. .. .................... . 3 .aau .— o .:uoc_.u - acuoc_.u ...:.... ao 2533.35.33 3 .8 .58.... . 3.... .8 3.: 8... . 25. 35:6... 3 am... .. . 23.5.5.3.um 3 up . «coon..- - usage... ...:.... aa 253353.33... 3 .8 23335.33... 3 .8 2333332333 3 .8 .583. . 3.... .88 aua. o . .....c~.a..>6c~ ._ ...... 3 ¢ .52.:. a 3.3.3: ...:.... ao 253.3333... 3 .8 .8...“ . 3.... .8 aw... 3... . 25. 33.65 3 ...... e . 233253...“ .8.. .— . .:uuc... - .cuec..u ...:.... .8 253.3333... 3 .8 233.3333... 8 .8 23323.33... 3 .8 ..:.... . 3.... .8 3... o . 233—33.65 3 ...a. 3 . 38...: . 38...: ...:.... ao 253233533 3 .8 ..:...-...“ . 3.... .8 3.: 3... . 25. 833.65 3 aw... .. . 23.8330muw 3 3 o «30...... a “cone:- .....!. ac 25.853.33.93 .8 2338:3533... 3 .8 23.833535 3 .8 ..:...-.... . 8.... .03 aw:— o A 22.-3co—na.>ocu 3. ...... .u o 330...: n .52.:: ...:.-a 8 25355.33 3 .8 .2.33... ... . 8..... ...:..- co .35....333 .30.. . 3...... .8 3... 3... . 25333:... 3 aw... .. . 23333...muw 3 3 o .52.:: a 3.3.3: ...:.... .8 253333.33 3 .8 233333.33 3 .8 233332.33 3 .8 .2233... 3 . 8.... ..:.l.- 8 32.8.32: .33 . 3..... .oo awa. a . .....cona.>oc~ .. ...au 2 o 3.30... a n .52.: ..:...... ao 25333.53... 3 .8 .36. ... . 8.... .8 a»... 3... . 25383.5 3 am... .. . ...-335.0%“ 3 2 o 330...: n .53.: ...:.... .8 25353533 3 .8 23323 .. 33 5 .8 233333.33 2n .8 ...... 8 . 3..... .8 3a. a . 23333.65 3 55.22.53.333 .3: fl — . on“: 00¢—3:3 ...... 2 o 382:. a .52.: ..:...-6 ac 253.3333... 3 .8 .58.. 5.83 . 3..... .8 ...... 3... . 253.5365 3 aug— o - ...-.csn-30uum 2 o .52.:: a «cone:- ......3. ac 253.5333... 3 .8 233.3533 8 .8 2.3333533:— sn .3 .53.. in . 3..... .oo aua. o 3 ...uvcsnu.>oca ...... .. . 38....- . 38...: ..:...-... ao 253333.33 3 .8 .882: . 3..... .8 am... 3... . 25383.5 3 a...» .. n 2333309.“ 3 o 3.8::- o 33...:- ...:§. .8 253333.33 3 .8 233333.33 o... .8 2333333... 3 .8 .6889. . 3.... .8 ...... a . 2333.35 .9. .— o «coca... - u¢9¢¢..¢ ..:...... ao 2:38.333... 3 .8 .88.. .83....33. . 3.... .8 ...... 3... . 25333.6... 3 am... .. . 2.333330%“ 2 o ecu-...: n 3.3....- ..:.S. .8 253333.33 3 .8 233333.33 5 .8 233333.33 3 .8 7.83 .83......-.. . 3.... .8 a3. a . 23333.65 .93 3 8 3.3.33 - «cone... ..:...-... .... 253333.33 3 .8 13.5533. . ...... .8 a... S... . 25333.5: 3 am... a .. 2333309.“ 3 c «co-...:. 6 3.80::- ...:.3. ac 253333.33 3 .8 $3333.53... 3 .8 2.3833233:— sn .3 . .:.:...chzp . 8..... .8 am... e A 23333.65 .8. 3 o 3.923: a 382:— ...:§. .8 253333.33 3 .8 .65 ..3 . 3..... .8 an... 3... . 25323.65 3 am..— 0 .. 2.33333.“ 2 o 3.02.:- n 3.92:.- ..2333 .5 2:333:53:— «o .3 2333335339 on .3 23333333... 3 .8 .8. ...o . 3..... .3 am... a . 2:33:35 .8. 2 o 3.08:. o 230...: .::..-3 ..o 253.34.33.33 3 .3 «Soo§3.¢==oc¢la. . 3..... .8 am... 3... . 25323.6... 3 .3..— o - 2.33.33...“un 3 o .53.: n .52.: ...:.... ao 253233.33 3 .8 .. 3233.33 3 .8 2.33.3353“... um .3 .682 .... c.3355. . 8t... .8 am... a a 253.2355 .3 "on... .. on": coo—3:3 53.32.592.545 "0.: 278 .8 .33 ... u 3.33.: «a u ...-.3 . .o u 533.... ..oo.ooo a. ...o.n. us<.9ua .3..:8 .- 8.885 ..:...... .88 .. c...o3.eu.u...u8 2 o .53.... .. .53.... ...:.!- 3 25833325... 3 .8 .52.... . ...... .8 ...... 3... A 25828.82. 2 aux. o - .A...c~.an..oc¢m .— .9. 2 o .53... n .53... ......l: .8 253233-58... .3 .3 2......~.fl..»<.3.o o. .3 .....c~—nn..»<.4..o sn .3 .cxocxc: . c...: .o. 2.2— a A .....c~.an..>oc~ .. ...... 2 o .52.... a .52.... .....5. .3 25828325... 3 .8 .85.. . ...... .8 ...... 3... A 25828.82. 2 ...... e . 2.35.3.3!“ 2 .9. 2 o .53... a .53... ...:.... .8 25828325... 3 .8 22823328... 3 .8 2.35.3328... .n .8 12.3 . ...... .8 ...... o A 2.35.3325 2 ...... 2 o .52.... u .33... .....3 3 258.23.21.33 3 .3 ....ucoc . . 3...... .8 ...... 3... A 25 .8335 .. ...... a A 2.38.23oflm .. 2 o .53... u .51.... ...:.... .... 258.8325... 3 .8 2.38.3328... ... .8 22.38.85.338... 3 .3 ....55. . 3...... .9. am... a A 2....8.£..>2~ .. ...... 2 o .53... u .58.... ...:.... .... 2588.32.33 3 .8 8.8:... 8 . ...... ...:.... 5 .3:-30.3.. ..ooc . 3...: .8 ...... 8... A 25.83.82. .. ...... o A 22.53.32“ 2 .9. .. . .52... . .82... ...:.... .... 2583.32.33 3 .8 228 32.8... a .8 2.35.2.8... ... .8 .8. .:o .o . 8.... .3...-:- 5 235523. 30.. . 3...... .8 ...... o A 2288.82. 3 ...... 2 o .50... . u .53... ...:.... ..o 2588.32.83 3 .8 .3..:8 8 . 8.... .8 ...... 3... A 258......3»3. : ...... o A 2.3583,...“ 2 .9. 2 . .53... u .59.... ...:.... .3 2583.32.38 3 .8 2:83.... .. ...... 2 .8 2.353328... 2 .8 .2: a. . ...... .8 ...... a A 22.58.82. 2 ...... 2 o .53... a .53... ...:.... .... 258.3328... 3 .8 .58.. . 3.... .8 a!» S... A ..:.. .3335 .— .....— o A 2.358.825 3 ...... 5.....225..=./.u ...: 3 an”: ostctfi. 2 o .53... a .53... ..:...... .6 258.3325... 3 .8 2.358325... 3 .8 2.353325... 3 .8 ...-8 $83 . 3..... .8 ...... a A 2.35.585 ...... 2 o .58.... a .53... ..:...... .... 258.8325... 3 .8 .8822 . ...... .8 ...... S... A 25853.8. .. ...... a . 2....Sfl3ofiw 2 o .53... a .53... ..:...... .a 2583.32.53 3 .8 2283.32.52. 8 .8 2.3:35.2.83 .n .8 .883... . 8..... .8 ...... o A 23533.5. ...... 2 8 .53.... n .59.... .3...-8 ..o 25.83.33.829 .3 .3 ...-Ex. soot-...... . 3.... .8 ...... 3.... A 25. .3335 2 ...... e - .....cmflusflw 2 o .53... a .53... ...:.... 3 258.3325... 3 .8 258.3328... 2.. .8 2......3935830 s... .3 2.33 .3...}...33. . 3..... .3 an... a A 2.3.5333... ...... o .59.... a .53... .2252; . 3..... ... A 2.... .3335 u. w... o - 2....53..on 2 o .53... .. .53... ...:.... ..o 258832.83 3 .8 2 58332.3... 3 .8 2....53..=.8.3 sn .3 . 2.2.5.2...- . 3..... .8 aw..— . A 25.53.33. ...... 2 o .53... a .58... ...:.... ..o 2583.32.33 3 .8 .8. 3m . ...... o .2 ...:.-z ..o 258332.33 ......nwzmw... a .0.. am..— 36 A 22.. 3335 u. an... o I 2......nfl..nflw 2 o .53... a .53... ...:.-3o .5 ..:....nna..=.8m_a 3 .3 2.333.333... on .3 2.353.238... s. .3 .3. 3° . 3..... .8 ...... o A 2....53..>2~ ...... 2 o .59.... A .53... 3.1.5. .8 2:333:35... we .3 .Soaxxsqczuaocuec. . 3..: .8 ...... S... A 2582582. 2 .3..— o - 2.353..»ch . a 2 o .53... n .53... ...:.... ..o 2583.32.33 3 .8 ...-83.32.83 9.. .8 2283.328... 2 .8 .58. 3.52888. . 8.... .8 ...... a A 2.35332... ...... 2 ¢ .58. . o .53... ...:.... .... 258.8325... 3 .8 ..8... 2.288 . 8..... .8 ...... S... A 25 .3325 2 ...... o .. ...-8.53”.“ mg .00... .. Gnu: 0033—30 5. gig—39:52.9 ...: 2E7!) 93 «on: . al.—u. 3:25. .8 . . 3..... u . ...:.... S . ...:. ...... .35....83 o. .8 2.3:. I..: 0.2.35.3... on .3 . 3.2.3 v3. 2.. £25. 3.... “3:5. .8 . . 3..... I I \o ...:.... 8 ..:...... 1.2.8.55... 3 .8 .:.:..... 1.28.38... nq .00 .A0.... .300... .3. Dou905500~ . 0:58 «fits. ..8 .3...-1.33»... s... 2.52.32. 3.35. 3...... "...:.-.- co . . 3..... ...:.... S 3..-6.1.9.3233... mm .8 .......uu1...8......3... ne .3 ..:...-.3. cu 9.7.33.2... 033:. 3..... “..:.... s . . 0:53 .. I «..:.!- 5 2.35.... .3 9332.33 3 .3 2x35... 3 953:3“; me .3 .3...-2. 3 .3 03.2.5 8...: 3.:... co . . 3..... 0\ ...:.... 8 2.3.3.3.3253 3 .8 2.3....3:33=.33 3 .3 2.26:3. ..:......83 .353. 3..... .3:-5. co . . 3..... no...l.o Co 2.42.33.33.53; no .3 23.33.333.33 3 .3 2.265... >333: .352. 3..... ...:..- co . . 3..... u ...:.... S ...-.:-:..»...Ea... 8 .8 2.3.; 3333:...«3 3 .3 ...: 333303.... 3.... 3..... 3...!. co . . 3..... . ...:.... S 2.3.-1.6.3.2....33 8 .3 2.3.3». cool-.2.33 3 .3 13:33:33»: 33.... 3.... 3.:-3. co . . 3..... «0...... co 2...“:39.I3u:<.33 on .3 2.333.133533a on .3 2.3333}. 0330......- quns... 8.... “3:5. .8 . . 3..... ...:.... S 2.3.3.8355; 3 .8 25.88.83.333 3 .8 18:18:89.8... ...... ...... 3.:!- co . . 3..... . ...:.... 8 ...:.-.:.:..1325... 3 .8 2.3.8... 52.3.3.8... 3 .8 18:8..383 ...... ...... "...:.-3 ca . . 3..... .. a": 823:3 5....a.s>..=.:u 8.: . ...:.... S 2.383.583... .. 3 .8 2.....88 58.8.3.8... .... .8 18:38.2... E8 . .... ...... no...l.. co . . 8..... . ..:...... 8 2.3.8». .5355... 8 .8 ...-:8... c3832....o 3 .8 ...-{$182.3 a... 8.... . ...—.5. co . . 8..... ...:.... 8 .:.:... 3...... ...:.... 8 .8 3.... ...:I: S .:.:.. . 3 .8 ...... . ~.. .8 3.35.2.3... . ...:.-8 5.33....” .8 ”a“ o 2... u . ..:.-3. co ... ....... ....................... 3 .3 8..... ..:...-3 co ..:...-2. .352 . .....— Cgu. o... .3 3..... . ...:.... 8 2:88:38... .... .8 38...: .:.:..... 2 .8 . .3.... . .8 8..... .....IS .8 .............-....-............. . ....a ...:.... S . 0 Guam . 2.93- 3833 2:: .N a ”war.“ 0 C o I \c .... gonna-ox .g . h 3:» . Nut-an 2:... ...: ax oooooooooooooooooooo....¢oo¢o¢¢ococo.oooooooo+¢ooooooooooooooooooooooooooo 3333!“ 32.2358“ 69009009900090.00000990600909000000.0000000000QOOOOOOOOO¢O¢O0909000O¢O¢OOO u 23;. 3...: .8 ...->3- ...3 ... .33.... 8. p .3 8..... ...:.... S . . 8.... “3..-5. Co 3.3:»)... 39.22 .9 u 3 326.- 5..qu o. N _3 8...... .96 «3:3 .8 . . 3..... 3.. 3.33.: 3 .. .. .a .3. :0... n A 39.-5c.- .— x 33...... - on u 9.3. c ......................... ................................. ...... 3. o .3......- n .32.; ...:.... S .&.-.o.£.=.&_a 3 .8 ......8..32.83 an .3 2.33..-:53... on .3 ...:.... 3 .3 3.... ...:.... S . ....... . 3 .8 ..-:.... a... .8 ..:.-.. 3 .8 3.... 8.. ...... a . 538.... .. ..u... a . 8.38.... ...:.“: ..~ o .31...- . .32.; ......s. cc ......o.n...<..u.o «o .o. .....o.a........o on .8 2.383.238... on .8 ..:.—2. 2 .8 8.... u..:.... go .. ...... . .o .o8 ....-.... a. .oo.. ...... . .n ... ....a .oa a... o . c...o.a. .. ..:..a~. ...:..a..aa...:..a .nn...c..aonn...c..amna...c..q.nn.. .c..uuau...c..amnu...c.. ..u...c..an ...:..a ...:....na.. .c.. ~.....c.. ......c . o.....¢.. on...c.. ...:..a.... .c..a.n...c..amn...c.. .....c..ann...c..a~n...c..a.n. . .....o... 2.38. .::..—323... .fl.....i§..2.a§..:.§fl.. c... ..:.... ..:.. ..:....Bu..:..a~u£:..:.—8.. 2... $3.23 .3...... 23:3. $223. 32:“...32 .... B..:....£..:...i..:. 3...... 2...... .... . ...-.8... 2:...9541435pan“;.38—flu;auguosngoaa-vcsflao 33.33411:53.233!!!“au.cnflu+=uvc~£uo::cpflao «savage-o. an .5 3233333335343 3233333659.? aua.§u.3u.¢3aonu=c3uoauagqosugdozgcas n c.3023 n3 nouns .- an“: Quota—36 gist—53:27.9 3.: APPENDIX D APPENDIX D W 280 FARM CODE: 5728 10/01/87 - 12/31/87 Page 1 End Rolling Current Current Quarterly SHIMS W ___Period Awe—raga. PLragze. .Gestating Females 422 405 267 Lactating Females 88 88 67 Open Saws 0 0 3 Cull Saws 0 14 8 Open Gilts 7 111 3 Boats 58 62 36 Pre-weaned Pigs 673 734 618 Pre-nursery Pigs 0 0 0 Nursery Pigs 907 763 615 Grower Pigs 795 677 518 Finisher Pigs 2,219 2,336 1,434 REPRODUCTIVE EFFICIENCY MEASURES 1. Pounds Marketable Pork Produced a. Per Quarter 598,780.00 507,733.00 339,983.33 b. Per Female 1,018.33 821.95 868.04 c. Per Litter 2,123.33 1,767.56 1,749.49 2. Pigs Produced a. Per Quarter 2,652.00 2,606.25 1,513.00 b. Per Female 4.51 4.22 3.86 c. Per Litter 9.40 9.07 7.79 3. Pigs Born a. Per Quarter 3,042.00 3,194.50 2,081.00 b. Per Female 5.17 5.17 5.31 c. Per Litter 10.79 11.12 10.71 4. Pigs Born Live a. Per Quarter 2,856.00 2,980.75 1,937.67 b. Per Female 4.86 4.83 4.95 c. Per Litter 10.13 10.38 9.97 5. Pigs Weaned a. Per Quarter 2,639.00 2,567.25 1,785.00 b. Per Female 4.49 4.16 4.56 c. Per Litter 9.36 8.94 9.19 6. Litters Weaned a. Per Quarter 245.00 236.00 185.33 b. Per Female 0.42 0.38 0.47 7. Total Female/Boar Ratio 8.98 9.92 10.98 FARM CODE: 5728 EEEICIEEQX LN EEED USAGE 1. Lactation Average lbs/female/day Total lbs fed 2. Gestation Average lbs/female/day Total 1bs fed 3. Starter Average lbs/pig/day Total lbs fed 4. Nursery Average lbs/pig/day Total lbs fed 5. Grower Average lbs/pig/day Total lbs fed 6. Finisher Average lbs/pig/day Total lbs fed EFFICIE CY IN FACILITY USAGE 1. Litters Farrowed/Crate 2. Pigs Weaned / Crate 3. Nursery Turnover 4. Grower to Finisher Turnover 281 EPO Current Period 8.99 72,000 11.18 418,015 0.35 24,012 2.37 173,049 2.23 143,000 7.26 1,542,612 3.20 29.99 2.23 0.65 10/01/87 - 12/31/87 Pa 2 Rolling Quarterly Average 9.49 76,000 10.04 370,012 0.58 38,503 2.44 169,512 1.89 116,250 5.97 1,269,670 3.26 29.17 2.17 0.55 Current SHIMS Axerage 5.53 33,685 6.99 169,825 0.31 17,509 1.45 81,203 2.29 108,050 4.02 524,121 3.04 27.89 1.72 0.65 FARMCODE: U 5728 W 1. Market Hogs Sold . Number Sold . Average Market Weight . Sales Price Per th . Marketing Costs Per th (1.0 do Feeder Pigs Sold Number Sold . Average Market Weight . Sales Price Per th . Marketing Costs Per th CLOO‘W Underweight Market Hogs Sold Number Sold . Average Market Weight . Sales Price Per th . Marketing Costs Per th QOO‘W Cull Sows Sold Number Sold . Average Market Weight . Sales Price Per th . Marketing Costs Per th 0.06m Cull Boars Sold Number Sold . Average Market Weight . Sales Price Per th . Marketing Costs Per th Q-OO‘ID Boars Sold for Replacement . Number Sold . Average market Weight . Sales Price Per th . Marketing Costs Per th 000‘” 282 10/01/87 - 12/31/8 Current Egrigd 2,457 239 43 0 100. 29. 156. 150. 49. 72. 486. 40. 557. 27. CDCDCDO Open Gilts Sold for Replacement . Number Sold . Average Market Weight Sales Price Per th . Marketing Costs Per th 900‘” 30. 276. 54. O Bred Gilts Sold for Replacement Number Sold . Average Market Weight Sales Price Per th . Marketing Costs Per th D-OO‘N 0000 .00 .16 .53 .00 00 00 66 .00 .00 00 67 .00 00 6O 87 .00 .00 50 98 .00 .00 .00 .00 .00 00 67 96 .00 .00 .00 .00 .00 Rolling Quarterly Average 2,050. 232. 50 0 214. 43. 120. .00 76. 465. 41. .00 0000 0000 50 30 .41 .00 25 22 59 .50 114. 37. .00 00 63 50 44 03 .50 397. 35. .00 50 72 .00 .00 .00 .00 .00 251. 62. .00 12 24 .00 .00 .00 .00 Current SHIMS ver 1,243. 240. 43. 0. 225. .40 98. .00 48 149 70 42 0000 10. 277. 54. .00 0000 e 33 05 14 00 33 16 .67 .25 49. .00 67 .00 439. 35. .00 74 38 .33 370. 37 .08 .00 .00 .00 .00 .00 33 19 27 .00 .00 .00 .00 283 fifllflfi QUARIEELX REPORT - Page 4 FARMCODE: 5728 10/01/87 - 12/31/87 Rolling Current Current Quarterly SHIMS CA§fl EX£E§§E§ PER CWT 2033 Period _Axgrggg_ Average Purchased feed expense 13.87 11.95 17.41 Non-feed expenses 1. Repairs and Maintenance 1.61 0.77 1.35 2. Veterinary care & drugs 0.30 0.34 0.57 3. Labor 3.42 2.85 2.40 4. Supplies 0.20 0.14 0.12 5. Fuel 0.35 0.32 0.25 6. Electricity 1.21 0.83 0.82 7. Telephone 0.00 0.00 0.02 8. Trucking 0.02 0.02 0.01 9. Marketing 0.42 0.43 0.52 10. Insurance 0.36 0.35 0.31 11. Interest 0.81 1.66 1.06 12. Taxes 0.11 0.29 0.06 13. Replacement females 0.00 0.00 0.00 14. Replacement boars 0.00 0.00 0.00 15. Other Expenses 1.85 3,28 1.13 Total Non-Feed Expense 10.66 11.28 8.62 284 U L PO - Pa e 5 FARMCODE: 5728 10/01/37 - 12/31/87 SLAUGHTER CBECK RESULTS Current Period Rolling Quarter Current SHIMS DATE: 11/03/87 Total Total # PIGS PERCENT # PIGS PERCENT # PIGS PERCENT PN ON none 26 43.33% 109 42.58% 166 25.00% mild 32 53.33% 131 51.17% 340 51.20% moderate 2 3.33% 14 5.47% 90 13.55% severe 0 0.00% 2 0.78% 68 10.24% TOTALS 60 256 664 Pleuritis l 1.67% 2 0.78% 10 1.51% Pericard. 0 0.00% 0 0.00% 2 0.30% LIVER SCARS none 56 93.33% 238 92.97% 596 89.49% mild 4 6.67% 15 5.86% 47 7.06% moderate 0 0.00% 3 1.17% 9 1.35% severe 0 0.00% 0 0.00% 14 2.10% TOTALS 60 256 666 ME none 44 100.00% 394 98.25% 475 95.57% mild 0 0.00% 6 1.50% 15 3.02% moderate 0 0.00% 1 0.25% 3 0.60% severe 0 0.00% 0 0.00% 4 0.80% TOTALS 44 401 497 RH NI S none 36 81. 2% 114 85.71% 180 57.14% mild 7 15.91% 15 11.28% 86 27.30% moderate 0 0.00% 2 1.50% 35 11.11% severe 1 2.27% 2 1.50% 14 4.44% TOTALS 44 133 315 Sep. Dev. 1 2.27% 17 12.78% 51 16.19% TATIOO LEGIBILITY good 44 100.00% 400 99.75% 474 95.37% marginal 0 0.00% 1 0.25% 18 3.62% illegible 0 0.00% 0 0.00% 5 1.01% TOTALS 44 401 497 FARMCODE: 5728 PI MORTA SUMMARY Number STILLBORN 138 MUMMIES 48 PREWEANED PIG DEATHS Crushed 105 Deformed 52 Scours 21 Starved 85 Sudden 64 Weak 63 Other 33 TOTAL 609 WEANED PIG DEATHS Pre-Nursery Weaned Pigs Chronic Poor Doer 1 Other 1 Nursery Pigs Pneumonia 2 Scours 1 Chronic Poor Doer 16 Sudden Death 2 Other 8 Unknown 17 Grower Pigs Injury 1 Abscess 5 Other 5 Unknown 1 Finisher Pigs Pneumonia 1 Lameness 2 Abscess 5 Chronic Poor Doer 1 Sudden Death 2 Other 19 Unknown 4 TOTAL 94 * Monthly Deaths as % of Average Inventory 285 S U T L 0R - 6 10/01/37 - 12/31/87 Herd %* 4.54% 1.58% .63% .29% .93% .75% .82% .78% 1,46% 24.78% NNWONE .00% .00% CO .08% .04% .66% .08% .33% .71% 000000 .05% .24% .24% .05% 0000 .01% .03% .07% .01% .03% .27% 0.06% 2.96% 000000 SHIMS §* .24% .67% HUI .01% .23% .61% .59% .69% 40% uabwblfl~<\o 41.76% .00% .00% OO .05% .00% .27% .05% .16% .14% HOOOOO .00% .13% .13% .77% 0000 .00% .02% .05% .02% .02% .14% 0,65% 3.60% 000000 S U E - a FARMCODE: 5728 10/01/87 - 12/31/87 B EEDING H REMOVALS Herd SHIMS ..LUHI—b§_t__ Peflent" .P_6r.c_ea£.t SOW OV Gulls 72 4.08% 5.96% Deaths 1 _Q..0_6.% __Oiléi TOTAL 73 4.14% 6.22% BOAR REMOVALS Culls 2 1.02% 3.11% Deaths 0 0.00% 0.00% TOTAL 2 1.02% 3.11% Herd SHIMS Number Percent** Pegcent** SOW SUMMARY Reproductive Prob. 12 23.53% 29.75% Lameness/Injury/Down 8 15.69% 6.01% Old Age 20 39.22% 10.44% Thin/Unthrifty 4.43% Mastitis/Poor Udder 3 5.88% 2.22% Abscess 1 1.96% 0.32% Poor Performance of Offspring 4 7.84% 45.89% Other 2 3.92% 0.63% Unknown 1 1.96% 0.32% SOW EA SUMMARY Lameness/Injury/Down 66.67% Unknown 1 100.00% 33.33% * Monthly Removals as % of Average Inventory ** Percent of Total Removals S S U FARMCODE: 5728 Summary Tab1e - Annual Measures Number of Quarters: 4 Pigs born/sow/year Pigs born live/sow/year Pigs weaned/sow/year Pigs produced/sow/year Pounds marketable pork/sow/year Litters weaned/sow/year Pigs produced/crate/year Annual nursery turnover Annual grow-finish turnover Whole farm feed efficiency EPOR - Farm 20. 19. 16. 16. 3,287. 118. 10/01/87 - 12/31/87 68 32 64 88 80 .52 47 .68 .20 .02 SHIMS 21. 19 16. 15 3,247. 97. 26 .60 97 .74 03 .74 27 .49 .46 .11 APPENDIX E APPENDIX E SWINE HEALTH INFORMATION MANAGEMENT SYSTEM VARIABLE COST ESTIMATIONS A. Initial visit—dollars/farm 1. personnel 2.5 hours travel + 25 hours on farm 5.0 hours total 5.0 hours * $20.00/hour = $100.00 2. travel 120 miles * $0.22/mile = $26.40 3. forms 30 pages * $0.05/page = $1.50 4. data entry.t()approximatcly 1081 individual data items per farm per initial v15: labor: 0.25 hours * $6.00/hour = $1.50 computer time: 0.25 hours * $1.30/hour = $_0_3_3 total = $183 5. data storage 12 months * $020/month = $240 B. On-farm data collection—dollars/farm/year 1. producer time 2 hours/month * 12 months * $5.00/hour = $120.00 2. forms 5 pages/month * 12 months * $0.05/page = $3.00 3. data entry (approximately 8640 individual data items per farm per year) labor: 1/6 hour/month * 12 months * $6.00/hour = $1200 computer time: 1/6 hour/month * 12 months * $1.30/hour = $ 2.60 total = $14.60 288 4. data storage $0.05/month * 12 months = $0.60 $0.05/month * 11 months = $0.55 $0.05/month * 10 months = $050 $0.05/month * 9 months = $0.45 $0.05/month * 8 months = $0.40 $0.05/month * 7 months = $0.35 $0.05/month * 6 months = $030 $0.05/month * 5 months = $025 $0.05/month * 4 months = $0.20 $0.05/month * 3 months = $0.15 $0.05/month * 2 months = $0.10 $0.05/month * 1 months = m total = $3.90 Slaughter health checks-estimated average costs for one health check per farm per quarter in dollars/farm/year (one check ranges from 100 to 240 pigs). 1. personnel 4 hours/quarter * 4 quarters * $15.00/h0ur $240.00 2. travel 80 miles/quarter * 4 quarters * $0.22/mile = $70.40 3. forms 5 pages/quarter * 4 quarters * $0.05/page = $1.00 4. administration telephone: 5 calls/quarter * 4 quarters * $2.00/call = $40.00 labon 0.25 hours/quarter * 4 quarters * $15.00/hour = 15.00 total = $55.00 5. report labor and data entry (approximately 26 individual data elements per farm per slaughter health check) 1 hour/quarter * 4 quarters * $15.00/hour = $60.00 computer time: $1.00/quarter * 4 quarters = $ 4.00 postage: $0.22/quarter * 4 quarters = $ 088 forms: 2 pages/quarter * 4 quarters * $0.05/page $ 0.40 total $65.28 290 D. Quarterly report—dollars/farm/year 1. personnel 1/6 hour/quarter * 4 quarters * $6.00/hour = $ 4.00 2. computer connect and computation: $3.02/quarter * 4 quarters = $12.08 printing: $0.19/quarter * 4 quarters = 5 0.76 total = $12.84 3. mailing labor: 1/12 hour/quarter * 4 quarters * $6.00/hour = $2.00 postage: $0.22/quarter * 4 quarters m total $2.88 APPENDIX F APPENDIX F .3... 22.5.... 3.039 2: 333 3... 2333.2. 33.3.. 2: 3.:. 23a!- 2: 3 .II.- 39. c.2332... v3.39. 2: I3. .. 2.33.... 2:. s. an o I 333° an: 5.35.: . 3:30 I 33.39 .3..— aagw— II ampxuo ‘— 3323 o .:.:.. I 3353 I ...—053.. .23 2:5... .83. 3.3.9... 2: 333 33 8.338 .33. 3.3.9:. 2: .2: 2.38... 2: e. .23 3... 5.3.2... .3.... 330.93 2: I.I. .. 3.32.... 2:. u— 93 a I 33.30 may 3353 . 3:5... I 3:53 am..— .n—zg IA 33a: 3- .33 22.3.... venue—9..... 2: I33... I»... 429.313 33» e... 2: 3.:. use. 2: 3 .3 3... 2:332! :83 9x. 2: 33 .. .33.... 2:. Ids-flu. 8:1!!3 an 3 =35 u: 5:: 33:22 I 3.: I0— ».933 3 3:32“ 0.: .pS—agf 9:9; .43» w..— 3 an I 5:; Imouuuxu so...) Ems—«.80.: «=0 u—fi:§_ al.—=53: "us—:5... p3: aw..— . I up; 3 am..— 30533 I :2: s— 3. I 953. I 32:23 $32.02.. v3.34. 9......- 2: ... an 393 u. :21 a... .:.:..... I 3 . 03: In . ... ....- Iuov 2: ... 9.9.... .e .835... ..I I. u. .33». . 2: c. In. vow-.3.... use 3.39. 33.3 33.3.... 2: I. 32: . 85 . 9..: . 2.3 I .:.:..... . «.::..—a . as! I 32: a... a. none. ....I II.I.:u.Iu. .II; . a... . use. - ....a . ....a I coax 33:26. 3: .33 ... 3.33 2: 33.3.3. 9......» . ..:...... I .03 : puma a“. .... .n~_ ....uI. . ..~. .....I. I .n~_ ... ... a o. . . n~_ Io. 339:9. I .:.:..—2 I 530:. 2:3: 3.. .33 333.3. A .32.. I 353— .. 3..—..:. 3.32.. 3.. .....o .33 333.3. 3.3.. I ..:.....— I 0.5.3— »..oucgc. ...: 3.5.03 .33 333.3. ........a. I ....un.“uw ...—.....au ....amao. ....a. ...uam.o...._o_.. ..I. u. u ...a. I ....... I a... o I as. . .. ...Nm . ..... . .. mag. .. m I man. I ...au.o. ....a. ..I "......“ ..I3 .....2. . ...:a. I I o I u. 3.3....) 30:932.. 2: 32.-...... 8.33 3.3:... 2:. ~ee “III. .- no... ooop.e..~e x...u:_s.’...:/.u .I... no—pI28n - plus a \ ma.ea I .~_.>—a 33.33 5.3 .22.... 3236.3... 3..-...:.. u on p I N- 8. 2.005 . a «was I H.309 25:3:— \ a \ 338...: I 2.335 I 23.8... x a: can. I 3.09; an. .xua gazeuea I ....~..oo... . .. . mm... I .~.>Iea.o¢e. Income: e. . I mm. no. .383: x 3:... I ‘33. «3.003. x can. I mucosa ~n. .... :32.32. I ....n..oo... I .. . «n... I .n.>Ieu.I:aI .3...-... e. . I ~n. 8. and; \ “cm... I iii: 3:83 \ ..:.. I 2.3:: :- puma a I 32.03. 03. o— p I 3. 8; on. :0: c I 32.3..- ..3. o: p I on. 8. :22. 323-... 3..-.::... 9: I «33:56 I 0359.50 9: I “mama—$50 I (88256 3 ~53 a I 3.358... a I 3..—3. a... \ up... \ 332:9... I 33.3.23 .3:-..:. .. 2:3“. .o .03.: 333.3. 22532.3 I 3.3:- e I an_v8_u..—a~o— n o. p I n. so. 3.:... 35.2... e. I..... .23 33.3.3. .~ \ 2:8... I 5.... I 5.3.: .....ooa. . ..m...a. I ...:...a. I .::Ia.ea .333. :2. c. :3... o. ..o 2. .3:. 333.3. a I 2......— o I 9......— p I 0.: ~92.— I g<= u.ua_. a. .o.e..o.u. ...ea.. I. ..:a...:a. ...ea.» «I ...>.o 2.9.. 3 I n ._-.=8-. «ugauzu. 90900000000900.0009000000600...00OOOOO§OOOOOOOOOOOO¢O¢¢¢oOOOOOOOOOOOOOOOOOOOO. IIIIIIIIIII 8:4.‘13 8.. 8‘ 5‘8: 0.3 a»; IIIIIIIII. OOOOOOOOQOOOOOO‘OOOOOOO0999009.600990000099.090.6099996.9000OOOOOOOOOOOQQOOOOo .8 «one; o. 83' 0002330 83.933343:an ...: 291 292 ...... 2:8... I .....I... .. I.. ..8. :8 I 3.3.. I 33:8: I 23:8: 3.0.. I 2.8. I 3.9.8 :8 I 3:83. I 50.8. I 2.85 I 3.8. .838. I eeoo I 3.2.2.33— I 3.8.2.35— .. ..3 .::... .528 ...:.. ..:.... ..:...... ..:...-..:.... ...... 3.3.. I 3.3. \ 2.8. I 3......2... I 533. .3...... I 9:3. \ 3:2. I 3.3.9.1 ““87... O I 0 a... v o. 3..! 8 I o. «3.3.x I ..I—8 3.9.- I 35:3: I 3.353.. mm. puma Ann—.05.. I 3.2.x I 3.3- 6.3. o— p I an. 8. o I 3..... gun I 3.:—mass» I 3.8.2.53— 23... 68.2.... .8... .3...... .552. ..:...-3.23.. ...... 5. gm ...—.03.: I 3.26.. I 3.8.. ...-3...: I 933.....- I 6.99.0.2: awa- a I 3:3. .- 333: I 3...: 3:3. I 3.228.. I 33.5.; on. bx! non—.11.. I «can. I 3.3 ..I. on . I On. 8. o I 3..: 3256 I 3.8.2.33— I 3.83335— 23! Igua III. .3:. .338. .3:—.3038 .3 .. 9.. 2:33... ...... .II... .:.:..... ....8... ..I... .3...... ....Szos... ..I. ..I... I. I 338...... .. ... .... ... ...... ___ a... ...: ...:.. .:.:... ..:. ..:...... ...: .:.:..... ...: ”2.3.8.2.... ..I. ...... .... .:.:..... .... ..:...... ..I... .. I 9..... .. .. ... . I ... 8. 28.38.... I .. . 3:33.... I 3.33... 3353:. a. 34... .. 3:88: ... I..—3.3.22... 2 I An... p 33.939 I a. x a. I 32.220 am:— 0 I 2 ...»..uou I ...:.—.33. .— —..II I 23...... I 32.—.:.:..“: I ...—.336 n o— p I a: 8. .8.}. II 3..— fiasoach 8.99-.34....33 3:. ...IIIII ...... .::8... .92... ... ...:.. .:.:..... .II. .23. .....53....u_3. ..8 .. as. ..:.... I 8.3.... I 3.22.. .cIuaa.uamu I .n.a.- ..:.....- I .82.: .. I ... I ... ...... 3:8... I 3.39 .. $2.33 In... ... .21... 3..-...:.. e I ..:.... . 338...:- II... ... .35.. 3..-...:.. o I .....3: o. .xun I32... 3... 3.3 .8.. 322...... o I 3.:... nO—nIo. 8. ..:...... ...:.}... ..:... ..:... .. ... .28. 3 x ..:...-..I . 2:2... I 2......— Iu... I ..:...... s— .8.... «3 IN. mpg. - I s. I s. 5:: v s. 3.1. on a...\—Iuoa ...-ag— oIagzma OI; OIL. .. I ... ..I... . II... I .:.:..... 2.... . I..... I ...-.8833... 3. ...... .3...... I 2... I ..I... .832... I I..... I I..... 3518.. . 3...... I .3...... .5583: . .:.:..... I .3...... 2 . 3&3... o. . I 3. 8. I I ..I... .. I «I... ...:.-.... 8.139 2: .26 92.3... In. . «I.I 3.3 a... .33. 3.3.2.. 2: 3.3.3:: ......93 2.3:... 2:. 3 =9: 2.... I ..:... x 23.03 I 332. I 33.8 n— on p I o. I.. .. in o I 3303 3.0 «9!: I 3303 I 3:63 am:— samxu— II 3.53 .- 3503 I :30... I ...—.03 I «at... .33 ..:...... .33. V2.32... 2: I333 I.I. 8..-...:. .I...II 92.33.. 2: .2: 238! 2: o. .23 I.I. 5.3.3.. .I...II $2.33.. 2: I.I. .. 9.28:... 2:. .- an o I 3330 a: 3:03 I 33030 I 330: ......— anpg II 3:08 .— t.u.nl.aa/..-—3.u no... ...:.-.3 32.5... 3302. II 8.2 9023:”. 293 I C 8 - U I d 1 I. 3 - 53 C I I I I I l- ‘- - II A. ....zo....u>«o ...oda...uu.a .d«u .«udu a.— —x_&ua . u.ua.a m¢ u¢___.¢~u au¢<=m aozxou maua_u «g .n.wu.¢a‘ mans.“ u¢ .n.Owu.maxgg .udax_u u< .n.g.aa 2.9 u.oa.. a¢ .n.¢g.aa.. udua_u m¢ .o,.m¢.wa .udaa_u m< .n...o¢ 3.9 as...“ a< .n.um‘:g uz_.mm m< .n.ao.a<.a 3.9 swam... u< .nvxmoa «wow—z. m¢ an..m¢‘x. «mum—z. m< .n..:¢z 2.0 mags.“ m< .n.auw.uaaa wdua_m a< .m.¢awu.mozm .w.uz_u m< .o..¢ua u x.a macs.“ «< .o..maxu .u.gz_u.a< .n.m._au ._a ..>.o ..g.a..o ..>.a. ...».o ..>_aw ..>_a:. >~.ua cam ux.a<.n «.3:. «caud ....u .o_uo. ..a_uo:. duo.ua on» wg¢duuo ...... o.. ....u ..~.n>a .._.>u .n>.u_ ..>uo_ua ...«uxu .uaaxa ...“). ._<_a on» u.<.uua II93.0339033399330033.33::.:.:..:3::333:39:33.3: 3 _ ‘ wads—u 3.::- . 93.303093933339393ooooozoooooooooooooooo:oo..::..:33:33:. can .:.:.525. 3832;. up; puma 023:: I _su>O_—o— awa— 3.25—3— A ”30.2: m— uss-:0» I 03:530— awzn 0522—0» v 25.—op ‘— = 93 ”323— I 3.25:0— u>o:o~ I GUS—pop .5:— p I upset s— fixquuto— . sum—o— . >wc—o— I 93.—o— 82=3>u¢ I 2": I >wc—o— 8p \ 3:32; I raga—t I 9:58..— I 8..—43w. 8— \ 33‘: I 2:: I edema—:3 I 35>: 92 pan: aagp \ uuhcnoouo: I. co..uII .:o..o. Ia». 233—=5 I 0?... mums-m—O—v I aanwU—smm I aww‘OCG—O—v O aaflvflU—flk5 I Owngpcuv I ggwumpap 32:3— . omega—o— . Gum‘s—=2 I Gum‘s- .nenudez I 3.2—mama: I cuumfii—o— 33:3: I 320332 I 3:033— :338: I 3:03:98 I Gum‘s-=6— 3- =3 A333<83 o m»<8:—o— I «pings—o— n on p I 3- as a I «38:3- 6 I sum—o— . ._ can a.oauoz— .o. u__.: >¢dua a. a...) a»:— . . u._¢:“o.. Ac... can .o_=m .o~_m>m .o_m>u .o>_u_ .o>ua_mu .oaa .n.n>a .n>_a. .n>wa_um .nz‘uxa .::.. ....waa.u_<_u ddu .~_u>u .~>_u. .~>wo_aa .~am .__m>m ..>_m_ ._>ua.um ...«uzu .::az .adomuo:—.u_<_u ddoou aux. asp...oo¢ I .s—_.>Iau .- . . .s... no: . .h...>.aa n on u I s: 8. ~: puma .. can .. can ..~._._..wau ...uamao_ ...uam ...wna.o..wu_a_.. ..‘u um.» .. a . a~._....una .3:-9:: ..uau . up... n .. mac. ..uqu . «no. . ..una.o- ...un. .mmu .~._.mu<¢a d.¢u a . .~......u¢a .~._.ua. \ .~...:am . It“ aw. o . .~—..ua. ._ .~..._.. . .~......unm nap—IN: 8‘ 3.905 . Apuuau I augus- 2—85 0 68.9.8: I edema..— agooguuaau I 5:2! “8:00; -- no": coopxo—wa 8:68-32:5/3 ...: 2SJ4 3.2:... um: .I n um: I.. a III I323 .II... II .3:-III 3:823 I... 5.5 I Ii... 3555?- EI :19. $33.2: I IIIQII III. vIIIguhl $33.9... IVS-:3 .33.. I nun-gum: .3:— n I moan-w: :— 5... "3.332. .I . vs. .II ..I! I n ~I_:m "3.5qu I 2323 PI. .38 I n Ila-=28? .5qu I — :5: ".382” I..: .II .II—3.2.. III..- £39. 3593 ..I III“. .0 833 :35 I :52 u 2:: :5: u 23:: “I I I..-III >395: .o .31! $33—95 SI... " 33.: I I 2.28 .....qu .o 34!: 3.8.2.. ...: u 23:: u.. I 2an.. ..:.-...:. _o .385: 2.3.9.: m. =2. an::e III :3:— II... I hz—um n— o— p I m- 8: 22.... :2 "$183.39.... II III: II I:- ofiaoz... 2: ..35 I 3.:: 52:8 .33.. I .3383. :5: n I Hagan: :— u «33.: “I I I: I =_:: I "as“; “I Vogue—9.: 2...: “3388.. .I 3.:! ..I! I I I 5...: ..ISZII—Ih .33 I ~ I ..:... IISZIIIIAI .3:! I p I 2:: 2:: 2.... I III: .:III .II IIIII... .3 I5. .I 3.52 385 I ..:... u 2:... . 5:: A.-. :35. I I I.I—.3:. 3.30 88.. >5- .8: I .2... n 5.! ~95: .. 2:: 3 I a: J I II: 5.5:: lac: II: 3 ..I... In: on I 5...: u 3.: 2:: £32339 SEI- _III:I I SCI... I....EII...&I I 332.. 2:53.... I 332.. .:.:..... 2&2: ..:II I 5:... .3222... 2.19:- IIIIIvoII I :32: ..:...:F. 2.19:- III>III I SCI—a Inocu- I.I._IvoI.. I 3.2.. Ice—II 9.2.3.. I :33 II_II_..IuII 3..... I .329 nut-:3: I I.IvQ-I I 3.53 II.II_..I9II :23: I 3.29 ..:...ogocn 3.9. I .329 I155: 313%.. I .82.. 2.3508 833.. I :53 a I gas—3— o I 0532.0— 0 I 8.3 «a I 339 no I QN—m>n .c I o—gn I I 0);— no I 232mm "a I 23me a I 33 "a I :3“ no I nN—gu no I n—n>n “a I nam— 5 I n>wemm no I 5293 30 "In: I. 38— 2562:35235 "I... 03:33: a I No.3 0 I :3“ soon: I 2:: ISNI I 2:56 ..8... I 0::58 ..:qu I 3::58 Lam: I 25.58 I :m>m no I ~35 no I ~22 no I ~>ua—mn no I ~288... Cor-... ..I. ..II— ?393: II 8.58. ‘33:: I 5...: 333393; I..: .I I >30... ..I]...- III III. .II...- 3 I359. v8.3.9.3 I 5...: I....IIII .. 8.2 82323 5.65.35.51.33: ... I >39... ...:....c: ..2. v3. .IcI.c: .0 359. $33.98 $05.8. 33.. I 32.333... 3.; n I 3.5333: I. ..:.... «3.383... ... v3.30 .II: I n $3.310... ...—vol I ~ Inst->330 :33 I p 2:: .IquII: coo. co I..... .o 353 I328 do. ... I $5.. :3... “.232. 2.823.... I..... I bana- I ha!— I —2—: I al.-L I ha—: u —I—: u— gm =35 I ~28— 2...‘ .II... I «33.33 am: n I Pagans ‘— I— an 5mm. 2mm. m5. «8:53- od-fi. up; 3 a; 3:5 n =3; 139.....- a: pa 0393.9... «:39 3:: mac—awn: :3 a— “5552 0.3 238.. 5:5 ..:.... "338...... ... «.2 pa rum um; um; nun xwm um; um; um.» sum In; an; nus x2 nut II“ 8630!. . .8:qu ..II: I n ..Icofiuouok. .88 I ~ $522,339 .IIqu I . ~23.— uxuvoua In 3 ~53 3 I333 385 I ..8 ..R. 8..... .2. 3...! $89. I.I I no»; ..I... «5.2. no; «If- ..quI I.I I I389; ..I: .35.. Iona—o... I I309; 33 ucIIIuIEI. $83—93 I 3:993 I.III. ISIIIIEI. 38913; I 35...: II. Venue-9... I 3:093 :38... $3910.; I 3593 I883... “.33—...... I III-III I..—.IIIII 3.:—I.I I 3.893 9.2.3... $38.98 I I213 2.2133 $83—98 I IIcIQII 3.9.33? “.33—I.I I I389; .II. VIII—I.I I 3.893 3%: 98910.; I 3593 8.1. 33019.. I IgI 9...... II III“. P3533) $33.98 an..- — I I..: I— am: u 0 33.53 s— I pg— : am 8—“0... I ~23— I ~23— pI—na 20:97.... I baa— anSmAAIIII Aoeama.ocu I :13- «ovum..... vauan. II I I pg— nnuuLAo u. . I he: 83582313253 «I... 296 . .. ac _asa_ so. unaa....... . a¢¢=a . «can. . .. ..u_.¢=aa.aa.a.._su.- . .znuaz. . ..uw._a . ulna. aqu .. uaodu .o. _xua ......ao .uu ... ..« ... pass. a o. n . .9. so. . .. m¢ _aga. co. .~.a....... . uc<=a . «can. . .. ..u_.¢=aa.a..u.a_au_. . .,~.a/. . ..uw._a . azuu. saga .. umodu . . .o. puma ..o..¢¢a «u ... a¢ ... paga— . a. — . ... so. . ...m< _aaa. so. ...:....-.. . ..<:a . nous; . .. .¢u_.¢=aa..._u.._au_. . 3,..az. . ..uu._o . n’uu. gage sea..uo.o.a _ouoI. . nuuaaomoaa swan u I wucaomooa m— m— can co. puma ~n00—ucguaxvuzmu I Aoo_.anuoaa n on p I oo- no; ICC—uo>sooao ..:uuas I uuucaoaooa aux» p I wuuaomooa 5. Juan I ucawo swap n I wuaaonaua: m- .. can .~ \ ....oog.. . duo_o_ . p‘.uo . n. unodu .n.u»g..ao ..:.u.. . auuuacaduaa auxp . . uuuaomdua: .. N. ufiOau .o..a¢...-.. u ..n...‘-u ..q..».xu ..n_.aaxu ..~..u¢.u ...—.ugxu .*o..uaxu u.o.mmxu .~. .:aa. ...agxu .Nvaguu .o.«.xu ..n.amxu ...meu .m.mauu .Nvmgx ...mmxu .~. .34.. u..oa .aa<.. ..n n..».a<.. ..n ...u¢. .~. .::.. .q .....a<.x ... ...u<. .n .....o«.a ..n ...94. ”~. .:aa. ~ ...».o¢.a A~ ..u.. .. ..>.0¢.. ..u.. ~. .342. a .~.>.a<.a H.“ .~.u<. .. u~.>.a<‘z H~.u<. H~. .:aa. w u~.».a<‘a .n .~.u¢. MM ~.x.o<.a .~.u¢. ~. _=¢a_ e .n ~ .. ”n.».o<.a H.. ”n.u<. .n.>.o.o¢.a ” m n.u<. .~. .:aa. . .n.>.a<$z . . ~—o ”coo. .. pend. oooasop\~¢ 9.... .323. .3359. scan»! .8 5...: . ocud sand ._~».na .«.>.na an. In. . ....ooa. .~..oo¢d .an..oog. ...xaa .~.xao .n.xaa .~. .35.. ...auumuoaa ..~.awu.maas...n.¢uu.aoxg .~. pans. Ix; ...ua ...ua .~.ua .n.ua ~. .::.. ~§ m< ~3¢a_ cam I—oa<.a u.. ...a¢. u.n ...».a¢.a ..n . ~ ...x.a<‘a .A~ ...m¢. ... .—.>Ia¢.a ..’ ...g‘. a ~v>.a<.a .am .~.g‘. .. .~.>.a<.a .. .~.a¢. n .~.».a¢.. ..n .~.a<. H.~ .~...a<.x u.~ .~.A<. . .~.>.a‘.a .. .~.;<. ..m .n.>.a¢.x ..n n..¢. q .n.>.a<.a ... n.a<. ..n .n.>.a<.a .n .n.¢<. ~ .n.>.a<.x ..~ .n.m<. .. n.>.a«.a ..w .n..<. .m..:¢a ”.n_.aa.a ”A“..¢.o ...—.35: ”....u¢.a .....cca .n..aaa .n_.ua‘a .n_.g.a n.~..a¢a .~..ag.a .~..a.o ....aaa ...—.«II. .....aao .o..aca ..c..m¢.a .«opvgua .o.:¢a ....aa.a ..o.a~o .ovama ..o.ua.a .o.g.a .~.aaa .~.u¢.a u.~.g.o u.o.a¢. .ovua.a ..o.¢.a .nvama ..n.um.a .n.¢.o .q.:¢a ....mg.a ....gaa .n.:aa ..n.m¢.a ....gao ..~.aaa u.~.u4‘a ..w.a¢a ...:aa ....ag.x n.nvgao a‘_ ... .uzux scam: .4gma g.~:.o_a c: .ua Isa: mega: ...«g.ox ..«.gg.aa .“n.¢ .aa ......O.......‘....O§ 33333333333333333333333 '2 cued ..:z. ..—.>.ax .«.>.ax an. Ion .:aa_ ....ooa. ..~..ooad ..n..ooa. ....xaa .«.xau .n.xam .:aa. a..a0wu‘maag ..~.aawm.mox¢ .n..auw.moas .3“:— gmx. ....ua ...ua ..~.ua .nvua . .:I.. «. m< .352. so& ._¢a.. . aqud_. . .I- . «_uu._a . s’uus saga . n. muodu Q Q s c Q 9 Q Ao—WN“AIOII u .m..m¢xu .w..u¢xu .n..mgxm a~..aaxu ....meu mo—.agxu .o.aaxu n. _:¢a_ .o.mmxu ..~.maxu ..o.mmxm ..m.maxu . u..¢a .c ..I.¢¢.x .. Avvmaxu .m.m;xu oz<._ .m ...».a¢.a .n .~.uax ....maxu ”n. .:a:. ...u¢. ..n .....a<.a ..n . ..u<. n. .252. ..u¢. .n. .:az. .~ ...).o<.. u.~ ...u¢. u.. ...>.a<‘. .., ...u‘. .n. _aaz. .m H~.>aa‘.a ..m .~.u¢. ... .~.>.o¢.a ... .~.u‘. .n. .353. .n .~...c‘.a ..n u~.u.. ..~ H~...a“a H.~ n~.u¢. ”n. _aa._ .. .~.>ao‘.a ... .~.u¢* .m n.>.a¢.a .n n.u<. n. _aaa. .e .m.xag<.a ... n.u<. H.n .n.>.a<‘a ..n .n.u<. .n. _aaa_ .~ n.>.o..z .~ .n.u«. .. .n.>.o<.x ... ...u‘. .n. _=La_ .m..:ma ..m..m¢.a H.n..o¢a ....aaa ”....ma.a ....o.o .n. _aaa. .n..a¢a u.n..m¢.a ..n.VO¢o u.~..3¢z .~..a¢.a u.~..o.o ”n. .:cx. ..,.zaa A’..M¢.a ...woco .o..3¢a ..o..ma‘a mo_.o.a .n‘ .3“:- .o.3¢. ..o.aa.a ..ovoaa ....aaa ..o.ua.a ..o.o.a .n. _aaa_ .svzaa .~.u¢.a ..svoao .o.naa .o.«a.a .o.o.a n. .342. .mvaaa ..m.a¢.a .n.o¢a ....agz ....«g.a ....oca .m. .:aa. .nvnaa ..n.ma.a ”...aao .¢~.:¢a .«uvog*a ..«.o.a ”n. .:aa. ...:ma ...ma.a Invoaa a;_ ... 03.. n. pass. ——6 "coat .- wouo— coop\°—\~O 8.0.08—xu/a_~:7uu «cg—m 297 m— can 9. cacao ct uaodu aux— — I u:§_ 3 IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII. 3: 23.83; 3.5.28. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII. I- can ~0— paw: Asa-vowwmmozmm I Asa—unaqumaza n o— p I so. can swap n I uua:0mouumz ‘— m— can . . . . II. .II: Auu_.Iouu.moaI no I. II .I bags. I a. . I on. Io. . .I II has.— Io. III..II-... I IIIaa I Ina-I I .. ..I—IIaaIIII_I.I_=u_I I I/IIIII I I_uu._a I I/«uI .qu Ica_.uI.III .ouQII I IIUI=OIIIIII awz_ ~ I guanoIaIIII I. I. can nos I .nIIIIIIIaaI I .nIIIIIIIaaI ~II I .~.Iauu.mozI I .~.Iouu.IIaI .II I ...IIIIIIIII I ...IIIIIIIII . . II unadu SI 2: ..2 2 .II 5%. “I II ~=Ia_ Ia. IIII.I I III... I III I I_uwa_a I I’III IIIo Ica.II>.IIna .IaIuII I IwuazoIIIIII aux. . I Iguaomoqua I. 5— can wu-ams I uu_¢mQOx—<. has I p3¢02~cou co.uuoo o¢.ao._o. cap. cco— \ annrIIIao _Iauuos I auuusaauux aw..— . I 3.332. I. m. can ua— puma Asa—«sacs I Asa—.ssuaxc n on p I so. no; no. hum: Ana—v.05 I goo—vgcc n— o— a I no. .05 awzp n I wacaomaoa u- m. can .I moodu co. ..I: .8339... .8 .II .II ..I .2... n o. . I co. co. .I II _aIa. Io. II_I.II.... I I.I:a I Inca; I .. ..II_I<=az.II_m.I_:u_I I III—oz. I I.uw¢.° I I’nuI sumo .I uao.u . . Io. puma ..I—VIII .Iu .II II .I .3::— n— o. n. I .o. no. _I II .:II_ so. IIIa.II.... I I.Isa I «coca I .. ...w_-<=oavua_a.u_xu_a I I’IIo/I I «_uuc.o I Ilqu III: —I woodu II. .II. ..o_.III .Iu .II .II ..I .:I2_ ~. I. I I .I. so. n—o «ones I. noun. Goo—\o—\~o l_qu8-aa/4-p=7uu «...; 858 I .22.. I 239...: IIIIiIII «2.3 ..:... I II I ...:.... I .2! IZSNB O a O ba‘OIOO AU"OODI ur—babcb «I . '.~I§ ..uOh I bI-g « b8-: u b8-: u b...‘ m IIIIIIIIIIIII II gh‘fi’“. ..:.; CAI... ad .3 I353... 3 232m III... I 5...: I . .........I............-.Ioo.. nouns. . I ...-I. I 22.: 6.6.3. ...:iIgI uI...- a... I: I SIZE... 3 5:3 III... 2 v3 ..:.. o I ..:... 3.3 an... I .IIIIIIIIIIIIIIIIIIIanu 0.0-...; ..26 .13... $33.98 .3 3.... I . I I 55:. .2.2I II2I.o. .III.IIIIIII Ia..... . . .2.II I.Ioon I emu..o.. .IIIIIIIII I..... I—3 bl—gu nIIIIIIIIIII-IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIodouo— III bl-g I IIIIIIIIIIIIIIIIIIIIIIIIIIIIIImIIIIIIIIIIIII‘CUO~ I I #8.: I.I: .2... I . .oao. I IIIau.I.o. IIMNthIIII. IIII.IIIIIIII I..... II.................... ........m@mm m mmemqa”www.mnnIIIIIIIIunmmmm .I.....I..I......-...I........I.... ..I ..an .::..o a . .III. I IIIII.I.I. I .I..III2I. .III.IIIIIIIIIIW._mm I II.-......----.......-.... . .Iao~ I IIIIIIu.o.. IIIIIIIIII I..... 2.»: .2.2I I .I......-......................-....IIIII .cIIIuI.aI2 I .2... . ..m.......-........IIIqu I .2.2I . .ooo. I I.Iau.I.o. I ....III2I. .III.IIIIIIII I..... I I .-....-.-............ . aoo~ I IIII2...o.. .IIIIIIIII I..... ..I. I..: I ..:.... 28.2.9. .. I..... .. ...,Imflmflruwmfiufix... .I . an“ I I . . I 2.3 22.! I II....:.....IXII—mm2¢m£mmmu2m8$axt“unInIaagIIumm.ms . II ...—..I ISM”... Influnng and: . I I II I . .... . . .--.......-...-.-..--......-........-.-. I 2 II ..I. I..... I I.....E.............I......m......Emu...wmImWI....I.__IIIIII..Im..I. I IfifiIIHIuIIIIII. In...» Q I IIIooooIIIoooocolollotollco I.I: .2.2I I .I.............mmmmm.m.mwmmm_mmmm.u.mmmwmemmwcmunucnIIIIIIIqummmm IImc..I.III IIIIa.oII .I u ...-I I .aoo. I I.Iau.I.o. I .o..IaI2a. .III.IIIIIIII I..... I I ........-.... .2_2I ..n.x.a2.a o. .IIIIIIIII II...- ‘-g -:-:n I.IIIIIIIOIIOIOIIOOIOO00000000000000.0000.IIguUOJI—C‘ I b8-: 8 IIIIItoIIIIIIIOIIIIIIIIOIOIIIII L..“:: I P.—: . .aoo. I IIIau.I.o. I ...IIIII. IIII.IIIIIIII I..... I I -..-.................... .~.I.II.I.o. .IIIIIIIII I..... 8.8 -.-: ” 0.0IOII.IOOIIIIIIDCIOIOIIIOIOOIOIIIOIOIOOIante-”Jab“ . F...‘ . IOIOIIOIIOOOIIIIIIOOII-jhu . ~‘-g . .aeo. I IIIau.I.o. I ...IIIII. .III.IIIIIIII I..... I I ..........-............-. ...:.ms.a.o. .IIIIIIIII I..... ‘-3 h...‘ u 0.00l00000000000000II000000000OOOOIOIOOIIIII§fi0.0~ . b.-g 3 IIIIIIMMMMODIIOOIL.“.C 5 I . h...‘ . .III. I IIIII.I.I. I ...II II. IIII.IIIIIIII I..... I I IwIII IIIIa III I I .2.II 2.3 3.: I 5.........................I.......I......»..u...uo.u I 22.! I .I.. .2nuu....mmna Iuouc. II... I.. ...I ..o.II<. .2_III ...... I....o. IIII.IIIIIIII M “.IIIOIO.IOOOIOIIIIOOOOOOOIOOOIIOOODOIIIOI|O—.UO— I .III. I I.Iau...o. I .II. I... IIII.IIIIIIII 0.00....III...‘....I.I.II.I...I..... ‘. “.0 h.‘“° .III. I I.I.I...o. I .m........ IIII.IIIIIIII N «.IIIIIIIIIOIIIIIIIIIOIIIIIIIIIIIIIIOLCOA ~¢0..U.—QUC .III. I I.Iau...o. I .....I.... IIII.IIIII.II “.IIII.OI...OI.OI....IIO.ICOIOOIII:¢!. ~c§0.‘ao. I II... I I.Ioo...o. I .n........ IIII.IIIIIIII 0'.UI0I.....‘I.-.I.'..-.|III.I..IOIICOUUUICCIOCI..‘._ aooo— \ u»¢au_&~0— c onmustv It....§‘§§‘as . .I...............-.....I.........--......-........I:I .III. I ...:....o. I ......III IIII.2.IIIIII I II-..............-........................-..II....:. I I .99.. I I.Iau...o. I .IIIIIIII IIunmunIIIIII 0.00....II.|.IO|OIIOOICCOIIOOIIIIOIOCIOUOOOOOIOI .999. I I.IIGII.0. I anIIqum. IIII.IIIIIIII “ N.|OOIIOIIIOIIIIOOIIIOIIIOIU.gI-u g ....“ 52.5...) .ooo. I u...u...0. I .u..a.xw. IIII.IIIIIIII II.............................IucI¢I.c.II ucI ..I... IIIcoaIu uIII.c02 uIIII_o.I .2..II I.....I..o. IIII.IIIIIIII I II-.-.............n.-...............-..-.....I.o. laficzoa I 2.... I 233.... IIII.III 3.... ...... I II I I..... ..IIII.... I I....a2.o.. IIII.IIIIIIII ” “...................................C..-I.....>S..Sa 2.... I I......... IIII.III I2.I: .2...I I II I I..... ..I....... I I.I.III.o.. IIII.IIIIIIII ” «....I..............-...I.....I.....IIICIIIIIOIhibu IRECJOQ I 2.... I I......... IIII.I.I II.I: .2.... I II I I..... .....u.... I I...2...o.. IIII.IIIIIIII “ “.........--..........-........'......-......h.“.c . IIIcIII. III. IIIII~III .. I2.I: .2....I.... I ..I..... .2...I I._.= .2....I.... I ..I..... .2.... II_I= .2...II.... I ..o..... .2.... uz_m: —x_¢¢dAIIII I ..I..... .2.... oz.«: —a_¢aa..III I ..I..... ...... II_I= .2...II.... I ..I..... ...... I2_I= .2....I.... I ..I..... .2.... I...: .2...II.... I ..o..... .2.... a2..: .2...II.... I ..IIIII. ...... I2.I: .2....I..-. I ..I..... .2.... I2..: .2....I.... I ..I..... .2...I .2_I= .2...II.... I ..I..... .2.... o2_I= .2....I.... I ..I..... .2.... I2_I= .2....I.... I ..I..... .2.... I._I= .2...II.... I ..I..... .2.... I2..: .2....I.... I ..I..... .2.... .III I....... .2.... I2..: .2....I.... I ..III I. .2.... I ...-.. I I....... .2..II I2.I: .2....I.... I I I.. I. .2...I laECJOM I a ~AIIII I I....... .2..II I2_I: .2...II.... I ..o.. I. .2.... 3 —AIIII I I....... .2.... I2_I= .2....I.... I I....... .2.... I ..I..... .2.... pa—uga I p3—8sd I —a_-¢4 I —a_-II I pa-um. IaN—vuaxu .3.... .2.... II...o. IIII.IIIIIIII II...........................................I.o. IIUIII I I .2.... I I.:.. IIII.III I2.I= .2...I I II I I...... 2.... I I....... I......... I..I= ...... I II I I..... II... I............ n “.................'.................‘.'..O..-‘ 5800‘ :Aulu . I2.I: .2....I.... I IImIIII. 2.... Inc. ....I I II... I. .2.... I ~AIIII I I....... .2.... I2..: .2....I.... I ..I..... .2.... IIuIIg Ion Iuczoa I .3..... ..I. I I.Iua2II. IIII.IIII I2.I: .2...I I II I I.I.II. I. I I........... IIII.IIII I2..: .2.... I II I I..... ..:...... IIII.IIIIIIII ” “..I..............'...........CO..'.....-.“8‘ ..‘b.x Ioacu>o¢ vo.uo.o.I .n saazu I owe Icons I. NoIo— 0.6—\0.\~o I I II... .2.... I .2....... I I....... .2.... I2II= .....II.... I I....... .2.... I I....... .2.... :—u.mx-:u/a_—=/Iu no... .2.... I..... I I....... IIIIIIIIII I II...-..-...........................-........I.I. II... I I....a2.o I IIIIIIIIII I II.........-....-....-.............-.......... .IIIax .IIII I I...o...o.. IIIIIIIIII I II..-.......-......--...........-........-......IIOIu .IIII I I...2...o.. IIIIIIIIII I II...-......--..-.......-....-................IzI.c I 19.3. 53959.3 to». 33a 9.. Is .2.... IIIoII. .2 IIIIIIIIII .onoo. 388... IIIIIIIIII I II...................-....................Im0g .IIIIx IIoc_.I..Ix vouuo.o.I .o .2.... I.I.2.I2.o.o. IIIIIIIIII I «I...-.....-..........--...-.......I..........>.oI.:x .~.2.I2.I.o. IIIIIIIIII I II.--...-....-..-..-..-...--.-.--........-......Izqu ...:....III. IIIIIIIIII I II...---........-..-...............I.....-....IgI_c I IIIIIo. I.Ioa 00.90 0.. .m .2...I I.....22 II I I “I...-.....-...-...--..........-.....II¢.¢I02 uIIII I.. .I .2.... I..... II... I.III I II..-.....-..--.-..-.-.....IIII. ....I Ic.uc. II... III .I .2.... I...... IIIIIIIIII I II......-.-..............--..I..-.....-.........II.:2 .III2. IIIIIIIIII I II...........-...--.............................I:o.u ....2. IIIIIIIIII I II........-...-....-...-......................I.I.c.. III..o.c0>c_ ICIucw voqu_o.I 2 II .-.--.-.--------.--..-.-...............-..-.-.....I 2 II ............................IIII. ..Iou ac. IIIIIII 2 II .-.....--...-.-...-.-....-..-............IaII: too. 2 II .......-...-.-........-.....III.II .IIIII III I I....... .2...I IIIIIIIII I... .. «IOuulno I.I: .II.I. IIcoQIu 2.... I.I .I....II.I..I.. IIII I.. .I....:I.I.... II 3.... I222 II I2... .I .352. 23.2. 3...... II .2.... ....CIOCOCICCOICOCIOCI.CCCCCOCCCCOCOCIOIICIOOCOOCCOCOI... C. .:..:o co.II.:I.I m:.=m II .2.... I .2.... .2.... 2.2. I I ......I I.IIIIIIII I.IIIIIII .2.... 2.2. u I ......II I I I2_I: .2...II.... I ..IIIII. .2...I I2_I: .2...II.... I ..I..... .2..II I2II: .2....I.... I ..I..... .2...I I2_I: .2....I.-.. I ..o..... .2IIII I I....... .2.... I2II: .2..III.... I ..I..... .2...I I2_I= .2...II.... I ..IIIII. .2.... I I....... .2.I.I I..": .2...II...- I ..I..... .2.... I2..: .2....I.... I I....... .2...I I2.I: .2....I.... I ..I..... .2.... I I.I..II. .2...I o2.I: .2...II.... I ..I..... .2.III I2II: .2....I.... I ..I..... .2.... I2II: .2...II.... I ..I..I.. .2.III I2.I: .2....I.... I I....... .2.... a2_I= .2..III...- I ..o—III. .2IIII .I I I.I..II. .2.... .2...I I ...aaIIIII.--. I ..I..... .2.... I.I.IIIIII...- I I....... ...... IIIIIIIIIIII.... I ..I..... .2...I I.IIIIIIII.... I III I... ...... II. .II II..... I I....... ”2.... AIIII IIIII.IIII .2..II IA~AIDIO I.IuauoIII Iao—III. .:.:.. usaIIIIIIIIIAIIII I .Ao—v-dp usuuaa 8 a As... a .A°p~.¢— —a_-md 8 I AIIII I Inc—~ICp pa-csd : I A...I I .aopv-¢— pl-Cmd I IIIIIIIIIIIIII a .Ao—v-cp ~3-csd a _a_-Ad I bx-umd Im—I-tt— u- .wpa-as— .- -ucxu —a_u¢d II .2.... I.I.I ...2.... .I I I..... ..I so... :I ...:..I I... I. O I a: I— I 00>» o. qu9. tag: I..:u IIIII II .IIc. .2... IIoII. .o no. I. ...c... .II I I pans. I —a_-c nasa- —a_-¢ —3_¢L aw..— — I .33.- .— h—o «one. .. poIo— oao—xo—INO 8—a.u8-auza_—:/Iu Io... "GQN'AIOII ... ..... . ...oo. . .a...o.u.o. . .....32.o. . .o........ .....I... .2..: .2... ........ .2... . ...... . .....o.. .....I.. .2..: .2... ......4. .2... . ...co. . .....o." .....2..: u2.m= .2... O .OIIIIOIIOOIOOIIII ..o . III Manny-(p uIII unnovo<. wIII unpav¢¢~ ua—cm ...ooo~ . o....=2.o.. wuuuuun.a u...: .2... III unn~VC<~ «III unnovu.0I.:a I III .amhu-<* «III unmoved: «III nanny-(b —3_-s . ....2..2.o.o. .uununu.. .2..: .2... ac. ..n..... .... ..no.... .... .......m .2... . ....2....a.o..mmnuu.u.. ...“: ”awn“ . .....o. ....a no.uo~o.. .. u .2... .2... .... ..n..... .... ....MflMwuum.m2.2... I “000. ... . ...2..2. ...a..... ...: ...a. ...............c.ca.2 no.u..o.. .. . .2... .2u.2. ........ .uao.. ...I.... ...2...¢.u. ........ .......>.. ..I».... .2... ..o ... ...:c..cou o. ...2. ..... .2u..:.2. AIIII . ...2. ..II.IIIIIII «I..: .2... ......4. .2... . .~o.. mmumwuaqunu.u2uaa~.2... I I AIOII 2..... ......a.... a2..: .2... ......«. .2... . .....2. ..........2. a2..: .2... ...-..I. .«.u. oc.u:. no.9..o.. .n a .2... .2... .... ........ .... ...o.... .... .2... ........ ...... 2... . .....2. ......II. .2..2 .2... ..-...............-.-......:2 c .2... . ..III ........ .... ..no.... .... .2... ...«.... .2... . .....2. ......II. u2.mm .2... C.-II..I.IO...IOC.OIIOOhghu . _.-.‘ .... .....a... 52.“: .2... ..o..... .2..... ... . ..o.. .....I... «2.»: .2... ........ .2... . ..>.o.mm ......“a wu..:..2... . sq «— ..... .... . ..2.... .....I... o2..: .2... ...-..........-.-.........c . n .2... .....o.c.>c. .:.uc. 20.9» 0.. .u . .2... .2.... ........ ..:u.. ........ .......a... ........ ......u... ........ .2... .2... ...... auucaauauaa .- .........................................-....»¢.ua I .2... ..u.=auoa2 .- .......-........-........-...I... ...ou ac. ...»... a..2... AI.-. uu-auuauu.a «I .........................................IIII: too. I .2... Ina-:oauua .- ............................Ioo..s ..q.II tea I .2... I.....Ia .I..II an... Incoanu I .2... one ...I. .- ...I. .....a..~a 2......2mi...:’uu ..... ......ao. ..I. .. a .2... .2... ... ......aa...2u_. .... ... ......aa...... .. ......aa no.0..o... .2... .3...... .222 .. SS. 3 ..8-32.. .82... ..:...... .. roman?“ .2... I.-... a p8—Cfi I..... II II I bz-dt I..... I bl-Cfi 8A.... . .2... IIIIIIII.-.. . .2... . ..u IIII. n. . .~ . ......w... . ~ . ....o... . ~ . ......aa. . u.o..o.a. ........o. - ....o. . ....o. . u>o..o. ua....>.. . ..>.. . >...o. co. . .2ao.... . mu...uo.... . ..2. . ...2.._2. co. . .2oo.... . .....uo.... . .o.. . ...2..o.. .2ao.... . .oo. . .....oox.... . .2....» . ...2.... co. . mu...uc.... . .2uos... . .2..2a . ua....2.. co. \ .u..... . .2.. . a.oau...a . ..>.. a..o.au . .....o...o. . ....o.. . ....o... co— puma «co-vcmmxu I sum—op I «um—o— aoco— \ m»ou u..u:a.. owe»... o.o.. o. ....o .o..m..u. Is. ..I: u¢.xo..o. I... o. 0. ~ I «a. so. AAnVUU_I&u IA.--- Owu$BDI—O—v I nanwU—Ctu I Gunmanfihchw I Aapqu-Cmn C w—IfiwwuuuaMuwufluwnm~°p ‘0... GummBDZ—O—v I aanUU-Ctm I OUU&O¢u—O—v O AA—qu—Btm l p0d§WUflM~vm “d““wfibwmb u . .... wqu—bbcbv O aanwU—Cmm I aww&O¢GbOhv O aanvNU—amm I OUU&fl:IhO—v I axwowM&—O» awu.¢:2~0p I oquO¢upop I Gum‘s—uho— I ommmp0— an~u>ua—ma I A—~30wummazs I thUOwwuau o>uo—au I apwcawmmmoaa I —ouu~na oaIuo_a .Iuo. ucIuaos. .03 I :5 .I.uI.. I.Iumogu.o Is. . g..x no.uo.o.a I.I noncoaxo Innauun >.Iu.ouoc I. co..uo. _ao..oI I‘». anvmpuo.mm I an—O—oa Anvm>.o.a.m. ........ ......w... ..I..... .2... ..u ... .suac..cou o. ...2. ..o.. a .2... ..:......o. ............a o2..: .2... ........ .2... . .e.......c. ............. o2..: .2... ....m.... .2... . ....ua...aa ........I... o2..= .2... ..o..... .2... . ....o....o. ......gagn... u2..= .2... ......................o. . .2... can. ... s .3... . .....u.... .3...... u2.m: .2... u .. . .9.... g .2... . . III .~m~v¢<— nIII manown..o.. .....2....... e2..: .2... . ......¢. .2... m ....>..aa ........I... «2.»: .2... ..o..... .._.. . .oo....>.. .....I....... ...»: .2... .I................oos ....I3 I . . . . . ..:co>.. no.9..o.. .o n .2... .2a.2. ..~..... .aqo.. ......4. .......o... ........ .......>.. ..I..... .2... ... ... ..:c..cou o. ...2. I.... . .2... ..coo. . .....oau.o. . a....=2.o. . ..2....... .....I... «2..: .2... waa «I.I; .. ~ouo— coo—\a—xwo 8—u.n8.aa/a.—azuu "Ia—I ......I. ....I.. 1 ......u.... I ..o........ ..II. IIIIIIII a2.I= :«.... ...... 1 .....o...oa. .III. IIIIIII I...: ..I..... .2.... 1 ......u.... I a...2...o.. ..II. IIIIuII. a2.ma .I...-....-........-1I...¢ . .....II. .IIco... III. II... I.. .I . ..o..... .2.... ...-.II. 1.2a.I ......I. ...>.¢. ..I. ..I...I. ......u... 1.0.1... .2.... 1 .2.... 1 .2.... 1 .2....1.2.... 1......xu . .2.... 1...... 1 .I.>....I. .III. IIIIIII. .2.I= ..I..II. .2.... 1 ....>.. I ......o... ..II. IIIIIII. 92.»: .M»I.II. ...... 1 ....>..a.. .III. IIIIIII u2..: I..... .2....1.I...o. .III. IIIIIII. ...": ....................I.o. ...... ..I... . I .2.... 1 1.2.. ..II.II. «I..: .2.... 1 1. I ..I.II. ..mwo.. I. I . 2.... 1 ..o..... .III. .III. I2.I= ...a.. 1 1. . I..... 1......" I III . a:ov.¢— .MII I an&w-<— uIII Inpovo..oa II. .III... u...= .2.... ..I. II. .2.... 1 .uo....>.. ..II.IIIIII«. a2.Ia .2.... .I............-..IIo. .I..I2 .....II. .2.... II::I>I. II.II.I1.. .. I .....II. .2.... ...... ...... I .....o.u.o. I a....=..o. I .......I.. ..IIIIII. wanna-«2.... Aooon . .2.... 1 ...... I .IIN.....I. I I....a2.o. I .o........ ..IIIIII. u...= .2.... ..I.... .2.... 1 .....au. .3 I .2.I= .2.... ......I. .2.... 1 ...... . .....o.. ..IIIIIa I..»: .2.... .I...................I.o_ I ......I. .2.... III IanQvO<—I .III ..nh~.....I. .III. IIIII. ...»: .2.... .I»..... .2...1 ..2..II .III. IIIIIa. o...: .2.... ... II. ....o Ic.uc. II.oI_o1. . .....II. ...... .2.... 1... ..mo..... ... ...M..... .... .2.... ...I.... .2...1 .....2. ..IIIIIa. u2..: .2.... ......-................1II1:2 ..o..... .2.... III Iamov-thI .II InnMVQC—I .III ~3—8‘d ...I.... .2...1 ........ 1...-.........-...-....1o:o1u I .....II. .2.... ....I ..IIIIII. o2.I= .2.... 1.8.... ......1 .8.. ....IIIIII. .2.... .2.... ..II.... .2.... 1 ......II ..IIIII. o...: .2.... ......I. .....1..2<.2u ..IIIIIa. I2.I: .2.... ..-..................1I.I.c . I ......I. .2.... I.I.1o.coI:. .:.uc. I..». o.. .~ I ......I. .2.... ...... .....II. .::o.. ......I.. .. .>ua. I... ..I..... ......u».. ..o..... ”uuuuu 1*...aoa...1.... ..o..... .2.... ..u.:omo¢..... ......I. .2.... “I“..aououu.1.... ..o..I.. ...... I...=o1u.1.... 1. .-.--.--. ......... ..........II.... ....II IcI I .....II. .2.... ......II ...1II II... II..... I ......I. .2.... tIIu1aoI I.I. .. I ..o..... .2.... .I ..-.....-.-.-...-.-.-...-..-...----...-....--..I... .I ...-.-........--.-.....-....II.I. g.III ICI IIIII.a IIIIIOIIIII.IIIOOIIII-IOOIICI'IOOOCOOICCO.2S 3“ _AIII. 2.... 1.. .I....:I.I..u.. .... 1.. .....Iaa.I.... ..I..Iao II.uI.o....m2.... AI... I.... 122.2 1I::. .9 .II-:2. ..II.II. .I.uu..a 1. ..quoo..a .....II. .2.... ~a_¢sd .IuIIIIIIIII..... <<<<<<<<<<<<<<< «4‘1144‘44uau044444‘444444<‘4“{‘<<4<4<<<<.Acpv.<— hl—Isd II“. I.... II I.Mo—~.<~ ha-usd 5.... .::.ac ca..I.:I.m mx_=m II I.«opvn<— ~a_¢Ia I I.... II IMMo—vddp ha—Cma IIuIIIIIIIIII.-.. .ao—wo<— ~8-fltd na-xsa COCICCCICIIOCIII...III...COCO...IICIIOOCCICCCCCCCOCICICIC .2.... 1 .2.... 1 .2...1 .2.... 2... o I ...2.... .. ......u ..c~....:u 1......2u .2.... 2.2... I ...2.... .. ......2. .....I.2u .2.... II .2.... I...) ...2.... .I I ...... I ....2. 1 .2... I .:.2. I .2... 1 .2... 2.:. . I ...... .. e I o: .— I In». Imu.x. gonzo cI .I.¢.I¢ I.I. I. I. ...cha o. .III. nag: ...:. I1o1. .3... .Ioaoa .o . o. .o.c..a .Im .... .I I .o I o: .I I III. .:n.:o I... .c..a o. a..: no» on .2... 1....ac.Ir ..I....... 2:..o. .. . II...c:1. .I1o. I .2... 1 .2......... 1 .2... I.. 1....13c..cau o. ...2. II... I .:.2. .2... 1.2..o..o. .III. IIIIIII. o2..= ..... ..II.... .2... 1 .o.u>o..o. .III. IIIIIII. o2.I: .2... .....II. .2... 1 .uao..o..I1III.IIIIII. o2..: .2... ..I..II. ...I. 1 ..Io..a. .III. IIIIIII. u...= .....1........ .2... ~o1I. .II..o..~o I.. 1.... -- I.I.II.2I....:219 1.... 303 c~nosm.p I gamma I nnsavn.n.vmxuv I ~oona.so- I «Imam I- azw :mz— — I __ m umaw nuqomn.p I gamma I canoe—...vIXUV I so—~.n~p. I I. a swap ~ I ._ I_ww.u ~o.o—oooooooonocoov.o I gamma I oqo—Io.~.vaxuv I -~mo.os. u «Imam 2.2. n I .. .. ...... .... .... II... In. pz_Iw¢ I..III...IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII.I...IIIIIIIII.I..IIIIIIIIII ua_—:O¢a=u m<-.uuo..o. .III.IIIIIIII o2.I: . .III.— .2.... 1 .qu..o.oI .III.IIIII I u2.ma 1......I .2.... 1 .IIo..o. .III.IIIIIIII I2.I= .2.... 1 ..II1II. I................IIIou I... .II .IIO .sauc. no... ... .... 1......— . .2.... 1.2a..o. I I.Iaa...o. I .I..o.. .III.IIIIIIII I2.I= .....II. .2.... 1 1o.o..o. I .IIIou...o. I .2..o.. .III.IIIIIIII .2.I= 1 . ...... .2.... 1 .42m.o.oa .III.IIIIIII u2_ma ..II.II. .2.... 1 .“uuuw. .III.IIIIII.M “an.” .I................. 1 I . c I . ..I...o. I .IIIII...o. I .I...I.2.. .III.IIIIIIII o2.I= .....II. .2.... 1 .o.a..o. .IIIoa...o. I .I...I.2u. .III.IIIIIIII .2.I= «~IIIII. I..... 1 ..I..I.2u. .III.IIIIIII o2.I: ..Im.... .2.... 1 ..I...I.2I .III.IIIIII I u2.ma .I.........I¢I.I. .II. ....o I ......I. .II..o. I .IIIau...c. I .I...I.2I. .III.IIIIIIII u2.Ia 1....II. .2.... 1 1a....oI I ...Iau...o. I .I...I.2.. .III.IIIIIIII u2_I= ......II. .2.... 1......I.2...1III.IIIIIII u2..: ..IM.II. .2.I.u.uom.whwnwmuw.mmII.IIIIII "swan.” .I......... I . I . .2...o. I .IIIou...o. I .I...I.2.. .III.IIIIIIII .2.I= .....II. .2.... 1 1o.a..o. I ...Iaa...o. I .a...m.x.. .III.IIIIIIII I2.I: «.III... .2.... 1 ..I..I.I.. .III.IIIIIII a2_ua ..I¢.II. .2.... 1 1.....I.II .III.IIIIII I u2..: .I.......II.III. .cquuo.ao¢ I ..o—va<— .2...o. I ...Iau...o. I .I...I.2I. .III.IIIIIIII u:..: ......I. .2.... 1 1a....o. I .IIIou...o. I . ...I.I.. .III.IIIIIIII .2.I= .....II. .I.... 1 ..I..I.Iu. .III.IIIIIII .2.I= ..I«.II. .2.... 1 ..I—..I... MIII.IIIIIIIM ou.m: .I......-.............-III . I . o. I.. .2...o. I .I. I IAo—v¢

.II.:a a . 8.8m. 1....u.... .III.III I2.I= .2.... 1 .I I I. ... I 1....II. III «and -<— mIII manhua.o<.s. v s. ._ gasp ..N. .n.>.o<.a . N. J. .. can qoaou. ...... ....~_ .n..:u‘. . .. x .. . ..~. .n..ao‘. sue. a . ..~_ .n.¢<. ..~.~.. mu“n««“u_anum.udmwgwwuw gag: . a:¢._ .- aus. ..~. n.>.a<.a . s. .. nua— o . ..~_ .n.>.a¢.a .— .. gag J. as» ._ can ~a¢¢zup . .~._u«uu<. . .~._u‘uu¢. ....~. .~..:u«. . .. ~ ..~. ~.~:u<..- . qa<¢xu~ aus— .u._az . ..~_ .u.».a<.a. y K. ._ nus. ..~_ .~.>.a<.a g s. .— ‘_ can qaaxua ua¢ <. .. .o...o¢o.. .o ao...u>aou. . An... ..._~. .~..:u<. . .. x .. . ..~_ .~..:u¢. aux. o v ..~_ .~.‘Jo ._ oo— \ ..~_ .~.g:u«. . ..~_ ~..:u<. .. as. ..—~_ .A.... ~..:u<. ...~_ .~._a<. ...~_ .~..>¢<. ...~_ .«.o.¢.._u.o... .d«u and» ..~. ~..>‘¢. . ..~_ J..:u‘. au=.o . az<._ ._ aux. ..~_ ~.».o<.a . s. .— aua. a . ..~. .~.>.o<.a J. b- can 3. can o~o ...-J .. ~ouo. oaa.so.\~o x.a.ux.au’4._:,uu «o..& sa-o<.a. v s. .. :w:~ ..~_ ...».a«.a . s. ._ ._ can sa<¢xu_ . ...—u¢uu<. . ....uauu¢. ...—~_ .,.~:u¢m . .. \ ._«_ 3929‘... . .qa4axuu u._az \ .A.~_ ....:u¢. . ._~. ..<.a. . ._~_ ....:u<$ au>axua ma< <. ._ zo._.oao.¢ .o ao_m.u>zou. ....-. ..._~_ ....:u<. . .. \ .. . ._~. ....:u«. au=_ o v ._~. ...<<. ...~_ .—.o.«‘._n_a_._ dg¢u mad» ..~_ ...>«<. . ...~.o ...:u¢. :. . aa«.. ._ .33 2“. 2:9: _. t = am:— e . ..~. ...>.a<*. ._ n o. _ . .~. .o. .q pan. 9 . .....ucuu<. n a. u .q no. a . qa¢xu_ o.n . .n ..~_=<. “o.~ . .a ....>¢<. "a . .n u.~o.<. ~ . .u ...:<. u. . .. ....><¢. no . .e ..od¢. a . .n ...ac. "n.~ . .n ....>‘<. «a u .n u..od«. a . .~ ....=<. .n.~ . .u ....>¢¢J ”a u .N ..od<. a.“ . .. ....:<. u..~ . ... :.>¢<. no u .. ...o.¢. .. a .n. ..a«. .o. ~ . .n .-o><<. .n.. u .n ”J. .<. ~ . q. .~..a‘. ._ . .. .n..»<<. .o . .. “~.od¢. .n . .m J..=«. ... . .n J..><¢. no . .n “N. J¢J n~ . .~. «..:<. ”n. _' a .« Jvo>¢<. no - .~ ~=o.<. .. . .. J..=¢. “o. s . .. ~..>‘<. ”a. . . .— .~;o.<. - . .n. c.3‘. “a. ~. . .n .n~.><<. ”q." . .n ”n.od¢. - . 3 £23. a. o . 3 335: J . 3 .33: .m . ..n n...¢. "a . .« .n..»<<. " n .n .n.o.<. n.m..~. .c;:.%..z 235.3~..~.n33. - . .. .n..=<. .n. ~. . .. .n..>¢¢‘ «e n a .— .n.od<. u.uz.a .a< .m..... ngau<. .m.ea.a .u< .n J..:<. .u4»x_a a¢ .n. J..><<. .udoa_u m< .m J.a.<. 2.: .m._ax .oomd .oz<._ J. ...<.a ..>.a<.a ..:ad .oaud ..._u¢uu<.. .“wflunu_uuu ...:od<> ..u ..<. pa.o_._ can ua¢duuo ooo¢¢o¢ooooooooooooo.o+0.ooo¢o¢o¢ooooooooo.¢o¢ooooooooooooooooooooooooooooooo. ua-naucnaa «(..muuao "No.mo-soo.~ I Ape» «Annosv. I AQNV» noos~psq. -~n—mv. I A¢~vp "Acqwnqw. 8...: . . .oo... u..~on~.. . .oo.. "onmohqa. . .oo.. «~n~.-o. . .I... “nooooo. . ‘00.. "~nsm.~o. . .mo.. «onooqn. I ..o.. “.omo-o. . .n... ~o°eoo~. . .~o.. up~oo-. . A...» nono~.a~. . .ao.. ~.nomnn~. . ..I.» -qnn.~. . .oo.. "esqoqoo. . .Io.m,u-m“o. . .oo.. .oqonoao. . .m... ano°~.o. . ..I.. “~o~oo~o. . ..o.. "ima¢«.o. . .~o._ “..~..Om. . ....» omo-ca. . .oo.. "oo~.o~m. . .95.. .n~oomon. . ..~.. ..QJIJmn. . .II.. Isqoqn. . .o~.. "oq~o..n. . .m~.. ..momqnn. . ..~.. .oo~«.~a. . .n~.. cocon~m. . .-.. "~oo~o.n. . ..N.» .m~qmm.n. . .c~.. “I¢¢m«.n. . .00.. oooooom. . .oo., uo~uqsom. . .No.. "..uomoa. . .oo.. "~o-.om. . .no.» omsamom. . ..o.. uo~o.~om. . .no.. unqom.on. I .~o.. n-oo.am. . ..o.» ..qooom. . .oo.. «_.oncom. . .om.. uoq-oon. . .om.. woo—_oom. . .sn.. .omooom. . .om.» us¢~ocon. . .mm.. ”cocoocm. . ..m.. un~oooom. . .nm.. «ooccam. . .~m.. "a. . ..n.. u . . .am.. " . . .o... ooooooe. . .I... "JIoooo.. . .5... u~.oooo.. . .0... unm~ooo.. . .u... o.qooo.. . ....» ".coooov. . .n... u~m-oo.. . .~... “oooooo.. . A...» commoov. . .o... uo~oooo.. . .on.. ”NmOJooq. . .nn.. "m~.o~o.. . .sn.. ~J~ooo¢. . .on.. "qn-mo.. . .mm.. "..JnvoJ. . ..n.. "n~.a~o.. . .nn.. I . . I . A A A A AA I Ace—vx «90. I A00»: «.0. I Aoovx «so. I Asovx n . I A06»: «0. I Amovx "we. I Aqovx "no. I Amour «~0. I A~ovx u—o. I A—ovx o. I Aoovx «on. I Aoovx "no. I And“: «no. I Asovx ”on. I Acne: no. I Annex “we. I Aqovx ”no. I Andy: u~o. I A~o~x "An. I Annex 0. I Aaovx "on. I A05»: "9A. I Aosvx «AA. I Ass»: «on. I Aosvu as. I AnAax "vs. I Awsvx «nu. I Ankvx “~A. I A~s~x ”As. I AAA»: 5. I Acnvx "00. I Aoowx "no. I Anew: «so. I Ase»: nod. I Aoovx no. I Ame»: "we. I Aqovx "no. I Annex « . I A~o~x n—o. I A—oux o. I Aeoex "on. I Aomvx "on. I Annex «Am. I Aumvx ”on. I Acne: mm. I Annex "cm. I Away: "mm. I Anny: "Nu. I ANmVx “An. I A—nvx m. I Aomsx now. I onvx "on. I Aavvx ”he. I Asqux "on. I Ace»: we. I Ame»: "we. I Anvvx «me. I Anqux «~Q. I A~c~x «Aw. I A—vvx v. I Ace-x «on. I Aonvx "on. I Ann»: as». I Asnvx «on. I Aanvx an. I Annex "cm. I Aonux "mm. I Anny: u~n. I A~nvx .An. I A—nvx n. I Aon.x no~. I Ao~vx uo~. I Ao~vx «An. I A-~x «on. I Aowvx mu. I Am~vx nc~. I Aq-x «n~. I Anwvx u-. I A-ux u—~. I Ap~vx ~. I A°-x "ou. I Acuvx “up. I Aopvx «An. I ANA»: uo—. I Ao—ux n—. I An—ux uvA. I qu.x amp. I An—vx «Np. I A~—~x app. I App»: —. I Ao—ex «06. I onx ”no. I Anya use. I Ase: nod. I onx no. I Anvx "co. I Acvx "no. I An»: ”No. I Anvx “.9. I A—vx u.ua_u «I .99... .uag... I< Ago..~ 2.; mugs—I a« A» —~.. I.. u..¢.a ..:od¢> u I <. ...a... .3» a-_ pa-muc macs—a ac ux___.<_u auuI ..I>I .>.I. .>uo.II .:.uxu .aaca ..I). «.III Ian I.. .a.Iuo IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII wz_A:O¢-:« u<-.aA¢Aa. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII. magma radius no ><¢a< waA «— A AaAun> awn umu¢Am IO uwnxzzquAuaavaAuafi 8mm Ana—:OIoAuaA tun Anus—IaAua: 8m“ A aw. Anus Ans-voAuo> I AA I HHAAoAuo> AI tuna A 0A A I apnea I HHA «Om I Acpuauvapua> I a—ua» xwa o I was A- A- can a—uo: I AAAcAun) uQA Anus Ansgua I Ana. \ Ans-AaAun>AA I Asa-voAuoa I AA I aHAAcAuo> A. au—u A 0A A . o—uou I as. so. AoAuaEAoAua> I a—uo» lwzp o I man u- u..¢.u .III .IIIII .I.un2 ......aa ...... ...uaz. .uo.ue .II a.. Ia.Iua IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII. wa_A:ouo:u a4...uopua. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII. on“ can Adwafin<.wo I ><¢¢< uu<¢OAu mo ao-«xuxAOA uuuAau. cUAa mxAA I An. AAa. maAAoA. ><¢¢< ua<¢ko I >Ao. mA<¢ Ana—:0 I >Ao>I uA<¢ AaaxA I ’A93. A006 \ AAA>AO I >An> QOAA Anwa Ana—AA>AO I >AaA I >AaA >Anu GA A I oo-- «an o I >Ao— AAoo- I A>An§A)Ao I >An=A I An I A>Aanv>pa I A>AouA>Aa oaas Anus A>AOA \ AccisA>Aa II... I 05.0.. AAaa- I AQOssv>Ao I Aaaa \ AA I oaqav>AoA I An I AcesaA>Aa I AceswA>Ao A>Aau 0A A I news so. >Aau \ >Ao I Anon >Ao3A >AAmo 9:» .IA A8AIw° ..II.I .>.aa ..I .>.. .I... ...a. .....o ...o. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII. uaAAaacoaa m<-.>A.ua. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII. can cam I... I a» ...... “I.. I ... ...:.. a I ... ...I... ..I. I I. ...... .I I ... ...IIII I. I ... ...I.. ..I. I ... ...II “I I ... ...IIII I. I ... ...I.. I.. I ... ...I. I... I :. ...IIII .. I .~. ...... I I ... ....I "I.. I ... ..IIII ... I I ... ..I.I Ina .IIII .. no... .II.Ie.I~o I.I.Ix.au,...:’.u .I..I I. I. I ... .. .II .I I . o. ...IIII .I. I I .e. “..I.a I. I I .A ...:. .I.. I .A ...IIII .o I .I ...I.o I.~. I .I ...ao I .I I .I ...III. .I. I I .. ...IJI I..~ I .I ...—II I .~. I .I ..III. "I.. I .I ...I.. I I .. ...:. "I.. I .A ...III. I. I .I ...... ..I I .A ....g. .I I .I ...III. ... I I .I ...I.a I. . I .I ....I I... I .I ...III. ... I I .. ...IJI .... I .A ...:. I... I .A ...IIII .. I .n ...I.. I.~. I .~. ....a .I.. I .~ ...IIII I.. ~ I .~ ...I.. I~I I.. ....II .~. ~. I .. ...III. .I. I I .. ..I.I ..I. I ... J..a. ”I I .I. J.II<. I. I .I. A..... ..I. I ... J.... "I I ... ...III. no I ... .~.I.a I.I. I ... .~..zo "I I .n. .~.I>¢o .o I .A. .~.I.o I I .~. .~...o I... I .~. ~.I><. no I .~. .~.I.o ~.I I ... «..I. "I I ... «.III. I .~ I ... ~.I.I ..I. I ... .~..ao .~.o I .o. .A..»II I I I . ”I.I.I I.. I .A ~..=. I... I .A «.III. I I I .~.o.a ..I. I .I ~..=o "I.. I .I «.III. I .I I .I .~.I.o I.o~ I .I .~._=. "I.~. I .I .~.I>¢a .I.I I .. .~.o.o I I .I ~..=. I... I .A .~.IIII I I .I .~.I.I I.. I .A .~..=. "I I .I .~.I><. I .~ I .I .~.I.o I... I .. ~..=a ”..I I ... I.III. I .I I .I .~.I.a ..I I .A .~..=. I. . I .A I.IIII I. I .n .~.I.I o.~. I .~. ~..=a I... I .~ .~.II<. ... I I .~ .~.I.o II I .. «..I. .~.~. I .. J.IIII II. I I .. ~.I.I ..I. I ... .A.... u .o I In. ..IIII .o I .I. .n.o.a I.I. I ... ....a. I. I ... ...IIII In I ... .n.I.o I.I. I ... .A.... "I I .A. ...III. I I .A. ...I.a I.I I .~. ...:. In I .~. ..IIII no I .~. ...I.a a. I ... n...a . .I I ..A ...IIII In I ... .n.o.a ..I. I ... ...—x. no. I ... ..IIII . .I I ... .n.I.o I.I I .A ...:. ”I.. I .o .A.I><. I. I .I u..... I.. I .A ....a "I I .I ...IIII . .~ I .I ...IJI I... I .I I.... I... I ... ..III. I .I I .I ...I.. ..I I .A I..Io II. . I .A ..IIII no I .. ...I.. I... I .~. ...:. . .I I .~ ..IIII ... ~ I .~ I.I.I IN I .. J..=a .~.~. I .. .m.IIIa .I.I I .. ...I.I .... ...IIII ...o... ..I... III a.. ...II. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII uaAAaoaaam u<.IAuunAo. OOIOOOOOOOOOOOOO§OOOOOO6099909000OOOOOOQOOOOOO9.9000O‘OOO’OOOOOO0000.90.00.00o .:a nun A. can A— can n. I AAA I 2332. I AAzaaz \ Aa>m I An>mw I N—n>aAA I >uaAua 3:8: \ Au>u I :«wxa auap 33:: I >AmA .— NA») 0 NAu>u I ~A¢>u A“) I An) I Nun) An> I Aa>u I An)“ A I >Aa- I >Aa— Am) I 0A“ swan can v Am) a- Am> I As“ an:— Axu A An) A. nno "ouIm I. ncqu Ono—IOA\~O SAuInx_:m/AAA:IIU ..AAA Io: IIIII II. .III IIIIIIIIIII .II .III. I.I..Ixx I. . I II. III .III.I_ II IIIIIIIIIIIIIIII .IIII.I. II IIIIIIIza..I:a I.III I.I..Iza .II .III. II II .III. III I.I:.I I III... I I.I I I...... I .III. IIII ~I mac... II. .... ”III: .II .III. .III “III. ...:.... IIIIII. .III ”I. .III. .III I.....Ia .IIIIIIoz IIIIIIII II .III. . III. .III. II..IIAII .IIIIIIII .IAIIIIII .II .III. I.IIIII. IIIIIII. II..oqu ...xaI IIIIII IIIIII .II .III. I.IIIIIIIII IIIIAIIIIII..II..IWII.II “II .III. .III .III; I.III .III. .III. II .III. w. u< 212— I: :39... I an... I If. I «Aqua—o I .III”... a; n. 30.5 . . I.wII.. .IAIIIII ..IIIIII ”I. .III. I.I.ooI. II..ooI. II..III~ ..IxaI IINIII IIIIII .II .III. I....IIIIII IIIIIIIIIII IIIIIIIIIII II .III. IIxI .III-x ...... .IIIII .IIIII .II .III. n‘ «1 A22— 8; ..:.—II I “mg: I If. I «Aqua—o I III”... :93 «33:5 I «8.3 I «mud: 2.430 I «8.... I «~33 . m- an a I nth-8 «..8—8 I an¢<8 “snout I as: 25:. o— I cwbafia :wmdu NO I ASA-l «show.- I “33:5 "ISA: I an; xwa o I 83816 m—wmau NO I Alb—I "15:! I “33¢; “unmet I ac: luxp o I ch¢<8 twwdu A0 I ASA—x “IRON: I nAxu<=a “sham: I u¢<3 :2: h I uwAch =wm.u 00 I ASA—a «INGAI. I “Ataga "IsoNI I ¢¢<8 swap 0 I ch¢I I.IIII II .III... I.IIII II II. I.I.IIIII x.. I..... .II..=I I..... ......I .II. ....II .II. ...... I.. ‘4‘{“{‘4‘{4‘1‘{{{““‘{ 1‘4“{ “{“‘{‘4“4‘1‘1‘ 8.. A123 III.IIII.IIII>I .I...I.. ...I.III II.:..II III III.III II..II IIIIII I.IIIIII III III.III A8 «0095 I. 3qu oooAonxg §Ip¢au9=§uu 3.: OI: pIplfia 2.. .222 .2 . .2....2 . 2 . ......2. . .. . .2..... .2 . .2....2 . 2 . ......2 . ~ . .2....2 . ~ . ......2. . .. . .2...... ..222. . .. . .2...... .......2.222 . ..222. 2.2. ..222. . ......_2.a22 .. ..u....2.22¢ . ..222. 222. ..222. . ..m....2.m.¢ .. .......2.m.¢ . ..222. 222. ..222. . .......2.u22 .. .......2.222 . ..222. .....222oo.u>< ......2 ..2222..~.2...o .222 .. .m...22 . .2222 . o 2.2 . 2.2 22.222. ..8. >2 2. . u 2.. 22. >2 . .22 .. .222 >. .222 2.222 . .>..2u . .>. .2...22 222. 2 .v ..>. .m...22 . .>..2u. .. 2.222 . .>...o . .>. .2...22 222. 2 .v ..>...u . .>. .m...22. .. 22 2. . a >. 22. 2.2.22.2222 22222. mac—...)- 3. >ao—~I:8m .. =3. .2 a .>. ..ow2n2u2 mac—pIzgs >aop~IZSm o I as: «a I was 3:833“ 2.3.. 230.06 23330.00 I 5:; as . ha... 2 >3: 2 us: . p3: « as: a .38.. u ...—an n 5;; . 22"“.an .22 ...-2 .. no... ouo..2..~2 noo.>¢.ua>._.2,«u no... map; :5. 4.3. u. .222 22 .222 222 . .~2 .2.... 22 o. . I 22 no. >2 2. ~ . 2. 22. cut: 9:;- 2. ~52 8.2. I :33 S I :38 us 2 p I ... 8‘ 2. .5: an n ..2 ...22 22 o. . u .2 2o. 38. I «:3 n I g p I <5- vé I 3..: e I 3:: 8— I 3: o . .2222 .2... . .2... 2.2.. . .2...-2.. .2 292.2 .2. .222 ..2...2.22.22 ... .222. .22.22.222 2. . . .2. 22. 33.2. 22 25.22.823.528! 2.82 .2a>22.222 ... .222. .2 m2 .222. 2a. ..22.. . 2.2... . .I. . 2.2.2.2 . ...2. 22.2 a. 222.2 . 0.. .222 .....222 .2 .222. 2.22222 2. . . oo. 22. 8. .222 .2....2222 .22 .222. 2.22222 2. . . no. 22. 2.22.2. a2 .m.<22>2..2222 .2.22.2. 22 .2.<2222.222 2.222 2.22222 .22 .222. o. 22 .222. 2c. .22.. . »~.... . ... . 2.2.2.2 . ...u. 2.22 .2 222.2 8. .2! ......222 .22 .222. 2.22.222 o. . u on. 22. n8 «coca -- neo— ooo—xo—xwo as.»¢._u5..——=’uu 2:: 310 “ 111 ‘4 “ { “‘ “1‘ ‘ “‘{““ 1 a II ax<1—_ . —_v 92.8). mood — o —_ I ~— 9 I u—ma o~no—. good .2 . .2.22.2 . 2 . ...22.2. . .. . .2...22 .2 . .2.22.2 . 2 . .2.22.2 . 2 . .~.22_2 . 2 . ...22.2. . .. . .2...... 2.222. . .. . .2...22. ....22.2.222 . 2.222. 222. 2.222. . ....22.2.222 2. ..2.22.2.222 . ..222. 222. 2.222. . ..~.22.2.222 2. ..2.22.2.222 . 2.222. 222. 2.222. . ..2.22.2.222 2. ..2.22.2.222 . 2.222. ...2.22.2222>2 ...22.2 ..2222.22.2..22 2222 .. .2...22 . .2222 . . 222 . 222 22_.2222 2.22222. _ >. .222 2.222 . .>..22 . .>. .2...22 222. 2 .2 ..>_ .2...22 . .>..22. 2. 2.222 . .>..22 . .>. .2...22 222. 2 .. ..>..22 . .>. .2....2. 2. 22 2. . . >. 222 2.2.22.2222 22222. .>2. . 2.. .2 2 ..22222 . .22 ..>2_.22. . .22 ..>2n“2"222 22 2. . . 22 222 2. 222 ...:.... n... a"... ...... u .2 ...“ ...... . a a ......2.232222252232222.....a......._.m.2 a... . :22. 9. I2 2.22 2. . . 2.22 . 2.22 22 ..22 222. 2 .. ..>2. . ~>2.. 2. 22. .222 xo— I ~>u— aus- e I «Axu2222 . 2~>u_.22u 2. )2 o— N I 8U- .02 p I N>w_ c~nop. co .2 . .2.22.2 . 2 . ...22.2. . .. . .2...2. .2 . .2....2 . 2 . .2.22.2 . 2 . .~.22.2 . 2 . ......2. . .. . .2...22. 2.222. . .. . .2....2. ....22.2.222 . 2.222. 222. 2.222. 2 ....22.2.222 2. ..2222.2.222 . 2.222. 222. 2.222. . ..~.22.2.222 2. ..2.22.2.222 . 2.222. 222. 2.222. 2 ..2.2..2.222 2. ..2.22.2.222 . 2.222. ...2.2222222>2 ...22.2 ..2222.22.2..22 2222 .. .2...22 . .2222 . . 222 . 2.2 222.2222 2.22222. >. .222 2.222 . .>..22 . .>. .2...22 222. 2 .. ..>. .2....2 . .>..22. 2. 2.222 . .>..22 . .>. .2...22 222. 2 .. ..>..22 . .>. .2...22. .. 22 2. . . >. 222 2.2.22.2222 22222. .>2. . 2.. 22 .222 ..22 ..>2..22. . 22222 . ..22.22. . .22222 . ... . .22 ..>2..22 22 2. . . 22 222 222222 . 22222 2222 2.. . 22222 222. >2 . 2 .v .. 2. ...22 ...22.2.22.222 2222 . . .2222 222. 2 .. ..2.22 . ..>2..2.. . .2>2..22. 2. 2. 222 22 ..22 222. 2 2 .22222 . .2222. 2. . . .2222 . .2222 222. 2 v ..2.22 . ..>2_.22. . .~>2..22. 2. 22. .222 22. . ~>2. 222. 2 .. ..22...2 . .~>2..22. .. >2 2. 2 . 22. 222 . . «>2. 22. .222 22. . .>2. 222. 2 . ..22..22 . ..>2..22. 2. >2 2. 2 . 22. 222 p I —>u— a II ~x¢t—- . p—v ua—s: on no¢.><2ma/2-—:’.u 2..-m 222 .2222 .. 22.2. 222.22.222 222..222222..22.2 2.2.2 222 2.2.2 .. 22.2. 222.22.222 311 00—3. a< a>¢0—3x3 23~0003. a< aubut3 .m000—3. a3 .0»¢0—38 20300.02 003<3a 303300 000003. ac a»¢0333. 0000—3. ac .0333. 0000—3. ac Avp<033 \0300.a\ 00¢<03 3.0 3000—3. ac .n02000. 3000.3. m« .0003 3000.3_ a< .0003 3.0 0.03.a a< .0000.0 0.03.a a< .0. n.a»<00.s 3.0 .2>.22 ...2. ..>.2 ...2.2 ..2.2. ....>.2 ..>.2. ..>.22. >.222 222 2222222 ..222 ..222.2 22.222 ....2.22> ..2.22. ..2.222. 222.22 222 2222222 22coco...222922..9.299920222222202o222222222290222...22.222222222222222222022. 3.a.ax.3a 30¢. 00pm<0<.-a. p303 ... . 222 2 ..2. ..22.222 . .>.2222 . .>..22 .22 . 22 2. .222 .>. .2..22 . .>.222 . .>..22 .22 2. . . 2. 222 .0 I .>.003 a3 0— p I 3. 000 3.. ~3.000 0.00.300 0.30300 0. 03.n00300a. ..222 ...22. 2.22.222 222 2.. 222.22 222 222. .2. . 22 22 .2. . .2222222. 222 222. .2. . 22 22 ... . 22 2. 22 2..22.. 22.22.22. 22. 2222 22..2..2..22 22222. .222. 2 .22.22.2 . .22...2... .. . 2222222 22.. 22.2.. .2.22 .2... .222 2.. 222..2222>2 22..222.. ... ... 222_.222.. 22 222222. .2... .2.22 . .2.22 ..2>2.22.2222222>2 .. . 2222 2. 22.. .2>2. .2.22 . . 22 22222 22...2. .2>2..2.2222222>2 22 2...: 22 22 .22.22 222 .222.. . 2222.2 . .2. . 2.222.2 . ..22. 2222 22 2 .22....2.2 . 22_.222. . 22...2. 22 22222 22 .222 000 2.0.0 .. no.0. 000p\0p\~0 a00.»<.00/...0/20 no... $0.222.2 .222 ..222. .222.222.2...222.2 22 2...: ..2>2_ .222.222.2.2.222.2 ... .. .222.222.2 ..2.222.2. .2.22 2 2. . . 222.222.2 222 .2.22 ..22 .2>2..22 ... .. .22 ..222. .2.22 .222.2. .22 2..22 .2>2.22.2222222>2 . .22 .2>2.222 . 222.2. 22 2. . . 22 222 22 22 .22.22 22. .222.. . 2222.2 . .2. . 2.222.2 . .222. 2222 .2222> 2 2.22.222.222 .222. .2.22 .2_22 ..2>2..2. 2. . 22..2222 22. 22 2222> 2.22.222.222 .222. .2.22 .2.22 n .2.22 ...22..2.2222222>2 .. . 2222 2. 22.. .222. .2... 22 .222 22.222.2 .222 ...>2. .22.222.2.2.222.2 2.. .. .22.222.2 2.22.222.2. .2.22 n 2. . . 22.222.2 222 .2.22 ".22.22 2.. .. .22 2.222. .2.22 22 2. . . 22 222 .I 20.03300 03— 00 a0p¢3.0¢000I 03.30 ~3.30 2.—>0.020 “I I 0.00—300 03. —¢ 30.20302 03» 00 002<>I b3.00 2 auu 222.. . 22..222 .2 . .2....2 . 2 . ..22..2. . .. . .2...22 .2 . .2....2 . 2 . .2....2 . 2 . .2.2..2 . 2 . ..222.2. . .. . .2_.222. ..222. . .. . .2...22. ....22.2.2.2 . 2.222. 222. 2.222. 2 ...222.22222 .. ..2..2.22222 . ..222. 222. 2.222. . ..2.2..2.2-2 .. ..2.2._2.222 . 2.222. 222. 2.222. 2 ..22.2.22222 2. ..22...22222 . ..222. ...22222222222 ..222.2 ..2222.22.2..22 2222 .. .2...22 . 22222 22..2222 2.22222. .>2. . 2.. .22.22 . .22 ..22wm2h222 22 2. . . 22 22. 0- 030 030 303— :3. I an 30 I08 I an 0— . 230.0t00I 3:3 303— I>I I an 30 I»: I au 0- au .Inaspv 03.33.00. 03— 3030 30_p<~.x_—¢O 3:003I p303. nS—ct «.>0083-1100m 60—<.00/..~:/20 2o... 312 p o s— I h- :3 v 5— mi; 8 0.. g,— . . . .::.ac. . ..:..ao.o. I .Iaa.=o.o. ..:aoau IaaooIg.o .=:. ....32. .::.ac. u:az....uo.ua ..I. .::... . ..:II..o. I ..:aa..o. .. can . I .2. ..I. m.328)... .9.. .38 ... 85.... I... I..-.333. v.63... . .2...... I 35 £85.... 3.. 2...... .38 838.8. ......- . .8.... . ......- I .2...... £852.. 8.. 2..-..I... .:8 5.3.3. ...-... . 3...... . 3...... I .2...... ... ...a .588. I 2.3.8... . .. . :3. . 3.2.333. 2.83.. o. . .. ... 8. ~.. .... .III-a .. ... ..8... . .u.. a... . 3.29.3... g I 2 neg.- I . ~23 o an:< ......a ..I.... ...:...a Ian I.. .2.... :33 Eng cg“; o— “:3. 3.—<~_8=8. swam—a— u< 33:30:: .832: 0‘ «Snug—3! \o—§=\ Guam-“8.80 AIIII 08.83.. -- no": Goo—3:3 «8.><.§=S/uu "0.: 313 ~><¢¢< macs-u 3 3339.3. “mega 2309333— 3 an; I >pa. In; at: I pa. >350 I >~9 .9.—(ma 3 «was: I .50. 3:35 ‘30— I >—oh. >534 33.0; I >po. w=u 2.825 I )3». wp25. .ooo I I..>_a I >Ia» oo.. .II: .oo...>.a I >.a. I >_a_ >Iaa o. . I on.. coI o I >.o_ ..oa. I .>Iaa.>.o . >_a=. I In I .>.ou.>.a I .>.a..>.a cogs Ixuz .>.a— I .aaI~.>_a I.... I IIIo. . ..ooo I Inasn.,—o . .999 I I. I oo-.>.a. I .o I .aoqq.>.o I .aoqq.>_a .>on o— . I cow. IoI _ . >Iax I .>.ax 33pr33 . >.au I >.a I .ooo u.—<_u .>Iau .Ia >_o .Ig.o .>.o— ...).I .>.o» .>.oz. >".Ia.uma a. I. a oIIIoIIIIIIIIIII.III...IIIIIIIIIIIIIIIIIIIIo...9o.I.IIIIIIIIIIIIIIIIIII+IIIII. m<¢.>—.wa wx__:c¢osa. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII. o.omoo:_ I «IIo.oOI I .I...I.¢a.oOII>¢ ...Ia. . ...ua. I ...II_a .~.Ia. . .~.ua. I .~.II.a 33... . 33.... I 3.:; oa533: A a. m— 5: puma - I asp—:5! am: 3:38: A C—_:<9. I. p I CZ:<9. I «23:9. n on p I =— 8‘ _ I ..I. .II. .IIIIIIII.»I9.I: I I. I. I. can . I ..I. I ..I. a I ..ooI ..ooI . ...ua. I ...uz. I.IoI I o.omuo=. I a.omoo=_ .:.ooI..I.u I I.I.aoa ..I..— ...I:=2§ I I. I. 0: fix: ..au I ...uaa. I .I.. ...upIou.I I .... ...Ipcau.I Ia.Io I ..ooI I ...ua. NS «..I; I. 3“: 0033—30 mafizusisfiu 3.: :3 I age I cute? I «rag :3 I 2:8: I 52005 I 8:8; I 2.85. I.I.ooI I .Ia.Ia I ...:.“..IIoI I ...:.ua_o.o_ I.I.Ia I I.I.Ia.o. I ._a.uo.o. . . ....ao. I a.I_:o.o. I I.I_=o_o_ .8 ..am II.I¢ .:.:.Ia az.uo ...>.o .....ao. ...:.-.>..Ia ..Iu ...... I I.Ia..o. I ..Ia..op Ia.ooI I Ia.Ia. I a.ooI I ...o..Ic.n I I.I.III 2385 I .233 x 233 I :3. 2:3 I 88.39 .II-Io. 2_Io.u:.!8 oI. I . I .II o I o. .~..=o. I ...... .~.ua. I .I.. .~.I»I¢I_I I .I.. .~.I.I¢I.I a: puma Ann—v03. I ~30... I 3::- 034 o— u I a: as a I 332- a~:8¢ I 03:53— I 0.9—33— 055250 I anx—ua—o—Op I sax-nanos— ..~.uza .oxuooaguo .oau. ..voxuu ..~..=o¢ ..«va.n..ua—ua ..Iu .~.a.- I o-ua._oI I oIoa._o_ 832 I 351283— \ any—8..: I «a: I «I: 3&8 I 2:352: \ any—Bu: I 3:33 I .3:—- InquI I .u.. .n.m»¢nu.I I .I.. .m...823 3 big a» u— A 359 Sun mug—u to music—93:53 tun page—99> 8nd past—I293 8w. >2 In I a: .Icaos I upxu 00. u— I «pu- n— I can: «n. I ~35: xw:~ v I saoaaa s— u— can 00. puma poooo. I goo-vawaa can: o» —¢<—ma I oo- .05 o I >2 II I a: Isnuos I n—xw co- mu I (pun ~— I can: no I p¢3 "a I a: us~aoa I oo. up I o I 62w: In I zu:—~ I poooo. I goo—vawaa arm: 0» u¢OOOSUm. I.... ..uuupa. «I ..I .IIII .IIIIII. «I can: ..III... II .III»: I~guo.c\ aI-Iaw “aaxau . I-... ..I «I ..I: ...II¢:a ...uua.o .IaoII .quIIa. II .II-Isa: I.Iuo..\ IIIIII aoxxao .~..I.¢I I.I I.I..u «I .o«.IIIa I.I u.oa.a «I Ion o...: 2.4 I.I..I «I .oI.IIII.. 2.9 .o..III=o 2.9 u.ua.u «I .c..ux I.I I.I..u «I .o..=u I.I I.I..I II .o...u I.I I.I-.I «I .oIIII I.I I.I..u II .o. .oIIII I.I I.u2.I II .II .e...~ I.I .I_ un—‘uo 4“‘111““1“{‘1‘1 ....II ....ux. a.oc_amu .:u I.I.uuo .uaI. ..aaII.a ....IIIIII. .uauII. .:u I.I.uua .III. ...IIIIII. «Iqu_ .:a IIIIqu poo "IoIs II «and. Ono.\c—\~o uanI—c0—-2 o. . I I.. no. >a I .II .. .II: I. ..I: «..I: . .>..au I .>. In...xx :uz. a II ..>. .I...xx - .>..ao. .. «.III I .>...u I .>. .....xx aux. a II ..>..Iu . .>. .m....x. I. cam: o. ..I—I. I >_ «a. I.I.«IIIaou nuwzu. .. I .II ..:...u . .>..ao. I .>. ...... I .>..Iu I .>.—...."“ . I I cam. o. ..<.Ia I >. go. >2 a. N I ._ «a. . >. ..I. .c I I». ...-I 9.... o. ...:.... I z 8. >2 a. I I ._ no. quau pl: cIu; IaIupmu ““‘41‘4‘ ““1{‘ Ii“ 4‘1 {“{41‘.‘{{ {{{44‘14““1““l III...) 30. 2.3.3.3 I ...:.. I.. I ...-I I Q... I 3.: I 3.: I 5.: I 5.... I ..:.... I ..:.... I ..:.... I 3“. mazes: 2.3. ...«u. as. I .~a .~...a cam; o. ..I—Ia I In so. >3 a. ~ I ~. so. :. pxw3 e I :23 o I :33 933 o— 2233 I :I 3‘ : puma ~ I ..q ....x any: a. .¢<.m. I .s so. pace. I I53 a I 33.3 0.. I 334 c I ~33..— 82 I :8: ua I U3 0 I plug: Ice IIoII .. IoIo. oao.\o.\~o .....Io.-3 In I ma ":23: I :xw 32.8. I :3 "on I 933 Isn I 2253 3a..» ~— I .2533 s— : 9.0 00- =3 3 I 30:33 33 on >553 I o: 85 n I >3 3 I ma ...:.—3.. I :3 2.8. I <5. Ion I 93 "on I 2253 3!; p. I .3833 u— : in a. :9. 8. I «00:53 a: on b2—u3 I oo— 85 n I >3 3 I «3 I..—3... I u—xw I3. I 3w. Inn I Gama Inn I p353 3w..— 3 I £533 5- : an a: :3 .8. I 30:33 a: on p553 I 00— St a I >3 In I «3 “392.. I «hum 38.38. I <5. 3n I 933 «2 I p553 3w..— 0 I 5833 m- : an oo- :3 an I .&_v3u§ 33 on pgum3 I oo- 83 o I >3 I9 I ma I....xu: I 33w :8. I gun I; I 933 SN I nguvi 3w..— 0 I $833 5— : 93 oo- =3 2 I goo—van: 633 0— p353 I oo- 85 o I >3 I.. I ma "Inxwa I :xm 38. I <5. Ism I gua 3N I pus-m3 3w:— 5 I g3 .— : 9m 02 =3 e— I 30:3ws 993 0— p333 I a: as o I >3 II I m3 3&qu I gnu 2.8. I <5. “mm I 933 3.." I ~3—m3 39: o I £633 3- : an 02 =3 as I goo-v3u3 I an. on —=—m3 I oo- 85 o I >3 Iv I ma I..—nus I :5 38. I Inw- I0— I 933 3.. I p553 39.— n I I633 5— n3 “Io-I. II 1.3— Quote—3o 33733343318 «0.: 317 I. as» n . good. am:— as. . a~ «a ;c. n «u ._ a . goo.— .w:— a». . .~ .0 .>. . a~ .. an a..a\.. oa_aa_au. us. 2a.. so._¢~_x._¢o sauna. .:ga. _a... ”updm.:._ _xua «_dua . .>..au . .’_ .__.ux sua— o .v ..>_ u...xx . .>..ao. ._ <~dua . .>..du - .>. .u...xx am:— 9 .. “.>..du . a>_ .a...xx. .. azwa o. .¢<_mz . >_ so. «_a.<._mzou uuuau. .>u. . a.- qq puma .~ \ ..quux . .sa ..>u..xx. . .aa ..>u_.xx can: a. _.‘—m. . a. so. a I g..— . am:— I»: I an 80 aha I an m— an .I~I\>v ea—Ia-Uu. map as. a——<~_8:8 Suuul =88— mww. «Iguana I; ...:.... pica n pass an..— o— IA u—mu m- u o u—wu I luau 29: o I 3): . ~>u: m- o I ~§= aw..— 0 AI 3): o gm: ‘— 80- paw: 8”: I N>u_ 8w..— 0 A Rafi—uua . a~>u:m: m- ): O» N I .3— 8. p I ~>w— — I Ngi mi! 8 p .. ~58: ou.‘_a . .. u .u__... .ua.x .a».._o ...<_w.._.-uuu<-. dd. _xua ‘_dua . .>..ao . .>. .u__..x an:_ e .v ..>. .....xx . .>..au. .— <~dua . .>...u . .>. .a...xx am:— a .v “.>..du . .>. .u...xx. ._ can: o. ..:.": a >_ «a. o...<¢_uaou unwau. .>u. . ... as puma ...q ..>u_.xx. . «sa.¢ . ..qq.ux. . .¢:.d< . ... . .qa .—>u..xx . azu- o— _.‘—ma . as no. a<=¢d< . <=gd< undo o.. . - . n .v __ .- ...xn ...ux.o.o¢_auu dd¢u . . _aao. am:— a .. ..<_w. . ..>u_.... . .~>u..m.. ._ swap 9 . ngood. .— .. as» p . ngao.. gm:— 6 .. .«xxgo . pxaau. .- . . basa. . ..:o. am:— e v ..«_u. . .—>u_.... . .~>u..‘.. .. cu. .xua xu_ . ~>u_ aux. a .v ..:u.... . .~>u_.... .. >3 o. N . so. so. . . ~>u_ so. .xua zu_ . .>u_ aw:— o . ..xu.... . ..:u..... .. >3 o. ~ . :u. so. . . .>u. o I mg: 3:! 8 ... _.ua on.._a . .. . ....... .uoIa .amI..a ...<_w.._.¢usu¢._ dd¢u ..:.s ......n . ..:...«uwnanuu. can. o— _-¢_na . .:_q «a. , . u.a . u.a ao._ua=. dd¢u. mac ”...; .. .ouo. ooo.\o.\~o ua...¢o~.u. ..... «mumps—2 .2: Inna «new... u¢ onus Imam... u¢ _-¢»ua \~uuo..\ am¢¢zw4uaxxao An... ..I I¢ ...: .A.I-¢=u Isaac—a .Iao-g ..wum»a_ u< cupc¢=az \.nuoa.\ nmuw:§ . ~25: I navux pun I In .3 plus “2 ..:xx 0 «>39! I n>=ux can "I... -- noun. goo—\o—\~o ua...go.-<’4_ua/uu no... :8 On p I .: 8; .o I azvux 3w: O» bud—u: I a an 29:23 3380 o— 3.25.9.3. a..xx ...ux. o_o¢_xuu can ... uzquo 9w 6:854? 3.. xw-xzr : gm NIga— tux—IIIIfluslcsIunu- o I g: .3:» I»: I a~ 3 .>.. I ua m— uu 32;. 2222an w..— .8: 8:<~_::8 .53”... RI:— p823 cwwu Inga—8.. .9.—:0— .I I 8:u 3:08:5- .: .II 96:33: mo awe-.II ht—g pg: «3: o.— m344> =< 3m: nun—s. “not: .3=u¢¢:~xu<¢— 443 ua _xu. -a .~>m_.xx . .~q.<_u..h .anq .~>w..xx ..u .. .~a ...xu. ~a_¢m can. o» pa u o_o.~auu.xoa bang. _a-¢ ...-g na~>w_.m. . . so_»uza& ux— Io mad<> a_o¢~xwu.aoa hwy-z .:.cI 5.: us puma ..~q.ux u. u .. “nu «..ux. pa... any: op p¢u_.m.. . I a.on»awu u=_ ~¢ nu_»uaa. map .0 u=4<>u »z_.g. ...-u .gaoauxvaau. u. .aaoau ...... u.-w_-<:aa.na¢=a “- ”cup-aau - “uhuuu_a u. ”nuancesau ...-m “ adu u:_»_o_ - .oe.<_u.._ no \ .ux___¢<_. . ux_~azu. . ux_p.o_ .ux_p . u:_»aau an.~_o . .. . ....... .ua.x .am;‘_a ..vm. . ”_— ua .xux Ans.ux . .ua —)u..xx 95: oh p53: I Na. 85 89.— o I 332:— u— 58 "Iona .. 83p so—xozmd wigs—3:256 «0.: 319 A.A . AAAAAA.A.. . AAaAaAAA.: . AAAAAA.A.A . AA.AA.AAA . AAAAAAA.AA . AAA .A..A= A. AAA. AA..AAAAA.A A AA.AAAAAAA.A . AAA . A. .A.AA: A A. A . A. AA. A. AAA. A.AAA.A=A.A A AAAAA A AA.AA . AA..AAAA. . AAA . A. .A..A: A. A. A . A. AAA A. AAA. AA..oAA . AA. .A.AA= AA A. A . A. AA. 099— \ 9.499.9—op I m»<9u.9:us~ .m.m><9u.9 o aNAmp<9o.9 o .pAm><9u.9 I m><9u.9—Op A. AAoAA AAAAAAAA.A .AAAAAAAA.. .AA.AAAAA.. .AAAAoA .A. A3... A. AA AA... AAA .AAA.. . AA.AAAA=a . AAAAA . .A. . AAAAA.A . .AAA. .A.. A. AAAAA AA.AAAAA. ;.AAAA .AAAAAAA.. ”A. AA... AA.AAAA.A .A....A .AAAAAA.A. A. A=... AA.A.A.AAA.: ..AaAAA.:. .A....3 ”A. AA... AA.AAAA AaAA AAA. .A. AA... AA.AAAo.AAA ..AoAAAA AAAAAAA. .A. AA... .AA.AAA..... A .AAAAA..A .AAA.§ $.AmAwAAAA .A.. AAAAA .A. AAAAAA ..AAAAAAA. .MAAAAAA. .A. AA... AAAAAAA AAAAAAAE AAAAAAA. 8;. :AAAAAA A~.AAA.A. ..AAAAA. .A. AA... ..AA. AA .AA..AAA. . AA. AA ”AAAAA ”AA. ......A.. .M A. .AA..AAA. .AA A.AAA .AA. .A.A.AAA. .A AA .AA..AAA. .AA .AAAAA .AA. .A....AA. .A AA ”.A.-AAA. ”AA ”AAAAA .A.. .A....AA. .. AA .A.-AAA. AA .AAAAA .AA. .AA.AAAA. .A AA .A....AA. .AA .A.AAA AA .A....AA. .M AA .AA..AAA. .AA .A.A.A A.AA. A...AAA.. A. .AAAAA ”A. An... AAAA.A. A . .AAAAaA. .AAA.AAA. .AAAAoAA .A. AA... AAA..." AAAAAAA. .AAAAAAA .AAAAaA. .AAAAAAA. ..AA.oAA .A. AA... AAA.2 .AAA..AA. AAA.AAA AA...A. ....AAA. .AAAAAA A. AA... “.AMAAAA. ”AAA... ..AAaA. ..~.AA.. .A«.oAA .A. AA... AA... AA.AAA. .AA.oAA AA. ... .A... "A. AA... 3.. o...A. ...:.: ..:....A. .95. A. 5.... A... ..:...... .35.... .52.... .A. 5.... . AAAA .A=.A ..A ..:... «AA... .MA.AAA ”A. AA... .AA.oo.A AAA.oo.A .AAA.ooA. AA..AA «AA..AA AA.AA A. AA... AAAAAAAAA.A A~.AAAAAA.A .AA.AAAAAA.A .A. An... AAAA. AAAA. .AAAA. .AAAA. .A. AA... A. AA 5.... AAA ..AAA... . ..:...... . A8! . .>.. . AAAAA... . .>.... .A.... AAAAAAA. . A. ... A..AAA AAA... AA AAAAA..AA ... 32.: AA AAA .A....» .... AAA..A AA AAA .A..... ..A 93 A0909 .. 8A3 stag 3..—8.3.333 AI... AAA..A AA AAA .A.... ... AAA... AA AAAAAAAA.A ... AAA..A AA .AA .AAAA ..A swamps. m. .n .n. ..9... 2.9 AAA..A AA AA A.AAA ..A «mom... a. .mAAaax 2.9 swam—z. m< .m-.w..x 8.9 u.oz.n a. .n—AOA9 8.9 swam... m< .nAo .92 2.9 AAAAA.. AA AA. .A. ..A AAAAAA. AA .A..ooAA ..A AAA..A AA AA..AA ..A 9.92.9 m. .m.9uw.m93a 2.9 AAAAA.. AA AA.A. ..A .Aa.. .AAAA.A .AAAAAAAAA AAAAAAA AaA AAA..A AA AA.AA: .AAAAAA. AAAAAA .axxou anA—a. m< —>w. A.... ..uuupx. 0‘ pl! Aube .uuuwpx. 9‘ 98m. ..uuu—x. ad p¢AA. AAA.AAAAAAA .A. A..A: A.A. AA AAA... . AA. AA. A. AA AAAAAA AAA AAAAAA.A .AAA .AAA.. . AAAAAAaa.AAAA=u . AAAAA .....- AA .AAAAAAAA.AAAAA.A..A.A . .A. . AAAA . .A. . AAAAA.A . .AAA. . AAAAAA.A A. AAA. A. AAAAA A. AAA. AA. .A . ...... .A.. .A....» .A.. .A.... .A.. ....A: .A. AA.A. AA. ...AAA. . AA...A.AA. . AA. .A . ...AAAA A...AAAAAAA . AA. A. ..A... . .m. ......» ..AAAA . AA.AA.AAA. . .A. ..A... . A. A...» BA. AA. ..AAA. . .. .2 A. AA AAAAAA AAA AAAAAA.A .A.. AAAA A a .. . AAAAAAAac . Anus. ...... AA .AAAAAAAA.AAAAAAAA.A.A A .A. . AAAA . .A. . AAAAA.A . .A. A. . AAAAAA.A AAAAAaa. AA AA . AAA.A . A. AAA A. A.A u...~9o»-<’..—a/Au A.... m... on ux.pa:§ 4<~o— um>¢u. AAAA. . .AAA. . AA. .A.AA=.. A AA. .A..AA.. AA. 999 A0909 .- mono. 990—x9—\~9 320 a a o. ..xua h. pi! AAA.A.AA. . ~.AA...-.. A A AAA.A.AA. . A.AA.A . AA . AAA. .A.... . AA. .A.AAAAA AAAA.A . .A.... A . ~.AA. .A.. A. A AA. .A . A...AA A. . AA. .A....» . A... .A.... . AA. A . .A.A.A. AAAA. AAA... . AA. .A...:. . AA.AAAAAAAA A AA. .A . ...... ..AA. .A.AAAA . A . AAAA. .A.. A . AA. A.AA. A. AAAA. A.AAAA . AA. .A..... . AA.AA.A.A. A AA. A.AAAA . AA. .A.... Aufix. A . ..AA. in: a» ..:.: I s. 8. 3:23: o— 3 9 822. I o. 8. 0. pa, 3.:—mu... . 2n. . 3.:—v.85 I 3 I 335...: am: on n25: I o. a. A . 8:... A. A.A A. AAA. A. AA... A. .AA. A . AA. .A.A . .A....A .A.. A A .A.. ..AAAA A. AA. ...: .A.. A. A AA. .23: A. A... «2A... .A.. ..:.... .35.... ..A. AA AAAAA. . A. AAA n. 3 5’. a. 33m... 38 . up!“ 0 I.I O Am-vfls 0 “88$ 6 Anna. AA AA . 35.33.5833... A .>.. o .28 A .>.. . ABA... A .>.“... - AAAAAA: — . .3:; Op 3 0 loss» I n. 8. 2w..— 3 o no... A can; A. AA. A A ..:.... .AA. .238... 939.9 A. AAA. A A AA. .A. A .AaA..A.A .A.AA A AA. .Mé fiA.AAAA A. . . ;A fiA.AAA .A.. A. v A. AAA.AAA A. AA. A A AAA.AAAA AA. .AAA.A.A. AA. AAA.AAA AA. .AAAAAA= An... 9.! 9A .55: I o. 8. n. A. 5.... .A.... 2 2x. o .. a A. 32.33 A «8A.. A .>.. o 3.3 A .>.. o 33.... A .>. AA. .38 23953333 I an AS! AAAAAAAA..A.AA . AAA .A.A.= AAAAAAAA.AA.AA . AAA .A.AA: an; I ..AAAAAA.A A AA...... ..AAAAA.... .A.. A AAaAaAAA.: A .A..AA.A.A A AA.AA.AAA A AAA.AAA.AA A AA.......... AaAAA. A AAAAAAAAAA . AAAAAAA..A . .....A . AAAAA..A.A A AAAAAAAaA ..... A.: A A.A.AAA.= . AAAAAA.=.A . AAA.AAAA.AAA . A.AA.AAA . AAAAAAA.A. . AAA A...: AAxaoA. \ AAAaAAA. A AAAAAAA..A A AA....... :9 A}. .. 3A3 Odo—3:3 3..—3:54:33 3.: BIBLIOGRAPHY BIBLIOGRAPHY Abkin, M11. and C. Wolf. Distributed delay routines: DEL, DELS, DELF, DELLF, DELVF, DELLVF. In: Computer Library for Agricultural System Simulation (CLASS), Document 8, Department of Agricultural Economics, Michigan State University, East Lansing, 1976. Adeyemo, R. Egg production on cooperative farms: An economic analysis. Agric. Syst. 19:67-75, 1986. Aho, P.W. and MB. Timmons. Simulation of heavy broiler production in areas of high or moderate summer temperature. Poultry Sci. 641623-1627, 1985. Allen, M.A. and TS. Stewart. A simulation model for a swine breeding unit producing feeder pigs. Agric. Syst. 10.193—211, 1983. Anderson, D.R., DJ. Sweeney and T.A. Williams. An Introduction to Management Science: Quantitative Approaches to Decision Making, 4th Ed, West Publishing Co, St. Paul, 1985. Anderson, JR. and WE. Griffiths. Production risk and input use: Pastoral zone of eastern Australia. Aust. J. Agric. Econ. 25(2):149-159, 1981. Antle, J.M. Incorporating risk in production analysis. Am. J. Agr. Ec. 65(4):1099-1106, 1983a. Antle, J.M. Sequential decision making in production models. Am. J. Agr. Ec. 65(2):282- 290, 1983b. Antle, J.M. and WJ. Goodger. Measuring stochastic technology: The case of Tulare milk production. Am. J. Agr. Ec. 66(3):342-350, 1984. Arendonk, J.A.M. van. A model to estimate the performance, revenues and costs of dairy cows under different production and price situations. Agric. Syst. 16:157- 189. 1985. Beattie, BR. and RC. Taylor. The Economics of Production, John Wiley & Sons, New York, 1985. Bellman, RE. and SE. Dreyfus. Applied Dynamic Programming. Princeton University Press, Princeton, New Jersey, 1962. Bellman, R. and R. Kalaba. Dynamic Programming and Modern Control Theory, Academic Press. New York, 1965. Bhide, S., F. Epplin, E.O. Heady and BE. Melton. Direct estimation of gain isoquants: An application to beef production. J. Agric. Econ. 31(1)29-43, 1980. 321 Biehl, L.G., ME. Mansfield, AR. Smith, G.T. Woods and RC. Meyer. Health and performance of commingled feeder pigs as affected by lincomycin and carbadox. Prev. Vet. Med. 3:489~497, 1985. Blackburn, H.D. and TC Cartwright. Description and validation of the Texas A&M sheep simulation model. J. Anim. Sci. 65:373—386, 1987. Blackie, MJ. and J.B. Dent. Analyzing hog production strategies with a simulation model. Am. J. Agr. Ec. 58:39-46, 1976. Boneschanscher, J., AD. James, AJ. Stephens and RJ. Esslemont. The costs and benefits of pregnancy diagnosis in dairy cows: A simulation model. Agric. Syst. 9.2934, Boyd, L.H. Preparing medicated feed labels. In: Feed Additive Compendium, The Miller Publishing Company, Minnetonka, Minnesota, 1988, pp. 33-39. Brockington, NR, C.A. Gonzalez, J.M. Veil, RR. Vera, NM. Teixeira and A.G. de Assis. A bio-economic modelling project for small-scale milk production systems in south-east Brasil. Agric. Syst. 12:37-60, 1983. Brockington, NR, A.G. de Assis and ML. Martinez. A bio-economic modelling project for small-scale milk production systems in south-east Brazil: Part 2—Refinement and use of the model to analyse some short- and long-term management strategies. Agric. Syst. 20:53-81, 1986. Brokken. Ray F. and A.C. Bywater. Application of isoquant analysis to forage:grain ratios in cattle feeding. J. Anim. Sci. 54:463-472, 1982. Bruce, J.M. and JJ. Clark. Models of heat production and critical temperature for growing pigs. Anim. Prod. 282353-369, 1979. Bywater, A.C. and J.B. Dent. Simulation of the intake and partition of nutrients by the dairy cow: Part I-Management control in the dairy enterprise; philosophy and general model construction. Agric. Syst. 1:245-260, 1976. Bywater, A.C. Simulation of the intake and partition of nutrients by the dairy cow: Part II-The yield and composition of milk. Agric. Syst. 1:261-279, 1976. Bywater, A.C. and R.L. Baldwin. Selected computer applications for research: modeling. I_11: Proc. NCCI Computer Applications in the Feeding and Management of Animals Workshop, North Central Computer Institute, Madison, Wisconsin, November 12-15, 1984. Chaing, A.C. Fundamental Methods of Mathematical Economics, 2nd. Ed. McGraw- Hill, New York. 1974. Chambers, R.G. Risk in agricultural production: Discussion. Am. J. Agr. Ec. 65 (4)1114- 1115, 1983. Chavas, J-P., J. Kliebenstein and TD. Crenshaw. Modeling dynamic agricultural production response: The case of swine production. Am. J. Agr. Ec. 67(3):636- 646, 1985. Christianson, L., G.L. Hahn and N. Meador. Swine performance model for summer conditions. Int. J. Biometeor. 26(2):137-145, 1982. 322 Clarke, S.E., C.T. Gaskins and J.K. Hillers. Systems analysis of beef production-effects of culling criteria on net income. J. Anim. Sci. 55:489-497, 1982 Close, W.H. and LE. Mount. Energy retention in the pig at several environmental temperatures and levels of feeding. Proc. Nutr. Soc. 3033A-34A, 1971. Close, W.I-I. and LE. Mount. The rate of heat loss during fasting in the growing pig. Br. J. Nutr. 34279-290, 1975. Close, W.I-I. and LE. Mount. The effects of plane of nutrition and environmental temperature on the energy metabolism of the growing pig. 1. Heat loss and critical temperature. Br. . N utr. 40:413-421, 1978. Close, W.H., L.E. Mount and D. Brown. The effects of plane of nutrition and environmental temperature on the energy metabolism of the growing pig. 2. Growth rate, including protein and fat deposition. Br. J. Nutr. 40423-431, 1978. Close, W.H. The effects of plane of nutrition and environmental temperature on the energy metabolism of the growing pig. 3. The efficiency of energy utilization for maintenance and growth. Brit. J. Nutr. 40433-438, 1978. Cochran. W.G. Sampling Techniques, 3rd Ed., John Wiley & Sons, New York, 1977, pp. 75-78. Congleton, Jr., WR. and RE. Goodwill. Simulated comparisons of breeding plans for beef production-Part 1: A dynamic model to evaluate the effect of mating plan on herd age structure and productivity. Agric. Syst. 5207-219, 1980. Congleton, Jr., WR. Dynamic model for combined simulation of dairy management strategies. J. Dairy Sci. 67:644-660, 1984. Congleton. Jr., WR. and L.W. King. Profitability of dairy cow herd life. J. Dairy Sci. 67:661-674, 1984. Davis, GB. Management Information Systems: Conceptual Foundations, Structure, and Development, McGraw-Hill, New York, 1974. Dent, J.B. The application of systems theory in agriculture. I_n_: Study of Agricultural Systems, GE. Dalton, Ed., Applied Science Publishers, Ltd. London, 1975. Dijkhuizen, A.A., J.A. Renkema and J. Stelwagen. Economic aspects of reproductive failure in dairy cattle. II. The decision to replace animals. Prev. Vet. Med. 3265- 276. 1985. Dijkhuizen. A.A., R.S. Morris and M. Morrow. Economic optimization of culling strategies in swine breeding herds, using the "PORKCHOP COMPUTER PROGRAM." Prev. Vet. Med. 4341-353, 1986. Dillon, J.L. Bernoullian decision theory: Outline and problems. In: Risk, Uncertainty, and Agricultural Economics, J. Roumasset, Ed., Iowa State University Press, Ames, 1979. Epplin, EM. and ED. Heady. Statistical estimates of optimal protein for beef feeding rations. Agric. Syst. 935-41, 1982. 323 Farver, T.B. Disease prevalence estimation in animal populations using two-stage sampling designs. Prev. Vet. Med. 5:1-20, 1987. Fawcett, RH, C.T. Whittemore and CM. Rowland. Towards the optimal nutrition of fattening pigs: Part I-Isoquants and isocompostion functions. J. Agric. Econ. 29165-173, 1978a. Fawcett, R.H., CT. Whittemore and CM. Rowland. Towards the optimal nutrition of fattening pigs: Part II-Least cost growth and the use of chemical value in diet formulation. J. Agric. Econ. 29175-181, 1978b. Fetrow, J., J.B. Madison and D. Galligan. Economic decisions in veterinary practice: A method for field use. J. Am. Vet. Med. Ass. 186:792-797, 1985. Flesja, K.I., J.B. Forus and I. Solberg. Pathological lesions in swine at slaughter. VI. The relation between some mainly non-environmental factors, diseases, weight gain and carcass quality. Acta Vet. Scand. 25309-321, 1984. Forbes, J.M. Models for the prediction of food intake and energy balance in dairy cows. Livest. Prod. Sci. 10.149-157, 1983. Forster, T.G., D.N. Mowat. S.D.M. Jones, J.W. Wilton and DR Stonehouse. Systems for producing leaner beef. Agric. Syst. 15:171-188. 1984. Fox, DO. and JR. Black. A system for predicting body composition and performance of growing cattle. J. Anim. Sci. 58:725-739. 1984. Gartner, J.A. and WA. Herbert. A preliminary model to investigate culling and replacement policy in dairy herds. Agric. Syst. 4:189-215, 1979. Gartner, J.A. Replacement policy in dairy herds on farms where heifers compete with the cows for grassland—Part 1: Model construction and validation. Agric. Syst. 7289-318, 1981. Gartner, J.A. Replacement policy in dairy herds on farms where heifers compete with the cows for grassland—Part 2: Experimentation. Agric. Syst. 8:163-191. 1982a. Gartner, J.A. Replacement policy in dairy herds on farms where heifers compete with the cows for grassland-Part 3: Revised hypothesis. Agric. Syst. 8:249—272. 1982b. Glen, 1]. A dynamic programming model for pig production. J. Oper. Res. Soc. 34511- 519, 1983. Gutierrez-Aleman, N.. AJ. DeBoer and RD. Hart A bio-economic model of small- ruminant production in the semi-arid tropics of the northeast region of Brazil: Part 1—Model description and components. Agric. Syst. 1955-66, 19863. Gutierrez-Aleman, Nestor, AJ. DeBoer and E.W. Kehrberg. A bio-economic model of small-ruminant production in the semi-arid tropics of the northeast region of Brazil: Part 2—Linear programming applications and results. Agric. Syst. 19159- 187, 1986b. Hale, O.M.. T.B. Steward and 0.6. Marti. Influence of an experimental infection of Ascaris suum on performance of pigs. J. Anim. Sci. 60220-225, 1985. 324 Halter, AN. and G.W. Dean. Use of simulation in evaluating management policies under uncertainty: Application to a large scale ranch. J. Farm Econ. 47:557-571, 1965. Harsh, S.B., L.J. Connor and GD. Schwab. Managing the Farm Business, Prentice-Hall, Inc., New Jersey, 1981. Heady, E.O. Economics of Agricultural Production and Resource Use, Prentice-Hall, Inc., New Jersey, 1952. Heady, ED. and J.L. Dillon. Agricultural Production Functions, Iowa State University Press, Ames, Iowa, 1961. Holt, John. Risk in agricultural production: Discussion. Am. J. Agr. Ec. 65: 1116-1117, 1983. Hulme, DJ, R.C. Kellaway, PJ. Booth and L. Bennett. The CAMDAIRY model for formulating and analysing dairy cow rations. Agric. Syst. 2281-108, 1986. Irvin, K.M., L.A. Swiger and DC. Mahan. Influence of dietary protein level on swine with different growth capabilities. J. Anim. Sci. 41:1031-1038, 1975. J anssen, L. and J.B. Hassler. A Forecasting-Programming Method for Swine Production- Marketing Decisions, Agricultural Experiment Station, Institute of Agriculture and Natural Resources, University of Nebraska, 1981, pp. 2-40. Johnson, G.L. Agricultural economics. production economics and the field of farm management. J. Farm Econ. 39441-450, 1957. Johnson, G.L. Stress on production economics. Aust. J. Agric. Econ. 7:12-26, 1963. Johnson, G.L. and LK. Zerby. What Economists Do About Values, Department of Agricultural Economics, Michigan State University, East Lansing, 1973. Johnson, M.H. and DR. Notter. Simulation of genetic control of reproduction in beef cows. 1. Simulation model. J. Anim. Sci. 65:68-75, 1987. Jolly, R.W., A.P. Sather, R.D. Patterson, B.H. Sonntag, A.I-I. Martin and H.T. Freeden. Alternative market weights for swine: Production economics. J. Anim. Sci. 51: 804—810, 1980. Jolly, R.W. Risk management in agricultural production. Am. J. Agr. Ec. 65:1107-1113, 1983. Kahn, HE. and C.R.W. Spedding. A dynamic model for the simulation of cattle herd production systems: Part l-General description and the effects of simulation techniques on model results. Agric. Syst. 12:101-111. 1983. Kahn, HE. and C.R.W. Spedding. A dynamic model for the simulation of cattle herd production systems: Part 2—An investigation of various factors influencing the voluntary intake of dry matter and the use of the model in their validation. Agric. Syst. 1363-82, 1984. Kahn, HE. and AR. Lehrer. A dynamic model for the simulation of cattle herd production systems: Part 3—Reproductive performance of beef cows. Agric. Syst. 13:143—159, 1984. 325 Klein, KK, R. Hironaka, C.H. Heller and BS. Freeze. Profit-maximizing linear program model for dairy cattle. J. Dairy Sci. 691070-1080, 1986. Koong, L.J., K.H. Falter and HL. Lucas. A mathematical model for the joint metabolism of nitrogen and energy in cattle. Agric. Syst. 9301-324, 1982. Kuester, J. and J. Mize. Optimization Techniques with FORTRAN, New York, McGraw- Hill, 1973. Leibenstein, H. A Branch of economics is missing: Micro-micro theory. J. Econ. Lit. 17:477-502, 1979. Levine, J.E. and W. Hohenboken. Simulation of beef cattle production systems in the Llanos of Colombia: Part 2—Results of the modelling. Agric. Syst. 7:83-93, 1981. Leuthold, R.M. On the use of Theil’s inequality coefficients. Am. J. Agr. Ec. 57344—346, 1975. Lindqvist, J-O. Animal health and environment in the production of fattening pigs. Acta Vet. Scand., Suppl. 51, 1974. Lindvall, R.N. Effect of flooring material and number of pigs per pen on nursery pig performance. J. Anim. Sci. 532863-868, 1981. Lloyd, J.W., J.B. Kaneene and SB. Harsh. Toward responsible farm-level economic analysis. J. Am. Vet. Med. Ass. 191:195-199, 1987a. Lloyd, J.W., S.B. Harsh, J.B. Kaneene, BJ. Thacker and GD. Schwab. Decision support for growing and finishing pigs: Application of computer simulation techniques to a bioeconomic system. I_n_: Economics of Animal Disease, Mather, EC. and Kaneene, J.B., Eds, McNaughton and Gunn, Saline, Michigan, 1987b. Loewer, 0.1., E.M. Smith, NO. and R. Fehr. Incorporation of environment and feed quality into a net energy model for beef cattle. Agric. Syst. 11:67-94, 1983. Lovering, J. and J.A. McIsaac. A forage-milk production model. J. Dairy Sci. 64:798-806, 1981. Macbeth, GM. and GP. McPhee. An economic evaluation of breeding systems with selection and crossing of large white and landrace pigs in a closed herd. Agric. Syst. 20219-239, 1986. Manetsch, TJ. Time-varying distributed delays and their use in aggregative models of large systems. IEEE Transactions on Systems, Man, and Cybernetics, SMC—6:547-553, 1976. Manetsch, TJ. and G.L. Park. Systems Analysis and Simulation with Applications to Economic and Social Systems, Michigan State University, East Lansing, August, 1982. Manetsch, TJ. Simulation as an aid to multicriterion decision making in dynamic systems with uncertain parameter values and exogenous inputs. Department of Electrical Engineering, Michigan State University, East Lansing, 1986. 326 McPhee, C.P. and G.M. Macbeth. Profit in a pig herd from performance testing and selection of breeding stock. The effect of variation in the accuracy and cost of testing. Agric. Syst. 15:137-151, 1984. Meek, AH. and RS. Morris. A computer simulation model of ovine fascioliasis. Agric. Syst. 7:49-77, 1981. Moser, R.L., S.G. Cornelius, J.E. Pettigrew, Jr., HE. Hanke and CD. Hagen. Response of rowing-finishing pigs to decreasing floor space allowance and (or) Virginiamycin in diet. J. Anim. Sci. 61:337-342, 1985. Moughan, P.J. Sensitivity analysis on a model simulating the digestion and metabolism of nitrogen in the growing pig. New Zealand J. Agricul. Res. 282463-468, 1985. NCR-89 Committee on Confinement Management of Swine. Effect of space allowance and antibiotic feeding on performance of nursery pigs. J. Anim. Sci. 58:801-804, 1984. NCR-89 Committee on Confinement Management of Swine. Effect of space allowance and tylosin feeding on performance of growing-finishing pigs. J. Anim. Sci. 62:871-874, 1986. Nix, J. Farm management: The state of the art (or science) J. Agric. Econ. 30: 277-291, 1979. Oltenacu, P.A., R.A. Milligan, TR. Rounsaville and RH. Foote. Modelling reproduction in a herd of dairy cattle. Agric. Syst. 5:193—205, 1980. Oltenacu, P.A., TR. Rounsaville, R.A. Milligan and RH. Foote. Systems analysis for designing reproductive management programs to increase production and profit in dairy herds. J. Dairy Sci. 642096-2104, 1981. Oltjen, J.W., A.C. Bywater, R.L. Baldwin and W.N. Garrett. Development of a dynamic model of beef cattle growth and composition. J. Anim. Sci. 62:86-97, 1986. Orsini, J.P.G. and G.W. Arnold. Predicting the liveweight changes of sheep grazing wheat stubbles in a Mediterranean environment. Agric. Syst. 2083-103, 1986. Parsons, T.D., G. Smith and DT. Galligan. Economics of porcine parvovirus vaccination assessed by decision analysis. Prev. Vet. Med. 4:199-204, 1986. Pope III. C. A. and ED. Heady. Economics of feeding corn silage versus corn grain to beef steers as affected by interest rates, liquidity preference and opportunity cost of capital. Agric. Syst. 10:65-74, 1983. Pindyck, RS. and D.L. Rubinfeld. Econometric Models and Economic Forecasts, 2nd Ed., McGraw-Hill, New York, 1981. Powley, 18.. PR. Cheeke, D.C. England, T.P. Davidson and W.H. Kennick. Performance of growing-finishing swine fed high levels of alfalfa meal: Effects of alfalfa level, dietary additives and antibiotics. J. Anim. Sci. 53:308-316, 1981. Quijandria. Jr., B. and O.W. Robison. Weight and backfat deposition in swine: Curves and correction factors. J. Anim. Sci. 33911-918, 1971. 327 Randolph, J.H., G.L. Cromwell, T.S. Stahly and DD. Dratzer. Effects of group size and space allowance on performance and behavior of swine. J. Anim. Sci. 53922-927, 1981. Reece, RN. and BD. Lott. The effects of temperature and age on body weight and feed efficiency of broiler chickens. Poultry Sci. 621906-1908, 1983. Rehman, T. and C. Romero. Multiple-criteria decision-making techniques and their role in livestock ration formulation. Agric. Syst. 15:23-49, 1984. Rehman, T. and C. Romero. Goal programming with penalty functions and livestock ration formulation. Agric. Syst. 23:117-132, 1987. Reyes. A.A., R.W. Blake, CR. Shumway and J.T. Long. Multistage optimization model for dairy production. J. Dairy Sci. 64:2003-2016, 1981. Riberio de Lima, F, TS. Stahly and G.L. Cromwell. Effects of copper, with and without ferrous sulfide, and antibiotics on the performance of pigs. J. Anim. Sci. 52:241- 247, 1981. Robison, LJ. and PJ. Barry. The Competitive Firm’s Response to Risk, MacMillan, New York, 1986. Romero, C. and T. Rehman. Goal programming and multiple criteria decision-making in farm planning: An expository analysis. J. Agric. Econ. 35:177-190, 1984. Root, MD. and DC. Mahan. Effect of carbadox and various dietary copper levels for weanling swine. J. Anim. Sci. 55:1109-1117, 1982. Rozzi, P., J.W. Wilton, E.B. Burnside, and WC. Pfeiffer. Beef production from a dairy fgan: A linear programming simulation approach. Livest. Prod. Sci. 11:503-515. 1 . Russell, NP. and T. Young. Frontier production functions and the measurement of technical efficiency. J. Agric. Econ. 34:139-149, 1983. Sanders, JD. and T.C. Cartwright. A general cattle production systems model. Part 2—Procedures used for simulating animal performance. Agric. Syst. 4:289-309, 1979. Sandiford, F. An analysis of multiobjective decision-making for the Scottish inshore fishery. J. Agric. Econ. 37:207-219, 1986. Schultz, T.W. Theory of the firm and farm management research. J. Farm Econ. 21(3), Part 1, 570-586, 1939. Schwab, G. and HG. Hogberg. Reproductive efficiency and mortality rates: A description of data from the Michigan swine record-keeping project in 1982. In: Report of Swine Research 1983, Michigan State University Agricultural Experimental Station, East Lansing, October. 1983. pp. 177-181. Sere, C. and W. Doppler. Simulation of production alternatives in ranching systems in Togo. Agric. Syst. 6:249-260, 1981. Singh, D. Simulation of swine herd population dynamics. Agric. Syst. 22:157-183. 1986. 328 Sonka, S.T., E.O. Heady and PF. Dahm. Estimation of gain isoquants and a decision model application for swine production. Am. J. Agr. Ec. 466-474, 1976. Stahley, TS, G.L. Cromwell and HJ. Monegue. Effects of the dietary inclusion of copper and (or) antibiotics on the performance of weanling pigs. J. Anim. Sci. 51:1347-1351, 1980. Straw, B. and N. Ralston. Comparative costs and methods for assessing production impact of common swine diseases. In: Economics of Animal Disease, Mather, CE. and Kaneene, J.B., Eds, McNaughton and Gunn, Saline, Michigan, 1987, pp. 165-180. Sutherland, J.W. Systems: Analysis, Administration, and Architecture, Van Nostrand Reinhold Company, New York, 1975. Talpaz, H. JR. de la Torre, PJ.H. Sharpe and S. Hurwitz. Dynamic optimization model for feeding of broilers. Agric. Syst. 20:121-132, 1986. Tess, M.W., G.L. Bennett and GE. Dickerson. Simulation of genetic changes in life cycle efficiency of pork production. I. A bioeconomic model. J. Anim. Sci. 56:336- 353, 1983a. Tess, M.W., G.L. Bennett and GE. Dickerson. Simulation of genetic changes in life cycle efficiency of pork production. 11. Effects of components on efficiency. J. Anim. Sci. 56:354-368, 1983b. Tess, M.W., G.L. Bennett and GE. Dickerson. Simulation of genetic changes in life cycle efficiency of pork production. III. Effects of management systems and feed prices on importance of genetic components. J. Anim. Sci. 56:369-379, 1983c. Teter, N.C., J.A. DeShazer and TL. Thompson. Operational characteristics of meat animals. Part I—Swine. Transactions of the ASAE 16:157-159, 1973. Thomas, W.C., E.L. Arobio, L.L. Naylor and RD. Stern. An alternative management system for Alaska reindeer herds. Agric. Syst. 11:1-16, 1983. Thacker, B.T. Personal communication, 1987. Thulin, A.J. Personal communication, 1986. Thulin, A.J., E. van Ravenswaay, J. Lloyd, S. Harsh, J. Kaneene and B. Thacker. Pig identification and swine health information management system (SHIMS) In: 1988 Research Investment Report, National Pork Producers Council, Des Moines, Iowa, 1988, pp. 38-40. Upton, M. The unproductive production function. J. Agric. Econ. 30179-191, 1979. United States Department of Commerce, Bureau of the Census. 1982 Census of Agriculture, Vol. 1. Geographic Area Series, Part 51, Washington DC, October 1984. VanDyne, G.M. and Z. Abramsky. Agricultural systems models and modelling: An overview. I_n_: Study of Agricultural Systems, G.E. Dalton, Ed., Applied Science Publishers, Ltd., London. 1975. 329 Verstegen, M.W.A, W.H. Close, LB. Start and LE. Mount. The effects of environmental temperature and plane of nutrition on heat loss, energy retention and deposition of protein and fat in groups of growing pigs. Brit. J. Nutr. 3021-35, 1973. Watt, D.L., J.A. DeShazer, R.C. Ewan, R.L. Harrold, D.C. Mahan and GD. Schwab. NCCI SWINE—Swine Growth Simulation Model, North Central Computer Institute, Madison, Wisconsin, 1987. White, D.H., PJ. Bowman, F.H.W. Morley, WR. McManus and SJ. Filan. A simulation model of a breeding ewe flock. Agric. Syst. 10:149-189, 1983. Whittemore, C.T. Development of recommended energy and protein allowances for growing pigs. Agric. Syst. 11:159-186, 1983. Whittemore, C.T. An approach to pig growth modeling. J. Anim. Sci. 63615-621, 1986 Zimmerman, D.R., D.P. Conway, D.H. Bliss, D.O. Farrington and HJ. Barnes. Effects of carbadox and pyrantel tartrate on performance and indices of M ycoplasma hyopneumoniae and Ascaris suum infections in pigs. J. Anim. Sci. 55:733-740, 1982 330