. Bus . miss .. e - . .0;me Pmmma am; A COMPUTERIzw-Eé R RES j ”EARQH F0 — _ w w ~ m. r w w _.. —. 7".1-99 "nay-yup;- uquuhny.n .. flaw“). :vV. Thesis for the Degree-of Ph 13-. : WERSIL” u TATE EDDY mmmmoua ~ 1971 EGAN 3' WE?! ,- .1. . .UJXN— .. . u. :huxx .fl/. .v...r..r . . 11. p 1.. . Irr/r. I6 laurffa' .1 fulfil. v v 1.2.1. .1 r. r. (Iv. 3.4110115; Ir {Currl‘ v mac.“ I. . v llrfrx. I II. . (1...! .s p 4.1.Ir‘li’ . . This is to certify that the thesis entitled A COMPUTERIZED FARM BUSINESS SIMULATOR FOR RESEARCH AND FARM PLANNING presented by Eddy Lorain LaDue has been accepted towards fulfillment of the requirements for Ph.D. Jegreein Agricultural Economics M61 ég:»\//(’ éécabc/ Major professor Date November 17, 1971 0-7639 ABSTRACT A COMPUTERIZED FARM BUSINESS SIMULATOR FOR RESEARCH AND FARM PLANNING By Eddy Lorain LaDue The scientific industrialization of Agriculture is evolving a commercial farming sector made up of individual units of ever increas- ing size and complexity. This complexity involves the effects of size itself, a rapidly increasing level of technical knowledge and an increased interdependence and interaction of farm firms with input supplies, product processors and retailers. As well as its effects on complexity, expanding size significantly increases the capital levels involved in most management (1901810115- Although techniques such as budgeting, COMparative analysis and linear programming have been deve10ped to assist managers in decision making, the increased complexity of the management function provides a need for even more sophisticated and sensative tools of analysis. Farmers, farm advisors and researchers all need tools to aid them in understanding the relationships and interrelationships that occur in the commercial farming sector. This study involved the development and testing of a computer- ized dairy farm business analysis model for use in both research and farm planning. The model was conceived within a systems framework with the farm business viewed as a set of acting and interacting SyStems. Simulation was selected as the most appropriate modeling technique to be used. ,5 _. Edd y Lorain LaDue The major focus of model construction was develOpment of a model which could realistically simulate the important physical and financial characteristics of individual farm businesses. The model developed can handle either a dairy-crop farm or a crap farm with only those cr0p enterprises usually found on dairy farms. Additional characteristics of the model include (1) a monthly time unit interval with variable values specific to both month and year, (2) a deterministic mode for all except the dairy herd itself, (3) choice of either a calendar or fiscal year basis, (4) focus on ”simulation for management” 33 Opposed to "simulation of manage- ment,” (5) 87 different Specific dairy, cr0p grow or cr0p harvest systems, and (6) user defined systems which can be used to represent systems not specified by the “del- Model coefficients are divided into two groups. The first group includes those variables which represent the characteristics of the Specific situation to be simulated. These coefficients must be input for every alternative simulated. The second group includes all other coefficients used in the model. These coefficients are assigned initial values by the model. Alternative values are input by exception. Control of the action of the model through a multi-year simu- lation occurs via coefficient change and management decision entries. These are coded input lines (cards) which instruct the model to carry out the appropriate transactions required to change a coeffi- Cient value, buy or sell land. livestock or machinery, or construct buildings Each instruction is dated as to month and year of occur- 0 rance o Ekidy Lorain LaDue Output of the model includes any combination of eleven reports which vary as to data included and degree of detail. Report formats corresPond to electronic farm accounting reports to the degree possible. Specification of the reports to be printed at the end of each year is controlled by the user. The simulator (FABS) is written in Fortran IV for the CDC 3600 icomputer in a batch processing mode. Simulator Operation requires aapproximately 25 to 35 seconds of CPU time per year simulated for typical situations. Use of the model on hypothetical situations designed to test the operationability of the user defined systems indicated that the underlying logic was sound and that systems not defined within the model could be simulated. A stanchion-parlor dairy system and eight- row crOpping systems were used. The model was used on two actual farm planning problems to illustrate its extension application. Three different alternatives were simulated for each situation. The general results indicated that specific farm situations could be simulated and that the generated data could be useful in making management decisions. A small research problem was conducted using the FABS model as the primary research tool. Although insufficient background research on the problem was conducted to provide an acceptable foundation for drawing conclusions from the data generated, the results did indicate that the model had sufficient flexibility to be useful to researchers for certain types of problems. A COMPUTERIZED FARM BUSINESS SIMULATOR IFOR RESEARCH AND FARM PLANNING By Eddy Lorain LaDue A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1971 r ‘ .p {If /? DEDICATION To my father George J. LaDue My first and foremost teacher in both Economics and Agriculture ii ACKNOWLEDGMENTS The author wishes to express sincere appreciation to Dr. Warren Vincent, Professor of Agricultural Economics, for his help and gui- dance as major professor and thesis advisor. The author is also grateful for the data and helpful suggestions provided by Professor Ray Hoglund, Dr. L. H. Brown, Dr. Larry Connor and Dr. Stephen Harsh of the Department of Agricultural Economics and Dr. Donald Hillman of the Dairy Mpartment of Michigan State University; also to Dr. Lester Manderscheid for his critical and helpful review of the manuscript. I would like to thank Miss Laura Robinson, Mr. Daniel Tsai and Mrs. Vanda Freeman for their assistance in programming and Mr. Robert Milligan for his help in running and testing the model. I am grateful for the financial support, encouragement and scholarly environment provided by the Department of Agricultural Economics and its chairmen during my tenure at Michigan State Univer- Slty, Dr. Lo Lo Boger and Dr. D. Ho Hathaway. Finally, a special acknowledgment and thank you to my wife, Lorraine, for her cheerful encouragement throughout my graduate program and for typing the model construction instructions, users manual and prelimenary drafts of the manuscript: and to my sons, Steve and Scott, for the sacrifices inherent in having a graduate student father. iii TABLE OF CONTENTS Chapter I. INTRODUCTION.COO...OOOOOOOOOOIOOOOOOO...OOOOOOOOOOOOOOOOO (flijecrbinnes............................................ thetkuuirilogy........................................... IILEJI Of? Future Chapters............................... II. PREVIOUS RESEARCH AND EVALUATION OF Tm SIMULATION MEYI‘HODOOOOOOOOO...OOOOOOOOOOOOOOCOCOOOOOOO Chanexzil Systems Theory................................ SimuiLation and Systems Analysis....................... §SianLation VS. Linear Programming..................... ESimuxLation.and Economic Theory........................ EHNEViOUS Simulation Research.......................... In Economics....................................... In Business........................................ In Agriculture..................................... .Final Evaluation...................................... III. MODEL DESIGN CONSIDERATIONS..o................."no"... Degree 0f Generalization.............................. Computer Language..................................... Time Interval......................................... Farm Planning VS. Research Emphasis................... Stochastic VS. Deterministic.......................... User-Simulator Interaction............................ Computer Selection.................................... Of or For Management.................................. IDPUt and outPUtoooooooooooooooooooooooooo000000000000 Large Models vs. Aggregation of Small MOdBlSooooocoooooooooooooocooooooecooooooooco .5“: IV. TIE MODELOOCIOOOOOOOOOOOOCOOOOOOOOOOOOOOOOOOOOOOOOOOOOIOO General Description and Important Model Concepts..................................... iv Page 1/ Chapter (§81U31§11.‘3Ut11JN3 Of “Odeloocoooeoooooooooocooooooooo Fiscal or Calendar BaSiSoooooooooooooooooooooooo Parameters...................................... Management Decisions and Decision Rules......... SPeCfiic Model Concepts............................ ESyEVtems......................................... (:ETIP Production Functions....................... M. Ifirvestock Production Relationships.............. Age Composition.............................. Culling Rates................................ Feeding Rates................................ r Machinery COStSoooooooooooo0.0000000000000000.00 —HLabor Requirements.............................. rCPIOdUCt Prices.................................. «’Machinery Set Specification..................... «iDairy Herd Initialization....................... "Building Capacities and Delays.................. IData,Sources....................................... Directions for USeeoooooooooooooooooooooooooooooooococ ‘V. TEETPING AND APPLICATION.-oooooooooooooooooooooooooooooooc General Problems Of Validation........................ If TWO General Approaches............................. Variable and Subroutine Testing....................... Test Of User Defined SYStemSoooooooooooooooooooooooooe Farm Planning Problems................................ A Research Problem.................................... VI. MODEL COST AND MAINTENANCE-0000.000.00.000000000000000... Development COStSooooooooooooooooocoooooo0000000000000 Operating COStSooooooooeococo-coco.00000000000000.0000 Maintenance COStSooooooooooooooooooooooooooooooooooooc User Charges.......................................... Page 105 108 108 110 111 112 115 118 119 121 125 127 130 132 133 134 136 138 140 142 142 145 152 154 160 16+ 167 167 168 172 174 Chapter VII. POSSIBLE EXTENSIONSooooooooo00.000000000000000...coco...o Possible Algorithm Modification....................... Price Level Change Parameters...................... Group Price Changes................................ Stochastic Elements................................ Heather............................................ Machinery Replacement Relationships................ Dairy Herd Characteristics IHPUDooooccoooooooocoooo Teletype Use.......................................... Separation Of Subroutines............................. Generalization........................................ VIII. SUMMARY AND MLICATIONSOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO Summary............................................... ImplicationS.......................................... For Extension...................................... For Research....................................... BIBLICERAPHYooooooooooccoo-00000000000000.0000.coco...0000000000 Appendix A. FABS USER MANUAL AND DATA INPUT FORMS.................... FABS User Manual...................................... Introduction....................................... Part 1, Required Input Data........................ Part II.ooooooonococoa-cocoooooooooooooooooooocoooo Part III-ooococo.coococoo-oooooooooooooooooooocoooo LiSt Of syStGMSooocoo.ooooooo00000000000000.0000... FABS Data Form looo.00000000000000.0000...000000000000 Part IO...OOOOOIOOOCOOOOOOCOOOOOOOOOOOO0.00.0.00... Part II...0......0......0..OOOOOOOOOOOOOOOOOIOOICCC 13. DETAILED DESCRIPTION OF FARM BUSINESS SIMULATOR (was) COMPUTER PROGRAM ROUTINES............... Program FABSoooooooooooooooooooooooooooooooooooooooooo Subroutine READI0.0......CO0.0000000000000000IOOOOOOOO Subroutine INPUToooooooooooeooooooooooooococoa-coco... Subroutine LANDoooooooooooooooooooeooooooooooooooooooo vi Page 175 175 176 177 178 179 180 182 182 184 185 187 187 190 190 191 194 211 211 211 213 226 285 297 303 303 318 321 321 322 324 337 kmnndix Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine Subroutine YLDADQJO...OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOCOO WOOOOOOO0.0.0...OOOOOOOOOOOOOOOCOOOOOO CORN0.00....CO...0.000COOOOOOOOOOOOOOOOOOOO MYOOOOOCOOOOOOOOOOOOOOOO...COOOOOOOOOOOOIC “WTOOOOOCCI...OOOOOOOOOOOOOOO0.0.0.000... OATSCOOCOOCCOOOOOOOOOOOOOOOOO00.000.00.000. FBEANSOOOO0......O...OOOOOOOOOOOOOOOOOOOOOC SBEANSOOOOOOOOOIOOOOOOOOOOOOOOOOOOOOOOOOOOO DAIRYOOOOOCO0.00.0000...OOOOOOOOOOIOOOOOOOO STORESOOCOOOC00.00.000.000...0.00.00.00.00. BUILDOCOCOOOOOOOOOOOOOOOOOOOOOOOIOCOOOOOOO. MACHOCOOCOOOOOOOOOOCOOOCOOOOOOOOOOOOOOOOOOC MOROOOOOOOOOOO0.000000000000000000COOOOOO ACOUNTOOO...0.0.0.000...OOOOOOOOOOOOOOOOIOO FINCEO...C0.00...0.00000000000000000COOOOOO TAXACCOOOOC0.0...IO...OOOOOOOOOOOOOOOOOO... PRINTROOOOOO0......OOOOCOOOOOOOOOOOOOOOOOOI (L DATA SOURCES FOR COEFFICIENTS............................ vii Page 340 345 346 352 358 361 367 370 399 416 422 445 456 462 473 476 487 Dflfle 4.1 4.2 5.1 5.2 5-3 5J1 6.1 B1 LIST OF TABLES Fertilizer Rates and Relative Yields...................... Grain Transition Constants................................ Comparison of Six and Eight Row Systems................... Comparison of Six and Eight Row Systems................... Comparison of Monthly Labor Requirements.................. Desired Age of Freshening................................. Estimated Computer Costs.................................. Variable Names of Parameters Described in Users Manual.... viii Page 117 126 155 157 158 165 171 332 Flgue 2.1 4.1 4.2 1.3 1h4 “.5 4.6 4.? LIST OF FIGURES Basic Input-Output System................................ Flow Chart of Simulator.................................. Generated Production Function............................ Effect of Culling on Daily Production.................... EXpected Distribution of a Herd by Production............ Machinery Systems Average Costs.......................... Effect of Machine Duplication on Costs................... Average Labor Requirements............................... Page 20 107 116 122 123 128 129 130 5o. INTRODUCTION The scientific industrialization of Agriculture1 is evolving a commercial farming sector made up of individual units of ever increasing size and complexity. This complexity involves the effects of size itself, a rapidly increasing level of technical knowledge and an increased interdependence and interaction of farm firms with input suppliers, product processors and retailers. As well as its effects on complexity, expanding size significantly increases the iamount of money involved in most management decisions. This changing situation is particularly true in the dairy sub- sector where farmers are presently evaluating dairy system invest- ments which frequently involve $40,000 to well over $100,000 for progressive dairymen2 and a minimum of $5,000 to $35,000 for average 1 Shaffer, James D., ”The Scientific Industrialization of the U.S. Food and Fiber Sector, Background for Market Policy," in Agricul- tural Organization in The Modern Industrial Economy, NCR-ZO-68, Department of Agricultural Economics, The Ohio State University, Columbus, Ohio, 1968. 2 Heglund, C. R., J. S. Boyd and J. A. Speicher, Free Stall Dairy figusing Systems, Research Report 91, Michigan State University Agricultural Experiment Station, East Lansing, Michigan, 1969. dairymen.3 The large array of combinations of technologiesl+ and the herd eXpansion that often accompanies the change make investment evaluation very complex. Because of the technological and capital-labor substitution characteristics of industrialization and the apparent financial incentives for increased size of farm unitsj it appears that farm decision making will be subject to increasing complexity. As pointed out by Babb and Eisgruber, "Not only is the complexity of business management problems growing, the nature and environment within which the problems have to be analyzed are changing at an ever increasing rate."6 Although techniques such as budgeting, comparative analysis and linear programming have been developed to assist managers in decision making, there is a further need for even more SOphisticated and sensative tools of analysis. Farmers and farm advisors need Hoglund, C. R., Characteristics of Newly Built Cold-Covered and Harm-Enclosed Dairy_Housing Systems, Agricultural Economics Report 129, Department of Agricultural Economics, Michigan State University, East Lansing, Michigan, May, 1969. Buxton, Boyd M., Alternative Dairy Technologies, A Comparison of Unit Cost, Net Return and Investment, Station Bulletin 490, Agricultural ExPeriment Station, University of Minnesota, 1968. Krause, K. R. and L. R. Kyle, Economics Factors Underlyinnghe lgghience of Large Farminngnits, The Current Situation and Probable Trends, Agricultural Economics Report 12, Department (fl'Agricultural Economics, Michigan State University, East Ikusing, Michigan, May, 1969. Babb, E. M. and L. M. Eisgruber, Management Games for Teaching and Research, Educational Methods Inc., Chicago, Illinois, 1966. ' ~ more powerful tools to assist them in COping with the multifacited problems of larger, more complex businesses. Researchers need tools to aid them in understanding the relationships and interre- lationships that occur in the commercial farming sector. The power Of the tools of analysis must increase with the magnitude of the problems being considerei. Objectives The primary objective of this study is to develop and validate a total farm business analysis model which can be used to deal with an array of research and farm planning problems. This model is to ‘be a tool which can be used by farm managers and farm management advisors to assist them in coping with the complex decision situations which farm managers face. The model should also offer promise to researchers interested in evaluating current or exPected changes either within farm firms or in the environment within which the farm firms Operate. More specifically, the primary Objectives are to: (l) deve10p a dairy farm business analysis model which (a) can reasonably represent Michigan dairy farms. (b) can be adjusted by the user to evaluate individual farm situations. (0) is flexible enough to assist in the design and evalua- tion of systems not now in Operation. (d) is designed to minimize difficulties in expanding or adapting the model to include other farm enterprises. [4 (e) will offer flexibility in use by offering a range in output Options with a corresponding range in input requirements. land (2) generate evidence as to the degree to whicn this model may be useful as a tool for research and farm planning. Although it was presumed at tne outset that the model developed would be a simulator designed such that management was viewed in a systems context, it was deemed necessary to critically examine that position. In line with this, secondary Objectives of the study were set forth. These secondary objectives were to (1) review systems theory and its relationship to the management function and (2) evaluate the simulation method as a potential tool for total business analysis. Methodology The methodology used in this study included an extensive review and evaluation of systems and simulation literature followed by design and development of a total farm business computer simulation model. The actual process used can be delineated as five steps: (1) problem identification, (2) establishment of a theoretical frame- *work for model development, (3) specification of the model, (4) satis- :faetion of data requirements and (5) model testing and application.7 7 The approach to and process Of model deveIOpment follows Closely Fkumtsch, T. J., Design, Develgpment and Use of Simulation Models for Systems Plannigg and Ma__nagement, Paper presented at the North Central Regional Farm Management Extension and Research Conference, Michigan State University, October 13-16, 1969. - .1 - . - l -| .. '2‘ . ‘1 . \ ..‘ k . . T r. . . . l ‘7 A .3 en .u. . \ "~ . \ ‘l K . In 7. n '0 b. . . a- Q 4 . As previously indicated, the environment in which farmers are and will be making decisions is becoming increasingly complex. The investment and disinvestment decisions being made almost daily involve large capital investments and cash flows of previously unheard of magnitudes. The rapid rate of technological progress provides new cr0p varieties, machinery systems and building systems at such a fast pace that a number of different methods of carrying out any one function exist at the same time, each with a different investment requirement, technical capacity and degree of obsolesence or potential obsolesence. Increased specialization makes the various parts of an individual business more interdependent and increases the impor- tance of specific technical relationships used by the farm business. The resultant high level of technical detail required to realis- tically handle any particular farm situation led to restriction of the problem to development of a model of analysis which would handle only one type of farm businesses. Because of the author's prior interest and experience in the dairy industry and the fact that dairy farming is the most predominant type of farming in Michigan, the type of farm business chosen was dairy. To develop a model which included more enterprises would have required either more time than was available for the present study and larger computer than was available on a reliable basis or £1 reduction in the technical detail and thus the realism of the model developed. Dairy farm managers are currently facing the problems involved it: changing to new types of housing and ways of handling cows. In addition, new more efficient and more specialized crOp growing and handling machinery is continually becoming available. Young men starting in farming frequently must evaluate alternative methods of developing a viable business which may involve eXpansion of the home farm business or formation of an entirely new business. The debt levels caused by these and other changes make investment analyses and cash flow estimation necessary parts of financial management. Older farmers must evaluate the effects of various business reorgani- zation plans (bringing in a partner, reduction Of the livestock enterprise) required to meet their changing financial needs and physical abilities. For these reasons, the problem was defined as a need to deve10p a business analysis model which would allow analysis Of investment, disinvestment and reorganization alternatives for Michigan dairy farms. The model must have sufficient technical relationships built into it so that changes which effect labor requirements, cash flow requirements and other technical input-output coefficients can be evaluated. FUrther, the model must contain all of the enterprises, livestock and crOp, normally found on dairy farms so that the inter- relationships between these enterprises can be included in the model. As defined, the problem focuses on the individual farm business and its problems. It is assumed that data input requirements must be such that any investment, disinvestment or reorganization problem is evaluated with respect to a specific farm situation rather than being generally valid for most situations. Viewed from a different perspective the problem may be defined as development of ”laboratory,” for the scientist, farm business manager and farm advisor, to be used in analyzing alternative investment, disinvestment and organizational strategies. The physical and financial impossibility of experimenting with an actual farm business to answer the problems of Specific farm busi- ness situations leaves these people with few alternative methods of business analysis. The problem is to design a model which makes use of the capabilities of the digital computer to provide the experimental abilities required to evaluate potential changes in individual businesses. Although any list of examples is necessarily incomplete, the types of problems which the model must be able to address can be indicated. In addition to those indicated above, examples include: (1) income and cash flow analysis of changing dairy housing systems, (2) comparison of marketing systems with different timing, prices and farm storage requirements, (3) analysis of different growth strategies (both production system and financing system strategies) and (4) comparison Of different strategies for increasing business size to include a son. The model need not have the capacity of selecting an Optimum ration or determining an optimum fertilizer program. Following problem identification a theoretical framework for model deve10pment was establised. The observation or assumption that the interrelationships between the various parts of a business Eire of paramount importance led to an in-depth review of systems 'thecmy and the systems approach as a basic theoretical foundation for a whole farm business model. Closely associated with this was an aleysis‘and literature review Of the simulation method as to its compatibility with a systems approach to management and economic theory, and as a technique for individual firm representation. After establishment Of a theoretical framework the model was specified in detail and the parameter data collected. Model specifi- cation was essentially a heuristic process of trying to deve10p ways 'to simulate the physical and financial characteristics of a farm business. Simfarm 18 and other simulation models develOped and being develOped by the Department of Agricultural Economics Farm Management staff provided ideas and guidelines for model design. Parameter data was collected from Michigan Telfarm records, DHIC records, Agricultural Economics research reports, Agricultural Experi- ment station bulletins, Department of Agricultural Economics publica- tions from other states (particularly Minnesota, Illinois and New York) and from faculty members in the Department of Agricultural Economics and other departments in the College of Agriculture and Natural Resources. The model was then programmed in Fortran IV for Michigan State University's CDC 3600 computer. Computer programs were written by the author and Department of Agricultural Economics computer programmers. The final phase of model develOpment involved testing and appli- cation Of the model. This phase included three different activities. TFirst, test farm situations were used to check out all of the user defined systems. Then, two actual farm planning situations were 8 Vincent, U. R., Agricultural Economics Report 164, Department of Agricultural Economics, Michigan State University, East Lansing: Michigan, in process. 9 simulated with opportunity for repeated feedback from each of the farm planners. And finally, a small research project was conducted. using the model as the primary research tool. Plan of Future Chapters The objective of this chapter has been to provide a general overview of the study by outlining its justification, objectives and methodology. Future chapters present the study and its results in detail. Chapters two and three provide the theoretical frame- work and background for the model with chapter two covering the general systems theory framework and previous research within which the model is develOped and chapter three Specifying the general characteristics of the model within that theoretical framework. In chapter four the model constructed is outlined followed by a discussion of the theoretical basis or justification for certain important model relationships and their method of representation within the model. The final section of this chapter includes directions for use of the model. Chapter five includes a discussion of the general problems of validation plus an enumeration of the testing and application of the model which was carried out. In chapter six estimates of deve10p- ment and operating costs are made and the problems and costs involved in.model maintenance over time discussed. Possible extensions or modifications of the model are discussed 111 chapter seven. Chapter eight includes the traditional summary and conclusions of the study. 10 Three appendices are included. Appendix A is a COpy of the user manual and input forms required for use of the model. Appen— dix B contains a complete description of the model in detail. These appendices should be referred to for all questions about the exact Operation of the model or the method of representing system charac- teristics. Appendix C lists the data sources for the coefficients used in the model. CHAPTER II PREVIOUS RESEARCH AND EVALUATION OF THE SIMULATION METHOD An appropriate theoretical framework for a management oriented business analysis model must contain both a suitable implicit view of management and the desired focus on important aspects of the business. The belief that the model must contain the complete farm business, and the observation that the interactions between the ‘various enterprises within the business are of paramount importance in evaluating alternatives, lead one to conclude that use of basic (economic concepts within a general systems framework may provide the ‘best theoretical basis for model development. The actual viability of such a combination of economic theory and systems theory, however, (depends on the usefulness of the system approach as a basis for a -theoretical framework and the existence of a technique which allows incorporation of economic concepts as well as viewing management in .a systems context. One technique which appears to meet these criteria is simulation. ,Although rapidly expanding use of this technique in business speaks ‘well for its potential application to farm business management, it is necessary to investigate that potential. In an attempt to evaluate systems theory and the simulation method the following discussion expflbres systems theory as it presently exists, investigates the 11 12 relationships between systems theory and simulation, looks at the theoretical economic implications of simulation, compares simulation and linear programming and reviews previous simulation research. General Systems Theogy The development of a systems theory for management, or even for the biological sciences, is still in its embryonic stage.1 At this point there is not even agreement as to what is meant by the term ”system.” Various authors have defined system in different 'though somewhat similar ways. Examples of these definitions include (1) ”A set Of Objectives together with relationships between the objects and between their interrelationships,”2 (2) "An aggrega- ‘tion or assemblage of Objects jointed in some regular interaction or interdependence,"3 (3) ”A set of elements so interrelated and integrated that the whole displays unique attributes,”u (4) ”A set HOpeman, Richard J., Systems Analysis and Operations Management, Charles E. Merrill Publishing 00., Columbus, Ohio, 1969. McMillan, Claude and Richard Gonzalez, Systems Analysis,_A Computer .Approach to Decision Models, Richard D. Irwin, Inc., Momewood, Illinois, 1968. Gordon, Geoffrey, System Simulation, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1969. Timms, Howard L., The Production Function in Business, Richard D. Irwin, Inc., Homewood, Illinois, 1966. 13 of interrelated parts”5 and many others.6 Whether "a" definition of system will evolve or not is questionable. A wide variety of disciplines are using the concept with each approaching problems and defining the term in such a way as to meet the needs of the particular discipline. Even those interested in systems from a management point of view have some differences in Opinion as illustrated by the above definitions. Although there is not complete agreement as to the definition of the term system, the definitions all contain the concept of interaction.7 Other words, such as interdependence or interrela- 'tionships, are often used but it appears to be the interaction (among a group of components and their attributes which make them a system. Illustrations of the forms that interaction can take include feedback mechanisms, catalysts, inhibitors and joint causality. There is, however, less agreement as to whether a system must have an Objective or not. Kennedy states that the concept of a ‘5 Tilles, Seymour, ”The Managers Job - A Systems Approach," Harvard Business Review, Vol. 41, No. l, p. 74, January - February, 1963. For examples, see Hare, Vancourt, Jr., Systems Analysis: A Diagpos- tic Approach, Harcourt, Brace and World Inc., New York, New York, 1967, and Forrester, Jay, Industrial Dynamics, M.I.T. Press, Cambridge, Massachusetts, 1961. Deutsch, Ralph, System Analysis Techniques, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1969. 14 system ”implies a goal or purpose."8 Wilson and Wilson9 indicate that the components of a system must be arranged to perform some wanted operation. Others10 take a much more theoretical approach and are concerned with general systematic relationships, or the black box to which Bouldingll refers. Businesses do have objectives and they are important in the design of systems. Whether the systems themselves have objectives is still a moot question. This author contends that business systems should be used to meet the objectives of management. As previously stated, a general systems theory is just beginning to evolve. The basic idea of a general systems theory stems from the "Gestalt” theory of psychology develOped by Wertheimer, Koffka and Kohler.12 The basic philosophy of this theory is that the whole is ‘more than just the sum of the parts.13 Thus one must consider the 8 Kennedy, John L., "Psychology and System DevelOpment" in Psycho- lggical Principles in System Development, edited by Robert M. Gagne, Holt, Rinehart and Winston, New York, New York, 1966. 9 Wilson, Ira G. and Marthann E. Wilson, Information, Computers and System Design, John Wiley and Sons, Inc., New York, New York, 1965. :10 Johnson, Richard A., Fremont E. Kast and James E. Rosenzweig, “Systems Theory and Management,“ Management Science, Vol. 10, NO. 3, p. 367, January, 1964. Boulding, Kenneth E., ”General Systems as a Point of View,” in Views on General Systems Theogy, edited by Mihajlo D. Mesarovic, John Wiley and Sons, New York, 1964. 11 :12 Kbhler, Wolfgang, Gestalt Psychology, New York, 1929. 153 Ruch, Floyd L., Psychology and Life, Scott, Foresman and CO., Chicago, Illinois, 1963. 15 'whcde as an interrelated system and study the individual parts as 'they effect and are effected by the system},+ The real force which gave birth to a general systems theory, Ihowever, was the desire to deve10p a unitary theory of science or as Boulding calls it a ”Skeleton of Science."15 The name and the :foundations for a general systems theory were first published in a series of articles in the late 1940's and early 1950's by Ludwig *von.Bertalanffy.l6 Von Bertalanffy was trying to deve10p a general tineory to explain what he viewed as the parallel evolution and iasomorphic laws in science. A set of system principles or charac- 'teristics were set forth. These included: 14 15 16 (1) Growth: A system grows as measured by certain attributes of the system. Growth may be either positive or nega- tive and is often typified by prOportional, eXpoential or logistic change. (2) Wholeness: The system behaves as a whole, the parts are interdependent and Often codetermined. Petermann, Bruno, The Gestalt Theory, Routledge and Kegan Paul, Ltd., London, 1932. Boulding, Kenneth E., ”General Systems Theory - The Skeleton of Science,” Management Sciencg, Vol. 2, No. 3, April, 1956. The two most commonly quoted which are in English are: von Bertalanffy, Ludwig, "An Outline of General System Theory,” The British Journal for the Philosophy of Science, Vol. 1, p. 134, 1950-51, and von Bertalanffy, Ludwig, "General Systems Theory: A New Approach to Unity of Science,“ Human Biology, Vol. 23, NO. u" p. 301, 19510 l6 (3) Independence or Physical Summativity: Variation in the complex equals the sum of the variation of the indivi- dual elements. Although this is the Opposite of whole- ness above, its presence only points out that both characteristics can and do appear. (4) Centralization: Certain elements of a system may have more effect than others. Some elements may have little or no effect. The variables with the most influence may change ani the order and procedure Of that change may be predictable or consistent. (5) Competition: There may be competition among the various parts of the system for the resources and the inter- mediate products of the system. There may be compe- tition between the various parts of the system to determine the specific role to be carried out be each part. (6) Hierarchical Order: The many system components are them- selves systems Of the next lower orders (7) Finality; Systems are seen as approaching three possible final states or solutions with the passage of time. Systems may asymptotically attain a stable stationary state: they may never attain a stable stationary state: or they enter a state of periodic oscillations. Ts 17 17 If one defines a theory, as Websters does, as "a systematic statement of principles involved" or as a "formulation Of apparent relationships or underlying principles of certain observed phenomena which has been verified to some degree," then von Bertalanffy's above set of principles is the closest approximation to a statement of a general systems theory that is presently available. However, Boulding18 indicates that the develOpment of a general field theory of the dynamics of action and interaction which would be involved in a general systems theory is a long way ahead. Instead, he prOposes a second possible approach to general systems theory which involves construction of a hierarchy Of systems. This hierarchy roughly corresponds to the complexity of the individuals of the various empirical fields. Thus, each field or discipline has its own hierarchy of systems parallel to the hier- archy of other fields or disciplines. The various system levels .as proposed by Boulding are listed below. (1) Frameworks: This is the level of the static structure and includes the geography of the Universe, the map- ping of the earth and the pattern of atoms in a molecular formula. ‘17, .1, : Websters New World Dictionary of the American Léggpggg, The World Publishing Company, Cleveland and New York, 1956. 18 B0u1d1n8, OE. Cite, p. 2020 l8 (2) Clockworks: The simple dynamic system with predetermined, necessary motions. This includes the great clock of the universe, the solar system. The greater part of economic theory falls in this category. (3) Thermostat: This level is that of the control mechanism or (4) Open cybernetic system. The transmission and interpretation of information is an essential part of the system. The system will move toward gpy_giygp.equilibrium within limits. Initial conditions do not necessarily determine the final state. The essential variable is the differ- ence between the ”Observed" and the "ideal" level. systems: Self-maintaining structures typify this level. At this level life begins to differentiate itself from non-life. Self-reproduction and self-maintenance in the midst Of a throughput of material which originates outside the system and is returned outside the system are salient features. (5) Plant systems: There is a division of labor among differen- tiated and mutually dependent parts. There is a dif- ference between genotype and phenotype. (6) Animal systems: This level is characterized by mobility, teleological behavior and self-awareness. More deve- lOped systems at this level have specialized informa- tion-receptors leading to an enormous intake of informa- tion and a brain which allows response to "image” as well as physical stimulus. 19 (7) Human systems: At this level the individual human being is considered as a system. This level includes self- consciousness in addition to self-awareness, realization Of knowing as well as knowing and an ability to absorb symbols as well as signs. (8) Social organization systems: This is the level of "roles" tied together with communication. This involves inter- action of groups of the next lower set of systems and the development of a set of value systems and a histori- cal record. (9) Transcendental systems: This is essentially the level of ultimates, absolutes and the inescapable unknowables. This, of course, is only a listing of a hierarchy of systems or Eulattempt at constructing a system of systems. It is not a set of prhmiples that could assist one in.predicting the outcome of the anion of certain actors or conditions within a system and thus be added a theory. The principles of production theory in micro- economics assist one in evaluating the results of producing under c=ertain conditions or having certain conditions change. This hier- arChy of systems does not provide a parallel set of principles for 3 theory of systems. That there is not at the present time a body of principles and ideas that could be called a systems theory is supported by Ansoff and Slevin. In their search of the systems literature with particular 20 emphasis on Industrial Dynamics they found no "statements which even begin to describe ID as a theory."19 What the work of Boulding and von Bertalanffy has provided is a.tesis for the development of a heuristic base which serves as the intellectual framework for systems analysis. The most important part of that heuristic base is what may be called the systems concept. This is not another definition of system. It is a theore- tical construct which allows elucidation of the skeletal framework of a system. Modern management organizations and businesses are generally accepted as being systems of the level of Boulding's Open systems. They are self-maintaining structures which receive material from and return material to their environment. "The business organization is .annn-made system which has dynamic interplay with its environment- f'improving, adjusting, replacing or repairing systems. What this heuristic base provides is an angle of perception or a'methei of viewing problems which may be superior to any previously deV810ped method for certain problems. As Boulding points out,3u "Perhaps one of the most valuable uses of the above (hierarchical) Scheme is to prevent us from accepting as final a level of theoretical \- 34 Ibide, Po 2070 .__..._- v 444W; 26 analysis which is below the level of the empirical world which we are investigating” and "Its emphasis on communication systems and organizational structure, on principles of homeostasis and growth, on decision processes under uncertainty, is carrying us far beyond the simple models of maximizing behavior of even ten years ago." Viewing the hierarchy of systems as the essential basis for a systems approach or systems analysis has been widely accepted.35 AlthOugh there is essential agreement that the heuristic base «mudined above provides a basis for systems analysis an agreed upon definition for systems analysis has not been found. The unsettled nahne of the definition of systems analysis can be illustrated by quoting from two texts with the word "systems analysis" in their intle. McMillan and Gonzalez36 very candidly admit that "To be frank tmaauthors are not certain what is meant by systems analysis." At alnmhldifferent extreme Hare37 states that "The scientific method of impury, which demands such relevant and dependable relationships for its results, i_s_ systems analysis in its broadest sense." N 35 For examples, see Johnson, H. A., F. E. Kast and J. E. Rosenzweig, Iflg_Theory and Management of Systems, McGraw-Hill Book Company, Inc., New York, 1963. Hall, Arthur D., A Methodology for Systems Igrlineerin , D. Van Nostrand Company, Inc., Princeton, New Jersey, 1962. Vincent, Warren H. and Larry J. Connor, "An Orientation for Fbture Farm Planning and Information Systems,“ Ag. Econ. Misc., 1268 - 5, Department of Agricultural Economics, Michigan State University, East Lansing, Michigan, 1968. Mcl'iillan and Gonzalez, op. cit. 37 Ihue, Van Court Jr., Systems Analysis: A Diagnostic Approach, Harcourt, Brace and World Inc., New York, New York, p. l, 1967. neg, . gnu, , P' , . um. ’. h‘ ‘ ;\ . . '- .. 9 u ~ 4 u --n 27 At this point in time it appears that systems analysis can best be defined as a method of analysis in which the interaction of the various components of a system are considered of paramount importance. What is called systems analysis will depend upon the Specific area of interest and level of focus being considered and the types of analysis historically carried out in that field. For example if a certain field of study had considered price to be a linear function of a certain demand shifter, an analysis which considered price to be the result of the interaction of the forces of demand and supply would be considered as systems analysis. On the other hand, in areas where systems are a historical method of analysis, systems analysis is said to occur only when even greater emphasis is given to the interaction of components.38 Using the broad definition systems analysis in economics might be viewed as a change from evaluating the effect of a variable by holding all other variables constant to an attempt to discern the effect of varying the variable when all other variables are allowed '50 Vary in their normal way. This would provide the advantage of allowing observation of the variable under more realistic conditions but the disadvantage that the specific conditions of observation are more difficult to quantify. \- 38 The author is indebted to J. B. Holtman, Departments of Agricultural Engineering and Systems Science, Michigan State University for his a‘BSistamce in clarifying this point. 9. we 28 Within the field of management Young39 states that what appears to be occurring with acceptance of systems analysis is that our "concept of the organization is changing from one of structure to one of process.” That is, organizations are viewed as a set of flows of information, men, materials and behavior, with time and change the critical aspects. Young feels that this new approach will prove to be more productive. The relative importance of particular advantages cited for systems analysis may depend on the specific system of concern. Ikmever, a general list of advantages is presented below. (1) Systems analysis allows determination of "better" solutions to problems for which an optimum does not exist or can not be found with Optimizing methods.)+0 (2) A larger number of heterogenous variables can be handled in a consistent manner. (3) Systems analysis allows explicit treatment of uncertainty and can handle different types of uncertainty simultaneously. This is particularly important for long-range planning. (4) Systems analysis may be conducive to the invention of new systems. (5) The criteria of analysis or the decision criteria can be broadened or increased in number. \— 39 Yoting, Stanley, Management: A Decision-Making Approac_hJ Dickenson Publishing Company, Inc., Belmont, California, 1968. 0 Iiitch, Charles, "An Appreciation of System Analysis,” ngrations Research, Vol. 3, No. 1+, November, 1955. 29 (6) The complex interrelationships between problem elements and the objectives of numerous functional units may necessitate the use of objective analysis of decision problems.“1 (7) It provides a method of reducing complex relationships to paper. A manager is able to see the various parts of a business in a more realistic perspective. It provides a basis for ”putting it all together."42 In the field of management there is wide variation in the level at which systems analysis is applied. In many cases the uninitiated reader is essentially led to believe that this level is thg_level at lunch systems analysis applies. Ewell43 includes an entire large corporation and its subsidiaries as his system for analysis. Neuschelm views the various functional units of a business, such maths production control systems, as the appr0priate level for Sfiflmms analysis. To Evenns systems analysis involves the study of N 1 (Deland, David I., and William R. King, Systems Analysis and Project mement, McGraw-Hill Book Co. , New York, New York, 1968. 2 King, William R., ”The Systems Concept in Management,” Journal of Industrial Engineering, Vol. 18, No. 5, May, 196?. Ewell, James M., “The Total Systems Concept and How to Organize for It,” Computers and Automation, Vol. 10, No. 9, September, 1961. N'euschel, Richard F., Management by System, McGraw-Hill Book Company, Inc., New York, New York, 1960. 45 Even, Arthur D., The Role of the Industrial Engineer in Systems I)BSign and Improvement,” The Journal of Industrial Engineering, V01. 8, No. 6, p. 370, November - December, 1957. ‘3? .ul" 0 ‘u .... ‘x. -... rt ...I n x 30 the system of engineering records and data processing or the manage- ment information system. At what may appear to be an extreme, Clippingerh6 views systems analysis as applying only to the confi- guration of computers and computer related equipment that is used by a firm. Others“7 assume a more general view of systems analysis and allow the problem situation to determine the appropriate level at which systems analysis is to be applied. Systems analysis has been used, is being used and has been pupposed for use in approaching a wide variety of problems, some of which have resisted solution by other methods. Although any short list of examples will leave out far more than it includes, a few examples are presented below. Drorl+8 suggests that systems analysis has potential for assisting in modernization decisions and the solution of certain development Problems of develOping countries. It has been suggested that systems analysis may provide a superior approach for certain social decisions where there are important externalities and the decision is made by \ Clippinger, R. F., ”Systems Implications of Hardware Trends," Systems and Procedures Journal, Vol. 18, No. 3, May - June, 1967. 7 Optner, Stanford L. , Systems Analysis for Business Decisions, Second Edition, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1968. De Masi, Ronald J. , An Introduction to Business jystems Analysis, Addison-Wesley Publishing Company, Reading, Massachu- Bette, 1969. Ellis, David 0. and Fred J. Ludwig, Systems Philo- E’Lhy, Prentice-Hall Inc. , Englewood Cliffs, New Jersey, 1962. 1+8 Dror, Yehezkel, "Systems Analysis and National Modernization Decisions,” Academy of Management Journal, Vol. 13, No. 2, June, 1970. 31 49 individuals or groups. Although care must be exercised in the use 50 of computers for social decisions, a systems approach to informa- tion for social decisions should improve the decision process.51 Systems analysis is presently being used by the government in support of the PPBS program and increased use is ”likely to lead to a higher order of rational decision-making and efficiency on the 0052 part of government. Systems analysis has been used on a wide variety of problems in the behavioral sciences.53 The Institute of Electrical Engineers have initiated publication of a Journal 54 with major focus on systems analysis. There has been very little use of systems analysis per se in management in agriculture. It has been prOposed as a useful approach for farm planning and is covered in a limited way in at least one h Bower, Joseph L., ”Systems Analysis for Social Decisions," (guputers and Automation, Vol. 19, No. 3. March, 1970. 0 5 Foster, David F., ”Computers and Social Change: Uses - and Misuses,” Computers and Automation, Vol. 19, No. 8, August, 1970. 1 Gavin, James M. , “The Social Impact of Information Systems," 99mputers and Automation, Vol. 18, No. 8, July, 1969. Black, Guy, ”Systems Analysis in Government,” Business Economics, Vol. 2, No. 2, Spring, 1967. 53 Buckley, Walter (ed.), Modern Systems Research for the Behavioral Seientist, Aldine Publishing Company, Chicago, Illinois, 1968. 54 , Systems Science and Cybernetics, The Institute of Electrical and Electronic Engineers Inc., New York, New York. A w *__._-—» 32 advanced farm management course.55 However, it has not appeared elsewhere in the literature. It should, of course, be noted that the general systems approach idea has been used by farm Operators and researchers for a long period Of time. However, this could normally be characterized by a farm Operator who Observes a certain technical research result and says "yes, but there are other things that you have to take into consideration as well." This is an informal recognition of a concept that has only recently undergone formalization. Simulation and Systems Analysis In a general sense, simulation means a representation of rmflity. As Chorafas defines it, "Simulation is essentially a working analogy. ... it involves the construction of a working mathematical or physical model presenting similarity of prOperties Orlmlationships with the natural or technological system under study.“56 Viewed in this manner simulation includes almost any Simplified representation Of the real world. In this study we will be concerned only with mathematical ‘Nflels which make use Of the computer. The difference between a \— 5 55368 Vincent, Warren H. and Larry J. Connor, "An Orientation for F‘uture Farm Planning and Information Systems,” Agricultural Eggggmics Mimeo, 1968 - 5, Michigan State University. These aMthors also teach an advanced farm management course which covers some Of systems analysis. 56 Chorafas, Dimitris N., Systems and Simulation, Academic Press, New York, New York, p. 15, 1965. 33 mathematical model and a physical model can be illustrated by considering the difference between placing a physical model Of an aircraft in a wind tunnel and developing a mathematical model Of the aerO dynamics involved. Simulating a model on a computer consists Of using a digital or analog computer to trace numerically or graphically the time paths Of all endogenous variables generated by the model.57 Given this more limited scape, simulation might be defined as dynamic representation of a system achieved by building a model and moving it through time.58 This closely corresponds to the definition of computer simulation Offered by Naylor, e_t.__a_l_: ”A numberical technique for conducting experiments on a digital computer, which involves certain types of mathematical and logical models that describe the behavior of a business or economic system (or some component thereof) over extended periods of real time.“59 Given the above definition it is Obvious that computer simu- lation is a technique which may be used in systems study or systems analysis. Because Of the dynamic characteristics Of simulation one M Cohen, Kalman J. , "Simulation Of the Firm,” The American Economic Review, Vol. 50, NO. 2, p. 534, May, 1960., Arthur, William, ”To Simulate or Not to Simulate: That is the Question,“ Educational Data Processing Newsletter, Vol. 2, N0, 1+, P0 9- 59 “aylor, Thomas M., Joseph L. Balintfy, Donald S. Burdock and Kong Chu, Computer Simulation Techniques, John Wiley and Sons, New York, p. 3, 1968. 1.7-2. 1‘ I . 1: ~ .\ \I .. J . \ “L V 3# might say that any simulation study is systems analysis to some degree, whether it is recognized or not. However, the use Of systems analysis does not require the use of simulation as a technique Of study. Other techniques and methods of analysis are widely used in systems analysis.60 There are, however, several characteristics Of systems which make computer simulation a good and Often the best technique to use in systems analysis. These include: 1. It may be impossible or extremely costly to Observe certain systems in the real world. Examples include determination of the effects of Space flight on man prior to the first manned space flight and determination Of the United States Gross National Product for the next five years.61 2. The system may be so complex, that is so large in terms Of the number of variables, parameters, relationships, and events to which the system is responsive, that it is very difficult if not impossible to analyze mathematically.62 3. The system may contain relationships between system entities and attributes which are not well behaved or not mathema- tically tractable. A solution to the model cannot be obtained 7“ 0 See Optner, Op. cit., and Lazzaro, Victor, Systems and Procedurgs, Second edition, Prentice-Hall Publishing Co., Englewood Cliffs, New Jersey, 1968. 61 HE"b'lor, et. al. Op. cit., p. 5. MCHiILan and Gonzalez, Op. cit., p. 26. 35 with straightforward mathematical techniques. This may be caused by either the inherent nature of the system or data limitations forced on the systems analyst. 4. There may be insufficient numerical data about a system available to allow verification of a mathematical model and its solution. Or, such data may be extremely costly to Obtain. 5. There are many systems, particularly social systems, which cannot be manipulated or experimented with to determine the impact Of changes in the system or its environment. In this case simulation can serve as a systems laboratory. Even in cases where manipulation Of the system is possible simulation may be much less expensive. 6. Most systems contain random variables, which are difficult or impossible to handle expeditiously with other types of mathe- matical models. 7. Real time for many systems may be either too slow or too fast to allow meaningful analysis of the system. Simulation allows one tO expand or compress time to the analysts' specifications. The presence Of any one of these characteristics in a system may provide sufficient justification for using simulation as the method of analysis. It should be recognized, however, that combinations of simulation and other research techniques may prove more useful in answering certain system questions than either method alone. est. .q ~~u~.—o' ‘ V: . ‘ I M.,... “' u . ..-, ‘- I: t. 3‘: 36 Strickland63 found combining simulation and linear programming to be a useful approach to studying questions Of farm firm growth. Simulation can be used to analyze systems from several different perspectives.64 One common perspective is to use simulation as a method of describing the action and interaction Of the system to assist in developing an understanding of the system itself. The process of modeling a simulated system requires detailed Observation Of the real world system which in itself may provide valuable new insights about the system being simulated. Second, simulation may be used to analyze the behavior Of the system. In cases where it is impossible to Observe the behavior of the system, a properly validated simulation model may be used to generate data about the system. This is particularly useful for evaluating hypothetical changes in a system and for projecting or predicting the behavior of a system into the future. The simulation model can be develOped and validated based on past and present func- tioning of the system and then used to evaluate changes or projec- tions into the future. Third, simulation may be applied in the design of systems. In many cases systems must be assembled and purchased as a unit for an essentially unique situation. Commitment to any one system may represent a large fixed investment. The nature Of the system and 63 Strickland, Roger, Combining Simulation and Linear Programming in Studying Farm Firm Growth, Unpublished Ph.D. Thesis, Department Of Agricultural Economics, Michigan State University, 1970. This section is based, in part, on Vincent and Connor, Op. cit., p. 290 37 the cost of the components may be such that no physical evaluation of possible systems is possible. In this case simulation of possible alternative systems may allow discovery of an efficient and accep- table system with minimum cost. In most cases where simulation has been used in the design of systems, decision makers have used a "satisfieing" criteria. The system selected was the best of those tested in meeting certain critical criteria. Fourth, simulation may be used to evaluate the Operator of the system. This may be done in two ways. The first is analagous to placing a pilot at the controls of a simulated plane as a means of evaluating his apparent ability to fly. If what constitutes desirable output or behavior of the system is known it is possible to place a manager at the controls of a simulated system and evaluate the results of his decisions. Similarly, the reactions of managers to particular new or unusual situations can be determined. 0n the other hand, the performance of Operators of systems may be evaluated by simulating the results of system Operation if de- cisions and decision rules other than those used by the Operator had been used. This approach is useful when the desirable behavior or output for a system are not known. Simulation vs. Linear Programming Another technique which has received wide use in management both in Agricultural Economics and Business is linear programming. In addition to its previous wide usage this technique has the advantage that it can be used on nearly any computer via canned programs. This means that computer programmer time for use Of 38 this technique is very low and the computer time required for any one program is quite reasonable. For these reasons a compari- son of simulation and linear programming is made to insure that the most appropriate technique is used. Linear programming has been defined as the Operations research technique which "deals with the problem of allocating limited resources among competitive activities in an Optimal manner,"65 and has been used in a wide variety of research projects both in agriculture66 and other fields.67 Although this might lead one to assume that linear programming could be used to solve practically all problems, simulation appears to have several advantages for a large class of problems. It should be recognized in any comparison of linear programming and simulation that linear programming can be and has been viewed as a simulation model. Although dynamic linear programming does not violate any assumptions or necessary major features of simulation, it is at most a very restrictive simulation model. Simulation as defined here refers to a much freer form of computer modeling without apriori software restrictions. As is pointed out below under previous 65 Hillier, Frederick S. and Gerald J. Lieberman, Introduction to Qperations Research, Holden-Day, Inc., San Francisco, 1967. For examples see Hutton, Robert F., "Operations Research Techniques in Farm Management: Survey and Appraisal,” Journal of Farm Economics, Vol. 47, NO. 5, December, 1965, p. 1400, and Shapley, Allen E., Alternatives in Dairy Farm Technology with Special Emphasis on Labor, Unpublished Ph.D. Thesis, Department of Agricultural Economics, Michigan State University, 1968. 67 Dorfman R., P. Samuelson and R. Solow, Linear Prqgramming and Eggnomic Analysis, McGraw-Hill Book Co., New York, 1958. 39 research, linear programming and simulation have been combined for certain applications; both as co-equals and with a linear program- ming model as a part of a simulation model. One advantage Of simulation is the ease with which it handles multiple goals. The single parameter nature Of the Objective function of simple linear programming forces maximization Or minimi- zation of one variable subject to certain constraints. Thus, a single goal is implied: optimization with reapect to one variable. Although it may be argued that a prOperly designed linear program- ming model can reflect several goals through the use Of restrictions, this is only partly true. To the degree that goals can be reflected by the use of minimums, maximums or linear tie-in relationships they can be included by using restrictions. If one has a maximum debt level which is not to be exceeded under any conditions, this can be reflected in linear programming. Or, if the maximum debt level is a linear function of income, this can also be reflected. However, if the variables within the multiple objective function are not linerally related, representation in a linear programming model becomes difficult and at best a step function approximation. Further, if there is a significant number of variables involved, a great deal Of ingenuity is required to get the apprOpriate relationships entered and a large prOportion of each model will consist of restrictions and dummy activities. The largest problem, however, is not the difficulty of entering these relationships, but the fact that different farm operators will have different variables and relationships in their objective #0 function and thus the model may have to be reformulated for each situation. Another problem is the lack of a common denominator for vari- iables in the Objective function. If an Objective function includes the probability of going bankrupt, the satisfaction of having the highest producing herd in the county and the level of monetary income, finding a common denominator, or common denominators, may be impossible apriori. In order to include relationships between variables of this type common denominators must be found, at least between successive pairs of variable, because all the relationships must be endogenous to the model. Associated with an ability to handle multiple goals is an ability to handle sequential decisions within the planning period, using different criteria.68 A logical progression of decisions and decision criteria can be built into a simulation model and is an assumed feature of many models. Simulation, on the other hand, handles multiple goals in two ways. First, the decision rules within the model can depend on a number of variables each of which may reflect different goals. For example, the simulator may borrow money for a certain Operation only if the expected income exceeded a certain level with that level itself dependent upon the current equity situation. In this case income and debt-equity goals are reflected in the decision rule. 68 Irwin, George D., "A Comparative Review of Some Firm Growth Models," Agricultural Economics Research, Vol. 20, No. 3. P. 82, July, 1968. 1+1 Second, the values of any number of variables can be printed out. Thus, for each policy or alternative being considered the variables to be used in the Objective function or as decision criteria are made available to the decision maker. One might view this as a menu of alternatives and the expected results of choosing those alternatives printed out for the decision maker to select from. The Objective function or decision criteria are exogenous tO the model. This allows any type of decision criteria to be used and does not require that the criteria be either determined prior tO simulation or capable of being stated in mathematically tractable form. Possibly the most important advantage of simulation is that any type of function or relationship can be included in the model. It does not require that relationships be continuous or linear. Step functions, conditional relationships, qualitative variables and indivisibilities, as well as continuous and/or linear equations can be included in any combination. It is likely that few real world relationships, particularly economic relationships, are actually linear. Linear programming becomes useful only for those cases where a linear approximation is acceptable or as good as the data being used. Associated with the ability of simulation to handle any sort of relationship is its ability to handle stochastic variables. In cases where only the probability distribution of a variable is lknown or where a stochastic relationship is considered a better representation of a variable, a random variable can be used. This .... e: ra' ' ..-, ‘. e- ‘a a a v. . n . In; a ‘ ’x’ v D \ 42 'feature also allows develOpment of completely stochastic models which can be used in Monte Carlo simulation Of alternatives. When more than one time period is used, dynamic linear program- ming must be used. If recursive programming is used it is possible that short run Optimization may lock out Optimum long run alterna- tives. Optimization in early time periods may cause purchase or sale of durable assets which use up limited credit and/or become fixed assets in later time periods. This may not be in the longer run interest of the firm and may preclude Optimal long run decisions. In this case proper selection of alternatives may allow a simulation model to provide better information as to the Optimum long run alternative than would be provided by an Optimizing linear program- ming model. It should be pointed out, however, that in cases where polyperiod programming can be used this criticism becomes invalid. Given the data problems involved in develOping a linear program- ming matrix and the sensitivity of many linear programming solutions to slight changes in the matrix there is a question as to the degree to which these solutions are Optimum in any real sense. What is set forth as the Optimal solution may be practically no more ”Optimal” than a number of other solutions, some of which may be much more acceptable. In comparison of simulation and recursive programming Lins found that although linear programming solutions often generated a.higher net worth, some "mathematically Optimum” solutions were :in fact not "logical optimums.”69 69 Lins, David A., “An Empirical Comparison of Simulation and Recursive Linear Programming Firm Growth Models,” Agricultural Economics Research, Vol. 21, No. l, p. 7, January, 1969. s.“ .... “3 Simulation, of course, also has large data requirements and thus data amassing problems similar to those of linear programming. However, simulation takes a less ambitious approach and does not attempt to Optimize or put forth an answer which is to be interpreted as 332 optimum. A final advantage of simulation is that it tells "how to get there from here.” A simple linear prOgramming solution usually presents an Optimal organization and allocation of resources but without indication of how the firm gets to that organization or even if it is possible. Even a poly-period model only presents a number of Optimum points along the path. Appropriate restrictions will insure that any of the points is possible but a great deal Of the detail of ”how to get there from here" is ommitted. A simulation model moves through time a step at a time and the values of the variables at each step can be printed out so that the decision maker knows how the final organization and allo- cation configuration is reached. Simulation and Economic Theory A cursory review of the literature indicates that simulation is an analytical method which may be used either to formulate and test economic theory or to apply economic theory to the solution cufreal world problems. As pointed out by Shubik70 there are many aspects of economic theory that are very incompletely develOped or not develOped at all. Examples include oligopoly, multiple product 7° Shubik, p. #15, 1970 no, ‘firms, welfare economics and marketing. The economics profession is continually attempting to deve10p or improve theories in these and many other areas. Shubik, Naylor gt;_§l.71 and Cyert and March72 all indicate that simulation is a tool which has great potential for assisting economists in develOping the required new theory. The primary advantage of simulation is that it can be used in both the formulation and validation of more complex theories.73 Traditional verbal, graphical and mathematical models used by econo- mists have Often been called "unrealistic" when applied to complex problems. This lack of realism has meant that theory is Often limited to the less complex situations or complex situations significantly reduced in scOpe by a number of assumptions. The simulation method forces very few restrictions on the form Of the model. By using large computers, large numbers of variables and relationships of any kind and combination can be handled. This frees research workers of many problems related to adequacy of repre- 74 sentation. Many of the previously required assumptions can be re- laxed e 71 Naylor et. al., p. 189, 1968. 72 Cyert, Richard M., and James G. March, A Behavioral Theory of the Firm, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1963. 73 Shubik, Martin, ”Simulation of the Industry and the Firm," 223 American Economic Review, Vol. 50, No. 5, p. 918, December, 1960. 7“ Orcutt, Guy H., ”Simulation of Economic Systems," The American Economic Review, Vol. 50, No. 5, p. 900, December, 1960. 45 A primary advantage of simulation over traditional econometric models is the ability of simulation to handle lagged variables endogenously. Econometric models are usually interpreted as one- period-change models. The lagged values of endogenous variables are "assumed to be predetermined by outside forces rather than by earlier applications of the mechanisms specified in the model."75 TO determine the values of endogenous variables for future time periods new values must be assigned to the lagged endogenous vari- ables for each period. "...it is assumed that each period any errors resulting from the ”determination" of last periods' endogenous ‘variables are corrected, so that there is a tendency for the one- period-change model to be kept on course by the fact that it always has a correct starting place."76 Computer simulation allows development of process models where the values of lagged endogenous variables are calculated within the model with no corrections for error. "The equations of the model, together with the Observed time paths of the exogenous variables, are treated as a closed dynamic system; each period, the values of the predetermined endogenous variables are the values generated by the model, not the known or actually observed values.“77 75 Cohen, Kalman J. and Richard M. Cyert, "Computer Models in Dynamic Economics," Quarterly Journal of Economics, Vol. 75, No. 1, February, 1961. Cohen, Kalman J., Computer Models of the Shoe, Leather, Hide Sequence, Prentice-Hall, Englewood Cliffs, New Jersey, 1960. 76 77 Ibide, Po 130 46 Computer simulation provides a laboratory for the study of economic systems which has not been previously available. In cases where considerable information is available concerning the components of a system, the system can be synthesized for study. When little is known about a system, a set Of relations can be derived which will exhibit the Observed characteristics of the system. This model can then be analyzed to "determine whether or not the behavior of the model corresponds with the Observed behavior Of the total system."78 A simulation model of an economic system allows detailed analysis of a representation of that system which can provide a basis for the develOpment Of theories about the system. Theories thus develOped can be evaluated with respect to real world data. On the other hand, theories can be tested or validated by using a simu- lation model to calculate the expected reactions Of the system to stimuli relevant to the theory. Although one must guard against a temptation to both develop and validate a particular theory with a single simulation model, a variety of models combined with the available real world data may allow both development and validation of theories via simulation. Use of only one model could provide a basis for refutation of more naive theories and would provide the first phase of validation for more sound theories. For the purposes of this study the most important relationship between simulation and economic theory is that economic theory can 78 Cohen and Cyert, Op. cit. p. 118. '\". ... . .. .... ... ..41. .1... .I. A. .v . u .. A e . . ~\ .... k . .-‘ A . .1 .I\ . a e ...«fi y-ve\ nel‘ Us. ~11|s.\\fi.\\ 47 be used in the development of simulation models designed to address particular questions and represent specific systems. The theory indicates many of the relationships required within the model. The theory may indicate the kinds of relationships involved, the relative magnitude of the relationships or the factors to consider in develOp- ing model interrelationships. Numerous examples of the use of econo- mic theory in computer simulation models appear in the review of literature below. Previous Simulation Research A review of all the simulation literature is a task beyond the sc0pe of this work. As indicated by such titles as Simulation in Social Science,79 Simulation of the Dynamics of Fluid Systems,80 Simulation in Systems Engineering81 and A Simulation Model of a Saturated Medical System,82 simulation has been used in a number of (indeed most) fields outside of Business, Economics and Agriculture. However, what is presented below is a brief review of some of the uses 79 Guetzkow, Harold (ed.), Simulation in Social Science, Prentice-Hall Inc., Englewood Cliffs, New Jersey, 1982. 80 Clymer, A. B., Simulation of The Dynamics of Fluid Systems, Eastern Simulation Council Meeting, Philadelphia, Pennsylvania, May, 1961. 81 Smith, E. 0., Jr., “Simulation in Systems Engineering,” IBM Systems Journal, p. 33, September, 1962. 8 2 Galley, John L., J. B. Hallan and A. H. Packer, ”A Simulation Model of a Saturated Medical System,“ American Institute Of Industrial Engineers Eighteenth Annual Institute Conference and Convention Prpceedings, p. 138, 1967. 48 of simulation in business and economics and a somewhat more detailed review of the use of simulation in Agriculture. Although any demar- kation between these fields is necessarily arbitrary, the material is presented under three headings for expository purposes. In Economics Except for a little work by Yule83 and Orcuttau on time series which possibly could have been called Monte Carlo simulation, there is essentially no use of simulation by economists prior to 1950. Since that time the use of simulation by economists has grown with the advancement of computer technology.85 A comparison of the number of economic models presented in Shubik's86 1960 bibliography and Naylor's87 1969 bibliography on simulation provide an indication of the rate of growth. A sampling is presented below. Simulation studies in economics have been focused at three different levels, the firm, the industry and the economy. At the 83 Yule, G., "Why DO We Sometimes Get Nonsense Correlations Between Time Series?” Journal of the Royal Statistical Society, Vol. 89, 1926. 8“ Orcutt, Guy, ”A Study of the Autoregressive Nature Of the Time Series Used for Tinbergen's Model of the Economic System of the United States, 1919-1932, Journal of the Royal Statistical Society, Series B, Vol. 10, 1948. 85 86 Shubik, Martin, "Bibliography on Simulation, Gaming, Artificial Intelligence and Allied Topics," Journal of The American Statisti- cal Association, Vol. 55, No. 292, p. 736, December, 1960. Orcutt, Op. cit., 1960. 87 Naylor, Thomas H., "Bibliography 19., Simulation and Gaming," Computing Reviews, p. 61, January, 1969. 49 level of the firm Chu and Naylor88 develOped a dynamic model of the firm using the model presented in Value and Capital by J. R. Hicks as the point of departure. Cyert and March89 rejected a major part Of classical theory and used simulation to develop a behavioral theory of the firm. Their model is an oligOpOly model with a number of quite realistic attributes. Naylor et. a1.90 present three varia- tions of the well known Cobweb Model; a stochastic model, a learning model and a model with stocks. A characteristic of many of the more comprehensive models of the firm is the inclusion of important theory from a number of disci- pline besides economics. For example, Bonini91 included theory from accounting, organization theory and behavioral science. Cyert and March use behavioral science, psychology, sociology and organization theory as well as economics. This apparent trend may spell the be- ginning of a multi-disciplinary theory of the firm. At the industry level Bladerston92 and Hoggatt carried out a simulation study of the United States West Coast lumber industry in 8 8 Chu, Kong and Thomas H. Naylor, ”A Dynamic Model of the Firm," Management Science, Vol. 11, No. 7, May, 1965. 89 Cyert and March, op. cit. 0 Nay10r etc 3.1., OP. Cite, Po 192. 1 9 Bonini, Charles P., Simulation of Information and Decision Systems in6the Firm, Prentice Hall, Inc., Englewood Cliffs, New Jersey, 19 3. 92 Bladerston, F. E. and Austin C. Hoggatt, Simulation of Market lProcesses, Institute of Business and Economic Research, Berkeley, California, 1962. 50 an attempt to determine the affect of economic forces on institu- tional alignments, decentralization of market decisions and limits on market information. Cohen93 develOped a model of the hide- leather industry including tanners, shoe manufacturers and shoe retailers. The time paths Of variables generated by a simulation or “process model” where compared with actual values for the 1930 to 1940 decade. A very close correSpondence was found between simulated time paths and actual time paths. Several computer models have been develOped for simulating a number of subindustries within the textile industry. Zymelman94 develOped a model of the cotton-textile gray-goods industry for the U.S. Department of commerce. Models have also been develOped for the tufted textile industry and the ladies seamless hosiery industry.95 Although Orcutt96 in 1960 and more explicitly in 196197 stated that simulation may be useful in alleviating some of the aggregation problems that have been plaguing economists for years, no general industry level models of a nature similar to the general firm models outlined above appear to have been constructed. Orcutt suggested that simulated micro level representative consumer or producer units 93 Cohen, op. cit., 1960. 94 Zymelman, Manuel, ”A Stabilization Policy for the Cotton Textile Cycle,” Managgment Sciencg, Vol. 11, NO. 7, March, 1965. 95 U.S. Department of Commerce, Civilian Industrial Technology Program in Textiles, Textile Industry Behavioral Information, Hashington, D.C., March, 1963. 96 Orcutt, 02. Cite, 1960. 97 Orcutt, Greenberger, Korbel and Revlin, Op. cit. u \5v. 1. ‘ “Ht.- ’us .I ...- 1 ‘ ‘ b...‘ a... 1v. . o ' . e ‘ O. ‘ u l ‘l '54" ' \ cr" 'v 51 could be added up directly and thus avoid the intermediate phase of specifying how the behavioral relationships are to be added. There is the problem that variables exogenous to the firm may not be exogenous to the industry98 which could require involved feed- back effects or a sort of iterative solution involving resimulation of the micro units if the aggregate industry level variables were inconsistent with the levels assumed in the exogenous micro level simulation. However, even recognizing this problem, it is surpris- ing that a general industry model has not been develOped. Many of the papularly known economy level or macroeconomic models are econometric or one-period-change models. Given the values of lagged endogenous variables for periods t-l, t-2 etc., the model can calculate values for t. Values for t+1 cannot be calculated until values for period t are available. Included in 100 this group are Klein's model,99 Christ's model and the Klein- Goldberger models.101 98 Manderscheid, Lester V. and Glenn L. Nelson, "A Framework for Viewing Simulation,“ Canadian Journal of Agricultural Economics, Vol. 17, No. l, p. 39, February, 1969. 99 Klein, Lawrence, Economic Fluctuations in the United States, 1921 - 19u1, Cowles Commission for Research in Economics Mono- graph No. 11, John Wiley and Sons, New York, 1950. 100 Christ, C. F., ”A Test of an Econometric Model for the United States,” Conference on Business Cycles, National Bureau of Economic Research, New York, 1951. 101 Klein, L., and A. S. Goldberger, An Econometric Model of the United States, 1929 - 1952, North-Holland Publishing Co., Amsterdam, 1955. 52 Simulation models of the economy are more recent and fewer in number. The Duesenberry, Eckstein and Fromm102 model focused on the impact of alternative time patterns of decline of fixed invest- ment and government purchases on national income when automatic stabilizers are put in a realistic macroeconomic setting. The Brookings - SSRC Quarterly Econometric Model of the United States is likely the most elaborate model of an economic system in exis- tence. This model is aimed at both understanding and predicting the United States economic system. Other macro level simulation studies include the Orcutt gt;_§;,103 demographic model of the United States household sector and Holland and Gillespie's101+ model of the Indian Economy. At an applied or specific problem oriented level economists have made numerous uses of the simulation method. Kain and Meyer105 indicate that simulation is particularly appealing when evaluating ”public investments characterized by important externalities, broad social objectives and durable installations." They cite computer simulation studies of regional and urban problems including regional 102 Duesenberry, James S., Otto Eckstein and Gary Fromm, "A Simu- lation of the United States Economy in Recession," Econometrica, Vol. 28, No. h, p. 7&9-809, October, 1960. 103 Orcutt, etc 3.1., 02. Cite, 19610 '10“ Holland, Edward P. and Robert W. Gillespie, Experiments on a Simulated UnderdeveloPed Economy: Deve10pment Plans and Balance of Payments Policies, The M.I.T. Press, Cambridge, 1963. ‘105 Kain, John F. and John R. Meyer, "Computer Simulations, Physio- Economic Systems, and Intraregional Models,” American Economic RBV1BW, V01. 58' NO. 2’ Pl 171’ May, 1968. 53 analysis, metr0politan growth, community renewal and river basin develOpment. Taylor106 used simulation to develop hypotheses relative to develOpment patterns. The impact of war on defense Spending patterns was studied by Galper.107 In Business The use of simulation in business has had two major foci. It has been used, both to develop and train managers and as a tool of management to assist in decision making. The role of simulation in the development and training of managers has occurred through the creation and use of management games. The term ”game" may be unfortunate. Although participation in the use of management games is often enjoyable, the primary objective is to educate. The name management "laboratory" has been suggested as a substitute. However, considering the long reign of the term "game" it appears likely to stay. Management games are the computerized descendents of the war- chess games of the seventeenth and eighteenth centuries.108 The first games were played on a board with pieces to represent the military items. In 1798 George Venturini introduced a game (”New Kriegspiel”) which replaced the board with a map. The map had a 106 Taylor, Lance J., ”Development Patterns: A Simulation Study," Quarterlnyournal of Economics, Vol. 83, No. 2, p. 220, May, 1969. 107 Galper, Harvey, "The Impacts of the Vietnam War on Defense Spend- ing,” Journal of Business, Vol. 42, No. 4, p. #01, October, 1969. 108 Longworth, John H., ”From Var-Chess to Farm Management Games," Canadian Journal of Agricultural Economics, Vol. 18, No. 2, p. 1' JUly, 19700 5“ grid of 3600 squares and there were 60 pages of rules. In 1811, ‘von Reisswitz, a Prussian Army officer, developed the first war game played in a sand box. This eventually received wide acceptance and war gaming gradually spread throughout EurOpe and North America. Efforts were made to make the games more realistic and include probability distributions. By the end of world War I, war games had develOped into Operational tools, being used to rehearse cam- paigns as well as for training. The German spring offensive in 1918 was planned and tested in a war game, as were the invasion of France in 19% and the invasion of the Ukraine in 1941.109 The publication of von Neumann and Morgenstern's classic book110 in 1994 placed new emphasis on war games and, more importantly, aroused interest in the subject of decision making under uncer- tainty. This area had been neglected by most researchers111 up to that point because it was considered to be an art and thus the ”exclusive province of men of 'experience'."112 In 1955 the Rand Corporation develOped for the Air Force what was to become the forerunner of business management games. This game called "monOpologs,” was a simulation of the Air Force supply 109 Thomas. Clayton Jo. "Military Gaming," PIOEress in Qperations Research, Russell L. Ackoff, Editor, John Wiley and Sons, Inc., New York, p. #21, 1961. 110 von Neumann, J. and O. Morgenstern, Theory of Games and Economic Behavior, Princeton University Press, Princeton, New Jersey, 194“. 111 Notable exceptions would include Knight and Hart. 112 Luce, R. D. and H. Baiffa, Games and Decisions: Introduction gnderitical Survey, John Wiley and Sons, p. 3, New York, 1957. :5: a... 55 system and was primarily concerned with the management of inventory in the face of uncertain demand. After observing this game the American Management Association develOped their T0p Management Decision Game. This was the first fully computerized business management game. In October, 1957. this game was used in a course on decision making for business executives at the Academy of Advanced Management. A facilitating factor in the develOpment of war games and business games during the 1950's was the develOpment of the digital and analog computers. This was essentially the period of takeoff in the develOpment of computer technology. The ability of computers to rapidly handle large quantities of data provided researchers with a capability that previously did not exist. This capability may have been a prerequisite for the development of realistic business games that were rapid enough and inexpensive enough for widespread business use. Although a number of non-computerized games were develOped 113 during this same period, even advocates of non-computer games stressed the advantages of computerized games.114 113 For examples see Vance, 8., Management Decision Simulation: A Non-Computer Business Game, McGraw Hill Book Co., Inc., New York, 1960, or Green, J. R., and R. L. Sisson, Dynamic Management Decision Games, John Wiley and Sons, New York, 1959. 11“ Andlinger, G. E., "Business Games-Play Onet” Harvard Busipggs ReVieW, v01. 36, N00 2, p. 115, MarCh'APrilg 19580 .sw 56 Three broad and often overlapping classes of business manage- ment games have evolved.115 The first class is aimed at general executive development and focuses attention on all aspects of the business at the upper-middle or t0p management level. The principles are assumed to apply for any particular type of firm. These are total enterprise games and are often quite complex. A number of games that would fall in this general class have been designed primarily for college instruction. Manuals for such games are often designed with classroom limitations in mind.116 The second class is industry or industry specific games. These games incorporate much of the language and institutional framework of the industry concerned. Industry games are usually develOped by peeple directly involved in the industry and are aimed at in-service training rather than general management education. An example is the insurance game.117 Functional games usually focus on decision making within one specific area, while taking cognisance of the remaining a8pects of the firm, or on illustration of a particular management technique. Games included in this third class are usually devised to provide a particular experience within a very Specific area. 115 Longworth, op. cit., 1970. 116 For examples see Smith, U. Nye, Elmer E. Estey and Ellsworth F. Vines, Intggrated Simulation, South-Western Publishing Co., Cincinnati, Ohio, 1968, or Darden, Bill R. and William H. Lucas, The Decision Making_Game, Appleton-Century-Crofts, New York, 1969. 1 l7 McGuiness, J. S., "A Managerial Game for an Insurance Company,” Opergtions Research, Vol. 8, No. 2, p. 196, March—April, 1960. 57 The number of business games was estimated in 1960 to exceed one hundred.118 Any cursory review of the literature indicates that the rate of develOpment and use of business games has been continually increasing throughout the last decade.119 The primary use of all management games has been to assist in management education. They often stimulate interest and make learn- ing more palitable, but this is of secondary importance. Although there are undoubtedly cases where games have been used to address specific problems, business games are generally designed to be most useful for teaching management skills. The second major use of simulation in business has been as a tool of analysis to assist in decision making. This might be charac- terized as use of simulation as a decision aiding technique. Although a few decision making simulation models have been develOped, other procedures such as linear programming and inventory control models appear to be more applicable for this type of problem.120 As of approximately 1950 simulation was a "technical curiosity which some prophesied would be a panacea for systems-analysis studies, and others viewed as a sort of mathematicians sedative, which held no technical promise, but consumed all too large a share of available 118 Richards, M. D., and F. H. Kniffin, "Business Decision Games - A New Management Tool,” Pennsylvania Business Survey, Bureau of Business Research, Pennsylvania State University, p. 7, June-July, 1960. 1 19 Graham, Robert G., and Clifford F. Gray, Business Games Handbook, American Management Association, Inc., 1969. 0 Sisson, Roger L., ”Simulation: Uses,” in Progress in Operations Research, Vol. III, Julius S. Aronofsky, (ed.), John Wiley and Sons, Inc., New York, P0 19’ 1969. 58 time, manpower, and money."121 Since then there have been thousands of applications of simulation for the purpose of aiding decision making.122 Many of these applications have been reported in the literature. The principal sources of these reports are Management Science, Operations Research, Behavioral Science, The Journal of the Association of ComputingyMachinery. Although it tends to empha- size engineering applications of computer simulation, the magazine Simulation publishes the results of simulation studies as well as technical research on simulation itself. A new Journal called Simulation and Games emphasizes simulation in the social sciences and may become an important source of simulation literature applicable to business. Simulation has been applied to a number of Specific and rela- tively separate management or problem areas. One of these areas is the design of communication and data-handling systems. Research and discussion in this area has concentrated on the develOpment of manage- ment information systems. The principal purpose of management infor- mation systems is to provide economically the information needed for planning, direction, evaluation, coordination, and control of the firm.123 This essentially amounts to the develOpment of a system 121 Morgenthaler, George H., "The Theory and Application of Simulation in Operations Research,” in Progress in Operations Research, Vol. I, Russell L. Ackoff, (edU, John Wiley and Sons, Inc., New Yuri: p. 363, 19610 122 Sisson, cp. cit., p. 30. 3123 Beged-Dov. Aharon G., ”An Overview of Management Science and Information Systems,” Mgnggement Science, Vol. 13, Noe 12, p. B-8l7, August, 1967. 59 of decision aiding techniques. As indicated above simulation may be one of these decision aiding techniques. However, simulation can also be used in the develOpment and evaluation of information systems for Specific problem situations.12u A second use of simulation has been in the design and evaluation of the management system per se. This involves simulation of the firm from the perspective of where decisions and policies are made and what factors are important in decision making at decision points. For example, in the simulation of a vertically integrated firm, Roberts gt;_§1.125 found that the particular performance measures used to monitor the progress of the firm strongly influenced the decisions made by managers of the various segments of the firm. Simulation is also used for capital allocation or investment problems. In this case simulation is used to deve10p information as to the expected results of various investment alternatives or combina- tions of alternatives. The manager can then select the portfolio he desires.126 As pointed out by Clarke,127 the primary advantages of 124 Vazonyi, Andrew, "Automated Information Systems in Planning Control and Command,” Management Science, Vol. 11, No. 4, p. B-2, February, 1965. 125 Roberts, Edward E., Dan I. Abrams, and Henry B. Neil, “A Systems Study of Policy Formulation in a Vertically - Integrated Firm,” Management Science, Vol. 14, No. 12, p. B-674, August, 1968. 126 Salazar, Rodolfo G., and Subrata K. Sen, "A Simulation Model of Capital Budgeting Under Uncertainty," Management Sciengg, Vol. 14, No. 4, p. B-675, December, 1968. :12? Clarke, Lawrence J., ”Simulation in Capital Investment Decisions," The Journal of Industrial Engineering, Vol. 19, No. 10, p. 495, October, 1968. d... l ' o. a. _ a A . t u ‘\., \ ‘. I A l I I Q . u' '1 60 simulation in handling the risk and uncertainty involved in invest- ment are (1) the forecast density function of the decision parameters do not have to meet any particular shape or mathematical equation, (2) the full range of data relative to each parameter is used and (3) management can evaluate the risks involved in alternative invest- ments and select the portfolio which is closest to its personal risk- return preference without attempting to communicate its risk-return preference to others. Economos128 found that the data generated in using simulation as a method of analyzing the risk of entering a certain business line is also useful for management control in hand- ling the investment if accepted. Simulation has also been useful in the design and analysis of facility layout. The primary advantage of this fourth use of simu- lation is the ability to evaluate facility layouts without incurring the actual cost of the physical facilities themselves. Examples include simulation of warehouse locations for large-scale, multi- 129 plant firms and an iterative model for determining the optimum location of a firm's facilities by selecting successively better sub- optimal solutions.130 128 Economos, A. M., ”A Financial Simulation for Risk Analysis of a Pr0posed Subsidiary,” Management Science, Vol. 15, No. 12, p. B—675, August, 1968. 129 Kuehn, Alfred A., and Michael J. Hamburger, ”A Heuristic Program for Locating Warehouses,” Managgment Science, p. 643, 1962. 130 Amour, Gordon c. and Elwood s. Buffa, ”A Heuristic Algorithm and Simulation Approach to Relative Location of Facilities,” Management Science, Vol. 9, No. 2, p. 294, January, 1963. I“. h-»- IIE INK ¥ I. I: u I ~ u s I. I h. v \ I .. N. I 3. M... .. .m I. 2,. .... ... N. u. e. on . 9. ... n u. ~\~ I Q u. n u h i o . I a «A. a . p i x c v . e nl§ .4 l a on . o . o u .n v c u . t . \ . . u I . .u ..... .... .2 a .. .e . .a . N ..I. ... .. .. . .. . .. .. t. .. 61 Use of simulation in personnel selection has occurred primarily through the use of management games as a method of evaluating a poten- tial employee's ability under certain Specified management situations. Data from game play is evaluated along with other more conventional sources of information. In a somewhat more comprehensive approach Smith and Greenlaw131 develOped a simulation model of the complete decision process of the personnel officer. A large variety of production management problems have been approached via simulation. One of the more famous early applications was the simulation of job-shop sequencing. Study of job-shop problems led to the develOpment of the Simscript programming language. One of the more recently developed job-Shep simulators is designed to deter- mine the optimum sequence of jobs for the next shift. At the begin- ning of each shift the model provides the foreman of each section with the priority of jobs currently in his section.132 Simulation has also received wide application in marketing. In a comprehensive review of marketing simulations, Kotler and Schultz133 point out four usages of simulation in marketing. These are: (1) Imitation of the essential behavioral characteristics of a system. 131 Smith, Robert D. and Paul S. Greenlaw, ”Simulation of a Psycholo- gical Decision Process in Personnel Selection,” Management Science, Vol. 13, No. 8, p. B-409, April, 1967. 132 Bulkin, Michael H., John L. Colley and Harry w. Steinhoff, Jr., ”Load Forecasting Priority Sequencing and Simulation in a Job Shop Control System," Management Science, Vol. 13, No. 2, p. B-29, October, 1966. 133 Kotler, Philip and Randall L. Schultz, ”Marketing Simulations: Review and Prospects,” The Journal of Business, Vol. 43, No. 3, p. 237, July, 1970. :.3. z .. . .. L. a..- ' . ... t 'n. a I a. fig ‘-. s ._ . . : '- . U . ’ \ v. n. . \ .- - - . n. ‘o h .- 62 This would include simulators such as the model of a market for frequently purchased packaged goods develOped by Lavington,134 (2) as a method of introducing and handling uncertainty, (3) as a computational technique for measuring parametric sensitivity and (4) as a heuristic technique for finding approximately Optimal solutions. The high level Of uncertainty and complexity involved in marketing management decisions has also precipitated attempts to simulate decision making in the marketing area.135 In Agriculture Previous use of simulation in Agriculture has concentrated primarily on business management games and simulators designed for Specific problems. The first simulator in the agricultural field ‘was a cheese plant simulator developed in the early 1960's by Glickstein gt;_§l. at Purdue University.136 Since that time interest in simulation has expanded rapidly. Interest in management games has Spanned the entire period Of interest in simulators in general. Prior to the develOpment Of agricultural games, non-agricultural business management games were 134 Lavington, Michael R., ”A Practical Microsimulation Model for Consumer Marketing,” Operations Research Quarterly, Vol. 21, NO. 1, P0 25, March, 1970. 135 Robertson, Gary N., c Geoffrey E. Fernald, and John G. Myers, “Decision Making and Learning: A Simulated Marketing Manager,” Behavioral Science, Vol. 15, NO. 4, July, 1970. 136 Glickstein, Aaron, E. M. Babb, c. E. French and J. H. Green, Simulation Procedures for Production Control in an Indiana Cheese Plant, Agricultural Experiment Station Research Bulletin 757, Purdue University, December, 1962. 63 used in some agricultural economics classes.137 The first computer- ized management game was the Purdue Farm Management Game develOped by Eisgruber138 at Purdue University. This game is a model Of a central Indiana mixed enterprise farm and has been widely used both in and outside Of Indiana. Fuller used this game as a prototype for develOping a Northeast Farm Management Game139 and a Poultry Farm Management Game.140 The farm management game, Simfarm, developed by Warren Vincent141 at Michigan State has been used extensively in classroom teaching and has received some research application.l’+2 Two other farm 143 management games are the California Farm Management Game and a game developed by Hutton in Pennsylvania. This latter game has 137 Garoian, Leon, ”Review Of Management Games for Teaching and Research by E. M. Babb and L. M. Eisgruber," Journal Of Farm Economics, Vol. 49, NO. 3, p. 765, August, 1967. 138 Eisgruber, L. M., Farm Operation Simulator and Farm Management Decision Exercise, Agricultural Experiment Station Research Progress Report 162, Purdue University, February, 1965. 139 Fuller, E. I., The Use of the Northeast Farm Management Game in Massachusetts, Mimeograph, Department of Agricultural and Food Economics, University of Massachusetts, March, 1968. Inc Fuller, E. 1., Massachusetts Poultry Farm Management Game, Players Information, Mimeograph, Department Of Agricultural and Food Economics, University of Massachusetts, Amherst, Massachu- setts, August, 1968. 141 Vincent, op. cit. 1&2 Strickland, Op. cit. 143 Faris, J. E. and J. Hildermuth, The California Farm Manggement Game,gSouthern San Joaquin Valley Farme, Participants' Manual, Giannini Foundation Of Agricultural Economics, University of California, Berkely, California, October, 1966. 64 been extensively used in a comparison with four methods of teaching farm business analysis to high school and adult students.lu# All of the farm management games develOped have a number of similarities. First, all models create uncertainty by incorporation of stochastic elements. Second, participating teams may, and usually do, consist Of one person. Third, the situation is constructed such that the teams are pitted against the real world uncertainties of the model rather than the actions Of each other. Fourth, the models all have a decision period of one year. 145 a monthly decision In a game recently develOped by Longworth, period is used. Although the author indicated that this contributed to the games ability to provide a valuable "exercise in the full mana- gerial process” involving both ”technical and institutional a5pects," the limited storage capacity of the computer used did not allow complete monthly interaction Of variables. At approximately the same time the Purdue Farm Management Game was develOped, three other agri-business games were develOped at Pur- due University.”6 These include a farm supply business game, a super- market game and a dairy processing game. The farm supply business management game attempts to duplicate the environment Of a farm supply business selling feed and fertilizer. The supermarket game simulates 1““ Curtis, S. M., ”The Use Of a Business Game for Teaching Farm Business Analysis to High School and Adult Students," American Journal of Agricultural Economics, Vol. 58, NO. 4, p. 1025, November, 1968. 145 Longworth, Op. cit., 1959. 146 Babb and Eisgruber, Op. cit. ‘-v , ...- r" t'g 65 an urban supermarket with four departments and emphasizes marketing decisions. Marketing decisions are also emphasized in the dairy management game. This game involves two to four competing dairies processing and selling wholesale and retail. A number of simulation models designed to address specific problems have concentrated on the ability of the simulation model 147 to handle uncertainty. In an early study Halter and Dean used simulation in a study of management policies under conditions Of weather and price uncertainty. At that time they concluded that "... it appears that simulation is a promising tool of analysis, particularly if uncertainty characterizes the decision-making environ- ment and a large number Of time related interrelationships among variables exist.“ Eidman et. a1.148 combined simulation and Bayesian decision theory in evaluating uncertainty involved in commercial turkey pro- 149 duction contracting. A 1965 study by Zusman.and Amiad and a 1968 study by Anderson150 develOped models for evaluation of crOpping 147 Halter, A. N. and G. H. Dean, "Use of Simulation in Evaluating Management Policies Under Uncertainty: Application to a Large Scale Ranch,” Journal of Farm Economics, Vol. 47, NO. 3: P. 557, August, 1965. 148 Eidman, Vernon R., Gerald Dean and Harold Carter, "An Application of Statistical Decision Theory to Commercial Turkey Production,” Journal of Farm Economics, Vol. 49, NO. 4, p. 852, November, 1967. 149 Zusman, Pinhas and Amotoz Amiad, ”Simulation: A Tool for Farm Planning Under Conditions of Heather Uncertainty," Journal of Farm Economics, Vol. 47, No. 3. P. 754. August, 1965. 2150 Anderson, Raymond L., ”A Simulation Program to Establish Optimum Crop Patterns on Irrigated Farms Based on Preseason Estimates of Water Supply," American Journal of Agricultural Economics, Vol. 50, No. 5, p. 1586, December, 1968. 66 patterns under weather uncertainty. Use of simulation to evaluate the combined effect of weather and equipment breakdown uncertain- 151 ties has been carried out for sugar cane and a model emphasizing the effect of weather uncertainties on the trade-Off between harvest machinery investment and crop losses is being develOped for corn.152 The ability of simulation to handle multiple goals was exploited in a study by Patrick and Eisgruber.153 Although they attempted to develop a weighting scheme for four defined goals in order to evaluate the satisfactoriness Of various growth plans, they point Out that ”Because Of the interaction among controlled variables, decisions made, goals, expectations and outcomes, it is not possible to analyze the effects of controlled variables without also taking into account the relationship between expectations and outcomes and the changes in farmers' goals over the course Of time." The time paths of vari- ables are known, whereas in standard linear programming and marginal analysis only the equilibrium positions are known. 153‘ Sorenson, Eric E. ani James F. Gilheany, ”A Simulation Model for Harvest Operations Under Stochastic Conditions,” Management Science, Vol. 16, No. 8, p. B-549, April, 1970. 152 Haltman, Jo Bo. K. Le Prickett, Do Lo Armstrong and L. J. connor' Modeling of Corn Production Systems - A New Approach, Paper No. 70-125 presented at the 1970 Annual Meeting of the American Society of Agricultural Engineers, Minneapolis, Minnesota, July, 1970. 153 Patrick, George F. and Ludwig M. Eisgruber, ”The Impact of Managerial Ability and Capital Structure on Growth Of the Farm Firm,” American Journal of Agricultural Economics, Vol. 50, N00 3, P. “91, August, 1968. 67 Support for the use of simulation in farm firm growth research per so was indicated as early as 1966.154 Since that time a number of farm firm growth models have been developed at Purdue University. These models have all been modifications of the Purdue Farm Manage- ment Game. Harshbarger's model emphasized land purchase strategies and loan limits.155 A version develOped by Harrison focuses on 156 A number of financial leverage and its influence on growth. other unreported versions have been or are being develOped. An interesting feature Of some Of the Purdue models is an attempt to build in a search routine for selection Of superior alternatives. This is accomplished by a subuprogram with built in decision rules which assigns probabilities to various activities and then readjusts those probabilities depending on the income generated when alternative combinations of activities are selected. Successive combinations chosen depend upon the updated probabilities connected with each activity. It has been suggested by a number of farm management researchers that linear programming might be included at a number of sub levels within simulation programs in order to use optimization at certain decision points. Methods of introducing Optimality into simulation 15“ walker, Odell L. and James R. Martin, ”Firm Growth Research Opportunities and Techniques," Journal of Farm Economics, Vol. 48, No. 5, p. 1522, December, 1966. 155 Irwin, Op. cit., 1968. 156 Harrison, Virden L., Management Strategies for the Growth Of Farm Firme, Mimeograph, Department Of Agricultural Economics, Purdue University, December, 1968. 68 models such as these appear to possess the capability of extending the usefulness of simulation models. Efforts to introduce Optimality exogenous to the model by develOping response surfaces and methods of selecting Optimum points from those have been made in two different studies. In a study by Zusman and Amiad157 the method Of steepest ascent was used to select optimum or near Optimum solutions from a surface generated by re- peated simulation according to a sample design. Candler and Cartwright158 have developed a performance function which is an explicit function of performance statistics generated by a simulation or budgeting model. This has the disadvantage that the functional form must be assumed and, like the Zusman and Amiad method, it requires a large number of simulation runs. Another group of problems within the field of agriculture to which simulation has been applied includes a group of problems to which Optimization essentially does not apply. Vincent159 in colla- boration with Sheppard has developed a poultry integrater simulator which is directed toward the design of equitable egg production 157 Zusman and Amiad, Op. cit. 158 Candler, Wilfred and Wayne Cartwright, ”Estimation of Performance Functions for Budgeting and Simulation Studies,” American Journal of Agricultural Economics, Vol. 51, NO. 1, p. 159, February, 1969. 159 Vincent, Warren H., "Simulation for Problem-Solving in the Poultry Industry,” in Agricultural Economics Report 157, Simulation Uses in Agricultural Economics, Department of Agricultural Economics, Michigan State University, February, 1970. 69 160 161 contracts. Tyner and Tweeten in 1968 and Schechter and Ready in 1970 developed simulation models of the farm economy in an attempt to assess the effect of government farm programs on farm prices, farm incomes, output, stock accumulations, efficiency and treasury costs. In each Of these cases there was a conflict (producer income vs. integrater income, farm income vs. government costs) which excluded Optimization in any realistic sense. The latter study did deve10p marginal rates of substitution among the reSponse variables which should be useful in policy making. 162 Upchurch states that the circumstances which presently promote vertical integration provide a situation where simulation and systems analysis ”offers the most promising approach for gaining meaningful insights into market performance." The USDA is presently supporting a hog and pork subsector study in which a simulation model Of the subsector is a major part. A major effort to apply Simulation to the problems of the agricul- tural economy of underdevelOped countries is being made by a team Of 160 Tyner, Fred H. and Luther G. Tweeten, "Simulation as a Method of Appraising Farm Programs," American Journal of Agricultural Economics, Vol. 50, No. l, p. 66, February, 1968. 161 Shechter, Mordechai and Earl D. Heady, "Response Surface Analysis and Simulation Models in Policy Choices,” American Journal Of Agricultural Economics, Vol. 52, NO. l, p. 41, February, 1970. 162 Upchurch, M. L., Recent Advances in Research Methods in Marketing, Paper presented at XIV International Conference Of Agricultural Economists, Moscow, August, 1970. 70 163 researchers at Michigan State University. Although they list lack of trained personnel and lack Of data in developing countries as limiting factors, the benefits they list include (1) clearer understanding of the economy, (2) acquisition of relevant information in an ”instant recall" model, and (3) a diagnostic tool for evalu- ating policies. In another study, Foster and Yostl6l+ used simulation in an attempt to evaluate the relationships between pOpulation growth, education and income in an underdevelOped economy. On the domestic rural development front, Edward165 develOped a small two region simulation model of the United States to assess the differential regional effects Of various income and pOpulation policies. The potential value Of the use Of simulation in extension was P°1nt°d out as early as 1963. At that time Babb and French166 indicated that large food processors had their own system simulators and that extension workers could assist decision making in smaller 163 Hayenga, M. L., T. J. Manetsch, and A. N. Halter, "Computer Simulation as a Planning Tool in DevelOping Economies," American Journal of Agricultural Economics, Vol. 50, NO. 5, p. 1755, December, 1968, and Halter, A. N., M. L. Hayenga, and T. J. Man- etsch, ”Simulating a Developing Agricultural Economy: Methodology and Planning Capability,” American Journal Of Agricultural Econo- mics, Vol. 52, NO. 2, p. 272, May, 1970. 164 Foster, Phillips and Larry Yost, ”A Simulation Study of POpulation, Education and Income Growth in Uganda,” American Journal Of Agri- cultural Economics, Vol. 51, No. 3. p. 576, August, 1969. 165 Edwards, Clark, ”A Simple, Two-Region Simulation of Population, Income, and Employment,“ Agricultural Economics Research, Vol. 22, NO. 2, P0 29, April, 1970. 166 Babb, E. M. and C. E. French, "Use of Simulation Procedures," Journal Of Farm Economics, November, 1963. 71 firms by making simulation procedures available. The first appli- cations involved the adaptation and use Of games for teaching 167 Although they stated a number of requirements management. for games and in extension, Hammond epp_§l.168 were very encouraged with the use Of a simplified management game for dairy plant managers. Extension workers at Purdue University have develOped extension oriented simulators which they used in their TOp Farmer Corn Hork— shOp in 1969 and their TOp Farmer Hog HorkShOp in 1970. These models were designed for use in group meetings and batch processing. The workshops were conducted as a series of meetings with farmers supply- ing data on their own Operations at one meeting and the extension personnel returning the results at the next meeting. Those involved with this effort were quite enthused with this approach. Michigan State extension workers have taken a somewhat different approach. They are developing a number Of initially smaller programs for use by farmers and extension workers on a demand basis with remote access to computers via touch-tone telephone and teletype. Over fifteen programs have been develOped which simulate a wide variety of limited size problems faced by farm managers. The primary benefit Of making simulation models available through extension is to allow firm decision makers to carry out exante 167 Babb, E. M., ”Business Games as a Marketing Extension Tool,” Journal Of Farm Economics, p. 1024, December, 1964. 168 Hammond, David H., J. Robert Strain and C. Phillip Baumel, "Simplifying Management Games for Extension Programs,” Journal Of Farm Economics, Vol. 48, NO. 4, p. 1026, November, 1966. :0... .~. . I“; .c. - . "o. . V I .- K A O .4 ‘ . K . ‘ 72 experimentation with a simulation model rather than forcing them to experiment within their real world environment.169 A problem involved in the extension application of simulation is the possibly more demanding requirements of the software develOped. Candler et. al.170 found this to be the case in their recent experi- ence at Purdue. Except for the above mentioned extension oriented simulation models, it appears that few models have been constructed which are general enough to be useful in approaching any broad spectrum Of problems. An important effort to deve10p general research or farm planning models has been conducted by Hutton of Pennsylvania. His first effort, which was also one of the early simulation models in agriculture, was a model Of a dairy enterprise.171 All important parameters were input by the user. The model stochastically simu- lated through any number of years desired. Emphasis was on dairy herd characteristics. The primary limitations Of this model were the exclusion Of all enterprises except the dairy herd and its heavy technical input requirement. 169 Walker and Martin, op. cit., and Babb and French, Op. cit. 170 Candler, Wilfred, Michael Boehlje and Robert Saatoff, "Computer Software for Farm Management Extension," American Journal Of Agricultural Economics, Vol. 52, NO. 1, February, 1970. :171 Hutton, Robert F., A Simulation Technique for Making Management Decisions in Dairy Farming, Agricultural Economic Report, NO. 87, Economic Research Service, United States Department of Agriculture, February, 1966. 73 More recently, Hutton and Hinman have develOped what they 172 call a "General Agricultural Firm Simulator.” This model is designed primarily for use in modeling farm firms but can accept and process data on a wide range of type and size Of problem situations. This model is designed in a linear programming format. Input data is arranged in tables with column headings indicating activities and row columns indicating inputs required or products produced. Allowance is made for input and product price trend, the introduction of stochastic price and purchase, sale Of capital items and development Of user defined subroutines. All activities are completely defined by the user. Production functions are linear and independent. This model has been used by one of its authors in a study of finance management practices,173 A more general perspective Of the usefulness Of Simulation which appears in the literature is that it may serve as the labora- 174 tory Of the social scientist. Burt Observes that "Simulation can be used to generate observations om complex phenomena much as a 172 Hutton, R, F, and H, R. Hinman, A General Agricultural Firm Simulator, Agricultural Economics and Rural SOOiOlOgy Bulle- tin 72, The Pennsylvania State University, University Park, Pennsylvania, July, 1969. 173 Hinman, H. R., AppraisinggResults of Alternative Finance Manage- ment Practices by Use of Simulation, Unpublished Ph.D. Thesis, Pennsylvania State University, December, 1969, reported in Hinman, H. R. and R. F. Hutton, ”A General Simulation Model for Farm Firms,” Agricultural Economics Research, Vol. 22, No. 3, July, 1970. 17“ Burt, Oscar R., ”Operations Research Techniques in Farm Manage- ment, Potential Contribution,” Journal of Farm Economics, V01. “'7, NO. 5, p. 1418, December, 1965. 74 traditional experiment is used." In a similar vain Irwin175 states that ”It seems to me that simulation is the nearest thing we have to the flexibility of the physical scientists' laboratory." Although this might appear to place simulation in the role Of the social scientists' salvation, Irwin goes on to say that "Simulation both encourages and terrifies. It entices with its flexibility but frightens with the realization that we must Specify both the kinds and forms of relationships. Our ignorance stands cowering in the spotlight.” This latter statement points out one of the disadvantages cited for simulation. This is the fact that develOpment of simulation models require a large investment of effort and money. The complex- ity Of most models forces a large data collection effort. Software development is time consuming and requires trained personnel. This has led a number of researchers tO indicate that other analytic techniques are preferred whenever they can handle the situation.176 Two other disadvantages or limitations Of simulation appear in the literature.177 First, most models are so complex that it is difficult to explain all the assumptions. This allows the economist to (either intentionally or unintentionally) build his own biases or 175 Irwin, George D., ”Discussion: Firm Growth Research Opportuni- ties and Techniques,” Journal Of Farm Economics, Vol. 48, NO. 5, p. 1533, December, 1966. 176 Irwin, op. cit., p. 94, 1968. 177 Suttor, R. E. and R. J. Crom, ”Computer Models and Simulation.” Journal of Farm Economics, Vol. 46, NO. 5, p. 1341, December, 1964. ...-...- n:..-.. 75 preconceptions into the model. Second, simulation is so well suited to specific problem areas that it may encourage more Specialization among economists and they may thus find it harder to keep up with developments outside their field of specialization. Final Evaluation The above discussion leads the author to conclude that simu- lation is the proper technique to use for the problem being considered. Although the systems approach does not provide the unifying theory that its proponents imply, it does provide a "systems concept” and the basis for a ”systems perspective" which focuses on the inter- active elements of a system. This appears to allow the desired management focus. The simulation technique does allow integration of basic economic concepts into a model within a general systems framework. Simulation is, in a sense, a systems approach to modeling. In addition, its ability to include any kind of relationship allows inclusion of theoretical economic concepts as well as concepts from other disci- plines. Although the model will be complex and the develOpment cost high, the investment levels involved in the decisions being considered by farm managers are of such magnitude that a small change in decision making efficiency could easily justify the model develOpment cost. In addition, the existence of sequential decision criteria or goals, the non-linearity of many of the relationships found on most farm firms and the need to be able to evaluate alternatives actually being considered by farm managers make simulation appear to be the technique to use. CHAPTER III MODEL DESIGN CONSIDERATIONS After development of an apprOpriate theoretical framework for model design and selection of a modeling technique, but prior to construction of a Specific model, a number of model design charac- teristics must be considered in detail. These characteristics relate not to the specific relationships to be included in the model but to the type of model to be constructed. They represent a general specification of the model and actually determine its effective design. Decisions relative to these characteristics must be made early in model design so that the appropriate specific relationships will be included in a desirable manner. Further, these general characteristics determine the specific method or approach to be used in reflecting and carrying out the general theoretical framework decided in the previous chapter. Those characteristics which appear to be most important for the model being developed are discussed in this chapter. Degree of Generalization Generalization of a model of the nature considered here can be accomplished in at least three ways. First, a model designed to address a particular problem can be constructed so that the model is 76 77 useful in addressing this same problem in other areas or so that the model is capable of addressing a number of different problems in a particular problem area. This can be illustrated by observing that a dairy enterprise model designed to answer questions for a particular farm situation can be generalized to handle either similar dairy enterprise questions on other farms or a much wider scape of dairy enterprise problems than those of initial concern. Second, a model may be designed to handle a wide variety of enterprise situations such that the model user or the model itself must choose those parts of the model which are apprOpriate to the problem being considered. A model which contained dairy, beef, swine, poultry, field crop and vegetable crop enterprise alternatives would fit this category. Most applications of such a model would use only a few of the enterprise alternatives. The third type of generalization involves develOpment of a model in which only the logic or the methods in which variables are used are specified within the model. The user specifies the activities and all the parameters relative to those enterprises. A typical example of this type of generalization is the general agricultural firm model developed by Hutton and Hinman.1 In this case the activities and the input and output coefficients relative to those activities are specified by the user. Although these three types of generalization represent a continuum in the sense that the amount and specificity of data contained within 1 Hutton and Hinman, op. cit., 1969- e.- s... ‘A 78 the model continually decreases, their uniqueness in other respects makes the categorization useful. The model develOped in this paper is generalized with reSpect to the first type of generalization. This model is designed to be useful for a wide variety of dairy farm problems and firm situations. The limited degree of generalization of the second type is indicated by the fact that the model is useful only for dairy farms and cr0p farms that grow only those crOps usually found on dairy farms. The primary justification for not generalizing the model in the type three sense was a desire to both reduce the burden of user input and make the model as realistic as possible. Realism requires specification of a number of very complex multivariate interrelation- ships which may be impossible to build into a model containing only the standardized logic required for type three generalization. Computer Language The languages available for use in develOping a simulation model include several general purpose languages, such as Fortran, Cobal or Algol, plus over #0 specific simulation languages.2 Although the general availability of Fortran compilers and computer programmers, a basic knowledge of Fortran on the part of the model designer, and the inherent flexibility of the Fortran language may lead to an apriori decision to choose Fortran over the other general purpose 2 Teichroew, Daniel and John F. Lubin, “Computer Simulation-Discussion of the Technique and Comparison of Languages," Communications of the Association for Computing Machinery, Vol. 9, No. 10, pp. 723- 739. October, 1966. 79 languages, the applicability of the Specific simulation languages must be considered in greater detail. The Specific Simulation lan- guages considered potentially useful for the model being develOped include only those most frequently used in management and agricul- tural economics and/or available at the MSU Computing Center. These are DYNAMO, CSMP, FORDYN, GPSS, GASP, SIMSCRIPT and SPURT. Specific simulation languages are generally designed to handle one of two basic types of simulation models. These are continuous- change models and discrete-change models. Continuous-change models are appropriate when the system to be simulated can be viewed as a continuous flow of information and material rather than a series of discrete events or transferrals. Models of this type are usually represented mathematically by differential or difference equations that describe rates of change of the variables over time. Although analytical or numerical methods are frequently used to solve this type of model, simulation is often required when analytical or nu- merical methods are either inappropriate or not powerful enough to reach a solution. Discrete-change models are appropriate when the system can be viewed as a series of discrete events. The discreteness may be caused by actual discrete events, measuring devices or measuring intervals which provide discrete data or intricate functions which can be most easily represented by discrete segments. Corresponding to the two basic types of models are two classes of simulation languages; continuous-change languages and discrete- change languages. Although a number of continuous-change languages 80 ‘were investigated in detail there is insufficient basis for selecting a continuous-change language. In a study of a tufted carpet mill using Dynamo, Yurow3 concluded that continuous representation is useful for obtaining only qpalitative managerial guidelines. He recommended use of discrete models when more precise measurement is required. Although growth of both plants and animals is certainly a continuous process, and continuous functions may best represent it, financing, debt repayment and crOp sales on a Specific farm are very discrete activities that could not be easily represented by continuous functions. Further, the intended extension appli- cation of the model being constructed requires more than "qualita- tive managerial guidelines.“ The decision between FORTRAN and one of the specific descrete languages rests on the relative advantages and disadvantages of each of the Specific simulation languages over Fortran. Fortran has the advantage that: (1) it provides practically complete flexibility, (2) programmers are available, (3) most computer facilities have Fortran compilers and thus the program would be adaptable to other research and farm planning situations, (h) the researcher was familiar with Fortran and (5) MSU'S other Forward Farm Planning programs and other research programs which might be used in conjunction with this model are in Fortran. 3 Yurow, Jerome A., ”Analysis and Computer Simulation of the Pro- duction and Distribution Systems of a Tufted Carpet Mill,” Journal of Industrial Engineering, Vol. 18, No. 1, January, 1967. eon H... v!“ ..a, 81 Although languages such as GPSS, GASP and Simscript may help in formulating a Simulation problem, they tend to force a certain degree of form on the model which appears inappropriate to the problem being considered.“ The model could be formulated in these languages. However, they do not appear to provide sufficient advantage to make any of them superior to Fortran for the parti- cular simulator being designed. Spurt, on the other hand provides some quite useful routines which could have been used in the model develOped. The decision not to use Spurt routines was made on a routine by routine basis. In each case another routine either previously develOped or develOped specifically for the model was considered either superior or easier to use. Time Interval The appropriate basic unit of time for a Simulation model depends primarily on the questions the model is being designed to address. Although the data used (both parameters within the model and initialization values) and the output variables are important, they can usually be adjusted to the requirements of the problems being addressed. The basic unit of time selected for the farm business simulator reported in this paper is one month. Action takes place on a monthly 4 For a detailed discussion and comparison of simulation languages see Teichroew and Lubin, op. cit., Krasnow, Howard and Beino Merikallio, ”The Past, Present, and Future of General Simulation Languages,” Management Science, Vol. 11, No. 2, November, 196“, and other references listed in the bibliography. ”it . , . I....._I r“ f O I Id. 1 n a u a V A V «s. . .‘ ' A ' l ‘C 1 \II ‘I C.‘ . . I: s. ..v .u. . . S. ... ... u c 3‘. ..J 2. be Ive .- , em . . ... u n n u s eh I q. .I‘ An. Al. N. u m ...n v . n.. M , u u ...k ..W “\ Hf Mia. . seen #1:. no.4 7. ..e u . . . u \ . . . . . e u u . .x. in: II. I .. I \ use 82 basis, coefficients are month Specific and output may consist of monthly values. This can be viewed as a state variable approach where the values or states of variables change monthly. Use of a monthly time unit has several advantages over the annual time period used in most previously develOped Simulators. More micro or tactical comparisons and analyses can be made. Many important tactical questions which hinge on the timing of Operations cannot be handled on an annual basis except by inserting the answer in the model before simulation. A monthly time period may allow one to address problems of this nature with the simulator. In some cases the mere shortening of the time period allows the analysis to be made. In other cases the shorter time period will make an analysis possible but additional data indicating the apprOpriate values for certain coefficients will also be required. Cash flows can be handled in a realistic manner on a monthly basis. Handling cash flows on an annual basis essentially forces one to net out many of the real problems. An annual cash flow statement tells little more about liquidity problems than an income statement. The real problems of cash flow usually occur within a year rather than between years. An advantage associated with cash flows is more realistic handling of credit, particularly Short-term credit. Without elaborate credit control procedures an annual model may have non—existent or ineffective credit limitations and/or incorrect interest calculations. For example, an annual model could Show a zero short-term credit balance at the beginning and end of the "Dov ~--- . 83 year but still have implied unlimited use of Short-term credit at some point during the year. A particular advantage of the one month time interval for farm planning is that Simulation of a problem can start in any month. A user can input the existing farm situation regardless of the time of year and simulate forward from that point. This feature does, however, have the disadvantage of complicating annual summary and tax calculations. A further advantage is the fact that interaction of monthly values of variables may improve the accuracy of variables being calculated. In many cases monthly parameter values are the same as annual values or the monthly values are one-twelfth of annual values. Thus, when these values are multiplied by monthly values of other variables which vary by month, the result is a better estimate than would have been generated using annual data. It should, of course, be recognized that this improvement in accuracy does not always occur. In cases where the value of a variable in one period is dependent on its value in previous periods the effect of an inaccurate parameter will be compounded. That is, if Yt - XY£_1, an inaccurate parameter value for X will make the value of Y more inaccurate if t is in months (and X is set equal X to IE) than if t is in years. An obvious disadvantage of the monthly time unit is the magnitude of the data requirements. Both the model builder and the user ex- perience a many-fold increase in the amount of data required. In 84 addition to the evident quantity problem is the fact that some rela- tionships may not have been previously Specified on a monthly basis. Thus, certain monthly variable values may be of relatively lower quality than their annual counterparts. It is important to point out however that many monthly variables in a Simulator can have no greater error than their annual counterpart and that as pointed out above monthly interaction of variables may in itself reduce error. A shorter time period also involves more develOpment and com- puter time and cost. It requires more professional and programmer time to deve10p a model with a shorter time unit and the resultant larger number of variables. In addition the computer time required for debugging and running such a model is longer. The longer run time increases the cost of using the simulator after it is develOped. Another possible disadvantage is the fact that most summaries of farm Operating data has previously been on an annual basis. Record systems such as Telfarm make most calculations on an annual basis. One alternative time period which could have been used is a quarterly or three month unit. Certainly sufficient precedent has been set by business for use of this time unit. The primary reason for not using a quarterly time period is the paucity of agricultural data on a quarterly basis. Most data which is not annual is monthly, weekly or daily. All in all, it appears to the author that the quarterly time unit offers insufficient gain over an annual time unit to make it worthwhile. .‘ié. ‘v .H’ a ‘ 5 C I;- I!) ..o 85 Farm Planningpvs. Research Emphasis As indicated in the title of this publication, the Simulator being develOped is designed for both research and farm planning. It is indeed anticipated that the model will be of genuine use for both applications. Nearly all model characteristics needed for research coincide with those of farm planning. However, conflict between these two applications does occur at times. The most important case of conflict for the present model occurred in the development of output forms. Whenever such conflict has arisen in the development of the present model, the farm planning application has received prior consideration. The primary justification for giving farm planning prior consi- deration is an effort to avoid any model deficiencies which might occur as the result of attempting to do two jobs and ending up’doing neither well. Justification is not based on any apriori judgement that either of the intended application areas is of superior worthi- ness. The farm planning emphasis was chosen because no whole farm simulation models develOped primarily for extension application had been constructed and it appeared to the author and others that such a model should be of considerable value. Stochastic vs. Deterministic The primary reason for inclusion of stochastic elements in simulation models is to allow realistic inclusion of variables for which only probability distributions of their values are known or for which the best estimate of their values are represented by estimates of their probability distributions. That is, variables which create 86 real-world uncertainty can be included in a manner which comes closest to reality and variables for which only limited data is available can be included (at least for study) if reasonable estimates Of their’density functions can be made. To be able to include the interactive effect of related random variables is a distinct advantage. Although the expected value of the product of independent variables equals the product of their reSpective expected values, this is not true where independence can- not be assumed. For any particular firm Situation there is good reason to believe that many groups of interacting environmental factors, interacting firm factors or interacting firm and environmental factors are not independent. The ranges, distributions and expected time paths for the variables resulting from Operation of the model can be calculated via Monte-Carlo Simulation. For many variables, such as income and *T). 123 a Number of animals Figure 4.4. Expected distribution of a herd by production. The influence of eliminating dry periods is automatically taken care of by the herd array matrix described in the above discussion of age composition. Also, the effect of culling at the historical rate is assumed in the level of herd production input by the user. How- ever, the effect of chagges in the rate of culling must be handled separately. To discuss this, Figure 4.4 will be used as reference and three 1, 31 and v1 will be used. 01, s1 and different rates of culling, U V are measured in terms of percent of average annual herd.size and represent percent of the herd that must be culled in order to cull all animals with production levels below U, S and V respectively. It is assumed that the lowest producing animals are always sold first. Thus, 0 s U g S gv 5 99. Reflection of the effect of changes in the culling rate is accomplished by assigning certain animals a production adjustment parameter. At the beginning of each lactation each animal is assigned 124 a random number from the range 0 through 99. If the culling rate is reduced, say from S1 to U1, cows with a random number between U1 and S1 indicating an implied production level between U and S will not be culled. However, they Should be if the average herd production is to be maintained. To represent the effects of this action the production adjustment parameter of each such animal is set equal to the difference between S and the production level implied by the number drawn. These differences are estimated in Table 75, Part II, Appendix A. On the other hand, if the culling rate is increased, say, to the level v1, animals will be culled which need not be culled to maintain production. When this occurs the animal cannot be assigned a positive valued production adjustment because it leaves the herd. The positive production adjustment is assigned to the next cow. The production adjustment parameter for each cow is added to its age determined production level to calculate the actual production level. The production adjustment parameter also indicates any genetic superiority or inferiority indicated for purchased animals. This procedure can be illustrated by assuming that the historical culling rate input was 30 percent and the user reduced the rate to 25 percent. Thus an animal assigned a random number of 26 should be culled to maintain production but will not. To indicate the decreased production the animal will be assigned a negative production adjust- ment of 2,000 pounds from Table 75, Part II, in Appendix A. This method will allow the effects of changing culling practices to gradually exert their influence over time; as occurs in the real world. A reduction of culling rate will only gradually lower 125 production, but the resultant lower production will linger after the culling rate is returned to its previous level. Changes in feeding rates are handled in a manner Similar to that used for fertilizer except that the user specifies a point on a surface rather than a point on a function. This can be visualized in three dimension by assuming the usual X, Y and Z axes with 2 representing milk production and X and Y representing quantity of grain concentrate and forage quality. By indicating historical production and feeding rates the user Specifies the coordinates of one point on the production surface. Using this point as a base the program superimposes the remaining portion of the surface. The shape of the surface is Specified by use of transition constants or rate of change parameters which represent the eXpected change in production for given small changes in either grain concen- trate quantity or forage quality for each level of the other factor.3 Different sets of transition parameters are used for different levels of initial actual production. If the level of grain feeding is changed, the level of grain feeding, the level of production and the forage quality would be used to determine the appropriate transition constants to use in calculating the new production and hay consumption levels. The trans- ition constants represent the change in milk production brought about by a 500 pound change in grain feeding. Thus, if the change in the grain feeding rate was 250 pounds and the transition constant was 3 The transition constants were develOped using unpublished data supplied by C. R. Hoglund, Department of Agricultural Economics, Michigan State University. 126 200 pounds the average rate of milk production could be changed by 100 pounds. If the change in the grain feeding rate is greater than 500 pounds, more than one transition constant is used. For example, assume a user has indicated historical grain feeding and production levels, while feeding medium quality hay, of 4,250 and 12,000 pounds reSpectively, and that for the present month being simulated, the grain feeding rate is to be increased to 5,250 pounds. The change in actual average annual production is calculated using Table 4.2 below. Table 4.2 is a reproduction of Table 73, Part II, Appendix A with parameter identification numbers ommitted. Table 4.2. GRAIN TRANSITION CONSTANTS Change in Level of Grain Feeding For- Level age of 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 Qual- Prod. to to to to to to to to to to to ity (000) 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000 7500 --------- change in milk production - pounds----------- ex. 13 450 uoo 300 250 250 250 200 200 150 100 50 ex. 11-13 uoo uoo 250 250 250 200 150 150 100 50 0 ex. 11 350 350 250 250 150 150 100 100 50 50 0 med. 13 600 450 450 350 300 200 200 200 150 150 0 med. 11-13 500 uoo too 300 300 200 200 150 100 100 50 med. 11 350 400 250 250 200 200 100 100 100 100 0 poor 13 700 500 500 #00 300 300 250 200 200 150 100 poor 11-13 650 500 450 350 250 250 200 200 150 100 100 poor 11 400 400 300 300 200 200 150 150 100 100 0 127 The forage quality and milk production levels indicate that the coefficients in line five are to be used. The grain feeding level change indicates that the coefficients of concern are 300, 200 and 200. The change in production - (500560”250) 300 + 200 + 1250563000) 200 . 150 pounds. When forage quality is changed the transition constant represents a change of one in the quality index instead of 500 pounds of feed and the transition constants in Table 74, Part II, Appendix A are used. If both forage and grain feeding are changed, the model evaluates the changes separately and sequentially. Machinery,Costs Machinery costs are divided into two groups; fixed and variable. Fixed costs include depreciation, insurance and interest. Deprecia- tion is handled in a tax depreciation manner and thus depends solely on the purchase price and expected life of each machine on hand. Although it is recognized that some portion of depreciation may be due to wear and tear, the difficulties of measuring and enumerating this and the small prOportion of depreciation usually represented by variable wear and tear makes the method used appear most appro- priate. Interest and insurance are calculated as functions of the depreciated value of all machines. The variable costs, which include repairs plus gas and oil, are Specific to particular systems. For each system, two coefficients indicating the repairs and gas and oil costs appear in the group two parameter list. These cost parameters represent the expected annual repairs or gas and oil costs per unit of that system. The coefficients 128 used by the model are found in Table 69, Part II of Appendix A. The economic implications of this procedure are Shown in Figure 4.5. Average variable cost is a constant and represents the sum of repairs, gas and oil costs. Average fixed cost declines in the usual manner and average total cost is the vertical sum of the other two. TC COST AFC)” ‘F—AI ‘vac OUTPUT 7 Figure 4.5. Machinery systems average costs. Although it is generally assumed that average variable cost curve for inputs is U shaped or at least declines over the left segment of the curve, there appears to be little justification for that in this case. The items included in variable costs for machin- ery vary directly with the amount a machine is used. Although there is time involved in preparing machinery for use and carrying out other functions related to getting a system going, these activities rarely use gas and oil or cause repairs in themselves (except possibly for the pick-up truck). Even the time required to get to the field with a given machine will represent making more trips for larger acreage rather than on making better use of the trips required for a small acreage. 129 It is the specification of all machines used by each system that makes use of a horizontal average variable cost curve reasonable. Average variable costs will change (decline) as the level of output increases when the increased output implies use of larger and more efficient machines. Whether average variable costs will change (decline) when the same machinery is used more hours is much less clear. This model assumes they do not. In this model the variable machinery costs do change when systems are changed but the change in systems is explicit and involves a change to a Specific machinery set. Duplication of machines is carried out on an individual machine basis. Each machine has a capacity coefficient which represents the number of acres of a crap or crOps the machine can handle. When- ever the number of acres of that crOp or crOpS exceeds the coefficient, or an integer multiple of it, another machine of that type is pur- chased. This will effect the average fixed but not the average variable machinery costs. Assuming that Q represents the capacity coefficient for this system and that Q is applicable for all machines in the system, Figure 4.6 indicates the cost curves that are implied. COST [ATC AFC-v? \ _ l. AVCJ‘ M Q OUTPUT Figure 4.6. Effect of machine duplication on costs. 130 The variable machinery costs for crOp grow systems are distri- buted throughout the year in the same prOportions as the labor required. Costs for harvest systems occur during the harvest months and are distributed in proportion to the acreage harvested. Labor Reqpirements Labor requirements are Specific to systems. Each system has an annual labor requirement which is presented in Tables 54, 55, 56 and 57, Part II, Appendix A. These, of course, can be changed as the user desires. The labor requirement listed for each system represents the expected labor required when the system's Size is at the level at which this system is usually Operated or would be constructed. This is equivalent to saying that the coefficients represent that level of Operation at which the Short run average labor requirement curve is tangent to the long run average curve or point A in Figure 4.7. If the wage rate were constant these curves would represent labor cost (with prOper axis identification). «5. Short run Labor or A Long run Labor Cost \ UNITS OF SYSTEM OPERATED *’ Figure 4.7. Average labor requirements. 131 Also included in the parameter list is a set of modification parameters for each crOp system (Table 72, Part II, Appendix A). These parameters indicate the percent of the average labor require- ment coefficients that would be incurred when the system is Operated at other than expected or "normal" levels. In general these numbers increase from 1.0 as the level of Operation is reduced from ”normal" and declines from 1.0 as the level of operation increases. This provides Short run average labor requirement curves of the form illustrated in Figure 4.7. Although these curves may turn up as indicated for the long run curve, there is little evidence to indicate that this actually occurs over relevant ranges of system operation. Actual curves for most systems appear to be quite flat or at least flatten out at relatively low levels of output. Distribution of labor required for livestock and crOp grow systems is carried out by use of distribution parameters (Table 71, Part II, Appendix A). These parameters indicate the percentage of the annual per unit labor requirement used during each month. The monthly per unit labor requirement is determined by multiplying the modified annual requirement by the distribution parameter. When a crop is planted in two or more months, even when the same machinery is used, each month is represented by a different system. Thus, the labor distribution parameters are different for different planting months. The total requirement may also differ by month of planting. Harvest system labor requirements occur during the months of harvest and are distributed in prOportion to acres harvested. 132 Product Prices Prices of most farm products used in this model exhibit an annual seasonal pattern. To represent this seasonal pattern a set of monthly percentage coefficients was develOped to indicate the expected relationship between the average annual price (weighted by months rather than quantity marketed) and individual monthly prices. Average prices for the most recent three year period were used to develop the monthly coefficients. Although it was recognized that this procedure would Often include some trend as well as seasonal fluctuation, it was assumed that relatively realistic prices would be obtained. Thus, for each commodity, thirteen coefficients are maintained; twelve monthly per- centage coefficients and the annual average price. To calculate the monthly price for any commodity the average annual price is multiplied by the percentage coefficient for the month in question. Using this procedure provides realistic monthly prices and allows changing the price level and the annual seasonal pattern separately. The coefficients used are presented in Table 53, Part II of the User Manual in Appendix A. The prices used are average market prices. It is assumed that the market will buy or sell at that price. The difference between purchase and sales prices is determined by the commercial hauling rate. The effective purchase price is the market price plus the hauling charge for moving the product from the market to the farm. The effective sales price is the market price minus the cost of haul- ing to the market. This is equivalent to saying that the difference 133 between the at the farm price the elevator Operator or feed mill will pay and the delivered price it charges is two times the haul- ing charge. Machinery78et Specification In order to reduce the quantity of input required for Simulation, the model user is given a choice of either Specifying the machinery on the farm being Simulated or allowing the model to select a repre- sentative machinery set. To select a representative machinery set the Simulator uses the three systems matrices (livestock systems matrix, crOp grow systems matrix and harvest systems matrix). These matrices contain the parameter values indicating which machines are used with which systems. For each machine the model first determines which if any systems being used require this machine. If any system in use requires this machine, the number units of each system using it, in conjunction with the maximum capacity coefficients described in the discussion of machinery costs, are employed to calculate the number of that machine required. The assumed initial cost of each machine is taken from Table 68, Part II of the User Manual (Appendix a). This parameter list also includes the expected life of each machine. The ages of the machines are assigned consecutively according to three investment classes. The three classes are delineated as machines with a new cost of (1) under $2,000, (2) $2,000 through $5,000 and (3) over $5,000. That is, the first machine with a new cost of more than $5,000 is assigned an age of one, the second, two. The present value or depreciated value of in. 3. III 7‘ .Lwh cl 134 each machine is then calculated using the straight line deprecia- tion method and the assigned age. After this procedure has been carried out for each machine, the farm business has been assigned a representative set of used machinery that includes only those machines required by the systems being used. Further, the number of each machine on hand depends on the number of units of each system (number of animals or acres) found on that specific farm. Additions or deletions can then be made to the machinery set in the normal fashion. The user Should recognize, however, that provision has been made to allow entry of the actual machinery owned by the farm busi- ness and that such entry is encouraged. The closer the Simulated machinery set replicates the real set, the better the simulated results should be. In fact the user can specify either just the number and kind of machines on hand or complete data about machinery on hand including purchase value, age and depreciated value. Dairy Herd Initialization Developing methods for generating appropriate starting conditions is often a problem in simulation model construction. This is particu- larly true when the system being investigated Operates continually in a steady-state condition. It is often advised that the data ob- tained during an initial time period be excluded from consideration.“ The expected uses of this Simulation model make discarding an initial time period of data impossible. The initial situation of the 4Hillier, Frederick S. and Gerald J. Lieberman, Introduction to Operations Research, Holden—Day Inc., San Francisco, California, PI “62, 196? I, 135 business being simulated is normally very important in determining the simulation results and the system being Simulated does not really ever reach a steady-state condition. Other methods of solv- ing the problem must be used. Starting conditions are not a problem in this simulator except for the dairy herd. The dairy herd is simulated by maintenance of the herd matrix as discussed above. The starting condition problem is caused by the method used to handle culling and freshening. At the time each animal reaches breeding age for the first time or freshens, a random number is drawn which is used to determine whether the animal dies, is culled involuntarily (mastitis, disease, injury), is culled for low production or completes the lactation, and if the lactation is to be completed another random number is used to deter- mine the next freshening date. All deaths occur at calving, involun- tary culls are sold during the fifth month of lactation and low pro- duction culls are sold in the ninth month of lactation. It is this assumption that each animal old enough to have been bred has been assigned sale or freshening dates that causes the problem. An animal in the tenth month of lactation obviously is not going to die or be culled that lactation (under model assumptions). The probability distributions used to determine the fate of that animal during that lactation must be adjusted to reflect that fact. A set of conditional probability distributions were develOped. The apprOpriate distribution to use depends on the number of months 136 the animals have been fresh. Let all - the probability of dying, F - the probability of being involuntarily culled, r - the culling rate, and - the number of months fresh Then if X s 5 the probabilities are calculated given that the animal did not die. The apprOpriate probability of being culled involuntarily is 14+. and the probability of being culled for low production is 112.5 1 -¢A If 5 < X 4:9, the probability of being culled involuntarily is zero and the probability of being culled for low production is X' l-d-p This represents the probability of being culled for low production given that the animal neither died nor was culled involuntarily. If X >.9 the animal is not to be culled this lactation. Use of conditional distributions in this manner allows Specifi- cation of the herd characteristics at the day the Simulation is started as needed for simulation. No warm up period is required for getting the correct starting conditions. Building Capacities and Delays Building requirements for both the dairy and storage are handled by use of capacities. The initial capacities are input by the user. Each time a factor changes which would require additional building 137 capacity, the capacity available is checked. The user uses decision rule parameters to specify whether buildings are to be built or animals and commodities to be sold when capacities are exceeded. For the dairy herd capacities are calculated as a function of, but are not necessarily equal to, the physical number of stalls avail- able and still not have capacity exceeded. The parameters used for calculation of capacity are listed among the group II Parameters. This procedure keeps temporarily greater than normal animal numbers from forcing building construction allows Specification of an animal to stall ratio other than one. In addition it allows tempor- ary relaxation of stalls per animal requirements while a building is being constructed. A.delay parameter is also used. This parameter is set equal to the number of months each dairy capacity must be exceeded before additional buildings are constructed. That is, if the parameter value is three, the number of cows must exceed capacity for three months before additional capacity will be automatically constructed. Use of a delay parameter allows representation of some of the delays involved in building construction and can be used to force some of the costs which often accompany the process of increasing capacity. However, as a method of including such items as the time lag involved between the decision to build and the actual completion of construction, this procedure falls short. Much of the lag involved in this type of situation implies expenditure of management time on discussions, calculations and direction of construction. There may be little else involved which would change the value of Simulation variables. The lag occurs more in the mind of the manager than in 138 alteration of physical or monetary characteristics of the firm business. For a contract job the additional facilities and the incurrence of their cost may indeed occur during the same month. Further, to assume that actually exceeding capacity would be an important decision criteria for deciding to build additional capacity assumes that the manager lacks the desire or intelligence required to anticipate and plan for future firm building needs. For a well managed business exceeding capacity, the availability of new capacity and the incurrence of the cost of the new capacity could easily occur during the same month. Data Sources An important dimension of the process of Specifying the parti- cular relationships or concepts included in a model is the data used. Inclusion of any relationship is always dependent upon finding data of sufficient quantity and quality to make model representation of the relationship realistic. In many cases the particular data available determines the exact way in which a relationship is included in the model. The major problems of data collection are represented by two different situations. The first is the lack of data and in particular the lack of data in the correct form. In a number of cases the data desired for develOpment of the model was not available because raw data had been analyzed to get averages rather than distributions or in some other manner which omitted the Specific numbers desired for the model. In these cases, re-evaluation of the raw data is required. In other cases raw data which contained the numbers required did not 139 exist. The relationships which could have been included had appro- priate data existed, of which weather is an example, were excluded from the model. Limited resources excluded the possibility of collecting the required data. The second and possibly more serious problem is conflict or inconsistency between different sources of the same data. In numer- ous cases the two of three sources available for a particular coeffi- cient or set of coefficients would vastly differ as to the correct value of those coefficients. Two different approaches to this problem were used. One is to deve10p a judgement weighted average of the sources to deve10p what is hOped to be superior coefficients. The second method is to accept one of the sources and properly footnote the data where ever it is presented. Both methods were used in collecting data for the FABS model. The large number of coefficients included in the FABS model and the existence of a number of relationships, which Should have been included in the model if there had been sufficient data, made the data collection process a lengthy one. A number of different sources were used. The sources used for the coefficients that are in the model are listed in Appendix C. Some of the other sources used are listed in the bibliography. The magnitude of the data collection process implies something about the ease of updating the model. This will be discussed in a later chapter. 140 Directions for Use Use of the farm business simulator as it is presently designed involves use of the User Manual and Data Form 1 found in Appendix A. The User Manual is divided into three parts. Part I contains the questions and data required for each business Simulated. This corre3ponds to Part I on Data Form 1 and the data for the business indicated in Part I of the User Manual is recorded on Part I of Data Form 1. Part II of the User Manual includes a listing of the parameters to be used by the Simulator unless they are altered by the user. It is suggested that each user read through this part to see if there are any parameters the values of which should be changed in order to accurately simulate the business being considered. User Manual Part III contains a discussion of the procedure used to change parameter values and make management decisions. The parameter value changes and management decisions for the alternative being simulated are entered in chronological order on Part II of Data Form 1. For each simulated month for which parameter values or management decisions are made, the entries on Part II of Data Form 1 indicating those changes must be preceded by a management decision record indicating the month and year in which the changes are to be made. If there are parameter values in Part II of the User Manual which Should be changed, then the parameters to be changed and their new values follow the first date record containing the first month and year simulated. Management decisions to occur during the first month are entered after the parameter changes. This is followed by 141 a date record indicating the next month in which parameters are to be changed or management decisions carried out which is followed by the Specific changes to be made. In turn these changes are followed by a record indicating the next month for which entries are to be made. All parameters in Part II of the User Manual and those in Part I that can be changed after initial entry are given identification numbers. These numbers are used whenever identification of a speci- fic parameter is required. If the value of a specific parameter is to be changed, the change is accomplished by entering the parameter identification number and the new value. When recording management decisions it must always be remem- bered that the model makes changes in the order in which they occur; even during one month. Thus, if a management decision requires new values for certain parameters these should be entered prior to entry of the decision itself. Conversly if a parameter is used for a management decision but the occurence of the decision makes a new value appropriate, the parameter value should be entered after the management decision. After Data Form 1 has been completed, eighty column Hollerith cards are punched directly from the form. A different Data Form 1 must be completed for each alternative being considered. CHAPTER V TESTING AND APPLICATION General Problems of Validation "...the problem of verifying or validating computer models remains today perhaps the most elusive of all the unresolved metho- logical problems associated with computer simulation techniques."1 Although it is recognized that an unvalidated computer Simulation model remains little more than a set of computer lanuage statements that will print out tables, graphs or plots, what constitutes vali- dation, or even what procedures Should be used during the validation phase, is yet to be conclusively determined. Several different areas of concern contribute to the validation problem. The validity of a model is affected by the purpose or use for which the model is constructed. A model that is relatively valid for one use may be totally unsatisfactory for another. For example, a model designed and used for classroom teaching of management principles may contain many characteristics which make it useful for its intended purpose but be void in certain characteristics necessary for research or farm planning purposes. Any model is 1 Naylor, Thomas H. and J. M. Finger, "Verification of Computer Simulation Models,” Management Science, Vol. 14, No. 2, p. B-92, October, 1967. 142 143 necessarily a simplification of its referent system and one useful method of Simplification is to abstract from those characteristics of the referent system which are not essential for the intended use of the model. It is usually difficult to determine the appropriate criteria to use in the model validation process. When a historical record on the system being simulated exists it is often recommended that the time paths of the values of important system variables be com— pared with the values of those variables as generated by the Simula- tion model. However, this presents a problem of "how close" the two time paths should be. Which variables measure the goodness of fit of the simulated data? Even if this problem is surmounted there exists the problem that the historical time series represents only one sequence of conditions. Although the simulator closely repli- cates the historical time series, would it have done as well had the sequence of causal conditions been different? This can be illustrated by using the equation Y’u 2X to Simulate the equation Y'- 2X + 4X2. 1 If during the historical time period X 1 2 was near zero, the simulator would do quite well. However, if the Simulator is used for time periods when X is large the model will do very poorly. Further- 2 more, the validation process can be expected to vary according to the specific criteria selected. Most simulation models are complex. The number of relation- ships and variables is usually large. This makes it very costly, time consuming and practically impossible to check out the values of all variables under all possible conditions. In fact the purpose of most models is to predict the actions of a system under conditions 144 that have not previously existed to see what the value of certain variables might be. This means that some limited set of variables must be chosen without the assistance of any generally applicable set of decision rules. Computer Simulation models are often developed to simulate systems that do not exist. The nonexistence of the real system may even be the primary reason the researcher has turned to the simulation method. In this case there is no real world system with which to make comparisons. Any validating process or criteria which requires data from a real world referent system is inapprOpriate. Use of validating criteria which require data from a real world referent system may also be inappropriate for systems that do exist. This occurs where the alternatives to be considered imply underlying conditions which differ widely from any that have been eXperienced by the real system. In this case there is no useful data for comparison. The appropriate level of validation requirements depends on the availability of alternative models or methods of providing the data required. If projections must be or will be made, the most valid model or method Should be used regardless of its absolute level of validity. Even the best method of weather prediction may be imper- fect, but because predictions are required, it is used. Thus, deter- mination of absolute criteria for separating valid from invalid mOdels is impossible. The extent and manner in which human participants are involved in the simulation influence the validation process. When human participants are used to represent actors in the reference system 145 their ”representativeness" must be validated. Placing individuals in unfamiliar roles, requiring an individual to play a role incon- sistent with his character or insufficiently motivating participants to act as they would in the real system (which may in fact be impos- sible) may all cause validation problems. When user-model inter- action is involved the human participants used during validation must be representative of those expected to use the system. In many cases ”representative" individuals may not exist, and even if they do a ”representative” individual may not really test the ex- tremes of the system. Observations or measurements on the real system may be in error. This is particularly true when proxies or aggregations of variables are used in the validating criteria. Any measurement on a system is only an assertion about reality. Many characteristics about a system may not be measurable by any absolute scale or may be measur- able only by indirect methods. Some undefined difference between the simulation system and the referent system can always be explained by measurement error. The magnitude of this difference influences the validity of verification measures. Two General Approaches Two reasonably comprehensive approaches to the problem of vali- dity have been developed by Naylor and Finger2 and Hermann.3 2 Ibid., p. B-95. 3 Hermann, Charles F., ”Validation Problems in Games and Simulations with Special Reference to Models of International Politics,“ Behavioral Science, Vol. 12, No. 3, p. 216, May, 1967. 146 The Naylor and Finger approach combines three methodological positions on verification to develop a multi-stage verification procedure. The first stage of this procedure calls for the formulation of a set of hypotheses or postulates describing the behavior of the system of interest. This makes use of the rationalist approach which holds that any model or theory is merely a series of logical deductions from a set of unquestionable truths or assumptions "not themselves Open to empirical verification or general appeal to Objective experience."u It is not implied that a complete set of unquestionable truths exists for any model nor that each assump- tion must be an unquestionable truth. It does require a diligent search be made, using all information available, for a set of postulates or assumptions about the system which come as close to being unquestionable truths as possible and that the model be develOped around these assumptions. This is essentially a rejection of Friedman's5 position that the validity of assumptions is irrelevant and a recognition that models can be expected to react properly under new or different conditions only if the causal relationships underlying the model are sound. Operationally, this stage involves use of the researcher's general knowledge of the system being simulated, any Specific infor- mation available about the system and the results of the simulation 4 Blaugh, M., Economic Theory in Retrospect, Richard D. Irwin, Inc., Homewood, Illinois, p. 612, 1962. 5 Friedman, Milton, "The Methodology of Positive Economics," in Friedman, Milton, Essays in Positive Economics, The University of Chicago Press, Chicago, Illinois, 1953. 147 of other "Similar” systems to Specify the components, select the variables and formulate the functional relationships to include in the model. The second stage of this multi—stage procedure involves an attempt on the part Of the analyst to verify the assumptions on which the model is based. Although using validation as part of the process of validation implies a certain level of circularity, what this stage calls for is the use of the ”best" available sta- tistical and non-statistical tests to empirically examine those aspects of the model to which such tests can be applied. It is not suggested that the researcher Should adopt an empiri- cist approach at this point and discard all assumptions that cannot be empirically tested. It is suggested that assumptions that can be tested be examined and that those found to be false by this pro- cedure be rejected or replaced. The third and final stage of this verification procedure consists of testing the model's ability to predict the behavior of the system under study. This stage represents the entire validation procedure as viewed by positive economics and assumes that the basic purpose of the model is prediction. In order to test the degree to which data generated by the Simulation model conform to observed data, two alternatives are available: historical verification and verification by forecasting. For historical verification the historical record produced by the system being simulated is used to check the accuracy of the simulation model. Verification by forecasting requires use of the model to forecast the future path of the system being Simulated and then comparing the actual path traveled with the model generated 148 forecast. The final decision concerning the validity of the model must be based on its ability to predict the time path of the system being Simulated. This multi-stage verification procedure treats validation as a continual process throughout model development instead of a test of the model after it is developed. Each stage of model construction involves a stage of validation. Although this method does not solve a number of the problems of validation it does provide a constructive approach for develOping more valid models. In an attempt to develop criteria with which to measure the validity of a model, Hermann presents five types of validity criteria. The first, internal validity, refers to the betweenerun variance when the initial conditions and any exogenous inputs introduced during Simulation runs are constant across all runs. The smaller the between- run variance the greater internal validity is assumed to be. When the observed results of an Operating model can be attributed to extraneous factors rather than internal model relationships, internal validity is low. The critical requirement for internal validity is that variation be accounted for by identifiable relationships within the Simulation. This criterion is of dubious value. Any model with stochastic elements will have between-run variance which is attributable to the interaction of those stochastic elements. This may legiti- mately be large if the variance of the model probability distribu- tions is large. Minimum between-run variance would always occur with a deterministic model even though it might be less realistic and could include inapprOpriate relationships. Thus, a check for 149 internal validity can at most involve an observation of whether the values of variables are always within the range of possibility given various values of other variables. The second criterion is face validity. Face validity is a surface or initial impression of a simulation's realism. A general impression that the model ”looks good" or that “things don't seem right” can be an important check on the real world representativeness of the model. Bayesian statistical concepts may be useful in evalu- ating face validity. The most severe limitation of this criteria is that in some cases the experimenter will not know what behavior is ”realistic", often because determination of realistic behavior of the system is the reason for constructing the model. Another problem is the lack of any explicit validity criteria. Variable-parameter validity involves comparisons of simulation variables and parameters with their assumed counterparts in the real world. This third criterion involves direct comparison of simulation values with real world values and sensitivity analysis to compare the model response to variable value change to the real world response of changing the value of that variable. The primary advantage of this approach is that it isolates individual components of the simulation. It is thus possible to determine which particular features may be reducing the representativeness of the model. The primary disadvan- tage of applying variable-parameter validity is that a large model will have numerous variables and sensitivity analysis becomes a laborious process. The fourth criterion employs "natural" events which occur in the referent system as criteria against which to compare outcomes 150 occurring in the simulation. Event validity may refer to either the magnitude or the occurrence of an event. Because most events are the result of the interaction of several relationships and variables, event validity may be quite useful for checking the total Simulation or major sectors of the model. It is less useful, however, in dis- covering the exact parts of the model in which problems occur. The second problem encountered with event validity is the selection of the appropriate events and dimensions of those events which Should be used in comparing occurrences in the simulation with its reference system. There is also the problem of determining the prOper weight to give the importance of each event. The last type of validity criterion is hypothesis validity. This is essentially a testing of the validity of the relationships in the model. If, in the real system X bears a certain relationship to Y, a corresponding relationship between model system X1 and I1 should be present. In this case it is the relationships between variables that is important rather than the variables themselves although there is a certain degree of correspondence between the variables and relationships. By combining these five types of validity criteria Hermann deve- lOps a unified approach to validation. ”In the initial construction of the simulation or game as well as during its Operation, face vali- dity is appropriate because of its ease and simplicity. To establish the degree of control and stability available in the Operating model, 151 internal validity should be conducted soon after the model's develOp- ment.”6 Following internal validity checks, event validity criteria are applied by using the model on a number of real Situations and paying close attention to all of the results generated. This phase may involve normal research or use of the model in its intended application. Whenever the results of a Simulation or number of Simulation runs produce results with an unacceptable divergence between the Operating model and the observable reference system, variable—parameter and hypothesis validity criteria Should be applied. Although these can be applied earlier to any variables or relationships which the researcher questions or is unsure of, general variable-parameter and hypothesis testing is not carried out because of the cost and size of the task. Both of the approaches to validation outlined above provide only a procedure to use and a general idea Of what to look for. Regardless of the approach or the criteria one only finds evidence to support or reject the realism of certain asPectS of the model. "Certainly, the establishment of validity for one variation and one set of conditions does little to establish the virtue of other variations of the model.7 This leads one to accept the Popper8 philOSOphy that a model attains a degree of validity as it passes 6 Hermann, op. cit., p. 226. 7 Conway, R. W., B. M. Johnson and M. L. Maxwell, ”Some Problems of Digital Systems Simulation," Management Science, Vol. 6, NO. 1' Po 104, OCtOber, 19590 8 Popper, Karl R., The Logic of Scientific Discovery, Basic Books, Inc., New York, 1959. 152 tests designed to examine its validity. The more difficult the tests and the greater their number the greater the validity attri- buted to the model. Only when the model fails a test is anything conclusively determined. In line with this basic philosophy, the validation procedures reported in this paper do not represent an exhaustive verification of the model develOped. That is left for future papers. An attempt has been made, however, to Show that the model does provide a reason- able representation Of certain situations and that it may indeed be useful for those Situations for which it was designed. The validation procedures carried out for the FABS model con- sisted of four separate activities. During and immediately following develOpment of the model a number of hypothetical problem situations were used to test the variables calculated for reasonableness and consistency. Two farm situations were developed which between them required use of all of the user defined systems. The model was used on an actual farm planning problem and an example research study was carried out using the model. These are discussed below. Variable and Subroutine Testing The model was develOped on a subroutine basis. After each individual subroutine was written it was run a number of times for given data situations and the results checked against the expected results which had been calculated independent of the model. This served two purposes. First, it was a check on the programming. NO subroutine was correctly written the first time. Most subroutines 153 were run several times with major programming corrections after the initial runs and minor corrections after later runs. Second, it served as a check on the model logic. Although considerable effort was made during conceptual develOpment of the model to design a complete and consistent model, a few cases were found where important variables or relationships were ommitted or only partially Specified. After each subroutine appeared to be working it was run with certain important subroutine variables set at extreme values. This was designed primarily to be sure that the model would not "blow up" when extreme values were used, that the model was reacting in the right direction and that the output variable values were reasonable in magnitude. After all of the subroutines were written and tested, the com- plete model was put together and tested. Three different hypothe- tical situations of increasing complexity and a number of variations of the more complex situation were used to test the complete model. This process focused on compatibility errors between the subroutines and prOper functioning of the interactive elements Of the model which involved more than one subroutine. This procedure required numerous runs and is in fact a continuous process which will continue as the model is used in more situations. A major problem which is encountered at this stage of model construc- tion is separating those relationships or variables which were ommitted from the model but Should be included if it is to function prOperly from those that ”it would be nice if they were included.“ 154 Test of User Defined Systems The user defined systems were develOped to allow complete latitude in the types of systems that could be handled by the model. However, the lack of definition means that these systems must be incorporated into the model in a general framework. To test the operationability of these systems, two different farm situations were developed. The first situation involved a large farm Operator who was faced with the problem of deciding whether to purchase six or eight row equipment. The model contains Six-row systems but not eight- row systems. Thus, to compare these alternatives the cr0p enter- prise user systems have to be defined as eight-row systems and the apprOpriate data develOped for input. The input data required for use of the user defined systems is a list of the appropriate values for (1) variables that are used only by the user system(s) selected and (2) those variables that are also used by standard systems for which new values are required to reflect the characteristics of the specific user system(s) selected. The variables specific to user systems are limited to question 52, 54, 56, 57, 65, 66, 68, 69, 7o, 71, 72, 80(a), 80(b), 80(c), 80(d), 81, 82 and 83 found in Part II of Appendix A. All Of these variables are entered as coefficient value changes at the beginning of (before) the first month simulated. The fact that the user defined systems have been chosen as the systems in use in Q.19, Part I will cause all of the input variable values to be used. For the eight-row system comparison, the data required consisted of (l) eight-row machine costs and life, (2) the labor requirements 155 for systems using the eight-row machines, (3) the Operating costs for the eight-row machines, (4) the capacity of the eight-row machines, and (5) Specification of the systems, which used the eight-row machines. The results which can be generated by the model for evaluating the effects of user defined systems is voluminous. Some factors which might be important for the six-row, eight-row comparison are labor requirements, labor distribution, machinery costs and income measures. Although this comparison was made only to check the ability of the model to handle user defined systems and the data generated is specific to a particular hypothetical situation, examination of some of the data provides interesting insights about the model. Selected financial data from the comparison of the two alternatives is pre- sented in Table 5.1. Table 5.1. COMPARISON OF SIX AND EIGHT ROW SYSTEMS Simulated Financial Data, 1971 System Hired Gas & Machinery Machinery Return to and Year Labor Oil Repairs Depreciation Labor a Mgt. Eight-row crOp systems Year 1 $13.800 $3.701 $2.692 $17,483 $16,035 2 13.800 3.765 2.727 17.483 13.171 3 13.800 3.753 2.721 17.483 18.542 4 13.923 3.924 2.813 17.483 17.789 5 13.945 3.977 2.841 17.483 22.571 Six-row crOp systems year 1 $14.952 $3.83t $2.844 $16,293 $15.220 2 14,909 3,927 2,878 16,293 11,108 3 15.158 3.915 2.872 16.293 16.317 4 15,536 4,087 2,964 16,293 16,683 5 15.791 4.139 2.992 16.293 21.421 156 Hired labor, gas and oil and machinery repair costs are lower with the eight-row system. Although increased machinery depreciation partially offsets this, return to management and labor is higher for the eight-row system. The differences in the first four columns listed do not sum to the difference in return to labor and management because of the differences in insurance, interest (both the total investment and the ratio between owned and borrowed capital vary) and miscel- laneous expense (which is a function of total other exPenseS). Gas and oil as well as machinery repair costs Show a negligible decline during year three. This is caused by a decline in the average number of heifers for that year which more than Offsets the Slight increase in cow numbers (see Table 5.2) and thus a lower machinery repair, gas and oil costs for the dairy enterprise. It Should be noted that these cost differences are exactly the same for both systems and thus do not effect the comparative results of the two systems. Although there are stochastic elements in the dairy subroutine, the random number generator always generates the same sequence of random numbers. This means that any two runs with exactly the same initial dairy situations and the same coefficient changes and management decisions will have identical dairy enter- prise numbers, costs and returns. Thus, a comparison of two crOp- ping systems such as this will have exactly the same dairy and crOp acreage situation. 157 Table 5.2. COMPARISON OF SIX AND EIGHT ROW SYSTEMS Simulated Physical Data, 1971 System Average Number Labor Hours and No. of of Surplus Hourly Year Cows Heifers Regular Hired Eight-row crOp systems year 1 128 72 2755 0 2 121 99 2722 O 3 122 90 2759 0 4 137 99 2091 62 5 140 98 1932 72 Six-row crop systems year 1 128 72 1696 461 2 121 99 1651 448 3 122 90 1770 531 4 137 99 1205 695 5 140 98 1123 783 The existence of identical dairy and crOp acreage Situations does not, however, limit the interactive features of the model. As indicated in Table 5.1, the labor cost declined Slightly in year two for the six-row system. No comparable decline was experienced for the eight-row system. The fact that the crOp acreage is the same for both systems could lead one to conclude that this decline was caused by the dairy enterprise, and it was. Dairy cow numbers and thus dairy enterprise requirements declined during year two (see Table 5.3). However, this decline occurred for both the Six and eight row systems. The decline in labor cost for the six-row system occurred because the decline in livestock labor requirements allowed the hiring of less hourly labor. There was no corresponding decline for the eight-row system because the lower total crOp requirements provided a Situation where no hourly labor was hired. The Operator and regular (full time) hired labor provided more than sufficient labor to handle both crOps and livestock for the first three years. 158 Table 5.3. COMPARISON OF MONTHLY LABOR REQUIREMENTS Simulated Data, 1971 Six-Row System Eight-Row System Year Livestock Labor Hourly Livestock Labor Hourly and Labor After Labor Labor After Labor Month Required CrOpS* Hired Required CropS* Hired Year 1 Jan. 524 881 O 524 895 0 Feb. 534 890 0 534 903 0 Mar. 555 792 0 555 821 0 Apr. 554 682 O 554 770 0 May 523 659 0 523 751 0 June 517 569 O 517 663 0 July 527 472 55 527 536 0 Aus- 535 552 0 535 678 0 Sept. 533 726 0 533 821 0 Oct. 527 269 258 527 756 0 Nov. 542 395 148 542 806 0 Dec. 559 769 0 559 786 0 Year 2 Jan. 560 881 0 560 895 0 Feb. 561 890 O 561 903 0 Mar. 564 792 0 564 821 0 Apr. 554 682 0 554 770 0 May 530 659 0 530 751 0 June 520 569 O 520 663 0 July 534 472 61 534 536 O Ange 533 552 0 533 678 0 Sept. 528 726 O 528 821 0 Oct. 512 269 243 512 756 0 Nov. 539 395 144 539 806 0 Dec. 529 769 0 529 786 0 * Hours of labor available after meeting crOp requirements (inequality of row totals is caused by rounding). 159 This example illustrates one of the advantages of a complete farm business Simulator. The effects Of a change on the entire farm business are calculated. An ordinary partial budget or machinery investment simulator would normally assume that the entire quantity of labor saved from using the eight row system would be reflected in a reduction of labor expense. This also illustrates the advantages of a detailed Simulator with detailed output possible. Without the degree of detail inher- ent in this model the reason for this decline in labor cost, or possibly even the decline itself, would have been lost. To check out the livestock user systems, a parlor-stanchion system was developed and input as a user system. This was compared with the stanchion system which is defined within the model. The results indicated decidedly lower labor costs which were partly off- set by higher machinery Operating costs and machinery depreciation, but a higher return to labor and management with the stanchion- parlor system. As was the case for the eight-row and six-row crOp systems comparison, the particular data generated is important only to the extent that it indicates the degree to which the user defined systems are Operational. The data generated for both of these comparisons were inspected by a Michigan State farm management Specialist and by the author. Visual inspection and comparison with budgeted data were used to evaluate the appropriateness of the results generated. In both cases the results were judged to be apprOpriate and apparently complete. Thus the user defined systems can be used to handle systems other than those specifically defined within the model. 160 ExPerience with these two Situations, however, did indicate that the volume of input data required to change a majority of the systems in use, such as all crop systems when the eight-row crOp machinery alternative was considered, is so large that such a change will likely be limited primarily to research application of the model. Cases where only a few user systems are required could be handled in many extension Situations. Farm Planning Problems A major develOpment focus of this model has been farm planning. To test the potential usefulness of the model for farm planning problems, the model was used in two actual farm planning situations. The problem situations involved two college seniors, one who was planning to return to the home farm and one who was planning to start farming on his own. The basic results of these two tests of the model were nearly identical. Thus, only the first Situation will be reported in detail. For this Situation, the farm, presently Operated by the father, was too small to support two families. The alternatives being con- sidered were two different approaches to increasing size of business. Although both alternatives involved increasing herd size, there were major differences in timing. The first alternative called for imme- diately building a new barn and buying bred heifers. The second alternative placed immediate emphasis on increasing youngstock numbers and delayed barn building for approximately one year. The labor, feed use, cash flow, debt and income characteristics were of primary concern. 161 The input data required for this problem consisted of one OOpy of the data indicated in Part I of the Users Manual (Appendix A) plus one set of coefficient changes and management decisions for each alternative being analyzed. Because of his familiarity with the actual farm Situation, the student had little difficulty in providing the data required. In fact, both students were able to provide the data required with relative ease. However, this may provide a weak test of the degree of ease or difficulty with which other potential users may be able to provide the data required. These students had been exposed to farm business simulators in a class they were just completing and both were practically college graduates. Many farm managers have neither of these characteristics. However, because this model will be used by only the larger commercial farmers, most poten- tial users would have either or both some college education and exten- sive extension experience. Thus, they may be just as capable of providing the data required as the students were. The output data which can be generated for a farm planning pro- blem, is indicated in section 21.1 of Appendix B. Much of the annual financial and physical data are output in the same format as that used by Telfarm and other farm accounting systems. Monthly physical and financial data are also generated. The actual data reported should depend upon the particular problem being analyzed and is determined by the user as part of the Part I (Users Manual, Appendix A) data requirements. For the problem considered, the simulator was instructed to report data on monthly labor use, monthly and annual feed use, monthly cash flow and annual debt and income. Examples of the types of information 162 generated that were considered important to the problem can be indi- cated. The monthly labor data Showed that the existing family labor would be able to handle the increased size of business regardless of the timing of the change except for the month of July when approxi- mately 100 hours of labor would be needed. The feed use data indi- cated that a considerable amount of oats and Shelled corn would have to be purchased in the spring and summer respectively after the herd size was increased. The cash flow summaries Showed that, although there was no build up of short term debt over time with either alter- native, making the change in the first year increased the total Short term over the first two years by approximately $8,000. Debt and income data indicated that although average incomes were only slightly higher (less than $1,000) for the first alternative and the maximum debt levels were approximately the same ($71,500 and $70,900) cattle numbers increased somewhat faster with the first alter- native. The most unfavorable equity Situation (.63) was achieved by the first alternative. This occurred during the first year, but is not considered an excessively risky situation. The lowest equity ratio achieved by the second alternative occurred during the second year when the equity ratio was .68. ExPerience in using the model on these planning problems exem- plifies the advantages of having experienced management personnel available to assist in preparation of input and interpretation of output. This is a learning OXperience similar to that of working through a budgeting problem. Having an experienced person available to provide guideline data and ideas and to question the validity of any data input which appears unreasonable helps individuals work 163 through the input and interpret the results to their problems. This is the educational role which could be played by extension personnel. For the two Situations discussed here, exact Specification of the input and inSpection of the output led to a number of changes in the alternatives being considered. Each problem used three com- puter simulation runs of each alternative. Although the third run was usually run out of curiosity and because it was free, the second was generally required for an acceptable answer to the problem. For example, the problem situation being discussed in detail started out with a ten percent culling rate and a three and one- half ton hay yield on land with a rental rate of three dollars per acre. Discussion of the probability of being able to maintain a ten percent culling rate in the future (it did accurately represent the most recent past) caused him to change the input coefficient. When he Observed the resulting production and crOp sale levels brought about by the hay yield level, greater effort was made to determine the actual average rate and the yield coefficient was adjusted to a more reasonable rate. Experience gained in working with these two Situations indicated that this model could be used to generate data for use in a manage- ment by exception context. Much of the data generated is in the same form as farm accounting system summaries. All of the data is generated to be specific to a future point in time, and thus is a relevant bench- mark against which actual accomplishment can be compared. Such bench- mark data would be both forward looking and Specific to the particular situation being considered. 164 A Research Problem A small research study was carried out to illustrate the useful- ness of the model for research. The topic chosen was a comparison of fall versus year-round freshening of the dairy herd. This makes use of the monthly time period of the model and is a real economic problem in dairying. The milk price advantage of fall freshening is easy to see. However, with year round barn feeding, production may no longer be influenced by season of freshening and labor, building and machin- ery utilization efficiency may be Significantly improved with year- round freshening. The initial farm Situation used was an actual farm business with a fall freshening herd. DHIA and Telfarm data were used to construct the initial farm Situation. It was assumed that there was no effect Of season of freshening on production. The desired seasonality of freshening was indicated by the freshening preference schedule. The freshening preference schedule is input in question 8, Part I (Users Manual, Appendix A) and indicates the desired age of freshening for animals by the month of year in which they are born. That is, if it is desired that an animal born in January freshen in July, the desired age of freshening is 30 (assuming that freshening at 18 months of age is unacceptable). The freshening preference schedules used are Shown in Table 5.2. All other factors in the business were the same for both alternatives. 165 Table 5.4. DESIRED AGE OF FRESHENING Month of Birth of Animal Freshening Pattern Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. --- ---------- Freshening Age Desired (months) --------------- Fall 30 29 28 27 26 25 24 24 24 23 22 21 Year Round 24 24 24 24 24 24 24 24 24 24 24 24 To avoid the problem of attempting to construct two comparable herd Situations for these two different situations, the same initial herd was used with the ages of the youngstock adjusted to reflect prior attempts to achieve the desired freshening pattern. A ten year run was made with the first five years assumed to be transition years and thus not used. The output reports or tables of simulator generated data which can be printed out for a research problem are the same as those avai- lable for farm planning problems. The format and data contained in these reports are indicated in section 21.1 of Appendix B. The results of this analysis indicated a Slightly higher average return to labor and management and a somewhat larger average herd size with year-round freshening. The first five years Simulated (transition years) Show this difference Slowly developing. However, it Should not be concluded from this data that year round freshening is superior. Insufficient prior study was carried out to be sure that all of the prOper variables were held constant or varied. The problem should be run a number of times under slightly different conditions to be sure that the freshening pattern is causing the difference. 166 Further, the results apply only to areas with blend pricing systems. As presently designed, the model will not handle problems using base-excess pricing except where the average price to be used can be input. The exercise was carried out and reported here only to illustrate technique. A second approach to research problems is to use the simulator as develOped as the basic model but make slight programming changes to allow more complete study of the problem being investigated. For example, one interested in looking at the effect of year—round versus fall freshening under a base-excess pricing system could modify the milk price generating procedure, to reflect base-excess pricing. This allows consideration of significant and varied research problems with little actual programming on the part of the researcher. CHAPTER VI MODEL COST AND MAINTENANCE There are three different types of costs involved in develOping and using a computerized simulation model. These are (l) develOpment costs, (2) Operation costs, and (3) maintenance costs. This chapter discusses these different costs for the FABS model. DevelOpment Costs The size and complexity of the FABS model made the develOpment costs high relative to a number of other computer models that have been constructed. The author Spent approximately one year develOping the model and getting it on the computer. In addition one programmer worked full time and two others worked part time for nearly four and one-half months. As previously indicated, a realistic estimate of the real cost of the computer use required for model develOpment is difficult to determine. Although the marginal cost to the Department of Agricul- tural Economics was estimated at less than $200, the Opportunity cost was undoubtedly much higher. To make a rough estimate of the minimum develOpment costs, the actual amount paid the author and an $8,000 per annum salary for programmers can be assumed. This provides a minimum cost of 167 168 approximately $10,850. Although a major portion Of this can be charged to graduate education, it is still a cost of model construc- tion. OperatingACosts Actual Operating costs can be expected to vary widely from computer facility to computer facility and from problem to problem. However, estimates can be made which provide an indication of the costs that are likely to be incurred. Operating costs can be divided into two cost groups: (1) compu- ter use costs and (2) user preparation and interpretation costs. Three major factors influencing the computer cost of using this model are the number of years Simulated, the quantity of print out- put and the number of alternatives considered. For most farm planning situations a three year Simulation length seems most likely. This provides one year of change, a year of transition and a reasonably ”normal” year of Operation after the change. The quantity of print required depends on the problem. A number of problems would require only one-fourth or one-half of the reports available. Others would require all or nearly all. For this reason cost estimates are made for twenty-five, fifty and one-hundred percent of the total estimated print. The problem assumed is a typical or model type of problem with only a moderate number of management decisions. The cost esti- mates will be made for Michigan State University's CDC 3600, the computer on which the model was develOped, and the Detroit based DACC teletype system. 169 Costs for Operation of the CDC 3600 were estimated at $4.10 per minute of computer time plus a card input charge of $0.50 per thousand cards read in and a print charge of $0.20 per thousand lines printed.1 Experience in using the model indicates that the computer time for a three year period is approximately 95 seconds. Assuming the model is on tape, card input would normally be under 200 cards (if tape is not used either a 9,000 card Fortran deck or a 5,000 card binary deck would be input). If all reports are printed, total print is approximately 600 lines per Simulated year. Thus, for a three year run there would be a $6.50 computer cost, a $0.10 card input charge and a $0.36 print charge for a total of $6.96. The cost, if the reports required amount to only one-half or one- fourth of total print, are $6.78 and $6.69 respectively. If two alternatives are being considered, the cost of analyzing the two situations would be approximately $13.44. The DACC system is a somewhat Slower system with an estimated CPU time of ten seconds per simulated year instead of the seven seconds estimated for the 3600. The costs of using this system are divided into three parts; CPU time, connect time and storage. The costs for these are $0.15 per second prime time ($0.12 per second non-prime time), $9 per hour and $1 per thousand characters per month after the first 75 K. The most difficult of these cost items to estimate on an indivi- dual use basis is storage charges. The total cost is a flat monthly 1 Estimate by Laura Robinson, Chief Programmer, Department of Agricul- tural Economics, Michigan State University. 170 fee based on the Size of the program irrespective of the number of times per month the program will be used must be made. Such an estimate is complicated by the fact that the total cost of using the program will influence the frequency of its use. To allow preparation of estimates it will be assumed that the program is used by ten users per month with an average of two alter- natives considered per user. Using an estimate of 256 K as the total program Size, the monthly storage charge is $181. This amounts to $9.05 per use. The duration of connect time required depends on the amount of input and the quantity of print. For the FABS model connect time per simulated year is estimated at ten minutes for input and processing assuming paper tape input, and sixty minutes for print assuming all reports are printed. For a three year run this means connect charges of $31.50 with all print, $18.00 with fifty percent of print and $11.25 with twenty-five percent of print. The corresponding total costs are $45.05, $31.55 and $24.80 respectively. Thus a two alter- native comparison would cost from $50 to $90. Table 5.4 presents cost estimates for a number of Simulation periods using similar calculations. Costs for a two alternative comparison would be approximately double the figures given. 171 Table 6.1. ESTIMATED COMPUTER COSTS FABS Simulator, 1971 Computer Number of Years Simulated and Amount Of Print 1 2 3 4 5 10 CDC 3600* All print $2.62 $4.78 $6.96 $8.78 $10.61 $17.70 5 print 2.56 4.66 6.78 8.54 10.31 17.10 t print 2.53 4.60 6.69 8.42 10.16 16.80 DACC** All print $21.05 $33.05 $45.05 $57.05 $69.05 $129.05 % print 16.55 24.05 31.55 39.05 46.55 84.05 I print 1&030 19055 24080 30005 35030 61055 * Computer run times assumed were 35, 65, 95, 120, 145 and 240 seconds for l, 2, 3, 4, 5 and 10 year runs respectively. ** Prime time rates. The cost advantage of using the CDC 3600 computer whenever batch processing and a one or two day delay in printing output will not cause problems is Obvious. However, the costs Of using the remote access teletype system are not of such a magnitude as to preclude its use. The costs indicated here would be more than off- set by a one percent saving on most investments presently being made. The second type of Operating cost is the user cost involved in preparing the data for input to the computer and interpreting the results. The first time the model is used by any one user, some time would be required to explain the model. Although this would provide a useful format for discussion Of the particular problem in question, this discussion might proceed faster if a known technique such as budgeting were used. On the other hand, because the input forms ask the questions and list the variables to be used, the 172 enumeration of the problem Situation and the alternatives to be considered may proceed faster than an Open ended technique such as budgeting. Other than these somewhat offsetting items the major preparation cost involved is punching the cards for batch processing or preparing the paper tape for remote access (or typing in the data directly if tape is not used). There are a minimum of 47 cards (or lines) of input data. Although there is no set maximum number of cards or lines, in most cases the number would not exceed 75. Punching and verifying this much.data should not require over one-half hour of clerical or user time. Interpretation of the results of the Simulator would normally require more time than interpretation of the results of a budget that had been developed by its users. The exact meaning of a number of the variables would need to be discussed. This is in Spite of the fact that an effort was made in designing the output reports to use variables and terminology as defined in Telfarm reports or as normally used by farmers. The degree to which this would increase interpre- tation time would depend upon the complexity of the farm situation and the user's degree of familiarity with the terminology used. Maintenance Costs There are two costs involved in maintaining a model such as this. One is the cost of maintaining the program to keep it consistent with the software and hardware changes made by the computer facility to be used. Practically every computer facility is continually improving its software. In addition, the constant develOpment of "new, bigger 173 and better” computers brings about frequent hardware changes. To keep a program working on a long term basis requires continual adjustment of the program to keep it compatible with the computers used. The costs involved in this type of maintenance depends on the computer facility used and the frequency and magnitude of changes made. Any cost estimate would necessarily be short range and would depend on changes programmed to be made. The second and more important maintenance cost is the cost associated with keeping the model updated. The coefficients included in the model must be periodically examined and changed to represent more recent experience and environmental conditions. AS pointed out in the discussion of model relationships and data sources in Chapter IV, finding appropriate coefficient values for a model as large as the FABS model is a difficult and time consuming task. Although the presence Of ”a value" which is referenced makes the updating job much easier than develOping the apprOpriate coeffi- cients when the model is initially constructed, finding new values for 3600 parameters remains a job of significant prOportions. Some coefficients will require little thought or will be easy to get: such as mature equivalent coefficients or data from Telfarm reports. New values of other coefficients will be as hard to get as the origi- male. The cost or time involved in updating this model is difficult to estimate prior to development of experience in updating models of this type. Based on the time required in develOping the model, a 174 rough minimum estimate would be two weeks time by a well trained Agricultural Economist familiar with the types of data required. User Charges A major factor influencing the demand for a computerized tool of analysis such as the FABS model is its cost to users. It is clear that the user Should be charged for the Operating costs, both for preparation keypunch work and actual computer and telephone charges. This is the actual cost incurred by the user and thus should be borne by the user. However, the proportion of the development and updating costs which should be incorporated into the cost of using the program depends on your frame of reference. Viewed as a commercial venture, all costs must be covered by the users. Thus, a charge for develOp- ment and updating must be included in the user charge. On the other hand model develOpment and updating can be viewed as a public good. The cost is incurred only once regardless of the number of times the program is used. Viewed from this point of view none of the development and updating costs Should be incorporated into the user charge. This difference in viewpoint provides the basis for the difference Of Opinion about apprOpriate user charges between universities, which view development and updating as a public good, and the commercial firms which would like Offer these services but find themselves in direct competition with the universities. CHAPTER VII POSSIBLE EXTENSIONS Several possible extensions or modifications of the present model could be made. Although some of these extensions could be expected to improve performance of the model for the purposes for which it was originally designed, most of the potential extensions discussed below would either extend the sc0pe of the model and make it applicable to additional problem areas or reduce the user burden involved in Operation of the model. The extensions discussed include possible algorithm modification, use of teletype, separation of the subroutines and generalization of the model to more enterprises. It should be pointed out that all of the possible modifications discussed in this section are dependent upon first placing the model on a larger computer, developing overlays for part of the present program or otherwise generating additional usable core Space. With the computer presently being used there is practically no available memory remaining for additional variables or program. Possible Algorithm Modification In any model of the type develOped here, many minor algorithm changes could be made which would make the model more appropriate to a Specific problem or a specific personal point of view. The 175 176 possible algorithm modifications suggested here include only those which would be expected to have a major effect on the model or use of the model. Price Level Chapge Parameters In the model as presently designed any change in price levels or price relationships must be input by the user. This has the advantage that prices are known and that any price level or relation- ship can be input that the user desires. Also, this represents a very reasonable assumption if prices are expected to be constant through time. However, if prices are likely to be continually chang- ing, as they have in the past few years, a large burden is placed on the user to input price changes for each year, or in some cases each month, if absolute prices are to be realistic. Even though the relative price relationships may remain correct through time the absolute values of variables will not. If absolute price levels become incorrect the output from the model becomes of little value as a data generator for management by exception. To handle situations where continuous inflation (or deflation) is expected in all or a number of variables price level change para- meters could be inserted. This would be accomplished by using para- meters which have an initial value of one and expected price level changes would be represented by changing (either endogenously or exogenously) the values of these parameters. Insertion of these parameters such that they enter into the calculations in a multipli- cative fashion allows use of constant prices by changing the values as the model moves through time. 177 By using a set of parameters with each parameter representing a different commodity or cost group, relative as well as absolute prices could be easily changed through time. The price change rates could be easily changed through time. The price change rates could be handled either as constants or entered as a series of index num- bers. Use of index numbers would require addition of a set of arrays to contain the numbers. Constants could be handled either on a monthly or annual basis, or annual coefficients could be used with the program using log functions to convert the rate of change to a monthly basis. Group Price Changes Use of a model of this type in areas with different technological or economic conditions requires that the values of a large number of variables be adjusted. AS presently constructed the FABS model requires an individual entry for each parameter which is to be given a new value. This procedure works well and is efficient when only a few values are changed and when there is no pattern of relationship between groups of standard values and corresponding groups of adjusted values. How- ever, in many cases it will be necessary to change groups Of parameters by a constant or a Specific percentage. A user may need all gas and oil costs to be raised by ten percent, and all machinery prices lowered by five percent. To reduce the user burden of making all of the required changes individually a set of group price change parameters could be used. These parameters would serve as flags to the program that the values for a certain group of parameters are to be changed. The value of the 178 parameter would indicate the magnitude and direction of value change. The parameter values would then be changed by the program before Simulation begins. If a set of price level change parameters, such as those described in the preceding section, were inserted in the model, the group price changes discussed here could be incorporated in the price level change parameters. Setting the first year level of the price level change parameter equal to 1.1 would raise the initial value of the parameter by ten percent. Then, if subsequent values of the parameter were cal- culated (or input if index numbers were used) relative to that initial value, both the change in level and the change through time would be accomplished. Stochastic Elements As indicated in chapter three there are several advantages of a deterministic model, particularly for extension application. There are, however, a number of possible applications of a stochastic model. A number of parts of a farm business do exhibit a large degree of variability and a stochastic model may best represent certain aspects of those situations. Further, a stochastic model may be very useful for a number of research and teaching applications. Additional stochastic elements can be added to the model by calculating a standard deviation for each price, yield or cost used and adding this, multiplied by random standard normal deviate, to each price where ever it is used. Monthly or annual selection of new random numbers makes the model Operate stochastically. This procedure 179 has the advantage that setting the random standard normal deviate at zero allows Operation Of the model in a deterministic mode. An alternate, and possibly superior method when stochastic elements are added to an existing model, is to use a price and yield generator. This is a routine which uses the input price, standard deviation and standard normal deviate or some other method to gener- ate the price, cost or yield value to be used for each month. The primary advantage of this procedure is that little of the existing program needs to be changed. Care must be exercised, however, to keep from destroying the input values that will be required for succeeding price calculations. Weather A major factor influencing the amount of work accomplished and the timeliness of that work as well as machinery and labor requirements is weather. Explicit handling of the effects of weather could improve the performance of the model relative to tactical questions of machin- ery purchase, timeliness, enterprise combination and labor utilization. There are presently at least two major problem areas which one must face if a realistic representation of weather is to be inserted in a model. The first is the problem of interaction between field days, non-field days, labor used and machinery required. For example, if there are insufficient field days to get a certain corn acreage planted, the acreage can be reduced, additional machinery could be purchased or a longer day could be worked. Different users may desire that different actions be taken for the same problem situation. Automatic reaction by the computer program requires that decision 180 rules be developed so that proper action is taken. A further dimen- sion of this problem is the fact that while a one-crOp model may be able to assume that all other activities are carried out when you are not planting or harvesting the crop being analyzed, a complete farm model must deal with the overlap and trade-Offs necessary to mesh a multiple crOp program. The second problem is the apparent lack of data required. Areas in which additional information is required are: 1. 2. 3. Field days available--more information is required on the distribution of field days for different Operations. The characteristics of harvest field days and planting field days may differ. Certainly hay harvest field days differ from corn plant field days. The relationships between these two different types of field days must also be determined. Field days required--the number, combination, and timing of different types of field days required for each crOp enter- prise must be determined. Losses or gains caused by timeliness-~the losses incurred by not carrying out an Operation at the Optimum time must be specified. Also the interactive effect of altering the timing of a number of different Operations on the same crOp must be evaluated. Machinery Replacement Relationships As presently designed the model handles machinery replacement in the normal fashion by replacing machines when their age equals their expected life. Although this appears to be a reasonable approach, 181 provides approximately correct answers and is indeed correct a large prOportion of the time, any astute observer knows that actual patterns of machinery replacement often differ significantly from the patterns that would be generated by this approach. Many machines are traded before their physical life expires: sometimes for technological reasons but often for other reasons. The economics involved may be Similar to that of the family car which is usually traded before its physical life expires. Assuming that a capital expenditure for replacing the car would not be required until the end of its physical life would be unrealistic. This observation leads one to conclude that possibly a routine could be develOped which would provide decision rules for machinery replacement. A study of management behavior patterns relative to machinery replacement decisions may be needed to provide a basis for develOping such a routine. Although casual observation indicates that the economic position of the firm and the level of profitability of the business during the year in question are factors, their Specific effect and importance are only partially known. One possible approach which would allow further study of the problem would be to deve10p a random decision rule generator. This could be done by using a truncated normal distribution or a gamma distribution with a range equal to the physical life of the machine and assuming that the machine will be traded at an age equal to one- half life plus a random or predetermined number of standard deviations (with maximum age equal to life). Experimentation with the distribu- tion and the number of standard deviations may produce a more represen- tative replacement pattern. 182 Dairy Herd Characteristics Input The model presently requires detailed input of the characteris- tics of the dairy herd. This provides excellent data with which to simulate individual farm situations. However, the input burden placed on the user is quite heavy. One possible way to make input easier would be to require input only of the number of animals found on the farm and generate the herd characteristics (individual animals) using typical age and freshening distributions. This would not be quite as accurate as input of the actual data, but the loss may be small enough that the generated numbers would be sufficient for most applications. One constructive approach for modifying the model in this way is to alter the input forms so that the user can input either the detailed characteristics of the herd or just the numbers of animals. A program segment would then be distributions to deve10p the information charac- teristics would provide. Implementation of this modification will require first determin- ing the typical distributions for individual herds. Although the data for these distributions is available the data appears not to be summar- ized in the required distribution form. Teletype Use As previously indicated, the FABS model has been develOped to make conversion to teletype simple. The width of reports has been limited to 72 columns and the model is designed to allow program interruption and data change between years. Utilization of this model as a tool to assist decision makers with specific management decisions likely depends on an ability to 183 access the model at a location within reasonable driving distance from the manager's place of business. Although other teaching (adult and classroom) and extension applications can be made using batch processing, the delays involved in batch processing make it less adaptable for this application. Further user-simulator interaction may provide an additional dimension of considerable use to managers. In cases where the proper sequence of events through time is totally unknown, the ability to work the model through time a step (year) at a time may allow discovery of superior alternatives. Other advantages and disadvantages of teletype use are discussed in chapter three. The eXpected process to be used in transferring the model to teletype has been discussed previously. If the decision to transfer to teletype is carried out, the possibility of placing the model on a nationwide network such as the General Electric system should be given careful consideration. A network system could extend the availability of the model to a number of states. By develOping different data subroutines containing all of the initial program values for each state (preferably by workers in that state) the model could be made applicable to a large area. Very minor program modification would allow only one of these data subroutines, depend- ing on a state code number input by the user, to be called. The major advantage of this method would be the reduced storage costs incurred for storage of the program. Each state involved would increase only the storage costs for its data subroutine and its share of storage costs for the program itself. 184 §eparation of Subroutines Any large model carries out a number of different functions. An effort was made during development of the FABS model to deve10p subroutines which contained specific functions or logical units of activity in their entirity. For example, the labor subroutine handles all labor requirement and cost calculations. With units of activity organized in this manner it would be possible to separate off certain of the subroutines, design appro- priate input data, select or design the desired output reports and use the subroutines as programs by themselves. In this way persons interested only in certain parts of the model could use those parts without being forced to use the complete model. The subroutines which appear to have the most potential as programs by themselves are labor, dairy, storage and sales, finance and machinery. The labor subroutine would allow a user to determine the expected labor requirements of alternative enterprise combina- tions. The dairy routine offers two possibilities. One alternative would be to separate off only the part of the routine used to generate animal numbers. The second approach would be to use the entire sub- routine to generate animal numbers, production and feed requirements when different raise, purchase, sale and feeding practices are used. Similar opportunities exist for use of the other subroutines mentioned. The primary problem that can be expected when these subroutines are adapted to Operate independently is the difficulty of develOping input that is compatible and complete. Much of the data which is now calculated using a number of variable values will have to be calcu- lated exogenous to the model and entered by the user. 185 At this point it appears that there is little justification for using the complete model to answer questions which require use of only a small part of the model. If a problem is limited to ques- tions of labor use, operation of the complete model is costly and the complete input compliment is still required to get dependable answers. A more apprOpriate approach would be to use a model de- signed specifically for labor problems, which does not require and generate data irrelevant to the problem. Generalization The final possible extension of the model to be discussed here is generalization to additional enterprise areas. Beef, swine, poultry, fruit and vegetable enterprises could be added to the model by develOping a subroutine to handle each enterprise added and appro- priately modifying the storage, machinery, labor and buildings routines to handle the needs of the new enterprises. This would allow use of the same model to consider nearly any farm firm problem situation that could be expected to arise in the northeast quarter of the United States. Switches from one enterprise combination to another or comparisons of enterprise combinations could be carried out with ease. A major question must be evaluated before generalization of this type is undertaken. That is, whether it is better to add more enter- prises to this model or use some of the ideas and routines from this model to deve10p new models to handle other enterprises. Although a number of farms shift the emphasis between their livestock enterprise and crop enterprise, few actually consider changing livestock 186 enterprises. If the number of users that would be making these livestock or major enterprise shifts is low it may make more sense to develop additional models rather than adding to the present one. Construction of additional models has the advantage of allowing use of a less complicated model. It also makes model develOpment somewhat less cumbersome. CHAPTER VIII SUMMARY AND IMPLICATIONS Summary The scientific industrialization of agriculture is evolving a commercial farming sector made up of units of ever-increasing size and complexity. The primary objective of this study was to develop an improved technique of analysis which would be useful in assisting farm managers and researchers in dealing with this dynamic situation. More specifically, the objectives involved develOpment and testing of a dairy farm business analysis model which (1) can reasonably represent Michigan dairy farms, (2) can be adjusted to evaluate individual farm situations, (3) allows evaluation of systems not now in Operation, (4) is designed to allow additional enterprises to be added, and (5) offers flexibility in respect to input and output. To provide an appropriate theoretical framework and background for development of the desired model, a critical review of the systems theory and simulation was conducted. Although no body of knowledge on systems could be found which could be called a theory, a systems concept which can provide a heuristic base for systems analysis, or a system approach to management, does exist. Use of this systems concept as an approach to management provides an analytical framework 187 188 for problem analyses which forces consideration of the whole business and focuses on evaluation of interactive elements. Simulation has been widely used in non-agricultural business management as a method of evaluating alternatives and systems. It is just beginning to receive widespread use in agriculture. As a technique of analysis, simulation provides the basic design and structural flexibility required to deve10p a model with emphasis on interaction effects without excluding other management concepts. That the model develOped can reasonably represent many Michigan dairy farm situations is indicated by the results generated for both the farm planning and research problems simulated. Both farm mana- gers and extension specialists found the simulated data to correspond closely with that expected for Michigan situations with the initial characteristics found on the farms used. The variability in the situations simulated indicate that with apprOpriate data input the model can be used to simulate a wide variety of situations in a large portion of the North Central and Northeastern United States. The required input of the basic data for the farm being simu- lated plus the ability of the user to change any parameter within the model allows adjustment of the model to simulate very specific individual situations. Little difficulty was experienced in simu- lating the special characteristics of those businesses simulated. In fact, the primary limitation on simulating individual situations is the lack of knowledge about the individual situation being simu- lated. Actual values for many parameters are often unknown. For this reason the parameters listed in Part II of the Users Manual (Appendix A) are organized with those variables likely to have the 189 most effect on the outcome in most situations and/or those variables for which the user is most likely to know the appropriate value placed first. The user defined systems which are an integral part of the model, allow simulation of any type of system for which the user can estimate the apprOpriate parameter values. User systems were develOped and simulated for a stanchion-parlor dairy system and an eight-row crOp- ping system. In both cases the results were in line with estimates made using other techniques. Although these particular systems are in use on some farms, a similar procedure could be used for systems not in use for which only engineering data is available. Although the model developed is designed to allow the addition of other enterprises, the ease with which this can be carried out is untested. The present enterprises are organized by subroutine so that addition of an enterprise would involve only adding a subroutine for the enterprise in question and making the apprOpriate addition or correction to the labor, machinery, accounting and possibly storage or building subroutines. The model user has nearly complete flexibility in input. Only the data specifying the basic characteristics of the situation to be simulated are required. The number of other coefficients to be input and the number and degree of detail of management decisions to be made is completely the user's decision. On the output side, there are eleven output reports which can be selected. These reports vary in both coverage and degree of detail. The user can select those reports which supply data relevant to the problem being evaluated. 190 Both farm planning and farm management research problems were simulated. Simulation of the farm planning problems indicated that alternatives could be simulated for specific farm situations and that the data generated could be useful in making management decisions. Simulation of a small research problem indicated that the model does provide a basic framework and tool of analysis which can be used to analyze certain types of problems. Although simulation of the situ- ations discussed above provided only limited testing of the model, the results were positive and reinforcing. Using the philOSOphy of Popper, the conjecture that the model can be used to simulate indivi- dual dairy farm businesses has been supported but not proven. Exten- sive use of the model will be required before its actual usefulness is determined. Implications For Extension This model has at least two possible applications in extension. First, as previously indicated, this and other similar type models can be used to compare and evaluate alternatives for individual farm businesses. This should allow farm managers to consider more alter- natives in greater detail and with considerably more speed than has historically been possible (particularly when remote access rather than mail is used). When used in this fashion, however, the role of the extension worker may change. He will be freed from calculating budgets and checking farmer's budgets. However, he will now be forced to spend more time specifying the exact alternatives to be evaluated and interpreting the output. He will also be required to Spend more 191 time in communication with the computer, either by mail or remote access teletype unit. To the degree that this allows or forces concentration on specification and analysis of alternatives rather than arithmetical calculations, this could improve the services of the extension worker. However, achievement of such improved services would likely require a period of intensive training on use of com- puter models for most extension workers involved. The second potential application is to use the model to provide data for management control or management by exception. A farm busi- ness could be simulated for some period into the future and the data thus generated used as a benchmark for evaluating the actual results achieved over that period of time. This should provide a better basis for management control than has been available in the past (such as comparison with other farm group averages) and should allow improved management of individual farm businesses. The application, which may have the most potential value, is a combination of the two listed above. Simulation of a number of alter- natives to determine the one to be selected and then using the simu- lated data as a benchmark for control during and following adaption of the alternative may be the most productive way the model could be used. For Research Two different areas of research are implied by this model. The first, which was discussed in the previous chapter, is to make addi- tions to the present FABS model. No simulation model is complete in every way and additional features which are useful for a certain 192 range of problems can always be added. As additional features are added, the realism of the model or range of problems which the model can be used to address, will improve. The second area involves use of the model as a tool in farm management research. The monthly time period plus the capacity of being able to change the value of any coefficient makes this model useful for addressing many problems of a highly tactical nature which could not be handled by previous simulation models. Thus, this model could be used to simulate the effects of a wide variety of initial characteristics and environmental situations. Different price or credit environments could be evaluated on either a monthly or annual basis. The results of ad0pting certain techno- logical advances could be estimated as soon as the engineering characteristics of the new technology were known. Use of the model in this way may reduce both survey and budgeting time as well as allowing earlier publication of the results. For research problems where the model as presently designed will not (1) handle certain variables in the required manner, (2) generate the apprOpriate variable values or (3) report the required information, changes can be made in the model programming. For a wide array of problems, making adjustments to the FABS model could provide a model with the required characteristics with a minimum programming effort. Appendix B should make this a rela- tively simple procedure. The FABS model can also be used as a tool for research in manage- ment per se. Different management strategies or decision rules can be evaluated under a variety of environmental situations. Alternative 193 management techniques may either use (such as management by excep- tion) or be evaluated by the model. If placed on a remote access or interactive computer, the model could be used to provide a realistic environment within which to study the decision process of managers. This could also be carried out on a batch proces- sing basis, but would require somewhat greater effort and a longer time span of study. BIBLIOGRAPHY BIBLIOGRAPHY Ackoff, Russell L., ”Management Misinformation Systems," Management Science, Vol. 14, No. a, December, 1967. Anderson, Raymond L., ”A Simulation Program to Establish Optimum CrOp Patterns on Irrigated Farms Based on Preseason Estimates of Water Supply,” American Journal of Agricultural Economics, Vol. 50, No. 5, December, 1968. Andlinger, G. R., ”Business Games-Play One!” Harvard Business Review, Vol. 36, No. 2, March-April, 1958. Ansoff, R. Igor, and Dennis P. Slevin, ”An Appreciation of Industrial Dynamics," Management Science, Vol. 14, No. 7, March, 1968. Armour, Gordon C., and Elwood S. Buffa, ”A Heuristic Algorithm and Simulation Approach to Relative Location of Facilities,” Management Science, Vol. 9, No. 2, January, 1963. Armstrong, David L., and Ralph E. Hepp, Simulation Uses in Agricultural Economics, Proceedings of Joint Conference of North Central Regional Farm Management Extension and Research, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 157, February, 1970. Arthur, William, ”To Simulate or Not to Simulate: That is the Question,” Educational Data Processing Newsletter, Vol. 2, No. b. Babb, E. M., "Business Games as a Marketing Extension Tool,” Journal of Farm Economics, December, l96h. Babb, E. M., and L. M. Eisgruber, Management Games for Teaching and Research, Education Methods Inc., Chicago, Illinois, 1966. Babb, E. M., and C. E. French, ”Use of Simulation Procedures," Journal of Farm Economics, November, 1963. Baker, Frank B., ”The Internal Organization of Computer Models of Cognitive Behavior,“ Behavioral Science, Vol. 12, No. 2, March, 1967. Beged-Dov, Aharon G., ”An Overview of Management Science and Information Systems,” Management Science, Vol. 13, No. 12, August, 1967. 194 195 Benjamin, G. L., and L. J. Connor, Economies of Size of Machinery Systems on Southern Michigan Cash-Grain Farms, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 112, September, 1968 Black, Guy, "Systems Analysis in Government," Business Economics, V01. 2, NO. 2, Spring, 1967. Bladerston, F. E., and Austin C. Hoggatt, Simulation of Market Processes, Institute of Business and Economic Research, Berkeley, California, 1962. Blaugh, M., Economic Theory in Retrospect, Richard D. Irwin, Inc., Homewood, Illinois, 1982. Bohm, David, ”On the Problem of Truth and Understanding in Science,” in The Critical Approach to Science and PhilOSOphy, edited by Mario Bunge, The Free Press of Glencoe, New York, New York, 1964. Bonini, Charles P., Simulation of Information and Decision Systems in the Firm, Prentice Hall, Inc., Englewood Cliffs, New Jersey, 1963. Boulding, Kenneth E., ”General Systems Theory - The Skeleton on Science," Management Science, Vol. 2, No. 3, April, 1956. Bower, Joseph L., "Systems Analysis for Social Decisions," Computers and Automation, Vol. 19, No. 3, March, 1970. Brown. L. H., Plan Your Cash Flow, Don't Let It Just Happen, Michigan State University, Department of Agricultural Economics, Report 180, June, 1970. Brown, L. H., and John Speicher, Telfarm Business Analysis Summary for Specialized Southern Dairy Farms, 1968, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 137, June, 1969. Brown, L. H., and John Speicher, Telfarm Business Analysis Summary for Specialized Southern Dairy Farms,g19§9, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 175, August, 1970. Buckley, Walter (ed.), Modern Systems Research for the Behavioral Scientist, Aldine Publishing Company, Chicago, Illinois, 1988. Bulkin, Michael H., and John L. Colley and Harry M. Steinhoff, Jr., ”Load Forecasting Priority Sequencing and Simulation in a Job Shop Control System," Management Science, Vol. 13, No. 2, OCtOber, 1966. Burt, Oscar R., ”Operations Research Techniques in Farm Management: Potential Contribution," Journal of Farm Economics, Vol. 47, NO. 5, December, 1965. 196 Buxton, Boyd M., Alternative Dairerechnologies,gA Comparison of Unit Cost, Net Return and Investmenp, Station Bulletin 490, Agricul- tural Experiment Station, University of Minnesota, 1968. Galley, John L., J. B. Hallan and A. H. Packer, "A Simulation Model of a Saturated Medical System,“ American Institute of Industrial Engineers Eighteenth Annual Institute Conference and Convention Proceedings, 1967. Candler, Wilfred, Michael Boehlge and Robert Saatoff, "Computer Software for Farm Management Extension," American Journal of Agricultural Economics, Vol. 52, No. 1, February, 1970. Candler, Wilfred, and Wayne Cartwright, "Estimation of Performance Functions Budgeting and Simulation Studies," American Journal of Agricultural Economics, Vol. 51, No. 1, February, 1969. Chorafas, Dimitris N., Systems and Simulation, Academic Press, New York, New York, 1965. Christ, C. F., "A Test of an Econometric Model for the United States," Conference on Business Cyples, National Bureau of Economic Research, New York, 1951. Chu, Kong, and Thomas H. Naylor, “A Dynamic Model of the Firm," Management Science, Vol. 11, No. 7, May, 1965. Churchman, C. West, "An Analysis of the Concept of Simulation," in Symposium on Simulation Models: Methodology_and Applications to the Behavioral Sciences, edited by A. C. Hoggatt and F. E. Balderston, South-Western Publishing Co., Cincinnati, Ohio, 1963. Clarke, Lawrence J., ”Simulation in Capital Investment Decisions," The Journal of Industrial Engineering, Vol. 19, No. 10, OctOber, 1968. Cleland, David I., and William R. King, Systems Analysis and Project Management, McGraw-Hill Book Co., New York, New York, 1968. Clippinger, R. F., ”Systems Implications of Hardware Trends,” Systems and Procedures Journal, Vol. 18, No. 3, May - June, 1967. Clymer, A. B., Simulation of the Dynamics of Fluid Systems, Eastern Simulation Council Meeting, Philadelphia, Pennsylvania, May, 1961. Cohen, Kalman J., Computer Models of the Shoe, Leather, Hide Sequence, Prentice-Hall, Englewood Cliffs, New Jersey, 1960. Cohen, Kalman J., "Simulation of the Firm," The American Economic R3V13", V01. 50, N0. 2, May, 1960. 197 Cohen, Kalman J., and Richard M. Cyert, "Computer Models in Dynamic Economics,” Quarterlngournal of Economics, Vol. 75. No. 1, February, 1961. Connor, Larry J., Costs and Returns for Major Cash Creps in Southern Michi an, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 87, November, 1967. Connor, L. J., G. L. Benjamin, J. R. Brake and W. F. Lee, Michigan Farm Management Handbook, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 36, October, 1967. Conway, R. W., B. M. Johnson and M. L. Maxwell, "Some Problems of Digital Systems Simulation,” Management Science, Vol. 6, N0. 1, OCLOber, 1959. Curtis, S. M. ”The Use of a Business Game for Teaching Farm Business Analysis to High School and Adult Students,” American Journal of Agricultural Economics, Vol. 58, No. 4, November, 1968. Cyert, Richard M., and James G. March, A Behavioral Theory of the Firm, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1963. Dailey, R. T., G. E. Frick and R. H. McAlexander, Agricultural Planning Data for the Northeastern United States, The Pennsyl- vania State University, Department of Agricultural Economics and Rural Sociology Bulletin 51, July, 1965. Darden, Bill R., and William H. Lucas, The Decision Making Game, Appleton-Century-Crofts, New York, 1969. DeMasi, Ronald J., An Introduction to Business Systems Analysis, Addison-Wesley Publishing Company, Reading, Massachusetts, 1969. Deutsch, Ralph, System Analysis Techniques, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1969. Dimsdale B., and H. Markowitz, "A Description of the SIMSCRIPT Language," IBM Systems Journal, Vol. II, No. 1, 1964. Dorfman, R. P. Samuelson and R. Solow, Linear Programming and Economic Anal sis, McGraw-Hill Book Co., New York, 1958. Dror, Yehezkel, ”Systems Analysis and National Modernization Decision," Academy of Management Journal, Vol. 13, No. 2, June, 1970. Duesenberry, James S., Otto Eckstein and Gary Fromm, ”A Simulation of the United States Economy in Recession," Econometrica, Vol. 28, No. 4, October, 1960. 198 Economos, A. M., ”A Financial Simulation for Risk Analysis of a Proposed Subsidiary," Management Science, Vol. 15, No. 12, August, 1968. Edwards, Clark, "A Simple, Two-Region Simulation of Population, Income, and Employment,” Agricultural Economics Research, Vol. 22, No. 2, April, 1970. Eidman, Vernon R., Gerald Dean and Harold Carter, "An Application of Statistical Decision Theory to Commercial Turkey Production," Journal of Farm Economics, Vol. 49, No. 4, November, 1967. Eisgruber, L. M., Farm Operation Simulator and Farm Management Decision Exercise, Agricultural EXperiment Station Research Progress Report 162, Purdue University, February, 1965. Ellis, David 0., and Fred J. Ludwig, Systems Philos0phy, Prentice- Hall, Inc., Englewood Cliffs, New Jersey, 1962. Erdmann, M. E., L. S. Robertson, R. L. Jones, R. G. White, M. W. Adams and A. L. Anderson, Field Bean Production in Michigan, Michigan State University Cooperative Extension Service, Extension Bulletin 513, December, 1965. Even, Aurthur D., ”The Role of the Industrial Engineer in Systems Design and Improvement,” The Journal of Industrial Engineering, Vol. 8, No. 6, November - December, 1957. Ewell, James M., "The Total Systems Concept and How to Organize for It,” Computers and Automation, Vol. 10, No. 9, September, 1961. Faris, J. E. and J. Wildermuth, The California Farm Management Game, Southern San Joaquin Valley FarmsL_Participants' Manual, Giannini Foundation of Agricultural Economics, University of California, Berkely, California, October, 1966. Forage Highlights, Proceedings of the Thini Research - Industry Conference, American Forage and Grassland Council, January, 1970. Forrester, Jay W., Industrial Dynamics, M.I.T. Press, Cambridge, Massachusetts, 1961. Forrester, Jay W., ”Industrial Dynamics - After the First Decade," Management Science, Vol. 14, No. 7, March, 1968. Foster, David F., ”Computers and Social Change: Uses - and Misuses," Computers and Automation, Vol. 19, No. 8, August, 1970. Foster, Phillips and Larry Yost, "A Simulation Study of Population, Education and Income Growth in Uganda," American Journal of Agricultural Economics, Vol. 51, No. 3, August, 1969. 199 Fuller, E. 1., Massachusetts Poultry Farm Management Game, Players Information, Mimeograph, Department of Agricultural and Food Economics, University of Massachusetts, Amherst, Massachusetts, August, 1968. Fuller, E. I., The Use of the Northeast Farm Management Game in Massachusetts, Mimeograph, Department of Agricultural and Food Economics, University of Massachusetts, March, 1968. Galper, Harvey, ”The Impacts of the Vietnam War on Defense Spending," Journal of Business, Vol. 42, No. 4, October, 1969. Garoian, Leon, "Review of Management Games for Teaching and Research by E. M. Babb and L. M. Eisgruber," Journal of Farm Economics, Vol. 49, No. 3, August, 1967. Gavin, James M., "The Social Impact of Information Systems," Computers and Automation, Vol. 18, No. 8, July, 1969. George, Frank, "Cybernetics: The New Science of Management,” Management Decision, Quarterly Review of Management Technology, Vol. 3, No. 1, Spring, 1§89. Glans, Thomas B., Grad Burton, David Holstein, William E. Meyers and Richard N. Schmidt, Management Systems, Holt, Rinehart and Winston, Inc., New York, New York, 1968. Glickstein, Aaron, E. M. Babb, C. E. French and J. H. Green, Simulation Procedures for Production Control in an Indiana Cheese Plant, Agricultural Experiment Station Research Bulletin 757, Purdue University, December, 1962. Goetz, Billy E., Quantitative Methods: A Survey and Guide for Ma ers, McGraw-Hill Book Company, New York, New York, 1965. Gordon, Geoffrey, System Simulation, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1969. Graham, Robert C. and Clifford F. Gray, Business Games Handbook, American Management Association, Inc., 1969. Green, J. R., and R. L. Sisson, Dynamic Management Decision Games, John Wiley and Sons, New York, 1959. Guetzkow, Harold (ed)., Simulation in Social Science, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1962. Hall, Arthur D., A Methodology for Systems Engineering, D. Van Nos- trand Co. Inc., Princeton, New Jersey, 1962. Halter, A. N., and G. W. Dean, "Use of Simulation in Evaluating Management Policies Under Uncertainty: Application to a Large Scale Ranch,” Journal of Farm Economics, Vol. 47, No. 3, August, 1965. 200 Halter, A. N., M. L. Hayenga, and T. J. Manetsch, "Simulating a Developing Agricultural Economy: Methodology and Planning Capability," American Journal of Agricultural Economics, Vol. 52, No. 2, May, 1970. Haltman, J. B., K. L. Prickett, D. L. Armstrong and L. J. Connor, Modeling of Corn Production Systems - A New Approach, Paper No. 70-125 presented at the 1970 Annual Meeting of the American Society of Agricultural Engineers, Minneapolis, Minnesota, July, 1970. Hammond, David H., J. Robert Strain and C. Phillip Baumel, “Simpli- fying Management Games for Extension Programs," Journal of Farm Economics, Vol. 48, No. 4, November, 1966. Handbook of Agricultural Charts, 1969, United States Department of Agriculture, Agriculture Handbook No. 373. Hare, Van Court Jr., Systems Analysis: A Diagnostic Approacp, Harcourt, Brace and World Inc., New York, New York, 1967. Harrison, Virden L., Management Strategies for the Growth of Farm Firms, Mimeograph, Department of Agricultural Economics, Purdue University, December, 1968. Harsh, Steve, Roy Black and A1 Tinsley, Using Time-Share Computers at the Area and County Agent Level: The Michigan Experience, Unpublished Mimeograph, Department of Agricultural Economics, Michigan State University, 1970. Hayenga, M. L., T. J. Manetsch, and A. N. Halter, "Computer Simu- lation as a Planning Tool in DevelOping Economies,” American Journal of Agricultural Economics, Vol. 50, No. 5, December, 1968. Hepp, R. E., and L. H. Brown, Telfarm Business Analysis Summary for Northern Michigan Dairy Farms, 1968, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 139, June, 1969. Hepp, R. E., and L. H. Brown, Telfarm Business Analysis Summary for Northern Michigan Dairy Farms, 1969, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 177. August, 1970. Hepp, R. E., and L. H. Brown, Telfarm Business Analysis Summary for Southern Dairinenera , 1968, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 138, June, 1969. lHepp, R. E., and L. H. Brown, Telfarm Business Analysis Summary for Southern Dairy General, 1969, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 176, August, 1970. 201 Hermann, Charles F., "Validation Problems in Games and Simulations with Special Reference to Models of International Politics," Behavioral Science, Vol. 12, No. 3, May, 1967. Hildebrand, S. G., R. W. Chase, M. H. Erdmann, L. V. Nelson and D. H. Smith, Jr., Field Crop Recommendations for Michigan, Michigan State University, Extension Bulletin 462, April, 1965. Hildebrand, S. G., L. V. Nelson, H. E. Henderson, Donald Hillman, C. R. Hoglund, R. L Maddex and R. G. White, Corn Silage Production-Harvest-Storage in Michigan, Michigan State Univer- sity Extension Bulletin E-665, September, 1969. Hildebran'l, Se Ce, Le Se ROber'Lson, Re C. White, No A. SMith and H. S. Potter, Seybean Production in Michigan, Michigan State University, COOperative Extension Service, Extension Bulletin E-362, July, 1969. Hildebrand, S. C., and E. C. Rossman, Michigan Corn Production, Sybrid Selection and Cultural Practices, Michigan State Univer- sity, C00perative Extension Service, Extension Bulletin 436, September, 1964. Hillier, Frederick S., and Gerald J. Lieberman, Introduction to Qperations Research, Holden-Day, Inc., San Francisco, 1967. Hines, Charles A. and Glenn A. Swanson, Michigan Agricultural Statistiee, Michigan Cr0p Reporting Service, Michigan Department of Agriculture, July, 1970. Hinman, H. R., Appraising Results of Alternative Finance Management Practices by Use of Simulation, Unpublished Ph.D. Thesis, Pennsylvania State University, December, 1969, reported in Hinman, H. R. and R. F. Hutton, "A General Simulation Model for Farm Firms," Agricultural Economics Research, Vol. 22, No. 3, July, 1970. Hinton, R. A., Farm Management Manual, University of Illinois College of Agriculture, Department of Agricultural Economics Report AE-4097. Hinton, R. A., and R. P. Kesler, Detailed Cost Report for Central and Western Illinois, 1964 and 1965, University of Illinois, Department of Agricultural Economics, AERR 85, June, 1967. Hitch, Charles, ”An Appreciation of System Analysis,” Qperations Research, Vol. 3, No. 4, November, 1955. Hoglund, G. R., Characteristics of Newly Built Cold-Covered and Warm- Enclosed Dairy HousingASystems, Agricultural Economics Report 129, Department of Agricultural Economics, Michigan State University, East Lansing, Michigan, May, 1969. 202 Hoglund, C. R., ”Economic Analysis of High-Level Grain Feeding for Dairy Cows,” Journal of Dairy Science, Vol. XLVI, No. 5, May, 1963. Hoglund, C. R., Economic Considerations in Selecting Silage Storage and FeedingASystems, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 84, September, 1967. Hoglund, C. R., ”Economic Effects of High-Level Grain Feeding," Journal of Dairy Science, Vol. XLVII, No. 10, October, 1964. Hoglund, C. R., J. S. Boyd and J. A. Speicher, Free-Stall Dairy Housing Systems, Research Report 91, Michigan State University Agricultural Experiment Station, East Lansing, Michigan, 1969. Hoglund, C. R., J. S. Boyd and J. A. Speicher, Milking Efficiency, Investments and Annual Costs for Milking Parlore, Michigan State University Agricultural ExPeriment Station, Research Report 93, September, 1969. Hoglund, C. R. and G. McBride, Michigan's Changing DairyAFarming, Michigan State University Agricultural Experiment Station, Research Report 96, January, 1970. Holland, Edward P. and Robert W. Gillespie, Experiments on a Simulated UnderdeveIOped Economy: DevelOpment Plans and Balance of Pay- ments Policies, The M.I.T. Press, Cambridge, 1963. Hopeman, Richard J., Systems Analysis and Operations Managemepp, Charles E. Merrill Publishing Co., Columbus, Ohio, 1969. Hundtoft, E. B., Harvesting and Handling High Moisture Corn, Cornell University, Department of Agricultural Engineering, Extension Bulletin 373’ JUly, 19660 Hutton, Robert F., A Simulation Technique for Making Management Decisions in Dairy_Farmipg, Agricultural Economic Report, No. 87, Economic Research Service, United States Department of Agricul- ture, February, 1966. Hutton, Robert F., "Operations Research Techniques in Farm Management: Survey and Appraisal," Journal of Farm Economics, Vol. 47, No. 5, December, 1965. Hutton, R. F. and H. R. Hinman, A General Agricultural Firm Simulator, Agricultural Economics and Rural Sociology Bulletin 72, The Pennsylvania State University, University Park, Pennsylvania, July, 1969. Ibach, D. E., and J. R. Adams, Cro Yield Res onse to Fertilizer in the United States, StatisticaI Bulletin £0. 431,’Economic Research Service and Statistical Reporting Service, United States Department of Agriculture, August, 1968. 203 Irwin, George D., "A Comparative Review of Some Firm Growth Models," Agricultural Economics Research, Vol. 20, No. 3, July, 1968. Irwin, George D., ”Discussion: Firm Growth Research Opportunities and Techniques,” Journal of Farm Economics, Vol. 48, No. 5, December, 1966. Jones, Curtis H., "At Last: Real Computer Power for Decision Makers,” Harvard Business Review, Vol. 48, No. 5, September - October, 1970. Johnson, Richard A., Fremont E. Kast and James E. Rosenzweig, "Systems Theory and Management," Management Science, Vol. 10, No. 3, January, 1964. Johnson, R. A., F. E. Kast and J. E. Rosenzweig, The Theory and Management of Systems, McGraw-Hill Book Company, Inc., New York, 1963. Kain, John F., and John R. Meyer, “Computer Simulations, Physio- Economic Systems, and Intraregional Models,“ American Economic R3Vie", V01. 58, NO. 2, May, 1968. Kearl, C. D., and D. P. Snyder, Field Creps Costs and Returns from Farm Cost Accounts, Cornell University Agricultural EXperiment Station, Department of Agricultural Economics Research Bulletin 308, November, 1969. Kearl, C. D., and D. P. Snyder, Livestock Costs and Returns from Farm Cost Accounts, Cornell University Agricultural Experiment Station, Department of Agricultural Economics, Research Bulletin 310, November, 1969. Keener, Harold M., and Warren L. Roller, Labor Economics of Milk Production Systems, 1969 Annual Meeting American Society of Agricultural Engineers, Paper Number 69-403, June, 1969. Kennedy, John L., ”Psychology and System DevelOpment" in Seychological Principles in System Develgpment, edited by Robert M. Gagne, Holt, Rinehart and Winston, New York, New York, 1966. King, William R., ”The Systems Concept in Management,” Journal of Industrial Engineering, Vol. 18, No. 5, May, 1967. Klein, Lawrence, Economic Fluctuations in the United States, 1921 - 1241, Cowles Commission for Research in Economics Monograph No. 11, John Wiley and Sons, New York, 1950. Klein, L., and A. S. Goldberger, An Econometric Model of the United Statee, 1929 - 1952, North-Holland Publishing Co., Amsterdam, 1955- Kohler, Wolfgang, Gestalt Psychology, New York, 1929. 204 Kotler, Philip and Randall L. Schultz, "Marketing Simulations: Review and Prospects," The Journal of Business, Vol. 43, No. 3, July, 1970. Krasnow, Howard, and Beino Merikallio, ”The Past, Present, and Future of General Simulation Languages," Management Science, Vol. 11, No. 2, November, 1964. Krause, K. R., and L. R. Kyle, Economic Factors Underlylng the Incidence of Large Farming Units, The Current Situation and Probable Trends, Agricultural Economics Report 12, Department of Agricultural Economics, Michigan State University, East Lansing, Michigan, May, 1969. Kuehn, Alfred A., and Michael J. Hamburger, "A Heuristic Program for Locating Warehouses," Management Science, 1962. Kyle, Leonard R., Hours of Labor Used bpronths on Farms of Michigan Telfarm Cooperators, 1965, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 54, July, 1966. LaDue, E. L., Farm Management Handbook, Cornell University, Department of Agricultural Economics, Agricultural Economics Extension Bulletin 440, October, 1966. LaDue, E. L., Free-Stall-Barn, Herringbone ParlorLingh-Silage Feeding Dairy Chore Systems: Comparison and Analysis, Cornell University, Department of Agricultural Economics, Agricultural Economics Research Bulletin 188, 1966. Lavington, Michael R., ”A Practical Microsimulation Model for Consumer Marketing,” Operations Research Quarterly, Vol. 21, No. 1, March, 1970. Lazzaro, Victor, Systems and Procedures, Second Edition, Englewood Cliffs, New Jersey, 1968. Lins, David A., "An Empirical Comparison of Simulation and Recursive Linear Programming Firm Growth Models,” Agricultural Economics Research, Vol. 21, No. 1, January, 1969. Llewellyn, Robert W., FORDYN, An Industrial Dynamics Simulator, North Carolina State University, Raleigh, North Carolina, 1965. Longworth, John W., ”From War-Chess to Farm Management Games,” Canadian Journal of Agricultural Economics, Vol. 18, No. 2, July, 1970. Luce, R. D., and H. Raiffa, Games and Decisions: Introduction and Critical Survey, John Wiley and Sons, New York, 1957. 205 Manderscheid, Lester V. and Glenn L. Nelson, "A Framework for Viewing Simulation,” Canadian Journal of Agricultural Economics, Vol. 17, No. 1, February, 1969- Manetsch, T. J., Design, Development and Use of Simulation Models for Systems Planning and Management, Paper presented at the North Central Regional Farm Management Extension and Research Con- ference, Michigan State University, October 13-16, 1969. Markowitz, Harry M., "Simulating with Simscript," Management Science, Vol. 12, No. 10, June, 1966. McDaniel, B. T., R. H. Miller, E. L. Corley and R. D. Plowman, DHIA Age Adjustment Factors for StandardizingALacpations to a Mature Basis, National C00perative Dairy Herd Improvement Program, Dairy-Herd-Improvement Letter, ARS-44-188, Vol. 43, NO. 1, February, 1967. McGuiness, J. S., "A Managerial Game for an Insurance Company," Qperations Research, Vol. 8, No. 2, March-April, 1960. McMillan, Claude and Richard Gonzalez, Systems Analysis, A Computer Approach to Decision Models, Richard D. Irwin Inc., Homewood, Illinois, 1968. Meggitt, William F., Weed Control in Field CrOps, Michigan State University, COOperative Extension Service, Extension Bulletin E-“34. May, 1970. Mesarovic, Mihajlo D., Views on General Systems Theory, John Wiley and Sons,Inc., New York, New York, 1964. Morgenthaler, George W., "The Theory and Application of Simulation in Operations Research," Progress in Operations Research, Vol. I, Ruzsell L. Ackoff (ed.7, John Wiley and Sons, Inc., New York, 19 1. Naylor, Thomas H., “Bibliography 19., Simulation and Gaming,” Computing Reviews, January, 1969. Naylor, Thomas H., Joseph L. Balintfy, Donald S. Burdock and Kong Chu, Computer Simulation Techniques, John Wiley and Sons, New York, 1968. Naylor, Thomas H. and J. M. Finger, "Verification of Computer Simulation Models,” Management Science, Vol. 14, No. 2, Neuschel, Richard F., Management by System, McGraw-Hill Book Company, Inc., New York, New York, 1960. Optner, Stanford L., Systems Analysis for Business Decisions, Second Edition, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1968. 206 Orcutt, Guy, ”A Study of the Autoregressive Nature of the Time Series Used for Tinbergen's Model of the Economic System of the United States, 1919-1932, Journal of the Reyal Statistical Sociepy, Series B, Vol. 10, 1948. Orcutt, Guy H., “Simulation of Economic Systems," The American Economic Review, Vol. 50, No. 5, December, 1960. Patrick, George F. and Ludwig M. Eisgruber, ”The Impact of Managerial Ability and Capital Structure on Growth of the Farm Firm," American Journal of Agricultural Economics, Vol. 50, No. 3, August, 1968. Peacock, D. L., and J. R. Brake, What is Used Farm Machinery Worth? Michigan State University Agricultural ExPeriment Station, Research Report 109, March, 1970. Petermann, Bruno, The Gestalt Theory, Routledge and Kegan Paul, Ltd., London, 1932. Pepper, Karl R., "PhiIOBOphy of Science: A Personal Report," in British Philosephy in the Mid-Century, A Cambridge Symposium, edited by C. A. Mace, George Allen and Unwin, Ltd., London, 1956. POpper, Karl R., The Loglc of Scientific Discovery, Basic Books, Inc., New York, New York, 1959. POpper, Karl R., The Open Society and its Enemies, Vol. I, Princeton University Press, Princeton, New Jersey, 1966. Popper, Karl R., The Poverty of Historicism, Basic Books, Inc., New York, New York, 1960. Bath, Gustave J., ”A Description of Spurt: A Simulation Package for University Research and Teaching," Northwestern University, Vogelback Computing Center, Evanston, Illinois, 1968. Richards, M. D., and F. W. Kniffin, "Business Decision Games - A New Management Tool," Pennsylvania Business Survey, Bureau of Business Research, Pennsylvania State University, June-July, 1960. Roberts, Edward B., Dan I. Abrams, and Henry B. Weil, ”A Systems Study of Policy Formulation in a Vertically - Integrated Firm," Management Science, Vol. 14, No. 12, August, 1968. Robertson, Gary N., C. Geoffrey E. Fernald, and John G. Myers, "Decision Making and Learning: A Simulated Marketing Manager," Behavioral Science, Vol. 15, No. 4, July, 1970. Ruch, Floyd L., Peychology and Life, Scott, Foresman and Company, Chicago, Illinois, 1963. 207 Salazar, Rodolfo G., and Subrata K. Sen, ”A Simulation Model of Capital Budgeting Under Uncertainty,” Management Science, V01. 11", NO. 1+, Decemmr' 1968. Shaffer, James D., "The Scientific Industrialization of the U.S. Food and Fiber Sector, Background for Market Policy," in Agricultural Organization in the Modern Industrial Econemy, NCR-20-68, Department of Agricultural Economics, The Ohio State University, Columbus, Ohio, 1968. Shapley, Allen E., Alternatives in Dairy Farm Technology with Special Emphasis on Labor, Unpublished Ph.D. Thesis, Department of Agricultural Economics, Michigan State University, 1967. Shaw, R. H., Estimation of Soil Moisture Under Corn, Iowa State University, Department of Agronomy, Research Bulletin 520, December, 1963. Shechter, Mordechai, and Earl D. Heady, "Response Surface Analysis and Simulation Models in Policy Choices," American Journal of Agricultural Economics, Vol. 52, No. 1, February, 1970. Shubik, Martin, "A Curmudgeon's Guide to Microeconomics," Journal of Economic Literature, Vol. VIII, No. 2, June, 1970. Shubik, Martin, ”Bibliography on Simulation, Gaming, Artificial Intelligence and Allied Topics," Journal of the American Statistical Association, Vol. 55, No. 292, December, 1960. Shubik, Martin, "Simulation of the Industry and the Firm,” The American Economic Review, Vol. 50, No. 5, December, 1960. Sisson, Roger L., ”Simulation: Uses,” in Pregress in Operations Research, Vol. III, Julius S. Aronofsky, (ed), John Wiley and Sons, Inc., New York, 1969. Smith, E. C., Jr., ”Simulation in Systems Engineering,” IBM Systems Journal, September, 1962. Smith, Robert D., and Paul S. Greenlaw, "Simulation of a Psychological Decision Process in Personnel Selection," Management Science, V01. 13, N0. 8, April, 1967. Smith, W. Nye, Elmer E. Estey and Ellsworth F. Vines, Integrated Simulation, South-Western Publishing Co., Cincinnati, Ohio, 19580 Sorenson, Eric E., and James F. Gilheany, "A Simulation Model for Harvest Operations Under Stochastic Conditions,” Mapagement Science, Vol. 16, No. 8, April, 1970. Speicher, J. A., and L. H. Brown, A Dairy Begget Guile, Michigan State University, Bulletin D-211, August, 1969. 208 Speicher, J. A., and C. E. Meadows, Milk Production and Costs Associated with Length of CalvingAInterval of Holstein Cows, Michigan State University, Dairy Department, Michigan Agricultural Experiment Station Journal Article No. 4242. Strickland, Roger, CombiningASimulation and Linear Programming in Studying_Farm Firm Growth, Unpublished Ph.D. Thesis, Depart- ment of Agricultural Economics, Michigan State University, 1970. Strommen, Norton D., and James E. Horsfield, Monthlprrecipitation Probabilitiee_py Climatic Divisions, United States Department of Agriculture, Economic Research Service, Miscellaneous Publication No. 1160, November, 1969. Suttor, R. E., and R. J. Crom, "Computer Models and Simulation," Journal of Farm Economics, Vol. 46, No. 5, December, 1964. Systems Science and Sybernetics, The Institute of Electrical and Electronic Engineers, Inc., New York, New York. Taylor, Lance J., ”DevelOpment Patterns: A Simulation Study," Quarterly_Journa1 of Economics, Vol. 83, No. 2, May, 1969. Teichroew, Daniel, and John F. Lubin, "Computer Simulation-Discussion of the Technique and Comparison of Languages," Communications of the Association for ComputingiMachinery, Vol. 9, No. 10, October, 1966. Thomas, Clayton J., ”Military Gaming," Progress in Operations Research, Russell L. Ackoff, (ed.), John Wiley and Sons, Inc., New York, 1961. Tilles, Seymour, "The Manager's Job - A Systems Approach," Harvard Business Review, Vol. 41, No. 1, January - February, 1963. Timms, Howard L., The Production Function in Business, Richard D. Irwin, Inc., Homewood, Illinois, 1966. Tyner, Fred H., and Luther G. Tweeten, ”Simulation as a Method of Appraising Farm Programs,” American Journal of Agricultural Economics, Vol. 50, No. 1, February, 1968. Upchurch, M. L., Recent Advances in Research Methods in Marketingg Paper presented at XIV International Conference of Agricultural Economists, Moscow, August, 1970. U.S. Department of Commerce, Civilian Industrial Technology Program in Textiles, Textile Industry Behavioral Information, Washington, D.C., March, 1965. 209 Van Arsdall, Roy N., Labor Reqelrements,_Machinery Investments, and Annual Costs for the Production of Selected Field Crepe in Illinois, 1965, University of Illinois, Illinois Agricultural ExPeriment Station, Agricultural Economics Report AE-4112. Vance, 8., Management Decision Simulation: A Non-Computer Business Game, McGraw Hill Book Co., Inc., New York, 1960. Vazonyi, Andrew, "Automated Information Systems in Planning Control and Command," Management Science, Vol. 11, No. 4, February, 1965. Vincent, W. H., Agricultural Economics Report 164, Department of Agricultural Economics, Michigan State University, East Lansing, Michigan, in process. Vincent, Warren H., Methods and Models in Managerial Economics: A Bibliography, Michigan State University, Department of Agricultural Economics, Agricultural Economics Report 108, July, 1968. Vincent, Warren H., Simulation for Problem-Solving in the Poultry Industry, Mimeograph, Presented at the North Central Regional Farm Management Conference, Michigan State University, October, 1969. Vincent, Warren H., and Larry J. Connor, "An Orientation for Future Farm Planning and Information Systems," Agricultural Economics Mimeo, 1968 - 5, April, 1968. von Bertalanffy, Ludwig, "An Outline of General System Theory," The British Journal for the Philos0phy of Science, Vol. 1, 1950-51. von Bertalanffy, Ludwig, "General Systems Theory - A Critical Review," in Modern Systems Research for the Behavioral Scientisr, by Walter Buckley, (ed:), Aldine Publishing Co., Chicago, Illinois, 1968. von Bertalanffy, Ludwig, "General Systems Theory: A New Approach to Unity of Science,“ Human Biology, Vol. 23, No. 4, 1951. von Neumann, J.,and 0. Morgenstern, Theory_of Games and Economic Bgfiivior, Princeton University Press, Princeton, New Jersey, Walker, Odell L., and James R. Martin, "Firm Growth Research Oppor- tunities and Techniques," Journal of Farm Economics, Vol. 48, No. 5, December, 1966. Websters New World Dictionary of the American Language, The World Publishing Company, Cleveland and New York, 1956. 210 White, Robert G., Selecting a Forage Harvesting System, Michigan State University, Department of Agricultural Engineering, Information Series Number 225, December, 1968. Wilson, Ira G., and Marthann E. Wilson, Information, Computers and System Design, John Wiley and Sons, Inc., New York, New York, 1965e Wright, K. T., Intended and Actual Tractor Purchases, Michigan State University Agricultural Experiment Station, Research Report 15, July, 1964. Young, Stanley, Management: A Decision-Making Approach, Dickenson Publishing Company, Inc., Belmont, California, 1968. Young, Stanley, "Organization as a Total System," in S stems, Qrganization, Analysle, Management: A Book of Readings, by David I. Cleland and William R. King,7eds.), McGraw Hill Book Co., New York, New York, 1969. Yule, G., "Why Do We Sometimes Get Nonsense Correlations Between Time Series?" Journal of the Royal Statistical Society, Vol. 89, 1926. Yurow, Jerome A., "Analysis and Computer Simulation of the Produc- tion and Distribution Systems of a Tufted Carpet Mill," Journal of Industrial Engineering, Vol. 18, No. 1, January, 1967. Zusman, Pinhas, and Amotoz Amiad, "Simulation: A Tool for Farm Planning Under Conditions of Weather Uncertainty," Journal of Farm Economics, Vol. 47, No. 3, August, 1965. Zymelman, Manuel, ”A Stabilization Policy for the Cotton Textile Cycle,” Management Science, Vol. 11, No. 7, March, 1965. APPENDICES APPENDIX A FABS USER MANUAL AND INPUT DATA FORMS FABS (EArm Business Simulator) USER MANUAL Introduction is manual is designed to be used in conjunction with FABS Data The manual is divided into three partse The first part is a of the basic information required about the farm to be simula- represents the minimum data required to simulate a business. sting corresponds to Part I of Data Form 1 on which the data for Lness is recorded. rt II is a listing of the parameters or numbers which will be the model unless they are changed by the user. It is suggested a user read through these to see if any should be changed. Each ient in Part II and most of those in Part I have an identifica- nber just to the left (in parentheses) or just above the value Use of these identification numbers is discussed in Part III. rt III contains a discussion of data required and the procedure sed in indicating management decisions and change of parameter Parameter value changes and management decisions are placed in agical order on Part II of Data Form 1. ietailed description of each of the systems used by the simulator in the appendix. For a detailed list of the machines used by stem, see the systems matrices at the end of Part II. 1 the information required for simulating an alternative is indi- 1 the FABS Data Form 1. It is suggested that all data for each 211 212 alternative be placed on a separate form and not on the user manual. The computer cards are punched directly from Data Form 1 which has card and column numbers under each coefficient. 213 PART I Required Input Data General 1. The first month to be simulated is (enter month by number and then year) 19 2. The number of years to be simulated is 3. Are summaries to be on a fiscal or calendar year basis. The fiscal year would start during the first month simulated (1 = calendar, 0 = fiscal). 4. The output desired is: (enter 1 if desired, 0 if not) ) labor summary ) annual financial statement ) annual crop production & feed utilization summary ) annual dairy cattle numbers summary annual income and expense statements ) annual production and yields summary ) brief monthly cash flow summary ) monthly crop production and feed utilization ) monthly dairy cattle numbers summary ) monthly cash flow statement ) brief debt and income summary (a (b (c (d e (f (s (h (i (J (k 5. Notes: 21“ .Umpdaaaam on on apnea pmham mnp ma hhdfiqwb_wa haze vows on Goo mmqflddmn npqoza H05 .HO OH m m u w m : (AWL N a ma. 3.. fl- ma- m7 .2. o? m. m- a- m- m- a- m- m- d. no“... whomwm Luv poo poo >02 can can pee .82 a? an: 83. .3. mg flow 80 82 «one .63 anomom .oopcaqfiam on 09 Apnea pmhfim 0p “Gama apnea wqfinmnmmnh Mo nanoa_ch nofipwpodd ha mzoo Mo Hmnfiflz .0 lg 215 7. (a) Number of Heifers by Age (months) (month prior to first month simulated) gelljeL3Ih56I78910n12 Numbe 1 r I I I Ea I 13 111L151 16 17 18 19 20 21 22 23 2h amber] I I Ea L25l26127i28l29l 30 31 32 33I34 35 36 mberr I l I r l 7° ( b) The Number of Dairy Steers by Age (months) gee 1|2|3|u15I6I7 8 9 1011 12 . umber I I I I I I 3'1311AI15J16117 18 19 20 21J22I23 211 1“er _r i 1 1 1 8 ‘ Freshening Preference Schedule Feb Mar Apr May June July Aug Sept Oct Nov Dec 9 .. The average value per animal is: Cows (1'13): Bred Heifers (l-lh) Open Heifers (overl year) (145): Calves (under 1 year) (1-16) 216 10. Historical production experience for herd input in question 6: Average annual actual production (lbs.) for this herd is (1-17) when forage quality is (l = excellent, 2 = medium 3 = poor) (1-18) and pounds of feed fed per cow per year is (l-l9) and culling rate is (percent) (1-20) 11. For the first period simulated: (a) Pounds of feed to be fed per cow per year (include HMO) (1-21) (b) Average forage quality to be fed is (1-22) (c) The culling rate to be used is (percent) (l-23) l2. Composition of concentrate fed (excluding high moisture corn and supplement fed separately) GrainLi Lbs. Ear Cornl (l-2h) Shelled Corn (1-25) Oats (1-26) Wheatl (1-27) Supplement (l-28) 13. (a) Is high moisture corn to be fed (1 = yes, 0 = no) (1-29) (b).££ so, the pounds fed per cow per day by month is: Jan. Feb. Mar. Apr. May June Jul Aug. Sept.Oct. Nov. Dec. Lbs2 1-30 1-31 1-32 1-33 l-3h 1-35 1-36 1-37 1-38 1-39 1-10 l-hl (c) The moisture content of high moisture corn as fed is (percent) (l-h2) 1. If ear corn or wheat supply becomes exhausted shelled corn will be used. 2. If there is insufficient HMC to meet this requirement, dry corn will be used instead. (d) Pounds of supplement fed with HMC per cow per day 217 (l-h3) 1%. Percent of Hay Equivalent From.Various Forages by‘Monthl Forage Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Hay l-hh 1-h7 1-50 1-53 1-56 1-59 1-62 1-65 1-68 1-71 1-7M 1-77 Hay Crop 1-h5 1-h8 1-51 l-su 1-57 1-60 1-63 1-66 1-69 1-72 1-75 1-78 Silage g?rn 1-h6 1-h9 1-52 1-55 1—58 1-61 1-6u 1-67 1-70 1-73 1-76 1-79 ilage 15. The average calving interval of this herd is (l-80) months. 16. The % of heifer calves to be kept for replacements is (1-81) . 17. (a) The maximum number of cows to be allowed is (1-82) (b) The maximum number of heifers over 1 year to be allowed is (1-83) (c) The maximum number of heifers under 1 year to be allowed is (l-8h) 18. (a) Type of bedding used for youngstock is (l = straw, 2 = other) (1-85) (b) Type of bedding used for dairy herd is (l a straw, 2 = other) (1-86) (c) if bedding other than straw is used, the average cost per animal per month is: Jan Feb Mar Apr May June July» Aug Sept Oct Nov Dec Cows 1-87 1-89 1-91 1-93 1-95 1-97 1-99 1-101 1-103 1-105 1-107 1-109 Heifers 1-88 1-90 1-92 l-9h 1-96 1-98 l-lOO 1-102 l-loh 1-106 1-108 1-100 1" lExclusive of H.E. from.pasture. 218 Machinery 19. The systems to be used on this farm.are (indicated by number). Use the last number (system) in each set of parentheses (user defined systems) only if you plan to define it. 1. Dairy Cows (1-7) (3—111) 2. Dairy Replacements (8-10) (3-112) 3. Corn Grow - April (l-h) (3-113; h. Corn Grow - May (5-8) (3-llh 5. Corn Grow - June (9-12) (3-115) 6. Corn Silage Harvest (1-5) (3-116) 7. Corn Grain Harvest (6-11) (3-117) 8. Wheat GrOW'gSept.) (13-15) (3-118) 9. Wheat GrOW' Oct.) (16-18) 3-119 10. Wheat Harvest (12-15) (3-120) 11. Oats Grow (Apr.) (19-21) (3-121) 12. Oats Grow (May) (22-2h) (3-122) 13. Oats Harvest (16-19) (3-123) lu. Hay Crop Plant (25-28) (3-12u) 15. Hay Crop Silage Harvest (20-2h) (3-125) 16. Hay Crop Maintain (29-30) (3-126) 17. Hay Harvest (25-28) (3-127) 18. Fieldbean Grow - May (31-33) (3-128) 19. Fieldbean Grow - June (3h-36) (3-129) 20. Fieldbean Harvest (29-32) (3-130) 21. Soybeans Grow - May (37-39) (3-131) 22. Soybeans Grow - June (ho-A2) (3-132) 23. Soybeans Harvest (33-35) (3-133) 20. (a) Do you want to enter the specific machines presently on this farm? (1 = yes, 0 = no) If you enter "no", a representative set of machinery will be assumed . (b) Do you.want additional (other than replacement)machine pur- chased as the program determines that they are needed. (1 = yes, 0 = no) (1-l3h) . 21. (a) The present value of owned land (excluding buildings) on this farm is . (b) The value of this land (by itself) in ten years is expected to be (1.135) .1 22. (a) Normal acreages of crops to be grown. Owned or Cash Share Rent Crop Rent Acreage Acreage Corn (1-136) (l—luh) l. .Jlf.a number less than 50 is entered this will be used as the expected annnual rate of inflation in land value. 219 22. (a) (con't) Owned or Cash Share Rent Crop Rent Acreage Acreage Hay Crop (including silage and pasture) (1-137)fl(1~lh5)m Oats (1-138): (1-1h6): Wheat (1-139):(l-1u7): Soybeans (1-1h0)”(1-1h8): Fieldbeans (1- lhl) (l-lh9)~ Government Programs (1-lh2)*(1-150) Total (l-lu3): (1-151) (b) Expected last year acreage.l Hay Crop Silage Corn Silage 23. Acres cash rented (included in above acreage) (1-152) Rental per acre (1-153) Buildings 2h. Purchase Present Average Average Number of Period Value Age(yrs.) Yrs. Deprec. Over Within last 5 yrs. 5-10 years ago {ore than lO_yrs. 25. Present Building Capacity is: (a) Number of free stalls (1-15h) (b) Number of stanchions (1-155) (c) Milking parlor capacity (no. of men)21-156) (d) Number of dry cows and/or heifers (max. ) (1-157) (e) Number of calves (maximum) (1-158) (f; Silage storage capacity-upright (tons) (1-159) (g Silage storage capacity-horizontal (tons) (1-160) (h) Hay and straw storage capacity (tons) (1-161) (1) Ear corn storage (bushels) (1-162) 3) Grain bin storage (bushels) (1-163) (k) High moisture corn storage (bu. dry equiv.) (1-16h) 26. (a) Do you want dairy buildings and silage capacity to be purchased as required? (1 = yes, 0 = no) (1-165) 1« .Actual first year acreages will be determined by simulation. 2- .A.l indicates a one-man parlor, 2 indicates a 2-man parlor, 3 indi- <2ates a one-man parlor and one 2-man parlor. 220 (b) Do you want grain storage buildings to be purchased as required (1 = yes, 0 = no) (1-166) . (If off-farm.storage is allowed, that will be used rather than building.) (c) Do you want hay and straw storage capacity to be purchased as required? (1 = yes, 0 = no) (1-167) . (If you answer no to question (a), (b) or (c), animals or crops in excess of capacity will be sold at freshening or harvest respectively.) (d) Off-farm storage is used for: (i) Fieldbeans (1-168) 1 (ii) Soybeans (1-169) (iii) Wheat (1-170) (iv) Shelled Corn (1-17lI (v) Oats (1-172) Labor 27. (a) Hours of Labor Available Per Month2 Number 1 Jan Feb Mar Apr May June'Jul Aug’Sept Oct Nov Dec I 173 Operator 181 189 197 205 213 221 229 237 2h5 253 261 269 17h 2 Family 182 190 198 206 21h 222 230 238 2M6 25h 262 270 175 3 Year 183 191 199 207 215 223 231 239 2H7 255 263 271 Hire (1) 176 h Year 181 192 200 208 216 22h 232 2&0 248 256 26h 272 Hire (2) 177 5 Year 185 193 201 209 217 225 233 2M1 2M9 257 265 273 Hire (3) Rate Per Hr.3 178 Hour1y(1)‘l 186 l9u 202 210 218 226 231 2M2 250I258 266 27h 179 Hour1y(2 ’ 187 195 203 211 219 227 235 2M3 251 259 267 275 180 Hour1y(3)7 188 196 20h 212 220 228 236 2AA 252 260 268 276 .1. Enter 0 if off-farm storage is not used for this crop; enter 1 if off- farm storage is used for all this crop; enter 2 if off-farm storage is used for any amount of this crop in excess of farm storage capacity. .All identification numbers in this table must be preceded by a l- to 'be complete. {The least expensive labor is used first. ZIt is assumed that this labor is hired only when needed. 28. 221 (b) All additional labor will cost (1-277) per hour. The wage rate is: Year hire (1) Year hire (2) Year Hire (3) per (l-281) l per (1-282) 1 per (1-283) 1 u u u A H I 16 _q u)- v estate Crops 29. 30. 31. 32. 33. 3h. 35. 36. Average yield of oats planted in April and harvested on time when (1-28h) pounds of plant food is applied is (1-285) bushels per acre. Average yield of corn planted during period2 (1-286) and harvested on time with (1-287) pounds of plant food applied is (1-288) bushels per acre. Average yield of wheat planted in September and harvested on time when (1-289) pounds of plant food is applied at planting and (1-290) pounds of nitrogen is applied in the spring is (1-291) bushels per acre. Average yield of hay when (1-292) pounds of plant food are applied and lst cutting is cut in June, 2nd cutting in July and 3rd cutting in August is: (1-293) tons first cutting (1-29h) tons second cutting (1-295) tons third cutting Average yield of fieldbeans planted in June and harvested on time when (1-296) pounds of plant food is applied is (1-297) bushels per acre. Average yield of soybeans planted in June 1 to 15 is (1-298) bushels per acre. The number of hay cuttings (including pasturage) is (1-299) (this number must be either 1, 2 or 3). The number of acres of hay crop pastured is: él-300) acres first cutting 1-301) acres second cutting (1-302) acres third cutting monthly, 0 2 weekly. before May 1; 2 = May 1 to May 15, 3 = May 16 to May 31; and after May 31. 222 37. (a) The amount of hay crop silage to be harvested is (1-303) 1 (b) The amount of corn Silage to be harvested is (1—30h) l (c) The amount of corn to be harvested as high moisture corn is (1-305) .2 38. The beginning inventory on hand is: (a) Hay crop silage (tons, 70% moisture) (b) Corn silage (tons) (c) Hay (tons) (d) High moisture corn (bushels) (e) Ear corn (bushels) (f) Corn grain (bushels) (g) Wheat (bushels) (h) Oats (bushels) (i) Soybeans (bushels) (3) Fieldbeans (bushels) (k) Supplement (ton) 39. (a) The amount of N, P205 and K20 (lbs./acre) to be applied by crop is: Lbs. N Lbs. P205 Lbs. K20 (1) Corn (1-306) (1-313) (1'320) (2) Soybeans (1-307) (1-3lh) (1-321) (3) Fieldbeans (1-308) (l-315)‘ (1-322) " (1) Wheat (1-309)_____3 (1-316)_____ 21-323)____ (5) Oats 1-310)_____ (1-317)___“_ 1-32h)_____ (6) Hay (seeding) (1-311)“ (1-318)_______ (1-325)________ (7) Hay (all acreage) (1-312) (1-319) (1-326) (b) The Agricultural Subregion for this farm is (see map) (l-327)____. 1. Enter -1 if enough is to be harvested to fill the silos. 30 Enter number of tons per cow (02$ tons £ 25) if a certain amount per cow is desired [up to silo capacity]. Enter number of tons (>’25) if a certain absolute tonage is to be harvested as silage [up to silo capacity]. Enter -1 if enough is to be harvested to fill the silos. Enter number of bushels (dry equivalent) per cow (O:é bushels é 200) if a certain amount per cow is desired. Enter number of bushels (dry equivalent) if a certain absolute number of bushels is to be harvested as HMC. Excluding topdressing. 223 ho. (a) Current Debt Situation er- - Amoun Type Term ,Mon 3 s Age Loan est ment of of of are to be mad 2 of Per- Rate yr. Payment Loan Loan 1 2 3 5 Loan iod no. mo . . (b) Current cash and checking account balance is (1-328)$ . 1. Includes principal and interest for equal payment loans but only principal for loans requiring constant principal payment plus in- terest accumulated. May be omitted if age of loan and loan period are both entered. 2. Enter months in which payments are to be made. Zero in column 1 indicates monthly payments. 3. 1 2 equal payments including principal and interest. 2 - amount of payment is principal, interest due is added. 11 = pay interest only first year and equal payments thereafter. 12 = pay interest only first two years and equal payments thereafter. 13 = pay interest only first three years and equal payments there- after. 1h = pay interest only first four years and equal payments thereafter. 15 = pay interest only first five years and equal payments thereafter. 21 = pay interest only first year and principal payment plus interest thereafter. 22 = pay interest only first two years and principal payment plus interest thereafter. 23 = pay interest only first three years and principal payment plus interest thereafter. 2h = pay interest only first four years and principal payment plus interest thereafter. 25 = pay interest only first five years and principal payment plus interest thereafter. h. 1 = short term, 2 = intermediate term, 3 = long term. 5. Required only when amount of payment is not entered and when type of loan is 11-15 or 21-25. This is the number of months between the date the loan was made and the first month simulated. 6. Years, required only when type of loan is 11-15 or 21-25 and when amount of payment is not entered. 224 hl. (a) Withdrawalsl for the farm families (family) is expected to be: Jan. Feb. Mar. A r. Ma . June With -329 ~33o -3 -3 - -3 drawal J A . S . Oct. Nov. -335 -3 -337 - -339 (b) Annual off-farm income is expected to be2 (1- 3&1) Number of exemptions to be claimed for tax purposes Il-3 E2)_ (c) Estimated gaxes to be paid on income from year previous to simulation (1-3h3) . (d) Estimated taxa 1e net farm income for first part of first year simulated (1-3hh) . #2. (a) Are dairy steers to be raised (1 = yes, 0 = no) (1-3h5) . If so: (b) What percent of the bull calves are to be kept (1-3h6) . (c) At what age (mo.) are steers placed on feeds (1-3h7) . (d) At what age (mo.) are the steers sold (1-3h8) . (e) Which ration is to be used during the feeding period6 (1-3u9)___, 1. Exclude income taxes but include personal insurance. 2. Taxes will be estimated assuming that all off-farm income is in the form of wages, tips, etc. 3. Required only if program starts in January or February. h. Required if program starts in any month other than January. 5. Until steers are placed on feed it is assumed that their require- ments are the same as heifers of the same age. 6. May be 1 through 6, as indicated in Q.51 Part II. 225 AGRICULTURAL SUBREGIONS OF MICHIGAN 226 PART II The statement and tables below define variables which are used in the FABS program, The value found in the upper half of the matrix cell or in parentheses preceeding the line is the identification number for that variable. The number in the bottom center of the matrix cell or on the line is the value used by the program unless it is changed. Zero means a statement is not used. The statements and tables in this part have been ordered so that those questions most important fer most problem Situations come first and those least likely to require change appear last. It is suggested that the first 58 questions be carefully evaluated by most users. 1. The opportunity value of operator's labor is (1-350) §5,000. 2. The opportunity value of family labor is (1-351) $2.25. 3. The opportunity rate of interest (percent) on equity investment in the farm business is (1-352) 219' Purchases and Sales h. Sell all hay in excess of (1-353) .5 tons per cow in month (l-35h) u . """" 5. In month (1-355) h sell all corn grain in excess of (1-356) __6__._9_ times the amount of corn used during that month. 6. In month (1-357) h sell all oats in excess of (1-358) h times the amount of oats used during that month. 7. (a) Sell all wheat in month (1-359) 2 1 b) Sell all corn in month (1-360; 0 1 (c) Sell all oats in month (1-361 0 1 (d; Sell all soybeans in month (1-362) 3 1 Sell all fieldbeans in month (1-363)"' ' 2 1 1. Use 13 to indicate sale of entire crop at harvest. 227 8. Purchase (1-375) 0 bushels of shelled corn grain in month (1-376) 0 - 9. Purchase (1-377) 0 bushels of high moisture corn in month (1-378) 0 . 10. Purchase (2-8893) tons of supplement in month (2- 889M) 0 . (If the number of tons entered is less than 2.0, that much supple- ment per cow will be purchased.) 11. In each year the following number of cows or bred heifers (l: bred heifers,2 hisht cows) (1-390) are to be purchased in the month indicated an average age of _(1-391) 27 months. Jan. (1-392) 0 July El-398) 0 Feb. (1-393) 0 Aug. 1-399) 0 Mar. 1-39h) 0 Sep. (l-hoog 0 Apr. 1-395 0 Oct. (l-h01 0 May 1-396) 0 Nov. (1-h02) 0 June (1-397 0 Dec. (1-1103) 0 Loan Terms 12. Standard loan terms for intermediate term credit used to purchase machinery and livestock are: (a) Period of loan in years (1-h23) 31. (b) Interest rate in percent1(l-h2h)* . (c) Type of loan2 (1- #25) (d) Payments per year (1-h2 26) 2 . 13. Intermediate term loans are defined as loans with loan periods greater than (1-h27) year(s) and up through (1-h28) years. Loans with shorter loan.periods are short term loans. Loans with longer loan periods are long term loans. 1h. Standard loan terms for long term credit used for land and build- ing purchase are: (a) Period of loan in years (l-h29) b) Interest rat in percent (l-h30) 28 c) Type of loan (1-h3l) l d) Depreciation life 3(buildi flfig)(l-h32) . (e) Payments per year3 (l-h33) w. 1. It is assumed that bred heifers are purchased the month prior to freshening and fresh cows the month at freshening. From (1.110, Part I. May be 1, 2, 3, h, 6 or 12. 228 15. Interest rate on short term capital in percent is (l-h3h) 8 . 16. (a) Minimum checking account balance to be maintained is (l-h35) §lOO . (b) Money remaining in the checking account at the end of the month in excess of (l-h36) §leOO is used to reduce debt. Cropping Program 17. The percentage of the hay crop that is reseeded each year is (1-597) 33 . 18. The cost, labor and machinery requirements for 2nd and 3rd cuttings as a percent of lst cutting requirements are: hgy hay crop silage 2nd cutting (1-598)'9§ (l-6oo7 75 3rd cutting (1-599) _9 (1-601) ES 19. Hay equivalent produced by pasture is used as follows: (a) lst cutting (1-379) 59% May (1-383) 59% June (1-387) 0% July (b) 2nd cuttingl (1-380) 33% July (1-38h) 3u% Aug. (1-388) 33% Sept. (c) 2nd cuttinge (1-381) 69% July (1-385) 99% Aug. (d) 3rd cutting (1-382) 69% Aug. (1-386) 59% Sept. 20. (a) The loss in feeding value when hay crop is harvested as pasture rather than hay is (2-2887) 8 percent. (b) The gain in feeding value when hay crop is harvested as hay crop silage rather than hay is (2-2888) 10 percent. 21. (a) The wheat straw yield is (2-2537) .5 . (b) The oat straw yield is (2-2538) .5 . 22. The percentage of total acreage planted or harvested during each month and the relative yield coefficients for different planting and harvesting dates are: l. 2 cut system. 2. 3 cut system, 229 %of Crop Relative Yield Corn planted in April LE 5) 5 (l- 518) 100 May 1-15 (1— #75) 55 (1-519) 1T2 May 15-30 (l-u76) 50 1-520) June (l-h77) O (1-521) 79 Oats planted in April (l-h78) lOO (1-522) 100 May (1- h79) (1-523) 1T0 Wheat planted in Sept. (l-h80) (l-52h) 100 Oct. (l-h81) 50 (1-525) 1T0 Soybeans planted in May’ (1-u82) 25 (1-526) 109 June 1-15 El-h83) (1-527) 100 after June 15 l-h8h) (1-528) 85 Fieldbeans planted in.May (l-u85) (1-529) 100 June (1-h86) lOO (1-530) lTO Corn silage harvest in Sept. (l-u87) (1-531) 100 1 Oct. (1-u88) 50 (1-532) 100 Nov. (l-h89) O (1-533)12: Corn grain harvest in Oct. (l-h90) 50 (l-53h)* Nov. (l-hgl) (1-535): Dec. (l-h92) l- 36) 90 Wheat harvest in July (1-h93) 100 (1-537) 100 Aug. (l-hgh) (1-538) lTO Oat harvest in July (1495) ""'5"‘ (1-539) “1100 Aug. (1- M96) 100 (l-sho) lOO Soybean harvest in Sept. (1-h97) (l-5hl) 100 1 Oct. (l-h98) 5O (l-5h2) 1T0 Fieldbean harvest in Aug. (l-h99) 0 (l-5h3) 100 1 Sept. (1-500) (1- 5th) lTO Oct. (1-501) (l-5h5) lTO lst cutting Hay Crop Silage harvest in.May (1-502) (l-5u6) lOO 1 June (1-503) 75" (1-5h7) _l____00 July (1-50u) O (1- 5&8) 100 2nd Cutting Hay Crop Silage harvested in July (1-505) (1-5u9) 100 1 Aug. (1-506) (1-550) 100 Sept. (1-507) 0 (1-551) 3rd Cutting Hay Crop Silage harvested in Aug. (1-508) (1-552) 100 1 Sept. (1-509) 20 (1-553) 100 “ lst Cut Hay harvested in May (1-510) (1-55h) 100 1 June 1-511) (1-555) 100 July (1-512) 20 (1-556) 100 2nd Cut Hay harvested in July (1-513) (1-557) 100 1 Aug. (1611+) 55 (1-558) 100 Sept. (1-515) 0 (1-559) ¥ 1. Relative yield for harvest of this crop must relate to the yield input in Part I. That is, if the yield input assumed harvest in a particular month (September) then the relative yield for that harvest month must equal 100. 230 22. (con't) % of Crop Relative Yield 3rd Cut Hay harvested in 1 Aug. (1-516) 80 (1-560) 100 Sept. (l-5l7) 20 (1-561) 100 23. Specific Variable Crop Costs Per Acre Cost Soy- Field- Seed Item Corn Wheat Oats beans beans Hay Hay Seed 1-562 1-567 1-572 1-577 1-582 1-587 1-592 3.95 6.50 3.9M h.50 3.69 5.58 ---- Pesticide 1-563 1-568 1-573 1-578 1-583 1-588 1-593 5,80 0 .21 5.00 5.66 ---- ---- Other Variable (grow)a l-56h 1-569 1-57u 1-579 1-58u 1-589 l-59h 2.89 1.99 l.h5 3.10 3.hO .32 1.52 Other Variable (harvest)a l-565 1-570 1-575 1-580 1-585 1-590 l-595 .80 1.0h .65 .90 .95 ---- 1.26 Other Variable (silage 1-566 1-571 1-576 1-581 1-586 1-591 1-596 harvest)a 1.28 ---- ---- ---- ---- ---- .86 a. Excluding labor, machinery, equipment and fertilizer. Qperating Expense and Income 2%. (a) Milk hauling Charge per cwt. is (l-h72) § .30 (b) Milk is picked up (1 = every other day, 0.5 = every day) (l-h73) 1 1. Relative yield for harvest of this crop must relate to the yield input in Part I. This is, if the yield input assumed harvest in a particular month (September) then the relative yield for that harvest month must equal 100. 25. 26. 27. 28. 32. 33. 3h. 35. 36. 231 Annual dairy herd costs per cow for breeding fees (l-h69) 7.50 Vet. Medicine (l-h70) 11.00 Other dairy (1-471) 15.00 Cost per newborn heifer calves for milk replacer, feed supplements, antibiotics etc. is (1-hh0) §2O Monthly utility expenses are (1-605) $1.20 per cow per month. Annual miscellaneous expenses are (1-606) $1.25 plus (1-607) 1.h percent of other cash operating expenses (excluding insurance . Property taxes are (1-608)l $3.50. Months paid are (1-609) 10 and (1-610) 2 . Expenses for conservation and fence repair are (1-611) $0.75 per tillable acre for conservation plus (1-612) §l.00 per acre of’crop land pasture. Land rent is paid in the months of (1-613) _2_ and (1-61h) ll . The landlord share for share rent acreage is (1-615) 33 . Price per pound for fertilizer .N is (lzélg) 7.3 cents P 0 is 1 17) 8.7 cents Kgosis (1-618) 5.3 cents Cost of hauling the following commodities is: Hay or straw (per ton) (1-36h) h.00 Corn grain (per bu.) (1-365) :£L_ Oats (per bu.) (1-366) .05 Wheat (per bu.) (1-367)755 Soybeans (per bu.) (1-368) .06 Field beans (per cwt.) (1-3E§)‘;19 Off-farm storage cost per month is: Corn grain (per bu.) (1-370) § .Oh Oats (per bu.) (1-371) .Oh Wheat (per bu.) (1-372)':5E Soybeans (per bu.) (l-37§7_.0h Field beans (per cwt.) (1437E)‘;9§ Custom harvest rates per acre are: (a) Corn silage E2-2359; 11.00 Eb) Corn Grain 2-2360 8.00 c) Wheat (2-2361) 5.00 1. Use a number between 0 and 10 for per acre owned value. If a number greater than 10 is used this will be assumed to be the total annual tax bill. (d) (e) (f) 37. (a) (b) (c) 38. (a) (b) (e) 39. (a) (b) #0. The the 232 Oats (2-2362) 6.00 Field Beans (2-2363) 7.00 Soybeans (2-236h) 5.00 Machinery is insured at (2-2316) 100 percent of depreciated value. iMachinery insurance cost as percent of amount of insurance is (2-2317) 0.65. Months insurance is paid (2-599)._;_ (2-600)_;g_. Amount of building insurance as a percent of new value is (1-633) 60 . Building insurance rate as a percent of amount of insurance is (1-63#) .65 . Fire insurance on buildings, livestock and produce is paid during the month of (1-635) 3 . Cost of insurance on livestock is (2-8889) §l.18 per $1000 value. Cost of insurance on produce and supplies is (2-8890) § .78 per $1000 value. market value of new buildings is (1-619) 60 percent of purchase price. hl. (a) Distribution of Building Repair Cost and Labor Requirements Month Jan Feb Mar Apr May June % of 1-620 1-621 1-622 1-623 1-62h 1-625 annual total 7 8 10 ll 8 5 July Aug Sept Oct Nov Dec % of 1-626 1-627 1-628 1-629 1-630 1-631 annual total 8 9 8 8 8 10 h2. h3. hh. #5. 233 (b) Annual building repair cost as a percent of new value is (1-632) 2.5 . Average sale weight per animal (pounds) is: (a) Dairy calves (2-253h) 100 (b) Cull cows (2-2535) 1300 (c) Dairy Steers (2-2536) 1000 Ag. program.payments are (1-602)1 2.00. Months payments are received is (1-603) 5 and (l 0 ll . Machinery sold is valued at (2-2318) 29 percent of its depreciated value. All machines have a (2-598) 10 percent salvage value. (a) Income from the sale of land is distributed over (2-8891) 3 years. (b) Annual income from the sale of land is received in month Mortality and Feed Reguirements h7. (a) Calf mortality rate is (l-hl9) percent. (b) Heifer mortality rate is (l-h20) 2 percent. (c) Percent of heifers sold for infertility is (l-h21) h . (d) Conception rate for heifers is (1-h22) 70 . #8. (a) Approximately (2-2865) 2.8 percent of the cow herd is expected to die each year. (b) Approximately (2-2866) h.l percent of the cow herd will be sold because of physical injury or disease each year. These animals are sold at one-half price eaCh year. #9. Dairy Herd Forage and Grain Requirements Level of Grain Feeding (lbs.)gp Tons H.E. Reguired 1 (2-601) 2000 (2-613) 7.0 2 52-602) 2500 (2-61h) 6.9 3 2-603) 3000 E2-615) 6.8 h (2-6oh) 3500 2-616; 6.6 5 (2.605) #000 (2-617 6.u 1. Use a number between 0 and 10 for per crop acre value. If a number greater than 10 is used this will be assumed to be the total annual farm Ag. program.payment. 234 M9. (con't) Level of Grain Feeding (lbs:) Tons H.E. Required 6 €2-606g #500 $3-618; 6.2 7 2-607 5000 2-619 6.0 8 (2-608; 5500 (2-620) 5.8 9 (2-609 6000 (2-621) 5.6 10 (2-610) 6500 (2-622) 5.3 11 (2-611) 7000 (2-623) 5.1 12 (2-612) 7500 (2-62h) h 8 50. (a) The average amount of feed in pounds fed per month to heifers under 12 months of age is (l-h37) 120 . (b) Annual hay equivalent requirements (tons) for heifers Birth to 12 months (1-h38) 1.5 12 months to freshening (1-H39) h.0 51. Feed Requirements for Beef Steers Lbs. Per Animal Per Day on Feed Ration Number Feed 1a 2a 3a ha 5b 6b 7b 8b Corn Silage 2-2319 2-232h 2-2329 2-233h 2-2339 2—23hh 2-23h9 2-235h #2 0 22 22 58 0 28 28 Hay Crop Sil- 2-2320 2-2325 2-2330 2-2335 2-23h0 2-23h5 2-2350 2-2355 age (70%) 0 36 20 0 0 50 26 0 Hay 2-2321 2-2326 2-2331 2-2336 2-23hl 2-23h6 2-2351 2-2356 0 0 0 7 O O 0 9 (Shelled Corn 2-2322 2-2327 2-2332 2-2337 2-23h2 2-23h7 2-2352 2-2357 0 3.5 1.75 1.7 O h.5 .. 2.52 Supplement 2-2323 2-2328 2-2333 2-2338 2-23h3 2-23h8 2-2353 2-2358 1% 0 0 0 1i 0 0 0 7 H 3. Per standard to good hOO pound holstein and beef type calves fed to 1,000 lbs. with expected daily gain of 1.9 lbs and feeding period of 316 days. For standard to good 650 pound holstein or beef type steers fed to 1,000 lbs with expected daily gain of 2.0 lbs and feeding periods of 225 days. 235 52. (a) Straw Requirements for Cows Per Animal Per Season System Number Season 1 2 3 h 5 #67 77d Winter (Oct.- 1-hh1 1-h1+5 1-l+1+9 1-153 1-h57 1.161 1.165 Apr.) .5 .2 .2 .2 .2 .6 0 Summer (May - 1-l+l+2 1-l+l+6 1-1+50 1-h53 1-h58 1-l+62 1-1166 Sept. 0 .05 .05 .05 .05 1+ 0 52. (b) Straw Requirements for Heifers Per Animal Per Season System.Number Under One Year Over One Year Blank Season 8' 9 10 8 9 10 Cells Winter (Oct.- 1.443 1.11117 1.1151 1-h55 1-h59 1-2.63 1-u67 Apr.) .2 .10 0 .ho .15 0 0 Summer (May- l-hhh l-hh8 l-h52 l-h56 l-h60 1-h6h l-h68 Spet.) .15 .05 0 0 0 0 0 .eaoneoo .ensooonm .o 236 .peonnoo .aneaano .nsoo nonowsoam .p .vopotha @5686 he. @3330: 03.3 d mmm can 30an 5.3.308 mo ommnoko pawflonpm w m.“ 03.3 owsnoné. ..m ma.mofl om.ma .oo.oam cm.mm mm.sm om.mm mo.mm mm.m op.» so. mm.a mo.H os.m aqooenm mMmmum ommmum semmum sosmum amsmum (mommum mmmmim meemum mmemam Hemnm mosmnm ommmum ssmmum omono>< oo.H mo.a mo.a Ho.H Ho.H so. oo.H mm. mm. oo.H Ho.a mm. so.a mmmm-m mamm-m 60mmum mommum omsm-m smmmum smsm-m Hasm-m msmnm maamum mosm-m mmmmum oemmuw .666 so. mo.H mo.H Ho.H Ho.a mm. so. em. mm. am. mm. mm. so.a HMmmum Ammam memm-m mmem-m mewmwm mommum mnemsm ossmum emamum demum Hosmum mmmmum msmwum .>oz mm. Ho.H mo.H Ho.H Ho.a mm. mm. mm. am. (am. am. mm. mo.a ommmum semm-w scam-m Hmew-m msem-m meow-m mmsmuw mmsmum msmum masm-m ooem-m smmmnm smmNnm .noo mo.H mm. mo.H Ho.a Ho.a oo.a mm. mm. mmn, om. am. am. oo.a mmmmum Ammum memmum omwmnm ewem-m Ammmmum Hmsmwm mem-m mmemwm masmum mammnmnmwmmnm mammuw .nmom mo.a om. so.a mo.a mo.a :o.a mm. mo.H mo.H mm. omw mm. m . [mNmm-m memm-m memm-m mmsm-m ssmnm mommum omsmum smsw-m emsmum Hawmum wmmmnm mmmm-m msmmum .ms< so.H om. mo.H mo.H mo.H mo.H swan mo.H so.a so. mm. mo.H mm. smmmum :Hmmum Hommum wmsmum meumum mmsmum mesmum msmnm, mmwmum oasmwm smmmnm emmmum Hammum sash mo.a oo.a Ho.a mo.a mo.a oa.a mo.a so.a so.H mo.H em. so.a smw {ImNmmum mamm-m 66mmum swemwm :eemum Hmnmum mesm-m mmsm-m mmsmum mosm-m ommmum mammum oemmum onus mm. so.a mm. oo.H oo.H mo.a oo.a mo.H Ho.m mo.a mo.a mo.H mm. mmmmum mammum mmemnm m-m mesmnm omemmm asemum memum Hmemmwnmonmum mammum mmmmum mommmw as: so. mo.a mm. am. am. Ho.a mo.H mo.a oo.a ao.a oo.a mo.H mmnl emmmum Hemmsm mmsmum m mum wssmum mnemum mum mmsmum omsmim eoemum :mmmum ammmum wommum .nm< mm. so.a mm. am. am. mo.a mo.a mo.H mo.H mo.a oH.H mo.a Ho.a mmmmum osmmum ammwnm smamum Hesm-m (mmswwm mnemnm mmumim mflsmum mosmum mmmmum owmmum emmmum .noz mm. mo.H mm. em. paw mm. mo.a mo.a mo.a HH.H so.H mo.H No.H mmmmum memmum moemum mmum-m osemam amemrm seem-m Hmsmum (maemum mosmum mammum msmmum mmmmum .nom oo.H mo.a mm. mm. mm. mm. so.a mo.H mm. ma.a so.a mo.H mo.a Hummum \mommsm mmsmnm mmemum omemnm (mMsum msemum omem-m sasmsm sowmum Hmmm-m msmm-m mmmmum .ooo Jase see ads 38 .22 3.3 ass Se 38 3e Ea 38 22 secs poms annum £50 3380 203m goo ham mason. modem .0550 Rooms snow as -oaaasm _ Masai assoc Janeen. oaaso 2 .aom has 72 naoaoq onenm one nonoeoameooo onena_aaeono2. .mm 237 5%. Labor Requirements Annual Dairy Cow Labor Requirements (Hours) No. of Dairy Cow S stem Cows l 2 ’ 3 h 5_ 67 7 less than 30 2-661 2-666 2-671 2-676 2-681 2-686 2-691 100 60 60 59 59 66 0 31-u9 2-662 2-667 2-672 2-677 2-682 2-687 2-692 90 52 52 51 51 57 0 50-79 2-663 2-668 2-673 2-678 2-683 2-688 2-693 80 A8 A8 #6 M6 53 0 80-1u9 2-66h 2-669 2-67u 2-679 2-68h 2-689 2-69h 70 #5 M5 M3 M3 50 0 150 and over 2-665 2-670 2-675 2-680 2-685 2-690 2-695 70 he #2 39 39 #6 O 55. Livestock.Monthly Labor Distribution Coefficientsa Portion of Average Monthly Requirement Animal Jan. Feb. 1 Mar. ‘ ,Apr. May. June Cows 2-696 2-698 2-700 2-702 2-70h 2-706 1 1 1 l l l Heifers 2-697 2-699 2-701 2-703 2-705 2-707 1.2 1.2 1.2 1.2 .8 July Aug. Sept. Oct. Nov. Dec. Cows 2-708 2-710 2-712 2-71h 2-716 2-718 1 1 1 l l Heifers 2-709 2-711 2-713 2-715 2-717 2-719 .8 .8 .8 .8 1.2 1.2 a. Equals portion of average month coefficient. fer the year must equal 1. chion systems for cows might use = 1.2 for months 11, 12, 1, 2, 3, h, and 8 for months 5, 6, 7, 8, 9, 10. weighted average value For example, data indicate that stan- 560 238 Annual Dairy Replacement Labor Requirements (Hours) Dairy Replacement System Number Animal Age 8 3 Under 12 months (2-720) 15 (2-722) 13 (2—72u) 0 12 Mo. to fresh (2-721) 10 (2-723) 8 (2-725) 0 57. Crop Systems Annual Labor Requirements System Hours Row GS-l Corn Grow, April, 2-row (24726; 11.31 1 GS-2 Corn Grow, April, lie-row (2'-727 3.56 2 GS-3 Corn Grow, April, 6-row (2-728) 2.76 3 GS-h Corn Grow, April, User £2429) 1+ GS-5 Corn Grow, May, 2-row 2-730; 5 08-6 Corn Grow, May, lt—row 2-731 3. 56 6 GS-7 Corn Grow, May, 6-row E2432; 2. _____'_7___6 7 GS-8 Corn Grow, May, User 2-733 8 GS-9 Corn Grow, June, 2-row (2-73h) E. 31 9 GS-lO Corn Grow, June, h-row (2-735) 3.5 10 GS-ll Corn Grow, June, 6-row (2-736) ______7__2. ll GS-l2 Corn Grow, June, User (2-737): 12 GS-13 Wheat Grow, Sept., 2-1+ row (2-738) 13 GS-lh Wheat Grow, Sept., 6-row (2-739) 1.181 1h GS-l5 Wheat Grow, Sept., User (2-714-0) 15 GS-16 Wheat Grow, Oct., 24+ row 2-71+1) 71316 GS-l7 Wheat Grow, Oct., 6-row 2-71+2) 1.1 17 GS-18 Wheat Grow, Oct., User 2-7h3)"""""' 18 GS-l9 Oats Grow, April, 2-h row 2-71m; 1.66 19 08-20 Oats Grow, April, 6-row 2-7h5 1.53 20 GS-2l Oats Grow, April, User 2-7h6) 21 08-22 Oats Grow, May, 24+ row 2-7h7) I i. 22 GS-23 Oats Grow, May, 6-row (2-716; 1. 53 23 GS-2h Oats Grow, May, User (2-7h9 21+ GS-25 Hay crop plant, direct, 24+ row (2-750) 25 GS-26 Hay crop plant, direct, 6-row (2-751) 26 08-27 Hay crop plant, companion crop $2452; .22 27 GS-28 Hay crop plant, User 2-753 28 GS-29 Hay crop maintain - fertilizer (2-751-1) .20 29 GS-3O Hay crop maintain - User (2-755)________ 30 239 57. (Cont'd.) System Hours Row GS-3l Field Bean Grow, May, 5-row' S2—756)3 31 08-32 Field Bean Grow, May, 6-row 2-757) 3.0 32 GS-33 Field Bean Grow, May, User (2-758) 33 GS-35 Field Bean Grow, June, 5-row (2-759) 3. 35 GS-35 Field Bean Grow, June, 6-row (2-760; 3.0 35 Gs-36 Field Bean Grow, June, User (2-7612" 36 GS-37 Soybeans Grow, May, 5-row (2-762) 3. 22 37 08-38 Soybeans Grow, May, 6-rOW' E2-763) 2. 57 38 GS-39 Soybeans Grow, May, User 2-765) 39 GS-50 Soybeans Grow, June, 5-row (2-765) 3. 22 5O GS-5l Soybeans Grow, June, 6-row (2-766) 2. 57 51 GS-52 Soybeans Grow, June, User (2-767) 52 HS-l Corn silage harvest, custom. (2-768 .50 53 HS-2 Corn silage harvest, l-row E2—769; 5.65 55 HS-3 Corn silage harvest, 2-row 2-770 5.01 55 HS-5 Corn silage harvest, S.P. chopper 52-771) 3.13 56 HS-5 Corn silage harvest, User 2-772) 57 HS-6 Corn grain harvest, custom, (2-773) .25 58 HS-7 Corn grain harvest, 1-row picker (2-775) 3.11 59 HS-8 Corn grain harvest, 2-row picker (2-775 2. 1 5O HS-9 Corn grain harvest, 2-row combine (2-776)w 2.28 51 HS-lO Corn grain harvest, 3-row combine 2-777 2.02 52 HS-ll Corn grain harvest, User (2-778) 53 HS—12 Wheat harvest, custom. (2-779) .20 55 HS-13 Wheat harvest, S.P. combine spread straw (2-780) 1.08 55 HS-15 Wheat harvest, S.P. combine bale straw (2-781) 2.05 56 HS-15 Wheat harvest, User E2-782) ' 57 HS-16 Oats harvest, custom. 2-783) .20 58 HS-l7 Oats harvest, S.P. combine spread straw (2-785) 1.16 59 HS-l8 Oats harvest, S.P. combine bale straw (2-785) 2. O7 60 HS-19 Oats harvest, User (2-786)w 61 HS-2O Hay crop silage harvest, mow, 1-row (2-787) 3.00 62 HS-2l Hay crop silage harvest, windrow, 2-row (2-788) 2.60 63 HS-22 Hay crop silage harvest, windrow, S.P. chopper (2-789) 2.50 65 HS-23 Hay crop silage harvest, windrow, l-row (2-790) 2.80 65 HS-25 Hay crop silage harvest, User (2-791) 66 HS-25 Hay harvest, mow, bale, load ' behind wagon (2-792) 5.66 67 250 57. (Cont'd.) System. Hours Row HS-26 Hay harvest, windrow, mow conveyor (2-793) 3.22 68 HS-27 Hay harvest, windrow, kicker (2-795) 3.72 69 HS-28 Hay harvest, User (2-795) 70 HS-29 Field Bean harvest, custom (2-796) .30 71 HS-30 Field Bean harvest, 5-row, bean head (24973 2.38 72 HS-3l Field Bean harvest, 6-row combine 2-798 1.28 73 HS-32 Field Bean harvest, User 2-799) 75 HS-33 Soybeans harvest, custom. 2-800) 75 HS-35 Soybeans harvest, combine (2-801) 1.57 76 HS-35 Soybeans harvest, User (2-802) 77 Building Investggnt Costs and quuirements 58. (a) Cow capacity in a stanchion barn will be defined as the number of stanchions plus (1-505) 15 percent if there is enough space for (1-505) 100 percent of the additional animals in ‘with the heifers. (b) Cow capacity in a free stall or user defined barn will be defined as the number of free stalls plus (1-506) 22 percent, if there is enough room for (1-507) 60 percent of these animals with the heifers. 59. (a) If dairy buildings are to be purchased by the program, they will be purchased (1-508) _9_ months after capacity is first exceeded and the cow building capacity purchased will equal (1-509) 20 animals, (1-510) 50 percent of the present building capacity or facilities for the animal numbers at the end of this month, which ever is larger. 60. When dairy building construction is calculated by the program, forage storage is constructed as follows: §ilos Storage (tons) Dry Hay Storage (tons) Cows (1é5ll) 12 (14515) 0 heifers over 1 yr. E1—5l2) 0 (1-515; 0 heifers under 1 yr. 1-513) 0 (1-516 1.5 61. The number of cows that can'be handled with One l-man parlor is 1-517) 100 One 2-man parlor is 1-518) 200 62. When grain storage capacity is to be built by the program, high moisture corn storage will be built only if at least (1-389) 60. percent of that capacity will be used in the year built. 251 63. (a) Cost of hay storage capacity (per ton) is (1-636) §25.00. (b) Cost of ear corn storage (per bushel) is (1-637) 21.50. (c) Cost of grain bin storage (per bushel) is (1-638) $2.00. 65. The cost of milking parlors is: (a) One man parlor (double-four herringbone) Building (1-639) §2,200. (b) One man parlor (double-four herringbonefiO Equipment (1-6 ) $6,200. (c) Two man parlor (double-eight herringbone) Building (1-651) $12,100. (d) Two man parlor (double-eight herringbone) Equipment (1-652) $10,200. (e) Life for parlor and barn equipment is (years) (1-653) 8 . 65. Heifer and Calf Building Costs Type Cost Per Stall of Conventional Free User Animal Pens Stall Defined Heifers & Dry Cows (2-1) $270 (2-3) $337 (2-5) 0 Calves (2-2) $203 (2-5) $253 (2-6) 0 66. Per Cow Cost of Building Equipment Includes Feeders, Ventilation System, Etc. Number [ Liquid rLiquid of Tie Stall Cold Warm Cold Warm Open 'User Stalls Stanchion Covered Covered Covered Covered Lot Defined l 2 3 ’5 5 6__ 7 Under 70 (2-7) (2-12) (2-17) (2-22) (2-27) (2-32) (2-37) 200 115 155 152 .12? 115 0 70-89 512-87' (2-13) 512-18) (2-237 (2-58) (2-387’ (2-3875 200 _flg 100 139 127 166 lOQ__V 0 90-109 552-9757 (2-15) 5(2-19) (2-55) (2-29) (2585) (2-39) 200 .19 117 93. 131 72_. 0 Over 109 (2-10) (2-15) (2-20) (2-25) (2-30) (2-35) (2-50) . 200 3§9 107, ,78 116 69 0 252 66. (Cont'd.) Per Cow Cost of Dairy Building Excluding Equipment [Liquid Liquid fie Stall Cold Warm Cold Warm Open User Stanchion Covered Covered Covered Covered Lot Defined l 2 3 5 _5 6 7 (2-11) (2-16) (2-21) (2-26) (2-31) (2-36) (2-51) 375 223 268 283 328 197 o 67. Upright and Horizontal Silo Costs 15,057 a. Number of tons capacity that must be built to achieve costs as low as that indicated under "cost per ton". 68. Machine Costs 253 Machine Purchase Price and Life Machine Purchase Life ionth of Machine Code Price (Years) Replacement 50 H.P. Tractor 1 (2-268) 5,130 (2-378) 10 (2-588) 10 51-60 H.P. Tractor 2 (2-269) 5,250 (2-379) 10 (2-589) 5 61-80 H.P. Tractor 3 52-270) 7,830 (2-380) 10 (2-590) 5 81-100 H.P. Tractor 5 2-271) 8,510 52-381) 10 (2-591) 5 100 H.P. Tractor 5 (2-272) 9,780 2-382) 10 (2-592) 5 6 £2—273) (2-383) (2-593 7 2-275) (2-385) (2-595) 8 (2-275) (2-385) (2-595) Pick-up Truck 9 (2-276) 2,880 (2-386) 5 (2-596) 3 Large Truck 10 (2-277) 5,600 (2-387) 5 (2-597) 7 11 (2-278) (2-388) $2-598) Unloading wagons 12 (2-279) 2,250 (2-389) 10 2-599) 9 Unloading Trucks 13 (2-280) 1,800 (2-390) 8 (2-500) 9 Small Spreader 15 (2-281) 1,090 (2-391) 10 (2-501) 10 Large Spreader 15 (2-282) 1,270 (2-392) 10 (2-502) 10 Liquid Spreader 16 (2-283) 1,725 (2-393) 5 (2-503) 12 Gutter Cleaner (30 cows)17 (2-285) 1,650 (2-395) 8 (2-505) 2 Scraper (loader) 18 (2-285) 890 (2-395) 10 (2-505) 10 Liquid Pump 19 (2-286) 1,725 (2-396) 5 (2-506) 12 20 (2-287) (2-397) (2-507) 21 (2-288) (2-398) (2-508) 15' Silo Unloader 22 (2-289) 1,330 (2-399) 8 (2-509) 10 16' Silo Unloader 23 (2-290) 1,550 (2-500) 8 (2-510) 10 18' Silo Unloader 25 (2-291) 1,575 (2-501) 8 (2-511) 10 20' Silo Unloader 25 (2-292; 1,620 (2-502) 8 (2-512) 10 25' Silo Unloader 26 (2-293 2,200 (2-503) 8 (2-513) 10 28' Silo Unloader 27 (2-295) 2,310 (2-505) 8 (2-515) 10 30' Silo Unloader 28 2-295) 2,365 (2-505) 8 (2-515) 10 Trench‘Unloader 29 2-296) 2,300 (2-506) 8 (2-516) 10 500 Gal Bulk Tank 30 2-297) 3,800 (2-507) 10 (2-517) 6 600 Gal Bulk Tank 31 (2-298) 5,575 (2-508) 10 (2-518) 6 800 Gal Bulk Tank 32 (2-299) 5,500 E2-509) 10 32-519) 6 1000 Gal. Bulk Tank 33 (2-300) 6,500 2-510 10 2-520) 6 1500 Gal. Bulk Tank 35 (2-301) 8,500 E2—511; 10 (2-521) 6 2000 Gal. Bulk Tank 35 é2-302) 10,000 2-512 10 (2-522) 6 Milk Transfer 36 2-303) 750 (2-513) 8 (2-523) 9 Barn Equipment 37 2-305; 1,500 2-515) 8 (2-525) 11 Parlor Equipment 38 2-305 6,200 2-515) 8 (2-525) 11 39 2-306 2-516 E2-526) 2 Row-Cultivator 50 2-307 520 2-517 10 2-527 6 5 Row Cultivator 51 2-308 1,010 2~518 10 22-528) 6 6 Row Cultivator 52 (2-309) 1,725 E2-519) 10 2-529) 6 Sprayer 53 2-310) 660 2-520; 10 (2-530) 6 2'15" plow 55 2-311) 375 (2-521 10 (2—531) 3 2-16" plow 55 2-312) 500 (2-522) 10 (2-532) 3 3-15" plow’ 56 (2-313) 755 (2-523) 10 (2-533) 3 68. (Cont'd.) 255 Machine Purchase Life Month of Machine Code Price (Years) Replacement 3-16" plow 57 (2-315) 800 (2-525) 10 (2-535) 3 5-15" plOW' 58 (2-315) 1,150 (2-525) 10 22-535) 3 5-16" plow 59 (2-316) 1,210 2-526) 10 2-536) 3 5-15" Plow 50 (2-317) 1,365 2-527; 10 i2-537) 3 5-16" plow 51 (2-318) 1,550 2-528 10 2-538) 3 6-15" plow 52 (2-319) 1,590 (2-529) 10 (2-539) 3 6-16" plow 53 (2-320g 1,580 2-530; 10 32-550) 3 7-15" plow 55 (2-321 2,300 2-531 10 2-551) 3 7-16" plow 55 (2-322) 2,525 2-532) 10 (2-552) 3 8-15" plow 56 (2-3233 2,700 (2-533) 10 (2-553) 3 8-16" plow 57 (2-325 2,850 (2-535) 10 (2-555) 3 58 (2-325) (2-535) (2-555) 59 (2-326g 22-536; 2-556) 10' Disc 60 (2-327 925 2-537 12 2-557) 3 12' Disc 61 (2-328) 1,150 (2-538) 12 2-558) 3 15' Disc 62 (2-329) 1,335 2-539) 12 E2—559) 3 16' Disc 63 (2-330) 1,520 2-550) 12 2-550) 3 18' Disc 65 (2-331) 1,755 2-551) 12 (2-551) 3 8' Harrow 65 (2-332) 160 (2-552) 15 (2-552) 3 12' Harrow 66 (2-333) 290 (2-553) 15 (2-553) 3 16' Harrow 67 (2-335) 500 (2-555) 15 (2-555) 3 20' Harrow 68 (2-335) 580 (2-555) 15 (2-555) 3 25' Harrow ‘ 69 (2-336) 555 (2-556) 15 (2-556) 3 70 (2-337) (2-557) (2-557) 2 row-planter 71 (2-338) 560 (2-558) 10 (2-558) 5 5 rOW'planter 72 (2-339) 1,250 (2-559) 10 (2-559) 5 6 row planter 73 (2-350) 2,120 (2-550) 10 (2-560) 5 Fertilizer Spreader 75 (2-351; 860 (2-551) 8 (2-561) 7 Grain Drill 75 (2-352 1,270 52-552) 12 (2-562) 3 76 (2-353) 2-553) (2-563) 1 ROW'Chopper 77 (2-355) 2,660 (2-555) 8 (2-565) 9 2 Row Chopper 78 (2-355) 2,990 é2-555) 8 $2-565) 9 2 Row-Chopper (S.P.) 79 (2-356)11,500 2-556) 8 2-566) 9 Blower 80 (2-357) 1,250 (2-557) 10 (2-567) 9 3 Row Corn Head (Grain) 81 (2-358) 3,150 (2-558) 10 (2-568) 10 1 Row Corn Picker 82 (2-359) 2,070 £2-559; 10 (2-569) 10 2 Row Corn Picker 83 (2-350) 3,550 2-560 10 (2-570) 10 2 Row Corn Combine 85 (2-351) 7,580 (2-561) 10 (2-571) 10 3 Row Corn Combine 85 2-352; 9,750 2-562) 10 22-572) 10 Grain wagons 86 2-353 550 2-563; 10 2-573? 7 Grain Elevator 87 2-355) 1,225 2-565 10 (2-575 7 2 Row Corn.Head (Grain) 88 2-355) 2,280 (2-565) 10 (2-575) 10 10' Grain Combine 89 22-356) 7,180 (2-566) 10 (2-576) 7 12' Grain Combine 90 2-357) 8,300 (2-567; 10 (2-577) 7 15' Grain Combine 91 (2-358; 9,530 (2-568 10 (2-578) 7 Dale Kicker 92 (2—359 1,270 (2-569) 12 é2-579) 3 PTO Windrower 93 {2-360% 1,370 (2—570) 8 2-580; 5 10' S.P. Windrower 95 2-361 5,350 (2—571) 8 (2—581 5 68. (Cont'd.) 255 Machine Purchase Life Mbnth of Machine Code Price (Years) Replacement 15' S.P. Windrower 95 (2-362) 6,560 (2-573) 8 (2-582) 5 WindrOW'Turner 96 (2-363) 170 (2-575) 10 (2-583) 5 Baler 97 (2-365) 2,100 (2-575) 10 2-585) 5 wagons 98 (2-365) 530 (2-576) 12 (2-585) 5 Hay Elevator 99 (2-366) 330 (2-577) 10 (2-586) 5 Mow Conveyor (50A,Hay) 100 (2-367) 1,000 (2-578) 10 (2-587) 5 Mower 101 (2-368) 650 (2-578) 10 (2-588) 5 Rake 102 (2-369) 620 (2-579) 10 (2-589) 5 Crusher 103 (2-370) 1,050 (2-580) 8 (2-590) 5 Hay Head (1 Row Chopper) 105 (2-371) 790 (2-581) 8 (2-591) 5 Hay Head (2 Row Chopper 105 (2-372) 1,185 (2-582) 8 (2-592) 5 Bean Puller Attach 106 (2-373) 550 (2-583) 10 (2-593) 8 Bean Puller Attach (6 Row) 107 (2-375) 785 (2-585) 10 (2-595) 8 Bean Combine 108 (2-375) 12,000 (2-585) 10 (2-595) 8 Bean Attach (Combine) 109 (2-376) 1,500 (2-586) 10 (2-596) 8 110 (2-377) (2-587) (2-597) 69. ,Annual Machine Repair and Gas and Oil Costs System. Repair Costs Gas & Oil System. Number Per Unit Cost Per Unit Dairy;Cows Stanchion or tie stall LS(l) (2-1577) $5.80 (2-1565) $5.50 Cold covered free stall LS(2) (2-1578) 5.28 (2-1565) 9.85 Warm.enclosed free stall LS(3) (2-1579) 5.28 (2-1566) 9.85 Cold, free stall, liquid Ls(5) (2-1580) 5.56 (2-1567) 11.50 warm.rree stall, liquid Ls(5) (2-1581) 5.56 (2-1568) 11.50 Loose housing, open lot LS(6) (2-1582) 5.65 (2-1569) 10.55 Dairy CoweUser LS(7) (2-1583) (2-1570) Dairy Replacements Conventional pens LS(8) (2-1585) 1.32 (2-1571) 2.52 Free stall LS(9) (2-1585) 2.50 (2—1572) 5.55 User LS(lO) (2-1586) (2-1573) 256 69. (Cont'd.) System Repair Costs Gas & Oil System Number Per Unit Cost Per Unit Corn Grow April, 2-row 03(1) (2-1587)$ 1.16 (2-1575) $1.79 April, 5-row GS(2) (2-1588) .97 (2-1575) 1.59 April, 6-row GS(3) (2-1589) .81 (2-1576) 1.25 April, User GS(5) (2-1590) (2-1577) May, 2-row Gs(5) (2-1591) 1.16 (2-1578) 1.79 May, 5-row GS(6) 2-1592) .97 (2-1579) 1.59 May, 6-row GS(7) (2-1593) .81 (2-1580) 1.25 May, User GS(8) (2-1595) (2-1581) June, 2-row GS(9) (2-1595) 1.16 (2-1582) 1.79 June, 5-row GS(lO) (2-1596) .97 (2-1583) 1.59 June, 6-row GS(ll) (2—1597) .81 (2-1585) 1.25 June, User GS(12) (2-1598) (2-1585) Wheat Grow Sept., med. GS(13) (2—1599) .76 (2-1586) .95 Sept., large GS(15) (2-1500) .72 (2-1587) .91 Sept., User GS(15) (2-1501) (2-1588) Oct., med. GS(16) (2-1502) .76 (2-1589) .95 Oct., large GS(17) (2-1503) .72 (2-1590) .91 Oct., User GS(18) (281505) (2-1591) Oats Grow April, med. GS(19) (2-1505) .76 (2-1592) .97 April, large GS(20) (2-1506) .72 (2-1593) .93 April, User GS(21) (2-1507) (2—1595) May, med. GS(22) (2-1508) .76 (2-1595) .97 May, large cs(23) (2-1509) .72 (2-1596) .93 May, User GS(25) (2-1510) (2-1597) May Crop Plant Direct, med. GS(25) (2-1511) .60 (2-1598) .97 Direct, large GS(26) (2-1512; .65 (2-1599) .90 Companion Crop GS(27) (2-1513 .07 (2-1600) .12 User GS(28) (2-1515) (2-1601) Hay Crop Maintain Fertilizer GS(29) (2-1515) .07 (2-1602) .13 User GS(30) (2-1516) (2-1603) Field Beans Grow May, 5-row GS(31) (2-1517) 1.36 (2-1605) 2.09 69. (Cont'd.) 257 System Repair Costs Gas & Oil System. Number Per Unit Cost Per Unit May, 6-row GS(32) (2-1518) $1.23 (2-1605) $1.91 May, User GS(33) (2-1519) (2-1606) June, 5-row GS(35) (2—1520) 1.36 (2-1607) 2.09 June, 6-rOW' GS(35) (2-1521) 1.23 (2-1608) 1.91 June, User GS(36) (2-1522) (2-1609) Soypeans Grow May, 5—row GS(37) (2-1523) 1.10 (2-1610) 1.65 May, 6-rOW’ GS(38) (2-1525) 1.06 (2-1611) 1.60 May, User GS(39) (2-1525) (2-1612) June, 5-row GS(50) (2-1526) 1.10 (2-1613) 1.65 June, 6-row GS(51) (2-1527) 1.06 (2-1615) 1.60 June, User GS(52) (2-1528) (2-1615) Corn Silage Harvest Custom Hs(1) (2-1529) .85 (2-1616) 1.77 1-row Hs(2) (2-1530) 2.83 (2—1617) 2.85 2-row Hs(3) (2-1531) 2.56 (2-1618) 2.79 2-row S.P. Hs(5) (2-1532) 2.56 (2-1619) 2.79 User Hs(5) (2-1533) (2-1620) Corn Grain Harvest Custom Hs(6) (2-1535) .06 (2-1621) .18 1-row pick Hs(7) (2-1535) .76 (2-1622) 1.58 2-row pick Hs(8) (2-1536) .67 (2-1623) 1.25 2-row combine HS(9) (2-1537) 1.05 (2-1625) .88 3-row combine HS(10) (2-1538) .89 (2-1625) .80 User Hs(ll) (2-1539) (2-1626) Wheat Harvest Custom. Hs(l2) (2-1550) .03 (2-1627) .09 Combine, spread straw Hs(l3) (2-1551) .60 52-1628) .58 Combine, bale straw Hs(15) (2-1552) .68 2-1629) .69 User HS(15) (2-1553) (2-1630) Oat Harvest Custom. Hs(l6) (2-1555) .03 (2-1631) .09 Combine, spread straw Hs(l7) E2-1555) .60 (2-1632) .58 Combine, bale straw Hs(18) 2-1556) .68 (2-1633) .69 User Hs(19) (2-1557) (2-1635) 69. (Cont'd.) 258 System Repair CoStS Gas & Oil System. Number Per Unit Cost Per Unit Hay Crop Silage Harvest Mow, l—row chopper HS(20) (2—1558) $1.89 (2-1635) $2.30 Windrow, 2-row chopper HS(21) (2-1559) 1.63 (2-1636) 1.59 Windrow, S.P. chopper HS(22) (2-1550) 2.09 (2-1637) 1.59 Windrow, l-row chopper Hs(23) (2-1551) 1.67 (2-1638) 1.62 User Hs(25) (2-1552) (2-1639) Hay'Harvest Mow, rake, bale HS(25) (2-1553) .93 (2-1650) 1.51 Windrow, thrower, mow-conveyor HS(26) (2-1555) 1.53 (2-1651) 1.25 Windrow, bale, thrower Hs(27) (2-1555) .70 (2-1652) 1.20 User Hs(28) (2-1556) (2-1653) Field Bean Harvest Pull, rake, custom combine Hs(29) (2-1557) .03 (2-1655) .09 Pull, rake, combine 5-row HS(30) (2-1558) .92 (2-1655) .75 Pull, rake, combine 6-row Hs(31) (2-1559) 1.13 (2-1656) .92 User Hs(32) (2-1560) (2-1657) Soybean Harvest Custom. HS(33) (2-1561) .03 (2-1658) .09 Combine HS(35) (2-1562; .58 (2-1659) .38 User HS(35) (2-1563 (2-1650) Distribution and Relative Efficiency Coefficients For Machine Costs and Labor The following tables contain the coefficients used for distributing machinery repair, gas and oil costs, and labor requirements throughout the year. labor requirements for different sizes of operation. Table 72 contains the relative efficiency coefficients for The labor distribu- tion and relative efficiency coefficients relate to questions (table) 57. 259 o mo.H mo.m sm.m em.w oeea-m mmaaum beeaum ameaum .mmea-m oaeanm mamaum maum aboanm womanm .ooo oa.m ma.m em.aa mm.w mm.w Tomeaum emea-m meeanm mmeaum Hmea-m moeanm papa-m m ma-m mama-m Home-m .eoz o o o mm.w mm.w pope-m omefl-m neeHum «meaum omeaum moeaum ombaum .xmmm.-m mama-m mmoHnm .poo o o o .w .m eoaa-m mmsa-m mead-m HmeH-N mane-m hora-m mmmaum mmmaum Hemaum mmoaum .saom o o o .m .w mmeanm emeaum msea-m omea-m napalm perm emmaum m ma-m cabana ymmwflnm .wa< 0:.mH mm.e .m m an mm.m mosanm mmeanm Hesaum mmeanm paeflum mead-m mmmaum mearm mmmaum em Hum ease mb.mm be.ma ma.b mm.m m.m emeaum mmea-m cred-m mmeaum mafia-m eoeanm mamaum pmmaum mmma-m .WMMHnm ease m.o: m.me Enema m .w m.w mmea-m Hmea-m mmeaum emea-m maea-m moeaum Hmoaum memaum nomaum mmmaum an: em.ma ee.em mm.mw nw.w .m mmeaum Omea-m mmea-m mea-m aaea-m move-m ommaum memH-m .mmmaum emmwwm .aa< mo.m NH.: me.:H am.m em.m Hmsa-m meea-m emeaum mmeaum mHeH-m Hoeanm mmmH-m sema-m mood-m Mmoeum .ndz o o o em.w em.m omea-m meaaum mmeaum meaum mesa-m coed-m mmmaum mama-m ambaum mama-m .noa o o o em.m a .m mmeeum e:ea-m mmeaum mmea-m Haeaum mmoaum ewma-m mama-m meme-m Hana-m .ese NH Henm m arm : mua oa arm Hasem 5 one Hanan memos you: $30." w mom: 309 o How: 30H 0 Moms ovum numb mofim no 0 Jim no aim no aim no mom 83055 83. .52 flame. mpaoaooddmwm goo amen 30.5 930 30.5 500 30.5 anon Eda meson sedate assesses: one Heo one new no soapsseneoan aeneqoz .oe 250 .omndq no .5302 to o o o o o o momn-m ommn-m mamn-m morn-m mmn-m memn-m ommn-m annum coma-m moan-m amen-m .ooo o o mm.mm mm.mm o o nomn-m mwmnum pawn-m mme-N mmwnnm nemnum mm n-m enmnnm momnum mannnm nwnnum .>oz o o o o nn.m: o oomn-w mmmn-m memn-m enmnnm mmmnum num m n-m n Hum :omn-m moan-m o enum .eo0 0 o o o as.mm mw.me mmmn-m emmnum mmmnum mmmnum nmmnim ommnwm amen-m mnwnum momnum nmnn-m mennum .eaom o mm.om o o o mm.mm mmmn-m mmmn-m enmnum momnum 0mmnum mmmn-m mmmwmm enmnnm mama-m omnnum .mennnm .wa< o we.ms o o o o emmnum mmmn.m mean-m Hmmnum m mn-m nmmn-m mmmn-m mnmn-m nonnam amen-m seenum ends o o om.m o o o .Iwmmn-m nmmnum Newnum ommn-m mumnum mmwnum m n-m mnmnum oownum Farm meannm cash 0 o oo.mm om.m o o mmmn-m mwmnum newn-m mmmnum ewmn-m mmwnum mm Hum nnmnnm mmen-m amen-m mean-m as: oo.oon o mm.mm mm.wm e.:n n:.:n :mmn-m mmwnnm oemn-m mmmnum Hum :mmnum mmmnum onwnnm mmenum mmen-m annum .nm¢ o o .e ew.nn mm.m m .m mmmnnm nwmn.m momnum ammn-m mnmnum mmwn-m nmwnum momnum nmnnum mmenum meenum .ns: 0 o o o o o mmmn-m ommn-m momn-m mmmnum enmnnm mmmnum mmmnnm mownnm omen-m amen-m mean-m .non o o o o o o Horn-N memnum emmnum mmmn-m mewnum nmmnum mnwn-m nownrm napalm mannum neen-m .sse mm ems mm-mm em mm-mm Hm omumn wn pn-mn mn annmn peso: numb gonad oo #3an now: A no 2 numb A no 2 numb HA no 2 numb A no 2 enena won Hanna .eoo .mWom mono ham zone undo keno mpdo keno 962E] kono p.025 A.o.pnoov .2. 251 o o o o o mmo~-m onomum mmmnum mmmn-m neon-m moon-m Oman-m man-m mmmnum anon-m .oon o o ea.n mw.w o nmomum meow-m emmnnm mmmn-m mean-m nmmn.m open-m nmmnum immon-m mnmnum .soz o o o o o omom-m moomnm moon-m emmmwm memnum .pmmn.m wsmn-m mmmnum .xwmmnnm mnmnum .uoo o o o o o anew-m wwwWIN mama-m mwmn-m nemnum mmmnum snmnnm .mmmnnm mmmnum nnmnum .pavm wn.m o ww.m n:.: o mnomum moomrm emmnum mmmnum oemnum mmmn-m bean-m nmmnum mmmnum onmn-m .ma< mm.:n oo.on mn.mn >>.nn 5 .mm anomum moom-m mmmnum nmmnum moan-m nmmnum mamnum mmmnum Hmonnm omen-m anon me.mm mm wrmmn m mmwm: m omwmn m :nmam mnom-m zoom-m m Hum om Hum mm Hum me Hum an aim m Hum om num n-m cane wr.ma mo.me e:.mm cm.Hm mm.nn mnom-m moon-m nmmnum mean-N noon-m mmmnum mean-m nmmnum mnmnum nommum no: mmwm me.mm “mom oo.mm o enomum moomnm ommnwm mama-m moon-m emmn-m mnum ommnum mnmnum .oomn.m .na< o o o a .m o mnomum noom-m mwmn-m nemn-m mmmnum mmmnum n mn-m mmmnnm anon-m moan-m .ndz o o o o o mnom-m 000mum wwmnum puma-m nomnum mmmnum pumnnm mmmnnm mama-m eomnnm .non o o o o o anom-m mmmnum anon-m memnum momnnm nmmnnm mmmnam emmnum mnmnum moan-m .nme me Hence mm mmsnm om mmsem mm mmunm cm seaoz now D 30m numb 30m nmuD 30m numb 30m numb . pnom who”. mno : Fm no: mnou gm and» s2 case a: encased: none gonnom Bone unannom zone Room Smog zone doom 3”lo mono room A.o.eaoov .on 252 mb.m no.m an.m an.m nw.m nm.: mo.m oemum wmorm .m:m-m ammum mmmnm onmnm .momnm omwum annum mmwnm cmmwm mmmum .mmmum enmwm .ooo no.m ao.m mn.: nn. m am. : :e.b om.mn tommum ammum mam-m mmmum ammum momum .momum mmmnm mam-m nomum annum ammum mmmnm mnmim .>oz no.m no.m em.n: an. m o o o mom-m ommum .ueonm mmmum ommum momum omm-m emmwm msmwm omwwm wawum .mmmum mmum mnmwm .eoo o o ow.mm mo.bn o o o emm-m mmmum muowm ammum mam-m somum .mmwum mmmmm Hemum mmmwm annum mmmwm mmmum nnmnm .eaom o o o be.nm em. m m:.m nw.m .lmbm-m emmsm meowm ommum amum .momum nomrm mmmum oemim .mmmnm .mamwm .emmuw mmmwm onwum .ma< o o o o no.en em.m mo.m mom-m mmmum namum mmmam enmum momum momwm nmw-m ommim emmnm memnm mmmum ammum momma anew em.on mm.e o o oe.om mm.mn swam amm-m mmmnm oam-m .mmmum .mnmum aomum mom-m own- mow-m ommwm nemnm mmmwm ommum .momnm ooze mm.mm me.m o o mm.em eb.mm ob.mn momum ammum mmm-m ammum mam-m momum namnm memqm emmim mmwum memum ammum mwmum now-m no: mm.mm mn.mm ee.> an. a mw.m oe.sn mw.:m .Immm-m cmmum mmmum .mmmum enm-m momum omwum memwm www-m emmrm mamnm ommwm .mnmnm mum .naa me.m Hm.mm mm.e mm.e mo.“ mo.m wo.m .1Hmm-m maoum ammum mmmnm mam-m nomum ommum annum mow-m mmmim annum ammum annum momwm .nsz Hm.m mw.: sn.: mn.: mm.m mm.m mm.m omm-m wamum .mmmum ammum mam-m oom-m wmmnm, mam-m ammum mmmwm oamnm .mmmmm .mnwum eowum .nom no.w, no.m an.m nn. m mo. m mo.m mo. m mmmum enmum mmmum mmmum nnm-m mmwum emm-m mew-m mmwum nmwum mmwum emmnm mnwum mowum .ase SCH 30H 30H 30H 30H .30.... 30H RFGOZ who who @96— who who 080 A080 now: d.m non: rum nomD JUN now: #aN numb :.m_ nomD .wmm numb :am as: henna Hanna henna .eoo esenm .smom easnm mane ensnm as: pecan annn< enonm zonw mpmo sono pmonz sonw anoo maopmhm zonc mono non nonmq no nonpdnnnpmnm_hflnpnoz .Hb 253 mm.n mm.n mm.m mw.m o o o mmnnum annnam monnnm omonum weomnm moonum .emon-m Neon-m omonum.mnonum moonum ammum mmo-m mn .omm mm.n mm.n mw.m mn.m o o o mmnn-m mannum nonnsm mwon-m aeonum moonnm mmon-m neonum muonum enonum moonum mmmum ammum an .eoz o o o o o o o smnnum mannum connum.mmon-m .meonnw.amonum mmonum onomnm .mmonum.mnonum noon-m mmm-m omo-m on .poo o o o o o o om.m mmnnum nannum mmon-m ewonum meonnm mmonum Hmonnm amon-m emonnm.mnon-m moonum ammum mam-m m .eaom mo.m o _m:.w mb.e o o em.aa mmnn-m onnn-m .mmon-m monum neonum moonum nmon-m mmonum omonnm snonum moonnm omm-m.mem-m .m, .mo< mm.em wo.en mm.mn eo.mn mm.mm o cm.e: nmnn-m monnnm. emon-m amonum meonum noonnm maonnm emonnm mmmmwm-mnon-m noonum mmm-m esmum e anon ::.om .mm.om mowmm mama om.oo o om.m omnn-m moan-m .mmonum amonnm .mmon-m omonnm meonum.mmon-m amonum mnonum ooonum www-m.mamum ease wmdmm mn.om mn.mm n.nm na.nn o o mannum eonnum moon-m mmon-m neonnm mmonnm neonnm mmonum mmonum anonum mmm-m ammum mam-m m are mm.m em.om on.m. eo.mn o mm.nm o .Imnnn-m monn-m soonum amonum oeonnm anon-m .meonum amonum mmonum onon-m .mmmum.mmm-m nmmwm a, .naa mn.m mm.m mm.s mm.: o me.w: o ennn-m monnum moonum Hmonum moonnm emonum mepnwm mmonnm nmon-m moonum.nmomum mwm-m memum m .naz an.m an.m ao.m no.m o o o .pmnnnum eonn-m «monum omen-m wmonum.mmmnum neonum mmon-m omon-m moonum.{mmmum awn-m mmmim m .non mw.n ww.n nw.m nw.m o o o mannum moan-m Hmonum meonum emonum mmonum meonum nmon-m anon-m noonrm mmmum_mmm-m nemum n .aoe II. 11. Ill Son H Ansoz son son non son monL o no now: no : now: no +~ news no :i newbie no in now: .pnom condo {N case henna as: banner case scene n82 pasAMI. 1:.enepennz non: -aaoo pecan 30no mqmoMNOm Sonw comm oaonm mono hem pnoam mono new A.o.esoov .nn 254 mn mn an on mg m a m m e m m n n mm. om. Hm. awn mmw mmwl, mall, mm», em. .1 1. enmnum onmnum momnum mmnnum immanum mmnn-m mennum anum nmnnum emnnum nannum omnnum mmnnum oom nose mm» am. ami, emu mm. m . am. am. am] mnmmnm momnum momnum mmnn-m m:mnn- nmnn-m eennum nonnnm omnnum Mann-m m.::HH- m:mnn- mmnn-m oom-nmn mm. mm. mm.! mm. em. m . 8. n 3.1 mm. mnmn-m mnum nomnnm Joanna nmnnum omnn-m mennum mmnnum mmnnum rmm. m:nn-m mmnnwm nmnnum cmnunon mm. mm. mm. oosn may mm. no:n am. pm. anmn-m aomn-m oomnum mmnn-m m:mnn- mnnn-m mennum moan-m manum amen-m aennum emnnnm omnnnm oon-me mm. em. mm. mo.n emu mm. mo.n oo.n mm. mnmnum momnnm .omnnum mmnnnm mwnn-m menn-m nannum smnnnm emnnum ommnum menn-m manum mmnnum meunm oo.n oo.n va mo.n oo.n mad, mo.n mo.n oo.n mnmnum momnum mannum nmnnnm emnnum eennum onnn-m mmnnnm annum mannum msnnum mmnn-m.mmnn- omnmm mo.n mo.n oo.n mo.n mo.n oo.n on.n mo.n so.n nnmnnm eomnum emnn-m omnn-m mannum cannum monnum mmnnnm mannum mennum_nannum emnn-m eman- mm 503 mmmH .Hme .uhwm .meD 30H m 30% me5 mm .HO 30H N 30H H hme 30% m .30." .J 308 N Mono :um Son m no 80930 monoe. snowmen: mwno comm Sons memo 595mm nnoo neonh qnoo mean an naoemnm no noeononnnm noemq oenpmnom .me 255 mm mm em mm mm am _om on ma an on ma an n oo.n em. nan. om. om. mm. mm. om. nmwi .Imomn-m nomnnm noma-m emmnum ommnum memn-m momn-m mmmn-m mmmnum newn-m mmmnum nmmnum ammnum oom noeo oo.n em. emu mm. no. mm. mm. Hm. nm. IIeOmnnm oomHum mmmnnm mwmn-m mama-m mama-m mmmnum mmmnum nmmn-m a:mn-m emmnum ommnum mmmnum oomunmn oo.n emu, em. awn] mmwl em. own _ mm. mm. .momnum mmmn-m momnum mmmn-m .memn-m nemnum emmnum emmn-m cmmnum mama-m .mmmnum mmmnum mmmn.m omnunon oo.n saw. um. _am. mm. _wm. em. mm. mm. .pmomn-m mmnum ammnum :mmn-m nemnum oemn-m momnum mmnum mawnum mmmnum mmmnum mmnnm nmmnnm oonmme oo.n m . em. oo.n no. oo.n mm. new. nmw .ymomn-m ammnum ommnum mmmnum memnum mmmnum.wmmn.m mmmnum mnum nemnum ammn-m nmmnum ommn-m maunm oo.n oo.n mm. mo.n oo.n no.n oo.n oo.n emw. momnum-mmmn-m ommnum mmmnum mamnum.mmmnum nmmmnm emmmnw snwnum,owmn-m mmmnum mmmnum mnmnum omnmm oo.n mo.n oo.n mo.n no.n moan mo.n mo.n oo.n momnnm mmmnum mmmn-m nwmn-m enmn-m noun-m ommn-m mmmnnm bemnum ommnum mmmnnm mmmn-m wnmnum mm flan». mmmd .Hmwd 50.2.8 30H m 39H ilnlmmd. 308 m 30H .3 Hmmd. ‘ 30H m .308 : Hmmfi 30H m 3.0M mono -maoo elm rum no pcmam mono hem 3onw mqmonhom 3on0 modem .333 3on0 p.893 mono< A.o.bsoov .me 256 mm am mm mm em mm mm Hm om mm mm am e mm. mm. 8% Wm. _ ad. 84 mm. 8. 84 mmmn-m mmmnum .msmnum nemn-m emmnum emmn.m ommn-m .mwmnum .mmmnum mmmn-m mmmnum mnmn-m oom noeo mm. mm. oo.n _ mm. nmw, oo.n mm. nan. oo.n nmmn-m emm,-m enmnum oemnum mmmnum .mmmnum mamn-m mnmn-m‘ mmmnum mmmnsm nmmn-m emmnum oomsnmn am. no. oo.n em. mm. oo.n no. mm. oo.n ommn-m mmmn-m .memnum ommn-m m mnum mmmn-m mn-m nemn-m mmn-m emmn.m ommnum mnmnnm omnunon no. em. oo.n mm. mm. oo.n mm. man. oo.n ommn-m mmmn-m memn-m .mmmnum nmmn-m_ :mmn-m nemn-m oemn-m mmmnum mmmnum mnmnum mnmn-m oonnme oo.n mm. oo.n bowl. mm. oo.n mm. mm. oo.n mmmn-m ammnum nemn-m emmn-m oomnum mmmn-m .memnum mmmn-m mmmnum mmmnum .mnmnum nnmn-m msunm no.n oo.n oo.n mm. emal oo.n mm. em. oo.n emmn-m ommn-m memn-m .mbmn-m mmmn-m mmmn-m_ mamn-m wmmnum nmmnum emmnum enmnnm onmnum omnmm mo.n mo.n oo.n oo.n oo.n oo.n oo.n oo.n oo.n bmmn-m mama-m memn-m mbmn-m mmmn-m nmmn-m_ eamn-m emmnum ommnum mmmnum bnmnum momnum mm _ comp mmoa non: .3on m 3on : Eopmdo now: vamp coonmm .aonmSO non: oddn ommnmw .EOpmso mono cane cane cane cane no 0.800 I800 I800 l800 _ mmno< mezwm dem UHmwm Pmmzdm #60 #mem p.005.» A.o.saoov .me 257 oo on we a: or on a: m: m: n: on om n no. .mm.n .11. no. mo. bow now won mow .wo. .mennum omnnum mmnnum omennm mean-m nann-m nmnnum nmmnnw, omnnnm. mnnnum onnum oomnum oom noeo no. oo.n mo. mow no. mow .mo. _ mo. mo. onenum woenum nonwwm amen-m ennnam cannum mmnmnm 1mmwmnm onnnum mnenum monnum .momnnm. com-non mo. oo.n no. no. no. mo. am5 now om. neenum emnn1m omnn1m moan-m .mnenum omnnnm mmnnnm. omen-m nnnnm nnannm. nonnum nomnum conunon mm. oo.n no. oo.n no. mo. oo.n oo. oo. menn-m .mmnn-m_ omnnum monn-m onennm .mmnnum nmnnwm men-m ennnnm onnnum moan1m momnrm oonnmo no. _ oo.n oo.n no.n wow, boq1. oo.n no. no. mann1m omnnnm..monnsm nonnnm. nunnww. emwnJm. omnnum moan-m .mnnnnm oonnnm Nonnum oomnum oeunm oo.n oo.n no.n oo.n oo.n mo. oo.n oo.n no. nannum nonnum eonnum omwnum meannm. omnwwm omen-m mmnme onnnnm wonn1m nonnum :omnum ooumm :o.n oo.n 1 oo.n ao.n _ oo.n oo.n oo.n oo.n oo.n oeanum moan-m boenum onen1m meanum omnnum .wmenum nmnn-m ennnam eonnum_ connnm momnrm mm 0.93. mmmfl _ nomb 930.800 ammné non: noxonm name. lnodom nmmD 3on .n nogmony 3on N 3on H mono 3on nqoo n28: 3on 4 .m.m 3on 302 no nouns. 3on teen: 3on zoqnz mono< -osnz _ soon: 1 pmo>nom somehow pmonnmm new pmo>nnm ommnnmmOmmwnmm A.o.psoov .me 258 o oon oon oon oon 08 com 8m 8m oo: 03 oonum omn1m annum annum oonnm conum n:n-m mmnum mmnuw annum oon-m nn can» moon noon oon oon con com com oom oom com co: cow. com nonnm omn-m ben1m emn1m .mm1um onnum oenum nmnum mmn-m mnnum eonnm mnunn noon oon oon com com com 0% 8m 8: 08 08 8M monum amnum men-m monum eonum wnnum oon-N omn-m nmnum mnnnm men-m mn no>o noon 0 8n 8n oon oon 8m 8m on com 03 com monum mmn-m een-m oonnm won-m nannm mmn1m omnum omnnm nnnnm won-m nn 3% mafia .608 cm oon 8n con 08 00m 8m 8m 8: 8: 8H nonnm mmn1m menum nmn-m oonum annum emn1m wNn1m onn-m onnnm nonum mnunn .oo3 o oon oon 00m com com com owm one an: com oon-m nmnum manna mmnnm non-m mnnnm mmnnm annum .mnn-m oon-m oonum mn nose .eoa o cm on oon oon con oon com com oom oom omn-m omnum nen-m men-m monum annum omn1m mmnum annum wonum oonm nn mane moo .x0 o oo oon con con com com com com 00: co: monum oennm oenum nmn1m mon-m men-m emnum omnnm 1mnnum eon1m worm mnunn .xu co oon con 8m oom com com com com 8: cm: emn1m wen-m omnum oon-m non-m annum mmn1m :mn1m onnum mon-m noum mn nose .xo meadow I nonpoddonm mam: an owqdno nn on o m a .m o a. m m n coon coon ooob coon ecoo oooo com: 000: ooom ooom ooom Aooov nan on on on on on on on on on on on eona undue coon econ ooom econ coco econ coo: OOom ooom comm ooom no owe menooon ansno no1no>on an omncno noson unon resonance ooneneqenn ensue unnnmnonpdnum nonpouoonm .mN. 259 mn nn on o m a m o a m m n e. 0 8m 8m 8m com 8m 8m 8m com 03 8: oon pom-m nomum oom1m oem1m memnm emmnm nmmsm ommum onm-m mnm-m somum nomnm nn many mood noomuuoz com com com 8m oom com 8: com 08 om: coo 8N. 8mm 0 mm omm mm m mum mmmnm ommm Ham-m mnmum mnm-m 8mm 8mm mn-nn 88-82 8m 8m 8m 0% com 03 com 8m coo 8m 08 oon. - ommm oomum momum a mnm numnm ommum omm-m lm1m.m...m 5mm nnmum mom-m oon-m mn no.6 .8818: 0 8m 8m 8m 8m 8m com 03 03 03 cm: on: emm-m mom1m momum bem1m oem-m amm1m wmm1m mmmum mnmum onmnm nomum wonum nn an». mmOH .dflfilJnm 8m oom 8m 8m 08 com com 03 oon So So 8a. m mum ammum nomm o m-m ommm mmmum nmmm nmm-m onmum oomum mom..m eon-m mnunn ous-dam oom 00m oon 0o com om 00m oon cor com oon com momm comm comm 13mm mmmnm mmmum ommm ommum :nmm momum mom-m monum mn .88 627.83. noegomVaoneoaoono nnnz on among 82. 82. 8mm 88 8mm 88 8o: 08: 8% 08m 8mm 88 A88 131% Acorn omunon no n33 an «wanna mandpuqoo acnpnmndna ommnom 3:5 750 260 Production Adjustment for Culling Difference between number ‘grawn and required_culling rate 76. (2-625) 2-626) (2-627) (2-628) (2-629) (2-630) (2-631) (2-632) (2-633) (2-635) (2-635) (2-636) (2-637) (2-638) (2-639) AAA/x HHHEH A'QU'IU) omooxlmmrwmw Mature Equivalent Coefficients Production Adiustment (lbs,) (2-650) 500 (2-651) 1000 (2-652) 1500 (2-653) 2000 (2-655) 2500 (2-655) 3000 (2-656) 3500 (2-657) 5000 (2-658) 5500 (2-659) 5000 (2-650) 5500 (2-651) 6000 (2-652) 6500 (2-653) 7000 (2-655) 7500 ——‘ Agéflof Freshening (months) __q (2-2867) .23 (2-2868) 25 -.35 (2-2869) 36 “.51 (2-2870) 58 - 59 (2-2871) 60~-.11 (2-2872) 72 - 191 (2-2873) 102 - 112 (2-2875) 120 - 131 (2-2875) 132 - 155 (2-2876) 156 -.259 M.E. Coefficient;:— ___ (2-2877) 1.35 (2-2878) 1.26 (2-2879) 1.15 (2-2880) 1.06 (2-2881) 1.02 (2-2882) 1.00 (2-2883) 1.01 (2-2885) 1.03 (2-2885) 1.05 (2-2886) 1.10 77. Milk Lactation Curves Percent of Total Lactation Production Produced Each Month 261 Month of Calvinngnterval (month) Lactationa 11 12 13 15 15 16“ l 2-2763 2-2780 2-2797 2-2815 2-2831 2-2858 5 5 5 5 5 5 2 2-2765 2-2781 2-2798* 2-2815 2-2832 2-2859 12 12 ___12__ 12___ 11 11 3 2-2765 2-2782 2-2799 2-2816’ 2-2833 2-2850 15 13 13 12 12 11 “5? 2-2766" 2-2783 2-2800 2-2817 2-2835 2-2851 13 13 12 11 11__, ll 5 2-2767 2-2785 2-2801 2-28187' 2-2835 2-2852 13 _412 11 11 11 10 6f“' 2-2768_' 2-2785 2-2802 2-2819 2-2836* 2-2853 _____ __, 12 _12 _. 11_, ,__10 10 10 7 2-2769 2-2786’ 2-2803 242820 2-2837 2-2855 ___ 10 10 __j1_ 9 9 £2 78 2-2770 2-2787 2-2805 2-2821 2-2838 2-2855 9 ,9 8 8 8 8 9 2-2771 2-2788 2-2805 2-2822 2-2839 2-2856 7 7 7 7 7 7 10 2-2772 2-2789 2-2806 2-2823 2-2850 2-2857 5 5 6 6 6 5 11 2-2773 2-2790 2-2807 2-2825 2-2851 2-2858 0 0 2 5 5 5 12 2-2755 2-2791 2-2808"’ 2-2825 2-2852 2-2859 0 0 2 5 5 5 13 2-2775 2-2792 2-2809 2-2826‘ 2-2853 2-2860 - 0 0 1 2 3 l5 2-2776’ 2-2793 2-2810 2-2827 2-2855 2-2861 - - 0 0 0 1 15 2-2777 2-2795 2-2811 2-2828’ 2-2855 2-2862 - - - 0 0 0 167’ 2-2778 2—2795 2-2812 2-2829 2-2856* 2-2863 - - - - 0 0 l7 2-2779 2-27967' 2-2813 2-2830 2-2857 2-2865 - - - - - 0 a. Assumes freshening takes place on the 15th of the month. Lactation month = Simulation month - month last fresh +'1. 262 78. Frequency Distribution for Lactation Length Calving Interval Length of Lactation (Months) impnth§)_ 11 12 13 15* 15 16 12.0 25 60 8 5 _3, 0 _12.1 23 59 9 5 5 0 12.2 21 57 10 6 5 1 12.3 19 55__, 12 7 6 1 "T 12.5 17 i2 L 15 8 6 2 12.5 16 50 ‘ 16 9 7 2 12.6 15 57 18 .__ 10___ _8, 3 12.7 12 55 ‘ 20 11 8 3 12.8 _9, 55 22 12 9, 5 12.9 7 52 25 13 10 5 2-655 2-656 2-657 2-658 2-659 2-660 13.0 5 50 25 15 10 __ __5 13.1 5 36 l 26 18 11 5___ 13.2 5 32 l 26 21 12 5 __13.3 i 27_ 1 29 22 13 5 _ 13.5 3 22 32 25 13 6 13.5 3 19 32_ 26 15 6 13.6 2 16 _32 g9 15 7 _137 4_1 M. L 0 fig .JZ. 7 13.8 4_ 0 12 28_ __ 36__ 16 _L8 13.9 o 10 I 27 38 _1_7 8 __15.0__, 0 7 ,__25_ 50 19 9 79. Fertilizer Rates and Relative Yields 263 ____ Agricultural SubregionLL 4_ Crop 39 *50 51 55 39____j50 __51 55 Lbs. of N, ngiand K20 Relative Yield Corn 2-2539 2-2567 2-2595 2-2623 2-2651 2-2679 2-2707 2-2735 0 0 0 0 100 100 100 100 2-2550 2-2568 2-2596 2-2625 2-2652 2-2680 2-2708 2-2736 138 150 138 127 151 158 158 157 2-2551 2-2569 2-2597 2-2625 2-2653 2-2681 2-2709 2-2737 261 273 261 215 179 163 170 167 2-2552 2-2570 2-2598 2-2626 2-2655 2-2682 2-2710 2-2738 321 333 321 275 187 169 175 175 Soybeans 2-2553 2-2571 2-2599 2-2627 2-2655 2-2683 2-2711 2-2739 0 0 0 0 100 100 100 100 2-2555 2-2572 2-2600 2-2628 2-2656 2-2685 2-2712 2-2750 78 79 78 0 138 152 155 100 2-2555 2-2573 2-2601 2-2629 2-2657 2-2685 2-2713 2-2751 125 122 122 0 156 163 171 2-2556 2-2575 2-2602 2-2630 2-2658 2-2686 2-2715 2-2752 158 152 152 0 167 177 180 264 79. (Cont'd.) _ Agricultural Subregion erp» 39 5o 51 55 39 ‘50 w51 55 Lbs. of N, P205 and K20 Relative Yield Field 2-2557 2-2575 2-2603 2-2631 2-2659 2-2687 2-2715 2-2753 Beans o o o o 100 100 100 100 2-2558 2-2576 2-2605 2-2632 2-2660 2-2688 2-2716 2-2755 57 57 57 57 116 119 118 115 2-2559 2-2577 2-2605 2-2633 2-2661 2-2689 2-2717 2-2755 91 91 91 91 121 123 123 121 2-2550 2-2578 2-2606 2-2635 2-2662 2-2690 2-2718 2-2756 120 125 120 120 125 126 126 125 2-2551 2-2579 2-2607 2-2635 2-2663 2-2691 2-2719 2-2757 156 157 156 156 126 127 127 125 2-2552 2-2580 2-2608 2-2636 2-2665 2-2692 2-2720 2-2758 156 167 156 156 128 128 128 126 Wheat 2-2553 2-2581 2-2609 2-2637 2-2665 2-2693 2-2721 2-2759 0 o o o 100 100 100 100 2-2555 2-2582 2-2610 2-2638 2-2666 2-2695 2-2722 2-2750 67 79 67 67 152 150 155 156 2-2555 2-2583 2-2611 2-2639 2-2667 2-2695 2-2723 2-2751 103 115 103 103 180 175 179 185 2-2556 2-2585 2-2612 2-2650 2—2668 2-2696 2-2725 2-2752 138 150 138 138 198 195 199 205 2-2557 2-2585 2-2613 2-2651 2-2669 2-2697 2-2725 2-2752 175 186 175 175 203 205 205 212 265 79. (Cont'd.) Agrieultural Subregion Crggr 39» *50 ‘51 _‘55 39 ‘50 51 55* Lbs. of N, 2205 and K20 Relative Yield Oats 2-2558 2-2586 2-2615 2-2651 2-2670 2-2698 2-2726 2-2753 0 o o o 100 100 100 100 I 2-2559 2-2587 2-2615 2-2652 ' 2-2671 2-2699 2-2727 2-2755 155 l 155 155 102 I 151 138 137 132 2-2560 2-2588 2-2616 2-2653 2-2672 l2-27oo 2-2728 2-2755 220 232 220 155 ‘ 167 I 161 158 155 2-2561 2-2589 2-2617 2-2655 ! 2-2673 2-2701 2-2729 2-2755 260 172 260 250 172 I 167 165 173 Hay 2-2562 2-2590 l2-26l8 2-2655 2-2675 2-2702 2-2729 2-2756 0 o o o 100 100 100 100 2-2563 I2-259l 2-2619 l2-2656 ' 2-2675 2-2703 2-2730 2-2757 86 ~86 86 86 I 183 t 290 250 186 2-2565 2-2592 2-2620 2-2657 2-2676 12-2705 2-2731 2-2758 153 153 153 153 1 206 330 290 221 I 2-2565 2-2593 2-2621 l2-2658 I 2-2677 2-2705 2-2732 2-2759 202 202 202 ‘ 202 217 I 360 310 236 2-2566 2-2595 2-2622 2-2659 2-2678 2-2706 2-2733 2-2760 255 255 255 255 228I 370 320 250 l 80. Animals and Acres Handles by User Defined Machines (a) Machine Capacities 266 Kachine Code Livestock‘__ Numbers Crop Grow Acres Harvest Acres 7 8 ll 20 39 58 70 76 110 80. (b) Tonage Handled by Individual Silo Unloaders (2-2257) 2000 (2-2258) 2000 (2-2259) 2000 (2-2260) 2000 (2-2261) xxxx (2-2262) xxxx (2-2263) xxxx (2-2265) xxxx (2-2265) xxxx (2-2266) 2000 (2-2267) 2000 (2-2268) 2000 (2-2269) xxxx (2-2270) 2000 (2-2271) 2000 (2-2272) 2000 (2-2273) xxxx (2-2275) xxxx (2-2275) 2000 (2-2276) 2000 (2-2277) 2000 (2-2278) xxxx (2-2279) 2000 (2-2280) xxxx (2-2281) xxxx (2-2282) 2000 (2-2283) 2000 Silo Size (ft{l 12 l5 16 18 2O 25 28 30 Tons Handled (2-2285) 106 (2-2285) 200 (2-2286) 261 (2-2287) 528 (2-2288) 583 (2-2289) 827 (2-2290) 975 (2-2291) 1290 .pcoaoammmoo esp o>ond copmaa mad gonads 039 p0 mpaMao Hsom pmna map haqo .opmamaoo op op um an Umcmomnm on page manna man» ma hoganq moapmoamapqmca £08m .n .mmoao aadm moan Ahawc 039 an cohadvoa nonawn can can mmono wqanmm msamohhawc on» an cohadoma gonads map mo aqaaxda on» on op cmESmmn on aaaz umaaddoa mModhp can mnOponnp mo nmgazq 039 .8 ma 5 ma 3 i mammaaaoa m m N. m m a m m a occm ooom ooom ooom ooom ooom ooom oocm coom ccom 000m 000m cccm ccom Iwmam mam cmam Nwam umamemam maam caam moam amom mwomxmbom owom mmom :mcm om wmom omom oa Mofihp.wa ooom coom coom ocom coom ooom ooom ooom oocm oocm occm coom 000m 000m Mushy Inmam Ram Sam 3% mmam mmam SAN 8am aoam 88 £8. Row 86m 68 mmom 30m smom mmow m 9-83 o o :mamxmmam w:am o:am mmam #mam.maamxmoam ooam mmom qmom whom mwom o m mmom :wom mmcm mom IIm 00m omm com cm» com om: coma coma cow ooza ooza com com com mwam mmam kaam omam amam mmam maam hoam mmom amom mmOm mbom bmom mmom amcm maom mmom hmom m +ooa mu: mmm mva 0mm _OOm cmm lccma coma cow 003a oosa cow com com mwam :mam oawm mmam omam mmam maam moam mmmm omom mmmm arom‘mmomymmom omom mmbm :mom mmcm :II, ooanaw cm: com oma com Acm: com coma coma cow oo:a ooza com com com amam mmam mwam pmam mmam amam maam moam boom mmom awom mhom mmom hmom mqom aaom mmom mmcm m omJHw om: com cma com cm: com coma coma cow oosa oo:a com com com omam mmam.maam mam wmam omam maam mcam mom wwom omom mhom.wmom7mmom7muom ozom mmom :mcm m kbmuadi cm: com cma coo cm: com ocha coma cum coma oOma 00ma ocma cow a: omam amam maam mmam bmam maamlaaam moam mmcm bwom mwcm abom mmom mmom >:cm mmom amom mmom a soap mmoa D .zomm 30h¢,309m 3. 30mm SOAWIkonm.ca m \Im h ImI m a, m m a coco mcasomz cmpmo>hom mammo _ sapmhw anadm oaanooz ho .m.m aanm mouod. maanmm monoGOU mdamdom mmhh 09am dwmogfim Umhvboo flownoddpm mmoog EH53 UHOU 33 UHOO mpcoEmocammm banana mkoo tango xaapdz mm: imcanooz mampmhm aoonammnaaa QHQ .: ma manna wasp you ocoo Hmaasn Nanpda one .d 272 a a a a a cm oaoamaamm aoaaam a a a a a a a a pm psoaaascm sham ,a cm meanness aaaz mm s = ccom :m = = 03H a a a a mm = = coca a mm aaom .aoo com a am aasm .aoc coo cm aasm .aoc cc: mm = oaam mesons QN : 2 a om hm hmcdoaab oaam .mm mm Hoodoanb oaam .dm oa mo mo co Imc mo :0 mo mo ac a c aaaopm aeom c eoa eaamaa caaaaa aaoem aasem aasam baa oooc osaaooz omam adqoap ammo aadpm aaopm omam. omam no amazon: nam>noo mnamdom comm omam cmmoaonm cmno>oo moanoqcpm omooa and; oaoc sass oaoc mpnmswooamom anama mkoo haacm ea.a.oaooo .ac 273 mm cm s eaanm .JN H H H 0.: : :WHIJ mm m: : :JHI: mm a a a 3 .. ..maum luwm I III II II ma 30am elaaum ON mi .30Hm“ ..QHIN OH 4: 3OHAH :JHIN ma a a a a a a a a a m: 9.98an Na a a a m: ~035ng 30.7w ma 1 li a a II 1H II 1 aa 833 uado Sonia m a a a a o: Mandingo 309nm ea mm ma ma Mocha mnaaamoaqc ma ma moms; mqaaaoaac a II I 1 I aa ca ca gonna omamaa. mo a a a a a a a a a m Mocha manuaoam co w No N. Polo II II II c mo m .. .m.m +ooa no a a a a .. .m.m coauaw mo a a a a a a a a a m __ .m.m ownao mo a a a a a a a a a m : .na.m cola: ac a a a a a a a a a a noponaa .m.m claco ma aa ca mo co co mo :0 mo mo ac ..a D 30.70 gonna 387m b 30.7w 38H 30am c 5?an 30.7: 307m 300 oqagooz 23730.8 980 mozzsoac choc aaamaanzoac 980 0530.62 Manama on: Eocagomz mSopmtam 3090 may .mw .m ma macaw 9.23 no.“ 350 Mocha: Kaunas one to ma. aaaam 5.95 a a a a a a +1. ~380an ......aom a a a mm .. 30.70 a a a ma. .. 30.7: a I II II a II a ab .aopqdam spasm or mm : aim we : .om H H H PW : .@H a a III a a a a Em zoaamm .ma mm 30.34% .m . :w : .ma a a a mm : .ma N0 = 1.2” a a a a a a aw omam .ma 00 : .0H mm omaa .w mm. Fm : :mle II hm 30am eaanw mm .. ..caIH :m = data. mm .. ..manm mm = .3le a a a am 30am emanm ma aa ca mo Imo ac bo mo ac mo mo ac a D aaoanmlaoaialzoam D 30.7% Sondra zoaam D Boahm 30.7: 307m mcoo masonecoao Faoc Icaamzéaoac Faoo Haaflflkoac Raoo 05:32 aa.s.asooc lac .mm N Om : ...:Hlm am a a a a a o: s ecaI: mm m: : :JHI: NN Nu: : ..lem am caI soam seawm cm m: a .-cam mH +3 .30HnH ...:HIN ma a a a a m: aoanaam NH Nd : 30Hl0 ma aa .385ng 33H lwIa. II II c: nonwbapaaac 309nm :a mm ma ma manna maaoooaac ma ma aomoz maacooaac IIHa aa ca ca gonna omaoa mo a a a a a a a a a a m xoaaa as-aoam 5 ”a co m ac a Imwc m. mo m .. .mfi +ooa ac a a a a a a e .m.m coauam mo a a a a a a a a a a m = .m.m_cmuac mc a a a a a m = .m.m cola: ac a a a a a a a a a a a nopooaa .m.m csuc .I, Imm am om mm am. mm mm am cm (ma ma aa ca ma :a ma a c ammac .ma .ooz c .ma .ooz .ma .ooz 2 .ma .ooz c .ma .coz ococ moaned: Goad—dam Wfidmmm 562 anon/Ia. 90903.00 .amnfiuw-Wmm OGHQOdS IEOU #omRHQ 30.60 me0 39am Rama» maaamw aasm cosam mono aom Naapmz mm: haocanomz mafipmhm 30.5 39 .mw 276 .m ma canon wasp now «coo nonasq Kaunas one .6 cm a a a a a ma aaaac 598 a: a a a a a :a aoaooaam.fio.a m: ma = son-c B: NF : 32-...— I ma aa nopooam soaIm 3 ca 4.: 00 : ..JN as cc .. .8 m: a a a a a ac .. .ca aa a a a a a mm 82am .ma 0: mm tonadm .m cm am a .ma PM N0 : cdH mm a a a a a am ooaclema mm 00 : uOH am on oaac .m mm mm mm Fm : :0le am IImm soamItaaum om mm : Swflnlh mm :m .. ..aaua mm mm .. ..canc EN Nm = ...:le mm a a a a a am“ aoaa ..maum mm am mm am am mm mm am om ma ma aa ba ma aa maI a c 398 53 .82 c .ma .8: 2 .ma .8: 2 .ma .8: c .ma .32 38 3382 noanm mcacomm has aaam¢ MCQOpoo amnfiopmmm amazed: IEOO vomhaa BOHG mpdo SONG Roma?» moaamm adsa unmam mono Mom oa.o.oaocv any .mc 277 mm Om : ...:Hlm am a a a a m: ._ scald mm m: = eaaa: mm a: .. ..caum am I I ma roam baaum ON md : ..WHlN mH .4: 3OHAH :JHIN ca a a a a a a a a a ma aocoaam NH H H H H N: : 38.-0 Loa a a a a LaI aopofiwamm noun: ma 3 nova: capo soanm :a mm ma ma aoaaa maaeooaoc ma ma , sows: moaaooaac aa aa ca ca Mafia ammoa mo a a a a a a a a a m Mega 9..on co m ac a ho b mo In. .. .m.m +ooa so a a a a a .. .m.m coauac m0 H H H H m : oMom OwIHW mc a a a a a a a a a m .. .m.m 8.3 ac a a a a II a a a a a notch... .m.m 91c ma Iaa oat aaI mm IaaI an Ina an mm mm aw oa Mal a D 30.7% Fonda D 30.7.0 ton-An 39.70 ROHHH D 5»ng 38H D ....Taflh 0.000 2550.62 28a. 3090 maopmom 3: 05:. has: 30.5 madam caoah .aagm 333: 5695.6: no.5 bum Names: on: bagged: 3.8936 30.5 on .mw .m ma cannon man», no.“ 350 .385: campus 25 ..c cm 3. aaann 5.98 m: a a a a a i. ~330an £73m m: a a a a ma .. 28.6 a: a a a a ma .. would E: III aa nopadam tonnm 9.. ca +3 mo .. .:m a: co .. .8 m: a a a a am .. .ma a: l l a a a J a mm bayou .ma 0: no gm N mm #0 2 uwH mm a a a a mo .. .ca Nam N0 = c+~H bm a a a a Hm omam ama mm 00 : -OH 1m 0m Oman .0 mm on Nm hm : :0le am mm 8am __aHIm cm mm .. ..maua mm :m : ...:HIN. mm mm : :mle EN NM : :JHIM cm a a a a a beam ..maa ma aFII ca mm) ma. 1 aflm Low InIn lam mm mm aaI cm mm a D 30.?” 38.-m D #086 koala D 30.7.0 koala D 3086 koala. D .Paona cacao «canon: ugh. No: 3.3. Na: .aoEBm Samoa: 395 333.com 30.8 363 caoam Ramadan: no.6 ham .A.e..eooo loo .ma am :w .aoo choc soaum mm a mm .Moam choc konum mm a ma .Moam choc koala am a ac A588 .cm naoc spasm mm a a a cw nwkoam mm a ma ._ .md noaum am a ma Hammono 309nm IINmm I. a Na hommono_3oana ma ma ma m: HohdHMm N..H N: s 30H lm I.ma I. ad. hapd>apadc soaua ma 0: novn>apa50 39H...m :a mm ma ma Mafia anacooasc ma a a a ma , homo: maaoooadc o, ”m aa I. aa ca ca gonna omama mo a a a a a a a a a m Moshe mfiuaoam mo m ac a be I Iw mo m .. .m.m +ooa :c a s .m.D coanaw mo a a a m .. .m.m chac NO H H H H H H H N : £on OOIHJ ao I. a a a a II. a gouache .m.m oawo aIaI ca mm mo I ac bc mo Ho mo mo ac a D unannoo ocansoc moam Moam _EopmSU D .m.m sounm koala ECPmSU ocoo oqanooz zonIm zonnm konnm koala 309nm omaQOdE pmm>Hcm nacho choc pom>hnm ommaam choc xaapdz omD hawaanooz mampmhm pmo>hdm Adv .mw .m ma macaw man» How ocoo ampasn Naapda one .6 mm oaa mm ooa 0.88835 aoom am ooa oaapaoo aoom Om FOH : : 30.7.0 ma ooa #2 aasm comm 309:: Da mca haoac soaumaom aom a: acadumono.30nhwqmcm ham ma mca absoa pacaoc ma mca Sam aa aca ago: mu, ooa Hoam>noo_302 m: mm, aoaw>mam mom a: mm mzownz ca am aoaom om cm nausea 39883 lmm no 856.883 .md .Iaa am am agoaosaz .m.m .ma cm mm 926.883 can .ma mm mm .8385. oaom .Jm Hm : : -:H mm a a II cm 2:88 cacao .ma mm mm osapaoc aaoac .ca am a mmaaaoaovam 88 8.7m cm a a a a am 888a”... 898 mm a a a a co coma: saoac mm mm, onaaaoo choc Bonum aa ca mo mo ac cc mo ac mo mo ac a c osaoaoc osaoaoc aoam aoam 888 c .m.m aoanm son-a aopaac oooc osaaooz 3on|m tonnm koanm noula koaum oqanocz pmmbhmm cacao choc pmo>hcm mwoaam choc éotbocv 3 .8 281 E :m 338 32$ ON mm : : 3OHIN mm mm £on 9:5 38H :m .5 660m goo bonum mm H H H H cm .33on mm H 2. .md Baum .8 H M? hommono tonum cm H H E. nommogc wouuH mH E. g m: uohdnmm S m: .. rogue bH HF 8%: pHsc 397w mH 3 nongpHsc tonum :H cm H mH Hog 338?: NH H H H H NH coma: qucuoHac HH HH S 3 Hana «was ac m H33. “Ha-H03 cc m c H. co 0 mo m .. .m.m +03 :c H H .. .mé ccHuHm MO H H H H. H H m : 0&0: OCIHW «c H m .. .m.m 8qu 8 H H H H H H H 1H H 8889 .m.m 91c Hm hm mm Hm cm @ bH LAW cH HH MH HWH H c Ammo moUnc mono mono 5.3m 23% 3o» c :25 3.29m a3 88 .33 sound .m.m sounm 39H..." oHMIm vacuum :36 Odom unnummanao 03:32 39833 to: flog mlmflflm No.8 ham 05 9.800 puwgm fic «fiasco vnobhmm pg c3932 on: hugged: mfiopmhm puosm 3v .3 282 .w mH medp man n0% ocoo umnad: ”Hands 039 .w mm OHH mm mcH A.Haocv.pp< scum Hm wOH qunaoo ccom Om FOH = : 30H:@ m: 00H pcmanodpp< .HHdm admm tons: m: H H mOH Ahommono konnmv udmm.>cm c: H :OH Nuommono konachdmm ham 0: H m0H nonOHpchoo m: H NOH «mam :: H HOH 99302 mall 00H momm>qoo_3oz m: H H mm gopm>on Ham H: H H mm mqowdz c: H H mm umHmm mm mm Hmchna konchz mm mm Hozonchz .m.m .:H mm :m\ = .m.m .mH cm H H H mm nmxoncaHz cam mm mm nmaoaae mHmm Jm Hm : : ..JH mm H H H H om .aoo quuw .mH mm mm .aoc quuc .cH Hm mm cdmm choc zonum 0m H H H H cw uouu>on qu90 mm H H H H mm mcowmz qunc \mm mm qunaoo tonum Hm mm mm Hm cm mH wH NH me mH HH mH mH H D mono mono mono mono D Aacnpm kwnpm Sop D. kmnpm sappm Hep mcoo oanocz sonuH .m.m aonnm SounH dem cwmnmm undo oHdm cwmnmmnmao qunodz zouchz 302 mcHDEOU ocHasoo pmo>hmm mmcHHm mono mam pmm>pmm pco pmm>hdm pwmnz GA.U.pnoov ADV .mw 283 um :w mcHnaoo apoo konlm mm mm : : 30HIN mm mm anOHm quoo onIH :m Hm ccoquuov 309nm mm cw nmonm mm mu nmcconc .m.m soy-m Hm mp nmmmono zonlm cm bu nommcnc_souuH mH mu mH m: nmhwnmm NH H N: : scale \mH H H HH, nonm>HcHsc zomuw mH c: moum>HpHsc :ouum :H mm MH mH Mocha manmoHsc NH NH dowdz wchdOHQD HH HH cH cH Hanna mwncH cc H H H H H H m Hague gs-H0Hm co a no u lmc mum mc m = .m.m_+ccH :c H = .m.m ccH-Hw mc m = .m.m cmch mc H H H H H H m = .m.m ccuH: Hc H H H H H H H H accomna .m.m cch mm mm mmw mm Hm cm mm mm mm mm mm H D maHQsoo Ecumso D _3ouuw zohu: mchEoo D uwaouna .>coolzoz uHmm econ uaHnodz .nsoo .gaoo acumdo oHdm Mekonne mxdm qunodz pmc>nmm campsom .1, exam cam HHcm soucaH: somcaHz 30: pmm>ndm scum cHon pmo>hdmlwdm anpcz omb.hnquDOdz mampmhm pmmbhdm HOV .mw .0 0H oHnwp 0.39 no.“ 0000 000,5: x2008 «HS. :0 281+ mm cHH mm H mcH H.paocv.nompp<_nmmm Hm H DOH 00.3800 0.03 Om H. FOH = = : .30le a: H H mcH .pp< HHsm ccom 309-: mmml mcH Hmonc scmumvcmmm 5mm c: :cH Hmcnc somuHchom Ham 3 H mOH 900033000 m: H H H H mOH 003m +3 H HOH H0302 mcH H OOH “059500 202 m: H H H mm mopa>mHm.Hmm H: H H H mm macwmz c: H H H pm ponm mm H H mm 900.39 30.853 pm mm .HmkoncaHz .m.m .:H mm H mm, = .m.m .mH cm H mm nmzomchz cam mm H H mm nmsouse «Ham 1m Hm : : ..JH mm H H cm qucaoc chmc .mH mm mm oaneoc chmc .cH Hm mm AcHwnwvcdmm €00 Son-m om H H H H H 5 .HofizfiHm 50.5 mm H H H mm 000mg» :Hdho me mP 039800 0.80 kohnm mm mm mm mm Hm cm mm \mm mm mm mm H D 003500 50350 D 30.70 307: 053500 D 0030.39 50001302 oHdm 0000 0030a: .9800 .9800 800050 3.3 0030.89 83m 0:30.02 pmmtwm admphom 93m 000 HHdm 30.833 30.553 302 pmmzwm 50m chHm pmmtdm 060m aAH.paocc Aoc .mc 285 PART III There are two ways that changes in the farm firm situation or management decisions can be reflected. One way is by changing the value of some of the coefficients in Parts I or II. The second is by purchase or sale of assets. These changes are accomplished by data in- put on Part II of Data Form.l immediately following input of the data required in Part I. Each numbered set of two lines on Part II of Data Form 1 is used for one record. Only one date, parameter change or man- agement decision is to be placed in each record. Date Records The date (month) during which each parameter change or decision is to take place is indicated by the date record which preceeds it. A date record has zeros in the first two spaces, the month of the year the change is made in the third and f0urth spaces and the last two digits of the year of the change in spaces five and six. For example, if the first changes are to occur during'March of 1972, the first record would appear as follows: month year 1. I 0.0.0.3.7l2| ....I, .§ 5 10 This record would be followed by decision records indicating the changes and decisions to be carried out for that month. All changes (decision records) following a date record should be listed in the order in which they are to occur. Only one date record is to be entered for each month changes are made. No date record is required for months in which no change is made. 286 Identification Numbers Each coefficient in Part II has an identification number just to the left (in parentheses) or just above (when date is in table or matrix form) the coefficient value listed. Similar identification numbers are placed to the left of the line or above the space provided for most coefficients in Part I of this manual. Each of these identi- fication numbers consists of two parts separated by a hyphen. That part of the number to the left of the hyphen is the identification number code. When identification numbers are being entered on Data Form 1 the code part of the identification number must be entered separate from the rest of the number. For example, if the identifica- tion number is 1-29, the code part of the number is l and the number would be entered in the proper spaces as code rest of number ”NW 4 11L I l2J9J Those coefficients in Part I which have no identification number can not be changed after the initial value is entered. In.most cases these coefficients are required to specify the initial situation for the business but will normally change through time. Examples include the beginning inventory values which are of value only as starting values. There would be no reason to specify a new beginning inventory after the first year. 28? Changes and Decisions There are eleven different types of changes or decisions that can be used. Each type of change is identified by a number and represents a different purchase, sell or parameter change action. The eleven types of eta nge are: Number Type of Change 01 Change in value of a coefficient 02 Building purchase (detailed) 03 Building purchase (brief) 04 Machine purchase 05 Machine sale 06 Livestock purchase 07 Livestock sale 08 Land purchase 09 Land sale 10 Machinery inventory input (detailed) 1.]. Machinery inventory input brief) For each decision or parameter change, one decision record must be entered indicating the data necessary to make that change. Each type of change and the data record required to represent that change is discussed below. The numbers below a data line indicate the spaces or columns in which the data is to be placed. 1. Change in Coefficient Value Data required: (a) Type of change number . 0 I1 I 2 I (b) Identification number of coefficient changed . I i I | I | 5 O (c) New value of coefficient | J l . L I L. J . Is” 20 an Example: ,_ J 1". Ll]- . L L2 19 L I A 1 1" o o 5 10 15 20 25 w b 288 Explanation: This changes the coefficient indicated to the new value entered. The new value will be used for the month in which the change is made (except for decisions entered prior to this one) as well as future months. Building Purchase4(Detailed) Data required: (a) Type of change number I OIg 2 (b) Building cost Lily, _ , LII 5 10 (c) Expected building life (years) . .. 12 (d) Loan period (years) I I 14 (e) Interest rate I I I I 1 17 (f) Loan type I l§ (g) Months payments made I | I II II I I ' 20 23 25 27 29 30 (h) Building capacity coefficient changed2 Lg: A . 1 . . 32 35 37 (1) New value of coefficient - _ ‘41 _ J_, . , 42 #5 51 (j) New value is O - new total capacity 1 - amount added to capacity I [I 41 Example: LQZ..1.QQENZQL11.6.LIZ.QLLQ...I 5 10 15 20 25 30 Linn.n5a..li.i.il...aerrni..cl 35 40 #5 50 55 60 1. 2. Use same loan type definitions and entry methods as shown in question From question 25 Part I. If more than one is changed, list the addi-' tional ones with number 01 changes. Leave blank if no capacity coef- ficient is changed. 289 Explanation: This will purchase the buildings and change the value of buildings, the financial position, the capacity value and the depreciation for this and future months as indicated. 3. Building_Purchase(Brief) Data required: (a) Type of change number I 2 (b) Building capacity coefficient changed:L ' W (c) New value of coefficient I l I I I | I 15 2 (d) New value is 0 new total capacity 1 amount added to capacity L4 27 Example: E;£L~ afl-—_JL_a-~1 c "\d ’4. #13...J'1_L;L5.’+ A4JAan. 1 ngmol L111: I; 5 lO 15 20 25 ,Explanation: This will purchase buildings to increase the building capacity indicated to the level or by the amount entered as the new value. Costs in- curred and loan conditions are those indicated in Part II. The value of buildings will be changed, depreciation will be changed and the financial situation will be adjusted. Note: The type of buildings purchased will depend on the sys- tems being used. If this represents a change in systems, be sure that the system change is indicated before the building purchase change. l. From.question 25, Part I. If more than one coefficient is changed, list the additional ones with number 01 changes. Leave blank if no capacity coefficient is changed. 1+. 290 8. Machine Purchase Data required: (a) Type of change number I I I 2 l (b) Machine code ISII f (0) Machine life (years) I I I 11 (d) Age (years) l—ffig (8) Purchase Pricez 144 LVLLII 1L4 18 27 (f) Code number of machine traded in3 I I I I II 30 32 (g) Cash (-1) or Loan (=2) L_, 35 Example: a b c d e W LM... 17.8. ..16.. .i0. . . . . L. ..3.1.2.5. Li 5 10 15 20 25 I .0. . drum. 1.? ... __, 35 no 5,4 f s 30 Explanation: This purchases the machine indicated, enters it in the machinery inventory and makes the appro- priate cash or loan transactions. If a machine is traded in, the oldest machine with the code number indicated will be traded. l. 2. From questions 34 or 55 Part 11. Enter -1 if a simulator generated price is to be used. in is involved, enter the "boot" value paid. Enter 0 to indicate no trade-in. If trade— If loan is indicated, standard loan conditions from question 15 Part II will be used. 291 Note: If purchase of the machine involves a change in a system being used, be sure to change the system's matrix 0 5. Machine Sale Data required: (a) Type of change number I OI 5, 2 (b) Machine code of machine soldl L_i_i_4 2 5 7 (C) Sale price .44.. . . . 10 16 Example: a b c i M ”M Lt. Lb: L 1 191311 | 141 l l-llLl Llf 5 10 15 Explanation: The oldest machine of the code indicated is sold at the price indicated. If sale of the machine involves a change in a system used, be sure to change the system's matrix accordingly. 6. Livestock Purchase Data required: (a) Type of change number IOI6I 2 (b) Number of animals I5I I I8, (c) Age (months) I I I I 3 12 (d) Price each I I I I I I4, 13 18 1. From questions 34 or 55 Part II. 2. Enter -1 if machine is to be sold at depreciated value minus 10%. 3. Enter -1 if a simulator generated price is to be used. 292 1 (e) Months since last fresh I E1. (f) Months until next fresh (if bred)2 I IuI 2 (g) Genetic superiority or inferiority (lbs. per lactation) 23 29 (h) Cash (-1) or loan (=0) I_J 31 Example: a b c d e f g “‘0 @1614 lilJL‘Lpll LIJ Lib-ILL; l 00 5 10 15 20 25 30 l .cno: 1'5? ‘v’ 35 h EXplanation: This purchases the number of animals indicated and adds them to the herd. If the next freshen- ing date is not known, one will be assigned. If bred heifers are purchased months until next fresh must be given a value. Not more than 10 livestock purchase decisions can be made during any one month. 7. Livestock Sale Data required: (a) Type of change number I9. 2, (b) Number of animals I I I I 2I 5 8 (0) Age (months)3 . I I . 10 12 (d) Price each I I I I I I 13 18 1. Enter 0 if fresh cows are purchased, -1 is animals have never freshened. 2. Enter -1 if the animal is to freshen in the month purchased, leave blank if the next freshening date is unknown. 3. Enter a negative value if animals sold are steers. u. Enter -1 if simulator generated price is to be used. a /-*—\ 293 Example: b c d M” W L“ L017: 1 1 l1 LZLL J11)“: l L L1|7r51 114% 8. 5 10 15 20 Explanation: This sells the number of animals indicated. The animals sold are those whose age is closest to the age indicated. If there are not enough animals of the age indicated available, those one month older will be sold, then those one month younger, then those two months older. This process continues until the required number are sold. If cows are being sold and there is more than one animal of that age, those fresh over ten months are sold first, followed by those fresh the shortest period of time. Land Purchase Data required: (a) (b) (C) (d) (e) (f) (g) Type of change number I q 8I 1 2 Value of land I_. I ,_,L, . n . 5 12 Cash down payment LIJ LIA . . . 15 22 New normal acreages (owned or cash rented only) Corn I I I I I (h) Soybeans I I I I I 25 28 41 44 Hay Crop L_L_L_I_J (i) Fieldbeans L_I_I_I_J 29 32 45 48 Oats (j) Gov't. programs I I I I I 33 35 49 52 Wheat I I I l I (k) Total L l l 1 _LJ 37 40 53 57 1. If borrowing is necessary, loan terms from question 16 Part II will be used. 29“ Example: b c d e a 1-~i ,.____—»“~_——-~\ ,---—/~——-"‘\ ,——¢--\’4~—' h. L018...“ .2LoiqoL0. 1.1L.1210L010.1. 119.91 .I 5 10 15 20 25 30 LleL 1 L212 1 12134 I l 01 I 151514 LL84 Uzljlfil I I I 35 40 “5 5 55 60 WW 6 f g h i j k Explanation: The value of the land minus the down payment will be added to loans outstanding. The down payment will be added to cash land expenditures. The acreages indicated will be the new tgtal_owned and cash rented acreage. If the share-rented acreage changes at the same time, this should be indicated by entering decision records with type 01 changes. 9. Land Sale Data required: (a) Type of change number .0i2. 2 (b) Value of land sold I I o. .1 1 I J 5 12 New normal acreages (owned and cash-rented only) (c) Corn I I I I I (g) SoybeansI I I I I 15 18 31 34 (d) Hay Crop I I I I I (h) Fieldbeans I I I I I 19 22 35 38 (e) Oats (i) Gov't. payments I I I I I 23 23 39 42 (f) Wheat I I I I I (j) Total I I I I I I 27 30 43 47 Example: a b c d e f I4" L0! 91 l l I l I lLolq 0! 0| J 1 ll 818LL I 71 214 12101 I [215‘ 5 10 15 20 25 30 295 '4 I215: l IL‘iolLl LOIJJZJZIOLLIJLLJ I I I II III 35 1+0 45 50 55 60 MW e h i j Explanation: The acreages indicated are the new total acreages of crops grown on owned or rented land. If the share-rent acreage changes at the same time this should be indicated with a number 10 type change decision record. The value received for the land will be distri- buted over the number of years indicated in question 54 Part II with not more than 30 per- cent of the total received during the first year and interest received as indicated in question 16 part II. 10. Machinery Inventory Input (Detailed) Data required: (a) Type of change number IlIg (b) Machinery code I I I I 5 7 (c) Approximate purchase price L_III_I I I I I (d) Life I I I 10 16 20 (3) A83 I_I._.: 24 (f) Present value L,. I I I . . I Example: 27 33 a b c d e f ""‘ rr-“—*\ I""'-*""“\ r-*~a r~*-\ r***"” “’0 LILOI I I l I lJ_I 1 LL L31 8l 5L0! l I ll q I l L 7| LLL I I I I 5 10 15 20 25 30 [SIOI OI J Lg I 35 f Explanation: 296 This decision record is used to input present machinery inventory when the answer to question 20 Part I is yes (-1) i3. when the user inputs the beginning machinery inventory. This can be used only in the first month of simulation. One record is used for each machine. 11. Machinery Inventory Input (Brief) Data required: (a) Type of change number IlIlI 2 (b) Machine code I I I I Example: a b f-‘- 7 ,—.~_.I )4. QLILLOIZILIIS 5 Explanation: 10 This is used to input the beginning machinery inventory when the user does not want to Specify the life, age and present value of each machine but wants the specific machinery contained in the beginning inventory. Values for these items will be assigned by the program. Type 11 changes can be combined with type 10 changes to enter the complete inventory. This type of change can be used only in the first month of simulation. 297 List of Systems Livestock Systems Dairy'Cows 1. In stanchion or tie stall barn with gutter cleaner, milk transfer system, bulk tank and silo unloader. 2. Cold covered free stall system, herringbone parlor, manure scraper and silo unloader. 3. warm covered free stall system, herringbone Parlor, manure scraper and silo unloader. h. Cold covered free stall system, herringbone parlor, 1iquid.manure and silo unloader. 5. warm covered free stall system, herringbone parlor, liquid manure and silo unloader. 6. Loose housing open lot, herringbone parlor, manure scraper and silo unloader. 7. User defined. Dairy'Replacements 8o 9. 10. Conventional pens cleaned by manure loader, silage fed to older animals. In outside lots during summer. Free stalls for animals over 6-9 months, manure scraper into con- ventional spreader, silage fed. User defined. 298 Crop Grow Systems Corn Grow - April 1. Two row system, 2, 3 and 1+ plow tractors, 3-16" plow, lO' disc, 12' harrow, l spray, l cultivation. Plant in April. 2. 1+ row system, 2, 3 and 1+ plow tractors, lIr-l6" plow, 12' disc, 12' harrow, l spray, 1 cultivation, fertilizer spreader. Plant in April. 3. 6 row system, 2, 3, 1+ and 5 plow tractors, 5-16" plow, 16' disc, 16' harrow, l spray, l cultivation, fertilizer spreader. Plant in April. 1+. User defined. C_qrn Grow - May 5. 2 row system. Identical to 1 except for timing. 6. 1+ row system. Identical to 2 except for timing. 7. 6 row system. Identical to 3 except for timing. 8. User defined. Corn Grow - June 9. 2 row system. Identical to 1 except for timing. 10. 1+ row system. Identical to 2 except for timing. ll. 6 row system. Identical to 3 except for timing. 12. User defined. Eh_eat Grow - September 13. Plant in Sept., 2, 3 and it plow tractors, L16" plow, drill, 12' disc, 12' barrow, compatible with 2 or 1+ row corn grow system. 11;. Plant Sept., large equipment (compatible with 6 row corn grow), 2, I+, and 5 plow tractors, 16' disc, 16' harrow, drill. 15. User defined. 299 Wheat Grow - October Plant in Oct. Same as 13 except for timing. Plant in Oct. Same as 1h except for timing. Plant April, 2, 3 and h plow tractors, h-l6" plow, drill. Compatible with 2 or M row corn grow system. Plant April, large equipment. Compatible with 6 row corn grow, 2, h, 5 plow tractors, 16' disc, 16' harrow, drill, 5-16" plow. Plant May. Same as 19 except for timing. Plant May. Same as 20 except for timing. 16. l7. 18. User defined. Oats GrOW'- April 19. 20. 21. User defined. Oats GrOW'- May 22. 23. 2h. User defined. Hay Crop Plant 25. 26. 27. 28. Direct seeding, 2, 3 and h plow tractors, h-16" plow, drill, 12' disc, 12' harrow, compatible with 2 or h row corn grow. Direct seeding, 2, h and 5 plow tractor, 5&16" plow, 16' disc, 16' harrow, drill. Compatible with 6 row corn grow. Companion crop, spring seeding, 2 plow tractor, seeder. User defined. Hay Crop Grow 29. 30. Fertilizer spreader for summer application. User defined. Fieldbean Grow - May 31. h row, plant May, spray, 1 cultivation, 2, 3 and h plow tractors, h-l6" plow, 12' disc, 12' harrow. 300 32. 6 row, plant May, sprayer, l cultivation, 2, 3, h and 5 plow trac- tors, l6' disc, 16’ harrow, 5-16" plow. 33. User defined. Fieldbean Grow - June 3h. h row. Same as 31 except for timing. 35. 6 row. Same as 32 except for timing. 36. User defined. Soybeans Grow - May 37. h row, 2, 3 and h plow tractors, spray, cultivate, 12' disc, 12' harrow, h-l6" plow, plant in May. 38. 6 row, 2, h and 5 plow tractors, 16' disc, 16' harrow, 5-16" plow, spray, l cultivation, plant in May. 39. User defined. Sgybeans Grow - June ho. h row. Same as 37 except for timing. #1. 6 row. Same as 38 except for timing. #2. User defined. 301 Harvestq§ystems Corn Silage Harvest 1. Custom harvest, all men and machines provided by the custom operator. 2. 1 row chopper, 2, 3 and h plow tractors, unloading wagons, blower. 3. 2 row chopper, 2, 3 and h plow tractors, unloading wagons and blower. h. Self propelled chopper, 2 and 3 plow tractors, unloading wagons, blower. 5. User defined. Corn Grain Harvest 6. Custom, custom combine and trucks, man and elevator required for unloading. 7. 1 row picker, 2 and 3 plow tractor, grain wagons, elevator. 8. 2 row picker, 3 and h plow tractors, grain wagons, elevator. 9. 2 row combine, 3 plow tractor, grain wagons, elevator. 10. 3 row combine, 3 plow tractor, 2 grain wagons, elevator. 11. User defined. Wheat Harvest 12. Custom hire of combining and trucks, one man and elevator required. 13. S.P. Combine, spread straw, grain wagons, 2 plow tractor, elevator. 11+. S.P. Combine, bale straw, grain wagons, hay wagons, 2 and 1+ plow tractors, grain elevator, hay elevator, baler. 15. User defined. Oats Harvest 16. Custom combine, one man and elevator required. 17. S.P. Combine, spread straw, grain wagons, 2 plow tractor, elevator. 302 S.P. Combine, bale straw, grain wagons, hay wagons, 2 and h plow tractors, grain elevator, hay elevator, baler. Mow, crush, rake, chop with 1 row corn chopper with hay head, 2 unloading wagons, blower, 2, 3 and h plow tractors. Windrower, chop with 2 row corn chopper with hay head, 2 unloading wagons, blower, 2, h and 5 plow tractors. Windrower, S.P. Chopper, 2 unloading wagons, blower, 2 and h plow Windrower, 1 row chopper with hay head, 2 unloading wagons, blower, Mower, crusher, rake, baler, wagons. Load behind the baler, Windrower, bale with kicker, use mow conveyor, elevator, wagons. PTO windrower, bale with kicker, place bales in mow by hand. 18. 19. User defined. Hay Crop Silage Harvest 20. 21. 22. tractors. 23. 2, 3 and h plow tractors. 24. User defined. Hay'Harvest 2S. elevator. Hand bale handling. 26. 27. 28. User defined. Field Bean Harvest 29. 30. 31. 32. Custom.combine, h row pull, rake, 2 and 3 plow tractors, grain elevator. h row pull rake, combine with bean head, 2 and 3 plow tractors, grain elevator, grain wagons. 6 row pull, rake, bean combine, 2 and 3 plow tractors, grain elevator, grain wagons. User defined. Soybeans Harvest 33. 3h. 35. Custom combine, 1 man and elevator fer unloading. Combine, elevator, grain wagons, 2 plow tractor. User defined. FABS (_F_A_rm Dusiness Simulator) DATA FORM 1 FABS DATA FORM 1 Note: Each number entered on this form should be placed in the right-hand portion of the space allotted. The Spaces are divided into segments and one number should be placed in each segment. If there is a decimal point in the number, it occupies one space. For example, the number 26 would be entered as Z / / )/ I/2/6 / and 13.2 would be entered as follows: //J / Lll3/.j2/. Part I Card 1 1. Month Year 1-2 3- 2. Years (5-6) 3. Fiscal or Calendar { / 7 h. (a) (8) (b) 9) (C) (10; (d) (11 (e; (12) (i (if?) 8:; 823 (J (17) 5. Notes: 303 301+ .dopdgfiu on. 0». specs puma.“ one a.“ gash. M.“ has come on. use awed—”duos memo: as as am 3m 8.. as E so an. as me as a s a 8.13 -3 -05 -5 is -5 .3 as as A: .3 .3 -03 -5 1; -d Assume \\ W\ W\\QW\ \W\\ \\ \\ EN \\ \\ \\ \\emwo \N\ \\ \\\ \\ \\\\ \\ \\ \\ \\ \\ \\ \\ \\ NH \ D\\ \\ \\ \\ \W\\ SN w D\\Q \\ \\ \w \ w \\\ W\ W\ \\ NV \\\\ ww \\#\ \\\ \\ NV \\a. \\\ w\ \\ \\ \\ DW\\j \\ \\ \\ \\ \\ \\3 m \w\ \\ \\ x D \\\ \\ \\ \\{\ \\ \W\\ \\m \\ \\\ \\ j\\\\ \\ \\ \\ \\ \\ \\ \\ \\: a \W\ \\ \\ \\ \\\\ W\ wwx \\ w \\ \\ \\m \\\ 3 \\ \\\‘ D\ \\\ j \\\ \\ \\ \\ \\N ‘W\\\\\{\\ W\ \\D\ \\ \\ \\\ \\ \\ V \\H onwwwm 5.. fi- mau NH- 3.. 0H- m- m- a... o- m- a- mu m- H., some. oewmwm poo >oz oon new pom no: nee are ones sass ms< poem poo eoz soon immm empegm on on £82 the op .85 goo: guesses no £82 as eofiepeeq .3 .58 no and; Halal" M—d'lnwbmm N Udh'd .m 305 Card 12 (76:86) 7. (a) Heifers Age 112,3256789f101112I Number/ /I/ / / / / / /‘/ // 1- <3- (5- ‘(7- (9- (11- (13- <15- <17- (19- (21- (23- 2) 1:) o) 8) 10) 12) 11+) 16) 18) 2o) 22) 22) Age 13 11+ 15 16 17'18119 20 21J22 23 28 ”m”? z 1 / / /]/ [1 / /I/ / 1 I25- <27- <29- (31- (33- (35- (37- (39- *ul- (h3- (us- (A7- 26) 28) 30) 32) 3A) 36) 38) no) )2) an) A6) A8) 6 Age 25 26 27 28 .29 30 31 32 33—1 34 4) 35 36 mm" / /‘/ / / / / 1) L. / / (no- (51- (53- (55- (57- (59 *161- (23. (65- (67- (69- 771- 50) 52) st) 56) 58) 60) 62) 6n) 66) 68) 7o) 72) Card 13 '(75286) 7. (b) Steers [Age 1238567891011I12 le“’// //// /z¢ //// //// /zI/z (1- (u- (7- 110- (13- (16: (19- (22- (25- (28- (31- ‘(3h- 3) 6) 9) 12) 15) 18) 21) 21)) 27) 30) 33) 36) Age 13 [1h'15 16 [17118119 20 21 22l23 r211 weer //H/l////l/M//j//// ////|//f// (37- (MO-‘(h3-‘(E6- (E9- (52- (55- (58:'(51- (5h- (67- 7(70- 39) A2) )5) A8) 51) 5h) 57) 6o) 63) 66) 69) 72) 306 Card 1h W7" 8. Freshening Preference Mo. of Birth Age Fresh.j///JL/ ////J (l- (3- *15- I7- (9- (ll- (13- (15- (17- (19- (21- (23- 2) u) 6) 8) 1o) 12) 12) 16) 18) 20) 22) 2h) 9.cows/1// (25-27) Bred heifers Jan Feb Mar Apr May June July .Aug Sept Oct Nov Dec 2 ~30 Open heifers / [_ / / 131-337 3 -3 10. Production (37-515 Forage quality {115) Lbs. feed 653-57; Culling rate -51 11. Lbs. feed to be fed (SE-555 Forage quality to use ) 5 57 Calves Culling rate to use 12. Concentrate lbs. Ear corn 1- Shelled corn 5 Oats LrfiwU Wheat (73-755 Supplement Zl‘é7fiéafié“ 307 Card 15 Z79-805 13. (a) Corn fed? {1) 13. (b) J an Feb March April May June 1bs./J/J/JZ//////L/////J/[// 12-67 (7-11) (12-167 (17-21) (22-267 727-317 July Aug S ept Oct Nov Dec lbs. J/1[//_/J////J/A//LAJJ (32-36) (37.11) (Die-1+6) 87-53 (52-5 (57-61) 13. (C)%moisture A / 1 / /J (62-66) 13. (d) HMC supplementL / L j j / (67-71) 11+. Forage H.E. percentages Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec (l-h) (5-8) (9- (13- *(17- (21- *155- 129- 33- 37- (£1- 5- 12) 16) 20) 2h) 28) 32) 36) ho) uh) h8) 308 Card 1 (79-80; 15. Calving interval / / Z_ [_1 / (1-5) 16. % heifer raises 17. Maximum number cows L Z A Z / (9-12) Maximum.heifers under 1 year 13- Maximum.heifers over 1 year / /__/ / / (17'20) 18. Youngstock bedding £21) Cow'bedding 22) C 3 Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec D 20 C 21 E (l- (a (ll- (15- T21-1726- 1.31- 136- (El- (116-(51456- 5) 10) 15) 20) 25) 30) 35) #0) #5) 5o) 55) 60) Card 22 79 19. Cows Z Z2 / 2. Heifers Z 3Z Z 3. Corn "' Apr.3 h. Corn.- May 5. Corn - June 8. Wheat - Sept. Z Z Z 15- 9. Wheat - Oct. Z Z Z 7- 10. Wheat - har. $19-20) 309 19. (Cont'd.) 11. 12. 13. 1h. 15. 16. 17. 18. 19. 2o. 21. 22. 23. Oats - Apr. 521-22) Oats - May {23- 2E) Oats - bar. ‘25-- 2)6 Hay plant Z272 Z Z HCS w-30) Hay grow 31-32 33-3 ) F. beans - May Hay 35-3 ) 37-38) 39- ) - ) 3- Soy. - har. (125-133; F. beans - June F. beans - her. 20. Enter machinery? ) ‘57 Buy machinery? (fiy/ 21. Land Land Card 23 {N05 22. Crop Corn Hay Oat s value, 10 year val . / [_ Z / Z Z 1 / us now (“9-55 5 - 2 Own or rent / / Z. [_ / (1.7.7 [ l / 19-12) 1 1.7-20) share rent % 3% 310 Year's 22. (Cont'd.) 2523 Own or rent share rent Wheat Z_ / Z Z / Z. / Z / / (ZS-28) (29-32) So beans Z Z Z Z Z Z, / / Z / y 33-3 (37:EO) Fieldbeans / / / / / Z, / / / / (El-Rh) (HS-H8) GOV't programs Z_/ Z / 2/ Z_ / / / / (h9-52) (SB-56) TWfl L] /Z_/ /// / (57-607 (61:6h) 22. (b) Hay crop silage 5- 7 Corn silage LZ Z Z Z Z (68-71) 23. Acres rented ‘/ / A/ / / 172-75) Rent rate / / / Z_ Z / (76:80) Card 2h T753557 2h. Value Age / / yams ZZ/ ZZ Z Z/ Z Z (146) ( years Z/ZZ (37-h2 7-12) yams ZZ/ Z/ Z_/ Z / /Z /_J / ZZZZ (19-2h) (25-36)’ (31 [Z/Z1Z//////Z ) (23.28) (“9 / -3 / / / / ZL/ / Z Z Z_ / (13-18) Z Z. / 6) / Z. / -52 Card 25 (79505 25. 26. 311 Building Capacity (3) (b) (C) (d) (e) (f) (s) (h) (J') (k) (a) (b) (C) (CD Free stalls / Z Z Z ZJ (1'5) stanchions Z / jl AZ 1 15 0) Parlor / / 1 / ZJ (ll-157 Heifers / Z/ / L] (16-25) Calves Z / ZJ ZJ (ii-2?) U-silo Z Zi Z Z Z (2635 H-silo/Z/Z/Z T3l-357 Hay / L/ Z / / T3630) E.cornZ/ZZZ/ (1.1-1+5) Grain / Z / / ZJ (113 ) -50 ENG L/ Z / Z / (5’1-557 Dairy buildings? (‘6/ 5 ) Grain storage? {__/ 57) Hay storage? {_8/ 5 ) (i) Fieldbeans Z__/ 59) (ii) Soybeans (50‘) (iii) Wheat (5'11) (iv) Corn Z524 (V) Oats V (63) 5A8 A863 RTE 829% 5-3 8:28 mm Aafl mm 398% Hm Awflmfll om ACRE mm $7.3m mm 393m am xahademli em mammal Undo nopfimooa 938962 30.3.00 am pfld ends m5 RAB Aomnwmv RNANV “om-mi Randi #3-»: and R fima 3 $672762 3 vhdom 3&4 Am v ohm A323 32% afissa .3293 SE. 2 3mm.” Saga ah. on? mums-24ml. .3an .FN 313 Card3h (79-805 27. (b) Labor cost Z_Z/ / Z_1Z / (1-57—' 28.Hire(l)/Z/ZZ/ZZ/Z// (6:10)“ (11.15) Hire(2)/Z/ZZ/ZZZZZ/ (16920)” (El-25) Hire(3) / / / Z Z_ / / / / / ZL/ (26230) ‘“(3l-3§) Card 35 (79-805 29. Plant food / / / / / 30. 32. u-uf Bu. oats Z Z Z Z Z -Z (5-9) Period 10) Plant food 3/ Z/ / / / (11.117) Bu. Corn Z, Z Z Z Z Z ‘PI15-19) . Plantfood Z/ ZZ/ ‘(20-237 Spring N 2 -2 Bu. Wheat / Z Z Z_ Z Z (27-317' Plant food / / / / / (32-35) Tons lst / .Z,Z Z Z (36-39) Tons 2nd .Z / Z/ Z Z THO-1+3) Tons 3rd / / ZZ Z Z (la-1+7) 31H 33. Plant food / -51 Bu. Fieldbeans leZ 3/ Z Z / 752-56) 3%. En. Soybeans / / / A/ / / (57-31) 35. Cuttings '(6§{ 36. Pasture lst LZ / L] / (63-67) Pasture2nd /JZ/ // (68972) Pasture 3rd ZZ Z Z Z Z (73-77) Card 36 (79-805 37. Hay crop silage / Z Z Z Z / (T-S) Corn silage Z._Z .Z Z _Z Z (640) H.M. corn / Z Z Z ZJ (ll-15) 38. (80 Hay crop silage Z Z Z Z Z Z (16-20) (b) Corn silage Z Z Z Z Z Z (QT-25f (c) Hay Z Z /J L/ (2630) (d) HMC Z Z Z Z ZZ (31-35) (e) Ear corn / ‘/ Z,Z Z Z ( 36-l+0) (f) Shelled corn Z Z / j / / (kl-MW (a) wheat /Z ZZZ / 1116-507 315 38. (Cont'd.) (h) OatSZZZ/Z/ (51-55) (i) Soybeans LZ Z Z / / (56e6o) (J) Fieldbeans Z_1Z, Z ZZZ Z (61-65) (k) Straw/ZZ/Z/ (66-70) (1) Supplement / / Z / Z / (71-757 Card 37 (79-85) 39. (a) Plant food P20 (1) Corn 1-3 (2) Soybeans Eta 10-12 13-15 1 (3) Fieldbeans -21 22-2 ...) \0 25-27 (h) Wheat 2 -30 31-33 3 (5) Oats 37-39 (6) Hay-seed ittitt) t: tat 9-51 52-5 (7) Hay iiii 55-57 5 - O 1- 3 (b) Region Z Z 5; 1+0. (a) Debt 316 Uw3>0 Balance Rate No. Amount HRH-4P3 zwbiea Month Eifi>t> candiehik: ‘35 326 no #1 #2 b uh #5 (79- 80) (1- 9) Card #6 (79-80) 41. (10- 1b) (a) Withdrawals (15- 16) (17- 2h) (b) Cash Z Z Z_2Z Z Z Z (h6;53) (25- (27)28-30-32-3h-36-38-(uo- (M3- 29 31 33 35 37 39 #2) #5) 26) ZZ / F7... Feb Mar Apr Ma June July Aug Sept Oct Nov Dec FT].- 5) (6- 10) Card h? (79:80) (b) Off-farm income / / Z / / / / (146) Exemptions (7-5) m- 15) (1- 20) (21- (262 25) 30) ‘(31- 35) (36+ #0) (ul- A5) (26. 50) (51- 55) (5 - 60) #2. (d) (a) (b) (C) (d) (e) 317 Estimatedtax / / / / Z Z (9-13) Estimated income / / / / / / Z (lit-l9) St r ? ee 3 Z20; Percent / Z Z Z Z (21-2H) Age on feed ‘Z,109 and 3 equals the number of the dairy cow system in use (SYS(I), Q.19, Part I). The building cost is the number of stalls times BC(5,j). B. CAPCT(3) (Q.25(c), Part I), and the number of "parlors" to be purchased is odd, one l-man parlor is purchased. If the number of "parlors" to be purchased is equal to or greater than 2 the number of two-man parlors is calculated by dividing the number of "parlors" purchased by two and rounding off to integer. The building cost equals the number of one-man parlors multiplied by PCOSTl (Q.64(a), Part II) plus the number of two-man parlors multiplied by PCOSTS (Q.64(c), Part 11). The parlor equipment cost is the number of one-man parlors 326 multiplied by PCOSTl (Q.64(b), Part II) plus the number of two-man parlors multiplied by PCOST4 (Q.64(d), Part II). Two variables, "one-man parlors" and "two-man parlors" are main- tained and the numbers built are added to the total. For both A and B above, the building cost is added to the building set matrix. If the value of EQUITY (Q.40, Part II) is less than 100, two building sets are added to the building set matrix. One set with a code value of 1 has a purchase value equal to EQUITY multiplied by the total building cost. The second set with a code value of 2 has a pur- chase value of the building cost minus the purchase value of the first set. For both sets depreciated value equals purchase value, age equals zero and life is equal to the depreciation life indicated in Q.l4, Part II. If EQUITY = 100, only one set is added and the code is l. The value of equipment purchased is added to the machinery inven- tory matrix. A code number of 111 will be used for barn equipment and 112 for parlor equipment. New value and depreciated value equal the cost. Life equals PLIFE (Q.64(e), Part II) and age is zero. For either A or B above the building cost is added to the equipment cost and the total is entered in the current debt situation matrix (DEBT(i,j)). The beginning balance equals the total calculated cost. Interest rate, number of payments, loan period and type of loan are taken from Q.l4, Part II and the term of loan is 4 for buildings, 5 for land, 6 for machinery, and 7 for livestock. C. CAPCT(4) (Q.25(d), Part I), the cost is equal to the increase in capacity multiplied by the (1,3) position of the HCBC matrix (Q.65, Part II), where: J = (replacement system no. (SYS(2), Q.52, Part 11)) - 7, 327 This cost is added to the building set matrix and the current debt situation is the same manner as indicated in (B) above. D. CAPCT(5) (Q.25(e), Part I), the cost is equal to the increase in capacity multiplied by the (2,3) position of the HCBC matrix (Q.65, Part II), where: j = (replacement system no. (SYS(2), Q.52, Part 11)) - 7. The cost is added to the building set matrix and the current debt situation in the same manner as indicated in (B) above. CAPCT(6) (Q.25(f), Part I), the silo to purchase is found by searching SC(i,l) (Q.67, Part II), for the smallest tonnage which exceeds the amount to be purchased. If this is SC(r,l), where r is a specific value of "i", the silo cost is SC(r,3) and the unloader cost is XMP(K,I) (Q.68, Part II) where K is the machine code number calculated below. The silo cost is entered into the building set and current debt by the same method as used in (B) above. The unloader is added to the machinery set. New value and depreciated value equal the unloader cost. Age is zero. The machine code number (K) is: 15 if r = l or 2 17 if r = 5 18 if r = 4 or 5 19 if r = 6, 7 or 8 20 if r = 9 21 if r = 10 or 11 Life is XMP(K,2) (Q.68, Part II). A "l" is placed in the system matrix cell LSM(machine code, SYS(l)), from (Q.81, Part II). The cost is added to the current debt situation matrix. The loan term, interest rate and loan type come from Q.lZ, Part II, and the pay- ment months and amount of payment are calculated as in (B) above. 328 F. CAPCT(7) (Q.25(g), Part I), the cost per ton is chosen by selecting the highest value of i possible for which the minimum tons still exceeds the tonnage calculated (Q.67, Part II). If that value of i = r, then SC(r,5) is the cost per ton. The cost of the silo is SC(r,5) multiplied by the tons to be built. The cost is added to the building set matrix and the current debt situation as in (B) above. G. CAPCT(8) (Q.25(h), Part I), cost equals the tonnage required multiplied by SCOSTl (Q.65(a), Part II). Cost is added to the building set and the current debt situation as indicated in (B) above. H. CAPCT(9) (Q.25(i), Part I), cost is the bushels multiplied by SCOSTZ (Q.63(b), Part II). This is added to the building set and the current debt situation as in (B) above. I. CAPCT(IO) (Q.25(j), Part I), the procedure is the same as that indicated in H above except that SCOST3 (Q.63(c), Part II) is used. J. CAPCT(ll) (Q.25(k), Part I), the size of silo to build is found by searching SC(i,2), i = l,...,7 to find the smallest number which is larger than the bushels of storage to be pur- chased. If that value of i = r, then SC(r,3) is the silo cost and MP(K,1) is the unloader cost where K is defined as in E above. These costs are handled in the same manner as the silo purchases in E above. For each of the above A through J the capacity coefficients should be changed to the new value when new capacity is constructed. 5.1.4 If the type of change number is 4, indicating machinery purchase, 329 life and age are taken from input. Purchase value is taken from input if a positive price is entered; or from Q.68, Part II if -I is entered in price. If the price is generated by the model, the amount of cash or loan paid is the appropriate price from Q.68, Part II (indexed as XMP (machine code, 1)) minus the depreciated value of the machine traded in; new value and depreciated value both equal the price generated (Q.68, Part II). If the purchase ("boot") price is entered, the new value and the depreciated value are the "boot" value plus the depreciated value of the trade-in and the cash or loan paid is the "boot" amount input. If cash is paid, the "machinery purchase" and "cash machinery investment" accumulation are increased by the amount actually paid, otherwise only machinery purchase is increased. If the machine is purchased on loan, the loan amount is added to the current debt situation. Beginning balance is the loan amount. Loan term, number of payments, interest rate and type of loan are taken from Q.12, Part II. 5,1,5 If the type of change number is 5, indicating machinery sale, a machine with the code indicated will be found (if there is more than one, the oldest is selected) and removed from the machinery inventory matrix. "Machinery sales," as accumulation, will be increased by the amount indicated or the depreciated value minus 10 percent (if -1 is input). If the sale value differs from the depreciated value, the difference is added to the accumulated depreciation, i.e. (depreciated value-sale value) is added to depreciation. figlgfi If the type of change number is 6, the data is put in a 10 x 5 matrix called STOCKH (livestock purchase hold) of animals to be pur— chased. For each group of animals input by a code 6, the matrix 330 contains (1) number of animals, (2) age, (5) months since last fresh, (4) month until next fresh, and (5) production adjustment. Each group of animals occupies one line of the matrix. The number of animals purchased is added to cows bought, heifers over 1 year bought, or heifers under 1 year depending on age and freshening. The cost of the animals is calculated by multiplying the price each by the number of animals. If the price is input, that price is used. If the price is to be generated (-1 in price entry) the number N of animals is added to the appropriate _purchase" group with cost calculated in storage and sales. "Bought" variables indicate animal numbers regardless of pricing mechanism. "Purchase" variables are messages to the storage and sales routine. Purchase groups are: (1) cows purchased, (2) bred heifers purchased, (3) open heifers pur- chased (over 1 year) and (4) heifers under 1 year purchased. If the price is given, the value of the purchase is added to "livestock purchases" and, if cash is paid, it is also added to "cash livestock investment." If a loan is used, the value is added to the current debt situation. The value is the beginning balance. Interest rate, loan term, type of loan and number of payments come from Q.12, Part II. Months of payment and amount of payment are calculated as in 3.1.2 B above. gglgz If the type of change number is 7, the animals to be sold are put in a "livestock sale hold" matrix (SLIVEH). This is a 10 x 3 matrix with each row containing (1) number of animals (in the group), (2) age, and (3) price each. §._1_._8_ If the type of change number is 8, the normal acreage coeffi- cients, CPACRE(8,2) (Q.22, Part I), are given their new values. The 331 down payment is added to an accumulation called "cash land investment" and the value of the land minus the down payment is the beginning balance added to the current debt situation. Interest rate, term of loan, type of loan and payments per year are taken from Q.14, Part II. Months payments are to be made and amount of payment are calculated as in C(2) above. The value of the land is added to PURLD (land purchase) and to VLANDM (the value of all land owned by the business) ‘gglgg If the type of change number is 9, the value of the land is added to SALELD (land sale income) and the new normal acreages replace the old ones in CPACRE (Q.22, Part I). The value of the sale is subtracted from VLANDM. 5.1,10 If the type of change number is 10, the machinery listed is put directly in the machinery inventory matrix. The values in each row of input correspond to the values to be put in rows of the machinery inventory matrix. This type of change can be used only in the first month simulated. 3.1.11 If the type of change number is 11, the machinery listed is put directly in the machinery inventory matrix. The values for life, age and depreciated value are calculated by the machinery routine. This type of change can be used only in the first month simulated. 342 Listed below are the variable names and identification numbers of all variables listed in Parts I and II of the Users Manual (Appendix A). 332 oo-n zH>q aa-a Hmmao< on Hoz Hm mH1H .sp na-a Aavmbqa> o amH1H mozwam any NH-H .ep H-H Amavmmmmm m mmazmz flavom AmmvmmmemH Anv mmH1m .ep HHH-m Ammvmmm ma AmmvmmammH Amvs OHH-H .eo am-a Ama.mvamoomm Ama.oavszOH m oo-n zommm m mm-H omamm ma AHHVBaBDOH a am-a x<2> soap soapsoamassooH manoams> soap anode 135,0 geszmz mmmm: zH ammHmommq mmmemz .Hm canoe 333 mmm1H .sp mmm1a Amvoagmm a AHHv>zHao on mom; 9% mmm1a omam 1 . mom1a >mmmo own H 85% m 1 1 mom a >mmmom an own a omam mom1H nomad who; 85mm m «on; 85% 8.1.4 Named 1 oom1H Hyman on man A mammxm a 1 mom A menu mm mm m1.” ransom m 1 1 mom H mQQMHm on Hmm H mmdqa> m som1a aqquw omm1a mmeqo> a 1111111111111 1HH omma1111111111 111111111 mmm1a mqoomm mm mmm1a mquHw osm1a onaqu Amvae mmm1H QOHmmm om mmm1a mm ooa» doapooamapsooH canoaao> soap 1 $50 .. mesa n.o.peoov Hm canoe 334 mam1a *zmonm mm Hom1a .es mom1a *Am.mvmooeao ma mam1a mmdmm mm som1a *mmmmmm ea 3m; 35mm one; Edam A3 2m; GEE S who; 2.33m $va mam1H mmzoom amm1H amem ame1a *Hmadm oma1a mmammm mm mma1a qummm NH maa1a mama Any moa1a .mp mmn1a Amavemmmzx msa1a gambam onam Hom1a ammod mam1a .es Nmm1H As.mvoo>m mm oom1H mmmozo Ha Hmm1a .eo asa1a mam.aavooaomo mm momm1m ozmam momm1m aqmwmo Aev momm1m amazoe OH smmmm 3863 A8 3 man; 9.15% 881m sigma 3V in; Sosa m smmm1m ammoqm AsVom man1a oozem mmm1a .eo osm1a *Aoavmmemem ma msmva amzmoo m mnonesz medz nonpaz muonssz meaz anEdz soapooamaosooH oaeoams> soap soapooamesooH oasoamm> soap 1 mesa 1 wood A.o.oeoov Hm canoe 335 mae1a .eo Ham1a Am.mvoqoo me moe1H .sp aom1a *Aevmaozoo Amvmm mmmN1m eammem mom1m .ep mms1m hasvmao an mmmm1~ 93306 mms1m .ss oms1m Am.mvmmm mm emmm1m eamqmo ma mae1m .eo mmm1m Ama.mvomzx mm mmm1H *msz Aev mom1m Hozsz on momm1m remozzo Amvmm samm1m *zmzHem Apv Nma1H ammozoo mHmN1N *szHaoa onsm Hma1a *ammsz ammm1m .ep mmmm1m Amvzoamao mm oma1a anemozm asm1a .ep 0sn1a Amvmmoem mm maa1a *qamoso as mmm1H .eo smn1a Amvgpam mm mamm1m mango on mam1a ammOHmm Hmmm1m mmwx some scammoamaosooH mammams> soap 1 mesa 1 mode A.o.oeoov Hm canoe 336 O®m1m .zp mm©1m *Aquam ms ammm1m .ep mmsm1m *Am.aavqu as mmmm1m .ep smmm1m Am.oavmse soap eoanmoHMamaooH oaooamm> soap 1modd 135$ A.o.peoov Hm edema 337 SUBROUTINE LAND This subroutine calculates government payments, property taxes, land rent, conservation expenses and land inflation or deflation. g,1 Income from government payments is calculated using GOVTPA (Q.45, Part II). If GOVTPA is S 10, multiply the number by the total acres from Q.22, Part I. If the number is > 10, the number itself equals government payments income. One-half of the calculated value of government payments is income in each of the two months indicated by GOVTMl and GOVTMZ (Q.43, Part II). Calculations are made during the months payment is received. g,g If PTYTAX islé 10, land taxes are calculated by multiplying PTYTAX (Q.29, Part II), by the total number of acres owned. Owned acreage is calculated as the total acres owned or cash rented (CPACRE(8,1) from Q.22, Part I) minus the acreage cash rented (ARENT from Q.25(a), Part I). If the PTYTAX is greater than 10, the number listed is the property tax expense. The amount of tax is divided equally between months TAXMl and TAXM2 (Q.29, Part II). Calculations are made during the months payment is made. 44;, The land rent is calculated by multiplying ARENT by RENTRT (Q.23, Part I). One—half of this is paid in each of the two months RENTMl and RENTMZ (Qnsl, Part II). Calculation is also made in these months. If the calculation in the latter month of the year is larger than that paid during the first month the difference is added to the latter months rent cost (if a new fiscal year is not started between the two months). 4,4 Conservation and fence repair are calculated by multiplying the number of tillable acres, CPACRE(8,1) + CPACRE(8,2) (Q.22, Part I), 338 by ZCONSl (Q.SO, Part II), and adding ZCONSZ multiplied by the maximum acreage of first, second, or third cutting pasture (Q.36, Part I). One-third of this expense occurs in April and the rest is evenly divided among the next five months. Each month is calculated separately during the simulation of that month. g,§ The monthly rate of inflation is calculated during each month of simulation. If VLANDZ (Q.21(b), Part I) is greater than 50 the monthly rate of inflation (r) equals (ln(Q21(b))- ln(QZl(a)) - 1 120 e If VLANDZ is less than 50, (r) equals <1n11921(b) (1 + (ggl(a))] - angl(a)‘) _ 1 12 e Each month the new value of land equals the old value (last month) multiplied by one plus the rate of inflation (r ). Land value is maintained for the first month (before calculations) and the last month of the year (after calculations). g,§ Variables calculated by this subroutine are listed below. 339 Variable Definition Name Dimension Codea 1. Income from government payments GOVTP (13) 2. Land taxes TAXLD ( l) 3. Land rent RENTLD ( 2) 4. Conservation and fence repair CONS (13) 5. Rate of inflation RINFLA ( l) 6. Land value--beginning of year LANDVB ( 1) -—end of year LANDVE ( 1) --this month VLANDM ( l) a. Throughout the description of this program, dimension codes indicate the dimension of the variable and will be defined as follows: ( 1) - Value is used only in this month or is maintained until changed ( 2) - Values for this month and an accumulated sum for the year are used (1 = this month, 2 = accumulated sum) ( 5) - Values for this month, the previous month and an accumulated sum are used (12) - Values maintained for each month of the year (15) - Values maintained for each month (i = 1, ..., 12) and for an annual total (1 = 15). 3&0 SUBROUTINE YLDADJ This subroutine calculates and adjusts crop yields based on standard yield and fertilization input data and actual fertilization rates. §,1 Crop yields are calculated in the first month simulated and adjusted whenever standard or actual data are changed (Q.29-54 or 59, Part I and Q.22, Part II). The present yield coefficients maintained include: YELDCl = Corn grain yield YELDOl = Oat yield YELDWl = Wheat yield YELDHl = Hay yield YELDFl = Field bean yield YELDSl = Soybean yield In the first month these are initialized from Q.29-54, Part I such that: YELDCl = YIELDC YELDOl = YIELDO YELDWl = YIELDW YELDHl = YIELDl + YIELD2 + YIELDS YELDFl = YIELDF YELDSI = YIELDS Each present crop yield coefficient is then divided by an appro- priate relative crop yield coefficient from Q.22, Part II so that the yields maintained are 100 percent of relative yield (and calculated relative to the selected period of planting). Old standard relative yield coefficients which represent the values of the relative crop yield 3A1 coefficients which were used to calculate the existing present crop yield coefficients are also maintained for all crops except hay (i,e, the relative planting yield for hay is assumed equal to 1.0). The variables used for this purpose are COOX, where X is the first letter of the crop name. In order to check whether the standard crop yield has changed or not the old standard crop yield levels are maintained as variables. These are initialized the first month by setting them equal to the input or new standard yield: SYELDC = YIELDC SYELDO = YIELDO SYELDW = YIELDW SYELDH = YIELDl + YIELD2 + YIELDS SYELDF = YIELDF SYELDS = YIELDS 5,3 Also maintained are the old standard plant food levels. These are initialized in the first month by setting them equal to the input or new standard fertilization rates from Q.29-34, Part I as indicated below: Old Standard Plant Fbod for Oats = FOODOl = PFOODO " " " " " Corn = FOODCl = PFOODC " " " " " Wheat = FOODWl = PFOODW u n " " " Hay = FOODHl = PFOODH " " " " " Field beans = FOODFl = PFOODF Also maintained are the present or old actual plant food application levels. These are initially set equal to the actual or new fertilization rate from Q.59, Part I. That is: FOODCZ = 2 Fert(l,J) 3 FOODOZ = 23 Fert(5,,j) 3 room-12 = 2 Fert(4,j) 3 FOODHZ = 2 Fert(7,j) 3 FOODFl = z Fert(3,,j) §,§ If during any month either the new standard yield, the new standard plant food level (Q.29-34, Part I), the relative yield coefficient (Q.22, Part II) or the new actual fertilization rate (Q.39, Part I) is changed, a new yield for that crop is calculated. Taking corn as an example, in the first month the yield (YELDCl) is calculated. In all following months (2,...) a new corn yield (YELDCl) is calculated if any of the following are true: 3 (l) Poonczgé .21 Fert(1,j) J: (2) SYELDC 1! YIELDC (3) FOOIIJl 7i PFOODC (4) coac 7! CROPCO(K,2) where K = the standard planting period for or corn (PERIOD) (5) PERODl g PERIOD Calculation of the yield involves sending to the yield adjustment Subroutine (YLDCAL) (l) the present yield coefficient, (2) the new standard yield, (5) the old standard yield, (4) the new standard fertilization rate, (5) the old standard fertilization rate, (6) the 343 old actual level of fertilization, (7) the new actual level of fertili- zation, (a) the first (i) value for that crop (in RELYLD(i,j)), (9) the last (i) value for that crop (in YELYLD(i,j)), (10) the new standard yield divided by the new relative yield coefficient and (11) the agricultural subregion (Q.39(b), Part I). For corn this would be (1) YELDCl, (2) YIELDC, (3) SYELDC, (4) PFOODC, (s) FOODCl, (6) FOODCZ, (7) E7): Fert(1,j), (a) l, and (9) 4, (lO)YIELDC/CROPCO(k,2) and (11) ASE. H The new present yield coefficient and new values for the old standard coefficients are calculated by calling the YLDCAL subroutine. Following the call of YLDCAL the old standard relative yield coefficient is updated by setting it equal to the appropriate present relative yield coefficient. A parallel procedure is used for oats, wheat and field beans except that no planting period is explicitly involved. Fbr hay the same procedure is used except the first, second and third cutting rates are recalculated by making them the same ratio to total yield as given in Q.52, Part I. That is: YLDHll = (“535585) YELDHl _ YIELD2 1111:1112 — (YLDTOT) YELDHl YLDHlES = (gLTEfl—Lg-SE) YELDHl where YLDTOT YIELDl + YIELD2 + YIELDS and YLDHll = lst cutting hay yield YLDH12 = 2nd cutting hay yield YLDHlS = 5rd cutting hay yield 344 The variables calculated and maintained for this subroutine are listed below: Variable Definition Name Dimension Code Corn Grain Yield Coefficient YELDCl (l) Oats " " YELDOl (1) Wheat " " YBLDWl (l) Hay " " YELDHl (1) Field Beans " " YELDFl (l) Soybeans " " YELDSl (1) Standard Plant Food Oats FOODOl (l) " " " Corn FOODCl (l) " " " Wheat FOODWl (l) " " " Hay PCODHl (l) " " " Field Beans FOODFl (1) Standard Corn Yield SYELDC (l) " Oats " SYELDO (l) " Wheat " SYELDW (l) " Hay " SYBLBH (l) " Field Beans Yield SYELDF (l) " Soybean " SYELDS (1) Present Plant Food Level Corn FOODCZ (l) " " " " Oats FOODOZ (l) " " " " Wheat FOODWZ (l) 11 11 11 11 Hay FOODHZ (1) " " " " Field Beans FOODF2 (l) lst Cutting Hay Yield YLDHll (1) 2nd " " " YLDHlZ (1) 3rd " " " YLDHLS (l) 345 SUBROUTINE YLDCAL This subroutine calculates and adjusts yields for all crops except soybeans. It is called only from the YLDADJ subroutine. 6,1 The yield is calculated using the Agricultural subregion (ASR) to indicate which of the first four columns (j) of RELYLD(i,j) to work from initially. 6&1,1 First find the two fertilizer levels which the new standard fertilization rate is between (within the two (i) values given). Call the lower "low" and the higher "high." Then , new standard fertilization rate - "low" % Increment = "high" - "10W" Using the same two rows, the relative yields are found four columns over (in j + 4 of RELYLD(i,j)). The standard _ relative yield + % increment (rel. yield rel. yield relative yield - for "low" high - low If the new standard fertilization rate is equal to or greater than the rate found in the row indicated by the highest (1) value, the relative yield used is the relative yield found in that row. 55142, The new relative yield is found in the same manner using the new actual fertilization level. Then the new value of the present yield coefficient is new relative yield > standard relative yield new standard yield ( Then (1) the old standard yield is set equal to the new standard yield (2) the old standard fertilization rate is set equal to the new standard fertilization rate and (3) the old actual fertilization rate is set equal to the new actual fertilization rate. 346 SUBROUTINE CORN This subroutine handles the calculations required for the production and harvesting of corn silage and corn grain (including high moisture corn). .1,1 The total acreage of corn is taken from Q.22(a), Part I. The acreage planted during each of the four planting periods is calculated using this figure and Q.22, Part II. The acreage of each of the three corn grow systems indicated in Q.19, Part I as being used are calculated from the same figures by combining the middle two plant periods into the plant May system. The rest of the corn grow system from GS(l),...,GS(12) are set equal to zero. The corn grain yield coefficient is calculated by the crop yield adjustment subroutine. The corn silage yield coefficient is the corn grain yield coefficient (YELDCl) divided by 5. The weighted average grow silage yield is calculated by multiplying the silage yield coeffi- cient by the relative yields according to planting date (Q.22, Part II) and weighting according to the percentage planted in each planting period. The average harvested yield is calculated from the average grow yield by using the relative harvest yields and harvesting percen- tages in the same manner as corn silage grow. This calculation is done in September. .1,§ If, corn silage is grown, the amount to be harvested is calculated during the first month (Sept., Oct. or Nov.) in which the percentage of l. The acreages for all of the grow systems are contained in the GS(42) array. 347 the corn silage to be harvested (Q.22, Part II) is not equal to zero. If the coefficient in Q.37(b), Part I is: (a) between 0 and 25, the silage requirement is the coefficient multiplied by the number of cows. (b) greater than 25, the coefficient equals the silage tons to be harvested. (c) -l, the silage to be harvested is the total silo capacity minus the inventory of corn silage and hay crop silage on hand (STORES, Q.38, Part I). The total acreage of corn to be harvested as silage is the minimum of the total tonnage to be harvested divided by the average yield and total corn acreage. The total acreage of corn grain is calculated by subtracting the acreage of silage from total acreage. 1,; The landlord share acres equivalent for corn is calculated as the corn share rent acreage from Q.22, Part I multiplied by landlord share coefficient from Q.52, Part II. If the landlord share equivalent acreage is less than or equal to the acreage of corn grain, "landlord corn grain" acreage equals landlord share acres equivalent and landlord corn silage acreage equals zero. If the landlord share equivalent acreage is greater than the acreage of corn grain, "landlord corn grain" acreage equals acres of corn grain and the difference equals "landlord corn silage." 1,2 In the month planting takes place, seed and pesticide costs are calculated as SVCC(1,1) and svcc(2,1), (Q.23, Part II), multiplied by the acres of that system. Other variable costs of growing are distributed in the same way as labor and calculated for each growing system each month as: 348 (number of acres of that planting system)(CPD(month,j))(SVCC(3,1)) Where CPD is from Q.7l, Part II, SVCC is from Q.23, Part II and j = 1 if system is April Corn Grow System No. 11—13 (Q.19, Part I) = 2 " April " l4 4 3 " May " 15-17 = 4 " May " 18 = 5 " June " 19-21 -.-. 6 " June " 22 Fertilizer cost is calculated using fertilizer quantities from Q.59(a), Part I and prices from Q.55, Part II. Zgg In September, October and N0vember the acres of corn silage har- vested1 are calculated using the total silage acreage from above and Q.22, Part II. The quantity of silage is calculated as the acreage harvested multiplied by the average grow silage yield and the relative harvest yield coefficient. Other variable costs for harvest are cal- culated by using acres harvested and SVCC(1,5). In October, November and December the acres of corn for grain harvested are calculated using the total grain acreage and the coefficient from Q.22, Part II. Other harvest costs are calculated using SVCC(4,1). All harvest costs, requirements and production occur during the month of harvest. The amount of grain harvested is calculated from the acreage harvested, the yield coefficient and the coefficient from Q.22, Part II. If the corn harvest system is 7 or 8 the amount harvested is multiplied by 2 and added to ear corn produced. Otherwise the harvested amount is added to corn grain produced. 1. The acreage of each harvest system used each month are contained in HS(35). This indicates the actual acres of that crop harvested that month. 349 The "landlords share of grain" harvested (bu.) in each month in which harvest takes place is the landlord corn grain acreage multiplied by the percent of crop coefficient for this month for corn from Q.22, Part II multiplied by the corn grain yield multiplied by the relative yield from Q.22, Part II. The "landlords share of corn sihige" is calculated in the same manner using landlord corn silage acreage and the corn silage percent of crop coefficients. If the corn grain harvest system is not 7 or 8 the amount of corn grain produced is then calculated as the total amount harvested for the month minus the landlords share. The amount of corn silage produced is calculated in the same manner. The landlords share is accumulated for the year for both crops. If the corn harvest system is 7 or 8 the landlords share of the grain is multiplied by 2 to get landlord share of ear corn and this is subtracted from ear corn harvested to get ear corn produced. 1,6 In the first month in which corn grain is harvested (corn grain produced has a positive value) the quantity of high moisture corn to be harvested is calculated. If Q.57(c), Part I is: (l) -1, the HMC to be harvested is the total HMO storage space from Q.25(k), Part I minus the quantity of HMO on hand from STORED (4,14). (2) between 0 and 200, bushels of HMC to be harvested is the coefficient multiplied by the number of cows. (3) greater than 200, bushels of HMC to be harvested is equal to the coefficient. The quantity calculated is compared with storage capacity Q.25(k), Par1: I. If Q.26(b), Part I is zero and the quantity to be harvested is 350 greater than storage capacity minus STORED (4), the quantity to be har- vested is reduced to storage capacity minus STORED (4). If Q.26(b), Part I is l, and the quantity to be harvested does not exceed the storage capacity available by more than Q.62, Part II of the smallest size silo SC(1,2), the quantity to be harvested is reduced to the storage capacity available. If the quantity to be harvested does exceed that quantity, the flag variable HMCEXD is set equal to l and the total calculated quantity is to be harvested. If the quantity of HMC harvested to date equals the quantity to be harvested the above section is skipped. The program maintains two variables "high moisture corn harvested this year to date" and "high moisture corn harvested this month." During each month that corn is harvested the quantity of HMC harvested to date this year is compared with the total to be harvested. If the quantity harvested to date is less than the quantity to be harvested, HMC harvested this month is set equal to the minimum of (1) "corn grain harvested" or (2) quantity of HMC to be harvested minus quantity of HMC harvested to date. Then HMC harvested this month is added to HMC harvested to date and subtracted from "corn grain harvested this month." If the harvest system for corn silage is 1(Q.19(6), Part I), the silage custom harvest cost equals the acres harvested this month multiplied by the corn silage coefficient in Q,56, Part II. If the corn grain harvest system (Q.19(7), Part I) is 6, the corn grain custom harvest cost is the acres harvested this month multiplied by the corn grain coefficient from Q,36, Part II. 351 Variables Calculated and Maintained for this Subroutine are: Variable Definition Name Dimension Code 1. Seed Cost SEEDC (l) 2. Pesticide Cost PESTC (1) 3. Fertilizer Cost FERTC (l) 4. Silage Produced (Tons) CSPRl (1) 5. Other Grow Cost OGROWC (l) 6. Other Harvest Costs OHRVC (l) 7. Grain Produced (Bu.) CORNG (l) 8. Acres of Corn Grow April XN(5) (l) 9. Acres of Corn Grow May XN(4) (l) 10. Acres of Corn Grow June XN(5) (l) 11. Acres of Silage Harvested ACSHRV (2) 12. Acres of Grain Harvested ACGHRV (2) 13. Ear Corn Produced ECORN (l) 14. High Moisture Corn Harvested (Bu.) HMCHRV (2) 15. Landlord Share - Corn Grain CGLS (2) l6. Landlord Share - Corn Silage CSLS (2) l7. Landlord Share - Ear Corn ECLS (2) 18. Acres of Corn Silage XN(6) (l) 19. Acres of Corn Grain XN(7) (l) 20. Custom Harvest Cost CUSTMC (l) 21. Acres of Corn Grow Systems GS(l),...,GS(12) 22. Acres of Corn Harvest Systems HS(l),...,HS(ll) 352 SUBROUTINE HAY This subroutine handles all calculations on the production and harvesting of hay and hay crop silage. 8,; Total acres of hay crop is taken from Q.22(a), Part I. The acres to be seeded is calculated in June if grow systems 25 and 26 are used and in March if system 27 is used. Acres to be seeded is calculated by use of total acres multiplied by the reseeding rate, RESEED (Q.23, Part I). Seed cost is calculated using the acres seeded and SVCC(l,6) from Q.23, Part II. This occurs in August with direct seeding and April with companion crop seeding. Pesticide costs for the direct seeded acreage are calculated using acres seeded and SVCC(2,6) from Q.23, Part II. This occurs in September. Other variable costs are calculated using SVCC(3,6) and acres seeded. These are distributed in the same proportion as labor and are calculated as: (acres reseeded) (CPD(month,j)) (SVCC(5,6)) Where SVCC(3,6) is from Q.23, Part II CPD(i,j) is from Q.7l, Part II and j = 13 for direct seeding 14 for companion crop seeding 15 for user seeding distribution. Fertilizer costs are calculated using actual application rates (Q.83, Part II), the acres seeded and prices in Q.33, Part II. 8,2 The acreage of the hay crop to be harvested as hay crop silage is calculated (in May) if one of the harvest systems 20 through 24 are being used. The average expected yield for each cutting is calculated 353 using the relative yield coefficients (Q.22(a), Part I) plus the present yield coefficients for each cutting and Calculating a weighted average with the percent harvested during each month as weights. The amount of silage to be harvested is then calculated using Q.57, Part I. If the coefficient in Q.37, Part I is (a) between 0 and 25, the H.C. silage requirement is the coefficient multiplied by the number of cows. (b) greater than 25, the coefficient equals the silage tons to be harvested. (c) -l, the silage to be calculated is the total silo capacity minus the inventory of corn silage and hay crop silage on hand. (Stores, Q.58, Part I). The acreage of first cutting harvested as hay crop silage is the minimum of (l) the total tonnage requirement divided by the average yield calculated above and (2) the total acreage of [hay-—(acres of lst cutting harvested as pasture)] from Q156, Part I. If this is insufficient to meet the silage requirements and Q.55, Part I is 2:2 the acres of second cutting harvested as silage is calculated as the minimum of (1) total acres of hay minus acres of second cutting pasture and (2) the [total H.C. silage requirement minus the first cutting silage harvested] divided by the yield of second cutting Hay Crop Silage. If this does not meet the silage requirements and Q”54, Part I is 5, Similar calculations are made for third cutting. The acreage of hay by cutting is the total acreage of hay minus (1) the acreage used for pasture and (2) the acreage harvested as silage. In May, June and July the acreage of Hay Crop silage to be 354 harvested this month is calculated by using the percentage of crop harvested from Q.22, Part II and the acreage of first cutting to be harvested as silage as calculated above. In July, August and September similar calculations are made for second cutting (if Qn55, Part I is=a 2) and in August and September similar calculations are made for third cutting (if 11.35, Part I is 3). In the same months the same acreage calculations are made for hay using the total hay acreage from above and percentage coefficients from Q.22, Part II. 8,; The landlord share acres equivalent for hay is calculated as the hay share rent acreage from Q.22, Part I multiplied by the landlord share coefficient from Q.52, Part II. The landlords share (tons) is calculated as the landlord share acres equivalent multiplied by the first cutting coefficient for that month multiplied by the corresponding relative yield (Q.22, Part II) multiplied by the first cutting yield plus (if Qp55, Part I is 2 or 3) the landlord share acres equivalent multiplied by the corresPonding second cutting coefficients plus (if Q.55, Part I is 5) a similar calculation for third cutting. This sum (landlord share of hay) is subtracted from hay harvested to get hay produced. §,g Hay crop pesticide cost occurs in May and is SVCC(2,7) multiplied by the total number of acres of hay. Other variable grow costs are distributed in the same manner as grow labor and is calculated as: (acres of hay crop) (svcc(3,7)) (CPD(month,j)) Where svcc(3,7) is from Q.23, Part II, CPD(i,j) is from Q.7l, Part II and j = 15 if system 25 or 26 is used 355 14 if system 27 is used C.» II 15 I! N 28 H H Other variable harvest costs for hay are calculated as [(Acres of first cutting harvested) + (acres of second cutting harvested) (CUTCOF (1,1)) + (acres of third cutting) (CUTCOF(2,1)] svcc(4,7). Where (1) the acres are calculated above (2) CUTCOF(i,j) is from Q.l8, Part II (5) SVCC(i,j) is from Q.23, Part II. Other variable costs for hay crop silage are calculated in a similar manner. That is, other variable harvest cost equals: [(Acres of lst cut Hcs) + (Acres of 2nd cut HCS) (CUTCOF(1,2)) + (Acres of 3rd cut Hcs) (CUTCOF(2,2))] svcc(5,7). These calculations are made during May through September. 8,8, The quantity of hay harvested in any month is calculated using the basic yield for each cutting as calculated above and multiplying this by relative yield for that cutting from Q.22, Part II and multiplying the result by the number of acres harvested. The quantity of hay crop silage harvested is calculated using the same basic yield coefficients, plus a percentage saving factor to allow for reduced harvesting losses, multiplied by 5. This product is then multiplied by the HCS relative yield coefficients for the month of harvest from Q.22, Part II and the result is multiplied by the number of acres of the cutting being harvested. 8,8 The amount of pasture hay equivalent produced is calculated using pasture acres from Q.56, Part I and the distribution of pasture use from Q.19, Part II and adjusting for the reduction in hay equivalent harvested because it is pastured (PLOSS). This is calculated for the 356 months of May through September and assumed to be zero during other months. The calculations for each month are as follows: May tons June tons July tons Aug. tons Sept. tons Acres of first cutting from Q.56, Part I multiplied by yield of first cutting as calculated above multiplied by the proper Coefficient from Q.19, Part II. Same as May except use June coefficient. Same as May except use July coefficient plus acres of second cutting from Q.56, Part I multiplied by the yield of second cutting multiplied by the July coeffi— cient in row 2 if Q.55, Part I is 2 of the July coefficient from row 5 is Q.55, Part I is 3. Acres of second cutting from Q.56, Part I multiplied by the yield of second cutting multiplied by the August coefficient in row 2 if Q.55, Part I is 2 or the August coefficient from row 5 if Q.55, Part I is 5 plus acres of third cutting pasture multiplied by third cutting yield multiplied by the August third cutting coefficient from Q.19, Part II. Acres of second cutting multiplied by second cutting yield multiplied by September coefficient from row 2 Q. 19, Part II, if Q.55, Part I is 2. 2; Acres of third cutting multiplied by the third cutting yield multiplied by the September coefficient for third cutting, if Q.35, Part I is 5. 8.1 Fertilizer cost for each nutrient is calculated as the quantities of N,P O and K20 from Q.59, Part I multiplied by their respective price 2 5 from Q.53, Part II, 357 each multiplied by the acres of hay. The grow fertilizer cost equals the sum of the cost of the three elements. 8.8 The variables calculated and maintained for this subroutine are: Total Fertilizer Cost Total Pesticide Cost Total Other Variable Cost for Growing Fertilizer Cost Seeding Pesticide Cost + Grow Pesticide Cost Seeding Fertilizer Cost + Grow Seeding Other Variable Cost + Grow Other Variable Cost Variable Definition Name Dimension Code 1. Acres lst Cutting Hay Harvested AlHAY (2 2) 2. " HCS AlHCS (2 ) 3. " 2nd " Hay " A2HAY (2 2) 4 o I! II II HCS II AZHCS (2 ) 5 " 3rd " Hay " A3HAY (2 2) 6. " " " HCS " A3HCs (2) 7. Seed Cost SEEDH (l) 8. Pesticide Cost PESTH (l) 9. Other Variable Cost (Grow) OGROWH (1) 10. Other Variable Cost (Harvest) OHRVH (I) 11. Tons of Hay Produced HAYPRO (1) 12. Tons of Hay Crop Silage Harvested HCSPRl (l) 13. Fertilizer Cost FERTH (l) 14. Pasture Hay Equivalent Produced HEPAST (2) 15. Landlord Share of Hay (Tons) HAYLS (2) 16. Acres of Hay Crop Silage HCSLS (2) 17. Acres of Hay XN(16) (1) 18. Acres Equivalent of HCS XN(15) (l) 19. Acres Equivalent of Hay XN(17) (l) 20. Acres of Hay Grow Systems GS(25),...,GS(50) 21. Acres of Hay Harvest Systems HS(20)...,HS(2B) 358 SUBROUTINE WHEAT This subroutine makes all calculations relative to the production and harvest of wheat and wheat straw. 8,1 The total acreage of wheat is taken from Q.22(a), Part I. Wheat planted in each planting month is calculated using Q.22, Part II and total acreage. Seed cost is calculated in each month planting occurs and is the acres planted (acres of each system) multiplied by the cost of SVCC(1,2) from Q.25, Part II. Pesticide costs are calculated as the cost in SVCC(2,2), Q.23, Part II, multiplied by acres. The cost occurs in May. Other variable growing costs are distributed as wheat labor is distributed and are calculated for each grow system used as: (acres of that system) (CPD(month,j)) (SVCC(3,2)) Where CPD(i,j) is from Q.7l, Part II, SVCC(3,2) is from Q.23, Part II and j 7 if system is September Wheat Grow 8 if system is October Wheat Grow 9 if system is User Wheat Grow Fertilizer cost is calculated using Q.59(a), Part I, the amount of spring nitrogen applied (SPRNW), and Q.53, Part II. It occurs in the month of planting and is the acres planted multiplied by the sum of the three nutrient amounts multiplied by their respective prices. 8,8 The acres of wheat harvested in any one month is the total acreage multiplied by the harvest coefficients in Q.22, Part II. The grow yield coefficient is calculated as a weighted average of the present yield coefficient for wheat multiplied by the relative grow 359 yields from Q.22, Part II. The weights are the percentage of the Chip that is harvested each month. The amount of wheat harvested is the number of acres harvested in that month multiplied by the yield. The yield in the grow yield coeffi- cient from above multiplied by the relative yield coefficient for harvest in that month (Q.22, Part II). ‘88; The landlord share acreage equivalent for wheat is calculated as the wheat share rent acreage from Q.22, Part I multiplied by the land- lord share coefficient from Q.52, Part II. The landlord share wheat (bu.) is calculated each month during which harvest takes place and equals the product of landlord share acreage equivalent, grow yield, percentage of crop coefficient and the relative yield coefficient (Q.22, Part II). The amount of wheat produced is the amount harvested minus the landlord share for that month. Landlords share is also accumulated. ,8,4 All harvest costs and labor occur in the month of harvest. Other variable harvest costs are calculated as the acreage harvested multiplied by coefficient svcc(4,2) (Q.23, Part II). If wheat harvest system 14 is used, the straw yield is the coefficient in Q.31, Part I multiplied by the acres harvested. If the wheat harvest system is 12 (Q.19(9), Part I) the custom combining charge is the acres harvested this month multiplied by the wheat coefficient from Q.36, Part II. 360 9.5 The variables calculated and maintained by this subroutine are: Variable Definition Name Dimension Code 1. Seed Cost SEEDW (l) 2. Pesticide Cost PESTW (l) 3. Fertilizer Cost FERTW (l) 4. Other Variable Grow Cost OGROWW (l) 5. Other Variable Harvest Cost OHRVW (l) 6. Bu. Wheat Produced WHEAT (l) 7. Tons Straw Produced STRAWW (l) 8. Acres of Wheat Harvested XN(10) (l) 9. Acres of Wheat Plant September XN(8) (l) 10. Acres of Wheat Plant October XN(9) (l) 11. Landlord Share -Wheat (Bu.) WLS (2) 12. Custom Combining CUSTMW (l) 13. Acres of Wheat Harvested This Month AWHRV (1) 14. Acres of Wheat Grow Systems Gs(13),...,GS(la) 15. Acres of Wheat Harvest Systems HS(12),...,HS(15) 361 SUBROUTINE OATS This subroutine makes all calculations relative to the production and harvest of oats and oat straw. ‘18,; The total acreage of oats is taken from Q.22(a), Part I. Oats planted in each planting month is calculated using Q.22, Part II and total acreage. Seed cost is calculated in each month planting occurs and is the acres planted (acres of each system) multiplied by the cost of SVCC(1,5) from Q.25, Part II. Pesticide costs are calculated as the cost in SVCC(2,5), Q.23, Part II, multiplied by acres. The cost occurs in June. Other variable growing costs are distributed as oats labor is distributed and are calculated for each grow system used as: (acres of that system) (CPD(month,j)) (svcc(3,3)) Where CPD(i,j) is from Q.7l, Part II, svcc(3,3) is from Q.23, Part II and j 10 if system is April oat grow 11 if system is May oat grow 12 if system is USer oat grow Fertilizer cost is calculated using Q,59(a), Part I and Q.35, Part II. It occurs in the month of planting and is the acres planted multiplied by the sum of the three nutrient amounts multiplied by their respective prices. 18,8_ The acres of oats harvested in any one month is the total acreage multiplied by the harvest coefficients in Q.22, Part II. The grow yield coefficient is calculated as a weighted average of the present yield coefficient for oats multiplied by the relative grow 362 yields from Q.22, Part II. The weights are the percentage of the crop that is harvested each month. The amount of oats harvested is the number of acres harvested in that month multiplied by the yield. The yield is the grow yield coefficient from above multiplied by the relative yield coefficient for harvest in that month (Q.22, Part II). '18,; The landlord share acreage equivalent for oats is calculated as the oats share rent acreage from Q.22, Part I multiplied by the land- lord share coefficient from Q.52, Part II. The landlord share oats (bu.) is calculated each month during which harvest takes place and equals the product of landlord share acreage equivalent, grow yield, percentage of crop coefficient and the relative yield coefficient (Q.22, Part II). The amount of oats produced is the amount harvested minus the landlords share for that month. Landlords share is also accumulated. 18,; All harvest costs and labor occur in the month of harvest. Other variable harvest costs are calculated as the acreage harvested multiplied by coefficient svcc(4,3). If oat harvest system 18 is used the straw yield is the coefficient in Q151, Part I multiplied by the acres harvested. If the oats harvest system is 16 (Q.l9(1l), Part I), the custom combining charge is the acres harvested this month multiplied by the oats coefficient from Q.36, Part II. 363 10,5 The variables calculated and maintained by this subroutine are: Variable Definition Name Dimension Code 1. Seed Cost SEEDO (l) 2. Pesticide Cost PESTO (l) 3. Fertilizer Cost FERTO (l) 4. Other Variable Grow Cost OGROWO (l) 5. Other Variable Harvest Cost OHRVO (1) 6. Bu. Oats Produced OATS (l) 7. Tons Straw Produced STRAWO (l) 8. Acres of Oats Harvested XN(15) (l) 9. Acres of Oats Plant April XN(11) (l) 10. Acres of Oats Plant May XN(12) (1) ll. Landlord Share - Oats OATSLS (2) 12. Custom Combining CUSTMO (l) 15. Acres of Oats Harvested AOHRV (2) 14. Acres of Oat Harvest Systems GS(19),...,GS(24) 15. Acres of Wheat Harvest Systems HS(16),...,HS(19) 364 SUBROUTINE FIELD BEANS This subroutine makes all calculations relative to the production and harvest of Field Beans. ‘11,; The total acreage of field beans is taken from Q.22(a), Part I. The acreage planted during each of the two planting periods is calcu- lated by multiplying the total acreage by the coefficients for field bean grow in Q.22, Part II. Seed cost is calculated in each month planting occurs and is the acres planted (acres of each system) multiplied by the cost of SVCC(1,5) from Q.25, Part II. Pesticide costs are calculated as the cost in SVCC(2,5) (Q.25, Part II) multiplied by acres. The costs occur in the month of planting. Other variable growing costs are distributed as field bean labor is distributed and are calculated for each grow system used as: (acres of that system) (CPD(month,j)) (SVCC(5,5)) Where CPD(i,j) is from Q.7l, Part II, svcc(3,5) is from Q.22, Part II and j 18 if system is May plant (4-6 row) 19 if system is May plant (User) 20 if system is June plant (4—6 row) 21 if system is June plant (User) Fertilizer cost is calcukited using Q.39(a), Part I and Q133, Part II. It occurs in the month of planting and is the acres planted multiplied by the sum of the three nutrient amounts multiplied by their respective prices. 11,8 The acres of field beans harvested in any one month is the total acreage multiplied by the harvest coefficients in Q.22, Part II. 365 The grow yield coefficient is calcukited as a weighted average of the present yield coefficient for field beans multiplied by the relative grow yields from Q.22, Part II. The weights are the percentage of the crop that is harvested each month. The amount of field beans harvested is the number of acres harvested in that month multiplied by the yield. The yield is the grow yield coefficient from above multiplied by the relative yield coefficient for harvest in that month (Q.22, Part II). ‘11,; The landlord share acreage equivalent for field beans is calculated as the field beans share rent acreage from Q.22, Part I multiplied by the landlord share coefficient from Q.52, Part II. The landlord share field beans (bu.) is calculated each month during which harvest takes place and equals the product of landlord share acreage equivalent, grow yield, percentage of crop coefficient and the relative yield coefficient (Q.22, Part II). The amount of field beans produced is the amount harvested minus the landlords share for that month. Landlords share is also accumu- lated. 11,; All harvest costs and labor occur in the month of harvest. Other variable harvest costs are calculated as the acreage harvested multiplied by coefficient svcc(4,5) (Q.23, Part II). If the field bean harvest system is 29 (Q.l9(l8), Part I), the custom field bean harvest charge for this month is the acres harvested this month multiplied by the field bean coefficient from Q.55, Part II. 366 11.5 The variables calculated and maintained by this subroutine are: Variable Definition Name Dimension Code 1. Seed Cost SEEDF (l) 2. Pesticide Cost PESTF (1) 5. Fertilizer Cost FERTF (l) 4. Other Variable Grow Cost OGROWF (l) 5. Other Variable Harvest Cost OHRVF (l) 6 Bushels Field Beans Produced FBEANS (l) 7. Acres Field Beans Planted May XN(18) (l) 8. Acres Field Beans Planted June XN(19) (l) 9. Acres Field Beans Harvested XN(20) (l) 10. Landlords Share - Field Beans FLS (2) 11. Custom Combining CUSTMF (l) 12. Acres of Field Beans Harvested AFHRv(2) (2) 13. Acres of Field Beans Grow Systems GS(Sl),...,GS(56) 14. Acres of Field Beans Harvest Systems HS(20),...,HS(52) 367 SUBROUTINE SOYBEANS This subroutine makes all calculations relative to the production and harvest of soybeans. .1211 The total acreage of soybeans is taken from Q.22(a), Part I. Soybeans planted in each of the three planting periods are calculated using the total acreage and the percent of crop coefficients for soy- beans from Q.22, Part II. The acreage for the May plant system comes directly from the May plant period. The June plant system acreage is a sum of the two June plant periods (June 1-15 and after June 15). Seed cost is calculated in each month planting occurs and is the acres planted (acres of each system) multiplied by the cost of SVCC(l,4) from Q.25, Part II. Pesticide costs are calculated as the cost in SVCC(2,4) (Q.25, Part II) multiplied by acres. The cost occurs during the month of planting. Other variable growing costs are distributed as soybean labor is distributed and are calculated for each grow system used as: (acres of that system) (CPD(month,j)) (SVCC(5,4)) Where CPD(i,j) is from Q.7l, Part II svcc(3,4) is from Q.25, Part II, and j 22 if system is plant May (4-6 row) 25 if system is plant May (User) ll 24 if system is plant June (4-6 row) 25 if system is plant June (User) Fertilizer cost is calculated using Q.59(a), Part I and Q.55, Part II. It occurs in the month of planting and is the acres multiplied by the sum of the three nutrient amounts multiplied by their respective 368 prices. 18,; The acres of soybeans harvested in any one month is the total acreage multiplied by the harvest coefficients in Q.22, Part II. The grow yield coefficient is calculated as a weighted average of the present yield coefficient for soybeans multiplied by the rela- tive yield coefficients for planting soybeans from Q.22, Part II for each planting period. The weights for the average are the percentage of the crop planted in each period. The quantity of soybeans harvested during each month is the product of number of acres harvested in that month, grow yield, and the relative harvest yield coefficient (Q.22, Part II). 18,8_ The landlord share acreage equivalent for soybeans is calculated as the soybeans share rent acreage from Q.22, Part I multiplied by the landlord share coefficient from Q.52, Part II. The landlord share soybeans (bu.) is calculated each month during which harvest takes place and equals the product of landlord share acreage equivalent, average grow yield, percentage of crop coefficient and the relative yield coefficient (Q.22, Part II). The amount of soybeans produced is the amount harvested minus the landlords share for that month. Landlords share is also accumulated. 18,8 All harvest costs and labor occur in the month of harvest. Other variable harvest costs are calculated as the acreage harvested multiplied by coefficient svcc(4,4) (Q.25, Part II). If the soybean harvest system is 55(Q.l9(21), Part I), the custom combining charge is the acres harvested this month multiplied by the soybean coefficient from Q356, Part II. 369 12.5 The variables calculated and maintained by this subroutine are: ,Variable Definition Name Dimension Code 1. Seed Cost SEEDS (1) 2. Pesticide Cost PESTS (l) 3. Fertilizer Cost FERTS (l) 4. Other Variable Grow Cost OGROWS (l) 5. Other Variable Harvest Cost OHRVS (l) 6. Bu. Soybeans Produced SOYB (l) 7. Acres Soybeans Harvested XN(25) (l) 8. Acres Soybeans Plant May XN(21) (l) 9. Acres Soybeans Plant June XN(22) (1) 10. Landlord Share - Soybeans SLS (2) 11. Custom Combining CUSTMS (l) 12. Acres of Soybeans Harvested ASHRV (2) 15. Acres of Soybean Grow Systems GS(57),...,GS(42) 14. Acres of Soybean Harvest Systems HS(55),...,HS(55) 370 SUBROUTINE DAIRY This subroutine maintains and updates the dairy herd character- istics, including the dairy beef-enterprise. It makes all calculations relative to production, purchase and sale of animals and determines feed requirements. This subroutine is divided into two parts; an entry point and the subroutine itself. The entry point enters the animals in the herd matrix. It is called from the READI routine and is used at the beginning of the first month only. The main part of the subroutine is called each month. The use of an entry point was necessitated by the need to conserve core space. It was not required to provide the appropriate flow of calculations. Sections 15.1 and 15.2 below are included in the entry point. 15,; It is assumed that (1) all animals freshened at the average "desired age of freshening (months)" (Q.8, Part I) plus one month and (2) that the length of each lactation of all animals was equal to the average calving interval. It is further assumed that the character- istics of the herd are input as of the month prior to the first month simulated. These assumptions plus the input data in Q.6,7, and 8, Part I are used to develop an array which represents the characteristics of the herd. The array format is as follows: Animal Month Next Month Production Production Number ,Age Last Fresh Fresh Level Adjustment 1 2 3 4 ‘ 5 l 2 5 371 One animal number (row) is assigned to each animal. For example, if there were 5 first calf heifers that freshened in the month prior to the first month to be simulated the (1,1) cell of the matrix in Q.6, Part I would have 5 in it. Thus rows 1, 2 and 5 would be assigned to those 5 animals. Age would be calculated by Age = (average desired age of freshening) + l (i - l) calving interval + j - 1 = 25 + 1 + (l - 1) 13 + l - 1 = 26 months This is rounded off to the nearest month. Age as calculated here is as of the month prior to the first month simulated. The main program will update this to the first month during the first month calculations which are common to all months. Last month fresh ("Last") = l - j = l - l = 0 Before calculating next month fresh the animal is subjected to a proba- bility of being culled at the end of this lactation. An integer between 0 and 99 is drawn from the uniform distribution. This number is used in conjunction with the appropriate probability distributions to determine the fate of the animal. The appropriate probability distribution to use depends on the number of months the animal has been fresh. Let C = calving interval = CALVIN(Q.15, Part I) a = probability of dying = CWMORT(Q.48(a), Part II) x 91%391 B = probability of being involuntarily culled = SINVOL(Q.50(b), Part II) x-91%%91 6 = culling rate used = CULL2(Q.11, Part II) x QL%%QI X = number of months fresh = j Then ifxés (l) and the number drawn is less than I5; , 372 the animal is sold during the fifth month of lactation. This is accomplished by placing a flag (100,000) in the production adjustment column indicating involuntary sale of the animal for beef in the fifth month of lactation. The animal is assigned a value of "next" (next month fresh) equal to the last month fresh plus the average calving interval, rounded to the nearest integer. . __3__ sea (2) and the number drawn is between 143 and 143 the animal is culled during the ninth month of lactation. This is accomplished by placing a flag (200,000) in the production adjustment column indicating that the animal is to be culled during the ninth month of lactation. The animal is assigned a value of "next" equal to the last month fresh plus the average calving interval, rounded to the nearest integer. If 5< X‘5 9 and the number drawn is less than 142—8 the animal is culled during the ninth month of lactation as indicated in the above paragraph. If Xt>9 the animal is not to be culled this lactation. If the animal is not to be sold the value of "next" is calculated. To calculate "next" a number between 0 and 99 is drawn from a uniform distribution and if the number is: 0 - 4, "next" = "last" + 11 5 - 44, " = " + 12 45 - 69, " = " + 13 7O - e4, " = " + 14 85 - 94, " = " + 15 95 - 99, " = " + 16 Use of a frequency distribution of length of lactation in this way, however, may result in "next" coefficient less than one. Thus if the calculation alone gives values of "next" less than one, they will be assigned coefficients according to the following table: 373 Number Next Month Fresh 0 - 4 5 5 - 44 2 45 - 99 1 15,8 All heifers are also listed in this array. A heifer is indicated by setting "last" equal to -100. Unbred heifers will have only the "last" and age columns non-zero. For bred heifers "next month fresh" will have a value. This value is calculated by first calculating the month in which the animal is born which = (first month simulated - l) — j 9; (first month simulated - l) — l + 12 where the month must be between 1 and 12. If the age of the animal is 2'[(the "desired age of freshening" for animals born in that month) - 9] the animal is assigned a freshening date. A number between 0 and 99 is drawn from the uniform distribution and the "age of freshening" for that animal is: (a) "desired age of freshening" if 0‘5 number15 69 (b) "desired age of freshening" + 1 if 70 5.. number if: 89 (c) "desired age of freshening" + 2 if 90:6 number 5 99 Then "next" = ["age of freshening" - age]. If "next" 1 assign it as follows: Number Next Month Fresh 0 - 24 5 25 - 49 2 50 - 99 1 18,; The steers are now added to the array. One row is assigned to each steer. The age is entered from input and the production adjustment column is flagged (400,000) to indicate that the animal is a steer. At this point the array contains the herd characteristics, except for production level, as of the month prior to the first month to be Simulated. 374 At the beginning of each month the number of steer calves, STERC and steers on feed, STERF, is calculated. This is necessary because the age of placing animals on feed can be changed at the beginning of each month. "Steer calves" is given an initial value in the first month simulated equal to the number of steers in Q.7(b), Part I whose age was less than FEDAGE (Q.42(c), Part I). "Steers on feed" is cal- culated in the first month as the number of steers whose age is equal to or greater than(Q.42(c), Part I). Note: The number of units of the dairy replacements system is calculated as the sum of the number of heifers under 1 year, the number of heifers over 1 year, number of steer calves and number of steers on feed. The variable indicating the beginning number of animals (CATN02(5)) and the beginning cattle inventory value is calculated at the beginning of the first month. Values for the number of cows (all animals that have freshened), heifers over one year of age, heifers under one year of age, steers on feed and steer calves are maintained at all times. These values are adjusted as each purchase, sale or freshening takes place. The variable CATN02(5) is used to maintain these values between months. 18,8 An average mature equivalent milk production lactation average for the herd is calculated before the production levels are assigned. This is accomplished by first calculating a herd average mature equiva- lent coefficient. The herd average mature equivalent coefficient is calculated by: (1) Finding the age at which each of the animals freshened, which equals (age) - (absolute value of last month fresh) 375 (2) As the age of freshening of each animal is calculated, accumulate a sum of M.E. coefficients and then divide the sum by the number of cows, M.E. Coefficients to be used are taken from Q.76, Part II. If no initial herd is input, the average mature equivalent coeffi- cient is set equal to one. This coefficient is calculated only during the first month simulated. 18,8 The following algorithm calculates the herd M.E. lactation average during the first month and whenever the grain fed or forage quality levels are changed in Q.ll, Part II. Define the pounds of grain fed, forage quality and actual production from Q.10, Part I as "grain fed," "forage quality 1" and "actual produc- tion 1" respectively. Define the level of grain feeding and the forage quality in Q.ll, Part I as "grain fed 2" and "forage quality 2." The production adjustment routine maintains three variables called "grain fed 5," "forage quality 5" and "actual production 5." These are initially (in the first month) set equal to "grain fed 1," "forage quality 1" and "actual production 1." These variables are also used to test whether a new M.E. production average should be calculated (i.e. if "2" values do not equal "5" values for grain and roughage quality the calculation should be made). 15,5,1 If "grain fed 5" does not equal "grain fed 2," the actual production is adjusted for grain feeding level. A. First the row (i) of the grain transition constants table to be used is calculated. The calculation is as follows: i = 1 if "forage quality 5" = l and "actual production" is > 13,000 B. = 2 if "forage quality 5" ___: 4 H :7 H :9 H The column calculated The column calculated H H H H H H 376 l and 11,0004- "actual production" é 13,000 = l and "actual production" is 4 11,000 = 2 and "actual production" is > 15,000 = 2 and ll,000 5 "actual production" 5 15,000 = 2 and "actual production" is < 11,000 = 5 and "actual production" is > 15,000 = 5 and 11,000‘2 "actual production" 13,000 = 5 and "actual production" is‘< 11,000 (J) indicated by the old level of grain feeding is using integer arithmetic as follows: J: "grain fed 5" — 1500 500 l é:J‘éll (K) indicated by the new level of grain feeding is using integer arithmetic as: K: "grain fed 2" - 1500 500 If "grain fed 2" is greater than "grain fed 3" (KS1 J), then when (l) K = J, the new "actual production 5" equals (action production 5) + GTC(i,J) ( where GTC( 1,.) :grain fed 2" - "grain fed 5" 500 ) is from Q.75, Part II. (2) K > J, values of L,M and N are calculated, using integer arithmetic for M, where M: L ("grain fed 5" 1000 ) 1000 "grain fed 5" — M ) 377 If L > 500 then subtract 500 from L 500 — L T : and N 500 Then P, Q and R are calcukited using integer arithmetic for P, where P =("grain fed 2" lOOO ) 1000 Q = "grain fed 2" - P If P.> 500 then subtract 500 from P .21. and R ’ 500 Then the new "actual production 5" equals K-l ("actual production 3") + (N)GTC(i,J) + (R)GTC(i,K) + 2 GTC(i,j) j=J+l If "grain fed 2" is less than "grain fed 3" (K‘5 J), then when (1) K = J, the new "actual production 5" equals "grain fed 3" - "grain fed 2") H 0 H ( actual production 5 ) - ( 500 GTC(i,J) NOTE: This is identical to D.(1) (2) K < J, values of L, M and N are calculated (using integer arithmetic for M) where H t M = ( grain fed 5 I 1000 ) 1000 L "grain fed 5" - M If 11> 500 subtract 500 from L .1. N _ 500 Then P, Q and R are calculated (using integer arithmetic for P) where H 0 H _ grain fed 2 ) P — ( 1000 1000 "grain fed 2" - P D II 378 If Q 7 500 subtract 500 from Q _ SOO-P) andR—( 500 Then the new "actual production 5" equals J—l ("actual production 3") - (N)GTC(i,J) - (R)GTC(i,K) _ E GTC(i,j) j=KTl After "actual production 5" is adjusted for the change in grain feeding level "grain fed 5" is set equal to "grain fed 2." 15,5.2 If "forage quality 5" does not equal "forage quality 2," the actual production is adjusted for forage quality change. A. First the correct column (J) is calculated using integer arithmetic where "grain fed 5" - 1250 500 J = 1 é J 2 12 B. If "forage quality 5" = l and "forage quality 2" = 2 and: (1) "actual production 1" >-l5,000, then the new "actual production 5" equals ("actual production 5") - FTC(1,J), where FTC(i,j) is from Q.74, Part II. (2) 11,0006 actual production l ‘5 13,000, then the new actual production 5" equals ("actual production 5") - FTC(2,J) (5) "actual production 1" < 11,000, then the new "actual production 5" equals ("actual production 5") - FTC(5,J). C. If "forage quality 5" = 2 and "forage quality 2" = l the adjust- ments are the same as for B above except that FTC(i,J) is EQQEQ to the old "actual production 5" to get the new value. D. If "forage quality 5" = 2 and "forage quality 2" = 5 and: (1) "actual production l".> 15,000, then the new "actual production 5" equals ("actual production 5") - FTC(4 J) 9 379 (2) 11,000 9 "actual production l"EE 15,000, then the new "actual production 5" equals ("actual production 5") - FTC(5,J) (5) "actual production l"‘c 11,000, then the new "actual production 5" equals ("actual production 5") - FTC(6,J). E. If "forage quality 5" = 5 and "forage quality 2" = 2 the same equations are used as in D. above except that in each case FTC(i,j) is 88828 to "actual production 5." F. If "forage quality 5" = l and "forage quality 2" = 5 and: (1) "actual production 1" > 15,000, then the new "actual production 3" equals ("actual production 3") - FTC(1,J) - FTC(4,J) (2) 11,000‘6 "actual production l" 9 15,000, then the new "actual production 5" equals ("actual production 5") - FTC(2,J) - FTC(5,J) (5) "actual production l":< 11,000, then the new "actual production 3" equals ("actual production 3") - FTC(3,J) - FTC(6,J). G. If "forage quality 5" = 5 and "forage quality 2" = l, the same equations are used as in F. above except that in each case the two FTC(i,j) are ggggg to "actual production 3." After "actual production 5" has been adjusted for the change in roughage quality, "forage quality 5" is set equal to forage quality 2. 15,5,5 The herd average M.E. lactation average is calculated after an adjustment for a change in grain feeding level or forage quality or both is made. The initial calculation of the M.E. lactation average is AVELAC = (ACTPR3) (-%-2- (AVGME) where AVELAC = herd M.E. lactation average C = calving interval (Q.15, Part I) and AVGME = herd average M.E. coefficient This adjusts the actual production for length of lactation and 380 average age of the herd. However, because the actual production level is achieved by sale of some animals prior to completion of the lactation (before the dry period), production must be adjusted for this. This is accomplished by multiplying the above calculated average by l _ |.Q_:_§ (5 - a _ 3) + C ' 5 (a + 3)] C C where the symbol definitions are those used in section 15.1. The production level of each animal for any lactation equals a/b where a = herd M.E. lactation average b M.E. Coefficient for that animal The production level for each animal that has freshened is entered at the beginning of the first month to complete entry of the dairy herd. This same equation is used to calculate the production during all future lactations. These calculations are carried out for each animal at the time it freshens. This procedure allows for a gradual influence of changes in feeding rates. To represent the lactation curve of the animals a matrix with row identification indicating month of lactation, column identification representing the length of lactation in months and the cells containing the percent of the total lactation milk production produced in that month is used (Q.77, Part II). If we call this matrix XLP(i,j), the amount of milk produced by any cow in any month would be: (Production level) (XLP(Simulation month - last month fresh, next month fresh - last month fresh)). 18,8 Each month the characteristics of the herd are updated. The age is advanced one month. If an animal becomes 20 years of age it is sold for beef. 381 As cows are sold, purchased, etc. a running total called present number of cows (COWS) is maintained which is the total of the number of cows that have freshened and remain in the herd. The initial value comes from the number of cows from Q.6, Part I. Each cow that is added increases this by one, each one sold decreases it by one. The number of "Heifers under one year of age" and "heifers over 1 year of age" are maintained in a manner similar to cows. The heifers born and raised are added to "heifers under 1 year of age." For each animal whose age is changed from 12 months to 15 months the "heifers under 1 year of age" is decreased by l and the "heifers over 1 year of age" is increased by 1. 15.6.1 As the ages of the animals are being updated, when an animal of 12 months becomes 15 months of age the animal is sold if the number of heifers over 1 year of age is equal or greater than Q.l7(b), Part I. If Q.26(a), Part I is zero and: (a) the number of heifers equals or exceeds 25(d), Part I, the animal is sold (b) the number of heifers is less than 25(d), Part I, the animals age is set to 15, "heifers under 1 year" decreased by l and "heifers over 1 year increased by 1. If Q.26(a), Part I is l and: (a) the number of heifers equals or exceeds 25(d), Part I and (l) DELAY(Q.59, Part II) - EXCEDZ is greater than zero, the animal is sold and HEFEXD is set equal to l (2) DELAY(Q.59, Part II) - EXCEDZ is less than or equal to zero, the animals age is updated as in (b) above (b) the number of heifers is less than 25(d), Part I, the animals age is updated as in (b) above. 382 When an animal is sold, the "heifers under 1 year sale" is increased by l, "heifers under 1 year" is decreased by 1 and the row is set equal to (-1, blank, blank, blank, blank) and heifers under 1 year sold is increased by 1. 15.6.2 If the animal whose age is being updated is a steer (as indi- cated by the flag in the production adjustment column) and the age equals the age animals are put on feed (Q.42(c), Part I) then "steer calves" is reduced by one and "steers on feed" is increased by one. If the age equals the sale age (Q.42(d), Part I) then "steers sales" is increased by one "steers on feed" is reduced by one and the animal is removed from the herd matrix. Bred heifers indicated to be purchased by Q.ll, Part II (Purchases made each year) are added to the herd by setting the age column of a previously empty row equal to the age indicated and setting the value of "next" equal to the present month plus 1. The number of animals purchased is added to a "bred heifers purchased" variable, heifers over 1 year bought and number of heifers over 1 year. Fresh cows purchased annually by Q.ll, Part II, are added to the herd by setting the age column of a previously empty row equal to the age indicated, setting the month of freshening equal to the present month and flagging the production adjustment column (600,000) to indicate that no calf is born. The number of animals purchased is added to "cows purchased," cows bought and number of cows. _L;,Z In the updating of the herd inventory all animals not to freshen this month (i.e. animals for which next month fresh does not equal this month) are updated first. Then those that freshen this month which have freshened before ("last month fresh has a value") are updated. Last 383 updated are animals freshening for the first time. 15,7.1 Prior to the calculations involved in cow freshening, calcu- lations as to whether there is space for her or not are made. If the system in use is l (i.e. Q.l9(a), Part I = l), the number of cows equals or exceeds capacity 1 and Q.26(a), Part I is 0 the animal is sold. If capacity is greater than the number of cows, the normal freshening calculations are allowed to occur. If the system used is 2 through 7 (Q.l9(a), Part I = 2 through 7), the number of cows equals or exceeds capacity 2 and Q.26(a), Part I is 0, the animal is sold. If not, the normal freshening calculations are allowed to occur. If Q.26(a), Part I is l and: (a) Q.l9(a), Part I = l and number of cows equals or exceeds capacity 1 or Q.l9(a), Part I = 2 through 7 and number of cows exceeds capacity 2 and: (1) DELAY - EXCEDl is greater than zero, the animal is sold by changing the row to (-1, blank, blank, blank, blank) and adding 1 to "fresh cows sale" if the animal has freshened ("last month fresh" has a value) or "bred heifers sale" if the animal had not freshened. Also if the animal is a cow add 1 to cows sold and subtract 1 from number of cows. If the animal is a bred heifer add 1 to heifers over 1 year sold and subtract 1 from heifers over 1 year. The flag variable called COWEXD which is initialized at zero is set equal to l. (2) DELAY(Q.15, Part II) - EXCEDl is less than or equal to zero the animal goes through normal freshening calculations and COWEXD is set equal to one. (b) 384 the number of cows does not exceed capacity 1, the cow goes through normal freshening calculations. Capacity 1 is the minimum of (l) the number of cows from Q.l7(a), Part I and (2) "stall capacity 1." Capacity 2 is the minimum of (l) the number of cows from Q.l7(a), Part I and (2) "stall capacity 2." "Stall Capacity 1" = cow stall capacity (Q.25(b), Part I) + K‘+ L Where K = and L = the minimum of (l) the number of cows that are allowed to be housed with the heifers which equals the product of the stall capacity for cows (Q.25(b), Part I), the percentage that number of cows can exceed capacity (COWCAP(1)) and the per- centage of that excess capacity that must be housed with heifers (C0WCAP(2), Q.58(a), Part II) and (2) the number of Spaces available for housing cows with the heifers which equals the total heifer and dry cow capacity minus the number of heifers. the amount by which the number of cows may exceed the number of cow stalls without any requirement that they be housed elsewhere. This equals the product of total existing cow stall capacity (Q.25(b), Part I), the percentage that number of cows can exceed capacity (COWCAP(l)) and the percentage of that excess that can be housed with the cows (1 - COWCAP(2), Q.58(a), Part II). "Stall Capacity 2" is calculated the same way except that (1) Free Stall capacity is used (Q.25(a), Part I) (2) The percentage figures in Q.58(b), Part II are used instead of those in Q.58(a), Part II. That is, C0WCAP(3) and COWCAP(4) replace C0WCAP(l) and COWCAP(2) respectively. 385 15.7.2 For each animal, if "next month fresh" is the month presently being simulated: A. "Month last fresh" becomes the present month. Note: Simulated months are numbered consecutively from 1 to the number of years Simulated times 12. B. If the production adjustment column is not flagged to indicate that no calf is born (600,000), a number Z, from O - 99 is drawn from a uniform distribution. If 0 6 Z < 0/2 the calf was a bull, but it died. It is added to bull calves born and bull calves died. e/2ts z < a the calf was a heifer, but it died. It is added to heifer calves born and heifer calves (heifers under 1 year) died. a 5 Z < [a + 19%51] the calf is a bull and is added to an accumulation variable indicating the number of bull calves that could be raised. Z 5 99 the calf is a heifer and is added to an accumu- lation variable indicating the number of heifer calves that could be raised. Where: a = calf mortality rate (0.47, Part II) The variable "calves born" is increased by one regardless of the fate of the calf. C. A new level of production is calculated from the age of the animal and the herd M.E. lactation average using the procedure indicated in 12.5 above. The production adjustment, if any, is then added to the production level. D. Prior to calculation of "next" (next month fresh) the animal is 386 subjected to a probability of dying or being culled. A second number, Z, between 0 and 99 is drawn from a uniflirm distribution. If: (1)03 Z40, (2)6Y-‘—-Z< (a the animal died this month, age becomes -1 (to indicate that the row is empty) and the rest of row becomes zero. A variable called "cows died" is increased by one, number of cows is decreased by one, and the animal is removed from the herd matrix. + B), the animal is to be sold at one-half regular cow prices in the fifth month of lactation. This is indicated by placing a flag (100,000) in the production adjustment column. "Next" is set equal to the present month plus the average calving interval. (5) (a + 8) 9 Z1: r, the animal is to be culled for low production (4) réz<8, and sold in the ninth month of lactation. This is indicated by a 200,000 in the production adjustment column. "Next" is set equal to the present month plus the average calving interval. and the culling rate used is less than the required rate, the animal should be culled if production is to be maintained but she is not so a negative production adjustment is assigned. The magnitude of the production adjustment is taken from Q.75, Part II. To represent the fact that the greater the difference between the required and actually 387 used culling rates the greater the effect on production, the magnitude of the production adjust- ment is determined by the absolute value of the difference between Z and the required culling rate. (5) 3‘22 Z ‘ 8 and the required culling rate is less than the used rate, the animal is culled at the end of the lactation but need not be to maintain production. A positive production adjustment, with magnitude determined as in (4) above, is added to an array of positive adjustments. For each animal that is updatedlnnznot culled a positive production adjust- is added to the production adjustment column if the array of adjustments contains any values. Where a = Percent of cows dying (CWMORT) (average calging interval) 3 = Involuntary sale (average calvinginterval) percentage (SINVOL) 12 3‘: Minimum of (i) culling rate required to maintain production (Q.lO, Part I) (ii) culling rate used (Q.ll, Part I) 6 = Maximum of (i) and (ii) above. E. The new value of "next" is then calculated in the same manner as when the array is being initialized (section 15.1) except that there will be no problem with the "next" value being less than one. 15,7,5 Open heifers (indicated by a -100 in the "last" column and a zero in the "next" column), whose age equals the desired age of freshening minus nine, are subjected to a probability of dying or being 388 culled for sterility and then given a date of freshening. This is accomplished by drawing a number, Z, from a uniform distribution (0,99) and if: 15,7,4 A. 0'5 Z1< a, the animal dies this month, "heifers over one year died" is increased by l and number of heifers over one year is decreased by one. aré Z < (a + B), the animal is culled for sterility in three months. This is accomplished by giving the animal a value of "next" equal to the present month plus three and flagging the production adjustment column (500,000). (or + 8)é Z < (a! + 8 ), the value of "next" is the present month plus nine. (0 + 8 + ) 5'Z < 91, the value of "next" is the present month plus 10. 91é Z < 100, the value of "next" is the present month plus 11. where: a = the mortality rate for heifers B the percent of heifers sold for infertility 8': conception rate for heifers Cows in their ninth month of lactation that are flagged to be culled (200,000) are sold for beef. That is, for each such animal, the variable indicating the number of cows sold for beef (cows sale - beef)1 is increased by one and the row values in the matrix are set to (-1, 0, O, 0, 0). However, because it is assumed that the animal is sold on the fifteenth of the month, the milk production for this month 1. Animal "sale" variables indicate the number of animals of that type to be sold (a value calculated for) by the STORES routine. Animal "sold" variables indicate the number of animals sold regardless of whether the value is input or calculated by the STORES routine. 389 is calculated and one-half of that amount is added to the total milk produced for the month. "Cows sold" is increased by one and the number of cows is decreased by one. Animals in their fifth month of lactation which are flagged to be culled for physical injury, mastitis, etc. are sold. The herd matrix row values are set to (-l, 0, O, O, O) and the number of cows sold for beef is increased by one-half. However, because it is assumed that the animal is sold on the fifteenth of the month, the milk production for this month is calculated and one-half of that amount is added to the total milk produced for the month. "Cows sold" is increased by one and the number of cows is decreased by one. Heifers that have been flagged to be culled for sterility (300,000), and for which the value of "next" equals the present simulation month, are sold. Open heifers sale is increased by one, heifers over one year is decreased by one and the row of the herd matrix is set to (-l, 0, 0, 0, 0). 15,1,8 Each animal which is not culled is assigned a positive produc- tion adjustment if the array containing positive adjustments contains any values. This is accomplished by adding the first number of the array to the production adjustment column of the herd matrix row for that animal and shifting the array of adjustments. 18,8 Animals indicated to be sold by input type of change 7 (Part III) as indicated in the "livestock sale hold" matrix are found in the herd matrix. If the number of animals to be sold is negative, the Sign of the number indicates that steers are to be sold and the magnitude indi- cates the number to be sold. If insufficient animals of the age indicated are available, those one month older will be sold; then those 390 one month younger, then those two months older. The age range is con- tinually widened until the indicated number of animals are sold or all animals of that sex are sold. If the number of cows (steers) to be sold exceeds the number of cows, heifers and heifer calves (steer calves and steers on feed) on hand, all those on hand are sold. If females are sold, milk production for this month (if any) is calculated and one—half of the amount added to milk production for the month. The row for that animal is changed to (-l, 0, O, O, 0). If price is input, the number of animals times the price is added to live- stock sales for this month. If price is generated (-1 entered for price) the number of animals is added to cows sale (dairy), bred heifer sale, open heifer sale or heifers under 1 year sale depending on age. If the animals sold have freshened "cows sold" is increased by one and "cows" decreased by one. If the animal was over one year of age "heifers over one year sold" is increased by one and "heifers over one year" decreased by one. If the anhnal is less than 12 months of age "heifers under one year sold" is increased by one and "heifers under one year" is decreased by one. If steers are sold and the price is input, the value of the steers price times number sold is added to livestock sales. If price is to be generated the number of animals sold is added to steers sale. If the age of the steers is less than the age at which steers are put on feed (Q.42, Part I) steer calves is reduced by the number sold. Otherwise steers on feed is reduced by the number sold. Regardless of age, steers sold is increased by the number sold. If the number of animals to be sold exceeds the number on hand, the unsold number is maintained as a variable, CATN05(1) for females 391 and CATN05(2) for steers. If these numbers are non-zero, calves that would otherwise be raised (calculated later) are sold. .88,8 Milk production is then calculated as indicated in section 15.5.5 for each animal and added to the total production for the month. This calculation occurs at this time to avoid inclusion of a complete months milk production for cows purchased or sold. 18,18 Following this, the animals indicated to be purchased by input type of change 6 (Part III) as indicated by the livestock purchase hold matrix, are added to the herd. One row in the herd matrix is assigned to each animal. Age is taken from input. If the animal has freshened, the value of "last" equals the number of the simulation month minus the "months since last fresh" from input. If fresh cows are purchased ("last" equals zero),1 freshening occurs in the same manner as indicated in section 15.7.2. If the animals purchased are to freshen this month ("next" equals -l), calving and freshening occur in the manner indicated in 15.7.2. If a bred heifer is purchased ("last" equals -1 and "next" is greater than zero), last is set equal to -lOO and next is calculated as the present month simulated plus the months until next fresh from input. If unbred heifers are purchased "last" is set equal to -100 and the number of animals purchased is added to heifers under one year or heifers over one year as determined by age . If "months since last fresh" has a value but "months until next fresh" does not the animal is subjected to a probability of being culled. If the number Z, drawn from a uniform distribution (0,99) is: 1. In this paragraph "last" and "next" refer to input values of "months since last fresh" and "months until next fresh" respectively. 392 A. O 5 Z‘ 0, then M(i) of the machines are sold. This is done by searching the machinery set to find M(i) machines with machine code (i) and adding Q.44, Part II percent of the depreciated value to "machinery sales" and adding (100 - Q.44, Part II) percent of the depreciated value to accumulated machinery depreciation. B. M(i) = 0, no immediate change is made. Following sale of unused machines, the following list of equations is used to determine the number of each machine required and to indi- cate when machines are to be purchased. If an equation calculates the number of machines required, that number is rounded up and the number of machines on hand (M(i)) is subtracted from it. This gives the number of machines to purchase. If the equation does not calculate the number of machines required, the discussion with the equation indicates ‘when purchases are to be made. When a machine is to be purchased, the code number of that machine is added to an empty cell of the first column of the machinery inventory 425 matrix. If this is the first month simulated and the answer to Q.20(a), Part I is 0, the new cost and life are taken from Q.68, Part II, machines are divided into 5 investment classes and age and depreciated value are calculated in the same way as is done if input code 11 is used in input (spelled out above). If this is not the first month or the answer to Q.20(a), Part I is 1, only the machinery code number is entered. The remaining elements of the row in which the new machine is entered are left blank. 1615 The equations listed below require the total number of acres handled by each harvest system. Because the HS array indicates only the number of acres harvested thi§:gonth, an array called TT is used to indicate the total acres harvested by each harvest system. For oats, wheat, field beans and soybeans the number of acres harvested equals the acreage planted. For corn, a variable called CSACRE is calculated by the corn subroutine which indicates most recent calculation of acres of corn silage harvested or to be harvested. CSACRE is originally estimated for the first year and input by the user (Q.25(b), Part I). Thus the TT variable for the corn Silage harvest system in use is set equal to CSACRE. The TT variable for corn grain equals the total corn acreage minus CSACRE. A similar variable, HSACRE, is maintained for hay crop sikige. This is initialized by Q.25(b), Part I. Thereafter it is calculated in the hay routine as the sum of (l) the acreage of first cutting hay crop silage, (2) the acreage of second cutting hay crop silage multi- plied by the appropriate coefficient from Q.lB, Part II, and (5) the acreage of third cutting hay crop silage multiplied by the appropriate coefficient from Q.lB, Part II. The TT variable for hay is calculated 1426 in similar fashion using the hay coefficient from Q.lB, Part II. Acreage of first cutting hay is calculated as the total hay acreage minus first cutting pasture and first cutting hay crop silage. Acreage of second and third cutting hay are calculated in similar fashion using second and third cutting pasture and hay crop silage. 16,5,1 For machines 1 through 6, 9, and 10, the number of machine i's required equals the maximum of (Di + Fi) and (Di + Si) where Si + + + + F1 10 1 D1 = 32:1 [LS-1(1,J)XLS(J) m ] = [(GS(l)JGSM(i,l) + GS(5)JGSM(i,5) + GS(9)JGSM(i,9) + TT(20)JHSM(i,20) TT(25)JHSM(i,25)) (EEB(%:11)) J [(GS(2)JGSM(i,2) + GS(6)JGSM(i,6) + GS(10)JGSM(i,lO) + GS(19)JGSM(i,19) + GS(22)JGSM(i,22) Gs(16)JCSW(i,16) + TT(21JGSM(i,21) + TT(25)JGSM(i,25) + GS(5l)JGSM(i,5l) GS(57)JGSM(i,57) + GS(l5)JGSM(i,l5) + GS(40)JGSM(i,40) + TT(26)JGSM(i,26) GS(54)JGSM(i,54)) (—-—JL-ja J TCR(i,l2 + [(GS(5)JGSM(i,5) GS(7)JGSM(i,7) + GS(ll)JGSM(i,ll) + GS(20)JGSM(i,2) + GS(25)JGSM(i,25) GS(l7)JGSM(i,l7) + TT(22)JHSM(i,22) + GS(52JGSM(i,52) GS(55)JGSM(i,55) + GS(58)JGSM(i,58) + GS(14)JGSM(i,l4) + GS(41)JGSM(i,4l) TT(27)JGSM(i,27)) (EEBK%:IE)) J [(GS(4)JGSM(i,4) + GS(8)JGSM(i,8) + GS(12)JGSM(i,12) + GS(24)JGSM(i,24) TT(24)JHSM(i,24) + GS(15)JGSM(i,15) + GS(55)JGSM(i,55) + GS(56)JGSM(i,56) GS(59)JGSM(i,59) + GS(42)JGSM(i,42) + TT(28)JGSM(i,28) + GS(2l)JGSM(i,21) GS(18)JGSM(i,18)) (TE§(%:IZ)) 1 (32(2) GS( ) CSI(i )) ( l ) . J V ,. _____ 3:25 J J 2000 = [(TT(2)JHSM(i,2) + TT(7)JHSM(i,7)) (TE§(%:15)) ] + [TT(5)JHSM(i,5) 1+2? + TT(8)JHS1‘4(i,8) + TT(9)JHSM(i,9) + TT(30)JHSH(i,30) + TT(54)JHSM(i,54)) 1—7—3-1 [(TT(4)JHSM(i,4) + TT(10)JHSH(i,lO) + TT(31)JHSI‘-vi(i,51)) (___-TCR(:]§: 17))l + + [(TT(5)JHSM(i,5) + TT(11)JHSM(i,11) + TT(32)JHSM(i,32) + TT(55)JHSM(i,55) 1 (TCR(i,18)j + 15 19 (__L.) y: TT(.j )w3.1(1,3) + 2 T‘I‘(j' )JHS-I(i,.)) + TT(1)JHSVI(1,1) 2000 3:12 3:16 4. TT(6)JHSM(i,6) + TT(29)JHSM(i,29) + TT(55)JHSM(i,55))] This algorithm essentially assumes that the number of machines required depends on the dairy requirements plus the Fall crOp require- ments or the dairy requirements plus the Spring crop labor. Of these time periods the one with the peak load determines the number of machines required. 16.5,2 The quantity of machines number 7's,which is a user defined machine, required is calculated using Q.80(a), Part II to indicate the number of acres or animals that one machine can handle. This same method is used for machines 8, ll, 20, 59, 58, 70, 76, 110. 10 No. Machine 7 = (UR§%I_1)) 21 (XLS)J)LSM(7’J)) + (URQ(1 2)) 9 J: ’ (422.: (GS( )JGSM n) ( 1 )(52? TT( )JHSI(7 )) . , .’. + __ . 1» ,. No. Machine 8 same as for 7 except 7 = 8 and URQ(i,j) = URQ(i + 1,3) 428 No. Machine 11 = same as for 7 except 7 = 9 and URQ(i,j) = URQ(i + 2,3) 10 16.5,5 No. Machine 12 = 1 if z: XLS(j)LS;‘~I(12,j) J=l V O and 35 >3 TT(j)JHSM(12,J) - J=l I O 0 if both sums above = 0 35 2 if )3 TT(j)JHSM(12,j) > 0 i=1 W 35 = 3 if 5: TT(j)JHSIvi(12,J) 7 150 J=l 35 4 if E TT(,j)JHSM(12,j) > 400 i=1 No. Machine 15 = same equation as 12 except 12 is replaced by 15 16,5,4 No. Machine 14 through 19 are calculated as: 10 No. of machine 1 = s: [XLS(j)LSM(i,j) i=1 10 [ 1 l§,5,§ Machine 20 = 2 XLS(j)LSM(20j ——-——] 3:1 ’ ) URQ(4,1) 1 1 DMR(i=l5,j) 16,5.6 Machines 21 through 28 (silo unloaders) is calculated as below: 28 10 If [Q.25(f), Part I minus 2: I: [XIS(j)LSM(i,J)TONS(i)] = Q 5 0, i=21 j=l where TONS(i) is from Q.80(b), Part II, then the number of each Size 10 unloader being used 2 XLS(j)LSM(i,j) > 0 is 1. If these machines 3:1 are not present in the machinery set, they are purchased and the program 429 moves on to machine 29. If Q is 7 0 a second calculation is made. T is calculated where 28 10 T = [Q.25(f), Part I minus 2 2 [XLS(j)LSM(i,j)TONS(i)M(i)] i=21, j=1 where TONS is from Q.80(b), Part II and M(i) is the number of machine 1's in the machinery set. If T‘$ 0, no additional unloaders are to be purchased. If T 3 Q, an unloader of size i (machine 1) is purchased if 10 [:2 XLS(j)LSM(i,j)] > O and M(i) = 0. That is a machine is purchased j=l of each size which is to be used but which is not in the machinery set. Then a new T1 is calculated using the same formula as for T. The additional unloaders to purchase are calculated by searching through the TONS(i) to find the one with the smallest value of i which exceeds 1 10 T and for which 2 XLS(j)LSM(i,j) 7 0 (machine i is used). When j=l this is found an additional machine i is purchased. If none is found the largest unloader (with largest TONS(i))in use is purchased, TONS(i) is subtracted from T1 and the searching process is started again. If T <.Q, the number of additional machines is found by searching as above but using T instead of T1. 10 1 if 2 XLS(j)LSM(29,j) 7 0 J=l 1611111 Machine 29 0 otherwise 16,5,8 Machines 50 through 55 are calculated as indicated below. 7 12.5(Q.24(b), Part II) ( 2 XLS(j)) = G (gallons of storage required. J=l 430 If 0:5 0 35 where Q = (G - 2 B(i)M(i)) and B(30) = 400 i=30 B(3l) = 600 B(32) = 800 B(33) = 1000 B(55) = 2000, 7 the number of each size for which 2 XLS(j)LSM(i,J) 0 is l. i=1 If this number of machines is not present, additional machines are purchased and the program moves to machine 56. If Q4> 0, then T is calculated where 55 T = (G - 2 B(i)M(i)) i=50 and M(i) is the number of machine i'S in the machinery set. If T f- 0 no additional bulk tanks need be purchased. The program moves to machine 56. If T a Q, a tank is purchased for each size (i) for which 7 Z XLS(j)LSM(i,J) > 0 and M(i) = 0. Then a new T1 is calculated using 3:1 the same formula as for T (last formula) and the additional bulk tanks to purchase is calculated by searching the B(i) for the smallest value 7 which exceeds T1 and for which 2 XLS(j)LSM(i,j) 7 0. When this is i=1 found the additional machine (i) is purchased. If none is found the 7 largest bulk tank in use ( 2 LS(j)LSM(i,j) > 0) is purchased, B(i) J=l is subtracted from T1 and the searching starts again. 7 _ . . l 16,5,9 Machine 56 — 2 [XLS(j)LSM(i,J) DMR(7,j) J i=1 431 where DMR(i,j) is from Q.80(d), Part II. Machine 57 same as 56 except DMR(7,j) is replaced by DMR(8,J). Machine 58 same as 56 except DMR(7,j) is replaced by DMR(9,j). 42 35 1§,_3_,_1_0 Machine 39 = 2 GS(j)JGS/I(l4,j) -—————l + 2 TT(j)JHSM(l4,j) ._ URQ(5,2) ._ J~l J—l ...;L___. URQ(5,3) where URQ(i,j) is from Q.80(a), Part II 16.5,11 Machine 40 through 42: First check to be sure that at least one cultivator of each size used is on hand. That is if 42 55 2 GS(j)JGSM(l4,j) + 2 TT(j)JHSiI(l4,J)> 0 3:1 le At least one machine 40 should be in the machinery matrix. If it is not, one is purchased. Machines 41 and 42 are handled similarly except that in the equations 14 is replaced by 15 and 16 respectively. 12 42 If 2 GS(j) + 2 GS(j) - M(40)XMACP(1) - M(41)XMACP(2) - M(42)XMACP5 = Q j=l j=51 where XMACP is from Q.80(e), Part II is: 5 0 no additional machines need be purchased. 2»0 purchase one machine of largest size in use (i.e. purchase 42 35 machine 42 if 2 GS(j)JGSM(l7,j) + 2 TT(j)JHSM(l7,j) = s > 0. J=l i=1 42 If s is not > 0, purchase one machine 41 if 2 GS(j)JGSIvI(l6,j) + J'=l 35 2 TT(j)JHSM(l6,j) is > O, similarly for machine 40. After 3:1 432 one machine is purchased recalculate Q and repeat the process. 16,5.12 Machine 45 = the maximum of 42 55 - .v ___1___) . . . ___1h___) (1) 3:1 GS(3)JGSII(18,j) (MACP(4) or (2) 3“)-:1 TT(J)JHs11(ls,J)(MACP(4) 16,5.15 Machines 44 through 57: 42 1. If 2 GS(j)JGSM(19,j) > 0, then m(44) should be‘3 1. i=1 If it is not, one is purchased. This is repeated for machines 45 through 57 with 19 replaced by 20 through 52 respectively. 2. If [(M(44) + M(45))(2) + (M(46) +14(47))(3) + (M(48)) + M(49))(4) + (1«1(50)+ M(51))(5) + (M(SZ) + M(SS))(6) + (M(54) + 14(55))(7) + (M(56) + M(57))(8)][lelACP(5)] 12 24 42 - 2 08(3) + >3 GS(J') + 2 GS(J')] = Q j=l j=l9 j=5l is 2 0 no additional plows need be purchased. <70 purchase an additional plow of the largest size indicated as in use and return to recalculation of Q. 16,5.14 Machine 58: 42 1 Number re uired = 2 GS ' JGSM 55 ° ‘——‘—**" 16,5.15 Machines 59 through 64: 42 1. If 2 GS(j)JGSM(54,j) > 0, then M(59) should beé 1. If it i=1 is not, one is purchased. This is repeated for machines 60 through 64 with 54 replaced by 55 through 59 respectively. 2. If [(M(59)(8) + M(60)(10) + M(61)(12)+ M(62)(l4) + M(65)(l6) + 16.5.16 16.5,17 15.5.18 GS(3)+ 2 GS(J‘)+ >3 08(3)] =Q l j=l9 j=5l 3 2 24 42 M(64)(18)) (XMACP(6)J — 2 Is 0 no additional machines in this group need be purchased. 0 purchase an additional disc of the largest Size indi— cated as in use and return to calculation of Q. Machines 65 through 69: 42 If 2 GS(j)JGSM(40,j).> 0, the number of machine 65's j=l should be i 1. If it is not, one is purchased. This is repeated for machines 66 through 69 with 40 replaced by 41 through 44 respectively. If [(M(65)(2.25) + M(66)(5) + M(67)(4) + M(68)(5) + M(69)(6)) 12 24 42 (XMACP(7))] - 2 05(3) + 2 05(3) + 2 GS(j) = Q J=l 3:19 3:31 is 2.0, no additional machines in this group need be purchased. ‘40, purchase an additional harrow of the largest size in use and return to recalculation of Q. 42 Machine 70 = 2 GS(j)JGSM(70,j) i=1 ___Ja___. URQ(7,2) Machines 71 through 75: 42 If 2 GS(j)JGSM(46,j) > 0, the number of machine 71’s should '=l ‘VCJ be 1. If it is not, one is purchased. This is repeated for machines 72 and 75 with 46 replaced by 47 and 48 respectively. If [(M(71) + M(72)(2) + M(75)(5)) (XMACP(8))] - 12 42 2 GS(j) + 2 GS(J) = Q i=1 3:31 16,5.19 l. 16,5.20 15,5,21 The 16,5,22 434 istfi'O no additional planters need be purchased. < 0 purchase an additional planter of the largest size in use and return to calculation of Q. Machine 74: 42 If 2 GS(j)JGSM(49,j) 7 0, the number of machine 74's (i.e. i=1 M(74)) should be a 1. If it is not one is purchased. 12 24 42 1 If ('2 08(3) + .2 GS(j) + .2 GS(J))(W) is >1, 3:1 3:19 3:51 the number is rounded up to equal the number required. Machine 75: 42 If 2 GS(j)JGSM(50,j) > 0, the number of machine 75's (M(75) J=l should be 3 1. If it is not, one is purchased. 28 If M(75) (XMACP(10)) _ 2 GS(j) = Q J=15 is 3 0 no additional drills need be purchased. <-O purchase 1 drill and return to the calculation of Q. Machine 76: 55 number required = 2 TT(j)JHSM(19,J) J=1 1 URQ(8,5) Machines 77 through 79: 55 If 2 TT(j)JHSM(20,j) > 0, then the number of machines 77's i=1 is 5‘- 1. If 14(77) is < 1, one is bought. This is repeated for machine 78 and 79 with 20 replaced by 21 and 22 respectively. “35 2. If [M(77)XMACP(11) + M(78)XMACP(11)(2) +.M(79)XMACP(13)(2) - hcres of corn silage from corn subroutine)] = Q is %.0 no additional choppers need be purchased. (.0 purchase the largest size chopper being used (largest machine no.) and return to recalculation of Q. 3. If [M(77)XMACP(12) + M(78)XMACP(12)(2) + M(79)XMACP(14)(2) - 24 (acres of hay crop Silage from the Hay routine = E TT(j))] = Q j=20 is 3 0 no additional choppers need be purchased. < 0 purchase the largest Size chopper being used (largest machine no.) and return to recalculation of Q. NOTE: Both 2 and 5 are carried out regardless of the results of the other. 16.5.25 Machine 80: 55 1. If 2 TT(j)JHSM(25,j) > 0, the number of machine 80's is 2‘- l. j=1 If that machine is not present, it is purchased. 2. If (Q.25(f), Part I) (EMXE%(15)) = Q is > 1, Q is rounded up to get the number required. 16,5,24 Machine 81: 35 If 2 TT(j)JHSM(24,j)> 0, the number of machine 81's = 1. i=1 Otherwise, it equals 0. 16,3.25 Machines 82 through 85: 35 1. If 2 TT(j)JHSM(25,j) > 0, the number of machines 2 1. If i=1 the one is not present, it is purchased. 436 ll 2. If [(acres of corn grain = 2 TT(j)) _ [M(82))C«1ACP(16) + J=6 M(85))04ACP(16)(2) + M(84)XMACP(17)(2) + M(85)XMACP(17)(5)]] = Q is $ 0 no additional machines need be purchased. > 0 an additional machine of the largest machine code no. (82-85) is purchased and the program returns to recal- culate Q. 16.5.26 Machine 86: 55 1. If 2 TT(j)JHS«1(29,j)> 0 the number required is e 2. If J=l they are not present (i.e. if M(86)'< 2), the difference between M(86) and 2 are purchased. 55 . 1 1" = 2. If 321 TT(j)JHSJ(29,j) EMACP(16) Q is 5 2 no additional wagons need be purchased. < 2 the number required is the maximum of Q and T where T = (2)(M(84) + was) + M(89) + M(90) + M(9l)). 16,5,27 Machine 87: 55 1 if 2 TT(j)JHSM(50,J) > 0. J=l The number required 0 otherwise. 16,5.28 Machine 88: 55 1 if 2 TT(j)JHSM(51,j) > 0 i=1 The number required 0 otherwise. 16.5,29 Machines 89 through 91: 55 1. If 2 TT(j)JHSM(52) > 0, the number of machine 89's required 9- l. i=1 437 If that one is not present, purchase it. 19 1 ll 0 if [M(81)+M(88)] =0 2. If 2 TT(j) —-—-———7] = Q and 2 TT(j) 1 if [14(81)+M(ss)]> 0 3:12 XMACP(19 3:6 35 + JESSTTUil (W) = T and [(M(B9)(l)+M(9o)(l.2)+M(91)(1.4))1 - (maximum of Q or T) = S No additional combine need be purchased if S 3'0. If S < 0 purchase the largest size combine (largest of 89-91) in use and return to calculation of S. 16.5.50 Machine 92: 55 If 2 TT(j)JHSM(55,J).> O the number of bale throwers required is J=l the maximum of l or M(97). 16.5.51 Machines 95 through 95: 55 1. If 2 TT(j)JHSM(56,j) > 0, the number of machine 95's is 2 1. J=l If that one is not present, purchase it. Repeat this for machine 94 and 95 with 56 replaced by 57 and 58 respectively. 2. If [(M(93)(l,2) + M(94)(l.2) + M(95)(l.4)) - 28 l 2 TN") (XMACP(20)) j=20 I .02) is 3 0 no additional windrowers need be purchased. 0 i=1 0 otherwise 101 through 105: Same as 100 except 45 is replaced by 44, 45, and 46 respectively. 16.5.56 1. Machine 104 and 105: 55 If 2 TT(j)JHSM(47,j) > O the number of machine 104's equals j=l the maximum of l or M(77). USe same procedure for machine 105 replacing 47 with 48 and 14(77) with M(78). Machine 106: 55 If 2 TT(j)JHSM(49,J) > 0 the number of machine 106's required 3:1 439 equals the maximum of (l) l, (2) the minimum of 32 m . l (a) 2 Tim) >C«1ACP(23)(4) and (b) M(41). j-29 16.5,58 Machine 107: 35 If 2 TT(j)JHSM(50,j) ) 0 the number of machine 107's required i=1 equals the maximum of (l) l, (2) the minimum of (a) M(42) and 32 (b) 2 TT(J) 3:29 1 )04ACP(25)(6) 16.5.59 Machine 108: 35 If 2 TT(i)JHSM(51,J) > 0 the number required equals J=l 32 2 TT(J’) 3:29 ____Ja____ XMACP(24) ' 16.5.40 Machine 109: 55 If 2 TT(j)JHSM(52,J) > O the number required equals the maximum 3:1 32 of (1) .2291T(j)m and (2) (M(89) +14(90) + M(9l)). J: 16,5,41 Machine 110: 35 . . l The number equals 2 TT(J)JHSM(55,J) ————-— . 3:1 URQ(95) 16.5.42 After all machines have been checked and additional ones purchased where necessary, machine 86 is rechecked. This is necessary because the quantity of other machines on hand enters into the calcu— lations used to determine the number of machines of this type required. 16,4 As is implied by the method of handling machine purchases discussed 440 in section 16.2, the above equations and procedures are also used to be sure that the machinery on hand is sufficient to handle the livestock and crops being grown and to be sure that the machinery on hand is used. If the answer to Q.20(b), Part I is l, the subroutine uses the check procedure described in section 16.4.1 to determine whether it is necessary to check the sufficiency and necessity of machines on hand or not. (If Q.20(b), Part I is zero and Q.20(a), Part I is zero, theseequations are used only during the first month simulated. If Q.20(b), Part I is zero and Q.20(a), Part I is one, these equations are never used.) If the system numbers in Q.l9, Part I are changed or the machines used within a system are changed, the input subroutine sets variables ICHSYS or MCYSYS equal to one, respectively. If either of these variables equal one and Q.20(b), Part I equals one the machines on hand are Checked. 16,4,1 To avoid calculation of the equations listed in section 16.5 when it is unnecessary,the following check procedure is used. A two column array is maintained (PRESET(10,2)). The rows of this array represent the number of acres of each of the six crops, total crop acres, acres of corn silage, acres of hay crop silage and number of cows. The first column represents the value of these items the last time machinery was checked. The second column indicates the present values of these items. In any month when the present value exceeds the column one value by more than the maximum of (1) the column one value plus 25 and (2) the column one value plus 25 percent, the machines on hand are checked. .1616 Machine codes 111 and 112 indicate milking parlor equipment and dairy building equipment respectively. These are always entered in the I I'll ..tllfl’l I. flir- 441 machinery inventory matrix by the BUILDI or INPUT subroutines. This subroutine replaces this equipment as it wears out and calculates depreciation. If age equals life for the milking parlor age is set equal to zero, life is set equal to PLIFE (Q.28(e), Part II) and both new value and depreciated value are set equal to the new price. The new price is calculated as PCOST2 (Q.64(b), Part II) if the new value of the old machine is less than or equal to PCOST2; if it is greater, the new price is PCOST4 (Q.64(b), Part II). The price calculated is added to machinery purchased. If age equals life for the dairy building equipment, age is set equal to zero, life is set equal to PLIFE and the depreciated value is set equal to the new value existing in the matrix. The new value is added to machinery purchased. For both machines 111 and 112 the unde- preciated value at the time age equals life is added to machinery depreciation. Parlors are replaced during month two and building equipment during month eight. .1616 Each month the program replaces machines for which age equals life and the month of the year being simulated equals the month of replace- ment for that machine (from Q.68, Part II). When a machine is replaced, the same row of the machinery set is used, the machine code is not changed. The purchase price and life come from Q.68, Part II, age is set at zero and depreciated value equals the new value. The amount paid to purchase the machinery is the purchase value minus the salvage value of the old machine purchased. This is calcu- lated before the new depreciated value is entered. It is added to the variable indicating the total value of machinery purchased this month. 16.6,1 As part of the search for machines to be replaced, a search is 442 also made for machine items that are to be purchased this month. This is indicated by rows in the machinery inventory matrix with only a code number entered. Column two (as well as three, four and five) are zero. If the month of purchase from Q.68, Part II equals the month being simulated for any such machine, it is purchased. The purchase price and life are taken from Q.68, Part II. Age is set equal to zero. Depreciated value equals the new value. The total purchase price is added to the variable indicating the total value of machinery purchased this month. 16,6.2 After all machine purchase or replace calculations have been made for the month being simulated, thetotal value of machinery purchased this month is added to machinery purchase and is entered in the debt array. One row of the debt array is used. The beginning balance equals the total value of machinery purchased this month. Interest rate, number of payments, loan period and type of loan are taken from Q.12, Part II. The term of loan is set to 6. 1611 Each December, age is advanced one year and depreciation is calculated for each machine. 90 percent of New Value Life Depreciation = If the calculated depreciation exceeds the depreciated value of the machine, depreciation is set equal to the depreciated value. Deprecia- tion is subtracted from the depreciated value to get the new depreciated value. The total amount of depreciation is accumulated for the year. 1616_ After all purchases, sales, depreciation calculations and age changes are made, the value of machinery is calculated by summing the depreciated values of all machines. This is done after machinery is entered during the first month and at the end of the last month of the 443 first year. The first is the beginning inventory and the second is the ending inventory. Each year a new ending inventory is calculated. The end inventory for one year is the beginning inventory for the next. .1612 Insurance cost is calculated as the depreciated value of all machines in the machinery inventory matrix multiplied by (Q.57(a), Part II) (Q.57(b), Part II) and the amount is calculated 6nd paid) in the month indicated in Q.44, Part II. .16119 The machinery repair costs for each system are calculated each month. This is done by multiplying the total annual machine repair cost per unit (Q.69, Part II) by the percentage of that requirement which occurs this month from Q.70, Part II and then multiplying that total by the number of units of the system for livestock (XLS(j)) and crop grow (GS(j)) systems and by the number of units harvested for the harvest systems (HS(j)). Thus the equations for livestock would be of the form Q = XMRGO(i,l)XMDMC(month,j)XLS(j) where i = system number j = correct column for that system from Q.70, Part II Q = machinery repair cost XMRGO is from Q.69, Part II and XMDMC is from Q.70, Part II. Similar equations for crop grow systems would be of the form Q = )04RGO(i + 10,1)104Dhc(Month,j)05(j) and equationsfbr harvest systems would be of the form Q = XMRGO(i + 52,1)104MC(Month,j)HS(j) These equations are calculated only for the systems indicated in Q.l9, Part I. The costs for all other systems are zero. The total machinery 444 repair costs for the month is the sum of the amount for all systems. The Gas and Oil costs for each system are calculated by the exact same equations except that the column identification of the XMRGO coefficient is 2 instead of 1. The annual gas and oil costs for each system are used from Q.69, Part II. Gas and oil cost is also accumu- lated for the month. .16111 The following list of variables are calculated and maintained by this subroutine. Variable Definition Name Dimension Code 1. Gas and oil cost CASO (15) 2. Machinery repair cost XMCHRP (l3) 5. Machinery insurance cost XMCHI ( 1) 4. Machinery depreciation DEPRM ( 2) 5. Inventory value of machines (a) Beg. XMCHVB ( l) (b) End XMCHVE ( l) 6. Machinery sold XMSLD (13) 7. Machinery purchased XMPUR (15) 445 SUBROUTINE LABOR This subroutine handles labor requirement and cost calculations for the entire farm business. .1111 Three matrices of labor requirements per month are maintained. These matrices contain the labor requirements for livestock systems, grow systems and harvest systems and are dimensioned lxlO(XLRL), lx42(XLRG) and lx55(XLRH) respectively. The value for each matrix cell is the labor required per unit of that system and is represented by an equation or set of equations with the month of the year as one of the variables. These equations are used to calculate the matrix values at the beginning of each month. Calculations are made only for systems being used (Q.l9, Part I). 17,1,1 For the dairy herd, a size of herd group number is first calculated as follows: if no. of cows is less than 50 if no. of cows is 51-49 JACOW = Herd group no. = l 2 5 if no. of cows is 50-79. 4 5 if no. of cows is 80-149 if no. of cows is 150 and over The equations are of the form DCSLJACOW.Q) (XMDC(1, month of year)) ( 12 XLRL(i) where i - dairy system number (i=1,...,7) DCS is from Q.54, Part II and XMDC iS from Q.55, Part II The equations for youngstock are LDRS(1,1))(xxl + LDRS(2,1))(YY1) XLRL(8) = (XMDC(2,month<>f year)) ( ZZ XLRL(9) = (XMDC(2,month of year)) (QRSHmWO 22(DRS(2421)(YY) ) 446 XLRL(10) = ()C-iDC(2,month of year)) (L’Rsuénm) EZWRSQJHYN) where XX = number of heifers under one year + number of steer calves YY = number of heifers over one year + number of steers on feed 22 = total number of heifers and steers DRS is from Q.56, Part II and XMDC is from Q.57, Part II 11.1,2 Equations for calculation of grow and harvest systems labor requirements per acre are listed below. Prior to the list of equations for each crop, a size code indicating the number of acres or units of that crop is calculated. Fbr Corn Corn Acres Code = 1 if acres of corn (Q.22, Part I) is less than 25 2 if acres of corn (Q.22, Part I) is 26-50 3 if acres of corn (Q.22, Part I) is 51-75 4 if acres of corn (Q.22, Part I) is 76-100 5 if acres of corn (Q.22, Part I) is 101-150 6 if acres of corn (Q.22, Part I) is 151-200 7 if acres of corn (Q.22, Part I) is greater than 200 and XLRG(1) = [CPs(l)] [CPD(month, 1)] [RLE(corn acres code, 1)] XLRG(2) = [CPS(2)] [CPD(month, 1)] [RLE(corn acres code, 2)] XLRG(5) = [CPS(5)] [CPD(month, 1)] [RLE(corn acres code, 3)] XLRG(4) = [CPs(4)] [CPD(month, 2)] [RLE(corn acres code, 4)] XLRG(5) = [CPs(5)] [CPD(month, 3)] [RLE(corn acres code, 1)] XLRG(6) = [CPS(6)] [CPD(month, 5)] [RLE(corn acres code, 1)] XLRG(7) = [CPS(7)] [CPD(month, 5)] [RLE(corn acres code, 5)] XLRG(8) = [CPS(8)] [CPD(month, 4)] [RLE(corn acres code, 4)] XLRG(9) = [CPS(9)] [CPD(month, 5)] [RLE(corn acres code, 1)] XLRG(10) = [CPS(10)] [CPD(month, 5)] [RLE(corn acres code, 2)] XLRG(11) = [CPS(ll)] [CPD(month, 5)] [RLE(corn acres code, 3)] XLRG(12) = [cps(12)] [CPD(month, 6)] [RLE(corn acres code, 4)] 447 where (l) CPS is the crop system's annual labor requirements and from Q.57, Part II (2) CPD is the crop system's distribution oflabor by months (monthly distribution of annual labor) from Q.7l, Part II. (5) RLE is the relative size efficiency from Q.72, Part II. For Corn Silage, the corn silage acres code (JCSAC) = and XLRH(1) and if if if if if if if \JOUU‘IPCNNH XIRH(2) XLRH(5) XLRH(4) XLRH(5) acres acres acres acres acres acres acres Corn Grain, if if if if if if if \IO)U]#-OJQDFJ XLRH(6) XLRH(7) XLRH(8) XLRH(9) XLRH(10) XLRH(11) acres acres acres acres acres acres acres " of of of of of of of corn corn corn corn corn corn corn [CPS(43)] [CPS(44)] [CP6(45)] [CPs(46)] [CPS(47)] silage silage silage silage silage silage Silage (CSACRE) (CSACRE) (CSACRE) (CSACRE) (CSACRE) (CSACRE) (CSACRE) [RLE(JCSAC, 5)] [RmmEmm,5H [RLE(JCSAC, 6)] [RLE(JCSAC, 7)] [RLE( JCSAC, 6)] is < 25 is 26-50 is 51-75 is 76-100 is 101-150 is 150-200 is > 200 the corn grain acres code (JCGAC) = of of of of of of of corn corn corn corn corn corn corn [CPS(46)] [CPs(49)] [CPS(5O)] [CPS(51)] grain grain grain grain grain grain grain [RLE( [RLE( [RLE( [RLE( < 25 26-50 51-75 is 76-100 is 101-150 is 151-200 is .>200 is is is JCGAC, 5)] JCGAC, 5)] JCGAC, 6)] JCGAC, 6)] [CP5(52)] [RLE(JCGAC, 7)] [CPS(55)] [RLE(JCCAC, 6)] For wheat, the wheat acreage code (JWAC) = and XLRG(15) and if if if if if if if \lmmrP-UINH xum04) xmmUs) XLRG(16) XLRG(17) XLRG(18) XLRH(12) XLRH(15) XLRH(14) XLRH(15) oats, if if if if if if if \30301P~0¢h3h‘ XLRG(19) XLRG(20) XLRG(21) XLRG(22) XLRG(25) XLRG(24) XLRH(16) acres acres acres acres acres acres acres 448 of wheat (Q.22, Part I) is of wheat (Q.22, Part I) is of wheat (Q.22, Part I) is of wheat (Q.22, Part I) is of wheat (Q.22, Part I) is of wheat (Q.22, Part I) is of wheat (Q.22, Part I) is [CPs(l3)] [CPD(month, 7)] [CPs(14)] [CPD(month, 7)] [CPs(15)] [CPD(month, 6)] [CPs(16)] [CPD(month, 9)] [CPS(17)] [CPD(month, 9)] [CPS(18)] [CPD(month, 10)] [CPS(54)] [RLE(JWAC, 27)] [CPS(55)] [RL2(JWAC, 26)] [CFs(56)] [RLE(JWAC, 29)] [CP3(57)] [RLE(JWAC, 30)] the oats acreage code (JOAC) = acres acres acres acres acres acres acres of of of of of of of oats oats oats oats oats oats oats is is is is is is is 4<25 26-50 51—75 76-100 101-150 151-200 >200 [CPS(19)] [CPD(month, 11)] [CPs(2O)] [CPD(month, 11)] [CPs(21)] [CPD(month, 12)] [CPs(22)] [CPD(month, 13)] [CPs(23)] [CPD(month, 13)] [CPs(24)] [CPD(month, 14)] [CPs(58)] [RLE(JOAC, 31)] .425 26-50 51-75 76-100 101—150 151-200 >200 [RLE(JWAC, [RLE(JWAC, [RLE(JWAC, [RLE(JWAC, [RLE(JWAC, [RLB(JWAC, [RLB(JOAC, [RLE(JOAC, [RLE(JOAC , [RLB(JOAC , [RLE(JOAC, [RLE(JOAC , 14)] 15)] 16)] 14)] 15)] 16)] 9)] 10)] 11)] 9)] 10)] 11)] 449 XLRH(17) = [CPS(59)] [RLE(JOAC, 32)1 XLRH(18) = [CPS(60)] [RLE(JOAC, 33)] XLRH(19) = [CPS(61)] [RLE(JOAC, 34)] If the acres of hay crop (Q.22 (a), Part I) multiplied by the coefficient in Q.18, Part II = < 25 the hay crop plant code (JHCPC) is 26-50 the hay crop plant code (JHCPC) is 51-75 the hay crop plant code (JHCPC) is 76-100 the hay crop plant code (JHCPC) is 101-150 the hay crop plant code (JHCPC) is 151-200 the hay crop plant code (JHCPC) is > 200 the hay crop plant code (JHCPC) is «JOECflPCNNI-J and XLRG(25) = [CPs(25)] [CPD(month, 15)] [RLE(JHCPC, 23)] XLRG(26) = [CFs(26)] [CPD(month, 15)] [RLE(JHCPC, 24)] XLRG(27) = [CP8(27)] [CPD(month, 16)] [RLE(JHCPC, 25)] XLRG(28) = [CPs(2s)] [CPD(month, 17)] [RLE(JHCPC, 26)] Fbr Hay Crop silage, the hay crop silage acreage code (JHCSAC) = if the acres of HCS (HSACRE) is 4 25 if the acres of HCS (HSACRE) is 26-50 if the acres of HCS (HSACRE) is .51-75 if the acres of HCS (HSACRE) is 76-100 if the acres of HCS (HSACRE) is 101-150 if the acres of HCS (HSACRE) is 151-200 if the acres of HCS (HSACRE) is >200 4030119me and XLRH(20) [CPs(62)] [RLE(JHCSAC, 39)] XLRH(21) [CPS(63)] [RLE(JHCSAC, 40)] XLRH(22) [CPs(64)] [RLE(JHCSAC, 41)] XIRH(25) [CPs(65)] [RLE(JHCSAC, 42)] XLRH(24) [CPS(66)] [RLE(JHCSAC, 43)] Fbr Hay, the hay crop acres code (JHCAC) = if acres of hay crop is 25 if acres of hay crop is 26-50 if acres of hay crop is 51-75 if acres of hay crop is 76-100 if acres of hay crop is 101-150 if acres of hay crop is 151-200 if acres of hay crop is > 200 \lmUlPCNNH 450 and XLRG(29) [CPS(29)] [CPD(month, 16)] [RLE(JHCAC, 12)] XLRG(50) [CPS(3O)] [CPD(month,19)] [RLB(JHCAC,13)] Acres of hay equals hay crop acres minus hay crop silage acreage. Hay harvest acreage code (JHHAC) = 1 if acres of hay is < 25 2 if acres of hay is 26-50 5 if acres of hay is 51-75 4 if acres of hay is 76-100 5 if acres of hay is 101-150 6 if acres of hay is 151-200 7 if acres of hay is > 200 and XLRH(25) = [CPS(67)] [RLE(JHHAC, 44)] XLRH(26) = [CPS(6B)] [RLE(JHHAC, 45)] XLRH(27) = [CPS(69)] [RLE(JHHAC, 46)] XLRH(28) = [CPS(7O)] [RLB(JHHAC, 47)] Fbr Field beans, the field bean acreage code (JFBAC) = l of acres of field beans (Q.22, Part I) is ‘< 25 2 of acres of field beans (Q.22, Part I) is 26-50 5 of acres of field beans (Q.22, Part I) is 51-75 4 of acres of field beans (Q.22, Part I) is 76-100 5 of acres of field beans (Q.22, Part I) is 101-150 6 of acres of field beans (Q.22, Part I) is 151-200 7 of acres of field beans (Q.22, Part I) is > 200 and XLRG(51) = [CPs(3l)] [CPD(month, 20)] [RLE(JFBAC), 17)] XLRG(52) = [CPs(32)] [CPD(month, 20)] [RLE(JFBAC, 16)] XLRG(55) = [CPS(55)] [CPD(month, 21)] [RLE(JFBAC, 19)] XLRG(54) = [CPS(34)] [CPD(month, 22)] [RLE(JFBAC, 17)] XLRG(55) = [CPs(35)] [CPD(month, 22)] [RLE(JFBAC, 16)] XLRG(56) = [CFs(36)] [CPD(month, 23)] [RLE(JFBAC, 19)] XLRH(29) = [CPS(71)] [RLE(FBAC, 35)] XLRH(50) = [CPS(72)] [RLE(FBAC, 36)] XLRH(51) = [CPs(73)] [RLE(FBAC, 37)] XLRH(52) = [CPS(74)] [RLE(FBAC, 36)] 452 in the hay subroutine and equals HS(SYS(15)). Dairy replacements equals the sum of the number of all heifers plus all steers. This is calculated in the dairy subroutine. The total hours of livestock labor required is the sum of the requirements for all livestock systems. The crop labor requirements is the sum of the requirements for all crop grow and all crop harvest systems. These two requirements are added to get the total labor required for the month. 1316_ The amount of operator labor is calculated as XLABOR(1, month + 1) multiplied by XLABOR(1,1). The XLABOR(i,j) coefficients indicated come from Q.27, Part I with "number" as the first column and January as the second, etc. The amount of family labor available this month is calcu- lated as XLABOR(2,1) multiplied by XLABOR(2, month + 1). The amount of year hire labor is calculated as: year hire 1 labor XLABOR(5,1) XLABOR(5, month + 1) year hire 2 labor XLABOR(4,1) XLABOR(4, month + 1) year hire 3 labor XLABOR(5,1) XLABOR(5, month + 1) The "regular labor" available is calculated as the total of the three year hire labor amounts calculated above. The sum of (1) regular labor, (2) operator labor and (5) family labor is subtracted from "labor required" to get "hourly labor used." If "hourly labor used" is less than zero it is set equal to zero. If "hourly labor used" is greater than zero, the hourly labor row (i) from Q.27(a), Part I with the lowest per hour rate is chosen. The labor available from hourly (i-5) is calculated as XLABOR(i,l) XLABOR(i, month + 1). "Labor hired" is set equal to "hourly labor used." 451 For Soybeans, the soybean acreage code (JSAC) = 1 if acres of soybeans is ‘ 25 2 if acres of soybeans is 26-50 5 if acres of soybeans is 51—75 4 if acres of soybeans is 76-100 5 if acres of soybeans is 101—150 6 if acres of soybeans is 151-200 7 if acres of soybeans is > 200 XIRG(57) = [CPs(37)] [CPD(month, 24)] [RLE(JSAC, 20)] XLRG(58) = [CPS(3B)] [CPD(month, 24)] [RLE(JSAC, 21)] XLRG(59) = [CPS(59)] [CPD(month, 25)] [RLE(JSAC, 22)] XLRG(40) = [CPS(40)] [CPD(month, 26)] [RLE(JSAC, 20)] XLRG(41) = [CFS(4l)] [CPD(month, 26)] [RLE(JSAC, 21)] XLRG(42) = [CPs(42)] [CPD(month, 27)] [RLB(JSAC, 22)] XLRH(55) = [CPS(75)] [RLE(JSAC, 46)] XLRH(54) = [CPS(76)] [RLE(JSAC, 49)] X1RH(55) = [CPS(77)] [RLE(JSAC, 50)] 111g Each month the labor requirements for each system in use (Q.l9, Part I) are calculated by multiplying the number of units of that system (except for hay and HCS harvest and dairy replacements) as calculated in the systems subroutines, by the labor requirements for that system as calculated above. For hay harvest the number of units harvested is the acres of first cutting plus acres of second cutting multiplied by the coefficient from Q.18, Part II(CUTCOF(1,1)) plus the acres of third cutting hay multiplied by the third cutting coefficient (CUTCOF(Z, 1)). This is calculated in the hay subroutine and equals HS(SYS(17)). Fbr hay crop silage the same calculations using the acreage of hay crop silage for each cutting multiplied by the correct coefficient in Q.18, Part II, i.e. acres first cutting + (acres second cutting) (CUTCOF(1,2)) + acres third cutting (CUTCOF(2,2)). This is calculated 453 If the "labor hired" is: (1) less than or equal to hourly (i-5), "labor hired" multiplied by XLABOR(i,l) is added to "hourly labor cost" (2) greater than the labor available from hourly (i-5), hourly (i-5) multiplied by XLABOR(i,l) is added to "hourly labor cost," labor available from hourly (i-S) is subtracted from "labor hired" and the program moves on to consider the next cheapest source of hourly labor. This process continues until all the hourly labor indicated in rows 6, 7 and 8 is used or hired labor equals zero. If all the labor from rows 6, 7 and 8 is used and "labor hired" is greater than zero, the value of "labor hired" is multiplied by the cost of all additional hourly labor (Q.27(b), Part I) and that value is added to "hourly labor cost." 1111 The wages paid year hire (1) regular labor are calculated as shown below. If the frequency of payment is: (1) monthly (1 is input), the wage indicated is multiplied by XLABOR(5,1) and the result added to "regular wages paid" (2) weekly (0 is input), the wage indicated multiplied by XLABOR(5, l) is multiplied by WEEKS and the result added to "regular wages paid" where WEEKS = 4 if month = 2 4 2/7 if month = 4, 6, 9 or 11 4 3/7 otherwise. The wages paid year hire 2 and year hire 5 are calculated in a corresponding manner with XLABOR(5,1) replaced by XLABOR(4,1) and XLABOR(5,1) respectively. 454 1116 Hours of surplus labor is calculated as the sum of operator labor, fanily labor and regular labor minus the total labor required. If this value is less than zero it is set equal to zero. "Hired labor" is the sum of "regular wages paid" and "hourly labor cost." The values of operator labor and family labor are calculated monthly and accumulated for the year. The equations are: Value of operator labor (VOPLB) = (XLABOR(1,1))(VOLABR/12) Value of family labor (VFMLB) = (Family labor hours)(VFLABR) where VOLABR is from Q.l, Part II VFLABR is from Q.2, Part II and family labor hours is from section 17.5 .1116 The variables calculated and maintained by this subroutine are listed below. Variable Definition Name Dimension Code 1. Livestock labor (hrs) LIVLB (l5) 2. Crop labor (hrs.) CRPLB (15) 3. Total labor (=labor required)(hrs.) TLB (13) 4. Operator labor (hrs.) OPLB (l3) 5. Family labor (hrs.) FMLB (l3) 6. Regular labor (hrs.) RGLB (13) 7. Hourly labor (hrs.) HRLB (15) 8. Surplus hours SURLB (15) 9. Regular wage paid ($) RGWAG (13) 10. Hourly labor cost ($) HRWAG (15) ll. Hired labor ($) HIRDLB (15) 12. Value of operator labor VOPLB ( l) 13. Value of family labor VFMLB ( l 17,1 SUBROUTINE CODES(X,J) is a subroutine called only by the labor subroutine which calculates the acreage or size code for the various crops. The acreage of crop is received as the first parameter (X) 455 and the code number for that crop is sent back to the labor subroutine as the second parameter (J). The relationship between the code number and the acreage of crop is found in section 17.1.2. 456 SUBROUTINE ACCOUNT This subroutine summarizes operating expenses and receipts and makes the accounting calculations required prior to debt repayment. 1611 Utilities expense for this month is calculated as the number of cows multiplied by the utility expense items in Q.27, Part II. A first calculation of "cash operating expense" is calculated by summing the following expenses: 1. Utilities expense 2. Total insurance (sum of building, machinery, supplies and live— stock insurance) 5. Total fertilizer expense (sum of fertilizer expense from all crop routines) 4. Total seed cost (sum of seed cost from all crop routines) 5. Total pesticide cost (sum of pesticide cost from all crop routines) 6. Gas and oil expense (from machinery routine) 7. Machinery repair expense (from machinery routine) 8. Hired labor expense (from labor routine) 9. Total other crop expense (sum of other grow costs and other harvest costs from the crop routines plus off farm storage cost from the stores routine) 10. Total breeding expense (from dairy routine) 11. Total vet expense (from dairy routine) 12. Total marketing expense (sum of milk marketing expense from dairy and crop sales hauling cost from stores) 15. Other livestock expense (sum of other dairy costs and calf costs from dairy plus straw cost from stores) 457 14. Rent paid (from land) 15. Taxes (from land) 16. Land, fence and building repair (sum of conservation and fence repair from land and building repair from buildings) 17. Total custom hire (sum of custom hire from all crop routine). Miscellaneous expense is calculated as the constant from Q.28, Part II plus the percentage figure from Q.28, Part II multiplied by "cash operating expenses" as calculated above. Then true "cash operating expense" is calculated by adding 18. Miscellaneous expense l9. Feed purchases (from stores) p161; Cash income is calculated by summing the following income items: 1. Milk sold (from storage and sales) 2. livestock sold (storage and sales) 5. Government payments income (from land) 4. Crop sales (from storage and sales) Total withdrawal is calculated by choosing the correct cell in Q.4l(a), Part I and adding "income tax" if the month is February. In year 1 the value of "income tax" is from Q.4l(c), Part I. In future years "income tax" = TAXI, which was calculated in December of the previous year in the TAXACC subroutine, is used. .1616 For those variables with a dimension code of 15, the thirteenth value is the total for the year. These thirteenth values for all variables are calculated by this subroutine. This is accomplished by monthly adding the value for this month to the thirteenth value. The equations for each variable are of the form X(l5) = X(15) + X(K) 458 where X is the variable name and K is the number of the present month. Total land purchases (PURLD) and land sales (TLDSAL) are also accumulated as the sum to date plus the value for this month. ‘1614 Total long term assets equals the sum of land value and building sale value. Intermediate term assets equals the sum of depreciated value of machinery plus value of cattle. Short term assets equals the inventory value of feed and supplies (crops). Total capital investment equals the sum of land value, building value, depreciated value of machinery, cattle value and value of feed and supplies. The beginning and ending value of these asset or investment categories is calculated using the beginning and ending inventory value, respectively, of the items that make them up. Average capital investment in land is calculated as the average of the beginning inventory land value and the end inventory value from the land subroutine. Average investment in machinery, livestock, buildings, feed and supplies (crops), short term assets, intermediate term assets and long term assets is calculated in a similar fashion. 1616 Increase in land value is calculated as the end land inventory minus the beginning inventory minus "total land purchased" plus total land sales. Increasein cattle (livestock) inventory is calculated as the ending inventory (at end of last month) minus beginning inventory (at beginning of first month of the year) minus livestock purchases (stores). Increase in feed inventory is calculated as the value of the inventory of crops from the beginning of the year minus the value at the end of the year (as calculated stores). 459 1616 Average crop yields for each crop are calculated in the twelfth month of the year. This is calculated by dividing total production of that crop plus the landlord share by the number of acres. Average milk yield is calculated as the total milk produced divided by the average of the 12 monthly number of cows values. ‘1611 Income from the sale of land is distributed over the number of years (TAXYRS) indicated in Q.46(a), Part II. This distribution is carried out so that whenever TAXYRS equals two or more years the sale qualifies as a sales contract. An array, CLDINC(5), is set up to contain the income from the sale of land for each of the next five years. Whenever land is sold (SALELD is positive) the amount of cash income from the sale of the land for each of the next five years is calculated and entered in the array. The interest income that would be earned on that portion of the income to be received in years other than the first is calculated using the long term interest rate from Q.l4, Part II. This is added to the value of the land before it is entered in the CLDINC array. That is if TAXYRS, (a) equals one, the total land sale income (SALELD) is added to CLDINC(1) (b) greater than one and less than four CLDINC(1) = CLDINC(1) + (0.5)SALELD) . (0.7)SALELD . . . CLDINC(l) = CLDINC(i) + TAXYRS_1 (l+J)1-l), 1-2,..., TAXYRS (c) greater than or equal to four SALELD [ TAXYRS (1+J)(i-l)1, i=l,...,TAXYRS CLDINC(i) = CLDINC(i) + where j = the rate of interest 460 If the present month being simulated equals the month in which land income is received (Q.46(b), Part II), CLDINC(l) is added to cash on hand and to the variable "cash land income." The values in the CLDINC array are shifted to the left one cell so that next years income now appears in CLDINC(l), year three income is in CLDINC(2), etc. 18,8 The variables defined and maintained by this subroutine are listed below. Variable Definition Name Dimension Code 1. Total capital invest (a) Beg. CAPVB ( l) (b) End CAPVE ( 1) (c) Ave. CAPVA ( 1) 2. Increase in land values VLNDI ( 2) 5. Increase in cattle value VCATI ( 2) 4. Increase in feed inventory VFEEDI ( 2) 5. Average Corn silage yield ACSYLD ( 1) 6. Average Hay silage yield AHSYLD ( l) 7. Average Corn grain yield ACYLD ( l) 6. Average Oat grain yield AOYLD ( l) 9. Average Wheat grain yield AWYLD ( l) 10. Average Hay grain yield AHYLD ( l) 11. Average Field beans grain yield AFYLD ( l) 12. Average Soybeans grain yield ASYLD ( 1) 13. Average Milk yield AMKYLD ( 1) 14. Utilities expense UTIL (15) 15. Total insurance TINS (15) 16. Total fertilizer expense TFERT (l5) 17. Total seed cost TSEED (15) 18. Spray (pesticide) cost total TPEST (13) 19. Total other crop expense TOCRP (13) 20. Total marketing costs TMKT (15) 21. Custom hire total TCUSTM (13) 22. Total breeding fees TBREED (13) 25. Total vet expenses TVET (15) 24. Other livestock expense OLIVSE (13) 25. Rent paid RENT (15) 26. Taxes TAX (15) 27. Land 2 Fence repair 4 Build repair CONSRP (13) 28. Livestock sold SLDLIV (13) 461 .Xariable Definition (Cont'd.), Name Dimension Code 29. Crop sold SLDCP (15) 50. Total long term assets (a) Ave. ASETLA ( l) (b) Beg. ASETLB ( 1) (c) End ASETLE ( l) 51. Total intermediate term assets (a) Ave. ASETIA ( 1) (b) Beg. ASETIB ( 1) (c) End ASETIE ( 1) 52. Total Short term assets (a) Ave. ASETSA ( 1) (b) Beg. ASETSB ( 1) (c) End ASETSE ( l) 33. Miscellaneous Exp. )CMISC (13) 34. Total withdrawal TOTW (13) 55. Average capital investment ACAPIV ( 5) where l = land 2 = buildings 5 = machinery 4 = livestock 5 = supplies 56. Annual cash land income CLDINC AA '65 U1 vv 57. Monthly cash land income CLI 462 SUBROUTINE FINCE This subroutine handles all borrowing, debt repayment and interest calculations. 1611 This routine maintains the current debt situation matrix. This is a 50x14 matrix (DEBT(i,j), set up as indicated in Q.40(a), Part I. Each row represents a specific loan. The initial debt.situation is entered in the matrix by the READI subroutine. At the beginning of the first month the amount of payment column (column four) is checked to be sure that the amount of payment has been entered for each loan entered from input. If the amount of payment has not been entered, it is calculated using the same equations as is used to calculate the amount of payment when part of a loan is prepaid (section 19.50). 1611 The first calculations made each month involve updating the DEBT matrix and calculating principal and interest payments to be made for this month. This is done by individually considering each row of the matrix that contains data. The age of the loan is increased by 1. If a payment on this loan is to be made this month, principal and interest calculations are made. That is, calculations are made if (1) amount of payment = DEBT(i,r) # O and (2) DEBT(i,j) equals either zero or the month being Simulated where j=7,...,12 and i = the loan row being considered. 19,2,1 The interest due on the loan is calculated as O C . i (DEBT(1,1)) (DEBT(1,2)) (months SlnC:zlaSt payment) Mbnths Since last payment equals one if DEBT(i,7) equals zero. If DEBT(i,7) is not zero, months since last payment is the number of months 463 between the present month of the year being simulated and the first previous month in which payment is to be mlde as indicated by DEBT(i,7),...,DEBT(i,12). That is, if payments are to be made in months three and nine, and the present month is one, months since last payment equals four. The interest due is added to: (1) "long term interest" if DEBT(i,6) = 3 (2) intermediate term interest if DEBT(i,6) = 2 (3) short term interest if DEBT(i,6) = l or 0 If the "term of loan" (DEBT(i,6), is zero, skip to the next debt row. 19.2.2 If the type of loan (DEBT(i,5)) is: A. l, 11 21 the "interest due" is subtracted from the amount of payment and the result subtracted from the balance due (DEBT(i,l). The result is also added to (a) long term principal paid if DEBT(i,6) = 3 (b) intermediate term principal paid if DEBT(i,6) = 2 (c) short term principal paid if DEBT(i,6) = 1. through 15, the same calculations as used in A above are used if the product of (l) the type of loan number (DEBT(i,5)) minus 10 and (2) 12 is less than or equal to the age. If age is less than the calculated number, no principal payment is made. the amount of the payment is subtracted from balance due (DEBT(i,1)) and added to one of the three principal paid categories as indicated for type 1 loan. through 25, the same calculations as used in C above are used if the product of (l) the type of loan number minus 464 20 and (2) 12 is less than or equal to the age. If age is less than the calculated number, no principal payment is made. 19.5 A first total of interest paid for the month is the sum of "long— term interest," "intermediate-term interest" and "short-term interest." A first total of principal paid is the sum of the three corresponding principal paid items. These two totals are subtracted from the "cash surplus" where cash surplus is the total cash income plus machinery sold minus the sum of (1) cash operating expenses, (2) withdrawals, (5) cash livestock expense, (4) cash machinery expense, (5) cash land expense and (6) cash building expense. The resulting "cash surplus" is added to the cash balance (Q.40(b), Part I). .1g,4 If the new cash balance is less than Q.16(c), Part II the difference is borrowed (added to the debt Situation) by setting up or adding to an operating short term loan. An existing operating Short term loan has a value for balance due and interest rate, and the term of loan (DEBT(i,6) is set at zero. If there is an existing short term operating loan, the quantity borrowed is added to the balance due for that loan. If there is no existing short term operating loan, one is established with balance due equal to the quantity borrowed, interest rate equal to Q.lS, Part II, and term of loan and age set equal to zero. The quantity borrowed is added to "borrowed-short term." If the cash balance is greater than or equal to Q.16(a), Part II and less than or equal to Q.16(b), Part 11, "cash on hand” is set equal to the present cash balance and the program moves to section 19.6. 19,5 If the checking account balance is greater than Q.16(b), Part II, 465 the following sequence of actions are taken: A. The program checks for operating short-term debt (DEBT(i,6)=0). If there is such short—term debt and its balance is (1) less than the cash balance minus Q.16(b), Part 11 (called "cash available") the short-term debt is removed (DEBT(i,l) is set equal to zero) and the amount of the debt is sub- tracted from the cash balance and added to "short-term principal paid." I! ll (2) greater than or equal to "cash available, cash available" is subtracted from short-term debt balance due and added to "short term principal paid." The cash balance is set equal to Q.16(b), Part II, cash on hand is set equal to Q.16(b), Part II and the program moves to section 19.6. If there is no operating short-term debt or if the cash balance is still greater than Q.16(b), Part II after paying operating short-term debt, the program checks for entries made this month (DEBT(i,4) has no value) and pays off those for which cash is available. A value of "cash available" is calculated as the value of the cash balance minus Q.16(b), Part II. This value (cash available) is subtracted from the cash balance. The program finds and pays off entries made this month in the following order until "cash available" equals zero or all potential loans entered this month are paid off. (1) Livestock loans (DEBT(i,6) 7) (2) Machinery loans (DEBT(i,6) e) (5) Building loans (DEBT(i,6) = 4) (4) Land investment loans (DEBT(i,6) = 5) 466 The action taken as each entry or potential loan is considered depends on the quantity of cash available. If cash available is equal to or greater than the amount of the entry (DEBT(i,l)), DEBT(i,1) is subtracted from the cash available, added to the appropriate cash livestock investment, cash machinery invest- ment, cash building investment or cash land investment variable and then set equal to zero. If the cash available is less than the value of DEBT(i,1), cash available is subtracted from DEBT(i,l) and added to the appropriate cash investment category. Cash on hand is set equal to Q.16(b), Part II and the program moves to section 19.6. If there were no operating Short term loans and no entries made this month, or if "cash available" is still greater than zero, debts contracted in previous months are paid until "cash available is expended" or all loans are paid off. These debts are paid off in the following order: (1) Short—term debt (DEBT(i,6) = 1) (2) Intermediate-term debt (DEBT(i,6) = 2) (5) long-term debt (DEBT(i,6) = 5) AS each loan is considered the interest due since the last payment was made is calculated first. This is a simple interest calculation and is the product of (1) the loan outstanding (DEBT(i,l), (2) the interest rate (DEBT(i,2)) and (5) the number of months since the last payment divided by 12. MOnths Since last payment equals zero if pay- ment has been made this month, i.e. if DEBT(i,7) equals zero or DEBT(i,7),..., DEBT(i,12) equals the present month simulated. If payment has not been made this month, months since last payment equals 467 the number of months between the month being simulated and the first previous month in which payment is to be made as indicated by DEBT(i,7), ...,DEBT(i.12). The interest due, as calculated above, is added to the outstanding balance of the loan (DEBT(i,l)). If this sum is less than the cash available the entire loan is paid off. The sum is subtracted from cash available and if the debt is: l. Short-term, the interest is added to "Short-term interest paid," the balance due is added to "Short term principal paid" and DEBT(i,l) is set equal to zero. 2. Intermediate-term, the interest is added to "intermediate- term interest paid," the balance due is added to "intermediate- term principal paid," and DEBT(i,l) is set equal to zero. 5. Long—term, the interest due is added to "long-term interest paid," the balance due is added to "long—term principal paid" and DEBT(i,l) is set equal to zero. If the sum of interest due and balance outstanding exceeds the amount of cash available, the percentage that cash available is of the sum is calculated. This percentage is then multiplied by the interest due to get the actual interest paid and by the balance outstanding to get the amount of principal to be paid. The sum of interest and principal actually paid is subtracted from cash available and "cash on hand" is set equal to the cash balance. The amount of principal paid is subtracted from DEBT(i,l). The actual interest and principal paid H are added to the appropriate interest paid" and " principal paid" variables (as indicated in the above paragraph) respectively. When part of a loan is paid off, a new "amount of payment" is 468 calculated. It is assumed that the same loan period will be used and the amount of payment is reduced to reflect the reduced principal and interest to be paid. The remaining years of the loan is first calcu- lated as the loan period minus one-twelfth of the age (which is in months). The new amount of payment is calculated as indicated below. If the type of loan (DEBT(i,5)) is l, the amount of payment equals " __i_ “ P Y? (K) l-t-t) l +— 1HA nogpo mfifixgl .pm> mcwomohm moao nm£yo Assam coow “mafiawphmm moodnquH coflpw>nomcoo oufim Bowmso gawdmm .eafism ufiwmmm humcwnods afio a mac HOQQH omammxm .zow: a coda .p.>oo mason oamwm mammphom pawns mpwo Chou ham mappwo xaflz meoqu H303 .08 .>oz .poo .Emm .m3. :3. .846 S: 3:3 £2 .pmm .52. fig .pz\.os on .hh\.oe no snow figmenmegwm 309m mméd MQIBZOS OH mama #85 vqwm co nmwo cozohnom Homfiocfihm Hmswncspfiz .umo>qH ammo mmflmmxm osoozH manpoe msoq .ch phomm coachgom .mGQH .ch phonm meflocfinm omam mmcfleaflsm haozfinomz Moopmo>fiq pcosamo>cH nmwo gong hAoGHnomz mmawm Hapfldao mcoqnpwohmch .chnpmonopQH phonmapmmgoan A.p:oov omamgxm pr08 .08 .>02 .poo .pmmm 03¢ .HSh .82. as: 33$ .3: 5mm .cdh ampH .h>\.os oh .h%\.oe mo snow A.pcoov Bzmzme