2' 09"? ABSTRACT A SIMULATION ANALYSIS OF POLICIES FOR THE NORTHERN COLOMBIA BEEF CATTLE INDUSTRY By Alvaro Posada The Atlantic Coast of northern Colombia (known as the Costa) supports between MO and 50 percent of Colombia's cattle population and, with easy access to domestic and world markets, is the most important of Colombia's five beef-producing regions. Because cattle raising is the main economic activity in the Costa and is an extensive operation with low technical efficiency, the region has been a priority target for cattle development programs. In the mid-19603, with the financial and technical assistance of several international agencies, the Colombian government started a cattle development program aimed at increasing beef production mainly on the Atlantic Coast. In the early 19705 this program was reinforced with a disease control program and then revised and issued as a national cattle development plan. The main instruments of this plan are credit, technical assistance, export subsidies and improved marketing and slaughtering facilities. Its long-term objectives are to increase the protein supply to the Colombian population and to generate foreign exchange earnings. \F. Alvaro Posada CIT The primary purpose of this study was to develop a system simulation model to (1) analyze the effects of production incentives on the decision of farmers to adopt new production methods, and (2) estimate the effects of the expanded regional production on the income of farmers, government revenues, Colombian beef consumption and sustained level of exports. Four alternatives to traditional production were considered. Alternative 1 considered the improvement of native and artificial grasses; alternative 2 considered the improvement of artificial grasses and the substitution of artificial for native grasses; alternatives 3 and LI added the production of forages and silage to the improvement of range lands in alternatives 1 and 2 re- spectively. At the present stage of the study, however, alternative 2 was the only one comprehensively tested and used as a base run for policy experimentation. The cattle system simulation model has five major components (including a cattle demography model) which (1) allocate land use according to the farmer's perceived profitabilities of cattle and crops subject to land and capital constraints; (2) calculate yield and output of cattle and crOps and their respective producer and market prices; (3) provide the instrumental linkages for government revenue, export trade policies, and production campaign policies; and (A) generate the performance criteria necessary to evaluate the impacts of alternative programs on the cattle economy through time. Alvaro Posada The five major sets of assumptions investigated were (1) disease control in the traditional herd, (2) alternative cattle industry taxing policies, (3) alternative development credit policies, (A) alternative levels of government production campaign promotion, and (5) alternative cattle pricing and export policies. The results of the cattle policy experiments were discussed in terms of the projected time paths (from 1966 to 1985) of five of the most important performance indices incorporated in the model: (1) regional cattle population, (2) Colombian beef consumption per capita, (3) regional farm income from cattle, (A) capitalized grazing land value per hectare, and (5) annual regional government revenue from cattle. Experiments with disease control and export promo- tion policies each used two indices instead of the above five: regional cattle population and extraction ratio for the disease control policies and domestic market price of finished males and export margin for the export policies. In general, the study demonstrated that (l) the projected outcomes with the government disease control campaign were greater than under precampaign practices in the traditional herd; (2) the projected outcomes with government programs easing development loan terms were in all cases greater than the base run which assumed current credit policies; (3) the projected area in improved land and the modern cattle population with government policies benefit— ing both the traditional and modern operations were in all cases lower than under policies benefiting only the modern Alvaro Posada operation; (A) the projected area in improved land with the increased land tax rate was greater than the base run which assumed current land tax rates; (5) the projected outcomes with the removal of special taxes on cattle were lower than the base run which assumed no removal of these taxes; (6) given the assumptions on farmers' decisions and accounting mechanisms in the model, availability of credit for land improvement does not seem to be a serious constraint to land modernization; and (7) the projected outcomes with a flexible exchange rate suggest that this is an effective incentive to export without involving large transfers from public revenues to exporters in the form of subsidies. The study indicated areas where more research and regional data are needed to improve the model's performance, and discussed possible extensions that could help analyze more fully alternative policy strategies for the Costa's overall development. Finally, the study demonstrated that the system simulation approach with a computerized model of the cattle economy which incorporated information from diverse sources and accounted explicitly for the dynamic interactions and feedbacks that might occur can be a very useful methodological tool for policy analysis. A SIMULATION ANALYSIS OF POLICIES FOR THE NORTHERN COLOMBIA BEEF CATTLE INDUSTRY By Alvaro Posada-V A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 197A To Maria Lucia and Clara Lucia 11 ACKNOWLEDGMENTS I express my gratitude to Professor Glenn Johnson for his guidance throughout my entire graduate program and for his encouragement. Special thanks are due to Dr. Michael Abkin for his assistance and critical yet perceptive comments since the early stages of this study. I also want to thank Drs. Thomas Manetsch, Marvin Hayenga and Leonard Kyle for their help as members of my thesis committee. I am thankful to Chris Wolf for writing the computer program; to Judy Pardee, Enid Maitland and Patti Stiffler for typing earlier drafts of the thesis; and to Addiann Hinds for her editorial assistance. I am indebted to the Rockefeller Foundation for financially supporting my studies at Michigan State University. The U. 8. Agency for International Development (under Contract AID/csd-2975) was also very helpful providing financial aid for the final stages of my research. A special debt is due to my wife, Maria Lucia, for her love, patience and support during this period of mutual knowledge enrichment. iii TABLE OF CONTENTS LIST OF TABLES . LIST OF FIGURES . . . PART I--INTRODUCTION The Problem . CHAPTER l--SCOPE AND NATURE OF STUDY Agriculture in the Colombian Economy Need for This Study General Systems Simulation Approach as a Tool for Beef Policy Analysis Purpose and Objectives . . . CHAPTER 2--A GENERAL DESCRIPTION OF THE COLOMBIAN CATTLE INDUSTRY . . Size and Location of Cattle Industry Production and Marketing Systems . Beef Consumption . . . . National Policies Toward the Cattle Industry . Taxation . . . . . . . . . Land Reform . . . . . . . . . Credit . . Disease Control . . Development Plan . Exports CHAPTER 3-—THE REGIONAL SETTING OF THIS STUDY . . . . The Geopolitical Setting . . . . The Population . . . . . . . . . . Ecological Zones . Climate and Natural Vegetation . . . $0118 0 O O O O I I O O O The Agricultural Economy . . . . . Cattle Production . . . . . . : Agricultural Services . . . . . Cattle Marketing . . . . . . . iv Page .viii PJH CHAPTER u--GENERAL SPECIFICATIONS AND PROCEDURE . . . . . . . . Area of Study . . . . . . . . . Farming Sectors . . . . . . . . Ranching Practices . . Modern Alternatives . . . . Static Restrictions Procedure . . PART II--MODEL DESCRIPTION Introduction . . CHAPTER 5--LAND ALLOCATION AND MODERNIZATION DECISIONS-~CROPS/CATTLE (LAMDAC) Land Uses . . . Availability of Agricultural Land Food CrOps . . . . . . . . . . Cash Crops . . . . . . Pasture and Cattle . . Alternatives . . . . . . Cattle Modernization Decisions Profitabilities . . . Promotion and Diffusion . Transition Responses Cattle Transfers . . . . CHAPTER 6--AGRICULTURAL PRODUCTION-— CROPS/CATTLE (AGPRAC) . . . . . . . CrOp Yields . . . . . . . . . Pasture Production . . . . . . Cattle Production . . . . . Demography . . Milk and Animals Output Market Model . . . . . . . . . Disease Control . . . . . . . . Agricultural Accounting . . . . . . CHAPTER 7--PRICE GENERATION (PG) . . . . Export and Market Price of Cattle . . Producer Prices and Price Averages . . CHAPTER 8—-POLICIES FOR THE CATTLE INDUSTRY Policies . . . . . . . . . . . . . Production Campaigns . . . . . . . . Taxes . . . . . . . . . . . . . Export Policies . . . . . . . . . . CHAPTER 9--ACCOUNTING AND PERFORMANCE CRITERIA (CRTACC) . . . . . . . . V 78 80 80 82 85 88 95 96 97 104 105 120 126 126 129 137 137 150 155 159 163 18A 18A 187 192 193 193 195 196 197 Page Budget Accounting . . . . . . . . . . . 197 Development Credit . . . . . . . . . 200 Commercial Credit . . . . . . . . 201 Aggregated Income and Consumption . . . . 20A Capital Formation and Export Incentives . 208 Performance Criteria . . . . . . . . . . . 215 PART III--VALIDATION AND TESTING Introduction . . . . . . . . . . . . . . . . . 221 CHAPTER lO--DATA USAGE AND MODEL TUNING . . . . 223 Model Data Requirements . . . . . . . . 225 System Parameters . . . . . . . 225 Technological Coefficients . . . . . 229 Initial Conditions . . . . . . . . . 230 Tuning . . . . . . . . . . . . . . . 231 Consistency Checks . . . . . . . . . 239 General Validation . . . . . . . . . . . . 2A1 CHAPTER ll--RESULTS OF SENSITIVITY ANALYSIS . . 2A6 Analysis of Results . . . . . . . . . . . 2A7 Methodology . . . . . . . . . . . . . 2“? Summary . . . . . . . . . . . . . . . . . 258 PART IV—-MODEL APPLICATION IN DECISION MAKING Introduction . . . . . . . . . . . . . . . . . 261 CHAPTER l2--POLICY EXPERIMENTS ON THE NORTHERN COLOMBIA BEEF INDUSTRY . . . . . . . 262 Policy Experimentation . . . . 263 Run Definitions and Organization . . 26A Policies Related to Disease Control . . . . . . 268 Policies Related to Cattle and. Land Taxes . . . . . . 273 Policies Related to Promotion and Development Credit . . . . . . 285 Policies Related to Domestic Supply . . . . . . . . . . 290 Policies Related to Crop Modernization . . . . . . . . . 300 Effects of Various Policy Combinations . . . . . . . 315 Policies Related to Export Promotion . . . . . . . . . . . . 319 Conclusions . . . . . . . . . . . . . . . 33A vi Page CHAPTER 13--SUMMARY AND CONCLUSIONS . . . . . . 3A0 Introduction and Summary . . . . . . . 3A0 Salient Features of the Costa Model . . . 342 Policy Implications from Simulation Experiments on the Costa Cattle Economy . . . . . . 3A6 Improvements and Extensions of the Model . . . . . . . . . 3A9 Needed Improvements in the Model . . . . . 350 Extensions . . . . . . . . . . . . . . . . 35A Concluding Remarks . . . . . . . . . . . . 357 APPENDIX: Computer Program . . . . . . . . . . . . 359 BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . A02 vii LIST OF TABLES Table 3 Page 1.1. Cattle Numbers in the National Herd and Export of Live Animals, 1960- 7A ('000 head). 0 o o o o o o o o o o o o 19 1.2. Costa-Land Classes and Recommended Use by Departments ('000 has.) . . . . . . . 53 1.3. Costa--Distribution of Farms According to Size, 1960. . . . . . . . . . . . . 5A I.A. Cattle Numbers in the Costa and Non— Costa Herd, 1968 ('000 head) . . . . . . 58 1.5. Surplus and Deficit Relationships in Beef Production Among Regions, COlombia’ 1969-19720 0 o o o o o o o o o 60 1.6. Costa-—Catt1e Distribution According to Size of Ranch, 1960. . . . . . . . . . 62 11.1. Selected Coefficients and Initial Values in the Land Allocation and Modernization Decisions Component (LAMDAC) . . . . . . 123 11.2. Average Annual Yield and Initial Costs by Farming Sectors . . . . . . . . . . 128 11.3. Maximum and Minimum Proportions of Cattle Sales . . . . . . . . . . . . . . 154 11.A. Perceived Changes in Cattle Output and Costs During the Planning Horizon. . . . 177 11.5. Selected Coefficients and Initial Values in the Agricultural Production Component (AGPRAC) . . . . . . . . . . . 181 11.6. Land in CrOps, Average Yields and Producer Prices in the Costa, 1960 . . . 188 11.7. Selected Coefficients and Initial Values in the Price Generation Component (PG) . . . . . . . . . . . . . 191 viii Table Page 11.8. Selected Coefficients and Initial Values in the Accounting and Performance Criteria Component (CRTACC). . . . . . . 219 111.1. Profitability Response Parameters for Traditional and Modern Grazing (Dimensionless). . . . . . . . . . . . 227 111.2. Cattle Production Parameters . . . . . . . 228 111.3. Pasture and CrOp Yields (tons/ha-year) . . 232 111.“. Mean Length of Cattle Production Stages (years) . . . . . . . . . . . . . 233 III.5. Selected Initial Conditions (1960) . . . . 233 111.6. Time Series Tracking . . . . . . . . . . . 237 111.7. Results of Consistency Checks. . . . . . . 240 111.8. Selected Results of Alternatives to Traditional Cattle Production--1985. . . 2A3 111.9. Results of Sensitivity Tests on Selected Parameters of the Costa MOdelo o o o o o o o o o o o o o o o o o 250 1V.1. Policy Simulation Runs . . . . . . . . . . 265 ix Figure 1.1. 11.1. 11.2. 11.3. 11.A. 11.5.a. II.5.b. 11.5.e. II.5.d. 11.5.e. LIST OF FIGURES Colombia—-the five beef cattle producing regions. . . . . . . . . . . . . . Ecological zones of Northern Colombia. Costa-~genera1 soil groups . . Hypothetical cross-section of alluvial valleys in Northern Colombia . . . . . Agricultural regions of Northern Colombia . . . . . . . . . . . A functional flow diagram of cattle production . . . . . . . . . . . Building blocks of the Costa beef mOdel O O O O O O O O O O O O O O O O The profitability response function. . The land "dropout" function. . . . The reversion response function. . . . . Cattle production cohorts. . . . . . . Traditional birth rate versus total digestible nutrients . . . . . . . . Modern birth rate versus total digestible nutrients . . . . . . . . . Traditional death rate of growing cohort versus total digestible nutrients. . . . . . . . . . . . . . . Modern death rate of growing cohort versus total digestible nutrients. . . Traditional death rate of producing cohort versus total digestible nutrients. . . . . . . . . . . . . . x Page 17 A6 ”9 50 68 71 75 106 115 116 138 1A2 142 1A3 1A3 1AA Figure Page 11.5.f. Modern death rate of producing cohort versus total digestible nutrients. . . . 1AA 11.6. The cattle sales function. . . . . . . . . 153 11.7. The on-farm resource use response function . . . . . . . . . . . . . . . . 172 11.8. Promotion and development credit profile. . . . . . . . . . . . . . . . . 195 111.1. Results of "coarse" model tuning-- cattle population in the Costa and Colombian beef supply time series against simulated series . . . . . . . . 235 111.2. Results of "coarse" model tuning--market price of finished males and land in crops time series against simulated series . . . . . . . . . . . . . . . . . 236 1V.1. Cattle pOpulation in the Costa with and without a disease control program, 1965-1985. . . . . . . . . . . . . . . . 270 IV.2. Extraction ratio from the Costa cattle herd with and without a disease control program, 1965-1985 . . . . . . . . . . . 271 1V.3. Cattle population in the Costa under various taxing policies, 1965-1985 . . . 280 IV.“. Beef consumption per capita of the Colombian population under various taxing policies, 1965-1985 . . . . . . . 281 IV.5. Aggregated farm income from cattle production in the Costa under various taxing policies, 1965-1985 . . . . . . . 282 1V.6. Average per hectare capitalized value of grazing land in the Costa under various taxing policies, 1965-1985 . . . 283 1V.7. Annual government revenues from cattle production in the Costa under various taxing policies, 1965-1985 . . . . . . . 28A 1V.8.> Cattle population in the Costa under various promotion and credit policies, 1965-1985. c o o o o o o o o o o o o o o 29]. xi Figure Page 1V.9. Beef consumption per capita of the Colombian population under various promotion and credit policies, 1965-1985. . . . . . . . . . . . . . . . 292 1V.10. Aggregated farm income from cattle production in the Costa under various promotion and credit policies, 1965-1985. . . . . . . . . . . . . . . . 293 1V.11. Average per hectare capitalized value of grazing land in the Costa under various promotion and credit policies, 1965-1985. . . . . . . . . . . . . . . . 294 1V.12. Annual government revenues from cattle production in the Costa under various promotion and credit policies, 1965-1985. . . . . . . . . . . . . . . . 295 1V.13. Cattle population in the Costa and the - rest of Colombia under various domestic supply policies, 1965-1985 . . . . . . . 301 1V.14. Beef consumption per capita of the Colombian pOpulation under various domestic supply policies, 1965-1985. . . 302 1V.15. Aggregated farm income from cattle production in the Costa under various domestic supply policies, 1965—1985. . . 303 1V.16. Average per hectare capitalized value of grazing land in the Costa under various domestic supply policies, 1965-1985. . . 304 1V.17. Annual government revenues from cattle production in the Costa under various domestic supply policies, 1965-1985. . . 305 1V.18. Cattle population in the Costa with and without a cr0p modernization program, 1965-19850 0 o o o o o o o o o o o o o o 310 1V.19. Beef consumption per capita of the Colombian population with and without a crop modernization program 1965- 1985 . . . . . . . . . . . . . . . . . . 311 xii Figure IV.20. IV.21. IV.22. IV.23. IV.24. IV. 250 IV.26. IV.27. IV.28. IV.29. Aggregated farm income from cattle production in the Costa with and without a crop modernization program, 1965-1985. . . . . . . . . . . . . Average per hectare capitalized value of grazing land in the Costa with and without a crop modernization program, 1965-1985. . . . . . . . . . . . . . Annual government revenues from cattle production in the Costa with and without a crop modernization program, 1965-1985. . . . . . . . . . . . . Cattle population in the Costa under various policy conditions, 1965- 1985 l o I o o o o o o o o o 0 Beef consumption per capita of the Colombian population under various policy conditions, 1965-1985 . . Aggregated farm income from cattle production in the Costa under various policy conditions, 1965-1985 . . . Average per hectare capitalized value of grazing land in the Costa under various policy conditions, 1965- 1985 o o o o o o o o o o o o o o 0 Annual government revenues from cattle production in the Costa under various policy conditions, 1965—1985. . . . . . . . . . . . . . . Domestic market price of finished males under various policy alternatives, 1965-1985. c o o o o o o o c o o o 0 Competitive position of Colombian cattle in export markets under various policy alternatives assuming high export target and moderate and low world beef prices, 1965-1985 . . . . . . . . xiii Page 312 313 314 320 321 322 323 324 330 331 P A R T I INTRODUCTION The Problem Although planning for development has been practiced in Colombia for over thirty years, it has played a small role in the preparation of economic policy, and the various plans have been conceived more as political than as economic documents. Plans have been criticized as being essentially tech- nocratic exercises; the general public has contributed little to plan objectives and serious intention to implement has been lacking. As a consequence, development plans have en- joyed minimal general support and have had little or no effect on changing the country's economic, social and polit- ical structures [26]. The general systems simulation technique, with-its special approach to analyzing the problems of development, could be helpful in solving the issues of feasibility, credi- bility, and general acceptance of the planning exercise. Yet its effectiveness as a tool for development will depend on the will of Colombian authorities to provide the necessary financial and institutional support for fulfillment of the plan's goals. 2 In Part 1, Chapter 1 discusses the scOpe and proce- dure of the study and briefly outlines the "systems approach" used. Chapter 2 is a brief descriptive account of Colombian beef production, distribution and consumption that will help clarify the problems of the cattle industry. Chapter 3 de- scribes the region studied and the characteristics of its agricultural production. Chapter 4 discusses the modeling specifications and procedure. Part 11 details the Northern or Costa model. Chapters 5 through 9 cover the land allocation and modernization de- cisions component, the agricultural production component, the price generation component, policy entry points and the criteria and general accounting component. Part 111 looks at testing and validation procedures and results. Chapter 10 discusses data needs and the processes of tuning the model to track time series of recorded behavior. The results and implications of sensitivity tests on model parameters are presented in Chapter 11. Part IV discusses policy applications of the model, conclusions and areas for further work. Chapter 12 presents the results and analyses of runs experimenting with various cattle development policy Options. Experiments include an investigation of the sensitivity of policy results to changes in certain parameter values. Finally, Chapter 13 presents summary and conclusions, and outlines areas for further work in refining, improving and extending the model. CHAPTER 1 SCOPE AND NATURE OF STUDY Agriculture in the Colombian Economy Although agriculture is Colombia's main economic activity, its rate of growth during the last two decades has been lower than that of the gross domestic product (GDP). In 1969 it contributed 30 percent of GDP but its share in the national output has been declining as industrialization has proceeded. Nevertheless, agriculture continues to be the main source of employment with over half the Colombian peOple directly depending on it for their living. Within the agricultural sector, livestock production occupies about 87 percent of all agricultural land, account- ing for about one-third of agricultural output, or approximately 10 percent of GDP. Beef is the primary product. But despite the agricultural industry's importance, the production of basic food crops has barely kept pace with a 3 percent pOpulation growth rate. Cattle slaughter per 1000 inhabitants has been declining since 1950. Colombia's economic growth has been responsive to changes in the performance of the export sector and this has been dominated by agricultural exports which accounted for 78 percent of total foreign earnings in 1970. While A Q offee has remained the country's major export, its share 0 fthe total value of exports has declined from 72 percent 71960 to 61 percent in 1970. As a proportion of all agri- o 6 (Qtural exports it has declined from over 90 percent prior 0 0‘5 .1965 to 75 percent in 1969 [41] . But Colombia's dependence (8 Qgri cultural exports which have unstable world markets ‘3‘. , coffee, bananas, sugar and cotton) has undesirable s 11 Sq u i librating effects which jeopardize development efforts Q create the necessity of finding new sources of foreign 9v 1; 3111.18 s . The development of the beef industry, for which he 01.11:: 1 ook in world markets is considered brilliant, will ac comp; 1 sh the aims of increasing the domestic supply of pro- tein to b an improved diet and of helping remove both the for- eign e3: Q lange and instability constraints. jet in order to fully exploit the natural comparative advaflta’g cue Colombia has for cattle raising and make it a lead- ifig Mid q stry that is competitive in world markets, a great rt effo 1:1 as to be made to overcome the traditionalism that ch r135 ab acterized the industry and to supply the necessary 1113 111p IE‘ or modernization. In recent years the Colombian government has revised <3 1159 p l :- cies toward the cattle industry and reoriented them 3rd to“ the attainment of increased beef production. These O1101 Q s 9 have been associated with credit and technical as disease control, land ownership rights, taxation or; and ert subsidies. The Colombian government has given priority in the o attle deve10pment program to the Atlantic plain of Northern 0 0Jornbia because of that region's favorable natural condi- 6 I 6 0&3, its accessibility to domestic and foreign markets, and Db alation, This also supports the decision to focus the Ben 17 study in this geographical region. N N for This Study Since cattle production is not merely an important Eco homi c activity but the only practical use for millions of bee tar-e S of agricultural land not suitable for crop produc- 1: 1°“ be c: ause of soil conditions, climate, floodings and/or distanc e from markets, its performance is and will be an importa— ht factor in the success of Colombia's efforts to foster a ound economic growth in agriculture. Because of its size, probably future demands for its not 1'08 g and the need for improved operation, the Atlantic lain b 40 Q fact that it has the largest share of the country's cattle p Qef production system's performance has had and will havea‘ gignificant impact on Colombia's agricultural economy. Several studies have been done on various aspects 0f the Colombian beef production and distribution systems. er Ge“ 3.1 descriptions of the industry and analyses of current afld 91-- A 98d $1. :1cu1ture Organization (FAQ) [66], the Caja Agraria C6], the World Bank [42] and the Instituto Colombiano o 9.91 Dec-‘-‘I.lario [31]. Production problems and projections have n ‘Oee I‘ecently analyzed by Henning [29] , Von Oven [60], and onsed policies have been done by Riley [61], the Food A tJKinson [67]. Bowser [5] attempted to make production pro- J ectzions by regions and establish surplus and deficit areas 4 b Oder two systems of management. Daines [68] is attempting o incorporate the cattle subsector into a broader sector Q 0 $3573.15 of Colombian agriculture. 51-- More specialized studies on diseases and reproduction O (1.0 bl ems have been done by the Instituto Colombiano Agropecuario 4) in and Gomez, respectively [31, 24]. Slaughtering, market- 8 n and Opportunities for exporting have been studied by A debso n [2'], Booz, Allen and Hamilton [4], Secretaria de gpicul 1: ura de Antioquia [64], and Garcia Samper [231- Profitability studies have been done by the World Bank C ’4 2 3 43], Federacion Colombiana de Ganaderos (FEDEGAN) [20]: and Instituto Colombiano de la Reforma Agraria (INCORA) [341’ M Care recently, the Centro Internacional de Agricultura c Trot?1 al (CIAT) [9] made a survey of the cattle industry in fioc'doebn Colombia in an attempt to gather basic information id and an ‘tify specific problems which are in need of further earch r85 'Zrhese have mostly been descriptive studies, and when pTOJect 1 one are included they are trend-like, straight forward, algebra 1 c estimates. The credibility of these estimates has always ‘3 een questioned because of their [reliance on time- series data which deserve a low degree of confidence. Yet 51139 have served the purpose of providing background informa- 6&0“ ab Q‘ut the cattle industry and a basis, though weak, for 9}” ing its development. But except for the Bowser, FEDEGAN, 7 And the CIAT studies, no attempt has been made to place the e attle industry in a regional context and assess the effect t brough time of alternative strategies of development on the Q Q) tainment of a multiplicity of objectives such as employ- @Q Y1“, farm income, government revenues, foreign exchange 06 hings, and others without neglecting the interactions with Sr subsectors of the agricultural economy. av This dissertation is an attempt to integrate the Q D I~la.t3 1e information into a computerized model that will r‘o vide the policy maker with a more informed basis for Dla “hing development strategies for the Colombian beef pro- du Ction system on the basis of the learning experience from the No:- 1: hern region. The basic parameters and structural relation ships estimated in this study can be utilized in the future P cr modeling the cattle industry in other Colombian and/or for developing a broader regional model of Colombia. “Ibis study has gained from experience with other 51"“11at:'=>r~s of cattle population and related activities 0 (18¢po d for Nigeria, Korea, Northeast Brazil and Venezuela 6 [53' 2 § 51, 55]. e ‘FJ’ by th erd Bank [43] also has provided an invaluable 1‘1 3x93 g h Qe . The cattle population simulator developed fleTRL Wystems Simulation Approach as a Tool for 969 icy AnaIysis In recent years there has been an increasing interest the i9 utilization of the systems approach for analyzing ' D I I u C. I“ . . . ,.--: - , " I‘ Q C . fl. .pn v..- ‘. 9" . .. o..¢ . - I'll I'll, . .“ ":~ I“ o... . . . )‘u . I" ‘A a.“ ': .. b . .fl “I ~ . u u, \ I ‘v " A "- A " .' ;-: v ‘.' . I .. h Q 0mFlex developmental problems. Computerized techniques We helped automate the hand calculation process and expand t 89 range of alternatives which can be examined. Policy $¢ ca‘i’er's and researchers have been placing more confidence QQ Q credibility on the general system simulation technique be I ts methodology and approaches for development have been 6 Db ter- understood and developed [25, 46, 47]. Public and 1 th va.1:: as decision makers have been presented with an approach Q t at: tempts to build a general model to trace the conse- (1115 1“04538 through time of following alternative courses of based on at least as wide a range of kinds and sources and information as decision makers use without Special 1 zing in any one technique to the exclusion of tech- niques fuequently used by decision makers. In addition, the approac 1') carefully avoids premature application of maximiza- tiof' te '2 lmiques in situations where decision makers realize [113,0 th 3 e ovyt multiplicity of goods sought and beds avoided has fl Ieen reduced to a common denominator. These charac- St ter1 1% a make the generalized, systems-science simulation approa Q h he to 17 Q escriptive and paper-and-pencil and desk-calculator very similar, though more comprehensive and complex, project 1 one which have maintained a high level of credibility 3190“ a Q cision makers [47]. Researchers have been provided with a technique for analyzlhg the problems of development without the method- ological and theoretical restrictions of more specialized n 106°“ Lg.\1es such as simultaneous equilibrium equations, 9 11Near programming (LP), benefit/cost ratio analyses, 1/ 1”thermal rate of return analyses, etc.— And as new con- 0 sits and experiences regarding the formulation and imple- Gfltation of systems approaches have evolved, many of the Q" 0 “1y objections of statisticians concerning the reliability (a Db the estimates of parameters, criterion variables, and <11 901’ :Lptions by users of the systems analysis are being A Sipa ted. The analogy between the general systems analysis 81 fr: Qla. t ion approach and the Bayesian approach to inference ha 8 been demonstrated by Ladipo [50], and Johnson [47, 49] has Elna. lgzed repeatedly the possibility of empirically vali- d ating a 11d verifying the normative concepts involved in Simulat; 1 on models. Econometric methods with probabilistically estimated paramet Q Is rely heavily on time-series and cross-sectional data not always available in developing countries. These teofiflqq «es also tend to be specialized on linear equations b and aha— Vioral assumptions involving maximization in accor- e danc W1 ‘th neoclassical theories 'of the firm and household; ec the thmic forces that link the various components of an on . e0011 y are assumed to be self-equilibrating as a consequence he of ‘3 maximizing activities for entrepreneurs and consumers. «eveb ‘ 140 a the validity of these two assumptions has been chal- ed. 1,696 where the findings of these kinds of studies were to /\ d t. e ~PIP/For a more detailed discussion of various special- .ybe “a thiques see Manetsch, et al., [53]. For particular 39? 6.1: ions to the Nigerian cocoa industry see Chong [10]. "H .- I“ n‘ . ' ’ 5... .. .- ‘ . . .1 . I. ‘ .r‘ -' ‘ .. ob.- . O\ ...-- ~. ‘r .0» vb - o» n a n l- . .1- '- v .- ' . ‘ . I *‘. .. , w u 'l 'V . ‘I, ‘ :I ‘§ ‘n . . s . n . u 1 0 'Q .‘fi: . .1 t .. . s.. t. ‘l " l §. '. . - 10 be Used for policy analysis and prescriptions [53]. These and other methodological difficulties often result in less bfi’Jiable parameter estimates than available from alternative lads and sources of data and information with less SOphisti- fl? ted estimation and approximation techniques. J Linear programming and benefit/cost ratios and ’2 ternal rates of return are other specialized techniques 10h have gained considerable prominence in recent years. :13 Is always used in an optimizing mode in solving the prob- SIDS o 1‘ resource allocation and policy analysis. The other two tee hniques have been used more for proJect analysis than for anal— lyzing alternative policies and programs. Basically, all the SE techniques suffer from a need for a common denomi- nator' a~1‘l':-ong the goods being sought and the bads being avoided. Margo"e b, the approach is quite mechanistic and may not allow rigorou 8 analysis or interaction between researchers and 909°” makers needed for a better understanding and improv- ing 0? the system. A consideration of the distinctive attributes of the Variou‘a approaches employed for studying development alter- native& in a variety of less developed settings has led to ”16pr Rent proposal to use the system simulation approach as a t Q) 91 for deve10pment planning and policy analysis of the cactlg industry in Northern Colombia. The general systems simulation approach, following 1396 pr- 3~r‘iciples of the scientific method and problem—solving es. 1‘35 th, is viewed as an iterative problem-solving process ’ 'I' a .4 ..o 1 _..-4 4 0!? ,.~ n n O l“ 1.. a Q ‘ .. n ‘I Q 11 that includes problem formulation, mathematical modeling, befinement and testing of a computerized approximation of t be mathematical model, and creative design and execution of 6‘ 9 (mulation experiments intended to provide answers to the O Gstions being asked by the decision makers involved.l/ a The general system simulation approach has been b S e Qially applicable for solving many of the problems of Q0 “01711 c deve10pment which do not meet the requirements for as D G 1Y1 mg the simple maximizing computations of static pro- 110 tLox-2 , consumption and welfare economics. As decision ma l(91"8 S eek so many different goods and avoid so many dif- feI‘ent b ads in developing the economy, it is very difficult for the m or anyone to find a common denominator to be maxi- mized or minimized. Problems of ordering and imperfect knowled gs about future consequences of present actions com- plicate the circumstances in which decisions are made. Yet weeping prescriptive conclusions to solve development prob- P lefls e guires development of positive and normative knowledge. met; The hodology used in the general system simulation approach owe. all the system analyst to maintain a philosophic orienta- s 131°In K). t‘ficiently flexible to permit analysis of questions 01v 11" ing both the normative and the nonnormative values 338 81“ present when considering the goals of economic deve10pment. /\ o l/For more detailed discussion of the philos0phy and if“ is miob1§ logy of the general system simulation approach for m\solvin g research see Manetsch et al. [53] Abkin PC} ’ and Rossmiller et al., [62]. ’ , all". - c‘ ,... l .. .. _..~ , ‘ III. - - .,'-uu-- . :- ~ . u .e. ..... a. . 5 . a. n: -. > . an. . s .4. 4., -. . ' ' - .a. I l.‘ ' . ‘ 0' ""Q I 'no.1_ VVV‘A" _ . f .... t... n ‘1 u .v x - c .‘ -o . _. . :. . H. _ . 8 .w‘ ’ . c 5", .‘i J ‘p I ‘- .- . . . n \.1‘ ~ 5 c , ’i. ,- _. ‘1 .,- . Q A,- ‘w‘ A . ‘ v V” \ ‘n. ‘1 a K. ’ 12 There are four distinguishing features of the general 8J’stem simulation approach particularly useful for the policy Q”c‘llysis of the Colombian beef economy. First, it is a 0 Qneralized approach which makes use of a wide range of 6 ‘2, {mary and secondary information from many sources includ- I; b 3‘ surveys, government records, experiments, business 8 Q01"(3 S, qualitative Judgments and insights of subject matter *0 Qrt: s , and descriptive work about the beef industry from at- Iou S disciplines. Also a wide variety of specialized so hni q ues are used from econometrics, linear programming, Dar tial budget, project analysis, etc. Since the research a hd mOd e l-building process is iterative and flexible, new informa t ion can be incorporated easily as it becomes avail- able, and the structure of the model modified accordingly. Second, the system approach can incorporate many ”peg 0 IE ‘ functional relationships into the model to closely £69601: the current or potential real system. These include {13ml C: dY interactions, curvilinearities, discontinuities, l time a‘% s, probabilities and irreversibilities. 'I‘hird, the approach does not have to assume (but does not prg Q lude) any profit or utility maximizing producers afld 001-1 S umers, or any self-equilibrating economic adjust- meflts. It does not necessarily involve a unique set of 013’f’1ml2 1 ng solutions based upon a common objective or a pre- determined singular goal, which does not exist in reality. co t in n bast, the approach is more guided by the problem er 910‘} jrr‘ivestigation. . " .m-r , ' ,, .o‘ ,'.;.AC “’0‘. 0" -. c u . .uunw ' .p.. 1‘ a . ‘ ..,‘ a " I! .. A‘- " —¢. _ .. u .""n ~‘_ ‘ -.. ‘- . e...’ ' fl .. c‘ . ': u '. . I.‘ u u .4 .. c 13 Fourth, the system simulation approach provides an experimental setting for exploring the consequences of a wide range of alternative plans or management strategies that ultimately will assist policy makers in determining the best course of action. Decision makers may be shown the consequences of alternative courses of action in terms of what goods or bads will be received by or imposed upon groups of people, when, and in what quantities. After such projec- tions are available, interaction among investigators and policy makers will lead to a better understanding of the trade-offs among the numerous goods and bads involved in the solution of the problem. Developing, extending and refining knowledge on the various goods and bads and learn- ing about the trade-offs is a way of solving the problem of finding an interpersonally valid common denominator. As stated earlier, this problem has contested the usefulness of the approach used by some quantitative techniques of analysis in examining the problems of economic development. Further, in the iterative process, decision makers and investigators can work interactively to solve the remain- ing;two major theoretical difficulties found in the analysis Of development when using some other problem-solving tech— niques, First, the sequence in which different action pro- grams should be undertaken can be established, thus resolv- ing the problem of how programs and projects (actions) can be ranked. Then, when consequences of alternative decision rules can be projected and studied, it is possible to develop yo J a': nu. cm: '0‘. '1 I" in a basis for choosing the best rule among the alternative courses of action being considered. This solves the planners' problem of choosing a decision-making rule, especially under conditions of imperfect knowledge and uncertainty. Purpose and Objectives The purpose of this thesis is to develOp a model to help evaluate policy decisions that might be made in expand- ing the production of beef in Northern Colombia through time. More specifically, the objectives are: 1) To deve10p a credible simulator that could eventually be extended to other beef produc- ing regions and be further integrated into a national model. 2) To use the simulator a) to analyze the effect of new production methods on the output of cattle; b) to analyze the effect of production incentives on the decision of farmers to adOpt the new methods; c) to estimate the effect of the expanded regional production on the income of farmers, government revenues, domestic beef consumption and sustained level of exports. The procedures used in meeting these objectives will be discussed in Chapter A. m..- ,- a... n\- ! I A. v Q. . dc . ‘1. CHAPTER 2 A GENERAL DESCRIPTION OF THE COLOMBIAN CATTLE INDUSTRY The characteristics of cattle raising in Colombia described in this chapter will help in understanding the multiple problems affecting the industry. Stock farming in Colombia is carried on in a variety of climates and ecological zones that give rise to a wide range of problems which limit beef yields and supply. The principal limiting factors are the heavy incidence of animal diseases, malnutrition, deficient marketing and slaughtering systems. Besides the technical factors, government decisions that affect the political, social, and economic environment also have a major effect on the industry's behavior. §ize and Location of Cattle Industry Of the 11“ million hectares in Colombia, only about “0 million are suitable for cr0p and livestock production; tnmeremainder is under forest or is wasteland. Cropland occuptes approximately 5 million hectares, which leaves 35 million hectares under livestock. Even if crop acreage increases at 7 percent per annum during this decade, which would be very rapid growth, there still would be 30 million hectares in 1980 with no practical alternative use but graz- ing. The nation has the choice of producing cattle on this 15 ‘ A .k. i .. ‘ ‘ ~.vo -' I: o; ..... I ..... . l.\- r «- ... ~ 'sl. .- ‘ an. v. c n .u. ‘ . ‘I \ . . 16 land or letting it go idle. Credit and managerial manpower are the only scarce inputs used by the livestock subsector; thus cattle production is not currently a competitor with crops for scarce land. Although statistical data are not extremely reliable, Colombia supports about 20 million head of cattle, including slightly more than 17 million beef animals. The rate of increase in cattle numbers has been low, about 2 percent per annum over the period 1950 to 1969. Since 1956, the rate has been close to 3 percent [Al]. Although Table 1.1 shows that herd numbers increased at about A percent per annum over the period 1965 to 1969, ICA [31] has projected an average rate of growth of 3 percent annually for the next five years. The majority of beef cattle are maintained in tropical zones which have been divided into five clearly differentiated producing areas (Figure I.l).l/ l) The Atlantic Coast at an altitude of between 0 and 500 meters includes Cordoba, Bolivar, Atlantico, Sucre, Cesar, Magdalena, Guajira, and Northern Antioquia. Average temperature exceeds 2A°C and annual rainfall varies between 250 mm and about 2000 mm. Cattle population is approximately 7.6 million head and area in pasture 9.7 million hectares.§ i/Max F. Bowser, "Prerequisitos y Potencial para la EXportacion de Carne en Colombia en la Decada de 1970," Agricultura Tropical, XXV (Bogota, Nov., 1969), 679- g/Cattle pOpulation and area in pasture for regions 1, 2: and 3 are taken from Caja Agraria [6]; for region A, from Bowser, ibid., 68“; and for region 5 from ICA [31]. lmml!” |“.;¥ , 41ml ..I..|,,||m Brazi l I AnAmIc COAS'I’ II CENTRAI. a. umn MAGDALENA VALlEY III CAUCA VALLEY IV EASTERN PlAINS V soumum REGION FIGURE 1.1. Colombia--the five beef cattle producing regions. l8 2) The Central and Upper Magdalena Valley at an altitude of less than 1000 meters includes Central and Southern Antioquia, Eastern Caldas, Tolima, Huila, the Santanderes, Central and Western Boyaca and Cundinamarca. Average temperature exceeds 2A°C and average rainfall varies between 2000 and “000 mm. Cattle popu- lation is approximately “.3 million head and area in pasture is 5.6 million hectares. 3) The Cauca Valley at an altitude of less than 1100 meters includes Valle, Cauca, and parts of Caldas, Risaralda and Quindio. Average temperature exceeds 2A°C and annual rainfall varies between 1000 and 2000 mm. Cattle popu- lation is approximately 1.25 million head and area in pasture is 1.2 million hectares. A) The eastern plains at an altitude between 500 and 1000 meters include Meta, Eastern Cundinamarca, Eastern Boyaca (Casanare), Arauca, Vichada and Guainia. Average temperature exceeds 2A°C and annual rainfall varies between 2000 and A000 mm. Cattle population is approximately 1.5 million head and area in pasture is 16 million hectares. 5) The South, at an altitude of less than 1000 meters includes South Eastern Narino, Caqueta, Putumayo, Amazonas and Vaupes. Average tem- perature exceds 2A°C and annual rainfall varies between 1000 mm and U000 mm. Cattle population is approximately .69 million head and area in pasture is 3.5 million hectares. Production and Marketing Systems Cattle production in Colombia is an extensive Opera— tion depending almost exclusively upon pastures as a source of feed inputs. Limited amounts of feed concentrates are being used in the more intensive dairy operations, and there are a few cases of confined feeding of steers. For the nation as a whole, the average carrying capacity is about 0.57 head per hectare, but there are wide differences in capacity among the various classes of pastures 19 TABLE 1.1. Cattle Numbers in the National Herd and Export of Live Animals, 1960—7A. (1000 head) No. of Change in Registered Year Cattle Inventory Exports 19603/ 15,329 529.0 1961 15,679 350.0 1962 15,979 300.0 1963 16,279 300.0 b/ 1969 16,58u 305.0 3.1— 1965 16,882 298.0 76.0 1966 17,372 u90.0 58.u 1967 18,082 710.0 19.8 1968 18,830 798.0 19.1 1969 19,576 796.0 58.3 1970°/ 19,792 166.0 125.8 1971 20,33“ 592.0 191.7 1972 20,99h 660.0 2h5.0_/ 1973 21,573 579.0 282.0 1979 22,328 755.0 329.0 a/ Cattle inventories between 1960 and 1969 are from World Bank Report [“1]. 7 0/ Exports between 196“ and 1971 are from Sarmiento [63]. c/ Cattle inventories between 1970 and 197A are projections by ICA [31]. g/ Exports between 1972 and 197“ are low target projections by Instituto de Comercio Exterior, INCOMEX [35]. .r‘ .n. I“ 20 and regions. The artificial pastures which make up about one-third to two-fifths of the total pasture area [61, 66] have the greatest carrying capacity: about 2.0 to 2.5 head per hectare under good management. These pastures have a carrying capacity 3.5 to 9.5 times greater than that of natural pastures. The highest stocking rates are in the Costa depart- ments: 1.35 to 1.A5 head per hectare; some of the lowest rates are in the Eastern Plains (Los Llanos) where a breeding cow and her calf are carried on up to 10 hectares. In the Magdalena and Cauca Valley regions the stocking rate varies from 0.79 head to 1.00 head per hectare. The national beef herd has been derived from "Criollo" breeds which still account for approximately 20 percent of the total. The remainder have been upgraded from "Criollo" by Cebu (mostly U. S. type Brahma) for up to three or four generations. The hybrid vigor of the first crosses and the adaptability of the Cebu to tropical conditions contributed to the popularity of this breeding practice. The size of producing units varies widely although accurate statistics on herd size distribution do not exist. Large units exist in the Atlantic Coast, Eastern Plains and South regions while in the mountain areas of Central Colombia there are many small units with less than 10 head of cattle per farm. Sixteen departments surveyed by the Departamento Administrativo Nacional de Estadistica (DANE) [1A] in 1960 had 98 percent of the cattle in herds of less than 200 head. ..- 1' . vu- ‘ a... In. -\ .. 0"- - Iv. v u!- .,. \ .F "c 21 As various measures of productivity show, the technical efficiency of the Colombian cattle industry is low. The "extraction rate," which is the proportion of the cattle inventory extracted for domestic slaughter and ex— portation, is approximately 13 percent annually. This extrac— tion rate compares with 40 percent in the United States, 29 percent in Australia and 29 percent in Argentina. The calving rate (number of calves born as a percent of females of breeding age) is undoubtedly very low. The estimates of the national average calving rate range from 50 to 70 percent as compared to 80 to 90 percent in countries with well—developed cattle industries. Death losses are relatively high, averaging at least 8 percent a year for all cattle. Losses are greatest among calves where mortality rates are often 20 to 30 percent or more during the first few months after the calves are born. The average growth rate is very low; slaughter age is about A years, although some of the better growers now market steers at 3 years of age. This is still high compared to the average marketing age of 1.5 to 3 years common for slaughter steers in the United States. Steers from La Costa are slaughtered at about ”50 kg. live weight while Los Llanos steers average 390 kg. Yield per animal slaughtered, in relation to the live weight of the animal, is barely 50 percent, as compared with 58 to 60 percent in countries where types specially bred for the production of beef are prevalent. Beef yields per hectare 22 and per animal are also low when compared with other countries. Von Oven [60], reported live weight yields per hectare of 62 and 11 kilograms for the Costa and Eastern Plains, respectively, as compared with 90 kilograms for the Buenos Aires province in Argentina. Live weight yields per animal unit were 83, HO and 117 kilograms for the Costa, the Easter Plains and the Buenos Aires province, respectively. Mortality and performance at all stages of growth are affected materially by inadequate health control measures. Major diseases or parasites which cause mortality or losses through the falling-off in production among the animals affected are endemic foot-and-mouth (types A and O), rabies, anthrax, brucellosis, septaecemia, ticks and tick—born fevers, black leg, screw worms, and a great variety of internal parasites. Althougheffective control measures that could be applied in Colombia exist for most of these diseases, treatments are not a common practice. Since it first appeared in Colombia in 1950, foot-and— mouth disease has caused significant losses that have been estimated by the Instituto Colombiano Agropecaurio--ICA-- at Ps. 332 million annually [31]. These losses are produced by death, reduced weight, retarded maturity, reduced milk production, and culled animals because of severe health injuries. Furthermore, foot-and—mouth disease constitutes an obstacle to trade between affected and immune areas, and precludes livestock and meat exports to countries which are free from the disease or on the way to eradicating it. 23 Brucellosis, or infectious abortion, is next to foot- and-mouth, among infectious and/or contagious diseases, in causing the heaviest livestock losses. Brucellosis affects about one-fifth of the cow population (1,136,000 head), and about 2 percent of the stud bulls (9,600 head) [31]. Losses in 1967 were estimated at Ps. 177.5 million and consisted of some 136,000 miscarriages, permanent sterility in about 22,700 cows, impossibility of using 9,600 sick bulls, deaths of about 5,500 cows, and permanent loss of milk in the affected cows. Losses due to parasites are probably equal to or greater than estimated losses due to disease. In many instances an animal may be sufficiently weakened by parasites to readily succumb to identifiable diseases. The incidence of internal parasitosis is enormous, especially among calves; losses may run as high as 15 to 25 percent. External para- sitosis is a disease almost entirely confined to animals in the subtropical and tropical zones, where it affects 75 percent of the stock. External parasites cause heavy losses by retarding growth, raising the mortality index and damag- ing the hides. Cattle production and yields are also limited by problems of nutrition. Seasonal fodder shortages coupled with deficiencies of minerals and vitamins lead to the diminution of milk and beef yields, to retarded growth and to death in some cases. Furthermore, the reproductive {Wumtions are affected, sometimes so seriously that the 29 animals become infertile or fecundity is reduced; and this in turn greatly lowers the birth rate. Gomez [29] reported up to 20 percent of cows between 2 and over 10 years of age as having permanent infertility, with trophic problems (associated with nutrition) as responsible for 85 percent of the cases. In addition to seasonal shortages, forage production is aggravated by the underdiversification of pastures and the absence of satisfactory rotation practices. Little attention is devoted to the management and care of pastures and they often deteriorate greatly. Obsolete and even primitive practices which prevail in many stock farming activities are responsible for the majority of drawbacks and deficiencies found in cattle pro— duction. Most stock farmers are slow to adOpt new techniques, and absenteeism on the part of landowners aggravates herd mismanagement and intensifies the managers' and herdsmens' tendencies to stick to traditional routine practices. But husbandry deficiencies are not the only example of poor management. Most ranchers do not keep accounting and production records and have scanty, imperfect knowledge of supply and demand trends as well as of the market situation. Defective management and extensive methods offer few opportunities for employment and higher salaries. Labor intensity in cattle production is 3 man-days per head-year or 1.71 man-days per hectare-year as compared with 50 to 25 65 man-days per hectare-year for most annual crops and with 120 to 300 for most vegetable and fruit crops [69]. Daines [70] gives another measure of the low labor intensity and high investment requirements to generate new mmfloyment in livestock. In livestockl/ labor is 1.0 to 9.3 percent of total costs as compared with 20 to 68 percent for most crops. And it takes from 6,300 to 26,255 dollars of investment to generate a new direct man-year of employment in livestock as compared with 300 to 3,270 dollars for most crops. The failure of the supply of livestock commodities to react to the high demand elasticity by which they are characterized is attributable not only to production difficul- ties but also to the problems created by current marketing systems. The deficiencies affecting the rounding-up and transport of livestock, as well as slaughter and beef dis- tribution, are manifold. The marketing system is extremely fragmented; many small buyers and commission agents serve the ranchers, and there are many slaughterhouses, many agents placing meat in slaughterhouses, and many small stores selling meat. Most animals are bought on the farm, and are usually purchased with little consideration of weight or quality. Cattle are shipped directly to "ferias" or stockyards, which are located throughout Colombia. Medellin is the most important market and often sets the price standard for the country. l/Includes wool, eggs, poultry, pork and beef. 26 The high cost of transporting livestock has been and will continue to be one of the most serious marketing prob- lems confronting the industry. Serious losses of weight occur during on-the—hoof movements. Cattle trailed for long days--from the Llanos for instance-~lose up to 15 percent of their weight, in addition to which mortality must be taken into account. Severe weight losses are also registered in animals taken by boat; in some cases the time between the departure from the farms, arrival at the port of loading and transport to the place of destination may be as long as 15 days or longer. Although truck and rail transport cause fewer losses, these means are deficient and costly and the animals are badly mishandled in transit. Methods of slaughtering and slaughterhouse services are extremely old-fashioned in most municipalities. Condi- tions are unhygienic, and as a general rule there are no veterinary services for proper inspection of the cattle on the hoof and the meat. One of the chief drawbacks is too many small slaughterhouses where the volume of operations is not large enough to finance the equipment, construction and services which would be required for efficient organization. Only about 5 percent of the slaughterhouses are located in major cities and provide technical and hygienic services. Among other serious deficiencies in the slaughter of livestock and the handling of meat are the inefficient utilization of slaughter by-products and the lack of 27 refrigeration facilities, even in torrid climates where meat spoils in a very few hours. Prices of cattle and beef have been rising with the general inflation that has prevailed in Colombia for several years. Prices of cattle at the ranch, on a liveweight basis, ‘were approximately 5.17 pesos per kilogram in early 1970, equivalent to U. S. $0.28 per kilogram liveweight. Deflated consumer beef prices have increased by 18 percent from 1969 through 1969, while prices at the ranch only increased by 13 percent [Al]. In addition to the rising secular trend, beef prices show both seasonal and cyclical variations with cycles averag— ing about seven years in length. Seasonal price fluctuations are caused directly by the occurrence of dry seasons and the lack of irrigated pastures and forage storage. Until 1964 the official exports of livestock products were negligible, but have since shown substantial increases. In 1970, these exports reached over U. S. $21 million [63]. In 1979 livestock exports are expected to range between a low target of U. S. $51 and a high target of U. 8. $107 million [35]. Exports of beef (frozen, refrigerated and chilled), viscera and processed meat have been increasing in importance. Estimated values for 1971 were U. S. $9.9 million for beef and U. S. $0.17 million for viscera and processed meat as compared with U. S. $2.9 million and U. S. $1.08 thousand in 1965, respectively. 28 Peru has been the most important market for live cattle followed by the Dutch Antilles, French Guiana and Venezuela. Illegal exports of live animals, predominantly to Venezuela, have been estimated between 100,000 and 300,000 head annually. Spain, Peru, the French Antilles and French Guiana have been the most important markets for beef. Beef Consumption Registered or controlled slaughter in 1970 was 2.366 million head, but total slaughter was estimated at 2.603 million head after increasing the former by 10 percent to account for unregistered or clandestine slaughter [63].;/ Although the trend in cattle slaughter has been upward, there have been significant variations [23, 61]. From 195A to 1957 slaughter increased 27.5 percent and then turned downward during 1958 and 1959. From 1960 to 196A it increased again by 30 percent. From 1965 to 1968 slaughter decreased by 3.3 percent and then turned upward again during the next three years. Slaughter of male cattle fluctuates less than that of females and averages about 60 percent of total slaughter. Female slaughter averages about A0 percent of total slaughter and has ranged from 3A percent in 195A to AA and A3 percent in 1951 and 1965 respectively [16, 61]. Consequently, the year-to-year variations in total cattle slaughter have been 1/ — This refers to domestic consumption. Official statistics usually include registered exports of beef and live cattle. 29 largely due to changing policies of farmers who withhold females for breeding purposes. Apparently, accumulation and liquidation phases in the cattle cycle are completed, on the average, every seven years. The trend in per capita consumption of carcass beef has been slightly downward due to the more rapid rate of growth in the human population than in total beef production. Registered cattle slaughter decreased from 123 head per 1000 inhabitants in 1951 to 110 head in 1970. Per capita con- sumption decreased from 29.6 kg. in 1951 to 22.A kg. in 1970,;/ but when unregistered slaughter is considered, per capita consumption in 1970 increases to 2A.6 kg. Yet, unequal distribution of income aggravates the nutritional problem, leaving peasants and low income urban groups with a consump- tion of 18.0 or less kilograms [31]. Undoubtedly beef con- sumption by the mass of the population is below the recom- mended nutritional requirements set at 28.0 kg. [9]. Increases in domestic demand will depend on popula- tion growth, per capita income and income and price demand elasticities. Assuming no price changes, domestic demand is expected to grow at approximately A.8 percent annually [31]. This assumes an income elasticity of .6, and annual rates of growth in p0pu1ation and real income per capita of 3.2 and 3.0 percent respectively [31]. 1/ - Estimate based on an overall dressed carcass average of 200 kg. 30 Riley [61] and ICA [31] have projected domestic con- sumption in 1975 using different estimates for the average consumption per capita. According to Riley, if per capita consumption remains at 23.75 kg. annually-—the average for the 1958-60 period--domestic consumption in 1975 would be 571 thousand tons or a 6A percent increase over the 1958—60 average of 3A7 thousand tons. If per capita consumption rises to 29.06 kg., domestic consumption would double the base period average. The ICA estimate shows that if per capita consumption is 25.9 kg. annually--the average for l96A--domestic consumption would increase to 6A0 thousand tons by 1975 or about 85 percent over the base period. The parameters determining the rate of growth in demand and the estimates of domestic consumption suggest the need for well coordinated government policies if the goals of improved nutrition, production incentives and increased foreign exchange are to be attained. If beef supplies are not increased substantially, the income of the lower income group is not raised, or beef substitutes are not available, large numbers of the population will continue to be under- nourished. National Policies Toward the Cattle Industry Taxation. Incentives for beef cattle production in Colombia are crucially affected by government policy. Cattle raising is subject to the same income and complementary taxes (net worth and excess profit) as any other economic 31 activity. But certain special provisions by which costs and income are computed favor the cattle producing taxpayer. The essence of this tax policy relates to the cost basis on which profits are calculated. For tax purposes, the cost of livestock sold is the purchase price only if acquired during the tax year. If cattle are sold in the year following that of purchase, then the approximate market value at the end of the previous year is taken as the purchase price. The difference between the purchase price and the assessed end-of—the-year market value is treated as an increase in capital and is not subject to income tax. The tax policy is also designed to encourage ranchers to engage in breeding activities or to hold females rather than males in inventory to build up the national cattle herd. A net worth tax exemption and two taxes support this policy. The first is a slaughter and export tax which differentiates between the sexes: 50 pesos per head for males and 100 pesos per head for females. The second is a selective inventory tax equivalent to the value of A kg. liveweight per head which applies only to males over one year of age. The amount of this tax varies from year to year. In 1971 it was 18.A0 pesos per head. A final element in government taxation of the live- stock industry is a general inventory tax. Any individual or corporation whose investment in livestock exceeds Ps. 15,000 at the close of any year from 1959 through 1980 is subject to a levy of 1 percent on the net investment. 32 Taxpayers who elect to subscribe for shares of Banco Ganadero and the Fondos Ganaderos at par, in an amount equal to the total tax due, are exempt from cash payment of the tax. This is in fact the customary form of payment, and it pro- vides an important part of the capital of these credit institutions. A property tax of A.2 mills on the assessed value of land is also levied on the cattle subsector. Additional surtaxes of three and two mills are levied on assessed properties in the areas comprising the Corporacion Autonoma Regional de Valle del Cauca (CVC) and the Corporacion Regional de la Sabana (CAR),l/ respectively. In 1971 the Colombian Government proposed the use of presumptive techniques for a more effective income taxation of agriculture, and finally in 1972 passed a law for approval by Congress [56]. Now, cadastral value of the land alone serves to assess farm income. Yet only a proportion of the cadastral value is used: (1) 50 percent for permanent cr0ps and cattle raising, (2) 75 percent for temporary crops, and (3) 80 percent for annual crops. The presumed income is 10 percent for all crOps and cattle fattening, A percent for cattle breeding,g/ and is also subject to the normal pro- gression of the income tax. The reformed tax law also phases 1/ -These are regional development corporations with headquarters in Cali and Bogota respectively. g/Javier Ayala, "Nueva Propuesta Sobre Ley Agraria," El Tiempo, (Bogota) January 20, 1972, p. 1. 33 out the special inventory tax on males and provides tax incentives on reinvestments in the farm. Fifty percent of farm income in excess of the presumed income is exempted from taxation if reinvested during the year following the fiscal year. Land Reform. Large holdings and extensive methods in cattle raising have made grazing lands an easy target for expropriation and land distribution schemes. Under the provisions of Law 135--the agrarian reform law--most pastoral estates are considered inadequately utilized and could be expropriated at the least favorable terms. With the increasing need for farm products, and considering that threats of expropriation have discouraged long-term investments and hampered agricultural development, the government in 1971 undertook a major revision of Law 135. The revised law,l/ now pending approval by Congress, provides for more protection against expropriation of adequately utilized farms and for more favorable compensation terms if expropriation occurs. The designation of farms as adequately utilized has been tightened; it now includes, among other things, the attainment of minimum levels of productivity, and the improve- ment of the level of living of the workers employed by the landowner. Cash payments for adequately utilized farms have ‘been increased to A0 percent of the land value if the value l/Ibid. 3A is 500,000 pesos or less, with this proportion decreasing gradually as the total land value increases. The balance will now be paid in five years with interest bearing and negotiable government notes. Credit. Government direction of agricultural credit is carried on through a complex of official rediscount facilities, reserve requirements and direct legislation. The Monetary Board, appointed in 1963, has legislative control of the banking system and is responsible for setting legal reserve requirements, interest rates and term of loans. Lending to the agricultural sector has been growing faster than in the economy as a whole. But within the agri- cultural sector, livestock increased slower than the growth in overall credit in the economy [Al]. The livestock portfolio's share of the total has been relatively constant, reaching a low of 18.3 percent in 1966 from a peak of 21.7 percent in 1963. Over the period 1958-1967 the livestock portfolio averaged 19.5 percent of the total portfolio. Among legislative measures, Law 26 of 1959 has in- creased the supply of credit to agriculture and strengthened the activities of the Banco Ganadero and Fondos Ganaderos through allocation of the general inventory tax. These credit institutions which specialize in livestock develop— ment, must loan not less than 70 percent of their funds for breeding and growing. The law also requires that commercial banks loan not less than 15 percent of their deposits for agricultural purposes. 35 At present, the Caja Agraria and Banco Ganadero are the two most important sources of credit to livestock producers. In addition, commercial banks are required by law to lend 15 percent of their deposits to agriculture, including loans for livestock development. In 1967, the Caja Agraria held A7.9 percent of the livestock portfolio; the commercial banks 30.0 percent; Banco Ganadero 18.6 percent; and Banco Popular 3.5 percent. Institutional credit is available to cattle producers at varied interest rates and terms. In general, interest rates charged to small and medium producers range from 8 to 12 percent annually, which are below the current commercial rate of 1A percent. Interest rates charged to large producers are more in line with the commercial rate. Terms for repayment vary greatly according to the purpose of the loan. For fattening activities terms do not exceed one year, while for breeding and land improvements terms range from three to twelve years. -Grace periods from one to four years have been introduced to accommodate better the repayment obligations to the slow return from invest- ments characteristic of the cattle industry. Special funds from foreign and domestic sources are administered by the Caja Agraria and Banco Ganadero as part of the overall cattle development plan. Small cattle pro- ducers within the INCORA-supervised credit programs receive loans, mostly in kind, from the Caja Agraria and Banco Ganadero. The Caja-INCORA scheme is financed by a loan from 36 AID to INCORA, and funds for the Banco program come from INCORA's Fondo Rotatorio. The Caja also administers a loan from the World Bank for livestock deve10pment programs. The Banco Ganadero has been using funds from the Inter-American Development Bank (IDB), the Dutch Government and AID for the same purpose. Loans from these programs are being devoted mainly to beef production in the Atlantic Coast and the Eastern Plains. Ranchers borrowing from these funds have to par- ticipate with 20 percent of the estimated cost, receive technical assistance and invest up to 70 percent of the loan on farm improvements. Interest rates are 1A percent annually, the term of repayment is up to twelve years with a three- to four-year grace period. Another form of credit quite common in Colombia, known as "cattle-in—partnership," is made available in the form of cattle for which the rancher provides pasture and supervision. Profits are shared when the cattle are sold. The cattle are financed by the private sector and the Fondos Ganaderos (livestock funds), for which financing is provided by departmental and national governments and by the Banco Ganadero and Caja Agraria. A usual profit sharing arrange— ment is 60 percent rancher, A0 percent financier. While such an arrangement has the advantage of not impairing the rancher's borrowing capacity, it is probably equivalent to a loan with interest between 15 and 20 percent (depending on the profit shared). 37 Despite the priority given by the government to agricultural credit and the increased channeling of resources to it, there is still an unsatisfied demand for long-term credit. Recent agricultural credit policies have been oriented toward increasing the availability of funds and raising the interest rates to ensure a better utilization of scarce capital resources. Changes in agricultural credit policies have included: (1) (2) (3) (A) (5) (6) (7) (8) Increased use of supervised credit. Credit is now considered an effective means of intro- ducing technological change. Increased terms and interest rate of loans under Law 26. Beginning in August 1969, the Monetary Board increased terms of repayment up to seven years with a grace period of two and one half to three years. Interest rates were changed from 8 and 9 percent annually to a variable rate that is 10 percent the first year and increases every year thereafter by one-half of 1 percent. Increased and preferential rediscount quotas for loans made by Caja Agraria and Banco Ganadero. Preferential rediscount rates for Caja Agraria, Banco Ganadero and INCORA. Obligatory investment by commercial banks for 32 percent of its loan portfolio for develop- ment. The latter includes Law 26 loans and other loans of the agricultural sector. Maintenance of subsidized interest rates for small producers. New program for credit to land reform beneficiaries organized in cOOperative or commercial operations. New program for personal credit to small farmers based on expected returns on the investments. Disease Control. With an international commitment to control foot-and-mouth disease (FMD) Colombia has entrusted to ICA the attainment of this goal and the eradication of 38 brucellosis. To meet its commitment, and with the financial assistance of the Inter-American Development Bank and the technical assistance of the Pan-American Center Against Foot-and-Mouth Disease, ICA prepared a two-stage plan beginning in 1971. During the 1971-1975 stage the campaign will be concentrated in the Atlantic Coast region where 83 percent of the cattle population will be treated by the end of the period. In the same year the proportion of cattle treated in the rest of the country will be approximately 58 percent. In the next period--l976-80--control measures will be intensified in all producing zones and the proportion of cattle treated will be very close to 100 percent. While the control of FMD is restricted to priority areas, the control of brucellosis will be spread over the entire country. The campaign aims at having 100 percent of the female population free of brucellosis by 197A. DevelOpment Plan. In 1972 the Ministry of Agriculture prepared a comprehensive livestock development plan for Colombia.l/ The objective was to establish livestock pro— duction goals for the next decade and then to outline in detail the necessary plan of action to help achieve the desired goals. The most important policy instruments are: (1) tax' incentives for breeding and farm improvements; (2) increased l/"El Gobierno Modifica Su Politica Ganadera," El Espectador (Bogota), November 7, 1972, p. l. 39 availability of credit and easier credit terms; (3) increased association of credit with technical assistance and sub- sidized technical assistance for small producers; and (A) protection against land expropriation if certain levels of productivity and use of resources are attained. Exports. General measures to promote exports are a more flexible exchange rate policy and a 15 percent tax bonus (Certificado de Abono Tributario-—CAT) incentive for all exports, except coffee, raw cattle hides, and petroleum. CATs may be traded at a discount or used after one year of issuance for tax payments. More specifically, the govern- ment has begun to promote beef exports through a semi-public lending institution, Corporacion Financiera AgrOpecuaria (COFIAGRO). About 80 percent of COFIAGRO's portfolio is in enterprises engaged in the export of beef, but it also lends to ranchers for fattening operations at one-year terms and at an effective interest rate of 16.28 percent. The government has recently taken two new measures intended to regulate the domestic and export markets. Begin- ning July 1972l/ beef has been banned two days a week from restaurants, hotels and similar public outlets. Beginning January 19732/ a quota system regulates exports to avoid l/Jaime Sotomayor, "Veda de Carne Dos Dias a la Semana," El Espectador, (Bogota), June 30, 1972, p. l. g/"El Gobierno Fija Cupos de Exportacion de Carne," El Tiempo (Bogota), December 11, 1972. no domestic shortages. This measure requires the gradual phasing out of export of live animals and an increase in beef and processed meat exports. Domestic marketing of beef is also being improved; the Banco Ganadero in cooperation with USAID has placed special emphasis on financing the modernization of slaughter- ing facilities. CHAPTER 3 THE REGIONAL SETTING OF THIS STUDY The Geopolitical Setting The states or departments of Atlantico, Bolivar, Cesar, Cordoba, Magdalena, and Sucre considered in this study and known as the Costa, are part of the Atlantic or Caribbean plain which is one of the five geographic regions into which Colombia is divided.l/ The capitals are Barranquilla, Cartagena, Valledupar, Monteria, Santa Marta and Sincelejo, respectively. In 196A these six states had a population of about three million within an area of 112,055 square kilometers; these figures were 18 and 10 percent of the total Colombian population and area, respectively [17]. The Atlantic plain is located between the Caribbean sea and the base of the Andean range in the northern part of Colombia. It is characterized by flat and swampy lands in the bottom of the alluvial valleys and the coastal plain, and by slightly undulating to rugged lands in the areas above the valleys floors and in the surrounding mountains. With the l/The other four are: Andean region, Pacific Coast, Orinoco region and Amazon region. Geographically the depart- inent of Cuajira and the Antioquian region of Urabé are included in the Atlantic plain, but for all practical purposes this study will refer to the six departments listed. A1 A2 exception of the Sierra Nevada de Santa Marta in the north- east, the altitude varies between 0 and 500 meters. The most important rivers are the Sinfi in the west, and the Magdelena with its three major tributaries, the San Jorge, the Cauca and the Cesar. The Magdalena, Cauca, San Jorge and Sinfi rivers are navigable and serve as impor- tant means of transport. The region has a relatively good network of roads which connect the main urban centers, but access roads to the agricultural areas are few and inadequate, especially during the rainy season. The railroad connects the port of Santa Marta with Bogota, Medellin and Cali. Air transportation is available both for passengers and cargo from the airports in the capitals and from air strips throughout the area. The Sea ports of Barranquilla, Cartangena and Santa Marta have modern fac- ilities and serve a substantial part of the Colombian export-import trade. The Population The Costa p0pulation has four major attributes, most of them characteristic of other regions in Colombia. First, the total population in the Costa has been increasing at an increasing rate. The annual average rate of p0pulation growth is estimated to be 3.23 percent.l/ If it continues at this rate, the population will double in approximately 22 years. l/Rate of growth estimated for the period 1938-196A. “3 Second, the population is unevenly distributed. The department of Atlantico has the greatest density (219 inhabitants per square kilometer) and Cesar the lowest (11 inhabitants per square kilometer). In 196A, about 61 percent of the population was urban, and approximately one- half of this was concentrated in the cities of Monteria, Cartagena, Barranquilla, and Santa Marta. Third, throughout the region the population has been shifting fairly constantly since 1938. These movements can be classified as: (l) permanent migration from rural areas to major towns of the region (population growth in the four major cities mentioned earlier is estimated to be near 5.0 percent annually); (2) migration from urban and rural areas to the neighboring labor—short Venezuela; (3) seasonal in- and out-migration of the rural labor force to accommodate the demand for harvest labor, especially for cotton, in the region and in the rest of the country; (A) migration from the rest of the country and from the region toward the new rural frontier areas along the Valle del Cesar, the Magdalena, and the low Cauca; and (5) out-migration toward other regions, especially the more developed urban- industrial departments. Fourth, education, occupational status, and income per capita are unevenly distributed, not only between the ‘urban and rural populations but among the departments in the region. Literacy ranges from A0 percent in the more agricultural departments to 62 percent in the more urbanized AA and industrialized department of Atlantico. The prepor— tion of economically active population engaged in agriculture, forestry, hunting and fishing ranges from 60 percent in the departments of Cordoba, Bolivar, Sucre, Cesar and Magdalena to 16 percent in the department of Atlantico [17]. Although the 196A census lists no figures on income per capita, income is probably higher in the urban than in the rural areas, and higher in the department of Atlantico than in the more agricultural departments. (These estimates are based on information collected by the Departamento Administrativo Nacional de Estadistica [DANE].) In 1970, DANEi/ examined the family income of urban and rural workers in the Atlantic region, in four other Colombian regions, and the city of Bogoté. For the Atlantic region, DANE estimates that 63 percent of the employed urban pOpulation and 8A percent of the employed rural population had a inonthly income of 1,000 pesosg/ or less. The 1970 DANE sample estimated over unemployment in the Atlantic region to be 10.96 and 7.73 percent of the economically active population for the urban and rural areas, respectively. But unemployment is more serious than these figures suggest. The number of people suffering from shortage of work is probably larger than the observed numbers actively l-/DANE, Encuesta de Hogares 1910, Bogota, June (1971), pp - 1-59 . g/One U. S. dollar equals approximately PS 20- A5 seeking work or longer hours, because the unemployed or underemployed not openly seeking work might do so if un- employment_decreased. Ecological Zones The Atlantic region can be divided into four distinct ecological zones characterized by the climate and natural vegetation: (l) the tropical dry-humid savannah in the littoral, east from the Sinu river outlet; (2) the tropical humid savannah in the center; (3) the trOpical dryahumid forest south of the humid savannah; and (A) the tropical humid forest in the extreme south (Figure 1.2).l/ In turn, each zone can be divided into two special natural regions-- the flood plains and the uplands--distinguished by their soils and the crOps cultivated. These ecological zones are identifiable and reasonably distinct, although the boundaries between them are arbitrary. The three geographical features that determine the agricultural activities in these zones are climate, soil moisture and soil types. Climate and Natural Vegetation Rainfall and temperature are the two most important climatic features. The region alternates between two contrasting rainfall patterns: a low rain or dry period from December through March, and a high rainfall period l/In this and the following two sections, I have drawn heavily on the Magdalena Mission Report [13]. A A 6 39:me :5 5:5 L. . .i A E (t (u ‘0 F ANTIOQUIA ! II 1|! Adapted from [13 , p.17] A, III I !. Ill 0 ‘1‘ w' UTANDER Ill Tropical dry-humid savannah E Tropical humid savannah Tropical dry-humid forest Tropical humid forest MM] FIGURE 1.2. Ecological zones of Northern Colombia. A7 from April through November. In general, rainfall increases and dry periods are shorter from north to south. ' In the dry-humid savannah of the north, total annual rainfall averages less than 900 mm. The humid savannah receives between 1000 and 2000 mm. annually, the dry-humid forest about 2000 mm., and the humid forest in the south over 2000 mm. An equally important feature is the seasonal distribution of rain. The littoral receives hardly any precipitation in the dry season, whereas the other zones receive a fairly substantial amount throughout the year (an average of 23 mm. per month during the dry season). The annual average temperature is about 27°C. Through— out the rainy season, the humidity is over 80 percent. Dur- ing the dry period, winds flow from the sea causing the temperature and humidity to drop slightly, but this effect decreases with increasing distance from the littoral. The natural vegetation of the Atlantic region can be divided into three basic categories: (1) the dry-humid savannah in the north characterized by xerOphitic and sub- xerophitic vegetation and grasses; (2) the humid savannah in the center characterized by a mixture of natural grasses, scattered shrubs and thin to thick forest in the more wet areas near the rivers and in areas with higher rainfall within the zone; and (3) the rain forest in the south. The distinguishing feature from north to south is the vegetation change from the sparse savannah to the lush rain forest associated with increasing abundance of precipitation. A8 In general, the four ecological zones provide a good habitat for grazing animals and crops. The dry-humid savannah in the north is more suitable for grazing, although annual crops are grown during the rainy season. Irriga- tion is required because of the dry periods and to allow for double cropping. The humid savannah, the largest and most important agricultural zone, produces most of the region's cotton. During the rainy season, cropping is safe; with drought-resistant and short-cycle crops such as sorghum, double cropping may be possible. This region also provides most of the grazing land. The dry-humid forest provides lush green pasture all year, but is considered too wet for annual crops other than rice. In the northeast banana belt, which is in the same climatic zone, the land is used mainly for grazing. The humid forest has the same land uses as the dry-humid zone. §2_1_1_§-. Semi-detailed soil studies of the Costa region have been made by the Instituto Geogréfico Agustin Codazzi (IGAC) [38], the Instituto de Fomento Algodonero (Cotton Development Institute) [36, 37], and the Mission for the Study of the Magdalena Valley [13]. These studies also contain informa- tion which correlates soil types and phases with potential use. Soils in the region can be divided into four general groups according to their origin: alluvial or flood plains, quaternary, tertiary and mountain (see Figures 1.3 and I.A). .masosw HHom assocmmIIMpmoo .m.H mmDUHm Mme .a .mm_ oeoe echo sooosoa . va\. <_=oo:z<_./.\.W NF «4383,; c <~¢<2 u<2¢wu<=o I A» 2.9 2.2:? D WW“ dmofluodokr . . as .1]. 50 .mHnanoo sposppoz CH mamaam> HmH>SHHm mo COHuoomlmmouo HMOfipospoamm .z.H mmach mHHom mamfipnop montage» humanopmsd mpfimoaoo HmH>SHHm ucooon ll <32 I m9 m8 51 Topography, fertility and use are closely related to these soil groups. Alluvial soils are characterized by slopes of 0 to 3 percent; soil textures vary from light to medium and heavy, with deep topsoil and drainage varying from well- drained to imperfectly and poorly drained. Often these lands have a high nutrient content with the exception of soluble nitrogen, which is low. Soil pH ranges between 6.3 to 7.3; it is lower in the poorly drained soils and higher in soils with some degree of salinity. Quarternary soils or terraces are old alluvial deposits characterized by 31Opes of 0 to 3 percent, and a hard or clay pan at varying depths. Soil textures are light and drainage is imperfect. Soil pH ranges between 5.0 and 6.0 and the nutrient content is low. Tertiary soils have undulating slopes ranging widely from moderately steep to steep (7 percent to 50 percent). Soil textures vary from light to heavy, and most soils are susceptible to erosion. Soil pH ranges between 5.5 to less than 6.0, and the nutrient content is low. The mountain soils in the region are characterized by steep to very steep slopes. Because of the excessive relief, most of them are erodible. They are formed chiefly from igneous and metamorphic rocks. Though the high nutrient content of some of these soils would ordinarily make them suitable for coffee and other permanent crops, because of their erodability their best recommended use is in forest. 52 Based on studies by the IGAC [38], the Magdalena Mission [12], and the Cotton Development Institute [36, 37], an inventory of the soil resource base by department was made (Table 1.2). This inventory includes the acreage of total land, soil classification according to origin, and land use. Table 1.2 also shows the region's natural endow— ment for raising cattle. The Agricultural Economy The Costa economy is basically agricultural, with cattle the predominant activity, whereas manufacturing is low and concentrated in the cities of Barranquilla and Cartagena. Agriculture employs 50 percent of the economically active population. The Costa agriculture is characterized by the same problems that affect agriculture in all of Colombia: (1) slow rate of growth; (2) low productivity and high cost per unit of production; and (3) unequal distribution of wealth. Private ownership is the predominant characteristic of land tenure in the region. A 196A survey showed that 60 percent of farms were privately owned and included seven-eights of the agricultural land [12]. Other striking features of the Costa's land tenure are the high degree of concentration and absenteeism. As Table I.3 shows, approximately two-thirds of the farms are less than ten hectares, while about 1 percent of the farms are over 53 .Aofiaom use couuo A.omz ooo.v .nucoEuawooo an on: crocoEEooom one momnsao ocwqaapmoo .m.H mqmee 5A TABLE I.3. Costa--Distribution of Farms According to Size, 1960 Area Occupied by Farms Size Categories Has. 1 Average (has.) No. Percent (000) Percent (Has.) Less than 10 120,793 6715 2A1 3.A 2.0 From 10 to 100 A3,7A1 2u.u 1,u91 21.3 3u.1 From 100 to 500 12,225 6.8 2,3A8 33.5 192.1 Over 500 2,356 1.3 2,928 Al.8 1,2A2.8 TOTAL 179,115 100.0 7,008 100.0 39.1 Source: CIDA [12, p. 72] 500 hectares and occupy A2 percent of the land in farms. According to DANEl/ 6,706 administrators Operate one-third ‘ of the total agricultural land, or an average of 370 hectares each (mostly pastoral states). The majority of landlords visit these haciendas infrequently--rarely on a weekly basis and in some cases only once or twice a year. Although crOps have been increasing in importance, land use is dominated by pasture. While in 1959 crOps occupied only 7.5 percent of all land and 12 percent of land in farms, pasture accounted for approximately two-fifths of all land or approximately three-fifths of land on farms [1A, 15]. Artificial grasses are a low prOportion of total , l/DANE: Censo Agropecuario 1960, Resumen Nacional, IBogota, February (196A), p. 21, 55 grasslands; 20 percent according to DANE [18], and A5 per- cent according to FAQ [66]. Cotton is the most important commercial crop; in 1969 the region had 137,000 hectares in cotton and produced 62 percent of the total Colombian production [7]. Sesame and rice are also important crops, accounting for 59 percent and 23 percent, respectively, of the total Colombian pro- duction in 1969 [7]. Sorghum has become increasingly impor- tant, particularly as a double crop with cotton; at the same time, the land devoted to raising bananas for export has de- clined from 20,000 hectares in the mid-sixties to approximately 5,000 hectares in the seventies. Sugarcane, tobacco, and coffee are also grown but to a lesser extent. The most important staple crops are cassava and corn, both in terms of the number of producers and the number of hectares. In 1969 [7] 79,000 hectares were planted in cassava and the production accounted for A0 percent of the total Colombian production. About 203,000 hectares were planted in corn which accounted for 26 percent of the total Colombian production. Plantain followed in importance (30,000 hectares), and still less land was used for beans and fruits. Although the introduction of commercial crops to the region during the past two decades has changed a number of traditional agricultural practices, average yields are still fairly low. Yet the potential for high yields clearly exists as has been demonstrated in properly managed commercial and 56 experimental farms. In the former, yields of cotton-seed, irrigated rice and sorghum have been doubled and that of corn tripled, while experimental yields for corn and irrigated rice have been 6 and 2.5 times as high, respectively [Al].l/ Low yields have been attributed to: (l) a large proportion of small holders producing under traditional methods;g/ (2) a lack of adaptive research and extension; (3) inadequate distribution and high cost of modern inputs; and (A) a lack of price incentives. Soil conservation practices are ignored and the con- tinual tillage of steep, erodible slopes with clean-cultivated crops is accelerating soil depletion. As pointed out, cattle raising is the most important economic activity in the Costa where the same general charac- teristics and problems affecting the Colombian cattle industry also apply. The major production problems in the region can be summarized as follows: (1) Management and economics (a) Lack of farm accounting and record keeping to establish cost relationships and operational efficiency l/Average yields (M.Ts/Ha) in the Costa for the period 1965-1969 have been: cotton-seed l.A, corn 1.11, irrigated rice 2.3, sesame .66, and sorghum 1.8 [7]. 2-/In 1960 according to DANE [15], 98 percent of sesame, 83 percent of cotton, 95 percent of rice, and 96 percent of corn were produced in plots less than 10 hectares. 57 (b) Lack of basic knowledge on returns to the different factors of production needed for an efficient allocation of resources and for considering organiza- tion alternatives (c) Inefficient markets for both products and inputs, including capital (2) Human (a) Low level of education (b) Lack of skills and training (c) Poor health (3) Technological (a) Inadequate soils and range management (b) Inadequate breeding, pest and disease control practices (A) Environmental (a) Poor use of natural resources (b) Downgraded quality of rural life Cattle Production Although there are not reliable time series estimates of the cattle population in Colombia, it seems that the Costa supports between A0 and 50 percent of the total Colombian cattle population. Table I.A shows the age and sex distribution of cattle in the Costa and the rest of Colombia according to the 1968 sample survey [18], but care should be taken when considering these figures. This survey, the sample surveys of 196A, and 1965, and the 1960 agri- cultural census made by DANE seemed to have underestimated the total cattle population by 2.5 million head [29]. 8 5 ma .9 .mau mzHHom mm.mmm ma.mma om.:H mw.mm mm.=m oofipcmap< "apnoo Qsaam u < ocmazoq _ _ . . L . _ . _ _ . E. a _ a _ . . _ — _ _ _ _ mocmaob . noanZOq _ . _ _ _ 69 Sector 1 or lowland is the flat land area in the valley floor formed by recent alluvial deposits. Of the two agricultural regions identified in this sector, region 1 includes lands which are permanently flooded or are subject to seasonal floodings and are used only for grazing. Region 2 is the flood-free area where cash and food crops compete with cattle for land and capital. Region 2 can expand into region 1 as the latter is drained and becomes available for cropping but cannot expand beyond the natural limit of lowlands. Sector 2, or upland, is the nearly flat to rolling land above the valley floor. It is formed of quarternary terraces and tertiary soils with slopes of 1 percent and over. This sector comprises agricultural region 3 and subregions l and 2 where farming is mixed, and includes cash and food crops and cattle production. Subregions l and 2 are roughly deter- mined by topographic conditions; subregion l is suitable for mechanized cropping and is the area where cash and food crOps compete with cattle for land and capital. Subregion 2 is characterized by a more rough and complex topography and is suitable for food crops and grasses which also compete for land and capital. Region 3 cannot expand beyond the natural limit of uplands, but in subregion 1, land in cash crops can contract and expand within its natural limit. These farming regions are not entirely internally homogeneous with respect to climate and cropping potentials. They occur within the four ecological zones but compromises were made to delineate these regions as homogeneous areas. «o 70 The primary reasons for this are twofold. First, despite climate variations, the pattern of farming is very similar in all ecological zones, and behavioral characteristics of farmers who control land use and modernization decisions are assumed to be identical throughout the four zones. Secondly, at the present state of aggregation of the model we are not interested in performing a separate accounting for each ecological zone. RanchinggPractices While ranching in these sectors is Of a mixed type, cattle breeding and growing is predominant in the uplands and fattening is done in the lowlands. But the cattle from the two sectors are aggregated into one herd when simulating the animal demography and computing the major outputs of the model. When the new alternatives of production are introduced, the cattle population of the Costa is disaggregated into two populations, one traditional and one using modern techniques (see Figure 1.6). The "traditional" cattle population is assumed to subsist on the flood-free (lowland and upland) areas during the rainy season. During the dry season, crop residues and additional grazing land which becomes available as the flood waters recede during dry months also add to the nutrient supply. It has been estimated that about A00,000 head from Sucre alone are moved from the uplands to the low— lands in search of water and forage as the dry season advances 1/ and food becomes scarce.- l/Personal information. 71 .=O«uosvouo oauuuo mo auuuauv soda "occuuussu < .o.~ “man—h fluid nu-ou assoc-u 33:32 «3333a «38 a 28. cot-Scuuvozllq _ coauofluuwonuwwauwcwnu .w a0no¢au¢> nsocouoxu I m vacuum lush .n 5...... as... . . a-..» s a see as... .flnfififi ”M E 2:: s “35 wings: «luau SHE—.83 ound-.838 a Isunhm no nuanced: OHuunu u 51»: 135 co co d a M u. stadiums" .— 3qu mm s w m a m ocuuuaom ooauwnom n unoluuquqz vac: Danna count « . a no: _ a _ _ "caucuses so. 7 « wousuuvaon cusses do A. 9.356 33898.: .155 «adage: h '0. BB umflug u... u 38: d .38: mg n w p .4. o n a w n 3 n .1 a 1 ad“: ZOHHQDQOHm u use 1 Sag .15.: .5: been“ mm<¢U xxhllli _ m\\ unease Emuwmns 3:31 \ _ a 9: 28888.. . Fl: :2ng nz<. [It n.-u ,IIIIHMMHMKI \ 38:55. an 45:85: :58 82029... 7 838 radii-n..— rli ac and“ mono nosed-om mono Iona any usausofi%v .33ch c a uncloudoox one: uauuwuwuu n sunbuo :o«»na:oo~ u ouuuuo use cocoon a couch noouu uo ouuum guano: a¢l«n< y co nousuavcoauu 72 Animals in the "modern" sector are assumed to be situated on the flood-free pasture lands where adequate nutrition is available from properly managed grassland and supplemental feed obtained from land devoted specifically to forage production. The level of husbandry is also assumed to be upgraded: diseases and parasites are controlled and improved breeding techniques are used. Modern Alternatives In considering the alternatives to traditional cattle production care has been taken to select those which embody a rather simple technology and are deemed to be both feasible and easily transferable given the resources at hand and the behavioral characteristics of ranchers in the Costa. Thus the alternatives considered are focused on investments in relatively simple improvements that will advance management and increase output. Outlays are spent on the most elementary of inputs: fences and stock water supply to permit the beginnings of managerial control; yards and corrals to offer the beginnings of health protection measures; seeds and fertilizers to begin to increase fodder production. Since a major problem for cattle in the region is a lack of adequate dry season nutrition resulting in substantial weight losses, lower calving rates, higher death rates, and "delayed" maturation, the alternatives emphasize methods of increasing pasture production and growing and storing forage. These not only improve nutrition but also step up 73 the average carrying capacity, allowing either for a larger or a constant cattle population in the face of expanding crops and shrinking pasture area. The modern alternatives evaluated in the model are: (l) Pasture lands are kept with the grass species already present. Fences, stock water supply and corrals are established to pursue the begin- nings of managerial control and health protec- tion measures. PrOper grazing rate and pasture rotation are introduced to increase fodder production and improve nutrition. (2) The same ranching practices as in Alternative 1 with artificial pastures substituting for natural pastures. (3) The same as in Alternative 1 with forage crops being used to provide feed during the dry season. (A) The same as in Alternative 2 with forage crops being used to provide feed during the dry season. At the present stage of development of the model, the modern alternatives are not competing among each other for land and capital; they are evaluated in separate computer runs, each one at a time. Static Restrictions Handling all the variables in unrestricted dynamics requires a team effort which is beyond the scope of this study. Thus, analytical restrictions are imposed that keep some of the variables fixed. Patterns of consumption and of ownership of resources, and hence an implied distribution of private real income is assumed fixed; the regional population is assumed constant, and the institutional set—up of the economy is assumed fixed. The implication Of these assumptions u 71: for the outcome of the study will be discussed in the last chapter. Another restriction imposed on this study is that its primary focus is on the beef production process with only general considerations of the related crop subsector, and rudimentary considerations of the marketing element of the beef subsector. Procedure To accomplish the objectives of this study, a multi— component but non-maximizing model of a micro—macroeconomic nature has been developed. The computer simulation model is composed Of five basic components or building blocks (see Figure 1.7) which are closely interrelated as the outputs from one block serve as inputs of others. Information- feedback mechanisms build into the system add to the dynamic interaction of variables within and among the various components. The first, the land allocation and modernization decisions component allocates land between crops and cattle in the regions of competing farming activities. Land use in food crops in all regions and cash crops in region 2 are exogenously determined, but in subregion 1 land use decisions are based on perceived relative profitabilities of the cattle and cash crops enterprises. Cattle modernization decisions are based on perceived relative profitabilities and the availa— bility of credit, investment capital, and information either from farmer-to-farmer in a diffusion process or from exten- sion agents as part Of modernization promotion efforts. Then, '75 uauuuuuaou ucoau-o>=a Inlsucou Ouwouu you moon saw: was moon scum oaoucw uuo< ucfiauum so moanuuun> meauosouo< .m cchflon nouns .q oauuoo mo endow .m moouua nacho van saw: .uuon mo newuusuoum .m apnea» undone: noouum housvoum pom nacho van ouauoom .H ufioouu was vowmuo>< hauowucuconxm .o moo u nous u . . 39662 convene zoanooE $4823 8x: a a so a n 0 1| coughs nexus: .~ some .m coughs vane: .a .c J mowom uaoacuo>ou .~ pang endear oucucuum unannoxu .H oammwwwfl nvcoaun ucoauuo>=H nouoaxu ucaauuocou unoauno>cH11 uuwvuuu ucoauuo>cH now would: .1 ao>wusuucw coauouacuovoz .m can nuowuaa uuooxm .q cacao-OOHH< pang .n savouu usuaoowwwuo Hm Lavouu can noxsh h c uoauwaunuuuuoum cOHuusvoum oouu .u o>wuoHox unaccounun .1 neuHcoEsu Houucou «mucous .p cowuusvoum oauusu .a wonmHuun zcua~b DRLAV total land in cash crOps if DCRU 1_DRLAV (has) a model parameter that controls the speed of land adjustment the absolute value. POsitive rates of transfer mean shifts from pasture and cattle grazing to cash crops; negative rates mean shifts from cash crops to pasture. 92 Land transferred from grasses to cash crops is immediately available for crOp production, but the rate at which land transfers from cash crops to grasses is de- layed to account for the time needed for grasses to become well established and to begin production at full grazing capacity. This time lag is simulated by the following distributed delay: CALL DELAY(AUXl(t-DT), AUX2, CROUTA, DELA, DT, KA) (5.22) where: AUXl a unlagged rate of transfer of cash crop land to grazing land: < 0 if DRLAV > DCRU = 0 if DRLAV < DCRU (has/yr) AUX2 . lagged rate of transfer of cash crop land CROUTA, DELA, DT, KA I elements of the DELAY subroutine defined earlier (p. 8A). Additions to food land in subregion 1 can come either from land in the least profitable of the remaining activities or from both cash crop and grazing land in specified prOpor- tions. Land also could be allocated to food from grazing land even if the latter is more profitable than cash crOps. Such allocation is performed in the model by means of the variable AUXA and the parameter CL2. If cash crops are more profitable than cattle, the rate of change of land in cash crOps is positive (RLCRU > 0) and all the increase in food land comes from land in grasses. 93 If cattle are more profitable than cash crops, the rate of change of land in cash crops is negative (RLCRU < 0) and the increase in food land comes from land in cash crOps and/or land in grasses depending on the value of the model parameter CL2(0 §_CL2 i 1): If CL2 = 0 all new land in food crOps comes from land in grasses, modern and traditional If CL2 = 1 all new land in food crops comes from land in cash crOps. The variable AUXA is then computed as: {CL2iDLFCUl(t), DRLAV 3 DCRU (5.23a) AUXA(t)-I 0 , DRLAV < DCRU (5.230) where: AUXA = transfer of land from cash crops to food (has) CL2 = a model parameter that allocates the change in food land between cash crOp land and grassland. Given the above allocation mechanism, total land in cash crOps in subregion l is: TLCRU(t) = max[min(TLCRU(t-DT) - AUXA(t) + DT*(AUX2(t-DT) + AUX3(t—DT), ALNDUl(t))), 0] (5.2A) where: TLCRU total land in cash crops in subregion 1 (has) AUX3 rate of transfer of grazing land to cash crops land (. RLCRU) if DRLAV < DCRU 0 if DRLAV > DCRU (has/yr). 9A Equation 5.2A essentially computes the time integral cfi'the total rate of change of cash crop land limited to preclude the possibility of negative land and expansion beyond the allowable land limits (ALNDUl--Equation 5.19). Given total allocatable land from Equation 5.19 and total land in cash crops from Equation 5.2A, the model computes total grazing land (in subregion l) as: TGLUl(t) = ALNDUl(t) - TLCRU(t) (5.25) where: TGLUl = total grazing land in subregion 1 (has). The rate of change in grazing land in subregion 1 is given by: where: RTGLUl = rate of change of total grazing land in subregion l (has/yr). The total grazing land in the Costa is simply the mmiof grazing land in each of the farming sectors and is given by the following equations: TGLU(t) = TGLU1(t) + TGLU2(t) (5-27) TGMt) a TGLL(t) + TGLU(t) (5.28) TGLR(t) = TGL(t) + TGLSF(t)*C9 (5.29) 95 where: TGLU = total grazing land in upland sector (region 3) (has) TGLUl = total grazing land in subregion 1 (has) TGL = total (flood free) grazing land in regions 2 and 3 (has) TGLR = total grazing land in the Costa region (has) C9 = a model parameter that adjusts seasonally flooded grasslands to permanent grazing land. Grazing land in region 1 is subject to periods of flooding that last from a few weeks to six months and longer. Since we are interested in the permanent stocking capacity of grasslands, seasonally flooded grazing land is adjusted in the model to a permanent grazing land equivalent. This is done by the model parameter C9 of Equation 5.29 which is a weighted composite of area and length of flooding. Alternatives In principle, every current land use is a conceivable alternative to every other present use in the same farming sector. In practice, however, certain assumptions can be “Ede which will reduce the multitude of alternatives and W111 simplify the model. Since the model is focused Indmarily on cattle production, only alternatives concerned '“Ih the introduction of technological changes in this a'CtiVity will be considered in detail. The alternatives to traditional cattle production included in this study are those described in Chapter A and considered as feasible in eVery agricultural region with the exception of region 1. 96 Pasture land in this region remains under traditional management throughout the simulation. Implicit in the allocation of land in region 2 and subregion 2 is the assumption that both food and cash crops are more profitable than cattle. Here, the allocation of land to food production has priority in order to meet the nutritional requirements and consumption preferences of the population. The remaining land is then first allocated to cash crops and finally to pasture. In subregion l a more mmnplex allocation mechanism has been described; land is first allocated to food production and then to cash crops and cattle based on their relative profitabilities. Although the present structure of the model re- stricts consideration of cattle production alternatives to one each simulation, future expansion could include compe- tition among modern grazing alternatives. The model also could be expanded to include competition among crops and a more realistic, though more complex, decision-making mechanism 1hr the allocation of land between individual crops and pasture in all regions. 923219 Modernization Decisions Land use decisions between cattle and cash crops have been discussed above in Equations 5.19 to 5.23. In this section we are mainly concerned with the more complex decision mechanism of cattle modernization. The rate at which cattle modePrdzation takes place depends on the relative profitability 97 cfi'each alternative, on modernization promotion efforts, on diffusion effects, on the availability of capital, and on the behavioral characteristics of the farmers making decisions. These considerations will be discussed in detail below. Profitabilities Farmers' decisions among the alternative uses for their land are based upon their perceptions of the relative profitabilities of the available alternatives. These decisions have been restricted to the allocation of land between pasture and crops in subregion l, and between traditional and modern cattle in all regions but region 1. In the first case, the relative profitability is given in Equation 5.21 above. The relative profitability of the modern cattle production alternatives is given by: PDR(t) = DRLAfiggAgflIgfi‘AT‘t) (5.30) where: PDR = the relative profitability differential (dimensionless) DRLAM = discounted sum of returns over the planning horizon for modern cattle production-- Equation 5.31 (PS/ha) DRLAT = discounted sum of returns over the planning horizon for traditional cattle production-- Equation 5.32 (Ps/ha). Land use profitabilities are defined as the present Value of the stream of net income farmers expect to receive 0 Ver- some relevant planning horizon. The model computes 98 the sum of the discounted present value of returns to a land use from the present to the planning horizon. This discounted sum is the "profitability" of that land use. But while expected revenues and costs from modern cattle vary over the planning horizon, those from traditional cattle and cash crops are assumed to remain constant. Equation 5.31 computes the profitability of modern cattle: n (TRLAMi(t) - TCLAMi(t)) DRLAM(t) = 1 (5.31) i=1 (1 + DIR) where: DRLAM = is defined in Equation 5.30 TRLAM - total revenue from modern cattle—- Equation 5.350 (Ps/haryr) TCLAM - total costs of modern cattle-- Equation 5.35d (Ps/ha-yr) DIR = the relevant discount rate (proportion/ year) i = indexes the n years of the planning horizon. The profitability of traditional cattle is given by; n ImLAT(t) = (TRLAT(t) - TCLATL(t))* X 1 1 (5.32) 1.1 (1 + DIR) Where ; DRLAT = as defined in Equation 5.30 TRLAT a total revenue from traditional cattle-- Equation 5.35a (Ps/ha-yr) TCLATL a exponential average of total costs of traditional cattle—-Equation 5.35b (Ps/ ha-yr) DIR i 99 = as defined in Equation 5.31 = indexes the n years of the planning horizon. The average profitability of all cattle used in Equation 5.21 is given by: DRLAV(t) = where: (DRLAT(t)*TTGLR(t) + DRLAMQtjiLTLMQggt) + TRSth)) TGLR(t7* (5.33) DRLAV = averaged discounted sum of returns over the planning horizon for cattle production (PS/ha) TTGLR = total traditional grazing land in the Costa region--Equation 5.51 (has) TLMOD = total grazing land in modern production-- Equation 5.N8 (has) TRSL = total land in transition from traditional to modern cattle production--Equation 5.h6 (has) TGLR = total grazing land in the Costa region-- Equation 5.29 (has). The profitability of cash crOps in the uplands is computed as: n DCRU(t) a (TRCRU(t) - TCCRUL(t))* Z 1 (5.3“) Where: DCRU TRCRU TCCRUL 1-1 (1 + DIR)i discounted sum of returns over the planning horizon for cash crOps production in the uplands (Ps/ha) total revenue from cash crOps——Equation 5.35e (Ps/ha-yr) exponential average of total costs of cash crOps--Equation 5.35f (Ps/ha-yr) 100 DIR = as defined in Equation 5.31. With the purpose of making all discounted present values comparable, the profitabilities are computed using a planning horizon common to all. In this case, this period of time is 12 years, the planning horizon selected for modern cattle. The discount rates used to compute the present value of future returns are behavioral parameters in the model. They reflect farmers' rates of time preference and the vary- ing risks of each alternative. In general, the more risky and unfamiliar the alternative, the higher the discount rate. Since we are concerned with farmer decision makers, the streams of future revenues and costs (Equations 5.35) used in the profitability calculations should reflect farnwrs' expectations. These expectations are assumed to be reflected in five-year exponential averages of recent Producer prices. Prices of cattle are determined endogenously (Equation 7.2) but prices of crops are determined exogenously and projected into the future with the same trend as costs. The form and computation of producer price averages and trends are discussed in detail later in the description of the price generating component (Chapter 7). Similarly, the stream of crOp yields farmers expect are the yields they currently experience rather than the potential production reported by experiment stations. Increased yields are considered later in Chapter 12 as part of the policy experiments. Additions to expected revenues are any cash and/or price subsidies which may be 101 offered as part of a modernization program, and the pay- ment of development credits. The cost side includes taxes on land and cattle, bicilogical, chemical, labor, and capital input requirements over the planning period. Associated input prices are exogenous in the model and are projected into the future accrxrding to rate of increase in farm costs. Production costs of crops are averaged and lumped in one figure while those of cattle are computed separately; but all costs are also exponentially averaged when they enter in the computation 0f profitabilities. Exponential averages of past costs are used here to reflect farmers' expectations of future cost streams. The computation of costs is discussed more fully in Chapter 6. Total revenue and total cost of traditional cattle are computed as: TRLAT(t) g (EPAP(t)gESLSPT(t) + PRMT*EQMT(t)) TTGLR(t) + AGSUBT(t) (5.35a) TCL - DT ATL(t) - TCLATL(t-DT) + —————*(TCLAT(t-DT) DELl9 - TLCLATL(t-DT)) (5.35b) Where: EPAP = the expected producer price of finished males-~Equation 7.5a (Pa/animal) 102 ESLSPT the expected animal sales-~(animals/year)l/ EQMT = the expected production of milk-~Equation 6.2lb (liters/year) PRMT = the price of milk (PS/liter) AGSUBT = subsidies paid to traditional cattle (Ps/ha-year) TTGLR = as defined in Equation 5.33 TCLAT = total traditional cattle costs (unlagged) (Ps/ha—year)l/ DT = time increment of the model (years) DELl9 = lag parameter (years). The stream of revenues and costs over the planning horizon for modern cattle production reflect different eXpectations according to the alternative adopted. Chang- ing expectations throughout the planning horizon are simulated by a set of coefficient arrays (SINCR, TMINCR) that increase output and costs over traditional cattle as perceived by farmers. Values of these coefficients for each of the four modern alternatives are shown in Table II.“ in Chapter 6. Total revenue and total cost over the planning horizon, i=1, ..., n, are computed as: TRLAM1(t) = (SINCR1*EPAP(t)*ESLSPT(t) + EPAP(t)*BINCRi *AUXL12(t) + TMINCRiiPRMTaEQMT(t))/TTGLR(t) + ELOAN1(t) + AGSUBMi(t) (5.350) l/For detailed computation of these variables see subroutine AGACC in the Appendix. 103 TCLAMi(t) = EOPCLM1(t) + EOCLNM1(t) + ETCEC1(t) + where: EDBSER1(t) + ETXCi(t) + EVLDTXi(t)*ECADEM SINCR TMINCR BINCR AUXL12 AGSUBM ELOAN EOPCLM EOCLNM ETCEC EDBSER ETXC EVLDTX ECADEM i (5.35d) expected increase in sales over the plannin horizon from the modern herd (dimensionless? expected increase in milk production over the planning horizon (dimensionless) expected increase in inventory over the planning horizon (animals/year) finished male equivalent of cattle prices (prOportion)l/ subsidies paid to modern cattle producers (Ps/ha-year) expected pa ments of development loans—- Equation 6. 0g (Ps/ha—year) expected operation costs of modern cattle --Equation 6.h0a (Ps/ha-year) expected operation costs of modern grazing land-~Equation 6.UOb (Ps/ha—year) expected total cash establishment costs-- Equation 6.u0f (Ps/ha-year) expected debt service of development credits--(Ps/ha-year)§/ expected taxes on modern cattle-eEquation 6.N0d (Ps/ha-year) expected taxes on modern grazing land-- Equation 6.HOc (Ps/ha-year) expected depreciation on equipment and improvements--Equation 6.N0c (Ps/ha—year). l/For a detailed computation of this variable see mnmoutine DEMOG in the Appendix. 2/ - For a detailed computation of this variable see mnuputine AGACC in the Appendix. 10“ Total revenue and total cost of cash crOps in the uplands are computed simply as: TRCRU(t) = EPCRPU(t)*EYLDCU(t) (5.35e) TCCRUL(t) = TCCRUL(t-DT) + DE%§§*(TCCRU(t-DT) - TCCRUL(t-DT)) (5.35f) where: TCCRU = total costs (unlagged) (Ps/ha—year)l/ EYLDCU = expected yield-~Equation 6.1 (Tons/ha-year) EPCRPU = expected producer price——Equation 7.5b (Ps/Ton) DEL22 = lag parameter (years). Promotion and Diffusion In the process of estimating the profitability differentials of the various alternatives, farmers need certain information. The information required by farmers include future producer prices, expected yields, government or private subsidy and loan programs, and expected costs. In the model this is provided as part of the promotion effort and in the form of "information units." These information units not only include extension agents, the main instru- ments of disseminating information, but also any other means of mass communication (radio broadcasts, films, and newspapers). l/For a detailed computation of this variable see smnputine AGACC in the Appendix. 105 While promotional information units (extension agent equivalents) are exogenously generated as a policy (Chapter 8), the model also computes (Equation 5.39) the demonstration effect of farmers learning from one another about alternative land uses. Transition Responses Changes in land use patterns reflect farmers' responses to the perceived profitabilities of the available grazing alternatives. The assumption is made that all farmers modernizing their systems of production, either because of promotion or diffusion, will receive the same type of public and private incentives. Therefore, the perception of the profitability of the new methods will be the same in both cases. The profitability response function (Equation 5.36) determines how many hectares of land an information unit can transfer per year from traditional to modern management. This calculation depends on the profitability of the alter- native and the behavioral characteristics of the farm deci- sion makers (see Figure II.l). PRl(t) = max[E7*(l-EXP(-E8*(PDR(t)-E9))), 0] (5.36) where: PR1 = a variable which introduces the effects of the profitability criterion, PDR, upon the adOption rate (prOportion) E7 = maximum prOportion attainable-—a model parameter 106 E8 = the rate of promotion response with respect to profitability (dimensionless) E9 = the promotion response threshold (dimension— less) EXP = exponential function max = takes the maximum of the term within the brackets PDR = the relative profitability differential-- Equation 5.30 (dimensionless). As shown in Figure 11.1, the parameter E7 determines the maximum value of the function. The threshold (E9) and response rate (E8) parameters reflect the farmers' attitudes and behavioral characteristics which affect the rate of their response to the relative profitabilities of their various alternatives. These two parameters represent a wide range of attitudes toward risk involved in the new methods, un- certainty related to social stability, government programs E7 Large E8 ‘1 Small E8 E9 PDR FIGURE 11.1. The profitability response function. 107 and promises in general, and the land tenure system. It is clear that a wide range of adaptor behaviors can be simulated by appropriately assigning values to these three parameters. Since farmers will have different attitudes toward extension agents (or other promotional efforts) than they will toward one another, the values of the parameters may be different for promotion responses than for diffusion responses. The profitability and behavioral criteria are instrumental in determining the rates at which farmers will respond in a diffusion process or to overt campaigns introduc— ing modern methods. The rate at which land enters a moderni- zation process as a result of overt promotion is given by Equation 5.37. RLMPI(t) = E3(t)*PRl(t)*EXTl(t) (5.37) where: RLMPI a rate at which grazing land enters moderni- zation due to promotion (has/year) EXTl = units of pre-campaign promotion—~a policy variable E3 = the maximum feasible adOption rate per unit of EXTl (has/year per unit of EXTl) PR1 = as defined in Equation 5.36. As the program progresses, the promotion effort becomes more efficient and the adoption rate likely will ‘be increased. This phenomenon is simulated by Equation 5.38. 3H9P6, E3 has its maximum value (E3l—E32) at the beginning of'a.campaign (TCAMaO) and approaches its maximum value 108 (E31) when TCAM is large. Again, a wide range of real-world situations can be simulated by appropriately assigning values to the model parameters. E3(t) = E31 - E32*EXP(-E33*TCAM(t)) (5.38) where: TCAM = the length of time the production campaign has been in Operation (years) E31. E32, E33 - model parameters. The rate of adOption due to demonstration effects depends on the differential between modern and traditional productivities and on the behavioral characteristics of farmers. The diffusion rate (Equation 5.39) is also a func- tion of the land which remains under traditional management (TTGL) and the land which has been modernized (TLMOD). If there is no land in either use, there is no demonstration effect and no diffusion, while the diffusion rate is greatest when there is as much land in the alternative use as in the present use. Thus, the rate at which diffusion takes place reflects the S-shaped curve of diffusion theory [I]. RLMDI(t) = PR1(t)*TT%g£B)'TLMODLtl (5.39) where: RLMDI = rate at which grazing land enters moderniza- tion due to diffusion (has/year) TGLO = total (flood free) grazing land at the begin- ning of the simulation (has) 109 The total land entering modernization, RLMI, due to the combined effect of promotion and diffusion is simply computed as: RLMI(t) = RLMPI(t) + RLMDI(t) (5.u0) The transition rate (Equation 5.41) is constrained by available capital and lagged to account for delays in establishing a modern alternative. Since the Costa is a surplus area (see Chapter 3), requirements of capital for buying cattle are not considered in the aggregate for the region, though this is an important factor in analyzing individual farms. Thus, the demand for capital for invest— ment is restricted to farm improvements. The capital available (NCFR) includes capital generated endogenously as income (after allowing for consumption), transfers from the crops subsector, and potential credit. The model does not consider competition for capital between investments and consumption; it is assumed that consumption is a first claimant to farm income and investment is treated as a residual. This oversimplification of the consumption/saving decisions is Justified here because of the lack of information on the consumption and saving patterns of farmers in the region. In addition, a realistic simulation of the consumption/saving and asset balance decisions of farmers requires the establish- ment of a preference function in order to maximize both satisfaction as well as returns from different investments 110 under conditions of changing interest rates, risk, and uncertainty. The availability of capital and credit will be dis- cussed more fully in the discussion of the Accounting and Performance Criteria Component (Chapter 9). The require- ments of credit for development (Equation 5.H2a) are deter— mined by subtracting from the total establishment costs the proportion of these costs met by the farmers' own resources. Establishment costs are reduced to an annual basis, so the credit required in the year the transition is made can be related to whatever credit funds are available that same year. CRM(t) = max{min{RLMI(t), [min(ARM1(t), ARM2(t)) + ARM3(t)]}, 0} (5.31) where: CRM = unlagged rate of modernization constrained by deve10pment credit and farmers' invest- ment capital (has/year) RLMI = unlagged and unconstrained rate at which grazing land enters modernization-- Equation 5.N0 (has/year) ARMl = allowable rate of modernization depending on availability of deve10pment credit-- Equation 5.D2a (has/year) ARM2 = allowable rate of modernization depend- ing on capability of farmer to meet his required share of the establishment costs-- Equation 5.u2b (has/year) ARM3 a allowable rate of modernization depending on farmer's capability to meet total estab- lishment costs without development credit-- Equation 5.2Dc (has/year) 111 min[a,b] = a function equal to the minimum of terms within the brackets max[a,b] = a function equal to the maximum of terms within the brackets. The variables ARMl, ARM2, and ARM3 (Equations 5.u2)l/ that determine the constraints to the rate of modernization require further explanation. If credit for development is not available (ARMI a 0; ARM3 > 0) the rate of moderniza- tion depends on the farmers' capability to meet the total establishment costs. If development credit is in ample supply but the investment capital of the participating farmers is not enough to meet their required share of the establishment costs (ARMl > ARM2; ARM3 = 0), the rate of modernization depends on the farmers' available investment capital. If development credit is available and the investment capital of the participating farmers is more than enough to meet their share of the establishment costs (ARMl < ARM2; ARM3 > 0), the model assumes that the remaining capital is reinvested in the farm and the rate of modernization is increased by an amount equal to the allowable rate without credit support (ARM3). This is to say that resources in modern cattle pro— duction are being used at a low opportunity cost. _ CREDT(t) ARMI - CRTREQ(t) (5.u2a) a NCFR(t-DT)*LT1 ARM2 TCEC(t)*RPTN (S'u2b) l/Equations 5.42b and 5.U2c have implicit in the denominator a one-year multiplicative factor that provides the desired units. ARM3(t) = 112 (NCFR(t—DT)-min(ARM1(t),ARM2(t))*RPTN*TCEC(t)/LT1)*LT1 where: ARMl ARM2 ARM3 CREDT CRTREQ LTl RPTN NCFR TCEC ’TCECTf) (5.h2c) allowable rate of modernization depending on availability of credit (has/year) allowable rate of modernization depending on capability of ranchers to meet their share of establishment costs (has/year) allowable rate of modernization depending on ranchers capability to meet total establish— ment costs without deve10pment credit support (has/year) credit available for modernization--Equation 9.u (Ps/year) per hectare credit requirement (Ps/ha) time over which development loans are paid-- a policy variable (years) farmers' participation of total establish- ment costs--a policy variable (0 i RPTN 3.1) --proportion net investment capital of farmers—-Equation 9.13 (Ps/year) total cash establishment costs-—Equation 6.37 (Pa/ha). The modernization process is simulated as a series 0f exponential delays which allow for the possibility of "dropouts" and represents the phenomena of random moderni- zation times for individual farms in the aggregate. Equations 5.h3 describe this process. R1(t) Rl(t-DT) + Y%%t*(CRM(t-DT) — Rl(t-DT)) (5.u3a) 113 XRl(t) = Rl(t)*A5 (5.h3b) R2(t) = R2(t-DT) + i%%E*(XRl(t-DT) - R2(t-DT)) (5.u3c) RLM(t) = RLM(t-DT) + i%%f*(R2(t-DT) — RLM(t-DT)) (5.u3d) where: CRM 8 as defined in Equation 5.hl RLM 8 average rate land leaves the modernization process and begins producing at modern levels (has/year) XDEL a one—third of the average time required for modernization (years) A5 = one minus the proportion of land that "drops out" due to shortage of technical assistance and credit--Equation 5.uu. R1, XRl, 32 = intermediate rates (has/year). The "dropouts" response function (Equation 5.hh) determines the proportion of land that remains, A5, after land "drops out" due to the shortage of extension workers and/or development credit. A5(t) = min(El2§g§$é%%%, l)*(AUX7(t) + AUX9(t)*min ACRDT(t (E13*55§5TTE)’ 1)) (5.4“) where: EXTR = extension workers (or the equivalent) re- quired to sustain the modernization program --Equation 5.h5 (man-years) EXTA 8 extension workers available (man-years) ACRDT a credit allocated for modernization--a policy variable (PS/year) 11h DCRDT = total demand for deve10pment credit—- Equation 9.3 (Ps/year) AUX7 = actual proportion of total land being modernized with resources other than develop- ment credit AUX9 = actual prOportion of total land being modernized with development credit resources E12, E13 = adjustable model parameters min[a,b] = the minimum of a and b. The calculation of A5 involves the combined effect of two functions which have the same response patterns. One depends on the ratio of available extension workers to the number of extension workers required to sustain the moderniza- tion programs; the other on the ratio of credit allocated for modernization to the total demand for deve10pment credit. As Figure 11.2 shows, the parameter E12 (E13) which governs the shape of the response function determines the threshold at which dropouts from the modernization process start and the dependence of the dropout rate upon the ratios EXTA/EXTR and ACRDT/DCRDT. The extension workers required (EXTR) are computed in Equation 5.u5 as the number of man—years needed to pro— vide technical assistance to the land in transition from traditional to modern management (TRSL). EXTR(t) = C2575TRSL(t) (5.145) 115 where: C257 8 the extension workers to grazing land ratio-- a model parameter (man-years/ha). In order to compute inputs required for modernization it is important to know how much land is in the modernization process at any given time. The land in modernization is simply the sum of the time integrals of Equations 5.u3. Equation 5.H6 computes TRSL--the land in transition from traditional to modern practices due to overt promotion and diffusion. TRSL(t+DT) = (XRl(t) + R2(t) + RLM(t))*XDEL (5.u6) A5 E12 (E13) = 2 *“E12 (313) = l D l EXTA/EXTR ACRDT/DCRDT FIGURE 11.2. The land "dropout" function. 116 A final economic decision to be made is whether some modern grazing land should be reverted to traditional practices if the profitability of modern methods drops significantly due to declining output prices, increasing input prices, etc. Figure 11.3 shows how the model (Equation 5.H7) handles this decision. It is clear that no reversion will occur unless the profitability cirteria relating modern returns to tradi— tional returns (PDR) drop below some threshold value, in which case, the reversion to traditional practices will occur at an increasing rate, up to a maximum, as the profitability con- tinues to fall. The adoption response function and the re- version response function, which are not symmetrical, attempt PR2(t) 1K E11 E81 small E81 large E91 PDR(t) FIGURE II.3. The reversion response function. to capture by 117 proxy the farmers' investment and disinvest- ment decisions. PR2(t) = max(E11i(l-EXP(—E81I(E91-PDR(t)))), 0) (5.“7) where: PR2 PDR 8 E11 = E81 = E91 = proportion of total land in modern production which reverse to traditional practice the profitability criteria of Equation 5.30 maximum proportion that will be reverted (proportion/year) a parameter regulating the reversion rate (dimensionless) reversion threshold (PS/ha). The rate by which modern land reverts to traditional uses is simply the product TLMOD(t)*PR2(t). where: TLMOD Given = the total land in modern production (has). this reversion rate and the rates land is being modernized by production campaigns and diffusion, it is possible to compute the total modern land, TLMOD, assumed to produce at modern productivities. This is done in the model separately for agricultural region 2 (lowland) and for subregions l and 2 of agricultural region 3 (uplands). Equation 5.u8 first allocates grazing land entering moderni— zation among the three regions in the same proportion graz— ing land in each region is of total flood-free grazing land, then subtracts the modern land reverting to traditional practices, and finally allocates any change in total grazing land over time between traditional and modern practices. 118 GLTOTi(t) TGL(t) TLM0D1(t+DT) = max{min[TLMOD1(t) + DT*(RLM(t)§ ENTiTLMODi(t)§RTGL1(t) _ u TLMODi(t) PR2(t) + GLTOT1(t) , GLTOTi(t)], 0} (5.u8) where: RLM = as defined in Equation 5.h3d GLTOT = total grazing land in the given agricultural region (has) RTGL = rate of change of total grazing land in the given agricultural region (has/year)l ENT = a model parameter (E6, E10, Elk) that determines the percentage of land entering or leaving pasture production that enters or leaves modern production TGL = total (flood-free) grazing land (has) i = indexes the agricultural regions, i=1, ..., 3. The inclusion in Equation 5.48 of the term involving RTGL requires further discussion. Since, over time, the land allocated by decision makers to cattle production will change, there is a question about how these changes should be allocated to traditional and modern production. The model formulation permits the user to make a number of assumptions about this through adjustment of the parameter ENT. For example if ENT = 0, the model allocates all increases and decreases in total land to traditional production. If ENT = l, the model allocates changes in land area to traditional and modern g lb/For detailed computation of rate of change in lowlands, subregion l and subregion 2 (RTGLL, RTGLUl and RTGLU2, respectively) see subroutine LANDAL in the Appendix. 119 production proportionately according to the percentage each is of total land. Further, if: ENT 1 when RTGL 1 0 and ENT = 0 when RTGL < 0, the model allocates net increases in total land proportionately to modern and traditional production and subtracts all de- creases from traditional production, etc. Total flood-free land in modern (TLMODR) and tradi- tional (TTGL) production in the Costa is computed as: 3 TLMODR(t) = Z TLMOD1(t) (5.49) i=1 TTGL(t) = TGL(t) - TLMOD(t) - TRSL(t) (5.50) where: TTGL = total flood-free grazing land in traditional production in the Costa (has). Finally, Equation 5.51 computes flood-free and seasonally flooded grazing land in traditional production (TTGLR) in the Costa. TTGLR(t) = TTGL(t) + TGLSF(t)§C9 (5.51) where: TGLSF = total pasture land in the seasonally flooded region--Equation 5.1“ (has) C9 = a model parameter adjusting seasonally flooded grasslands to permanent grazing land. 120 Cattle Transfers As pasture land is modernized and forage production increased, cattle are moved to graze in these lands under improved husbandry practices. The rate that animals are added to the modern grazing lands is a function of the rate of increase of their nutrition levels and the relative difference between the achieved nutrition and the desired one. This is computed by Equation 5.52. RTDN(t) + C12*TOPOPM(t)*(TDNAM(t) - TDNREQ) RAA(t) = TDNREQ (5.52) where: RAA = rate animals are added to the modern sector (animals/year) RTDN = rate of increase of TDN in the modern sector (tons/year—year)l/ TDNAM = TDN per animal in the modern sector (tons/ animal-year)l/ TDNREQ = desired TDN per animal in the modern sector (tons/animal-year) TOPOPM = total animal population in the modern sector (animals)l C12 = a model parameter that determines the influence which the difference between the achieved nutrition level and the de- sired nutrition level in the modern sector has on the rate animals are added to the modern sector (proportion/year). The rate at which males and females, summing to RAA, are added to the modern population is given by Equations 5.53. It is assumed that the sex ratio of transferring animals is l/ - For detailed computation of these variables see subroutine DEMOG in the Appendix. 121 the same as that of the traditional population (this could also be a policy variable). It is also assumed that only fertile females are transferred from the producing cohort. a RFGTT(t) = RAéégngisggt) (5.53a) RFPTT(t) - RAA(t)*FERT(t) (5 53b) ' TOPOPT1(t) ' RMGTT(t) = RAQéggggggggfil (5.530) * RMPTT(t) - RAééggpgffiigt) (5.53d) where: RFGTT = rate growing females are transferred out of the traditional sector (animals/year) RFPTT = rate producing females are transferred out of the traditional sector (animals/year RMGTT = rate growing males are transferred out of the traditional sector (animals/year) RMPTT = rate producing males are transferred out of the traditional sector (animals/year) FERT = total traditional fertile producing females --Equation 6.18 (animals) TOPOPTI = total cattle population in the traditional sector net of females unsuitable for re- production--Equation 5.5M (animals). The rates animals leave the modern sector are the negatives of RFGTT, RFPTT, RMGTT and RMPTT (negative departures are arrivals): RFGTM(t) = -RFGTT(t) (5.53e) RFPTM(t) = -RFPTT(t) (5.53f) RMGTM(t) = -RMGTT(t) (5.53g) 122 RMPTM(t) = -RMPTT(t) (5.53M) The traditional cattle population base for these transfers (TOPOPTl), computed in Equation 5.54 below, excludes all females which are unfit for reproduction, i.e., old cows, infertile cows, and cows with severe cases of mastitis. TOPOPT1(t) = TOPOPT(t) - OLDFT(t) - FINFT(t) - FMAST(t) (5.54) where: TOPOPT = total cattle population in the traditional sector (animals) OLDFT = traditional population of old females-- Equation 6.12 (animals) FINFT = traditional producing females which are infertile--Equation 6.17a (animals) FMAST = traditional producing females with mastitis --Equation 6.17b (animals). The preceding equations define the most relevant variables and structural relationships of the land allocation and modernization component. The interested reader can find the complete list of equations performing the land allocation and modernization decisions described here in subroutines LANDAL, MODCRD and MODRAT of the computer program shown in the Appendix. The output of component LAMDAC is used as an input to the component AGPRAC described in the next chapter. Table 11.1 at the end of this chapter shows the values of a selected number of variables used in component LAMDAC. TABLE II.1. 123 Selected Coefficients and Initial Values in the Land Allocation and Modernization Decisions Component (LAMDAC). Definition (Equation No.) Value DT Determines the time increment of .25 (5.1a) the model (years) 'AL2 Determines the rate of growth in .03 (5.4) food crOps land (proportion/year) CFDl Determines the allocation of food .5 (5.8) crOp land to subregion l (proportion) CL1 Determines the speed of land .03 (5.21) adjustment in subregion l (proportion/year) CL2 Determines the allocation of food .5 (5.23s) crOp land between cash crop and grasslands (prOportion) C9 Determines permanent grazing land .55 (5.29) equivalent (prOportion/year) Cl2 Determines the speed of animals 1 (5.52) transfer (prOportion/year) C249 Determines the allocation of .8 (5.6) food crop land to region 3 (proportion) C257 Determines extension workers .0005 (5.45) requirements (man-years/ha) E7 Determines maximum adOption rate 1 (5.36) (proportion) Ell Determines maximum proportion of .5 (5.47) reversion to traditional practices (proportion/year) E12 Determines the extension "drop- 1.2 (5.44) outs" response threshold (dimensionless) E13 Determines the credit "drOpouts" l (5.44) response threshold (dimension- less) E31 Determines maximum value of E3 4,000 (5.38) when accumulated time is large (has/year per unit of extension) 124 TABLE 11.1. (continued) Definition (Equation No.) Value E32 Determines minimum value of E3 2,000 (5.38) at time zero (has/year per unit of extension) E33 Determines the rate at which E32 .3 (5.38) decreases over time (dimension- less) . RLCRL Determines the rate of growth in 4,500 (5.11) cash crOp land in region 2 (has/year) RLDRN Determines the rate flooded land 13,500 (5.3) is drained (has/year) RPTN Determines farmers' participation .2 (5.42b) of total establishment costs (proportion) EXTA Determines extension workers 250 (5.44) available (man-years) TDNREQ Determines TDN desired for modern 1.85 (5.52) animals (tons/animal-year) PRM Determines the price of milk 1 (5.353) (PS/liter) XDEL Determines one-third of the time 1 (5.43a) to complete modernization (years) TLAVLO Initial flood-free agricultural 533.3 (5.1a) land potentially available in region 2 (thous. has) TLAVL(O) Initial flood-free agricultural 431.0 (5.1a) land actually available in region 2 (thous. has) TLAVUOl Initial agricultural land 2,169.06 (5.1b) potentially available in sub— region 1 (thous. has) TLAVU1(0) Initial agricultural land 1,720.73 (5.1b) actually available in subregion 1 (thous. has) TLAVU02 Initial agricultural land 1,676.0 (5.1c) potentially available in subregion 2 (thous. has) 125 TABLE 11.1. (continued) Definition (Equation No.) Value TLAVU2(O) Initial agricultural land 1,293.02 (5.1c) actually available in subregion 2 (thous. has) TGLSFO Initial potential grazing land 3,137.95 (5.1d) in region 1 (thous. has) TLDRNLO Initial land capable of drainage 3,137.95 (5.2) (thous. has) TLFCU1(0) Initial land in food crops in 40,915 (5.20) subregion 1 (has) Source: [13, 15, 36, 37, 38, 42, 53, 58, 59] and initial guesstimates and model tuning. CHAPTER 6 AGRICULTURAL PRODUCTION—~CROPS/CATTLE (AGPRAC) Component AGPRAC generates the production of crOps, pastures and cattle, and determines the yields of farm crops and the sales of cattle. Crop Yields Crop yields are a composite of the major crops grown in the Costa and are assumed to remain constant throughout the simulation. Increases in crop yields over time are allowed in the model as part of the modernization policies that will be discussed in Chapters 8 and 12. Cash crop yields are computed for each agricultural region according to the crop mix assumed in Chapter 5. Further, it is assumed that yields in the more fertile low- lands (YLDCL) are 10 percent higher than in the uplands (YLDCU). With these assumptions and using the average yields derived from the Caja Agraria crop reports [7], it is possible to compute the average yield of individual commodities for each agricultural region. Then, using the same crOps allocat- ing weights of component LAMDAC, it is possible to compute a composite yield for each agricultural region. Table 11.2 shows the computed average yield of each commodity, and Table 111.3 on page 232 shows the composite yields used in 126 127 computing crop production and income below (Equation 6.29s). The computation of food crop yields (YLDFC) is simply a composite of the average yields of plantain and cassava as reported by the Caja Agraria for the period 1965-1969'[7].l/ The five-year average yield of cash crops in the uplands used in the land allocation decisions of component LAMDAC is computed as: EYLDCU(t) = EYLDCU(t—DT) + fi%%8*(YLDCU(t-DT) - EYLDCU(t—DT)) (6.1) where: EYLDCU exponential average of cash crop yields in the uplands, used in Equation 5.35e in LAMDAC (tons/ha-year) YLDCU = average yield of cash crops grown in the uplands (tons/ha-year) DEL8 = average lag (years). The increase in crop yields as a result of crop modernization efforts is computed in Equations 6.2. Here, it is assumed that crOps reach their maximum target yield gradually, over a period of several years (DEL9 and DELlO). This length of time can be interpreted as being responsive to the crop modernization campaigns and could be a policy l/ — Average regional yields (tons/ha-year) of each commodity are: sesame--.66; cotton--l.4; corn-~l.ll; sorghum --l.8; rice-~l.9; p1antain--8.38; and cassava--8.28. TABLE 11.2. 12 8 Average Annual Yield and Initial Costs by Farming Sectors. Yield Cost* (Tons/ha-yr) (Ps/ha-yr) Cr0p Uplands Lowlands Uplands Lowlands Sesame .63 .693 400 408 Cotton 1.34 1.47 1,639 1,672 Corn 1.08 1.19 313 319 Sorghum 1.73 1.9 564 575 Rice -- 1.9 —- 1,270 Plantain 8.38 600 Cassava 8.28 787 *Costs are reported for 1970 and adjusted to 1960 prices. Source: [7, 57, 69] 129 variable. Target or desired yields have been derived from targets set by the Ministry of Agriculture for Colombian major crops [58]l/ and then weighted by the crop allocating weights of component LAMDAC to obtain a composite average for each agricultural region. YLDCU(t) + -93—*(DYLDCU - YLDCU(t)) (6.2a) YLDCU(t+DT) DEL9 YLDCL(t+DT) YLDCL(t) + fi%%§*(DYLDCL — YLDCL(t)) (6.2s) YLDFC(t) + --IlT—*(DYLDFC — YLDFC(t)) (6.2c) YLDFC(t+DT) DEL10 where: YLDCU the projected yield of cash crOps in the uplands (tons/ha—year) YLDCL = the projected yield of cash crops in the lowlands (tons/ha-year) YLDFC = the projected yield of food crOps (tons/ ha-year) DYLDCU = the target yield of cash crops in the uplands (tons/ha-year) DYLDCL = the target yield of cash crOps in the low- lands (tons/ha-year) DYLDFC = the target yield of food crops (tons/ha—year) DEL9, DELlO = average lag (years). Pasture Production Before generating the production of cattle, component AGPRAC determines the output of fodder as total digestible l/Target yields (tons/ha-year) for the major crops grown in the Costa are: sesame--.75; cotton--l.8; corn—-l.6; sorghum--3.8; rice--4.l; p1antain--10; cassava--10. 130 nutrients (TDN) from traditional and modern grasslands. First, the component computes (Equations 6.3) the regional average TDN yields of artificial and native grasses under both traditional and modern practices. Production of artificial and native grasses is estimated as an average of dry and rainy season yields based on the permanent carrying capacity of these pastures. Traditional artificial pastures in the uplands under continuous grazing and without fertilizer, yield 5.5 tons of dry forage per hectare annually or approximately 3.48 tons of TDN (l kilogram of dry forage produces .633 kilograms of TDN [33].) This is enough feed to support 1.9 head of cattle throughout a year on the basis of an average nutritional requirement of 1.82 tons TDN/head-year. Further, it is assumed that artificial grasses in the more productive lowlands (mostly para grass) yield about 10 percent higher than upland pastures (mostly guinea and puntero grasses). It is also assumed that native grasses yield two-thirds less than artificial grasses [61, 66]. Improved pastures are estimated to have a carrying capacity 40 percent higher than pastures under traditional management. The average yields used in these computations are shown in Table 111.3 on page 232. cco(t) a LCGOUSITTGLU1(t)_i TTGLU2(t))_+ CGOL§TTGLL(t)) TTGL(t) (6 ) .3a _ (CGOU1*(TTGLUl(t) + TTGLU2(t)) + CGOLl!TTGLL(t)) CG°1(t) ‘ TTGL(t) (6.3b) CG1(t) = CG2(t) = where: 131 (CGU1!(TLMOD2(t) + TLMOD3(t)) + CGleTLMOD1(t)) TLMOD(t) (6.3c) (CGU2*(TLMOD2(t) + TLMOD3(t)) + CGL2*TLMOD1(t)) CGO 0601 C61 C62 CGOU CGOUl CGUl CGU2 CGOL CGOLl CGLl CGL2 average grasses average grasses average grasses average TLMOD(t) (6.3a) TDN yield from traditional artificial in the Costa (tons/ha-year) TDN yield from traditional native in the Costa (tons/ha-year) TDN yield from improved artificial in the Costa (tons/ha-year) TDN yield from improved native grasses in the Costa (tons/ha-year) average grasses average grasses average grasses average traditional artificial (tons/ha-year) TDN yield from‘ in the uplands traditional native (tons/ha-year) TDN yield from in the uplands TDN yield from in the uplands improved artificial (tons/ha-year) TDN yield from improved native grasses in the upland (tons/ha-year) average grasses average grasses average grasses average grasses TDN yield from traditional artificial in the lowlands (tons/ha-year) TDN yield from traditional native in the lowlands (tons/ha-year) TDN yield from improved artificial in the lowlands (tons/ha-year) TDN yield from improved native in the low lands (tons/ha-year) TTGLL, TTGLUl, TTGLU2 = total land in traditional grazing in region 2, subregion l and subregion 2, respectively (has) 132 TTGL = total Costa flood-free land in traditional pasture production-~Equation 5.50 (has) TLMODI, TLMOD2, TLMOD3 = total land in modern grazing in region 2, subregion l and subregion 2, respectively-- Equation 5.48 (has). Total digestible nutrients available to the traditional sector come from pasture in the flood-free and in the seasonally flooded lands. Crop residues, particularly of cotton, are added to the nutrient supply. Since there are indications of overgrazing in the region, the deteriorating effect of this practice is introduced into the model by Equations 6.4. g TTGLR(t) GRT(t) TOPOPT(t) (6.4a) where: GRT = grazing rate in the traditional sector (has/animal) TTGLR = total traditional grazing land in the Costa-«Equation 5.51 (has) .TOPOPT = total traditional cattle population (animals)l/ RCON(t+DT) = max(RCON(t) + DT&CS§(GRT(t) - GRE), .1) (6.4b) where: RCON = range condition (a dimensionless number) GRE = equilibrium grazing rate (which results in constant range condition) (has/animal) C5 = a parameter that determines the extent of influence of grazing rate upon range condition 1/ - For a detailed computation of this variable see subroutine DEMOG in the Appendix. 133 max(a,b) = the maximum of a and b. Range condition is prevented from diminishing below an un- realistic limit by establishing a lower bound for RCON. The above equations stipulate that range condition increases or decreases over time if GRT is respectively greater or less than GRE. Given range condition, it is now possible to com— pute the total TDN available from the flood-free traditional grazing land. TDNTG(t) = RCON(t)*(CGO(t)*CPLPTnTTGL(t) + CG01(t) *(1 — CPLPT)*TTGL(t)) (6.5) where: TDNTG total traditional TDN from flood-free grasslands (tons/year) CPLPT = proportion of artificial grasses in traditional grazing land CGO = as defined in Equation 6.3a CG01 = as defined in Equation 6.3b TTGL = as defined in Equation 6.3a. Total TDN available from crop residues and seasonally flooded lands is computed as: TDNRE(t) = C7§C220*(TLCRU(t) + TLCRL(t)) (6.6) TDNSF(t) = TGLSF(t)*C9*ClO (6.7) where: TDNRE = TDN available to traditional animals from cr0p residues (tons/year) TLCRU land in cash crops in the uplands--Equation 5.24 (has) 134 TLCRL = land is cash crops in flood-free lowland-- Equation 5.11 (has) TDNSF = TDN available to traditional animals from seasonally flooded land (tons/year) TGLSF = total pasture land from seasonally flooded areas—-Equation 5.14 (has) C7 = TDN yield of crop residues (tons/ha-year) C220 = a model parameter determining the proportion of cash crOps producing residues used to feed traditional animals 09 = proportion of time that flooded land is available for grazing 010 = TDN yield of grasses from seasonally flooded lands (tons/ha-year). Finally, the total TDN available annually to the traditional sector, TDNT, is simply computed as: TDNT(t) = TDNTG(t) + TDNRE(t) + TDNSF(t) (6.8) Total TDN available to the modern sector depends on the alternative adopted. Land in transition from traditional to modern practices (TRSL) is considered part of the modern sector, but as producing forage at a rate intermediate between traditional and improved pastures. Briefly, alter- natives l and 3 consider the improvement of both native and artificial grasses, as well as the production of forage crops in the latter to supplement nutrition during the dry season. Alternatives 2 and 4 consider the improvement of artificial pastures and the substitution of improved artificial grasses for traditional native grasses. In addition, alternative 4 includes the production of forage crops to supplement nutri- tion from grasses. The component computes the average yield 135 from improved and transition grasses as well as the average yield from forage crops in the form of silage. Given the average yields and the land in pasture and forages, it is possible to compute the total TDN available to the modern sector from each alternative. Forages are planted to the extent needed to make up for the deficit in nutrition as shown by the difference between the TDN obtained from grasses and a target level of TDN (TDND in Equation 6.9f). Although TDND could be a policy parameter, the model assumes this target is set at the nutrition level required to support four head per hectare, the current average carrying capacity in the Costa during the rainy season. Equation 6.9g, which computes area in forages, implies that land is taken out of modern production and transition in the same prOportion. Mathematically, these computations are carried out in Equation 6.9. CGA(t) - CGl(t)*CPLPT + CG2(t)*(l — CPLPT) (6.9a) CTR(t) = CG3*CPLPT + CG4*(1 - CPLPT) (6.9b) TDNF g CZEZ*%§§3*TDNSG (6.9C) Further, CGA(t) CGI, and CTR(t) CG3 for alternatives 2 and 4. where: CGA = TDN yield from improved grasses (tons/ha-year) CTR = TDN yield from grasses in transition (tons/ ha-year) 136 CG3 = TDN yield from transition artificial grasses (see Table 111.3) (tons/ha-year) CG4 = TDN yield from transition native grasses (see Table 111.3) (tons/ha-year) TDNF = TDN yield from forages (tons/ha-year) TDNSG = TDN yield from silage (tons/ton of silage) C250 = a model parameter to account for weight losses when green forage is converted into silage C253 = yield of green forage (tons/ha-cutting) C254 = the number of times forages are harvested during the growing season (cuttings/year). In computing Equation 6.9c it has been assumed that forages are grown without irrigation and the growing season is thus restricted to the rainy period. TMPL1(t) = TLMODi(t) + TRSL1(t) - TLF1(t) , (6.9h) where: TMPL = total land in pasture (has) i = indexes the alternatives-~i-3,4. Finally, TDN available to the modern sector from alternatives 3 and 4 is computed as: TDNMi(t) TDNF'TLF1(t) + TDNG1(t)*TMPL1(t), i=3,4 (6.91) where: TDNM = TDN output from modern land (tons/year). 137 Cattle Production Demography Cattle inventories and output are modeled dynamically as populations distributed over time and stage of production. The demographic model of the cattle population is divided into three age cohorts for females and two age cohorts for males (Figure 11.4). The respective cohort lengths reflect the three production stages the model identifies: a growing stage, a producing stage, and a stage in which animals with- out reproductive capabilities remain. The aging of animals through the first two cohorts is modeled by distributed lags.l/ When females finally enter the old age cohort, their aging rate is no longer modeled, and cows remain there affected by deaths and sales through herd management policies. Based on the census data available [15] the model assumes that all animals two years old and less are included in the growing cohort; the producing cohort includes those animals over two years. The preceding production process is simulated by four calls to DELDT subroutine [52, 54], one for each sex cohort. Since the structure of the subroutine is alike for each cohort and differences in output arise only from dif- ferences in inputs, only the production of growing females l/The distributed lag model used here has been adapted following Abkin [1, Chapter 3]. 138 Out— transitiong: rate ‘\\ K Birth Growing Producing 0 Females Females Females Birth Growing Producing Finished Males Males Males Males Out547 i// i// transition‘ rate FIGURE 11.4. Cattle production cohorts. will be discussed in detail here. The growing males cohort uses the male birth rate as an input, and the producing cohorts use as an input the output of the growing cohorts. Equations 6.10 define traditional or modern populations depending upon whether DELDT is supplied with traditional or modern data. CALL DELDT(BF(t-DT), RFOUl(t), RINTF1(t), DGROF, IDTFl, DT, KGROF) (6.10a) where: BF 8 rate females enter the first cohort, i.e., the female birth rate-—Equation 6.19b (animals/year) RFOUl a rate growing females leave cohort l and enter the producing stage (animals/year). 139 DGROF = average length of time females remain in Cohort 1 (years) KGROF = a parameter that determines the probability distribution for the length of time individual females remain in Cohort l RINTFl, IDTFl = other variables associated with the use of the DELDT subroutine DT = time increment of the model (years). The purpose of this call to subroutine DELDT is to compute RFOU1(t), the rate females leave Cohort 1. This rate minus any losses (due to deaths, sales and transfers) becomes the input to Cohort 2, the producing stage, RFOUPl(t): RFOUPl(t) = RFOU1(t) - RFOU1(t)*(DRL1(t) + PSFG(t) + PPFGT(t))*DT (6.10b) where: DRLl death rate of the growing population-- Equation 6.16 (proportion/year) PSFG = proportion of growing females sold (prOportion/year)l/ PPFGT = proportion of growing females transferred out of a given seitor (modern or traditional) (proportion/year)_/ The output of the male producing cohort, RMOU2, is the number of finished males available annually for immediate consumption, and the output of the female producing cohort, RFOU2, is the rate at which females leave the producing stage l/ - For detailed computation of these variables see subroutine DEMOG in the Appendix. 140 as a result of old age. Given the basic model of the cattle demographic process, it is possible to compute the population in each cohort, total population and other variables of importance in the model. The number of animals in the female and male cohorts are computed as time integrals of pOpulation flow rates. As stated earlier, old females are not transferred but stored for sales decisions based on herd management policies. Since the structural equations are alike for Cohorts 1 and 2, the computation of the number of growing females will be shown in Equation 6.11 below. Yet it must be remembered that each equation uses the variables relevant to each cohort, and that the producing cohorts use as an input the output of the grow- ing cohorts. Equation 6.12 computes the population of old females. Total cattle populations in the traditional and modern sectors and in the region are computed simply by adding the pOpulations from each cohort. PFG(t+DT) = PFG(t) + DT*(BF(t) - DRLl(t)*PFG(t) - SLSFG(t)- RFGT(t) - RFOUPl(t)) (6.11) where: PFG = pOpulation of growing females (animals) SLSFG 8 sales of growing females--Equations 6.22 (animals/year) RFGT = rate growing females are transferred—— Equations 5.53 (animals/year) RFOUPl = rate females leave the growing stage and enter the producing stage-~Equation 6.10b (animals/year). 141 OLDF(t+DT) = OLDF(t) + DT*(RFOUP2(t) - SOLDF(t) — DRL2(t) *OLDF(t)) (6.12) where: OLDF = population of old females (females which have concluded the reproductive life) (animals) SOLDF = sales of old females-—Equation 6.22e (animals/year) RFOUP2= rate females leave the producing stage and enter old age--Equation 6.10b where transfers = O (animals/year) DRL2 = death rate of the producing pOpulation-- Equation 6.16 (prOportion/year). Once cattle demography has been simulated, the model computes the variables affecting the transition rates of this population. Live birth rates and death rates are computed as a function of the level of nutrition (TDNA). Death rates are computed separately for the growing and producing popula- tions as shown in Equations 6.13. The table look-up functions [52, 54] used in these equations compute traditional or modern birth and death rates depending upon whether the VAL arrays are supplied with traditional or modern data. Figures 11.5 graphically depict these functions. BR(t) = TABLIE(VALB, SMALLB, DIFFB, KB, TDNA) (6.13a) DRG(t) = TABLIE(VALDG, SMALLDG, DIFFDG, KDG, TDNA) (6.13b) DRP(t) = TABLIE(VALDP, SMALLDP, DIFFDP, KDP, TDNA) (6.13c) 142 BR .8P 57' VAL1(1) = 0.00 VAL1(2) = 0.25 6- VAL1(3) = 0.55 . VAL1(4) = 0.63 VAL1(5) = 0.70 'Sb VAL1(6) = 0.75 .4- .3- .2- .1- 0 l I I 1 1 TDNA k / 1 1 360 720 1080 1440 1800 2160 grs- an ma -year FIGURE 11.5.a. Traditional birth rate versus total digestible nutrients. BR ,9, .8- VALM1(1) = 0.00 - VALM1(2) = 0.30 '7 VALM1(3) = 0.60 VALM1(4) = 0,70 .6- VALM1(5) = 0.80 VALM1(6) = 0.85 .5- .4- .3- .2P .1- . l 1 l J l 0 3 0 720 1080 1440 1800 2160 TDNA kgrso/animal-year FIGURE II.5.b. Modern birth rate versus total digestible nutrients. 143 DRG .8' .7- VAL2(1) = .76 - VAL2(2) = .17 - VAL2(3) = .10 '6 VAL2(4) = .066 VAL2(5) = .041 .5- ‘ VAL2(6) = .029 .47 .3- 42- .1— l l l I - 0 360 720 1080 1440 1800 2160 TDNA kgrS'/animal FIGURE 11.5.0. Traditional death rate of growing cohort versus total digestible nutrients. DRG .8- U?‘ VALM2(1) = .70 VLAM2(2) = .15 .6' VALM2(3) = .08 VALM2(4) = .057 VALM2(5) = .032 ~5" VALM2(6) = .022 .4- fi3- J?” .l - l 1 1* 1 ' TDNA kgrs./animal-year 0 360 720 1080 1440 1800 2160 FIGURE II.5.d. Modern death rate of growing cohort versus total digestible nutrients. 144 DRP .8" .7'” 6" VAL3(1) = .46 VAL3(2) = .10 5__ VAL3(3) = .06 VAL3(4) = .04 VAL3(5) = .025 7,— VAL3(6) = .018 .3 - .2 - .1 ' l l 1 ' TDNA k rs /animal- ear 0 360 720 1080 1440 1800 2160 8 ° y FIGURE 11.5.e. Traditional death rate of producing cohort versus total digestible nutrients. DRP .8" .7 ‘ 6 _ VALM3(1) = .45 VALM3(2) = .09 VALM3(3) = .047 5 ' VALM3(4) = .027 VALM3(5) = .012 4 — VALM3(6) = .01 .3 - .2 .1L 1 l l TDNA kgrs./animal-year 0 360 720 1080 1440 1800 2160 FIGURE 11.5.f. Modern death rate of producing cohort versus total digestible nutrients. 145 where: BR = live birth rate-—pr0portion of producing females calving per year. In the model is also taken as a pregnancy rate DRG = death rate--proportion of growing popula- tion dying per year DRP = death rate--pr0portion of producing p0pula- tion dying per year TABLIE = a simulation subprogram which approximates arbitrary functional relationships by straight line segments VAL = an array of numbers which defines the depen- dent argument of the function SMALL = smallest value of TDNA in the data which defines the function DIFF = the fixed differences between values of TDNA K = the number of line segments used to approximate the birth or death rate functions [TDNA = total digestible nutrients (tons/animal-year) --the independent argument of the function.l/ In reality, births and deaths do not change instanta- neously with changes in nutritional levels and/or population sizes, but rather lag behind changes in these variables. The variables BR, DRG and DRP must therefore be Operated on to introduce these lag effects. Equation 6.14 shows this computation for birth rates: BR2(t) = BR2(t-DT) + 5%%T*(BR(t-DT) — BR2(t—DT)) (6.14) l/ — For a detailed computation of this variable see subroutine DEMOG in the Appendix. 146 where: BR2 = actual live birth rate (prOportion/year) DELI = lag parameter (years). The actual death rate of the growing population, DRl, and the actual death rate of the producing population, DR2, are com- puted using similar equations to the one above. Nutrition and management are not the only factors affecting birth and death rates. Diseases have a major role in determining the value of these variables, and we are particularly concerned here with brucellosis and foot- and-mouth disease (FMD) which are epidemic in the region. The effect of brucellosis on birth rates is introduced into the model by the variable CBANG which depends on the pro- portion of cows treated. This is shown in Equation 6.15 where the variable BR2 is taken as a pregnancy rate. BRL2(t) = BR2(t)*(l — CBANG(t)) (6.15) where: BRL2 the effective live birth rate (proportion/year) CBANG proportion of pregnant cows aborting due to brucellosis, where 0 1 CBANG : .04._/ The effect of FMD on death rates is introduced into the model by the variable DRA which depends on the prOportion of animals treated annually. This effect is shown in Equa- tion 6.16 for the growing cohort. l/ - For a detailed computation of this variable see subroutine DEMOG in the Appendix. 147 DRL1(t) = DR1(t)*DRA(t) (6.16) where: DRLl the effective death rate of growing animals (proportion/year) DRl = the actual death rate of growing animals-- Equation 6.14 (proportion/year) DRA = prOportional increase in death rates due to FMD, where l i DRA 1 1.33. Before computing total births it is necessary to determine the number of cows capable of calving found in the population of producing females. This is done in the model with Equations 6.17 by computing the number of infertile cows and those affected by severe cases of mastitis, caused by FMD, that have to be discarded from the breeding herd. Equations 6.17 are the time integrals of female population flow rates where affected animals come out from each of the transition rates in the same proportion. Equation 6.17a incorporates both the effects of malnutrition and infectious abortion on fertility (see Chapter 2). The effect of mal— nutrition is introduced by the variable PIFNU which depends oh the level of nutrition available and a rate of response to changes in nutrition. The function has an upper bound that is reached when nutrition is below a predetermined level; the lower bound indicates that a minimum of infertility is always present (due to heredity and/or other causes) despite high levels of nutrition [24]. The effect of brucellosis is introduced by the variable, CB(t) - BR2(t)§CBANG(t), which determines the proportion of cows aborting (see Equation 148 6.15); it is also assumed that 10 percent of the cows abort- ing become infertile [31]. FINF(t+DT) = where: FINF PFINF PFP DFP RFOU2 SLMAS SLINF CB PIFNU RFOUl RFPT SLFER PFINF(t)§[PFP(t) - DT§(DFP(t) + RFOU2(t) + SLMAS(t))] - SLINF(t)*DT + DT§(CB(t)!O.lO + PIFNU(t))*[(PFP(t) - DT*(DFP(t) + RFOU2(t) + SLMAS(t))*(1 - PFINF(t))) + DT!(RFOU1(t) - RFPT(t) - SLFER(t))] (6.17a) producing females which are infertile (animals) proportion of producing females which are infertile population of producing females--Equation 6.11 (animals) actual deaths of producing females—- Equation 6.20b (animals/year) actual rate at which producing females leave the producing stage--Equations 6.10 (animals/ year) sales of producing females with mastitis due to FMD--Equations 6.22 (animals/year) sales of infertile females--Equation 6.22 (animals/year) producing females aborting per year (proportion/year) producing females becoming infertile due to malnutrition where .01 g PIFNU 1 .05 (proportion/year) actual rate at which females leave the grow- ing stage--Equations 6.10 (animals/year) rate producing females are transferred out of a sector--Equations 5.53 (animals/year) sales of fertile producing females—~Equations 6.22 (animals/year). 149 Equation 6.17b below assumes that 18 percent of the cows without treatment against FMD get the disease; further, it is assumed that 5 percent of the cows getting FMD are affected by severe mastitis [31]. This equation also implies, as a simplification, that treatment against FMD is applied to animals in each cohort in the same prOportion. FMAS(t+DT) = PFMAS(t)*[PFP(t) — DT*(DFP(t) + RFOU2(t) + SLINF(t))l - SLMAS(t)*DT + (1 — PATAF(t)) *DT*0.18*0.05*[(PFP(t) — DT*(DFP(t) + RFOU2(t) + SLINF(t)§(1 — PFMAS(t))) + DT*(RFOU1(t) - RFPT(t) — SLFER(t))] (6.17b) where: FMAS = producing females with mastitis due to FMD (animals) PFMAS = proportion of producing females with mastitis PATAF 8 animals treated against FMD--Equation 6.28c (proportion/year). Finally, the number of fertile cows is simply computed as: FER(t) = PFP(t) - FMAS(t) - FINF(t) (6.18) Given the population of fertile females and the effective birth rate, it is possible to compute total births. It is assumed in the model that births are evenly distributed between males and females. 150 BA(t) = BRL2(t)*FER(t) (6.19a) BF(t) = 0.5tBA(t) (6.190) BM(t) = BA(t) - BF(t) (6.190) where: BA = total animal births (animals/year) BF = total female births (animals/year) BM = total male births (animals/year) BRL2 = as defined in Equation 6.15. Animal deaths are computed for growing and producing animals and for each sex category. Equations 6.20 show this computation for females only. DFG(t) = PFG(t)*DRLl(t) (6.20a) DFP(t) = PFP(t)§DRL2(t) (6.20b) where: DFG = actual deaths of growing females (animals/year) DFP = actual deaths of producing females (animals/year) PFG = population of rowing females-- Equation 6.11 animals) PFP = population of producing females-- Equation 6.11 (animals) DRLl, DRL2 = as defined in Equation 6.16. Milk and Animals Output Next, the model computes the output from cattle in the form of milk and total sales, and the marketing equations 151 used in the generation of prices. The quantity of milk produced depends on the number of fertile cows, cows getting foot-and-mouth disease (FMD) and the level of nutrition. It is assumed that cows affected by FMD produce 15 percent less milk than healthy cows [31]. QM(t) = [FER(t)*(PATAF(t) + (1 — PATAF(t))*0.82 + (l - PATAF(t))*O.18*0.85)]*PFLAC(t)*C202*YMA*TABEXE (VAL6, .31, .31, 4, TDNA) (6.21a) where: QM quantity of milk produced (liters/year) PFLAC = prOportion of females lactating YMA = average milk output per cow (liters/cow—year) C202 a model parameter determining the number of lactating cows which are milked (proportion) TABEXE(VAL6, ..., TDNA) = a subprogram which introduces a milk pro- duction factor determined by the level of nutrition--TDNA. The expected production of milk used in Equation 5.35a of component LAMDAC is computed as: EQMT(t) = FERT(t)*PFLACT(t)*CT202*YMAT (6.21b) where: EQMT 8 the expected milk production in the tradi— tional sector (liters/year). Cattle sales are computed for each cohort and are part of the herd management policies introduced into the 152 model. Although the sales policies have been designed with enough flexibility to permit simulation of farmers' behavior the current formulation allows little supply response. Cash flow imbalances that might induce sales are not considered as a factor influencing sale decisions, and the model assumes that these decisions depend on prices and the level of nutri— tion. Price changes may have a short—term response effect but, in the long run, sales seem to be dominated by nutri— tional considerations. As a general sales policy the model assumes the following priorities (for other than finished males): (1) old cows; (2) cows with reproductive problems; (3) growing males; (4) growing females; (5) fertile cows; and (6) producing males. According to the preceding assumptions, sales are used to control the cattle population to maintain a prescribed level of nutrition, and animals exceeding carrying capacity are marketed following the order discussed earlier. If pas- tures are being undergrazed, the decision mechanism operates to reduce sales and increase the retention of animals until the appropriate grazing rate is achieved. This nutrition effect is introduced in the sales equations by the variable PAN. This variable is recalculated each time a sale is performed. The sales equations describe a family of supply curves which first are completely inelastic, then are positively s10ped, and finally become completely inelastic (Figure 11.6). The inelastic portions of the curve place an upper bound to sales preventing the herd from being liquidated, and a lower 153 PAP(t) PAPO (L L - 1 I , AMN BMN BMX AMX Proportion of animals sold FIGURE 11.6. The cattle sales function. bound indicating that a minimum of animals are marketed from the herd despite low price incentives and/or excess carrying capacity. These bounds are set by the model parameters BMN, BMX, AMX and AMN which permit simulating farmers' behavior and herd management policies (see Table 11.3). Since there are similar relationships between management of the tradi- tional and modern herds and for each of the six sale groups, the sale of old cows will be shown in detail here. Response PAP ELAS to price is given by the relationship -———} in Equation PAPO 6.220, and nutrition relationships are given by gggg in Equation 6.22d.£/ l-/C0HS is used here as a general form to indicate the number of animals in a given sales group. 154 TABLE II.3. Maximum and Minimum Proportions of Cattle Sales. Management Practice Sales Group and Parameterl/ Traditional Modern 01d Cows: AMXl 0.75 1.00 AMNl 0.50 0.90 BMXl 0.75 1.00 BMNl 0.50 0.90 Intertile cows: AMX2 0.25 1.00 AMN2 0.15 0.80 BMX2 0.18 0.95 BMN2 0.15 0.80 Cows with mastitis: AMX3 1.00 1.00 AMN3 0.70 0.80 BMX3 1.00 1.00 BMN3 0.70 0.80 Growing males: AMX4 0.23 0.30 AMN4 0.11 0.11 BMX4 0.14 0.20 BMN4 0.11 0.11 Growing females: AMX5 0.21 0.25 AMN5 0.13 0.13 BMX5 0.17 0.19 BMN5 0.13 0.13 Fertile cows: AMX6 0.06 0.06 AMN6 0.02 0.02 BMX6 0.05 0.05 BMN6 0.02 0.02 Producing males: AMX7 0.20 0.20 AMN7 0.10 0.10 BMX7 0.15 0.15 BMN7 0.10 0.10 l/Amx1,BMX1and AMNé, BMNi are model parameters ou determinin upper and lower nds 1to cattle sales (Equations 6.220 and .22d)--proportion/year. Source: Guesstimates and model tuning. 155 TOPEQ(t) = TDN(t)!NREQ (6.22a) PAN(t) 8 TOPOP(t) - TOPEQ(t) (6.22b) where: TOPEQ = total animal population in equilibrium with nutrient availability (animals) TDN total digestible nutrients (tons/year) NREQ the reciprocal of the TDN required per animal (animal-year/tons TDN) PAN = defines the difference between the current animal population TOPOP and the equilibrium population TOPEQ (animals). PAP(t) PAPO ELASl ] ]} (6.220) PRESl(t) = min{BMX1, max[BMNl, PRSIli[ PSFO(t) . min{AMX1, max[AMNl, PRESl(t) + C206igégé%%7]}(6.22d) SOLDF(t) = OLDF(t)*PSFO(t) (6.22e) PAN(t) = PAN(t) - SOLDF(t) (6.22f) Market Model Component AGPRAC computes the demand and supply of beef which are part of the simple market model used in deter- mining the price of cattle. First, the model determines the number of animals marketed for consumption and/or export from the Costa herd (Equation 6.24). Although some of these animals are finished in other regions, it has been assumed they are slaughtered and enter the retail market as they leave the Costa farms. Nevertheless, this simplifying 156 assumption does not greatly alter the total supply of beef as computed in the model. SUPCTA(t) = SLSCCT(t) + SLSMLT(t) + SOLDFT(t) + C212&(SLSCCM(t) where: + SLSMLM(t) + SOLDFM(t) + C213*(TDTHST(t) + TDTHSM(t)) + C214*(SLFERT(t) + C2l2*SLFERM(t)) + C215§(SLSFGT(t) + C212§SLSFGM(t)) + C216! (SLSMGT(t) + C212*SLSMGM(t)) (6.24) SUPCTA SLSCCT, SLSMLT, SOLDFT, TDTHST, SLFERT, SLSFGT, SLSMGT, SLSCCM SLSMLM SOLDFM TDTHSM SLFERM SLSFGM SLSMGM supply from the Costa herd (animals/year) sale of cows with reproductive problems traditional and modern, respectively (animals/year) sale of mature males traditional and modern, respectively (animals/ year) sale of old cows traditional and modern, respectively (animals/ year) total animal deaths traditional and modern, respectively (animals/year) sale of fertile cows traditional and modern, respectively (animals/ year) sale of growing females traditional and modern, respectively (animals/ year) sale of growing males traditional and modern, respectively (animals/ year) 157 C212 a parameter accounting for heavier animals from the modern sector (dimensionless) 0213, 00., C216 = parameters determining the proportion of sales which is consumed (proportion). Non-Costa cattle population, TOPOPK, and supply are exogenously determined in Equations 6.25. Cattle population is assumed to grow in a non-cyclical, exponential fashion and its rate of growth could take different values in order to test the effect of government policies on the development of the non—Costa cattle economy. Beef supply is computed as the off-take for exports and slaughter from this pOpulation, where the extraction ratio reflects the oscillations of the long-term cycle (see Chapter 3). This cycling effect is approximated in Equation 6.25b by a TABLIE function which completes a cycle every seven years with the extraction ratio reaching a simulated peak at .17 and a bottom at .118. TOPOPK(t+DT) = TOPOPKO§EXP(C217*t) (6.25a) SUPB(t) = SUPCTA(t) + TOPOPK(t)*TABL1E(VAL9, 0, 1, 7, AMOD(t,7)) (6.25b) where: TOPOPK total non-Costa cattle population (animals) TOPOKO non-Costa cattle pOpulation at the beginning of simulation (animals) EXP = the exponential function rate of growth of non-Costa cattle popula— tion (proportion/year) C217 158 SUPB 8 total Colombian supply of beef (animals/ year) TABLIE(VAL9, ..., AMOD) 8 a subprogram which introduces a cycling factor on extraction ratios determined by the length of the long8term cycle--seven years t 8 simulated time in the model (years). Domestic demand for beef is computed in Equation 6.26a and its growth is due to population, income and price effects. DEM(t+DT) = DEM(t) + DT*(DEM(t)*RDEM(t)) (6.26a) RDEM(t) = ELASI*C237 + ELASP*C238 - ELASD* PA(t) - PA(t-DT) PA t-DT *DT (6.26b) where: DEM domestic demand for beef (animals/year) RDEM 8 the rate of growth of demand (proportion/ year) PA 8 market price of finished males-~Equation 7.2 (Ps/animal) ELASI 8 income elasticity of demand for beef ELASP 8 population elasticity of demand for beef ELASD 8 price elasticity of demand for beef 0237 8 rate of increase in income (proportion/year) 0238 8 rate of increase in population (prOportion/ year). Total demand for Colombian beef, TDEM, is simply computed as the sum of domestic demand, official exports, 159 EXPL, and illegal exports, UNEXPL. Due to the lack of statistics on illegal exports, these are handled as a constant throughout the simulation. Yet it would be realistic to treat UNEXPL as a variable since it can be expected this border trade will be responsive to market conditions in Colombia as well as in the neighboring countries (mainly Venezuela and Ecuador). Official or registered exports are computed from recorded statistics between 1964 and 1971, and from projected targets from 1972 forward (see Table 1.1, p. 19 for figures and sources). TDEM(t) = DEM(t) + EXPL(t) + UNEXPL ‘ (6.260) Disease Control Since foot-and-mouth disease and brucellosis seriously impair cattle production, and the Colombian government is committed to their control and eradication (see Chapter 2), it is relevant to the model to include some equations to test the effect of control measures. Component AGPRAC includes a simple exogenous model which permits evaluation of disease control policies. Here it is assumed that all the effort is directed toward the traditional sector and that all animals in the modern sector are treated according to recommended practices. Further, it is assumed that before the campaign starts, animals are treated at a constant proportion, but afterwards (i.e., after 1971) this prOportion gradually in- creases until it reaches the value one. At this point the model indicates that all the cattle population is being 160 treated. This effect is introduced in the model by the exponential function involving the variable TCAD(TCAD 8 t -TDO) where TDO is the year at which the disease control program starts. The treatment against brucellosis is shown in Equations 6.27 below. This treatment is applied only once when heifers are three to six months old, but for simplicity the model assumes that heifers are treated at birth. ATABTT(t) = BFT(t)*max[1 — C198*EXP(-Cl99lTACAD), C242] (6.27a) where: ATABTT 8 heifers treated against brucellosis in the traditional sector (animals/year) BFT 8 total traditional female births-~Equation 6.19b (animals/year) C242 8 prOportion of heifers treated without the campaign C198 8 proportion of heifers left untreated (0198 - 1 - 0242) 0199 8 model parameter regulating the shape of the exponential curve. The movement of the treated heifers is tracked through the growing stage until they reach the producing stage, and this allows the model to compute the proportion of cows treated, PCTAB, which is needed in determining the variable CBANG used in Equation 6.15. The equations computing PCTAB have the same structure for both the traditional and modern sectors, with the exception that the latter keeps track of the treated females transferred from the traditional sector. Equations 6.27b through 6.27d show this computation for the traditional sector. 161 PGFTB(t) 8 ATABTT(t)*DT + PGFTBT(t-DT)&(PFGT(t) - BFT(t-DT)§DT) :11 (6.27b) PFGT(t) CTABT(t) 8 RFOUTl(t-DT)§PGFTBT(t—DT)*DT + POTABT(t-DT)* (PFPT(t) - RFOUTl(t-DT)*DT) (6.270) a CTABT(t) PCTABT(t) PFPT t (6.27d) where: PGFTBT 8 prOportion of growing females treated against brucellosis in the traditional sector PFGT 8 p0pu1ation of growing females in the traditional sector--Equation 6.11 (animals) CTABT 8 cows treated against brucellosis in the traditional sector (animals) RFOUTl 8 rate females leave the growing stage-- Equation 6.10b (animals/year) PCTABT 8 proportion of cows treated against brucellosis in the traditional sector. Although the campaign against FMD and brucellosis is being carried out simultaneously, the data available [31] do not allow a breakdown of expenditures between the two programs. This problem was simplified in the model assuming that treat- ment against FMD was the only one depending on government expenditures. But since farmers have been and continue to treat a part of the herd on their own, the model computes both animals treated privately and by campaign personnel. Equation 6.28b implies that eventually all animals could be treated by the campaign, in which case farmers who have been 162 neatnm their herds privately will be charged for the service. The current program involves charging services to nwdhmr-and large-size farmers, and providing subsidized services to small farmers [31]. Since treatment against FMD is applied two or three :imes every year to all animals, the computation of the >roportion of animals treated, PATAFT, used in determining imavariable DRA in Equation 6.16 is more straightforward hen the preceding for brucellosis. 3 EXPAFT(t) TAFT(t) min[ COSTFT , TOPOPT(t) (6.28s) lere: ATAFT 8 government treated animals against FMD in the traditional sector (animals) EXPAFT 8 government expenditures against FMD--a policy variable (PS/year) COSTFT 8 government cost of treatment against FMD (Ps/animal-year) TOPOPT 8 total traditional cattle p0pu1ation in the Costa (animals). [FPT(t) = (TOPOPT(t) - ATAFT(t))*max[l - C2008EXP (-0201*TAOAD), 0244)] (6.28b) AFT(t) = W (6.28c) re: ATAFPT 8 privately treated animals against FMD in the traditional sector (animals) ATAFTT 8 total privately and government treated animals against FMD in the traditional sector (animals) 163 PATAFT 8 prOportion of animals treated against FMD in the traditional sector C244 8 prOportion of animals treated privately without the campaign 0200 8 prOportion of animals left untreated (C200 8 l - C244) C201 8 model parameter regulating the shape of the exponential curve. uatun16.28b above also implies a promotion and/or diffusion fect among farmers due to the campaign. As time of campaign sses, the proportion of animals treated privately increases proaching one. ficultural Accounting Finally, component AGPRAC performs the macroeconomic ounting for the agricultural production. This section 0 simulates farmers' varying expectations about the account- variables during the planning horizon. These expectations introduced in component LAMDAC for the computation of :ounted profitabilities. First, revenues and costs of as production are generated. Costs are a composite of the n? crops grown in each of the agricultural regions and are ulted using the same crop allocating weights of component .AC. It is also assumed that costs increase over time at inflation.rate for farm inputs. Table 11.2 on page 128, s tflne computed average cost of each commodity, where 3 111 the lowlands are slightly higher to account for eased harvesting costs because of higher yields. Equa- : 6.229 show these computations for cash crops in the K13. 164 lCRU(t) 8 TLCRU(t)flYLDCU(t)*PCROPU(t) (6.29a) ere: ARCRU accounting revenue from cash crOps in the uplands (PS/year) TMHU = total land in cash crops in the uplands-- Equation 5.24 (hectares) HDCU 8 the projected yield Of cash crops in the uplands--Equation 6.2a (tons/ha-year) ITR0H18 the projected producer price Of cash crops from the uplands-~Equation 7.4a (Ps/ton). ((t) = TLCRU(t)*CSTHCU(t-DT)!(1 + DTiRCST) (6.29b) ACCRU 8 accounting cost of cash crOps in the uplands (PS/year) CSTHCU 8 the average cost of cash crops in the uplands (Ps/ha-year) RCST 8 the rate of increase in farm costs (proportion/year). Next, the model generates costs and revenues from Operating costs have been computed separately for sure lands and for the herd. The Operating cost of the traditional and in the modern sectors are com- Equations 6.30 below. Equation 6.30a is flexible 1t::for- the seasonally flooded lands in TTGLR where rely that expenditures on range management are kept mum o = TTGLR(t)*CLNDT(t)!C267 (6.30a) 165 where: (WMNT 8 total operating costs of traditional grass— lands (Ps/year) TTMR 8 total traditional grazing land in the Costa --Equation 5.51 (hectares) CLMW 8 average operating cost of traditional grass- lands (Ps/ha—year) C267 8 a model parameter controlling total land where costs are incurred (proportion). Emunion 6.30b has been designed with enough flexi— bility to be used in any alternative. If the model is supplied with data for alternatives 1 and 2, this equation reduces to TLMOD(t)*CLNDM(t). Maintenance costs in lands in transition (TRSL) are included in establishment costs. OPCLNM(t) = TLMOD(t)*CLNDM(t)*(l — CPLF(t)) + TLF(t-DT)* CSRFGH(t) + TLF(t)*CSHARV(t)*0253&C254 (6.300) vhere: OPCLNM 8 Operating cost of modern grasslands (PS/year) TIWKN) 8 total land in modern grazing--Equation 5.49 (hectares) CIJHJM 8 the average operating cost of modern grass- lands (Ps/ha-year) (ZPIJ? 8 proportion of modern land in forage crOps-- Equation 6.9f 111R 8 total land in forage crOps--Equation 6.9g (hectares) CSRFGH 8 cost Of replanting and growing forages (Ps/ha—year) CSHARV 8 cost of harvesting and storing forage (Ps/ton) 0253 8 a parameter determining the yield of forages per cutting (ts/ha-cutting) 166 0254 8 a parameter determining the number of cuttings per year. Operating costs per animal include labor, drugs, supplemental feed if any, and other miscellaneous costs. The computation of total animal costs for the traditional and modern sector use the same structure shown in Equation 6.31 for the traditional herd. OPCLAT(t) = TOPOPT(t)*CSTANT(t) (6.31) where: OPCLAT 8 operating costs Of traditional cattle (PS/year) TOPOPT 8 total cattle population in the tradi— tional sector (animals) CSTANT 8 average operating cost of traditional animals (Ps/animal-year). Another component Of operating costs is depreciation of grazing land capital and equipment, as well as taxes on land. Because of lack of data on initial capital stock, the model simplifies the computation of replacement investments in cattle production by a lump annual sum per unit of land in production. Total value of depreciation is determined exogenously in Equation 6.32 for the traditional and modern sectors. The value of EQLM varies with the alternative chosenl/ and the corresponding value (EQLT) for the tradi- tional sector is adjusted by a model parameter to account for the flooded grasslands (see discussion for Equation 6.30s l/EQLM - 110; 120; 150; 170 Ps/ha-year for alter- natives l, 2, 3 and 4, respectively. ‘F*===il n ‘t ,fiiu. 167 ). The value of the property tax is based on the sed capitalized value of land (see Chapter 2) and is ted in Equation 6.33 for the traditional sector. M(t) = TLMOD(t)*EQLM (6.32) CAPDEM modern sector replacement investment in grasslands and equipment (PS/year) EQLM 8 capital costs for modern cattle produc- tion (Ps/ha—year) TLMOD 8 as defined in Equation 6.30b (hectares). 1(t) = VLANDT(t—DT)*TAXLND*C248 (6.33) VLDTXT 8 value of taxes on land in traditional cattle production (Ps/year) VLANDT 8 capitalized asset value of land in traditional cattle production--Equation 9.15 (Pesos) TAXLND 8 the land tax rate (proportion/year) 0248 8 a model parameter adjusting the capitalized value of land to the cadastral (assessed) value (proportion). Finally, the special taxes on cattle discussed in er 2 are computed in Equations 6.34. The general inven— ;ax (TAXC3) based on the net investment on cattle has ;he most difficult to estimate because of the complexity 'ed in the accounting of assets and liabilities of 's. This problem was circumvented in the model by ,ng a constant tax rate per animal estimated from 168 1/ :try of Agriculture sources.— The assessed liveweight 'ate (PKGR) used in computing the selective inventory TAXCl) was recorded from values set by the government een 1967 and 19702/ and then extrapolated by means Of a IE function [52]. These computations for the traditional (r are shown below: ‘l(t) = (PMGT(t)*C222 + PMPT(t))*0223*PKGR (6.34a) TAXCTl 8 the traditional cattle selective inventory tax (PS/year) PMGT, PMPT 8 traditional growing and producing male population, respectively--Equation 6.11 (animals) PKGR 8 the assessed liveweight tax rate (Ps/ kilogram-year) C222 8 a model parameter determining the prOportion of growing males over one year of age (proportion) C223 8 the animal-liveweight conversion factor (kilograms/animal). ‘2(t) = FEMSCT(t)§C277§SFTAX + SLSMLT(t)*C278*SMTAX (6.34b) TAXCT2 8 the traditional cattle export and consump- tion tax (PS/year) 1-/Ministerio de Agricultura, "Estudio Sobre la Renta ntiva." Bogota, 1971, pp. 1-111. (Mimeographed.) g/The Ministry Of Agriculture sets the liveweight at the end of each fiscal year. The values used in the were obtained by personal information. H. 169 FEMSCT 8 total traditional females sold for con- sumption (animals/year) SLSMLT 8 total traditional adult males sold for consump- tion (animals/year) SFTAX 8 the female consumption tax rate (PS/animal) SMTAX 8 the male consumption tax rate (PS/animal) 0277 8 a model parameter determining the proportion of females sold for immediate consumption (proportion) C278 8 a model parameter determining the proportion of males sold for immediate consumption (proportion). Equation 6.34b above needs a further discussion. First, it implies that the tax on animals sold for export is paid by the producer. The coefficients C277 and C278 intro- duce flexibility into the model to determine those animals which are sold to be finished in other regions and whose tax is not paid by the Costa producers. The variable FEMSCT includes cows with reproductive problems, Old cows, fertile cows and heifers; and SLSMLT includes finished steers and males sold out of the producing cohort. TAXCT3(t) = TOPOPT(t)*C279 (6.34c) where: TAXCT3 8 the traditional general inventory tax (Ps/year) TOPOPT 8 total traditional cattle population (animals) C279 8 the estimated general inventory tax rate (Ps/animal-year). After computing operating costs, the model generates tune cost of establishing any modern alternative at market 170 factor prices, and then determines the actual cash outlays made by farmers. Equation 6.35 is a composite Of costs of improving native and artificial pastures, planting artificial pastures and forages, and building storage for forages. It is clear that not all these costs apply to every alternative; a subprogram in the model assigns the relevant costs to each alternative, and in addition, computes costs per unit of land weighted by the proportion of land in artificial pastures and forages. TEC(t) 8 CSIMNP(t) + CSIMAP(t) + CSPLAP(t) + CSPLFG(t) + CSTGH(t) (6.35) where: TEC 8 total establishment costs at market prices (PS/ha) CSIMNP 8 average cost of improving native pastures (Ps/ha)l/ CSIMAP 8 average cost of improving artificial pastures (Ps/ha)l CSPLAP 8 average cost of substituting artificial for native pastures (Ps/ha)_/ CSPLFG 8 average cost Of establishing forage crOps (Ps/ha)l CSTGH 8 average cost of building forage storage (Ps/ha)l 1/ - For a detailed computation of these variables see subroutine MODCRD in the Appendix. ’ 171 Equation 6.35 above is the general approach of account- ing for establishment expenditures at their Opportunity cost. Yet some of the inputs required can be supplied on the farm at no extra cash expense, decreasing the need for the use of credit and/or savings. Examples of these inputs are family labor, materials for fencing and building, existing tools and equipment, etc. The function ALPHl computed in Equation 6.36 is an attempt to simulate the response of farmers' behavior to changing profitabilities. This behavior includes changing attitudes toward work and leisure, a more efficient use of the inputs at hand (including management), and incentive to utilize more fully the farm natural resources. As shown in Figure 11.7 ALPHl depends on a profitability threshold (C235) below which there is no incentive for farmers to use their resources intensively. As profitability increases, farmers exploit their resources more fully until they reach a limit (0234) where it is assumed that the ability to use on—farm resources has been exhausted. A parameter (C236) determines how rapidly the attitudes change with changes in the pro- fitability criterion. It is clear that a wide range of farmers' behavior can be simulated by appropriately assigning values to these three parameters. ALPH1(t) 8 C234 + min[l - C234, (1 — C234)*EXP(-C236* (PDR(t-DT) - 0235))1 . (6.36) 172 ALPHl(t) ‘I 1.0 I I 026 I /-—- 3 I I I I C234—““"I“'" “ _____ I A, 0 0235 PDR(t) FIGURE 11.7. where: ALPHl PDR C234 C235 C236 min[a,b] EXP The on-farm resource use response function. outlays for establishing an alternative (proportion) 8 the relative profitability differential of Equation 5.30 (dimensionless) a model parameter determining the minimum prOportion of establishment costs met with outside resources (prOportion) the on-farm resources intensity of use response threshold (dimensionless) less) the minimum value between a and b 8 the exponential function. a variable which introduces the effect of the profitability criterion PDR upon total the rate of on-farm resource use response with respect to profitability (dimension- 173 Finally, the total cash requirements for establishing an alternative, TCEC, are simply computed as: TCEC(t) = TEC(t)*ALPHl(t) (6.37) Given the components that enter in the formation of cattle production costs, it is possible to generate the accounting costs, ACLA. Equation 6.38 makes this computation for the modern sector. Accounting costs in the traditional sector are computed using only the first five terms of Equation 6.38 below. ACLAM(t) = OPCLAM(t) + OPCLNM(t) + CAPDEM(t) + VLDTXM(t) + TAXCM(t) + ALINT(t) + ALREP(t) + (TCEC(t) - TRSL(t) ALPHl(t)*CSTGH(t))*3*XDEL + RPCAPT(t) (6.38) where: ACLAM 8 total accounting costs of modern sector (PS/year) OPCLAM 8 operating costs of modern cattle--Equation 6.31 (Ps/year) OPCLNM 8 as defined in Equation 6.30b (PS/year) CAPDEM 8 as defined in Equation 6.32 (Ps/year) VLDTXM 8 value Of taxes on land in modern cattle production—-Equation 6.33 (PS/year) TAXCM 8 total special taxes on modern cattle (TAXCM(t) = i TAXCMi(t))--Equations 6.34 (PS/year) 1-1 ALINT 8 interest payments on development credit-- Equation 9.8b (PS/year) 174 ALREP 8 repayment of development credits-~Equation 9.5 (PS/year) CSTGH 8 cost Of building forage storage (Ps/ha) TCEC 8 total cash establishment costs--Equation 6.37 (PS/ha) ALPHl 8 as defined in Equation 6.36 (prOportion) TRSL 8 total land in transition from traditional to modern practices--Equation 5.46 (hectares) 3*XDEL 8 time required to complete a land improvement program--Equations 5.43 (years) RPCAPT 8 the rate farmers' cost is increased by execution Of additional storage capacity (PS/year). Accounting revenues from cattle are computed from sales of milk and animals and increased by any direct subsidy paid to farmers. In the modern sector, revenues are also increased by the payment of development loans, but due to the difficulty in allocating commercial loans between the two sectors, these are computed in a more aggregated accounting in Chapter 9. Since the market model only generates the price of finished males, the pricing of other animals sold is com- puted as a prOportion of the price of finished males. This computation is done with a set of coefficients estimated from time series recorded by the Central Bank and published by Garcia Samper [23]. Equations 6.39 show the computation of revenues from the modern sector. SLSPM(t) a (SLSCCM(t) + SOLDFM(t))*C224 + SLSMPM(t)*C225 + SLSMFM(t) + SLFERM(t)uC226 + SLSFGM(t)*C227 + SLSMGM(t)§C228 (6.39a) 175 YAM(t) = PAP(t)*SLSPM(t) (6.39b) where: SLSPM 8 sales from the modern sector weighted by price relationships (i.e., finished males equivalents) (animals/year) SLSCCM, SOLDFM, SLFERM 8 as defined in Equation 6.24 (animals/year) SLSMPM, SLSMFM 8 as defined in Equation 6.24 (animals/year) SLSFGM, SLSMGM 8 as defined in Equation 6.24 (animals/year) YAM 8 income from sales of modern animals (PS/year) PAP 8 producer price of finished males--Equation 7.3 (PS/animal) C224, ..., 0228 8 model parameters determining price relation- ships between finished males and other sale groups (proportion) ARLAM(t) 8 YAM(t) + YMM(t) + ALOAN(t) + AGSUM(t) (6.390) where: ARLAM 8 accounting revenues from modern cattle (PS/year) YMM 8 income from milk in the modern sector (PS/year) ALOAN 8 credits paid for farm deve10pment——Equation 9.4a (PS/year) AGSUM 8 subsidies paid to the modern sector (PS/year). Accounting costs and revenues provide estimates at a given point in time. Yet when farmers are considering the adOption of a new method they require an estimate of the future stream of revenues and costs throughout a relevant 176 planning horizon. These projections into the future and within a planning horizon are simulated in the model by assigning a weight to each year and for each accounting variable involved based on farmers' past experience and on their judgment about changes brought about by the new methods. As can be expected, each alternative produces a set Of dif— ferent expectations and therefore the above—mentioned weights vary accordingly (see Table 11.4). Expected revenues and costs are computed in Equations 6.40 below: CSTANTL(t)*CA1NCRi EOPCLM(t) 8 GRE (6.40a) EOCLNMi(t) 8 CLNDTL(t)*CL1NCR1 (6.400) EVLDTX1(t) 8 VLTXTL(t)*VLTXTP1 (6.400) ETXCi(t) 8 TAXCTL(t)*TXCPi (6.40d) ECADEMi(t) 8 EQLT*C268*CAPDTPi (6.40e) where: EOPCLM 8 expected Operating costs of modern cattle (Ps/ha-year) EOCLNM 8 expected Operating costs Of modern grasslands (Ps/ha-year) EVLDTX 8 expected taxes on land in modern cattle production (Ps/ha—year) ETXC 8 expected special taxes on modern cattle (Ps/ha-year) ECADEM 8 expected capital depreciation in the modern sector (Ps/ha-year) CSTANTL the exponential average Of traditional animals cost (Ps/animal-year) 177 Hmsmozv coanom wsHCCMHm .sm.m sm.m sm.m sm.m sm.m sm.m s:.m sm.H : wm.m nm.m 8m.m sm.m mm.m sm.m 8:.m um.H m as.H us.H as.H us.H s8.H mm.H s:.H 8:.H m sm.H wm.H N©.H mm.H um.H 8m.H mz.H mz.H H no>HpmchmuH¢ mpmoo HmEHcm CH mmcmco moCHEEOpOO Awo:.mvmozHHpmchpH< popcc>cH CH owcmno mocHEsmpoc Homm.mvmosz :.m :.m :.N :.m :.m z.m mm.H w.H mm.m mm.m mm.m mm.m mm.m mm.m m.H 8m. 8m.H 8m.H sm.H 8m.H 8m.H mm.H mm.H mm.H :.H :.H :.H z.H :.H mm.H mm.H mm.H uc>HpmchpH¢ poopso xHHE CH owcmno nooasaoooo Aomm.mvm02Hze : a z a a : w.m m.H H H z z z a z : >.m m.H H H m.m m.m m.m m.m m.m m:.m ~.H mH.H H H m m m m m m.H m:.H mH.H H H nm>HpmcpmuH< mmHmm CH omcmno nanosecooe Aomm.mvmosz NH HH on a w a a m A.oz .como coaoacauoo noucemnmm .cONHsom wchcmHm on» wcHszo mpnoo can usgpso oprmo CH mmwcmco Um>Hoonwm .:.HH mamae (continued) TABLE 11.4. Ln N N mmmN com-3::- ma-xoxo Ln :rm H o o o o o o o o o o o o HHNN HHHH HHNN Nmmm Ln N H mmmN mmzzr mzxoxo Ln :rm H o o o I o o o o o HHNN HHHt—I HHNN Nmmm in N o mmmN coma-:1- makoxo In :00 H 0 o o o 0 o o o o o o o HHNN HHHH HHNN Nmmm Ln N mmmN com-3: m.:r\o\o Ln :rm HHNN HHHH HHNN Nmmm A m In N (:3 mmmN coma—:1- mzxoxo L0 :06 g HHNN HHHH HHNN Nmmm v Ln N «31‘ c: :rsz mmzzr meoxo L0 :00 g HHNN HHHH HHNN Nmmm H bkolnm 8 :ranN NNmm Nmmm Ln :rm m HHNN HHHH HHNN Nmmm on Ln N mm 5 :r:r oo NNmm Non-:rzr Ln :rm 5% HHNH HHHH HHNN Nmmm cc mm in N Hm E NNooxo NNmm Nm moo 0\ HHHH HHHH HHNN NNMN LnLn ox NNKO-‘J meoxo meow HHHH HHHH HHHH NNNN \ommm MN HNmm LOKO m HHHH HHHH HHHH HHNH HHHH HH HHHH HHHH HHHH HHHH <1) 60 K $2 I (d (d Cd 43 .C H m m can mo 0) (D13 CD (Dd) ,‘ C c:: aux C A H q-Ict) (Dcd w-IQ. c; EmHNmzeHHNmerJ-lHNmzea) HNm.:r hp :4 H £413 2 m w 044 E¢v m . 480" 4.30.. L4H" 49H .. £2 (DOG) (D (D (1)-PG) (I)!!! (1) c‘ 'U :> 'U 90> U43 .‘> UH 23H (Dec-H H -H 31 H.515 r881» 1304: n.9, +3 .00“) cases Cd 0:0 «3 I: OHS: o>c1 fife-cc: 00 $3 $40 :1” S4 81' n “cos. :1- $4 0H °C® one o o .g m \O-r-I-P \O-H-P :ralp \D'H +3 ”4" v H v H .gH V H 0? mw< cum< mH< cum < 5,, Dim e«w ~Im e w $40.4 ZS ><£2 04> D2153 <60 th BR) 0 D-ICUO I146: >111: an >0 E-I-r-I 0043 [42] and initial guesstimates and model tuning. Source 179 GRE 8 equilibrium grazing rate (has/animal) CLNDTL 8 the exponential average of traditional grasslands cost (Ps/ha-year) VLTXTL 8 the exponential average of traditional land tax (Ps/ha-year) TAXCTL 8 the exponential average of special taxes on traditional cattle (Ps/ha-year) EQLT*C268 8 capital costs for traditional cattle production--Equation 6.32 (Ps/ha-year) CAINCR 8 the expected increase in animal costs (dimensionless) CLINCR 8 the expected increase in range management costs (dimensionless) VLTXTP 8 the expected increase in land tax (dimensionless) CAPDTP 8 the expected increase in capital costs (dimensionless) i 8 indexes the planning horizon-—i8l, ..., n. Expected establishment costs are computed as equal allotments during the years required to complete a modern alternative. A model subprogram allocates these values among the relevant years in the planning horizon. as...) = ass-Is (6....) where: ETCEC 8 expected cash establishment costs of a modern alternative (Ps/ha-year) TCEC as defined in Equation 6.37 (Ps/ha) 3*XDEL time required to com lete a modern alter- native--Equations 5. 3 (years). The expected debt service, EDBSER, is generated by Elandel subprogram that first computes interests paid on the 180 entire credit received for development during the period of establishment and the grace period. Next the subprogram computes repayments in equal allotments during the repayment period, and charges interest on the unpaid balances. Finally, the expected credits paid are computed in a way similar to ETCEC above. The time during which credits are paid (LTl) may not necessarily be equal to the time required to establish the alternative (3*XDEL). A model subprogram allocates these values among the relevant years in the planning horizon. ELOAN(t) = TCEC(t)?(l - RPTN) (6.40g) LTl where: ELOAN 8 expected credits paid for farm deve10pment (Ps/ha-year) RPTN 8 farmers' participation of total establish- ment costs-8a policy variable (0 i RPTN i l) (proportion) LTl 8 time deve10pment loans are paid—-a policy variable (years). Table 11.5 at the end of this chapter shows the values Of a selected number of variables used in component AGPRAC. 181 TABLE 11.5. Selected Coefficients and Initial Values in the Agricultural Production Component (AGPRAC). Definition (Equation NO.) Value DYLDCU Determines target yield of cash 1.65 (6.2a) crOps in the uplands (tons/ha— year) DYLDCL Determines target yield of cash 2.9 (6.2b) crops in the lowlands (tons/ ha-year) DYLDFC Determines target yield of food 10 (6.20) crOps (tons/ha-year) GRE Determines the equilibrium graz- .74 (6.4b) ing rate (has/animal) CPLPT Determines proportion of .45 (6.5) artificial grasses TDNSG Determines the average TDN from .2343 (6.90) sorghum silage (tons TDN/ton silage) TDND Determines target yield Of TDN 7.4 (6.9f) of grazing lands (tons/ha-year) NREQT Determines appropriate tradi- .55 (6.22a) tional animal nutritional re- quirements (animal-year/ton TDN) ELASI Determines income elasticity of .6 (6.26b) demand for beef UNEXPL Determines illegal cattle exports 300,000 (6.260) (animals/year) COSTFT Determines cost of treatment 4.5 (6.28a) against foot-and-mouth disease (Ps/animal-year) TAXLAND Determines the land tax rate .0042 (6.33) (proportion/year) SFTAX Determines the female consumption 100 (6.34b) tax rate (PS/animal) SMTAX Determines the male consumption 50 (6.340) tax rate (PS/animal) CT202 Determines number of traditional .4 (6.21b) lactating cows milked (prOportiOd TABLE 11.5. ( 182 continued) Definition (Equation NO.) Value 0217 Determines rate of growth of non- .0285 (6.25a) Costa cattle population (prOportion/year) C223 Determines the liveweight tax 4.0 (6.34a) rate (kilograms/animal-year) 0224 Determines price relationship .68 (6.39a) between finished males and cull cows (proportion) C225 Determines price relationship .92 (6.39a) between finished and producing males (proportion) 0226 Determines price relationship .88 (6.39a) between finished males and breeding cows (proportion) C227 Determines price relationship .42 (6.39a) between finished males and growing females (proportion) C228 Determines price relationship .535 (6.39a) between finished and growing males (proportion) C237 Determines the rate of increase .1 (6.26b) in income (proportion/year) C238 Determines the rate of increase .032 (6.26b) in population (prOportion/year) C242 Determines proportion of heifers .05 (6.27a) treated against brucellosis without campaign (prOportion) C244 Determines proportion Of animals .3 (6.28b) treated against foot-and-mouth without campaign (proportion) 0248 Determines the assessed value of .5 (6.33) land (proportion) C250 Determines the relationship 1.2 (6.90) between green forage and silage (tons forage/ton silage) 0253 Determines yield of green 40 (6.90) forages (tons/ha-cutting) 183 TABLE 11.5. (continued) Definition (Equation NO.) Value 0254 Determines the number Of forage 3 (6.90) cuttings (cuttings/year) C279 Determines the cattle general 3.1 (6.340) inventory tax rate (PS/animal- year) CSTHCU(O) Initial cost of cash crops in 560 (6.29b) the uplands (Ps/ha-year) CSTHCL(0) Initial cost of cash crops in 1,097 (6.29b) the lowlands (Ps/ha-year) CSTHFC(0) Initial cost of food crops 723 (6.29b) (Ps/ha-year) CLNDT(0) Initial Operating cost Of 35.8 (6.30a) traditional grazing lands (Ps/ha-year) CLNDM(0) Initial Operating cost of modern 100 (6.30a) grazing lands (Ps/ha-year) CSRFGH(0) Initial cost of replanting and 570 (6.300) growing forages (Ps/ha-year) CSHARV(0) Initial cost of harvesting and 6.25 (6.30b) storing forages (Ps/ton) CSTANT(0) Initial operating cost of 24.14 (6.31) traditional animals (PS/animal- year) CSTANM(0) Initial operating cost of modern 56 (6.31) animals (Ps/animal—year) CSINPH(0) Initial cost of improving native 576 (6.35) grasses (PS/ha) CSIAPH(0) Initial cost of improving 576 (6.35) artificial grasses (Ps/ha) CSPAPH(0) Initial cost of planting 660 (6.35) artificial grasses (Ps/ha) CSPFGH(0) Initial cost of establishing 590 (6.35) forage crops (Ps/ha) CBSTG(0) Initial cost gf building forage 8.5 (6.35) storage (Ps/m ) Source: [19, 20, 23, 31, 32, 33, 42, 57, 58, 61, 66, 69, 71] and initial guesstimates and model tuning. CHAPTER 7 PRICE GENERATION (PG) Component PG generates world prices for beef and market and producer prices of cattle, cash crops and food crops. In addition, five-year exponential averages of the producer prices are computed for use by component LAMDAC in the profitability calculations for the land allocation decisions. Export and Market Price of Cattle Colombian beef exports have been mainly live cattle, but the government has announced plans to export only dressed animals after 1974. The model assumes this change in export policy will be effectively implemented, and that the relevant world price is for frozen carcass beef. For the period 1964 tO 1974, prices are for a composite of live animals and frozen carcass beef. Although it has been assumed that exports will continue in the form of frozen carcass beef, it is clear that the export price will be modified if chilled, refrigerated or processed beef is exported. All export pumices are given as live animal prices; carcass beef is con— ‘verted to live animals by use of the factor 4.3 which is the runnber of animals to produce a metric ton of carcass. World pricesrfor cattle (U. S. $/anima1) are exogenously generated 184 185 by Equation 7.1 below. Since cattle exports were negligible until 1964, no world prices are generated for the years prior to 1964. For the period 1964 to 1970, prices are a composite of live animals and carcass beef as reported by Sarmiento [63]. From 1971 to 1974 these composite prices are projections based on Instituto de Comercio Exterior (INCOMEX) estimates [35]. Finally, after 1974 world prices are projected assuming various trends that will be discussed in Chapter 12. '0 O < t < 4 117, 115.5, 127, 147, 164, 163.5, 174 u i t i 10 WPB(t) a JWPB1970*(1+WPBR*(t-10) 10 < t 1 1n WPBC§t) t > 1“ (7.1a) WPBC(t) = WPBCl970*(l+WPBCR*(t-10)) (7.1b) where: WPB = world (FOB) price of beef (US$/anima1) WPBC world (FOB) price of frozen carcass beef (US$/metric ton) WPBl970, WPBCl97O = recorded world price in 1970 WPBR a rate of change of world price after 1970 as a proportion of 1970 price. This is a composite of live animals and frozen carcass beef (proportion/year) WPBCR = rate of change of world price of frozen carcass beef after 1970 as a prOportion of 1970 price (proportion/year) d ll simulated time (t=O is l960)--years. 186 The market price of cattle is computed in Equation 7.2. This equation, which generates the market price of finished males as a function of excess demand, is derived directly from the definition of demand price elasticity e: was where Aq = qt - qt-DT and Ap = pt — pt-DT and the ratios are taken relative to the initial price and quantity 1/ demanded, pt-DT and qt-DT'— PA(t) = PA(t-DT) + DT*C219*PA(t-DT)* (TDEM(t-DT) - SUPB(t-DT)) (7 2) ELASD*TDEM(t-DT) ° where: PA = market price of finished males (PS/animal) SUPB = total Colombian supply of beef--Equation 6.25b (animals/year) TDEM = total demand for Colombian beef-—Equation 6.26c (animals/year) ELASD = price elasticity of demand for beef 0219 = a model parameter regulating the beef price response to excess demand (prOportion/year). Equation 7.2 assumes that the target change in (quantity, Aq, will be the excess demand in the previous period and that the equilibrium price will not necessarily ‘be reached in one period, i.e., if DTflC219 < l. The l/For a detailed derivation see Abkin [1, Chapter 5]. 187 domestic price of other types of cattle are computed as a constant proportion of the price of finished males. This computation will be more fully discussed in Chapter 9. Producer Prices and Price Averages Next, producer prices are computed for cattle and crops. Producer prices of crops are exogenously determined and are a composite of the major crops grown in each of the agricultural regions. The model assumes a constant profit margin in the production of crops and projects increases in their prices over time at the same rate that costs of production are increased. Producer prices in the base year (1960) are as reported by the World Bank report on Colombia [41] and weighted by total yields in each agricultural region. Weights are derived from hectares in production reported by DANE [15] and average yields reported by the Instituto de Mercadeo Agropecuario (IDEMA)l/ as shown in Table II.6. Further, it is assumed that 50 percent of the harvested yield of sesame and sorghum, 35 percent of that of cotton, 2/ and 70 percent of that of corn come from the uplands.— l'/IDEMA. Informe Sobre la Superficie Sembrada, Produccion Total y Rendimientos 83:103 Productos Basicos. Bogota. Mayo 1971. Unpublished Report. g/Weights used for 1960 are, for cash crops in the lowlaxfls: sesame--.Ol2; cotton--.l3; corn--.24; sorghum-- .015; and rice--.6. For cash crops in the uplands: sesame-- .018; cotton—-.ll; corn--.85; and sorghum--.O23. For food crOp: plantain--.48; and cassava--.52. 188 TABLE II.6. Land in CrOps, Average Yields and Producer Prices in the Costa, 1960. CrOp Area Planted Average Yield Price (has) (tons/ha-yr) (Ps/ton) Sesame 9,787 .59 1,519 Cotton 38,815 1.34 1,726 Corn ' 162,806 1.19 47M Sorghum 2,952 2.46 369 Rice 74,588 1.98 883 Plantain 48,578 8.38 224 Cassava 53,243 8,28 303 Source: As indicated above. Price of sorghum was obtained by personal information. The producer price of cattle is computed in Equation 7.3 below: PAP(t) = PA(t)*(1-MKM) (7.3) where: PAP a producer price of finished males (PS/animal) MKM = the marketing margin for cattle--proportion (O i MKM i 1) (see Table III.2). Because of a lack of information on producer price changes, crop prices are increased over time at the same rate of increase in farm costs (RCST). The assumption of ‘maJJTtaining a constant profit margin throughout the la - A\\ \a 9 w 189 simulation is an accounting simplification that finds justification in the secondary concern of the model on the crop subsector. Equations 7.4 compute these prices as: PCROPU(t+DT) = PCROPU(t)*(1+DT*RCST) (7.4a) PCROPL(t+DT) = PCROPL(t)*(1+DT*RCST) (7.4b) PFCROP(t+DT) = PFCROP(t)*(l+DT*RCST) (7.4c) where: PCROPU = the projected producer price of cash crops from uplands (Ps/ton) PCROPL = the projected producer price of cash crops from lowlands (Ps/ton) PFCROP = the projected producer price of food crOps (Ps/ton) RCST = the rate of increase in farm costs (proportion/year). Exponential price averages are computed in Equations 7.5 for use in determining land allocation decisions (Equations 5.35a and 5.35e). EPAP(t-DT) + 5%%§*(PAP(t-DT) - EPAP(t-DT)) (7.5a) EPAP(t) EPCRPU(t) = EPCRPU(t-DT) + 5%%7*(PCROPU(t—DT) - EPCRPU(t-DT)) (7.5b) where: EPAP = exponential average of finished males producer price (PS/animal) EPCRPU = exponential average of upland cash crOps producer price (Ps/ton) 190 DEL5, DEL? = averaging lags (years). The values of a selected number of variables used in generating prices are shown in Table 11.7. 191 TABLE II.7. Selected Coefficients and Initial Values in the Price Generation Component (PG). Definition (Equation No.) Value WPBR Determines change in live-carcass .1lg/ (7.1a) world price of beef (proportion of 1970 price/year) WPBCR Determines change in frozen .3842/ (7.1b) carcass world price of beef (proportion of 1970 price/year) ELASD Price elasticity of demand for 1 .7 (7.2) beef (dimensionless) RCST Determines change in cost of .12 (7.4) farm inputs (proportion/year) SUPB(0) Initial total Colombian beef 1,889,100 (7.2) supply (animals/year) TDEM(0) Initial total demand for 1,889,100 (7.2) Colombian beef (animals/year) PCROPU(0) Initial composite price of cash 630 (7.4a) crops in uplands (PS/m.ton) PCROPL(0) Initial composite price of cash 892 (7.4b) crops in lowlands (PS/m.ton) PFCROP(0) Initial composite price of food 265 (7.40) crOps (PS/m.ton) PA(0) Initial price of finished males 1,067 (7.2) (PS/animal) WPBC1970 World price for Colombian 588 (7.1b) carcass beef in 1970 (dollars/ m.ton) a/ Based on price increase between 1964 and 1968 and assuming a gradual increasing weight of carcass beef on exports from 1970 to 1974. b/ Based on 1970 price reported by Sarmiento [63], and on 1972 price (US$1040/m.ton) reported by El Espectador, Bogota, April 8, 1973. Sources: [15, 16, 23, 41, 63, 67] CHAPTER 8 POLICIES FOR THE CATTLE INDUSTRY In a policy-oriented model, there are a number of places in which the policy maker can interact with researchers to perform experiments in a simulated system. These experi- ments may involve changing system parameters and technological coefficients to see the effect on the model's performance or direct policy experimentation. In the latter course, policies and programs are specified explicitly and the con— sequences are simulated as a result of the system structure of the model. When experimenting with different values of system parameters and/or technological coefficients, the policy maker acquires a better judgment about those parameters to which the model is insensitive and about those which sig- nificantly affect the system performance and therefore would play a role in future policy and planning decisions. .In addition, technological research may be suggested by policy runs speculating on the likely consequences of the introduc- tion of an innovation which may not actually be developed at the moment. Simulation runs testing parameter sensitivity and conducting direct policy experiments are discussed in Chapters 11 and 12. 192 193 Policies Three basic policy strategy alternatives are structured in the simulation model. Others could be added, but the three included seemed to be quite relevant for Colombian policy makers at the time the model was defined. Policies may be set and experimented with in any one or a combination of the following: production campaigns, tax policies and export policies. Production Campaigns Production campaigns make up the first class of policies which may be investigated. Promotion efforts aimed at moderniz- ing cattle production can generate substantial returns to both the public and private sectors. Such modernization may entail the introduction of better animal husbandry and/or the encourage- ment of improved pasture management practices and increased fodder production. The increase in output can then result in higher incomes for the farmers, increased availability of basic food for the mass of the p0pu1ation, and increased tax revenues and foreign exchange earnings for the public sector. The nonagricultural sector, though not modeled, could be expected to grow also as a result of increased demands from the agricultural sector. Associated with this modernization effort is a policy of providing credit for farm improvements at special lending terms. These terms include the interest rate, the grace period, the repayment schedule, the farmers' participation 194 on the total cost of the project and the technical assistance. Both the promotional information units discussed in component LAMDAC, Equation 5.37, and the funds for development credit discussed in component CRTACC, Equation 9.4, are generated exogenously using a promotion and credit allocation routine. The maximum yearly size (VMAX) and time spans (T0, T1, T2, TF) of these services may be specified by the experimenter, and the model generates the time profile shown in Figure 11.8.l/ Other policies related to production campaigns are the control of foot-and-mouth disease and brucellosis, and the improvement of crOp production. Currently, the model takes the disease control campaign as aimed toward the -traditional sector since it is assumed that animals in the modern sector are apprOpriately treated as part of the im- proved husbandry adopted. As indicated in Chapter 6 (Equations 6.27 and 6.28), animals are treated regardless of their pro- fitability. All that is required is an exogenous rate of vaccination (C199 and C200) which can be set as a policy. Government expenditures in the Costa on control of foot-and- mouth disease, EXPAFT in Equation 6.28a, are generated using a TABLIE function which steps up these expenditures from the years preceding the campaign until they reach a maximum, and 1/ ‘— This routine has been adapted from the moderniza— tion budget executive routine used in the simulation of the Nigerian Agricultural Economy [53]. 195 Campaign promotion (units/yr.) Credit funding (Ps/yr.) (i VMAX —---- tTime N—-———- TF FIGURE 11.8. Promotion and development credit profile. then the function projects them at a constant annual rate. The values used in this function have been approximated from estimates supplied by ICA [31] and show the more limited efforts that have been achieved in this program prior to 1971. The profile generated by the table function could be changed to one similar to Figure 11.8 as part of the policy experiments. Finally, the improvement of crops is determined exogenously using the simple model described by Equations 6.2, and its effect is measured in the land allocation decisions and in farm income. Taxes The second major policy which can be investigated with the model is the area of taxing policies. Taxes are 196 levied on net income, net worth, cattle, and land, but we are concerned here only with the last two categories. Taxes on cattle as described in Chapter 2 affect the cost of production and therefore cause adjustments in the use of factors of production that affect farm income and output. Taxes on land are amortized to its value and therefore de- crease the asset position of farmers. Yet both cattle and land taxes are a main source of government revenue. With simulation runs incorporating different levels of tax rates for both cattle and land, questions can be answered regarding the likely consequences these policies will have on produc- tion levels, agricultural income, and other relevant economic performance criteria. Export Policies Finally, the model allows experimentation with several kinds of export policies aimed at generating foreign earnings and regulating domestic supply. Specifically, targets can be set on cattle exports, exchange rates can be varied and subsidies can be paid to exporters. In addition to these, the effects of different levels of exports on domestic con- sumption and price can be investigated and their consequences projected. Further, the value of transfers from public revenues to the private sector in the form of an export subsidy can also be examined. CHAPTER 9 ACCOUNTING AND PERFORMANCE CRITERIA (CRTACC) Component CRTACC completes the farm accounting and computes the performance indices used to evaluate the out— come of the cattle and crops policy experiments. Budget Accounting Given incomes, costs of crops and cattle and the rate of land improvment, it is possible to determine credit require- ments, debt service, investment constraints and farm income on a regional basis. The farm development budget is modeled dynamically as cash flows distributed over time. The develop- ment budget model is divided into three stages of varying lengths. The respective stage lengths reflect the three investment periods which the model identifies: a period of expenditures on farm improvements and credit payments, a grace period, and a period of credit repayments. These lengths (LT) are policy variables which allow testing the effect of different credit schemes. 9 The preceding budget flow is simulated dynamically by three calls to BOXC, a "boxcar train" subroutine, one for each stage. This subroutine is used to delay a flow for a considerable period of time, with no outflow until the delay time is over [21, 52]. The credit-investments period 197 uses 198 as an input the outlays required for farm improvement, net of farmers participation and use of private funds. These requirements are determined by the rate at which land enters modernization (Equation 5.41) and the establishment costs (Equation 6.37). The remaining stages use the output of the preceding stage as an input. Equations 9.1 describe this CALL CALL CALL process. BOXC(AUX5(t-DT), BOUTl(t), TRAINl, NCOUNl, NOCYl, LTl, SUMIN) (9.1a) BOXC(BOUTl(t-DT), BOUT2(t), TRAIN2, NCOUN2, NOCY2, LT2, SUMIN) _ (9.1b) BOXC(BOUT2(t-DT), BOUT3(t), TRAIN3, NCOUN3, NOCY3, LT3, SUMIN) (9.1c) where: AUX5 credit needs for land entering modernization (Ps/year)l BOUTl = rate credit investments leave the first stage (this is the output variable of the first call to BOXC) (Ps/year) BOUT2 = rate credit investments leave the second stage (this is the output variable of the second call to BOXC) (PS/year) BOUT3 = rate credit investments leave the third stage (PS/year) tLTl = time development loans are paid (years) 1/ — For detailed computation of this variable see subroutine MODCRD in the Appendix. 199 LT2 time after completing farm development adding up to the grace period (LTl + LT2) (years) LT3 = time deve10pment loans are repaid (years) TRAIN, NCOUN, NOCY, SUMIN = other variables associated with the use of the BOXC subroutine. The purpose of these calls to subroutines BOXC is to compute TRNSL(t), the sum of credit invested in each stage. These levels are computed in Equations 9.2 as time integrals of credit flow rates. TRNSLl(t+DT) = TRNSL1(t) + DT§(AUX5(t) - BOUT1(t) - TRAINl(3)*(1 - A5(t))) (9.2a) TRNSL2(t+DT) = TRNSL2(t) + DT*(BOUTl(t) - BOUT2(t)) (9.2b) TRNSL3(t+DT) = TRNSL3(t) + DT*(BOUT2(t) - BOUT3(t)) (9.2c) where: TRNSLl = total credits paid during the first stage-— the period when improvements are implemented (Ps) TRNSL2 = total credits completing the grace period (Ps) TRNSL3 = total credits that have to be repaid (Ps) AS the proportion of land remaining in the program after drOpouts--Equation 5.44. The term involving TRAIN1(3) in Equation 9.2a needs further explanation. The credit rates that flow through the first 200 BOXC delay are adjusted to allow for the possibility of "dropouts" from the program. This dropout effect is intro- duced by the variable A5 discussed in Chapter 5. Equation 9.2a also implies that dropouts, if any, occur during the first year after entering the modernization program. Development Credit Once the total outlays of investment credit are generated, it is easy to determine the credit constraints to development and the debt service. First the model com- putes demand for credit, DCRDT, and availability of credit, CREDT, which are used to determine the credit-based rate of modernization in component LAMDAC (ARMl in Equation 5.41). _ TRNSLl(t) DCRDT(t) - LTl (9.3) CREDT(t) = max(ACRDT(t) - DCRDT(t), 0) (9.4) where: DCRDT = demand for development credit from ranchers already in the program (PS/year) CREDT = credit available for additional modernization (PS/year) ACRDT = total credit allocated for modernization-—a policy variable (PS/year). Next, the model computes the debt service on develop- ment loans. As a simplification, the model assumes that all farmers entering a modernization program receive credit if it is available. This is to say that: ALOAN(t) = min(CREDT(t), DCRDT(t)) (9.4a) 201 = TRNSL3(t) ALREP(t) LT3 (9.5) ALOANA(t+DT) = ALOANA(t) + DT*ALOAN(t) (9.6) ALREPA(t+DT) = ALREPA(t) + DTfiALREP(t) (9.7) where: ALREP = development loans repaid (PS/year) ALOAN = development loans paid (PS/year) ALREPA = accumulated development loans repaid (Ps) ALOANA = accumulated development credits paid (Ps). Interests on development loans are charged on the outstanding debt balance. These are computed in Equations 9.8 below: DBTOUS(t) = ALOANA(t) - ALREPA(t) (9.8a) ALINT(t) = DBTOUS(t)*RINTL (9.8b) where: DBTOUS = the outstanding development debt (Ps) ALINT = interest payments on development loans (PS/year) RINTL = interest rate on development loans--a policy variable (proportion of debt/year). Commercial Credit Commercial credit as used in the model is short- term credit, usually for one year provided through the development and private banks under a variety of government- regulated schemes (see Chapter 1). Since interest charges 202 on these loans vary widely, the model uses an estimated average rate (RINTC) when computing the short-term debt service. As a general policy, short-term credit is supplied to cover operating costs and buying feeder cattle, although the latter use has been ruled out in the model (see Chapter 5, p» 109. Regional allocation of commercial credit by the banking system determines its availability to farmers and this could be an important constraint in the model. Yet lack of information on this matter led us to the simplifying assumption that the only constraint to the use of commercial credit was the farmers' capacity to provide an acceptable security. It is clear that with more information the alloca- tion of commercial credit by the banking system, ACRDTC, could beta policy variable. Equations 9.9 below compute the availability of commercial credit and its debt service. CRDAV(t) = min(PEQCR*EQPOS(t), ACRDTC(t)) (9.9a) EQPOS(t) = VLAND(t) + VACAPL(t) - CDEB(t) - DBTOUS(t) (9.9b) where: CRDAV = commercial credit available to the cattle sector-(Ps/year) EQPOS 8 equity position of cattle producers (Ps) ACRDTC a commercial credit allocated (Ps/year) PEQCR = proportion of equity which can be used as a credit base (proportion/year) VLAND = capitalized asset value of land in cattle production-~Equation 9.150 (Ps) VACAPL = value of cattle inventories--Equation 9.14 (Ps) CDEB DBTOUS 203 commercial debt of cattle sector-- Equation 9.9f (Ps) as defined in Equation 9.8a (Ps). Total demand for commercial credit, TDCRDC, is determined by the difference between net income from cattle, increased by internal transfers of capital from the crops sector, and the expenditures for consumption. TDCRDC(t) = maX(-FARIL(t) - C239*FARMIC(t) where: + EXPLIV(t), 0) (9.9c) TDCRDC = total demand for commercial credit by the ' cattle sector (PS/year) FARIL = aggregated net farm income from cattle—- Equation 9.10c (PS/year) FARMIC = aggregated net farm income from crops—- Equation 9.11 (PS/year) EXPLIV - aggregated consumption expenditures of cattle producers——Equation 9.12a (PS/year) C239 = a model parameter determining the proportion of income from crops internally transferred to cattle production. Commercial loans paid to cattle producers, CLOAN, are computed as: CLOAN(t) = min(CRDAV(t), TDCRDC(t)) (9.9a) CREP(t) The repayment of the commercial debt is computed as: = max(CREPR*CDEB(t-DT), 0) (9.9e) 204 where: CREP = commercial loans repaid (PS/year) CREPR = repayment rate (proportion of debt/year). Given credit payments and repayments, it is possible to compute the outstanding commercial debt and interest pay— ments by the cattle sector. CDEB(t+DT) = CDEB(t) + DT*(CLOAN(t) - CREP(t)) (9.9f) CINT(t) = RINTC*CDEB(t) (9.9g) where: CDEB = commercial debt of cattle sector (Ps) CINT = interest payments on commercial debt (PS/year) RINTC = interest rate on commercial loans (proportion of debt/year) Aggregated Income and Consumption Component AGPRAC generates revenues and costs of traditional and modern cattle (Equations 6.38 and 6.39) disregarding the accounting effect of commercial credit. Now it is possible to incorporate this effect into the general accounting and determine farm income on a regional basis. First, Equations 9.10 compute aggregated income from cattle. ARLSK(t) = ARLAT(t) + ARLAM(t) + CLOAN(t) (9.10a) where: ARLSK = aggregated revenues from cattle (PS/year) 205 ARLAT = accounting revenues from traditional cattle-— Equation 6.39c (Ps/year) ARLAM = accounting revenues from modern cattle-— Equation 6.39c (PS/year) CLOAN = as defined in Equation 9.9d (PS/year). ACLSK(t) = ACLAT(t) + ACLAM(t) + CREP(t) + CINT(t) (9.10b) where: ACLSK = aggregated costs of producing cattle (PS/year) ACLAT = accounting costs of traditional cattle—- Equation 6.38 (PS/year) ACLAM = accounting costs of modern cattle--Equation 6.38 (PS/year) CREP = as defined in Equation 9.9e (PS/year) CINT = as defined in Equation 9.9g (PS/year). Finally, aggregated net farm income from cattle, FARIL, is simply computed as: FARIL(t) = ARLSK(t) - ACLSK(t) (9.10c) Next, aggregated farm income from crops is computed from net income of cash and food crops (as discussed in Chapter 6). The value of prOperty tax used in this computa- tion is based on the assessed capitalized value of all land in crops as will be discussed later in this section. FARMIC(t) = FARICC(t) + FARIFC(t) - VLDTXC(t) (9.11) 206 where: FARMIC = aggregated net farm income from crops (PS/year)_/ net farm income from cash crops (Ps/year)l/ 1/ FARICC FARIFC = net farm income from food crops (Ps/year) VLDTXC = value of taxes on land in cr0p production (Ps/year)l/ Due to a lack of data on family expenditure patterns and statistics on the number of family heads operating cattle farms, the computation of living expenditures posed a dif— ficult problem. This was circumvented in the model by developing a simply income-consumption submodel which uses estimates based on experienced judgments. The number of cattle farm operators was roughly estimated from the number of farms supplied by DANE [14] and an annual minimum consumption expenditure was set for the region based on reasonable living requirements per family.g/ The income— consumption equations attempt to incorporate into the model consumers' behavior related to income elasticity of demand, wealth effects and Engel's law. Savings in the cattle economy are implied when the combined income from cattle and crops exceed consumption expenditures. Equations 9.12 determine the income-consumption relationships discussed above. l/For detailed computation of these variables, see subroutine AGACC in the Appendix. 2/ — The assumed number of farm families is 40,000 and the minimum expenditures for consumption Ps 5,000/family-year. 207 EXPLIV(t) = max(ALPH2(t)*GINC(t), EXLMIN) (9.12a) where: EXPLIV living or consumption expenditures of cattle farmers (PS/year) GINC = aggregated gross income frfim sales of animals and milk (Ps/year._ ALPH2 = as defined in Equation 9.12b (prOportion) max(a,b) = the minimum value between a and b. ALPH2(t) = C261 + (C262 — C261)§EXP(—C263*C264*max (GINC(t) - EXLMIN, 0)) (9.12b) where: a variable which introduces the effect of income upon consumers' behavior (prOportion) ALPH2 C261 = a model parameter determining the minimum proportion of income which is consumed C262 8 a model parameter determining the maximum prOportion of income which is consumed C263 8 the rate of consumption expenditures response with respect to changes in income (dimension- less) C264 8 a scale factor max(a,b) I the maximum value between a and b EXP = the exponential function. Equations 9.12 imply that a minimum level of con- sumption always takes place despite low incomes. Further, when income is low a higher proportion of it is consumed, i/For detailed computation see subroutine AGACC in the Appendix. 208 but as this increases the proportion consumed decreases until it is stabilized when high levels are attained. This expenditure pattern reflects consumers' behavior with respect to changes in income. It is clear that by assigning dif- ferent values to the model parameters in the function ALPH2, a wide range of consumers' behavior can be simulated. The preceding computation can be improved and probably re- formulated as more information on this subject is available. Once aggregated income and consumption are determined, the model generates the farmers' investment capital that is used in component LAMDAC as a constraint to the rate of land modernization (Equations 5.41 and 5.420). This is computed in Equation 9.13 below, which assumes an internal transfer of capital from the crops sector to the cattle sector. NCFR(t) = max(FARIL(t) + C2390FARMIC(t) - EXPLIV(t), 0) (9.13) where: NCFR = net investment capital of farmers (PS/year) FARIL a as defined in Equation 9.100 (PS/year) FARMIC 8 as defined in Equation 9.11 (PS/year) EXPLIV = as defined in Equation 9.12a (PS/year) C239 = a model parameter determining the prOportion of income from crops internally transferred to cattle production.- Capital Formation and Export IncentiVes Two measurements of internally generated capital by the cattle subsector are considered in the model: the value 209 of cattle inventories and the value of land. Changes in asset value of cattle over time reflect changes in both prices and cattle population. The asset value of land considered here is based on its capability to generate an income stream independent of location, population pressure and other external factors. Any increase in the asset value of cattle and land not only increases the "wealth level" of farmers but also the collateral value of their assets, enabling them to borrow more capital for further agricultural expansion. The value of cattle in the model is related to the price of finished males since this is the only price generated by the market model (see Chapter 6, Equation 6.39a for a detailed discussion). It is clear that an expanded market model pricing of each animal category will provide a better estimate of the value of cattle inventories. The model accounts for a likely higher value of animals in the modern sector, and assumes that finished males are not kept in the herd but are marketed as soon as they complete the fattening period. VACAPL(t) = PAP(t)*[C227n(PFGT(t) + 0212nPF0M(t)) + 0226* (PFPT(t) + C212flPFPM(t)) + C224§(OLDFT(t) + C212iOLDFM(t)) + C228*(PMGT(t) + C2125PMGM(t)) + C225§(PMPT(t) + C212*PMPM(t))] (9.1a) 210 where: VACAPL = value of cattle inventories (Ps) PAP = producer price of finished males-- Equation 7.3 (PS/animal) PFGT, PFGM = population of growing females, tradi- tional and modern, respectively—- Equation 6.11 (animals) PFPT, PFPM = population of producing females, traditional and modern, respectively —-Equation 6.11 (animals) PMGT, PMGM = population of growing males, tradi- tional and modern, respectively-- Equation 6.11 (animals) PMPT, PMPM = population of producing males, tradi- tional and modern, respectively-- Equation 6.11 (animals) OLDFT, OLDFM population of old females, traditional and modern, respectively—-Equation 6.12 (animals) C212 = a parameter accounting for heavier animals from the modern sector C224, ..., C228 = model parameters determining price relationships between finished males and other sex and age groups (pro- portion). The asset value of pasture land used in the model is its capitalized value which is obtained by dividing the annual average returns in a hectare of land by the prevailing interest rate. The total capitalized value in the Costa is the sum of the values of the total land in the traditional and modern sectors. It should be mentioned that the capitalized value of a hectare of agricultural land can be increased by the increase in output, output price and decrease in the cost of production. Furthermore, the change in the interest rate in the economy 211 affects the capitalized land value. In the model, when the average returns from traditional cattle become negative, the land is valued at a salvage price assumed to be one peso per hectare. But when average returns from modern cattle or crops are negative, the assumed salvage value of land is that of land in traditional cattle production. The capitalized value of land in cattle production is computed in Equations 9.15 below, where RINT attempts to represent the opportunity cost of capital rather than the interest rate of bank loans. VLANDT(t) = max(SVALT*TTGLR(t), FAgifigflt)) (9-153) where: VLANDT = capitalized asset value of land in traditional cattle (Ps) TTGLR = total Costa land in traditional cattle-- Equation 5.51 (hectares) FARILT = net farm income from traditional cattle (Ps/ year) RINT = the current rate of interest (proportion/year) SVALT = the salvage value of traditional land (Ps/ha.). The computation of FARILT does not account for income and/or liabilities arising from credit. This accounting pro- cedure implies that borrowing does not affect land values and the procedure is also applied in the modern sector. The capitalized value of crop land, VLANDC, is computed in a fashion similar to traditional pasture and is not shown here. Yet there are two exceptions: the salvage value of land discussed earlier, and the farm income that is an aggregate of all cash and food crops. Capitalized values per unit of 212 land are simply computed dividing total value of land by total land in each use. VLANDM(t) = max(VLNDHT(t)*(TRSL(t) + TLMOD(t)), FARILMlLt) RINT (9'15b) VLAND(t) = VLANDT(t) + VLANDM(t) (9.150) where: VLANDM = capitalized asset value of land in modern cattle (Ps) VLAND = capitalized asset value of land in cattle in the Costa (Ps) VLNDHT = the per hectare capitalized value of the traditional land (PS/ha.) FARILMl = farm income from modern cattle net of credit accounts (PS/year) TLMOD = total grazing land in modern production-- Equation 5 4 (hectares) TRSL = land in transition from traditional to modern practices--Equation 5.46 (hectares). Finally, the model generates the variables associated with the export sector which are needed to evaluate policy alternatives toward cattle exports in Chapter 12. Since the instruments of export promotion have been mainly the payment of subsidies and adjustments in the exchange rate, we will be concerned with these two policy elements here. Export subsidies are paid as a proportion of the peso value of ex- ports and are computed as follows: SUBSE(t) = FOREX(t)*EXCHR(t)*EXSUB (9.16) 213 where: SUBSE = export subsidies paid to cattle sector (PS/year) FOREX = foreign exchange earnings from cattle exports--Equation 9.17 (US $/Year) EXCHR = the official exchange rate-—a policy variable--Equation 9.18a (PS/dollar) EXSUB = the export subsidy-—a policy variable (proportion of value of exports). Foreign exchange earnings are simply computed as: FOREX(t) = WPB(t)§EXPL(t) (9.17) where: WPB = world (FOB) price of beef-~Equation 7.1a (US $/anima1) EXPL = official cattle exports--Equation 6.260 (animals/year). Exchange rates are computed for the relevant period of cattle exports starting in 1964. Between 1964 and 1966, exchange rate values are those recorded by the International Monetary Fund [44], and average values for 1967 and at the beginning of 1968 are those recorded by the Central Bank [3]. From 1968 forward the exchange rate is projected at the rate of increase observed during the period 1967 to 1972 [45]. These computations performed in Equations 9.18 below in- corporate into the model the effect of a fluctuating exchange rate introduced by the Colombian government in March 1967. ’0 (i.e., undefined) 0 < t < 4 EXCHR(t) = 112.77, 13.5, 13.5, 14.5, 15.76 4 i t i 8 (9.18a) kEXCHR(t—DT) + DT*RCHEX(t-DT) t > 8 where: EXCHR = as defined in Equation 9.16 (PS/dollar) RCHEX = the rate of change in the official exchange rate--Equation 9.18b (PS/year). RCHEX(t) = AleEXCHRl968xEXP(AL1*(t—8)) (9.186) where: ALl = the annual exchange rate growth rate (proportion/year) EXCHR1968 = the official exchange rate at the begin— ning of 1968 (PS/dollar) EXP = the exponential function t = time (years). Given exchange rates, export subsidies, and domestic and world price of beef from component PG, the model computes the export margin, EXMAR, as a proxy for the competitive position of Colombian beef in international markets. Another way of looking at EXMAR is as the profit for beef exporters. When exports are made in the form of carcass beef it is assumed that revenues from viscera, hides and other by— products cover slaughtering and handling costs. In Equation 9.19 below, the export subsidy, EXSUB, is reduced by a factor of .8 to account for the discounted price at which the tax certificates used to pay the subsidy are sold in the market. A negative export margin indicates that Colombian beef is priced out of international markets at the going effective rate of exchange. EXMAR(t) = WPB(t)*EXCHR(t)*(1 + 0.8*EXSUB) - PA(t) (9.19) 215 where: EXMAR = the profit margin of beef exports (Ps/ animal) EXCHR = as defined in Equation 9.16 (Pa/dollar) WPB = world price of beef—-Equation 7.1a (US $/ animal) EXSUB = the export subsidy--a policy variable (proportion of value of exports) PA = market price of finished males-—Equation 7.2 (PS/animal). Performance Criteria Equations 9.20 through 9.23 compute a number of performance variables of the Costa model. These include: (1) farm assets and income; (2) foreign exchange and govern- ment revenues; (3) government expenditures on modernization campaigns; and (4) beef consumption. Other performance measures which may be useful in evaluating alternative modernization policies include output variables from other components. Examples of these are total cattle population, extraction ratios, and animals treated against contagious diseases. Equations 9.20 compute value of capital in cattle production and farm income. VALCAP(t) = VACAPL(t) + VLAND(t) (9.20a) FARMI(t) = FARIL(t) + FARMIC(t) (9.20b) FARMIA(t+DT) = FARMIA(t) + DT*FARMI(t) (9.200) FARILA(t) + DTxFAR1L(t) (9.200) FARILA(t+DT) 216 DSREVL(t+DT) = DSREVL(t) + DT*FARIL(t)*EXP(-DIR*t) (9.208) where: VALCAP total value of animal population and graz— ing land (Ps) FARMI = total agricultural income (PS/year) FARMIA = accumulated agricultural income (Ps) FARILA = accumulated farm income from cattle (Ps) DSREVL = discounted future returns from cattle (Ps) DIR = the discount rate (proportion per year). Foreign exchange earnings and government revenues are computed by Equations 9.21. FOREXA(t+DT) = FOREXA(t) + DT*FOREX(t) (9.21a) GOVREV(t) = TAXCT(t) + TAXCM(t) + VLDTAX(t) (9.21b) GOVREVA(t+DT) = GOVREVA(t) + DTfiGOVREV(t) (9.210) where: FOREXA = accumulated foreign exchange earnings from cattle (Ps) GOVREV = government revenues from the cattle sector (Ps/year) TAXCT, TAXCM = value of taxes on traditional and modern cattle, respectively--Equations 6.34 (PS/year) VLDTAX = value of taxes on land based on the aggregated capitalized land value--Equations 6.33 and 9.150 (PS/year) GOVREVA==accumulated government revenues from the cattle sector (Ps). 217 Government expenditures on modernization campaigns, Equation 9.22, include allocation of funds for development credit and control of foot-and-mouth disease (FMD). ACRDTA(t+DT) = ACRDTA(t) + DT*ACRDT(t) (9.22a) EXPDS(t+DT) = EXPDS(t) + DTfiEXPAFT(t) (9.22b) where: ACRDTA = accumulated funds allocated for develop- ment credit (Ps) EXPDS accumulated public expenditures on control of FMD (Ps). Finally, beef consumption is computed in Equations 9.23 for the total Colombian population. POP(t) = POPO!EXP(C282*t) (9.23s) _ C281*(SUPB(t)- EXPL(t) - UNEXPL) PERCAP(t) - POP(t7~ (9.23b) where: POP = total Colombian p0pu1ation (habitants) POPO = total Colombian population at the begin- ning of simulation (habitants) C282 = the rate of growth in population (por- portion habitants/year) PERCAP = the Colombian per capita beef consumption (kgs/habitant-year) SUPB = total Colombian beef supply--Equation 6.25b (animals/year) EXPL = registered beef exports-—Equation 6.260 (animals/year) UNEXPL = non—registered beef exports—-Equation 6.260 (animals/year) 218 C281 = the average dressed carcass weight (kgs/animal). Table 11.8 shows the values of a selected number of variables used in component CRTACC. 219 TABLE 11.8. Selected Coefficients and Initial Values in the Accounting and Performance Criteria Component (CRTACC). Definition (Equation No.) Value LTl Determines the time development 3 (9.1a) loans are paid (years) LT2 Completes the grace period 1 (9.1b) (years) LT3 Determines the time develop- 8 (9.10) ment loans are repaid (years) RINTL Determines the interest rate on .14 (9.8b) development loans (proportion of debt/year) PEQCR Determines the proportion of .5 (9.9a) equity which can be used as a credit base (proportion/year) CREPR Determines the repayment rate on 1 (9.9e) short term loans (proportion of debt/year) * RINTC Determines the interest rate on .1 (9.9g) short term loans (proportion of debt/year) EXLMIN Determines the farmers aggre- 200,000 (9.12a) gated minimum living expendi- tures (thous. Ps/year) SVALT Determines the salvage value 1 (9.15a) of traditional grasslands (Ps/ha) RINT Determines the opportunity cost .18 (9.15a) of capital (porportion/year) EXSUB Determines the export subsidy .15 (9.16) (proportion of value of exports) ALl Determines the rate of growth .0728 (9.18b) in the exchange rate (pro- portion/year) C212 Determines increased weight of 1.4 (9.14) "modern" animals (dimensionless) C239 Determines internal transfer of .1 (9.90) capital from crops to cattle production (proportion) 220 TABLE 11.8. (continued) Definition (Equation No.) Value C261 Determines the minimum consump- .25 (9.12b) tion from gross income (pro- portion) C262 Determines the maximum consump- .75 (9.12b) tion from gross income (pro- portion) C263 Governs response rate of consump- .3 (9.12b) tion to changes in income (dimensionless) 0264 A scale factor (dimensionless) 10"8 (9.12b) C281 Determines the average dressed 200 (9.23a) carcass weight (kgs/animal) C282 Determines the rate of growth in .032 (9.23a) population (proportion habitants/year) . EXCHR1968 Official exchange rate at the 15 beginning of 1968 (PS/dollar) POPO Initial Colombian population 15,415.7 (9.23a) (thous. habitants) Sources: [3, 5, 17, 41, 42, 45] and initial guesstimates and model tuning. P A R T I I I VALIDATION AND TESTING Introduction Model testing is an ongoing process which should continue even after a model is implemented and in routine use. Testing, refining and validating a model are closely connected processes. A simulation model is tested both to check its internal consistency and to assure that it is an adequate representation of the complex processes of the real world. The validity of a model has to be established with some degree of confidence before a decision maker can base policy decisions on the experimental results of that model. There are primarily three ways in which a model may be validated. The first method compares the structure of the model and its simulated output, using alternative assump- tions about its behavior established by experts and from other published sources. This test uses the intuitive knowledge and expertise of people who have experience in Colombia and other developing countries. The second approach attempts to compare the behavior predicted by the model under various conditions with what actually occurs as real time passes under the same condi- tions. Or alternatively, the model can be used to reproduce 221 222 historical data from the real world which are not used in the construction of the model. Once the model has been implemented, it is tuned and updated as an ongoing process with such comparisons. Finally, sensitivity tests, which identify which of the model's parameters outcomes are most sensitive to their value changes, can also be conducted to validate the logic and internal consistency of the model. Chapters 10 and 11 briefly discuss the model's data requirements and problems and examine two of the approaches commonly used to deal with these problems. These are tuning the model to track recorded time series and analyzing the model's sensitivity to variations in parameter value. CHAPTER 10 DATA USAGE AND MODEL TUNING Data requirements for the model are extensive. Data were obtained from a diversity of sourcesl/ that included Ministry of Agriculture reports, FAO reports, World Bank reports, FEDEGAN reports, INCORA reports, Caja Agraria reports and statistics, DANE and Central Bank statistics, other published reports and informal guesstimates. Other data used were "synthesized" or "simulated" from various combinations of data. Often costs were one point estimates obtained from published sources that were later converted to base year values by means of indexes reflecting the rate of inflation in prices of farm inputs. Aside from the secondary sources, some informal primary information was obtained from a two-week survey of the Costa made in the summer of 1971. Yet, the data problems encountered were many and ranged from nonexistent information to unreliable and contradictory estimates. In Colombia, as in other developing countries, existing statistics on agricultural production are so deficient and deserve so low a degree of confidence that they create a problem in planning for 1/ — Detailed references are found in Chapters 1 through 3. 223 224 agricultural development. A few exceptions include estimates on area harvested and of the production of cotton, coffee, tobacco, bananas for export and sugarcane for sugar pro- duction. In the case of other crops and cattle the estimates are no more than conjectures [40]. However, researchers, planners and policy makers cannot wait for accurate and reliable data to recommend, plan, and make decisions on policies and programs for develop- ment. Models have to be designed on the basis of the best information that is readily available, and techniques may be used not only to improve the quality of data but also to make best use of the data available at the time. The system simulation approach offers three ways of 00ping with the information problem. Sensitivity tests (discussed in the next two chapters) can reveal the implica- tions of parameter variability both for the validity of the model and for policy formulation. These tests can also provide guidance for determining priorities for data gather- ing activities. Secondly, given coarse probability distri- butions for a set of key parameters, running the model in a Monte Carlo mode can generate directly output statistics reflecting data uncertainties [1, Chapter 4]. Finally, the model may be tuned to track a number of recorded reliable time series by adjusting uncertain parameter values. This procedure will be discussed later in this chapter. The entire data input to run the model can be found in various chapters where this information is relevant and 225 and in the Appendix. Tables present the numerical values of the parameters and coefficients used to run the model in its deterministic mode, since time constraints precluded use of a stochastic determination of their values. Model Data Requirements Data for the Costa cattle/crops model fall into four general categories: system parameters, technological coef- ficients, initial conditions, and historical time series. The data requirements of the first three categories are extensive and obtained from a diversity of sources includ— ing descriptive information and guesstimates from knowledgeable persons. In this section, we will briefly discuss the first three categories of data and their sources. Historical time series, used in tuning the model, will be discussed in the next section. System Parameters Fundamentally, system parameters reflect the behavioral characteristics of the system being modeled. These parameters and the interconnected basic equations, in fact, define the system. A few examples of the many system parameters of the Costa model are: l. the land modernization and disinvestment pro- fitability response parameters (E9, E8 in Equation 5.36 and E91, E81 in Equation 5.47); 2. the land use response rate parameter (CL1 in Equation 5.21); 3. the profitability discount rate (DIR in Equation 5.31); 226 4. the many delays and averaging and smoothing lags of the model (e.g., XDEL in Equations 5.43, DEL15, l6 and 17 in Equations 5.1); 5. the sales restriction parameters (BMN, BMX, AMX and AMN in Equations 6.22); 6. the farmers' resource use profitability response parameters (C235, C236 in Equation 6.36); 7. the income elasticity of expenditure parameters (C261, 0263 in Equation 9.12b). There is little or no information on most of the behavioral system parameters and acquisition of this type of data would entail survey research that has never been conducted. The values used in the early stages of building and testing the model were educated and intuitive guess- timates acquired from various secondary sources [e.g., 29, 42, 53], from experiences in other developing countries (mainly experience acquired by the Michigan State University simulation team for Nigeria) and from such primary sources as interviews with Colombian officials and farmers in the Costa. Although values of selected system parameters are shown in Tables 111.1 and 111.2, we will take a close look here at the pasture land modernization and reversion transi- tion response thresholds as an example (E9 and E91 in Table 111.1). The value of E9 shown (.5) means that the alter- native to traditional grazing must be at least 50 percent more profitable before farmers will transfer the land to modern management. And the value of E91 (.3) means that the profitability of the modern operation must be at most 30 percent higher than that of the traditional one before farmers will reverse the modernization process. The relative 227' TABLE 111.1. Profitability Response Parameters for Traditional and Modern Grazing (Dimensionless). Variables (Eon. No.) (definition) Present Uses Alternative Uses Modern Grazing Traditional Grazing E9 (5.36) (response threshold) E91 (5.47) (response threshold) Traditional Grazing Modern Grazing E8 (5-36) (governs response rate) E81 (5-47) (governs response rate) Traditional Grazing Modern Grazing DIR (5.31) (discount rate) .15 .15 Source: Initial guesstimates and model tuning. 228 TABLE 111.2. Cattle Production Parameters. Definition (Equation No.) Value MKM Marketing margin .15 (7.3) C213 Proportion of dead animals 0 (6.24) consumed C214 Proportion of fertile cows sold .6 (6.24) for slaughter C215 Proportion of growing females .2 (6.24) sold for slaughter C216 Proportion of growing males 0 (6.24) sold for slaughter C242 Proportion of heifers treated .05 (6.27a) against brucellosis C244 Annual proportion of animals .3 (6.28b) privately treated against foot-and-mouth disease Source: [29, 31, 63, 64] and initial guesstimates and model tuning. A 229 values hypothesize different farmer attitudes (e.g., risk aversion and uncertainty as discussed in Chapter 5) toward cattle modernization. Despite the lack of accuracy in parameters such as those highlighted above, they play an important role in the validation of the model. Some of them provide a range of values which may be tested in tuningthe model to track historical time series and to improve the model's behavior in comparison to "reality." Some others, as shown by sen- sitivity tests, are not crucial to the model's performance and therefore the results are more sensitive to other elements in the system. Technological Coefficients Technological coefficients are probably the easiest to obtain and handle in the model. The various sources of data include Ministry of Agriculture reports, FAO reports, World Bank reports, INCORA reports, FEDEGAN reports and many other published reports [e.g., 5, 9, 20, 29, 31, 32, 33, 42, 43, 57, 58, 60, 61, 66]. The existence of data for these parameters does not mean they are completely reliable. In- stead, more research and field work will be necessary to increase their level of confidence. Some examples of technological coefficients used in the model are: l. crop yields (YLDCL, YLDCU and YLDFC in Equations 6.2); 2. pasture yields (e.g., CGOU, CGOUl, CGOL and CGOLl in Equations 6.3); Almost all throughout production inflation, 230 costs of production (e.g., CSTHCU, CSTHCL and CSTHFC in Equation 6.29b); average carcass weight per slaughtered animal (C281 in Equation 9.23b); . mean times spent in the cattle production stages (DGROF, DGROM, DPRODF and DPRODM in Equation 6.10a). the technological coefficients remain constant a simulation run. Some exceptions are costs of and price of crops that change with domestic and crop yields. Learning curves for yields are discussed in detail above in component AGPRAC, Equation 6.1. Values of selected technolgoical coefficients are presented in Tables 111.3 and III.4. Initial Conditions Initial conditions (1960) define initial values of all levels (and some rates) that must be given before the first cycle of model computations can begin. Since their values change during the course of a run they must be reset at the start of each run. Some of these include: 1. 4. land usage (e.g., TLFCO, TLCRL and TLCRU in Equations 5.4, 5.11 and 5.24); cattle population in each cohort (PMG, PMP, PFG, PFP and OLDF in Equations 6.11 and 6.12); crop prices and price averages (component Price Generation, Chapter 7) total demand for beef (TDEM in Equation 6.260). Some of these variables present no data problems. For instance, assuming all cattle population at time zero (1960) is traditional, we determine that modern population in each cohort is zero. But the model is quite sensitive 231 to the initial cattle inventories, as we shall discuss later (Chapter 11), so more complete and accurate cattle population estimates would increase this model's accuracy. Values of selected initial conditions are shown in Table 111.5. It must be stressed that the model can be useful for planning economic development, in spite of imprecise parameter estimates, for it is not necessarily the aim of a development model to forecast in absolute terms the values that will be attained by certain variables at a specified time. The aim is to design a strategy of deve10pment by experimenting with the model under various assumptions and then by comparing alternatives. Tuning The major components of the Costa model were programmed, simulated and tested individually as part of the overall model— building process. During this process, conceptual and pro- gramming errors were detected and corrected, and then the components were integrated into the Costa model. Extensive model tests were performed on the larger model to eliminate programming errors and inconsistencies between related model components, and to examine its correspondence with the real system. Checking the model against time series of past behavior and adjusting the values of certain system parameters, adding new mechanisms, or modifying structural relationships is what is known as "tuning" the model. These checks are made before the model is implemented and they suggest which 232 TABLE 111.3. Pasture and Crop Yields (tons/ha-year). Pastures (Average TDN Yields)‘ (Variable) (Ean. No.) Uplands Traditional artificial (CGOU) (6.3a) 3.48 Traditional native (CGOUl) (6.3b) 1.16 Modern artificial (CGUl) (6.30) 5.0 Modern native (CGU2) (6.3d) 1.7 Lowlands Traditional artificial (CGOL) (6.3a) 3.8 Traditional native (CGOLl) (6.3b) 1.26 Modern artificial (CGLl) (6.30) 5.1 Modern native (CGL2) (6.3d) 1.7 Land in Transition Transition artificial (CG3) (6.9b) 3.3 Transition native (CG4) (6.9b) 1.16 Crops Cash crops in lowland (YLDCL) (6.29a) 1.56 Cash crops in upland (YLDCU) (6.29a) 1.11 Food crOps (YLDFC) (6.29a) 8.3 *TDN = Total digestible nutrients Sources: [7, 33, 61, 66] and Instituto Colombiano Agropecuario (ICA), Dia de Campo-Pastos y Forrajes--Ganado de Carne. (Monteria, September 12, 1970), 28-58. "Algunos Aspectos de la Fertilizacion de Pastos." Pastos y Ganados_para la Costa Atlantica. Boletin Tecnico No. 15 (Bogota, 1967): 33-“2. . E1 Pasto Puntero. Hoja Divulgativa No. 029. (Bogota, April, 1970), 1-4. . E1 Pasto Guinea. Hoja Divulgativa No. 029. (Bogota, March, 1971), 1-4. 233 TABLE 111.4. Mean Length of Cattle Production Stages (years). Production Cohorts* (Variable)(Egns. No.) Growing females (DGROF) (6.10a) 2.5 Growing males (DGROM) (6.10a) 2.5 Producing females (DPRODF) (6.10a) 10.0 Producing males (DPRODM) (6.10a) 3.0 *Traditional and modern Sources: [5, 29, 61, 66] TABLE III.5. Selected Initial Conditions (1960). Cattle Population (thousand head) (Variable)(Eqn. No,) Growing males (PMGT) (6.11) 1,174 Growing females (PFGT) (6.11) 1,183 Producing males (PMPT) (6.11) 943 Producing females (PFPT) (6.11) 2,424 Old cows (OLDFT) (6.12) 427 Land Use (thousand hectares) (Variable)(Eqn. No.) Cash crops in lowlands (TLCRL) (5.11) 140.5 Cash crops in uplands (TLCRU) (5.24) 281.0 Food crops (TLFCO) (5.4) 101.73 Export banana (TLBAN) (5.13) 20.0 Grazing land in region 1 (TGLSFl) (5.1d) 2,510.36 Grazing land in region 2 (TGLL) (5.16) 319.8 Grazing land in subregion l (TGLUl) (5.26) 1,471.7 Grazing land in subregion 2 (TGLU2) (5.18) 1,252.3 Sources: [Table 1.2, 8, 15, 16, 29] and initial guesstimates and model tuning. 234 parameters need adjustment, or where a structural relation must be added to the model to improve its behavior in com— parison to "reality." Despite the deficiency of Colombian statistics on agricultural production, four time series (1961—1970) were used initially in tuning the Costa model: Colombian supply of beef, market price of finished males, land in crops, and cattle population in the Costa. Since the modernization campaign promotion started in 1965, the tuning process in- cluded many of the parameters and structural relationships used in the modernization decisions, and the simulated series reflect the effects of the first five years of campaign implementation. The combination of traditional management before 1966 and modern management with improvement of arti- ficial grasses and substitution of artificial for native grasses (alternative 2) from that year on was used as a standard run for tuning the model. Alternative 2 was selected because it patterns more closely the modernization program currently carried out in the Costa. Plots of the above simulated series along with the actual ones are de- picted in Figures 111.1 and 111.2. Table 111.6 displays the four time series resulting after the initial coarse tuning. Data values generating this fit were used in the policy runs discussed in Chapter 12. Although mathematical measures of the goodness-of- fit could have been used as a criterion to measure past behavior characteristics [1, 21, 53], at this stage in the 235 .L000 map no use on» no coxmo ohm mofiLOoco>cH oapowo .mofisom oopmasefim omcfimwm weapon oEHp madman moon cmHnEoHoo 00m momoo on» CH soapmasooa mfiuumouuwcHCSp Hence somamoos mo mpHSmom .H.HHH opswfim ome. mmma 000a mood 000a mood zoma mmma mmma Hmma . _ _ a a a A) a i haddsw oopmHSEHm 0.m >H003n Hmsoo< 0.m T. 1 0.m 0.: l_ 0.0 cofiomHSQOQ ooowasswm : ,\m.\ , c\0 o\t\\ . coaoeesood $32 1 0.0 0;. .. Lea A000: coaHHHEV zaaasm 000m Acme: cowaaasv coaumasaoa mappmo 236 .onLom popmasefim pmcfimwm moficom oEHu mqoso CH Gama one moans oocmflcfim mo ooflpa poxamEIleHCSp HoooE sompooos mo muHSmom .m.HHH oaswfim E13 003 003 $3 002 _ mm? :03 m0? . N03 33 a (A _ 4‘ _ . _ a d w I. Q a 0m: moan Hmsoo< .Ill/ 0 .9000 H o ‘0‘ 00m 1 L 00m.H coats poumasefim moan H03p0<.ll)l 000.m \\ \ \ \ \ \ moan omuwasefim \\ 1 ooqu \ \ 1 000.m . r l Anon .msocpv mdopo CH 0:04 cams Umcmficfim\mm mafipm 237 .mmuwmw .dd so oooeoaoee we. "nooosom manmafim>m no: .m.: 50m.m mm~.m s.ms0 .n.c mmm.a omo.m oae.m omm.m cams mmm.m mmm.m s.mme 0.mmm moe.~ oom.a mm:.m em0.m some mme.m mom.m a.mme a.mmm smm.~ owe.s mmm.m oem.m mama emo.m mem.m 70.0mm 5.0H0 HOH.s 00H.a moo.m mom.m Nome mHa.H mmo.m 3.0H0 a.ea0 smo.a ema.0 0ma.m mmm.m mama mme.H mmm.a o.mmm e.mH0 mmm.0 eom.e emm.m mee.m meme mmm.a esm.H a.amm .e.c 0m0.0 000.0 omm.m wae.m sema mam.H NHH.H 5.05m m.mmm mme.0 .oem.e Nam.m mom.m moms 0mm.a emo.H m.mem m.wem 0mm.0 ooe.0 mma.m msa.m meme omH.H mmo.H H.mmm m.mmm mmm.0 oom.e osm.a mmm.H Hema omeequHm .fioocnooae m:.mm oua.~ 000.m :mm.m 000.H 0mm.m 0H0.m N.mm0 H.mmm 0m~.0a o>fium:hopa< 0a.mH 0am.m oma.m Hm~.m onm.mm mam.e o.mma a.mm0 o Ham.a o>aoectooae 0.0H 0»m.0a :mm.m Hmm.m 000.0m mmH.m 0 0.0m0 0 NNO.~ o>HumcpopH< Amwxv Ammv Amm Haaev Amm HHHEV Am: HHfiEV Amm: msocuv Ana: msonuv Ana: msocuv Ame: msocpv Aomoc msocuv acmEoww:mz mqum <>mm>00 <4Hmapwcpopa< no muasnom oopooamm .m.HHH mqm Ammo 0a mH.n o.au nm.n ma.| .o as. w.HI «. m0. 0.0m: noduosvoun oaupau o» naouo Eouu Hmufiaao uo councmcu Hacnouca uo COHpuoaoum ammo m H0.~u 0.0: m.m~i ma.mn .0 -.au ~.mn ma. mm. o.om+ Ahoo>\:o«u:oaouqv :onauoc weaccmaa .ocu Lo>o was: ucsoomao man 0 m. m. ~0.00+ oHoudo camooe :« ucospno> Hm.m- ~H.0~- m.m=u m.m- so. m.~ m.s~ (chase co oneooaot co caozmounu apfiflanmuaaopa Ham m. o.~ o.oo~+ oauoso cameos on Hacoa» [Hanna uo uncommon ho oaocaotco modaanooacoum mm s Ho.m- ~.oau H~.Hm- ~.~u .0 mo.. mm.ou a. 4. mm.mm- tapped ctocoe oo accoduauonu go oncoanop co open spfiflaoooficoua mm 0 3.: 0.m~u 3.35: m~.~n w. 0.3m: 0.HHu 00m oma 0.0m- Anamz\caoc 0:0» nsossv assodxo HemoaaH qumz: m mo.Hu 0.0m: 0.-n 50.: .0 mm.u 0m.u 0.H m. 0.0m: nuances: namedcm co soapstosc co Saddam mac n 5.0: ~.~mn mw.mon Hm.omn )0. m.0an 0.0m: me. ~. mm.mmu ocsumaa amatucdupw ca ocaa wCanaLw AoooH. «sanded uo :oHpquocm emqmo m a. m. o.m~+ ooxads nzoo m.~a No.0: m.0au 0:. mg. 00.0 03.3: :cuooosx uo :OaELOQOLm Nomxo a. m. o.m~+ ooxfiae nroo :Hsco«o nausea. co coaotoaotm women m csz Omam scum ohsuuaaoo ucoouom osaq> 03~s> cam omam ceauacauoo pounce cam cam cam Eon: nauseous: Louoeouam owum pave owcoco use» am am um mm mm imms«:¢ ucvoLom -coetoo\nm: .Haae .Haae .Haae .Haae .Haae .msoeu sa.o: oom.mo Hom.e Hma.m oao.em om~.~m Ham.o osaa> csx audm m az<4> <>mm>oc <0H2xauancom no uuasnom .0.HHH m4m<9 251 Hm.m =.~=H m~.m m0.n 1 H.2H . on.a Nam omo nmoa 00m H.e =.wH BQOH 00m Na. an.” 0H.H now 000 mmoa aw.+ 0H.0+ 0:.I sm.m~+ ms.0~+ sw.oa+ m0.oa+ mm.m«+ ~m.wa+ mo.m~o ~0.o~n “essay les\e0sc nacho :nao ocaazoa go oaoaa vwouo>a Hm«u«:H geomav Aasxcoov nacho :mwo ocaaas ho uaoaa owopo>a Haauacm Aoooav Acooxnav naouo ammo vcmaroH yo vodka owoco>a Hoaua0H Aoeofiv Acooxnav nacho ammo ncwaa: 00 00:90 owaco>m HaauacH Assess Anaes-me \nmv adOpo cmuo ocmaon ho unoo oumno>a HwapacH leeaac Ataos-ee \nmv odouo :mmo ocean: uo unoo owmco>m awauacH boomav Acme; ua:\nmv vcsa wdaumnm Ho:o«u«os:u uo unoo wca nuauoao owmco>o am«p«:H Aomoav Auaomuaasaca\nmv :0uooo Hacoauaosuu :« uaoo a:a:«:0uo> HaauacH howmflv Ahmozuaweacw\umv hauoOm aizoauqdopu :« gonna go unoo «capacH gossaa Anaemia \alv waste coo: uo unoo owmpo>s HaauficH Aooefiv Anoos-sc \nmv oqoco ammo ocoaxofl uo unOo owmpo>o Hw«u«:H Aomeav Ateos-sn \umv anono coco ocean: uo unoo owono>a HaauacH Anus» \:o«usoaoun0 ounce and: :« onouuoca go your flawed» ADUQA> Jmomom Dmomom aumfimo Doxfimo 80240 o Bamako mokao dozfimo Dorfimo emu: ca :3: uncm sock ohsuusnoa accouo; Loom ICOmLoQ\nwx 00.0H mm .Haafi um .Haua oom.mo me.o ad .Haaa H-.~ om .Haas oao.0m an .-«E 0mm.mm massaco .nsocu aHN.o 03Hu> cs: onom m oz <>mx>oo cam onom osau> cam amok :3: onam noun owcozo ucoopom co«u«C«uoo Lugoadndm cosmos Levesque: “vascuucoov .o.-~ mam<9 252 a m m 0.0:: Amnaoxv mumoo echo nncu cacao: chance: you vowdco>o haaaaucccoaxo ho asap ho camcoa owmno>< mmqma m m 0.0:: Ancmoav ocaa MCanoLw aaCOauaoaLu go nuaoo wcuuocoao ocmuoo: Lon vowmno>m Adanaucoconxo ao mean :0 cameo: owato>< qumo m m 0.0:: Amnmoav Hmeuca floccuuuowpu Lon nunoo wcuomcodo vommno>a zaamupcoconxo uo 08“» ho camcoa owmcv>< omqmo m m o.oe- Antoosc nunoo wc«»ohoao oauuco «acodouuanu chance: Lon otwwno>a maaofiucoconxo no 06“» co camcoH owcco>< magma Annwomv xmp peed HmcoasfiUMLu chance: Lon comras>s seawaucccoqxo go :0 teas co sowcoa omato>< magma m m o.o:- “mental sauuso aMCOHuaoaLu co mono» vacuum: cod powdcs>m magmaucocoaxo co 02:» co cameo: owmto>< waqmo m m 0.0:: Acwozv noavaz none :mso ocean: oowmco>w aflmaaucocooxo go we“: go camcoa owmno>< mama m m 0.0:: Amnmozv wound anoco cmao ocean: ovmsuo>c haaadococoaxo go oedu go camcoa cwwno>< name m m 0.0:: nmcmcxv moasn odouco Hacodoaomcu cowacv>o hamauucocoano he use» 00 camcoa owmco>< came m m 0.0:: AmLuomv mound couscous oauumo vowano>o xaamascocoaxo co teas co cameo: omcto>< mama 0H :sz onam soak ocsuusdoo ucoocom ozaa> 05~a> cam onwm :o«u«:«uoo 000308 :3: cam cam Eocm Louoadumm Louoadham swam pooh owcwco and» mm mm mm mm mm massacm acoohom IconLoQ\mmx .aaue .H~«E .Mafis .Hflaa .Aaas .mzocu 00.0H 00m.mo acm.o Hm~.m 050.0m omm.mm Ham.o osaa> cam unam m Qz<4> <>mm>oo <0H2xaca .:oapau:omnfio .0.:m vocnfiansacsv .ucoemo~o>00 o“EOCOom 0:: «macmcsocopmonucm .:o«umE:ou:H .m«:::o .m ”0840 \o .am .Ammma .oozmaanzacav :m>0 :o> :ofipooox an .moa:oe< :uzon :H 000:: oooooaom :« noaudafinanmom u:mmdoao>oa 0:0 I Hauaawo :o :szom mmwuomsv:H caupmo poem on» wcfiuoouu< uncuomm :a .Louhomwm oauuwo a no manEoHoo .mou:o«ELcm .< acuoozu \n madame Lon :oapdeanOo use: csanEoHoo I m I m ocaa ensues: mo osam> condaapaaso I Qz oauuao 50:0 osco>ou pcoficpo>ow uo Mahmouca osuu I<>mm>oo noono Eon» 0800:“ saw: :0 ampwouca 05H» I<0Hzm .mcoa outpoe I 0:0 mua:: uzwuoz 0:: nomad caaoeo~oo :H use mosao> 2:000:05 4H< .Ammmav :3: :ofiuoassam :moanmm a 00 0:0 0:» as one mosau> \m : 0N0: 000: 0.0:- Ao0aav “no: oseesoesv m codwotnsm :H ncma Hauspasofi:ww HmauficH ~o=> osam> cam emom :anficauoo bounce cam cam :3: 20:: Louosdham Louuadudm 00am once owcwco :00» mm am n: 00 mm massaca 0:00:00 -eomtodxnmx .Haae .laae .Haae .aaae .:::e .nsoeo 09.0: cem.00 H0m.a Hwa.m eao.00 00m.mm :Hm.e osas> cam onom m<0mmm m 02 <>mm>oo <0Hzm\.i...~ ZCH H N «Ev m. .3 C M» > pt I U FL . v:-.~..~ r. v) .. \Mv Government Revenue (million Ps/yr.) 295 Run Definitions 1. Disease control, modernization of cattle pro- duction, current credit terms and continuation of present trends and policies (base run). 6. Reduced interest rate on development credits from year 1966. 7. Increased terms of repayment on development credits from year 1966. 8. Increased funding for development credit from 1966 to 1976. 9. Increased units of modernization promotion from 1965 to 1973. 220T 2004» Run 6 180.. u” 9 160 "' 'un 7 190.» Runs 1,8 120-» 100‘» 80" 604- uo4. All runs 20 o 1965 1970 1975 1980 1985 Figure IV.12. Annual government revenues from cattle production in the Costa under various promotion and credit policies, 1965- 1985. 296 targets are tested in the model. The base run continues projecting exports at a low level (329 thousand head annually), Run 10 makes projections at an intermediate level (M96 thousand head annually), and Run 11 at a high level (692 thousand head annually) [35]. Run 12 compares the effect of attaining a high export target at the same time that the cattle herd in the other four producing regions is growing at 5 percent annually. At the time the simulation runs were completed, details of the new cattle development plan were released setting a revised export target at one million head annually by 1990, but due tc'time constraints this was not included in the study.l/ Illegal exports are kept constant in all runs at 300 thousand head annually. The effect of setting up low, intermediate and high export targets accompanied or not by programs to develop the cattle herd in the rest of the country results in striking differences in outputs. In Figure 1V.1“ we see that the major contributor to high consumption per capita is the development of the cattle herd in the rest of the country beginning in 1975, even after sustaining the export of 692 thousand head annually. The lowest consumption per capita is attained in Run 11 with high eXport target and "normal" growth of the non-Costa herd. l/El Espectador, (Bogota), July 8, 1973- 297 Total (national) cattle p0pu1ation is the highest in Run 12 as expected, despite a substantial decline in the Costa inventories after 1975 (Figure IV.13). Yet, the non- Costa cattle projections for this run reveal unrealistic internal inconsistencies. In one account, the same condi- tions that discourage cattle production in the Costa might exist in the other producing regions, resulting in a lower or even zero rate of modernization. Likewise, the 5 percent annual rate of growth assumed in the government plan seems to be unrealistically high since the growth of the Costa herd in the base run has been about 1.6 percent for the period 1960-1985, and about 2.2 percent annually during 1960-1975, the period of the fastest growth. The rising trend in the Costa cattle population of Run 12 (Figure IV.13) tapers off after 1975 and turns down after 1980 ending the projection with 8 percent fewer cattle than the other runs. The substantial increase in beef supply after 1975 causes a sharp decline in cattle prices (price of finished males are 95 percent lower in Run 12 than in Run 1 by 1985) that first reduces the comparative advantage of the modern Operation and later turns it into a loss. At this 7 point land and cattle are transferred back to traditional practices, and the nutritional imbalance caused by these shifts fosters sales that further depress prices. We must recall that the modernization mechanism in the model will return land to traditional management when the perceived relative profitability (net present values) becomes negative 298 indicating that the discounted returns at the base year for traditional cattle are higher than those of modern throughout the planning horizon. Therefore, the declining profitabilities discussed above, through their interactive effects in the model, result in reduced and early cessation of modernization, reversion of land and animals from modern to traditional, and finally in lower cattle inventories. Despite larger incomes from higher cattle prices in Runs 10 and 11 than in Run 1, the projected rates of land modernization are very close and, as a result, the Costa cattle population is virtually the same in these runs. The reason for this is twofold: (l) discounted net returns in each base year from traditional and modern cattle experience about equal proportional increases for higher prices apply to sales from both operations. Consequently, the perceived relative profitabilities that are part of the modernization's decision mechanism remain very close in their time paths, and (2) the final rate of modernization is dominated by the behavioral responses of farmers despite the greater availability of investment capital arising from higher incomes, especially in Run 11. This effect is the same as that of hav- ing more credit available for modernization. Yet we can speculate that projections after 1985 might show a higher population in Run 11 as modernization expands because of the increased liquidity of farmers at a time when that of the other runs has been reduced. It is also worth noting that the higher average discounted returns from cattle in Runs 10 and 11 also have an effect on land use, reducing the area in crops in 299 the region. This estimated effect, through the land allocative mechanism in the model, and its possible inac- curacies will be discussed in the next section on crOp modernization. The most dramatic effects of regulating prices through domestic supplies are seen in the other three perfor- mance variables. Aggregated farm income from cattle (Figure IV.15) is the highest when the high export target is attained, followed in order by Runs 10, l and 12. Farm income in Run 12 not only falls sharply after 1975 when increased cattle outputs from non-Costa regions are marketed, but also becomes negative during the run's last five-year period. As income drastically drops, farmers have to resort to short-term credit to cover their Operating costs and living expenditures. As short-term indebtedness increases, debt service expenditures further reduce farmers' liquidity. Although under these circumstances farmers reorganize their business, returning land and cattle from modern to traditional management, the model does not provide adequately for reorganizing production by reducing expenditures or, more drastically, going out of business. Contrarily, the upper bound on sales and the dominance of nutritional factors built into the sales mechanism preclude a liquidation of the herd and force farmers to stay in business even at a loss. This behavior points out the 300 need to refine the model so as to better handle this investment and disinvestment.l/ The capitalized value of land (Figure IV.16) follows the projected time paths of farm income. However, Run 11 shows the effect of a higher value of traditional land on the weighted average of land in the region. The value of land in Run 12 decreases sharply after 1975 until it reaches the salvage value of one peso assumed in the model when income becomes negative. Government revenues (Figure IV.17) are again the highest in Run 11 and the lowest in Run 12. Higher govern- ment revenues in Runs 10 and 11 than in the base run arise from higher assessed land values since cattle inventories and sales remain about the same. The loss in revenues in Run 12 is produced by reduced cattle inventories and mostly by lower assessed land values. Policies Related to Crop Modernization Run 13 attempts to highlight the interactive effects of increasing the production of cash and food crops via extension efforts to introduce new seed varieties and improved cultural practices, improving average cash crop yields in lowlands and uplands to 2,900 and 1,650 kg./ha., respectively, and average food crOp yields to 10,000 kg./ha. This run l/In turn, this need indicates a disciplinary need for economists to develop a user cost theory as a basis for improved modeling of investment and disinvestment. Cattle Population (million head) 30 2O 10 301 Run Definitions 1. Disease control, modernization of cattle pro— duction, low export target and continuation of present trends and policies (base run). 10. Cattle exports set at intermediate target from year 197a. 11. Cattle exports set at high target from year 1974. 12. Cattle exports set at high target from year 1974 plus modernization of rest of national herd from year 1975. 17 Run 12 1 Runs 1,10,11 i :F Non-Costa Population it Runs 1,10,11 J» Run 12 uns 1,10,11,12 Costa Population w ‘r % : i —: 1965 1970 1975 1980 1985 Figure IV.13. Cattle population in the Costa and the rest of Colombia under various domestic supply policies, 1965—1985. Carcass Beef Per Capita (kilograms) 3O 25 2O 15 10 Run 302 Definitions 1. Disease control, modernization of cattle pro- duction, low export target and continuation of policies (base run). present trends and 10. Cattle exports set at intermediate target from year 1974. 11. Cattle exports set at high target from year 197A. 12. Cattle exports set at high target from year 197M plus modernization of rest of national herd from T year 1975- Run 12 i -r- A11 runs (r / Run 1 0 Run 10 V Run 11 w ~4- %* j % 444 1965 1970 1975 1980 1985 Figure IV.1A. Beef consumption per capita of the Colombian p0pu1ation under various domestic supply policies, 1965-1985. Cattle Income (PS/yr.) 303 Run Definitions 1. Disease control, modernization of cattle pro- duction, low export target and continuation of present trends and policies (base run). 10. Cattle exports set at intermediate target from year 197A. 11. Cattle exports set at high target from year 197A. 12. Cattle exports set at high target from year 1974 plus modernization of rest of national herd from year 1975. 3,0001- Run 11 2,500«L " Runs 11,12 2,000-~ Run 10 1’5004' . Run 1 1,000 fir- 500 «II- RUI’I 12 Runs 1,10,11,12 #- .L : : —: 1965 1970 1975 1980 1985 Figure 1V.15. Aggregated farm income from cattle production in the Costa under various domestic supply policies, 1965-1985. Capitalized Grazing Land Value (PS/ha.) Run Definitions 3014 1. Disease control, modernization of cattle pro- duction, low export target and continuation of present trends and policies (base run). 10. Cattle exports set at intermediate target from year 197U. 11. Cattle exports set at high target from year 197A. 12. Cattle exports set at high target from year 197” plus modernization of rest of national herd from year 1975. V A/\ 3,0001” Run 11 2,500‘" 0 Run 10 2,000 ‘“ Runs 11,12 0 Run 1 1,500‘* 1,000s~ TL Run112 500. Runs 1,10,11,12 1965 1970 1975 1980 1985 Figure IV.16. Average per hectare capitalized value of grazing land in the Costa under various domestic supply policies, 1965-1985. Government Revenue (million Ps/yr.) 2201 200~ 180- 160‘ 1904 1204 100‘ 80« 60‘ M0- 204 305 Run Definitions 1. Disease control, modernization of cattle pro— duction, low export target and continuation of present trends and policies (base run). 10. Cattle exports set at intermediate target from year 197A. 11. Cattle exports set at high target from year 1974. 12. Cattle exports set at high target from year 1974 plus modernization of rest of national herd from year 1975. r t Run 11 Run 10 - Runs 11,12 Armin 1 Run 12 F 19 65 \ Runs 1,10,11,12 l 1 19'70 1915 19'80 1§85 ] Figure 1V.17. Annual government revenues from cattle production in the Costa under various domestic supply policies, 1965-1985. 306 assumes that yield targets are attained at the end of five years with the crop modernization effort starting in 1972 while cattle improvement programs are still in effect. Run 13 examines the effect of a program to modernize cash and food crop production on the performance of the cattle industry. In general, the modernization of crOps has an adverse effect on all performance variables but cattle population which shows a slightly higher trend in Run 13 than in the base run between 1980 and 1985, although for practical purposes the results can be considered the same (Figure IV.18). When cash crops are modernized, their profitability increases relative to that of cattle and more land is transferred from cattle production to crOp pro- duction. As a result, total grazing land declines over time. But the land allocation and modernization mechanisms in the model interact to step up the proportion of modern land in total grazing land. The effect of the land allocative mechanism is to take out land from the least profitable cattle activity--the traditional in this case—-and transfer it to the most profitable cash crop activity. Meanwhile, the modernization decision mechanism continues to shift land from traditional to modern cattle management. If the latter effect outweighs that of the land allocation, as in Run 13, this results in a higher proportion of modern pasture land relative to total pasture land that compensates any loss of grazing area. Yet it is likely that, in the long run, differences in cattle population will widen as more grazing land is 307 modernized in Run 13 after 1985 at a time when modernization has ceased in the base run. The reason for this is the greater availability of capital for investment brought about by larger revenue transfers from crops to cattle production as total income in the crops subsector increases. We should remember that the model assumes a 10 percentl/ internal transfer of net revenues from crops to cattle; and that, for the period 1972-1985, net crop income is greatly increased, being 190 percent higher in Run 13 than in the base run in 1985. While this effect has its major impact in the absence of development credit and when the liquidity of farmers is low the projected rates of land modernization are very close in the two runs, despite larger internal capital transfers in Run 13. As discussed, this result indicates that the final rate of modernization is dominated by similarities between the way farmers' behavioral responses are modeled in both runs. Although in Run 13 there are conditions for a larger and faster shift of land from cattle production to crOp pro- duction, the assumptions underlying the land allocation mechanism prevent profit incentives from exerting a greater impact on land use. It is assumed that cash crops in the lowlands expand at a constant H,500 hectares annually throughout the simulation; this seems to be an unrealistic assumption since it is likely this rate would increase as the profitability l-/The accuracy of this estimate is highly uncertain. The rational for using a low prOportion of capital transfer at this point is the current low level of reinvestment in cattle raising. 308 of crOps relative to cattle becomes greater. In the uplands (subregion 1), however, we have reasoned that farmers' deci- sions to expand (or contract) their cash crop acreages are based on their perceived differences in net present value of future income streams from land in different uses, and on a response rate parameter. In the land allocative mechanism, we see that the effects of revenue differences between the allowed land uses are reduced by the response rate parameter which is assumed constant in the projected time paths for the two runs. This also seems to be an unrealistic assump— tion since it is likely that farmers will accelerate the rate of land transfer from pasture to crops as their perceived relative profitability becomes larger. As a result, the impact of relatively greater returns from crOps on land allocation is minimized and the final estimate of land in crOps is biased downwards. However, this transfer could be tapered off or even reversed if the average returns from cattle approximate or outweigh those from crops. Although cattle population is slightly higher in Run 13 than in Run 1, the resulting per capita consumption is lower during the last five years of the run (Figure IV.19). This apparent paradox is explained by a higher pasture-to- animal ratio toward the end of Run 13 that results in a lower extraction ratio. In the sales mechanism of the model, the short-term response in off-take is dominated by nutritional conditions. Yet it is likely that the off-take in Run 13 will increase in the long run when modernization is completed 309 and the cattle herd is in equilibrium with the forage available. As expected, aggregate farm income from cattle and the resultant capitalized value of land follow the same pattern. Income from cattle (Figure IV.20) follows almost the same time path in the two runs until 1980 indicating, again, the behavior and interactions discussed in previous sections. But after 1980, the two runs diverge widely and, while Run 1 reverses its downtrend, Run 13 continues to fall sharply. The reason for this is found in the greater avail- ability of capital for investment brought about by increased revenue transfers from crop to cattle production-~which enables farmers to continue investing in farm improvements for longer periods after the credit program has been cut off. This downtrend could be tapered off or even reversed if moderniza- tion slows down or stops altogether either for lack of in- centives, lack of outlay balances for modernization, or lack of traditional land. The capitalized value of land per hectare is a re- flection of what happens to farm income (Figure IV.21). How— ever, it is worth noting that in Run 13, after 1980 the decrease in land values is less than that of farm income. This is ex- plained by the increased weight of modern land in the average capitalized value of land in the region resulting from the expansion of modern pasture and the contraction of traditional one. Finally, government revenues are lower in Run 13 after 1980 as a result of lower tax collections from cattle sales and assessed land values (Figure IV.22). Cattle Population (million head) 310 Run Definitions 1. Disease control, modernization of cattle pro— duction, no modernization of crops and continua- tion of present trends and policies (base run). 13. Modernization of cash and food crops from 1972- 1976. lllF {AAA 1 _l_ t 1965 1970 1975 1980 1985 J Figure IV.18. Cattle population in the Costa with and without a crop modernization program, 1965-1985. Carcass Beef Per Capita (kilograms) Run Definitions 311 1. Disease control, modernization of cattle pro- duction, no modernization of crops and con- tinuation of present trends and policies (base run). 13. Modernization of cash and food crops from 1972- 1976. 25}? «L __ Run 1 20‘L Runs 1,13 1. Run 13 151’ ‘r 101- 5*” ‘1 1965 1970 1975 1980 1985 Figure IV.19. Beef consumption per capita of the Colombian population with and without a crop modernization program 1965—1985. Cattle Income (million Ps/yr.) 312 Run Definitions 1. Disease control, modernization of cattle produc- tion, no modernization of crops and continuation of present trends and policies (base run). 13. Modernization of cash and food crops from 1972- 1976. 2,5001 __' 2,000 41- Run 1 1,500. 1 000. ’ 6 Runs 1,13 'un 13 500‘- 1 l _1 1965 1970 1975 1980 1985 Figure IV.20. Aggregated farm income from cattle production in the Costa with and without a crop modernization program, 1965-1985. A . .3: m. L V .03 40> U: 54 Ma: RN fieNU C .0 N. w- 5 3.,“ H Q 30 Capitalized Grazing Land Value (Ps/ha.) 313 Run Definitions 1. 13. 2,500jf Disease control, modernization of cattle pro- duction, no modernization of crops and con- tinuation of present trends and policies (base run). Modernization of cash and food crops from 1972- 1976. 2,ooo-- Bun 1 Run 13 1,500-- f» Runs 1,13 1,000- 500. # + + 4 1965 1970 1975 1980 1985 Figure IV.21. Average per hectare capitalized value of grazing land in the Costa with and without a crop modernization program, 1965—1985. Government Revenue (million Ps/yr.) 31“ Run Definitions 1. Disease control, modernization of cattle pro- duction, no modernization of crops and con— tinuation of present trends and policies (base run). 13. Modernization of cash and food crops from 1972- 1976. 2201r 200" 180‘* Run 1 160 “ 140 Run 13 120 100 80 60‘ U0 l l J_ I 1965 1970 1975 1980 1985 Figure IV.22. Annual government revenues from cattle production in the Costa with and without a crop modernization program, 1965-1985. 315 Effect of Various Policy Combinations The set of runs which investigates the consequences of various combinations of policies and programs includes Runs 1, 1h, 15 and 16 as defined in Table 1V.1. Briefly, Run 1 projects present trends and policies (the base run); Run 14 implements the taxing policies of Run A with the credit policies of Runs 6 and 7; Run 15 investigates the effects of increasing the funds for deve10pment credit coupled with a 50 percent increase in the promotion effort; and Run 16 implements the programs of Runs 1“ and 15 concomitantly with the development of the non-Costa cattle herd and the increase of beef exports from a low to a high target. In general, the more micro—economic oriented policies of Run 1” have the greatest long-run impact on the performance variables in the model, followed by those of a more macro- economic nature (Run 15). The combination of these policies in Run 16, coupled with a program of expanding exports and growth of the cattle herd in the rest of the country, produce varying results that go from a steady increase in consumption per capita to a gradual decline in cattle population in the Costa and a sharp decline in farm income, land value and government revenues. By 1985, total (national) cattle population is 42 million head in Run 16, about 50 and 40 percent higher than in the base run and Run 1“, respectively. The cattle popula- tion in the Costa in Run 16 at first experiences a faster rate of growth than in the other runs, but this tapers off 316 after 1975 when the development of the cattle industry in the rest of Colombia begins, and in the long run the growth rate will likely turn down (Figure IV.23). Although the negative effects of increased beef supplies from the rest of Colombia on land modernization and cattle population in the Costa have been discussed earlier, further discussion is necessary at this point to better understand the changes in cattle inventories in Run 16. Between 1975 and 1985, cattle population continues to grow at a decreasing rate although no more traditional land enters the process of modernization and conditions exist for a reversion of land from modern to traditional management. The reason for this is found in farmers' delayed response to falling prices and income which is built into the model. Since the perceived relative profitabilities are based on exponentially averaged costs and prices of the preceding five years, it takes several years before the decline in price has its full effect on expected returns. Increasing prices before the turning point in 1975 (see Figure IV.28) have the effect of increas— ing or maintaining averages in the following base years. Since the perceived relative profitability in 1985 continues to be positive and higher than the threshold parameter, it prevents the operation of the land reversion mechanism although modernization of new land is stopped by the other constraints imposed on the modernization decision mechanism. Nevertheless, the land in process of modernization from previous years (the land "stored" in the modernization delay) 317 completes its development and enters full modern production a few years after modernization of new traditional land has been stopped; as a result, the area in modern land increases beyond 1975 and with it the more productive modern herd. However, it is to be expected that in the long run this process would reverse as discussed earlier in the section on domestic supply policies in connection with Run 12. It is highly unlikely that farmers would wait ten years before changing their expectations from profit to loss and start reorganizing production. This weakness of the model should be considered more carefully and corrected. Run 1“, which combines the incentives provided to modernization only, shows a steady uptrend in cattle popula- tion as a result of a higher and sustained rate of land modernization (Figure IV.23). Run 15, which combines the credit funding and promotion policies of Runs 8 and 9, results in virtually the same cattle population of Run 9 alone (about 9.8 million head in 1985) indicating that adding in— creased availability of development credit to the promotion effort does not affect the output of the model. This result was explained in the section on promotion and development credit policies. Consumption per capita is the highest when the cattle herd in the rest of Colombia is developed, even after sustaining a high level of exports. When the cattle deve10p- ment program is limited to only the Costa region, Run lu yields the highest domestic supplies (Figure IV.2M). 318 As depicted in Figure IV.25, aggregated farm income from cattle is greatly influenced by export policies and development programs which affect cattle prices and costs as well as expenditure commitments for farm improvement. Initially, farm income is inversely related to the rate of land modernization with Run 1 at the top followed by Runs 15, in and 16 respectively. But between 1970 and 1975, this order is completely reversed by the effect of the various policies followed. Run 16 shows the effect of increased prices because of high exports coupled with increased sales and cost reductions arising from the development programs. When the export target is low and domestic price drops as in Runs 1, 14 and 15, the production incentives provided in Run 1n result in the highest farm income.* The changing trend in farm income in Runs 1, 1“ and 15 after 1975 is explained by different levels of expenditures on land modernization at the end of the credit for development program. Farm income in Run 16 sharply decreases after 1975 and becomes negative after 1980 when increased supplies from the rest of the country greatly depress prices-—more than offsetting the positive effect of the other policies implemented in this run. In general, the capitalized value of land (Figure IV.26) follows the projected time paths of farm income. In the initial period 1965-1970, the order differs from that of farm income because of varying proportions of traditional and modern land in the total grazing land that affect the average value of land in the region. Although farm income 319 in 1985 is lower in Run 1“ than in Runs 1 and 15, the capitalized value of land is higher because of the larger value of traditional land which has the greatest weight in the average value of land. The value of land in Run 16 de- creases sharply after 1975 until in 1985 it reaches the salvage value of one peso per hectare assumed in the model when income becomes negative. Government revenues (Figure IV.27) are the highest when development is promoted while maintaining the special taxes on cattle (Run 15). When the special taxes are cut off, government revenues are sharply reduced even though the other policies implemented in Run 1N substantially increase revenues from the property tax. But government income is the lowest in Run 16 because of the lost revenues from cattle taxes and very low assessed land values. Policies Related to Export Promotion Policies aimed at the promotion of beef exports are examined in the last set of runs, Runs l2, 17, 18, 19, 20 and 21. Although the present structure of the model does not pro- vide a feedback linkage between the export sector and the domestic market except for the simple effect of increasing the total number of animals demanded, the output of the study is appropriate for exploring the likely outcome of some world market conditions and/or domestic policies on the competitiveness of the Colombian cattle industry. The export promotion policies now in operation were introduced in 1967 under Decree-Law HUM, and those examined Cattle Population (million head) 320 Run Definitions 1. Disease control, modernization of cattle pro- duction and continuation of present trends and policies (base run). 14. Combines Run 9, Run 6 and Run 7. l5. Combines Run 8 and Run 9. l6. Combines Run 14 and Run 15 plus modernization of rest of national herd from year 1975 and high export target. 12 1* Run 14 11‘” Run 16 lo q— Run 15 9.- Run 1 8'+ 7 cu- All runs 6 «- 2% 1965 1970 1975 1980 1985 Figure IV.23. Cattle population in the Costa under various policy conditions, 1965—1985. Carcass Beef Per Capita (kilograms) 35 30 25 2O 15 10 Run 321 Definitions Disease control, modernization of cattle pro- duction and continuation of present trends and policies (base run). 1U. Combines Run 4, Run 6 and Run 7. 15. Combines Run 8 and Run 9. l6. Combines Run 14 and Run 15 plus modernization of rest of national herd from year 1975 and high export target. T 4» Run 16 11- .p Run 1“ ‘;‘///A11 runs a. Run 15 Run 1 «b + <; 1965 1970 1975 1980 1985 Figure IV.2U. Beef consumption per capita of the Colombian population under various policy conditions, 1965-1985. Cattle Income (million Ps/yr.) 322 Run Definitions 1. Disease control, modernization of cattle pro- duction and continuation of present trends and policies (base run). in. Combines Run A, Run 6 and Run 7. 15. Combines Run 8 and Run 9. 16. Combines Run 1“ and Run 15 plus modernization of rest of national herd from year 1975 and high export target. 3,5001' 3,000" 2,500‘P 2,000‘“ Run 15 1,500.- Run 1 9 Run 1“ 1,000"Run 1 ’ Run 16 l 500‘_ Run ll A11 runs 1965 1970 1975 1980 1985 Figure IV.25. Aggregated farm income from cattle production in the Costa under various policy conditions, 1965-1985. Capitalized Grazing Land Value (PS/ha.) 323 Run Definitions 1. Disease control modernization of cattle pro- duction and continuation of present trends and policies (base run). l4. Combines Run 4, Run 6 and Run 7. 15. Combines Run 8 and Run 9. 16. Combines Run 14 and Run 15 plus modernization 0 of rest of national herd from year 1975 and high 3’75 " export target. 3,500__ 3,000.. 4L 2,SOO._ Run 14 2,000.- Run 15 Run 1 1,500-- "Run 1 Run 16 -Run 500 . Run 15 All runs 1965 1970 1975 1980 1985 Figure IV.26. Average per hectare capitalized value of grazing land in the Costa under various policy conditions, 1965-1985. Government Revenue (million Ps/yr.) 324 Run Definitions 1. Disease control, modernization of cattle pro- duction and continuation of present trends and policies (base run). 14. Combines Run 4, Run 6 and Run 7. 15. Combines Run 8 and Run 9. 16. Combines Run 14 and Run 15 plus modernization of 240 rest of national herd from year 1975 and high 14 export target. 200 «r Run 15 160 4!- Run 1 1201F Run 1 9 Run 14 80.. Run 16 Run 14 (40 «r- / A11 runs 1 i i I 1965 1970 1975 1980 1985 Figure IV.27. Annual government revenues from cattle production in the Costa under various policy conditions, 1965—1985. 325 here at the tax credit certificate (CAT) discussed in Chapter 2, and a fluctuating exchange rate. The present 15 percent rate of return accruing to exporters through the CAT scheme is subject to annual adjustments depending on the competitive position of Colombian exports in foreign markets. Although sales of live cattle have dominated Colombian exports, it is assumed that after 1974 only dressed animals will be exported in the form of frozen carcasses. Therefore, the three levels of world price considered here are for frozen beef. The high price assumes that after 1974 the world price will continue the trend for the period 1970- 1972, one of rapid rising prices (during this two-year period, the price of Colombian frozen beef increased 38.4 percent on a yearly basis). The low price assumes that after 1974 the rising trend in world price will be approximately one- eleventh of the 1970-1972 period. And finally, the moderate price assumes a rising trend in the world price of about three-eights of the 1970-1972 period. In all cases the per ton price of carcass beef has been converted to a live animal equivalent by a factor of 4.3. Runs 17 and 18 in- vestigate the effect of low and moderate world prices, respectively. In the remaining runs, Runs l2, 19, 20 and 21, policies related to exchange rate, export subsidy and promotion of cattle production in the rest of the country are examined under conditions of low world price. Run 19 investigates the effect of cutting off the export subsidy 326 after 1975 while maintaining a flexible exchange rate. Run 20 speculates on maintaining export subsidies while returning to a fixed parity against the dollar when this reaches an approximate value of Ps 25. Run 12 combines the effect of maintaining export subsidies and fixing the exchange rate with an increased growth of the non—Costa herd at 5 percent annually after 1975. And finally, Run 21 combines the effect of cutting off export subsidies after 1975 and main- taining a flexible exchange rate with an increasing cattle population in the rest of the country. In general, the international competitive position of Colombian beef is investigated assuming the least advantageous conditions in both the domestic and world markets. In the domestic market we always assumed high export targets which are reflected in higher domestic prices, and in the world market we assumed low prices in all cases but in Run 18. In the study, export margins, defined as the difference between the pesos equivalent of the world price per animal increased by the export subsidy and the domestic price per animal, are used as a proxy for competitiveness in world markets. The subsidy to exports considered in the study is that enacted by Decree—Law 444 of March 22, 1967. Although Decree 444 also introduced a fluctuating exchange rate, the study assumes a return to fixed parity against the dollar when this reaches an approximate value of 25 pesos (Runs 12 and 20). Exchange rate values are trend-like projections computed exogenously in the model by Equation 9.18a on page 213. 327 As Figure IV.28 shows, the domestic market price per finished male is greatly affected by policies related to exports and production expansion. When exports are at a low level and modernization is limited to the Costa, policies to increase production (Run 14) result in lower prices than the base run. If modernization is carried out only in the Costa with the same development policies as in Runs l, 10 and 11, the larger export target in Run 11 results in the highest price. The major contributor to lower prices is the expansion of cattle population in the rest of the country as depicted by Run 12. Although Runs l, 10, 11 and 14, show a steadily rising trend through the period 1965 to 1985, the varying time path at each five-year interval shows the effect of the seven-year cycle of beef supply from the rest of the country., Run 12 experiences a sharp drop in price after 1975 when the increased production from the rest of the country herds is marketed. But an upward trend follows after 1980 which coincides with a declining path in the long-term supply cycle. Since cattle production is discouraged in the Costa by low prices and domestic demand is always increasing, it is likely that in the long run the price cycle in Run 12 will have a raising trend. From Figure IV.29 we can see that export margins are consistent with world prices and with policies affecting domestic 328 prices and the pesos equivalent value of exports.l/ Since Colombia's current and projected share of the international beef market is relatively small (the high export target sustained after 1975 is equivalent to approximately 161 thousand metric tons or about 10 percent of the total beef traded interna- tionally),g/ we assume an infinitely elastic demand for Colombian exports. This means that Colombian sales to the world market do not have an impact on world prices and that beef exports will be traded at the going world price. However, if Colombia greatly increases its share of the world market in the future, the above assumption would not hold and conditions of world supply and demand would have to be more carefully considered. When domestic price is high (because of high exports) and world price is low as shown in Run 17, export margins are negative for a period of about ten years, even if export subsidies are paid and the pseo gradually depreciates against the dollar. But further depreciation of the peso, to the point when the exchange rate reaches a 1985 value of Ps 53.98 for each dollar, results in a rising export margin that becomes positive during the last three years of the run. If world prices are moderate, export subsidies are paid, and the exchange rate is flexible, as shown in Run 18, export l-/0ne way of looking at export subsidies is by chang- ing the effective exchange rate at which certain commodities are traded. 2/ - Australian Bureau of Agricultural Economics, "World Production and Trade," The Beef Sutiation, No. 15 (May, 1971), 12-14. 329 margins are always positive and by 1985 could be as high as Ps 15,160/head. Runs 19 and 20 project the effect of two alternative policies for export promotion under conditions of high domestic price and low world price. Run 19 shows that if ex— port subsidies are cut off in 1975, a flexible exchange rate is not enough to produce positive export margins, although the upward trend of the last ten years suggests that in the long run the margin will be positive as the peso further depreciates. Yet, as shown in Run 20, exporters are worse off when a subsidy is paid but the exchange rate is fixed at about Ps 25 to the dollar. The downward trend in this case indicates that the negative margin will continue to widen as increases in domestic demand bid up the price. Run 21 shows that under conditions of low domestic prices resulting from increased supplies from the rest of the country and low world prices after 1975, a flexible exchange rate alone is sufficient to cause export margins to change the downward trend and become positive. And finally, Run 12 shows that if a fixed exchange rate is used in combination with an export subsidy the export margin takes an upward trend from 1975 to 1980, but after 1980 it again shows a downtrend when domestic price rises after increases in demand more than offset the impact of an enlarged supply. It is clear in this run that export margins follow a reciprocal pattern to that of domestic prices, and therefore the margins are affected by the long-run supply cycle as well (see Figure IV.28). Price of Cattle (Ps per finished male) 330 Run Definitions 1. Disease control, modernization of cattle pro- duction, low export target and continuation of present trends and policies (base run). 10. Cattle exports set at intermediate target from year 1974. 11. Cattle exports set at high export target from year 1974. 12. Cattle exports set at high target from year 1974 plus modernization of rest of national herd from year 1975. 14. Combines Run 4, Run 6 and Run 7. 12,0001" Run 11 Run 10 10,000 *' Run 1 Run 14 8,000 .- 6,000 4- Runs 11,1 ? Run 12 14,000 + All runs 4+ 2,000 *- % 11 § rd 1965 1970 1975 1980 1985 Figure IV.28. Domestic market price of finished males under various policy alternatives, 1965- 1985. Export Margin (PS/head) 331 Run Definitions 12. Same as Run 1 with high export target and low world price after 1974 plus fixed exchange rate at Ps 25 and modernization of rest of national herd from 1975. 17. Same as Run 1 with high export target and low world price after 1974. 18. Same as Run 1 with high export target and moderate world price after 1974. 19. Same as Run 1 with high export target and low world price after 1974 plus export subsidy cut off at year 1975. 20. Same as Run 1 with high export target and low world price after 1974 plus fixed exchange rate at Ps 25. 21. Same as Run 1 with high export target and low world price after 1974 plus export subsidy cut off and modernization of rest of national herd after 1975. 16’0001' 1u,0001. Run 18 l2,000<> 10,000 w 8,000 « 6,000 # Run 21 4,000 a All runs Run 17 2’000 0 Run 12 O «1 gr -2,000 0 " Run 19 Runs 19,21 -4,000 " Run 20 -6,000 0 % % t -% 1965 1970 1975 1980 1985 Figure IV.29. Competitive position of Colombian cattle in export markets under various policy alternatives assuming high export target and moderate and low world beef prices, 1965-1985. 332 Two comments on the assumptions about the exchange rate are relevant to this analysis. First, when the exchange rate is flexible or fluctuating, it is assumed to depreciate at 7.28 percent annually precluding any acceleration or tapering off of this rate over time. Second, when the exchange rate reassumes its fixed parity against the dollar it remains at about Ps 25 from approximately 1973 until the end of the run in 1985. This extended period of fixed parity is likely to result in an overvaluation of the peso that would eventually affect Colombian foreign trade leading to balance of payment problems and eventual further devaluation. The above considerations make the assumptions related to exchange rates in the model likely unrealistic. The annual value of the subsidy paid to exporters will depend on the volume of exports, the world price and the exchange rate. Given the same world price, the higher the exchange rate the higher the subsidy per animal exported. Although annual payments vary from year to year, at the end of the simulation in 1985 they are the highest when the world price is high, the exchange rate flexible and the export target high (Run 11) and amount to P8 5,179 million. But subsidy payments drop to P3 1,168 million when world price is low (Run 17) while exports and exchange rate are the same as above. If world price is high, the exchange rate flexible, but the export target is low as in the base run, subsidy payments are Ps 2,462 million. And finally, subsidy payments are the lowest amounting to P3 546 million when the 333 exchange rate is fixed and the world price is low even though exports are high (Run 20). The value of foreign exchange earnings will depend on the volume of exports and the world price and, therefore, its annual projection will experience a changing pattern that will also differ among the various runs according to their underlying assumptions. If exports are sustained at a high level, the projected value of foreign exchange earnings in 1985 will be $144.3 million when the assumed world price is low; $307.5 million when world price is assumed moderate; and $639.7 million when world price is assumed high.l/ If world price is assumed moderate, the projected value of foreign exchange earnings in 1985 will be $146.2 million when exports are maintained at a low level; and $220.4 million when exports are intermediate. The foregoing examples indicate that changes in world price provide the government with more latitude in the choice of policies for the attainment of foreign earning targets without greatly impairing other desirable goals. Recalling earlier discussions we see that increasing the level of exports reduces consumption per capita unless other measures are taken to increase domestic beef supply (see Figure IV.14). Yet it is also true that higher prices brought about by l/At the end of the run, the projected world prices per live animal based on the price of frozen beef are: low $208.5; moderate $444.4; and high $924.4. 334 enlarged exports could create the incentives for farmers to expand cattle production with the long-run effect of increasing the number of animals marketed for slaughter. Likewise, government revenues from the domestic sector are enhanced as profits, cattle inventories and land values are increased. Conclusions We have made eight major inferences from Our cattle policy experiments. First, given the assumptions behind the model, the availability of credit for development does not seem to be as crucial as originally thought. Although farmers use credit whenever it is available, there are substantial unused credit balances at the end Of each year between 1966 and 1976. Furthermore, the model indicates that, in the aggregate, farmers could cover establishment costs of the land being modernized with resources generated internally in the agricultural sector. This is shown in the model by the allowable rate Of modernization depending on the farmers' capability to meet the total establishment costs without credit support (ARM3 in Equation 5.42c) being higher than the combined rate of modernization due to promotion and diffusion (RLMI in Equation 5.40). As seen throughout this analysis, farmers' response to economic Opportunities determine their use of private and public savings for farm improvement. However, cash flow problems developing late in the simulation suggest the need for a comprehensive credit 335 policy. These outcomes are seen more explicitly here when increasing the competitive edge of the modern over the traditional Operation; in such cases, easing the credit terms, as in Runs 6 and 7, encourages modernization more than increasing development credit funding as in Run 8. And the sharp decline in annual income in all runs after 1975 is an indication of farmers' unbalanced cash flow. The preceding results imply that providing credit assistance for a longer period might be more effective than increasing the volume Of funds within the ten-year period (1966-1976) set in the model. Second, the model's output indicates that the performance indices depend heavily on farmers' attitudes toward adoption and continued use of the new production methods. This behavior is simulated in Equations 5.36 and 5.47 using principles of diffusion theory. But modeling farmers' decisions to expand or contract production on the basis of diffusion theory is a poor proxy to sound economic principles explaining such processes. Until more adequate user cost, investment, and disinvestment theory is developed for use in modeling expansion and contraction of agricultural production, the model would benefit from more research and experimentation on farmers' response patterns under the diffusion theory used in this study. Third, the benefits of long-term output responses in modern pasture acreage expansion are reinforced by policies directed toward the modern sector only. Otherwise, the 336 relative profitability differential of improved over traditional cattle production is decreased and the adoption rate of improved management practices is reduced accordingly. The effect of production incentives and the production campaign, particularly promotion, are highly complementary in encouraging the farmers to modernize their system of cattle production. Fourth, cattle inventories and output in the Costa depend decisively on controlling diseases in the traditional sector. But at the national level, the major effect is Obtained when cattle production is also modernized in the other producing regions. Fifth, the cumulative effects of policy combinations (Runs 14, 15 and 16) on the model's output are, in general, greater than those of single policies. Positive effects of single policies are reinforced by complementary measures when simultaneously applied. Reinforcing and offsetting policy effects are important characteristics to be considered by policy makers when designing strategies for economic development. Sixth, the competitive position of Colombian cattle in world markets is decisively dependent on world price and policies concerning domestic supply and export incentives. In this analysis, a negative export margin indicates that domestic price is higher than world price as viewed by Colombian exporters and implies that they cannot compete in world markets at the price beef is being internationally traded. The results shown in Figure IV.29 suggest that the 337 need for and the amount of transfers from the rest of the economy to cattle exporters in the form Of export subsidies can be examined and determined in the face of both domestic and world prices and exchange rate policies. Seventh, an alternative course to policy makers' active participation in the model was to assume certain policy changes during the period 1966-1973. In this period various programs were implemented (cattle develOpment and animal disease control), legislation was prOposed (increased property tax, presumptive taxation Of agriculture and elimination of cattle selective inventory tax), and important decisions were made (extension Of general cattle inventory tax after its expira- tion in 1970). Or, using another interpretation, retrospective policy experimentation is a demonstration of how the model could have been used as a planning and decision-making tool had it been designed at the time decisions were made and/or policies proposed. Quick legislative action, assumed in some cases in the model, could possibly have been obtained by the use Of the model. Models designed to predict the outcome of alternative policies have the additional application Of prompt- ing government Officials and legislators to action. The Costa model attempts to evaluate the effects Of an early implementa- tion of various proposed policies and the likely effects of having started cattle develOpment programs applying different measures to those used at the time of their introduction. Initial time Of alternative policy implementation can easily be changed to 1973 or any other future year. The model's 338 structure is flexible enough to allow these changes. Given the structural relationships and assumptions behind the model, it can be expected that its output variables will experience the same trends as shown for the period 1966-1973. Changes in initial time of policy implementation were planned in additional experiments with the model but were not accomplished because of time constraints. Meanwhile, it is believed that extrapolating results from policy implementation during the period 1966-1973 will produce legitimate predictions for another future period, say 1973-1992. Finally, throughout this study we have demonstrated that a simulation model provides a useful experimental setting in which policy makers and researchers can interact at different stages in model creation and develOpmental planning. This interaction is instrumental in redefining goals and policies, and reformulating parameter values and interrelationships. With different and improved sets Of goals, policies, parameter values and interrelationships, new experiments can be carried on and new outcomes analyzed. This interactive, iterative process can and should continue until results are judged to represent the real system reasonably well and until decision makers are satisfied with their goals and the display of pro- jections indicating the effects Of following their various development strategies. In our case, each Of the policy runs have projected different outcomes at different time phases of the planning horizon. These projected differences provide policy makers 339 with some basis for selecting a prefered policy option on the basis of tradeoffs among perceived values for the economy. For example, if the perceived dominant values for the cattle subsector during the next 20 years are farmers' income and the foreign exchange generated from cattle exports, Run 11 (which has the high export target) may be preferred. 0n the other hand, if a perceived paramount value is government revenue, Run 5, which gives the highest annual revenue and accumulated funds for government, is the logical Option. How- ever, if the dominant value is to ensure consumers a higher availability Of beef, without impairing farmers' and government revenues, Run 6 (with the reduced interest rate feature) would be recommended. If the Objective were to in- crease per capita consumption of beef up to or above the recommended nutritional requirement of 28 kilograms, the selected Option would be Run 16 (with the non-Costa cattle modernization feature). But this run, even though it maximizes foreign exchange earnings from cattle exports, seriously impairs farmers' and government revenues. Hence, in this particular situation, the tradeoffs are between a loss in government revenue and an increase in output and personal income of farmers in the cattle economy; and between an increase in beef consumption and a loss in foreign exchange generated from cattle exports, government revenue and farmers' income. CHAPTER 13 SUMMARY AND CONCLUSIONS Introduction and Summary Colombia, as many other developing nations, is facing a new pattern Of food scarcity in the decade ahead. The increased demands of her rapidly expanding population are added to the impact of rising affluence on demand for food. To meet domestic needs and take advantage of world market Opportunities to increase foreign earnings required to support development, Colombia must make a great effort to encourage agricultural production, particularly the output Of the protein-rich food which is in greatest demand. With vast natural resources suitable for cattle pro- duction, this industry has the potential for becoming a lead- ing sector in the Colombian economy. To this purpose the Colombian government has committed resources for preparing and implementing a nationwide plan aimed at the development Of beef cattle production. With about half the cattle popula— tion and with a regional comparative advantage for grazing, the Atlantic or Caribbean plain of Northern Colombia is re- ceiving most Of the development effort. With the preceding considerations in mind and realiz- ing the experienced usefulness Of the systems simulation approach in overcoming many Of the complexities of develOpment 340 341 planning, this dissertation developed a simulation model which: (1) focused primarily on the production of cattle in the six departments of Northern Colombia (the Costa) that include most of the Atlantic plain, and (2) included only rudimentary considerations on the related production Of crOps. The model thus developed is capable of exploring the ramifications of the proposed government strategy and their resultant interactions and feedback effects. This study is divided into four parts incorporating basic background information and material relevant to the model building and analysis. Part I describes the general problems of producing cattle in Colombia and the physical and economic setting of the Costa region, discusses the justification for using the systems simulation approach, and finally sets the model's specificationsand procedure. Part 11 details the five components used to simulate the production Of cattle which: (1) allocate land use accord- ing to the farmer's perceived profitabilities of cattle and crops subject to land and capital constraints; (2) calculate the yield and output of cattle and crops and their respective producer and market prices; (3) provide the instrumental linkages for the government revenue, export trade policies, and production campaign policies; and (4) generate the performance criteria necessary to evaluate the impacts Of alternative programs on the cattle economy through time. Three major policy entry points are considered; production campaigns can be specified, cattle and property taxes can 342 be levied, and export targets and incentives can be regulated. Part III discusses data needs and methods used in dynamic system models to determine the correspondence between the model and the system being represented. These methods in- clude sensitivity analyses, tuning the model to track recorded time series and general validation procedures. Sensitivity analyses not only reveal logical or theoretical inconsistencies but also can provide an indirect way to test policy options and suggest data collection priorities. Part IV demonstrates the model's applicability to policy formulation. Chapter 12 analyzes the results of 21 runs that examine combinations Of policy options which have recently been considered in Colombia. Salient Features Of the Costa Model In this summary some of the salient features of the Costa model are discussed. Then some policy implications and areas for additional research will be discussed. First, the model is mathematical. With a mathe— matically formulated model, assumptions about behavior, technology and institutions are translated into the universal and precise language Of mathematics which makes them relatively explicit and open for examination. Second, the model is operational. That is, it is a computerized model and can be Operated without much dif- ficulty and at very low cost. Using an operational model is a major step forward in the task of modeling sectorial and/or regional economies. Experiments can be performed and 343 new improvements in the model introduced if needed. Re- peated runs using refined data and structural relationships improve the model's representation Of reality and its over- all performance. Without an Operational model, it would be difficult, if not impossible, to accomplish these ends. Third, the model to a large extent is data based. That is, many of its relationships have been formulated and tested using actual data. The model's outputs can be checked against actual data. These points are important because the degree of confidence we have in a model depends to a great degree on the extent to which it is able to explain past variation of variables and to predict future variation. As discussed in Chapter 10, when regional data are poor in quality and quantity, vast data requirements make the task of formulat- ing and implementing a model with measurable quantities particularly difficult. Although models can be built even when information is poor, the system simulation approach pro- vides means for dealing with the data problem by indicating where improved regional information would yield high returns in terms of a better understanding of regional phenomena and superior models. Fourth, significant steps forward are made in the modeling Of cattle demography. Three age cohorts for females and two for males with age-specific birth rates and age-sex— specific death rates, sale rates, transfer rates, and disease treatment rates have been employed which make the model's outputs change in extremely significant ways in 344 response to changes in the age and sex distribution of the cattle population. Such changes feed back in the cattle demographic component through time delays and induced behavioral effects to influence the pattern Of cattle popu— lation development that produces further changes, and so on. Those endogenous interactions between cattle demography and performance appear to be vital elements in the process Of regional economic growth. Fifth and last, although the model as presented here needs further work and is not ready for implementation, it affords a good example Of how the system simulation approach provides an analytical framework within which researchers and policy makers can interact while formulating alternative cattle policies. We were specifically interested in evaluat- ing the long-term economic impact of modifying cattle prices through exports, revising tax policies, and the proposed government production campaigns to expand cattle production in Northern Colombia. Crop improvement and export policies were included as a secondary Objective. To this end, the computerized cattle simulation model provided a very useful and a convenient means Of predicting and comparing the outcomes Of various combinations Of cattle programs and policies. Based on the predicted time paths Of the various performance indices Of the cattle subsector, the merits of various policy alternatives were discussed. This capability of the model to project the time paths Of various performance indices can be used to give the policy 345 maker a clear picture Of the range of possible outcomes Of each proposed policy. In addition, policy makers not only benefit from the analysis of potential policy results but also from the process of formulating the simulation model itself. Planning Officials taking part in the model development process are forced to specify, examine and study their assumptions, data sources, underlying interrelationships and impact Of each policy upon the model structure and parameters. Thus the planners may refine and improve their decision process and the information used in it. As the process of simulation model development and experimentation proceeds, both re- searchers and planners gain greater insight into the mechanisms and likely patterns of change within the system being modeled. Further, the decision maker can play a more active role in the experimental system by making exogenous policy decisions at the end of any time period and allowing immediate feedback on the results of alternative decision patterns and policy choices. This iterative process involving close interaction among decision makers and system analysts engages decision makers in investigation activities that lead them to perform as researchers as well. Hence, the simulation model becomes not only a valuable analytical tool in helping decision makers in their planning, policy formulation, and program development activities, but also an educational tool that enhances their planning capacities [28]. 346 Policy Implications from Simulation Experiments on the Costa Cattle Economy There are six major inferences from the simulation experiments which may throw light on questions Of public policy concerning a cattle deve10pment program. First, investments in government disease control programs are justifiable since the projected cattle output, given the same model assumptions, was higher when improvements in range and herd management were accompanied by disease control in the traditional sector. Nevertheless, long-run assessments of the pay-Offs to "traditional" farmers require a more accurate accounting Of costs with and without the extended treatments. Second, measures aimed at improving the profitability Of modern cattle alone are the most effective in encourag- ing cattle output and increasing farmers' incomes and govern- ment revenues. Easing the debt burden, particularly interest payments, had the greatest long-run effect on all performance variables. Although credit has been considered crucial for development, the results point out that farmers' attitudes constrain the use of capital resources to a point that in- creased credit funds went largely or totally unused. The pay—Offs of "educating" the farmer, demonstrating the profit opportunities of the new practices and creating a socio- economic environment amenable for investments seem to be very high. Further, the profitability Of the cattle sub- sector relative tO other sectors in the economy must be carefully considered if capitalization Of the former is a 347 preferred goal. Otherwise capital will likely be diverted from the cattle subsector to more profitable sectors of the economy. Third, increasing the domestic price level Of cattle by increasing exports from low to high targets is not by itself an effective tool for expanding cattle modernization and output. While it is true that higher prices greatly in— crease farmers' income and wealth, they also have the effect Of curtailing the incentive to modernize by reducing the profitability differential between traditional and modern practices. Consequently, if modernization is to be enhanced by taking advantage of the farmers' expanded revenue, it is necessary to implement other policies that would increase the profitability of the modern operation relative to tradi- tional. Yet, when total Colombian cattle Output is greatly expanded because of developmental efforts in the non-Costa regions, the simulation analysis indicates that a pricing policy through the export market is crucial for sustaining modernization and preventing farmers from incurring heavy income and capital losses. Likewise, government revenues would not likely be improved because Of farmers' higher income unless there are more effective ways of taxing farmers. Fourth, special taxes on cattle are the main sources of government revenue (not including income tax). Cutting Off cattle taxes, in addition to removing this source of revenue, has the same counteracting effect on modernization as price increases (discussed above). However, any loss 348 in government revenue that might occur from cutting Off special cattle taxes could be compensated by more effectively taxing the farmers' increased income and the asset value of their land; or by taxing their increased purchases of pro— ducer and consumer goods. Although increasing taxation on land has, from the point Of view Of government revenue, an equivalent effect to levying taxes on an expanded cattle population, the likely allocative effect of land taxation makes it a more preferred policy Option. These results suggest the need for a careful reassessment of the taxing policies toward the cattle subsector and the consideration of alternative policies that will not impair government revenues and will not interfere with the allocation of re- sources on the farm. Fifth, if improved cattle productiOn is to be used to bring some sort Of redistribution of income in the region, it is a requisite that the medium and small farmers are not left behind in the modernization effort. Alternatively, modernization might be accompanied by a change in the pattern Of land ownership that prevailed in 1960. According to the agricultural census Of that year, three-fourths of the cattle farms were less than 100 hectares; they controlled one-fourth Of cattle and had an average inventory of less than 50 head Of cattle [14]. Finally, the world price Of beef greatly affects foreign earnings. It is, therefore, very important that the government secure the highest world price for exports. 349 This can be attained by exporting high quality dressed and/or processed beef to bring higher prices in inter- national markets. In addition, this measure might increase the Colombian value added Of exports with spill-over effects on the economy. The policy experiments show that the profit margin of cattle exporters varies according to domestic and world price conditions and measures affecting the effective rate of exchange. The combination Of a fluctuating exchange rate and an export subsidy seems to give an ample profit margin to exporters. This suggests that the subsidy could be adjusted periodically based on cattle and foreign exchange market conditions. An appropriate exchange rate could main- tain a competitive edge in international markets, thereby eliminating or reducing the need for largetransfers from public revenues to exporters. Since cattle exporters com- pete with suppliers to the domestic market, the foreign earning targets set by the government may affect the nutri— tional levels of the Colombian population. By the same token, exports have a regulatory effect on domestic prices that in turn affect incentives to produce. Improvements and Extensions Of the Model The simulation Of dynamic human systems is a pro— cess of trial and invention that can never be completed. Each simulation result teaches and prompts additional ques- tions leading to an iterative procedure that helps sharpen data and verify structural and causal relationships. It 350 also may disclose problems in the original formulation of goals, feasibility and methodology that might need refine- ment and reformulation. Revisions may also be required by the changing needs Of planners and policy makers. The extent Of these experiments and changes demand costs that must be weighed against the expected returns of increased model accuracy, flexibility and relevance before a decision is made to proceed with the modifications. A number Of areas in the current Costa model need further attention in order to improve its performance. These are discussed below. Experiences with other regional models [1, 27, 53, 62] have suggested possible extensions to enable it to better address some Of the major problems Of economic develOpment. These will also be discussed. Needed Improvements in the Model There are several aspects of the Costa model which need further development and verification. First, it is not certain that the model of the domestic cattle price mechanism (Equations 6.39a and 7.2) adequately or even realistically represents the actual operation of that market. In particular, the pricing Of cattle other than finished males is an oversimplification Of supply and demand for the various sale groups. Furthermore, the link between the Colombian market price and the regional market price is not clear since regional prices are sensitive to short-term fluctuations caused by seasonal changes in pasture yields. 351 In addition, the supply response to price changes is not yet very well understood. Since the model is fairly sensitive to cattle prices, further research and eventual modification of this aspect of the model may be indicated. A second area that could call for further work is the simulation Of cattle demography. Although the use of different demographic groups is an improvement over previous regional cattle models [51, 53, 55] a further step could be to calculate the productivity Of each age group relative to the feed resources consumed. This will permit more precise computation Of feed requirements and total herd productivity (kilograms of gain per animal-year). The latter will enable the model to estimate the nutritional contribution to the Colombian population more accurately.l/ If policy makers and planners feel this is an area they would like to investi- gate more fully, revisions of the model will be necessary. A major feature of the model which needs theoretical and empirical verification is the modernization decision mechanism (Chapter 5), particularly the value of parameters that determine the adOptors' behavior. A development program taking place currently in the region and resembling that described by alternative 2 (improvement and substitution Of artificial pasture for native pasture) provided both an empirical base for assigning values to these and other model parameters as well as justification for focusing the simulation 1/A detailed demographic model Of this sort is dis— cussed by Johnson et al., in [48]. 352 experiments on this alternative. Yet other alternatives including forage crops, though with greater impact on the model's output and performance, were disregarded because Of inconsistencies probably arising from the simulation of farmers' behavior in the face Of the higher risk and un- certainty involved in these alternatives. Additional research is necessary to better understand the nature of this problem before further modeling work can be done on it. Another unrealistic aspect Of the decision mechanism is the assumption that capital is employed at a low Opportunity cost in cattle production. This does not effectively represent the capital market in Colombia where transfers of capital out Of cattle farming are certainly occurring with the subsequent effect of impairing the farm improvement effort. It also seems unrealistic that the private capital constraint on land modernization decisions (Equation 5.41) is practically inactive during most Of the simulated time and only becomes effective toward the end of the simulation period when con— sumption expenditures are greatly increased. It is more likely that the capital outflow effect discussed above- coupled to the demand for capital to buy cattle on an individual farm basis, which is not in the model, would put an earlier constraint on land modernization decisions. Other constraints, which are not in the model at all, are the allocation of commercial credit through the banking system, the availability Of labor (including management) and the availability Of other inputs (fertilizers, 353 herbicides, seeds, and drugs, primarily). Thus, it may be desirable, if the model is to be implemented, to give high priority to modifying the model to realistically reflect actual input constraints on land allocation and production decisions. Related to these possible shortcomings of the model are the problems Of investing and disinvesting and the genera- tion and use of farm-produced capital. The former is approxi- mated in the model by the land modernization and reversion mechanisms of Equations 5.36 and 5.47, and the retention or sale Of cattle accompanying the increased or reduced carry- ing capacity brought about by these land transitions. Yet, as pointed out by Johnson [47], a solution to these problems needs more development in economic theory concerning user costs which partially determine how many units Of productive services are generated from fixed durable inputs in the intricate process of agricultural growth, change, and/or deterioration. Two examples of other structures of the Costa model which may require further verification are the on—farm re- source use response and the living expenditures adjustment mechanism. First, it may be questioned whether Equation 6.36, in assuming the functional relationship shown in Figure II.7 between the perceived relative profitability differential Of traditional and modern cattleCNIthe one hand and, on the other, the extent to which farmers make 354 use Of their on—farm resources, realistically or even adequately determines their preference function. Secondly, further information may indicate weak- nesses in the formulation of living expenditures, particu— larly if disaggregation by income groups is accomplished. Likewise, this formulation does not model income effects on consumption brought about by changes in the real income Of farmers. Finally, this model may always be improved by re- fining the data that go into it and by a closer interplay among specialists from related disciplines and policy makers. Extensions Seven additional ways in which the Costa cattle simulation model can be extended for policy analysis will be listed here. First, it should provide a more compre- hensive basis than it currently does for analyzing govern- ment revenue and budget expenditures for agricultural moderni- zation programs. The present model was not able, nor built, to generate the effect Of a graduated income and wealth tax for cattle producers whose efficiency could be compared with alternative consumption, land and special taxes. Likewise, the present model did not provide comprehensive grounds for balancing and allocating the government budget for different modernization programs whether on a regional or national basis. 355 Secondly, to discuss meaningfully the distributional impact of government policies on investment in production campaigns, exports and public revenues, and the response Of farmers to these policies, the cattle subsector may have to be disaggregated by farm size. Since farmers in each size category have a different command over resources and their own beliefs and values, such disaggregation may be helpful in analyzing problems Of income distribution, and in simulat- ing more realistically their decision making. It may be useful to further disaggregate the region by ecological zones and to subcategorize the cattle industry by type of activity, i.e., breeding, growing, fattening, and their combinations. A third possible problem area which could call for an extension of the model is the question of growth and age distribution Of the rural population. This is closely related to the problems Of employment, labor supply, and pressure over natural resources, which in turn involve aspects of income distribution, rural-urban migration and land ownership distribution. The dimension of income distribution, a pervasive one in all develOpment plans, would be an important output criterion for evaluating alternative policies. Fourth, as part of the process of improving the land use decision mechanism of Chapter 5, the crop subsector may have to be disaggregated by competing commodities in order to determine a more realistic crop mix. This will 3S6 allow the more accurate simulation of the competition between crops and cattle and, in addition, may suggest policies toward improvement of individual crops and regional specialization. Likewise, the model may be extended to relax the constraint Of testing each modern alternative one at a time and include mechanisms for the adOption Of various alternatives simultaneously. This may reflect more realis— tically the various responses Of farmers with respect to profitability perceptions. The two extensions suggested here may have Substantial impact on the performance variables that could be Of interest to policy makers. Fifth, the sensitivity of the model to cattle price changes suggests the need for including a semi—automatic decision-making mechanism whereby the government export policies in any one year depend on the interaction between the prevailing world and domestic price of beef. Export targets, subsidies and exchange rates could be set with the aim at maintaining both a price incentive to producers as well as a competitive position in international markets. The major benefit of such a model extension would be to help determine a more flexible governmental export policy which would stabilize farmers' income and net government revenues from exports, given the fluctuations Of beef prices. Sixth, one extension of a technical nature men— tioned in Chapter 10 is running the model in a Monte Carlo stochastic mode rather than deterministically. Although there are various theoretical problems involved in the 357 use Of Monte Carlo techniques [1, Chapter 11] such stochastic runs could be useful in dealing with data problems and in evaluating the relative stability and sensitivity Of policy alternatives in the face of uncertainty. Giving a proba- bility distribution to some of the data instead of a mean value, it is possible to incorporate methods of statistical sampling and inference into the outcome Of the model. Such statistics would permit the evaluation Of ranges and dis— tributions of possible outcomes for different policy Options rather than point predictions of absolute output levels. Finally, there are extensions on the scope of the Costa model that would be relevant to policy makers in their task of solving the problems Of development. First, the present version of the model can be easily adapted to the other four Colombian cattle producing regions and further integrated into a national subsectorial model. Secondly, based on other experiences, it could be expanded into an agricultural sector regional model or even a com- plete regional model including both the agricultural and nonagricultural sectors. The experience gained from these extensions and the information they contribute are of un- questionable relevancy for development planning in Colombia. Concludinijemarks We have discussed some Of the shortcomings of this study and have suggested means by which they can be dealt with in order to improve the model's predictive and prescrip- tive capabilities. As we have seen most Of these shortcomings 358 arise from data problems and from deficiencies in the economic theory required as a basis for modeling many of the activities found in developing economies [47]. The shortcomings we have encountered, however, also affect the accuracy of models built using simple, paper—and- pencil techniques, or the more complex and specialized techniques discussed in Chapter 1. Nonetheless, we have reasoned that the systems simulation analysis as used here, with its flexible approach to many of the methodological problems found in studying economic development, provided an improved framework for policy, program and project prob- lem analysis. It must be stressed that the Costa model yields usable estimates of the consequences Of following several policy strategy alternatives over a periodof several years. Furthermore, the present work constitutes a major improve- ment over other cattle production studies made in Colombia as well as a useful contribution to the study Of likely con- sequences upon regional growth Of developing a leading agri- cultural industry. In the future, when more and better regional statistics and research and more advances in our knowledge Of regional develOpment are available, still more information could be introduced into the model to correct current inadequacies. The experience and lessons learned in the present work and others like it will be valuable in future modeling efforts. APPENDIX 'COMPUTER PROGRAM PROGRAM SIHCOLCINPUY.OUIPUT.YAPE131NPu1.7AP52.ouYPUvi sxncoL cannon Icowan/ 7.0'oDUNoIRUN.BEGPR1.PRYCHO.PRTVL1oPRYVLz,lvn|uf. ca; 1 HALoT‘OUoYDO cu! READ9000NRUV sxncou DO 200 lRUN-ioufluw sxncoL CALL RUNDAT SIHCOL CALL PRISET SlflcoL CALL LANSET SIHCO' CALL CAYSET SIHCOL CALL cansev suncog CALL PROSET SlHCOfi CALL ACCSET SIHCOL CALL HODSET sxncog CALL CRTSET SIHCOL PR? - uEGPRY SIHCOL PRTVL I PRTVLI CNS T i 0. S‘HCOL 20 IPRINT . 0 since; T a Top? SIHCOC IFIT.LT.PRT)GO Y0 40 SIHCO£ IPRINT I 1 S!HCOL IFCT.EO.PRTCHG)PRYVLIPRIVL2 CH2 PR7 I T‘PRTVL Slflcog 40 CALL PRIGEN sxncoL CALL LANDAL stucog CALL AGPROD Stucog C‘LL DEHOG snacou lr(T.6E.Tnon)CALL noncnu SIHCOL CALL noacc sxncog CALL HODRAT SIHCOL CALL CRTACC SIHCOL 1r(7.LT.DUR)co to 20 sunco; 200 CONTINUE SIHCOL STOP SIHCOL 900 FORMATCli) SIHCOL END SIHCOL .DVOUONFN 36() d susuourlws nuwnar . RUNDA! COMMON ICONVRL/ Ton'oOURoIRUNcaficPRYoPRYCHOQPRTVLiu'RVVLZoIPRllTa CH5 1 HALbTHODofDO CH5 NAHELIST INAHRUN/ nT.DuflolEGPR1.PRTCHa.PRYVLIQPRTVL20"AL.YHOD.TDO CNS tr:lauw.ar.i)co to so ‘ nuwoat Dun . 10. Ruuoat PRTCHG - 106. cu: PRTVLZ I 5. CH2 PRTVL1 a 1. CH5 BEGPRT I 1. RUNDAt THOD I 100. RUNDAf "AL I a RUNDA? D? a .25 RUNDA' READ(1:NAHRUN) nuuoat HRITE(2.NAHRUN) RUNDAt RETURN RUNDAt END RUNDAt OOOUOUOQONMR SUOROUYINE PRIGEN COMMON [ACCOUN/ D'L‘TIINLOYI‘CL‘VIVLANDYoVLD'XYoTAXCTIAGSUT. D'L‘"I‘"LOHIOCL‘"IVLIHD‘IVLD'XHIY‘XCHILGSUHa D'LAV.DCRU.ranHIC.DWYDUS.Dant.RCSTDY.ExILlv.ucrn. ALPHII'ECoTCEC.YRNSLS.7L7.TLFPaCPLraR1NTIRINYLo C“ED'ID‘QICZSO.C253ICZ5‘ILY10L720L730CBSTGDCSYGHo OIabt-NMI REAL NCFR COMMON ICATYLE/ JI‘O‘UI&OI~FP COMMON ICONYRL/ 1 090 'ORHAY¢86H1001PUY or suonouvtus PItDEw at vtns.ro.2: 010 fonnavc1H0.ox.2HIA.101.3HRAP.0x.4HexIL.II.3HDEH.9x.aHYD§n.Ix. 0HEIcnvuzax.ouPcnoru.0x;cuPcnopL.ox.aunrcnorI1H0.0x.9£12.00 ENTRY Inxsev PCIOPU . 036. PcaorL . 000. PrCIoP . 2‘90 EICIIU I PCIOPU PA I 1067. Yuan I 1009 00. Dew . 17301 0. SUPB - 1009100. PIP 0 ’070 1 REAL ”KN EPcRPJ.PCBOPU.PCR)PL0P'CROP. ‘ EYLDCJo'LDCUoYLDCLoVLOFCaDYLDCUonVLDCLaOYLD'C Irc1.reut.PrIt;PH37.PHPY.IDPDPY.YDPOPR. PIG". PrPH.PH3n.PnPI.YDPDPH.VACaPL.auxttz. erenr.sLsccr.sDLDrY.SLSHLT.SLSPY.SLSPYP.rEHscv. SLrERI.sL9ccn.sDLDrn.SLSMLM.SLSPH.SUP8.rEHSCH. pprctr.vIrPtt.IIHDTT.IPIPVT.PPVGVH. PPrPrn.PPHDVH.PPHPTH.anTv.0791!. augvr,nnptt.arcvn.nrprn.RHGVH.RHPYH. Yout.rD~ar.tDNn.IDNMP.Iowan.PaYaDY.DHN.DRY.PFLACY. YNA'.’AIP‘PoPAPpIPROV.E‘PLIUNEXFL0C72020C214I32“ 1.D'.DUR.IRUN.sEGPRr.IRVCH0.PRYVL1.PRYVLZ.IPRINY. "‘LIT‘OUI'DO DIMENSION VaLsic11) DAYA c219.c237.c230 I 1.. .1. D‘Y‘»EL‘SDIEL‘S!OFL‘S' I 0,. 060 10 I DAYA HKH.UHEXPL I .15. 000000. I DAYA DEL? I 5. , Dara snaLL55.Dxrrsa.xso I 4.. 1.. 10 I , DAYA vaLsD I 3160.. 70023.0 50375.. 19040,. 1y140.. 50270.. {25700.; 191700.. 205000.. 202000.. 329000. I I DAYA PAPO / 957.] EPCIPU I EPCRPUI¢PCIOPUIEICRPUJGUY/DEL, PCROPU I PCIO'UIRCSVDY PCROPL I PCIOPLIRCIYDY E'CIOP I PFCIOPIRCIVDV PPA I PA PA I PAIC1..621IICYOEQIQUPO’POYI(YDEHIELASD)i PAPP I PAP PAP . Ia-cxtoan: PHAT 0 Par/Ian; _ EDEN I ELAS 0628705LA5P0¢830IILA800(Pa-PPAiliPPAIDTi EXPL I D. _ . Ir¢1.0£.4.)IxPL-tastilcvauso.DHALL50.Dtrr90.x§i.Y: TDEM I DEMoIXILouHEIIL lrclIRlNT.Lv.1)IEVulN anuraoo.r ' _ . anwroao.Pa.IaI:exIL.D£I.rosn.elcnpu.pcnopu.IcIDIL.prcnor REYUIN 3(11 .032 I PRIDE! ACCDUV aCCOUV ACCOUV accouv cu7 accouv ACCOUH ACCDUH CAYYLE CAYYLE CAYTLE CAYYLE CAYTLE CAYYLE CAYYLE CATYLE CAYTLE CH5 CH5 PRIGEH PRIGEH PRIGEV PRIGEH PRIGEH CIS PRIGEH P0105» PRIDE! Patsev PRIGEH CH4 CH4 CHO IRIGEH PRIDEH IH166H PfllcEH PRIGEH PRICE! PRlGEV PRIDE! PRICE! IRIGEH PRICEV PRIDE! IRIGEV Intseu PRICEV PRIDE! PRICE! PRIDE! PRICE! PRICEV DHIGEH PHIDIH PRIGEV PRICE! IRIGEV PRICE! PH!DE\ N‘DC‘OOIINlPBifla‘UOWIOOIN1..‘i”1lbh"0~ wan” canau FW‘ lulu-Inna 362 RETURN END PRHBEN 46 PRIGEN 07 20 .“~0‘ .0 363 SUOIOUYINE LANDAL CDHIDH IcowtaLIt 7.0'000“. IRUH.0EDP0t.Iltcuo.IntVL1.IIYVLI.IPIINY. HaL.tIDD.tDo tDL.tt0L.ttDLL.7Y0L01.TYDLuz.tILIFD. YIIL.YLIOD.7L0000.tLHDDz.thons.YYOLR.t0LI0 tLCIL.thoo.thnuP. tDLI'.t0LuiI.YLtc.YLc. xlg.R1,H2.PR2.PDl.CRH. RLM.RLHI ,ILcnu.aux1.auxa. AIH1.a R82.ARH8.05.09.0Rf.cPLIt.xDEL.RP1~.IIE DIMENSION cnout0(3).CI00101¢00. VALocti! ”‘7‘ ‘LZ.¢2"OC'D‘.CL2 I 0.3. :.O 0,. O, I Data vaLa9 I 20000.. 10920.. 0050.. 7000.. 5000. I Data DEL4.DGL11 I1. 5. 10.I Data DEL15. pEL10.DEL17.DEL10 I 405. I Data K4.K11.K49.Dlrr40.snaLLooI 3. 0. 4. 1.. 0. Data tcho. tsLsr0.tLDRNL0 I 101700.. 0137050.. 0037050. I Data YLAVLD. tLavuo1.tLavuoz I 530300.. 2109000.. 10700000 1 DOT. xDELORLDRNIRLcRL I to. 1”..II ‘5000 I Irct. Lt. tnooioo to 20 TRSL I (XRioRZORLIDIXDEL EO . 10 . érngOLL.LY.0..AND.YTCLL.CT.DYO(IRTOLL)AEGIit 1 3 10 éffifiTGL01.LY.5..AND.TYCLJL.OT.DTI(IRYDLUA))EafiIov 1 ' 10 IFLRTGLUZ.LY.O..AND.TTGLUZ.07.0"(IRYCL02))ESQIC0 YLHoD1 . anax1¢anlw1ctLIoD1th.1nLnotDLLItDL.tLHDD1.P02.e¢ontDLLo TLH001/YGLL). tDLL). 0.) COMMON ILINO/ 1 IYLHODZ I AHAXICAH!N1(YLHODZIDTO¢RLN'YOL91ITOLIYLHOD2"IIIIYBLU$' EiGOTLH002/78L0130 tDL01). 0.) 'LHODJ I AHAX1¢AHINICYL"0030070(RLHIYCLUZIYCLIYLH0030'N20RTCLUZ' 1 £14IYLH0D3ITDLUZ). TDLUZ). o.) 'LHOD I YLHODioYLHoDZIYLHODJ CDNthUE YLAVL s tLAVLoctLAVLD-YLAVL00DtIDEL15 *Lav01 - tLavu191tLaqu1-TLAVU10007IDEL10 YLAVUZ I TLAvuzItVLAVJDZ-YLAV02)IDTIDGL17 RLFC I ALZItchfioExPCALZIT) TU'C I YLFCODYORLFC TLFCU 0 C249-TLFC CALL DELAVLILDRN.aux10.CRDUV11.DEL11.ot.Ki1) TLDRN I AHIN1CTLDINthoadXID. YLDRNLO) TLFCL I AH!N1(TLFCpYLfcu. YLAVLDIYLDRN) YLsau . 20060. lrct.0£.0.)tLaaw . tABLIELVAL49.SHALL09.Dxrr4I.K00.t) ALNDL I tLAVLotLDPNvTLFCL-TLDAH . 'LCRL I ANIN1(YLC'LIDYIRLCRL. ALNDL) YLCRLR I YLCRLOYLFAN TGLLP I TGLL TGLL . anaxi(YLaVL-tchL-ILCRLR. ..t RTGLL I (YGLL-YGLLP3/DY ICLSF: I YGLSF1¢(tcLsro-teLsr1gthIDEL10 tsLsr - tDLsr1-3LDRN 'LFCUiP I TLVCU1 TLfDUl I antwilcrn1otchu. DLPCUI I YL'CULIYLFC01P ALNDUA I TLaVU1vtchu1 AUXI I D. _ IFIRLCRU. LE. 0. )auxa-cLzoDchu1 TLCIUP I TLCRU thIu - aHa11¢AHx~1(TLCRU-Aux0.Dt-¢aszoauxst. ALNDUI’. 0., YGLUIP I YOLUI YLAVUI) LANDAL CH5 CH5 LAND LAND LAND LAND LAND LANDAL LANDAC LANDAL CHO LANDAL LANDAL LANDAH LANDAL CH4 LANDAL LANDAK LaNDaC LANDAL LANDAQ LANDAL LANDAL Lanoau LANDAL LANOAC LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL Lanna: LANDAL LANDAL Lanoag LANDAL LANDAL LANDAL LAHDag LANDAL Launac LAMDAC Lauoag LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAC LANDAL LANDAL LANDAL ..V...‘..-l‘~ 000 tcLu1 a ALNDUi-YLCRU LANDAL 50 RtsLu1 . (TGLU1-TGL01’)IDT _ LANDAL 50 CALL DELAYcAUX13AUx2.CROJYQ.DEL4.DY.KI) LANDAL 0| tucuz - Amman-frou-YLVCU. 'Lawm Lauoaq 01 TGLU2? - tDLuz Lanna 02 tcLuz . tLavu2-thcuz LANDAL 03 RTGLUZ . ctnLuz-tnL0291IDt Lanna; 00 thc . TLCRLRIYLCPU LANDAL 6| th . tch.vLcc Lanna; 00 tcLu . 10101010102 LaNDaL 67 T0L . tsLLotDLu Lauoac 00 ttGLL a TGLL-YLHOO1 LANDAL 69 ttDLu1 . tcLu1-1LI002 LANDA' 70 ttDLuz . tsLuz-thun3 LANDAL 70 TYGL I TGL-tLHon-tRSL LANDAu 72 ttDLa - ttDLotDLsr.c9 Lanna; 73 tan . VGLITGLSFICO LAMDAC 70 lrclrnlwt.Lt.1)uetuRw LAMDAC 75 PRIHt090.t LANDAL 70 anwt900.aLNDL.tLAVL.ALNDU1.TLavui.YLaV02.TLDRH.TLBaN LANDAL 77 Innut900.tLICL.Ychu1.tchuz.tchu.tLtc.thnL;tLCRLR.YLcsu.TLCC. LANDAL 70 1 th LANDA 79 Pntwtozo.ttoLL.ttsLU1.ttaLuz.tcLU.t0L.ttsL.t0L0r.ttDLR LANDAL 60 PRlHt92I.RtnLL.PTaLu1.R[0L02.Rch.auxa.aux4.aux10 LANDAL 61 Pnlnt93o.cnoutazcnout11 LANDAL 02 lrtt.Lt.tHDD)R£tunw ,, LANDAL 03 PRIN7950.TLHOD.YL"OP1.TLHODZ.TLMODJ.YISL.!CLL.TCLUI.VOLUR.YOLR LOND‘L 6‘ ustunn LANDAL 05 090 FonnaT(30H4nutpu1 or suuRDUYlNe LANDAL at t¥ns.r0.21 - LANDAL 00 900 FORHAY(1H0.9X.5HALNDL.7I.SHYLAVLo7X.6uALNDU .0x.0HtLAVU1.0x. LANDAL I7 1 6H1L0v02201.5HYLDRN.7X.5HYLBANI1HD.9x.7¢£11.001x0l LANDA GO 900 FORMAT(1H0.9X.5HYLFCL.7X.0HYLFCU1.0!.0HYLFCUZ.0X.5NVLFCU.7!. LANDAL 0’ 1 athrc.0§.5w1LckL.7x.outhnLn.0x.sthcRu.tx.4HYLcc.ll. LAMDAC 90 2 3HtL0I1Hc.9x.101e11.4.1x)l , , LAMDAC 9A 910 FOHHA711H0.9X.5HTVGLL.7X.OHYTCL0106X.ANYTGLUZ,6!04HYCLU.OH03NYCL0 LANDAL 92 1 9x.4HTYGL.Rx.5H70LSF.7X.5H7?6LR/1H5.CX.IELZ.4’ . LANDAL 93 924 FDHHAT(1H0.ox.5HRtDLL.7x.buntGLu1.0X.auntsLuzk0X.4HnLFc.lx.IHAUXZ. LANDAL ’0 1 Bx.4uaux4.ax.5HAUX10I1Ha.9X.71511.4.1x01 LAHDAQ 95 930 F0RHAT(1H0.9X.6HCIoUTI.aoX.7HcR00711/{HD.9x;9¢§13...1133 LANDAL 90 950 Fonna1¢100.9x.sthHon.7X.0HtLHoDL.0x.0HYLHDD2}0X.0HtLHoD3.Ix. LANDAL ’7 1 autng.0x.4HVDLL.0x.5HtDLU1.7x.SHtDLua.7X.IHtOL811HD.01. LANDAL 9! 2 9612.4! ' LANDAL 99 ENTRY Lanset LaHDaq 100 LANDAL 101 VALUES row LAND aLLocaton Lanna; 102 - . LANDAQ 103 YLAVL . 431000. LANDAL 104 TLavu1 . 1720729. Lanna 109 TLAVU2 - 1293017. Lanna 100 'GLSFI I .CIYGLSFD LANDAL [I7 tLDnu . 0. - Lanna“ 100 TLuau . 20000. LANDAL 100 TLCRL I 140300. CH4 0 TLcnu - 201000. CHI 7 RLCRU . 3500. cuo 0 Vch . 101730. Lauoag 113 *chux . 40915. LANDAL 110 71c . tLaANothnLothsuotch LANDAL 11s TLHOD - 0. LANDAQ 110 TLHOD1 I 0. LANDAL 117 YLnooz . o. LANDAL 110 990 365 'LH003 I 0. TRSL I 0. TGLUI - 1471730. TGLU2 I 1252325. TGLL I 319004. ’YGL01 I TGLUi YYGLUZ I YGLU? *TCLL I TGLL RTGLUi I -0937. RYCLUZ I I1037. RYGLL I -10076. TGL I TGLLIYGLuioTGLU2 TGLR I TGLIYGLSFDIC9 TYGLR I 76L» AUX! I 0. AUXZ I 0. _ AUX3 I 7000. 992 ' 00 R2 I 0. at." . 00 in I 0. C0007411) I f. CPOUY‘CZ) I c. CFOUY‘CS) . 00 DO 990 1.1.5 CPout11(l) 0 27.3. CONTIFUF RLYURH END LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAL LANDAg LANDAL LANDAL LaNDAL LANDAL LANDAfi LANDAL LANDAL LANDAL LANDAL LANDAL LANDAg LANDAu Lanna; LANDAL LANDAL LaNDAL LANDAL LANDAL LANDAL LANDAL LANDAL 11° 120 121 122 123 124 125 120 127 120 129 130 131 132 133 134 135 130 137 130 139 140 141 14? 143 144 145 140 147 30 40 50 CO 100 140 366 SUBROUTINE AGPROD COMMON [ACCOUN/ )\I.Hufllfl REAL NCFR COHHON ICATTLE/ muoagumn COHNON ICONYRL/ COHHON [LAND] :OUNUF H DQL‘T0AVLA70ACLOVIVLANDY0VL07170YAXCT0lOSU'I DOL‘HD‘RL‘nI‘CL‘nOVLAHDqIVLDYNNOT.xCNO‘GSUNI DBL‘VIUCRUIVARHICIDQYOUSIDCRDT.PCSYDYIEX'LAvo~cruo ‘LPH1OYbCOYCECIVR‘SLSIYLFCYLFPOCPLFIR‘NYORINYL. CREvT.DlR.C250.C253.C254.L71.L720L73.CBSTCaCSYGH. EPcRPJ.PCRDPU.PCRJPL.IFDRDP. EVLPCJ.VLDCU.YLDCL.YLDFC.DYLDCU.DVLDCL.21LDrc PFGY.VERY.PFPT.PHGY.PHPT.TOPOPT.YOPDPR. PFG". PFPH.PH3H0PHP~010POPH0VACAPL0AUXL120 SLFERY.5L5CC7050LDFY.SLSHLY.SLSPT.SLSPTPorEHSCY. ersRI.0Lsccn.soLDrH.SLSHLH.SLSPH.SUPB.FEHSCH. paystt.wprptt.ppnstt.nPnptt.PprctH. PPFPT‘.PPHGTHIPpNPT*Ian'rORrPTYI RHQYT.HHPTY.RFGYH.RFPtH.RHGTH.RHPYH. Thnt.YDNAY.VDNH.YDNHP.YDNAH.PATAOT.OHH.DHY.PPLACY. vuat.va.PaP.PapP.Pnat.exPL.DHExpL.07202.0214.0244 TOD'ODUNO‘RUNDBEGPRTOPRYCHGDPPTVL1OPRTVL20lpa‘u'. HILIT'OUaYDO IntOTTGLIIYGLLgt'GLU1.YYGLUZI'GLS'OI TQSL.YL"000VLH0010TLH0020TLHOQSIYYOLRIYOLR. TLCRLaTLCRU.YLcRUP.YGLSF.YGL01P.TLFC.TLC. XR10910R20PR20PORICRH09LH0RLH10RLCRUI‘UXI0‘UX30 ARM!0‘R"20ARH3.‘5IC9IGR10CPLPYQXDEL0RP'NIGRE Data coou.cocU1;cnu1.c002 I 3.40. 1.10. 5.. 1.7 I Data c00L.000L1LCDL1.csL2 I 3.0. 1.20. 5.1. 1.7 I DATA €5.67.C9.C1D.CZ2D I 00030 10160 ”‘7‘ CPLPT.GRE.TDNDITDNSG I 0450 DAY! C030C6‘ I 3.30 .21 I 02343 I .5502... 07907090 1016 I Data tnoDc.DEL02DEL9.DEL10 I 100.. 305. I can . (coon-1ttDLuiottDLuztoccaLottDLL)IttDL c001 . (ccou1otttsLu1ottcLuz).c00L1-ttsLL1IttcL Rec» 0 anax1(Rcouopt005-10Rt-DRE). .10 YDNYC - RCONI(CODICPL’YIYYCLICGDIIC1.-CPLPT)IYYCL) TDNRE s 07.0220-(7LCRJ oYLcRL) TDNSF I YGLSFICOIC1G TDNT 0 tDNtcotnunE.tDHSr FtLDcu . EtLDcu.1ancu-EtLDcu1.DtIDEL0 Ir¢t.Lt.tnoDc1oo to 30 tLDcu 0 YLDCUILDYLDCU-YLDCU)IDT/DEL9 YLDCL 0 YLDCLILDYLDCL-YLDCL)IDT/DEL9 tLDrc - tLDtCo:Danrc-YLDrcrthIDEL10 CONYINUE IF(Y.LT.THOO)GO 70 200 TDNHP I TDNM lrctLHoD.LEID.1ao to 40 CD1 I (CD010:YLHDDQOTLHODBDoCDL1otLfloollltLHDo 002 - (C5020!TLHODonLHODCDICCL2IYLH001117LHOD GO 70 5° €01 . C32 ' CI Ir(HAL.20.2Ion.naL.eo.a100 to .0 CGA I caxocILPT0cozoti.-CPLPY) ctn . assoc-197.004o11.-cPLPt1 60 YO 100 CGA I C01 CtR I C03 IrLHAL.Dt.2bco to 140 YDNN I COAITLHODOCTRIYPSL GO 70 200 IVCTLHODITRSLoLE.00)GO 70 145 AGPROo ACCOUN accouu ACCDUN ACCOUV CH7 ACCOUV ACCOUV ACCDUV CAVYLE CAYTLE CAYTLE CATTLE CATTLE CAYTLE CAYYLE CATYLE cattLE CH5 CIS LAND LAND LAND LAND LAND AGPROD AGPROo AGPRDo ADPROD ACPHOo ACPROO ADPRoo ACPRDo AGPRDo AGPROo ABPRDo AGPROo AGPRDD ACPROo AGPRDD ADPRDD ADPRDo AGPHDo ABPROD ACPROD AGPROD ACPRDD AGPHDu ACPRDD ADPRDD ACPHDD ABPRDo AGPROD ADPRDD ACPHDD ABPHDD ABPADD AGPRDD ACPRDo AGPRDD asraoo OOIVOF'JIUPONFIOIDQ‘UO\I.tflNIDOMHP\fl.(flNI’ 000 367 YDNG - (CCAoTLnoDoCTRoINSLilttLfioaoIRsL) Go To 150 1" TONS 3 00 150 TDNF I €2530C2540YDNSG/CZSD CPL? 0 (TDND-TDHG)/(TDhF-TDNG) TLFP a TLF TLF 0 CPLFoCTLHDDoTRSL) TMPL I TLHODOTRSL-TLF TDNM 0 TDNroYLFoThncofHPL 200 IFCIPRINT.LT.1)REYURN PR1NTD9c.7 PRINY9DG.Rc0N.CGD.c501.IDNTG.YDVREoTDNSF.TDNY PRINT910.EYLDCUZYLDCU.YLDCL.YLDFC IFCT.LT.THOD)RETURN PRINT950.CGA.CYR.PPLFoILFDTNPL.YDIH RETURM 390 ronnA1(36H4nuYPHT or SUHRDUTINE AGPROD A! T!H£.FD.2) 900 Fopnat(1no.9x.4ukcu~.9x.3HCGD.9X.4«CGC1.ax.sutuutc.7x.5uvouns.7X. 1 5HTD~SF,7X.4HTOHV/1Hco9xo7(E11.401X)3 91D Vonnavc1Ho.gx.DHEVLDCJ.DX.5HVLDCU.7x.sHYLDCL.7x.5HYLDFCI1HD.Dx. 1 0612.4) 950 FORMATC1HD.9x.3HCGA.9X.JHCTR.9x.4ACPLr.ox.3HtLr.9x.4HYnPL.lxo 1 4H70Nh/1H9.5¥.5E12.4) ENTRY ansev VALuEs FOR PRODUcTIflV GRT 0 (TCLoTCLsrc0C9)/10PDPT RCON ' 1. TDNM 8 6. YLF ' 8. TLFP I 3. usruan END AGPROD AGPROO AGPROD AGPROO AGPROo AGPROD AGPROD AGPROo AGPRDD AGPROD AGPROo AGPROD AGPROo AGPROD AGPROO AGPRoo AGPROo AGPROo AGPROD AGPROD AGPROo AGPROo AGPROD AGPROD AGPROD AGPROD AGPROo AGPROD AGPnno AGPROD AGPROD AGPROD AGPROo AGPROD 268 QUOROUTINE nEH06 COHHON ICATTLE/ 9‘GTgFERT.PF9T0°HGT.PHPT,TOPO°T0T0P0°R0 O‘OO'UNI‘ P’SH. PFPH,PHGH,PHPH0TOPOPH.VACA°L0AUKL120 SLFERT0SLSCCT.§OLDFT,SLSHLT0SLS°T0$LSPTP,FEHSCT0 )L’ERH.SLSCCH0$OLDFH,SL5HLH0$L$PH0$UPB.FEHSCH0 PPFGTT.PPFPTT,PPHGTT,PPHPTT,PPFGTH, DPPPTHgPDHGTH,PPHDTH9PFGTT,RFRTT, RHGTTgflHPTTgp‘GTH,RFPTH.PHGTH,PHPTH, TOHT,TDNAT,T0NH,TONprTONAH9PATADT,1HH00HT.PFLACT, YHATgpA.PAP,PAP°,P°AT,EXPL,UNEXPL06T2020021Q,CZQE CONNON ICONTRLI T0DT00UR019"",BEGPPT0PRTCHG0PRTVL10pQTVLZ0IPRINT0 1 HAL.THOO.T"0 OOHHOH ILAHO/ TGL.TTGL0TTGLL,TTGLUlgTTGLuszGLSFO0 C‘de TRSLpTLH000TLHOO1.TLH0020TLH003.TTGLRgTGLR0 TL’QL.TLCPU.TLCRUP,TGLSF,TGLUIP0TLF00TL00 XRl0R1.RZ,PR?.PD°.CRH0RLH09LHI0RLCRU.‘UX1.AUXT. ARngARH20ARH30A5.C90GQT00PLPT0XDEL0RPTN06QE "IHEN910N VAL1T5)0VAL2(6).VAL3(6)0VALhl?).VALS‘209VAL6(5)0VAL9153 DTHENglflN VALH1(5)0VALHZI6)0VALH3C6) 71HEH§IOH VAL7(I) DIHENQION VAL9(I) "INEHSION RINTFTiClS’0RINTFHIC15’0°INTFTZ(15).PINTFHZ(15) OTHEN9ION QINTHTIC15)0RIHTHH1C15).RINTHTZ(15).RINTHHZ(15) QEAL DATA DATA DATA GATA WATA flATA DATA "ATA OATA DATA OATA OATA flATA "ATA OATA DATA OATA DATA DATA OATA 1 QATA 1 DATA DITA DATA DATA "ATA OATA DATA OATA flATA HATA 1ATA DATA QATA ”ATA NREQT.N’EOH 012 I 10 I 6198,0199,CZDC.62u1007222,CHZDZ/.95. 200 070 050 0H0 0~I 0296002070C203002390C21C0C211I 100 100 100 100 100 10I 0212032130c2150C2150C2160C217 I 10H0 L00 060 02050000235, 02250022‘062260C2270C225 I 0660 0920 00.0 0920 0535 I 923C032310CTZ3Z0CH23200233I00100550 1001050 10C9I 372310CW2310:2520CZK30CZQB0C255I 0900950 055030003000/ 32b6,CZIO,CZD3 I 100 100 30 I . Ppngl0ELAST10‘NXT10‘HNTI0‘HN'10'HX71I05010007502.050075I DQSITZ.ELIQTZ0‘HXT20HHN'20EHN12P4H‘T7I010070025001500150010I ”RSIT30EL‘ST30AHXT30AHNTJ03”NTJ0°NXT3I0.010010007007010, “RSITbgrLDSTb,AHXTk,hHNTk,BHNTB,8HXTh/.1008002302‘0110015 I °RSIT505LA¢T50AHXT50AHNT509HNT50BH1T5I0130005002102’0130017 I oRSITS0€LAST§0AHX760AHNTS03HN7608H‘76I0802007000502.00200as, °R$IT705LAST70AHXT7,IHNTT,QHNT708HXT7I01300600202’0A0015I 99‘1"!0ELASH10‘FNH10AHXH1pHHNfl198HAHII05010009010009010 I °°SIHZ,ELISM’0AMN520AHXH208nNNZpaHXHZI050070080100.B0095I 0°91H30ELA§H30AHN“30AHXH30BHNH30BHXH3I08010005010006010 I °9§1HbgiLAfiMb,AHNH80AHXHh,HHNHB,BHXHBI.10000011003 90t1002’ 9951‘50EL‘QH50‘HNH50QHXH503HNH50BHXHQI01300850013002500130 019 I 9°31H603LI§H60‘HNH60HHXH603HNH60BHXH6I00020 070 0020 0360 0920005 I 3°§IH705L‘SH70AHNH70‘HXH705HNH708"AH7I0130060010020010015 I IOTFI0IOTFZ0TDT”10IOTHZ I 20 20 20 7’ (GIfiF,(”ROOF.‘G°0H0KPQOOH I 100 60 100 “I 0520',DDRHDF09620~0nP°OOH I 2050 1100 2090 303’ SHHLL10nIFr10(1I 0350 0350 SI SHALLMI0DIF‘H10VHLI 0160 0360 5 I SF°LL30qlrr20IS TPLATAEHT TACAD 8 T'TDO ATABTT s flrTOAHAX1(1.00196'EY91-C199'TACADI0 0262) ATAHHT : "PH ATAD I ATADTT0ATAHHT CTABT = DFOUT1'pGFTQT’OTODCTABT’(PFPT-QFOUT1'DTD STAB“ : (?FOUH1'DSFTqH-RFPTH’DCTADT)‘DTOPCTABH’1PFPH-(QFOUH1' 1 RFPTH)'DT! , CTAH 8 AHIN110TADTICTADH, °EPT§PEPH1 TFtDFDH.LE.C0130 TD 62 pCTABH 8 CTA‘HIEEPH 60 TD 63 PCTA‘H 8 0. PCTABT : CTART/DVDT IFC°E6H.LE00.)50 T1 65 PGFTDH 8 C(ATARHT-DFfiTH'EGFTbT)‘DT096FT3H'(PFGH-(BFH-RFGTHD‘DT))I 1 Frau GO TO 66 PGFTQH 2 00 DGFTBT 8 (ATAQTT’3T0PGFTBT’(FEGT-BFT’OT))IPEGT FOOT AND HOUTH T°EATHEHT 5x905 8 EX°D§OOT'EX°AET EXPAET 3 00 IE(T0550T00)EXPAFT8TADLIE(VAL609HALL60DIFF60H69T1 ATAFT 8 AHTH1(E!°A‘T/COSTFT0TOOOPT) ATAFPT 8 (TODOPT-ATAFT)’AHAX1110‘CZDD'E‘P(°C20I'TACA910 A'AFTT I ATAETOATAEPT ATAF 8 (ATAETT‘T0939H1'CZD3 PATAFT 8 ATAETTITOPOPT IF(TofiioTDDDPATADT8(ATAFTTOATABTT'(10-PATAFT))ITOPOPT 0266) ADJUSTMENT 0E IHTEQHEDTATE RATES RCHNfif I 10°(DQL71IDSEGTOPPFGTTD’DT RCHNGH I 10-(0RLH10PSFGH0PPFGTH)'DT no 100 I810K6°0F PINTFT1¢Ib I RINTET1(I)’RCHN6T RINTFH1(I) I RTNTFH1(I)‘RCHN6H CONTINU? QCHNGY I 10-(DdLTZOPSFPTOPPFDTT)’DT PCHNGH I 1.-(DRL"2¢°SFPH0PP¢PTN)'DT 00 120 II10K°PDDF RINYFT211) I RINTFY2(II'RCHNGT RINTFHZ(1) I RIHTEHZ(I)'RCHN6H CONTINUE RCHNGY I 1.-(DQL110PSH6109PNGYT!'DY °CHH6H 8 10-(ORLH10PSHGH0PPH6TH)'07 DD 160 I810K6°OH RTHTHT1(I) I PINTHT1111'RCHH6T °IMTNH1(I) I PINTHH1(I)'RCHH6H CONTIN09 RCHNGT I 1.-(fl°L120PSH°ToPPHPtT)'DT RCHNGH I 10-(DRLH2695HDH0PPHPTH1‘DT no 166 II10KPPDDH R1NTHTZ¢11 I RINTHT?(I)0RCHNGT RINTH12111 I RIN11H2(I)'PCHNGH DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEHOG' DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEHDG 173 176 176 176 177 176 179 160 161 162 163 166 165 166 167 166 169 190 191 192 193 196 195 196 197 196 199 210 201 202 293 206 205 206 207 206 209 210 211 212 213 216 215 216 217 216 219 226 221 222 223 226 225 226 227 226 229 230 231 232 233 0, :0" 153 175 1’9 1'0 1‘) “ANT TN"? CALL DELfiTtfifT. Q'JUT1. PINTFT10 DGROF. TDTF10 0T. KGROF) CALL 3‘LDTIP'nuPYl0 P'OUTZ0 RINTFTZ0 DIaoor. IDTFZ. 0T0 KPQOD') c‘LL "quTAdNTp EHOUT10 RIHTHTlo OGROH0 IOTH10 OT. KOQOH’ CALL WELDTCQ'OJPI10 RHOUTzo pIHTHTZ. D'QODH. 10TH2. OT, KPQOOH) TFCT.LT}THDJ)GO 7" 161 3'0N ICTUNH-TGNHPTIWT 2AA I C°TJV051?’TO°OPH‘(TDNAH—YDNREfliiITDNPEO 7000911 0 oratorgfit0PfiT §F6TT 8 QAI'DFGTITODOPT1 ’3??? I °A|'FE’TITOPOPT1 OHGTT 0 °AA8°H6TITOPOPT1 inDTT 0 9AA8PHPTIT0°0PT1 aF618 I -°FG?Y JIFTH I -°'=TT 9&6!" I -°HG!! Quavu . -DIDVV PPFGT? 0 PFGYTlorcY DPFPTT s IrvttIProt DONG?! 0 =NGTTIP81Y PPH°TT 0 IapTYIPIIY PPFGTH 2 PFGTHIP'GH TF1°FGH.LE06.1P996TH 8 00 ”FEDTH I °E°THI°EPH TF¢°F5H0L503.)°°E°TH 8 00 ‘PHGTi I ’HGTH/PHGH IE(PH6H0LE.:0)°°"6TH 8 00 ponotq 0 anornIIon 1'69H’H0150‘10’PP3PY‘ ' 00 TFCPFGH 06T0“00AN0091NTFH111)0EO000360 TO 170 ”ALL OSLDTIUEH. QFDHHx, RINTVH1. DGROF. IDTF1. 0T. (6R0?) CALL 7F!"T(PF0U°H10 'FOUHZ. QINTFH20 DPRODF. IOTFZ. DT. HPR00') CALL DELDT161H, °HOUH10 RINTHH10 06R0H0 IDTH1. OT. KGROHI CALL DELDTCIHOU°H10 RHOUHZ. PINTHHZ. DP?OOH, IDTH20 0T. KPRODH’ 60 T0 160 00 172 I810K6°0F ’1NTFH1T') 8 DFGHICDGROF'IDTF1’ ”ONTTNU? RFOUHx I °F6HIDG°0F no 176 [81,H900T‘C QINTFH?(11 8 PFPHI(OPROOF’TDTE2| EONTTNU2 °FOUHZ a PFPHIDP’ODE ‘0 176 1810K6P0“ ”TNTHH1111 8 PHGHICDGQOH’TDTH11 TONTTN"? snoth I PHGHI‘GPO‘ no 070 tI1,KPOOOM QTNTHHZ‘I) I PHPHICDPEODH'IOTHZ’ SONTINU? OHOUH? I DHDHID9PODH QDNTTN'I? @IQT4 °ATES AND HIRTHS - TRADITIONAL CDANG! a TAILIstva-A. SHALLB. Diffs. K0. PCTABT) 8912 0 09170wflnfL1'CBPI-RRTZ) Dar . vau11c¢v011. SHALL10 oxrr1. K10 YONIT’ «01!: - 0°12‘(1.-:DA~C?) 9|? I 88172'F'°’ 1" g . (00A? 235 236 ‘0 VI 26) 261 262 263 266 265 266 267 ?66 269 290 231 292 2‘! COO 0630 0001 1551600 211 ’36 26) 373 QHT I HAToRFT CDT 8 8°T2‘CQANQT DIQTH RATE9 AND BIRTHS - HODERN TFCT.LT.THOD’60 T0 210 -CPAN6H 8 TABLIE(VAL60 SHALL60 OIFE60 K60 PCTADH) BRHZ 8 9RH2ODTIDEL1'CBRH-BQH21 TF1T0909H00706018368TADL1E(VALH105HALLH1001FFH10KH10TDNAH) HPLHZ 8 9°H2'110-CSANGH) BAH I RQLHZ’FERH RFH 8 05‘9AH BHH 8 BAH-99H CON 8 RRHZ'CDANGH CONTINUE DEATH RATES AND DEATHS - TRADITIONAL DRAT 8 TA9LIE¢VAL50 SHALLS, DIFF50 K50 pATAFT, n9T1 8 DPT1*DTIDEL2'(O°6T-D°T1) ONT? 8 DDTZODT/DEL3'(DPPT-DRT2) OPGT : TABLIE(VAL20 cHALLZ, DIFFZ, K2, TDNAT) ORPT 8 TAQLIE(VAL30 SHALL3. OIFF3. K3, TONAT, DRLT1 8 DRT1’DRAT DRLTZ 8 DRT2’DQAT DFGT 8 PFGT'ORLT1 DEPT 8 DFPT'DRLTZ DOLDFT 8 OLOFT'DQLTZ OHGT 8 pHGT’OELT1 DHPT : PHPT‘DPLTZ TDTNST 8 DFGTODFPTOOHGT+DH°T+DOLDFT DRT 8 TDTHSTITODOPT DEATH RATES AND DEATHS - HODERN IF‘T0LT0TH00160 TO 263 ORAN 8 TABLIE(VAL50 SHALL50 OIFE50 K50 FRTRFH) 0°~1 3 ORH1 OPT/"5L2'10RGH-ORH1’ OR"? 3 Op"? ODTIOEL3’10RPH-ORHZ’ IF‘TOPOPH0GT0001DR3H8TABLIE(VALHZ0SHALLHZ0UIFFH20KH20TDNAH) IF‘TOPOPH0GT0D0)DRPHiTAOLIETVALH30SHALLH30DIFFH30HH30TDNA") DRLHl 3 DRH1'O’AH ~ ODLHZ 8 "DH2'OQAH OFGH I pEGH’DDL'". DFPH 8 DEPH'DRLH? "OLDF! 8 OLOFH'ORLHZ DHG' 8 PHGH'OQLH1 OH?" 8 PHPH'DRLHZ TDTHSH 8 OFGHOOFPHOOHGHOOHPHOOOLOFH IF‘TOPOPH0LE0D0TGO TO 256 DRH I TDTHSHITOPOPH GO To 260 DP" 3 00 OOHTIHUE SALES . TRADITIONAL 0L0 FEHALES 6P1 8 PRAT”ELAST1'PRSTT1 PREST: I AHIN1(HHXT10 AHAX1CDHHT10 0P1), DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEHOS DEH06 DEH06 DEHOS DEH06 OEHOG DEH06 DEH06 DEH06 DEH06 DEH06 DEHOS DEH06 OEHOG OEHOG DEH06 DEH06 DEH06 DEH06 DEHOG OEHOG OEHOO DEH06 DEH06 OEHOG DEH06 DEH06 OEHOG DEH06 OEHOG 296 295 296 217 296 299 300 331 332 303 306 335 306 307 336 309 310 311 312 313 316 315 316 317 316 319 320 321 322 323 326 325 326 327 326 329 330 331 332 333 336 335 336 337 336 339 360 361 362 363 366 365 366 367 366 369 350 331 352 353 356 GOO 000 L1 051 51133 61 0,0 .3 n 0 3714 A91 8 PREST100206’PANTIOLDFT pSPOT 8 AHIN1CAHXT10 AHAX11AHNT10 30Lfl'T 8 OLDFT'PSFOT “ANT 8 RANT-SOLDET A911) INFERTTLE AND HASTITTS 9P2 8 PRAT"ELAST2'PRSIT2 RRESTZ 8 AHIN110HXT20 AHAX110HNT2. AP2 8 PREST2OC207‘PANTIFINFT PSINFT 8 AHIH11AHXT20 AHAX11AHNT20 SLINFT I FINFT'PSINFT PANT I PANT-SLINFT 893 8 PRAT"ELAST3’RRSTT3 °REST3 8 AHIN1CBHXT3, AHAX11BHNT30 693 8 PREST306266'PANTIFHAST l’SHA9T’ 8 AHIH11AHXT30 AHAX11AHNT30 SLHAST 8 FHAST'PSHAST 9ANT 8 RANT-SLHAST GRONING HALES 9P6 8 °RAT"ELAST6'°RSIT6 PPE9T6 8 AHIN1TDHXT6. AHAX11RHNT60 696 8 pRE9T606206‘PANTIPH6T oSHOT s AHIN1CAHXT60 AHAX11AHNT6, SLSHGT I PHGT'PSH6T 0ANT 8 PANT-SLSHGT 6R3NIN6'FEHALES 995 8 pRAT"ELAST5'PRSIT§ PRE9TS 2 AHIN118HXT6, AHAX1CBHNT50 6P5 8 PREST50CZB9’PANT/PF6T pSFGT 8 AHIN1¢AHXT50 AHAX11AHNT5, QLSFGT 8 PFGT'PSFGT 9ANT 8 DANT-SLSFGT FERTILE FEHALES 996 8 P’AT"ELAST6‘PHSTT6 °REST6 8 AHIN1¢9HXT60 AHAX110HNT60 A96 8 9°?ST6OC?1."ANTIFERT PSEERT 8 AHTN11ANXT60 AHAX11AHNT60 SLFEPT 8 FERT'PSFERT PANT 8 PANT-9LFERT PRODUCING HALES 1P7 8 PQAT"ELAST7’PRSIT7 °°E9TT 8 AHIN1IBHXT70 AHAX1TBHNT7, AP7 8 PRE9T'IC211‘°ANTIPHPT °SHPT 8 AHIN1(AH!T7. AHAX11AHNT7. SLSHPT 2 PHPT'PSHPT MISCELLANEOUS 9L§HFT : RHOUT2'110~10RLT20PSHPTOPPHPTT)’OTi CLSHLT 8 9LSHRTISLSHPT 9LSHT 8 SLSHLTOSL9H6T. 9L9CCT 8 SLTNFTOSLHAST ' 6P2), AP21) 693)) AP3)! 696D) AP6’) 695)) APB.) 69611 APO!) 6P7)! AP71) DEH06 DEHOG DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEHOG DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 DEH06 OEHOG DEH06 DEH06 DEH06 DEH06 DEH06 OEHOG OEHOG DEH06 DEH06 DEH06 356 356 357 359 339 369 351 352 363 366 365 356 367 356 369 376 371 372 373 376 375 376 377 376 379 360 361 392 363 396 365 396 357 366 399 396 391 392 393 396 395 396 397 396 399 600 601 602 603 666 605 606 667 606 669 610 611 612 613 616 615 000 GO GOO 000 GOO GOO 37’5 SLSEPT 8 SLSC'TOSLEERT IsFPr I SLsrPtIprIr EEHSCT 8 SLSCCTOSOLOFT00216'SLFERTOCZ15'SLSE6T SLSET 8 SLSFPTOSOLDETOSLSEGT SLST 8 SL9FTOSLSHT 3L¢PTP 8 9L9PTITTGLR SLSPT 8 (SLSCCT0SOLDFTD89226OSLSHPT80225OSLSHFTOSLFEPT’02260 1 SL9E6T8022705LSH67'C226 SALES - noncou Irct.Lr.tIonaco v0 so 0L) FEHALES 0H1 8 PIAT9’2L69H1’PRSTH1 PPESH1 8 AHIN119H1910 AHAX1CDHNH10 NH!!! AH1 8 PRESH19C256’PANH/0LOEH PSFOH 8 AHIN11AHXH1. AHAX11AHNH10 AH1)! SOLO‘H 8 DLDFH'PSFOH “AN” 8 RANH-SOLDFH INFERTILE AND HASTITTS 962 I PRAT9'ELA9H2'PRSTH2 PRESH2 I AHTN1(DHI12. AHAX116HNHZ. 6H2). IN: I PRESIZoczar‘PANH/FINVN PSINFH I AHIN1(AHIHZ. lulx11AHNH2. Ant!) SLINFH 8 FINFH’PSINFH 00"» a PINH-SLINF6 ON: I PRAT8'ELASH3'PRSIH3 ”RES”! I 0ntu1tanx03. AHAX116HNH3. 6H3!) Al! I P'ESH300266'PANHIFHASH PSNASH I Afl1N1CIHxn3. AHA!1¢AHNNS. A!!!) SLNASN I FNASH'PSHASI ' DAN" I PANI-SLNISH CRONING HALES 9H6 8 PRAT"ELASH6’PRSTH6 ’RESH6 8 AHTN116HAH6. AHAI1CHHNH60 IH611 AH6 8 PRESH60C236'PANHIPH6H ESHGH I AHIN1CAHXH60 AHAI11AHNH6. AH6AD QLSH‘H I PHOH'PgHCH PANH I PANH-SLSHGH CRONING FEHALES CNS 8 RRAT"ELASH5'RR3TH5 ’RE3H9 8 AHTN1CIHIH50 AHAX110HNH50 6H5!) AH5 8 PRESH500209’PANHIPECH PSEEH I AH1N11AHXH30 AHAI1CAHHH5. AH579 SLS'CH 8 RECH‘RS'38 PANH 8 'ANHISLS'CH FE‘TTLE 'EHALES 6N6 I DRA188ELASHDOPRSYN6 PRESH6 I A61N11661660 AHAI1CIQNHA. 6N61I 6N6 I P6636606212'PANII'III 8672RH I II!N1(IHI160 AHAI1¢ANNHO. 6H6)! CLIERH I PER-8087266 616 617 616 619 620 621 622 623 626 625 626 627 626 629 636 631 632 633 636 635 635 637 636 639 666 661 662 663 666 665 666 667 666 669 650 651 652 653 656 655 656 657 656 659 666 661 663 666 665 666 667 666 669 676 671 672 673 676 675 676 f§76 Iann - 8I68I§LFE°5 990000166 66163 1633 667 8 P9679’616567'PK9767 666567 I 61351106197. 66661166667. 66777 667 I 66699766211'966616666 “8696 I 66361166397. 66661166667, 66717 5L56°6 I DHPH'PS6P6 6156611666005 0030 SLSHFH 8 RNOUHZ'C1o-(DPLHZOPSHP6OPP6P761'OT) QLS6L6 8 SL56966§L56F6 SL566 I SLSMLMISLS‘GP 5LSCC6 8 StINF6OSL6IS6 SLSFP6 8 SLSCCHISLFtni IFCPF’FoLE.0.)GO 70 50 PSFPH 8 SLSFDHIDFPH 60 TO 59 39 95696 8 6o 59 666806 I SLSCCHIéOLOFHOCZ16'SLFER660215'515666 66686 I FENSCTorénscfl 51366 8 SLSFDHISOLDFHOSLS'GN 3136 I SLSFHISLSHH 731$ 8 918109156 31896 8 (QLSCCHISOLOF61‘CZZ6OSLSHP6’CZ25OSLS6FNOSL66R6'C226O 1 SLSFGM'CZZ'ISLSHGH’CZZB 63 EDITINUE 6ILK PRODUCTION 0‘90 FFLICY . Ioer'cTzsz . out ICFERT’CDAYIFIICI.-PA!IFY)’.020(1.-Pltl'118.10'.05)D'PFL|078 1 67,028YNIYOVI9EXECV0L6..31..31.6.7066!) one? - 9‘1!(812.2'210.‘PFLICI’FERT) xrtnnL.Lt.1)co to 300 PFL006 . I'an'nnzIt oqn - «rean-«Int-rno¢1.-IAtArn»0.02.:1.-IAVIrnnI.1o-.as»tIIILIcn' z nnzaz-vnan'rtaaxecano..31..31.~.touanp ancn . 066!(CHZIZ’ZSO.'PFLACH8FEP61 310 eoutxvus 6116601106 66710 660 70361 SUPPLY 0‘30 SUP676 8 SLSRCYOSL56L7930106700212'(SL360603L36L6960L066196213' 1 (7076319707636100216’(SL666706212'316666)86215’15L36679 2 0212’5L5636)00216.(SL366796212'5L5666) 90666 8 90°CYIIVGL 709096 8 30°OKJ'EIPCCZ17‘71 3069 8 9090760709936‘766L16CV6L9.$66L19903669g69.660017.7.D1 7060’ 8 70°OP°OTOP0PK 665 I SUP9IYOPOP 6667 I 6&67'8°LT’IYOPOP7-DQT 667 8 (SLSF'OSLS6filTOPOPT 6'96 8 5596.9616217090P6-066 6F6 8 (3L86605L36611709066 06' 8 170765707076961170P06P 6'69 8 (6621’6RL1266666'96L62)[TOPOPfl-OIR 696 8 (5L57051361IYOPOP! 6.6 I (96766661716697.6666, 06606 06606 05606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06506 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 06606 66606 677 665 666 667 666 669 690 691 692 693 696 695 696 697 696 699 5J0 501 502 533 556 505 506 507 506 509 516 511 512 513 516 515 516 517 516 519 526 521 522 523 526 525 526 527 926 529 536 531 632 533 536 535 536 537 0630 37 7 667716 560706 P9167766 1'63"6~TIL'013657"9~ EN'Q' 067'Q' .ptfl7503gtg'667ger5tp'fl‘ST.'Eltg'F’T.°Lo'39"CV.P663gPflptg7OPOPV .':"‘9695L5FGVISLI~639SLfl‘SYQSLFERTQSLS'P‘QSOLDF'.FENSCVISLSHGY. 1 SL369395L53 'P!67900,SLSCCI.SLSHFT.SLSHL7.31567.81861gSLSPY.SUPCII.SUPagsuflou 9!!67912,091.B9Z.0°3.896.085.9P69097.PP'67,88?CT Putut91~.Iaesvz.PQISYz.Incsva.II£31~.IIss15.90:516.Pa£s17.IIncr, PP'PT ' .'I”19169691.692,6’3Q6P6’6’596P696P793090P‘97090P 90161919,PSFOY,PSINFT.PSHIST.PSHGI.PSFGtgPSFERT.PSHPI.PSFPY PD!6'922,A!ABIY.CTAGV.°GFIBI.9CYIOI,AIAFT.IYIFP1.AVA'IV.8176?!. 1 65°08 96161926.ERT.E891.ERS.PFINFY,P86637.PIFNUT,TONAT.PI1T ..6NV5369.6795679567.59L32069729666N639067’0663 .Rx“?93“,n°.t’DRT1’ORL'1.DR'ZQORL'260R6660RP'QDRI 76(7oL7oTNOflbRETU°9 .RINTQS6’PFG~9'INF‘Q'H65".PEPH’p‘PHQOL0'",PFCNQPHGHIPWPH9609WPH 66167950.SLSF66,SL16F”.816196.SLFER6.SLSFPN.SOLOF6,FENSC6.SL$966. 1 5L56P699L55 ( "IN3962'70666,961HISLSCCH.SLSHFH.SLSHLNQSLS'H,SLSHHISLSPH P8167963,RFGYT,RF°7T.RHGYT,R6911.R706.RAI ”'6“: 659P°FGYTgDFFP'TQPpflctfgppflptfgppfctflgPP‘P'"QP°HG'"."HPTN Parnvge098H1'662'8‘3,9"6.°~599H69BH79PP‘GH.PPFCH 8&1673 0.925861.Pfiisfiz.9R:863.PRE566.PRESHS.PRESHO.°d£867.PPHGn, 1 P969" 6636797bgl619662.0639666.665.666.667 P6163976s95606993166".956636995666,PS'65.PSFE6N.PSH9H.PSF'H P6167909.ITIBHY.CIIBH,PGFIQH.PCYIBH.PFIN’6.PF60569P16606 963N3966,666,666pBafioaRLflzoapnzoCQ6NGH'6669066" 8R1679°Z.0R66.0261.0°L61.0Q86.DQHZ.OQLHZ.006.ERH.ERPH 96161996,0°°.0&R.ERQoERPkoATA!.AilF.ISLS.FEHSC.TOPOPR '67UQN 936 '666671356130320T 9' 3066003366 06606 6' 73669660211669560 6N6'6796X956'1N'Tp76.5666.33.76,6HFER7'6636H9667.6K. 5601067,7x,¢69663.6!.66P667.6l.66P6P7,Oig667090PTI16099X. 101611.6g1‘77 936 5066671166.9696NSL5'6796X9665L3667IOXQOHSLHIST96596N$L6667.6!. 5HSLSFPT.55,66$0L06706696H66656790',6NSLSHGTI659065LSH'39 5‘96HSL57/146995310161106.1677 5J6 '0666711NJQ9X. 6HSLSCCT.CXIGHSLSHf'IOXQQHSLSNLVp66. 5NSLSF7g7X95HSLSH'p7X. SHSLSPI.7X.GNSUPCVA.OX. 663096,0!.5690PBH/160.SX.9612.61 Oil IORH67116099"3HQ°1g9X93HBP299Xo36393.9593HBP609X93H3P599X.JHUP.’ 5‘93"8°7921X.56PPFGT.7ngflPPFCTIIH6.669761206012692612067 516 'OQHITC1H0.9'.6HPQEST1961,6HPRESTZ’6‘.66'66533'6'Q6HPQ6336966. 66°RESVS968.66P96336.61.66966377gtlxg56PP667.7X.56PPHP1/ 1H6965.7612o6912X.Z612067 516 80!611¢160.9x,3ulpt.9x.JHIPZ.9I,36193.91.36186.9x.3H8P!.9x.36186. 9'.366979216o6H7OPOPK95X'5HYOFOPI1HOQ6XI7E1ZI6Q12XOZE12063 516 5°666711KB99‘,5695507.7Xg66951667.66.6HP36637'6Xp5693667'7‘. 5N95667.7!95695666795595HPSNPT.7X’5H'S'PTI1H699‘.61611.69 1X77 522 '066671160'9'V66676677o6695663607,76.6N’6'367.66.66667667066. 5667663g7X96667669396Xg66676'73p66966963667966.5666605’ 1H696695612067 926 '0'”6711"0'9‘.3HE°795XI6”ER'7966g3"!“5.56.66963667966966666657. 61.66636607,61.56306639769669667/166.6Xg6612063 6" ~68 N68 UNI. lit. 68 IIII u.-- no 06606 DEMOG 06606 05806 06606 06606 06606 06606 06606 05800 06606 08600 09600 96606 06606 96606 06606 06606 06606 OEHOG 06606 06806 DEMOG DEMOG DEMOG 06606 DEMOG 06606 06606 06606 OENOG 06606 06606 06606 DEMOG DEMOG 06606 06606 06606 0:606 0:606 06606 06606 OEHOG 0:606 DEMOG 0:106 02606 06606 06606 06606 DEHOG 06606 06600 OEHOG DEMOG OEHOG 06606 06606 06606 0:606 562 563 566 565 566 567 556 569 570 571 572 573 576 575 576 577 576 579 560 563 566 565 556 567 566 569 590 591 592 593 596 595 596 597 599 603 606 605 606 617 606 609 610 611 612 613 616 615 616 617 616 619 626 621 62’ 623 626 625 626 627 526 378 4333 '0'~.T61"009Xp3“9‘709‘o3”BNYQQ‘.3HBRT.96.5H9917297"6H8R1296', 1 66606661,61.360'1.0X,660667116G.QIglello6.1X)i 9‘0 FO&HIT(160.9X,4HORIT. 0!.660671,0X.560RL71.76.6NORTZ.0!.5606LTZ9 1 7fl,660967,Si.660RPI,0X.66091 [161.96.6(511.6g1!)) as» FOPHIT(160.9!,6HPFS6,a!,56F1656,7!,56F6I56.7X.6HFER6.66,66P'P6.IX. 1 560L056,TX.66P566,BX.669166.OX.66PHPH.6X.661090P6I 2 1H6996915‘E11o601x’3 956 '0'"6'11H0.9X,6HSL5FG~96',6H9Lt~rngsxgEHSLH‘SH.6X,6HSLFERH.6‘, QHSLCFP1.66.5650L0F6gbl,66'56506g61,GHSL566696X.6HSLSHPH, 519668L56/140991.16512.b) 952 'ORH"11H399',3HT°NI",7X,6HP‘NH¢6‘96HSLSCCH96X,6HSLSN‘H96X. 1 5“SL‘HLH06X.5‘5LSPH97X9545LSHH97‘QsHSLSPH/1HO’6X,5512.63 353 ‘ORNIY11H699695H?‘GTT97XQ5HRFPTT.76.5HR‘GtTp7x95HRHPYtg7XQ6HQ'O". 1!,VHQII/1H?.9X.6(C11.6,1!)v 95’ FORMATC1HS,9!.6HP°¢611.61,SHPDFPIT,6X,56PPHGTV.6X.66PPHPIT.6X, 6H°°FGT596696NDP:PT”906,6HPPHGYH96X’GH’PHPTH’1H099X. 515110691¥33 955 FORH6711H099X93H95199X93H8H2'QXQ3HBH399X03Hafi6.9X93H9N599X'3HQH6' ' 91.36067,’1X.SHPPFGH,7X,SHPP506116C.0!,7£12.6,12X.2£12.6D 970 FORHA'(16699X,GHPQES61.6!,66PRESNZ.6X,66PP£SH3.6X.66°QESH6.6X. 5HPR'. S";goxosHDRESHbg6!,6HPDESN7’1OXQSHPPHGH’ 7X95H°pHPHI 1H0, 6!,7L12.6.12X.2£12.6) 9’6 FORHAYI160:QX,36011.9X,36A62.9X.36163.9X.JHAH6,9X.36695.9X.36166. 9‘93H6n7’1H509X’7(E110601X’3 975 FOQHATClflz,Q!,5H°SF06,7X.66PSINF6.6X,66PSH|SH.6X.56P5666,7x, 5HDSFG H, 7x.56PSFER6,6x.SHPSHP6.7!,SH°S‘F61160.QX. 51'.1106:1X’, 96, FOQH‘T(1HO99‘96H6163"796X95HC769"' 7x.oHPGFTBH,6X,66PGTIBH.SX. 6HPFYNFP’6X9 SHPFH‘SH.6XQGHPIFNUH’1H0.6X,7E1ZQ~’ 955 EORNIV‘1H099X93HBFH'QX’3H“HN.9X93HBQH99X,5HBRLHZ’7XQ6H0RHZQOXQ 66‘8A666.6X,36166,9x,&606661160, 9x.0(E11.6,1X)) 992 FOR667¢163,01,660066.81.660961gax. SHURLH1g7‘,6"OR°H96XQ~HDRH’.6X’ 5H”°L"2.7'.SHOqug"3HER”QQX’~HERP"’1“0’6‘69812. 6’ 956 FO’HAYG1HC,9!.3HSR°.9X.3HDRR. 9!.365RR,9X, 6HERPR,6‘,6H6T‘995X, 1 6H6TIF967,64TSL596X95HFEdSCg7Xg6HTOPOPR/1Hc,6X9951206’ ENIRY 611551 ’56! 8 1176330. PMPT 9 963003. 9'53 3 1153000. 9'97 3 2626306. 01°F? 3 627C000 TOPOP! a PFFTOPFPTOOLDFTOPHGVOPHPT b N» n N» P N» p N» DSFGT 3 013 PSFPT a .012 PSHGT a .1 °SHP7 . 0135 °SFGH a PSFP6 8 95666 8 PSHP“ : 0. SLSFG! I PSPGT!PFGT SLHISY I .006’9FPY SLINFT . .ucu~vrdr SL‘ERT I .OCZ'Prfif SLSFPT I SLFSPTOSLHIST0SLINFT SOLDF? I .5’0L0FT SLSFT I SLS’GTOSLS‘°T¢SOL0’T $1866? I PQHGY’Pflfif SLSHPT I PSHPT'PNDT SL567 I PHOUY2OSLSHGYOSLSHPT SL897 I (SLINFTOSLHhSTOSOLOFT1‘CZZ6OSLSHPT’6225OSLFERY'CZZ6O 1 SLS‘GT’0227OSLSHGT‘CZZ6 0R6? 8 .061 0271 3 .061 05606 06606 DEMOG DEHOG 06606 DEMOG 06606 DEMOG DENOG 05500 DEMOG 05606 08606 05606 05600 05606 05005 06006 05605 DEMOG DEMOG DEMOG 06605 05600 06606 DEMOG 05606 DEMOG 08906 05606 05606 06606 0E“OG DEMOG DEMOG DEMOG 05006 DEMOG 05506 DEMOG DEMOG "5606 DEHOG 02606 08606 DEMOG DEMOG OEHOG 05606 05505 DEMOG DEMOG 06606 OEHOG DEMOG 05506 06606 DEMOG DEMOG 15606 UENOG 621 630 631 632 636 635 636 637 636 639 660 661 662 663 666 665 666 667 666 669 650 651 652 653 656 655 656 657 659 650 660 661 66? 663 666 669 670 671 672 673 676 675 676 677 676 679 660 661 662 663 666 665 666 667 666 699 690 691 692 693 696 10 12 lb 16 379 "'LTI ' 035 0.97 I .025 DIV? I .025 DILTZ ' .03 OFF? I 0RLYZ'PFPT "' 3 .7 ”R72 ' .625 “'7 I P’PV'.6'.5 out I IF? CF? I .925 PGFYOT I .09 907.87 I .1 EXPIFT I o, EtPOS 3 0. PIVIF' I .3 Pl'lDT I CZBB PFHISY I 000126 PFIN" I 013 ’IFNUY I 0. DO 10 IIngGROF RINTF11(I’ I PFG'ICOGROF’IOYFI’ QINYFHICI’ I o. CONTINUE RFOU'I a PFG'IOGPO‘ DO 12 IIigKPROOF RINVVTZCI’ I P'PTICDPROOF'IDTFZ! RINY'HZ(I’ I 0. GON?INUE RFOUVZ I PFP'IOP°OO' DO 16 IIIoIGROH RIN'flTICI, I PHGT/(DCROH'IOYHI) RINVNH!!!) I 0. CONYINUE RHOUV! I FHCTIOG’OH 00 16 IIIQKP'OOH 'INTNTZ(ID I 'flPT/(DPRODH'IDTHZ, RINVHHZ'!’ I .9 CONVI'UE RIO"?! I PHPYIOPRO)" ’FGH I F'PH I OLDF! I PRC" I PHPH I do SLSFGH I SLS'PH I SLSHSH I SLSHPH I SOLD'H I 0. SLS'H I SLSHI I SLQQSH I SLIN'H I SLFERM I 0. SLSCCQ I O. SLSHLN I Go [HIGH I 0'32 0'". 00‘: - "I"! I 0'tfl1 IDQGH OR”! I URL"? I DRP" um I I. TD'HSH I .9 OR” I .0 BR"? I 9" 0'" I DH” I .0 can I o. P‘FTDH I PCTIOH I PX'NUN I 'FH‘SH I P'IN'N I 09 9.7.?” I 10 RICH"! I RHOUHQ I {FOUHI I RFOUHZ I 09 RFGT? I ate!" I RFPY' I If?!” I RHGT' I .HGYH I RMPTT I QHPTH I .0 PPF6TY I PPFP'Y I PPHGTY I PPNPTT I 09 PP'GTH I PPFP'H I PPHGTH I PPHPTH I l. RETURN "5105 DEMOG DEH06 DEMOG DEH06 DEH06 OEHOG DENOG DEMOG DEMOG DEMOG DEMOG OENOG OEIOG DEH06 DEH06 OENOG DEH06 DEMO? DEH06 DEMOS DEH06 DEH06 DEH06 DEMOS DEMOG DEMOG DEH06 DEH06 OEIOG OENOG DEH06 DEMOG DEH06 DEH06 DEH06 DEH06 DEH06 DENOG "ENDS DEH06 DEH06 UEIOG OENOG DEH06 DEH06 DEH06 DEH06 "EROS ”EH06 DEMOG DEH06 DEH06 DEH06 DEHOG DENOG "EH06 DEH06 DEH06 DEMOG "EH06 SIS 999 997 693 999 733 791 702 ’03 705 ms 736 797 799 799 no u: 7:2 7&3 ’16 719 719 7:7 719 719 729 721 722 72.7 729 729 725 727 729 729 no 731 732 773 73h 739 739 737 739 739 790 7n 792 77.3 7b“ 77.5 796 797 no 759 799 751 752 793 75:. 799 380 DEH06 756 381 susnouvlue noncun 'CDHHON IAccnUN/ DILAT.ANLAYAACLAY.VLANDYAVLDVXY.TAXCYAADSUVo D“L|H.ARLAM.ACLtH.VLAnDH.VLDVXN.tAxcn.Acsun. ALPH1ATtCaTCEC.VRJSL3.TLF.YL'P.CPLF.R!NYARINTLA 'm.--‘J” E’cRPJ.PCROPJ.PCR3PLAPFCROP. E'LnCJo'LDCUoYLDCL:YLDFC.DVLDCUADVLDCLADYLD'C REAL NCFR COMMON ICONYRL/ T.DVAOUNAIRUN.BEGPRY.PRYCHG;PRIVL1.PR7VL391PRINY. 1 fllLoT*002100 ‘TnL.TYGL.YYGLL.Y73LU1.YYBLUZ.YOLS?OA YQSL.YLHOD.thooi.fLNonz.1Ln003.Y19LR.TsLR. TLCRL.YLCBU.TLCRUP.TGLSF.TBLUiP.TLPCoVLCA xn1.R1,N2.PRZ.PDR. CRH. RLN.RLNI.RLCRUokuxtoAUXBo APHIoARH2.ARH3. A5.c9.aRY. CPLPT.XDELoRPYNAGRE DIMENSION TRAIN1(6)2T?AIN2(2). YRAINJLED) DIVA LT1.LT?.LYJ.VDCY1.NOCYZoNDCYS I 30 1. .0 40 40 4 I 0A7A 0239.c235. c23¢.c257. £12.513 / .o. .5. i9. .0005. 1 z. D‘Y‘ YCJ. TCi.TCZoTcF.CRUHAX I 600 1300 1500 16.0 106.000.00 DATA RPIN.EITA I .2. 250. I TRNsLa . ru~5L19n7.¢Aux5-30u11.rRAlN113)0¢1.-A5)3 TRNSL2 I TRNSL29DYo(BOU11-80012) TRNSL3 . TRNSL39DYc(BDU?2-BDU?3) TRAIN1(3) - TRAIN1¢3)0A5 - CALL BoxccAux5.aour1.tRA!81.NCOUN1.NOCY1.LY1.SUH!N1) lrtL72.LT.1)GO TO 50 gALL noxccauu11zaou12.TNAINZ.ucouu2.Nocv2.er.suntuz) o 70 100 COHHON ILANfl/ .udfifl‘ 1 I 3 I 50 BOUT2 I BOUY1 100 108 110 CALL uoxccanutzzenu73.tnAxus.Ncouus.uoc73.L73.suan3) 1Auxs . (can-AnnsoAnIN1c1.. RLNl/(AHINi(ARH1. AnnzioARH3777ovceco (1.-RPYN) ALPH; . 0234¢AHIN1¢1.-C234. (1.-C234)oExPC-CZSDICPDR-CZJS))) csxupu . csxupu.nrsrnr CSIAPH : CSIAPHoRCsYDT CSPAPH - cspAPHoncsvor cspron . csercnoncsfnr casvc . cas7c-Rcsvnv csanP . (at-chr).chPtocsxAPH cserc . CPLrocsprcu CSYGH s CPLr9CBSTGc?. «62530625410250 IFCHAL.ED.1 on. HAL so. 3700 to 100 CSPLAP . :1..chr7.c1..cPLPt:ocSPAv~ co 70 110 _ csxnuw - (1..CPLr7.t1..cPLP17-cslupu 7&6 I Cs]"NPICSIHIP’CSPLIPOCSPLVGICSYOH 7cec . ALPHioTEc carneo . tcec-tz.-RPY~)ILI1 AcanrA . AcaoVAont.Ac=Dt CALL onAput7co.7c1.rc2.7cr.canntx.t. ACR07) ncnor - TRNSLl/LT! cnenr - AHAY1¢ACRF1’DCRUY. o.) Ann1 - cnen7/catnro Annz - NCFR9L11/(TCEc-RPTN) anus - (NCFR-AHIN1(AR‘1A Anna).YcecoRPtV/LtiboLr1/Ycec AUX? I Aux7o(Auxo-AUX7)'DT/KDEL AUX9 - 1.-Aux7 EXTR . czsrgTPSL _ 17(Exrk.LE.o..ou.nCRDV.LE.o.)Go T0 120 _ A5 - AH1~1¢FIZIEXYAIEXTNa 1.,0(AUX7°AUX90AHIN1¢E130ACRDYIDBR072 D’L‘VADCRUOFIRH'CADB'OUSADCRDT.RC3YD'OEX'L1VANCFRA caenr.ona.czso.c253.C254.L1i.L72.LT3.cosvcacsvcu. MODCRD ACCDUV ACCDUN ACCOUV ACCDUN CH7 ACCOUV ACCDUV ACCOUV cus C95 LAND LAND LAND LAND LAND NODCRD MODCRD NDDCRo MODCRD MODCRD NODCRu MODCRD MODCRD MODCRD MODCRD NDDCRD HDDCRo MODCRD MODCRD MODCRD MODCRD NDDCRo MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD NDDCRo NDDCRD MODCRo MODCRD NDDCRo NODCRo NODcno NDDcno noocao NDDcno NDDCRo MODCRD MODCRD MODCRD HODCNo MODCRD MODCRD IODCRD (DO‘UOW’UI511IDNII(DCHUFIUU.(dflifl 000 382 1 1.)) no to 125 120 As I 1. _ 125 tr(An:N1(Ann1.Ann2:oAnnJ.LE.o.)co 70 130 Auxo . ARMS/(AHIN1clflflto AnnonAnwst so 70 135 130 Auxo I o. 135 Auxa I 1.-Auxo trctrnIN7.L?.17ne7unu PR‘NY9000Y ' PRINY9OS.YRNSL£37RNSL2.7RN8L3.ARH1.ARn2.Ann;.aoutt.00072.90073 Putu791o.ALIH1.tec.7cec.cnvneo;Acnov.AcnotA.ocaot.caent PRIN7915.EXYR.A5.Aux5.Auxo.Aux7.Auxo.Auxv Paxur9zo.cslupuzcsxAPu.csnAru.csprou.casve.c51nur.csxHAP.csPLAP. 1 cvar020376h RETURN 900 FORMAvcaouonTIur or suanourlue noocao A7 vine.ra.27 - 90! FORIA771uo.9x.aurnu5L1.ox.outnusL2.ox.9979N3L§.ox.AuAnng.ax. 1 AuAanz.ax.4uAR~3.Ix.suaou11.7x.5neouvz.7x.suaouvslxuo.ax. 2 951224) . 910 EORHAY¢1N0.Ix.sHALpH1.7x.antec;9x.auvcec.ox.oucnvnso.ox.suAcaov. 1 7x.oHAcnnTA;ox.5uocnor;7x.sucneot/1H6.ax.061254) 91’ KORNIY‘1H009XI4NEXYR0UXIZHA5016XI4HAUXSOOXICH‘UX60.104H‘UX700‘O ‘ 4H‘UXOIOXI‘H‘U‘9’1HOO9X.7‘5110‘01‘,, 92° FORHAYciflo.9xoowcstPH.ox.oucsxAPH.6x.oucsPAIu.ox.Aucsrrcu.ox. 5HCBITG.7X.6HCSIHNP.61}5HCSIHAPAOXoGHCSPLAP.¢XIOHESPL'BIGXA 1 z sucs7cH/iuo;ax.1oexz.4: thYIAL VALues roa CREDIY ENYRV CROSS? TRNSL1 I o. TRNSL2 I 0. TRNSLJ I D. AUX5 I o. 800'; I .9 80072 I 0. BOUT: . .0 R1 I o. ‘5. 10 DO ‘00 1.1.. TR‘INI‘l) I D. 400 CONYINUE Do 410 1.1.10 TRAINJCI) I 0. 410 CONTINuE TRAIN2¢13 I D. TRA1N2(2) I D. NCOUN: I O NCOUN2 I o NCOUN3 I o SUHIN1 I 0. SUMINZ . .0 SUHIN3 I 0. can I o. AIH1 I 0. AR"? I 0. ANN! RLHI ncrn AU!‘ noocao IDDcno Ioncno IDDcno Noucno NDDCRD noocno NODClo noncno NDDCRo NDDCRo noocno NODCRo MODCRD MODCRD MODCRD Hoocno noocno NDDCRD Honcno NODCRo MODCRD MODCRD MODCRD MODCRD MODCRD NDDcno noocno NDDCRo noncno MODCRD MODCRD NDDCNo HDDCRo MODCRD NDDCRD NDDCRD NDDCRD NDDcao HODCRD NDDcno MODCRD MODCRD HODcno NDDCRo MODCRD MODCRD noncno NDDCIo NDDCNo noncao noocno NDDCID NDDcno noocno NODCRo NDDCRD NDDCRo NDDCRD NDDcno MODCRD 500 510 383 AUX? I 0. ACRDT I 0. ACRDTA I o. PDR ' 0. CSINPH I 576. OsthH I 576. CSPAPH . 665. CSPFGH - 595. C8870 . 805 CPLF I 0. CSTGH I CPL'ICBSYGI2.ICZSSICZSIICZSJ CSPLFG - CPLr-csprcfl CSIMAP - (1i-CPLf1oCPLP'ICSIAPH Ir(HAL.Eo.1,0k.HAL.Eo.3)ao to 500 CSPLAP I (1.ICPLF)I(1o-CPLPV)IC5PAP4 CSIMNP . OI Go To 510 _ CSIMNP I (1..CPLF)ot1..CPLPT).CSIMP4 CSPL‘P ' OI YEC I CSIMNPICSIHAPOCSPLAPICSPLFGICSTGH YCEC I 766 RETURN END NDDCRD MODCRD HDDCRo MODCRD NDDCRo MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD MODCRD NODCRo HODC“D MODCRD NDDCRo MODCRD MODCRo MODCRD MODCRD 112 113 114 115 116 117 115 119 120 121 122 123 124 125 126 127 128 129 7130 131 132 15: 13¢ S'l'ld- OUT I Hf IGAC? 3814 IONLOM IACC"UNI D=LAT.A~LAT.ACLAT.VLAuDT.VLoTXT.TAch.AcsuT. LILAN.A~LAH.AcLAn.vLAqDI.VLovxn.TAxcn.Ansun. ’U ‘w~.-" NEAL HCFR FnHMON ..aUI-O - ‘7.) u... ICATTLF/ COHHON ICUHTRLI 9 FOHHOH ILlnnl #0.:ng O-I DIHFNSInN B!NCR(12).C‘1NCBC12).CAPDYPc12).CLINCR(12).EDBSER¢12). 2 DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA nATA DATA DATA DATA DATA DATA TMTNCA I EYXC‘ I EVLDTA I D’L‘V.DURU.'ARHIC.D)YOUS.DCRDY.RCSYDY.EXPLivoflcrfl. ALP“1.TtC.YCEC.'RNSL3.YLF.7LFP.CPLF.RINToulNTLI CDEFT.DIQ.C259.C253.C254.L71.L72.L13.CBS'G.CSYGH. EDCHPJO“CROPUIPCR)PLDPFCROPI EerCJO'LOCUDVLDCLOVLD'SOD'LDCUODanCLDD‘LDrc P'G'o'E"'oPFPT.PH3T.PnPT.repeat.Topnpn, Prgfl. SLFERT,SLSCCY;SOLDFT.SLSHLY,$LSPY.SLSPTP.FENSCT. PrPN,9n3H.PNP1.TOPOPH.VACAPL.AUXL12. SLFER“.SLSCCH.SOLDFI.SLSHLH.SLSPH.8UPB.rEHSCH. P=rGTT.PPrPTT.P°HaTT.PPIPTT.PPrcTH. P°rPT*."PMGTH.PPHPTM.RFSTT.RFPTT. nucTT.RIPTT.chTH.RrPTH.RHGTH;RHPTH. TruT.TD~AT.TDN".ToVIP,TDNAH;PATADT.nHM.pHT.PrLACT. V'AT.’A-PAP.PAPP.PRAT.EXPL.UNExPL.CT202.C214.c244 T.DVa0U".TRUN.BEGPRT.PRTCHG.PRTVL1IPRTVLZ.[PRINT. H‘LOYqUUD'DO TQLITYGLIYVGLL.'Y7L01AY'GLUZIYGLSFOI TQSLITL"000TLH001IYLNODZITLMOD3IYYGLRITGLQO TLCRLATLCRU.YLCQUPITGLsroTGLUiPIYLFCIYLCI x91.R1.H2.PR2.PDR.can,RLH.RLHt.PLCRU.AUX1.AUX3. A9").IR”2.ARH3,A5.C9pCRY.CPLPY.XDEL.RPYNIGRE PIHLNSION BINCRA(12.4).CAINCA(12.4).CLIVCAC12.A).ECADEAT12o4). ETXCA(1?.4).[VLDYA(12.4’.SINCRAt12odi.THINCAC12o4) ELOAN:1?).EYCtC(12).SINCR(12)aTHINCRC12).TXCP(12). VLTXTP(12).EDLHCA).VAL3¢(4) EDLH I 116.. VAL36 I ‘.70 ‘05,. SVAc.SVALT2SVALH.AGSUBI.STGLR I 301.. zoo. I PINT.RINTL3!?HoDlR-3CST I .15. .i‘. 12. .15. .12 I SITAX,§HTAX.FOLY.YAKLND I 10100 5000 350I 000‘? , VL"CU1.TLDCL1.VLDFC1 I 1.11. 1.5.. 0.3 I 07LocuzoVLDCL.DYLDFCI 1.6;. 2.9. 10. I PR".EXLHIN I 10' 2000000000 ’ SIHCRA I 1.. 100 1015! 104501065. 2.1090 5.200 2.1.. 101,0 1070 200 20250 2.45} 5.2060 c222.C223.C2‘80C259 I .‘7.‘0. .5. .4 I c2530c252.C2‘JOCI5‘0c261IC26206263’1.2003.40.0300.?5o0750.3I C264.C267.C26806269o0270.6271/.06000601. 5-1. I C2740C775.C276 I3I015 I C277.C278.6279 I 100 100 301 I DELSIDELfingL17.DEL1300£L14 I 5.5. I DEL19095L2C.DEL21.UEL22 I ‘05. I DIFF3OASHALL360K36 I 1.0 700 3 I 1?000 15000 1700 I 4.6. 4.6 I 2.10. 1050 2070 302. 3090 5.4.0 2.1.. 105' 2070 3020 3090 6". I 1010 ’02. 2.10250 3.10350 5.1.‘I 1.2. 1.220 10290 10360 {.43. i.‘60 10530 901.57. 1.3. 1.370 10570 1030 2.0 2.20 6.2022. 1.37. 1.1. 1.1. 1.1. 1.390 10‘. 10°20 2020 7.2.4 I Iolbo 1.250 1.350 1.10 10350 3.1.. 3.1020 6.1.30 1.19. 1.21. {.26. 1.27. 1.20. 501.3. 1030 10330 1.350 10360 10370 5"... 1.60 2.0 2.4. 2.55. 602.60 1060 200 204. 2.95. 6.2.6 I 3.10. 3.10250 6.10350 3.1.. 3.1.3. 6'tg‘. AGACC ACCOUV ACCOUV ACCOUV ACCOUV Cd7 ACCOUV ACCDUV ACCOUV CATTLE CATTLE CATTLE CATTLE CATTLE CATTLE CATTLE CATTLE CATTLE C05 CIS LAND LAND LAND LAND LAND AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC O:I\JO\IOWthNIflCIO..\JO\IOIdRDO(I\IPIICMaRJN 15 385 3 3-1.. 301.32. 601.42 I DAYI LLADEA I 1.. 1.5. 2.. 902.5. 1 1.. 10.0 20,0 20.0 9.30. 3 1.1. ?.. 2.0. 3.. 0-3.4. 6 1.1. 1090 2070 2.90 3.3.3 I D‘T‘ C"NC‘ I 10170 102’. 10‘2. 1042. 3.‘0570 5.1.0,! 1 ‘0‘7. 10320 2.‘O‘7. 301.62. 5.10’70 ' 2 1.20 1.670 1097I 20‘70 2.370 3IQ’0 6.30270 3 1020 10670 ‘0970 20". 2087. 30070 6.332,, DATA CLINCA I 2-1.. 2'1.29. 3o;.4. 50;.5. 1 201.. 201.29. 301.4. 501.5. 2 1.. 1.30 1.60 1.80 2.0 7.2.50 " 1.0 1020 ‘0‘. 1060 1080.7.202 I D‘Y‘ BINCR‘ I 9.0 .05. 01,0 0.. 0‘50 2.000 .10 “000 1 9.0 .68. 0220 2..150 01. 2..050 4.000 2 900 .35. 0". 0,. 0‘. 2.020 5.0.. ‘ 0.0 .350 04,0 .5. 0‘0 20.20 5'0. I TAXCTL I TAICYLOCYA‘CIT-TAXCTL)IDTIDEL12 VLTXTL I VLTXYL¢(VLTXHT‘VLTXTLgobT/DEL13 !F¢T.LT.7.)GO T0 15 PKGR I TABEXECVAL36.S‘ALL36.DIF736oK36.V) TAXCTI I (PMGTIC2220PIPT)06223.PK3R TAXCTZ I FEMSCTIC?77ISF[AIOSLSHLVICZ7DISHTAX CONTINUE TAXCT3 I C279ITOPOPT TAXCT I TAXCTIITAICYZOTIXCTS TAXCHT I TAXCTITTGLR CTLBLT I CTLBLTIRCSVDY VETRAC I VEYRAconcsTDT VETRAD I VETRACIC1.°(’AIAOY-CZ44)I(1.-6244)D CSTANTL . CSTANTL.¢CSTANT-CSTANTL)IDTIDEL20 CSTANT I CTLBLTOVFTRAD CLNDTL . anotLo.oT/oEL§1 CLNDT I CLNDTIRCSTDT OPCLAT I TOPOPT-CSTANT OPCLNT I TTGLRICLNDTICZO7 TAT I SLSPTIPAP YPT I QHTIPIH AGSUT I AGSUBTITTGLR CAPDET I BOLT-TTGLRIC26O ASVLNT . VLANDT-C248 VLDTXT I ASVLNTITAXLND ASVHT I ASVLNT/(TYGLRICZ70) VLYXHY I VLDTXY/(TTGLRICZVI) ACLAT I OPCLATIOPCLNTOYIXCYOCAPOEVIVLOTIT ARLAT I YAYOYHT9AGSUT FARILT I ARLAT-ACLA' VLANDT I AHAX1¢SVALTIVTGLBI FARILT/RINT) VLMDHT I VLANDT/(TTGL'.C269) EPAP I EPAP.(PAPP-EPA9)IDTIDEL5 ESLSPT I FSLSPTo(SLSPTP-ESLSPT)IDT/DEL6 t0"? I FERTIPVLACTICT2OZIVHAY TCLATL . TCLATLotchAt-TCLATL).DTIDELi9 TCLAT I ACLAT/TTGLR TpLAT I EPAP.ESLSDTO(°RHOEOHT)/TT3LROIOSUBT DRLAV I (TRLAT-TCLAYL)ITCDlR IF(T.LT.TMODTGO TO 700 CAPT I TLF0C25300?54/2290 lr(STDRG.LTZCAPT)no To 100 RPCAPT I 0. RLTNPS I 0. AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGICC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGICC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGICC AGACC AGACC AOICC AGACC AGICC AGACC AGICC AGACC 100 110 120 125 130 132 135 386 Go To 110 RPCAPT I (CAPT-STORG).cuSTGI2./C252 STORG I STORGIDToccAPT-STORGI[6252 RLINPS I (CAPT-STORE)ISFSLBIZ.IC252 CONTINUE ALOANA I ALOANAODT.|L1AN ALREPA I ALIEP1ooT.ALaew DFTOUS I ALOANA-ALREPA ALINT I DBTOUS-nlnyL ALOAN I AHIH1IDCRPT. :RhDTI ALREP I TRNSL3/LT3 TAXCH1 I (PHGNIC2?2OP“PHIIC223.PK3R TAXCM? I FEMSCHIC?77osrTAXISLSNLN0027IISNTAX TAXCH3 I C279-TOPOPP TAXCM I TAxcn1ovAxcrzotAXCH3 CSTANH I CSTANHIRCsTDT CLNDN I CLNDHIRCSTD' OPCLAM I TOPOPHICQTANI CSRFGH I CSIFGHIRrleT CSHARV I CSHAPVIRCSTDT OPCLNN I TLMODICLNDNII1.-CPLFIoTLFPICSRFGHOTLFICSHARVIC253IC254 TAN I SLSPHIPAP vnn I ann.pnn AGSUM I AGSHBHIITLHCDITNSLI CAPDEM I TLMODIEOLHIHALI ASVLNH I VLANDM-6248 VLDTXH I ASVLNHITAann ASVHH I ASVLNN/(TLHODOTNSLI VLTXHN I VLnTXHI(TLNODIIRSL) ACLAHI I OPCLAMoO’CLNIICAPDENOVLDTXN ACLAM I ACLAN1ITAXCH¢ALINTOALREPICTCEC’ALPHiICSYGHIIYRSL/I3.IXDELI 1 ORPCAPT ARLAn1 I VAM.YHN.AGSUI ARLAM . ARLAH1oALOAN FARIL" I ARLAH-ACLA" VLAND" . AHAX1¢VLN0NT°¢!R8L°TLHODI.IARLAN1-ACLAH1-TAXCH1IRINT) VLNDHN I VLANDM/(TLIODITRBL) DO 120 II1.LT1 ELOANIII I TCECII1.-R°TNIILT1 CONTInUF ICEC I 3.0anL Do 125 II1.ICEC ETCECIII I TCEC/ICEC CONTINUE TCREC I TCEc.(1,.IpTNI DO 130 II1.LT1 EDBSERIII I TCRECIRINTLIIILT1 CONTINUE IL2 I LT1ILT2 IFILT2.LT.1IGO To 135 I2 I LT101 00132 II12.IL2 EDBSER(II I EDBSEPILT1I CONTINUE 13 I IL201 IL3 I IL2¢LT3 UNPBAL I TCOEC TPAVHT I TCDECILT3 no 136 1.1321L3 UNPBAL I UNPBAL-TPATHT EDBSERIII I YPAYHT.UN’BAL0RINTL AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC CI? AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC 104 105 106 107 100 109 110 111 112 114 115 116 117 116 11¢ 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 136 139 140 141 142 143 144 145 146 147 146 149 150 151 152 153 154 155 156 157 156 159 160 161 162 163 164 138 387 CONTINUE IFIY.E0.THOD)PRINT979 DRL‘H I 00 no 150 II1.IPH ETXC I TAXCTLITXCVII) EvLDTx I vaxTLIVLTIT’Il) ECADEH I EOLTIC266.CA°DTP(II EOPCLM I cstnTLIC1INCF¢IIIGRE EOCLNM I CLNDTL-CLINCVIII TCLAH . EOHSER¢|).ETCECCIIIEOPcLHIEOCLNI.sTchEVLDTXIECADEN TRLAH I EPAPI(SINCa¢I)ItSLsPTIeINCRIIIIAUXL121ITNINCRIIIIPRNIEONTI 1 TTGLRIELOANCIIIAGSUBH DRLAH I DRLAHIITRLA"-TCLAH)I(1.°DIR)III IFIT."£.THODIGO TO 150 PRINT980.EDHSIR(I).ETCEC(II.EOPCLH.EOCLVH.ETXC.EVLDYXIECADEM. 1 SINCRIII.T"IVCN(I).ELOANIII.TRLAH.TCLAH 150 200 1 PRINT908.FANHICLFAR”ICA.VLANDC}ASVLNC.VLDTXC.AICBL.ACCRL.CSTHL. 1 1 1 PPINT932.PKGR.TAXCT1.VAICIz.IAXCY3.TIICHT.VLNDHT.ASVH7.VLTIHT CONTINUE CONTINUE DRLAV I(DRLATITTGLRIDRLANI(TLHODITRSLII/TGLR ASVLNC I VLANOCIC755 VLDTXC I AvancoTAxLND ASVHC I ASVLNC/TLC VLTXHC I VLDTXC/TLC TRCRU . EPcuPu.EvLucu CSTHCU I CSTHCUIRPSYDT CSTHCL I CSTHCLIRCS'DT CSTHFC I CSTHFCIRCSTDT CSTHU I CSTHCU.(1.IC27IIITanu-TLDCUiI/(DTLDcu-TLDCU1II CSTHL I CSTNCLII1.9C275'IYLDCLITLDCL1I/IDYLDCL-YLDCL1)I CSTHF I CSTHFCII1..C276I(YLDFC.VLDFC1IIIDTLOFC-VLDFC1II TCCPUL I chnuLotTcCRJITCCRUL).oT/DEL22 chnu I csTuu .vLTxuc DCRU I (TRCQU-TCC30LIITCDIR ARCRU I TLcnUIYLDCUIPCROPU ARCRL I TLCPLIYLDCLIPCROPL ARFC I TLFCIYLDFCIPFCROP ACCRU I TLCPUICSTNU ACCRL I TLCHLICSTHL Acrc I Tch.CSTHF FARICC I ARCRU.ARCRL-ACCRU-ACCRL FARIFC I Anrc-Acrn fARHICA I rARHICA.pT.fARHIC FARHIC I rAnxccorAaIFCIvLDTxc VLANDC I AHAX1IVLNDHTITLC. rARHICIRINII VLNDHC I VLANDC/TLC GINC I VATIVHTIYAN.THI ALPH2 I C2610!C762IC261IIEXPIICZOSICZGIIAHAX1IOINCIEXLHII. 031’ ExPLIV I AHAX1¢AL°M2ISINC. EXLnINI IFIIPRINToLT.1INETURN PRINY9°°OY PRINT904.DRLAV.DCRUIYRCWJIYCCRULITCCRU.ARCRUIACCRU0CSYHU0CSYNCU0 GINC CSVHCLLALPH2 PRINT912.FARICCSFARIFC.VLUDHC.‘SVHC.VLTXHC.ARFC.ACPC.CSTUFICSTNFC. FXPLIV PRINT920.DRLAT.TRLA'.YCLATL.VCLAToESLSPY.EPAP.SONT.VARILY PRINT924,ARLAT.ACLAT.7A1.VH'.IGSUYIOPCLII.OPCLNT.YIIC'.C.PDEY PRINT92I,csrANthsTANTL.CTLBLT.VETRAD.VETRAC.CLNDT.CLNDTL.VLANDT. ASVLNT;VLDTXT AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC IGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AcAcc, AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC CH2 AGACC AGACC AGACC AGICC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AOICC AGACC AGACC AGACC AGACC AGACC 165 166 167 166 169 176 171 172 173 174 175 176 177 176 179 160 161. 162 163 164 165 166 167 166 169 190 191 192 193 194 195 196 197 196 199 266 201 262 263 204 205 266 267 209 210 211 212 213 211 21! 216 217 216 21’ 226 221 222 223 224 229 000 388 IF(YILT.7H09)FEYU’N PRINT954.DRIAHIFAIILHIANLAHIAcLhHIAILAH1IACLAH1ICAPTIRPCAPTISYORGI 1 RLINPs . 991NT956IVAMIVMMIIgSUI.ALOANIALOAIAIALREPIALREPAIDOTOUSIALINY PRINT962IOPCLAM;09cLN6ICSYAVHICLNDMICSRFBHICSHARVIVLANDHIASVLNHI 1 VLDTXH PRINT966ITAxCHITAlcfl1ITltcnzITAxcwsITAXCTLICAPnEHIVLTlTLIVLHDHHI 1 Asvun.vaqu RETURN 900 FonnAr¢3Iu2nuvru1 or SUUROUYINE AGACC AT TIHEIroIZI 90‘ FORHAYI1H0I9xI5HD°LIVI76I4HDCRUIOXISHTRCRUI7XI6HYCCRULIGXISHTCCRUI 7xI5HAPcvuI7IISflACCRUI7xISHCSTuUI7xI6HcsTNCUI6XIIHOINC/ 1H0IUXI1061204’ 906 FORHA711H0. 9XI6NFARNICI61.7HFARHICAISXI6HVLANDCI6XI6HASVLNCI6XI OHVLHYXCIOX, SHIRCRLI7xI5H‘CCRLI 7‘I5HC37HLI7XI6"CSYHCLI6XI SNALPN2/1H5 .PXI16E12I4I 912 IoRHAT(1HOI9XI6HFARlc:.OKIQHFARIFCI6X.64VLNDHCI6XISHASVHCITII IHvLyxuc.Ix:4MAchI6xIIuAcrcI6xISHCSTHFI7XI6RCSTHICI6XI 6HEX°LlV/1“cI6XI 10612.4) 920 FORMATI1HII9X SHDQLITI 7KI9HTRLATI7XI6HTCLATLI6XI5HTCLATIYXI 6HESLSPTI6X. IHEPAPIIXI4qu~rI61IIHrARILYIIHoIIXI6612I4) 924 FORMAT(1H0I 9x. suAflLITI 7'I SHACLATI 7XI3uvATI 9xI3HflHTI9XI5HAGBUTI7XI 6H0PCLATI6X.6H3rcIuTI 6x. SHTIxcrI7xI6HCAPDETl1HoIIII9E12I4I 926 FORMATI1H0I91I6HCSTANY,6!.7HCSTAN7LI51I6HCYLBLTI61I6HVEYRADIGXI 6HVETRACI61' 5HCLNDTI7XI6HCLNOTLI6XI6HVLANDTI6XI6HASVLNTI “XI.“VL07xt/1HOIU‘I1051264, 932 FORH67(1H0. 9XI4JP¥BRI 6XI6HTAXCT1I6XI6HTAXCT2I6XI6HTAKCTJI6XI NP H 0" Al." .~” NP NF. 24XI3E12I 4’ 954 FOR”‘Y‘1H‘O°XO5HD'L‘"07'I“fir‘a1Lnto‘05H‘RL‘nt7‘6,"‘CL‘"IIXO OH‘RLln1I6XI bH‘CLAH1I‘XIQHCAP'I .‘IOHRPClP'IQXISHSVORBI7‘I GHRLINPS/1HEIOXIIOE12I4) NO‘ 950 OORHATI1H0I °XI3HYAHI9XI JHYHHI9XI54A65UHI7XISHALOANI7‘06HIL0ANAI6XI I... SHALREP, 7x,IuALkePAI6x.Iu09TousI6x. SHALINT/1HOI6XI9612I4) 962 FORMATI1H0I IonuoPcLAI. 6x. 6u0PcLNII 6xI6ICSTANH. 6xI5HCLNpHI7XI 6HcsnrnH.6x,6HcsnARVI16x.6HVLANDII6XI6HASVLNHI6xI6HVLoTXH/ 1H0..x06F1,.‘012‘035120‘, 966 FORHATtlnochISHTAxcnI7xI6HTAxcH1I6xIINTAICHZIIXIIHTAXCISIIXI A)” ND." 6HVLTXHH/1HEI6II10812I4) 979 FORMATI1H1I 6HEDHSFRISII SHETCEC. 6XIQHEOPCLHISXI6NEOCLNHISII 1 4HETXCI7X.GNEVLDV‘I’xIOHECADEHI5‘I5H31NCR66xIOHYN1NCRI’xO 2 SHELIAN.oxIINTRLAHI6KI5HTCLANI 96o FORHATI1H6I12E11I3I ENTRT ACCSE? CPU’S RC‘TDV ' 1.007.9CST VLANDC U 962445800I CSTHCU . 56°I CSYHCL ' 1°97I CSVNFC I 723. YLDCU I YLDCUI ETLDCU I YLDCU VLDCL I YLDCLI VLDFC I VLD'C: chRU I CSTHCUIVLANnCICZSSITAXLND/TLC TCCRUL I TCCRU FARMIC I o._ rARHICA I O. 6N7AlC“?I30XIOHVLVDH'I6XI5HA3VHYI7XIGNVL'XH'IIHUIUIISE12I4I 6H¥AXCYLI61.6H3APDEHI6X.OHVLVXYLIOXIOHVLNOHHIG‘ISUICVHHI7‘I AGACC AGACC AGACC AGICC AGACC AGACC AGACC AG‘CC AGACC AGACC AGACC AGACC AGACC AGACC AGOCC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AG‘CC AGACC ABACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC hGACC AGACC AGACC AGACC AGACC AGACC AGICC IBACC AGACC AGACC AGACC AGACC ABICC AGACC AGACC AGACC AO‘CC 226 227 226 229 230 261 232 233 236 235 236 237 236 239 240 2‘1 242 2‘3 244 245 246 247 246 249 250 251 252 256 256 256 256 257 256 230 266 261 262 263 266 266 266 267 266 269 276 271 272 27! 270 275 276 277 276 279 260 261 262 266 266 269 266 000 388 ITCTILTITHOWIFETU’H PPINT954IDRTAHITA°|Ln.AHLANIACLAHIAILIfltIAcLAniICAPYIRPcAITISTORoI 1 “LINPS ””1”7953IV6"IVHFI‘bSU‘I6LOANIAL06'AIALRE'IALREPAIDBTOUSIALINT PR!N7962IUPCLAHI0?CLN*ICSTAVHICLNDMICSRFBHICSHAFVIVLlNDNIASVLNHI 1 VLDTXH PRINT906ITAXCHITAICN1ITQKCH2IT‘XCWJITAXCTLICAPDEHIVLTITLIVLNDHHI A3VHH.VLTxHH RFTURN 9oo rORNAT¢3IH2nuTPuT or SUUROUTINE AGACC AT TIHEIF6I2I 96‘ FORHATI1H0IOXI5HD°LAVI71I4HDCRUIOXI5H_YRCRUI7XI5HTCCRULI6‘I5HTCCRUI 7XI59APCVUI7'I SHICCRUI7xI5HCSTHUI7xI6HcsTHCUI61IIMOINCI IHOIUXI1UE120 4) 906 FORMATI1H0I9XI6NFARNICI6XI7HFARHICAISXI6HVLANDCI6XI6HISVLNCI6XI OHVLfiTXCI 6!. suARCRLI7XI5HAccRLI 7‘I5HCSTHLI7XI6HCSTHCLI61I SHALpu2/1H5 .PxI10212I4) 912 ‘OORH‘T(1N0I9XI6HFIR'C: 66x6 Our‘RlVCbeI 6“VLNDHCI 6!I5H6$VHCI7XI GHVLIXHCIQX.4H|RFCI8XIQHICFCI.II5HCSTurI7‘IOHcs'H'CIGXI 6HFXPLlV/1HEI6K.10612I41 920 VORHATI1HII9X SHDRLITI 7II5HTRLAT, 7XI6HTCLATLI 6XISHTCLATI TXI 1 6HESLSPTI6X, ‘HEPIPI 6XI4HEOHTI61I6HVARIL711H0.6XIREIZI 41 924 FORMAT¢1HOI91.5HAILAT,7I suAcLATI 7XI3uTATI9xI3H¥HTI 9XI5HAGSUTI 7x. 1 6H0PCLATI6X,6H3PCLNTI 6X. SHTAXCTI7XI6HCAPDETI1HOIOKI9612.4I 926 FORHATI1H0. 9XI6NCSTINT. 6!. 7HCSTANTLISXI6HCTL8LTI6XI6HVETRADI6XI IHVEYRACI61. 5HCL~0TI 7XIIHCLNDTLI6XI 6HVLANDTI6X-6HASVLNTI 5X0.“VLDTX7/1NOI5‘I105129" 932 FORMATI1H0. 9xI4MPVBR. 6XI6HTAXCT1I6XI6HTAXCT2I6II6HTAXCT3I6lI NH NH" .3,” NO. ~II 24XI3512I 4’ ’5‘ FORH‘Y11H'O9xasunnL‘HOIx.‘Hr‘nanlbeSH‘RL‘HD7‘6’"ACL‘"I IX. 6”‘.L‘”1I6XI6HICLIH1I6XI‘HCAP'I .‘IOHRPCAP'I QXISHSTORGI7XI 6HRLINPSI1H5IIXI10212I41 NH 950 VORHAT(1H0I9XI3HYIHI9XIJHTHHI9XI54AGSUHI7XI5HALOANITXIGHOLOANAI6XI III SNALREP,7XI5HALFIPAI6XIIHDQTOUSI6XI5HALINT/1H0I6XI9512.4) 962 FORMATI1H0I9XI6HOPCLA'I6KI6H0PcLNWI6XI6HCSTANNI6XI5HCLNDHI7XI 6HCSIFGHIIIIONCSHARVI16x.6MVLANDII6xI6HASVLNII6XI6HVLDTXH/ INOI HXIOF17. ‘I 12‘036120‘, 966 FORMATI1H0I 9XI5HT6XCHI7XI6HTAXC~1I6XI6HTAXCH2I6XI6HTAXCN3I6XI .ur- qu- IHVLVXHH/1HEI6II10212I4) 979 TORHIT¢1H1IIHEDHSERISI IHETCEC. 6xI6HEoPCLHIstoqucLNHstI 1 4HETXCI7X.GHEVLDTIISXI6NECADEH.51I5HSINCRI6!.6HTHINGRI SKI 2 SHELIAN.IxIsHTRLAHI6II5uTCLAHI 96o FORMATI1H9I12611I3I ENTRY ACCSET c9095 RCSTDT I 1IIDTgpc3T VLANDC I 962446600. CSTNCU ' 5.00 CSTHCL I 1097I CSTHFC I 723. YLDCU I YLDCUI EVLDCU I YLDCU 'LDCL I YLDCL1 YLDFC I VLD'CI TCCRU I CSTucUIVLAuncocz550TAXLND/TLC TCCRUL I TCCRU FARMIC ' 06- rARHICA I o. 6“7AICNTI30X96HVLVDH'I6XI54A3VNTI7XI6HVL'XHT/1H0I.‘6’E12IQI 6H¥AXCTLI 6x, INCAPDEHI6X.6HVLTXTLI6xI6HVLNDHHI6XI5UA6vunI7x. AGACC AGACC IG‘CC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGlCC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGICC AGACC AGACC AGACC AC‘CC AGACC AGICC AGACC AGACC AGACC AGACC AGACC AGACC ABACC AGACC AGACC AGACC 226 227 226 229 236 23! 232 233 234 235 236 237 236 239 246 241 242 zIJ 244 245 246 247 216 249 250 251 252 256 256 255 256 257 256 259 266 261 262 263 264 265 266 267 266 269 276 271 273 276 275 276 277 276 279 200 201 202 263 266 265 286 (1‘30 0‘10 1020 389 TWAflITIOH‘L LIVESTOCK PKGR - c. 'AXCTI I o. TAXCTZ - o. TAXCHT I £279ITOPflPT/TTGLH TAXCTL I TIxcuT canLr I 1624 VETRAC - 6.10 CSTANT - CTLBLTIVFTRAC CSTANTL I CSTANT CLNDT I 35.8 CLNDTL - CLNDT _ VLDTXT I 378000;. VLTXHT - VLDT‘TICTTGLQICZ70) VLTXTL I VLTXNT vCLIr I topoPTonsTINTlTTGLRICLNDTOCZ67°TAXCHTIEOLT06266IVLTXHT TCLATL - TCLAT EPAP I 907. ESLSPT - sLspT(TTcLR VLANDT - 1I?c;ououi. AssueT - o. HODFRN LIVESTOCK ‘LOANA I 0. ‘LO‘N 3 OI lLNEPA I 0. ALREP I OI STORG I 0. AGSUM I 0. CSHARV I 6.25 csnrcH : 576. CSTANH - so; CLHDH I 100. VLANDM I 0. VLDTXP I o. YAXCH . OI VA" . 00 TN" I a. ARLAM I 0. ‘CL‘H I OI DRLIH I o. TCDIR I OI no 1022 1-1;IPH ELOAN¢IT I g. ETCECTI) I o. EDBSERIIT I t. TCDIR I TCDIRI1I/T1IIDINTII! CONTINUE lrtNALIEOIOT RETURN DO 1060 lotIlPH SINCRTIT I SINCPATTIH6L’ THINCPCID I TF‘NCI‘30nAL’ TXCPTIT I ETXCATIINIL’ VLTXTPTIT I EVLDT‘0lO“L, CAPDTPcl) I ecAnenclIHAL) CAINCR(I) I CAT”C‘('I‘AL) CLINCPCIT I CLTNCATII06L) BINCRtl) I HINCNACIIfilL’ AGACC AGACC IGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC. AGACC AGACC AGACC 6N1 AGACC AGACC AGACC AGACC AGACC AGACC CN3 AGACC AG‘CC AGACC AfilCC AGACC AGICC AGACC AGACC AGICC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC AGACC ABACC AGACC AGACC 207 280 209 290 291 292 293 294 295 296 297 290 299 300 301 302 303 304 305 306 307 300 309 310 311 312 313 314 315 316 317 310 319 320 321 322 323 326 325 326 327 320 329 330 331 332 333 334 335 336 337 330 339 340 341 342 343 344 345 1060 CONYINUE RETURN END 390 AGACC 3‘0 AGACC 347 AGACC 340 200 210 920 220 230 300 .AAAHA n 3 9 l SUBROUTINE HODRAT COMMON IACCOUN/ DRLAT.A"LAT.ACLAT.VLANDY.VLDYXT.TAXC'IAGSUY: OJ‘JNH REAL NCFR COMMON [CATTLE] I‘d’fi‘éflNH COMMON COHHON DATA [CONTRL/ ILAND/ CL1.E7}ER}E9.E11 I DRL‘HIANL‘HANCL‘"OVLANDHpVLD'XNolecnolGSUHo DaL‘VIDCRUoFARHICoDBTOUSoDCRDT.RCSTDYIEXPLlVoNCFRo ALPH1.TtC.Tcec;TRNSLS,TLF.TLFP.CPLF.RINT.RINTL. cneor.oza.c256.c253.C254.Lti.L12.Lts.casvc.csrcu. E°cRPJ.PCBOPU;PCROPL0PFCROPL EVLDCJoYLDCUoYLDCLoYLDFC.DYLDCU:DYLDCLoDXLDFC PFGT.FERT.PFPT.PHOToPHPToTOPOPTaTOPOPRo prcn. PFPH,PHON.PHP*.TOPOPN.VACAPL0AUXLiZn SLFERT.SLSCCT;s0Lor7.SLSMLT;SLSPT.SLSPYP.rsHscT. erERI.sLsccn;soLorn.SLSMLM;SLSPH.BUPB.rEHSCH. pprGTT.PPPPTT;pPHGTT.IPIPTT;PPFGTM. PPFPT'.PPNGVHQPPHPTH.RFGTTaRFPTTa nugtT.RnPtT.chrH.RrPrH.RHGTH;RHPYH. Yan.tnuAr.tDNn.TDNHP.TONAH;PATADT.0NH.ONT.PFLACT. YNAToPAoPAP.PAPPoPRAT.EXPLnUNEXPLaOT2023C214.€244 T.DVoOUN.IRON;BEGPRT.PRTCHG}PRTVLioPRTVLZ.[PRINTu HAL.TIOU.TDO TGL.TTGL.TTGLL,TTGLU1.TTGLUZ.TGLSFo, TRSLIYLnoooYLHOOIITLHODZITLHOD30776LR0TGLR! TLCRLOYLCRUOTLCRUPO'GLS'DYGLUiPO'LFC'TLC. x91.R1.N2.PRZ.PDR.cRH.RLH.RLHI.RLCRU.Aux1.Aux3. A9H1.ARHZaARHS.A5.C9:GRTaCPLPT.XDEL.RPTNoGRE 0030 1:0 0150 .5005 / DATA 231.532.E33.F31.591 I 4006.. 2309.. .3; g.. ,3 I DATA TEO.TEi.TE2.TEFoEXHAI I 5.. 7.. 11.. 133. 500. I GRT - TTGLR(TOPOPT IFTDRLAV.GE.DCRU)GO TO 200 XTLU I TGLUIP 00 T0 210 eru I TLCRUP IF(T.GT.DT)co TO 220 CL1 I RLcnquas¢DCRU)/CITLUI(DCRU-DRLAV)) PRINT920.CL1 FORMAT(5HUCL1I.511.4) on T0 230 RLCRU I CL1IXTLUo(DCRU-DRLAV)/ABS(DCRU) Aux: I AHINQTRLCRU. o.) AUX3 I AHAx1(RLCRU. o.) 1r(1.LT.1Hon)Go to 300 ETCAH I TITMOD RLH I RLnocez-RLH).vT/quL R2 I R26(XR1-92)IDT/XDEL R1 I R10tCRM-R1bonT/XDEL XR1 I R1IA5 PDR I (DRLAH-DRLAT)IABS(DILAT) E3 I £31-E32IEXPt-e33IETCAH) PR1 I APAX1(670:1.pEx’t'EO'CPDR°E9))i. 00’ CALL GRAPH¢YEb.TE1;TEZ.TEE.EXHAX.T.EX11) RLHPI I £3I9R1IFXT1 RLHDI - PR1-TTGLITLHUOITGL0 RLH! I RLNPIIRLND! PR2 I APAX:(E11I(1.-EXP¢-l010(e91-PDR:3). 0.) can - AHAX1¢ANIN1(RLH!. AHIN1CARH10 ARM2)IARN3)0 Gnu I (TLMODITRSL’IYOPOPH ORR - TGLR/TOPOPR CONTINUE IF¢TPRINT.LY.1)NETURN PRINTOOCoT NODRAT ACCOUN ACCOUV ACCOUN ACCOUV Cd? ACCOUV ACCOUN ACCOUV CATTLE CATTLE CATTLE CATTLE CATTLE CATTLE CATTLE CATTLE CATTLE CH5 CH5 LAND LAND LAND LAND LAND noonAt HODRA' HODRAt HODRAt MODRAT nonaAt HODRAt HODRAT HODRA! MoDRAt HODRAt "DORA, noonAt HODRAt HODRAT HODRAt HODRAt HODRAt noDRAt HODRAt HODRAt HODRA NODRA HODRAt HODRAI HODRAT HODRAt HODRAT MODRAt nonaAt "ODRAt MODRAt HODRAT IODRAt IonnAf IODRAt OOVOWOUNNPOOOVOWOUNOON’UOUNN 392 PRINT910.CR?.RLCRU.AuxaoAUXS NODRA 48 lrtT.LT.rnoo:R£3u~u , , IODRAi 44 PR1N7950.Png.ea.Pa;.Ex71.anPl.ILuux.nLnt.rna;can NODRA 49 P91N7955.R1.Rz.xax,aLI.gnn.oan NODRA' 46 RETURN NODRA' 47 900 FORNAT(36N200TPUT of SUIROUTINE NODRAT AT TIME.FI.2) NODRAT 40 910 FORMAT!1N0.’X.3N0RTo9fla9HILCRu;7i.QHAUX1.0XL4HAUX311H0091.4(E11.4. NODRA' 49 1 1x») _ NODRA' so 950 TORNAT¢1Hn.ox.3NPnR.9x.zula.10x.3H991.CI.AHEXT1.OX.5NRLNPI.7x. NODRA 5A 1 SHRLnDI.7x.4NnLHT.Ix.3upR2.9x.sucanliud.9x;9(811.9.IX)) NODRA 92 959 EORNAY(1N0.IX.ZHR1.10!.zucz.1dx.3uxn1.9x.3HRLH.9x.3u6RN30x.3HoRRI HooaAt 5: 1 1No.9x.6(611o4.11)! NODRAt SI ENTRY HOUSE? NODRA 5! teLo I TGL NODRA so RETURN NODRAT 57 END NODRA' 5. T93 SUBROUTINE CRTACC CRTACC 2 COMMON IAccnunx DILAT.Angat.AcLAT.VLAMDT.VLDTXT.TAXCT.AGSUT. ACCOUV 2 1 D“LIH.ANLAH.ACLAH.VLANDN.VLDTXN.TAXCH.ACSUN. ACCOUV 3 ? OILAV.OORU.FARnIc.OHTous.ucnor.nc5ToT.ExILtv.Ncrn. ACCOUV I ' ALP“1.TtC.TCEC.TRHSL3.TLF.TLfP.CPLF.RINT.RINTL. ACCOUH 9 4 CQEDTIDIRI6250.C2530c254IL710LY20L730CBSYGICSYGHI CH7 ‘ 5 EDCHPJ.POROPU.POR3PL.IFOROP. ACCOUN 7 . EYLDCJ.TLDCU.YLDCL.YLofC.DYLDCU.DYLDCL.DYLDFC ACCOUV a REAL ucrn Accouv 9 COMMON ICATTLEI PFgT.FENT.PFPT.PHGYIPHPT.TOPOPT.TOPOPR. CATTLE 2 1 Pro". PFPH,PHGH.PHPI.TOPOPN.VACAPL.AUXL12. CATTLs 3 i SLrERT.SLSCOT.sOLorT.SLSMLT.SLSPT.SLSPTP.rEHscT. CATTLE I 3 SLFER“.SLSCCH.SOLDFH.SLSHLH.SLSPH.8UPB.FENSCN. CATTLi 5 4 PDrGTT.PPrPTT.anGTT.pPIPTT.PprOTM. CATTLE o 5 pIrPTM.PPHGTH.PPHPTH.RrGTT.RFPTT. CATTLE 7 b RHcTT.RHPTT.RFGTN.RFPTP.RHGTH.RHPTH. CATTLE a 7 TDnT.TDNAT.TDNH.TDNHP.TDNAH.PATADT.OHH.ONT.PFLACT. CATTLE 9 ‘ THAT.”A.PAP.PAPP.PRAT.ExPL.UNEXPL.cT202.C214.6244 CATTLE 1O COMMON ICONTRL/ T.oT.DUN.TRUN.BEGPRT.PRTCHG.PRTVL1.PRTVLZ.TPRINT. CNS 1 1 HAL.T*OU.TDO CH5 2 COMMON ILANO/ TnL.TTOL.TTOLL.TTaLu1.TTcLu2.TcLSFO. LAND 2 1 TPSL.TLHOD.TLN001.TLNoDZ.TLH003.TTGLR.TGLR. LAND 3 ? TLCPLITLCRUO'LCRUPOTGLSFITGLU{POYLFCD'LCO L‘ND ‘ 3 x91.R1.N2.PR2.POR.CRN.RLH.RLHI.RLCRU.AUX1.AUX3. LAND 5 4 APH1.ARH2.ARN3,A5.c9.GRT.CPLPT.XDEL,RPTN.GRE LAND 6 DTHENSION VAL51¢5).VAL52(7) CRTACC 7 DATA £239.CP72.C273.6281.C282 I .1. 1.. 1.. 2.6.. .632 I CRTACC 6 DATA SHALL51.DIFF51.K51 I 4.. 1.. 4 I CRTACC 9 DATA SHALL5?.DITF520K52 I 4.0 1.0 6 I CRTACC 10 DATA VAL51 I 12.77. 13.5. 13.5. 14.50 15.76 / CRTACC 11 DATA VALSZ I 117.. 115.9. 127.. 147.. 164.. 163.5. 174. I CRTACC 12 DATA 4L1.EXCHNI.I .0729. 15.76 I CRTACC 13 DATA HPBI.HPBCI.HPBR.HPUCR I 174.. 565.. .1i. .304 I CRTACC 14 DATA P090 I 15415760. I CRTACC 19 DATA CRFP",950CRAQINTC I 100 ISO 01 I CH6 ‘ CPEPP I cnep CRTACC 17 CREP . AHAx1(CREP°ICDEBI o.) CRTACC 1o CINT I RINTCICDEB CRTACC 19 CDEB I CDEBIDTITCLolN-CNEPP) CRTACC 20 CCDT I DBTousI(H1NT-RTNTLTOCDEBITRTNT-RINTC) CRTACC 21 POP I rOPoIEthczaon) CRTAcc 22 PERCAP I c2n1-(SUPB-erPL-UNEXPL)IPOP CRTAcc 23 ARLSK I ARLATOAFLAH'CLOIN CRTACC 2c ACLSK I ACLATIACLAH*CREP°CINT CRTACC 25 FARTLA I FAQTLAIDTIFARTL CRTACC 26 rARTL I ARLSK-ACLSK CRTACC 27 OSREVL I DSIEVLIOT.TARTLIEXP¢IOTRIT) CRTACC 28 FARHTA I FANHIAIDTIFARHI CRTACC 2° FAR"! I FARTLOVAK"IC CRTACC 30 VLAND I VLANDTIVLANDH CRTAcc 31 VALCAP I VACAPL.VLAND CRTACC 32 VLDTAX I anTITIanTx* CRTACC 33 VLANDH I VLAND/(TnLnoczlz) CRTAcc 34 VLDTXH I VLnTAXI(TGLRICZ73) CRTACC 3! COHLOX I VLnTXH/VLAN04 CRTACC 30 cnvnEvA I GnVPEVAIDYIGOVREV cRTAcc 37 GOVREV I TAXCTITAxcHIvLuTAx CRTACC 38 EQPOS I VLANDIVACAPL-Cbta-DBTOUS CRTACC 39 ACRDTC I PEOCH.FO°oS CRTACC 4o CPDAV I AMIN1(PPOCRIEOP0$. ACRDTC) CRTACC 41 Nch I AHAX1(VAFILoC239'FORNICoEXPLIV. o.) CRTACC 42 GOO 1’3“ TDCRDC I AHAx1(erAnlL-CZJOIFANHTCIEXPLIv. o.) CLOAN I AHTN1(OPOAV. TDCRDC) IFTT.LT.4.)OO TO 350 IFTT.GT.O.)GO TO 290 EXCHR I TABlIFIVALSI.SHALL31.017751IK51IT! 60 TO 295 29a EXCHR I ExcuRIOToncHEx 295 RCHEX I A11.excunx.£xI¢AL1o(T-a.)1 300 FOREXA I FOIEIA.OT.TOIEI xrtT.LT.4)Oo To 325 IFTT.GT.10.)GO TO 310 ups I TABLIETVAL52.SHALL52.DIF752.K52.TT GO TO 320 31a [FTT.GT.14.)GO To :15 MPG I UPBIot1.oHPPRI(TI10.)) Go To 320 315 HPBC I NPBCIIT1.IHP8CRITTI10.)) N98 I NPBCI4.3 320 IF(T.E0.7.)FXSua . .19 sann I upe.excun..1...o.exsue:~PA Foaex I HPBIEXPL suase I rOREXIExcunoexsue SUBSA I suesA.oT.5ues sues I SOBSEoCOOToAOsuToAOsun IFTIPPTNT.LT.1)RETUPN PR!NT900.T PRINT910. CR!P.C!NT' OOEO.EOPos.cROAv.cLOAN. TDCR00.NGFR.CCDT anuTvzo.TAIIL.rARxLA.rIRnx. FARHIA.DSREVL.ARLSK.ACLSKIPOP. PERCAP pnquvao, VALCAP' VLAND.VLDTAX.VLANDH.VLDTXN.CONLDX. OOVREv.OOVREVA PRINT940. suas.su95A.suass. ROHEx.ExcHR.rOREX. FOIEXAIEXNARINPB RETURN 900 FORMAT¢36H20UTPUT or suunourtue CRTACC AT Txn2.ra.2a 91o FORMATT1HO. 9x.4ucneP. OX.4HCTNT. ax.4uco£a. ax. SNEOPOS.7X.5HCRDAV.7X. SHCLOAN. 7x. aNTOCIOO.ox.4uNch.IX.4HOCOTI1HO.OX.9§12.4) 920 FORMAT(1H0. cx.5urARIL.TX.INFARILA.IX.5HFAINT.7x.6HrARHTA.o!. IHOSIEVL. 6x. SNARLSK. 71.5uAcLsx. 7X.3HPOP. 9X.6NPERCAPI1HOI ax. 9612.4) 930 TORHNTCIHO. CXIGHVALCA'IONISNVLINDD7NOGHVLDTINICNOO"VLAND“I.NO IHVLngnzox. IHcOHLOx.ox.ouoovasv.ox.7HoovaevAI1ua.ax. 8612. 4) 940 FORMATT1NO.OX.4HSUBS.BX.SNSUBSA.7X.5NSUBSE.7X.BNRCHEXI7!.5HEXCNR. 1 7x.5uronex.7x.OHFOIEXA.QX.5HEXIAR.7x.3uuPOI1uo.Ox.OE12.4T avrI I- ”0" INITIAL VALUES '0' INVESTMENT ACCOUNTING ENTRY CPTSET EXSUB I 00 r‘RlL . 0. TARILA I 0. FAR"! . 00 FARNIA I 0. GOVREV . 0|- GOVREV‘ ' 0. USREVL C 0. SUBS I O. SUBSN I 0. TONE! I 0. FOREXA I o. EXCHR . 0. NPB I o. CREP I 0. CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTAcc CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTAC: CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC CRTACC cues . o CLOAN I 5 DBTOUS I 5 RETURN ' E no 395 CRTACC CRTACC CRTACC CRTACC CRTACC 104 105 106 107 106 396 SUOHOUTTNE GRAPHCTithoFZITPIVHAXITIOUT’ 17(T-T0’4oo‘0015 1r¢T.T1:so.Io.26 IFTT‘TZ)GGIG003 IFTT-TF)70.IO.4 OUT 3 U. RETURN OUT I VHAXITT-TETITT1-To) RETURN OUT I VHAX RETURN OUT I VHAXITTF-T)I(TF-T2) RETURN END GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH GRAPH 397 FUNCTION TARLIETVALISRALLIDIFF.K.DUMMY) DTNENSION VALT1) DUN I AHIN1«ANAX110UNIT-SNALL.6.O).rLOAT(x).o;rr) 1 I 1..nUNIO!rr 1"!OEUIK.1,'.K TABLIE I (VALTII1).VAL(I))ITDUHIFLOATI1-1)IDIFF)IDIFFIVAL(|) EEEURH N TABLIE TABLIE TABLIE TABLIE TABLIE TABLIE TABLIE TABLIE OOVOU.UN 398 rUNCTION TAGEXECVALISNALLIDIPF,KIDUHHV) DIMENSION VAL‘1) DUH I DUMMY-SHALL I I HIN0("AN1(1.OnU"/DT'FIII’IK’ _ TABEXE I (VAL(!‘1’-VAL")"(DUH"LOAT(T'1).DIPP)/DTPFTVAL(I) RETURN END TABEXE TABEXE TABEXE TABEXE TABEXE TABEXE TABEXE OVOUOGN 3‘99 SURROUTINE OELOTTIINR.RUUTR.OROUTR.OEL.lOT.nT.K) DTNENSION CROUTRTI) DEL1 I DELIILOATTIOT)I¢FLOAT(K:IDT) ROUTR . 0 I Do 2 JI1.TDT RTN I RTNR/anATTIoT) DO 1 l'10“ Aac I cROUTR(l) CROUTRTI) I AROITRlN-AOOTIDEL1 RTN I ABC ROUTR I ROUTRIONOUTRTIT RETURN END DELDT DELDT DELDT DELDT DELDT DELDT DELDT DELDT DELDT DELDT DELDT DELDT DELDT 10 1300 SUDRDUYINE DELAvganR.ROUYR.CRoUTR.DEL.0T.K) DIMENSION CROUTntl) DEL: I DEL/(FLOAT(K)IDT) RIN I RINR 00 10 ‘.10K ABC I cnouvncl) cnourRCI) I Aaco(n;N-ABC)IDEL1 RIN I AFC ROUYR I CROUTR(K) REYURN END DELAY DELAY DELAY DELAY DELAY DELAY DELAY DELAY DELAY DELAY DELAY f‘HOfi ”Hooovouoan 8| “()1 Iuououvxus oncillul.Ioutl.vnalu.ncouur.uocv.L?3Iuntu3 hiatusxou VIIxoiz: ICDUNY I NCOUNYIi lunlu I suntuollun arcncouuv.ul.uch)nsvunu IOUTR I rnazu¢1seroat¢~oeV) no so :I2.Lt ruaxucx-x: I YlAtlcl) IIAINKLY) I sun!» DUNE" I DI NDDUNY o I. RETURN END DDXC BOXC DDXC BOXC DDXC aoxc DDAC DDXC DDXC DDXD DDXC DDIC DDXC 1cm BIBLIOGRAPHY 10. 11. BIBLIOGRAPHY Abkin, Michael. Policy Making for Economic Deve10pment: A System Simulation Model of the Agricultural Economy of Southern Nigeria. Staff Paper Series 72-H. East Lansing: Department of Agricultural Economics, Michigan State University, 1972. Anderson, A. W. La Industria de Carne en Colombia. Bogota: Ferrocariles Nacionales de Colombia, 1961. Banco de la Republica. Revista del Banco de la Republica. Vol. XL (Jan.-Dec., 1967). Bogota. Booz, Allen, and Hamilton, Management Consultants. "Meat Packing Industry Program for Colombia." Report to the U. S. Operations Mission to Colombia, Chicago, August 31, 1961. Bowser, Max F. "Prerequisitos y Potencial para la Exportacion de Carne en Colombia en la Decada de 1970." Agricultura Tropical, XXV (Nov., 1969), 675-716. ‘ Caja de Credito Agrario. El Ganado Vacuno en Colombia. Bogota: Departamento de Investigaciones Economicas, 1970. Estimativos Sobre Areas Cultivadas y Produccion Obtenida Para los Principales Cultivos. Afios: 1965-1969. Carta Agraria, No. 2AA. Bogota, 1970. . Calculos de Produccion Agricola de 1958 a 1963. Carta Agraria, No. 13h. Bogota, 1964. Centro Internacional de Agricultura Tropical. "Aspectos de la Ganaderia Vacuna en las Llanuras del Caribe en Colombia," Cali, 1972. (Mimeographed.) Chong, Kwong-Yuan. "A Simulation Policy Analysis of the Western Nigerian Cocoa Industry." Unpublished Ph.D. dissertation, Michigan State University, 1973. Comision de Reforma Tributaria. Report of the Commission. Bases Para Una Reforma Tributaria En Colombia. Bogota: Banco Popular, 1969. U02 12. 13. 14. 15. l6. 17. 18. 19. 20. 21. 22. 23. 2U. “03 Comite Interamericano de Desarrollo Agricola (CIDA). Tenencia de la Tierra y Desarrollo Socio-Econgmico del Sector Agricola: Colombia. Washington, D. C.: Pan American Union, 1966. Currie, Lauchlin. Programa de Desarrollo Economico del Valle del Magdalena y Norte de Colombia (Informe de Una Misionj. Bogota: Ministerio Obras Publicas, 1960. Departamento Administrativo Nacional de Estadistica. Directorio Nacional de Explotaciones AgrOpecuarias (Censo‘AgropecuariOI1960: Resumen Nacional, Segunda Parte. Bogota, 196D. Directorio Nacional de Egplotaciones Agropecuarias (Censo Agropecuario) 1960: Departamentos de Atlantico, Bolivar, Cordoba y Magdalena. Bogota, 196D. . Pronosticos y Estimaciones Agricolas 1971. Bogota, 1971. . XIII Censo Nacional de Problacion (Julio 15 de 196H). Bogota, 1967. Encuesta Agropecuaria Nacional 1968. Bogota, 19%0. Departamento Nacional de Planeacion. "Plan de Desarrollo Economico y Social 1970-1973," Tomo I, Bogota, 1970. (Mimeographed.) Federacion Colombiana de Ganaderos. La Rentabilidad Ganadera en el Pais. Bogota, 1971. Forrester, Jay W. Industrial Dynamics. Cambridge, Massachusetts: The Massachusetts Institute of Technology Press, 1961. Fromm, Gary. "Implications to and from Economic Theory in Models of Complex Systems." American Journal of Agricultural Economics, 55 (May, 1973), 259—71. Garcia Samper, Alfredo. Perspectivas de Colombia en el Mercado Internacional de Carne de Res. Bogota: Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, 1970. Gomez, Luis Jair. Aspectos Reproductivos en Rabafios Bovinos Tipo Carne de Colombia. Publicaciones Tecnicas No. l. Medellin: Fondo Ganadero de Antioquia, 1968. 25. 26. 27. 28. 29. 30. 31. 32. 33. 3M. 35. 40A Gresford, Guy B. "Systems Approach for Development." IEEE Transactions on Systems,AMan, and Cybernetics, SMC-2 (July, 1972), 311-18. Griffin, Keith B., and Enos, John L. Planning Development. London: Addison-Wesley Publishing Co., 1970. Hamilton, H. R., et a1. Systems Simulation for Regional Analysis--An Application to River-Basin Planning. Cambridge, Massachusetts: The Massachusetts Institute of Technology Press, 1969. Hayenga, M. L., Manetsch, T. J., and Halter, A. N. "Computer Simulation as a Planning Tool in Developing Economies." American Journal of Agricultural Economics, 50 (December, 19687, 1955-59. Henning, Robin G. "Ganado y Carne, Colombia: Analysis de la Produccion y Prospectos para Exportacion," Bogota, Ministerio de Agricultura, 1971. (Mimeographed.) Hirschman, A. O. Journeys Toward Progress. New York: Twentieth Century Fund, 1963. Instituto Colombiano Agropecuario. "Proyecto de Sanidad Animal: Combate de la Fiebre Aftosa y Control de la Brucellosis-Primera Etapa 1971—75," Bogota, Nov., 1970. (Mimeographed.) . "Silos y EnsilaJe." Technical Bulletin No. 8, 1-16. "Analysis Quimico en Base Seca, de Gramineas y Leguminosas Adaptadas a las Condiciones de Colombia." Informacion Preparada para los Programas de6gastos y ForraJes, Nutricion Animal y Suelos, l9 . Instituto Colombiano de la Reforma Agraria. "Productividad de la Ganaderia de Cria y Consecuencias Sobre los Programas de Credito Ganadero," Bogota, 1970. (Mimeographed.) ~ Instituto de Comercio Exterior. Bases para un Plan Cuatrienal de Exportaciones de Productos Seleccionados,_l97l—l97u-Resumen. Documento para Comision Mixta de Comercio Exterior, Bogota, 1971. 36. 37. 38. 39. NO. 41. U2. “3. UN. “5. A6. “7. U8. 405 Instituto de Fomento Algodonero. Estudios Preliminares de Suelos del Departamento de Cordoba, by L. F. Irusta and E. A. Fortoul. Bogota, 1959. . Estudios Preliminares de Suelos del Departamento del Magdalena y la Intendencia de la Guajira, by L. F. Irusta and E. A. Fortoul. Bogota, 1957. Instituto Geografico "Augustin Codazzi." Levantamiento Agrologico del Departamento del Atlantico. Bogota: Departamento Agrologico, 1960. . Atlas de Colombia. 2nd ed. Gobota: Litografia Arco, 1969. Inter-American Committee for Agricultural DevelOpment (CIDA). Inventory of Information Basic to the Planning of Agricultural Development in Latin America-~Colombia. Washington, D. C.: Pan American Union, 196“. International Bank for Reconstruction and Development. Economic Growth of Colombia: Problems and Prospects. Baltimore: The John Hopkins University Press, 1972. "Second Livestock Development Project: Colombia." Appraisal report of a Bank's mission to Colombia, Washington, D. C., September 3, 1969. (Mimeographed.) "Use of Simulation in Appriasing a Livestock Breeding/Fattening Project." Economics Department Working Paper No. 56, Washington, D. C., January 1970. (Mimeographed.) International Monetary Fund. International Financial Statistics. Vol. 20, (December, 1967). Washington, D. C. . International Financial Statistics. Vol. 26, (April, 1973). Washington, D. C. Johnson, Glenn.. "Alternatives to the Neoclassical Theory of the Firm." American Journal of Agricultural Economics, 54 (May, 1972), 295—303. . "General, Systems-Science, Simulation Analysis-- An Introduction," Michigan State University, 1973. (Mimeographed.) Johnson, Glenn L., et al. "A Simulation Model of the Nigerian Agricultural Economy: Phase I--The Northern Nigerian Beef Industry," Michigan State University, 1968. (Mimeographed.) “9. 50. 51. 52. 53. 5M. 55. 56. 57. U06 Johnson, Glenn, and Zerby, Lewis. What Economists Do About Values: Case Studies of Their Answers to Questions They Don‘t Dare Ask. East Lansing: Michigan State Agricultural Experiment Station, 1972. Ladipo, Olasupo. "General System Analysis and Simulation Approach: A Preliminary Application to Nigerian Fisheries." Unpublished Ph.D. dissertation, Michigan State University, 1973. Lehker, J. N., and Manetsch, T. J. Systems Analysis of Development in Northeast Brazil: The Feasibility of Using Simulation to Evaluate Alternative Systems of Beef Production in Northeast Brazil. Technical Report G-l3, Division of Engineering Research, Michigan State University, 1971. Llewellyn, Robert W. FORDYN-—An Industrial Dynamics Simulator. Raleigh, North Carolina: By the Author, Box 5353, 1965. Manetsch, T. J., et al. A Generalized Simulation Approach to Agricultural Sector Analysis with Special Reference to Nigeria. East Lansing: Michigan State University, 1971. Manetsch, T. J., and Park, G. L. System Analysis and Simulation with Applications to Economic and Social Systems-—Part II. East Lansing: Department ofII Electrical Engineering and System Science, Michigan State University, 1973. Miller, S. F., and Halter, A. N. "Systems Simulation in a Practical Policy-Making Setting: The Venezuelan Cattle Industry," American Journal of Agricultural Economics, 55 (August, 1973), 20-32. Ministerio de Agricultura. Proyecto de ley por la Cual se Introducen Modificaciones a las Leyes 200 de l936,_l35 de 1961 y la de 1968, se Crea la Jurisdiccion Agrariayyyse Establecen Disposiciones sobre Renta Presuntiva. Bogota: Banco Ganadero, 1971. "Estudio para la Produccion de Maiz de Exportacion en la Costa Atlantica," Medellin, 1970. (Mimeographed.) ,l 58. 59. 60. 61. 62. 63. 6A. 65. 66. 6?. U07 . "Programas Agricolas para 1972," Bogota, 1972. (Mimeographed.) . Informe del Comite Evaluador de la Reforma Agraria? Bogota: Banco Ganadero, 1971. Oven, Roderick von. "Factors Affecting the Beef Cattle Industry's Return on Capital and Development Possibilities in Selected Areas in South America," Institute of Agricultural Production Economics, University of Goettingen, West Germany, 1968. (Mimeographed.) Riley, Harold M. Beef Production in Colombia. Palmira: Facultad de Agronomia, Universidad Nacional de Colombia, 1962. Rossmiller, G. E., et al. Korean Agricultural Sector Analysis and Recommended Development Strategies, 197IL1985. East Lansing: Michigan State University, 1972. Sarmiento, Hector. "Una Decada en la Industria de Ganado Vacuno para Carne en Colombia," Agencia para el Desarrollo Internacional, Bogota, 1972. (Mimeographed.) Secretaria de Agricultura de Antioquia. Sacrificio de Vacas en Antioquia, by Luis Jair Gomez. Publicacion Especial No. 75. Medellin, 1966. Shapiro, Harold T. "Is Verification Possible? The Evaluation of Large Econometric Models." American Journal of Agricultural Economics, 55 (May, 19737, 250-58- United Nations Food and Agriculture Organization. Livestock in Latin America, Status, Problems and Prospects: Colombia, 1962. United States Department of Agriculture. Economic Research Service and Ministry of Agriculture and the Central Planning Agency of Colombia Cooperating. Changes in Agricultural Production and Technology, in Colombia, by L. Jay Atkinson. Foreign Agricultural Economic Report No. 52. Washington, D. C.: Government Printing Office, 1969. 408 68. United States Department of State. Agency for International Development. Bureau for Latin America. Colombia Agriculture Sector Analysis, Working Document Series. Washington, D. C., 1970-72. 69. . Colombia Agriculture Sector Analysis. Agricultural Production Cost Data, Averages py Wage Rate Regions-—l970, by S. R. Daines and G. V. Poynor. Working Document No. lA. Washington, D. C., 1971. 70. . Colombia Agriculture Sector Analysis. Preliminary Analysis of Costs of Employment Generation in Agriculture, by S. R. Daines. Working Document No. 2A. Washington, D. C., 1971. 71. . Colombia Agriculture Sector Analysis. Data and Preliminary Analysis of Livestock Production, by D. R. Steen and S. R. Daines. Working Document No. 12. Washington, D. C., 1971.