5555555555555555555 5555555 555555 555555 55555555555 3 1293 10408 9465 ___ I an 3.3 LLwfiR Mathias” grate ‘ University \r This is to certify that the dissertation entitled A Field Machinery Selection Model for Wheat Producers in the Andes Pre-Cordillera of South Central Chile. ‘ presented by Edmundo J. Hetz has been accepted towards fulfillment of the requirements for Ph.D. degree in AK. Engr. Tech. Major professor Date / 0/45/82. MS U is an Affirmative Action/Equal Opportunity Institution 0-12771 MSU RETURNING MATERIALS: Place in book drop to LJBRARJES remove this checkout from 5 your record. FINES will ‘ be charged if book is returned after the date stamped below. ~ 5 .’ r’ 3'. 0 ‘ ' 11‘9“,“ ’ — } r a 5 5‘ c) d( 3’ '1 ' "z 4‘ \I 2 1 §.. A FIELD MACHINERY SELECTION MODEL FOR WHEAT PRODUCERS 139 TTHE ANDES PRE-CORDILLERA OF SOUTH CENTRAL CHILE By Edmundo J. Hetz A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Engineering 1982 ABSTRACT A FIELD MACHINERY SELECTION MODEL FOR WHEAT PRODUCERS IN THE ANDES PRE-CORDILLERA OF SOUTH CENTRAL CHILE By Edmundo J. Hetz Chile's most important crop is wheat, which with other small grain cereals account for 802 of the area planted with annual crops. However, the area seeded yearly with wheat has been experiencing a steady decline. It has gone down from 780,000 hectares in 1966 to 546,000 hectares in 1980. Wheat output has fallen to less than 40% of domestic usage versus 752 a decade ago. Wheat production costs in Chile are heavily influenced by the cost of owning and operating agricultural machinery. It has been esti- mated that the agricultural machinery cost component can vary between 30 to 362 of the total wheat production cost. Crop rotations and tillage intensity have important effects upon machinery system requirements and, consequently, production costs. This research project focused upon the development of a computer model to aid the selection of machinery systems for wheat producers in Chile, evaluating the effects of crop rotations and tillage systems upon machinery requirements and machinery related production costs. A systems analysis approach was used as the analytical and problem solving tech- nique. Field work was carried out in Chile in order to collect agro- meteorological, agronomic, economic and agricultural engineering data to develop the computer model. Model validation was conducted with data collected through field surveys carried out at the farm level. The most important conclusions derived from the survey of wheat Edmundo J. Hetz ‘producers and the computer simulation analysis were as follows: The large majority of farmers (852) owned one (602) or two (25%) two-wheel drive tractors. A very good correlation (r 8 0.95) was found between the yearly seeded area and the total power available on the farm. The individual tractor power range was found to vary from 37.3 to 73.1 PTO-kW, with an average power per unit cultivated area of 0.55 kW/ha. Computer predictions of days suitable for fieldwork at the 0.70 probability were matched very closely by the results from the farmer's survey. For 10 of the 12 biweekly periods the 0.70 design probability values were found to be within 10% of the farmers' estimates, with a correlation coefficient r - 0.91. Ownership cost was consistently the largest of the system cost components, with values ranging from 41 to 362 (75 to 45% for the har- vester) of the total system cost. Labor and timeliness costs had the lowest relative importance among the cost components. As the number of crops in the rotation increased to include oats and lentils, machinery requirements decreased along with the costs per hectare. Diesel fuel requirements per hectare were affected by both the tillage level and the crop rotation. The effect of the first factor can be more important than the effect of the crop rotation, generating savings of up to 27.0 L/ha inpa 110 hectare farm. 1 Approved 1] AA W Major Professor l false Department, irman Dedicated to: Jean-Hendrik Hetz van Kapel, and Caterina Shelby Hetz van Kapel ii ACKNOWLEDGEMENTS The author wishes to express his sincere gratitude to the following: Dr. Merle L. Esmay, the author's major professor and committee chairman, for providing continuous encouragement and guidance with unfailing courtesy. Dr. Robert H. Wilkinson and Dr. C. Allan Rotz, who served on the author's guidance committee, for their opportune advice and suggestions. Dr. Warren H. Vincent and Dr. Fred V. Nurnberger, who also served on the author's guidance committee, for their helpful suggestions and continued interest. Mr. Jorge Chavarria, Extension Specialist of the Quilamapu Experiment Station, Chillan, Chile, for his generous and invaluable assistance during the data collection process in Chile. The Food and Agriculture Organization of the United Nations and the University of Concepcion, Chile, for providing the financial support during the three year sojourn at Michigan State University. iii TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES Chapter 1. 2. INTRODUCTION 1.1. Agricultural Overview of Chile 1.2. Problem Statement 1.3. Objectives LITERATURE REVIEW 2.1. Mechanization and Wheat Production in Chile 2.1.1. Agricultural Mechanization Development 2.1.2. Wheat Production and Characteristics of the Andes Pre-Cordillera 2.2. Systems Analysis in Agricultural Engineering 2.2.1. The Systems Approach 2.2.2. Farming Systems, Models and Simulation 2.2.3. Application of Systems Analysis to Agricultural Engineering Problems 2.3. Agricultural Machinery Productivity 2.4. Estimation of Days Suitable for Field Work 2.4.1. Observed Field Work Data 2.4.2. Generated Field Work Data 2.4.2.1. Precipitation-frequency analysis 2.4.2.2. Soil moisture content budgeting 2.5. Field Machinery Selection Criteria 2.5.1. Physical Performance iv Page viii xi ll 13 13 15 20 22 24 26 27 28 31 34 34 Chapter 3. 4. 2.6. TABLE OF CONTENTS (Continued) 2.5.2. Economic Performance Field Machinery Complement Selection Procedures MODEL DEVELOPMENT 3.1. 3.2. 3.3. 3.4. 3.5. 4.1. Field Work in Chile The Wheat Production System The Weather Model 3.3.1. Soil Moisture Content Budget 3.3.2. Suitable Day Criteria 3.3.3. Expected Number of Fieldwork Days at Selected Probability Levels Wheat Production Machinery Selection Model Costs Analysis 3.5.1. Cash Flow Method 3.5.2. Ownership Cost 3.5.3. Fuel and Lubrication Costs 3.5.4. Repairs and Maintenance Costs 3.5.5. Labor Costs 3.5.6. Timeliness Costs 3.5.7. Machinery System Cost DESCRIPTION OF THE MODEL Program.WEATHR 4.1.1. Subroutine INFILT 4.1.2. Subroutine EVAP 4.1.3. Subroutine RUNOUT 4.1.4. Subroutine GODAYS 4.1.5. Subroutine HVDAYS 4.1.6. subroutine SUM 4.1.7. Subroutine HARVEST 4.1.8. Subroutine WEEKS 4.1.9. Subroutine SORT 4.1.10. Subroutine SMOOTH 4.1.11. Subroutine INTERP Page 36 39 45 45 46 51 53 56 58 59 69 70 73 74 75 76 76 78 79 79 79 81 81 85 85 88 91 91 93 93 93 TABLE OF CONTENTS (Continued) Chapter Page 4.2. Program TRIGO 99 4.2.1. Subroutine TIMEAV 102 4.2.2. Subroutine COMBINE 102 4.2.3. Subroutine PLOW 107 4.2.4. Subroutine DISK and DISKZ 107 4.2.5. Subroutines HARROW and HARRZ 107 4.2.6. Subroutines SEEDER and SDR2 116 4.2.7. Subroutine FERTIL 116 4.2.8. Subroutine SPRAYR 116 4.2.9. Subroutine COST 120 5. MODEL VALIDATION 124 5.1. Available Field Working Days 125 5.2. Farm Survey of Machinery Systems and Model Results 138 6. SENSITIVITY ANALYSIS 148 6.1. Machinery System Requirements 148 6.1.1. Effects of Cultivated Area 149 6.1.2. Effects of Tillage Intensity Level 151 6.1.3. Effects of Crop Rotation 153 6.1.4. Effects of Available Time for Fieldwork 153 6.1.5. Diesel Fuel Requirements 157 6.2. Machinery System Cost Analysis 160 6.2.1. Machinery System Cost Components 160 6.2.2. Effects of the Cultivated Area 163 6.2.3. Effects of Tillage Intensity Level 165 6.2.4. Effects of Crop Rotation 165 6.2.5. Effects of Available Time for Fieldwork 168 6.2.6. Effects of Wheat Yield and Wheat Price 171 6.2.7. Effect of Fuel Cost 171 6.3. Summary 175 7. CONCLUSIONS AND RECOMMENDATIONS 176 vi TABLE OF CONTENTS (Continued) Chapter 7.1. Conclusions 7.1.1. In Relation with the Farmer's Survey 7.1.2. In Relation with Time Available for Fieldwork (Simulation Results) 7.1.3. In Relation with Machinery System Requirements and Costs (Simulation Results) 7.2. Recommendations for Future Work APPENDICES A. Data Collection Methodology and Worksheets B. Sizes and Prices of Machines Available from the Agricultural Machinery Dealers in Chillan - Chile C. FORTRAN Program Listing D. Effective Field Capacities and Power Requirements of Implements and Machines REFERENCES AND SELECTED BIBLIOGRAPHY vii Page 176 176 177 178 180 182 202 211 229 232 Table 2.1. 3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 5.1. 5.2. 5.3. 5.4. 5.5. 5.6. 5.7. LIST OF TABLES Agricultural Tractors in Chile Field Operations and Calendar Date Constraints for the Low Intensity Tillage Level Field Operations and Calendar Date Constraints for the Medium Intensity Tillage Level Field Operations and Calendar Date Constraints for the High Intensity Tillage Level Distribution of the Number of Tractors Among the Farmers Surveyed Agricultural Engineering Data Collected in Chile Economdc Information. Owning and Operating Agricultural Machinery Costs Expected Number of Days Suitable for Soil Engaging Operations, in Eastern Nuble Province, Chile. Farmer's Survey Results Farmers' Survey and Computer Model Results for Expected Number of Days Suitable for Soil Engaging Operations, in Eastern Nuble Province, Chile Expected Number of Days Per Week Suitable for Soil Engaging Operations in Eastern Nuble Province, Chile Expected Number of Days Per Biweekly Period Suitable for Soil Engaging Operations in Eastern Nuble Province, Chile Expected Number of Days Per Week Suitable for Above Ground Operations in Eastern Nuble Province, Chile Expected Number of Days Per Biweekly Period Suitable for Above Ground Operations in Eastern Nuble Province, Chile Expected Number of Days Per Week Suitable for Cereal Harvesting Operations in Eastern Nuble Province, Chile viii Page 48 49 50 65 67 72 126 127 129 131 132 134 135 Table 5.8. 5.9. 5.10. 5.11. 5.12. 6.1. 6.2. 6.3. 6.4. 6.5. 6.6. 6.7. 6.8. 6.9. 6.10. 6.11. 6.12. .LIST OF TABLES (Continued) Expected Number of Days Per Biweekly Period Suitable for Cereal Harvesting Operations in Eastern Nuble Province, Chile Effect of the Precipitation Pattern upon the Expected Number of Workdays in two Contiguous Periods. Probability Level - 0.70 Average Yearly Seeded Area and Power Source Characteristics. Farmers' Survey Results Percent of Wheat Producers Reporting Use of Different Machines. Farmers' Survey Results Comparison of Farmers' Machinery Systems and Model Results Effect of the Yearly Seeded Area Upon Machinery Requirements Effect of the Tillage Intensity Level Upon Machinery Requirements Effect of the Crop Rotation Upon Machinery Requirements Effect of Work Hours Per Day Upon Machinery Requirements Effect of the Design Probability Level Upon Machinery Requirements Fuel Consumption as Influenced by Tillage Intensity and Crop Rotation ' Relative Importance of the Machinery System Cost Components Effect of the Yearly Seeded Area Upon the Machinery System Cost Effect of the Tillage Intensity Level Upon the Machinery System.Cost Effect of the Crop Rotation Upon the Machinery System Cost Effect of Work Hours Per Day Upon the Machinery System Cost Effect of the Design Probability Level Upon the Machinery System Cost ix Page 135 138 139 142 143 150 152 154 155 156 159 161 164 166 167 169 170 LIST OF TABLES (Continued) Table Page 6.13. Effects of Wheat Yields Upon Machinery System Cost 172 6.14. Effects of Wheat Prices Upon Machinery System Cost 173 6.15. Effects of Fuel Cost Upon Machinery System Cost 174 Figure 1.1. 2.1. 3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 3.7. 4.1. 4.2. 4.3. 4.4. 4.5. 4.6. 4.7. 4.8. 4.9. LIST OF FIGURES Map of Chile Diagramatic Representation of the Methodology for simulation. Average Monthly Distribution of Rainfall in Chillan, Chile General Flow Diagram for the Weather Model Wheat Production Machinery Selection System - Initial Model Diagram Input-Output View for the Wheat Production Machinery Selection Model Simplified Flow Diagram for the Wheat Production Machinery Selection Model Flow Diagram for the Wheat Production Machinery Selection Model Flow’Diagram.for the Economic Analysis of the Machinery System Flowchart for subroutine INFILT Flowchart for subroutine EVAP Flowchart for subroutine RUNOUT Flowchart for subroutine GODAYS Flowchart for subroutine HVDAYS Flowchart for subroutine SUM Flowchart for subroutine HARVEST Flowchart for subroutine WEEKS Flowchart for subroutine SORT xi Page 19 52 54 60 61 63 64 71 80 82 84 86 87 89 92 94 96 Figure 4.10. 4.11. 4.12. 4.13. 4.14. 4.15. 4.16. 4.17. 4.18. 4.19. 4.20. 4.21. 4.22. 4.23. 4.24. 5.1. Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart Flowchart LIST OF FIGURES (Continued) for for for for for for for for for for for for for for for subroutine subroutine SMOOTH INTERP program TRIGO subroutine subroutine subroutine subroutine subroutine subroutine subroutine subroutine' subroutine subroutine subroutine subroutine Expected number of field Probability level xii TIMEAV COMBINE PLOW DISK DISKZ HARROW HARRZ SEEDER SDR2 FERTIL SPRAYR COST workdays at the 0.80 Page 97 100 101 103 104 108 110 112 113 115 117 118 119 121 122 137 1. INTRODUCTION 1.1. Agricultural Overview of Chile Chile is located on the southern Pacific coast of South America. It is a narrow, ribbonlike country, averaging 175 km. in width, and extending 4200 km. in length from 17° 30' S to 55° 59' S. The total area of continental Chile is approximately 756,000 square kilometres (CORFO, 1965). Northern Chile is one of the driest places in the world and has one of the few weather stations at which no rain has ever been recorded. Southern Chile is one of the rainiest parts of South America, where glaciers descend from snowbcovered mountains to a deeply fiorded coast. Between these two extremes is middle Chile, the center of population con- centration, intellectual, social and economic activity. Middle Chile contains the Central Valley, a narrow fertile depression between the Andes Mountains on the east and the Coastal Mountain Range and the sea on the west. The Central Valley has a mediterranean climate with rainfall increasing gradually from the northern transitional desert region in Coquimbo to the southern boundary of the region at Puerto Montt (See Figure 1.1.). The climate and the fertility of the Valley's soil provide ideal conditions for intensive vegetable farming, orchards and vineyards, cereal crops, legumes, sugar beet, sunflower, soybeans, rice, maize, ‘rapeseed, potatoes, and livestock. With the advantage of a harvest season when Europe and North Aunerica enter the winter months, the Central Valley offers Chile a 1 70' CONC .0" ‘°"""‘."~L L 5*qu 75'. 5 5 m m .. . m 1 "Am UK 0 \— ' CHILE AISEN ‘0‘ 75' -.—.< - -— -—~ . Ill. ‘ ANIOIAGASYA - .---- .IMAPACA -__—— f. . I 10‘ 00.0000 5 5 A. un- .- AYACIAEA l __5 cocoa-co Figure 1.1. Map of Chile. potential source of foreign exchange through the export of high—quality fruits, vegetables, seeds, wines, dairy products, and other specialty crops. 1.2. Problem Statement Chile has 11 million hectares of arable land, 19 million hectares of grasslands, and 23 million hectares of woodlands. There are also 22 million hectares with no agricultural or forestal aptitude. They add up to 75 million hectares which corresponds to the geographic area of con- tinental Chile (CORFO, 1965). If only the five million hectares without limitations, Soil Class I, were considered and the coefficients developed by Revelle (1976)* are applied, Chile would be able to feed about 100 million persons. Further- more, if only the 1.3 million irrigated hectares, whose quality is among the best in the world, were considered, Chile could feed about 25 million inhabitants. However, with a population of only 11 million people it has been importing, since 1939, an ever increasing amount of food that in 1980 reached a value close to 1,000 million dollars (USDA, 1981; FAO, 1981). Chile's most important crop is wheat, which with other small grain cereals account for 802 of the area planted with annual crops. However, the area seeded yearly with wheat has been experiencing a steady decline. It has gone down from 780,000 hectares in 1966, to 546,000 * These coefficients estimate that 24 human beings can be fed ade— quately from one hectare of high quality farmland worked at a level of agricultural technology comparable to that practiced in the Midwest of the USA, hectares in 1980, a decrease of 30%. At the same time, wheat consumption in the country has gone up from 155 kilogram per person per year in 1965, to 205 kilograms per person per year in 1980. According to Fouchs (1981), wheat output has fallen to less than 40% of domestic usage versus 752 a decade ago. This situation has forced the country to import large amounts of wheat. In 1980, it was necessary to import 955,000 tonnes of wheat and in 1981 wheat imports reached 1.2 million tonnes (FAO, 1981; Fouchs, 1981; USDA, 1981). Only a mosaic of reasons could explain completely this paradigm of a declining area seeded with wheat and the importation of nearly half the demand for the product. Important reasons why farmers are not seeding as much wheat as during the 1960's are the stead rise in production costs, the relatively low priCe of wheat in the international market, and a changing economy. The main reasons for the high cost of wheat production in Chile are the high cost of agricultural machinery, fuel, and fertilizers. Agricultural equipment in Chile is very costly, both to purchase and to operate, due to high costs in the manufacturing countries (up by 1002 between the Spring of 1977 and the Fall of 1980, according to Mayfield et a1. [1981]), transportation costs, import tariffs, and the wide pro- fit margin of machinery importers and distributors. Also, according to the Chilean Ministry of Foreign Relations (1980), more than 65% of the fuel used in Chile is imported and its price to the farmer is almost double that paid by a farmer in the USA. Although labor is, compara- tively, cheap in Chile in the case of wheat production it does not help a great deal because small grain cereals are highly mechanized crops. Several authors, Singh (1978), McIsaac and Lovering (1977), van lumpen (1973), Moore (1980), have pointed out that field machinery costs are a major component of the total farm budget, with a value in the range of 20 to 25% for the USA and Canada, in general. In the case of Saskatchewan farms, Brown (1981), estimated that machinery and implement operating expenses and depreciation charges can account for as much as 452 of the total farm operating expenses and depreciation charges. Chilean wheat producers also have a high machinery component in their production cost. According to Franco (1981), the agricultural machinery cost component can vary between 30 and 36% of the total wheat production cost, depending upon the cultural practices and machinery field efficiency. Moreover, the great majority of farmers in Chile still use conven- tional tillage systems which demand many passes over the field. Research comparing tillage systems which differ in tillage intensity has been carried out in the wheat growing area by the.University of Concepcion. The reduced tillage systems have been proven successful to farmers with adequate farming and management skills. Under these circumstances, a farmer who wants to stay in the farming business has to be a very good manager. Unfortunately, he does not have at his disposal the tools that would help him make sound deci- sions concerning machinery management and other related activities. Furthermore, the 'Ingeniero Agronomos' with whom he could consult are not adequately prepared to deal with these problems. The University of Concepcion, at Chillan, is strengthening the Agricultural Engineering Department which offers undergraduate instruction and master of science programs to train students to deal with these and other shortcomings affecting the Chilean farmers at the present time. Agricultural mechanization in Chile has progressed to the point wmere the application of the principles of scientific management is a necessity. With only 18% of the population living in the rural areas, the Chilean farmer has become increasingly dependent on his machinery set to carry out his work on schedule. This is especially true for the cereal producers because these crops are almost completely mechanized. The selection of a machinery complement is a complex problem involving many economic, biological, physical, and social factors, such as weather uncertainties, timeliness, sequential and parallel operations, soil type and conditions, type of crops and rotations, management prac- tices, and labor and fuel supply. Machinery selection decisions are among the most important that producers must make in today's agriculture. The importance of these decisions stems from the relatively high proportion of total costs attributable to or related to machinery and the infre- quency and irrevocability of such decisions. The importance of machinery sizing decisions and general machinery management cannot be overstated. Mayfield et a1. (1981), have determined that the total cost of owning and operating the typical farm tractor increased approximately 1002 from the Spring of 1977 to the Fall of 1980. During the past decade the systems analysis approach has been successfully applied to agricultural machinery management and to other agricultural problems. Several authors, Brown (1981); Burrows and Siemens (1974); Danok et a1. (1980); Doster et a1. (1980); Edwards and Boehlje (1980); Hughes and Holtman (1976); Hunt (1977); Krutz et a1. (1980); Loewer (1980); Muhtar (1982); Osborn and Barrick (1970); Wolak (1981); Pfeiffer and Peterson (1980); Von Bargen (1980), have proposed a variety of methods to select crop production systems and the associated machinery complement for agricultural enterprises. Computer models have proven to be a very useful analysis tool for the selection and scheduling of agricultural machinery, prediction of available time for field Operations, and for the economic analysis of the machinery investment. A computer model which would address the machinery selection and management needs of the Chilean wheat producers, would be a most useful educational tool that could be used in the new Agricultural Engineering Department of the University of Concepcion, at Chillan. 1.3. Objectives The global objective of this project was to develop a computer model to determine least cost machinery systems for the wheat producers located in the Andes Pre-Cordillera area of the province of Nuble, in Chile. The model was designed for use as an educational tool for the students of the Agricultural Engineering Department of the University of Concepcion and to assist farmers in their machinery purchase and management decisions. The specific objectives of the project were: 1.3.1. To use climatological records to estimate the expected number of days suitable for fieldwork in the eastern part of the province of Nuble, at selected probability levels. 1.3.2. To develop a computer model to aid the selection of field machinery systems for the wheat producers in this region. 1.3.3 To compare production systems differing in tillage intensity level and crop rotations including wheat, oats, lentils, and subterranean clover, with respect to machinery requirements and total costs including nachinery, labor, timeliness, and fuel costs. 2. LITERATURE REVIEW 2.1. Mechanization and Wheat Production in Chile 2.1.1. Agricultural Mechanization Development In the Latin American panorama, Chile is one of the countries that has mechanized its agriculture to a high level. Most of Chile's modern agricultural machinery has been imported. The importation of tractors was initiated around 1930, but not until the 1950's did the number of tractors in use in the country reach a significant value, as shown in Table 2.1. TABLE 2.1. Agricultural Tractors in Chile. Year Working Tractors 1930 660 1936 1,560 1940 2,750 1944 3.880 1948 5,400 1955 14,180 1963 16,500 1970 20,000* 1975 23,000* 1980 20,000* Sources: CORFO (1969); *Estimates by Ibanez et a1. (1979) The 1975 peak was reached with the importation of about 8,000 tractors, Belaruz MTZ-SO and Universal 650-M, from the USSR and Rumania respectively by the Allende Government. After 1975 these tractors were left without spare parts and at the present time few of them are still working. Before the establishment of the Plan Chillan to develop agricul- ture in south-central Chile in 1954, with the assistance of the USA's Point IV Program, no research or extension work on the use of agricultural machinery had been carried out. Research on agricultural mechanization has been meager. Prior to 1963 not more than 10 significant experiments related to agricultural machinery had been performed (Ulloa, 1969). Like most South American countries, except Argentina and Brazil, Chile depends on importations for tractors and other agricultural equip- ment. Importers have seldom considered the needs of the Chilean farmers, and the main criteria to decide on what to import have been profits and the initiative of dealers and distributors. The result has been inade- quate equipment and proliferation of makes and models (CORFO, 1969). Importation of agricultural machinery has represented about 52 of the total value of imported goods. Until 1966, the USA and the UK were the source of 762 of all the imported agricultural machinery (CORFO, 1969). However, at the present time Argentina and Brazil have replaced the UK becoming important sources of agricultural machinery for Chile. The national production of agricultural machinery has been small, :representing between 5 to 72 of all machinery purchases (UN, 1968). Equzlpment manufactured in small quantities in Chile includes plows, barrows, tool bars, Sprayers, ditchers, lime applicators, fertilizer broadcasters, dryers, maize shellers, sunflower headers, hammer mills. 10 wagons, animal drawn equipment and hand tools. Only electric motors are made in significant quantities in Chile. Until 1975 the Andean Group of Free Trade represented an attrac- tive market for Chilean manufacturers of agricultural machinery. However, in 1975 Chile ceased to be a member of the Group, consequently the market was lost to the national manufacturers. Chile is still far below the mechanization level of more developed countries. In 1963 there were 16,500 tractors, 274,450 horses, 291,930 oxen to work 2,317,800 hectares (UN, 1968 and CORFO, 1969). If all the work was to be done exclusively with tractors, each tractor working 25 hectares, which is the average for nine Western European countries, and working only the full capacity of Chile's five million hectares of Class I soil, a total of 200,000 tractors would be required. A more realistic approach was presented by Stenstrom (1959), in his Report to the Government of Chile. He indicated that one tractor per 100 hectares under cultivation is an adequate ratio for developing coun- tries when the tractor is used mainly for the heavy farm work. Increasing the mechanization level to include the rest of the farm work, one tractor per each 50 cultivated hectares would then be adequate. According to this last ratio and considering only the cultivated area in Chile a total of 66,000 tractors would be needed at the present time. Furthermore, if we consider the thesis developed by Giles (1967), that 0.5 HP of effective capacity per cultivated hectare is a minimum iPOVer requirement for developing countries and considering the 3.3 million Ihectares under cultivation. 33,000 tractors with a 50 HP effective capa- <31IY.* would be required in Chile at the present time. This quantity of Effective horsepower capacity is defined as the measured, rated draw- bar horsepower, not engine horsepower or advertising claims by the manufacturers . ll tractors is much larger than the latest estimation by Ibanez et a1. (1979), of 20,000 as working in Chile in 1980. The number of working tractors in 1980 is smaller than the number in 1975 because: 1) they have not been replaced by small farm operators‘ who are the beneficiaries of the land reform program due to a large increase in the price of tractors; and 2) the lack of subsidized credit. All the numbers presented previously show the need to increase the power available for agricultural production in Chile. Although it is recognized that mechanization alone is not the answer to the problem of adequate food supply it is equally true, however, that without the power and the proper tools to perform the production operations in a satis- factory and timely way, much of the potential benefits of improved crop varieties, increased use of fertilizers, increased water availability, and improved cultural practices cannot be achieved. Under dryland farming conditions with low and/or very seasonal rainfall, timeliness of operation and particularly of seeding is very important. Only with power equipment can this timeliness be achieved over the large areas concerned (Kitching, 1968). 2.1.2 Wheat Production and Characteristics of the Andes Pre-Cordillera The VIII Region, located in south-central Chile, comprises the provinces of Nuble, Bio-Bio, Concepcion and Arauco (See Figure 1.1.). 13m5Andes Pre-Cordillera area of the provinces of Nuble and Bio-Bio covers .about 640,000 hectares, of which no less than 300,000 hectares have agricultural aptitude, being classified as Soil Classes II, IV and VI (INIA, 1980). The soils typical of this area have developed from recent volcanic 12 ashes (Dystrandept) and they have been classified as Santa Barbara Serie. They are deep soils, with a loam to silt loam texture, brown to dark gray in color, and have a rolling hills topography with an 8 to 12% representa- tive slope. These soils are very permeable, with a very high organic matter content, very low bulk density and high total porosity. They have good to excellent internal and external drainage characteristics. Phos- phorus fixation capacity is high due to the presence of oxides of iron and aluminum, and their infiltration coefficient and basic infiltration velocity are also high (Bernier, 1966; Mellado, 1981). The Andes Pre-Cordillera area of the province of Nuble has a temperate mediterranean climate, rainy, with one to three dry months. Frost-free season longer than 4.5 months. Temperatures for the coldest month go from -2.5° to -10° C. Maximum average temperature for the warmest month is 21° C. The average annual rainfall and average winter rainfall are 1305 and 760 mm., respectively (Pena, 1978). The agricultural production alternatives for this vast area include cereals (wheat, oats, barley, rye), lentils, rapeseed, natural and artificial pastures. All these crops have to be grown under dryland conditions. By far the most important crops are winter wheat and cats, with an average of 35,000 and 10,000 hectares seeded yearly, respectively (INIA, 1980). As stated earlier, wheat has always been Chile's most important crop. However, since 1940 the country's wheat production has not been enough to satisfy the demand. The central and southern parts of Chile have good wheat growing conditions, but the average yield for the country, at 1500 kg/ha., can be considered very low when compared with the average of 3650 kg/ha. obtained during four years in more than 50 demonstration «:enters, located in the Andes Pre-Cordillera of the province of Nuble 13 (INIA, 1980). The main reasons for the low average wheat yields in Chile seem to be: inadequate varieties and poor seed quality, insufficient fertili- zation, untimely tillage, seeding and harvesting, insufficient use of grain drills, inadequate weed control and crop rotations (INIA, 1976). Furthermore, wheat production in the Andes Pre-Cordillera of Nuble is also negatively affected by diseases, especially by foot rot (Gaemannomyces graminis, Fusarium sp) and by rusts (Puccinia gp). The best ways to reduce the effects of these diseases are to use resistant varieties, adequate crop rotations, early seeding and timely spraying (INIA, 1980). Kitching (1968), indicated that since wheat is the staple food in the developed countries, the cultural practices and machines best suited to producing high yields at low cost under varying soil and climate con- ditions are well known. In theory, it would only remain to apply these methods and machines to production in the less developed areas. Moreover, since wheat and rice are the world's basic.food crops, together making up approximately 41% of the total human food consumption, it is important that priority be given to means of increasing the production of these crops. 2.2. Systems Analysis in Agricultural Engineering 2.2.1. The Systems Approach Modern systems for producing and processing food, fiber, and forest products are complex syntheses of modern science and technology. Complexity and size have, in recent years, lead to the application of systems analysis to agricultural problems. 14 A system has been defined, by several authors (Manetsch and Park, 1977;' Naylor, 1971; Gordon, 1978), as a collection of objects, called components, which interact synergistically to perform a given function or functions. The components are differentiated into: 1) exogenous or environmental variables; 2) endogenous or controllable input variables; 3) input parameters; and 4) output variables. Smerage (1979) indicated that a system resides in an environment that stimulates it by external, independently generated forces. It responds in some manner, over time, to those forces and the system may, in addition, respond to a nonequilibrium, internal condition or state. Dent and Anderson (1971) emphasized that the systems‘v'i’ew is a holistic one, which implies that an isolated study of parts of the system will not be adequate to understand the complete system. This is because the separate parts are linked in an interacting manner. A system implies a complex of factors that are interrelated, it implies interaction between these factors and it implies that a conceptual boundary may be erected around the complex as a limit to its organization autonomy. Churchman (1979) and Rountree (1977) pointed out that the system analysis approach is a method of problem solving which attempts to study the whole, its parts and their interrelationships. Systems analysis is concerned with the analysis of system behavior and the compositional basis for behavior. A system is analyzed to predict its responses to specific stimuli and its general behavioral properties. The medium for this analysis is a model that is obtained, first, by a separate analysis of system composition. The essence of the systems concept is to describe a situation with many interacting elements where, to be understood, any individual element in the system must be viewed in the context of the whole. This . «N .5... D ‘vn. 1. I" 15 fact has important implications for model construction since all relevant elements must be represented in the model (Dent and Anderson, 1971). 2.2.2. Farming Systems, Models and Simulation Farming systems are characterized by the fact that man is at- tempting to control biological systems in an uncertain environment to achieve some goal which is predominantly economic in nature. For this reason, they are frequently referred to as bio-economic systems. The degree of control exerted over the system can vary considerably from extensive pastoral farming, which is essentially a harvesting operation, to an intensive system (such as poultry farming) where management control can be almost complete. In most farming systems, however, management control is not complete and the biological goals of the plant and/or animal subsystems often conflict with those of management (Wright, 1971). Dent and Anderson (1971) indicated that the environment of farming systems is probably best considered in two distinct parts, reflecting the fact that weather and prices constitute the two major sources of uncer- tainty for management. The climate influences plant and animal produc- tion and may provide essential system inputs, such as water. The socio-economic environment provides system inputs in the form of goods and services and determines the economic outcome of the system's operation. Socio-economic conditions also influence the farmer, who is a component of both the farming system and a wider socio-economic system. Farmer's goals are often poorly defined and much analytical work has been based on the simplifying assumption that they are profit maximizers, whereas a more realistic assumption would be that they are risk averters. Wright (1971) stated that the complexity of farming systems and 16 the uncertainty associated with the decision making process are features which indicate that a systems approach to research could be particularly useful. There seems to be a growing recognition of the need for such an approach to the study of farming systems, both in systems analysis and in systems design. Dent and Blackie (1979) indicated that systems research relies to a considerable extent on the use of models because it is often impossible or impractical to study the real system. If the research is concerned with the design of new systems, then by implication the corresponding real systems do not yet exist and models must be used. Even when the real system exists, experimentation may not be feasible due to factors of time, cost, disturbance, etc. Gordon (1978) stated that a model is a system which is an abstrac- tion of a real world system; therefore, a model is not only a substitute for a system, it is also a simplification of the system. He defines a model as the body of information about a system gathered for the purpose of studying the system. Since the purpose of the study will determdne the nature of the information that is gathered, there is no unique model of a system. Different models of the same system will be produced by different analysts interested in different aspects of the system or by the same analyst as his understanding of the system changes. Smerage (1979) stated that a model has two parts: 1) the con- ceptual model, and 2) the mathematical model. The conceptual model expresses the perceptions of its author about the components of the real system and their structural arrangement to form that system. It is usually expressed by a schematic with supporting narrative. The behav- ioral properties of each component are described by relations between its relevant attributes (parameters). System structure dictates the l7 interactions between components; it is described by another set of equations expressing constraints between the attributes of individual components. The mathematical model describes the properties of its behavior as a single entity. It consists of a set of equations for certain attributes of the components and the environmental stimuli. It is formulated, by alternative approaches and in alternative formats, from the component and structural descriptions of the corresponding conceptual model. The precise nature of an 'adequate' model will be dictated by the purpose of the investigation and the kind of problems to be solved. According to Gordon (1978) and Naylor (1971) models can be used in a number of ways, but a basic distinction can be drawn between descrip- tive and normative applications. When used for descriptive purposes, the model acts as a framework for the identification of system components and relationships and the determination of satisfactory functional forms of these relationships. The descriptive use of models is mainly a tool of systems analysis where the objective is to gain a better understanding of the system. Models are used in a normative fashion in an attempt to solve problems; the problem may be the derivation of decision rules that willlassist a decision-maker in making an optimal decision. A normative model thus requires some objective function to evaluate different decision rules. Descriptive models are not primarily concerned with solving prob- lems, so they do not usually include an objective function. Simulation, however, uses descriptive models to study decision-making problems. The Imodel merely describes the behavior of the system under a given set of assumptions; yet, by experimenting with the model, approximate solutions to problems can be obtained (Dent and Blackie, 1979). 18 The term 'simulation' like "system" is sometimes a source of some confusion. In its widest sense, to simulate means to duplicate the essence of a system without actually attaining reality itself. Naylor (1971) presents a useful applied definition of simulation, as 'a technique that involves setting up a model of a real situation (system), and then performing experiments on the model'. That is, simulation is essentially a two-phase Operation involving modeling and experimentation. The real system is replaced by an analogous, but abstract, system in order to overcome problems of physical experimentation. It is not the concept of simulation that is new, but rather the use of computers to run mathematical analogues of real systems, and the emphasis on whole or total systems. In this sense, the development of electronic computers has been a necessary prerequisite to the development of simulation as a systems research technique (Manetsch and Park, 1977). A general methodology for simulation is illustrated in Figure 2.1. The main feature is the feedback to any previous step, which is characteristic of the almost cyclic nature of many simulation studies. The diversity of fields in agricultural research means that the development of simulation models is always likely to be a major task for an individual. Wright (1971) pointed out that the most practical solu- tion is the adoption of an interdisciplinary approach utilizing the specialist knowledge of researchers in the relevant fields. Although the lack of data seems to be a major limitation to the development of satisfactory models, the mere attempt to develop models can play a useful role in terms of highlighting the sort of information that is lacking. In most experimental work there is a problem of relating the results to the real system because the experimental environment is not the same as that in which the results are to be applied. This problem 19 THE TEN PHASES OF A SIMULATION STUDY SPECIFICATION OF THE PROBLEM 1 LEARN ABOUT THE SYSTEM _ l FORMMLATION OF INITIAL MODEL 1 DATA COLLECTION L 1 FORMULATION OF DETAILED MODEL I PROGRAMMING J VALIDATION I EXPERIMENTATION J ANALYSIS OF RESULTS 1 PUBLICATION ] "‘5' ”1'55 ”iv 5i "'15 “L 5'5" 51—5 ,1. '1- ' Figure 2.1. Diagramatic representation of the methodology for simulation. (Adapted from Wright, 1971). 20 of interference is particularly true of simulation models in that the results are obtained from a mathematical rather than a physical model. The process of evaluating the model in relation to reality is referred to as the verification or validation stage of simulation. Dent and Blackie (1979) and Wright (1971) have stated that 'veri- fication' and 'validation' are often used synonymously in relation to simulation models, although each term does have a distinct application. Literally, to verify means 'to establish the truth or correctness of‘ so that verification of a model is concerned with establishing whether the model is a true or correct representation of reality. On the other hand, validation is not so much concerned with the correctness of a model, but rather whether it is effective or suitable for a specific purpose. Thus a model is 'validated' in relation to the purpose for which it was con- structed, whereas a model is 'verified' in relation to absolute truth. Although validation implies some sort of comparison between model and reality, there may be little quantitative information about the real system that can be used as a basis for comparison. Therefore, a con- siderable degree of subjective judgement may be necessary. The crucial test in validation of system models should be whether the model leads to better decisions that can be obtained by using other techniques (Dent and Blackie, 1979). 2.2.3. Application of Systems Analysis to Aggicultural Engjneering Problems As early as 1934, attempts were made to develop systematic pro- cedures to select farm.equipment (Carter, 1934). In a more general view of agricultural problems, Finches (1956) suggested the need for agri- cultural engineering research with explicit application of 'agricultural 21 systems engineering' to agriculture. He concluded that agricultural systems engineers should seek to bring the farm's resources of soil and water, machinery, structures, livestock, and labor into a condition of better operational balance. Such an integrated approach would challenge the technological validity and biological necessity of every operation. It would evaluate critically the type, scale, timing, sequence, nature, and purpose of every element of the process. Sammet (1959) stated that a useful alternative to experimental comparison of entire systems is the representation and comparison of alternative systems through synthesis or model building. He described a planned approach to systems studies at two levels. Systems analysis, comprising the study, definition and description of processes, and the establishment of optimum relationships; and systems design and develop- ment, including research and development oriented to methods' improvement and the execution of plans of action based on results of systems analysis. Rockwell (1965) stated the importance of using simulation methods for solution of operational system problems in almost every field of the economic and social activities. An analogy was established between indus- trial and agricultural production systems to encourage the application of systems analysis to agricultural production. The same year, however, Stewart (1965), observed that the appli- cation of systems analysis (operations research) to agriculture had lagged behind the advances made in other areas. He noted that American agriculture has become a highly complex undertaking where man, machines, noney, biology and environment interact to produce food and fiber at a profit. His question was: is agriculture too complex to profit from the successes of systems analysis in industrial and military enterprises? Esmay (1974) described an applicable systems analysis approach 22 and proposed a plan for development of a standardized approach for all engineers involved in feasibility studies and the development of recomr mendetions for selective agricultural meachanization. Friedley and Holtman (1974), used systems analysis to predict the socio-economic implications of mechanization. They concluded that a careful systems analysis approach could maximize benefits and minimize adverse effects. Link and Splinter (1970) and Loewer et a1. (1980) in their survey of simulation techniques and applications to agricultural problems con- cluded that the fears expressed by Stewart (1965) could be put aside. Simulation had become one of the most powerful research tools in agricultural engineering and other fields of agriculture. 2.3. Agricultural Machinery Productivity Singh (1978) stated that the design of field machinery systems involves calculation of machine productivities, estimation of days suit- able for field work, selection of an appropriate performance criterion and development of an adequate procedure for optimizing performance criteria subject to specified constraints. Hunt (1977) and Bowers (1975b) indicated that the major factors influencing the productivity (performance) of agricultural machines are size (width), operating speed, field efficiency and energy requirements. According to White (1978) operating width, under most circum- stances, is a design factor, and cannot be readily changed once the Imachine has been purchased, but for a few exceptions. Operating speed :for most machines can, at least theoretically, be varied over a wide trange. Practical limitations, however, such as operator ability, tractor 23 power and speed ranges, topography, crop and soil conditions, and machine performance characteristics, tend to set both lower and upper limits. Available sizes of various machines can be obtained from the literature published by the several agricultural equipment manufacturers, such as the Whole Goods Price List, or the Implement & Tractor Red Books. Typical ranges in operating speed and field efficiency for most types of machines are presented in the Agricultural Engineers Yearbook (1982) and in most of the agricultural machinery text books and extension bulletins. Field efficiency is undoubtedly the most elusive factor in esti- mating farm machinery productivity. It is a measure of the relative productivity of a machine under field conditions. White (1978) defines field efficiency as the ratio of the theoretical productivity of a machine to its actual productivity. It accounts for failure to utilize the full operating width of the machine and for time losses due to turning, idle travel, material handling, cleaning clogged equipment and field repair and maintenance. Hunt (1977) analyzed the various factors affecting the field efficiency of a machine. Field machinery energy requirements consist of functional require— ments and rolling resistance requirements (Kepner et a1., 1980). Func- tional requirements are those that relate directly to the processing of soils, seeds, chemicals or crops. Rolling resistance power requirements arise from the necessity for moving heavy machinery over soft field surfaces. Functional requirements depend upon soil and crop conditions which are highly variable. According to Singh et a1. (1979) tillage draft varies with soil type, soil moisture, root development, organic unatter content and depth of penetration. Forward speed also significantly affects plow draft. Draft of tillage implements is normally reported per unit of 24 effective width or per row. Hunt (1977) presents functional and rolling resistance requirements combined for most tillage and ground driven machines. Ranges of draft or energy requirements for most field machines are listed in the Agricultural Engineers Yearbook (1982) and elsewhere (Hunt, 1977; Bowers, 1975b; White, 1977, 1978). Agricultural machinery productivity has been thoroughly studied by many researchers. Recently, Renoll (1981) developed a mathematical expression to predict rowbcrop machine performance rate, which uses 14 specific input coefficients including items relating to the actual machine and its use, field size, shape, physical condition, and machine manage- ment. Predicted values were compared with actual capacity and found to be at least 95 percent accurate. 2.4. Estimation of Days Suitable for Field Work The farmer is the world's most interested weather observer. He scans the skies upon arising in the morning and he speculates on the next day's weather as he goes to bed. He listens avidly to radio and tele- vision weather broadcasts and he can recall years later the unusual weather associated with a particular planting or harvesting season. weather is the variable in farming over which he has no control. It is a variable of such importance that it can either bankrupt or enrich him. For efficient machinery management, a farmer needs information on the number of days suitable for field work available in order to prop~ erly balance between the high timeliness costs of a small machinery complement and the inflated costs of overinvestment in machinery (Elliot et a1., 1977). Hunt (1980) pointed out that only the most unusual weather 25 interferes with mechanical operation of field machines. Torrential rains, freezing temperatures, high winds, and blowing snow can, in a few instances, impede crop gathering mechanisms and conveyors, interfere with lubrication and hydraulic control, and cause a loss of traction and steering control. But generally, most tractors and implements are capable of operating over a wide range of weather conditions. Instead, it is the effects of weather on soils and crops that control field machine Operations. It is the farm operator's task to recognize and, if possible, pre— dict these effects. He must make and continuously review a decision as to whether today's and tomorrow's field conditions are such that machines can operate. The farmer being only an amateur weatherman will usually resort to a trial field operation of his machine to test whether the soil and the crop are ready for field work (Hunt, 1980). Van Rampen (1971) stated that the moisture state of the soil or crop is usually the most important factor affecting machine operations. Soil moisture constrains machine operations in relation with tillage and trafficability. Responsible management should limit traffic to the same moisture content as that for tillage. Working the soil at field capacity or above, whether from tillage tools or rolling wheels, causes a puddled condition which may reduce soil productivity for years afterwards. Crop moisture contents must be limited to well-known percentages for safe, dry storage and the limits often dictate the moisture content at which field machines operate. High moisture harvest and storage is one way to beat the weather in that field operations are constrained only by the trafficability of the soil. Tulu (1973) indicated that the integration of soil and crop con- ditions with past, present, and anticipated weather can lead to that 26 single most important measure of weather to a farmer--the likelihood that a working day has arrived. The actual arrival cannot be related to a specific calendar date but depends on both past and present weather. Seasonal accumulations of temperature, precipitation and evapotranspira- tion affect the initial state of both crops and soils on any particular day. Should seasonal weather indicate the arrival of a working day, bad weather for that day or hour can still cancel the arrival event. Given that the season indicates a working day, an experienced farmer designates a day as a working day only after considering many factors and exercising many agronomic skills. He considers yesterday's weather, today's conditions and tomorrow's forecast. He must recognize the arrival maturity of crops and the losses of quality and quantity from untimely field operations. He monitors the growth of weeds, insects, diseases, and judges the potential for damage from soil manipulations at a non-optimum.moisture content. And then he declares a working or non~ working day (Hunt, 1980). There is an abundance of literature concerning days suitable for field work. Two categories of workday data have been reported: 1) observed data for a location and year, and 2) generated data using weather and soil parameters. 2.4.1. Observed Field Work Data The papers in this category are few. Link (1968) reported on days suitable for field work developed from the observations kept in the personal diary of the manager of the Agronomy Farm, at Ames, Iowa. The record presents working conditions in the fields of that farm from 1932 to 1962, except for 1940 and 1941. 27 Morey et a1. (1971) reported daily work data which was collected by the department of agricultural statistics at Purdue University, in central Indiana from 1952 to 1968. Fulton et a1. (1976) reported observed number of suitable days in Iowa at four different probability levels for field operations throughout the crap season based on records of the Iowa Crop and Livestock Reporting Service. The data for each week were ranked and the minimum.number of suitable days was determined under each probability level to permit estimates to be made according to an acceptable risk. 2.4.2. Generated Field Work Data Hunt (1980) concluded that until working day forecasts are per- fected, simulation models of weather will be used by researchers to estimate field machine performance. These models generally assume a moisture content of soils and crops at some initial date. Mathematical statements of the change in moisture resulting from.weather factors are derived and applied to a moisture accounting procedure. Various weather conditions are entered for each day as a passing of the season is simu- lated. The number of days when the calculated state of the crop or soil is favorable for machine operations are counted as days suitable for field work. The varying answers from repeated simulations leads to probabil- istic statements of the number of working days. Most simulations are site and soil specific. In one of the earliest publications, Shaw (1965) estimated the moisture content in the top 15 cm of the soil considering precipitation and evaporation. A working day was defined as one in which the soil was not frozen and the available soil moisture in the top 15 cm of the profile 28 was less than 19 mm. He compared the number of predicted days suitable for field operations to the record of suitable days from the Agronomy Farm, Ames, Iowa (Link, 1968). The correlations between the observed and predicted number of days during March, April and May ranged from 0.87 to 0.93. 2.4.2.1. Precipitation-frequency analysis. Models for hay making and other crop harvesting operations have tended to depend onlycxirainfalll Von Bargen (1966) presented the 'Open Haying Day' criteria defined as: ". 1 .;less than 0.1 inches (2.5 mm) of rainfall on that day, less than one inch (25 mm) of rainfall the previous day, and greater than 702 sunshine on that day." Probability theory has been used extensively to analyze precipi- tation patterns and to predict sequences of wet or dry days. Weiss (1964) used a Markov Chain Probability Modeltx>fit sequences of wet or dry days to records of various length and for several climati- cally different areas of the USA and Canada. A Markov Chain is defined as a type of time-ordered probability process which goes from one state to another according to probabilistic transition rules that are determined by the current state only (Jones et a1., 1972). A convenient nomograph was presented by Weiss (1964) relating probability, length of sequence, and cumulative probability distribution, for dry or wet sequences. The author concluded that the Markov Chain model might be used to indicate the rainfall or drought probability regime of a station and from the results from many stations to specify it over a wide area . Colville and Myers (1965) and Feyerherm et a1. (1966) and Dale (1968) studied the weather of Nebraska, Michigan and Indiana, respectively. 29 They developed probabilities of wet or dry days from past weather records. Initial probability, used when no information exists on the previous day, and transition probability, computed whether the previous day is known to be wet or dry, were calculated considering four amounts of precipitation to define a wet or dry day. The probabilities were grouped for each seven-day period (climatological week) of a year. Dry and wet values are given for initial probabilities. Sequences of dry/dry, wet/dry, dry/wet, wet/wet probabilities are presented for transition probabilities. Pro- cedures for determining the probability that a particular day or group of days will be dry or wet are explained along with a method for checking computations. MacHardy (1966b) and Wiser (1966) applied the Monte Carlo method to the study of probability urn models of the precipitation process and the sizing of farm machines for weather dependent operations. The Monte Carlo method is a simulation technique which uses a series of random numbers to create statistical distribution functions. This method is used to study stochastic models of physical or mathematical processes and is based on the fact that the probability distribution of the random variable is known. Three different urn models were tested: the Bernoulli model, the Polya model, and the Markov model. Results showed that the Bernoulli model was the simplest but the least precise in determining expected values of precipitation and applies only to independent events. The Markov and the Polya models were not suitable when weather persist- ance extended over several periods. If this is not the case, the Markov model was superior in obtaining expected values of precipitation. 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F2 :5. _ e n.23v zc~h Soil engaging operations criteria ///fREAD 17 years of daily —///f weather data SM‘l operations READ in hours of sunshine. Apply @| criteria for cereal harvesting _ SMQQTWP 'workday Sum workdays for each year for all combinations of crops, operations _ rkd and time periods no WC ay Compute expected number of work- N days at selected probability levels N for all time periods _ PP‘<2 mm no-workday & t Y ‘ Store expected number of workdays for use in machinery selection N no-workday STOP no-workday workday <:> Harvesting operations criteria workday Figure 3.2. General Flow Diagram for the Weather Model. 55 reached water no longer enters the soil but instead is lost by surface runoff or ponded on the soil surface. Saturation for this soil, deter- mined by Bernier (1966), is 113 mm/lSO mm layer. Field Capacity is defined as the amount of water left in the soil after subsurface drainage or the emptying of macropores has been come plated. This remaining water is, in part, the stored water available for plant use. Field Capacity for this soil is 54 mm/ISO mm layer (Bernier, 1966). Permanent Wilting Point (PWP) is defined as the moisture content of the soil at which a plant will wilt and no longer regain turgor. Permanent Wilting Point for this soil is 19.5 mm/lSO mm layer (Bernier, 1966). Evapotranspiration is defined as the total water lost from the soil due to the combination of evaporation from the soil surface and transpiration from the growing plant. Available Water Capacity (AWC) is defined as the amount of water a soil holds between Field Capacity and Permanent Wilting Point (Hassan and Broughton, 1975). Soil moisture for day t, SM(t), is calculated by the following equation: SM (t) - SM(t-1) + Infiltration(t) - Evapotranspiration(t) - Drainage(t) (3.1) An initial soil moisture content is needed, however. Soil mois- ture is initialized at Permanent Wilting Point at the start of each computational year (March 1), since the weather records show that January and February are consistently hot and dry. Infiltration is set at 0.90 of precipitation (BB), based on results obtained by Mellado (1981), and to cease once saturation is reached. Evapotranspiration is computed daily using the evaporation values 56 recorded from a USA Weather Service class A pan. Pan evaporation is considered to be greater than evaporation from a free water surface, hence a pan coefficient of 0.7 is used (Schwab et a1., 1966). Crops do not generate the same evapotranspiration as from a free water surface depending upon crop development. Crop coefficients used were 0.36 for bare soil (Tulu, 1974), 0.50 for emergence to 20% cover, 0.90 for 20% cover to maturity, and 0.5 for maturity to plowing (Pair et a1., 1976). One additional coefficient is used as a zone coefficient since we are only dealing with the upper 150 mm of the soil profile. During the peak growing season only 602 of the moisture used is taken from the upper zone since deep roots draw water from the lower soil horizons (Baier and Robertson, 1966). Thus for the peak growing season, a zone coefficient of 0.60 is used, else the zone coefficient equals 1.0. Soil moisture lost then through evapotranspiration is calculated as pan evaporation times pan coefficient times crop coefficient times zone coefficient. Evapotranspiration is assumed to be zero when the soil moisture content reaches Permanent Wilting Point. .Whenever the soil moisture content is greater than Field Capacity a drainage quantity is calculated for use in the following day's moisture budget. It is known that complete drainage of a satur- ated soil down to Field Capacity occurs in 48 hours (Schwab, et a1., 1966). Therefore the maximum drainage per day cannot exceed 29 mm of water. Drainage is assumed and defined as not occurring below a mois- ture content of Field Capacity. 3.3.2. Suitable Day Criteria Once the soil moisture content is computed a set of criteria is employed to determine if a day is a suitable workday. Three types of 57 suitable days exist according to the field operation, namely: 1) soil engaging (tillage, seeding); 2) above ground (fertilizer broadcasting, spraying); and 3) cereal harvesting. A day suitable for soil engaging operations must meet the fol- lowing two criteria: a) soil moisture content less than 99% of Available Water Capacity. Although these soils classify as loam their high plastic limit extends the trafficability period to high moisture contents (Rutledge and McHardy, 1968; Rosenberg et a1., 1981). In the opinion of the farmers surveyed they would wait two days after heavy rains to start working their soils again. b) less than 2 mm precipitation (Frisby, 1970). Suitable days for above ground operations require less than 2 mm precipitation on the day in question as well as unsaturated soil on the previous day (Hunt, 1980; Frisby, 1970). Days suitable for cereal harvesting differ from the other two types of field operations in that the amount of daily sunshine is also considered as a criterion. It should also be noted here that only the period from December 20 to February 15 is considered as appropriate for cereal harvesting in the study area. A day suitable for cereal harvesting has to meet all the fol- lowing criteria (Wolak, 1981; Von Bargen, 1966; van Kampen, 1971): a) eight or more hours of sunshine, unless the soil moisture content is at PWP; b) less than 2 mm precipitation; c) less than 12 mm precipitation on the previous days. 58 3.3.3. Expected Number of Fieldwork Days at Selected Probability Levels The design of field machinery systems often requires a prob- ability level higher than the mean. It is, therefore, necessary to estimate the expected number of suitable fieldwork days at different probability levels (Fulton et a1., 1976). After all the daily weather data has been transformed into a series of work-no work days for the 17 years on record, estimates of the minimum number of suitable days at the 0.50, 0.60, 0.70, 0.80, and 0.90 probability level were derived using empirical cumulative probability distributions (Rosenberg et a1., 1981). The empirical cumulative distribution approach was selected because: a) Determining the best theoretical probability distribution is beyond the scope and time constraints of this project; in using the cumulative probability distribution the theoretical probability distri- bution does not need to be assumed. b) Histograms of expected number of fieldwork days suggests that different periods have different theoretical distributions; the cumula- tive probability distribution captures different theoretical probability distributions. 1 c) General experience suggests that for small samples, observations generated using the empirical cumulative probability distribution are often more reliable than those generated using an estimated probability distribution function. In part, this occurs because estimates of the parameters of probability distributions take away degrees of freedom. The empirical cumulative distribution was constructed from sequences of work-no work days in a four step process, as follows: 59 1) the number of workdays for each time period and year are summed to form observations of the number of fieldwork days in each time period; 2) workday frequencies are ordered from smallest to largest and assigned a probability using the rule that the Kth ordered observation is a measure of the K/(N+1) fractile (Anderson et a1., 1977); 3) the probability assigned to a given number of suitable workdays is smoothed by averaging the probabilities associated with tied values; 4) the number of suitable days at the 0.50, 0.60, 0.70, 0.80, and 0.90 probability level is now obtained by linear interpolation. The 0.70 probability level, for example, represents the minimum number of suitable days that can be expected to occur seven out of 10 years. 3.4. Wheat Production Machinery Selection Model In the initial stages of model development the hierarchical structure of systems and subsystems was defined. Figure 3.3 shows the initial model diagram with a crop production system and a machinery system. Within the machinery system two smaller subsystems are identi- fied as: a) tillage and seeding subSystem; and b) harvesting subsystem. All the components and their relationship are also shown in Figure 3.3. From this initial system diagram model development progressed into an input-output view of the machinery selection model. Figure 3.4 shows the inputs to be used, system parameters, and expected outputs for the model. All systems parameters and desired outputs were develoepd from the data collection process carried out in Chile. Three main criteria were established in order to further develop the model and arrive at a machinery system selection strategy: a) only machine types and size available to the farmers in the study 60 .Eouwmaa Hove: Hofiuacu I Boummm cowuoodom huooasomz mowuoavoum amon3 .m.m ounwam .ooumu sowuoausa use unououau «mumoo gonna one Hush “sumo oosoauomuom one oaaocouo mumsfinomz mumoo umo>um: aoumoo Ho>oa oocooamcoo, P _Em>m§m ham—ZEUS; §m>mm3m 7629595 homo . V . u e s \ ./ mama I ,/ msamsazm eoxmuam .1 _ anode : \\ :1 1:.1 oasoonum I— mcoauoho.. houmm>hmz aceasco roumswunmm EonXmosm mcwooom can «measfie 8qu BB 301 cougumodosa no.8 28338 980.. 61 .Hovoz coauooamm zuoafinomz :owuonooum upon: may you 3mw> usau30Iuna:H .¢.m unawfim .oasoonom maoauouoao .souauo; wafisaoaa .ououoow wooed can doom .mucoaofimuooo mandamuoaoa mom oufimaou .omao> owm>~om .oaooosom newshoa .moowua "mama ofiaoaoom muoafinomz .moufim oaseawo>o .muas: umzoa moo undue .mouooofioammo .ooooam "moon oocoauomuom zuoafisooz message .9me mEooca mmouw acoam>woom Hmaca< .oH you» pom pom: ammo: u0uomue .a umoo umo>uo£ Boumoo .m - umo>umn BOumao you uOuomm xmfim pom» pom cofiuaasmooo Hush .5 moofiuouoao guano one mafiumo> oumuoon Hoe umoo amummm myocazomz .0 game: than .msqzoad you moo Hoe muse: xuoz awnmuoaso can .ooomcmucfima w onHomqum mono mam ufioaou .mmoowaosfiu .uonoH.Ho=u mo umoo .m .uonma .Hoom .zuooanoma How couuoauoH msmumAm Ham “on umoo wmmszuaaoo Hoodoo com umoo umom OH Houoe .c onHoanomm mono you oofiua vouooaxm mamumzm mama» oouooaxm huocfiaoma umoo umooa unfinu one vcooom .m Hm huocucoma ouflm amass“: .N umoo wooed comes one woumo>umz V umoo doom dooofia mafioEoo .uoxouam .uoumooomouo HoNfiHfiuuou mommom Sound: 3mg: .uoomom .3ouuma :uooubmxuam .3ouum; Ho>oH oosoeuucoo humane: xmao .Boaa .muouoouu no oufim new Hones: mHHuooH .umonz .muoo no“: voodoo oou< wsaosaoofi .soummm xuocwnooapumoo umooa .H mmomnvummmmm mom: aouumuom {It .m~ .Nm .HH .OH .m .m .n .o .m .q .m .N .H mHDmHDO mBDmZH 62 area would be considered; b) there would be time constraints for all operations, according to agronomic data, within which operations must be finished. That is, in a first step a minimum size machinery system capable of doing all the work in the time allocated would be selected. c) from this minimum size machinery system a size incrementation process would be used to search for a least cost system by determining the trade-offs among timeliness costs, ownership costs and the other components of the total system cost, which is used as the basis for selection. Figure 3.5 presents a simplified flow diagram of the machinery selection model, showing the general approach to the problem. Figure 3.6, on the other hand, presents a more detailed view of the structure of the model showing, in particular, the system size incrementation process. The machinery system selected includes the following components; tdewheel drive tractor(s), disk plow, off-set disk harrow, spike-tooth harrow, grain seeder, centrifugal fertilizer broadcaster, boomrtype field sprayer, self—propelled combine harvester and transport wagon. Results from the farmers' survey, presented in Table 3.4, indi- cate that selection of up to two tractors would address the problem of 852 of the mechanized wheat producers in the study area. Survey results also indicated that farmers owning two tractors would use a (larger) tractor for tillage (plowing, disking, harrowing) and another (smaller) for seeding, fertilizing and spraying. Owning two tractors only becomes very important for large producers at seeding time when disking or harrowing and seeding are to be carried out during the same period, according to the field operations calendar presented 63 READ user's Data Initialization L Weather Model (Suitable Days) 1 Select Minimum Size Machinery System 1 Economic Analysis Increment System Size Cost<<:Stored Cost All Sizes Y S Select Combine Harvester tore New Cost and ' and wagon. Calculate Cus- Machinery System tom.Harvest Cost PRINT Machinery Systems and Costs qr ( STOP > Figure 3.5. Simplified Flow Diagram for the Wheat Production Machinery Selection Model. Initialize equipment , Subprogram 1 co sts available time Determine plow EFC Power sizing )L Weather Model (Suitable Days) a Determine disk & seeder EFC Power sizing Determine harrow & seeder EFC. Power sizin 4’lDetermine fertilizer EFC I {Determine sprayer EFC I ' __Subprogram 2 [Total annual equivalent ’ cost for machinery system Cost Analysis 1 Store - ° “ > new cost L L Ispr-lspr'fl - W Subprogram 3 _ N Y Combine Harvester and L! "fit-"m” tom Harvest Cost. h-i lhatr-lharrfl k IhWth L Totalize Costs I - A ‘Y , 4E h—leiskz-ldiskfi ‘ W / PRINT Machinery j 'Y Systems and Costs 'Y ”""'{lpan-uflowfifl ‘ ‘<-llnfl:::EIBBS- STOP Figure 3.6. Flow Diagram for the Wheat Production Machinery Selection Model . 65 earlier in Tables 3.1 to 3.3. TABLE 3.4 Distribution of the Number of Tractors Among the Farmers Surveyed* Number of Farmers Tractors . Z 1 60 2 25 3 10 4 or more 5 *Source: Farmers' Survey. The selection of two tractors is, therefore, approached in such a way that, when the seeded area demands it, a second tractor is sized to power the grain seeder, fertilizer broadcaster, field sprayer and transport wagon. The essential methodology used in this model for matching the size of soil engaging implements to tractor power and for calculating their productivity has been developed from the Agricultural Engineers Yearbook (ASAE, 1982). Productivity data are required to establish the power needed of the tractor and the work capacity of machines: PTOkW = D * S * W / LF * TE * CONV * CI (3.2) EFC = S * W*Eff / C2 (3.3) Where: PTOkW - tractor power takeoff power (kW) D - implement draft (kN/m) implement speed (km/h) U! I W = implement width (m) pr 35 Vi r-fl 66 LF = tractor load factor (decimal) TE - tractive efficiency (decimal) CONV - tractor PTO to axle power ratio (decimal) C1 - dimensionality constant (Cl-3.6) EFC - effective field capacity (ha/h) Eff - field efficiency (decimal) C2 - dimensionality constant (C2=10) Table 3.5 presents four factors developed from data collected in Chile that can be used to solve Equations 3.2 and 3.3. Table 3.5 also presents the sizes of machines available in the Chilean market as well as the logical increment units to use in the model. The load factor (LF) is the tractor design loading rate. It reduces tractor wear (Bowers, 1978) while providing extra power for dif- ficult field conditions briefly encountered. LF has been assigned a value of 0.77 in this model, based partly on values reported by White (1977) and the elevation being greater than 500 metres above mean sea level, for the location of these farms. The PTO to axle power conversion factor (CONV) has been given a value of 0.96 (ASAE, 1982). Tractor power is determined by the size of the plow or the disk harrow, unless two tractors are selected, whereas the power of the second tractor is determined by the size of the seeder. The selection of the combine harvester was originally approached in a different way. Because this operation can be customized, it seemed appropriate to develop an independent model. The model calculates the cost of eleven harvesting alternatives. The first alternative is to Customize all the area, the next five alternatives represent the use of flme.five sizes of combines available in the market (12 to l6-foot), plus Custom cost if necessary; the last five alternatives represent the use 67 m5o>usm .muoauom mmawsoIooHH«£0IaoHoamoooo mo huwmuo>fiaaIuaoEuuoaon moauooswmam Housuasofiu0< .Awaaav .Hm um possum “Assasv .ucam “Aaaaav .musez "Ammaav ..m.<.m.< mosaeoIamHHasoImuoamma eumafiaomz Hmuaoasuauaa on mzmfi>umuaH unmounomm osoou 00.0 moacou 0 I N. IIII 0.0 IIII IIII comma uuoamooue m Auoow H0 6 0000.0 0000 0H I 0H 05.0 0.0 IIII IIII Houmo>uon Auoou H0 a ma¢n.o “use as I «H m0.o m.s I--- I--- emsauaoualmnmm m mauve 00.H mouooa 0H I OH 00.0 0.0 00.0 0.0 soummovoouo nouaaauuom 5 ounce 00.H moauuoc «N I 0H 00.0 0.0 00.0 0.0 muuoa 00.H moauuoo 0H I «H 00.0 0.0 00.0 0.0 nommuam camam 0 Asou H0 e 055H.0 msou 0H I OH 00.0 0.5 00.0 0.H novoom camuu 0 pumps 00.0 mouuoe 0 I 0.0 00.0 0.5 00.0 0.0 soups: auoouumsaam a Axmfl0 H0 mauve m~.0 a0avmxmfio 00 I A00~H 00.0 0.5 00.0 A0.m.mvn.0 Aswan av «Home m~.o Assemamau mm I Aevma mm.o n.o ms.o as ammavm.s saunas Axmfiv H0 shame 00.0 Acavmxmuo 00 I A00~H 00.0 0.0 00.0 A.o=um00.0 xmwv woquwo m Axoav H0 ouuoa 00.0 meoav 0 I N 00.0 0.0 00.0 5.0a 30am 0000 N A00 H0 33 05.0 30090 05 I mm IIII III IIII IIII nauooua H unmaouooH asawxozIasaHsfiz Aaoauooov An\axv afloaaooov Aa\zxv aowuoouuausova use use: xuos mouam odomaao>< .oamum 00000 .oaumm amen: Hones: magnum: cause xuos o>wuomu9 ou unease 1“ *.oaano ea uuuooaaoo some mcaumocwwcm Hmuauaaoaum< .m.m names 68 of two harvesters: one l6-foot combine and a second one changing in size from 12 to 16 feet. While experimenting with this model, three facts became clear: a) a risk factor needed to be introduced in the custom cost calculation to have a fair comparison with alternatives contemplating ownership; 2) unless unrealistic changes in prices were made, most of the time the least cost combine was the minimum size harvester. This is due to the large increase in purchase price as combine size is increased; c) be- cause this model could handle very large areas, over 800 hectares, it is not fully compatible with the rest of the wheat production machinery selection model. It became necessary then to develop a new combine harvester selection algorithm, which would be compatible with the larger model. Considering the farmers' survey results showing that only 10% of them have two combines and considering that custom harvest is a common prac- tice in the area a new approach was devised, in such a way that calcula- tions proceed, first to establish the cost of customizing all the area and, second to select a combine harvester and establish its cost. In this way the user can specify the risk he associates with the custom harvest, therefore, increasing the cost, and can compare this cost with the one related to ownership of a harvester. Custom cost is calculated using the following equations: CUSTPR - 192 * PRICECR (3.4) cusrco - AREAT * CUSTPR * RISK *2 113”? (3.5) Where: CUSTPR - custom price ($/ha) I92 - cost in kilograms per2 hectare (actual charge is 3.0 metric quintals per 15,625 m 2) 69 PRICECR I crop price ($/kg) CUSTCO I custom cost ($/years analyzed) AREAT I area being custom harvested (ha) -RISK I risk factor associated with custom harvest (dimensionless) INMA I annual inflation rate for machinery (Z) IF I farmer's rate of return (Z) n I number of years analyzed Combine harvester capacity is determined by matching required effective field capacity to the capacity of harvesters available in the Chilean market. Engine power, on the other hand, is calculated from the results of the farm machinery dealers survey (Appendix B). A transport wagon is also considered with the combine selection algorithm, in such a way that 12 and l3-foot combines are assigned a four tonne wagon and 14, 15 and 16-foot machines are assigned a five tonne wagon. This approach is based on survey results and machine tank and work capacities. A better approach to the wagon selection would require a more complex model using distance, yields, cycle time, speed, and other data not available at the present time. It is felt that the gains in accuracy would not fully justify the degree of complexity required of the model. 3.5 Costs Analysis The cost analysis method used by most Agricultural Engineers is called the fixed/variable cost method. The primary advantage is its simplicity. However, the cash flow method of cost analysis is better suited than the fixed/variable cost method to model inflation's affect on costs since all costs are modeled as they occur. 70 In this model the cash flow method of cost analysis is used, following the basic methodology presented by Rotz et al. (1981), who demonstrated that their model is most useful for comparing machines or systems of machines available to farmers. Changes in the methodology include the addition of timeliness costs, elimination of tax benefits, modification of repair, maintenance and shelter calculations, and the inclusion of two interest rates and four different inflation rates for machinery, fuel, labor and crop prices. Figure 3.7 presents the flow diagram of the cost analysis algorithm. A linear relationship between size and purchase price of machines is assumed in this model (Hunt, 1967, 1977; Melsaac and Lovering, 1974, 1976, 1977; Singh, 1978). Purchase price: is predicted by regression equations developed from data obtained through the farm machinery dealers survey, in Chile. The results from the survey and the regression equations are presented in Appendix B. Other economic data used to compute owning and operating costs of agricultural machinery are presented in Table 3.6. 3.5.1. Cash Flow Method Smith and Oliver (1974), took an annuity approach to model the cost of machine ownership. They broke the initial cost of the machine down to a series of equal annual costs. A similar approach has been used to determine the annual equivalent cost of owning and operating agricultural machinery (Rotz et a1., 1981). The annual equivalent cost is determined by multiplying the initial capital cost by a capital . 11 ‘ START > 4’///r : machine typel///1 DATA:remaining l value,R&M coef. Y _1 READ: power, price, READ: size, price, hours use hours use Determine fuel and lubri- cation cost v ' FE Seeder I_Determine labor cost J: [Determine repair and main- tenance cost Q I:Determine ownership cost Increment to next year I= ", [zzgzrmine tim;:iness [Determine this year's 6081: J IAccumulate costs N ,//:;/ik\\\\\_Y - More Y ‘\\\\\:::::/’i Machines N //rPRINT: Cost Summary / STOP Figure 3.7. Flow Diagram for the Economic Analysis of the Machinery System. .Haaaav mmfiom cam umemnH .mnfiaouamnnflao IaoHoa0oaoo mo huHmu0>HnaImoHaocoom HmuaanoHu0< 0:0 0=Hu00nHwnm HmusuHsoHuw< mo muomsuumm00 “00095000 72 0N000.0 5 0H 50.0 000 00N.0 0H comma 000000009 00000.0 0 0H 50.0 000 000.0 0H H0000>u0£ maanfioo 00HH0aouaI0H0m asooo.o H OH mm.m oma oom.H NH Hmummuamoun u0uHHHuu0m 00000.0 0 0H 00.0 00H 000.H NH umhmuam 0H0Hm 00000.0 0 0H 00.0 00m 000.0 NH .umvmwm awmuw 00000.0 0 0H 50.0 00H 000.H 0H Bound: auoouuoanam 0N000.0 0 0H 00.0 000 000.0 NH 30HH0£ ease somumwo 5N000.0 0 0H 50.0 000 000.0 0H 30H0 0000 NH000.0 NH 0H 00.0 000.H 000.NH NH Houomua Mao: H00» 00 N N munom muaom mu00» coaumoHMHus0vH A00H00 000:0“:0 00 N0 .0=H0> .aoH00Ho0u0 .000 00HH 0cHnosz 0oHnomz u0uH0£m 0:0 0onmnoucha .muH0000 0:H:H0800 I00 Hmaca< Hmaca< «.mumoo >u0anowz HmuauHooHu0< 0sHumu000 0:0 0oH=30 .aowumauomaH odaonoom .0.0 0H085 N ET'O |Y ' DA1§;260 SOLCOF-O. 6 I l carcorao. 36 6 Figure 4.2. Flowchart for subroutine EVAP. 83 r career-o. s r— | CRPCOF-O.9 ‘ ‘ CRPCOFIO.5 {C} i 7 i ' 4 ETIPANEVP(I, J)*O. 7*CRPCOF*SOLCOF 0L lCHECKISOLMOS-PWPJ RETURN Figure 4.2. (Cont'd.). 84 C m) 41 READ YEAR(I), DAY(J), SOLWAT, INF,/ DRAIN, SAT, F. C, PWP g? I SOLWAT(I,1)IINF+PWP _ ‘ DRAIN(I,1)IO 'TOTWAT SOLWAT(J-l)+INF-DRAIN(I,J) TOTWATI INF+PWP l TOTWAT>83 » DRAIN(I,J)I0 l EMIN(I,J+1)I29 1 V l DRAINCI,J+1)IT0TWAT-FC 'Vl RETURN Figure 4.3. Flowchart for subroutine RUNOUT. 85 4.1.4. Subroutine GODAYS At this point the program is ready to apply the suitable day criteria developed in Section 3.3.2. Subroutine GODAYS does this work for soil engaging operations (i.e. tillage, seeding) and for above ground operations (i.e. fertilizer broadcasting, spraying). A one (1), for workday, is assigned to any day in which all conditions that make up the criteria are met. For soil engaging opera- tions (called BELOW in the program), soil water content must be less than 0.99 of available water content and precipitation for the day must be less than 2 mm. For above ground operations (called ABOVE in the program), soil water content for the day and the previous day must be less than saturation and precipitation for the day must be less than 2 mm. When these criteria are not met the day is assigned a zero (0), for no-workday. All these data are then used in the summation and probability calculation subroutines. Figure 4.4 presents a flowchart for subroutine GODAYS. 4.1.5. subroutine HVDAYS Subroutine HVDAYS carries out a function similar to the one performed by subroutine GODAYS, except that now the suitable day criteria for cereal harvesting are applied. These criteria include the amount of daily sunshine and consequently these values are read in for the harvesting period between December 20 and February 15. Figure 4.5 presents a flowchart for subroutine HVDAYS, based on the criteria developed in Section 3.3.2. 86 ( START ) ' READ YEAR, DAY, SOLWAT, PRECIP, PANEVP, SAT, FC, PWP, ch . SOLWAT(J)<:O.99*AWC BELOW(I,J)I1 BELOW(I,J)I0 J N N SOLWAT(I,J—1)<:SAT Y N PRECIP 2 Y ABOVE(I,J)I1 ABOVE(I.J)-0 __.l WRITE I, J, ABOVE(I,J), BELOW(I,J). PRECIP(I.J:;///' PANEVP(I,J), SOLWAT(I,J) RETURN Figure 4.4. Flowchart for subroutine GODAYS. 87 C w) v READ YEAR(I), DAY(J), SOLWAT, PRECIP SAT, FC, PWP, SUN ® PRECIP(I,J-l)<12 N Y f“ Y l HARVST(I, J)-1 [ HARVST(I,J)I0‘I Figure 4.5. Flowchart for subroutine HVDAXS. 88 4.1.6 Subroutine SUM This subroutine sums up the number of days suitable for soil engaging and above ground operations for all crops, rotations, and time periods. Two types of workday data are used as follows: a) IADAYS (17,365) are workdays for above ground operations; and b) IBDAYS (17,365) are workdays for soil engaging operations. March 1, 1965 has been assigned Julian date number 1. Therefore, all calendar dates for the different cultural practices previously presented in Tables 3.1 to 3.3 have been transformed into their respective Julian dates. Other nomenclature used in this subroutine include the following: c) EWKDY(I,J)Isuitable days for soil engaging operation I in year J; d) EPWKDY(I,J)Itime available in the optimum period plus the penalty period for soil engaging operation I in year J; e) AWKDY(I,J)Itime available for above ground operations. Soil engaging operations start on day ESTART(I). The optimum finishing date for soil engaging operations is EENDR(I). The following code designates each operation: 1 I plowing; 2 I first disk pass; 3 I second disk pass; 4 I third disk pass; 5 I fourth disk pass or harrow and seeding. Above ground operations start and end on days ASTART(I) and AEND(I). No penalty period exists for these operations and the fol- lowing code is used: 1 I Nitrogen application; 2 I spraying; 3 I transport oats; 4 I transport wheat; 5 I cutting lentils; 6 I transport lentils. A flowchart for subroutine SUM is presented in Figure 4.6.. 89 C m) {I Initialize IADAYS(17,365), IBDAYS(17,365), IHDAYS(16,58), EWKDY(5,17), EPWKDY(5,17), AWKDY(6,17), ESTART(S), EENDR(S), EENDPN(5) , ASTART(6) , AEND(6) I < .__.‘:>— + <<:J 4e——-—-— 1, I? :>*"'” EWKDY(I,J)IO EPWKDY(I,J)IO - AWKDY(I,J)I0 20 10 ‘53\ \w’ <(Jenn-ml, 174,/’ AWKDY(6,J)I0 _ 300 K(—ESTART I EENDR I EWKDY(I,J)IEWKDY(I,J)+ IBDAXS(J,K) 400 '6; ' 4: I '_ P I EPWKDY CI,J)IEPWKDY (I,J)+ IBDAYS .1 RX 43:; 200 .100 Figur. 4.5. Flowchart for subroutine SUM. 9O 700 K(-—- ASTART (I) , AEND(I) AWKDY(I,J)IAWKDY(I,J)+ IADAYS (J,R) 600 RETURN 500 Figure 4.6. (Cont'd.). 91 4.1.7 Subroutine HARVEST Subroutine HARVEST does for harvesting operations what SUM does for soil engaging and above ground operations. This subroutine uses the data for workdays and no-workdays stored in IHDAYS(16,58) to sum the number of suitable harvesting days for each crop. As in subroutine SUM the workdays of each year for each crop harvesting period are placed into a variable and this variable is returned to the main program where expected number of workdays at selected probability levels are worked out . Beginning and ending dates for each harvesting operation are stored in START(I) and HEND(I). The dates for ending with penalty are stored in HENDP(I). The following code is used: 1 I harvest oats; 2 I harvest wheat; 3 I harvest lentils. Other variables in this subroutine include HWKDY(I,J), which is the number of days suitable for harvesting operations that occur in each of the years on record, I being the crop and J the year; HPWKDY(I,J) which is the number of suitable days for harvesting operations that occur in the optimum period plus the number in the penalty period. A flowchart for subroutine HARVEST is presented in Figure 4.7. 4.1.8. Subroutine WEEKS This subroutine sums up the number of suitable workdays for 52 climatological weeks, starting on March 1 of each year, for soil engaging and above ground operations. Suitable harvesting days are summed for an eight week period between December 20 and February 13. The following new nomenclature is used in this subroutine: 92 C “$3 Initialize HWKDY(3,16), HPWKDY(3,16), I START(3), HEND(3), HENDP(3), IHDAYS(16,58) \k < 10\ I(._l, 3 / \F 20 _T HWKDY(I,J)IO HPWKDY(I,J)IO do} do; V so I.¢______. 1, 3 60 <<: J 1 L HWKDYH ,J)II-IWKDY(I ,J)+ IHDAYS (J,K) i L HPWKDY(I ,J)IHPWKDY(I,J)+ IHDAYS (J,K) 50 Figure 4.7. Flowchart for subroutine HARVEST. 93 EWEEK(I,J) is the number of suitable days for soil engaging operations in week (I), year (J); AWEEK(I,J) is the number of days suitable for above ground operations in week (I), year (J); HWEEK(I,J) is the number of days suitable for cereal harvesting operations in week (I), year (J). A flowchart for subroutine WEEKS is presented in Figure 4.8. 4.1.9. Subroutine SORT This subroutine sorts the number of suitable workdays for each of the operations, crops, rotations and time periods. The year with the maximum number of suitable days is given top rank, and the year with the minimmm.number of suitable days is given the lowest rank. A flowchart for subroutine SORT is presented in Figure 4.9. 4.1.10. Subroutine SMOOTH This subroutine takes the ordered years from subroutine SORT, assigns each a probability value, smooths the data and then sends the data to a linear interpolation subroutine. PROB(I) contains the (R/N+l) cumulative probability value for each year. K is the rank of a given year assigned by subroutine SORT. Figure 4.10 represents a flowchart for subroutine SMOOTH. 4.1.11. Subroutine INTERP Subroutine INTERP locates a specific value of suitable workdays for a given probability level. In this subroutine, X is the probability level input and Y is the number of suitable workdays found by linear 94 C mg 3 Initialize EWEEK(52,17), AWEEK(52,17), HWEEK(8,16), IADAYS(17,365), IBDAYS(17,365), IHDAYS(16,58) - I ¥%‘\\_ <<:1I‘%--d, 5g,/' ’jb j, (I... 1.2%,} EWEEK(I,J)IO AWEEK(I,J)-O ‘__ 10 < h I {-— 11 s/ W L 40 < Jg—l, 16/ j HWEEKCI,J)I0 30 .r EWEEK(I,J)IEWEEK(I,J)+ IBDAYS(J,KK) (I,J)IAWEEK(I,J)+ IADAYS(I,KK) IKK+1 300 'é’fi Figure 4.8. Flowchart for subroutine WEEKS. 95 400 I... I, 1. m 500 I H1, 8 600 x (_1, 7 HWEEK(I,J)IHWEEK(I,J)+ IHDAYS(J,KK) KKIKK+1 600 500 RETURN 400 Figure 4.8. (Cont'd.). 96 C m) READ WRKDAY, N 20 J (___ 1, (N-l) WRKDAY (J) READ DAY70, DAYSO, DAY90, WRKDAY, N - {I Initialize SMPROB(17), SMDAY(17), IENTRY, PROB(17), TOPROB(17), PROB70, PROBSO, PROB9O I DENOM-N-tl I I4g__..1, N’ ,r [Eton (I) - (N- (I-l )YDENOM] I 1 W310“ um I é C i ”‘\ (._1,Nf v~ WRRDAY (I )gTREMAx IENTRYI IENTRY+1 IWRKIWRKDAY (I)+O . 5 TOPROB (IENTRY) IPROB (I) COUNT' 1 V CALL INTERPCPROB70, DAY70) CALL INTERP(PROB80, DAY80) 0 CALL INTERP(PROB90, DAY90) RETURN Figure 4.10. Flowchart for subroutine SMOOTH. 98 In N Y L JWRK=WRKDAY(J)+0.5 1 WRKDAY(J)I999 COUNTICOUNT+l TOPROB(IENTRY)IPROB(J)+TOPROB(IENTRY) w SMDAY (IENTRY)IWRKDAY (I) _-I SMPROB(IENTRY)ITOPROB(IENTRY)/C0UN;| ' €10; ® 0 Figure 4.10. (Cont'd.). 99 interpolation. Subroutine INTERP is connected to subroutine SMOOTH by a common block. A flowchart for subroutine INTERP is depicted in Figure 4.11. 4.2. Program TRIGO Program TRIGO consists of a main program and 12 subroutines. The program has been designed for interactive use and, consequently, on each run it provides directions to the user on how to enter all the required information, which has been shown on the left side of Figure 3.4. The main program of the model controls the operation of the sub- routines by means of indexes, flags, logical expressions and by direct calls to the proper subroutines. A flowchart for program TRIGO is presented in Figure 4.12. Initially, the main program calculates the areas over which the different field Operations are to be performed, according with the nature of the crops, rotation and tillage intensity level being used. The main program also handles the correlation equations, presented in Appendix B, which predict the purchase price for all sizes of machines available from the local farm.machinery dealers, in Chile. The main program controls the Operational flow during the machinery system size incrementation process. During this process the main program uses a specific algorithm to compare, sort and store the three machinery systems with the lowest total present value cost. All the subroutines, except COST, have been provided with data specifying the effective capacity of all sizes of machines, according with equation 3.2 and the data presented in Table 3.5. Also, relevant 100 C D L READ PROB LEVEL(X) WORKDAYS(Y) [Initialize SMPROB(17), SMDAY(17), I ENTRY, CSMPB(18), CSMDY(18) CSMPB(1)IO CSMDY(1)IO l CSMPB(I+1)ISMPROB(I) CSMDY(I+1)ISMDAY(I) 50 YI(((CSMDY(1+1)-CSMDY(I»*(X-CSMPB(I)))/ (CSMPB(I+1)-CSMPB(I)))+CSMDY(I) @ Figure 4.11. Flowchart for subroutine INTERP. 101 < START > L L READ FARM MANAGEMENT DATA; SYSTEM DESIGN PROBABILITY AND WORKDAY HOURS DATA; ECONOMIC DATA 0 AL , [ INITIALIZE AVAILABLE MACHINERY SIZES, l WORK INCREMENT UNITS, PRICES CALCULATE AREAS FOR DIFFERENT FIELD OPERATIONS All ; CALCULATE TIME AVAILABLE FOR ALL FIELD J OPERATIONS. CALL TIMEAV 4! SELECT COMBINE AND WAGON. DETERMINE HARP ST CUSTOM COST. CALL COMBINE, CALL COST AIL SELECT MINIMUM.SIZE MACHINERY SYSTEM. CALL PLOW, DISKCDISKZ), HARROW(HARR2), SEEDER(SDR2), FERTIL, SPRAYR, COST A: INCREMENT SIZE OF EACH IMPLEMENT INDIVIDUAL- LY BY ONE WORKING UNIT AND CALCULATE NEW COST. CALL AGAIN EACH ONE OF THE SUBROUTINES. CALL COST AFTER EACH INCREMENTATION STEP L SORT MACHINERY SYSTEMS AND STORE THE THREE SYS TEMS WITH THE LOWEST TOTAL PRESENT VALUE COST L WRITE MINIMUM SIZE AND THREE LOWEST COST MA- CHINERY SYSTEMS: HOURS OF USE; FUEL CONSUMP- TION; LABOR HOURS; ALL COMPONENTS OF TOTAL SYSTEM COST Figure 4.12. Flowchart for. program TRIGO. 102 subroutines (PLOW, DISK, DISK2, SDR2, COMBINE) have been provided with data establishing the PTO power equivalent needed by each size of the different machines. These power requirement data were obtained using equation 3.3 and the data presented in Table 3.5. Resulting effective field capacities and power requirements were discussed with farmers and extension personnel working for the Technology Transfer Program and modified according to their suggestions during the data collection process in Chile. Effective field capacities and power requirements of different machines used in the model are presented in Appendix D. 4.2.1. Subroutine TIMEAV This subroutine handles selected output from program WEATHR, which will be used in program TRIGO. The time available, in days, for each field operation at 0.70, 0.80, and 0.90 probability level is stored in this subroutine. These available days are transformed into hours using variables WKHRSl and WKHRSZ, which are sent to each of the machinery selection subroutines. A flowchart for subroutine TIMEAV is depicted in Figure 4.13. 4.2.2. Subroutine COMBINE Subroutine COMBINE selects a self-propelled combine harvester and a transport wagon according to the algorithm presented in Figure 4.14. This subroutine also calculates the cost to the farmer if he chooses to hire a custom operator to harvest his crops. The area used to size the combine harvester is that area which 103 C m) L {READ PROB, NPASS, WKHRSI, WIQIRSZ/ PROB‘O.7 l Assign values to TIMEP, TIMDPl, DAYFl, TIMESD, TIMOPé, DAYFA, TIMEF, TIMESP, TIMEW, TIMOPB, Assign values to TIMEP, TIMOPI, DAYFI, TIMESD, TIMOPé, DAYF4, TIMEF, TIMESP, TIMEW, TIMOP8, DAYFB Assign values to TIMEP, TIMOPl, DAYFl, TIMESD, TIMOP4 , DAYF4 , TIMEF , TIMESP, TIMEW, TIMOPB, DAYF8 l Assign values to TIMED(J), TIMER v Assign values to TIMED(J) , TIMER Assign values to TII‘EDU L TIMER k _ NPASS'3 Assign values to TIMED(J), TIMER sign values to IMED (D . TIMEH Assign values to TIMED J‘ DAYF8 I NPASSIZ TI Assign values to TIMEDU) , TIMER Assign values to y!’J TII, [Use WKHRSl and WKHRSZ to transform.workdays into hours] Figure 4.13. Flowchart for subroutine TIMEAV. (:: STARTfE; \ll READ CROP AREAS, TIME AVAILABLE, RISKQ] INFLATION & INTEREST RATES, PRICES 1L_L [Initialize EFCC(S), KW(S) r_;k IAREATIAWHEAT+AOAT+ALENT l \l/ [ CUSTPRs192*PRICEw TCOST1=O I :1<$-—-—-1, 101/' T] CUSTCOaAREAT*CUSTPR*RISK* A , ((l+INM)/(1+IF))**N I CUCOST-TCOSTl l AL ' J; ' I TCOSTl-TCOST1+CUSTCO I AREACaAWHEAT+2.*AOAT/3 ] 5 L I EFCREQ-AREAC/TIMEW ! Figure 4.14. Flowchart for subroutine COMBINE. 105 k—O IPOWERPKW(ICOMB) 1 IEFC'EFCC(ICOMB) ] I- FOR ICOMB-l, 2,-3, 4, OR OR 16, RESPECTIVELY UI FEET EQUAL 12, 13, 14, 15,_l I HRLENT'ALENT*2.5 ] TE- I HROAT-AOAT/EFC I If HRUSE-HRLENT+HROAT/3+AREAC/EFC __I I INCOST-85805+11g96*FEET I CALL COST Y _ @ STORE NEW COST G) N ' ASSIGN 4 TONNE WAGON To 12 OR l3-FOOT COMBINES; ‘ ASSIGN 5 TONNE WAGON TO 14, 15, OR 16-FOOT COMBINES I CALL COST I - it - . I LCOSTc-LCOSTC+XCOST I , gr ‘ ___ I AECCOM?LCOSTC*(IF*(1+IF)**10/((l+IF)**lO-l)I GED Figure 4.14. (Cont'd.). 106 must be harvested between January 10 and February 15. This area in accordance with the field operations calendar, includes all the area with wheat and two thirds of the area with oats. The logic behind this approach is that this is the most intensive work period, therefore, any harvester capable of handling this area in the time available would be able to harvest without any delays the rest of the area with oats and the small area seeded with lentils, which does not exceed 152 of the total cultivated area. This calculated required effective field capacity is compared with the effective field capacity of each of the harvesters from the smallest to the largest size, until the capacity of one of the machines is equal or greater, in which case this harvester is chosen as the mini- mum size combine. Subroutine COST is called to determine the harvesting cost using this machine size. In the next step, the subroutine tries the combine one size unit larger, determines the cost and compares it with the previous machine's cost. If the cost is smaller the size incrementation process continues, otherwise the program selects the smaller lower cost harvester. The hours of use for the combine harvester are calculated from the total harvested area of wheat and oats and the effective field capacity of the selected machine. Two and a half hours per hectare are used to determine the hours needed to harvest the lentils (Ibanez and Rojas, 1979), because this crop has been previously cut and the combine is fed by workers with forks during the harvesting operation. 107 4.2.3. Subroutine PLOW This subroutine selects a disk plow based upon the area to be plowed, calculated by the main program, and the time available. The subroutine also determines the hours Spent plowing and the power required by the selected plow. A flowchart for subroutine PLOW is presented in Figure 4.15. 4.2.4. Subroutine DISK and DISKZ Subroutine DISK selects an off-set disk harrow; when the number of disk passes is greater than two and only one tractor will be necessary the subroutine also selects a grain seeder, otherwise the grain seeder is selected by subroutine HARROW or SDR2. Subroutine DISK may stop the program whenever the seeded area increases to such an extent that the largest disk will not handle all the work in the time available. This subroutine may also send the pro- gram into the two tractor situation whenever the largest disk and largest grain seeder are not able to do all the work in the time available during the seeding period. An iterative process of disk size incrementation is carried out later by the subroutine in order to allocate time to select the grain seeder. This process occurs when three or four disk passes are used and the last pass must be carried out during the seeding period. The subroutine also determines the hours spent disking, the power required by the disk harrow selected, and will update the power required by the system every time the power required by the disk is greater than the power required by the plow that was selected previously. C STfT D / READ Amir, TIMEP / Initialize, EFC(S), PKW(5)I I EFCP - AREAP/TIMEP I EFCP>EFC (NPLOW) l PRINT 'Largest plow will not work this area in the time available' 35% STOP I POWER-PKW(IPLOW) I I HRPLOW-AREAP/EFC(IPLO;)I I EFCPL=EFC(IPLOW) I i RETURN Figure 4.15. Flowchart for subroutine PLOW. 109 A flowchart for subroutine DISK is presented in Figure 4.16. subroutine DISKZ is used only when two tractors are required. The subroutine selects a disk harrow in accordance with the algorithm depicted in Figure 4.17. Disk size, tractor power required and hours spent disking are determdned by subroutine DISKZ. 4.2.5. Subroutines HARROW and HARRZ Subroutine HARROW selects a spike-tooth harrow and a grain seeder whenever the number of disk passes is two and only one tractor is required. The subroutine starts by selecting a harrow using 0.30 of the time available during the seeding period. This is only a starting point based upon the effective field capacities of harrows and seeders con- sidered in this model. The rest of the time is allocated to size the grain seeder and while doing this the size of the harrow may be further increased to free more time for the grain seeder. When there is not enough time for both operations to be performed, the harrow size incre- mentation process may send the program into the two tractor situation whereas other subroutines are used. A flowchart for subroutine HARROW is presented in Figure 4.18. Subroutine HARRZ is used whenever two disk passes are used and two tractors are required. The subroutine selects a spike-tooth harrow in accordance with the algorithm depicted in Figure 4.19. 110 START I//EEAD AREAD, AREASD, TIMED(A), POWER, NPASS, ICOUNT __,///r L INITIALIZE EFC(9), EFCD(9), EFCS(9), PKW(9), EFCMAX I €---’1, NPAS§,/'_’ :1L_ IfivEFCAPAREAD/TIMED(I) IDISK-O St N IDISKPIDISK+1 I * RINT*, 'Cannot disk all , this area for winter wheat IDISKPIDISKPI 1th largest disk availa- C Is in the market' 10 IDISK:>NDISK CALL DISK2 Two-.TEUE. Figure 4.16. Flowchart for subroutine DISK. 111 I TD-AREAD*(1/EFCD(IDISK)) I I TSDE-TIMED(NPASS)-TD J ML I EECSD-AREASD/ TSDR J e I POWERPPWRNEW ] J: I HRDISKPAREAD*NPASS/EFCD(IDISK) I RETURN Figure 4.16. (Cont'd.). A C START) I 112 AREAD, TIMEDM) , POWER, j NPASS, ICOUNT i: Initialize PKW(9), EFC(9), EFCD(9) I EFCMAx-O. O 10 I; L IDISK-o ] I‘E--d, NPAS§/' ‘9' I EFCA-AREAD/TIMED (I) EECMAWCMNDISE) Y N PRINT*,'Cannot disk all this area for winter wheat with largest disk available in the market' i IHRDISKPAREAD*NPASS/EFCD(IDISK)I RETURN I POWER-PWRNEW j STOP 10 Figure 4.17. Flowchart for subroutine DISKZ. 3 C D I READ AREAH, TIMEH, AREASD, TIMESD,/ NPASS, ICOUNT J! [Initialize EFCH(8), EFCS(9)I PRINT*, 'Cannot harrow all this area for winter wheat with largest harrow available in the market' STOP Figure 4.18. Flowchart for subroutine HARROW. 114 I IHARR-IHARR—l J L IEARR- IEARR+1 ] -Y 1 LTHARR-AREAH*(1/EFCH(IHARRfl I mg mg]: I l mum-m5 1 ($ij A J I EFCSD-AREASD/ TSDR ] I ISEED-O —I II I ISEED-ISEED+1 I EFCSD>EFCS (ISEED [mm-ml EFCH(IHARR) 1 ~ (9 ‘ RETURN ' Figure 4.18. (Cont'd.). 115 C SW D L I READ AREAH, TIMEH, NPASS, ICOUNT, EFCH(8) [EFC-AREAH/TIMEH ® I PRINT*, 'Cannot harrow all this area for 'winter wheat with largest harrow availahl in the market' IHARR-O STOP F I IHARPIHARRfid I N I HRHARRPAREAH/EFCHCIHARR)I RETURN Figure 4.19. Flowchart for subroutine HARRZ. 116 3:2.6. SubroutinesgggEDER and SDR2» Subroutine SEEDER is used when only one tractor is required and its function is to determine the hours spent seeding and to provide the seeder's effective field capacity needed in the timeliness cost calcu- lation. A flowchart for subroutine SEEDER is presented in Figure 4.20. Subroutine SDR2 is used when two tractors are required and its function is to select a grain seeder in accordance with the algorithm presented in Figure 4.21. Since the power required by the largest Seeder is 28.6 kW and this number is only slightly larger than the power of the smallest tractor available in the market, this power is assigned to the second tractor which also powers the fertilizer broadcaster and the field sprayer. 4.2.7. subroutine FERTIL This subroutine selects a fertilizer broadcaster upon the basis of the area to be fertilized. hectares seeded with wheat and oats, and the time available. The number of hours spent fertilizing are also calculated and provided for the cost calculations. A flowchart for subroutine FERTIL is presented in Figure 4.22. 4.2.8. subroutine SPRAYR This subroutine selects a field sprayer based upon the area to be sprayed and the time available. The number of hours spent spraying are also determined and provided for the cost calculations. 117 ( sum D /READAREASD, ISEED, ICOUNT/ L I Initialize EFCSC9) I EFCSD'EFCS (15-) I l I HRSEED-AREASD/ EFCS CISEED) ] RETURN Figure 4.20. Flowchart for subroutine SEEDER. 118 C 5m ) READ AREASD, TIMESD, ICOUNT, EFC(9) A ICOUNT=1 Y I EFCSIAREASD/TIMESD I Ech:>EFC(NSEED) PRINT*, 'Cannot seed all this area with winter wheat with largest seeder availabl in the market' If: ' ST” I ISEEDIISEED+1 I I POWERPZB.6 I _ if , - IHRSEED-AREASD/EFC(ISEED) I _ 4L _ I EFCSDRPEFC(ISEED) I I RETURN > Figure 4.21. Flowchart for subroutine SDR2. 119 C m D I AEAD AREAF, TIMEF, ICOUNT7 J: I Initialize EFC(7) I N ® Y IJFCF- AREAF/TIMEF ‘I EFCF‘EFCWFERT) l I IFERT-NFERT 7 i A LFERCAP-EFC (NFERT) *TIMEF ‘I PRINT 'Area fertilized with largest broadcaster is' FERCAP _ LHRFERT-AREAF/EFC (IFERT) 7 Figure 4.22. Flowchart fOr subroutine FERTIL. 120 A flowchart for subroutine SPRAYR is presented in Figure 4.23. 4.2.9. subroutine COST The total present value cost of a machinery system is calculated, using the cash flow method proposed by Rotz et al. (1981), by subroutine COST in accordance with the algorithm depicted in Figure 4.24. Five components make up the total cost of a machinery system. They are the fuel and lubrication cost, labor cost, timeliness cost, repairs/maintenance and shelter cost, and the ownership cost. Only repairs/maintenance and shelter and ownership costs are calculated for all machines in a system. Fuel costs are calculated only for machines with an engine. Labor costs are calculated only for the tractor, grain seeder and combine. As suggested by researchers in Chile and because of the lack of good data on timeliness losses for other operations, timeliness costs are calculated only for plowing, seeding and harvesting. Subroutine COST is called first to determine the cost of the minimum size machinery system. During the system size incrementation process subroutine COST is called each time a machine size is increased in order to establish the cost of the new system. Following this pro- cedure the model is able to determine the minimum.aize system with its cost and the three lowest cost machinery systems. 121 C m) I [READ AREASP, AREAT, TIMESP, ICOUN3/ AL I Initialize EFC(7) I -‘ J; I—WISPRAY-NSPRAY I I I SPRCAP'EFC(NSPRAY)*TIMESP PRINT 'Area sprayed with largest sprayer is' . SPRCAP HRSPRY= AREAT/EFC(ISPRAY) RETURN Figure 4.23. Flowchart for subroutine SPRAYR. 122 C sum 3 READ MACHINE TYPE, HRUSE, POWER, EFC, AREATO, INCOST, PRICES, 0RKHOURS/DAY,AVAILABLE TIMES,COEFRM(J), INFL. & INTEREST RATES 30 N‘(--l, lgx’I ISL, Calculate Fuel Cost COSTFL-COSTFL+1.15*PRFUEL*POWER*FCF*HRUSE*((1+INFU)/(1+IF))**N TRACTOR, SEEDER, COMBINE ‘Calculate Labor Cost I COSTLA=COSTLA+1.10*WAGE*HRUSE* 1+INLA)/(1+IF))**N PLOW, SEEDER, COMBINE Calculate Timeliness Costs. Assign K Factor Values, Probability of a WCrkday (DAYF). Calculate Time used by Disk/Harrow and Seeder. if I AREAOP'TIMEOP*EFC I l_v L EXCES s-AREATOv-AREAOPI I ARPDAY-EFC*HRDAY I J I AVARDY-ARPDAY*DAYF I L F DAYs- (EXCESS/ARPDAY) /DAYF I 1 41 I w- ((l+INCR)/ (1+IF) ) **N I Figure 4.24. Flowchart for subroutine COST. 123 ICOSTTM?COSTTM+AVARDY*ID*K*YIELD*PRICE*WI as; Calculate Repairs, Maintenance and Shelter Costs I COSTRMfiCOSTRM+INCOST*COEFRM(J)*HRUSE*((1+INM)/(1+IF))**N a 6‘21 Calculate Ownership Costs 202 Down Payment; S-year loan J Calculate Remaining Value A RV=0.1*INCOST*((1+INM)/(1+IF))**10 J L USN-((1+IF)**S-l)/(IF*(1+IF)**5) I - L CRF-IB*(1+IB)**5/((1+IB)**5-l) I J I COSTOWiO.2*INCOST+O.8*INCOST*CRF*USPW-RV I I I XCOST-COSTFL+COSTLA+COSTTM+COSTRM*COSTOW RETURN Figure 4.24. (Cont'd.). 5. MODEL VALIDATION One important step in modeling is to establish how well the model represents the actual system under study, which is referred to as model validation. Validation is generally thought of as a two phase process: a) verification of the model being the process by which the programming logic is compared with our intentions--that is. the programming logic of the model should accurately do what we intended for it to do (Loewer et a1., 1980); and b) the model validation phase during which the model assessed in relation to its prescribed use; this could involve comparing the performance of the model either against recorded data for the system or against a subjective judgement of what the output should be, given a broad understanding of the system which the model represents (Dent and Blackie, 1979). Thus, a model is verified in relation to absolute truth, whereas the model is validated in relation with the purpose for which it was constructed. The model verification procedure followed here consisted of testing all the subroutines, previous to their final assembly, in order to detect and correct all anomalies and errors in sintax and program logic. During the validation process data collected in Chile through the farmers' survey, relating to available working days and machinery systems. were used to analyze the model behavior. 124 125 5.1. Available Field Working Days Original results from the farmers' survey, concerning available field working days, are presented in Table 5.1. Considering that tillage and seeding in the fall and spring are the most critically time con- strained operations, of which farmers are aware, farmers and machinery operators were asked during the survey to estimate the average number of days suitable for soil engaging operations by half-month periods, between April 1 and September 30. The results show that there seems to be a continuous deteriora- tion of the weather conditions from April to July, with the later part of that period being the worse, and from then on a continuous improvement on the weather conditions related to soil engaging operations. The method proposed by Fulton et al. (1976), was used to allocate the number of working days in the halfdmonth periods to the adjacent biweekly climatic periods which are used in the computer model, and thus be able to compare the farmers' survey results with the computer prediction. Table 5.2 depicts a comparison between the results of the farmers' survey and the computer output at the 0.50, 0.60. 0.70, 0.80, and 0.90 design probability levels. The opinions of the surveyed farmers were matched most closely by the 0.70 probability level. This shift can be interpreted in the context of the conservative nature of farmers the world over, their innate desire to reduce risks, short tenm records versus human memory and the model's suitable day criteria being slightly off target. For 10 of the 12 biweekly periods the 0.70 design probability values are found to be within 102 of the farmers' estimates, with a correlation coefficient r = 0.91. 126 o.H N.H N.H 5.H N.H 5.H ¢.H ¢.H N.H N.H N.H N.H N N.HH H.0H N.m 5.5 H.N N.N N.N N.N «.5 N.N N.HH N.NH .M NH NH OH OH N N N N N CH HH HH NN NH HH HH N N N N N N N HH NH «N NH OH N c N N N N N m 0H . NH NN HH N N N 5 5 5 5 5 5 HH HH NN NH oH OH N N OH 0H 0H 0H OH NH NH HN NH HH HH N c N N N N N HH NH 0N NH 0H 0H N 5 5 5 5 N m HH NH NH HH m m m 5 5 5 N N m NH NH NH NH OH N N N N N N N CH OH OH 5H OH OH oH N N N N N OH OH OH OH NH OH oH N 5 N N N N N OH OH HH NH HH N N N N N N N N N HH NH NH NH HH HH N N N 5 5 5 N 0H NH NH HH m m m 5 5 5 5 5 m NH NH NH HH NH OH 5 N c c N 5 5 NH NH HH HH HH oH oH N N N 5 OH OH NH NH OH NH oH N N N c c N N NH NH NH m HH m m m 5 5 N N N m NH NH N NH OH CH N q N N 5 5 N m NH 5 NH m N N 5 5 5 5 N N HH HH N OH 0H 0H N N N N N N N NH NH N NH NH NH OH oH N N N N 5 NH NH c NH HH HH OH OH oH N OH OH NH NH «H N NH NH OH N N N N N N OH 0H 0H N N N N N N N N N NH NH NH NH H ONINH NHIH HNINH NHIH HNINH NHIH ONINH NHIH HNINH NHIH oNINH NHIH .oz mmmzmemmm HNDGD< VHDN mzah >4: AHmm< unouaoammm .ouHsmoN ao>u=m .mumaumm .oHHso .oo:H>oua mHasz cumummo cw .msoHumuooo Ncwwowso HHom you oHnouHam mhmv mo Hanan: vouooaxm .H.N MANNH 127 o N CH N N muHammu >o>u90 mo NOH aHNqu mvoHuoa mo umnaoz «.0 0.0 0.0 0.0 «.0H u--- 0H .000 u 50 “00500000 0.0 0.0H H.HH 0.HH 0.0H «.0H 0~u0H nonsouaom 0.0 H.0 0.0 H.0H. 0.HH «.0 NH .0000 n 00 000000 «.0 0.5 «.0 0.0H 0.HH 0.0 00-0H 000000 0.0 «.0 0.5 H.0 H.0 ~.a 0H-~ 000000 0.H 0.« 0.0 H.“ 0.0 0.0 H 000000 . 0H 0H=0 0.0 0.0 0.0 0.0 H.0 0.0 0Hu0 0H=0 0.0 0.0 0.0 0.0 0.0 0.0 « 0H=0 . H0 0:00 H.0 H.0 0.0 0.0 «.a 0.0 00-5 0:00 0.0 H.0 0.0 0.0 0.0 0.0 0 0:2. .. «0 00: 0.0 0.« 0.0 H.0 0.0 0.“ 00-0H 00: 0.« H.0 «.0 0.0H 0.HH «.0 00: u 00 HHu0< 0.0 0.0 0.0H 0.0H 0.HH 0.0H 0~-~H HHu0< 0.0H 0.HH ~.NH ~.~H H.0H n--- HH HH00< . 00 0000: 00.0 00.0 05.0 00.0 00.0 0m>u=0 uoHuom HQ>UH shagginmfioun— UGUQUN—ucfi um GUHSQUH H0602 .muwewh . hau—Ogfim . anmaHHo .mHH:o .mo:«>oua oHaoz :uoumoo :H .maoHuoumao NaHwaoo HHom you oHnmuH30 mamv mo amass: omuoooxo you muHsmmu Hoooa nousnaoo coo >o>u=m .muosumm .N.N NHNoH muHHHnmnoua ON.o ecu um mzovxuos NHmHm mo Hogans wouomaxm .H.N ouanm 8H NmunH «<5 85mm >mew3|HN .mmwmaeae0nee00ewwxw.wwm0 N . e ....>.% ...... . N . . . .... . O NH 3 00200.00. .BBHBE H om N>NN< 138 TABLE 5.9. Effect of the Precipitation Pattern upon the Expected Number of Workdays in two Contiguous Periods. Probability Level s 0.70 Expected Number of Workdays and Mean Rainfall for Climatic Periods: May 24-30 Manyl-June 6 June 7-13 Expected Workdays 2.1 3.7 1.9 Mean Rain- fall (mm) 51.5 24.2 55.1 Aug 30-Sgpt 12 September 13-26 Sept 27-Oct 10 Expected Workdays 9.0 11.1 9.0 Mean Rain- fall (mm) 39.6 27.6 36.1 5.2. Farm Survey of Machinery Systems and Model Results Twenty farmers were randomly selected among the ones actively participating in the Technology Transfer Program being carried out by I.N.I.A. The only condition was that they owned at least one tractor. A summary of the survey results, concerning tractor characteris- tics, is presented in Table 5.10 and it shows a very diversified and aging agricultural machinery system in much need of replacement. The average tractor age is 10.5 years, with 11 makes imported from seven countries being represented. The power range goes from 37.3 to 73.1 PTO-kW (50 to 98 PTO-HP). The mean tractor PTO power is 50.2 kW with a standard deviation of 8.5 kW (67.5 and 11.3HP, respectively). Average power per unit cultivated area was found to be 0.55 139 0«.0 ....“00.0 00 0.00 000H 000-00 H «0H 0H 00.0 00.0 NNH «.«0H 00 0.00 000H 00-00: 0000H00 00 0.00 000H 03000 0H>00 0 00H «H 00.0 «0.0 mmH 0.00 00 0.«« 000H 00«-0H0000ez H0 0.00 000H 0000:0000 0 00H 0H H0.0 00.0 mmH 0.«0 00 0.00 000H 003100 00 0.H« 000H «00:00 0 00H 0H 00.0 ««.0 H0 0.00 000H 0000-0000 H H0H HH «0.0 0«.0 00 0.00 000H 00-00: N00300 H 00H 0H «0.0 0«.0 00 0.00 000H 00-00: 0000H00 H 00H 0 «0.0 0«.0 00 0.00 000H 00:00: 0000H00 H 00H 0 00.0 0«.0 00 0.00 «00H 00H0 00000 0000 H 00H 0 00.0 00.0 H0 0.00 000H 0000:0000 H 00 0 00.0 0«.0 00 0.00 000H 0000: 0000000 H 00 0 00.0 00.0 00 0.«« 000H 00«-0H0H0002 H 00 « 00.0 00.0 00 0.0« 000H x 000-H0000>000 H 00 0 H0.0 00.0 00 0.00 000H 00002 0000000 H 00 0 0H.H 00.0 00 0.00 000H 00H0um0000 0000 H 00 H 00\0m 0:\3a 00 ex 000» 000: 00000000 H000 0000 .02 muu< vmvmom\umzom hosomloem mo .02 vacuum N Euom .mUHamoN >o>u=m .mumauoh .moHumHumuooumno mousom uoaom can «004 vacuum NHuwow onuo>< .oH.N NHNHumuoaolou 0x .N5NH ooaHm 50H>Huom HmHSuH=UH0Nm 0H: NHnmumNHmaoo Nwoouou am: New ucmsouwa m omHm mH :omuoa mHne an 140 00.0 H0.0 mmm. 0.000 00 0.H« 000H «00:00 00 H.00 000H 000:00 H0 0.00 000H 0000:0000 00 0.00 000H 00«0:00000 0000 00 0.00 «00H 00«0:00000 0000 0 000 00 00.0 00.0 WNW 0.0«H 00 0.00 000H 0000: 0000000 H0 0.00 000H 0000:0000 00 0.00 HO0H 00:00: 0000H00 0 000 0H 00.0 00.0 .Nwm 0.00H 00 0.«« 000H 00«:0H0000az 00 H.00 000H 000:00 00 0.H« 000H «00:00 0 0«0 0H 00.0 ««.0 mmw 0.00 00 0.0« 000H 0000: 0000000 00 0.00 000H 000:0000 0 000 0H 00.0 «0.0 mww H.00 00 0.00 000H 00:00: 0000H00 00 0.00 000H 000H cowswuom 50000: N 05H NH 00000 00\30 00 30 0000 000: 00000000 0000 0000 .02 mmu< Nowumm\uwsom HoBomloem 00 .oz Novomm M. 6000 00000000000 .0H.0 0H0<0 141 kW/ha (0.73 HP/ha), which is far above the country's average of about 0.30 kW/ha (Ibanez et al., 1979), and falls in the upper part of the range proposed by Giles (1967), of 0.5 to 0.8 HP/ha, as the minimum power necessary to obtain respectable yields. Sixty percent of the farmers report owning one tractor and seeding 121 hectares or less per year, with the exception of one tractor owned in a co-operative fashion that reportedly works 164 hectares per year. Twenty five percent of the farmers own two tractors and seed up to 225 hectares per year. Only 15% of the farmers own three or more tractors seeding between 242 and 550 hectares per year. A very good correlation (r = 0.95) was found between the yearly seeded area and the total power available on the farm. Equation 5.1 can be used to predict the power required in relation with the proposed yearly seeded area, based upon the results from the farmers' survey. Y(kW) - 9.999 + 0.489 x Seeded hectares (5.1) No acceptable correlation (r I 0.08) was found between the total seeded area and the power available per hectare. This fact seems to indicate a lack of sound management practices among the surveyed farmers. The presence of other machines needed to produce wheat and other small grain cereals, among the surveyed farmers, is reported in Table 5.11. Only 65% of the surveyed farmers reported owning a fertilizer broadcaster and a combine harvester. This low percentage can be ex- plained, in the case of the fertilizer broadcaster by the fact that many small farmers apply the nitrogen by hand broadcasting. The low overall percentage of combine harvesters reported seems to be caused by the lack of combine ownership among farmers seeding less than around 100 hectares per year. This situation agrees well with the model prediction which 142 establishes a lower cost for custom harvest than for combine ownership for areas smaller than about 125 hectares, depending upon the risk factor assigned to the custom harvest. TABLE 5.11. Percent of Wheat Producers Reporting Use of Different Machines. Farmers' Survey Results. Machine Percent of Farmers Disk plow 95 Off-set disk harrow 95 Spike-tooth harrow 90 Grain seeder 90 Fertilizer broadcaster 65 Field sprayer 70 Combine harvester 65 Transport wagon 80 A comparison between the machinery systems reported (SR) at 18 farms and the model results (MR) for the same farms including the rotation being used, is depicted in Table 5.12. Farms number one (1) and 20 of Table 5.10 were not used in this comparison because they are not wholly representative of the wheat producers addressed by program TRIGO. This comparison is intended as a validation of the model, although several factors work against a close correlation of survey and simulated results. Most of the machinery reported in the survey was acquired between seven to 10 years ago, when the decision of what and how much to buy was overwhelmingly done on the basis of purchase price and the avail- ability of subsidized lines of credit with the government. Therefore, during that period many farmers bought machinery without giving a great deal of thought to their real needs. 143 0.0 00.« «H 0 «H 0H 00.0 0H « 00.0 0 00.H 0 0.00 .0.H« 00 0.« 00.0 0H 0 0H 0H 00.0 0H 00 H0.H 0 00.0 0 0.00 .0.00 02 0 00H 00 0H 0.« 00.0 0H 0 0H 0H «0.0 0H 0 00.0 0 00.H 0 0.00 00 0.« 00.0 0H 0 0H 0H 00.0 0H 00 H0.0 HH 00.H 0 0.00 0: 0 H0 0« HH as as a: as a: w an Nm.o q N.NN mm 0.« 00.0 0H 0 0H 0H 00.0 0H 00 00.0 0 00.0 « 0.0« 0: 0H 00 0H 0H 0.« 00.0 0H 0 0H 0H 00.0 0H 0 00.0 0 00.0 « 0.00 00 0.« 00.0 0H 0 0H 0H 00.0 0H 00 00.0 0 00.0 « 0.0« 0: 0 00 00 0 0.« 00.0 0H 0 0H 0H 00.0 0H 0 00.0 0 00.0 « 0.00 00 0.« 00.0 0H 0 0H 0H 00.0 0H 00 00.0 0 00.0 « 0.0« 0: 0 00 0« 0 0.« 00.0 0H 0 0H 0H H0.0 0H « 00.0 0 00.0 « 0.00 00 0.« 00.0 0H 0 0H 0H, 00.0 0H 00 00.H 0 00.0 « 0.0« 0: 0H 00 00 0 0.« 00 00 00 0«.0 «H « 00.0 0 00.0 « 0.00 00 0.« 00.0 0H 0 0H HH 00.0 0H 00 H0.H 0 00.0 0 0.00 0: 0H 00 00 0 00 00 00 00 0«.0 «H 0.0 00.H 0 00 0.00 00 0.« 00.0 0H 0 0H 0H 00.0 0H 00 00.H 0 00.0 0 H.00 0: 0H 00 00 0 0.« 00.0 0H 0 0H 00 «0.0 0H « H0.H 0 00.0 « 0.«« 00 0.« 00.0 0H 0 0H HH 00.0 0H 00 00.H 0 00.0 0 H.00 0: 0 00 00 « 0.« 00 00 00 V0H.0 0H 00 00.H 0 00.0 « 0.0« 00 0.« 00.0 0H 0 0H 0H 00.0 0H 00 00.H 0 00.0 0 H.00 0: 0 00 0 0 00 00 00 00 H0.0 0H « H0.H 0 00.0 0 0.00 00 0.« 00.0 0H 0 «H 0H 0«.0 «H 00 00.H 0 00.0 0 H.00 0: 0 00 0H 0 2.0 000 300 000.0030 000 000 000 000 000 000 000 000 :3 000 H a 0 .02 am: moHnaoo Howmumm umuHH powwow .000: numm.onn 3on upsom Hmnv mmu< 8000 :00000 .muHsmmN Havoz New maoumzm 500:05002 .uumaumm No comHumaaoo a. I .NH.N MHN0sm u MN .ouHooou Hocoz n N: .30000: meN o No NooumnH .NaHvoom ouomon 3ouuon :uoouloxHQo o om: 0 uaomoua you n a: .mHHuaoH uH muoonz u3 mouoo no N .uaouoaoo uaox o0o3 ooHnoHuo> uoaaH 0ozuo HHo .No.H Hoswo uouoow onu uoo>uoz aouozo o .Ho>oH oNoHHHu NuHocouoH achoa .Ho>oH zuHHHnonoua ameov mm 05.0 NaHos many HH<« o.oH NN.0 «H N NH 0H 0N.N 5N 0.0 oN.< HH.oH NH.N m N.5N .N.NN .N.NN MN 0.« NN.N NH N NH 0H ON.N NH a: N5.N NH 00.H N N.NN .N.NN m: 0 CNN 0 0H N.NH oN.5 «N N NH 0H NH.N N 0..» NN.N .NH.N CNN OH N.H.0 .5.00 .H.N5 mm 0.0 NN.N NH N NH .NH oN.N NH a: N5.N NH 00.H N N.NN .N.NN N: NH NON 5N NH 00 00.« «H 0 0H «H 00.0 0H 0 0H.«. 0.0 00.H 0 0.0« .0.00 00 0.0 NN.N NH N NH NH oN.N NH 0: NN.N oH CN.H N N.NN .N.NN m: NN OCH OCH 5H o.N NN.N 0H 5 0H NH NN.N «H N N5.N NH oN.H N N.NN .N.NN mm 0.0 NN.N NH N NH NH No.N 5H 0: NN.H N NN.o 0 N.NN .N.N0 NE NH oN N5 NH 0.0 00 0 «H 0H 00.0 0H 0 00.0 0H 00.H 0 0.00 00 o.q NN.N NH N NH NH oN.N NH 0N 5N.H N N5.N N H.NN m2 ON ON 05 NH 0.« Nm.N NH 5 0H NH oN.N NH 0 No.N a oN.H N N.NN .N.NN am o.0 NN.N NH N NH NH 0N.N NH as HN.H 5 N5.N N N.NN .5.NN 0: ON ON NN 0H 05 NJ» 0H N NH NH NN.N 5H.NH N20 5N.N N.N NN.H N 5.0.0 N.NN mm o.0 NN.N NH N NH NH 5N.N NH 0: HN.H 5 N5.N N N.NN .5.NN NE NH 0N NN NH H0.0 000 300 000.0002 000 5.00 03.0 000 :5 000 0.00 0.00 0300 000 H 2 o .02 :N3 ocHAEoo uomoumm nouHH uovoom .000: whom.onQ aon nosom Honv oo0< anon :Hu0om 00000000000 .0H.0 0H0<0 145 Agricultural machinery dealers, on the other hand, would also suggest to the farmers the acquisition of power sources larger than really necessary. As it can be seen in Table 5.10, the majority of the tractors, 522, is in the range of 52.2 to 55.9 kW (70 - 75 HP). Ford- 5000 and Belaruz MTZ-SO tractors make up more than 33% of the tractors reported in the survey. A large number of Ford-5000 tractors were brought to the country through the Alliance for Progress and the Agrarian Reform Program of the Frei Government. All the Belaruz MTZ-SO tractors were imported from the USSR to serve the goals of the Agrarian Reform Program of the Allende Government. As stated in the first chapter of this dissertation, the area seeded yearly with wheat in Chile has decreased from 780,000 hectares in 1966 to 546,000 in 1980. Many of the farmers in the survey acquired their machinery systems in the early seventies when they also were seeding a larger area. Therefore, in general most of the machinery systems reported in the survey seem oversized for the area they work now. The high power/area value for these farmers (0.55 kW/ha), is evi- dence of the reduction in cultivated area together with an initial purchase of a large power source. Rotations seem to have changed slightly, also, during the last five years. The percentage distribution of area per crop, found in the survey is as follows: 65% wheat, 28% oats, 52 lentils, and 2% other crops (barley, rapeseed). These percentages are somewhat different from the ones found in the region in 1976 and reported in INIA (1980), which present values near 682 for wheat, 21% for cats, and 11% for other crops (lentils, barley, rapeseed). The survey results show an increment of the area seeded with oats and lentils and a reduction in the area planted with wheat. These results were corroborated by Chavarria (1981), who 146 also reported a strong reduction of the area planted with rapeseed. Despite the many factors pointing towards a changing technology and economic environment, the values presented in Table 5.12 do compare reasonably well in a general way. As pointed out by Wollak (1981), it is important that model results be in the ballpark of what is expected by the farmer. When this occurs, it is possible to rationalize the dif- ferences between actual and generated machinery systems, commonly caused by different management alternatives. The main discrepancies between actual and simulated machinery systems occur with the power level and the plow size, for which the model consistently predicts smaller units. The discrepancy with the seeder size is opposite, since the model chooses, in many cases, a larger seeder than the one reported by the farmers. The difference in the power level has already been analyzed and most of the farmers would agree with the statement that they tend to oversize their tractors. The case of the plow is different. Plows appear oversized because many farmers do not use the recommended period for plowing but a shorter one for reasons, it appears, of pasture utili- zation (Chavarria, 1981), needing therefore, a larger effective field capacity which usually also means a higher power level. In the case of the seeder, the model goes to larger sizes because of the timeliness costs associated with late seeding, to which the farmers do not respond in the same fashion. Farmers tend to have a larger disk, which also increases the size of their tractors. Confronted with unsuitable weather and late seeding many farmers would resort to spring seeding despite the lower yields and drought risks. In other aspects, the behavior of the model is quite acceptable. Using the 0.70 design probability level for available days and the most 147 common management practices followed by the farmers the model starts selecting two tractors once the cultivated area exceeds 121 hectares/ year, coinciding with the number of tractors reported by the farmers in the survey. Although the model selects a combine harvester for all areas inputted, it also calculates the cost of the custom harvest alternative and these results agree very well with the combine ownership pattern among the farmers, as it was pointed out earlier. The presence of many lA-foot combine harvesters among the surveyed farmers has, most likely, been caused by the massive importation of John Deere-960 model combines during the late sixties. The extra capacity provided by a lA-foot harvester is used by the farmers to do some custom work among their neighbors who plant smaller areas and do not own a combine harvester. 6. SENSITIVITY ANALYSIS 6.1. Machinery System Requirements As the values presented in Table 5.12 are in indication of adequate behavior of the machinery selection model, program TRIGO was used to analyze the effects of various factors upon the machinery system requirements and their respective costs. The following tables present the machinery systems selected for different areas, tillage intensity levels, crop rotations, workhours per day, and design probability levels, under a standard set of common conditions encountered among the wheat producers of the region. Information presented in later tables show the fuel consumption and the costs associated with the different machinery systems. These results should be seen as examples of the kinds of analyses that can be made with model TRIGO using the inputs of interested farmers. Program TRIGO prints out four machinery systems with their cor- responding partial and total costs, as well as other types of informa- tion: 1) least cost system; 2) second least cost system; 3) third least cost system; and 4) minimum size system. This has been done in order to be able to use the program as an educational tool later in Chile. The discussion that follows is based upon the least cost machinery system. 148 149 6.1.1. Effects of Cultivated Area Table 6.1 depicts the variations in machinery system requirements in relation to changes in the cultivated area from 50 to 230 hectares. Sizes of all machines increase with increments in the area, with the exception of the spike-tooth harrow which decreases in width as soon as two tractors are selected. The largest change in size occurs in the case of the disk plow which goes from 0.72 m (3 disk plow) to 1.44 m (6 disk plow). As expected, total power increases with the area from 32.1 kW to 92.5 kW. However, the power per unit area initially decreases as the area increases and later is stabilized at a value close to 0.45 kW/ha. Tractor use per year increased with the cultivated area, except for a small reduction at 110 hectares for which implements much larger than the ones used at 90 hectares were selected, demanding consequently less hours per year on the power source. Fertilizer broadcaster and field sprayer sizes do not change a great deal with changes in the cultivated area. These two machines have large effective field capacities, low purchase price and negligible power requirements, therefore, the model selects larger than minimum sizes to save on field hours, fuel consumption and labor costs. For all inputted areas, program TRIGO selects a combine harvester and calculates the cost of custom harvest. The asterisks in the column before the last on the right side of Table 6.1 indicate those areas for which custom cost is less than combine harvester ownership, for a risk factor of 1.05. For the areas that can be worked with two tractors (up to 270 hectares for 10 work hours per day) the program selects a 3.65 m wide combine (12-foot). This seems to be caused by the large amount of time available for harvesting (negligible timeliness cost) and by the 150 numoo HonoH "H\m NN.0looHun Hoom HomoHo "Nx\m NN.0IooHun mono mon\wx 00NNuoHoH5 moons onu um acoumcoo uaox one moHanum> onu mo umou ego .oonHooem omHsuonuo mmoHc: .mcou Houaeaoo HHm cH .N0.HuuouomN meH umo>um£ acumoo “NNHuooHun mono no GOHuonna "NoHuuonoH no coHumHmoH “NNHuHosm co cOHuoncH “NNHumuoaHnooa so coHumHmcH "NNHIcuauou No menu m.uoauom «NNHuumououcH soon mun\m 00.H "moon> NcHsoHHom .ooum mHsu HHn comm cu oHno on a» woven: one >oo\u£ 0.N mamas can moumuoon 0NN onu How ueooxo 0.Num5moxmuaosxu03 “05.0uHo>oH mocoonoou assume: “cowumuom ousummmluoonz “Ho>oH muHmcwucH onHHHH 30H .mHsmuocao wouoo>unm ooHaaoo cmnu mmoH umou aoumoo « 0.N NN.N NNOH 0«.0 N.NN NH 0N.N N.N N0.N NN.H 0 0NN 0 0.« NN.N NNN NN.0 N.NN NH 0N.N N.N NN.N NN.H 0 0HN 0 0.« NN.N 00m No.0 N.NN NH 0N.N N.N N0.N NN.H 0 0NH 0 0.« NN.N NNN NN.0 N.HN NH 0N.N N.N NN.H 0N.H 0 05H 0 0.« NN.N NN5 NN.0 N.NN NH 0N.N 0.N NN.H 0N.H 0 0NH 0 0.« NN.N N05 NN.0 N.NN NH 0N.N 0.N NN.H NN.0 0 0NH 0 0.0 NN.N HON NN.0 N.NN NH N0.N N.N NN.H NN.0 0 0HH 0 0.« NN.N NNN NN.0 H.NN HH NN.N N.N 5N.H N5.0 0 0m 0 0.« NN.N 00N NN.0 H.NN 0H HN.N 0.N 5N.H N5.0 0 05 0 0.« NN.N HNN NN.0 H.NN 0H N5.H N.N 5N.H N5.0 0 0N 0 80 05 0.520 2:: H30 05 H5 05 c5 3 H 3 o oomm3 ocHoaou om: uosom .uuom noooom .uuo: men son Amnv mou< wouooua .mucoaouHovom NuocHnomz con: oou< ooooom hHuoow one no soouwm .H.N MHN nonuo HH< 0.N I mwoxmuoo: Mao: 05.0 n Ho>oH oucoofimcoo Hosanna anemone uoc n a: NNN om.o N.NN N m NH NN.N a: HN.H NN.o oNH oN mm mm H«0 eme NNN om.o N.NN N m NH NN.N a: HN.H NN.o omH oN mm mm Ame aaHeoz «co mN.o H.NN H m NH oN.n o.o NN.H NN.o omH oN mm mm ANN aoH onNoH nouuoua Houoa oNoHHHa .mucoaouHooom huocHsooz con: Ho>oH zuHmcoucH oNoHHHH onu mo uoomwm .N.N MHN nonuo HH< anemone uoc n a: 0.N u No0\muoo: Hues 05.0 I Ho>oH oooooncou assume: NNN H«.c N.NN N N NH NN.N a: HN.N NN.H oHN oN mm mm aumustuo NNN H«.o N.NN N N NH NN.N a: HN.N NN.H oHN o NoH NoH aumusao NNN ««.o N.NN N N NH NN.N 9: «N.N ««.H NHN o 0HN o Numuz HN>NH NNHNzNNzH NNNHHHN szamz NNN N«.o N.HN N N NH NN.N a: NN.H NN.N oNH oH oN oN N-N-zuHuo NNN N«.o N.HN N N NH NN.N a: NN.H NN.N oNH o NN NN Numuzuo HNN «N.N N.HN N N NH NN.N as NN.N NN.H omH o NNH o aumuz HN>NH NNHNzNNzH NNNHHHN NNHN HNN HN.o H.NN H N NH NN.N N.N .NN.H NN.N oNH oN NN NN anauzano «oN HN.o H.NN H N NH NN.N N.N NN.H NN.N oNH o NN NN Numuzuo NNN NN.N N.NN H N NH NN.N o.N NN.H NN.H oNH o oNH o N-N-N HN>NH NNHNzNNzH NNNHHHN soH NN« N«.o H.NN H NN NH N«.N .a: NN.H NN.N oN ON NN NN Numnanuo HN« N«.o H.NN H N NH N«.N a: NN.H NN.N oN o NN NN Numuzuo NNN N«.o H.NN H N oH N«.N a: NN.H NN.N oN o oN o aumus HN>NH NNHNzNNzH NNNHHHN zaHNNz NNNNNNV NeNsx stv .umue Nev Nev Nev Nev Nae Hay Ange H 3 o aoHuNuoN on: nosom .oz .zuem .uuom .uom .uuom xmwn son oou< Away mou< wouooua . 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Weather Confidence Level I 0.70 Oats-Wheat-Pasture Rotation. All other variables kept constant. 170 TABLE 6.12. Effect of the Design Probability Level Upon the Machinery System Cost. All Values in U. S. $. 01* fi Total Mach. System Annual Equiv. Cost Area (ha) Area Prob. Pr.Val.Cost* Cost per ha. Increment 0 w L (ha) Level ($/10 yr) <$/ha)* ($/ha)** ($/ha) 25 25 0 50 0.70 58.594 261 524 '0 25 25 0 50 0.80 59.190 263 526 2 25 25 0 50 0.90 88.249 393 656 132 35 35 o 70 0.70 71,109 226 428 0 35 35 0 70 0.80 75,268 239 441 13 35 35 0 70 0.90 100,570” 320 522 94 85 85 0 170 0.70 156,477 205 318 0 85 85 0 170 0.80 165,362 216 329 11 85 85 0 170 0.90 Not enough time to seed all this area. * Combine harvester cost not included. ** Combine harvester cost included. #Needs two tractors. Medium tillage intensity level Work hours/day - 8.0 Oats-Wheat-Pasture Rotation All other variables kept constant. 171 6.2.6. Effects of Wheat Yield and Wheat Price The effects of wheat yield and wheat price upon the machinery system cost are depicted in Tables 6.13 and 6.14. Table 6.13 shows that as the wheat yield increases from 1500 to 5100 kg/ha the machinery system cost only increases by 6.0 $/ha, from 354 to 360 $/ha. The effect of the increment in wheat yield is only evident in the timeliness cost, which increases proportionally with the increments in yield. The other cost components remain at the same value regardless of the wheat yield. The last two columns in Table 6.13 show an increment in the annual gross income and a reduction in the annual equivalent cost/gross income ratio proportional to the changes in the yield. The effect of the wheat price is similar to that of the wheat yield. Table 6.14 shows the changes in timeliness cost, system‘s cost per hectare, gross income per hectare, and annual equivalent cost/gross income ratio, as the wheat price changes from 0.15 to 0.39 S/kg. The effect of both wheat yield and wheat price upon the machinery system cost is not very large because they only affect one of the com- ponents of the total cost and also because for these areas there are no timeliness costs for the combine harvester. 6.2.7. Effect of Fuel Cost Table 6.15 depicts the effects of changes in fuel cost upon the machinery system cost. Again, only the fuel cost component is affected in the same proportion as the fuel cost changes. 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Summary The machinery selection model (Program TRIGO), was developed to analyze the impact of different tillage intensity levels and crop rota- tions upon the size, number and cost of machinery systems used by wheat producers in south central Chile. It is a heuristic model in which field operations must be done within specific calendar periods. The model can be used by farmers in their decision-making process because it gives the user a range of alternative machinery systems that are not necessarily profit maximizing or cost minimizing but close enough to be a ballpark optimum. This machinery selection model (TRIGO) has been designed as an educational tool to help college students, instructors, extension agents and farmers to improve on some farm.management aspects and to select a machinery system for wheat production using specific tillage practices and crop rotations. The model matches machine productivity to available time. The model selects the most economical machinery system that can finish all required field operations within specified time constraints. Machinery and timeliness costs are established each time a different machinery system is tried out. The system that proves the least cost, considering ownership, labor, timeliness and operational cost, is selected. Only machine sizes available in the Chilean market are considered in the selection process. 7. CONCLUSIONS AND RECOMMENDATIONS 7.1. Conclusions From the results obtained in this research project the following conclusions are made: 7.1.1. In Relation with the Farmer's Survey b) e) The large majority of the surveyed farmers (85%) owned one (602) or two (252) two-wheel drive tractors. The machines represented at the lowest percentage level were the fertilizer broadcaster and the combine harvester, both being reported only by 652 of the farmers. Other machines were reported by a large percentage of the wheat producers (90-95%). The machinery system owned by the surveyed farmers is old, with tractors having an average of 10.5 years of use. This fact reflects the difficult economic times these wheat producers face, brought about to a large extent by rising production costs and low crop prices. A very good correlation (r = 0.95) was found between the yearly seeded area and the total power available on the farm. The individual tractor power range was found to vary from 37.3 to 73.1 PTO-kW, with an average power per unit cultivated area of 0.55 kW/ha. This value puts these farmers.at a high mechaniza- tion level, far above the country's average power of 0.30 kW/ha. 176 177 d) Near 125 hectares seems to be the upper limit to the area that can be cultivated with one tractor, and around 225 hectares when two tractors are available. These results agree very closely with the computer model predictions which give a range, including both limits, in accordance with the time available used as input in each run. e) The crop rotation used by the farmers in the study area has changed somewhat in the last five years. The main changes relate to an increment in the area seeded with oats, from 21 to 282 of the total area, and a slight increment in the area seeded with lentils. Both these changes have been recommended by the . Extension Service personnel for some time and these results are seen as positive extension accomplishments. The area seeded with rapeseed has decreased notoriously. 7.1.2. In Relation with Time Available for Fieldwork (Simulation Results) a) In general, time available for fieldwork was found to be abundant in the eastern part of the province of Nuble. However, the results also show that during the seeding period (May-June) the expected number of suitable days for fieldwork is greatly reduced, making necessary a careful approach to the selection and manage- ment of agricultural equipment in order to complete field opera- tions on time. The number of days suitable for fieldwork does not increase substantially until September. b) Computer predictions of days suitable for fieldwork at the 0.70 probability were matched very closely by the results from the d) 178 farmers' survey. For 10 of the 12 biweekly periods the 0.70 design probability values were found to be within 102 of the farmers' estimates, with a correlation coefficient r - 0.91. The values of suitable days presented at different probability levels in Tables 5.3 through 5.8 are to be interpreted as the mdnimum.expected number of days suitable for fieldwork for that many years out of one hundred. These results should only be used in connection with field operations performed upon the loam textured soils of eastern Nuble province. The computer program counts only whole suitable days, not frac- tions of days. Therefore, farmers can expect to have a somewhat larger span of time available considering that they would, most likely, work quarter and half-days, or as long as the weather permits at critical times. Farmers should be prepared to perform a large amount of work during the important seeding period of the first week in June. Because of the specific precipitation pattern in the area, this week shows consistently a considerable larger amount (an average of 852 at the 0.70 probability level) of time suitable for field- work than' both the previous and following weeks. 7.1.3. In Relation with Machinery System Requirements and Costs (Simulation Results) a) The wheat production machinery selection model, program TRIGO, is a powerful and useful analytical tool that can be used success- fully to study the effects of cultivated area, tillage intensity level, crop rotation, available time for fieldwork, and an array b) C) d) e) f) g) 179 of economic factors upon the machinery system requirements and its associated costs. The model predicts the number and size of machines required for a given set of conditions, estimating the system's cost and pointing out relative differences in management strategies. Absolute values are only as reliable as the quality and reliability of the input data. Ownership cost was consistently the largest of the system cost components, with values ranging from 41 to 36% (75 to 45% for the combine harvester) of the total system cost, in accordance with the cultivated area. Fuel and Lubrication and Repairs and Main- tenance costs, which together make up 502 of the system's cost, followed ownership cost in relative importance. Labor and time- liness costs had the lowest relative importance among the cost components. Machinery requirements and total system.cost increased with increments in the cultivated area. However, costs per hectare decreased as the cultivated area increases. The number of tillage Operations affects both the machinery system size requirements and fuel use. The effect was especially notorious as the change is made from the low tillage intensity level to the medium and high tillage intensity levels. As the number of crops in the rotation increased to include oats and lentils, the machinery system size requirements decrease along with the costs per hectare. This result is caused by a reduction in the amount of work to be performed, especially plowing. The number of working hours per day and the design probability 180 level affected both the machinery system size requirements and its associated costs. The effect was so extreme at the 0.90 design probability level that its use in the machinery selection process is open to debate. Results using different amounts of time available for fieldwork also point out the need for grain seeders with larger work capacities, of which the farm machinery dealers seem to be aware. However, this would require the elimination of obstacles (stones, stumps) in many fields and improvements in the rural roads. h) Diesel fuel requirements per hectare were affected by both the tillage intensity level and the crop rotation. The effect of the first factor can be more important than the effect of the crop rotation, generating savings of up to 27.0 L/ha in a 110 hectare farm, when changing from high to low tillage intensity level. 1) Changes in wheat yield and price affected only to a small extent the machinery system cost. The system size requirements were not affected. Changes in the fuel cost had a more important impact upon the system's cost. However, when these changes were kept within expected variations the effects were much smaller than the effects produced by changes in the tillage intensity, crop rotation or time available. 7.2. Recommendations for Future Work It is suggested that future research be carried out along the following lines: a) Set up a network of field observers and rain gages to collect b) e) d) e) f) 8) h) 181 data on days suitable for fieldwork. Analyze the cost of performing work with one and two tractors on critically sized areas. Enlarge the machinery selection model to include the operations necessary for spring wheat production and other production inputs (i.e. seed, fertilizer, chemicals, etc.) in order to estimate net returns to land, through a complete analysis of each crop production system. Develop a specific algorithm to analyze custom harvest work operations. Adapt program WEATHR to estimate the number of days suitable for fieldwork for other soil types and areas, especially in the Central Valley. Collect more data related to implement draft, work speed, field efficiency, slippage, and tractive efficiency in order to improve the predictions of effective field capacities and power requirements. More agronomic experiments are needed in order to collect better data on timeliness losses for different crops, operations and regions. Develop specific algorithms to analyze the machinery system requirements and their associated costs for other crops grown under irrigated conditions in the Central Valley. APPENDICES APPENDIX A Data Collection Methodology and Worksheets 182 APPENDIX A Data Collection Methodology and Worksheets A. Data Collection Methodology. The data collection process was carried out in November and December of 1981, in the Andes Pre-Cordillera area of the provinces of Nuble and Bio-Bio, and in the City of Chillan, Chile. During this phase of the project the author joined the extension specialists of the Technology Transfer Program of the Quilamapu Experi- ment Station of the Institute of Agricultural Research (INIA). Much of the fieldwork was carried out with the help of Mr. Jorge Chavarria of INIA. Worksheets to collect the necessary data had been.prepared pre— viously at Michigan State University in East Lansing. From among the farmers participating voluntarily in the Technology Transfer Program completely random samples of different sizes were taken. Twenty farmers were sampled to be interviewed using Worksheets No. 4 and 6. Twenty five farmers and machinery operators were interviewed using Worksheet No. 5. All agricultural machinery dealers (6) represented in the Chillan area were interviewed using Worksheet No. 3a, in order to obtain the sizes of machines available and their respective costs. Fifteen researchers and faculty members of the Quilamapu Experi- ment Station (INIA) and the College of Agriculture of the Univerity of Concepcion, at Chillan, were also interviewed using Worksheets No. l, 2, 3b, 3c, and 4. All the existing climatological records (June 1964 to February 183 1982) of the Agrometeorological Station of the University of Concepcion, at Chillan, were collected and brought to East Lansing to be used in the computer model that estimates the time available for fieldwork (Program WEATHR). 184 "uHaHH oHumea HHom m.wuonuouu< NNH eon Nuo> I own I oumuoooa I ooow I ucoHHooxo "oNooHouo HHom HHH HmEHooo "uooHonmooo soHumuuHHmcH A0H as "nommH so NH Home: .ucHom NcHuHHz unmanauom um acoucoo ousumHoE HHoN Am ea "HomoH so NH Home: .muHoomou oHon um acousoo ououmHoa HHoN AN as “uoon so NH Home: .cOHumusuoN um ucoucoo ousumHoa HHom a5 oo\N “NuHmooo Hook HHom aN 8NN II "SEEN 3H3 HHoN G N ”zuHmouoe Hmuou HHom Ac N comm “IIIII.N uHHo “IIIIII. 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HHOH3 36m I I I I I I I I I q I I I I I I I I N I I I I I I I I I N I I I I I I I I H A50 nuoH3 Nonnoz new» Heooz\exez Bonnen eOooquHHeN HeeaeHueoov .ON .oz neenexnos q I I I I I I I I I I I N N H Heonnouv NuHoemeo oeoH nee» Hoooz\exez nowea unonenens q I I I I I I I I I I I I I I I N N H 200 Hum no 80 nuoHa Nonnoz Ammv noBom enHwnm nee» Heooz\exez neuee>nem enHNEoo q I I I I I I I I I I N N H A50 :uon NonnoS neew Heooz\exez neueeooemmm . neuHHHunem HoennHunoov .UN .02 neenexnoz 201 lllll'll'lll'IIIlll IIIIII'IIIII’IIIIII HHH HOH AN AN H5 AN AN AN AN AN IIIIIIIIIIIIIIIIII HH Amann .nuoHa .neammw muHoemeo Nonnoz new» HmOozNusmz eanoez .Anesuo one uneaanve NnHme: .uneaanue nOHnoenu HeaHnev mmszuez mmmao HONOOHH:6O0 .00 .oz neenexnoz APPENDIX B Sizes and Prices of Machines Available from the Agricultural Machinery Dealers in Chillan - Chile Sizes and Prices of Machines Available from the Agricultural 202 APPENDIX B Machinery Dealers in Chillan - Chile Tractor Power - Price Relationship. Power at P T-O Price Tractor Identification kW HP (US $)# M.F. - 240 (United Kingdom) 28.13 37.70 17,880 J.D. - 1040 (Germany) 32.06 43.00 17,940 D.B. - 1190 (United Kingdom) 33.79 45.28 18,120 Ford - 4600 (Brazil) 38.02 50.94 18,750 M.F. - 265 (Brazil) 39.18 52.50 19,668 J.D. - 1640 (Germany) 40.30 54.00 21,480 M.F. - 265 (United Kingdom) 41.42 55.50 21,540 D.B. - 1390 (United Kingdom) 47.17 63.21 23,160 Ford - 6600 (Brazil) 49.28 66.04 20,420 M.F. - 290 (Brazil) 52.48 71.00 21,564 M.F — 290 (United Kingdom) 55.62 74.52 24,000 J.D. - 2140 (Germany) 63.36 84.90 28,728 D.B. - 1690 (United Kingdom) 72.51 97.17 33,612 J.D. - 3140 (Germany) 73.79 99.05 36,168 #Source: Agricultural Machinery Dealers - Chillan - Chile. r - 0.94** a - 4993.91 b - 379.14 I1N3) . 4993.91 + 379.14 x Power in kW 203 TABLE B-Z. Disk Plow Size - Price Relationship. Number Working Price# Plow Identification Disks Width (m) (US $) Pascualli (Italy) 2 0.48 1,206 Ramsomes-3 (Chile) 3 0.72 1,990 M.F.-204-3 (United Kingdom) 3 0.72 2,292 Jumil (Brazil) 3 (rev.) 0.69 2,950 Ramsomes-4 (Chile) 4 0.96 2,490 M.F.-204-4 (United Kingdom) 4 0.96 2,820 Bamford-634 (United Kingdom) 4 1.20 3,324 Ramsames-S (Chile) 5 1.20 2,700 M.F.-206 (United Kingdom) 5 1.20 3,720 Bamford 635 (United Kingdom) 5 1.20 3,950 #Source: Agricultural Machinery Dealers - Chillan - Chile. r - 0.82** a - 284.3 - 647.3 I-<) I 284.3 + 647.3 x Number of Disks TABLE B-3. Off—set Disk Harrow Size - Price Relationship. 204 Number Working Width Price# Harrow Identification Disks Disks Metres (US 5) Giambenedetti G-H6 (Argentina) 12 6 1.37 3,560 Breuer (Chile) 14 7 1.60 3,995 TATU—Marchesan GNL-C (Brazil) 16 8 1.83 5,568 Giambenedetti G-H8 (Argentina) 16 8 1.83 4,435 TATUNMarchesan GNL-C (Brazil) 18 9 2.06. 6,120 John Deere 225 (USA) 18 9 2.06 7,176 Connor Shea (Australia) 18 9 2.06 7,260 Bamford (United Kingdom) 22 11 2.51 7,560 John Deere (USA) 22 11 2.51 8,268 Bamford (united Kingdom) 24 12 2.74 8,688 #Source: Agricultural Machinery Dealers — Chillan - Chile. 0.94** H I -l906.5 907.7 *4) II ~1906.5 + 907.7 x Number Working Disks 205 TABLE B-4. Spike-Tooth Harrow Size - Price Relationship. Working Width Price# Harrow Identification (Metres) (US $) Local Manufacturing (Chile) 2.40 700 P.J. Zweegers (Holland) 2.44 892 Local Manufacturing (Chile) 3.20 1,000 P.J. Zweegers (Holland) 3.66 1,235 P.J. Zweegers (Holland) 4.88 1,560 Local Manufacturing (Chile) 6.40 1,625 P.J. Zweegers (Holland) 6.40 1,970 #Source: Agricultural Machinery Dealers - Chillan - Chile. O.96** '1 I a - 220.68 253.14 0‘ I 220.68 + 253.14 x Working Width in Metres 206 TABLE B-5. Grain Seeder Size - Price Relationship. 4 Number Seeding Width Price# Seeder Identification of Rows (metres) (US $) Connor Shea (Australia) 10 1.78 6,240 M.F.—33 (United Kingdom) 15 2.66 8,220 Connor Shea (Australia) 14 2.49 8,400 John Deere 8250 (USA) 14 2.49 8,880 M.F.-33 (United Kingdom) 17 3.02 9,576 Connor Shea (Australia) 18 3.20 9,840 John Deere (USA) 18 3.20 11,868 #Source: Agricultural Machinery Dealers - Chillan - Chile. r - 0.91* a - 699.29 b - 548.38 *4) II A 699.29 + 548.38 x Number of Rows 207 TABLE B-6. Fertilizer Broadcaster Size - Price Relationship. Working Width Price# Broadcaster Identification (metres) (US $) VICON PS - 302 (Holland) 12 1,272 LELY 1000 (United Kingdom) 12 1,740 VICON PS - 402 (Holland) 14 1,794 P.J. Zweegers Vebrax 400 (Holland) 14 1,980 VICON PS - 602 (Holland) 16 2,160 VICON PS - 802 (Holland) 18 2,280 #Source: Agricultural Machinery Dealers - Chillan - Chile. r - 0.87* m I O" I 134.41 '<) I -55.61 + 134.41 x Working Width in metres 208 TABLE B-7. Field Sprayer Size - Price Relationship. f Number of Working Width Price# Sprayer Identification Nozzles (metres) (US $) Parada (Chile) 14 7 2,690 Hatsuta H-320 (Japan) 14 7 2,862 Tecnoma T-400 (Brazil) 16 8 3,084 K.O. - 400 (Brazil) 16 8 3,540 Hatsuta H-420 (Japan) 18 9 4,490 Tecnoma T-600 (Brazil) 18 9 4,150 Parada (Chile) 20 10 4,340 F.M.C. - D010150 (USA) 20 10 5,302 K.O. - 540 (Brazil) 24 12 4,656 Vi #Source: Agricultural Machinery Dealers - Chillan - Chile. r = 0.84** a - -261.66 b = 234.18 r<> II -261.66 + 234.18 x Number of Nozzles 209 TABLE B-8. Combine Harvester Size - Price Relationship. Combine Harvester Size - Power Relationship. Cutting width Engine Power Price# Combine Identification ft m HP kW (US $) MF - 310 12 3.65 105 78.30 45,000 MF - 3640 12 3.65 105 78.30 64,200 New Holland-Clayson 1530 13 3.96 111 82.77 64,920 HD - 960 14 4.26 116 86.50 54,000 HD - 955 14 4.26 116 86.50 54,000 John Deere 4420 14 4.26 120 89.48 82,800 New Holland—Clayson 4040 15 4.57 144 107.38 85,588 John Deere 6620 16 4.87 ' 150 111.85 105,600 #SOurce: Agricultural Machinery Dealers — Chillan - Chile. 8 0.77* - -85805.48 11295.92 ($) . -85805.48 + 11295.92 x Width in Feet Size-Price Relationship: :<) a‘ m n I = 0.93** = -27.38 = 8.55 (kW) - -27.38 + 8.55 x Width in feet Size-Power Relationship: P<>O‘ (D "1 210 TABLE B-9. Transport Wagon Load Capacity - Price Relationship. Load Capacity Price# Wagon Identification (Tonnes) (US $) Coloso (Chile) 2.0 2,100 SOGECO T - 25 M (Chile) 2.5 3,180 Coloso (Chile) 4.0 3,240 SOGECO T - 40 S (Chile) 4.0 3,300 Gehl - 500 (USA) 5.0 4,880 SOGECO T - 60 S (Chile) 6.0 6,030 Coloso (Chile) 8.0 8,140 SOGECO T - 80 S (Chile) 8.0 8,310 #Source: Agricultural Machinery Dealers - Chillan - Chile. r - 0.98** a - -151.78 1022.64 r<)0" I - -151.78 + 1022.64 x Load Capacity in Tonnes APPENDIX C FORTRAN Program Listing ..u'c'x'crtt'lbc'illfl'Pl'ct | not-anonnonnnnnnnnnnnnnnnnnnnnnnnonnnnnnnnnnnnnnonnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn PROGRAM T:lG0(lNPUT‘OUTPUT, .TAPEG-OUTPUT) RGRAH TRG 211 THIS PROGRAM SELECTS FIELD MACHINERY FOR HHEAT PRODUCERS LOCATED IN SOUTH-CENTRAL CHILE. THE MACHINERY SYSTEM CON- SISTS OF TRACTOR(S). DISK PLOH OFF- -SET DISK HARROH SPIKE-TOOTH HARROH GRAIN SEEDER, FERTILIZER BROADCASTER, FIELD SPRAYER. SELF- -PROPELLED COMBINE HARVESTER AND HAGON. VARIABLE wDICTIONA ALENT- AREA SEEDED HITH LENTILS (HA) AOAT- AREA SEEDEDH HITH OATS ( A) AREAc- AREA USED TO SI IZE THE COMBINE. EQUIVALENT TO 2/3 AOAT PLUS AHHEAT AREAD- AREA TO BE DISKED éHA) AREAF- AREA TO BE FERTILI ED HITH THE DROADCASTER AREAH- AREA TO BE HARROHED (HA) AREAP- AREA TO BE PLOHED (HAM AREASP- AREA TO BE SPRAYED AREASD- AREA TO BE SEEDED (HA) AREAOP- AREA COMPLETED IN HE OPTIHUH PERIOD (HA L AREATI- AREAP HHEN J-I(PLOHING), -AREASD HHEN J- E(SEEDING): (HA) AREATo- TOTAL AREA HE HANT TO PLOH. SEED OR WARV ARLAST- AREA LEFT UNDONE AFTER USING INTEGER PART OF VARIABLE DAYS ARPDAY- AREA HORKED EACH SUITABLE DAY (HA) AVARDY- AVERAGE AREA HORKED EA WC? CALENDAR DAY (HA) AHHEAT- AREA SEEDED HITH HHEAT HA COSTFL- FUEL COST COSTLA- LABOR COST COSTTM- TIMELINESS COST COSTRH- REPAIR S MAINTENANCE COST COSTOH- OHNERSHIP COST CRF- CAPITAL RECOVERY FACTOR. TAKES A PRESENT VALUE COST AND DISTRIBUTES IT INTO ANNUAL E UIVALENT COSTS OVER THE . GIVEN NUMBER OF YEARS HITH COHPO ND INTEREST CONSIDERED CHTFLI- FUEL COST FOR LOHEST COST SYSTEM STLAI- LABOR COST FOR LOHEST COST SYSTEM CSTTMI- TIMELINESS COST FOR LOHEST COST SYSTEM CSTRMI- REPAIR 8 MAINTENANCE COST FDR LOHEST COST SYSTEM CSTOHI- OHNERSHIP COST FOR LOHEST COST SYSTEM CSTFLT- FUEL COST FOR 2ND LOHEST COST SYSTEM CSTLAT- LABOR COST FOR 2ND LOHEST COST SYSTEM CSTTMT- TIMELINES COST FOR 2ND LOHEST COST SYSTEH CSTRMT- REPAIR C HAINTENANCE COST FOR 2ND LOHEST COST SYSTEM STOHT- OHNERSHIP COST FOR 2ND LOHEST COST SYSTEH STFL - FUEL COST FOR 3RD LOHEST COST SYSTEM STLA - LABOR COST FOR RD LOHEST COST SYSTEM M - TIHELINESS COST FOR RD LOHEST COST SY STEH STRM - REPAIR E MAINTENACE OST FOR 3RD LOHESTE COST SYSTEM TOH - OHNERSHIP COST FOR RD LOHEST COST SYST DAYFI- PROBABILITY OF A SUI ABLE DAY FOR PLOHINGH PENALTY PERIOD DAYF - PROBABILITY OF A SUITABLE DAY FOR SEEDING PENALTY PERIOD DAYF - PROD. OF A SUITABLE DAY FOR HARVESTING (PENALTY PERIOD) DAYs- CALENDAR DAYS USED TO FINISH "EXCESS" AREA EFCA- TEMPORARY EFFECTIVE FIELD CAPACITY (HA/HR) EFCAP- EFCPL HHEN J-I(PLOH): -EFCSD HHEN J-4(SEEDER) (HA/HR) EFCMAx- TEMPORARY EFFECTIVE FIELD CAPACITY (HA/HR) EFCP- PLOHING EFFECTIVE FIELD CAPACITY NEEDED (HA/HR) EFCPL- EFFECTIVE FIELD CAPACITY OF SELECTED PLOH (HA/HR) EFCSD- EFFECTIVE FIELD CAPACITY OF SELECTED SEEDER (HA/HR) EXCESS- AREA COMPLETED OUTSIDE OPTIMUH PERIOD (HA) FERCAP- WORK CAPACITY OF FERTILIZER (HA) HRCOMB- HOURS HORKED BY THE COMBINE HR/YR) HRDISK- TOTAL HOURS SPENT OISKING (HR/YR HRFERT- TOTAL HOURS SPENT FERTILIZING {H /YR HRHARR- TOTAL HOURS SPENT FERTILIzING HR/YR HRPDAY- HOURS HORKED PER DAY HRPLOH- TOTAL HOURS SPENT PLOHING (HR/YR HRSEED- TOTAL HOURS SPENT SEEDING HR/YR HRSPRY- TOTAL HOURS SPENT SPRAYING (HR/Y ) HRUSE- HRPLOH HHEN J-I; -HRDISK HHEN J-Z: -HRHARR HHEN J-3- RT HHEN J- : -HRSPRY HHEN J-6 HRUSE- HRSEED HH EN J-4 -HRFE HRUSE- HRPLOH+HRDISK+HRHARR+HRSEED+HRFER +HRSPRY+HRCOMB HHEN J-7 HRUSEI- HOURS OF USE FOR THE LOHEST COST SYSTEM HRUSEz-HOURS OF USE FOR 2ND LOHEST COST SYSTE HRUSEa- HOURS OF USE FOR RD LOHEST COST SYSTEM HRUSE - HOURS OF USE OF H GDN ID- BANN'S INTEREST ICOUNT- FLAG TO SELECT THE sMIngIUM SIZE COHBINATION (-I) AND TO CARRY OUT INCREMENTATION OF SIZES IDAYs- INTEGER PART OF "DAYS'S USED TO FINISH "EXCESS" AREA IDAYL- DAYS "ARLAST" HILL BE PEN ML ZED IDISK- SIZE OFA AGIVEN DISK (6 TO )4 DISKS) [I‘ll II'I ttbtr'tccr'ctcCRIIr' nnnnnnnnnnonnnnnnannnnnnnnnnnnnnnnnnnnnnnnfinnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn F- FARMER'S RATE OF RE TU RN NFU- FUEL INFLATION PERCENT/YR 212 NCR - CROP INFLATION PERCENT/YR NCOST-PURCAHSE COST OF A MACHINE S) FERT - SIZE OF A GIVEN FERTILIZER METRES) HARR- SIZE F A GIVEN HARROH )HE RES) NLA- LABOR INFLATION (PERCENT YR NM- MACHINERY INFLATION (PERCE R) T- FER ILIZER SIZE OR a D LOH 5 COST SYSTEM (METRES) HARR- HARROH SIZE FOR D DHEST COST SYSTEM ( PLOH- PLOH SIZE FOR 3R LOHEST COST SYSTEM (D ) SEED- SEEDER SIZE FO 3RD L HEST COST SYSTEM ( OHS) SPRY- SPRAYER SIZE FOR 3RD LOHEST COST SYSTEM (METRES) DISN- DISN SIZE FOR 2ND LOHEST COS SYSTEH s TFERT- FERTILIZER SIZE FOR 2ND LOHEST COST SYSTEM (METRES) THARR- HARROH SIZE FOR 2ND LOHEST COST SYSTEM (MET ES) TPLOH- PLOH SIZE FOR 2ND LOH ST COST SYSTEM TSEED- SEEDER SIZE FOR NO LOHEST COST SYST H SPRY- SPRAYER SIZE FO 2ND LOHEST COST S STEM (ME ES) J- MACHINE IDENTIFICATION NUMBER: I-PLO : 2-DISK: -HARROH: h-SEEDER; 5- ERTILIZER; 6-SPRAYER: a-TRACTOR:D -COMBINE - G . KJ- INDICATES MACHINE SIZE I UNITS NDIgNs. METR S OR ROHS LCOSCH- LEAST COST OF BI E AND LCOSTz- 2ND LOHEST TOTAL COSTS SY EM 03?)( LCOSTg- RD LOHEST TOTAL COST SYSTEM ( LCOST - EAST TOTAL COST SYSTER HITHOUT COMBINE AND HAGON ($) NCOST- L T OTAL COST s STEH HITH COMBINE AND HAGON (S) PAss- INDICATESN Nu BER OF DISKS ASSEs (2 2 O PKWEI; TD PNH ;- POHER RE UIRED BY THE OIFF RENT PLOHS NH PNH I TO PNH - POHER RE UIRED BY THE DIFFERENT DISNS NH POHER- POHER N TO OPE ATE AN IMPLEMENT (NH)T POHE ”4 . I)-c TRACTOR POHER FOR THE LEAST COST SYS H(NH) ER - mo POER FOR THE 2ND LEAST COST SYSTEM (NH) POHER - TRACT R POHER FOR THE 3RD LEAST COST SYSTEM (NH) PRCRO - CROP RICE ( /NG) PRFUEL- PRICE OF DIE EL FUEL (S/L) RI H- PRICE OF HHEAT (S/NG) PROB- PROBABILITY LEVEL TO SELECT SUITABLE DAYS P N CALCULATED NEH POHER NH) Rv- REMAINING VALUE OF A MACHINE (IO PERCENT OF INCOST) N- ROT ION USED (I. 2 OR 2 SPRCAP SPRAYER HORN CAPACITY (H TCOSTc- TEM RA TORAGE FOR TOTAL SYSTEM COST ($) CSTFL- TOT FU COST (5 TCSTLA- TOTAL LABOR COST ( ) CSTO TOTAL OH ERSHIP CO ($) TCSTRM- TOTAL REPAIR s MAINTENANCE COST (5) T:S TOTAL TIMELINESS COST (Sh TIARR- INITIAL TIME ASSIGNED TO ARROH A GIVEN AREA (HR) T -T HE AVAILABLE FOR DISNING (DAYS) T HED(4 - TIME AILABLE FOR EACH OF THE 4 DISNING OPERATIONS (DAYS) T MEF- IME AVAILABLE FOR FERTILIzING (DAY T MEH- IME A L BLE FOR HARROHING (DAYS) T HEOP- TIME AVAILABLE IN THE OPTIMUM PERIOD (DAYS) T MEP- TIME AV I ABLE FOR PLOHING DAYS) T MESD- TIME AVAILABLE FOR SEEDING (DAYS) T MESP- TIME AVA ABLE FOR SPRAYING (DAYS) T E TIME AVAILABLE FOR HARVESTING HHEA (DAYS T MOPI- TIME A AILABLE FOR PLOHING IN THE OPTIMUN PERIOD (on S) T MOPu- TIME AVAILABLE FOR SEEDING IN THE OPTIMUM PERIOD AYS T MOPB- TIME AV ILABLE FOR HAVESTING IN THE OPTIMUM PERIOD (DAYS) TSDR- TIME ALOCATED TO THE SEEDER (HR) USPH- UNIFORM SERIES PRESENT HORTH FACTOR.(THE RECIPROCAL OF CRF). ONVERTS FUTgRE UNIFORM SERIES OF COSTS INTO PRESENTV VALUE HAGE- LABOR COST ( /HR) HNHRSI- HORN HOURS PER DAY FOR PLOHING AND HARVESING NHRsz- HORN HOURS PER DAY FOR OTHER OPERAT O S XCOST- TOTAL COST FOR IO YEARS OF AGIVEN MACHINE ($) DISN - NINE SIZES OF DISNS xFERT - SEVEN SIZES OF FERTILIZERS XHARR - EIGHT SIZES OF HARR OH xPLOH - FIVE SIZES OF PLOHS EED - NINE SIZES OF SEEDERS xSPRAY )- SEVEN SIZES OF SPR RAYERS IELO YIELD OF HHEAT (KG/HA) YLDHH- YIELD OF HHEAT (KG/HA) MIN l3-LOGICAL VARIABLE - TRUE IF MINIMUH SET IS BEING COMPUTED LLg. .u-COST OF COMBINE ANDH AGON TCDI L-CDST OF FUEL FOR COMBINE TCO.'LA-CDST OF LABOR FOR COMBINE AND HAGON TCUI n-C ST OF TIMELINESS FOR COHBINE (I.I/scripts: nnnnnnnnnnnnnnnnnnnnnnnnnnnnnno TCOSTOH-COST OF OWNERSHIP FOR COMBINE FEET-SIZE OF LEAST COST COMBINE 213 CUCOST-COST OF CUSTOM HARVEST AECCOM-ANNUAL E UIVALENT COST OF COMBINE AND WAGON HRCOMB-HOURS RE UIRED FOR COMBINE POHERCOIPOHER R UIRED FOR COMBINE THO-LOGICAL VARI BLE - TRUE IF THO TRACTORS ARE REQUIRED POHERl-POHER SENT TO COST POHERZIPOHER OF ECOND RACTOR COSTILA-COST FO ONE LA ORER FOR COMBINE ICOSTl-COST OF C MBINE ITHOUT HA GON PPRHA-COST PER H CTARE LON T L E OND D L INDEXES FOR THE E C R IMUM B N E (X.Y) XII FOR x-2 FOR I FOR X FOR XI5 FOR INE GPRYRE§§5)-FUEL SUMPTION PER YEAR FOR EACH SET I G VARIABLES: E ST COST SET EAST COST SET T B H FOR TEN YEARS N ‘L‘ EAST COST SET SET F T M C M S D E FD IR S H IN O CO GPRHA -ANNUAL FU L CDNSUHPTIDN PER HECTARE AEC(5 -ANNUAL E UIVALENT CO MST- MR -ANNUAL E UIVALENTC DST PER EsTAQE- (g. .2) HRTRC(5.2 -HOURS E UIRED FOR TRACTOR HOU URS R QUIRED FOR TRACTOR} 2R HRLABR(5. 2)-HOURs OF LABOR RE UIRED FOR TRACTO ‘I- x,I -HDUR F LABOR RE UIRED FDR TRACTOR 2- x.2 TLABR(5. 2)-TOTAL HOURS OF LABR 1% TOTAL HOURS OF LABOR PER ECTARE- (x. 2) REAL IB. IF INH INFU INLA INCR. INCDST LCOSTCO LCOSTC. LCOSTz. LCOST3, +LCOSTSI LCOSTSi LCOSTH LCDSTSH, LCOSTS. OSTI REAL HCSTFL MCSTLA.HCSTTH, HCSTRH, HCST Hm LOTEEER MégRLG.M DIMENSION TIHEDEA}.XPLOH(5).XDISK(9).XHARR(8),XSEED(9). +XFERT(?).XSPW ) GPRHR(§)§£EC(5 2) HRTRC(S 2) HRLABR(5. 2). DIHENS ON GPRYR +TLABR(5 2) POHER é)P /TI ME AYFA TIHDPI, TIHOPA TIHOPB. PRICEH, YLDHH NM INFU, INLA, INCR, HAGE PRFUE COMMON HLI/DAY COMMON/CD STC law F.I PRCRDP AHHEAT, ADA LE WI TIHEH, TIRESO. TIHEF. TIHESP. TIHEH CDHHON/TEHP/TIHEP 5TIHED COHHON /ZZZ/ HRHARR HRDS COHHON/COMPCOM/COSTILA.ICOSTI“ CDHHON/FLAG/THO COHHON/M/HINFLG DATA IDASH, GPRYR, GPRHA. AEC. HRTRC. HRLABR. TLABR, POHERP. PPRHA FRINT? 'ENTER ROTATION TO BE USED' PRINTR' 'OATS- LENTILs-HHEAT-PASTURE ROTATION-II PRINTR’ 'OATS-HHEAT-PASTURE ROTATI DN- 2 PRINTR’ 'HHEAT-PASTURE ROTATlon-3' READR, ROTA TN PRINTR. 'ENTER AREA OATS AREA HHEAT AND AREA LENTI ILS' PRINTR. 'ENTER ALL ON ONE LINE SEPARATED BY COHHAS AS REAL ' RRIHTR’ :NREBERS LENTILS NOT TO ExCEED IO PERCENT OF TOTAL' READR, AOAT AHHEATA LENT REAORA' .N'ENTER NUHBERE OF DISN PASSES 2. 3. OR A AS INTEGER' PRINTR. 'ENTER HEATHER PROBABILITY LEVEL 70. 80. OR 90- PRINTR. .PéAg INTEGER REAORA’HNRRSER NO. OF HRS. PER DAY FOR PLOHING AND HARVEST' PRINTR. 'ENTER NO. OF HRS. PER DAY FOR OTHER OPERATIONS' READR, ’HNHRsz PRINTR. 'ESEER YIELD OF HHEAT EXPECTED IN KG/HA' READR, YL PRINTR. 'ENTER PRICE OF HHEAT EXPECTED IN SIKG' READR, PRICEH PRINTR. 'ENTER THE PRICE OF FUEL IN $/L' READR, PRFUEL . PRINTR. 'ENTER HAGE IN $/HR' READR, ’HAGE ~ PRINT’ R. 'ENTER BANN INTEREST. FARHERRS RATE OF RETURN INFLATION +FOR HACHINERY FUEL INFLATION. LABOR INFLATION. AND HCROP INFLATION + ON ONE LINE SEPARATED BY COMHAS AND IN DECIHAL FD READ *.IB,IF,INM, INFU, INLA. INCR CC 'ENTER RISK FACTDR' PRINTR RISK 214 READ* A A ZE THE LEAST COST THOI ALSE. C THE FOL LLOHING THREEL LINES I | I Y HIGH FIGURES. NI C STORAGE VARIABLES HITH ARBITR LCOST I DO LCOSTZIIOOOOOOOOOO. LCDST?IIOOOOOOOOO. LG I.TRUE. ERPIU, 2)IO TIAL ARIL mm—l-‘UMM>—I-I om m-nz 402! I If dddd‘ zzzz——————-o: TIMO'UM‘HMIU‘UO;8 xmmowrmzun p I TCO TCIO. O C CALCULATE AREA FOR EACH IMPLEMENT IF(A ADA AT GT. AHHEAT)THEN AREAPIA AOAT ELS AREAP-AHHEAT (ROTATN .ER. I)THE AREAD-AOAT+ LENT+AHHEAT AREACIA RE D AREAFIAOAT+AHHEAT AREASP? ELSE F(ROTATN. .Eg. .ZITHEN AREHDIAOAT+A AREIIHIAREA AREHSDIAREAD AREMCIAREAD AREHFIAREAD QEEHSPIAREAD AREHDIAHHEAT AREHHIAHHEAT AREHSDIAHHEAT AREHCIAHHEAT AREHFIAHHEAT AREMSPIAHHEAT EN F AREIITIAHHEAT+AOAT+ALENT C INCOST(J)IINITA L COST OF PLOW,DISK ......TRACTOR C THE ESUATIONSRE THAT PREDICT THE COST OF DIFFERENT SIZE C IMPLE ENTS HER WA DFROMA ASURVEY OF FARM MACHINERY C IN ggILE IN DEC.D I BY EDMUNDD HETZ. I XPLJ+]J) I28h. 3+6h *KJ C T OF DISK CALCULATE INITIAL COS DO 2 JI 1.9 CALCULATE?IRT§I236C3+T°3F7H§RROH DD 3J nu KJ- -J+II XHA RR(J J)-220 .68+2?3. Ih*0. *KJ CAchéAIEM INITIAL COS 0F SEE ER fiJD h 2 .8* KJ C CAchLAi INT -§?2L g+g% FERTILIZER APPLICATDR é CALCULAEEM IN figIRLGRDR “OF‘SERAYER nnnnnnnnnn KJIM+II XSPRAYIH)-- -261.66+2 h.IB*KJ 215 CALL TIHEAv(PRoa NP SS, HAHRSI HKHRsz CALL C0HEINE z! x c n» as > I“ -I I — I I x > am a m m nv > C) c: Ulc- E. .AND.J H ARR-o. g.IJ.EQ.1 .g§:7)fiEQ 2. OR.J .EQ.3)) THEN 0) THEN OHERZ IIPONER ...-"‘23....... 222i 2! :u ”—3! F F F F F F F F F F F F F F F 0 L O L O N —MMMMMAZLLLLLl—LLLLL OJ IH L H+H K+HRfl PHET JRSI SH HRPLOH+HRDISK+HRHAHR+HRSEED+HRFERT+HRSPRY+HRCOHB .NOT.THO.AND.§.E3. .8) COT THO.AND.J.EQ. ) RUSEFIHRSEED+HRFERT+HRSPRY+ PASS.NE.2 .AND J. E . )no To 21 Two.Ann.J .EQ. 3)TH EN 3 CAL COST(JJ, HRUSEF. Powst. EFCAP AREATI.INCOST xc05T. OSTTA. COSTRH. c05T6w) E CALL COST(J HRUSE. POHERI EFCAP AREATI INCOST§¥88§T. —U'II'I'IUIA “VIM—m 'I'I m. 2 C ( NA: OM I: ENDI TCOSTCITCOSTC+XCOST TCSTRHITCSTRQICOSTRH TCSTOHITCST0H+COSTOH CONTINUE IF (HINFLC) THEN HPLOW "-7 l .NO wPOHERZIPONE GPRYR h. rITw IOHRPLOW DISK)*O. 26*POWER+(HRHARR*O. I7)*POHER GPR RYR A: 2I IHRSEED+HRFERT+HRSPRYI*POHER 4. IF 7NOT. THO PGPRYREH. IgIGPRYR(h, ,I)+GPRYR(A, 2) IFw E.NOT. THO GPRYRh 253223 E:OSTSHI_ COSTH+LCOSTCO O L ,I- 2 IHRSEED+HRFERT+HRSPRY+HRCOHB HRTRC(h. 2 -o hfiI)§HRTRc(h, ,I)+HRTRc(h. 2)+(HRCOHB*6)+HRSEED EN IF(TCOST..L¥5LCOSTC) THEN vnnunnrufio :3: an: -4? x I _' c: u: a: 217 +HRS GPRYRSI.lg-GPRYR(1,1)+GPRYR(1.2) GPRYR 1.2 -O *o. 6*POHER+(HRHARR*O.17)*POVER PRY)* (1,2)+(HRCOHB*6)+HRSEED THEN *0.26*POWER+(HRHARR*O.I7)*POWER +HRSPRY) ,lg-GPRYR(2,I)+GPRYR(2.2) ,2 -0 .U (2.2) 339 333 338 500 + nnnvv——unn_______r EOHERP(2 I" 3)IPOHER2 T 218 J 'OV :HRTRCZ 2]; 22 )IHRLABR(2, I) :HRUSE HRSEED+HRFERT+HRSPRY+HRCOH8 40) RHRTRC(Z RTRC(2 1H flRTRC(2, 2)+(HRCOHB*6)+HRSEED TC. LT. LCOS 3)THEN "I WWW”? or: MWMID'UOU‘ MOI .v-‘w . .fl-I I J 1 F“MMM£‘<' mmmoommt 44:: :>rwr-mxw- cudmemxu UK 1mm>—t-C G -‘ wanna: DHC gammoz—iaxx ) POWERZIPOHER HRPL mo DISK)*O. 26*POHER+(HRHARR*0. l7)*POHER HRSEED+HRFERT+HRSPRY RY) 1w GPRYR I IGPRYR I +GPRYR 2 JD mGP§m “E; M} (3.) (3 ) IPOHERZ ' L O _1_1 w ....J .... I "v.- -In I II I nnno>>>>>>>>>>nn O‘u'oommmmmmmmmmzuo 221:0 nnnnnnnnnn-n >4 ."":_‘;“( J '8: S¥+HRFERT+HRSPRY+HRCOMB C 3, T$+HRTRC(3. 2)+(HRCOHB*6)+HRSEED RC ( COMBINATIONS 0F MACHINES SYSTEM )IHRCOMB IPOWERCO*O. 22*HRCOHB HRCOMB*POWERCO*O. 22 £A8R(J,])+l.I*HRLABR(J,1)*HAGE*((1.+|NLA) )ITLABR(J.1)/AREAT (l. +|F4*:égE/((I.+|F)**10.-l) DS c:(1?.I)/AREAT .1)/AR£AT snwcnr :(h, I)/AREAT CDSTCO*CRF ;(5.1)/AREAT géiEPRYR J. I; +GPRYR (J. 2) IGPRYR J.3 /AREAT 551 360 600 601 602 603 60“ 605 606 607 608 609 610 611 612 613 616 615 616 617 618 619 620 HRngd -HRLADR J I AR AT 219 fifié Eff-TLABR(J(1;/64EA% LCOSTS1-LCOSTC+LCOSTCO LCOSTSZ-LCOST2+LC OSTCO LCOSTSB-LCOST3+LCOSTCO POHéRPéJfl zi-O END IF IF(NPASS.NE.2)THEN HH RR-O A IFIIaazs I HARR-o IF PPRHA -LCOSTc0/AREAT RH -LcD DSTN/AREAT PPRNA g -LcOST /AREAT PPRNA -LcOST /AREAT - PPRHA I LCOSTC/AREAT c NON PRINT OUT THE TABLE WRIT546. .600) FORM A (1H1 5? X.'COHBINE'.8X 'NININUN'.9x,'FIRST' +aéi+§E 0631i1lx.'THIRD') FORNATéIZ .1?2(1H-)///) wRITEI , 02) EET.NPLDN.NDISK.NNARR.NSEED,NFERT.NSPRY I2PLow.I2DISK.I2HARR.I25EED,I2FERT.I2$PRY.ITPLON.ITDTSK.ITHARR. +ITSEED.ITFERT.1TSPRY,| PLow, +l DISK,IaHARR.I;SEED,I FERT.|3SPRY F RHAT(I o,on, MACHIN COHBINATION' 2hx x. F3. 0, - FOOT'. +2x,h(Ig.gx II.Ix.II.Ix.II.Ix II,Ix l1.h xi 0 V 9 L NRITE( o§)IOASH LOOSTN LcoéTc cbstL DST +EDRNSTéI§g iox,'c05T OF COMBINATION'.26x.7x. I.7x. wRITE( ' 06 FORNATéTH .on.'(wITHOUT COMBINE AND wAcON) ) . D co 2, LCD wRITE 60 )IDASN LCOSTSH, Lc STSI, L STS STS +50R§AFigHg,%g§y'cOST OF c6NOINATI6N', 26x. 7x,A NRTTE 606 FORMATéTN .on,'(wITH COMBINE AND NAOON)‘ S) WRITEé 607)CUCOST IDASH. IDA ASH .IDASH, IDAs FORNA é1H0 on.'cuéT0 nc6s ST' 3? 2buxH Hu(7x, AI ,6x)) wRITE( .603)IcDSTI IDASH. IDAé I6A Aafi GSTTETéIZS§TOX"c°éT OF EONBINT ,2 X,F1052,h(11X.A1.2X)) FORHATé1H ,on.'(wITNOUT NAGONL) NRITE .610)LCOSTCO IDASH IDAs ,IDASH IDASH 636T? é‘ETITOX"c°s DST 0F c6NBINE .28x. TIO.2 .h(11x, AI .2x)) FDRNA éTH .on.'(wITH NAOON)' ) NRITE( .612)TCOSTFL,MCSTFL.CSTFL1.CSTFLT,CSTFL3 FORHATé1HO,10X.'COST OF FUE . 1x,%(F10.2.gX)) wRITE 613)HRLABR(R I) ,HRLAB (h, ).£HRLA R(J 1).J-1,3) FORNA éTHO 10X,'HOU 6 OF LABOR' zgx.5 FIO.2,5x$; NRITE( .eIL)HRLA BR( ,2), HRLADR(L. ).(HRLABR(J.2 ,J-1,3) +F?§?ST(1Hgi10X. HOUS OF LABOR PER HECTARE'.17X. ITE 6,615;lDASH.MCSTLA,CSTLA1..CSTLAT.CSTLA£ 53R¢e(éI20G;ox.ICDST OF LABOR', 3OX.7X.A1,7X, (FIO.2 5x)) FORMATé1H .on,'(wITHOUT COMBINE AND wAcoN)') wRITEI ,617)TCOSTLA,T BR(h,1).(TLABR(J,1).J-I,g) GSRNQTé1208;OX,ITOTAL LCOST 6F LABOR',26X,5(F10. ,5x)) FORMATéIH .IOK.'(WITH COMBINE ANDN GON)') wRITE( ,619)T:OSTTN, s we 5% MT,CS EN; FORMATé1HO.10K.'TIMELINESS'COST',2X(F10.2 )) wRITEI ,62 )T:OSTRN MCSTRH ESTRNI. CST NT,c5TR +§O§NATéIgoiIo:i;cos ST OF REPAIRS AND MAINTENANC w ITET6,621ST:DSTDN, MCSTOW, CSTowI CSTowT.CSTOw3 FORMATé1HO,10Kw 'cosT OF owNE 2ZX,5(F10 2.5x)) RITE( ,622)P3WERCO,P0WERP(LR1).(Péw RP(J. I) SN 6” 6H 65 6% 6H 6% M9 6% 6N 632 633 6% 635 1000 PORNAT(IHO on, IPONER REQUIRED BY TRACTOR #II,15x. +eéFlo.%,gX); 220 ITE( . 23 IDASH, POWERP(h, 2). (PONERP(J 2) J-I 3) +§6Rg§TéIpOéng é IP6wER REQUIREO BY TRACTOR #2I, 2 . wRiTEi6.62A)HRCONO, HRTRC(h, I). (HRTRC(J I).J-I,3) +F???ST(IH2$IOX, 'HOURS USED BY TRACTOR #II.I9x,) 3RITE162£253IOASH.HRTRc(h.2) . (HRTRC(J. ) J-I +£OR3QT£1§O61gX,;??URSU USEO BY TRACT OR ',25x? wRiTE26.626)AEE(5,I).AEc(hiI). (A AEC(J I). J-I .3) F?§?3T(Iflgilox,'ANNU L EQUI VAL WE WéT .2Ix' ITE¥%:227;AEC(§.2).AEC(h, 2). (A Ec(J2 mg +PORNS (1H2 on.I NNUAL EQUIVALENT COéT PER H CTAREI,9x. aRITEgg'gzégPPRHA(§).PPRHA(h).(PPRHA(I).I-1,a) FORHA éiHo,on.ICO T PER HECTARE FORWNYEAS'.13X,5(FIO.2.5X)) wRITE( 629)|DASH,GP YR(A,I).(GPR YR(J. I). J- I.3) PORNAT(iHO,on,IANNUAL FUEL CONSUM MPTIONI .2ox, +7X.AI.ZX h(F10.2.5X)) URITE( .630) FORMATélg .on.I 0F TRACTOR #1 (LI ITREs)' I” NRITE( 31)IOASH.GPRYR(A 2).(GPRYR J. 2 ,2) +§2R£AI§3H2.%2§$I FUEL CONSUNP I6NI, '26x. . wRiTE 6 6 2 PORNATéi23,on.IOP TRACTOR Y#2 (LI RES)‘ ) wRITE( 33)CPRYR(%.E)m h. ). (GPRY RYR(J 3). J- g) +£3RHQI(3H2.Igf$'TO A ANNUAL (FU L CONsu SUHPT ON( (LITR S)‘, WRi%E(6.68£?GPRYR(a.h),F GPRYR(h, 6). (GP RY R(J u), J-163) +FgR?AT(IH .on.IAN UAL FUEL CON su PTI ON N' ,26x,5 (F RITE(6 6 ) F0RHAT(‘H3?IOX,' IPER HECTARE (blTRES/HA)‘ //) ggéng-AAHHEAT*YLDWH)+(AOAT*. *YLDHH) OO IOOO N-I IO A-((I.+INCR)/(I.+IF))**N PROFIT-PROFIT+TYIELD*PRICEH*A CONTINUE ECP-PROFIT*CRF PRINT *.'AE GROSS INCOHE- ,AECP PRINT* . 'TOTA AL YIELD EXPECTED WHEAT AND OATS KG.- ME LD PRINT*. 'TOTAL GROSS RETURN FOR TEN YEARS FROM SALES I Y'.PROF|T ENO sua BROUTINE TIHEAV(PROB. NPAss, NKNRSI, NKNRsz COM ON /TEHP/ TIMEP. TI MED. TI NEH HTINESO. TIMEFT COMMON/TIHELl/DAYFI, DAYFh, 'OAYPB. TIHOPI, TINOPL,T INTEGER PROBN PASS REAL TINEO OIL) IF(PROB WEE .70)THEN OP I-2 26. 6 SP TI NEH NOPé PRICEH, YLONH moqqqqaqquaq > I. —- , 3<33m< m-no E IF%PROB.EQ.80) THEN x5 CID—0"“ I- Iw VIM OM. WM _.10 \n \D #13 Ital I0“ I 3-<3 Gnu-ammo ‘0 0:3me E30 .8 L IF(§ROB.EQ.90) THEN <33m<333 ‘U I >——r>———— “HORN?!“ d I O E33. 3 221 mzomxr>————>—— zzthzzzzzbzzxzzhxxzzzAu mmmmmmmmmmmmmmmmmmmmmm‘u C?.E987O.AND.NPASS.EQ.2)THEN 2 -2 I —' —a 5(égoa.EQ.80.AND.NPAss.EQ.2)THEN $25-- E%§3236EQ.90.AND.NPAss.EQ.2) THEN 2(ER33. EQ. 70. AND. NPASS. £0 3) THEN 2 -% . ’g -o.' NEH-o .EEDIF(PROB. .EQ. 80. AND. NPASS. EQ 3) THEN .ucoo fauna ¥uuac :ooocw—o . w - - cu — E-m .SE IF PROB.E . O.AND.NPASS.E . THEN MED (I2h.0 Q 9 Q 3) IZhiO S|F(PR83. .EQ. 70. AND. NPASS. EQ. h) THEN -w. .SED IF(PROB. 2 g :az g -9 .SEH |F(PROB. 8EQ. 90. AND. NPASS. EQ. h) THEN Hm HEDiz Rs NT*, 'REENTER NPASS AS 2. 3. OR h' 0* NPASS EQ.80.AND.NPAss.EQ.u) THEN 44a4aa444mg»vE;444;m;444:m4444;m44444m444a4m444aqmaa444m44444m44444—mnwvmu4444c44 3 "I U I U! \D n C an an F-TIHEFfiwKHRSZ 222 TIME TIHESP-TIHESP*HKHRSZ TIHEH-Tm KHRS Eéno Pg-TIMOP8*HKHRSI END SUBROUTINE PLDNIAREAP TIHEF. PDNER, IPLDH. HRPLDH. EFCPL. ICOUNT) SIESNSIDN EFc(5).P H(§) INIEééLIzg EFFECTIVE FIELD CAPACITY OF THE 5 PLDHS (HA/HR) EFC 2 -. 2 38212 EFC - WTIAL?ZE ZgHER REQUIRED BY EACH OF THE 5 PLDHS (Kw) PKN I -28.2 PNN 2 - 2.i EN: 2 '22'2 PKN - I EFCPEAREiPaTIHEP NF( CP. CT.EFC(NPLDH ))TH EN RINT* 'ONE TRACTDR HILLo NDT PLDH ALL THIS AREA DETHEEN +§ggEH HDER I AND JANu RY EN DI IF (ICDUNT.EQ.I)THEN $3.593? IPLDN IPLDw énéEECP. .GT. EFC(IPLDH))CDTD I PowER-PKN(IP DH) HRPL ow-AREAP/EFC(IPLDH) EIFIITW °> END SUBROUTINE DISK (AREAD. TIMED, PDHER, EFCSD. IDISK. AREASD,HRDISK, +ISEED HRSEED.NPASS. ICDUNT) DIHENSIDN TIHEDIh). PKW(9), EFC(9). EFCDI9) EFCS(9) LDCICAL Two figHggN/FLAC/Two NSEED-E INITIALIz EFFECTIVE FIELD CAPACITY DF THE 9 DISKS FDR THE FIRST PASS AFTER PLDNINC (HA/HR) EFC I -o.gg Egg 2 -?°DI EFC 3 -l:lz EFC z -I.2 EFC -I. 9 EEE é 'I' I. EFC -II INITIAL?ZE EZZECTIVE FIELD CAPACITY OF THE 9 DISKS FDR THE 2ND. 3RD AND uTH PASSEg (HA/HR) EFCD I -o. 2 EFCD 2 -o.3 EFCD 2 -I. EFCD -I.2 EFCD g -I. EFCD -I. o EEES 5 '1' i EFCD -I'é INITIALI E Po ER REQUIRED BY EACH DF THE 9 DISK SIZES (Kw) PNN I - g.h 2 2.; mg“? PKH 2 - :2 NW é-I PKN -7 'h INIEI3§?2E gFFECTIVE FIELD CAPACITY OF THE 9 SEEDERS (HA/HR) EFCSézi-o.g; EFCS 3 -o.9 . m \.I B \I A R 2 m m a A R / T u u H A on o o o H NE c c R I-\ P In .I. R OP 9 9 A S S DI. S s H w R K s S I. o E 55 A A 9 R D R P P D R E .555 N N S A E MI 9 D E H... s R K K H AE s 5 II T E F" D D D G I 9T R R D ..I N EU H H s E 00 o 9 A F AR R R E F O . "B E 2 E R 0 U W H 3 .1“ A) Y. ES 0 O 9T n T R I P T I P o O ) "N ..I- AS NS 9 S o T T K EU I C I. ERK o RK S "0 c A K" 05 G 05 o o II 'C A P 5.]. )CD ) co 0 ll 9 ’9 A c on \IAII ) A'- A \.l \..I ( HS( c N K O K m 9 E \.l \.l N D Ass D FA N s D s N D R D D E c EAc D .l- o E I- E .l E E A E E H of RP...» L E w . x)” 00H 0 HUN *0 E E T E ANE E I. 50 N N I .l T“. )T s s \.l I I.\ O .I- F ER N E I) T (N T ‘I. N II R S D O F SRNE m (x D 0 DE K 9 K)RI-\ 1‘ )E 5 "5) E SAEH D coo CH son 5505 s K" A 09.38 E v AHHT .ISHC EEE E) DEE DA F. ..r ID. N REH .l T H)‘ 05". .NR 1- .2.-I INNR '9: E «IE 0 o .3 A Dc T c 2T2 0 $2 0‘ E T A +T 0. + o A (ND 0 N 0 IT” 0 ”OF c Big-$2 O 0Q 01-223“ EP oA GUI.\ KGTK KTUI\ ”(ST T (GE A EEOC EglhggFgg‘zzzNop-LE o o o o o no .NDTC 002 o s 065 5602 o CDAG EG “ a" E EE WA F o o o o o o 0 0F c o o o o o o o OATL o 1111]] 0T OAGF xV-KE llx oll I. oVIKE FEE o E o Kw“ R "SN L Incl-122232.000] 11111 H. 0.] ...-..ON]E.E AISU E ED SDEDKISU EHRD ESD PE A IRO F E------.- unsung-uuTESN “Sb-[BOIXOIIACX C*D N N NICAINI|*DTN oTIc NIc "WI KN éAN I IElzzmfingIZRJuggnuE-IAO EA?» I FAFFT RF'K'KFPK|KDT oRF] I DFDIDFFFE RFSR ODNCORD IERPPC SSSSSSH'IAEH'ENLWU'mSTSENSTCu'NL I U|(RSEETEEIIIINPEIIAIU REE'MREIHHHHHHHHLSSSSSSSSSOSN' ccccccc{\ C(CDI\'L TD IHI-((IINIII‘IILOTDIDC(ENE(DD "DDTDBENG AELCCCCCCCCACCCCCCCCCH (l\ FEEEFFFFOFEENFRAWENODODEFDODFRAWENDSFFSOSENN FONRENUSIOOHSAFFFFFFFFIFFFEFFFFF AF:- EEEEEEEIDE'EEIPC RECICI'IICIIPC RETTEIICIIEEPIPEHRESHDLCNNHEEEEEEEETEEEEEEEEE“ I.I | E . I. N "S N I.‘ ”U cl 2 3 I“. c c cc nn— nnnN IO THARR-O. *TIHEH EFCIAREA /THARR 224 IHARR-O CONTINUE INCREASE SIZE UNTI IL HARROH CAN HANDLE TOTAL AREA IN AVAI ILABLE TIME. IN RR-IHARR+I IF IHARR. GT. NHARR THEN PRINT*, 'YOU NEED 0 TRACTDRS' CALL HARR2(AREAH. TIHEH, IHARR. HRHARR. NPASS, ICOUNT) TVO-. TRUE. RETURN END IF(EFC. GT. EFCH(IHARR))G0 To I IHARR-IHA RR- CONTINUE IHARR-IHARR+I CUigTEm TIHE USED BY HARROH THARR-(I. IEFCHIIHARR SE SIZE UNTIL THERE IS ENOUGH TIME FOR RHARROHING AND SEEDI ING. HHH 8.3:: 223m... CABL IISEZIA REAH. TIHEH. IHARRs HRHARR. NPASS, ICOUNT) 253??" ' CALCULATE TIHE LEFT FOR SEEDING. E%E§3*ARE"§E“‘§B IF(EFCSD. GT. so{TCSIIHSEEDHGO To 2 lSEED-O HHN: .... éfiéfgcsn.GT.£rcs(Isaeo))Go To 3 HRHARR-AREAH/EFCH(IHARRI ENDIF EEEURN SUBROUTINE SEEDE§(AREASD.ISEED.HRSEED.ICOUNT.EFCSD) DIMENSION ng5(9 arcs I -o. I arcs 2 -o. _ arcs 2 -o.3 arcs -I. EFCS -I.I arcs -I.2 arcs a -I.2 arcs -I.z EIEgogeFéé seen) HRSEED-AREASD/EFCS(ISEED) SUBROUTINE FERTIL(AREAF,TIHEF.IFERT.HRFERT,ICOUNT) DIMENSION EFC(7) NFERT-7 EFC 1 - .2 EFC 2 - . g EFC 3 - . . .33 EFC 2 -h. EEE ““33 éééIZREIIIIIII 1"" IF(EFCF.LE.EFC(NFERT))THEN lFERT-O Hews...“ IF(EFCF. GT. EFC(IFERT))GO To I 5% FERCAP-EFC(NFERT)*TIHEF PRINT IO, WERC EIBIF HRFERT-AREAF/EFC(IFERT) FORHAT('AREA sxcaens LARGEST FERTILIZER BROADCASTER CAPACITY. +HAxIHUH AREA FERTILIZEDI'.F5.O) RETURN EN ND SUBROUTINE SP?AYR(AREASP, ,TIHESP, ISPRAY. HRSPRY. ICOUNT,AREAT) DIpENSION EF C I I .16 2 3:33 25 3‘» III MN aw ON ZOU ETHEN IARE ESP NSPRAY))THEN 4xMM- > INUE AY-ISP RAY+ FCS. GT. EFC(ISPRAY))GO TO I AY-NSPRAY AP-EFC NSPRAY)*TIHESP T IO. SPRCAP IF HRSPRYIQREAT/EFC§ISPRAY{ IO FORMAT AREAE CEEDS ARGEST SPRAYERS FCAPACITY +22¥$Rgfl AREA SPRAYED HITHOUT PENALTY“. mmvm—m—-n——m-mmnmmmmz -zn:nmm:n ‘ zzxvmr-«momm-fl-fl-fl-fl'flm-fl-flmm DD-Z‘UMA'UZ'UAOAGOGOOO ENO suaROUTINE COST(J HRUSE FONER. EFC, AREATO. INCOST.XCOST. +NKARSI NNNRs OSTFL. NCOéTLA.C65TTA. COSTRA, COSTON I CALCULATEs’ COST Ofic AACA J- FLON, J-z DISK NARRgN J-h SEEDER. wJ-gN FERTILIZER. J SPRAYER. J-g TRACT R, J- COAOINE. ”2 DAY], DAYh, DAY ARE COEFF 0F SUITABL 0 Y/TOTAL DAYS +ggA€gu '¢T6HEL|/ DAYF]. OAYFA, DAYF ,TIAOFI. TIAOFA. TIAOPB, COAAON '/C05TC/IE. IF. INA, INFU, INLA.INCR, NACE, FRFUEL COAAON/FLA C/TNO com 0N /ZZZ/ HRHARR NROISN NPASS COAAON/COAFCOA/COSTTLA.ICO§TI COAAON/A/AIN LOOICAL TNO, AINFLC REAL Ia, IF, iNA. INFU, INLA.INCR. K, INCOST, HRUSE INTECER FR63 nnnn OIAENSION COEFRm OATA COEFRA I-I 3) /0. 60002;. 0.00023. 0.000h. 0.00036. 0.000h7 +.0.000c.000150.6 020.0000 / C IB:BANK IN R0 T C IF :FARAER's RATE OF RETURN C INH:INFLATION FOR MACHINERY C INFU: FUEL INFLATION C INLA:LABOR INFLATION C INCR:CROF INFLATION C FRFUEL:FRICE OF FUEL C COEFRAzREFAIR ANO AAINTENA ANCE COEFFICIENT C INCOST:INITIAL COST 0R FRI ICE 0F CHINE COSTFL-0.O . XCOST-O. COSTLA-O. COSTTA-O. COSTRA-O. 0(30 N-I IO . J.58 66 GO TO II C CALCULATE 0 OF FUEL: COSTFL C FCF- FUEL CONSUAFTION FACTOR F0R LON AE0IUA AND HIGH C LOAgéi_3AE;ICAN SOCIETY OF AGRICULTURAL ENGINEERS YEARBOOK 1982 IF J.E '6 .0R.J .53. .2) FCF-O.26 IF J.E . ) FCFw . x- (I.+INFU)/(I.+IF MN C I.I ASSIGNS I? FERCEN A0RE FUEL COST TO COVER LUBRICATION COST OSTFL-COST L+I.I5*PRFUEL*PONER*FCF*HRUSE*X C CALCULATE LABOR COST: C0 STLA II IF J.EQ.h .0R. J.E9. {*m OR. J. E0. 8) THEN , Y- (I.+INLA)/(1 «H C I.I ASSIGNS I0 PERCENT) A0RE LABOR HOURs TO ACCOUNT FOR TIAE SPENT C CONNECTINC ANO DISCONNECTING IAFLEAENTs ANO AACHINEs COSTLAICOSTLA+I.I*NAGE*HRUSEW COSTILA-COSTLA ENDIE C CALCULATE TIHELINESS COST 226 C ARE ATO:TOTAL AREA NE HANT TO PLOH SEED OR HARVEST c ARE A0P:AREA COHPLETED IN OPTIHUH PERIO C EXCESS:AREA COMPLETED OUTSIDE 0E OPTIHUH PERIOD C AVARDY: AVERACE AREA HORHED EACH CALENDAR DAY C DAYF: SUITABLE DAYS/TOTAL DAYS C COSTTH:TIHELINESS COST C TIHEOP:TIHE DURINC OPTIHUH PERIOD (NO PENALTY) C Kz‘TIHELINESS EA CT OR HRPDAY-HH KHRSZ &F(J651 9 .0R. J. EQ. 8)HRPDAY~HNHRSI IE J.E .3 K-0. 002 IE J.E . K-O. 00A IE J. E . DAYE-DAYEI IE (3. Q I DAYE-DAY A IFEJ.E .8 DAYE-DAYE IE J.E .I TIHEOP-TIHDPI IE (. NT 0 THEN PASEPNSSSET' .2.AND. J. E0. A) THEN THE EOLLOHINC E UATION REDISTRIBUTES THE HOURS AVAILABLE C EOR SEEDINC BAS D UPON THE AMOUNT OE TIHE SPENT DISKING DURINC THE SAME PERO TIHEOP -TIHOPA-TIH0PA*(HRDISK/PASS)/ +((HRDISK/PASS) +HRU SE) ELSE IE(NPAss. E 2.AND.J .A) THEN THE EOLLOHINC E iON REDIST iBUTES THE HOURS AVAILABLE C EOR SEEDING BAS D UPON THE AMOUNT 0E TIHE SPENT HARROHINC DURING THE SAHE PERIO INSET? -TIHOPA-TIHOPA*(HRHARR/(HRHARR+HRUSE)) ENDIE IE(J. M8 .8) DTIHEOP-TIHDPB IE (w 0. “13';E .A) qTIHEOP-TIHOPg IE(J. E0 Q.A .O R. ) THEN AREAOP-EEC*TIm IE TIHEDP.LT.HRUSE) THEN EXCE s-AREAT -AREAOP IF(EXCESS. LT. O. ) CEss-O. C CALCULATE AREA PER DAY AND DAYS REQUIRED TO COHPLETE HARVEST ARPDAY-EFC*HRPDAY AVARDY-ARPDAY*DAYF DAYs-(EXCESS/ARPDAY)IDAYF DAYS IDAYSI C CALCUL TE TOTAL TIHELINESS COST: COSTTH H- (I. .IDNCR)/(ls MIF)) IS COS NICOSh H+AVARDY*ID*K*YLDHH*PRICEH*N ARLAST-EXCESS-AVARDY*IDAYS lDAYL-IDAYS+I EggTIH-COSTTH+ARLAST*IDAYL*K*YLDHH*PRICEN*N ENDIF C CALCULATE .C?3; 0?] 2?? IRS AND MAINTENANCE 2I COS RH-Cos RH+INCOST*C0EERN(J)*HRUSE*z 30 CONTINUE IE ES. .8) COSTLA-COSTLA* C CALCULATE O NERSHIP COST: COSTN Rv-O. MNCOT ”((1 IHI/(I .+IE)) **IO. USPw-((I .+IE **5 - (/(IF*)(I .+IF)* 5.) CRE-IB * (i. .+IB)**?h I .+IB COSToa-o. 2*INCOS +0. *iNCOST* §F*USPw-RV C CALCULATE TOTAL COST 0E THEH NE XCOST-COSTFL+COSTLA+COSTTH+COSTRH+COSTON RETURN EN 0 SUBROUTINE COHBINE(TI HEN RISK LCOSTC +TCOSTLA, TCOSTTH, TCOST ON, T +HHHRSI ,NKHRsz. HRUSEI,PO' ER DIHENSION EECC(34 KH(5) REAL LCOSTC ICO i TCOSTEL, COSTAH, ,EEETI. CUCOST.AECC0H. REAL INCOST, NH, HRUSE REAL IB,IE, INN, INEU, INLA. INCR INTECER PROB CDHHON/TIHELI/DAYEI DAYEA. DAYE8 TIHOPI, TIHOPA. TIHOPB. PRICEH. YLDHH COHHON/COHB/YIELDH, PRCROP’ AHHEAT, AOAT,A LENT COHHON/COSTC/IB INN, INEU, INLA. INCR. NAOE, PREUEL COHHON/COHPCOH/COSTILA’ ICOSTI IIII IIIgélgggIzIIIIIIIIII:IIIIII; LCOSTC-I OO HRUSE *COHPUTE CUSTOM COST CUSTPR-I 2. *PRICEN AREAT-AN EAT+AOAT+ALENT TCOSTI-O. DO I N-I, IO CUSTCO‘AREAT*RISK*CUSTPR*((I .+INH)/(I .+|F))**N TCOSTI-TCOSTI+CUSTCO I CONTINUE CUCOST-TCOSTI *NON SELECT COMBINE ANO CALCULATE COST BY CALLING *SUBROUTINE COST. AREAC-ANHEAT+2.*AOAT/3o EECREQ-AREAC/TIHEN I on 2 CONTINUE lCOHB-ICOHB+I I666 II g6°I °III EgFCREQCM T. FCCIICO ) so TO 2 -EFCC ICOHB Icons. E .I FE ICOHB. E .2 FE ICOHB. E .2 E ICOHB. E . FE ICOHB.E figs FE RLENT-ALENJ HROAT-AOAT/E HRUSE-HR ENT+HR AT/ +AREAC/EFC IN80$T-- 5805. I II 95. 92*FEET CALL COST(J. HRUSE RONER EFC.AREAC, INCOST.XCOST, NKHRSI .NNNRsz. +COSTFL. COSTLA.COS sTT. MSTRH. Tow) IF (xcésT. LT. LCOSTC 'TNEN Lc05Tc-xc05T TCOSTFL-COSTFL TCOSTLA-COSTLA TCOSTTH-COSTTH TEOSTou-EOSTON TCOSTRH-COSTRH IEOSTI-EOSTTL+c05TILA+E0$TTN+E05TRN+E05T0N HRUSEI-HRUSE 'II-n-n-n-nmom-n fit IF I p E I I I I I H PONERCO‘PONER GO TO 2 ENOIF 222 CONTINUE SIGN APPROPRIATE NAGON AND COHPUTE COST HRUSE-HRUSEI PONERI-O. EFCIO. lNCOST-RBBO. IF (ICON. EQ. I. OR. ICOH. EQ. 2) INCOST-3270. J cXEL COST(J, HRUSE PONERI EFC AREAC INEOST,XE0$T.NNHR$I.NNHRsz. +COSTFL, COST LA.COS sTTN. cosTRN.EOST0NI LCOSTc-LCOSTC+XCOST TCOSTFL-TCOSTFL+COSTFL TCOSTLA-TCOSTLA+COSTLA Tc05TRn-Tc05TRN+c05TRN TCOSTTH-TCOSTTH+COSTTH TEOSTow-TEOST0N+EOST0 CRF-IF*(I .+IF)**IO. /((I .+lF)**IO.-l) AECCOH-LCOSTC*CRF RETURN SKZIAREAD. TIMED, IDISK, POWER, HRDISK. ,RKN ED 0(3L’ (9). .§§C(9)6Ercn(9) 53 W's“ I NDISK-9o .7 / 2 351.1°?I;222§5236§7.§.62.96,652III}? EFCA IF ICOUNT. E .I) THEN DOI I-IoNP A S o W‘ EFCA-AREAD/TIHED(I; IF (EFCA.GT.EFCHAX THEN 228 EEcHAx-EEcA ENDIF IF (EECHAx.ET.EEcU(NUISK;) THEN PRINT* 'CANNOT DISK ALL HIs AREA FOR NINTER HHEAT HITH +LARsEsT DISK' STOP ENDIF CONTINUE IDISK-O CONTINUE IDISK-IDI I+ éfinfEFCHAX.GT.EFCD(IDISK)) so To 2 RHRNEH-wa (I n I sx) IF (PNRNEH.GT.P0HER) THEN POHER-PHRNEH ENDIF . HRDISK-AREAD*NPASS/EFCD(IDISK) RETURN END SUBROUTINE HARR§(AREAH.TIHEH,IHARR.HRHARR.NPAss.ICOUNT) BA¥§"EéEfi/§‘§"I L 2 I 2 A 2 7 3 o 3 3 3 6/ o.cgogogo’ogo'o NHARR-a IF {NPASS.E .2) THEN IE ICOUNT. 3.1) THEN EEE-AREAH/TI EH IF (EEc.cT.EEcH(NHARR)) THEN PRINT* 'CANNOT HARROH ALL THIS AREA FOR HINTER HHEAT HITH +§¢ggEST HARROV' ENDIF IHARR-O CONTINUE I HARR-l HARR‘H IF l(EFI.'..GT.EFCl'I(WIARRH GO TO I END F HRHARR-AREAH/EEEH(IHARR) ENDIF +§gsBS¥TINE snR2(AREAsn.TIHEso,ISEEU.PoNER.HR5EEU.EEcsn. UIHENSIoN ESC(8) DATADEFC/O. I. .89.o.97.1.05,1.13.I.2I.I.29.I.39.I.A5/ UNT.E .1) THEN AéD/ IHE "I :0 -mm AREA HITH HINTER HHEAT HITH mxmx'um——n-mmr'v-m—z gmmwozmmomzd>xmmmm APPENDIX D Effective Field Capacities and Power Requirements of Implements and Machines 229 APPENDIX D Effective Field Capacities and Power Requirements of Implements and Machines D-l. DISK PLOW. Plow N° of Working Effective Field PTO Power Equivalent N° Disks Width (m) Capacity (ha/hr) kW HP 1 2 0.48 0.25 21.3 28.6 2 3 0.72 0.37 32.1 43.0 3 4 0.96 0.50 42.6 57.1 4 5 1.20 0.62 53.2 71.3 5 6 1.44 0.75 63.9 85.7 D-2. OFF-SET DISK BARRON. Harrow' N° of Working EFC (ha/hr) PTO Power Equivalent N° Disks Width (m) Pass 1 2,3,4 kW HP 1 12 (6) 1.37 0.76 0.82 31.4 42.1 2 14 (7) 1.60 0.88 0.95 36.7 49.2 3 16 (8) 1.83 1.01 1.09 41.9 56.2 4 18 (9) 2.06 1.14 1.22 47.2 63.3 5 20 (10) 2.29 1.26 1.36 52.4 70.3 6 22 (11) 2.51 1.39 1.50 57.6 77.2 7 24 (12) 2.74 1.52 1.63 62.9 84.4 8 26 (13) 2.97 1.64 1.77 98.1 91.3 9 28 (14) 3.20 1.77 ].90 73.4 98.4 fi *Numbers in parentheses refer to the number of disks in each gang used to obtain the working width. 230 D-3. SPIKE-TOOTH HARROW. Harrow Working Effective Field PTO Power Equivalent N° Width (m) Capacity (ha/hr) kW HP 1 2.5 1.50 2 3.0 1.80 3 3.5 2.10 4 4.0 2.40 5 4.5 2.70 6 5.0 3.00 7 5.5 3.30 8 6.0 3.6 25.6* 34.3 '7 *Smallest tractor available on the market has 28.1 kW, which is used in all cases where power required is less than this value. D-4. GRAIN SEEDER. Seeder Number Working Effective Field PTO Power Equivalent N° of rows Width (m) Capacity (ha/hr) kW HP 1 10 1.78 0.81 2 11 1.96 0.89 3 12 2.13 0.97 4 13 2.31 1.05 5 14 2.49 1.13 6 15 2.67 1.21 7 16 2.85 1.29 8 17 3.02 1.37 27.0* 36.2 9 18 3.20 1.45 28.6 38.4 *Smallest tractor on the market has 28.1 kW. 231 D-S. FERTILIZER BROADCASTER AND FIELD SPRAYER. Fertilizer Broadcaster Field Sprayer Fertilizer Working EFC Sprayer Working EFC No. Width (m) ha/hr No. Width (m) ha/hr 10 3.30 1 6 2.16 11 3.63 2 7 2.52 12 3.96 3 8 2.88 4 13 4.29 4 9 2.97 5 14 4.62 5 10 3.30 6 15 4.95 6 11 3.63 7 16 5.28 7 12 3.96 D-6. COMBINE HARVESTER. 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