LIB TTTTTTTTTTTTTTT SIT TTT .TTTCTGTTTTTTTTTTTTTTTTTTTTTTT 3 1293 0696653? 5.127- 5: r" x s. a...--..::n¢ (3‘ .- '¢:$‘=" h" i «I van E! ‘O ”a“. n4 2;? '1: IS I' '59 I a I; J This is to certify that the dissertation entitled Alternative Tillage Systems for Corn Production in Michigan. presented by Shapoor Rowshan has been accepted towards fulfillment of the requirements for Ph-D- degreein Ag. Engz. Iech ' r profess a?“ (I 9 MS U is an Affirmative Action/Equal Opportunity Institution 0-12771 MSU RETURNING MATERIALS: Place in book drop to ”33.411155 remove this checkout from —__. your record. FINES win be charged if book is returned after the date stamped below. ALTERNATIVE TILLAGE SYSTEMS FOR CORN PRODUCTION IN MICHIGAN BY Shapoor Rowshan A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Engineering 1986 WW :3 76 ABSTRACT ALTERNATIVE TILLAGE SYSTEMS FOR CORN PRODUCTION IN MICHIGAN BY Shapoor Rowshan Different forms of conservation tillage systems have been developed in recent years to reduce soil and water erosion problems which result when a conventional tillage/planting system is used. Conventional tillage normally refers to a full or maximum tillage program, while Conservation tillage, in contrast, is a form of non- inversion tillage which reduces soil and water loss. Conservation tillage has been encouraged in the Sagniaw Bay watershed by a cost-share program administered by the 'Agricultural Stabilization and Conservation Service (ASCS). This study was conducted to determine the impact of conventional and conservation tillage systems for corn production when related to erosion, machinery, labor, and timeliness costs. Computer models were developed based upon the constraints at the farm level. Machinery, agronomic, and economic proper sets of data were collected and used for development of the models. A machinery replacement model analyzed the economic feasibility of a conservation tillage system through determining the switching and trading times when switching from a conventional to a conservation tillage system. Linear programming models were also formulated to determine the optimum machinery sets for commercial corn tillage systems and as input set of data for the machinery replacement model. The extra amount of corn residues on the soil surface can be reduced by an optimizing linear programming model. The economics and cost advantages of chisel plow tillage systems were compared to conventional systems for common crop rotations. The results indicate that conservation tillage systems have economic advantages to farmers. This can be, for example, indicated by the machinery replacement model presenting that conventional tillage systems are not profitable for farmers to continue due to higher machinery costs and the reduction in soil productivity from soil erosion. Approved M jor r ess Approved AC“ P5271: 1 . H! "i: F .r 1.?! '3¢ x1, I would 11k. to .,,.-.. -~_=« T.‘. W ”Minot: who 110359. . . El“ _ m 'J V',' £3:0It3§ and writing ”F”: H! HQAHQR Tr“. "J.:. ‘P IN or professor, Dr ‘. 'Ation, friendship, xwfi . - ~9 Mar; an I! graduate p: tails» 9. 9140: :N; .v - ihhnquunhun ’ érctotui thanks are .{;.mr, , . , .. HI: 'T‘.‘ W“ “pent-oz, for Lt» .i. «i? - .t . ”Tm for dive10M2n£ =,.-- $Oto thanks are Ollnflfiwfi-i =~ -mau in ;‘.n« c- T “about this vast. '1. *‘%3‘ “‘i “u" . 1::ng 3! var: than“ N. M. Mr camera“ 5:- 23. , . . i- 5% T toot and willingness. after; ‘15! 3f: semi-mt»: .‘g- 9». 3- for This "inane airfiyfifijk skier“ g'.$.:'.m.y&gM' ACKNOWLEDGEMENTS I would like to express my sincere thanks to all individuals who helped and encouraged me towards my research and writing of this dissertation. Foremost, my major professor, Dr. Larry J. Segerlind, for his motivation, friendship, and willingness to always help during my graduate program and on the course of this dissertation. Grateful thanks are extended to Dr. J. Roy Black, Project Supervisor, for his valuable help and constructive suggestions for development of this research. Sincere thanks are extended to Dr. Robert Wilkinson, Co-advisor, for his valuable efforts and encouraging directions throughout this work. I extend my warm thanks to Dr. Donald 0. Headers for his interest and willingness, being on my guidance committee, his guidance and encouraging comments for developing and improving this dissertation. iii TABLE OF CONTENTS List Of Tables. 0 I O O O O O C O O I O O I I O O O I v List of Figures . . . . . . . . . . . . . . . . . . . x CHAPTER 1. INTRODUCTION . . . . . . . . . . . . . . . 1 CHAPTER 2. OBJECTIVES . . . . . . . . . . . . . . . . 5 CHAPTER 3. LITERATURE REVIEW. . . . . . . . . . . . . 7 3.1 Conventional Tillage . . . . . . . . . . . . 7 3.2 Planting Date. . . . . . . . . . . . . . . . 11 3.3 Plant Population . . . . . . . . . . . . . . 14 3.4 Fertilizer Application . . . . . . . . . . . 16 3.5 Chiseling System . . . . . . . . . . . . . . 21 3.6 No-tillage System. . . . . . . . . . . . . . 24 3.7 Weed Control . . . . . . . . . . . . . . . . 29 3.8 Insects and Diseases . . . . . . . . . . . . 33 CHAPTER 4. LINEAR PROGRAMMING MODEL . . . . . . . . . 36 4.1 Introduction . . . . . . . . . . . . . . . . 36 4.2 Model Formulation. . . . . . . . . . . . . . 40 4.2.1 Objective Function Coefficients . . . 47 4.2.2 Model Constraints . . . . . . . . . . 72 4.2.3 Variable Coefficients . . . . . . . . 79 4.3 Power Requirement. . . . . . . . . . . . . . 80 4.4 Model Results. . . . . . . . . . . . . . . . 85 4.5 Timeliness Costs . . . . . . . . . . . . . . 90 CHAPTER 5. MACHINERY REPLACEMENT MODEL. . . . . . . . 98 5.1 Model Description. . . . . . . . . . . . . . 98 5.1.1 Program TRDMACH . . . . . . . . . .‘. 100 5.1.2 Subroutine EROS . . . . . . . . . . . 103 5.2 Model Equations. . . . . . . . . . . . . . . 104 5.3 Model Parameters . . . . . . . . . . . . . . 109 5.4 Model Inputs . . . . . . . . . . . . . . . . 115 5.5 Model Assumptions. . . . . . . . . . . . . . 126 5.6 Model Results. . . . . . . . . . . . . . . . 127 5.7 Sensitivity Analysis . . . . . . . . . . . . 133 5.8 Summary. 0 I O I O I O O O O O O I I I I O O 137 iv CHAPTER 6. IMPACT OF CONVENTIONAL AND CHISELING TILLAGE SYSTEMS ON MACHINERY SIZE FOR COMMON CROP ROTATIONS. . . Introduction . . . . Multiple Crop Machinery Selection. . 6.2.1 Machinery Selection 6.2.2 Farm Parameters . . . 6 6.3 CHAPTER 7. 7.1 7.2 7. 7 3 7 4 CHAPTER 8.1 8.2 8. APPENDIX A MACHINERY REPLACEMENT MODEL: .2.3 6. 2.4 CORN STOVERS . . Introduction . . . . Model. 0 Q I I I O 2.1 Constraints . Economic Parameters . Machine Parameters. . Results and Discussion . . . O O 7. 2. 2 Objective Function. . 7. 2. 3 Variable Coefficients Solution to the Model. . . . Sensitivity Analysis . . . . 8. SUMMARY AND CONCLUSIONS. . Summary. . . . . . . Conclusions. . . 3 Recommendations for Future AND DEFINITIONS VARIABLES . . . . APPENDIX B LINEAR SYSTEM APPENDIX C LINEAR SYSTEM APPENDIX D LINEAR SYSTEM APPENDIX E LINEAR SYSTEM APPENDIX E LINEAR SYSTEM PROGRAMMING MODEL: AND COARSE SOIL. . PROGRAMMING MODEL: AND MEDIUM SOIL. . PROGRAMMING MODEL: AND FINE SOIL. . . PROGRAMMING MODEL: AND COARSE SOIL. . PROGRAMMING MODEL: AND MEDIUM SOIL. . Algorithm Research. 0 I O O I O I O O FORTRAN CODE CONVENTIONAL TILLAGE O I I O CONVENTIONAL TILLAGE CONVENTIONAL TILLAGE CEISELING TILLAGE I O O I . CHISELING TILLAGE 138 138 139 139 139 139 140 140 168 168 169 178 191 198 198 202 202 207 208 210 217 220 223 226 229 APPENDIX G LINEAR PROGRAMMING MODEL: CHISELING TILLAGE SYSTEM AND FINE SOIL. . . . . . . . . . . . . . . 232 APPENDIX E LINEAR PROGRAMMING MODEL: NO-TILLAGE SYSTEM AND COARSE SOIL O O I O O O O I O C C C O O O O D 235 APPENDIX I LINEAR PROGRAMMING MODEL: NO-TILLAGE SYSTEM AND MEDIUM SOIL . . . . . . . . . . . . . . . . . 237 APPENDIX J LINEAR PROGRAMMING MODEL: NO-TILLAGE SYSTEM AND FINE SOIL . . . . . . . . . . . . . . . . . . 239 APPENDIX K LINEAR PROGRAMMING MODEL: CORN STOVER SYSTEM . . 241 REFERENCES. I O O O O O I I l C I O I O O O O O O O O 242 vi LIST OF TABLES Table 4.1 Machinery Speeds and Capacities for the Coarse Soil. . . . . . . . . . . . . Table 4.2 Machinery Speeds and Capacities for the Medium Soil. . . . . . . . . . . . . Table 4.3 Machinery Speeds and Capacities for the Fine Soil. . . . . . . . . . . . . . Table 4.4 Projected System Costs (Coarse Soils). . Table 4.5 Projected System Costs (Medium Soils). . Table 4.6 Projected System Costs (Fine Soils). . . Table 4.7 Estimated Costs for Tractors . . . . . . Table 4.8 Draft and Power Requirement Parameters ' for the coarse $0.11. 0 o o n a o a o o 0 Table 4.9 Draft and Power Requirement Parameters for the Medium Soil. . . . . . . . . . . Table 4.10 Draft and Power Requirement Parameters for the Fine 5011. I I Q 0 I I I O I I I Table 4.11 Economic Parameters of Machines. . . . . Table 4.12 The Maximum Annual Coverable Land (Hectares) in a Suitable Time Period . . Table 4.13 Total Suitable Days of Field Operations 49 51 53 55 57 59 61 62 64 66 68 74 on Three Soil Types and Three Probability Levels (in Bad Axe Michigan) . . . . . . . Table 4.14 Total Suitable Hours for Field Operations on Three Soil Types and Three Probability Levels (in Bad Axe Michigan) . . . . . . . vii Table Table Table Table Table Table Table Table Table Table Table Table Table Table 4.17 4.18 Labor Hours Used for Operations in Different Years in Michigan. . . . . . . The Optimum Number of Machines for a 400 Hectare Conventional Corn Farm with 80% Weather Probability Level and Three Soil Types . . . . . . . . . . . . . . . The Optimum Number of Machines for a 400 Hectare Chiseling Corn Farm with 80% Weather Probability Level and Three Soil Types . . . . . . . . . . . . . . . The Optimum Number of Machines for a 400 Hectare No-Till Corn Farm with 80% Weather Probability Level and Three Soil Types . Average Annual Machinery Costs for Three Tillage Systems. . . . . . . . . . The Daily Planting Timeliness Cost for Chiseling System After May 4 (400 Hectares) I I I I I I I I I I I I I I I I The Daily Planting Timeliness Cost for Chiseling System After May 4 (400 Hectares). . . . . . . . . . . . . . . . The Daily Planting Timeliness Cost for the No-Till System After May 4 (400 Hectares) I I I I I I I I I I I I I I I I The Daily Harvesting Timeliness Cost for Two Different Combines After October 24 (400 Hectares). . . . . . . . . . . . Machine Initial Cost and Repair Factors. Economic Parameters. . . . . . . . . . . Codes Used for Soil Types. . . . . . . . Critical Bulk Densities for Each Family Texture Class. . . . . . . . . . . . . . Coefficients of Equations Used for Calculating Sufficiency of Bulk Density. viii 78 87 88 89 91 93 94 95 96 110 111 112 113 114 Table Table Table Table Table Table Table Table Table Table Table Table Table 5.6 5.7 5.8 5.9 5.10 5.13 5.14 5.17 5.18 Adjustment Factors for Calculating Sufficiency of Bulk Density. . . . . . . . Soil Weighting Factors for 100 Centimeters of Soil Layers . . . . . . . . . . . . . . Characteristics of shebeon Loam Soil (0-2% Slope) in Saginaw Bay. . . . . . . . A Machinery Set Used for a Continuous Corn Rotation when Switching from Conventional to No-Till System (150 Hectares). . . . . . . . . . . . . . . . . A Conventional Set of Machines Used in a Continuous Corn Rotation (150 Hectares). . . . . . . . . . . . . . . . . A Set of Machines Used for a Corn, Navy Bean, Sugar Beet Rotation when Switching from Conventional to the Chiseling System (500 Hectares) . . . . . . . . . . . . . . A Conventional Set of Machines Used for Corn, Navy Bean, Wheat, Sugar Beet Rotation (500 Hectares). . . . . . . . . . A Conventional Set of Machines Used for a Corn, Navy Bean, Sugar Beet Rotation when Switching from Conventional to the Chiseling System (500 Hectares). . . . . . A Conventional Set of Machines Used for a Corn, Navy Bean, Sugar Beet Rotation (500 Hectares) . . . . . . . . . . . . . . Erosion Parameters for shebeon Loam' Soil (0-2% Slope) in Saginaw Bay . . . . . Machinery Costs and Replacement Times for a Conventional or a Modified No- Till System Using the Continuous Corn Rotation . . . . . . . . . . . . . . . . . Reduction of Soil Productivity due to the Moldboard Plowing System . . . . . . . Erosion Rates Resulted from Several Tillage Systems and Crop Rotations . . . . ix 116 117 118 119 120 121 122 123 124 125 128 129 131 Table 5.19 Table 5.20 Table 5.21 Table 5.22 Table 6.1 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 7.6 Table 7.7 Table 7.8 Table 7.9 Table 7.10 Table 7.11 A Comparison of Annual Equivalent Cost of Conventional and Modified No—Till Sys t em I I I I I I I I I I I I I I I I I I Machinery Costs and Replacement Times for a Conventional or a Modified Chiseling System for the Corn, Navy Bean, Sugar Beet Rotation (500 Hectares) . . . . . . . Machinery Costs and Replacement Times for a Conventional or a Modified Chiseling System for the Corn, Navy Bean, Wheat, Sugar Beet Rotation (500 Hectares) . . . . A Comparison of Three Replacement Times for a Modified No-Till System. . . . . . . Economic Advantages of Conservation Tillage vs. Conventional Tillage for Selected Rotations . . . . . . . . . . . . Variables and Operations Indicated in the “Odel I I I I I I I I I I I I I I I I I Suitable Days Estimated for Harvesting Corn and Stover in Michigan (Bad Axe) at 0.5 and 0.8 Probability Levels . . . . . . Fertilizer Recommendations for Corn Grain, Stover, and Corn Silage (kg). . . . Storage Sizes Used for Stalkage. . . . . . Stover Yield and Losses. . . . . . . . . . Corn silage and Grain Yield (30-40% D.M. for Green Weight) . . . . . . . . . . Corn Cob Yield and Production Costs. . . . Machinery Complements Which can be Used for 400 Corn Stover Farm. . . . . . . Machine Capacities and Power Requirement . The Time Study of Transporting Round Bales to Storage . . . . . . . . . . . . . Corn Stover Stacks for Different Harvesting Dates . . . . . . . . . . . . . 132 134 135 136 163 170 172 173 174 175 176 177 179 180 182 183 Table Table Table Table Table Table Table Table Table Table 7.12 7.13 7.14 7.15 7.16 7.17 7.18 7.19 7.20 7.21 The Transporting Time to Storage for Different Wagon Sizes. . . . . . . . . . Harvesting Systems and Projected Costs . The Daily Corn Silage and Chopped Stover Intake for Milk production. . . . Milk Production and Return for Corn Silage and Stovers . . . . . . . . . . . Corn Stover Transport to Storage . . . . The Estimated Total storing and Feeding Cost per ton of Corn Forage. . . . . . . Annual System Costs. . . . . . . . . . . The Effective Yield Capacity of the Largest Machine in the System. . . . . . Activities in Solution . . . . . . . . . Activities in Solution (300 Hectares). . xi 184 186 192 193 194 195 196 197 199 200 Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure 6.3 6.5 6.6 6.7 6.8 6.9 6.10 LIST OF FIGURES BREE Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Continuous Corn. . . . . . . . . . . . . . Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Continuous Corn. . . . . . . . . . . . . . 143 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Corn, Soybean. . . . . . . . . . . . 144 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Corn, Soybean. . . . . . . . . . . . 145 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Navy Bean. . . . . . . . . . . . . . 146 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Navy Bean. . . . . . . . . . . . . . 147 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Corn Navy Bean . . . . . . . . . . . 148 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Corn, Navy Bean. . . . . . . . . . . 149 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Corn, Navy Bean, Wheat . . . . . . . 151 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Corn, Navy Bean, Wheat . . . . . . . 152 xii Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6.20 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Navy Bean, Sugar Beet. . . . . . . . 153 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Navy Bean, Sugar Beet. . . . . . . . 154 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Navy Bean, Wheat Sugar Beet. . . . . 155 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Navy Bean, Wheat, Sugar Beet . . . . 156 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Navy Bean, Soybean, Sugar Beet . . . 157 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Navy Bean, Soybean, Sugar Beet . . . 158 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Corn, Navy Bean, Sugar Beet. . . . . 159 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Corn, Navy Bean, Sugar Beet. . . . . 160 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conventional Corn, Soybean. . . . . . . . . . . . . . . 161 Annual Costs of Three Machinery Sets for Various Farm Sizes Using Conservation Corn, Soybean. . . . . . . . . . . . . . . 162 xiii CHAPTER 1 INTRODUCTION Different forms of conservation tillage systems have been introduced to farmers in recent years due to the soil and water erosion problems that resulted from the conventional or moldboard plowing system. Conventional tillage is the combined primary and secondary tillage operations normally performed in preparing a seedbed for a given crop in a given geographical area. Conventional tillage also refers to a full or maximum tillage program consisting of both primary tillage (moldboard plowing) and secondary tillage (disking, harrowing or cultivating). Moldboard plowing is mainly used on fine-textured soils in the northern corn belt. It produces considerably high. yields due to simplicity of secondary tillage operations, but on the other hand damages the soil structure and increases the erosion (Larson and Hanway 1977). Conservation tillage is any tillage sequence which reduces loss of soil or water relative to conventional tillage. Various forms of conservation tillage can reduce erosion on many soils 50 to 90 percent (Myers, 1983). Conservation tillage is a form of non-inversion tillage that retains protective amounts of residue mulch on the surface throughout the year (Cook and Robertson, 1979). 1 2 Conservation tillage has been primarily accepted by farmers for the following reasons. 1. There is a large amount of soil loss each year from the cropland. The farmers are looking for techniques to conserve both soil and soil moisture. 2. Conservation tillage provides benefits to the farmers: lower costs for equipment, labor , and fuel; increases soil moisture retention and the greater land use flexibility. The rate of soil erosion depends mainly on tillage practices and amounts of crop residue on the soil surface. Soil erosion increases as the number of tillage operations increases, and decreases as the amount of crop residue increases. The success of conservation tillage especially no-tillage, depends on many factors such as the quantity and amount of plant residues on the soil surface, soil texture and moisture (Fenser 1977). The large volume of crop residue may intensify the soil moisture problem and retard evaporation rates (Robertson et. a1., 1976). It may keep the soil cooler during the spring which can delay planting (Newcomer, 1978). 3 The heavy concentration of crop residues on the soil surface may adversely affect the equipment performance making it difficult to control the seed depth. Excessive residue absorbs soil applied herbicides, interferes with desired operation of the fluted coulter, and results in poor seed soil contact. The chisel tillage is an effective method for reducing wind and water erosion because it leaves a major portion of crop residues on the surface and often provides a rougher, more porous surface. This system is less costly than conventional tillage and it is adapted to well drained soils. Chisel plow systems are the most extensively used conservation tillage systems in Michigan. The no-tillage system is another form of conservation system that greatly reduces soil erosion. The seed is directly planted into untilled sod, stubble or residue from the previous crop. The economic analysis of conservation tillage systems indicates that the no-till is the most cost effective of any practice commonly used. Factors that will influence the rate of adoption of no-till practice include technology development and transfer, economic advantages, education and training programs, and the gradual change in farm culture. 4 Voluntary adoption of conservation tillage by farmers is uncertain because of the lack of knowledge of the economic impacts of such practices by farmers (Rotz, et. a1., 1983). Several research needs for conservation tillage practices were identified by Muthar (1982) such as: 1. There are other conservation tillage systems on the market that may prove to be beneficial and need to be tried. More machinery management data is needed, such as fuel consumption, draft, speeds, and slippage, etc. for various soils and tillage systems. This data needs to be collected. The question of machinery rotations needs to be answered. Is there a need for a multiple machinery system on one farm? We need to know more definitely how and when to make the transition from the present farm system to a newly proposed one. The economics of this question need to be resolved. CHAPTER 2 OBJECTIVES The objective of the research was to study conventional and conservation tillage practices for corn production in Michigan. This study evaluates the impact of alternative tillage systems on machinery complements, labor, time— liness, and soil erosion on the fine texture soils of Saginaw Bay. To develop the research the following specific objectives were defined: 1. To formulate linear programming models to optimize machinery sets for alternative commercial corn tillage systems including conventional (using moldboard plows), Chiseling (using chisel plows), and no-tillage. 2. To determine timeliness costs based upon planting and harvesting time constraints. 3. To develop a machinery replacement model for the process of switching from conventional to a conserva— tion tillage system and specify the switching and trading times. 4. To evaluate the economic differences between conventional and chisel plow tillage systems for common crop rotations in Michigan. 5. To formulate a linear programming model to optimize the amount of corn residue (corn stover) on the soil surface and select the optimum harvesting systems. 6 This study is limited to the on farm economic and environmental issues of the problem. Although, off farm social and political basis are among the key factors, they are beyond the scope of this study. The economic and environmental perspectives of the research are conducted within a framework of cost analysis for tillage systems about machinery utilization and soil erosion. CHAPTER 3 LITERATURE REVIEW 3.1 MW Conventional tillage is the combined primary and secondary tillage operation normally performed in preparing a seedbed for a given crop grown in a given geographical area (Cook and Robertson, 1979). Following the corn harvest, the stalks are shredded and primary operations started using a moldboard plow and a tandem disk to till the soil. Tandem disks and spike tooth harrows are used for pre-planting operations in the spring. Rotary hoe and sweep cultivators are used for cultivating operations (Jolly, et. a1., 1983). Conventional tillage is also referred to as the moldboard plowing (about mid-April) and disking (about mid-May) as pre-planting operations. Weeds are controlled with cultivators and herbicides in pre-emergent and post-emergent spray form. Nitrogen and phosphorus should be applied adequately for the optimum production (Burwell and Kramer, 1983). Moldboard plowing (fall plowing) is mainly used on fine-textured soils in the northern corn belt. This method produced high yields due to the simplicity of secondary tillage operations. On the other hand, this method damages the soil structure and increases the erosion (Larson and 7 8 Hanway, 1977). Oschwald (1973) showed that moldboard plowing combined with secondary tillage implements in Illinois had a higher grain yield on poorly drained soils compared to other methods. A six year study of corn production in Ohio from 1962 - 1967 indicated that the conventional method using a plow had a lower average yield than the non-plow method (Triplett et. a1., 1969). Moldboard plowing is a suitable method for the level fields, but the crop will be exposed to weather damages until it has established a desirable cover. The soil moisture losses can also be excessive due to secondary tillage operations after moldboard plowing. The success of secondary tillage practices depends directly on the soil texture. Producing large clods on the soil surface and especially the hard layer below the surface, which is due to heavy equipment utilizations and excessive operations, are the other important problems indicated in moldboard plowing. Soil crusting is usually produced in very fine textured soils due to moldboard plowing practices (Erdmann, et. a1., 1981). A study of a four year conventional corn planting in Indiana showed that the yields from spring moldboard plowing are equal or greater than other non-moldboard plowing methods such as strip rotary and no-tillage. The _higher yield advantage from moldboard plowing is especially 9 due to better weed control practices (Griffith, et. a1., 1973). Many research studies have been done to compare the fall and spring plowing. The results indicate a small difference in favor of fall plowing. Fall plowing allows farmers to have an earlier spring planting than when spring plowing is required. Winter weather usually improves the 3011's physical conditions, especially when the soil has been relatively wet or dry at the plowing time. A desirable soil structure is produced due to continuous freezing and thawing, wetting and drying of large clods during winter. Such a situation cannot happen in spring, and the spring plowed soil remains cloddy. The high silt content soils should not be fall plowed. The soil granules are weakly held together so that in the .spring they are separated and destroyed and, therefore, a very compact soil layer is produced (Aldrich, et. a1., 1976). Larson (1973) studied the conventional corn production and Allarmous, et. a1. (1972) compared the corn yield of fall plowing with spring plowing. They found that the fall plowing also had a higher yield than spring plowing in southwestern Minnesota and eastern South Dakota. The temperature in fall plowed soils was also higher. Robertson, et. a1. (1977) studied the relation between tillage systems and soil air space in Michigan soils. The 10 loam soil before plowing had 23% (3.8 centimeters) soil air space. When applying only a moldboard plow for a minimum tillage, the soil air space was increased to 48% (11.4 centimeters). This method effectively reduced soil and water erosion so that the soil was able to absorb more than 10 centimeters of water. Thus, moldboard plowing can also be considered as a temporary method for reducing the erosion. With the possibility of plowing and planting in one operation, the soil dries rapidly and, therefore, prevents the new weed germination. Moldboard plowing also has some disadvantages such as high power requirements, undesirable performance due to speed fluctuations, and equipment calibration. The equipment transports large volume of soils with forward, lateral, and upward motions which increases the power requirements. Moldboard plows are usually designed to work at a particular speed. At low speeds, the incomplete soil cutting surface results, while at high speeds the soil particles are scattered over a greater area. Equipment calibration and adjustments need good training programs for operators before operation. Increased power requirements, incomplete soil fracture, excessive wear, and incomplete cover residues are the result of improper equipment calibration (Robertson, et. a1., 1979). '1lll 11 Moldboard plowing is also a combination of cutting, lifting, shearing and turning the top soil layer. The soil moisture content at plowing time should be adequate, especially in dry soils to provide a desired tilled layer. The heavy grass sod needs a long moldboard plow in order to turn over the furrow slice. The research experiments in the eastern part of the United States show that the optimum depth for moldboard plowing is about 20 centi- meters. The yield may also increase slightly for depths greater than 20 centimeters, but the optimum depth is indicated to be from 20 to 25 centimeters (Aldrich, et. a1., 1976). The experience shows that a three percent slope is the estimated limit for fall plowing in non-contouring soils. However, in the northern corn belt this level is increased to seven percent for contour lands (Aldrich, et. a1., 1976). 3.2 nt'n t Scientists have found that early corn planting has better yield advantages for farmers. The early planting date has been practically increasing because of effective application of chemicals for weed control, seed treatment, and the improved seed varieties. However, the best criteria for planting time is the soil temperature. ‘The optimum soil temperature for corn planting is 15°C at 12 the 7.6 cm depth. Rossman, et. a1. (1966) found that the average corn yield over a 10 year period in Michigan was 9% higher for a lst through the 9th day of May planting time than for the 12th to 20th of May, 16% higher than planting between May 22 to 31, and 27% higher than June planting times of June 1 through 11. The recommended planting time in lower Michigan for sandy soil is April 15th to May 5th and for other soil types, it would be April 25th to May 10th. The corn planted in April produces less vegetative growth (plant residues) than the late planted corn. The corn silking on July 15th receives about 26,200 Langely energy units in 55 days when compared to corn silking on August 15th which absorbs only about 20,800 units. The corn planted on April 20th will utilize only about 56 degree days by May 4th. The adverse weather conditions after May 4th require early planting (Lucas et. a1., 1978). The problems related to early planting include poor soil and seed stand due to the cold and the soil moisture, weed problems when the wet soil prevents cultivation and frost injury. The earliest planting date in Michigan starts on April 16th. The crop yield will decline if the planting operation starts after May 12th. The weed problem can be improved through effective application of _pre-emergence herbicides. Damages resulting from the wet l3 soil and frost have been relatively lowered in recent years because of modern seed treatment techniques. However, the inadequate stand can be improved by a replanting operation which is an efficient method if the first planting operation was not satisfactory. The modern hybrids are resistant to undesirable conditions such as soil disease and cold, so that they facilitate the early planting practice (Aldrich et. a1., 1976). The depth of planting usually depends on soil and weather conditions. Considering soil moisture content and temperature, an optimum depth is required for a high percentage of germination and emergence. Allesi, et. a1., (1971) made a growth room experiment and found that an 80% emergence needed a time period from 4 to 24 days. A time period of about 8 to 13 days was observed in field experiments in North Dakota for 80% emergence. Increasing the seed depth by 2.5 cm lowered the emergence by one day. Aldrich, et. a1. (1978) indicated that in the corn belt area with adequate soil moisture content, a 5 cm depth would be optimum, based upon the average planting time. Thirty years of planting data at Michigan State University (1948-1980) show the yield advantage for corn planting in late April or early May. The results also recommend corn planting in late April. The average 14 corn planted in early May is about 100 bushels. The yield decreases by one bushel per acre per day for delayed planting. Early planting results in earlier maturity in the fall which reduces the cost of drying. The weather data indicates that the first week of May is usually drier than the second week and, therefore, more suitable days are available for early planting. For early planting, the corn seed should have the highest quality with the cold test germination of 70 percent or better. The seed population should also be 15 to 20 percent higher than the desired plant population. Spring frost causes severe damage to the plant if the growing point remains below ground with the soil temperature below 32°F. Since the cultivated organic soils are cool and dry the frost damages to the planted corn are greater than if the soil is left undisturbed (Erdmann, et. a1., 1981). The optimum seed depth for early planting is found to be 1.9 to 3.8 centimeters for cool soils. A deeper seed placement of 3.8 to 6.4 centimeters is recommended for late planting in fine textured soils and also the deeper ranges are considered for coarse texture soils (Erdmann, et. a1., 1981). 3.3 W The corn population for grain production depends on several factors such as genetic characteristics, climatical 15 conditions, soil textures, row width, soil fertility, and moisture content. Larson and Hanway (1977) found that the optimum population varies from about 40,000 to 100,000 plants per hectare. Lucas, et. a1. (1978) recommended 64,246 plants per hectare at harvest for irrigated soils with 75 centimeter rows. The average yield of this population is estimated to be 11.6 tons per hectare. However, the 54,362 plants per hectare are recommended for those areas with a water shortage and problems with irrigation schedules. To obtain the desired plant population at harvesting time, an increase of 15 percent for seeding rate is necessary. Erdman, et. a1. (1981) found that the optimum plant population in Michigan soils ranges from 44,478 to 49,420 plants per hectare. The results of a five year study of irrigated and non—irrigated corn with four different plant populations of 37,806; 47,443; 57,574; and 67,953 per hectare indicated that the irrigated corn with the 57,574 plant populations per hectare had the highest yield of 10.7 tons per hectare (15% moisture) while 37,806 had the lowest yield of 5.7 tons per hectare. stickler (1964) found that the maximum yield for non-irrigated corn in Kansas resulted from the 40,000 plant population per hectare and for the irrigated corn, it was indicated to be from 48,000 to 59,000 plant population per hectare. 16 The optimum plant populations for the northern states and Canada have been higher than those in the southern states. Early planting is an important factor in increasing the plant population. A two year research in Minnesota showed that through providing the crop water requirement, plant population increased from 44,640 to 74,100 per hectare (Hicks et. a1., 1970). 3.4 W The use of commercial fertilizers has increased rapidly in recent years. The fertilizer supply in Michigan usually includes 97.3 kg N, 37.3 kg P, and 50 kg K/ha (Hargett, 1973). The rate and method of fertilizer application depends on the materials, farmers' preferences and cultural practices. For the cool regions with early planting practices, N, P, and K fertilizers are applied in bands 5 cm to the side and 3 to 5 cm below the seed at planting. Fertilizers are applied in various methods such as broadcasting using moldboard plows or disks before planting, injecting the gas into the soil before or after planting, or sidedressing between the rows after planting. The foliar application is an efficient method after the leaf development process is completed (Larson, et. a1., 1977). Soil sampling and testing are also a reliable method for providing the adequate information for 'fertilizer application and recommendations. 17 Results of the studies on nitrogen fertilizers in irrigated areas indicate that pre-planting and sidedressing are more effective than the broadcasting method which is applied in the fall (Larson, et. a1., 1977). A nitrogen fertilizer study in Ontario showed that the spring—applied N, produced from 370 to 2610 kg/ha more yield on clay soils than fall-applied N (Stevenson, et. a1., 1969). The research in Illinois indicated that the spring applied N at application rates of 67 and 134 kg/ha produced 10 to 20% higher yields than the fall applied. Yields were the same for the application rates of 201 and 268 kg/ha in several other locations (Welch, et. a1., 1971). Vitosh, et. a1. (1979) studied the economics of fertilizer N on different soils in Michigan. The results indicated that most profitable rates of nitrogen for the loamy sand soil, sandy loam, and clay loam with the potential yields of 4.4, 6.3 and 8.2 tons per hectare were 89.7, 131.1, and 162.5 kg per hectare respectively. He also indicated that soils with higher yield potentials needed more N fertilizers. Research in Michigan has showed that spring applications of ammonia N are more efficient than the fall applications. Fall losses on fine and medium textured soils are 5-10%, and for coarse textured soils are about 10-30%. Fall applications of nitrogen, especially on coarse texture soils causes groundwater contamination 18 and the fall applications should only be used on fine and medium textured soils (Vitosh, et. a1., 1979). Nitrogen fertilizers are acid forming and affect the phosphorus availability in the soil surface due to change in 8011 PH. It is recommended that lime be applied more frequently or to use moldboard plowing every three to four years to mix the lime and nitrogen fertilizers (Vitosh and Warncke, 1981). Anhydrous ammonia can be easily incorporated into the soil. It reduces the need for lime application. This is an efficient method for no-till systems. Applicators should have a rolling coulter ahead of each knife and a packer wheel behind to lower the ammonia losses into the air. When heavy residues are available on the surface, ammonium nitrate can be the most efficient fertilizer (Vitosh and Warncke, 1980). Organic residues provide a cool temperature medium that reduces evaporation or volatization. On the other hand, a major amount of the nutrients are lost through run-off and leaching. Therefore, 10 to 20 percent more nitrogen may be required when the plant residues are considered (Vitosh and Warncke, 1980). Superphosphates and ammonium phosphates are the two important sources of phosphorus fertilizers for corn (production. The commercial superphosphates contain about 19 8.8% P (20% P205) and concentrated superphosphates contain 20 to 22% P(45 to 50% P205). Superphosphates are mainly used as a single source of P while the ammonium phosphates fertilizer is a combination of different materials produced by the ammoniation of phosphoric acid. Phosphorus fertilizers are applied in broadcast form in the fall or spring, when using plow or disk at the tillage. Side dressing is also used at the time of planting for row application. If the soil has a moderate fixing capacity, the phosphorus can be applied prior to planting and if the soil has a large P fixing capacity, phosphorus should be applied in a band five cm to the side and five cm below the row (Larson, et. a1., 1978). The phosphorus requirement of corn could be estimated by soil test and crop response concerning the other factors such as yield desired, solubility of the phosphorus in the fertilizer, and method of application. Phosphorus fertilizers call for fast growth of small seedlings, especially at low soil temperatures. For corn and other grain corps, when the soil temperature is low, at least 28 kg P205/ha is recommended in the early stages which could be applied in bands near the seed. In the successive stages of growth, the plants will be able to properly utilize the phosphorus available in lower depths .(Warncke and Christensen, 1981). 20 Most of Michigan's soils have a high amount of phosphorus and are well-suited to production of no-till corn. Soil test results indicate that over 50 percent of all soils have phosphorus levels greater than 67.3 kg per hectare. This level of phosphorus is estimated to produce 6.3 tons of corn per hectare. When the solid phosphorus level is medium to high, all the phosphorus requirements should be applied in bands five cm to the side and five cm below the seed at planting time (Vitosh and Warncke, 1981). Potassium chloride is the commercial form of K which can be used as a single or a mixed fertilizer. It can be used before planting in broadcast form and mixed into the soil by plows or disks. It can be applied also in bands near the seed at the time of planting. Potassium fertilizer is usually applied based upon the soil fixing ability. It may be applied every year or in larger amounts once in several years. The side dressing is more effective for low application rates (Larson and Hanway, 1977). The crop seedling requirement for potassium is less than for phosphorus. The crop potassium utilization increases when the plant starts to grow rapidly. Potassium removal is also high when the corn plant is removed for Silage or Stover feedstuff. The row application of potassium is more effective than the broadcast form. The .amount of potassium applied near the seed should not exceed 21 a certain level due to salt injuries to the seed (Warncke and Christensen, 1981). 3.5 CW Chisel tillage is a less costly method than the conventional system and is well adapted to soils with good drainage characteristics. It is an effective method for reducing soil and water erosion problems because it keeps a considerable amount of plant residue on the soil surface. Chisel tillages leave a rough or porous soil that reduces soil and water losses. Chisel plows are heavy pieces of equipment with shanks spaced at 30 centimeters apart and equipped with 5 centimeter chisels and up to 46 centimeter shovels. The chisel points may be single or double-pointed, shovels, spikes, or small sweeps (Mannering and Fenser, 1983). The success of conservation tillage for controlling erosion depends upon the proportion of plant residue saved on the soil surface. The amount of plant residue left depends upon the type of chisel plow used and the crop residue. Using a 10 centimeter twisted shank on a corn field, 10 to 20 percent of the'residue left, while with narrow points, this amount may exceed 50 percent (Moldenhauer, et. a1., 1983). The results from a four year grain yield research in .Indiana indicate lower yields on fine soils for chiseling 22 than for moldboard plowing, but on coarse soils the yield resulting from the chiseling method was significantly higher than from moldboard plowing (Larson and Hanway, 1977). Soil moisture characteristics are considered the major criteria for dividing the soils into several management groups. These soil groups are defined as well-drained, moderately well-drained, somewhat poorly drained, and poorly drained (Cosper, 1983). Each tillage system causes limited physical changes in the soil due to the soil and residue mixing operation. However, reduced crop yields resulting from conservation systems are related to the limitation of soil physical properties. These include drainage problems, soil wetness levels, degree and frequency of wetness, structural stability, water percolation, impervious or restrictive layers 1J1 the profile, and surface soil texture (Cosper, 1983). Chisel plowing is the most extensive conservation method used in Michigan. The crop yield from the chiseling system is not different from moldboard plowing. Chisel plows significantly reduce water and soil losses, especial- ly from sloping lands. The rate of soil and water losses through chisel plowing depends directly on the proportion of crop residues left on the soil surface and the number 23 and kind of operation after moldboard plowing (Cook and Robertson, 1979). Conservation tillage systems may affect the implement in two ways: the type of implement used and the way they are adjusted and operated. The physical characteristics of the soil before tillage may be different. The plant residue on the soil may block the implement or affect the operation of the machine. Tillage equipment can be modified for operating in plant residues by adding rolling coulters to cut residues. Rolling coulters can be flat disks, with either smooth, notched, rippled, or fluted edges, or they may be concave disk blades with smooth or notched edges (Erbach, et. a1., 1983). Conventional planters can be practically modified to conservation planters in order to improve their operation. The commercially available coulters can be placed in front of the furrow opener to cut the plant residue and provide a narrow strip of soil for seed placement. Such coulters can be in various shapes such as smooth, notched, rippled, and fluted forms. The capability of coulters depends upon the residue and soil conditions. A rippled coulter is recommended for soft soils while a fluted coulter is practically suitable to work on hard soils (Erbach, et. a1., 1983). 24 3.6 W No—tillage is a procedure whereby a crop is planted directly into a seedbed not tilled since harvest of the previous crop, and no-tillage occurs during the growing and maturing season. More specifically, no-till is the planting of a crop into sod, previous crop stubble or a cover crop where only the immediate seed zone is disturbed (Anon., 1983). A narrow slot is provided by the no-tillage method in undisturbed soil so that the seed can be placed. No other tillage operations are necessary. No-tillage practices can be used when proper herbicides are used for weed controls (Nelson, et. a1., 1976). No-till is considered as one of the most effective practices developed in corn production for controlling wind and water erosion. Such practices result in the conserva- tion of soil nutrients and in the reduction of air and water erosion problems. In no-till production, frequency of farming operations are lowered so that the time, labor, and energy requirements are greatly reduced (Robertson, etc 31.: 1976)- Various studies and research experiences show that no-till is best suited to coarse and medium textured soils with well-drained characteristics. However, no-till practice may not be successful when one or more of these soil conditions are present: (1) fine texture soil; (2) poor structure; (3) inadequate drainage; (4) underestimated *organic matters; (5) eroded soil; (6) low fertility levels 25 and soil acidity; (7) herbicide ineffectiveness due to soil texture and weather conditions. These conditions can be evaluated with soil test levels (Robertson, et. a1., 1976). Several factors have increased the rate of adoption of no-till such as improvements in planting equipment, new chemicals, technology transfer, and environmental con- cerns. Other factors like economics of agriculture, educational methods, and gradual change in farm culture will have major roles on the development of no-till farming. Economic analysis of various conservation tillage systems indicate that no-till is the most cost-effective method commercially used (King, 1983). Nowak (1983) identified problems for management decisions on adoption of conservation tillage. Such problems are directly related to the timing and sequence of operations including incorporating chemicals and nutrients relative to the amount of crop residue left on the soil surface, controlling pest and weed problems adjusting or modifying implements, and selecting new seed varieties adaptable to different environments. No-till planters should be capable of performing planting operations under various soil conditions. No-till planters also need special features in addition to those for conventional seeding. These features include: 26 l. A rolling or fluted coulter is placed ahead of the furrow opener which will cut through the crop residues and penetrate the soil to a uniform depth of 5 to 6 centimeters. 2. A seed opener with a positive planting depth control is used to place seeds at an optimum depth. 3. A press wheel is placed behind the planter to firm the soil over the seed. 4. A separate coulter is also used to put the fertilizer properly in banded forms during planting operations. The no—till planters put the seed in undisturbed soils under conditions which are different from the conventional system. The soil is usually covered with residue or sod which is wet, firm, or rough. Thus, the planter should be capable of planting through residues with a uniform depth and with good seed to soil contact. The planter should be heavy and strong enough to cut through crop residues properly (Anon., 1983). Coulters will work successfully if they are operated at seed depth. If coulters are not penetrating enough, residues are not cut, and additional weights are required to provide a downward force for desired penetration. Such a force can be provided with weights, tanks of water, weighted frame members, or with transfer of planter weight from transport wheels to the coulters. A force of 2224 N 27 per blade in hard soils is needed for an optimum penetration (Erbach, et. a1., 1983). If fluted coulters fail to cut the plant residues due to inadequate penetration, the reside may enter the soil opening. Fluted coulters will hairpin the tough residue into the soil when the soil is wet. Therefore, the seed will be in close contact with the residue which will result in poor germination, emergence and early growth. Phytotoxic materials that are released from the residues will damage the seed growth. Phytotoxicity is produced when continuous corn, sorghum, or wheat is planted (Erbach, et. a1., 1983). No-till planters should be operated at a speed of 4.8 to 5.6 km/hr comparing to 7.2 to 8.0 km/hr for a conven- tional system. The planting hopper should be larger to maintain the same seeding rates. Fertilizer attachments should be adjusted to place fertilizer 2.5 to 5 centimeters to the side and 5 centimeters below the seed (Nelson et a1., 1976). Soil moisture is increased when using no-tillage because of the crop residue which reduces surface run off. The residue lowers the rainfall intensity and increases the soil infiltration. Soil temperature is also lowered which reduces moisture evaporation and improves soil moisture capacity (Knapp, 1983). 28 No-till farming is not always an effective method for controlling surface runoff. Many research reports from the corn belt indicate that the water runoff resulted from no-tillage was almost equivalent to the conventional systems. A no-till system will produce a firm layer which is impervious to water and increases the surface runoff. Such conditions will happen when the soil surface does not contain adequate crop residues to lower the flow velocity. A no—tillage method may not be effective to control erosion when a complete harvesting of crop residues for animal consumption occurs (Lindstrom and Onstad, 1984). Conservation tillage, especially no-till, provides a suitable environment for growing pests and disease organisms due to the crop residues left on the soil surface. In a conventional system, the soil is turned over by moldboard plowing which destroys the insects. Such a soil inversion operation will not occur in no-till which keeps the insect larvae on the soil surface for growing (Anon., 1983). A no-tillage system is primarily accepted as an effective method for reducing erosions on sloping lands. The intensity of erosion depends on the length of slope, slope gradient, soil properties such as texture, structure, organic matter, rainfall intensity, and cropping and tillage systems. Sloping lands will not have any drainage 29 problem because the water accumulation in rainy seasons is prevented (Phillips, et. a1., 1984). 3-7 liked—Calm. No—till farming needs better management and planning than a conventional system. Weed control practices cannot be done by cultivators after the planting operation. A farmer must know in advance about the types of weeds on his farm in order to prepare a herbicide program to match the weed problems. A successful weed control program should meet several criteria: (1) controlling existing weeds, (2) controlling germinating weeds and especially root growing weeds, (3) avoiding injuries to the present crop, and (4) preventing injuries to the succeeding crop. These objectives are met if the following procedure is considered (Cook and Robertson, 1979): 1. Proper herbicides should be selected to control weeds on the farm. 2. Sprayers should be properly calibrated to provide a uniform herbicide application throughout the planting seasons. The effect of herbicides on weed control practices depends primarily on the chemical properties of herbicides, rate of application, soil PH, soil organic matter content, amount of surface plant residue, temperature, rainfall and microbial decomposition. The production of continuous 30 no-till corn lowers the soil PH compared to the conventional system. The amount of residue on the soil surface depends on the type of crop material remaining from the previous crop or from the existing crop. Plant residue burning is also a common practice for removing extra plant material from the land surface and is an effective method to control some of the weeds that cannot be controlled by herbicides (Philips, et. a1., 1984). Two types of herbcides are usually required for a no-till planting system. The first type is called a contact herbicide which controls the existing residues and the second type is called residual which controls grass and weeds that may germinate after the crop is planted. Paraquate and Lorox are the two types of contact and ‘residual, respectively, used for soybean weed control. Other pre-emergence herbicides like Lasso and Amiben are recommended on sandy soils where Lorox may produce injuries to the crop (Clapp, 1972). Herbicides used for no-till can be used in conventional, but the reverse is not always true. Some herbicides should be mechanically incorporated into the soil and are not suitable for no-till systems. The no-till herbicides should be applied without soil incorporation.) In addition to the contact and pre-emergence, the post-emergence herbicides are also used in no-tillage 31 practices. Following is a short description and some examples of several herbicides used in no-tillage (Anon., 1983). 1. Contact herbicides are used before, during, or after planting, but before crop emergence, such as: HIRBIQIDI________QQIIBDLH Paraquat Emerged annual grasses, broadleaf weeds Roundup Emerged annual grasses, broadleaf weeds 2,4-D Broadleaf weeds Pre-plant or pre—emergence residual herbicides are applied before crop emergence. Incorporation is not usually required except for some of them with rainfall. A list of these herbicides are indicated as: IIIBISIDE: .SDHIBDLS Altrazine Annual broadleaves and grasses Bicep Annual broadleaves and grasses Bladex Annual broadleaves and grasses Dual Most annual grasses Dyanap Broadleaves, some grasses Lasso Annual grasses, nutsedge, nightshade Lorox Broadleaves, some grasses Princep Annual broadleaves, some grasses Prowl Most annual grasses, some broadleaves Ramrod Annual grasses, certain broadleaves Surflan Annual grasses, certain broadleaves 32 3. Post-emergence herbicides are used after crop emergence. They may be used over the crop or to the weed. W Altrazine Annual broadleaves, some grasses Banvel Broadleaf weeds Bicep Annual broadleaves, grasses Dual Most annual grasses Lasso Annual grasses Lorox Broadleaves, some grasses Prowl Annual weeds Ramrod Annual weeds 2,4-D Broadleaf weeds The rate of herbicide application for a conservation tillage depends upon the weed problem, crop rotation, knowledge of the manager, and the timeliness of opera- tions. A conservation tillage farmer may not have many options available to correct mistakes. A combination of herbicides and cultivators may be recommended for weed control programs in conservation systems (Hayes, 1983). The timing of an operation is an important factor in herbicide application programs. Weeds should be at the proper stage of growth in order to be controlled with the contact or translocated herbicides. The herbicide effectiveness may be reduced for tall weeds, or clipped weeds or when the weeds are drought-stressed. The pre-emergent residual herbicides should be applied at the planting time. The early application of the pre-emergent 33 herbicide may reduce the length of control while the late application may allow the weeds to germinate and emerge. Certain weed problems such as purple nutsedge and horsenettle cannot be controlled in conservation tillage with herbicides. These problems are controlled by incorporating herbicides and soil through other tillage methods before conservation tillage practices are attempted (Anon., 1983). 3.8 W Conventional and conservation tillage systems have similar pest and disease control problems, except for those insecticrdes which require soil incorporation. Farm managers and farmers may use different techniques and cultural practices on insecticides and pest management programs. Many sources like extension specialists, farm chemical suppliers, crop consultants, and agricultural colleges are available to provide suitable information on insecticides and pesticide problems. Major no-till corn insects are described under two categories of soil insects and above-ground insects. Seed corn maggot, wdreworm and seed corn beetle are the major soil insects. These pests attack the corn seed when the cool soil temperature causes slow germination. Insecti- cides can be applied in attachment units at the planting time to control these insects. Rootworm, white grub and 34 sod webworm are the other important soil insects which may destroy the corn plant completely. Root worm is found in the corn belt when the continuous corn crop is grown. Crop rotation is considered the best method to control rootworm. The life cycle of rootworm is broken when corn is rotated with another corp like soybean. Rootworms can be controlled with insecticides which should be applied in a band and incorporated with soil. The soil incorporated insecticides may be difficult to use in no-till method (Anon., 1983). The late seed germination resulting from the lower soil temperatures provides a suitable environment for seed corn beetle and seed corn maggot. The larva development of these two pests starts at 10°C and higher. Early organophosphate seed treatments will provide satisfactory results on controlling these two pests (Phillips, et. a1., 1984). Major above-ground corn insects are indicated as armyworm, cutworm, common stalk borer, and European corn borer. Foliar-applied insecticides are used to control these insects with the same methods used in a conventional system. The most appropriate method to control above-ground insects include a proper scouting program and the personal knowledge to identify the pest and the method _ to apply the insecticide (Anon., 1983). 35 In addition to insecticides, other methods and practices are recommended to control insects under a no-tillage cropping sequence. These methods include: (1) possible increase in predator and/or parasite activity; (2) selection of resistant varieties; (3) using a multiple crop rotation sequence; and (4) proper fertilizers with increasing seeding rates and lower row spacing (Phillips, et. 81., 1984). CHAPTER 4 LINEAR PROGRAMMING MODEL 4.1 Illnesses-Jen Linear programming models are means for conducting research studies associated with policy issues for resource utilization and allocation. Linear programming deals with problems of limited resources among competing activities in the best possible (optimal) way. One of the important decisions of the farmer is to select the machinery complement required for annual operation within a suitable time period. Failure of timely operation for planting, cultivating, and harvesting results in certain amount of crop losses. To make a reasonable comparison between conventional and conservation tillage systems, optimum sized machinery complements are needed for each tillage system under a suitable time period. Interactions among machines, land, weather, and capital investment create a problem which can be properly solved by a Linear Programming Model. Studies on implements used on farms indicate that machines are not properly matched to one another nor to the available power on the farm. Farmers usually do not buy a complete set of well-matched machines for their farming operations at a particular time. They buy machines 36 37 when needed and attempt to match the new machines to their present machinery sets (Rotz et. a1., 1983). Several machinery selection models have been developed to deal with the machinery complements required on a farm. Muhtar (1982) developed a computer simulation model to select a set of machines for a group of crop rotations in Michigan. Computer simulation models for machinery selection have some limitations which are complex due to computer algorithm and very expensive data processing. Computer simulation models usually do not generate optimum solutions. Amir, et. a1. (1978) formulated a mixed integer programming model to select an optimum dry hay system among the various alternative systems. The model maximizes profit for annual harvesting of dry hay relative to a series of constraints. It is used to examine the interaction of six different hay packing methods in Southern Ontario. Various quantities of dry hay ranging from 100 to 1500 tons were evaluated relative to operating costs and the resultant benefit. The mixed integer model was a proper method for determining the feasibility of machinery complements. The harvesting system include nine operations starting from cutting to feeding and each operation consisted of many activities representing machinery and complements. 38 Danok, et. al. (1978) made a regional study to develop a linear programming model to select a set of machinery complements for a cropping plan from several resources. The mixed integer programming model maximizes profit from harvesting crops related to the costs of implements and tractors used. The resource constraints are considered to be land, irrigation, available power sources, and the minimum number of implements required. The model estimated a major reduction in hired temporary labor achieved by substitution of capital through mechanization of harvesting crops and through planning of better utilization of permanent labor. The optimal results showed a higher farm income with less hired labor requirement than for the real situation. In addition, comparison between the optimal and actual results indicated more machinery complements with a lower number of tractors required for optimal situation. Yang and Sowell (1981) developed a mixed integer programming model for scheduling the harvest of flue-cured tobacco. The model maximized profits from the harvested crop, harvester, and storage capacities. Leaf harvesting is a sensitive process in a flue-cured production system which affects the quantity and quality of the product. This situation calls for a desired harvesting schedule. The model objective function presents the total net return from tobacco harvested from all fields. The barn capacity 39 was considered to be a major constraint due to the capacity and the curing time for tobacco. The net return was the difference between the total gross return and the harvesting cost. Witson, et. a1. (1981) developed a profit maximization model to include weather risk for machinery selection. The model was used for two crops of cotton and grain sorghum for farms of 1000 hectares or larger. The result was non integer solutions for machinery complements generated by the model. A linear programming model is formulated to select optimum machinery complements for conventional, chiseling and no-tillage practices as a basis for economic comparison. This method is used as an alternative for a simulation model that is expensive and complex algorithm. The proposed linear programming model has been developed based upon the following objectives: 1. To select optimum machinery sets for commercial corn tillage systems such as conventional, chiseling, and no-till in Michigan. 2. To compare the tillage systems and determine the most feasible system using an economic comparison. Capacity and power match, and cost analysis are major factors in the selection process. Capacity matching represents the interrelation between operation and time. _ In sequential operations, the time should be independently 40 divided between them. Parallel operations should be done within the same time period so that the timerequirement for individual operation is equal. Farm implements should match the power size of the tractor as well as possible. Oversized and undersized implements result in inefficient use of the tractor and damages to the machines. If several implements are pulled by the same tractor, their power requirement should be similar. The timeliness is defined as the loss in crop value when the farm operations, especially planting and harvesting are not completed within the same time period. The planting and harvesting machines selected by the model are used to calculate the daily timeliness costs. It is also possible to develop individual linear programming models based on single constraints like suitable weather, land, labor, capital investment, or power requirement but the results of the model are more realistic ‘when the interaction between several constraints is considered. 4.2 Winner; The proposed linear programming model has the general formulation minimize subject to and in which Xij - Cj 8 Bi I Aij - 41 m n 131 jil m n X z AinijfBi for 121 to m, j-l to n 1-1 j-l xijZO for i=1 to m, j-l to n Variables which refer to the number of market size machines ranging from the highest to the lowest size. The total average annual cost of each machine. It includes annual ownership and operating cost. Resource constraints that include suitable hours, land, minimum size tractor, labor, and machinery investment. Variable coefficients which represent maximum annual operating hours, maximum annual coverable land, power requirement, annual operating hours, annual ownership cost, and annual repair and maintenance costs for individual machines. W Variable x1 x2 x3 x4 x5 XG X7 X8 X9 X10 X11 X12 Moldboard Plow lZ-Bottom 9-Bottom 7-Bottom S-Bottom 3-Bottom 2-Bottom Tandem Disk 4.9m 4.3m 3.7m 3.0m 2.4m 1.8m :2 WM Variable Spring Tooth Harrow x13 6.0m x14 5.5m x15 4.9m x16 4.3m x17 3.7m x18 3.0m Row Cultivator x19 12-Row x20 8-Row x21 6-Row x22 5-Row x23 4-Row Fertilizer Spreader x24 6.0m x25 5.5m x26 4.9m x27 4.3m x28 ' 3.7m x29 3.0m Sprayer x30 11.0m x31 10.7m x32 9.2m x33 8.3m x34 7.6m X35 6.4m Field Cultivator x36 7.9m x37 6.0m x38 4.9m x39 4.3m x40 3.7m x41 2.4m Sub Soiler x42 3-units x43 2-units x44 l-unit Combine x45 12-row X46 8-row x47 6-row W 43 variable Combine X48 5-row X49 4-row N33 Applicator X50 10-knives X51 9-knives X52 8-knives X53 7-knives x54 6-knives X55 S-knives Row Planter X56 12-row X57 8-row X58 6-row X59 S-row X60 4-row Tractor C M F X61 40kw 40kw 40kw X62 50kw 45kw 50kw X63 55kw 50kw 55kw X64 60kw 60kw 60kw X65 70kw 70kw 70kw X66 85kw 75kw 85kw X67 95kw 80kw lOOkw X68 115kw 85kw 115kw X69 125kw 90kw 125kw X70 150kw 95kw lSOkw C, M, and F indicate Coarse, Medium and Fine Soil respectively. WW Chisel Plow variable X1 X2 X3 X4 X5 X6 7.9m 4.9m 3.0m 2.7m 2.4m 1.8m 4: WW Variable Tandem Disk X7 4.9m X8 4.3m X9 3.7m X10 3.0m X11 2.4m X12 1.8m Row Cultivator X13 12-row X14 8-row X15 6-row X16 5-row X17 4-row Fertilizer Spreader X18 6.0m X19 5.5m X20 4.9m X21 4.3m X22 3.7m X23 3.0m Sprayer X24 11.0m X25 10.7m X26 9.2m X27 8.3m X28 7.6m X29 6.4m Field Cultivator X30‘ 7.9m X31 6.0m X32 4.9m X33 4.3m X34 3.7m X35 2.7m Sub Soiler X36 3-units X37 2-units X38 l-unit Combine X39 12-row X40 8-row X41 6-row variable Combine X42 5-row X43 4-row NH3 Applicator X44 10-knives X45 9-knives X46 8-knives X47 7-knives X48 6-knives X49 S-knives Row Planter X50 12-row X51 8-row X52 6-row X53 S-row X54 4-row Tractor C M F X55 40kw 40kw 40kw X56 45kw 50kw 50kw X57 50kw 55kw 55kw X58 55kw 60kw 60kw X59 60kw 70kw 70kw X60 65kw 85kw 85kw X61 70kw 100kw 100kw X62 75kw 115kw 115kw X63 80kw 125kw 125kw X64 85kw 150kw 150kw C, M, and F indicate Coarse, respectively. 45 WW Medium and Fine Soil WWW): Field Cultivator variable X1 X2 X3 X4 X5 X6 OOOOOO 46 W1 Variable Fertilizer Spreader X7 6.0m X8 5.5m X9 4.9m X10 4.3m X11 3.7m X12 3.0m Boom Sprayer X13 11.0m X14 10.7m X15 9.2m X16 8.3m X17 7.6m X18 6.4m Row Cultivator X19 0 X20 0 X21 0 X22 0 X23 0 No-till Planter X24 12-row X25 8-row X26 6-row X27 5-row X28 4-row NH3 Applicator X29 lO-knives X30 9-knives X31 8-knives X32 7-knives X33 6-knives X34 5-knives Combine X35 lZ-row X36 8-row X37 6-row X38 5-row X39 4-row 47 Subsoiler X40 3-units X41 2-units X42 l-unit Tractor C M F X43 40kw 40kw 40kw X44 45kw 50kw 50kw X45 50kw 55kw 55kw X46 55kw 60kw 60kw X47 60kw 70kw 70kw X48 65kw 85kw 85kw X49 70kw lOOkw 100kw X50 75kw 115kw 120kw X51 80kw 125kw 125kw X52 85kw 150kw 150kw C, M, and F indicate Coarse, Medium and Fine Soil respectively. 4.2.1 W The linear programming model is a cost minimizing model and the objective function coefficients represent the total annual equivalent cost (including ownership and operation) of machines. The machinery cost model, a computer simulation model that includes inflation, is used to calculate the annual equivalent cost of individual machines (Rotz, et. a1., 1981). Major economic input parameters which determine the annual costs are initial prices, discount rate and inflation rates. Machine initial costs were provided from commercial suppliers of farm machinery. A survey was taken on initial prices for various market size implements (Tables 4.4-4.7). Tables 4.1-4.3 and 4.8-4.10 indicate 48 speeds, draft, and power requirement. Machine inflation rates, discount rate, and interest rate are given in Table 4.11. Machinery prices will also increase over a period of time. The major costs of agricultural machinery determined by the machinery cost model are capital investment, interest, property tax, insurance, shelter, repair and maintenance, and fuel and lubrication. The income tax deductions represent a benefit or saving to the owner. The cost to the owner for owning the machine is determined as the sum of the down payment plus all principal interest payments for the purchase of the machine, minus the remaining value at the end of its life. The down payment is determined in terms of the present value. Average annual costs represent a uniform series of costs that can be converted to present value. The remaining value of a machine represents a cost in the future that can be discounted to the present value. 49 . Table 4.) Machinery Speeds and Capacities for the Coarse Soil peed*------ (Km/hr) --o---s Max. Ann. Use 200 (Ha) (Hours) #00 (Ha) EFC (Ha/hr) H8. Plow(n) Low Med. High FOO Size (m) Implement 0'0. ' .I' gamma” tttttt tote-utter. 000000 «44444 "I" Tandem Dis Offset Dis Chisel Pi eeeeee) Zgzgl gzhguooooooo Sibznflgwnoag. .thhg \Ml Spring Too ltivt IIIIIII “halibut“ 0000000 0000000 8888888 aeeeeee" r6§666 r Ehhgmogha Mpg-[Old ”018 0:30 0775]; 0:61.410 eeeeeeee(eeeeee(eeeeee( eeeeee 0000M§31§Z§ IIIIIII ‘ -Row ZIP ......... .m (021.16.“ 833/“. 2.46611: 701905 145.2207 £0682 «(70113 “01800 88012.“ 70205 el-‘l-ellse' e'ellllle'z 86063 “01.30 cccccccccccc “our“; E32] .tazdatéoz Cibzibtfb 777777 88883 088888 222222 huh-“uh 66626 as“; 666666 111111 axvexvoxv 000000 000000 666666 088888 0720.“ 8% ”03%)Zhhg .w r 0 Boom Sprai Field Cult Subsoiler() 87)“ 08140 1.63 8.150 1.113 1.12 3 000 as; 600 20 1.23“ 7% £17.40 222 0.aU.n~nU. 777 “lion“ 888 2222 5; 33 666 “141“!“ 888 6666 000 0000 888 7777 000 £0.90 321.. coitus ) m ( ...mwwwm ”HHDRRRR 000m. . . . . . . .1865 3141C} 50 Table h.l Machinery Speeds and capacities for the coarse Soil ------ Speed*------ Max. Ann. Use I l S FE H' h (Km{hr) M d EFC “00(hours) ”p "m (J. ° (3) g (Ha/hr) (Ha) (Ha) Comb ne m): h- ' ( 3.0 70 6.h 3.2 h.0 0.8 500 250 i33x AppLicator(m)° 2 0 2 O 1 - K". O O O O O -knife 3.2 O . . .2 2.0 20 lo Z-Knife . O . . 2.2 1.Z 232 11 ~Knife 3.; 0 . . .2 l. 2 1h 5-Kni e O . . 6.2 1.0 hOO 20 Ng-Till Pl nter(m): 12'383'8 2% -: 2% “-2 .3 *2. 1 -Roon. Z. 6 In .32 22 33 lg El g-Row.0 .h h.2 .2 .h 1 2 -Row.o h. 'g .h h.2 .2 1.2 21 10 £-Row.0. ;. .h h.2 .2 1. 26; 12 an r IZYR .0. 12. 2 10.0 . 6.h . 80 to lZ-Rg:.0. 1 10.0 . 6.“ .8 10 l -row.0. 2 6 10.0 . .h .2 13$ g3 g-RWQOE 10.0 0 oh . I -Row.0 h. i; 10.0 . .h 1.§ 210 10 z-Row.0. 10.0 . .h 1. 2§o 1g; -row.0. 10.0 . .h 1.2 3 3 1 * Source: wh te.1978: Hhite.l977: Hunt.1 77: Self, .138 a; Vaug han. 19182 Frisby,197:Smithh.1980;Fornstrom,l8; : Kepner. l 7 Wilkinso 33h Agricultural Engineering Yearbook 19 ; John Deere Publications,1 Ann. Use 200 (He) (Haurs) Max. (Km/hr) Low 51 Table h.2 machinery Speeds and Capacities for the Hedium Soil ------Speed*------ High (In) Size Implement OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO :7u1227711 111111 222222 222222 222222 888888 6666666 666666 222222 222222 000 0000 Eggggggggéhhhh 2222222 777777 888888 777777 000 2222 ....................................................... O O O . 0 C . 145141.70.“ 5232) 52259 “hid-“L7H 212.323.) “hunk-4h“ 1451451414 “5651““ 72/777 555 3333 000000 222222 000000 000000 hhhhhlfl Egg 2423 3%33 9010264 222 1413““ 88838888888888888888888777777732/888888 IIIIII 888888888666/0 111111! Kg 000000 000000 000000 000000 0000000 000000 000000 000000 0 883888833838888888888888888888886666666666668888888 70 70 70 0 778007 0‘70h8 9.11.670“ 3071483030 Zilrbogomog 021.26..“ 909337“ 000 003 C O O O O C O O O O O O C O O O O O O O O O I I) O O I O O O O O O O I O .( OOOOOOOOOOOOOOOOOOOO 637.210 553.5] 8203‘ ZgZIWIbpzug 22hgr62hg 100.870)761Q:H32 37.] 21.43 B m r \-I d II\ P m I r 00 ee .0 a ( e ) o 6]]‘66)OOOOOO) ) H rug/6666 pl m t Ehhgmozgmggh mglshogo “7777777.”.03270(07225 "0602 .. 00 e e e e e e( e e e e o e o e e e o e( eeeeeeeeeeeeeeeeeeeeeeeee PI eeeeeeeeeeee ) )000000k23l1k8763 ”lg-1.1! gghgaooooooo eghgelogz.lghhg m .. (m 999999 ’ ’ « V w "\I.‘ t ( \ml el- ell cl 0' II Pl WMMWMWMD D P T. t 2 r. U c (\ tttttt .' OOOOOOOOO p c I M ‘tttttt 9.. el 9 U I S .Ittt poooooom a .. n cwwwwmmw- a 91-.-.-wwww BEBE-Bad S S .l RRRRRRR: W .I 3 n n NbRRRR ....—..n f .I r W.......ru C bUUUW-.—. azgi f h p 208623 0 .l U. . . 7365 H] T 0 C S R111 F B F 5.52ch Low (Km/hr) 52 ------Speed*------ Table h.2 Hachinery Speeds and Capacities for the Hedium Soii High (In) Size Implement - a . . 0 “5|“; 2020 6‘h6530. 5 79'82 6802605 “6797..“0. — - . p a 0 80273.“. 9.70080 1181060. o “2‘62 26030 91.08.3580. 5 11223.“ Illa/.225 Ill-1122b" . u . u 8 7.|8“‘9 2&2208 “17'6hou 0 221110 £21110 “3752'“- h 0 0 O O O 0 0 0 if E. - hhhuhhh 888838“ . c n c 0 000000 93:52; rug. 7 666666 6666666 662666" . u 0 2kg; 2160/80 2160680“ 3 gcglzeoZQZha‘ta ZOZha‘e’" ) \MI - m l\ . ( r e. a r e \II c O t m . t n l\ . co . 266/0666?! . ) c Ila/7777773 — m 0' P OOOOOOOOOOOOOO - ( 1.333... 0000000 mooooooo" "Wfff-‘Ifu‘l‘l OOOW’OOWOa "ONOWQOOOO. .‘W‘nnn:nonn.°|www WWOPOOOWWWW- a«3««««««q««aaaaa «manaarm Mb" 335°62ng 2086:!“ . * Source: See Table h.i 53 Table h.) Machinery Speed: and Capacities for the Fine Soil Hax. peed*------ (Km/hr) ------5 Ann. Use- (Hours) hOO (Ha) H3. Plow(m) 200 (Ha) FE High Law Med. EFC ( ) (Ha/hr) (m) Size Implement 6]]‘66‘IOOOOOOMI abhgmozha Mia/o.“ ”26748 030 07777777DO§O(0722.H "3% ...... O O O C O O) O O O Harr( ) 1W 2222222 2222222 ....... hhhhhhh 888888 0000000 888888866 cocoon-(ea. reg/0666 r. .mnmnwnmmnmmfiiuumkimkifimesuugmmmmmmmmm mmmwwmm m m n u u uuuuuu .. .. a. m. ....... n oooooom e e n ewwwwwww- 3333386 3 3 .l RRRRRRR?» . . . u - . n ll .I P. W. . . c - g - r 223' f h D. zgzc .l T 0 C S Raul] F O O O O 1"" I‘ll-II Field Cult'vt i E 888 0000 666 huh!“ 888 2222 I O O C \II II\ \H“ r. m c ( “ton-Ohm 0".ll'.|wwwm ‘nnanRRR buuum- . o u u. g c 4665 sglc‘l 554 Table h.3 Machinery Speed: and Capacities for the fine Soils ------s eed*------- ( m/hr) FE High Law Med EFC ( ) (Ha/hr) (m) Size Implement . c n 0 708372 71613426 90.58500. 5 70111462 690070 09'05- . - c — 0 “023“ gIZh] 800—5800. 0 5003.“ aileron-.1 169000. c c . - 8 GOZZS 023?; “51-7108“ - . . - 0 222222 880308 gig}. a 00000000000000000000 c u“ C‘AE‘AEEJ iiiii‘eia hhhuhhh“ . u 2 008 2222222 7Zl-I777. eeeeeeeeeeeeeeeeeeee - 3 “huh-“I“ 332% $23“ . c u I“ 8800 huh-“hunk. 72,7777" 6 gang “huh-“ha“ hhhuhuh" . u u 0 000000 pal—2‘22?) 52222;. 7 666666 6666666 6666666" . u 0 2kg; 21.60680 21.60680" 0 00000000000000000000 3 8623.022“; 22kg" a. )el ell m m u r C \II n O t m . t n II\ a so ' 36666666»! . ) c 177777773 . m cal P 00000000000000 o I\ 1.30.3. 00000 000m0000000- m ”lift-Cuff! o 0 two”, 999999999 . imAnnnnnnimmwoawwpmwwwwww. bR KKRKKKTRRRRRRR Ran—KRRW" m.3........-: cu. - “Hogzsgzgg 2086514 . c “all "all"! Reluelugll . a * Source: See Table h.l 55 Table 10.5 Projected Systu Costs (Coarse Soils) 16222 132142 8951015 7081.050 16‘18 1.128? 191.6 7252 208 8633 1680.314 6137 13.6262 86163 08626 008 701-9 . o . u an. 118uho g2 ‘83 h 1277 séhhs 2kg 351-2 lug 3 37.“ V n o 2323 2222 cl. E1!” AA " 1I‘I.||2 . 609'; 2300.“ 7879‘s 1360] "IIJ1II 31327 “phi-(67 26"] 336614 802 008 o o gal-cl.“ 00072 $122 1821-8 0 2,02 93606 .8637 82‘.“ 1?; ‘06 2729 tv 11:21.00 egg £13 013% oz 22 £9,863 3629 “28“] $29) 21.3 9.1.29 ) o o ‘8?07 ghgl 1862; lb“; "§ 3““; “3? 3‘2 [bu-“32 22.“ 8501 5 TP 22232 2"“ ul- 26" ( "12 ‘ t ‘ r 362 o 0 9km...” c b .......................................................... 6 . 825' u. L 15. . t 3 66'.“ Y .I all ............................................................... £6 u 0.66 d F 22; C t c . .IJ a. O n r h . .' u . n c 825“] 20g §?7 “2786‘ 105 Ill l’i". 180612.“ Gal-5‘10 05385 21.1.1.3 32 307 . PI . 2&2: Ens «(goo 21%] I-V‘vos “an“Z‘Z) 07.0187 361.3 81% 819 ‘08 o . c “2308 0‘23]. 03283 6326’ gvlth 03'2988 82%2 7.2-“gal 8089 1.60 [4665 o R n 21.3“ 3121' g, 22% “quhwg 2222222 353 IIIIIIIIcI. ‘12‘11 225 20h8 . " 221§J “6 o p c .m u ’t. 2““; gang 80"th “222 203l5 616163 62“; 8665‘ 335 223 ‘2’“ . ‘o 6708; 1232 029081- 1123 218-1}. 2233 “2% alga “16179 81-9 287 pl 0 - “81‘0“ ““780 81625 $615 3822 $20 2320.“ oazlho 31.03 7.18 2; "C. 3221! 0222' 172% 832‘ 0866-?“ Dal-£32 g ‘1 hi2 7“th '1 gho W n 2"] .I 1:2] cl 1 3.18 c * o )- Kw“ . - 7g; 3&3 80"“.‘0 “£25 660?...) 0023 03“; “00°00 aha-kl 35 0000 w c. 678; 11232 02;] Illa-“6 £18; 003'; §§ 652% E7666 8; 0000 .I . g1ho.“ “14780 81625 glib-IS 15822 2176; 1'60 5 3‘2 8]“186 3 0600 N r o 329“] 0222‘ 172-(2 832' 08665“ ‘86th gZIII “3“th 822‘ III 0%] S . 6142210 5.4.5.521 87503:. ZgZIIWIDSBHg ZZth’bzhg 1:09n0.i:.nv\l~!..bm 145.52 .52.... 2143 r \I d ( f m I r. no on . ( . ) o 61‘166)000000\I \I H r; r m t . éhhglmozglfigghhagzahogo ”lighhvglMO-Izgh "8;“ .. ......................................................... .)000 0000 5h332 I £8753 «Z32 22 9.65.4 h 33 88 000000 fizhgoloa-Ibiqlbkhg‘m .. M h OOOOOOO ' w v ” .ynlu‘l. '- I“ \W W m M w M M MD 0 P T t 2 r U I l\ ttttttt g m 0 O O O I D 'I a”. c Inf-fut m . ttttttt t .l .l .I n 932:3” .. .. n c333? a oasiwmmw M. . 333333 d S I .I RRRRRRR t m .l 8 n n anRRR . ....... n f .I r. M ....... f e bUUU w. . . . .I 3291-5111 I f h D. 208,02 0 0 .| U. . . 9865 H1 T 0 C S R111 F 3 F 5.321....1 56 Table 0.0 ......... f[SJSSESE-5Z3329-ESEE!-$SSEEEE-§Eil§l--- ------_-_ ................................ Prcjected System bCosts£$)------------ Implement Sife _ New Owner. Repairs Fu Le bor Aver - -- - 5'3 -Ezissiélt---52:£ ..... 5:122: .............................. 522221- Nagm “AP?! c.t°r(n). 3. 0 60700 56217 187800 03880 27165 228950 25089 8 ”32:3: 2:: 333 3:3 333 g g 33:33 3333 i§:§;: 322 288 (333 20 :2 : I 3 133 2332 33:33:“ 3 3 3:: 3: 3:0: 3:7. - - .27. 33:; “I 0 Cf“! 5:23;: 2:3 3:33 32:“ :333 : : 23:36 3 1 Row. 0. 2 200 2 12% 2 3 I I 333 g 3 g; Row.0. 000 I 3 2 391 _ - § 1:32.: 3§ :$:°: :03: 33:: - - 3333 a: 3-937'0. )3 i. 0 95 0 85 0 ,§;.3 - - A 521; ? -Row?8t. (’ 12. 2 2 000 2 0 3131 - - 1 0 2116 3:52.23: 3:: 33°33 :§§3 :9 ° : : 3§§2 32:3 g-Rawio. I 300 :30 :3 5? - - 223 2 0 ::~-3: 0% :33 '32 ' s : : '3 '33 g-rg::0. .0 8700 3 I; 3; ?9 - - 301 2 3503 * Source: Fern Hechinery Dealers.1983. 57 ----Projected System Cost($)-':::::::::::: Table 0.? Projected System Costs Medium Soils) Aver. An Fuel Labor Tot. 9 Repair 8 h»- prams» {Mm ' S ( Implement nual 3 . 1418:1220 2131‘). 142220 lab-M 10882] glut» {18143142 12.251422 Zn»; 3? : . 2.523%.) 222222 .l P.V. Haunt. Eost __ 3f- . o 833 2823 633 Iém‘ go Ids-i108; “3‘67 36111 0608‘ “g 003 . £670 2?; Sig-“2 ‘87)- ..ngJ 326 2627 532‘“ 917827 all. 29,29 . 537; 8.209 zzzzz 0‘7“: lag slog-1.8 3629 “23" 222014 2’5 229 . lap-{I07 8‘21 ”2'9 Zia-“g 0822 Bzzhhla “33' 3‘2 6‘2“; 22..“ 8; .Isllazo 6221.12 2327 “2786‘ 0&7 222627 62gslo 0‘55 1.221 0; 8° 7 . “slag “883‘ 0223 2‘51! 2271.5 2‘30? OZ‘ISQI 3‘3 809% 2‘9 ‘08 . 78“; 2‘18 wkl?o 682; 181.8% .3125 82%: 7.2th 028 272 “665 . 2‘15 32? “5.02, 22% '25“; 22223} 2;}:- sllll..|||s| 212112 225 2 03 22% 1“.in I; 3.328 80".“le “5‘22 66 9| 6‘6163 6252’ 82.191- 235 223 Ila: 22:2 flzé OJ‘IEO hail-[OJ 8‘9 2“, 31‘0“ ““780 81625 glib-I5 £322 £320 320.“ oszlho 3‘05“ 73 an; ““8221! 02221! Icing 832' 08125“ Oégz gslslln “32". Ian-1a." .lsl gho 7151; gas 80'5‘0 “a; 6609-05 67321 ‘23,. 0278‘ 1:122 2187’. SI 78 6 £68296 3 00 “8150.“ “14780 81625 $615 £822 lg 160?.) p.312 816786 E 3221 02221.. ‘7..ng 8321! 08665“ 12.8.32 32‘]: “lung: Run-(“3' .I.‘ fem-lam? 2"" I a el- 1 1' pg .0 778007 01103 370:“ 573000.30 gigogomog 011.22.“ 3;“ 000 003 O O I O O O I O O O O I O O O O O C O O O I O O) C O O O C O O O O C O I .( OOOOOOOOOOOOOOOOOOOOOOOOOOO 632‘0 {‘3‘ 82.3 ZEZ'IWGp-‘hg ZZhgomszlflg 1032.2. “kg ‘2] 2‘3 r \I d ( r I O .o f .0 .. .0 . ( . ) o 611%)000 00) \.l " P062266 r a t oooooooooooooooooooooooooooooooooooooooooooooooooooooooo )000000kzilnaz; “lg-‘1' gaug'ooooooo 6253.10376‘Zhhgm o. h ....... . ' w v n .‘I t r w w a m m a a no 0 P 7 z z r u a ( _ tannuu t a m ........ h c Itttm _ II- t III el. e. Poooooou a .. n 033:”: a o.-. .2-3: 33333 BM 3 8 RRRRRRR! .I 3 n n n Rana ....... f .l f W.......r I WUUU.... _ and; U (I h p 286‘!“ C . . . 7865 H‘ T 0 c s all] I. B F Sideline} 58 TIbIe 5.5 Projected System Costs (Medium Soils) ------------ Projected System Costs($)-------------- Size New Owner. Repairs T0t. Aver. implement (m) Price($)* Cost Haint. Fuel Eabo: _ P. !. Annual b-m 3.0 60700 562i] l878hb £3880 27l65 22895h 25h89 NH} KApplicatorh): IO-K nlf e 3.22:3: §§ £33 “I? 33132 E E iiiié I31; Wm 35; 333 33.5.3 333? E : 35%; 33%. I z-Row I2. 2 800 2 “h 6 I - - “OZIS bk 32:33:23 3 $3333 333 3;: ° : : 33323 35 §:33:EE3 Z .333 :2 . 33 : : a; 2.3 a§5§§3§iu M 3:3 ‘338 3‘53 @332 : : is: I :23 3.23333: 3°°3 3“ .883 : : 33°38 3'3 32333.: 23 £33 3?? €332 s 2 33.? £33 32323: 3.33 33.3 3.33 : : .3 33 5333 * Source: Farm Hachinery Dealers,l983. 5!) § 2 3 E é § 8 § 32 é a O7 % é é % 8 ? z 3 Aver. Annu 2 2 2 I I l I 1 ‘1‘2 . 2720lfl9 71521-6 801‘23 1.1.8601. QtJWIU ‘0 «(ilkqlog “51.27 gel-,1- .21862 “a 000.8 .- “$5 821% 6132/ 18723 20.“! . gbkog 26627 53].!“ “.512 ‘68 221.9 818 .lo 5‘; 0‘7“; 38827 2? 88639 “2.16“!- lflhgs 886 nmJlDfi-Dfig 2 207.844....9 Zhhg loaning 0.86% “3? 32;.)- (thg‘ 22..“ 33 3 12 ;;;}§;IIIIIIIIIIIIII .. I\ ...... n" 2101433 7.3.162 37961.3] 142785] Can-«H.165 9231.252 625210 004285 563259 27 £07 3:!" g; a fig 32 35 A? 2.3 a 23 y 5% 32 ? 3§ iii 2? 32 i; 5 éS as 15 6 §? kl £3 p Ownrcshi (m) Pricc($)* Cost ---u Projected System Cost; .00.... can... one... concoo)oooooo cocoa-0‘ ooooooooooooooooooooooooooo 1| glul S d ivator(m cocoooooo(oooooo(oooooo( ooooooooooooooooooooooooooooooooooooooooo z i 2 h 3 2 3 3 5 a z a a I . luau): tttttt tttttt lnplcncnt OH( on on on on am cm 0 Chiscl Plow : Boom Spra‘ F old Cult i t t t n - aw Row Row . . (agile! . Pl 0 o o o o o T: an i Offset D s 60 Table 5.6 Projected System Costs (Fine Soils) --------------- Pr rojected SystembCostséS)------------ 22'2"" 2:2- 22:25). 2222’- :22222 F . 22:52. HHS: supp! icetor (III)' E; .0 6070: 56%17 ”gig; 103880 27165 2239:; 251029 lO-K 2 0 0 - - H 12 2 2:2 22 2 2a 2 2 222 22: 2; 2 222 : : 22 22 2:23:52 PM!" ("012 2 800 gzgu o 3 - - 1.2 Z 1.71.0 22 22 2 22 22 2 a 22 22 E2222” 2:: 2222 22220 222232 : : 222222 22222 2:22;: '22 2222: 222 ‘22 : : 22°22 222° 22222 '2 22 22 22 :22 322 s 5 22% 22 2:22.222 2. 2,22 2222 .2222, : : 22222 2292 * Source: Fern Machinery Deelers,1983. 61 Table 5.7 Estimated Costs for tractors Tractors Initial Price (Kid) (5) 2° 22 22 8 22330 38 $1000 000 6 000 h 000 g h 00 2 22 32 130 g 00 10 00 11% g§00 2% 61088 1 8 8000 3 0 i 5000 Source: Farm Machinery 0ealers,l983. Implement qt. Power Re (Kw) Table k.8 ------KH/m--------- High Low Median 62 Draft and Power Requirement Parameters for the coarse soil _ --~----KN/m*-------- High Low Hedian Size (m) Implement “223.50; 023207. 2217700 3&2? 0730130 16.5002) 87.065.» 00232 gird/00 0 8&321 ZIIII 755322 632211 “33222 2111 2 11111 Q76§Lla6 . c . u g c - . u . u . c - u . . 222222 ““““““ 088088 00800 666610 lllllllllllllllll 222222 000 88880 . 111111111111 222 p . u " 000000 111111 666666 66260 000000 2‘67; ““““““ 111111 660666 72’ . . . . . u as; 11111 a; 2.3391; ““““““ 0000000 000000 000000 IIIIII 2" n - 660660 2‘23 ““““““ 0008 222222 2222222 000000 2 IIIIII 777 88888 c eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee . 000000 11111 6106666 000000 03/ 2222222 22; Egg 666666 53 08888 a rug; ‘‘‘‘‘‘ 222222 222222 222222 222 . . . . u u 23 06620 666660 000000 ““““““ 006’ 666666 00064 33 000 ‘‘‘‘‘ n 6666106 111111 123.) ““““““ 2.1; 0000000 000000 000000 £222.) 000 522; . " "giggoooooogggg IIIIII gi..... " 222222 000000 111111 222222 25 0000000 000000 000000 000000 660 n " 888888 222222 888888 888888 999999 0000000 222222 777777.888888 000 lllll ..................................... O 0 0 . 0 0 0 I O 0 0 O 0 0 . C 0 0 0 0 0 I O O I I - 222222 ““““““ 203 a]??? g; 1111111111111111111 g In“ ‘5th . 222222 222 c . n ee " 20007 02“8 p070“ 0030.530 zgogowogio 0230“ 027“ 000 207 " 632210 5.4332] 875332 Z221xm16§hg Zihgulbshhg quuozbimZhhz’Z 321. 765.43 - pl ) d 1‘ \II . r m I r m n 0 l.\ . \II a II\ . 6‘1166)000000) H r2226 r. M r “ 5“““2..MIM\030“0 W070.“ \Wgzahog “$50§(£210“ m0?“ My. eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee e e e e e e e " ”000000 “KI-“21.0702 Z221. 092“?“0000000 r02“g.]000 “Z““2IW “ .(mmwmmmn- a- ' m I- . . 0 r cl . W 0 0 P T t 2 r. U 0 .l. .0... . tttttt ' O O O O O O O.‘ p c ell pf‘lfff .P000000 0 C n c .I d °.i.l.lAflflnnn . 0000000 0 0 .I RRRRRRR‘ W .l 0nnn KKRKK soc-c..." f I r .......—l 0 00003....- .B228 3. h p W620i. 0 I U. . .Hoaz . H1 T 0 C 5 R111! F B F 52]"...- 63 Tabla a. 8 Draft and Pouar Raquiranant Paranatara for tha Coaraa Soil . ------- KN/ll*"‘-f --------- KH/m -------- Implwant Implalant Suaa High Low Hadnan High Low Radian Powar Raqt. (In) (MI) MN Ap licator(u): 53xn3?. 2.7 5.1 - 5.1 8.8 - 8.8 23 Nz-Tillo P1 ntar(a): Rou,0 . 12.2 1.2 - 1.2 2.0 - 2.0 26 lz-Rou.0. .1 1.2 - 1.2 2.0 - 2.0 1 1 -Rou. . .6 1.2 - 1.2 2.0 - 2.0 I; onou. . 1.2 - 1.2 2.0 - 2.0 1 ‘RW.O. “:102 - ‘02 20° - 20° f‘nwzo: g: ‘02 - ‘02 20° - 20° -Rou 0 0 1.2 - 1.2 2.0 - 2.0 R P1antar(m): ' -Rw.°. 12a 2 10‘ Ga 00 .0 O. ‘.2 ‘? 12-Rau.0. .1 1.1 0. 0. .0 0. 1.2 1 1 -rou, . .6 1.] O. O. .0 O. 1.2 -Rw.°. la‘ 00 00 0° 00 ‘02 -Rau. h.1.1 0. 0. .0 0. 1.2 E-Rou,o. g. 1.1 0. 0. .0 0. 1.2 E 'PW. a O 10‘ o. o. .0 o. ‘92 * Sourca: Saa Tabla 5.1 Implament Reqt. (KN) 0H8? Low Hadian P ------KH/n--------- High Table “.9 64 Draft and Powar Requirement Parameters for the nadium soil KN/m Low Hadian High 513’ rDIi!l"b\l AXUGXUOTI C(95flzjiflw oI‘IOERU-EJiiUTLVBHW. Plow(n) lZ-Bottom.0 3 -80ttma 0 lupialant 1'1111 ‘1]‘1‘ . O O O O C -"=‘,=l’s ,:l’=‘,=‘ C‘DK‘DK" AYOnXUn:U I‘l‘ilcl.‘ 222222 pO‘KUJmW“ ASO‘U7L:U_:.U“Q3,LU a aa)aa Pl Offset 0i: Chiaal Harr( gigué S A’uXU7LIU HAUKKS‘filD aaao aaaaahaaa 1(“1‘l’:|fl0‘f’“1i§ .w I U 9b Spring Too al9=l,:"=‘ Agonxgonmu O O O O 0 ‘111111 {(3:523R" .hfiFBLFHL7h I O O o O IchIIqu-‘l‘ql. Q‘,:":‘,:‘ 307Lybflffl a“hi’“h7“ aaaaaa “:0flxunxu nQNHXUOXU lijliJiifi nXUnXUnXU 11311111, nXUnXUnXU ’:":":‘ a o o a a alualal-IIaI-al. QQILRULXJnvnnzbbljfiLU .(. . .lebl{59. 7l§l£¢921mtgfifi?hliJ q‘Q1I0L29!1}D:fi?h2£J 0.] (I) -Rau. 0"! -Row. -RW. -Rou. -Rou 1 -RW. '§ 2 ITLITLlfiLIfiI lnzunXUnXUnv {2.7 vgf:) Fartii PIUC{9~2{JV AUQ‘L’hé‘ 211111 1.1111] ‘11111 .0.... nXUnXUn:U ...... I‘l‘l‘ 1 icator (a) : aijzlnnhruhZ“ aaa aaaaa ,=‘,=‘9s I]]‘l' A Kn?g a-Knife -knife l-Knife -Knifa IO 65 Tabla b. 9 Draft and Pawar Raquiramant Paramatars for tha medium Soil . ------:KN/m* ............... KH/m ........ 'mp‘w.nt Implaunnt filf. High Low Hadian High Low Hadian P07fia; Raqt. M NH) KApp1icator(m): ifa 2.7 6.2 2.3 5.8 12.h 5.8 8.9 2h H§-Ti11° P1 ntar(n): -Rou.012.2 2.6 - 2.6 2. - 2. g 12-Rou.0. .1 2.5 - 2.“ 2. - 2. 1 -Rou.0. .6 2.6 - 2.B 2. - 2. 2 -Ro~.0. . 2.5 - 2.6 2. - 2. I; 'ROH. 0 he 20“ - 20“ 20 ' Z. ‘ z‘ROH.8: go 20“ - 20b 20 - 2. '8 -Rou.0 .0 2.“ - 2.6 2. - 2. Rzu Plantar(m): Rm 12.2 1.2 0. 1.0 2. 0. 1. 18 12- Roufi .1 1.2 D. 1.0 2. 0. 1. 1k ‘ -rw:0a a6 ‘02 o. 100 20 o. lo ‘I -Rou.0. . 1.2 0. 1.0 2. 0. 1. ~Rou. h. 1.2 O. 1.0 2. 0. 1. z-Rou.0 g. 1.2 0. 1.0 2. 0. 1. -row.0 .0 1.2 0. 1.0 2. 0. 1. 5 O r * Sourca: Saa Tabl 66 Table 6.10 Draft and Power Requirement Parameters for the fine soil -------KN/m*-------- Implement Power Re ------KH/m--------- qt. H8. Plou(m) Low Median High Low Median High Implement (KW) Size ); 11'1“] 1‘32] 111111 222222 e a hhhhhuua ‘II‘II 11"]] 6‘ ‘12)000000) Ehhgmozha (”bud-.3070“ ”060KB." 00000k2310£a§ 0 9 I 0‘ mmmmmmo tttttf— tttttt 000000“ 0000000 .-—...fl 29223.” ...... Offset Dis Chisel Pl ...... Z321. 906;"0000000 626g"1000.|20“hg m Spring Tooh “bu-“ha“ “$6661“ IIIIII] ogt I‘I‘I‘l‘ “M n. “U. Q -Rou. -Rou ‘ti I Fertilizer 8266.).“ 001.22 610-60 221: 69907“ 2 ‘‘‘‘‘ 0065“ 963 0065 3.25 888888 111111 .666 fig; iiiiiiiiiiii fig; 000 6141414..» llllll 331)..) .ll.lu.|.l 141414661“ llllll 222222 9019 . . . . . 000000000000 O O O O O O O o 0 000000 000000 3.5113.) 000 000000 334 000000 777 gig) ..... I O O O O 0 O O Q 0 O I I I O O I O O O O 33% Egg llllll 888 146614.“ 614565..» 5:25 11111 666666 666666 888800 222 353 O O O 0 O O O O I O 0 O O O 0 O I O 0 O ..... 000000 000000 6166666 666 77777 . . . . e e e e e e eeeeeeeeeeee e s e . 000000 000000 222222 888 222222 222222 666666 000 $3 llllllllllll 666666 888 ZI~I~I7 llllll lulu“ 0729966 0% 000 3.4567 ”0026).!6663 .521. 7623 m h ea PI m ‘1 o ( M r. 5012(02‘1614 m0?» W I t ( C .YI I r e- r. U C 10...! D. c .1 Pfffff s elitttPIIOI-alle'e' d O.l.l.l.Annnfln .I Innn KKKKK . WUUUB. . . . . .l . . .Hogz B F 531"] 67 . . Implement Median Power Reqt. W Low Median High Table b.lO Draft and Power Requirement Parameters for the fine Soil -------KN/m*------- Low Implement (KW) High . . . . . . 0 81616.51 261418;. - a c . . S 1366:“th 008088“ a“ 3% lllllll g .I . c . c . will/2]., 808888. — e e e e e e e eeeeeee - 0000000 0000000 . 5 226.664 “huh-“Id.“ I“ 323.53 “nu-““61““. 3 0000000 52‘? 7 2222222 IIIIIII 088 0. u eeeeeee e e e e o e e— 0000000 0000000 .— u 3 2222222 hhhhhhh” eeeee e e e e e e e e e 7 332; 3323. o c . “I o 7 216000 2600”“ . ....... I C . . 0 0 O 20.22“; 22kg" . ee )1 el- all .0 am am . a 1‘ PI ee .T pl . \.I c O t m .0 t n ( u. . 066666669: .5 c I‘ll-[77.1]. . cl. P e e eeeee t a e e e e e- ee .l. 0000000 "0000000. 0 pf‘DIODDDO.DOODODO.c Dal]. l. .r. Aniwmwwwwmrwmwmwww. u KTRRRRRRR RR rRRRr. o 3......u..~l.°-.—..-s "50:00:!“ 200")“. 68 Economic giglfietél; of Machines "iRESJ.Ii§§'E§Z;""""""T'2'3"" Down Payment .20 Current Interest Rate .lh Discount Rate .l2 Machine Inflation Rate .lO Fuel Inflation Rate .l5 Labor Inflation Rate .08 Source: Dr. Rotz,l983: A riculturaI Engineering Department. ichigan State University. 69 The machinery cost model (developed by Rotz, et. a1., 1981) calculates the annual cost based upon the following equations The future cost in year j is Future costj a current cost (1 + Inflation Rate)j 1 The present value of a cost in year j is Present valuej a Future cost/ (1 + Inflation Rate)j or current cost (141W)3 2 1 + Discount Rate The relationship for determining the ownership cost before income tax deductions is indicated by ownership - DB + P RV( + W) 3 i(l+i) (1+1 _where DP a downpayment P a principal and interest loan payment m a loan term in years i = annual discount rate a . annual inflation rate for general equipment, insurance, etc. and RV = remaining value - Rv1(nv2)“(IC) 4 where RVl and sz - remaining value factors IC 2 initial cost of machine n = number of years analyzed (machine age). 70 The tax, insurance and shelter cost is considered as a constant portion of the initial cost of the machine. It is the current value which should be inflated to future cost and discounted to present value Tax, insurance and shelter 8 S(IC)Z:(1+ + i)j 5 where S a portion of initial cost. Repair and maintenance costs are determined in terms of current value. Repair and Maintenance = Rclj zgikiooo )«C2 (WCZ ] (1 - i 6 where R01 and RC2 - repair and maintenance constants USE 8 annual use of machine (h) Fuel costs are calculated as the product of fuel price, fuel consumption factor for the tractor or self- propelled machine, power rating of the machine and its annual use. Fuel and Lub = 1.15(rp) (HP) (FF) (033120.442 7 j=l l - i3 where PP current fuel price HP 2 power of tractor or self-propelled machine PF - fuel consumption factor b - annual inflation rate of fuel 71 The machine labor requirement is given by Labor 8 1.1(W)(USB)§E l_:_é>j 8 I=1 1 + i where w - wage rate C - annual inflation rate of labor costs The income tax benefits received by the owner are determined by Tax benefits - n . . C+tz 12;] + 1;] + (115j+fiflj)(1+g)3 + Ej(1+p)3 + ”(14913 9 1-1 (1+ilj where C 2 investment credit (0.10 * initial cost) t a income tax rate of owner Dj - accelerated cost recovery deduction during year j Ij a interest paid during year j TIS - current cost of tax, insurance, and shelter RM 2 current cost of repair and maintenance P a current cost of fuel and lubrication L a current cost of labor Subtracting the tax benefits from the sum of all costs produces the net present value cost of owning and operating of a machine during its lifetime. Thus, (10) is a combination of equations 3 through 9 72 pvc - up + p (1+11m-1 - RV(l:s “ [i(1+i) ] 1+i n . . +Zma¢mmJL_m1_m__Lm_£L+3+ +3'+'+j j-l (l+i)j n . . -C - t2: '-+ ' '+’ + J + ' + j + + j (10) 381 (1+1) where PVC 8 total present value of all costs and benefits. Multiplying the present value cost by the capital recovery factor results in the annual equivalent cost ABC . PVC 1(1+13“ (}l+i) -P} The result of the machinery cost model is presented in Tables 4.4:- 4.6 which include projected system costs of ownership, operating, and the average equivalent costs for different sets of equipment. 4.2-2. undel_cen§traint§ Major constraints included in the model are suitable days, land, labor, capital investment, and power require- ments. A proper set of data on each of the resources and constraints were collected and used for the formulation process. Weather variation and its impact on the number of suitable days available for soil preparation, planting, spraying, cultivating, and harvesting, is a primary factor 73 in a machinery selection model. Machinery complements are selected based on a framework through which the assigned operations are done within a predicted time period with certain probability levels. The probability of 70%, for example, means 7 out of 10 years. Rosenberg, et. al. (1981) developed a computer model to simulate the impact of weather variation on the number of suitable days available for field operation in different locations in Michigan. As a part of the analysis, the model also considers the various soil textures. The data generated from the Rosenberg model are used by the linear programming model for various field operations. These operations are indicated as soil preparating, planting, spraying, cultivating and harvesting- This data is given in Tables 4.12, 4.13 and 4.14. The labor constraint consists of total annual operating and hired labor used for primary and secondary farm operations. Data on labor were collected for three average sizes of large, medium, and small corn farms in Michigan, Table 4.15. Such farms do not have the same set of operators each year. The model assumes a budget as a constraint for annual machinery investment. This value represents the purchasing power of a farmer on machinery investment which depends on the farm size. 7'4 Table #.IZ The Maximum Annual Coverable Land(Hectares) in a Suitable Time Period. _ --------------------- Sci] TYP. ------------------ Implement Sife Coarse Medium Fine m MB. Plow(m): lZ-BottoM.0.z6 6. #00 #00 #00 -Bottom.0. l g. #00 #00 #00 -Bottom.0.#l . #00 #00 #00 ~Bottom.0.#l 2.0 #00 #00 ? l ~Bottom.0. 2 1.0 2 2 2I é -Bottom.0. 0.7 l 5 l# l Tandem Disk(m): 3.00 2.0 #00 #00 #00 . 0 . #00 #00 #00 . O . #00 #00 #00 . 0 . #00 #00 #00 .#0 .# #00 #00 g# l.80 1.8 290 25# 2 Offset Di.§ m): . 8. #00 #00 #00 Z. l. #00 #00 #00 . . #00 #00 #00 .3 .3 00 #00 #00 . . #00 #00 #00 .# .# #00 #00 #00 Chisel Plow(m): Z. Z. #00 #00 #00 . . #00 #00 #00 g. g. #00 #00 .Z .Z #00 #00 #00 2. 2. #00 #00 #0 s . T '88" (1‘8 352 3&0 32 prin oo arr : g 6.0 6.0 #00 #00 #00 f. g. #00 #00 #00 . . #00 #00 #. #. #00 #00 # . . #00 #00 #00 . . . boo boo 271 R Cultivator(m): l -Rou, O. 12.2 #00 #00 #00 lZ-Rou. 0. .l #00 #00 #00 l °Rou, 0. .6 #00 #00 #g -Rou. 0. . #00 3 # ? -Rou. 0. #. g 6 2 g E-Row. 0. . 2 11 -Row, 0. .0 2 l 9 l 3 Fertilizer Sgreader(m): 6. 6.0 #00 #00 #00 2. 2. #00 #00 #00 . . #00 #00 #0 #. #. #00 # 0 g. g. #0 g 2 - - 33 ‘5 7 Boom Sprayer(m) 1.0 11.0 #00 #00 #00 l0.l 10.; #00 # 0 £6 3. a. #00 0 -2 -2 35° :2 2 a 6.# '6.# 3 66 2 0 Field Cultivator(m : Z.3 .8 #00 #00 #00 . . #00 #00 #0 t. t. :08 #80 g g. g. 32 326 . . 3 l9 0 Subsoiler(m): -Unit 9.0 #00 #00 #00 -Unit .0 #00 3?; ?g0 l-Unlt 1.0 232 0 Combine(m): 1 -Row 2.0 #00 #00 #00 -Row .0 #00 #00 #00 -Row #.3 h h # -Row . 6 6 6 -Row .0 2i 2] 21 75 Table 1.. 12 ° The Maximum annual Coverable Land(Hectares) in a Suitable Time Period. 'f ------------------- Soil Type ---------------- Implement Size Coarse Md Fine m 15733222333. """"""""""""""""""""""""""""""" lO-Kn Pf #00 #00 #00 8- Knife 6.3 #00 #00 #OO - ni e . K f #00 #0 #00 22m- . i2). ii. i n: e . 5- Kni 3, ; no 109 lo M -TiII cPl nter(m): l -Row.0.12.2 #00 #00 #00 IZ‘ROH.O. .l #00 #00 #00 'g-zwoa- .. :33 :33 33° - ow. . . -Row.0. #.§ #00 31 gig z-Row.0. i. # 0 ; -RWQOe .0 3 h h 207 R Planter(m): iz'fiw'S' ”'2 1:88 1:88 1:88 - W. C O , a... . . 1.00 #00 I. o — 6H. . #. #00 #00 g # - ow. . g. # 0 #00 32 -row.0. .0 3 # 309 2 76 Table #.l Total Suitable Days for Field Dperat7ons on Three Soil Types and three Probabi ity Levels (in Bad Axe Michigan). ......... 3c - - ., 53 _ 3c ------- a a t e e a Time .Fine Medium Coarse Fine Medium Coarse Fine Medium Coarse 5;? fig 2:3 2% “2% $23 32% 2:2 2:3 3:; g: Y - O O O O O 0 e O O I- I e e e e ‘ e e e e e 33%. 2 -7 I2. I . l .0 I .6 ll. Ii. Ii. I .0 l .2 June I - . . . . 10. II. II. I .0 I .h July - I . II. II. I . I2. 12. l2. l .0 I . July I - I2. I2. I2. I2.2 12. I . l . l .2 I . Aug. l- l2.0 l2.l l2.2 l2.2 l2.# I .6 I . l . l . Aug. l6- II.0 II. l2.2 I2.# I2.6 I . I . I . I . Sept. l- lI.l II. II.# II. II. I . l .0 I2. l2. Sept.l6- . . .8 ID. 11. 11. 12. 1 .o 1 . Oct. l- . . l .0 l0.# I0. II.# II. I . I . 0ct. I6- 3. l . II.8 II.I II. II. II. l2. l '8 Nov. - 8 . ?. g. I0.0 I0. II. II. l2. I . "We 7 -3 Os 0 e7 302 3a he 502 Se 3 Modified from: Rosenberg.l982. * Estimated based upon he other values. 77 Table h.lh Total Suitable Hours for FieldOOperations on Three Soil Types and three Probability Levels (In Bed Axe Michigan). --------- 80-;-------" "-;---'--50-'------'-- -'-;-"'---30-'--'----;- 1:391:91. 12 1° :1 31 3 2°° 3 a 6: $111.29; 2} 2g, 32 ii; 155 13. 5 2 31 21:13:11?- $3; $3 $3 1;. 1§ 2.1 .3 2.3 :22 21:21:29 23 10% s 1.3 1 128 12 1;. “E Row Eulg. l 156 lhg l g l 8 16: 16 I g 15 151111....223 311 31; ii. 131 11. 23; 122 121 Modified from: Rosenberg.l982. * Estimated based upon he other.values. _ . (Fert. Appl.- Fertlizer Application. Fall Disk.-Fall Dishing. Spr. Disk.-Spring Disking. Field Cult.-Field Cultivating. Row Cult.-Rou Cultivatin . H3 Appl.-NH3 Application. Chis. Plou.-Chisel Plowing. pr. T. Harr.-Spring Tooth arrow). 78 Table b.l Labor Hours Used for Corn Operations 1n Diffrent Years in Michigan 1g; 18° 1;. .11 13° .1-1° 1 1563 21 5 35 5 7 6 55018§ 1gp lg): 52 °'. 5°12 111:1; l 6 l 3 III 290 M: 512. I I 62 2 h 26 2 I02.I6 iggé {235 2 g; ztio 70%; $76176 1§§§ 111° %8 °; 2111 .3132 1 2 22 2155 3 9“ 37“ 5 3- 5 l l 8 l )6 2. 2 £33 £1. .2§§ .zii izié £3111 l§§8 l‘ééé 2&3 ill}. ‘3‘: i°§z$i 1 0 1957 I; 3 3&12 312 57 . 9 l§§8 11°; 13 1113 2311 151:3: 1 0 235 2999 250 7 57 6 5-23 I l l0h 6 h I 8 6. 6 13% ......... 1.51%----“532----.§§§----2§§€ ........ 51:31 ............ Sour rce: A ricul tural Economic Reports(l973- -8I) .Tel Farm. Department of Agricu tural Economics. Michigan State niversity. 79 Another constraint included in the model is the minimum size tractor for each tillage and utility tractor. 4.2.3 Will. The first set of variable coefficients are the maximum annual operating hours which each implement can use. These values are determined by the machine effective field capacity, BBC, 3 WEE EFC 10 where EPC - Effective Field Capacity of Machine (Ha/Hr). S a Speed of Operation (km/hr). E a Field Efficiency (%). Maximum Annual Use (hours) - A/EFC 'where A - Land (hectares). The second set of coefficients are the land covered by each machine within a time constraint. Annual Land Covered (ha) a EPC x H where H - Annual hours available for each operation. The total amount of operating hours for tillage and utility tractors are set equal to the operating hours of_ tillage and utility operations and are the other set of coefficients. 80 The annual repair and maintenance and ownership costs, and the implement power requirements are among the major variable coefficients used by the model. 4.3 W The power requirement is a critical parameter in a farm machinery selection model. Farm implements should be properly well-matched to the tractors based upon their individual power requirements. Oversized implements will cause tractor overloading, excessive tire slippage, higher incidence of tractor breakdowns, and unsatisfactory operation. Undersized implements on the other hand, will result iJI inefficient operation, low production, and increased costs. Tractors are the power components in machinery systems. If several machines are pulled by the same tractor, their power requirements should be similar. This relation causes a constraint on the implement selected. The maximum drawbar power is normally a good criterion on the performance of farm tractors. The drawbar pull is affected by soil type and soil conditions. Soil conditions in the field vary from firm, compact soil to very loose, freshly tilled soil. It may also be dry or wet at different times of the year. Timeliness is a major factor in estimating the size of equipment which directly specifies the tractor capacity. 81 Larger size machines and tractors are required to do the job when only a few days are available for field operations. It is also difficult to obtain proper data on the power requirement for various machines and soil types for one location. Many sources, including textbooks and research papers are available to provide such data, but they do not present a standard form for various machines. Data collected from several sources shows a wide range of variation between machines and across soil types and will lead to inconsistency. Unrealistic results on the number of machines selected by the model will be obtained due to inaccurate values on the collected data. A small error on power requirement data will result in considerable changes on the number and sizes of machinery selected. Field measurement on power requirement is considered to be the best method for a location under the study. Many scientists have performed research studies on power requirement measurements in various locations. Vaughan, et. al. (1978) made a set of tests to measure the energy requirements of several primary tillage machines including moldboard plowing, chisel plowing, disking, and no-tillage planting both with and without under-row ripping. Fuel and labor were considered to be the main inputs used in the system. The time constraint of the 82 field operation or timliness was included in the study. The results on fuel requirements ranged from 18.0 to 68.1 liters of diesel fuel/hectacre. The study also indicated that the moldboard plowing system with under-row ripping needed the highest overall energy requirement (3492.5 kwh/ha or 307.1 liters/hectacre of diesel fuel), while no-tillage production required the least (2944.9 kwh/ha or 258.9 liters of diesel fuel/hectacre). The indirect energy inputs for pesticide, fertilizer, and machinery production were also considered in this system. Self, et. a1. (1983) made a field measurement on draft and power requirements for various implements related to the area of minimum tillage in Oklahoma soils. The primary and secondary tillage implements used in the experiment were (a) the moldboard plow, (b) a chisel plow with points on 30.5 cm centers, (c) a chisel plow with sweeps on 30.5 cm centers, (d) a chisel plow with points on 51 centers, (e) a tandem disk, (f) an offset disk, and (g) a vhblade plow. The machines and tillage systems used in each location were similar to those that are used in this study. The fuel consumption was chosen as a basis for power measurement. The fuel consumption equation developed by Self, et. a1., (1983) 18 MW Hectare (ha/h)2.7(kwh/L) 83 PTO (kw/m) is found by dividing drawbar kw by the width of the implement in meters and by a constant used for soil texture and tractor. These constants for firm soils and tilled soils were estimated to be 0.64 and 0.55, respectively. The test results on the silt loam soil indicated that the moldboard plow needed the highest power requirement (16.9 kw/m at 25-28cm depth); and the chisel plow with 51 cm centers required the lowest power requirement (3.9 kw/m at 13 cm depth). The chisel plow with points, chisel plow with sweeps and tandem disk drawbar powers consisted of 7.6, 7.8, and 9.1 kw/m, respectively. The results on the sandy loam soil showed that the offset disk at 15 cm depth and tandem disk at 8-10 cm depth required about 7.3 and 3.7 kw/m drawbar power, respectively. The drawbar required for VéBlade on loamy fine sand at 8 cm was 10.3 kw/m and at 8-13 cm depth was about 7.3 kw/m. On the silt loam soil at 8-10 Cm depth, the offset disk used 7.8 kw/m while the tandem disk used 8.8 kw/m. V-Blades at 10 cm needed about 11.0 kw/m drawbar power. The comparison of primary tillage implements for the four locations consisted of 15.6 kw/m for moldboard plow (the highest drawbar power needed) and 3.9 kw/m for chisel plow (the lowest drawbar power used). Frisby and Summers (1978) tested the energy requirements of tillage and planting implements operated on 84 Missouri soils. A modified strain gauge dynamometer was used to measure the drawbar power. The three soil types clay, loam, and sand were tested for implement operation. The implements studied were (a) a moldboard plow (1.07 m width and 20.51 cm depth), (b) a chisel plow (3.07 m width and 30.76 cm depth), (c) a field cultivator (3.69 m width and 20.51 cm depth), (d) a tandem disk (3.97 m width and 10.25 cm depth), (e) a row planter (3.89 m width and 5.12 cm depth), (f) a grain drill (2.33 m width and 5.12 cm depth), (g) a row crOp cultivator (1.94 m width and 7.69 cm depth, and (h) a seed-bed ripper (0.97 m width and 41.02 cm depth). The draft comparison results showed that the moldboard plow had a greater energy requirement (17.3, 15.2, 14.4 .kN/m on clay, loam and sandy soils, respectively) than the chisel plow (6.0, 6.0, 3.5 kN/m on clay, loam, and sandy soils). The seed-bed ripper had 15.0, 6.1, and 8.8 kN/m energy requirements for clay, loam, and sandy soils, respectively. The row crop planter also had a greater energy requirement than the no-till planter. The draft and fuel consumption of the field cultivator were greater in sandy soils than in clay soils and it was the reverse for the chisel plow. Pornstrom and Becker (1977) studied the energy requirements and machinery operation for four summer fallow 85 methods based on the soil moisture content. A large variation in the power requirement was observed when the machines were used on the same soil. Data from various sources on speeds and draft across the soil types were collected and pulled together. The median values were selected as a proper set of parameters for determining the implement power requirement based upon the following relation DPB 8 2‘5 3.6 where DBP - Drawbar power requirement of the implement (kw/m) D - Draft requirement (kN/m) S 8 Speed of operation (km/hr) The drawbar power requirements obtained from the above relation were used as a set of variable coefficients by the model. The tractor size for utility and tillage operations was determined using Tractor size (kw) = 1.25 x Implement Power Requirement (kw). Tables 4.1 - 4.3 and 4.8 - 4.10 summarize the data on speeds, draft, the power requirement. 4.4 HQ§£1_R§§fllL& The optimum machinery complements selected by the model for conventional, chiseling, and no-tillage on three 86 types of soils are presented in Tables 4.16, 4.17, and 4.18. These are the results of interaction of all resource constraints used by the model. Changing one or more of the constraints will effectively change the size and number of machines. In comparison with simulation models, linear programming provides results in an optimum basis which is a realistic approach to solve farming problems. Tractors and implements are well matched together based on their power requirements across the soil types from coarse to fine texture. In conventional corn, for example, a 90 kw tillage tractor is selected to do all of the tillage operations on the coarse texture soil. The field cultivator (6m), used on coarse texture soils, is the largest power requirement unit which needs 75 kw draft power. The field cultivator also specifies the size of tillage tractor. In medium and fine texture soils, the implements are also properly matched. The tillage tractors selected for medium and fine texture soils are 95 and 120 kw respectively. The 3 unit subsoilers used in medium and fine soils of the conventional system are required to have the largest power units. Utility tractors are also properly matched to the utility implements used in the system. Similar conclusions can be drawn for chiseling and no-tillage systems. 87 copumth xu._.u: tauumuk oom__.h toucmvm 30m tosnu__a «-mxz uc_nE0u tu_.om now toun>.u_:m v.0.“ Loxmt w Eoom toumotom ton.._utom caum>.u_:u 30¢ sorta: cuoo» .Lom ammo Euocmh zo_d1mz uc_cum: 3x1oo _ 3x10 _ 3x1 . 2x1o~_ _ 3x1m _ 3x1 _ zo¢1~_ _ 30 1M. _ 30¢1m _ E 4.0 _ E . _ E a. _ zom1m_ _ 30¢1m. _ zom1 _ mu_CM1 _ mu_CM1 _ mumc31 _ E .4 _ E .: _ E o.o _ E o... _ E o... _ E o.w_ _ E 0.0 _ E o.o _ E m. _ zom1~_ _ 30¢1~_ _ zo¢1w _ in. n in. ” n...” w E ~.m _ E .m _ E m.m . o~_m LonEaz o~_m toneaz o~_m tonEaz ac.“ E3_uuz outmou 1111111111111111111 moxh ._0m1111111111111111111 Cebu .moaxk __0m ootzw1ocm _o>04 xu._.nmooLM1tocumoz «om cu.n Eta“. u o _mco.uco>c0u otmuuo: oo: < to» moc_cum: no tonEaz E3E_ 2... 033 «sh 88 2g-o¢ _ 2x104 _ 3x104 _ rosumtp »L___u= 2x-mo_ _ 3x-mo_ _ 3x1mm _ tosuate oun___e 30 1 _ _ 30 1N. _ 30 1 _ _ toucm_m 30¢ a . _ e .5 _ e . _ touou__an«-m:z 30¢-m. _ zom-~_ _ 20¢- _ _ oc_neou ms_cm- _ mu_c=-~ _ ms_c - _ ro__om saw a .4 _ e o.o _ e .4 . rou~>_u_:u n_u,. e o... _ a o... _ a .o. _ anagram soon E 0.4 _ E o.¢ _ E .4 _ toomotom tuum._utom zo -N. _ 3o -N. _ zo -m. _ roua>_s.au 30¢ _. 3 _ E m... _ ”w." I 2 use a o. _ e .4 _ . . ----mmmw--ummymm-----mmmm--ummmmw------mmmm- runes: .c_;u.= oc_m E3_uut umLmOu 1111111111111111111uo>h __om1111111111111111111 -1-1----ummmNH-Mwww-mmumH1wmm-mumm11NwMMmmmmmum-“mammmm1mmw1mmw1-mumu- 1 ct0u uc__um_cu otmuuo: com < Lac muc_numz mo tunEoz :Emu 0 ask _.4 «_nmh 89 3x-om _ 3x-m; _ 2x- a _ .ouuaLF »u___u= 2:-m__ _ 3x-mo_ _ 3x- a _ Louuogh oua___» mu_c=- _ mu_c=- _ mu_c=- _ .u__om 93m 30 IN- _ 3omlw _ IOMIN— — Gaza—=00 e .m _ s . _ e .5 _ Lounum_na<-mzz zom-~_ _ som-m _ zom-~. _ .ouca a __h-oz e o... _ e o.__ _ e o... _ ..o a. m 200m E o.o _ E o.m _ E o.m . Loumoan Lo~___uguu m~_m Lunsaz o~_m LmnEDz u~_m Luneaz oc_numz uc_m E3_uuz omgmou nunuuuuunnusnunuuunonxh __0munuunnnuuu uuuuuuuu a --:-AmamameuflflfimflmwmuflEmma-“mammmwmmdqfim----- cLOU ___huoz eunuuu: 00: < Lo» moc_zum: uo Lonsaz E3E_u o on» m..: o_nME 90 An economic comparison of three tillage systems indicates that chiseling and no-tillage have the most cost advantages to farmers (Table 4.19). Machinery costs are reduced when changing from the conventional to the no-tillage system. No-tillage has the lowest annual ownership and operating costs of all three types of soils. The total annual system cost for no-tillage on coarse, medium, and fine soils are 85, 100 and $lO7/ha respectively, compared to 123, 130, and $153/ha for the conventional system. The chiseling system has a medium cost value compared to the other systems. Machine costs across the soil textures from coarse to fine texture increase because of larger tractor size requirements. 4.5 W Timeliness costs were calculated after the optimum size of planters and combines was determined using the model. Edwards, et. a1., (1980) studied the agronomic loss factors for different planting and harvesting times for corn production. These factors were used to estimate the planting and harvesting timeliness costs. It is indicated, for example, that there is no corn yield reduction for corn planting before May 4. Timeliness costs were also determined indirectly by machine capacity for daily planting and harvesting operations. The timeliness costs were estimated based on the relations 91 50. _n_ mmp can; 00. 0.. on. ea_uoz mm 50. m~_. outnou -Uflwumm----:-mmflmflmm-------Mmmmmmmwmmm:-----mmm-flmm: mEoumxm oam___h coach Lot Amvmumou xtoc_zumt _macc< uooto>< m_.: u_nmp 92 DC = R x Y x P TC 8 A_x DC x TI D where DC - Timeliness cost factor (dollars per hectare per day) . 2 Yield reduction (% per day) 2 Yield (tons per hectare) a Corn price (dollars per ton) Timeliness costs (dollars) - Area (hectares) uwgwmw I - Harvesting or planting period (days) TI - Time increment (days) The daily timeliness cost for planting and harvesting times are presented in Tables 4.20 - 4.23. The timeliness costs are low at the early days of operation and increase relative to a daily time increment. The average timeliness cost of conventional corn planting on coarse soil is about 56.5 dollars per hectare while for the no-till and chiseling systems, the cost is 39.9 dollars per hectare. The daily timeliness costs of planting on coarse, medium and fine soils are 39.9, 43.2 and $56.5/ha respectively. The average timeliness cost of no-till on medium and fine soils is greater than the average timeliness cost of conventional and chiseling systems on similar soils. The no-till system uses smaller size 93 Table b.20 The Daily Planting Timeliness Cost for The Conventional System After May (400 Hectares). -9331!-95:E33139 ......... Egggggf ........ §§1153 ....... ggé?ggf-- 3 gégizg 383:1 g§§:§8 2 ,g :23 :wozgé .g :23 g isgéi§§ iggigé iegéigg 11 13232 in I 5 182322 12 1?2?. 2620.3 1?2?. 8 12 522 323 : 2&2 323 ........12 .............. ééaégia--_-_-__--_-2----___-%e_é;i§--- Total($): 22610.00 17290.00 22610.00 * Using An B-Row Planter. ** Using A IZ-Row Planter. 94 .Table 5.21 The Daily Plantin Timeliness Cost for The Chiseling System After Hay (hOO Hectares). -9311!-93:Ei§i€9 ......... §§$§:§ ......... §§?§§§ ....... g§§?§§--- g gigs; .iiézg 3.2.; a 12 222 1 1I§ l1é§lzg 3 A? h; 1 gzgé mg :.8 11 3260200 342 23% 132§IZ3 ii 3 2" °i éiiiiza 2 ........ :ééégia--- 1232191"------------l§§§9;99-------12329z99-----33519;99--- Average(S/Ha) 39.90 h3 22 56 50 Each Soil Type Used A lZ-Row Planter. 95 Table 4.22 The Daily Plantin Timeliness Cost for The No-Till System After May (“00 Hectares). -9211!-9?i§35135 ......... §§$f§§ ......... ?EE%EE-------;Ei?E§--- i 33%;.32 §§§2§é §%%2%i % i%%33%§ '%%gz%% 333323: 13 in 82 1h6h:%3 1EZOI°§ 333 33% 3% : 3&3: 3%” i3 3 5% 9.33 3533533 :iEEEEEECIIIIZIZIi5§§§Z§§IIIEiééilééiiiiiééiiéiéé: Average(S/Ha) 39.90 59 90 63 18 Each Soil Type Used A lZ-Row Planter. 96 Table 5.23 _ The Daily Harvesting Timeliness Cost for Two Different ---22921222-5:£5:-95£223:-3§-5299-§sssszssl; ................ -92ilz-§::!s:£izs .............. Eig%!2 ............... §§§%§§-- % $2333 a 332% 5% 35%;; 5 55 :2 32%.:33 3 333232 33323 :3 323%; 33. :23 :2 i 3:3 3.23323 :3 {333. 2 - 3 3?: ....... 223i: Tota1($) 33250.00 22610.00 97 planters which results in longer time with greater timeliness costs. Harvesting timeliness costs were estimated based on different harvesting and planting times. A timeliness cost is estimated when harvesting starts from October 24th relative to the planting time of May 25th-June 4th (Table 4.23). CHAPTER 5 MACHINERY REPLACEMENT MODEL 5.1 Wu Conservation tillage practices have been considered as effective methods in reducing soil and water erosion problems resulting from practices traditionally used by farmers. Farmers may not be certain about the voluntary adoption of conservation practices because of the lack of economic knowledge and management information. Thus, a study was conducted to do an economic analysis for machinery replacement problems. Perrin (1972) formulated a general mathematical model to study replacement problems. The decision criterion was maximizing the present value of future earnings. The purpose of this study was to develop a computer simulation model as a useful tool for researchers, extension agents, and farmers for machinery replacement problems based on the following objectives: In To make economic assessments for switching from conventional to conservation systems so as to verify the best time to switch to a conservation system and the optimum replacement time to keep the modified or new tillage system in service. 2. To determine reduction in soil productivity resulting from moldboard plowing or other tillage systems. 98 99 Farmers use various sets of machines for their annual farming practices and they need to know the length of time to keep the machines in service. The machinery useful life can be considered as an index for keeping a machinery set in service. Machines also can be kept for a longer time by increasing the annual repair and maintenance costs. The commercial useful life of a machine may not be a good criteria for keeping a machinery system because it does not indicate a relation between time and costs. The economics of continuing with a conventional set of machines or switching to one of the commercial forms of conservation tillage needs to be verified. Conservation tillage lowers the soil and water erosion, but in some regions it may still be profitable to continue with Conventional systems. In switching from a conventional to a conservation tillage system, farmers need to know whether to purchase a new set of machines or trade or modify some of the conventional machines. The model algorithms are so developed that one can have various choices when switching from conventional to a conservation system such as: 1. To keep the old conventional system and sell some of the extra machines like the moldboard plow, the field cultivator, or the row cultivator and purchase some new implements like chisel plows and sprayers. 100 2. To use a combination of old (conventional) and new machines for the conservation system. 3. To replace the whole conventional machinery set with a new conservation set. Soil erosion resulted from the utilization of tillage systems, especially moldboard plowing, lowers the top soil which reduces the soil productivity. Thus, the reduction of soil productivity is considered as a portion of the total annual cost. 5-1-1 ELQSLBEL_IBDMAQH The TRDMACH Model determines the switching point and trading time based on an economic analysis for a set of crop rotations through a cash flow method (Fig. 5.1 and 5.2). The linear programming model described in Chapter Four and the multiple crop machinery selection model (Rotz, et. a1., 1983) will provide a proper set of input data for the model. The process of cost analysis starts with computing the initial costs of keeping or new machines. Keeping machines are the existing machines used for a continuous conventional system or for a conservation tillage system without modifications (except for conven- tional row planters). New machines are purchased from the market and used for the conservation tillage system (chisel plows, sprayers, etc.). Row planters can be modified to no-till planters by adding some attachments like roller Read Machine and Soil Specifications 1 Determine Total Remaining Values of Extra Machines Determine Machine Initial Cost. Yes Determine Additional ] Cost of Planter Determine Ownership.0perating and Total Net Present Cost. 1 Determine System Annual Equiv. Cost and Replacement Time Erosion Ho Calculation Determine Soil Productivity L._ Determine grosion Rate and Reduction in Soil Productivity 1 Determine Annual Equivalent Cost Output Fig. 5.l Flowchart of Machinery Replacement Model. 102 00 10 101.!“ 00 15 l-1.Nfl Determine Machine Initial Cost Specify the Conventional ystem. Determine Total Remaining Values of Extra Machines to A Necessary D0 20 l-1.MM Set ill-l2 Years (New Syst-l SeecHy the or New or Modified Sat MR-ll-M Years(Mddifled System) System DD 25 l-I.MM mlepiacemant tin L tall subroutine [ROS STOP Fig. 5.2 Flowchart of Machinery Replace-ant Program “'1“? Determine Annual Iouiv. Costs I Determine Ownership Costs 00 35 J-flY.M Determine Repair snd Maintenance Costs Determine Other Operating Costs Determine the Total Net Present Costs 103 colters to the units. Extra machines are not used for a conservation system and removed from the system (moldboard plows, field cultivators, etc.). The total present values of a machinery set including ownership and operating costs are determined for life cycles from 1 to 12 years based upon an infinite time horizon (Perrin's principle of cash flow cost analysis). Ownership costs are considered as down payment, loan payments, tax, insurance and shelter. Operating costs include repair and maintenance, fuel and lubrication, and labor. Tax benefits are considered as incomes to the owner and deducted from the total present value of machinery sets. The total net present value of each set of machines (after deducting from the total remaining values of extra machines) is annualized for each replacement time cycle to present the annual equivalent costs. Subroutine EROS calculates the erosion rate and change in soil productivity on an annual equivalent cost basis. The switching is needed if the summation of two annual equivalent costs of machinery and reduction in soil productivity for a conventional system is greater than the total annual equivalent cost of a conservation system. 5-1-2 S!2£22L1D§.£BQ§ Subroutine BROS quantifies soil productivity based on Pierce's model which describes soil as a major determinant *of crop yield due to the environment it provides for root 104 growth. Several soil parameters, sufficiency of available water, sufficiency of bulk density (adjusted for permeability), sufficiency of pH, and weighting factors are used in BROS subroutine to determine the soil productivity index of each horizon. The soil productivity is also a summation of horizons productivity indexes. The changes in crop yield can be estimated due to soil erosion resulting from tillage systems (Fig. 5.3). The economic value of soil productivity is determined by the gross return of the crop. The net present value of soil productivity is calculated by discounting the soil productivity at the end of each year to the present. The reduction in soil productivity is annualized based upon the difference of the original and present value of soil productivity for the subsequent years of using moldboard plowing. The annual equivalent cost is also determined relative to each replacement time of a machinery tillage system. 5-2 MQQEL_BQB§§120§ The first set of equations used by the model are cash flow equations to calculate ownership and operating costs and annual equivalent costs of machines (described in Chapter Four). Initial cost of each machine is estimated by Initial Cost $ = A + B x S Determine the Soil Horizon Reduction Determine the Erosion rate 105 Read Soil Characteristics J DD 60 K-l.5 Determine So'l He' ht' factors HF(|) .' I". Determine Soil Permeeolity and Adjusuoent factors Determine Soil Horizon Productivity index P(I) Determine Sufficienc of Available Hater.AS(l Calculate the Soil Productivity l Determine Sufficiency of Suit Density.lS(i) I Determine Sufficiency of Soil Reaction.PS(I) -Determine the Reduction in Soil Productivity 00 35 1101.MYR5 RETURN Fig. 5.3 Flowchart of Subroutine EROS. Determine Soil Prod. in each Year Determine the Annuai touivalent Cost 106 where A and B are initial cost factors (Table 5.1) and S is the machine size. The second set of equations are related to soil productivity and erosion calculations. The soil productiv- ity index is described by Pierce, et. a1. (1983) as r H . F (A81 x 351 x Psi x wri) =1 where A51 2 Sufficiency of available water. 331 s Sufficiency of bulk density (adjusted for permeability). ’ Psi s Sufficiency of pH. WPi - Soil weighting factor r number of horizons in depth of rooting. Response of each soil parameter was normalized to range from 0.0 to 1.0. The variables used in the soil productivity calculation are AS - 5 x AW where AW'I Available water (if available water is greater than or equal to 0.2, then AS 8 1.0. If available tagger is less than or equal to 0.03, then AS a BS - (l-adj) + (adj x c) C - slop.l x BD + Inter.1 107 where BD < CBD BD = Bulk density CBD - Critical bulk density and C a Slop.2 x BD + Inter.2 where BD > CBD If BS is greater than 1.0, then Bs a 1.0. If BS is less than zero, then BS 8 0.0. The sufficiency of pH is determined by the following set of equations: PS 8 1 (pH > 5.5) PS a 0.12 + 0.16pH (5.0 < pH < 5.5) PS 3 -1.31 + 0.44GPH (2.9 < 98 < 5.0) PS a 0.0 (pH < 2.9) where pH - Soil reaction in each horizon. The weighting factor for any horizon is the integral of the curve between the upper and lower boundary (cm) of the horizon. wr . 0.35 - 0.152109 (D + (D2 + 6.4511/2 ' where: D x Depth in centimeters 108 The soil loss equation due to water erosion was determined by Tilman (1976) as A - R x K x LS x C x P where A - Amount of soil loss in tons per acre per year R - Rainfall factor K 8 Soil erodibility factor L8 = Topographic factor C - CrOpping management factor P - Erosion control practices factor The rainfall factor, R, is a composite measure of the annual average intensity, duration and erosive force of rainfall. This value ranges in Michigan from 40 to 155. The R value can be estimated by examining five soil properties: percent silt and very fine sand; percent sand (coarser than very fine sand); percent organic matter: soil structure, and soil permeability. L8 is the interrelation between slope-length factor, L, and the slope-gradient factor, S. The L and 8 factors can be combined into a composite topographic factor, LS.' The cropping-management factor, C, is the effect on soil erodibility from the kind of crop, tillage operation, length of exposure, vegetation or cover on the site. This is the most sensitive factor which changes the landscape .characteristics affecting soil erosion. 109 The erosion control practices factor, P, represents the influence of various erosion control devices and procedures such as diversion ditchs and counter plowing. If there is no erosion control devices used the P value becomes unity (P 8 1) and AA = A x (0121) so where AA = Top 8011 reduced due to erosion in centimeters. PRLND = YILD x PRCE where are l. 2. 3. 4. 5. PRLND = Land productivity ($/ha) YILD = Crop yield (t/ha) PRCE = Crop price ($/t) 5.3 Medel_£arameters Machinery and soil parameters indicated in the model listed as: Machine initial cost and repair factors (Table 5.1). Economic parameters (Table 5.2). Codes used for various soil types (Table 5.3). Critical bulk densities for each family texture class (Table 5.4). Coefficients of equations for calculating sufficiency‘ of bulk density (Table 5.5). 110 ' Table 5.1 Machine Initial Cost and Repair Factors Initial Cost Factors Repair Factors Implement Code A B A B (S) (S/m) ESQEiEQ'---"-""'i """"" §§§3€-'-'§§ZBT§ """""""" 6:15-"'521"' Bean Puller 2 L367 1378.0 0.20 1.6 Beet Topper 3 #000 16h0.h 0.26 1.6 Beet Lifter h 5500 2952.8 0.23 1.h Soil Saver 5 1h000 h593.2 0.19 1.h Subsoiler 6 179A 1230.3 0.38 1.h Fert. Spread.* 7 1236 1788.1 0.95 1.3 Chisel Plow 8 -1606 2793.5 0.38 1.A Mo1dboard Plow 9 -2128 5213.3 0.33 1.8 Disk Harrow 10 -3791 2693.6 0.18 1.7 Heavy Disk 11 -1906 2601.7 0.18 1.7 Field Cultivator 12 -3h91 1563.3 0.30 1.h Grain Drill 13 1236 1788.1 0.55 2.1 Row Planter 1h -h520 0038.7 0.5L 2.1 Min-Till Planter 15 -270h h0h5.3 0.5h 2.1 Sprayer 16 606 275.6 0.h1 1.3 Row Cultivator 17 -163A 1099.1 0.22 2.2 Ammonia Applic.* 18 60h 275.6 0.38 1.h Uti1ity Tractor 19 0 500.0/KW 0.012 2.0 Tillage Tractor 20 0 500.0/Kw 0.012 2.0 Source: Black et. a1 (1980) . . . * Initial cost factors are estimated for fertilizer spreader and ammonia applicator. 111 Economiglgaéaéeters EQFQRQEQF """"""""""""" (£73; """"" 832313;“6;;FGTEYF;""""""""157.25; """" Fuel Price $0.32/Liter Labor Price $7.7/hour Remaining Value Factors A 8 Combine 0.75 0.88 Tractor 0.75 0.87 Implements 0.70 0.90 Source: Black et. a1 (198h) 112 Table 5.3. Codes Used for Soil Types Coarse loamy 2 Fine Loamy 3 Coarse Silty h Fine Silty 5 C1ayed:35-h52 6 7 113 ‘ Table 5.h Critical Bulk Densities for Each Family Texture Class Family Texture Critical Bulk Class Density(g/cm3) $.13----------------------------------;-g§ """" Coarse Loamy 1.63 Fine Loamy 1.67 Coarse Silty 1.67 Fine Silty 1.5L Clayed: 35-h52 1.h9 >153 1 39 Source: Pierce et. al (1983) 114- Table 5.5- Coefficients of Equations Used for Calculating Sufficiency of Bulk Density Family Texture Low High Class ---------------------------------------- ..................... §1223-----122555523-------§lees-----12£s:sse Sandy '1 933 9.093 '5 163 9 551 Coarse Loamy -1.160 2 717 -h.859 8 7A6 Fine Loamy -0.829 2 210 -7 509 13 866 Coarse Silty '0.725 2 037 '6 883 12 321 Fine Silty '0.870 2.166 ’7.509 12.389 Clayed: 35-h58 ‘1.933 3 706 -9 178 1b 500 >152 -1 933 3 513 -10 325 15 178 Source: Pierce et. a1 (1983) 115 6. Adjustment factors for calculating sufficiency of bulk density (Table 5.6). 7. Soil weighting factors for first 100 cm of top soil (Table 5.7). 8. Characteristics of shebeon loam soil (0-2% slope) in Saginaw Bay (Table 5.8). 5.4 W Two different sets of data inputs are used by the model. Machinery inputs are provided by the Linear Programming Model (described in Chapter 4) or the multiple Crop Machinery Selection Model (Rotz, et. a1., 1983). The output of those two models can be used as proper sets of input to the model. Examples are indicated in Tables 5.9 - 5.14. The second set of input data includes soil factors that are provided by soil management groups and soil conservation service publications (Table 5.15). The rainfall factor (R) varies across the state but it can be considered uniform over a county wide area. The soil erodibility index (k) measures the influence of physical and organic properties on a soil's susceptibility to erosion. Loam and silt loam soils and sandy loam soils are considered as the most erodible soils while loamy sands and sands are the least erodible soils due to coarse texture and high permeability. The topographic factor, LS, is a 116 Table .6 Ad%ustment Factors for Ca culating Sufficiency of Du k Density Family Texture Permeablity(in/hr) Class --------------------------------------------- < 06 06-.2 2- 6 6-2 >2 Fine Loamy 1.0 1.0 0 9 0.7 0 5 Coarse Silty 1.0 1.0 l 0 0.9 0 7 Fine Silty 1.0 1.0 0.9 0.7 0.5 Clay: 35-60% 1.0 0.9 0.7 0.6 0 5 >60: 1 0 0.8 0 6 0.5 0 L Source: Pierce et. a1 (1983) 117 Table 5.7 $011 Weighting Factors for 100 Centimeters of Soil Layers cn wr tn wr an HP 0 0.000000 31 0.199657 68 0.793882 1 0.011522 35 0.509912 69 0.801180 2 0.063886 36 0.520011 70 0.808111 3 0.083008 37 0.530057 71 0.815587 1 0.101611 38 0.539951 72 0.822699 5 0-119703 39 0-559737 73 0-329751 6 0.137211 10 0.559110 71 0.836713 7 0.151180 11 0.568976 75 0.813678 8 0.170655 12 0.578136 76 0.850551 9 0.186675 13 0.587791 77 0.857371 10 0.202276 11 0.597052 78 0.861137 11 0.217189 15 0.606212 79 0.870815 12 0.232311 16 0.615276 80 0.877198 13 0.216861 17 0.621216 81 0.881097 11 0.261073 18 0.633125 82 0.890612 15 0.271990 19 0.611911 83 0.897135 16 0.288631 50 0.650611 81 0.903576 17 0.302011 51 0.659229 85 0.909965 18 0.315152 52 0.667758 86 0.916303 19 0.328057 53 0.676205 87 0.922591 20 0.310711 51 0.681570 88 0.928829 21 0.353211 55 0.692856 89 0.935018 22 0.365186 56 0.701062 90 0.911159 23 0.377566 57 0.709192 91 0.917251 21 0.389161 58 0.717216 92 0.953296 25 0.901179 59 0-725225 93 0-959293 26 0.112727 60 0.733132 91 0.965211 27 0.121111 61 0.710966 95 0.971119 28 0.135337 62 0.718729 96 0 977009 29 0.116111 63 0.756123 97 0 982823 30 0.157336 61 0.761018 98 0 988593 31 0.168120 65 0.771605 99 0.991318 32 0.178765 66 0.779096 100 1.000000 33 0.189276 67 0.786521 Source: Pierce, et. a1. (Jan. - Feb., 1983) 118 Table 5.8 characteristics of Shebeon Loam Soil (0-22 Slope) in Saginaw Bay. Horizon Dept? Bulk Density Perm. Avail. water cm i """""" 553"”""‘i?'5'§"""""'i """"" 5.135 """"" 2 30 5 1.63 0.155 3 25.1 1.63 1 0.135 1 68.6 1.91 1 0 0.060 119 Table 5.9 Set used for a Continuous Corn Rotation in from Conventional to No-Till System A Machiner When Switc (150 Hectares . Machine Till. Tractor Util. Tractor Combine Fert. spread. H8. Plow Field cult. Row Planter Row Cult. NH} Applicator Sprayer 100.1 101.1 *1,2,and 3 indicate extra machines ,keeping machines,and new machines respectively. 120 ‘ Table 5.10 A Conventional Set Of Machines Used in A Continuous Corn Rotation(150 Hectares). Machine Size(m) N0 Hours State* HU"¥F§EESF"m"7ETS’RQ"""T"""'-315'5"""mi'm Util. Tractor 35.8 Kw 1 210.2 2 Combine 3 O l l21 6 2 Fert. spread. 12 2 1 19.1 2 MB. Flow 2 O 1 117.7 2 Field cult. 1.7 1 90.9 2 Row Planter 3 0 1 85.5 2 Row cult. 3 0 1 105.6 2 NH3 Applicator 3 0 1 101.1 2 * See Table 5.9 121 Table 5.11 A Set of Machines Used for A Corn-Navy Bean-Su ar Beet- Rotation When Switching from Conventional to t e Chiseling System(500 Hectares). Machine Size(m) N0. Hours State* iiiiT'iFQEZSF""'16§:§'1'.r1 (a 0 00000 65 70 75 80 85 60 90 21.4 DATA DUMMY4/0,0,.7,.9,.7,.6,.5/ DATA DUMMY5/0,0,.5,.7,.5,.5,.4/ DATA SOIL WEIGHTING FACTORS DATA (WF(I),I-l,100)/.044522,.063886,.083008,.101641,.119708, +.137211,.154180,.170655,.186675,.202276,.217489,.232344,.246864, +.261073,.274990,.288631,.302014,.315152,.328057,.340741,.353214, +.365486,.377566,.389461,.401197,.412727,.424111,.435337,.446411, +.457336,.468120,.478765,.489276,.499657,.509912,.520044,.530057, +.539954,.549737,.559410,.568976,.578436,.587794,.597052,.606212, +.615276,.624246,.633125,.641914,.650614,.659229,.667758,.676205, +.684570,.692856,.701062,.709192,.717246,.725225,.733132,.740966, +.748729,.756423,.764048,.771605,.779096,.786521,.793882,.801180, +.808414,.815587,.822699,.829751,.836743,.843678,.850554,.857374, +.864137,.870845,.877498,.884097,.890642,.897135,.903576,.909965, + .916303,.922591,.928829,.935018,.941159,.947251,.953296,.959293, +.965244,.971149,.977009,.982823,.988593,.994318,1./ READ(5,*)R,BK,BLS,P,CC DO 60 K-1,4 IF(PERM(K).LE..06)THEN DO 65 KK-1,7 ADJ(KK) I DUMMYI (KK) ELSE IF(PERM(K).GT.0.06.AND.PERM(K).LE.O.2)THEN DO 70 KR-1,7 ADJ(KK) I DUMMYZ (KR) ELSEIF(PERM(K). GT..2. AND. PERM(K). LE..5)THEN DO 75 RK-1,7 ADJ(KK) ' DUMMY3 (KR) ELSEIF(PERM(K).GT..6.AND.PERM(K).LE.2.)THEN DO 80 KK-1,7 ADJ(KK) - DUMMY4 (KR) ELSEIF(PERM(K).GT.2.)THEN DO 85 RR -1,7 ADJ(KK) - DUMMYS (RR) ENDIF AS(N)-5.*Aw(x) IE(Aw(N).GE.0.2)AS(Nl-1.o IE(Aw(R).LE.o.03)AS(x)-o IE(ED(R).LT.CED(J))TREN c-(SLPL(J)*EDlx)+xINTL(J)) ELSEIP