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' ‘~ 51-75.. m t“ - ES: 32"»; l ‘ J a '16: 2’ .‘w 'rrn'“ “r. ufiu ‘ :5 — - v ' .‘. .. .‘ .._,,"_~’1 2.-.”, ’1' o ...A, L BEARIES l lit llllll ll‘lll‘ w ll \‘l‘ ll This is to certify that the dissertation entitled Simulation Model For Field CrOp Production Machinery System presented by Wan Ishak Wan Ismail has been accepted towards fulfillment of the requirements for Ph ' D degree in __A_g£i_Q_._EDgr . 24,.“ KM Major professor Date 3/1// 7/ MSU LI an Affirmative Action ’Equal Opportunity Institution 0-12771 Helmet! 1 ‘Michigan Quart, J L University ii PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. DATE DUE DATE DUE DATE DUE ii MSU Is An Affinnetive Action/Equal Opportunity Institution cMMunS-M SIMULATION MODEL FOR FIELD CROP PRODUCTION MACHINERY SYSTEM By Wan [sink Wan Ismail A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in Agricultural Engineering Department of Agricultural Engineering 1991 ABSTRACT SIMULATION MODEL FOR FIELD CROP PRODUCTION MACHINERY SYSTEM By Wan Ishak Wan Ismail The Crop Production Machinery System (CPMS) program developed during this research is able to evaluate machinery systems for various tillage systems and crop rotations for different locations, soil types and farm sizes. Users can determine the combinations of crops in a rotation which minimize the machinery costs, evaluate different farming alternatives, or scale up or down a farm operation. The Machinery Selection Model is able to recommend tractor and implement sizes for a power- compatible machinery system that is able to complete the field operations within specified time constraints. The Machinery Cost Analysis Model is able to evaluate machinery costs when the use of implements is shared in several enterprises, when the same machinery is used under different conditions, or when machinery costs are compared for different situations such as farm size and production practices. The CPMS program was developed to be run on most microcomputers and to have potential for modification to be transferred to and evaluated for other geographical locations and situations. The locations, crops and equations used in this program could be changed as described in this dissertation. This program helps users to become more knowledgeable about a problem as they interact with the program. The program can be used with external data files to facilitate a sensitivity analysis or other experimentation with this model. The predicted draft and fuel requirements from this model were tested statistically against a set of data collected during field experiments performed at Michigan State University and in Clinton County, Michigan. The fuel consumption rates for the chisel plow, moldboard plow, disk harrow, field cultivator, row crop planter and grain drill, and the draft requirements for all of these implements, except the disk harrow, correlated well with the measured data. . The implementation of the model shows that producing more crops in a rotation reduced the costs per hectare. It was cheaper to produce corn and wheat in a C-C-FB-W crop rotation as compared to the same crops produced as a single crop operation. The one-tractor operation required larger implements and saved on costs per hectare as compared to the two-tractor operation. Approved by: 31m.“ flatMa/w— ajor Professor Eng/f Department Chairperson This dissertation is dedicated to my mother, Hajjah Wan Minah; my father, Haji Wan Ismail; my wife, Nomi; my sons, Nuaim and Najib; and my daughter, Nazeeha. Thanks for their moral support, prayers and encouragement throughout my studies. iv ACKNOWLEDGEMENTS In the name of Allah, the most merciful and the most beneficent. All praises and thanks are due to God the almighty, for His divine direction throughout my study. I would like to express my sincere appreciation to all those people who provided encouragement and assistance throughout my graduate program. I am indebted to my major professor, Dr. Thomas Burkhardt for his invaluable friendship, professional guidance, patience and encouragement. He is more than generous with his valuable time, even giving up his precious time during the weekend for his editorial help while writing this manuscript. Words cannot express my deep gratitude to him. Without the willing of Allah, in the first place, and the help of Dr. Thomas Burkhardt, this dissertation would not have been completed. Sincere appreciation and gratitude are extended to the other members of my guidance committee, Dr. Robert Wilkinson, Dr. Roy Black and Dr. Gerald Park for their valuable supports, contributions, advice, and constructive comments to the study. I am also grateful to Leslie Mack for his friendship and encouragement. Thanks are due to the Government of Malaysia and Universiti Pertanian Malaysia, for their financial supports. I am forever indebted to my wife, Nomi; my sons, Nuaim and Najib; and my daughter Nazeeha. who made great sacrifice for me so that I could obtain my degree. TABLE OF CONTENTS page LIST OF TABLES ........................................................................................................ xii LIST OF FIGURES ........................................................................................................ xviii GLOSSARY ...................................................................................................................... xx 1. INTRODUCTION .............................................................................................. 1 1.1 Objectives ............. i .................................................................................. 4 2. REVIEW OF LITERATURE ............................................................................ 6 2.1 Machinery Selection and Cost Models ............................................ . ......... 7 2.2 Tractor and Implement Matching ............................................................ 9 2.3 Cost Analysis ........................................................................................ l 1 2.4 Tillage System ......................................................................................... 16 2.5 Crop Rotation ....................................................................................... 17 2.6 Instrumentation ....................................................................................... 18 3. METHODOLOGY ............................................................................................. 21 3.1 System Description ................................................................................. 21 3.2 System [imitation .................................................................................. 23 3.3 Research Procedure ................................................................................ 25 3.4 Simulation Model ................................................................................... 26 3.4.1 Development of the Model ...................................................... 26 page 3.4.2 Verification ............................................................................... 30 3.4.3 Validation ................................................................................. 30 3.4.4 Sensitivity Analysis .................................................................. 31 3.5 Instrumentation ................................................................................. 31 SYSTEM MODEL ............................................................................................. 34 4.1 Crop Production Machinery System Simulation Model .......................... 34 4.2 Machinery Selection Input Data ............................................................ 37 4.2.1 Location ...................................................................................... 38 4.2.2 Soil Texture ................................................................................ 38 4.2.3 Tillage System ............................................................................ 39 4.2.4 Crops and Crop Rotation .......................................................... 39 4.2.5 Farm Size .................................................................................... 39 4.2.6 Calendar Dates of Farm Operation ............................................ 40 4.3 Tractor and Machinery Selection Model ............................................... 40 4.3.1 Machinery Selection Algorithm .................................................. 42 4.3.2 Confinuous Operation .................................................................. 50 4.3.3 Individual Operation .................................................................... 50 4.3.4 Multiple Crop Rotations ............................................................... 52 4.4 Machinery Selection Procedures ............................................................ 53 4.4.1 Procedures to Determine Drawbar Power .................................... 54 4.4.2 Procedures to Determine Fieldtime .............................................. 55 4.4.3 Procedures to Determine Implement Draft and Width .............. 56 vii 4.5 4.6 4.7 page 4.4.4 Procedures to Determine the Probability Working days ............. 71 4.4.5 Procedures to Determine Implement and Tractor Size ............. 73 4.4.5.1 Procedure TILLAGE ................................................... 74 4.4.5.2 Prowdure IMPNUM ................................................... 75 4.4.5.3 Procedure IMPCALC .................................................. 77 4.4.6 Procedures on Crops .................................................................. 83 4.4.7 Procedures on Implements ......................................................... 83 4.4.8 Procedures on Tractors ............................................................... 86 Machinery Cost Analysis Input Data .................................................... 88 4.5.1 Estimation of Costs .................................................................... 88 4.5.2 Capital Cost ................................................................................ 90 4.5.3 Taxes, Insurance and Shelter Cost .............................................. 91 4.5.4 Repair and Maintenance Cost .................................................... 92 4.5.5 Fuel Cost ..................................................................................... 94 4.5.6 Oil Cost ....................................................................................... 95 4.5.7 Labor Cost .................................................................................. 96 4.5.8 Timeliness Cost .......................................................................... 97 4.5.9 Machinery Cost .......................................................................... 97 4.5.10 Tractor Cost ................................................................................ 98 Tractor and Machinery Cost Model ...................................................... 100 4.6.1 Cost Analysis Algorithm ............................................................ 101 Machinery Cost Analysis Procedures ..................................................... 109 viii page 4.7.1 Procedure for Implement Cost .................................................. 109 4.7.2 ProcedureonTractorCost ........................................................... 111 4.7.3 Procedure CROPTYPE ............................................................... 112 4.7.4 Procedure CON CLUDE ............................................................. 113 4.8 Assumptions ........................................................................................... 114 5. EXPERIMENTAL PROCEDURE .................................................................... 115 5.1 Calibration of Transducers .................................................................... 116 5.1.1 Verification of the Engine RPM ............................................... 117 5.1.2 Verification er the Wheel Speeds ................................................. 118 5.1.3 Verification of the Fuel Flow Meter ........................................... 1 18 5.1.4 Verification of the Ground Speed ................................................ 119 5.2 Model Equations ....................................................................................... 119 5.3 Field Experiments ................................................................................. 122 5.3.1 Data Acquisition and Analysis ..................................................... 124 5.4 Validation of the CPMS Model ........................................................... 128 6. IMPLEMENTATION OF THE MODEL ......................................................... 145 6.1 CPMS Executable Files ......................................................................... 145 6.2 Implement and Tractor Sizes Selection ................................................... 147 6.3 Implement Analysis .................................................................................. 156 7. EXAMPLE FARMS ............................................................................................. 171 7.1 Two Tractor Operation for Corn - Corn - Field beans - Wheat Crop Rotation. .......................................................................................... 172 page 7.1.1 Problem Methodology .............................................................................. 172 7.1.2 Machinery and Tractor Cost Analysis ......................................... 209 7. 1.2.1 Single Cropping and Multiple Cropping ......................... 209 7.2 One-Tractor Operation For Corn - Corn - Field beans — Wheat Crop Rotation. .......................................................................................... 215 7.2.1 Dates of Operation ....................................................................... 215 7.2.2 Determine the Minimum Width for Each Machine .................... 217 7.2.3 Determine the Implement Power Requirement ........................... 220 7.2.4 Determine the Implement Field Time ......................................... 222 7.2.5 Implement Field Time Algorithm ................................................ 224 7.2.6 Cost Analysis ................................................................................ 227 7.2.6.1 Single Cropping and Multiple Cropping ......................... 227 7.2.6.2 Machinery and Tractor Cost ............................................ 234 7.3 Single Crop, One-Tractor Operation ........................................................ 239 7.3.1 Corn Crop ..................................................................................... 239 7.3.2 Field Beans Crop ........................................................................... 244 7.3.3 Wheat Crop ................................................................................... 248 7.3.4 Summary and Discussion ............................................................. 251 7.4 Single Crop, Two Tractor Operation ....................................................... 255 7.4.1 Corn Crop ..................................................................................... 255 7.4.2 Field Beans Crop ........................................................................... 261 7.4.3 Wheat Crop ................................................................................... 266 7.4.4 Summary and Conclusion ............................................................ 271 X page 8. SUMMARY AND CONCLUSIONS ................................................................ 274 9. SUGGESTIONS FOR FURTHER WORK ...................................................... 282 APPENDIX A. Field Experimental Data .......................................................................... 284 REFERENCES ............................................................................................................. 299 LIST OF TABLES page Table 5. 1 Regression Equations for the Transducers .................................................... 1 17 Table 5.2a List of Implements used for the Field Experiments at Michigan State University Farm ................................................................................... 124 Table 5.2b List of Implements used for the Field Experiments at Clinton County, Michigan ....................................................................................................... 125 Table 5.3 Experimental and Model Draft and Fuel Requirements for Moldboard Plow on Owosso-Marlette Sandy Loam ................................. 126 Table 5.4 Summary of the Regression Analysis for the Implements ......................... 134 Table 5.5 Summary of the Regression Analysis for the Moldboard Plow ................... 135 Table 5.6 Summary of the Regression Analysis for the Chisel Plow ....................... 135 Table 5.7 Summary of the Regression Analysis for the Disk Harrow ....................... 136 Table 5.8 Summary of the Regression Analysis for the Field Cultivator ..................... 137 Table 5.9 Summary of the Regression Analysis for the Row CrOp Planter .............. 138 Table 5.10 Summary of the Regression Analysis for the Grain Drill ...................... 139 Table 6.1 Implement Size Determined by the Model for a 100-Ha Corn Farm with Coarse Sofl .......... --- - . - - - ........ - 150 Table 6.2 Implement Size with the Required Time to Complete the Field Operations for a 100-Ha Corn Farm with Coarse Soil ................. 151 Table 6.3 Implement Size and Field Time for a 100-Ha farm with Different Soil Textures - - -- - - -- - 153 Page Table 6.4 Maximum Implement Width Based on Available Field Operation Time for a 100-Ha Farm with Coarse Soil ................................ 153 Table 6.5 Implement Size and Field Time for the Individual Farm Operation ................................................................................................ 157 Table 6.6 Dates of Operation and Field Operation Time .......................................... 163 Table 7.1 List of Implements for Each Tractor and Each CrOp .................................. 172 Table 7.2 Dates of Operation. ........................................................................................ 173 Table 7 .3 Adjusted Dates of Operation ......................................................................... 175 Table 7.4 Parameters Used for Each Implement. ...................................................... 176 Table 7.5 Implement Size, Power Requirement and Field Time for Each Implement. .............................................................................................. 180 Table 7.6 Total Implement Costs for Each Implement for All Crops in a Rotation ...... 202 Table 7.7 Total Tractor Costs for Each Tractor for All Crops in a Rotation .......... 203 Table 7.8 Summary for Fixed and Variable Costs for Each Tractor ........................ 203 Table 7.9 Summary for Machinery and Tractor Cost for Each Crop inaRotation ................................................................................................... 204 Table 7.10 Implement Costs for Each Crop Assumed as a Single Crop Operation. ...... 207 Table 7.11 Tractor Costs for Each Tractor for Each Crop Assumed as a Single Crop Operation ................................................................................................ 208 Table 7.12 Summary for Machinery and Tractor Costs for Single Cropping Using Two-Tractor Operation .......................................................................... 208 Table 7.13 Adjusted Dates of Opaation ......................................................................... 215 Table 7.14 Implement Size, Power Requirement and Field Time for Each Implement - . - - 221 Page Table 7.15 Total Implement Costs for Each Implement for All Crops in a Rotation. ........................................................................................................... 228 Table 7.16 Implement Cost Analysis for Each Crop Assumed as a Single Crop Operation ................................................................................................ 230 Table 7.17 Total Tractor Costs for All Crops in a Rotation ........................................ 232 Table 7.18 Costs for Each Tractor for Each Crop Assumed as a Single Crop Operation ........................................................................... 232 Table 7.19 Summary for Machinery and Tractor Cost for Single Cropping Using a One-Tractor Operation. ..................................................................... 235 Table 7.20 Summary for Fixed and Variable Costs for a One-Tractor Operation ........... 235 Table 7.21 Summary for Machinery and Tractor Costs for Each Crop in aC-C-FB-W Crop Rotation. .............................................................................. 237 Table 7.22 Summary for Fixed and Variable Costs for One-Tractor and Two-Tractor Operations for a Multiple Crop Farm ......... 238 Table 7.23 Implement Size, Power Requirement and Field Time for Each Implement - - . - - - - ..... 240 Table 7 .24 Mplement Costs For Each Implement for Corn .......................................... 242 Table 7.25 Tractor Costs for Corn. .................................................................................. 243 Table 7.26 Implement Size, Power Requirement and Field Time for Each Implement ...... - ....... 244 Table 7.27 Implement Costs for Each Implement for Field Beans ............................... 246 Table 7.28 Tractor Costs for Field Beans ....................................................................... 247 Table 7.29 Irnplement Size, Power Requirement and Field Time for Each Implement. ...................................................................................... 248 Table 7.30 Implement Costs for Each Implement for Wheat. ....................................... 250 Table 7.31 Tractor Costs for Wheat .............................................................................. 251 xiv page Table 7.32 Summary for Machinery and Tractor Costs for Single and Multiple Cropping Using One Tractor ........................................................................ 253 Table 7.33 Summary for Fixed and Variable Costs for Single and Multiple Cropping Using One Tractor ..................................................................... 254 Table 7.34 Implement Size, Power Requirement and Field Time for Each Implement. ........................................................................................... 256 Table 7.35 Implement Costs for Each Implement for Corn .......................................... 258 Table 7.36 Tractor Costs for Each Tractor for Corn ..................................................... 259 Table 7.37 Summary for Fixed and Variable Costs for Corn Production Using Two Tractors ................................................................................................. 260 Table 7.38 Implement Size, Power Requirement and Field Time for Each Implement. ..................................................................................................... 261 Table 7.39 Implement Costs for Each Implement for Field Beans .............................. 263 Table 7.40 Tractor Costs for Each Tractor for Field Beans .......................................... 264 Table 7.41 Summary for Fixed and Variable Costs for Field Beans Production Using Two Tractors ..................................................................................... 265 Table 7.42 Implement Size, Power Requirement and Field Time for Each Implement. ........................................................................................... 266 Table 7.43 Implement Costs for Each Implement for Wheat. ...................................... 268 Table 7.44 Tractor Costs for Each Tractor for Wheat ................................................... 269 Table 7.45 Summary for Fixed and Variable Costs for Wheat Production Using Tonractors .................................................................................................. 270 Table 7.46 Summary for Machinery and Tractor Costs for Single and Multiple Cropping Using Two Tractors ..................................................................... 272 Table 7.47 Summary for Fixed and Variable Costs for Single and Multiple Cropping Using Two Tractors ......................................................................... 273 Table A.1 Experimental and Model Draft and Fuel Requirements For Chisel Plow on Capac Loam Soil ................................................................. 285 XV Page Table A2 Experimental and Model Draft and Fuel Requirements for Disk Harrow on Capac Loam Soil ................................................................ 286 Table A.3 Experimental and Model Draft and Fuel Requirements for Field Cultivator on Capac Loam Soil ........................................................... 287 Table A.4 Experimental and Model Draft and Fuel Requirements for Field Cultivator on Capac Loam Soil ........................................................... 287 Table A5 Experimental and Model Draft and Fuel Requirements for Grain Drill on Capac Loam Soil after Moldboard Plowing ........................ 288 Table A.6 Experimental and Model Draft and Fuel Requirements for Grain Drill on Capac Loam Soil after Chisel Plowing .................................. 288 Table A.7 Experimental and Model Draft and Fuel Requirements for Chisel Plow on Owosso-Marlette Sandy Loam ............................................ 289 Table A.8 Experimental and Model Draft and Fuel Requirements for Disk Harrow on Granby Loamy Sand Soil ................................................... 289 Table A9 Experimental and Model Draft and Fuel Requirements for Disk Harrow on Palms Muck Soil ................................................................ 290 Table A.10 Experimental and Model Draft and Fuel Requirements for Disk Harrow on Metamora-Capac Sandy Loam Soil. .................................. 290 Table A.11 Experimental and Model Draft and Fuel Requirements for Corn Planter on Palms Muck Soil ............................................................... 291 Table A.12 Experimental and Model Draft and Fuel Requirements for CornPlanteronGilford SandonamSoil ................................................... 291 Table A.13 Experimental and Model Draft and Fuel Requirements for Corn Planter on Gilford Sandy Loam Soil ................................................... 292 Table A.14 Experimental and Model Draft and Fuel Requirements for Seed Drill on Granby Loamy Sand Soil ...................................................... 292 Table A.15 Experimental and Model Draft and Fuel Requirements for Grain Drill on Palms Muck Soil ................................................................... 293 Table A.16 Experimental and Model Draft and Fuel Requirements for Grain Drill on Palms Muck Soil (Table A.15 Cont) ......................... Table A.17 Experimental and Model Draft and Fuel Requirements for Grain Drill on Granby Loamy Sand Soil ............................................ Table A.18 Experimental and MOdel Draft and Fuel Requirements for Grain Drill on Granby Loamy Sand Soil ............................................ Table A.19 Experimental and Model Draft and Fuel Requirements for Disk Harrow on Granby Loamy Sand Soil ....................................... Table A.20 Experimental and Model Draft and Fuel Requirements for Disk Harrow on Palms Muck Soil ..................................................... Table A.21 Experimental and Model Draft and Fuel Requirements for Chisel Plow on Metmora-Capac Sandy Loam Soil ............................ Table A.22 Experimental and Model Draft and Fuel Requirements for Moldboard Plow on Metamora-Capac Loam Soil .............................. Table A.23 Experimental and Model Draft and Fuel Requirements for Field Cultivator on Capac Loam Soil ................................................ Table A.24 Experimental and Model Draft and Fuel Requirements for Disk Harrow on Capac Loam Soil ..................................................... Table A.25 Experimental and Model Draft and Fuel Requirements for Corn Planter on Capac Loam Soil .................................................... Table A.26 Experimental and Model Draft and Fuel Requirements for Seed Drill on Palms Muck Soil ....................................................... Page ......... 293 .......... 294 .......... 294 .......... 295 .......... 295 ............ 296 ............ 296 .......... 297 .......... 297 ......... 298 ......... 298 LIST OF FIGURES page Figure 3.1 Conceptual Model of a Crop Production Machinery System ...................... 29 Figure 4.1 Models in a Crop Production Machinery System Model ............................ 36 Figure 4.2 Machinery Selection Algorithm ...................................................................... 44 Figure 4.3 Machinery Selection Algorithm (cont) .......................................................... 45 Figure 4.4 Machinery Selection Algorithm (cont) ........................................................... 46 Figure 4.5 Machinery Selection Algorithm (cont) ........................................................... 47 Figure 4.6 Machinery Selection Algorithm (cont) .......................................................... 48 Figure 4.7 Machinery Selection Algorithm (cont) .......................................................... 49 Figure 4.8 Machinery Cost Analysis Algorithm ............................................................ 103 Figure 4.9 Machinery Cost Analysis Algorithm (cont) ................................................. 104 Figure 4.10 Machinery Cost Analysis Algorithm (cont) ............................................... 106 Figure 4.1 1 Machinery Cost Analysis Algorithm (cont) ............................................... 108 Figure 4.12 Machinery Cost Analysis Algorithm (cont) ............................................... 110 Figure 5.1 Comparison of Draft Requirement for Moldboard Plow on Owosso-Marlette Sandy Loam Soil ............................................................... 130 Figure 5.2 Comparison of Fuel Requirement for Moldboard Plow on Owosso-Marlette Sandy Loam Soil ........................................................... 131 Figure 6.1 Farm Operations on 100-Ha Coarse Soil Using One Tractor .................... 159 Figure 6.2 Farm Operation on IWHa Coarse Soil Using Two Tractors .................... 160 Page Figure 6.3a Field Time Algorithm for Corn Farm Operations on 100-Ha of Coarse Soil Using One Tractor ....................................................................... 164 Figure 6.3b Fleld Time Algorithm Using a Second Tractor for Secondary Tillage and Planting ................................................................................... 165 Figure 6.3c Field Time Algorithm for Corn and Field Beans Using One First Tractor ..................................................................................... 166 Figure 6.3d Field Time Algorithm for Corn and Field Beans Using Second Tractor Operation. ...................................................................... 167 Figure 6.3c Field Time Algorithm for Wheat Production on Coarse Soil Using Two Tractors ............................................................................... 169 Figure 7. 1 Field Time Algorithm for Four Crops in Rotation Using Two Tractors ................................................................................................. 185 Figure 7.2 Field Time Algorithm for Four CrOps in Rotation Using One Tractor ................................................................................................... 225 Figure 7.3 Field Time Algorithm for Corn Using One Tractor .................................... 241 Figure 7.4 Field Time Algorithm for Field Beans Using One Tractor ......................... 245 Figure 7.5 Field Time Algorithm for Wheat Using One Tractor .................................. 249 Figure 7.6 Field Time Algorithm for Corn Using Two Tractors .................................. 257 Figure 7.7 Field Time Algorithm for Field Beans Using Two Tractors ....................... 262 Figure 7.8 Field Time Algorithm for Wheat Using Two Tractors ............................... 267 APT OP CC CCT CI CN CP CRF CRFI‘ DBP DBPT DH EF EFC EPTOP FC GLOSSARY Area,ha Tractor Available Power-Take-Off Implement Capital Consumption Cost Tractor Capital Consumption Cost Cone Index Dimensionless Ratio Chisel Plow Implement Capital Recovery Factor Tractor Capital Recovery Factor Draft, kN Implement Drawbar Power, kW Tractor Drawbar Power, kW Disk Harrow Field Efficiency, dec Implement Field Time, h/ha Effective Field Capacity, ha/h Implement Equivalent Power Take-off Power,Kw Fuel Price,$ Fuel Cost.$ FCN F.Cult FI'IMP PTO GD LC LM LT LPM MC Fuel Consumption,l/h Field Cultivator Implement Field Time For One Crop,h Field Time Required by Implement, h Field Operation Time,h Fuel Use, 1 Grain Drill Hours Available per Day, h/day Total Hours of Machinery, nth year Total Hours of Machinery, n+1 years Total Hours of Tractor, nth year Total Hours of Tractor, n+1 years Interest Rate Number of Implements Expected Life,yr Labor Wage, 3 Labor Cost, 5 Machinery Expected Life,yr Tractor Expected Life, yr Machinery List Price,$ Tractor List Price,$ Total Machinery Cost,$ Oil Price,$ OC = Oil Cost,$ OCN = Oil Consumption, l/h OU = Oil Use, 1 PPM = Machinery Purchase Price, $ PPT = Tractor Purchase Price, 3 PWD = Probability Working Days R&M = Repair and Maintenance Cost,$ RCPL = Row Crop Planter RF1,RF2 = R & M Factors for Tractor or Implement RMN = Implement Repair and Maintenance Cost,$ RMT = Tractor Repair and Maintenance Cost,$ RMCMl = Implement Repair & Maintenance Cost, nth yr RMCM2 = Implement Repair & Maintenance Cost, (n+1) yr RMTl = Tractor Repair & Maintenance Cost, nth yr RMT2 = Tractor Repair & Maintenance Cost,(n+1) yr RVN Machinery Remaining Value, nth yr RVM = Machinery Remaining Value, n+1 yr RVNT = Tractor Remaining Value, nth yr RVMT = Tractor Remaining Value, n+1 yr S = Operation Speed, km/h S. = Ground Speed. km/h 8,, = Wheel Speed. km/h SL = Wheel Slippage Machinery Salvage Value,$ Total Machinery and Tractor Cost, $ Accumulated Repair and Maintenance Cost at End of n th year,$ Accumulated Repair and Maintenance Cost at End of (n-l) year,$ Annual Tractor Cost, $ Tractive Efficiency Total Field Time ,h Implement Taxes,lnsurance & Shelter Cost,$ Tractor Taxes,lnsurance & Shelter Cost,$ Compatible Implement to Tractor Size, kW Tractor Size, kW Accumulated Hours Used at End of n th year,h Accumulated Hours Uses at End of (n-l) year,h Implement Width, m Working Operation Depth, m Dynamic Wheel Load, kN Number of Rows For Planter or Grain Drill Number of Tines for Chisel Plow or Field Cultivator 1. INTRODUCTION Agricultural production in the United States has increased many fold in response to an ever growing demand for food by the domestic and world population. Scientists, engineers, industries and farmers are challenged to increase agricultural production without increasing the use of energy inputs. Machinery contributes a major capital input cost in most farm businesses (Chen, 1986; Ozkan and Edwards 1986; Singh and Holtrnan 1979a; Mayfield et a1. 1981). Rotz et a1. (1983), found that the data collected on actual farms indicated that machinery used on the farms was not always properly matched to the available power, and in several cases machinery was oversized with respect to the area farmed. To increase the profit, producers need to cut production costs. Profits can be increased by selecting the right complement of equipment for their farm size, crops and tillage system. Singh and Holtman (1979a) stated that field machinery cost per unit crop land area can be reduced either by reducing the amount of work done (eg. reduced tillage) for a particular crop or by using a crop rotation that distributes the work more evenly over the cropping year, improving machinery utilization. The crops were assumed to use same machines. The success of many farm-level production systems depends on wise selection of machinery systems. The application of machines to agricultural production has been one of the outstanding developments in agriculture. Many factors are involved in the process of 2 equipping a modern farm for field operations. Agricultural production is seasonal, leaving machines standing idle much of the time. Some machines which are extensively used will also stand idle much of the time if not managed properly. There is a need for timely operations in agricultural production because of the seasonal requirements of the crops. The main aim of tractor and machinery selection studies is to complete a certain field operation during a specified time and at a minimum total cost (Ghassan Al-Soboh et al. 1981). Tractor and machinery selection is an important part of machinery management. Wu and Persson (1986), stated that the matching of machine width and tractor power has an important effect on the time and fuel requirements per unit land area. Oversized implements results in a loss of productivity and fuel efficiency. In practice, farmers depend on their experiences or recommendations of other farmers and machinery dealers when matching tractors and implements. To adequately evaluate crop production and to be able to choose alternative crop production or tillage systems, information needs to be collected. Among the information is the implement draft requirement on different soils of major crop production systems. Soil types, soil conditions, operation depths, operation speeds and type and size of implements will determine the draft required and the traction ability of the tractor in the field. Implement draft requirement is an important consideration in selecting implements, tillage systems and a tractor size that is compatible with the operation. In addition to the requiredtractorsize, implementdraftwillalsobeusedto determinethefuel consumed for an operation. A technique is needed in an easily accessible form to match tractor/implement combinations for optimum performance. The complexity and magnitude of the machinery 3 selection problem in the analysis of crop production systems have led to numerous efforts to develop models as a decision aid. A model is required to evaluate alternative machinery systems and to select a machinery complement for different tillage practices for a variety of crop rotations. Number of crops in a rotation, different tillage systems, different land size for different crops in a crop operation, and the use of the same implement with different speed, depth and tractive efficiency complicate the selection of machinery. Large numbers of variables and relationships are involved, thus requiring a computer simulation model for definitive answers. A simulation model for crop production machinery systems to be developed will allow an individual farmer to budget farm operations and evaluate different farming alternatives on a micro computer. Such a model will require selection of machinery complements for different tillage systems for a variety of crop rotations. It must also include a machinery cost analysis. Systems research relies to a considerable extent on the use of simulation models because it is often impossible or impractical to study the real system. Simulation is a very useful tool that saves time, rather than experimentation on actual farms. In order to exhaustively study a system one must rely on computer simulation. Currently, many agricultural specialists are developing expert systems which will be more effective and comprehensive in dealing with agronomic problems (Downs et a1. 1990). Expert systems allow users to become more knowledgeable about a problem as they interact with the program. Simulation models will describe in a general algorithm which can be transferred, modified and evaluated for other geographical locations and situations. There is a need 4 to develop a simulation model that can be modified to be used in Malaysia. Technological change in the agriculture of Malaysia is a matter of importing new equipment from developed nations. While this continues to some extent, the question of appropriateness of the imported agricultural machine has been a focal concern. Agricultural machine if not properly introduced and applied, can lead to serious financial and economic losses and bring about undesirable social consequences. A simulation model that could determine the machinery requirements and sizes, and could estimate the cost of production will assist the policy maker to develop the country’s agricultural mechanization policy. 1.1 Objectives The objectives of this dissertation are: To develop a machinery selection model which utilizes parameters collected from field tests and from pertinent literature to determine near optimum machinery sets for various field crop production systems. To develop the machinery cost analysis model to evaluate machinery costs after a complete set of implements and tractors is selected. The machinery cost analysis must be able to evaluate machinery costs when: (a) the use of machinery ' is shared in several enterprises; (b) the same machinery is used under different conditions (eg. speed, depth, field efficiency, tractive efficiency); and (c) machinery costs are compared for different situations such as farm size and production practices. 5 To develop a machinery selection simulation model and machinery cost analysis simulation model that is user friendly, microcomputer-based program. Beside helping the user make proper decisions, the system must provide education through explanatory and feedback features. It must include programs for the users to create external data files. To compare different farm sizes, tillage systems, and crop production system with respect to costs and machinery requirements. Thus, the user can use this model to determine the most profitable combination of craps in rotations to evaluate different farming alternatives and to scale up or down his farm operation. To design the model to have potential for modification to be transferred to and evaluated for other geographical locations and situations. To carry out field experiments to measure the fuel and draft requirements. To validate the predicted or model fuel and draft requirements with the experimental fuel and draft requirements. 2. REVIEW OF LITERATURE The Crop Production Machinery System (CPMS) simulation model was developed to evaluate machinery systems for crop production. The computer program consists of the Machinery Selection Model and the Machinery Cost Analysis Model. The Machinery Selection Model will be used to predict the size of tractors and implements required to complete the farm operation during a specified duration of time and to determine the compatibility of tractors and implements by properly matching the available tractor power. The Machinery Selection Model also computes the field time required for the machinery set to complete the tillage and planting operations. The Machinery Cost Analysis Model will be used to estimate the annual cost for the tractors and implements selected in the Machinery Selection Model. Machinery requirements (number and size of each) and costs will be determined for each crop production system with two alternative tillage systems, conservation or conventional tillage systems. The users will have the choice of five different locations in Michigan, three soil textures, different farm sizes, and a maximum of five crops in a single rotation. The design of the CPMS simulation model involves calculations of machine productivity and costs. The implement’s required drawbar power is compared to the tractor’s drawbar power. The field time required by the implements to complete a farm operation is compared to the available field operation time. When a well-matched system 6 7 is selected, the total annual cost of owning and operating the set of machines is then determined. Several well-matched systems can be found for the same farm, thus cost analysis can be useful to select the "least cost" or "maximum profit" system. The CPMS model is a computer interactive model developed based on the concept of expert systems which allows the user to interact with the program. The user enters the required inputs and the program will carry out an iterative search. This interactive computer model identifies and searches for the number and type of crops used. For each crop the program identifies and searches for the number and size of the tractor used, and for each tractor, the program identifies and searches for the number and type of implements used. 2.1 Machinery Selection and Cost Models Several approaches to machinery requirements and associated costs have been developed. 1) The enterprise budgets and custom hire rates; 2) Whole farm, profit maximizing linear programming and mixed integer linear programming models; 3) Cost minimization model which seek a least cost machinery complement; and 4) Heuristic models for selecting multiple enterprise machinery sets. All those models were reviewed by Wolak (1981) and Muhtar (1982). 'l‘heenterprisebudgetandcustomhireapproachprovideauseful approximation of capturing cost difference for labor and machinery. It is limited by failure to adequately address the interaction of weather patterns, operations schedule, and crop rotation. Linear programming modelsareusefulfororganizingtheenterprisemixtomardmizeretums given a current machinery complement. These models are less effective for application 8 to search strategies. The cost minimization models select a minimum cost for a given farm size and crop rotation. It is limited by the number of crops, one or two crops at most. The heuristic machinery models developed at Michigan State University view timeliness as a constraint. Machine productivity is matched to available time during schedule calendar periods such that all operations are completed on time. The heuristic machinery models are restricted to only one type of soil, limited farm land size and the farmer is restrained to buy new machines. In many machinery selection studies the main objective has been assumed to be the completion of certain field operations before a given set of dates at a minimum cost (Hughes and Holtman,1976; Ozkan and Frisby, 1981). The time constraint algorithm described by Hughes and Holtrnan (1976) was further developed by Singh (1978). Wolak (1981) further developed the Singh’s (1978) machinery selection model. Rotz et al. (1983) developed a time constraint algorithm to include cost analysis cropping plans. A number of computer programs have been developed to select an optimum machinery set for a particular farm. Ozkan and Edwards (1986) developed a micro computer model for machinery selection for corn and soybean farming. Singh (1978) and Rotz et al. (1983) were among the researchers that developed a multiple crop machinery selection models. Chen (1986) developed a general crop budget model and a budget generator model to allow individual farmers to budget their farm operations and evaluate different farming alternatives. Doster et al. (1990) used the linear programming to determine the machinery size. The microcomputer program crop budget was used to test ways to improve crop mix, machine size, tillage system, and/or farm size. Currently, many agricultural specialists are developing expert systems to allow 9 users to become more knowledgeable about a problem as they interact with the program. Besides helping the user make proper decisions, the expert system can provide education through explanatory and feedback features. The importance of expert systems for machinery management was well described by Oskoui et al. (1990). Jones et al. (1987) use expert system concept in an overall systems approach for solving agricultural problems. Downs et al. (1990) developed an expert system and knowledge base to provide information that a farmer would need in making a typical management decision for tractor-implement systems. Kotzabassis et al. (1990), developed a software package to serve as an expert decision aid for cost effective farm machinery selection and management. 2.2 Tractor and Implement Matching Various methods and guides are available to match farm implements and tractors. A number of computer programs have also been developed to select an optimum machinery set for a particular farm. Capacity and power matching of farm machinery have been described in detail by Hunt (1983). Capacity and power matching model have also been developed by others (Rotz et al. 1983; Ramp and Siemens 1990, Downs et al. 1990). Calvin et al. 1989 developed an interactive computer model, TERMS, to predict farm machinery field capacity and fuel use. Their model was validated with experimental data and found to have a high correlation between the measured and predicted values. The effective field capacity of an implement and the field operation time are important in determining the number and the sizes of implements and tractors required to complete the job within a specified duration of time. An accurate method for 10 predicting machine performance rates is an important farm management tool. It would enable a farmer to more accurately determine the total machine time required to handle the farm crop operations, and would also be an important tool to help select the correct machine size. The energy requirements in terms of draft or drawbar power are important to determine the compatibility of the tractor-implement relationship. The size of implements, speed of operation, and field efficiency are required to determine the implement effective field capacity or the time required to complete a field operations. The commercially available sizes of various machines can be obtained from many sources such as the 1989 Agricultural Equipment Buyer’s Guide (Case 1.1. 1989), or from Implement and Tractors Red Book (1988). Field efficiency is a measure of relative productivity of a machine under field conditions. Hunt (1983) described various factors affecting the field efficiency of a machine. Typical ranges in operating speed and field efficiency for most types of machines are given in the ASAE STANDARDS (ASAE, 1990). The field time required for each operation is determined by dividing total crop area by the effective field capacity of the operation. This result is compared with the available field time within the specified calendar date constraints. Suitable work time available is generally hard to obtain because of the long period of time required to obtain such data. One source for suitable day data is computer simulation. A model developed by Rosenberg et al. (1982) can be used to generate probabilities of suitable work days (pwd) from weather data for a specified location. Wu and Persson ( 1986) developed a modelling relationship for farm machinery performance based upon machinery management standards published by ASAE. Their model examined the effect of variable machine width on time and fuel use when given 11 a machine type, tractor power and set of field conditions. Required minimum capacities for each machine are calculated by the CPMS model based upon the operations required, the area covered by each operation, and the time constraints for the operation; all of which are inputs to the model. Based upon the minimum capacities, a set of tractors and implements is selected from commercially available sizes. After the minimum sized set of machinery is selected, all operations are scheduled according to the hours available. The implement drawbar power requirement or draft is an important factor to determine if the tractor can pull the implement. ASAE STANDARDS (ASAE, 1990) presents draft and power requirements for most field machines for various soil types. Factors often included in the equations are implement speed, depth and width. Implements draft and power requirement were reported by various researchers (White, 1977 and 1978; FMO 1987; Hunt 1983; Self et al. 1983; Zwilling and Hummel 1988). Draft requirements of various machines under Michigan conditions were also reported by a group of scientists at Michigan State University (Rotz and Black, 1985). Summers et al. (1986), stated that draft requirements of tillage equipments are an important consideration in selecting tillage systems. Draft is the major component of forces between the tractor and implement, and in parallel to the soil surface and to the direction of travel. The ability to predict draft, power and fuel requirement for tillage operations is an important consideration in selecting tillage and planting systems. 2.3 Cost Analysis After scheduling of all operations is completed by the CPMS model, the total 12 annual cost of owning and operating the complete set of the machines is determined. The cost analysis determines the costs of fuel, oil and labor for each implement used by each crop and the implement’s and tractor’s repair and maintenance, capital consumption, and taxes, insurance and shelter for each crop. The analysis consists of determining the costs for (a) each implement used per crop, (b) each implement used for all crops, (0) all implements used per tractor, (d) all implements and tractors used per crop, and (e) total implement and tractor costs for all crop operations. This model summarizes the implement and tractor costs for each crop. It calculates the cost of operation per hectare and cost of operation per hour. The machinery cost analysis model also summarizes the total labor cost, fuel cost, oil cost, repair and maintenance cost, capital consumption cost, and taxes, insurance and shelter cost from all the implement for each tractor. After the first set of machines and the associated costs are determined, a check is made to determine if a lower cost set of equipment can be determined. The effect of increasing the sizes of some of the tillage implements of the farm economy is also investigated. The cost analysis by the CPMS program constitute of machinery cost. Machinery costs are the implement’s and tractor’s fixed and variable costs. The implements used in this machinery cost analysis consists of moldboard plow and chisel plow for primary tillage, disk barrow and field cultivator for secondary tillage, and row crop planter and grain drill for planting. The repair and maintenance, oil, fuel and operator’s labor costs are proportional to the machine use. The capital consumption and taxes, insurance and shelter costs are the fixed costs, which remain relatively constant regardless of use, but are dependent on calendar-year time (Hunt, 1983). The oil, fuel and operator’s labor costs are actual operating costs and are directly related to the field time used by the l3 implements. The repair and maintenance, capital consumption, and taxes, insurance and shelter costs are the costs being shared by other crops in a crop rotation. The shared cost for each implement is calculated based on the proportion of time used by each crop to the total field time used by all the crops. The costs equations used in the CPMS model are obtained from the ASAE STANDARDS (ASAE, 1990). Simplified equations derived from the ASAE STANDARDS by Kepner et al. (1978) and Hunt (1983) are also used. These costs equations calculate the annual cost of production for the machinery used for a particular year of operation. The ASAE STANDARDS (ASAE, 1990) suggest that in determining the fuel cost for a particular operation, the fuel requirement should be based on the actual power required. The oil cost is determined by the volume per hour of engine crankcase oil replaced at the manufacturer’s recommended change interval. The cost of labor varies with geographical location. For owner operators, labor cost should be determined from alternative opportunities for use of time. For hired operators, a constant hourly rate is appropriate. Labor cost in the CPMS model is computed based on recommendations for local conditions which involve one operator for each tractor. The model allows the user to enter the local fuel price, oil price and labor wage rate to compute the above costs. Depreciation and interest on investment are the most important items of fixed costs. Two methods commonly used are capital recovery with return method and the straight line depreciation and interest on average investment method. Bartholomew (1981) found out that the capital recovery with return method will be a more accurate measurement. Assuming no salvage value, he defined the annual cost for the capital 14 recovery with return as cost of asset times the annuity factor. Black (1991) described the case where the capital asset has a salvage value and that the value can be represented as a percent of the purchase price. The present value for the salvage was calculated based on the interest rate and expected life of the machine. The annuity factor was modified to include the present value for the salvage. Hunt (1983) used the machinery purchase price, salvage value and the annuity factor or capital recovery factor (CRF) to estimate the capital consumption of farm equipment. The capital consumption equation by Hunt (1983) is used in this CPMS model. An older machine with relatively low cumulative use may have a higher salvage value than a newer machine with high cumulative use. The salvage value of all the machinery used was assumed to be ten percent of the purchase price in the CPMS model. Farm machinery is taxed at the same rate as other farm property. Wide variations exist from state-to-state in the amount of sales tax and in the method of determining the assessed valuations. The property tax rate can vary widely within a state. Tax benefits may favor some machines more than others. If one does not want to complicate machinery selection with tax considerations, the rate can be set to zero (Rotz and Black, 1985). It is not a universal practice to insure machinery against loss by fire or Windstorm, but the insurance charge is justifiable. Machinery shelter has not been shown to increase machinery life, but it can increase a machine’s resale value (Hunt, 1983). A charge must be made against machines for shelter. The ASAE STANDARDS (ASAE, 1990) states that the taxes, insurance and shelter costs can be estimated as percentages of the purchase price. Rotz and Black (1985) suggested that cost analysis of agricultural machinery required parameters to describe the remaining value of the machines at the end 15 of the period of analysis. The above costs in this CPMS model make use of the implement and tractor remaining value. The remaining values as a percentage of the list price at the end of year it for the implements and tractors are obtained from the ASAE STANDARDS (ASAE, 1990). Repair and maintenance costs are highly variable and unpredictable as to time of occurrence. Accumulated repair and maintenance costs at a typical speed can be determined using the repair and maintenance factors, purchase price in current dollars and the accumulated hours of use of the machine. The repair and maintenance cost equation used in the CPMS model is obtained from the ASAE STANDARDS (ASAE, 1990). For convenience of the model, variable cost items such as repair and maintenance, fuel, oil and labor are expressed in terms of today’s dollars. Consistency demands that capital consumption cost and taxes, insurance and shelter cost be also expressed in terms of today’s dollars. In times of substantial monetary inflation, a machinery manager must include the effects of inflation on machinery planning. Inflation causes increased prices fm' goods and services in future years (Hunt, 1983). ASAE STANDARDS (ASAE, 1990) stated that in times of rapid inflation, the machine purchase price must be multiplied by (1 + i)“ where i is the average inflation rate and n is the age of the machine. Various machinery costing techniques, assumptions, and computer programs have been developed for budgeting and economic analysis. Robb et al. (1990) developed a microcomputer spreadsheet program to help analyze the economic and budget aspects of field machinery costs. Mayfield et al. (1981) developed a method to estimate the total cost of owning and operating selected types of farm machinery using a depreciation schedule based on current value of machinery, fuel, equipment and interest rate. Edwards 16 et al. (1990) used net present value algorithm for computing operating and ownership costs for agricultural machinery. They used the time value of money by estimating all future costs and discounting them to their present value. Future costs were estimated in nominal values first, then discounted by a nominal discount rate that converts all values to current dollars. 2.4 Tillage System Tillage practices vary widely in terms of equipment, tillage depth, and amount of soil pulverization. The two tillage systems defined here, conventional tillage system and conservation tillage system are based on the type of implements used. A conventional tillage system uses a moldboard plow and a conservation tillage system uses a chisel plow for their reSpective primary tillage operation. Other implements used with either system include disk harrow, field cultivator, row crop planter and grain drill. The primary difference between conservation tillage and conventional tillage is the percent of soil surface covered by crop residue after planting. Conservation tillage is any tillage and planting system in which at least 30% of the soil surface is covered by plant residue after planting (Michel, et al. 1985 and Muhtar, 1982). Whereas, under conventional tillage, fields are plowed with a moldboard plow or otherwise worked sufficiently to cover nearly all of the previous crop residue. A conventional tillage system frequently uses fall plowing with the moldboard plow followed by a disk harrow and/or field cultivator operation in the spring. The conservation tillage system or chisel plow-based tillage systems can also include vertical tine tillage and roller harrowing before planting. Magleby and Schertz 17 (1988) reported that the adoption of Conservation tillage has been increased from one percent of planted cropland area in 1963 to nearly one-third of United States planted cropland areas in 1986. Conservation tillage practices in crop production save soil, lower energy consumption, reduce machinery investment, and facilitate the return of some marginal land to row crop production (Magleby and Schertz,l988 and Hamlett et al. 1983). Because fewer passes are made over the field than with conventional tillage, both fuel and labor costs are reduced. The practice also reduces the time required to complete tillage and planting operations. 2.5 Crop Rotation Corn, soybeans, field beans, wheat and oats were chosen for this CPMS model based on Michigan’s farmers choices as indicated by previous researchers such as Singh (1978), Muhtar (1982) and Rotz et al. (1983). These crops use similar machines for the operations. The choice of planting equipment is based on the choice of crops. The number of crops using the same tractor and implement determines the implement field time available to complete the operation. The implement field time is used to calculate the implement size. Singh (1978), found that crop rotations have a strong influence on field machinery requirements. A balanced crop rotation increases machinery utilization and decreases machinery requirements, machinery investment, and annual machinery related costs on a unit crop land area basis. Singh and Holtrnan (1979a), concluded that using a balanced crop rotation can almost double the area under cultivation without a significant increase in the size of machinery. If a single crop is grown on the farm, then the whole farm area 18 must be covered within a shorter time constraint which requires larger equipment. As more crops are added, the farm is divided into smaller blocks of land which are worked at different times. Smaller areas done within the same time constraint require smaller equipment. Rotz and Black (1985) stated that selecting machinery for farms which produce several crops is a complex problem. Interactions among machines and between the machinery subsystem created a problem because of the large number of variables and relationships. Chen et al. (1985) stated that the advantages of double cropping increased the expected net returns because of the distribution of fixed costs associated with land and machinery over more crops and more efficient use of labor. The disadvantage that must be considered was more intensive management required where timeliness appeared to be the key ingredient for success. 2.6 Instrumentation Microcomputers were increasingly utilized in the acquisition and processing of implement - tractor performance data. The construction, capacity and versatility of the instrumentation packages varies according to individual data collection. The system designed for this research used an Apple IIel microcomputer for collecting data on-board the tractor and an IBM microcomputer for data processing. The Apple IIe data acquisition system was developed by earlier researchers (Tembo, 1986; Guo, 1987 and 1 Trade names are used in this dissertation solely to provide specific information. Mention of a product name does not constitute an endorsement of the product by the author to the exclusion of other products not mentioned. 19 Mah, 1990) at Michigan State University. The Apple He was chosen for its compactness and durability in adverse physical conditions as observed by Carnagie et al. (1983) and reported by Tembo (1986). Thomson and Shinners (1987) reported using a portable instrument system to measure draft and speed of tillage implements. Measurements were taken and stored using a data logger, then transferred via magnetic cassette tape to a microcomputer for further processing. Microcomputer - based data acquisition systems have emerged as relatively inexpensive alternatives to instrumentation - type tape recorders or strip chart systems. Lin et al. (1980), Carnegie et al. (1983), Clark and Adsit (1985), Bowers (1986), and Grogan et al. (1987), were examples of researchers who developed microcomputer - based data acquisition systems for measuring in - field tractor performance. Carnegie et al. (1983) reported the use of an Apple IIe personal microcomputer for data collection and analysis. They concluded that Apple IIe personal microcomputer was versatile, yet inexpensive, and performed well under adverse field conditions. In summary, the CPMS model deals with geographical locations with known probability working days, different soil textures, different crops in a multi-crop farm, different farm sizes, and different types of tillage systems with various combinations of implements. Five different locations in Michigan were selected, and any number of locations can be added in the future. Six different types of implements were available for various combinations of implements to be used on a farm. Five different crops are available with a maximum of five crops in one crop rotation. A maximum of four tractors can be used on one farm operation. 20 The CPMS model matches the power requirement of the implement and the available power of the tractor, and makes use of field time constraints to complete the farm operation. The model was evaluated on different locations, soil textures, tillage systems, numbers of crops in a crop rotation, and farm sizes. Cost analysis was carried out on several well-matched systems. The draft and fuel requirements for the model were validated with the experimental values. 3.METHODOLOGY 3.1 System Description Farming systems are characterized by the fact that people are attempting to control biological systems in an uncertain environment to achieve some goal which is predominantly economic in nature. Thus, it is essential to think of the farm as a system made up of subsystems or components. Such subsystems can be isolated and studied by researchers; however, solutions suggested must bear in mind the impact on other components. The systems concept involves a most careful search for interrelationships and interdependencies among interacting components. As these relationships are explored, the boundaries of a given system, its complexity, and the difficulties in its study may grow at an alarming rate. The system boundary arises from the need to define the system or sub-system. The setting of study limits and system boundaries must be closely related to the research objectives (Dent et al. 1979). The placing of a boundary is of considerable importance in modelling a system since it determines exactly which subsystems must be explicitly represented within the model structure. Across the boundary there is assumed to be no interaction. Many farm management decisions will influence the farm system as a whole. The system bounds in this example are the farm as a whole. The subsystems to be studied explicitly will be the tillage and planting operation of the farming system. 21 22 Several constraints and assumptions are made to better define the system boundary. Constraints considered in the study include: (a) type and number of crops to be planted; (b) the predominant soil type of the farm; (c) soil and weather condition; (d) tillage implements to be evaluated; (e) planters to be evaluated; (f) tillage systems to be evaluated; (g) the area of operation; (h) type of previous crop; (i) dates of operation; and (i) locations. The Machinery Selection Model and Machinery Cost Model consist of developing a mathematical model of a system suitable for operation on a computer. This CPMS model design is to a large extent dependent on the data available or on the feasibility of generating data within the time limits. The inputs to these models include those that are controllable and non-controllable. One of the environmental inputs that affects the system but is not, in turn, significantly influenced by it is weather. The weather conditions coupled with soil type and conditions will estimate the suitable field work days. The technical and economic selection of field equipment is a complex problem that has some unique characteristics compared with other industries. First, farms have diversified enterprises, and are subject to many special local conditions; thus each farm must be treated as a special problem. Second, since agricultural production is seasonal, equipment will necessarily stand idle much of the time. Also, most field implements are operated by a shared power unit, the tractor; and a change in one tractor-implement Operation will affect the whole system. Consequently, the complete system of implements must be considered. Third, the supply and abith of farm labor, which usually includes management personnel, is quite varied. Finally, a characteristic that is widely recognized but difficult to analyze is the need for timely operations because of the seasonal 23 requirements of craps. Thus, the problem of selecting field machinery efficiently is therefore one of adjusting the factors of implement performance, power availability, labor, timeliness, and costs until an optimum economic retum results. 3.2 System Limitation Few field operations are completely independent of other production operations. Typically, production farming involves the operation of a system of machines. As a result the field efficiency of any single machine may be limited by the capacity of other operations in the system. The timing of one individual operation with respect to another is the feature that defines a machine system. Timeliness of a field operation is a significant factor in field machinery selection. Timeliness is one of the major constraints and its costs arise because of the inability to complete a field operation within a specified duration of time. The timeliness value assumes a reduction in yield for working outside of the most favorable time period for an operation. Weather is one factor of the timeliness that contributes to the risk in operation. It affects the risk due to late planting and harvesting which in turn affects the yield and machine performance. Timeliness in completing a certain job, such as planting, within a certain time period after which equivalent losses become greater through either reduced yields or a less efficient harvest. If lost time is calculated on certain high-importance operations, like planting, with timeliness figured in, it becomes easy to see that delays or undersized machines can cost hundreds of dollars per hour instead of just a few dollars per hour in unused labor. In fact, a good ’rule of thumb’ when considering buying a new machine with little cost difference is to purchase 24 the machine that affords more timely operation (FMO,1987). The total time required for a field machine operation depends on the capacity of the machine and the number of available working days. Each region of the country has a unique climate, and different machine operations will have different criteria as to what constitutes a working day. Wet soil and wet crops are the usual deterrent to field machine operations. Nevertheless, timeliness of operation for a given farm size varies with the size of machinery which is largely determined by the power requirements during the peak work season. The timeliness of operations, for a given crop rotation and tillage system, also varies with the farm size. An essential reason for designing and building a simulation model I is that information may not be desirable from an experimentation on the real system : either the system as such may not be in existence or experimentation may be too costly, time consuming, or ineffective. Appropriately designed experiments with a simulation model can enhance understanding of the relationships and behaviors of the system and can reveal how management might control the system so as best to achieve certain goals. Major economic decisions made by a farmer raising row crops include selection of machinery complement and choice of enterprise mix. Year-to-year variation in weather and its resultant impact on the number of good days available for soil preparation, planting, spraying, cultivation and harvest has been a major uncertainty in making good selections and choices. To control this problem, scientists have adopted a framework in which machinery complements are selected such that completion of the assigned tasks is accomplished within an allotted time period for a known percentage of the growing 88880118. 25 Rosenberg et al. ( 1982), developed an algorithm to provide suitable days data for farm operations based upon weather information and soil structure for an area. These probabilities provide estimates of how many suitable days a farm manager can expect for performing field operations. The design probability is determined by the available work day data set. A work day set at the 80 percent level implies that the given weekly available field work time would occur during or be exceeded eight out of ten years. In general, size of machinery and tractor decrease as confidence level of available suitable work hours change from 80 to 50 percent. 3.3 Research Procedure In this particular study, the following parameters were dealt with: a) Combinations of five crops in different rotations. These crops are com, soybeans, field beans, wheat and oats. b) Three soil types, namely fine textured (clay loam), medium textured (loam), and coarse textured (sandy) soils. c) Five different locations in Michigan. These locations are Kalamazoo in Southwest , Adrian in Southeast, East Lansing in Central, Bad Axe in East and Seney in the Upper Peninsular. (1) Two tillage systems, namely conventional tillage system or moldboard-based tillage system and conservation tillage system or chisel plow-based tillage system. The implements considered are the moldboard plow, chisel plow, disk harrow, field cultivator, row crop planter and gain drill. Tractor sizes can be better determined when good draft and power requirement data exist for desired tillage tools in the specified soil types. Implement energy requirement varies geatly with soil types and conditions, previous treatment, gound 26 cover, speed and tillage depth. Field measurements for the draft and energy requirements of all operations will be determined experimentally. Bowers ( 1986), states that the draft and fuel data measured in field studies can be more reliably utilized in determining tillage energy requirements for crop production systems. The first part of the research was to determine the field measurements for draft and fuel requirements for major crop production tillage and planting operations. The tests for measuring power and energy requirements were conducted on the farnns at Michigan State University and in Clinton County, Michigan using various types of tillage and planting equipments. Experiments were conducted on different soil series at different depths and speeds using various conservation tillage and conventional tillage implements. The draft and fuel consumptions were determined for the moldboard plow, chisel plow, disk harrow, and field cultivator at various numbers of test runs also consecutive operations (e. g. chisel plow followed by disk harrow twice). The draft and fuel requirements were also be determined for row crop planters and gain drills. They were compared for individual implements and for the total tillage system for each crop. The field experimental data were compared to the prediction equations as provided by the Crop Production Machinery System Model. 3.4 Simulation Model 3.4.1 Development of the Model This CPMS model is to determine the tractor and implement compatibility, operation timeliness, and cost of production. The first step in the development of the 27 model was to develop a conceptual model which best describes the crop production system. A conceptual model is the pictorial representation of the system using block diagrams indicating major components and linkages, as shown in Figure 3.1. The field crop production machinery system consists of seven components namely: manpower, land, tractor, machinery/implement, tillage, planting and harvesting. The harvesting component is not considered in the CPMS model. The arrows into the building blocks of the components are the inputs. The farmer provides labor and management to all the components. Among the inputs are tractor and machinery purchase price, fuel and oil price, soil type, crop type and land size. Among the outputs for the CPMS model are sizes and number of each implement and tractor for each operation, machinery costs, and salvage machine. Linkages are the discernable paths of flow among the system components. Figure 3. 1 shows an example that the tractor component provides power and ernergy to tillage and planting implements. The initial selection of components and relationships should be related to the problem specification. The next step was the modelling phase of the simulation model. The modelling phase of simulation consisted of developing a mathematical model of a system suitable for operation on a computer. A pictorial representation of the system is useful. Initially, a series of simple symbolic representations in diagarnmatic form was drawn. Finally, a detailed diagram was constructedwhichisreadilytranslatedtoacomputerprogam. Selectionofthemost suitable computer progamming language was also a major decisiorn. The model design is, to a large extent, dependent on the data available or on the feasibility of generating data within the time limits (Dent et al. 1979). The mathematical relationships used in these models were primarily obtained from the ASAE STANDARDS (1990) and Hunt 28 (1983). The developed model was based on the concept of expert systems. Expert systems provide information and interact with the user to solve problems. The first thing the models do is to interact with the users and check which parameters need to be entered. The user enters the inputs for the farm where the model processes the input and initialize the farm constants. The model deals with the calculations based on user’s input, stored data and stored equations. In using the model, the user may want to input computed data or omit any of the field operations, therefore the model should have the capacity to handle this option. The ASAE published equations for fuel efficiency, tractive efficiencies, oil consumption, draft and power requirements that can be used in the model (ASAE,1990). Fuel efficiency was calculated based on type of fuel and percent load, where percent load is defined as the ratio of equivalent PTO power to maximum available PTO power from the tractor. The ASAE STANDARDS (ASAE,1990) also provides tables to determine repair and maintenance factors, field efficiency and field speed for tractors and implements. Thus, the operating costs including repair and maintenance, fuel, oil and labor costs and interest on operating capital were calculated for each field operation for each crop. Fixed cost per hour of use was calculated as suggested by Hunt (1983). Repair and maintenance costs were based on the accumulated use of the machine. The CPMS model was divided into two models: The Machinery Selection Model and The Machinery Cost and Analysis Model. The Machinery Selection Model was developed to determine the compatibility of the tractors and machinery used to properly match the available power and to select ”best" machinery complements. The Machinery entry oporat. I on cost 8 of units tlnaty cp'n H weather-,crop _> araamol I type.» our“. .“g I! I. labor-mint labor rain. feat-muon- tlmely cp‘n flatbll prim w of V24 ollflml an: we WWW macs —'> .- a . operation cont L— - ‘ of unit. twat-Int. plant-net- I t 0mg warm. I on can: labor nu —> I.” €08! softractfi In board,chtaal d . hart-awnI .cult ”1““,3“, ~> run cost +pnrcha-o 8 Figure 3.1 Conceptual Model of a Crop Production Machinery System. 30 Cost and Analysis Model was developed to calculate the production cost for evaluating the tillage system. The users have the option of using these models independently or interactively. 3.4.2 Verification Verification was done to provide results which are mathematically and logically correct. Its purpose was to check the logical approach of the model and the correctness of the simulation outcome. To verify the results, a hand calculation was carried out for both the machinery selection model and the machinery cost and analysis model. Section 7.1.1 shows sample calculation to determine field time and power requirement for the implements and tractors, implement width and tractor size. These calculated values were used to verify values calculated by the computer model. Calculations to determine the fixed and variable costs for each implement and tractor as shown in Section 7.1.1 were also used to verify the computer model. 3.4.3 Validation The CPMS model needs to simulate the real system sufficiently well to fulfill the purpo&s for which it was developed. The use to which this model will be put and the purposes for which it was designed is the framework within which we had to make the assessment of model validity (Dent et al. 1979). The machinery cost model was validated using the "Machinery Cost" program developed by the author’s major professor (Burkhardt, 1990). The draft and fuel consumption were validated using the experimental values carried at Michigan State University and Clinton County in 1989 and 1990. The 31 validation of the model is described in detail in Section 5.4. 3.4.4 Sensitivity Analysis Sensitivity analysis is a procedure carried out to explore the operation and performance of the model. A sensitive parameter is one which causes a major change in model output; the model is said to be sensitive to such a parameter. Sensitivity analysis is an on-going procedure even when the model is finally approved for application. A sensitivity analysis was carried out to study the reaction of the model to changes in (a) dates of operation, (b) mode of operation, (c) tillage systems, (d) type and nnunber of implements, (e) type and number of crops, (f) sizes, age and purchase price of tractors and implements, (g) farm size and (h) soil texture. Sections 6.2 and 6.3 describe the sensitivity analysis of all of the parameters described above. This analysis was done by changing each parameter or a goup of parameters at a time and noting the effect it has on the tractor and implement selection and its cost. Application of the model may indeed be considered as an extension of sensitivity analysis. 3.5 Instrumentation An on-board computer data acquisition system developed by previous researchers at Michigan State University such as Tembo ( 1986), Guo (1987) and Mah (1990) was used. A commercially available Dickey john Tractor Performance Monitor 11 (DjTPMII) was alsoemployedtomeasuretheenginespeedandgoundspeed. Anenginerpmsensor wasusedfordetenniningtheenginespeed. AsinglebeamDopplerradarunitwasused for determirning true ground speed. A magnetic pickup sensor was used to measure the 32 front and rear wheel rotational speeds. This sensor was used in conjunction with the radar unit to determine percent drive wheel slip. The fuel consumption was measured using an EMCO pdp-l fuel flow meter attached to the engine fuel line. The amount of fuel and time consumed was recorded directly to the data acquisition system. Fuel consumption in liters per hour was calculated during tillage, planting and idling. The draft of the tillage and planting equipment was determined using strain gages attached to the drawbar of the tractor. The draft was also recorded directly using the data acquisition system. The signal conditioner, analog-to-digital converter, computer, battery power source, and their mounting inside the tractor cab were described in detail by Tembo (1986). The sensors which utilized signals from the DjTPMII were also described in detail by Tembo, 1986 and Mah, 1990. The procedures to calibrate those sensors were the same as described in Tembo’s thesis. The data acquisition system is powered by 12VDC-120VAC, 60 Hz, 500 watt sinusoidal voltage converter. Input power to the converter is supplied by a 12 VDC batterywithfreefloatingground. The signal from eachsensorispassedthroughasignal conditioner and through an analog-to-digital converter. The data were stored as ASCII code in the Random Access Memory (RAM) of a microcomputer which was later transferredtoafloppydisk. Asecondcomputerwasusedtoconvertthedatafrom ASCII code to numerical values for analysis. In summary the system bounds for the CPMS model will be the farm as whole. The subsystems to be studied explicitly will be the tillage and planting operations of the farming system. The components of the system include the land, tillage, planting, power 33 unit and machinery. Linkages to the components include labor and maintenance, timely operation, number of implement and number of tractor. The inputs to the system include the farm, soil type, weather, crop type, type of implement, labor rate, shelter, purchase price of implements and tractors, age and expected life of implements and tractors, oil price, oil price and interest rate. Among the output considered are the operation cost and number of tractors and implements required. A conceptual model diagram was drawn followed by a mathematical model of the system. Finally, a computer model was developed. Verification and validation of the model were later carried out. Sensitivity analysis was also carried out to explore the operation and performance of the model. The development of the model is outlined in Chapter 4. Chapter 5 describes the field experiments. The verifications of the sensors and the validations of the draft and fuel requirements are described in Chapter 5. The implementation of the model with various sensitivity analyses are described as in Chapter 6. The machinery selection and estimation of costs for four example farms are presented in Chapter 7. 4. SYSTEM MODEL 4.1 Crop Production Machinery System Simulation Model A computer simulation is a valuable tool for solving the problems of agicultural production systems. With increasing power and availability of personal computers, it is becoming a more available tool. . Developing a simulation model of an agricultural Operation is very time consuming and requires much experience. With the use of a computer, relationships for machine capacity and power requirements can be properly balanced to provide a well matched machinery system. The Crop Production Machinery System (CPMS) program is a software package for analyzing and evaluating machinery selection and cost. The machinery selection process is subjected to the area of operation, calendar date constraints and the tractor drawbar power. The progam was designed for use with microcomputers to assure wide distribution and portability. The progam is written in Turbo Pascal, and is organized into blocks which are algorithms working on data structures. Organizing a pascal progam into procedures is a powerful aid in writing progams. Procedures and functions resemble the subroutines and sub-progams of other programming languages. Calls to procedures are simply ways to access blocks of code in a more convenient and clear way in the progam. The CPMS model was developed based on the concept of expert systems which 34 35 are thought to be more effective and comprehensive in dealing with the problems. An expert system is a computer program that solves problems in the same way as a human problem—solving expert. Expert systems also allow users to become more knowledgeable about a problem as they interact with the program. A related objective for the expert system is education. Beside helping the user with decision making, the system can provide education through explanatory and feedback features. The Crop Production and Machinery System consists of five executable files namely MACHZA, MACHCOST, INTRO, DATA and DATECROP. The CPMS main program, namely MACHZA, consists of Tractor and Machinery Selection Model and Tractor and Machinery Cost Analysis Model. The second model requires the output from the first model. The user has the option of using only the first model or executing both of the models using MACHZA. Figure 4.1 shows a simplified block diagam of the Crop Production Machinery System Model. It explains the input and output of both the Machinery Selection Model and the Tractor and Machinery Cost Analysis Model. MACHCOSI‘ is a subsidiary progam, independent of MACHZA main program. It is created to allow the user to do a cost analysis for a known farm operation. MACHCOST consists of only the Machinery Cost Analysis Model and is similar to the Machinery Cost Analysis Model of the MACHZA. It requires the implement data and field time data, which are outputs from the Machinery Selection Model. INTRO contains the introduction to the CPMS progam. The DATA file will enable the user to create an external data file for implement parameters. The DATECROP file will enable the user to create an extemal data file for the dates of operation for a maximum of five crops. The user has the option of using the DATA and DATECROP external files in the main 36 choices of locations soil textnre type type a 8 of crops known lurknoen inpl a tractor labor wage oil price Interest rate Inpl In tractor price 800 expected life rates of operation Nday area of operation aloflnpl . Machinery Selection Model fuel price __> l v Machinery Cost Analysis I fuel conaunption coats of labazfuelmil Inpl coats of ram,cc,tia tractor costs of rhm,cc,tis coat of production Figure 4.1 Models in a Crop Production Machinery System Model. 4 Inpl a tractor size: —> t of tractors Inpl a tractor field time power req 'ment 37 program or entering this innformation interactively during execution of the progam. These external files are created to allow the user to rerun the progam and to do a sensitivity analysis conveniently. The CPMS progam was created to enable users to easily carry out a sensitivity analysis. Even in using the external data files, the users can change the parameters or values in the main progam. Changing the dates of operation, mode of operation, tillage systems, type and number of crops, or size and soil texture of the farm affects the tractor and implement selection. The sizes, ages and purchase prices of tractors and implements along with the fuel price, oil price, labor wage rate, interest rate, and the type and number of crops in a crop rotation affect the tractor and implement cost analysis. 4.2 Machinery Selection Input Data A primary function of the main progam is to innitialize most of the user’s inputs. The user’s inputs consist of: (1) Geogaphical locations (2) Soil texture (3) Type of tillage system (4) Number and choices of crops in crop rotation (5) Landareaforeachcropinthe rotation (6) Farm operation calendar dates (7) Choicesofkrnownorcomputedfirsttractorsize (8) Choices of known or computed implement sizes 38 4.2.] Location The users have the choice of five different locations in Michigan. These locations are Kalamazoo in Southwest, Adrian in Soutlneast, East Lansing in Central, Bad Axe in East, and Seney in the Upper Peninsular of Michigan. The suitable days information for these locations was obtained from the weather data computer simulation work carried out by Rosenberg et al. (1982). Rosenberg used a probability of 0.8 indicating that during 8 years out of 10 the indicated portion of days will be available for field work. The predicted portion of days suitable for field work, the number of working days and the number of hours per day are used to determine the field time available for operations. The probability of working days (pwd) varies with the months of operations and the locations. This progam is designed such that other locations can be added if the pwd are known. 4.2.2 Soil Texture The choice of soil texture consists of fine, medium and coarse soils. Soil texture is used to determine the drawbar power required by the implements for a farm operation and combines with location to determine the suitable day probabilities. The equations for irnplernernt draft for different soil textures are obtained from the ASAE STANDARDS (ASAE,1990) and FMO,(1987). The draft of an implement is calculated based on soil texture, speed of operation, depth of operation and width of an implement. The progam selects the draft equations based on the choice of soil texture and the implement to be used. The draft is used to determine the required drawbar power of the implement. The required drawbar power of an implement is compared with the available drawbar power 39 produced by the tractor to find their compatibility. 4.2.3 Tillage System The user has the choice of two tillage systems, the conventional tillage system and the conservation tillage system. The primary difference between these tillage systems is the percent of soil surface covered by crop residue after tillage. The conventional tillage system uses a moldboard plow for primary tillage whereas the conservation tillage system uses a chisel plow. Other implements used in the model for both systems include a disk harrow, field cultivator and row crop planter or grain drill. In the program, users will have the choice of using all or any of the implements in the tillage system selected. 4.2.4 Crops and Crop Rotation This model selects machinery complements for different tillage systems for a variety of crop rotations. The user has the choice of using corn, soybeans, field beans, wheat and oats in various crop rotation combinations. Increasing the number of crops in a crop rotation reduces the machinery cost by increasing machinery utilization. The number of crops also affects the field time of operation and implement field time. The typeofcropdetenninesthetypeofplantingequipmenttobeusedandalsoassistsin detenniningthetypeoftillagesystemtobecarriedout. 4.2.5 anSize Theuserhastospecifythefarnnsizebyenteringthelandareaforeachcropin the rotation. The farm size affects the field time required by each implement to complete 40 its field operation within the time constraints. Subsequently, it is used to determine the number and size of tractors and implements to be used. The farm size is also used to determine the approximate minimum size of implements to complete the farm operation. 4.2.6 Calendar Dates of Farm Operation The user has two choices on the mode of farm operation. The first option is an individual farm operation of primary tillage, secondary tillage and planting. This option allows for the individual farm operations to be in different seasons. The second option is a continuous farm operation covering from the primary tillage through the planting operation. A continuous farm operation is usually carried out in one season. The user is required to enter the beginning and ending dates of the farm operation, and the number of hours per day available. The number of days and the hours available per day for the farm operation are used along with the pwds to calculate the available field time for that operation. The dates of the farm operation are used along with the farm location to determine the pwds. The available field time of operation is used to compare with the field time required by the implements to determine if the operation is completed within the date constraints. 4.3 Tractor and Machinery Selection Model The functions of the Tractor and Machinery Selection Model are: (a) To determine the tractor and implement optimum size. (b) To determine the compatibility of the tractor and machinery to properly match the required power with the available power, and (c) To complete the field operation during a specified time. 41 The main program executes based on the choice of implements selected earlier by SELECT. It displays the implement and tractor variables and parameters. If the implement size and parameters were not selected earlier, the program uses the recommended maximum implement size which was computed based on tractor size. The program allows the user to change the implement parameters. The users are advised to use the commercially available tractor sizes and implement sizes in the main program. The tractor and implement sizes recommended are to be used as a guide to select the commercial sizes. Using the recommended minimum implement size will utilize all of the available field operation time. Using the recommended maximum implement size will utilize all of the power available from the tractor. Thus, a compromise must be taken in selecting the commercial implement size to enable utilization of the field time and tractor power. The first objective of the Tractor and Machinery Selection Model is to match power required by the implement to the available tractor power and to complete the farnn operation within the time constraints. The factors affecting the implement’s power requirement include implement size, speed and depth of operation, and soil texture. These values are used to calculate the draft and the drawbar power of the implements. The drawbar power requirement of the implement is compared with the available drawbar power from the tractor. The required drawbar power of the implement must be less than orequaltothedrawbarpowerofthetractorforthetractortobeabletopulltlne implement. If the required drawbar power of the implement is higher than the available drawbar power of the tractor, the implement is too large or the operation is too deep m toofast. Thedrawbarpowerrequirements mustbemetbeforetheprogamwillproceed 42 to the next implement. The user has the options of not using the implement or changing the implement size, operation depth, operation speed, or tractor size. 4.3.1 Machinery Selection Algorithm Selection of a farm machinery set by the selection algorithm is based on a capacity and power match. The implements are sized to match the power available from the tractor and also sized to complete the job in the time allotted to each operation. Properly sized machinery should match the power available from the tractor as well as possible. Implements cannot be sized larger than the associated tractor can pull. Likewise, implements should not be undersized because this causes inefficient use of the tractor. If two implements are individually pulled by the same tractor, then their power requirements should be similar. Capacity matching is complicated by the fact that many operations are interrelated by time. When two or more operations are done in sequence, time devoted to one operation takes time away from another. Time, therefore, must be properly divided between sequential operations. Similar operations may also be done parallel to one another when there are two or more crops in a crop rotation. Thefirststepoftheuacmrandmachineryselecfionalgodflnmiswdetenninedne minimum and maximum implement size for a particular operation. The minimnun implement size is the smallest implement which is able to complete each field operation within the time interval specified. The maximum implement size is the largest implement which can be pulled by the associated tractor. In the process of determining the minimum implement size, the program also calculates the tractor size. The recommended minimum 43 and maximum implement sizes are to be used as a guide to select the appropriate commercial sizes of the implement in the main program. Figures 4.2 to 4.7 show the diagrams of the machinery selection algoritlnm. Figures 4.2 and 4.3 show the algorithm where the inputs are initialized. Figures 4.4 and 4.5 show the algorithm where the program calculate the minimum and maximum implement and tractor sizes. The second part of the algorithm is the implement analysis program. In this part of the program, the users have to enter commercial implement and tractor sizes. Selecting the recommended tractor and implement sizes ensures compatibility between the tractor and implements, and that the operation is completed within specified date constraints. Figures 4.6 and 4.7 show the algorithm to compare the power and field time requirements of the implement with the available tractor power and the available field operation time. Figure 6.1 in Chapter 6 shows an example of the implement algorithm to determine the tractor and machinery compatibility for properly matching the available power and for completing the farm operation during a specified duration of time. The drawbar power required by the moldboard plow is compared with the drawbar power of the tractor. Once the drawbar power requirement is met, the program then calculates the field time required by the moldboard plow to complete the farnn operation. The field time required by the moldboard plow to complete the operation is compared with the available field operation time. Once the field time requirement is met, the progam then proceeds to the next selected implement which is the disk harrow. After the drawbar power and field time requirements for all implements selected for the first tractor and for the first cropoperationaremet, the progamproceedstothe secondtractorfortlnefirstcrop. A maximum of four tractors is allowed in the progam. If another tractor is not required, Kalil-zoo Choice of Atrian loations ht Lansing ans Axe Seney flrie SOI l , medlun sol I ti l conveh ons coarse sol I Incl d disk harrow f. cultivator [planter/g.drll Choice of conventlonal tillaa ente- conservatlo comervatlon chisel disk furrow fieldasarB f. cultivator soybeans lplanter/g.drl I l corn oats wheat Figure 4.2 Machinery Selection Algorithm. 45 Figure 4.3 Machinery Selection Algorithm (cont). nclude unused FT nb/cp d.rarrov f.cult plt/gxti I D have ist tsz yes i enter tSZ have Yes use ext Yes inclement? fl '9 no LB. no 28 Figure 4.4 Machinery Selection Algorithm (cont). 47 y/ n recs-stud output to: min width of” "IX Width enter new es “223° ’ ... ar ‘ area 1 no ( exit ) C se'lne’ote ¢ i 4A Figure 4.5 Maclninery Selection Algorithm (cont). 40 default selected entered enter values Y9“ parameters no output: enter 1: conpatlble ‘—> ’lst tsz ' dbp ,tsz I tsz dip no A tsz 32 (hp output Scbp s,te,a E inc slze Figure 4.6: Machinery Selection Algorithm (cont). 49 GB ' as... 5:. 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A30... .820 8...... 8.... .xmoaa... - .85.... u... can mom .53 8.6 K... 5.8.. + So... u > .8... .5... 3.822.. .3... 8.... .5983... - .5... u.» can mom 88.. 8.8 ~— 50328.50 .3 .56....80 5.23....— =o.mmo..wo~. 86833. 8%... new ......o 58.. 2.. 5.. $9.35.. 8.223.. a... .o 388% a... 2...... 140 correlation of 0.935, showing a very close association between the experimental fuel and the predicted or model fuel. Table 5.5 show the regression analysis for each experiment carried out using the moldboard plow for each soil type. Two different sizes of chisel plow were used to validate the draft and fuel requirements. The 2.5-m chisel plow was experimented on Metamora-Capac Sandy Loam. The 2.2-m chisel plow was experimented on Capac Loam Soil. All experiments were canied out at different operation speeds and depths. Table 5.4 shows the regression analysis. for the chisel plow operation. The model output for the draft and fuel requirements for the chisel plow operations do not differ significantly from the field data. There was a high correlation of 0.974, showing a very close association between the experimental draft and the predicted or model draft. The correlation coefficient was calculated to be 0.742, showing a close association between the experimental fuel consumption and the predicted fuel consumption. Table 5.6 shows the regression analysis for each experiment canied out using the chisel plow for each soil type. Three different sizes of disk harrow were used to validate the draft and fuel requirements. The 3.04-m and 3.96-m disk harrows were tested on Palms Muck Soil and Granby Loamy Sand Soil. The 4.27-m disk harrow was tested on Capac Loam Soil. All experiments were carried out at different operation speeds and depths. Table 5.4 shows the regression analysis for the disk harrow operation. The F - statistic shows that the model output for the draft requirement is statistically different from the field data. There was a low correlation coefficient of 0.362, showing a low association between the experimental draft and the model draft. The model draft equation obtained from the ASAE STANDARDS (ASAE, 1990) and FMO, 1987 is dependent on speed and does not 141 include the operation depth, as depicted by the experiment. The model output for the fuel requirement does not differ significantly from the field data. There was a correlation of 0.776. Table 5.7 shows the regression analysis for each experiment carried out using the disk harrow operations on different kind of soil. Experiments using the field cultivator were carried out on Capac Loam Soil on an untilled area, after moldboard plowing and disk harrowing, and after chisel plowing and disk harrowing. Table 5.4 shows the regression analysis for the field cultivator operation. The model outputs for the draft and fuel requirements for the field cultivator operations do not differ significantly from the field data. There was a high correlation for the draft requirement of 0.869, showing a very close association between the experimental draft and the model draft. The correlation coefficient for the fuel requirement was 0.572. Table 5.8 shows the regression analysis for each experiment carried out using the field cultivator operations on different kind of soil. Experiments using the row crop planter were carried out on Capac Loam Soil, Palms Muck Soil and Gilford Sandy Loam on no till and previously plowed areas. Table 5.9 shows that the experiments carried out using the row crop planter show a high correlation of 1.00, showing a perfect association between the experimental draft and the model draft. The model output for the draft requirement does not differ significantly from the field data. The model draft equation obtained from the ASAE STANDARDS (ASAE. 1990) is dependent on width and independent of the operation speed. Thus the model draft obtained was at zero slope. The data obtained from the experiment and the model for all the experiments using the row crop planter were combined and analyzed. Table 5.4 shows that the model output 142 for the draft requirement for the row crop planter operations does not differ significantly from the field data. The correlation coefficient was calculated to be 0.831, showing a close association between the experimental draft and the model draft. The F-statistic shows that the model output for the fuel requirement for the row crop planter Operations is statistically significant from the field data. The correlation coefficient was calculated to be 0.339. There was a low association between the experimental fuel requirement and the model fuel requirement. Table 5.9 shows that correlation coefficients for the individual row crop planter operation on different soils were 0.849, 0.891 and 0.953. Those coefficients show that there were close association between the experimental fuel requirement and the model fuel requirement. The above results indicate that the regression analysis for the row crop planter operation on specific soil type gave a higher correlation. Three different sizes of grain drills were used in the experiment to validate the draft and fuel requirements. The 2.84-m grain drill was tested on previously moldboard plowed and previously chisel plowed areas on a Capac Loam Soil. The 3.05-m grain drill was tested on a Grandby Loamy Sand Soil that was previously moldboard plowed and disk harrowed. The 1.83-m grain drill was tested on a non tilled Grandby Loamy Sand Soil. Table 5.10 shows that the experiments carried out using the grain drill show a high correlation of 1.00, showing a perfect association between the experimental draft and the model draft. The model output for the draft requirements does not differ significantly from the field data. The model draft equation obtained from the ASAE STANDARDS (ASAE, 1990), is dependent on width and independent of operation speed. Thus, the model draft obtained has a zero slope. 143 The data obtained from the experiments and .the model using the grain drill were combined and analyzed. Table 5.4 shows that model outputs for the draft and fuel requirements for the grain drill operations do not differ significantly from the field data. The correlation of coefficient for the draft and fuel requirement regression equations were 0.847 and 0.860 respectively. There were a close association between the experimental values and the model values. In summary, the model outputs of the moldboard plow operations, chisel plow operations, field cultivator operations, row crop planter operations and grain drill operations for the draft requirements do not differ significantly from the field data. There were very close association between the experimental draft and the predicted draft for the experiments of the above implements. The model output of the disk harrow operations for the draft requirement is statistically different from the field data. There was a low association between the experimental draft and the model draft for the disk harrow operations. The low coefficient of determination indicates that the variation of the predicted draft requirement is not well explained by the experimental draft requirement. Research should be carried out to determine the disk harrow draft equation that also depends on the depth of operation. The model draft regression equations for the row crop planter and the grain drill show a zero or almost zero slope. The drafts stay constant which is independent of the operation speed. There was a perfect association between the experimental draft and the model draft for the individual row crop planter and grain drill operations. The correlation of coefficient was reduced when all the experiments were combined. Research should be 144 carried out to determine the row crop planter and grain drill draft equations to reduce the variability among the experiments. The model outputs of the moldboard plow operations, chisel plow operations, field cultivator operations, disk harrow operations and grain drill operations for the fuel requirements do not differ significantly from the field data. There was very close association between the experimental fuel requirement and the predicted fuel requirement for the above implements. There was a low correlation between the experimental fuel requirement and the model fuel requirement for the overall row crop planter experiment. There was a high correlations between the experimental fuel and the model fuel for the individual row crop planter experiments. The variations of the experimental draft affect the model fuel consumption. Thus, further research must be carried out. 6. IMPLEMENTATION OF THE MODEL 6.1 CPMS Executable Files The CPMS program was written in Turbo Pascal programming language, with executable files which are program files that can execute independently without the use of any programming language. The CPMS program consists of five executable files, namely INTRO, DATA, DATECROP, MACHCOST and MACHZA. The CPMS batch file was created to access the above executable files. The user has the option of using the CPMS batch file or executing directly from each program file. The CPMS batch file is accessed by typing ”CPMS" at the prompt. The program will execute the "INTRO" file which asks if the user would like an introduction to the program. The next executable file will be the DATA file followed by the DATECROP file. The CPMS batch file will finally execute the MACHZA executable file, which is the main program. The DATECROP file enables the user to create an external file of the operation dates foramaximumoffivecrops. Thefivecropsselectedinthispmgramconsistof corn,fieldbeans,wheat, oatsandsoybeans. Theprogramcanbechangedtoother crops ifnecessary. Theuserwillhavethechoiceofusing anyorallofthecropsgiveninthe program. For each crop, the user will enter the dates for the continuous operation from primary tillage to planting or the dates for the individual operation for primary tillage, 145 146 secondary tillage and planting. The DATA executable file enables the user to create external data files for known implement parameters. The user has the choice of using metric units or English units in creating DATA files. The English units are converted by the program to metric units for use by the main program; The parameters needed are implement width, ground speed, tillage depth, field efficiency, tractive efficiency, purchase price, age, and expected life. Other parameters required are the fuel price, oil price, labor wage, and interest rate. The MACHZA is the main program consisting of the Tractor and Machinery Selection Model and Tractor and Machinery Cost Analysis Model. The outputs of the former model are the inputs to the latter model. The implement and tractor field times and power requirements from the Tractor and Machinery Selection Model are used to determine the costs in the Tractor and Machinery Cost Analysis Model. Theuserhasthe optionofusingexternal filesorusingthecomputerkeyboardto enter the values required to execute the main program. The external files include the DATECROP file and DATA file, and these files must be given a file name before being retrieved into the main program. The DATECROP file consists of the dates of operation. The dates of Operation cannot be changed in the main program when using the external DATECROP file. The implement parameters retrieved from the external DATA file into the main program will be saved in the computer memory. The user can change the implement parameters in the main program. This flexibility allows the user to determine the most suitable implement size to be used for the crop production. The main program also allows the user to save the implement parameters into a file similar to the external DATA file. Simultaneously, it will also allow the user to save the implement field time, 147 implement power requirement, and tractor size to an external file. Both files can be saved after the tractor and machinery selection for each crop and also at the end of the main program. Saving the files at the end of the tractor and machinery selection for each crop will save the implement operation speed, operation depth, width, field efficiency, tractive efficiency, tractor sizes, and tractor and implement field time. Saving the files at the end of the main program will save these data as well as the fuel price, oil price, labor wage, interest rate, and the tractor and machinery purchase prices, ages and expected lives. The DATA file and the tractor and implement field time file are to be used for the MACHCOST program. The MACHCOST program is used to analyze the tractor and machinery cost of the selected tractors and implements, whereas the MACHZA main program analyzes the machinery selection as well as machinery costs. The user is not required to save the outputs from the Tractor and Machinery Selection Model to be used for the Tractor and Machinery Cost Analysis Model using the MACHZA main program. The MACHCOST is an executable file to analyse the cost for the tractors and implements selected in the main program, MACI-IZA. It is similar to the Tractor and Machinery Cost Analysis Model of the MACHZA program. It requires the data obtained from the Tractor and Machinery Selection Model to be saved from the MACHZA program. This program is provided for the convenience of a user who does not want to continue the cost analysis provided in the MACHZA program. 6.2 Implement and Tractor Sizes Selection The methodology to determine the minimum and maximum implement and tractor 148 sizes were discussed earlier in Section 4.4.3. The methodology Of determining the implement size depends on the available field operation time, area Of operation, tractor size, and type and number of implements to be used. When the tractor size is unknown, the program will calculate the approximate tractor size based on the number Of implements to be used. The recommended implement and tractor sizes may not necessarily be commercially available. Example 1: Location: East Lansing Soil texture: Coarse Crop: Corn Date of operation: Beginning primary tillage operation : April 11 Ending planting operation Number Of hours per day Area of operation: 100 ha May 22 12 From the above dates of operation : Field Operation time = d*pwd*hd [(19 * 0.34) + (22 * 0.663)] * 12 = 252.5 h. assumedspeed =7.2km/h depthofoperation =10cm Field efficiency = . tractive efficiency = 0.75 149 Table 6.1 shows the implement sizes determined by the program. The first column indicates the type Of implements used on the farm. Columns (a) to (d) show various combinations of implements to be used for a farm operation. The program determines the size of each implement in a set of implements selected. The implement size changes with the number and type of implements used. Columns (3) and (b) indicate that implement size decreased when fewer implements and fewer tillage operations were used. More field operation time was available with fewer implements and fewer passes across the field for a given crop. Replacing the moldboard plow with a chisel plow increased the size of other implements (a and c). Replacing the row crop planter with a grain drill increased the size of other implements (c and d). Thus, different implement combinations required different size implements for the same dates and area of operation. The above implements, when chosen, utilized all of the available field Operation time. Smaller implements required more time and larger implements required less time to complete Operations for the given farm. Larger implements required a larger tractor. The tractor size was calculated based on the primary tillage implement size. In each case (a through d) the selected tractor size was compatible with all implements selected for the continuous farm Operation. Table 6.2 demonstrates the effect of the area of Operation and tractor size on the implement sizes. Comparisons were carried out for the conventional tillage system using moldboard plow, disk harrow, field cultivator and row crop planter for different sizes of farm. Column (a) here was Obtained from column (a) Of Table 6.1. It shows the recommended implement sizes and tractor size for a 100-ha farm. Using the recommended implements will utilize all of the available field Operation time and all of 150 Table 6.1 Implement Size Determined by the Model for a 100-Ha Corn Farm with Coarse Soil. (3) (b) (c) (d) Chisel Plow 1.79 1.82 Moldboard Plow 2.02 1.19 Disk Harrow 6.49 3.82 6.95 7.06 Field Cultivator 1.67 1.79 1.82 Row Crop Planter 4.79 2.82 5.13 Grain Drill 4.64 Tractor Size,kW 24.41 14.38 26.14 26.58 the power available from the tractor. The recommended or selected tractor sizes as in columns (a) and (c) are able to pull any of the implements in their respective column. Column (b) shows the implement field time for the respective implements to complete the 100—ha farm during a specified duration of time. The total field time required to complete all the operations was 252.49 hours. Column (c) shows the recommended implement sizes based on a tractor size of 40 kW. Column ((1) shows the implement field time for the implement sizes Of column (0) for a 100-ha farm. The total field time required to complete the 100-ha farm was calculated to be 154.10 hours. Column ((1) demonstrates that there were 98.46 hours unutilized when using larger implements for a 100-ha farm. 151 Table 6.2 Implement Size with the Required Time to Complete the Field Operations for a 100-Ha Corn Farm with Coarse Soil. ‘ (a) (b) j (c) ((1) size time size time Moldboard Plow 2.02 85.79 3.32 52.35 Disk Harrow 6.49 26.76 10.63 16.33 Field Cultivator 1.67 103.73 2.74 63.33 Row Crop Planter 4.79 36.21 7.85 22.10 Area ,ha 100 163.89 Tractor Size,kW 24.41 40 Total Field Time,h 252.49 154.10 The program recommended the land area of 163.89 hectares to fully utilize the recommended implement sizes Of column (c). Columns (a) and (0) show that the larger farm size required larger implements. The larger farm size and larger implement size required a larger tractor. Using a larger tractor with smaller implements will waste the tractor power. Using implement sizes larger than recommended will not utilize all of the available field operation time. Table 6.3 shows the implement sizes, implement field times, and tractor sizes required for each soil texture to complete a 100-ha corn farm. The cumulative field time 152 for all implements computed for each soil texture indicates that these combinations of implement sizes can complete the farm operations on time. The fine soil required a larger tractor but not necessarily larger implements. Table 6.4 demonstrates that the program also determines the minimum implement width based on the available field time left by an earlier implement Operation. This example demonstrates the operation using the moldboard plow (M ’board), disk harrow (Harrow), field cultivator (F.cult) and row crop planter (Planter). The available field operation time was calculated to be 252.5 hours. Column (d) shows the implement sizes recommended by the program for a 100-ha farm. These implements when chosen, utilized all of the available field operation time. In this example it was assumed that the user used all of the recommended implements and also wanted to know the option of other implement sizes based on the unused field time left by the earlier implement Operations. The Procedure MOLDBOARD in the main program calculated the field time required by the 2.02-m moldboard plow and also recommended the minimum moldboard plow size to complete the farm operation based on the available field operation time. In this case, the available field operation time Of 252.5 hours was used to determine the minimum moldboard plow size. Column (3) shows that the 0.69-m moldboard plow required 252.5 hours to complete the farm Operation. The 2.02-m moldboard used about 85.79 hours leaving 166.71 hours available for the other implement operations. The Procedure HARROW then calculated the field time required by the 6.49-m disk harrow and also recommended the minimum disk harrow width based on the available left by the moldboard plow. Column (b) shows that the minimum disk harrow width of 1.04 meters 153 Table 6.3 Implement Size and Field Time for 100-Ha Farm with Different Soil Textures. rt coarse rsoil medium soil fine soilIfi size time size time size time M’board 2.02 85.79 2.16 80.26 1.51 115.3 Harrow 6.49 26.76 3.32 52.24 2.92 59.39 F.Cult. 1.67 103.7 2.20 78.94 3.00 45.68 Planter 4.79 36.21 4.23 41.08 5.39 32.21 Time,h 252.5 . 252.5 252.5 L Tractor Size,kW 24.41 30.32 49.90 ll Table 6.4 Minimum Implement Width Based on Available Field Operation Time for a 100-Ha Farm with Coarse Soil. (a) (b) (c) ((1) ll M’board 0.69 252.5 2.02 85.79 2.02 85.79 2.02 85.79 Harrow 1.04 106.6 6.49 26.76 6.49 26.76 F.Cult. 1.24 140.00 1.67 103.8 Planter 4.79 36.20 Time,h 252.5 252.40 252.60 252.22 ll 154 used up the 166.43 hours unused from the moldboard plow operation. Column (b) shows that the 2.02-m moldboard plow and 1.04-m disk harrow used all of the field operation time. The 6.49-m disk harrow used about 26.76 hours. The 2.02-m moldboard plow and 6.49-m disk harrow used a total Of 112.55 hours leaving 139.95 hours for the field cultivator and row crOp planter Operations. The available field time unused after the operation of the 2.02-m moldboard and the 6.49-m disk harrow was then used to calculate the minimum width for the field cultivator. Column (c) shows that the minimum field cultivator width of 1.24 meters used up the 139.95 hours unused from the previous implement operations. Column ((1) shows that the 2.02-m moldboard plow, 6.49-m disk harrow, and 1.67-m field cultivator used a total of 216.35 hours leaving 36.15 hours for the row crop planter. The minimum row crop planter width Of 4.79 meters used up the 36.15 hours from the previous implement operations. Example 2: Location: East Lansing Soil texture: coarse Crop: corn Individual dates of operation (a) Primary tillage Operation Beginning dates of Operation : October 20 Ending dates of Operation : November 30 Number of hours per day : 10 Field operation time,hours : 146.60 (b) Secondary tillage Operation Beginning dates of operation : April 15 155 Ending dates of Operation : May 10 Number of hours per day : 12 Field operation time,hours : 140.76 (c) Planting Operation Beginning dates of operation : May 11 Ending dates of operation : May 22 Number of hours per day : 12 Field Operation time,hours : 87.52 Area Of operation: 100 ha assumed speed = 7.2 km/hr depth of Operation = 10 cm field efficiency = 0.8 tractive efficiency = 0.75 Example 2 shows dates for the individual operations different from those used in Example 1. The primary tillage operation was carried out in the Fall with a total field operation time of 146.60 hours. The secondary tillage and planting Operations were to be carried out in the Spring with total field Operation times of 140.76 hours and 87.52 hours, respectively. The program advises the user not to overlap the dates of Operation, especially for Operations using the same tractor. The user planned to start secondary tillage on April 15 and to complete the planting operation on May 22. The user had 11 156 days to complete the planting Operation. If the secondary tillage was completed earlier, the user could use the available field time unused for tillage to complete the planting operation. Table 6.5 demonstrates the effect of implement sizes on field Operation time that is not continuous. For the individual farm Operation, the 1.18-m chisel plow required 146.60 hours, the 1.23-m disk harrow required 140.76 hours, and the 1.98-m row crop planter required 87.52 hours. The chisel plow, disk harrow and row crop planter were found to fulfill the field time requirements for each Operation. For the individual operation as shown in column (a), the recommended tractor size of 17.28 kW was based only on the primary tillage implement, namely the chisel plow in this example. Column (b) shows the maximum implement sizes calculated for the computed tractor size of 17.28 kW. The field times unused after the chisel plow, disk harrow and row crop planter Operations were calculated to be 0 hours, 26.35 hours and 25.58 hours, respectively. These selected implement sizes utilized the maximum power available from the tractor but did not fully utilize the available field operation time for secondary tillage and planting. The implements recommended above are not necessarily available in the market. Thus, the user had to select commercially available implements to be used in the program. For this example farm, the selected commercially available implements had to be at least as large as the implement sizes in column (a) to complete the operations within the time constraints. 6.3 Implement Analysis Examples in this section demonstrate the implement analysis using flow charts. 157 Table 6.5 Implement Size and Field Time for the Individual Farm Operation. (a) (b) size time size time Chisel Plow 1.18 146.60 1.18 146.60 Harrow 1.23 140.76 1.52 114.41 Planter 1.98 87.52 2.85 61.94 Are'a,ha 100 Tractor Size 17.28 Recommended Area,ha 100.03 Selected Tractor,kW 17.28 This example was for a 100-ha of corn farm with coarse soil located at East Lansing. The implements used were a 6-bottom moldboard plow, 2.74-m (9 ft) disk harrow, 2.74m (9 ft) field cultivator, and a 12-row planter. The field times required by the moldboard plow, disk harrow, field cultivator and row crop planter were calculated to be 73.19, 70.19, 47.71, and 28.48 hours, respectively. The power requirements calculated for the moldboard plow, disk harrow, field cultivator and row crop planter were 60.66 kW, 25.41 kW, 56.91 kW and 48.5 kW, respectively. 158 Figure 6.1 demonstrates the four implement operations using one tractor for a continuous farm Operation. The selected 65 kW tractor was able to pull all of the required implements. The available field operation time was calculated to be 223.99 hours, and was compared to the field time required by the moldboard plow to complete the farm Operation. The field time unused by the moldboard plow operation was then compared to the field time required by the disk harrow to complete the farm Operation. The field time unused after the disk harrow Operation was compared to the field time required by the field cultivator, and finally, the field time unused after the field cultivator Operation was compared to the field time required by the row crop planter. Figure 6.1 shows that the field time of moldboard plow, the cumulative field time for moldboard plow and disk harrow, the cumulative field time for moldboard plow, disk harrow and field cultivator, and the cumulative field time for moldboard plow, disk harrow, field cultivator and row crop planter were all less than the available field Operation time. The field time unused after the planting operation was 4.43 hours. Thus, this set of implements selected was suitable, although the tractor power was under utilized in pulling the disk harrow. Figure 6.2 demonstrates the use of two tractors with individual dates of farm operations in the same season. The Operation was carried out on a 100 ha-farm of coarse soil at East Lansing. The first tractor was used only for the moldboard plow, and the second tractor was used for the disk harrow, field cultivator and row crop planter. The tractor and implement sizes were kept the same as in the previous example for the field time comparison purposes. The field operation time for the primary tillage was calculated to be 144.43 hours. The field time unused by the first tractor was 71.24 hours indicating 159 /m-m/ flamm/ r mus-mus) and. '- yes yes yes yes Figure 6.1 Farm Operations on 100 Ha of Coarse Soil Using One Tractor. Begin date of operation : April 18 Ending date of planting : May 22 I-I/day = 12 Field operation time : 223.99 hours Tractor size selected : 65 kW 160 “0:162. 39 fun-144. 43/ /"’°'7' 961‘ tto=106.1 fists-=65n/ flames" / / tto=u.5 lino-ill :- tune-II. u no dbp 3139:2443 d.harrov(9tt) . 51:11:21“ fteF73. 19h p ttsq=70.19h tsrzs=60.66 t3130826.41 dbp sue-O. 1“ storm. 19!: “.9“, 711. “01-30. m tans-3!. 11 tags-56. 9!. dbp