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F or illustrations that ca n n o t be satisfactorily reproduced by xerographic means, photographic prints can be purchased at additional cost and inserted into y o u r xerographic copy. These prints are available u p o n request from the Dissertations C ustom er Services D e p a rtm en t. 5. Some pages in any d o c u m e n t may have indistinct print. In all cases the best available copy has been filmed. University M icrdfilm s International 300 N. Z eeb Road Ann Arbor, Ml 48106 8303830 M uhtar, H annibal A. AN ECONOM IC COMPARISON O F CONVENTIONAL AND CONSERVATION TILLAGE SYSTEMS IN T H E SOUTHEAST SAGINAW BAY COASTAL D R A IN A G E BASIN Michigan State University University Microfilms International PH.D. 300 N. Zeeb Road, A nn Arbor, MI 48106 1982 AN ECONOMIC COMPARISON OF CONVENTIONAL AND CONSERVATION TILLAGE SYSTEMS IN THE SOUTHEAST SAGINAW BAY COASTAL DRAINAGE BASIN By Hannibal A. Muhtar A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Engineering 1982 ABSTRACT An Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin. by Hannibal Muhtar There is concern over the quality of Michigan lakes and and over decreasing soil productivity due to the los3 of top soil as a result of water and wind erosion. cultural This concern has prompted the Agri­ Conservation Program (ACP), a USDA agency, to promote, through cost share programs, conservation tillage practices that reduce waterways, erosion and the associated pollution. ever, is uncertain because of the lack impacts of such of practices on farmers. are known to Voluntary adoption, how­ knowledge of the economic Therefore, this study was ini­ tiated to make comparative economic analysis of the conservation tillage systems being encouraged to the tillage practices traditionally used in the Southeast Saginaw Bay Watershed area. Cooperating farmers were asked to set aside a parcel of land (2 4 hectares) and to to prepare half of it with a normal method of tillage and the other half using conservation tillage equipment like the regular or modified chisel plow. Data on machinery management, agronomic requirements and crop performance were collected from these side-by-side plots. Results of the first two years of a three year study show that con­ servation tillage performed as well or better than conventional tillage in most areas. servation No increase in pesticides use was required due tillage. Seed moisture at harvest to con­ was not significantly different between the two types of systems. to plants for a longer period in conservation tilled plots. no statistically discernible difference 1980. Soil moisture was available In the 1981 in yield between There was systems in season differences were statistically discernable between individual plots due to abnormal weather patterns. A machinery selection model requirements for different was tillage developed to systems. The determine the optimum size machinery for conservation analyze machinery model was used to and conventional tillage based upon performance and economic criteria. Only input/output items that differ across both systems sidered. were con­ Partial budgeting techniques were used to evaluate the econom­ ics of conservation tillage systems relative to conventional systems. Results for different crop sequences on different farm sizes depict that conservation tillage can always provide a lower cost same crop or crop sequence. of producing Conservation tillage costs $13.55 to $59*96 less per hectare and can withstand a loss in yields of 1.9 to cent (depending the 9.5 per­ on the cropping sequence) before it loses its economic advantage over conventional tillage. Approved Department Chairman DEDICATED TO THE ONE WHO: Knew me when I was lost, deserving hell and had compassion over me Loved me beyond human comprehension that He willingly laid down His life on the cross that I might have life Rose triumphant over death and is alive today, side of the Father interceeding for me sitting atthe right Still calls sinners and all that are burdened and heavy laden to Him promising them forgiveness and rest Is not willing that any should perish but of God come tothe savingknowledge Is the author and finisher of my faith Is God almighty To my Lord and Savior Jesus Christ, to Him the glory, power and dominion for ever and ever. ACKNOWLEDGMENTS I thank ray Lord and Savior Jesus Christ who arranged for me to pursue this advanced degree and set up precious people and events to interact so that my sojourn at MSU though short, was full of valuable experiences. I would like to thank Mary, ray loving wife, for her encouragement, support and patience over these three years and for helping me make this venture a success; and ray father and mother who labored, suffered and sac­ rificed a lot to give their children what they themselves were not able to have: good schooling and education. I also would like to thank: * A1 Rotz, my academic advisor, for his invaluable, friendship, profes­ sional guidance, his willingness to always take the time to listen and share, and for his editorial help while writing this manuscript. 0 Roy Black, the project supervisor, for his analytical insight and sense of direction in the course of the project and for the long hours he put willingly into editing and improving this manuscript. * Tom Burkhardt, who served on my guidance committee, for his efforts in making this project the success it is, and for his willingness to take time to help clarify and edit this manuscript. * Don Christenson and Bob Wilkinson, who took time to help in the project and served on ray guidance committee, for their valuable and encouraging comments and help throughout the project. * Mike Score, Elizabeth Lake and Bruce Bedford, for their dedication and help in field related data collection. *Mark Haney for his help in developing the machinery selection 0 model.. Dr. D.L. Robertson, Mr. D. Quisenberry, Mr. H. Rouget and Dr. G. Schwab for their editorial comments. Last but not least I wish to thank the EPA, ECMPDR, funding the project and the Tuscola and Huron county CES who provided invaluable guidance and field assistance. AGP and ASCS for and SCS personnel Table of Contents Page List of Tables.................................................. viii List of F i g u r e s .......................................... CHAPTER 1. xiii INTRODUCTION....................................... 1 1.1 Reason for Conducting the Survey....................... 1 1.2 Definition of Terms ................................... 2 1.3 Traditional Tillage Methods ........................... 6 1.4 Soil Management Groups in the Project Area.............. 6 1.5 Geographic A r e a ....................................... 8 1.6 Work Plan.............................................. I0 CHAPTER 2. OBJECTIVES......................................... 11 CHAPTER 3. LITERATURE REVIEW ................................. 12 Tillage Systems of the Northern C o m B e l t .............. 14 3.1.1. 3.1.2. Conventional Tillage........................... Conservation Tillage........................... 14 14 Effects of Conservation Tillage....................... 17 3.2.1. 3.2.2. 3.2.3. 3.2.4. 3.2.5. 3.2.6. 17 19 22 29 31 35 3.1 3.2 3.3 Economics of Conservation Tillage ................... 3.3.1. 3.3.2. 3.3.3. 3.3.4. 3.4 Soil Temperature............................... Soil Moisture................................. Y i e l d ......................................... Management..................................... Pest Control................................... Soil Aggregation............................... . 36 Labor ......................................... Equipment..................................... Fuel........................................... Fertilizer. ................................... 36 38 38 40 Literature Summary.................................... 42 CHAPTER 4. METHODOLOGY....................................... 45 4.1 Systems Approach...................................... 45 4.2 Economic Approach..................................... 46 4.2.1. 46 Definition of Terms ........................... iv Page 4.3 4.4 Agronomic Practices .................................. 50 4.3.1. 4.3.2. 4.3.3. 4.3.4. 4.3.5. 4.3.6. 4.3.7. 51 52 52 57 57 58 58 Machinery Selection................ 58 4.4.1. 4.4.2. 4.4.3. 58 63 64 CHAPTER 5. 5.1 Model Requirement............................. Machinery Selection Model Development Criteria , Previous Investigation ....................... MACHINERY SELECTION PROGRAM....................... Model Description.................................... 5.1.1. 5.1.2. 5.1.3. 5.1.4. 5.1.5. 5.1.6. 5.1.7. 5.1.8. 5.1.9. 5.1.10. 5.1.11. 5.2 Crop Population (afterfull emergence). . . . . . Crop Residue and SoilCover.................... Growth Stages................................. Soil Moisture................................. Soil Analysis................................. Crop Moisture................................. Crop Y i e l d ................................... MACHSEL............................. Program Subroutine READIN.............................. Subroutine INIT................................ Subroutine MINCAP............................. Subroutine MINTRAC ............................ Subroutine IMPSEL.............................. Subroutine SCHED .............................. Subroutine NEXTWK............................. Subroutine CUSTOM. .......................... Subroutine T0TC0ST ............................ Subroutine FUELFIG ............................ Model Equations...................................... 67 67 70 73 73 74 76 76 78 78 81 81 84 84 Machinery Productivity Parameters.............. Timeliness Cost............................... Fuel Consumption.............................. 84 86 86 5.3 Machinery Parameters andTheirS o u rc e s................. 87 5.4 Model Assumptions.................................... 99 5.2.1. 5.2.2. 5.2.3. 5.4.1. 5.4.2. 5.4.3. 5.4.4. Management Assumptions ........................ Agronomic Assumptions.......................... Machinery Assumptions.......................... Economic Assumptions .......................... CHAPTER 6 . MACHINERY PERFORMANCE ANDMODELVALIDATION ......... 6.1 99 104 108 Machinery Performance................................ 108 6.1.1. 6.1.2. 6.1.3. 6.1.4. HI 116 116 116 Fuel Consumption......................... Field Labor................................... Field Entry Data .............................. Tractability and Ease of Operation ............ v Page 6.2 Model Validation....................................... 117 6.2.1. 6.2.2, 117 132 CHAPTER 7. Sensitivity Analysis........................... Simulated vs. Real Farms....................... AGRONOMIC RESULTS (1979-80 AND 1980-81 CROP YEARS) . . 138 7.1 Crop Residue........................ 139 7.2 Plant Population and Early Season Growth Rates ......... 141 7.3 Plant Population ....................................... 141 7.4 Fertilizer R a t e ....................................... 144 « 7.5 Weed Control and Herbicide Rates. .................... 147 7.6 Insect Populations ..................................... 152 7.7 Crop Diseases......................................... 152 7.8 Crop Yield............................................ 152 7.9 Grain Moisture at Harvest 158 ............................. 7.10 Soil M o i s t u r e ........................................ CHAPTER 8 . ECONOMIC COMPARISON OF CONSERVATION AND CONVENTIONAL TILLAGE............................................ 8 .1 Machinery Complements 8.2 Assumptions for Economic Analysis 8.2.1. 8.3 ..................... Synthesis of Information from Literature and Field Research ......................... Comparative Economic Analysis 164 165 174 178 ......................... 181 Methodology ................................... Projected Impact on Annual Machinery and Labor Costa . . . . . . Projected Impact on All Costs ................. Risk........................................... 181 Summary............................................... 189 8.3.1. 8.3.2. 8.3.3. 8.3.4. 8.4 ................................. 158 CHAPTER 9. SUMMARY 8 CONCLUSIONS 9.1 Summary ................... 9.2 Conclusions ............... 9.2.1. 9.2.2. Scope and Limitations Future Research Needs REFERENCES......................... vi 183 184 188 Page APPENDIX A ....................................................... A_1 APPENDIX B ........................................................ B_1 APPENDIX C ........................................................C-l APPENDIX D ........................................................D-l APPENDIX E ........................................................E-l APPENDIX F ........................................................F-l APPENDIX G ........................................................ G~1 vii LIST OF TABLES Page Table 3.1 Influence of Tillage Treatments on C o m Yield in the Western C o m Belt................. .. . , , 23 C o m and Soybean Yield Response to Tillage Treatments Under Different Crop Sequences , . . , , 25 C o m Yield Response to Tillage Systems Under Different Soils * ♦ ........... 27 Influence of Tillage System on the Use of Herbicide. Figures are in Dollars per Hectare. , , .......... 30 Estimates of the Effect of Different Tillage Practices on Insect Populations in C o m ................... 34 Estimates of Labor Hours Required Per Acre in Con­ ventional Tillage Relative to Conservation Tillage, 37 Estimates of Machinery Costs for Conventional and Conservation Tillage Per Harvest................. 39 Fuel Requirements for Conventional Tillage and Con­ servation Tillage............................... 41 Estimates of Costs per Hectare for Conservation Tillage and Conventional Tillage for Selected Crops 43 Table 4.1 Coded Crop Growth S t a g e s ........................ 53 Table 4.2 Cropping Sequences Considered in the Model........ 60 Table 4.3 Calendar Days Within Which Field Operations Can Be Performed Using Conventional Tillage.............. 61 Calendar Days Within Which Field Operations Can Be Performed Using Chisel Plow Alternative .......... 62 Table 5.1 Power Requirement for Implement in kw/meter . . . . 88 Table 5.2 Field Efficiency of Implements U s e d .............. 89 Table 5.3 Average Allowable Operating Speeds for Implements . 90 Table 3.2 Table 3. 3 Table 3.4 Table 3,5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 4.4 viii Page Table 5.4 Size Increments of Power Units and Implements Available on the Market in Michigan......... , . , , 91 Remaining and Repair Values for Power Units and Implements......... . . . . . . . ............... 92 Available Suitable Hours for Field Work per Week for Three Levels of Risk and Three Types of Soil . . . . 93 Purchase Price of Implements and Power Units (In Dollars Per Meter of Width)................... 94 Implements Used in MACHSEL and Their Corresponding C o d e ............................................. 95 Table 5.9 Custom Rate in Michigan........................... 96 Table 5.10 Timeliness Costs for Planting and Harvesting Operations 97 Table 5.11 Average Yields Reported for the Project Area and Market Price of the Seven Crops Studied (1981)*. . . 98 Table 5.12 Number of Working Hours Assigned to Field Operations 100 Table 5.13 Calendar Dates Assigned to Week Codes............. 102 Table 5.14 Operation Assignment to Power Source ............. 105 Table 5.15 Selected Input Prices Adjusted for Inflation . . . . 106 Table 6 .1 Observations on Selected Field Operations Performed in Spring (1980)................................. 109 Table 6.2 Fall 1980 Field Operations....................... 110 Table 6.3 Spring Field Operations (1981)................... 112 Table 6.4 Corn Harvesting Operations Fall (1980)........... 113 Table 6.5 Draft and Fuel Consumption of Selected Implements on a Sandy Clay L o a m ............................. 115 Example of How "MACHSEL" Iterates and Changes Sizes Until the Least Cost is Arrived At— Three Areas Are Shown. . . . . . . . . . . . . . . . . . . . . . 120 Machinery Selected for a 200 Hectare Farm Under Three Types of Soil............................... 123 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 6.6 Table 6.7 ix Machinery Selected for a 400 Hectare Farm Under Three Different Soils ............................. 124 Machinery Selected for a 600 Hectare Farm With Three Different Soils ............................. 125 Influence of Area on Machinery Utilization and Efficiency......................................... 127 Machinery Selected for a 200 Hectare Farm Under Three Weather Confidence Levels ......................... 129 Machinery Selected for a 400 Hectare Farm Under Three Weather Confidence Levels ................... 130 Machinery Selected for a 600 Hectare Farm With Three Weather Confidence Levels ................... 131 Comparison of Simulated and Real Machinery for a 100 Hectare Continuous C o m F a r m ............... 134 Comparison of Simulated and Actual Equipment For a 400 Hectare Com-Com-Navy Bean-Wheat Farm.......... 135 Comparison of Simulated and Actual Equipment for a 360 Hectare Com-Com-Navy Bean-Sugar Beet Farm . . . 136 Crop Residue Cover................................. 140 Stages of Growth for C o m Grown in 1981 Season. . . . 142 Stages of Growth for Dry Beans Grown in 1981 Season . 143 Seeding Rate and Percent Germination for Farms Growing C o m Spring (1980 and 1 9 8 1 ) ............... 145 Seeding Rate and Percent Germination for Farms Growing Beans (Spring 1981) ....................... 146 Target Rates and Kinds of Fertilizers for C o m Used and Yields Obtained— Rates and Kinds were the Same For Conventional and Conservation Tillage Systems . . 148 Target Rates and Kinds of Fertilizers Used for Beans and Yields Obtained in 1980-1981 Season. Rates and Kinds Were the Same for Conventional and Conservation Tillage ................... . . . . . . .......... 149 x Page Table 7.8 Table 7.9 Target Rates and Kinds of Herbicides Used on C o m and Yields Obtained— Rates.and Kinds were the Same on Con­ ventional and Conservation Tillage Systems .......... 150 Target Rates and Kinds of Herbicides Used on Dry Beans and Yields Obtained in 1980-81 Season. Rates and Kinds Were the Same for Conventional and Conservation Tillage 151 Table 7.10 Target Rates and Kinds of Insecticides Used on C o m and Yields Obtained. Rates and Kinds were the Same on 153 Conventional and Conservation Tillage .............. Table 7.11 Target Rates of Insecticides Used on Beans and Yields Obtained in 1980-1981 Season. Rates and Kinds were the Same for Conventional and Conservation Tillage. . 154 Table 7.12 Comparative C o m Yields on Conservation vs. Convention­ al Tillage for 1980 and 1981 Seasons (Tonnes/hectares] 155 Table 7.13 Bean Yield on Conservation Vs. Conventional Tillage for 1981 (Tonnes/hectares) ......................... 157 C o m Moisture Content at Harvest for 1980 and 1981 Seasons ........* ........... .................. 159 Table 7.15 Dry Bean Moisture Content of Harvest (1981 Season). . 160 Table 7.16 Analysis of Variance of Interaction of Depth, Drying Days, and Tillage System on Moisture in Clay Soil . . 161 Average Values of Moisture Content in Clay Soil at 15.2 and 76.2 cm Deep, Sampled Every Day Two Days After Soil Saturation . ........................... 162 Comparison of Costs and Machinery Sizes for a ComNavy Bean Farm at 160, 240 and 320 Hectares. Soil is Fine and Confidence Level is 80% ............... 167 Comparison of Costs per Hectare for a Com-Navy Bean Sugar Beet Farm at 160, 240 and 320 Hectares. Soil Is Fine and Confidence Level Is 80% ............... 168 Comparison Costs per Hectare and Machinery Sizes For a Com-Com-Navy Bean-Sugar Beet Farm at 160, 240 and 320 Hectares. Soil is Fine and Confidence Level is 80% 170 Comparison Costs per Hectare and Machinery Sizes For a Com-Navy Bean-Wheat-Sugar Beet Farm at 160, 240 and 320 Hectares. Soil is Clay and Confidence Level is 8 0 % .............................................. 172 Table 7.14 Table 7.17 Table 8.1 Table 8.2 Table 8.3 Table 8.4 xi Page Table Table Table Table Table Table !.5 1.6 1.7 1.8 .9 .10 Influence of Area on Machinery Number and Sizes in a Com-Com-Navy Bean-Sugar Beet F a r m ............ 173 Probability of Recurrence of the 1980 and 1981 Years Over a 30 Year Period ........................... 177 Estimated Influence of Yield for Conservation al Tillage for Saginaw Literature Studied and 179 Moisture and Soil Type on C o m Tillage Compared to Convention­ Valley, Michigan (Based on Field Research) ............ Estimates of Cost Advantage for Conservation Tillage Over Conventional Tillage for Four Rotations and Three Farm Sizes for a Medium Textured Soil Under All Conditions, for Fine Textured Soils Under Dry and Average Conditions, and for Coarse Textured Soil Under Wet Conditions............................. 185 Estimates of Cost Advantage for Conservation Tillage Over Conventional Tillage for Fine Textured Soil Under Wet Conditions............................. 185 Estimates of Cost Advantage for Conservation Tillage Over Conventional Tillage for Coarse Textured Soils Under Dry Conditions............................. 185 Table .11 Estimated Cost Reduction That Would Result from the Adoption of Conservation Tillage on Coarse Textured Soils and the Percentage Reduction in Conservation Tillage Yields, Relative to Those Projected, That Could Occur Under Conservation Tillage Before Profitability Would be Equal Between Conservation and Conventional 187 Tillage ......................................... Table .12 Estimated Cost Reduction That Would Result From The Adoption of Conservation Tillage on Medium Textured Soils and the Percentage Reduction in Conservation Tillage Yields, Relative to Those Projected, That Could Occur Under Conservation Tillage Before Profitability Would be Equal Between Conservation and Conventional 187 Tillage ......................................... Table .13 Estimated Cost Reduction That Would Result From The Adoption of Conservation Tillage on Fine Textured Soils and the Percentage Reduction in Conservation Til­ lage Yields, Relative to those Projected, That Could Occur Under Conservation Tillage Before Profitability Would be Equal Between Conservation and Conventional 187 T i l l a g e , a . xii LIST OF FIGURES Conventional Tillage Mold Board Plow in Action. Full Coverage of Crop Material. Extremely Small Amounts of Residue 3 Conservation Tillage: Modified Chisel Plow Allows Crop Residue to Remain on the Surface, . . . . . . 4 Conservation Tillage: V-Ripper. Subsoiling Leaves Crops Residue on the Surface 5 Locations of Farms Cooperating in the Economic Assessment of Conventional vs. Conservation Tillage in the S.E. Saginaw Bay Drainage Basin.......... 9 Hypothetical Probability Distribution of Net Farm Income Under New Farming Technology. Benefits From System "BM Are Clearly Superior to Those From System "A". ............................. 47 Hypothetical Probability Distribution of Net Farm Income Under New Farming Technology. Benefits From System B Have A Higher Average But Decision Making In This Case Is More Difficult ................. 48 Growth Stages of Corn ......................... 54 Growth Stages in Wheat, Oats, Barley, Rye . . . . 55 Growth Stages of the Sugar Beet ................ 56 Flow Chart of "MACHSEL" Program ................ 71 Flow Chart of Subroutine 75 MINCAP................ Flow Chart of Subroutine IMPSEL ............... 77 Flow Chart of Scheduling Subroutine ............ 79 Flow Chart of Subroutine NEXTWK ............... 80 Flow Chart of Subroutine CUSTOM ............... 82 Flow Chart of Subroutine TOTCOST................ 83 Flow Chart of Subroutine FUELFIT............... 85 Selected Input Prices Adjusted for Inflation. . . 103 Number of Days per Week in Which Field Work Can Be Conducted................................... D-4 xiii Figure G. 1 Questionnarie Used for the Survey: xiv CHAPTER 1 INTRODUCTION 1.1, Reason for Conducting the Study The focus of thi3 study was to compare the profitability of conven­ tional tillage to conservation tillage for crop sequences grown on the fine textured soils in Tuscola and Huron counties that southeast Saginaw Bay. The coastal drain into the drainage basin of the southeast Saginaw Bay ha3 been selected by the Agricultural Stabilization and Con­ servation Service (ASCS) of the U.S. Department of Agriculture, as an agricultural water pollution control site. authorized of ASCS, devoted This special project was and funded under the Agricultural Conservation Program (ACP) The project area was 96,800 hectares, to intensive agriculture. of which 87,200 Under this program, oost-share pay­ ments were offered to farm owners and operators a3 incentives conservation practices were to adopt which minimize agriculturally related contribu­ tions of sediments and nutrients to surface waters. There were 1,850 farm owners and operators in the project area. The project was approved for announcements of April. to tillage was to as April 1979, and cooperate shortly thereafter. one of the conservation practices encouraged through cost-share incentive payments. ACP in the farmers in the area were sent during the *lth week Farmers began signing up Conservation implementation These practices were defined by systems which reduce the theoretically calculated erosion rates to less than one-half of that estimated to be tolerated for soil productivity. tive economic maintaining Thus, the goal of this study is to make a compara­ analysis of the 1 conservation tillage systems being 2 encouraged and the tillage practices which are traditionally used in the project area. 1.2. Definition of Terms Conventional tillage refers to the traditional method of the seedbed for planting. preparing It can include chopping stalks (if present), plowing, disking, harrowing and planting. There are variations in the number of operations, especially in disking, harrowing, and cultivating. There are also differences choppers, example, for in the in place kinds of of a machinery used; stalk disk for cutting 3talks or a spring-tooth harrow or field cultivator in tillage. the same: a smooth seedbed that is free of The results are place of a disk for secondary residue and trash, Figure 1.1. Conservation tillage systems are those which inversion of the soil. surface. secure seed not cause total Required amounts of residue are left on the soil Tillage operations are reduced to good do germination the minimum necessary and an adequate plant population. Weed control is achieved primarily by properly applied herbicides, except the ridge-till systems where Figures in cultivation is practiced, (Quisenberry, Tillage System Definition, prepared for MSU-SCS 1981), to Tillage Crop Budgets, 1.2 and 1,3 depict two conservation tillage implements in action. Residual plant matter remaining on the surface of planting the soil after is the "criterion" for evaluating whether conservation tillage has been achieved. For the tonnes/hectare plant residue are required on the surface to qualify of soil types in the drainage basin, as conservation tillage, subject to modification for site-specific 1.7 soil Fig. 1.1. CONVENTIONAL TILLAGE MOLD BOABD PLOW IN ACTION. FULL COVERAGE OF CROP MATERIAL. EXTREMELY SMALL AMOUNTS OF RESIDUE 4 Fig. 1.2 CONSERVATION TILIAGE MODIFIED CHISEL PLOW ALLOWS CROP RESIDUE TO REMAIN ON THE SURFACE 5 Fig. 1.3 CONSERVATION TILLAGE.* V-RIPPER. SUBSOILING LEAVES CROP RESIDUE ON. THE SURFACF 6 types (ECHPDR, 1980). Specific tillage implements are not a condition of the conservation tillage system. 1.3. Traditional Tillage Methods A baseline survey was conducted in the winter of 1980 to determine the tillage methods commonly used by farmers producing corn, navy beans, 3ugar beets and soybeans in Tuscola County, Michigan. Farmers were asked about implements used, timing of operation, and frequency of use. Eighty-three percent^, 89.4JE, 68 .8% and 59.9% respectively of the farmers sampled who grew corn, navy beans, sugar beets and soybeans U3ed conventional tillage methods. Seventeen percent, 10.6%, 31.2% and 40.551 respectively used a chisel plow. None of the farmers sampled followed a no-till system (see Appendix G for questionnaire and detailed The high percentage of farmers chisel plowing navy bean fields was a reflection of the status of the field after harvest. was almost negligible results). The crop residue and the soil surface was already disturbed only once by the bean puller. 1.4. Soil Management Groups in the Project Area The soil management groups are a primary determinant tillage systems. For example, a than with conventional tillage. candidate review of the literature indicates yields under conservation tillage methods on compact more of soil are reduced Excessively compact soil must be loosened to be successful with conservation tillage methods. This 1 Since some farmers 3tated that they practiced more than one operation in one season, for example disc till and moldboard plow the same field, percentages reported here are based on total number of operations performed and not on total number of farmers surveyed. can 7 be best done in conservation tillage with a chisel plow when the soil is relatively dry. Fall chisel plowing can be done with little or no soil erosion on farm3 with land or water erosion problems which associated with moldboard plowing. tion tillage are those that penetrate the are compact zone. closely The best chisel plows for conserva­ heavy enough and strong enough to Thus the approach to conservation tillage must be cognizant of the soils of an area. no-till techniques have been disappointing management are Similarly, experience with on fine textured clay soils as contrasted to well drained, coarse-textured soils (Robert­ son, 1976). Descriptions of the soil farms are presented association 20, and Iosco, and in in management Appendix F. include groups the cooperating The principle series are in soil Simms, Parkhill, soil association 21 for Pawpawlin, which occurs along and includes Wisner, Thomas, and Essexville soils, which Capac, and the SaginawBay, are limey on the surface (White3ide, et al., 1968). The soils of drainage conditions material. lime. this division from developed under clay loam, or silty clay loam parent with .46-1.07 Closely associated meters are level, various sized principle The topography is with some low depressions and narrow sandy ridges. hazards sub of loamy sand or sandy loam3 covering the of this land was wet, swampy, and heavily timbered in its native The natural They are moisture retentive, have good natural fertility, and are clay loams or silty clay loams (Iosco or Essexville). nearly poor The soils are relatively high in organic matter, nitrogen and durable under cultivation. areas loam, were Most state. to crop production are naturally poor drainage 8 and poor tilth (soil outlets is structure). When tile drainage with adequate provided, the soils are very productive because the surface is deep, fine textured, and well supplied with humus. The soils do tend to be cold in the spring. 1.5. Geographic Area The project area is described in Figure 1.4. The primary agricul­ ture in the area i3 cash crop, with corn, dry beans, small grains, sugar beets and soybeans being sequences the principal crops. Crops are grown in adjusted from year-to-year based on deviations in prices, and as a result of labor scheduling, need to avoid adjustment also 3oil compaction. This hep3 partially mitigate disease and weed problems, and to reduce price and yield risk management. Nine percent of the crop land average in 1978 was in farms1 with 20 to 39 hectares, 16.516 with 40 to 79 hectares, 36.77? with 80 to 199 hec­ tares, 23.8? with 200 to 399 hectares, and 14.1? with 400 hectares or p more. farmers Farmers' average age in 1978 was 49.4 being between 25 and 64, years, with 83? of the Twenty-five percent of the crop land area in 1978 was farmed by full owners, 68 .2? by part-owners and 6.5? by full tenants. Farm has been (arbitrarily) defined as being at least 20 hec­ tares. Total crop land area would be 4.6? larger if the Census of Agriculture definition of a farm were used. 2 Hatching Census of Agriculture and Michigan State University Statistics baseline data to the project area i3 a problem since the project area is defined in the context of the Saginaw Bay Drainage Basin, not according to political jurisdiction boun­ daries. Nevertheless, Tuscola county data provides a useful per­ spective. 9 McKinley m Winsor ^ North 1 Haven Sebewa Brookfield a -rj •- ‘ sm ^ e fc g *■/1TTc.l 1*h 3 j o =5— ..' Elmwood fldMSEJ Ikiv ;• '--u t r_ |y] _ 7l-— .^i*. iA T I * ( -j ■ ji Columbia : Gilford fj>nS'-p- \ 7 & ; J - T 3 © f e i ' tE:zb^imuBieEr Denmark _ t- r-r- n- Fig. \ A LOCATIONS OF FARMS COOPERATING IN THE ECONOMIC ASSESSMENT OF CONVENTIONAL Vs CONSERVATION TILLAGE IN THE S.E. SAGINAW BAY DRAINAGE BASIN* *The numbers on the watershed map represent the farms cooperating with the project. 10 1.6. Work Plan The project thrust has a whole farm perspective. The logic Is that the primary purpose of the project i3 toreduce pollution of the Saginaw Bay which comes from agricultural sources. as the Since crop sequence aswell tillage system may be a controlvariable, the methodology, par­ ticularly in machinery selection and economic analysis, permits an exam­ ination of both tillage system and crop sequence and their effect on the economic performance of the farm. The Water Quality Planning Agency and the involved branches of United States Department of Agriculture are interested in methods which will ensure the continued use of agricultural production tices the control prac­ by farmers after the cost-sharing incentive payments have ceased. While U3e through of conservation cost-share tillage practices is currently achieved incentive payments, continued voluntary adoption is uncertain because of lack of knowledge of the impact on the farmer. The results are preliminary 3ince they represent only the first and second year of a three year study; most agronomic experiments mu3t be repeated over a minimum of three years because of year-to-year variation in weather and the fact that "treatment" differences (e.g., conservation tillage versus conventional tillage) weather many scenarios. Also, often differ alternative treatment effects are cumulative; the experimental effects (positive or negative) may take develop. under several years to CHAPTER 2 OBJECTIVES The overall objective of the study was to do a comparative economic analysis of conservation tillage systems encouraged in the project area to traditional tillage practices in the area. In order to meet the overall objective, specific objectives were: 1. To extensively review the literature for information on conser­ vation tillage relevant to the corn belt and Michigan areas. 2. To measure under bo^th types of tillage systems on actual a) crop residue on farms the soil surface b) plant population, c) plant growth, d) incidence and damage of pe3ts, e) crop yield, f) grain moisture at harvest and g) machinery requirements. 2. To develop a machinery selection model which ters collected in utilizes parame­ the field and from pertinent literature to select near optimum machinery 3et3 for various production sys­ tems. 4. To determine production costs and returns under both tillage systems types of and analyze the sensitivity of the returns to various levels of crop yield. 11 CHAPTER 3 LITERATURE REVIEW The primary objective of farmers should be to getthe maximum sible profit from their resources. In these days thisobjective 13 cou­ pled with a conscious effort on the part of the farmer to the positive impact on the environment. pos­ help improve Negative impact is due to chem­ icals in the drainage flow, and wind and water erosion with the result­ ing sedimentation in lakes and waterways. Major variables which are influenced by tillage systems andhave an important impact on yield include: a. Soil temperature during the first three weeks following ing; b. Soil moisture availability throughout the growing season; c. Weed and insect population; d. Soil aggregation; e. Erosion losses; f. Crop residues on the soil surface; s * Fertility and pH of the soil; h* Planting dates; i. Length of growing season; j. Soil organic matter; and k. Soil compaction. This review of literature will consider the impact of tillage conservation practices on the above factors for crops primarily grown in the northern corn belt regions, and in particular the study area gan. 12 of Michi­ 13 Moldboard plowing and subsequent tillage has been method in the traditional U.S. of working the soil in order to provide a uniform, structured seedbed. control, the This permits easier planting, more effective and more flexibility in management of crop rotations. pest It also permits easier placement of fertilizers and pesticide sprays for maximum effect (Constein et al., 1976). Adoption of conservation tillage practices has been slow because they create rough, porous surfaces very different from conven­ tional tillage. tially inverted Such surfaces make it harder to plant, and the par­ soil results in high concentrations of fertilizers and lime in the top eight centimeters of the soil profile, soil primarily acidity (Crosson, 1981). which increases Conservation tillage systems may require increased chemical applications for adequate control of weeds and other pests (Constein et al., 1976; Crosson, 1981). Recent concern of production agriculture regarding government agencies adverse consequences of conventional tillage have resulted in the development of alternative Such and tillage methods for crop production. alternate methods reduce fuel consumption, soil erosion, machinery costs, and labor requirements by eliminating some field operations and leaving crop residues on the soil surface after planting while maintain­ ing yields in most soils (Cros3on, 1981). face Crop residue on the soil sur­ reduces crusting and increases infiltration. also reduces erosion and is reported maintain, where suitable, (Griffith, et al., 1973). yields to reduce comparable Conservation tillage soil compaction, and to conventional systems 14 3.1. Tillage System of the Northern Corn Belt Tillage practices vary widely in terras of equipment, tillage depth, and amount of soil pulverization. Host farmed areas receive some form of primary tillage to a depth of 20 to 25 centimeters (Griffith et 1977). Tillage planting al., systems that have been evaluated in research trials and are currently used by some farmers in Michigan are as fol­ lows: 3.1.1. Conventional Tillage This refers to the traditional method of preparing the seedbed planting. for It can include chopping 3talk3 if present, plowing, disking, harrowing, and planting. There are variations in the number tions, especially in disking, harrowing and cultivating. of opera­ There are also differences in the kind of machinery used; stalk choppers,1 for example, in place of a disk for breaking up corn stalks prior to plowing. ultimate results are the same: The a smooth seedbed that is free of residue and trash (Quisenberry, 1981). 3.1.2. Conservation Tillage Several systems have guidelines been developed under conservation including no-till planting, ridge-till planting, chisel plow tillage, disk till, tandem disk and offset disk tillage servation tillage tillage systems are systems. Con­ defined as those that do not result in total inversion of the soil and allow required amounts of residue to implements like flail choppers that will chop without disturbing the soil the crop residue be IS left on the soil surface after planting operations should to erosion. Tillage be reduced to the minimum necessary to secure a good crop seed germination and an adequate stand. by reduce Weed control is achieved herbicide application except in the ridge-till systems (Quisenberry, 1981). Major conservation tillage systems U3ed in the project area include the following: 3.1.2.1. No-Till. Planting is in narrow 3lots opened by a narrow chisel, fluted, ripple, or smooth coulter or other device in undisturbed residues of the previous crop. Also, planting could be in soil covered with manure, when applied after corn is removed for silage, or following a winter cover crop. spring prior to percent of the soil planting Residues may be 3hredded in the fall, planting or may be left unshredded. surface is disturbed. are done in one operation. surface during the growing season. due is cent Seedbed winter, or No more than ten preparation and Crop residues are left on the soil The minimum amount of surface resi­ 454 kilograms of corn residue per hectare equivalent or 30 per­ of ground cover. Weeds are controlled by herbicides (Quisenberry, 1981). 3.1.2.2. Ridge-Tlll Planting. Ridge-till planting is planting the crop on ridges built with a special cultivator during the previous grow­ ing season. the The seedbed is prepared with a sweep that cuts ridge top and all crop residue between the rows. tillage before planting. Seedbed preparation and and pushes There is no other planting are accom­ plished in one trip over the field (not counting ridge formation). ^In some cropping sequences rye is used as a cover crop after fall plowing. It is sprayed by a contact killer in spring. Crop 16 residue from the previous crop is left on the soil surface (Quisenberry, 1981). 3.1.2.3. Chisel Tillage. seedbed by mixing Chisel plows are used to prepare the soil and the residue without total inversion of the soil; the entire soil chiseling is operation surface performed; is disturbed. it Usually cultivator, is has one disk used to prepare the field for planting. preparations and planting are done in different plow only one is typically done in the fall. Secondary tillage, usually a spring operation with a tandem field the operations. or a Seedbed A chisel or more rows of shanks with straight or twi3ted teeth or shovels on the shanks (Quisenberry, 1981). The chisel plow system i3 the most extensively used of the vation tillage conser­ systems in Michigan (Cook and Robertson, 1979). Chisel plowing is preferable on soils that benefit from fall tillage and water and wind erosion hazards are high (Amemiya, 1977). where It is effec­ tive in loosening compacted soil and it work3 reasonably well on fields. stoney Chisel plow systems are ideal for locations where soil freezing is common because it increases the water intake of the frozen layer operation, chisel plows (Pappendlk and Miller, 1981). To eliminate a separate residue chopping are combined chisel shanks. (helical) with a gang of disks or straight coulters ahead of the This combination is gaining chisel wide acceptance. blades are also replacing straight blades. Twisted This give the chisel plow an added ability for partial inversion of the soil while maintaining the needed crop residue cover on the surface. 17 3.2. 3.2.1. Effects of Conservation Tillage Soli Temperature An obstacle to adoption of conservation tillage Saginaw the Southeast Drainage Basin is the impact of reduced soil temperature in the top ten centimeters of the soil at planting time and six in week3. during the first This temperature reduction is due to the mulching effect of crop residue on the subsequent crop. Van Bavel (1972) defines mulching as the "providing or maintaining of a relatively thin surface layer of some suitable material on the soil surface*'. corn Research done in West Virginia showed soil temperature in sod-planted in grass to be 10 degrees Celcius (°C) lower than soil temperature under conventional tillage 1976). Various (Bennet, and Sperow, tillage practices affect soil temperatures differently according to Willis and Amemiya, (1973). warms Mathias, Fall plowed soil, for example, up more quickly in the spring than soil which is not fall plowed. Also, soil temperature in the top ten centimeters under fall plowing is approximately 3°C higher in May than under grass sod (Emerson and Olson, 1970). Schuler, (1979) reported that soil temperature taken two weeks after spring moldboard plowing was 5°C higher in the top ten centimeters than in the conservation tilled soil. large amounts of this effect is the The difference was attributed crop residue on the surface. increased water retention to The primary reason for in soils with higher organic matter levels due to the crop residue. Lower soil temperature in sod may reduce germination early growth and suppress (Griffith, et al., 1977), especially when crops like corn 18 are planted early to take advantage of the full growing season 1977; Amemiya, 1977). Lower soil temperature will have a large impact on the growth of stems and root3 of 1962). When the (Bennet, a wide range of plants (Canam, temperature drops below 4.5°C, growth of most plants practically stops (Weaver, 1926, as quoted by Willis and Amemiya, 1973). As a result, it may states of the corn belt (1980), stated that be necessary to delay planting in the northern fora few day3 (Bennet, 1977). Glere, et al., W.M. Lewis, an agronomist at North Carolina State University warns that due to this drop in temperature, planting in servation tilled soil con­ in the Northern corn belt states may need to be delayed by as much as ten days in relation This recommendation, however, is changing. to conventional tillage. Robertson (1982) stated that there are now new corn hybrids on the market that are cold tolerant. He also stated that with proper chemical treatment seeds will no longer rot due to prolonged periods in moist and cool soil. Personal communication with farmers practicing conservation tillage revealed the same. Allmars et al., (1964), Jones et al., Medreski et al., (1963) Ketchenson (1970), andWilliam et al., (1967) conducted experiments in which heating cableswere placed in temperature (1963), the soil and used to control of the top ten centimeters of the soil. Willis and Amemiya (1973), citing literature written on these experiments, stated that typical the response to temperature was an increase in germination, rate of emergence, nutrient uptake, yield, increased the to the optimum. weight and height as temperature Also, according to Knoll et al., (1964) a 15°C root temperature for fifteen affected production of dry matter. days early in the growing season 19 The root system of a plant is affected by soil temperature in ing up nutrients, absorbing tak­ water, producing metabolites for growth, and, providing a storage place in the above-the-ground plant (Willis and Amemiya, 1973). portion of the Moncrief et al., (1979). found a sta­ tistically discernible relationship between reduced yield in conserva­ tion planted corn and a drop in soil temperature. 3.2.2. Soil Moisture. There is scarcely a year when available soil moisture is sufficient for optimal crop growth at all points in the growing season. duction depends on water availability. drought Periods of high rainfall and occur in many area3 within the growing season of the crop, each with an adverse effect on yield and quality of the crop. more Crop pro­ To assure a uniform supply, water that is in excess of plant needs at one time must be conserved for later use through storage in the soil. irrigated conditions, water must be Even under temporarily stored in the soil. High soil moisture evaporation rates and low storage efficiencies, ever, have defeated how­ efforts to increase crop production in many areas (Amemiya, 1977). Lemon (1976) classified evaporation of water from 3oil stages. three In the first stage water loss is relatively fast and depends on the evaporative demands of the above-ground environment. characterized by a rapid decline The second is of water loss and is controlled by unsaturated conductivity rather than evaporation potential of surface. into the soil In the third stage, water losses are relatively low and essen­ tially constant, and: 20 "are governed by absorption forces of molecular distances at the soil liquid-solid interface. During this stage, a solid layer of increasing thickness, which approaches air-dryness, forms at the surface. Water lost by evaporation in this stage may diffuse a3 vapor through dry soil." (Conservation Tillage, 1973, P. «2). * It is specified that the highest first stage, and, evaporation loss occurs during the therefore, the greatest potential for reducing eva­ poration lies in this stage also. Conservation tillage utilizes the surface impact of solar radiation and wind movement. residue to reduce Thi3 reduces the rate of surface evaporation and permits the water to penetrate deeply soil where it is less affected by evaporation. into surface (Unger The value of the systems used in conservation tillage is closely related to the amount of crop residues left on the soil face, and the Evaporation reduction is a function of the percent of the crop cover on the soil and Parks, 1976). the the number of treatments after plowing. sur­ Moisture is closely related to the amount of crop residues left on the soil surface and the number of field treatments after plowing (Cook and Robertson, 1979). Surface residues increase infiltration and reduce evaporation (Ben­ net, 1979; Reicosky et al., 1979; and Griffith et al., 1977). This results in more water availability for plant growth throughout the grow­ ing season, which reduces the need for supplemental moisture. Hence it is easier for plants to use moisture from the small rain3 because grow near the soil surface under the mulch. roots Larson (1979) states that the primary benefit of conservation is reduced irrigation water usage. Researchers have found that 3oils in mulch tilled higher corn plot3 are in moisture than those in conventionally tilled plots during the 21 same growing season (Hayes, 1971; increase is not always Triplett, 1968). Even though the statistically discernible (Schuler, 1979) the fact that standing stubble will hold more snow than cultivated fields is also an important consideration when looking at the impact of tillage treatments on moisture. stubble Slow drying characteristics in spring under the continue throughout the season "which is the secret to moisture conservation" (Klocke, 1979). A primary advantage of conservation tillage infiltration (Reicosky, 1979; methods is Griffith, et al,, 1977). more water for transpiration by plants. increased This provides Reicosky (1979) also reported that evaporation was 2.4 times higher under conventional than under con­ servation treatments in maury silt loam for the period between September. and This was translated into an 18 percent higher water availa­ bility for transpiration for the no-till corn. indicated May that while surface Griffth et al., (1977) roughness promotes better infiltration, most research points out that surface residue is the most important fac­ tor for this increased infiltration and that more than 50 percent of the soil surface should remain covered with residue to get significant bene­ fits. Van Doren, Triplett and Henry (1975) reported that in Ohio a covering 70-80 percent of the surface improved mulch corn yields in the Wooster silt loam soil but not on fine textured 3oils such as Hoytville. The benefits of the mulch were associated with increased soil moisture. In well drained, sandy loam, conservation tillage had a ture striking mois­ advantage; as much as 1.3 centimeters more moisture were available in the top 15 centimeters when conservation tillage was used. 22 3.2.3- Yield. Adoption of conservation tillage has increased crop yields on some soils and locations and decreased yields on others. Gen­ erally, have occurred improved on yields better with drained resulted conservation soils. imperfectly or poorly drained soils. in tillage practices Yield decreases have occurred on Yields of corn following 3ome other crop in rotation on such 3oils have been in general, equivalent to or better than yields with late spring tillage (Van Doren et al., 1976). Yields with no-tillage or reduced cantly when compared being soil type. resulted in with The use of reduced tillage systems reduced tillage yields; Corn yields are lage where these soils favor yields are likely to however, on some soil eastern corn belt al., shallow tillage where and excessively drained 1976; Glere et surface residue but these factors create a negative Most erosive soils in favorable for conservation tillage. includes the rolling soils subject to water erosion and has typically increased under conservation til­ decline are types different crop rotations might influence due to shallow tillage or crop residue. the signifi­ conventional practices, the major variable improve lower yields on such crop3 (Van Doren et al., 1980). vary coarse in southern This areas textured soils subject to wind erosion (Griffith et al., 1977). Table 3.1 depicts the impact of tillage systems on corn yield, in the western corn belt. Several other crops have been successfully produced with tion tillage systems. Schuler conserva­ (1979) reported that potatoes yielded highest in conservation tilled plots. Larson (1979) and Wilkes and Underbrink (1979) reported that cotton growth under conservation tillage Table 3.1 Influence of Tillage Treatments on C o m Yield In The Western C o m Belt* - - - - -Tillage System- - - - Location Source Conventional Conservation Tonnes/hectare Tonnes/hectare Wittmus, et al 1971 Nebraska (average of 15 locations) 7.9 8.0 Rehm, Moomaw 1976 Concord, Nebraska 6.4a 6.8b Witmuss 1972-75 Lincoln, Nebraska S.3C 5.9d Ames, Iowa 9.6e 8.8f Erback 1971-75 ♦Adapted from Amemiya, 1977. £ Disk, moldboard plow, disk plant bShred stalks, till plant CChop stalks, disc, plow in fall, disk, plant ^Coulter chisel in fall, disk, plant in spring eFall mold board plow, disk, harrow, plant f Fall chisel plow, field cultivate, plant 24 produced just as well as conventional tillage. chiseling study where deep was used as a conservation tillage system, and tested on soy­ beans and corn, results showed that deep yields In a that chiseled treatments produced were 18% and 50% higher for both corn and soybeans respec­ tively (Camp et al., 1981). In the same experiment, conservation til­ lage with the chisel plow produced higher yields than other deep tillage treatments for soybeans but not for corn. A seven year study in Indiana compared yields chisel, and textures. no-till systems. under conventional, There were four sites with variable soil The difference in latitude between plots was 280 kilometers. In northern Indiana the plot3 were close to a Michigan latitude. Yields were 8.3 tonnes/hectare for conventional and 8.5 tonnes/hectare for con­ servation tillage systems, respectively on a well drained sandy loam. On a poorly drained sandy loam, no-till corn yields were tonne/hectare at least 1.0 lower than conventional tilled plots (Griffith, Mannering and Moldenhauer, 1977). Table 3.2 summarizes influence of tillage treatment by soil type on corn in the corn belt region. It is clear that on poorly drained soil3 conventional tillage practices outyield conservation tillage systems. In Ohio, the previous crop significantly affected corn yields where no-till was practiced. No-till and conventional till systems were com­ pared in a 12 year study for the following crop sequences: continuous corn, corn tion. No-till produced significantly higher yields on a silt loam an unstable after soybeans, and corn after meadow in a three year rota­ surface, while poorly drained clay soils. with conventional tillage surpassed no-till on When corn followed soybeans on a Hoytville Table 3.2 C o m and Soybean Yield Response to Tillage Treatments Under Different Crop Sequences* - - — Source Crop Sequence Crop Location Tillage System - - — Conventional Conservation Tonnes/hectare Tonnes/hectare Randel 5 Swan 1976 C o m after soybean Com Waseca, Minn. 7.2a 7.8b Van Doren Q Triplett, 1975 C o m after soybean Com Ohio 9.5a 10.3C Ross, 1974 C o m after soybean Com Sutherland, Iowa 7.3a 7.3d Amemiya, 1975 C o m after soybean Com N.K. Iowa 7.1a 7.1® Randel 6 Swan 1976 C o m after Com Com Waseca, Minn. 6.7a 4.7b Van Doren G Triplett, 1975 C om after Com Com Ohio 9.1a 10.2* Ross, 1974 C o m after com Com Sutherland, Iowa 6.8a 6.7d Amemiya, 1975 C o m after com Com N.W. Iowa 6.5a 6.6® Randel & Swan Soybean after com Soybean Waseca, Minn. 2.9a 2.9b Ross, 1974 Soybean after com Soybean Sutherland, Iowa 2.4a 2.3d Amemiya, 1975 Soybean after com Soybean N.W. Iowa 2.3a 2.3® ‘Adapted from Amemiya, 1977. aFall mold board plow,-field cultivate, plant bFall chisel plow, field cultivate, plant ^o-till ^Spring disk ^ill plant 26 clay, the decrease in yields was less than the decrease in continuous corn. When corn followed meadow, no statistically discernible differ­ ence in yield occurred between the two tillage systems (Griffith et al., 1977). Table 3.3 depicts the influence of tillage system and crop sequence on the yield of corn and soybeans. Miller and Shrader (1976) developed yield response curves for mois­ ture and for estimating the potential effect of conservation tillage systems on corn yields in western Iowa. Their data showed that when soil moisture levels were 100 percent of plant available water capacity, tillage practices had little effect on yields. average spring moisture levels, At average and below conservation tillage increased yield estimates over those obtained with conventional tillage. A USDA Agrisearch Report (1981) indicated that McGregor and Creer after working for three years on different tillage systems on grain corn and sorghum in the Mississippi Valley silty uplands reported the follow­ ing: Erosion and Watershed plots planted no-till or reduced till had better yields than conventionally tilled plots. The average yield over three conventional corn, 7.8 years was 7.5 tonnes/hectare for tonnes/hectare for no-till corn, and 8.3 tonnes/hectare for reduced till corn. Wittmuss and Yazar (1981) reported that conservation tilled plots in Nebraska had the highest and conventional plots the lowest four average yields. year One conservation treatment was 76% higher than conven­ tional control plots. With conservation tillage in Quebec, Canada, three consecutive years on a clay soil. of moderate and regular rainfall corn was grown for Results showed that in a season conservation tilled plots produced Table 3. 3 C o m Yield Response to Tillage Systems Under Different Soils* - - - - -Tillage System- - - - Soil Type Source Location Conventional Conservation Tonnes/hectare Tonnes/hectare Tracy sandy loam N.W. Indiana B.3a B.5b tl Runnymede loam N.W. Indiana 8.5 a 8.8b ir Blount silt loam E.C. Indiana 8. la 7. 3b m Bedford silt loam Southern Indiana 6.3a 6.9b Flanagan silt loam Illinois 10.6C 10. ob Catlin silt loam Illinois 10.5a 10.4C Wooster silt loam Ohio 10.2d 9. ld Hoytville silty clay Ohio 8.7d 7.4d Griffith, et al. 1976 Oschwald & Seimus 1976 tl Van Doren fj Triplett 1975 it *Adapted from Griffith, et al, 1977. aSpring plow, disc twice, plant bFall chisel, field cultivate, plant Fall plow, disc twice, plant Equal stand, good weed control, continuous c o m 28 higher yields than conventional tilled plots. Whereas in a season where rainfall at certain times was very high, yields were as percent lower than conventional tilled plots. much as eight The reason was attributed to increased water in the soil: "study of bulk density and moisture data showed that the overall soil volume occupied by soil particles decreased by about two percent" with conservation tillage management and in the "wet year", air was a limiting factor in the soil under study". This meant that there was more space for soil and water to share and therefore in a year in which rainfall was normal, soil had adequate air supplies. However, in a high lower and approached al., 1981). reported zero rainfall air-filled porosity was in the conservation tilled soil (Taylor et This yield differential frequently year in literature. due to moisture fluctuation is Studies in Iowa on Moody Silt loam lasting eleven year3 showed that in severe water deficits lister planted (a conservation tillage method) corn out yielded conventionally planted corn by 2.8 tonnes/hectare. Under favorable weather conditions, there was little difference (Amemiya, 1977). Unfortunately, crops other than corn which Saginaw Bay prominent in the Watershed are the crops with the least amount of available conservation tillage yield data. by are Soybeans and corn yields were reported Phillips et al., (1980) to be as high or higher than on conservation tilled soils when compared to conventional tilled soils on large area3 of agricultural lands. Robertson et al., (1979) conducted a study tillage comparing conservation on dry beans and sugar beets, in the Saginaw Valley area. locations were chosen for the study. Dry bean plots harvested Four showed 29 that conservation kilograms/hectare. of the tillage outyielded conventional tillage Conservation tillage also improved the by 740 germination dry beans and gave a superior plant growth for all varieties at all locations. In the same study sugar beets yielded 16 percent more, on conservation tillage plots. 3.2.4. Management. All conservation tillage systems require a higher level of ment manage­ skill than conventional tillage (Cook and Robertson, 1979). These factors must be recognized when making changes in tillage systems. ing a positive attitude is important to make the system work. Hav­ With this frame of mind, a farmer will maintain if not improve, on suitable soils, current yield levels while reducing erosion and improving water quality of the rivers and lakes (Cook and Robertson, 1979). Conservation til­ lage allows for very few errors. 11Clean till lets a farmer correct a maximum number of mistakes with another trip across the field. With conservation tillage the farmer cannot afford such practices'1 (Kelly, 1977). Many farmers report poor stand problem can with conservation tillage. often be traced to poor equipment adjustment, inexperience with planting in residues, poor seed placement, or improper ticides This (LeGlere, 1981). Indiana U 3e of pes­ studies (Griffith et al., 1973) of tillage systems on five soils showed few stand differences on sandy loam soil but up to 15 percent variation on silty clay loam. were always within 5 percent of conventional stands. planting rates are recommended For No-till stands this reason to be at least 10 percent higher than conventional (Robertson et al., 1979). Table 3.4 Influence of Tillage System on the Use of Herbicide. Figures are in Dollars per Hectare _____ Crop Source -Tillage System- - Conventional Conservation N.T.* % Increase Over Conventional Tillage 39.13 350 Wheat Taylor, 1979 CTexas) 8.75 Com Doster, 1973 (Indiana) _ C o m (in a Com-com-soybean sequence) Walker, 1977 (Iowa) 74.0 Sorghum Crosson, 1981 16.58 16.58 58.45 — — _ _ Phillips, 1974 * No tillage 8.75 50 100.7 ------ 36 - 253 50 31 To use conservation tillage techniques most effectively, a farmer must know his soil types and must be able to match them with appropriate tillage practices. use He thus needs to command greater technical skills to these methods compared to conventional tillage practices. However, the costs of acquiring the necessary 3kills are low and not an important obstacle to the spread of conservation tillage (Crosson, 1981). farmer using conservation tillage has less margin of often cannot go back over with adoption of conservation tillage (Glere et al., 1980). conservation tillage he Greater economic risk is thus asso­ should be emphasized during this transition period that because a field with a cultivator to control weed problems not handled by herbicides. ciated error The of tillage It systems may produce lower yields until farmers gain experience with more variables introduced by the system. 3.2.5. Pest Control 3.2.5.1. control Herbicides. Under conventional tillage systems, farmers weeds by plowing them under with the use of a tillage implement before planting, and by spraying operations. When tillage is herbicides during secondary tillage reduced, alternate weed control methods mu3t be implemented to accomplish this essential step of early reduction in weed population. Conservation tillage systems rely primarily tions to check weed establishments. on chemical applica­ Other forms of conservation tillage may include 3ome cultivator, but most usually require more kilograms herbicide for weed control than of conventional tillage (Crosson, 81). However, judging from literature this increase is highly variable as can be seen from Table 3.4. Crosson (1981) gave three major reasons for 32 thi3 increase in quantity: 1) substitution effect, where due to tillage, chemicals should handle a larger population of weeds; 2) effi­ ciency effect where new herbicide mU3t be applied level reduced to achieve a given of weed control because some of the herbicide gets tied up by the crop residue; and 3) environmental effect where increased moisture in the conservation tilled soil improves the conditions for germination and growth of weeds. Paraquat and Sod planting under no-till utilizes a Atrazine to control weeds in corn. combination On rough tilled sur­ faces, pre-emergents such as Atrazine, Lasso and Amiben are tive. Pre-plant incorporated (PPI) relatively well prepared surface to herbicides obtain of not effec­ must be applied on a uniform effectiveness. A trashy, cloddy surface will inhibit PPI performance. Post emergent herbicides are most effective when conservation lage is used. til­ This, however, may cause problems especially where early crop growth is suppressed by low temperatures because a height differen­ tial between crop plants and weed3 is required for effective results. This limits good control early in the season when weed detrimental to crop yields. growth is most Slow germinating weeds, or weeds which grow at the same rate as the crop cannot be controlled effectively with post emergents (Erbach and Lovely, 197*0. Weed control by mechanical cultivation is difficult in heavy due when tools such as resi­ sweep cultivators, and rotary hoes are used. Rotary tillers and disks work in heavy residue but they bury much of the residue, reducing the conservation values of the system. A rolling cul­ tivator works well in crop residue, and only buries a small fraction the residue (Erbach and Lovely, 197*0. of 33 3.2.5.2. severe Insecticides. Insect and disease problems in conservation tillage than in conventional tillage. Conserva­ tion tillage may require heavier application of insecticides and cides to achieve proper control. are fungi­ This is attributed to the crop residue left on the soil surface which provides a favorable habitat for 3ome insects and diseases. Researchers are divided on stated this idea. Philips et al., that this varies with conservation tillage practices. state that because of higher soil plants are moisture and less soil (1980) They also compaction healthier and can resist insect and disease pressure (Cros­ son, 1981). On the other hand, Kelly (1977), reported black cutworm no-till crop production, particularly when corn followed soybeans. is due to the insect affinity for soybean and high matter. moisture conditions resulting stubble, from lower in This temperature, increased soil organic Seed corn maggot and seed corn beetle are also favored in cold wet springs under conservation tillage. Root aphid and white grubs were found in higher populations in con­ servation tillage. Overwintering insects were not killed because they were less exposed under conservation than conventional pests systems. Other seen in higher populations are armyworms, slugs, and flea beetles (Constein et al., 1976). Control of insect populations under the U3e of chemicalsis Rains after insecticide effective or dependent on and as the tillage through the climate to a great extent. applications may render ineffective; decrease seed treatment conservation number becomes veryimportant. the insecticide less of tillage operations This treatment should most Table 3.5. Estimates of the Effect of Different Tillage Practices on Insect Populations in C o m .*a Spring Plowing Fall Plowing Seed-corn beetles 0 0 ? 4* Yes Seed-corn maggots 0 0 ? + Yes Wireworms 0 - 9 + (sod) Yes 9 + Csod) No 9 + (sod) 9 _? +? + (com) Yes White grubs 0 - C o m root aphids - - C o m rootworm _ 9 Reduced Tillage Effective Chemical Control Pest No-tillb Black cutworms ? ? ? + Yes Billbugs - - - + (sod) Yes European c o m borer - - + + Yes True armyworms - - - + (sod) Yes Common stalk borer - - - + No Slugs - - - + No Mice “ “ “ + (sod) Yes *The practice will increase the population or the potential for damage by the pest (+); it will reduce the population or potential for damage (-); no effect on the pest (0); effect unknown to the pest (?). University of Illinois, Circular 1172. bThe preceding crop will have a direct influence on the pest problem(s) in no-till com. 35 be coupled ters. with Anticipation an increased understanding of pest population parame­ of where potential insect problems will occur becomes crucial in pest control under conservation tillage practices. The University of Illinois <1979) found a direct relationship between tillage systems and insect management (Table 3*5). According to this table "reduced tillage" may increase populations of corn rootworm relative to conventional tillage. Where cutworms and wireworms are a fertilizer-insecticide tive. combination They should be applied by planting, and immediately of problem, spray3, granules or Aldrin or Meptachlor are effec­ broadcast on the surface prior to incorporated with a field cultivator within the upper eight or thirteen centimeters of the soil (Constein et al., 1976). Granular pesticides should be applied to control rootworms in a 13 to 17 centimeters band behind the planter shoe, but in front of the cov­ ering device and packer wheel. All 3oil applied insecticides are more effective when incorporated to a one to two centimeter depth and packed. With no-till equipment, placing granules directly in the seed furrow one of the only choices available. cides are regulated for such use. the furrow is Only a limited number of Insecti­ Granules must be lightly covered and sealed for this method to be environmentally safe (Constein et al., 1976). 3.2.6. Soil Aggregation Soil aggregation is an index of 3oil resistance to dispersion, com­ paction, plant emergence, soil aeration, drainage, water Intake and soil 36 ero3lon (Griffith et al.f 1977). Soil aggregation was studied for con­ servation and conventional systems used with continuous corn in Indiana, After five years, results showed that aggregation increased decreased. In most cases as tillage aggregation was higher in the zero to five centimeter zone than in the five to fifteen centimeter zone with no­ tillage (Mannering et al., 1976). When crop residues are incorporated in the top soil, regardless how they are managed, soil decrease is due to cementing which stick soil attack agents particles 0.84 ram in diameter. erosion is immediately produced by reduced. microbial of This organisms together, forming aggregates greater than Aggregation declines as other micro-organisms these products breaking into friable, erodible humus (Chepil and Woodruff, 1963). 3.3. Economics of Conservation Tillage Several researchers have published dealing with of their experiments various tillage systems and the impact of such systems on crop production. differences results Detailed farm budget studies could describe between the cost conventional and conservation tillage technologies under the variety of conditions in which they are actually used by farm­ ers. Information about differences between these two tillage systems In quantities of resources used and yields obtained is presented below under labor, equipment, fertilizers and fuel. 3.3.1. Labor. There is agreement that less labor per hectare is needed with servation tillage. Even con­ though harvest activities show no difference 37 Table 36. Estimates of Labor Hours Required Per Acre in Conventional Tillage Relative to Conservation Tillage*+ Comment No-till, crop or other details not specified Ratio, Conventional Tillage To Conservation Tillage As much as 3.0 2.1 2.1 2.4 2.4 Allen, R. et al 1976 Irrigated winter wheat, area not specified No-till "Limited" till Triplett and Van Doren, 1977 USDA, 1975 Data for 1969, area not specified Com Sorghum Soybeans Cotton Source 2.0 1.4 C o m in Nebraska 2.0 Derscheid et al C o m in Michigan 1.7 Mannering, J. and Burwell, 1968 Doster, H. and Philips, J. 1973. C o m in Central Indiana Chisel Plow Till-plant No-till 1.6 2.S 2.3 C o m in Piedmont in North Carolina 2.7 Dryland continuous grain sorghum, Texas Panhandle Dryland wheat-grain sorghum rotation, Texas Panhandle No-till spring wheat in southern Alberta 1.6-1.7 Shiply and Osbum, 1973 1.75 1.25-1.40 Zenter, and Lindwall, 1978 +Adapted from Crosson, 1981 ‘Differences are assumed to refer to pre-harvest labor requirements. The literature is not always clear on this. NOTE: Research results received too late for detailed consideration here show that labor required for conventional tillage of c o m exceeded that required by various conservation tillage systems by 30 to 50 percent. However, these estimates evidently are total labor required. Most estimates in this table appsmetly are pre-harvest labor only. 38 between systems, preharvest activities show a reduction of one half requirement {Crosson, 1981). Table 3.6 shows estimates of the labor required in conventional tillage relative to conservation tillage. 3.3.2. Equipment. A survey of literature on systems shows that data on conservation machinery and conventional investment tillage costs for the two classes of technology are meager, scattered and specific to soil location and farm size. These data, however, are almost unanimous that conservation tillage costs are less Table 3-7 types, than conventional (Crosson, 81). shows a summary of estimates of machinery costs of both sys­ tems. Machinery requirements per hectare are less with conservation til- * lage for a farmer who converts completely to this system. annual costs per hectare, the saving is on the order dollars. of In terms of three to ten However, many farmers likely will want to retain the option of conventional tillage and for them machinery costs likely would be higher than for farmers who forego this option (Crosson, 1981). 3.3.3. Fuel Conservation tillage requires less pre-harvest tional tillage because of fuel fewer passes over the field. than conven­ Conservation tillage saves ten to thirty liters of diesel fuel equivalent per hectare relative to conventional liters of fuel per hectare. tillage. No-tillage save3 thirty to forty Table 3.8 depicts fuel requirements for the two tillage systems based on literature published. Table 3.7 Estimates of Machinery Costs for Conventional.and Conservation.Tillage Per Hectare*+ Conventional Tillage Source Fall Plow Spring Plow Conservation Tillage Chisel Partial Chisel Disk Chisel Coulter Chisel 77.75 62.15 81.90 49.78 82.63 58,60 Disk TillPlant Limited No­ till Minimum Till Siemens 6 Oschwalb 1978° 200 hectares 400 hectares 76.90 61.25 73.38 61.78 74.13 55.20 Dobster 5 Phillips 1973b 20.55 33.40 20.95 63.03 50.18 62.05 41.65 15.40 16.00 Taylor, Reneau 6 Trimble 1979 Furrow-irrigated winter wheatc Dryland grain sorghum^ Walkere, 1977 27.10 20.60 15.73 37.15 22.43 12.93 79.73 73.90 104.70 ♦Data are not comparable among sources. tAdapted from Crosson, 1981. aC o m and soybeans in Illinois. ^ C o m in central Indiana; 240 hectare farm. cBushland, Texas; size of farm not given. Based on average yields 1974-1976. ^Rio Grande Valley, Texas; size of farm not given. eSouthwest Iowa: Based on average yields, 1974-1976. corn-com-soybean rotation; 128 hectare farm. 40 3.3.4. Fertilizer There are differing views about whether conservation tillage conventional tillage have different fertilizer requirements. problems in assessing the literature on this point is that of fertilizer requirements standpoint, the definition often is not defined. presumably is and One of the the concept From the farmer's economic: The fertilizer required is the amount that will yield a return equal to the cost of the fertilizer, allowing for ri3k. his fertilizer To be sure, the farmer may not define requirements in these terms, but as a profit maximizer, that is what he has in mind (Crosson, 1981). The definition of fertilizer requirements in the literature while unclear, is not the economic definition that one would expect farmers to employ. It is instead a technical definition reflecting agronomists and soil scientists on under given conditions of soil type, and tillage are not given. of the amounts of fertilizer needed structure, available requirements of nutrients. the fertilizer requirements of judgements temperature, moisture Often, the yield responses to conservation tillage and conventional Clearly, if conservation tillage systems require more fertilizer than conventional tillage systems, but there is an off­ setting increase in yield, the difference in requirements has no bearing on the farmer's choice between the two types of technology (Crosson, 1981). The evidence requirements in between the literature on differences in fertilizer conservation and conventional tillage is not ade­ quate to reach a firm conclusion. Table 3.8 Fuel Requirements for Conventional Tillage and Conservation Tillage* Liters Diesel Fuel Per Hectare Comment Tillage System Conventional Limited-till No-till 68.14 41.64 26.50 Furrow irrigated continu­ ous wheat, southern High Plains Allen, et al. 1976 Tillage System Conventional Disk and Plant Till-plant No-till 38.42 15.52 14.29 10.13 Com Witmuss et al. 1975 Tillage System Conventional Till-plant No-till 50.16 23.28 8.52 Crop not specified USDA, 1975 Tillage System Conventional Reduced-till No-till 53.00 42.59 27.44 C o m in South Dakota Derscheid et al. 1980 Tillage System *Adapted from Crosson, 1981. Source 42 Allen et al., (1976) reported that corn production tillage nearly equaled the co3t for conventional tillage. and overhead costs were lower but fertilizer costs were fith costs for no­ Labor, fuel higher. Grif­ et al., (1977) reported that even though conservation tillage sys­ tems are likely to reduce production costs "Maximum savings for versus fall plowing tonnes per hectare. are no-till not likely to exceed the value of .35 to .70 As a summary Table 3.9 estimates costs of conven­ tional and conservation tillage systems for some selected crops in 1979. 3.4. Literature Summary In a report on model development to determine a "low cost for reducing agricultural non-point pollution in Lake Erie", Forster (1979) indicated that yield indices used in his model were conservation Ohio. compared to strategy 100-105 for 100 for conventional tillage for Indiana and For Michigan he used the same yield indices for both tillage sys­ tems indicating that there were no significant yield differences between them. Griffith et al., (1977) summarized the factors that influence crop response to conservation tillage as follows: a. Soil Drainage. Shallow tillage and/or surface residue are more likely to succeed on well drained soils. systems b. Previous Crop. Shallow tillage and no-till for corn are more likely to succeed on poorly drained soils when corn follows anything but corn. o. Soil Structure. organic matter tillage. Corn on poorly structured soils with low is likely to react positively to conservation Many researchers have reported that the immediate benefits farmers to of conservation tillage are increased yields from moisture Table 3.9 Estimates of Costs Per Hectare for Conservation Tillage And Conventional Tillage for Selected Crops* Total Costs*3 Labor Machinery Conv. Tillagea Cons. Tillage Conv. Tillagea Cons. Tillage Crop 412.50 385.00 33.10 16.55 Sorghum 285.00 252.50 33.58 Wheat 197.50 170.00 Soybeans 262.15 237.50 Crop Conv. Tillage3 Fuel Pesticides Cons. Tillage Conv. Tillage3 Cons. Tillage Conv. Tillage3 Cons. Tillage 90.80 78.30 22.55 17.55 21.80 29.08 16.80 87.88 75.38 26.65 21.65 7.70 10.28 23.13 11.58 63.25 50.75 13.93 8.93 3.03 4.03 30.53 15.25 78.20 65.70 17.08 12.08 22.83 30.43 *Adapted from Crosson, 1981. Source: Conventional tillage from U.S. Senate, Committee on Agriculture, 1979. Conservation tillage: labor costs are assumed to be one-half those for conventional tillage; machinery costs (annual) are assumed to be $5 less; fuel requirements (diesel equivalent) is assumed to be 7.6 liters less at $0.26/liter; pesticide costs are assumed to be one-third more (see Walker, "An Economic Analysis of Alternative Environmental and Resource Policies”). All costs other than those listed are assumed to be the same for both tillage systems. Estimates by USDA of costs per hectare in 1979 for each crop nationally. The estimates thus reflect costs of conservation tillage as well as conventional tillage since about 25 percent of crop land was in conservation tillage in 1979. ^Exclusive of land. 44 saved, reduced crop losses from wind and water erosion, and in some cases, labor and energy savings CPappendik and Miller, 1981; Unger et al., 1977). yield is Others emphasize the tillage influence on usually more important than any possible cost savings in determining profits (Griffith likely that et al., 1977). Farmers are not to adopt conservation tillage when there is a risk of lower yields even though costs are lower. CHAPTER 4 METHODOLOGY 4.1. Systems Approach It is essential to think of the farm a3 a system made up of subsys­ tems or components. Such subsystems (for example: machinery, soil type and suitable work days) can be isolated and studied by researchers; how­ ever, solutions ponents. for a suggested must bear in mind the impact on other com­ The following example depicts how a decision to use a remedy problem cannot be treated in isolation from the rest of the sys­ tem. A farmer would not spray without thinking of next year's crop. if there would the herbicides time kill this year’s A farmer would not spray for leaf hoppers on not be for the weeds residual effect this herbicide would have on a alfalfa positive cost-benefit effect to offset the expense incurred through spraying. enough to pesticide If this same farmer does to be not allow broken down, he cannot feed a freshly sprayed, cut, and baled alfalfa without worrying about the level of pesticide in the milk. This illustrates that many farm management decisions will Influence the farm system as a whole. The system bounds are the farm as a whole. the economics of the whole farm and will not take into consideration outside environmental effects. The focus of this chapter is description of the methodology used in the analysis. three components: The study will focus on (1) economics, (2) selection. 45 The discussion is divided into agronomics, and (3) machinery 46 4.2 Economic Approach The methodology employed differs from most other studies, the focus is that on the development of a series of "representative” farms which are typical of the evaluations in area, as of all sample farms. contrasted to providing detailed As such, rather than reporting on all practices and economics for all individual sample farms, the practices and procedures are aggregated to provide a sequence of farms representa­ tive of the area. The impact on average net farm income and on the variability of net farm income are among the most important performance measures the farm family considers when evaluating the adoption of management system. The be 4.1. easy to new technology (Tillage under the new farming nearly always larger than under the existing system. the System B) new system has a higher system is The choice becomes more difficult under the conditions depicted in Figure under or estimate under the conditions depicted under Figure In this case net farm income income technology hypothetical probability distributions of net farm income under a new farming system would a 4.2. average, instances where values occur lower than those under the Net farm but there are currently used farming system. 4.2.1. Definition of Terms The concept of net farm income U3ed in this 3tudy is defined in operational manner. minus cost. easy Net farm income, for our purposes, is gross revenue Gross revenues (e.g., price times to identify. an Costs are more difficult. yield), are relatively For our purposes, we will Probability 47 Tillage System A Tillage System B Net Farm Income Figure 4.1 Hypothetical Probability Distribution of Net Farm Income Under New Farming Technology. Benefits From System "B" Are Clearly Superior to Those From System "A". 1 48 K Tillage System A Tillage System B Net Farm Income Figure 4.2 Hypothetical Probability Distribution of Net Farm Income Under New Farming Technology. Benefits From System B Have A Higher Average But Decision Making in This Case Is More Difficult. 49 focus on the net return to land; the costs subtracted from gros3 revenue to arrive at this estimate will include those costs incurred in crop production (variable costs), costs incurred irrespective of whether pro­ duction takes place (fixed cost), and the cost of "labor". Labor is singled out because it must be priced on an opportunity cost basis. labor If is saved, does it have an economic value in alternative use3 or a minimum "reservation" price? izer, herbicides, Variable costs will include seed, pesticides, fuel, and repairs. include capital costs on machinery, shelter, management charge should be and fertil­ Fixed costs will insurance. Also, a imposed; however, at least initially, we will abstain from making that assessment. Labor will be priced at a value of $4.50 per hour to reflect average earnings in alternative uses. Time will be considered from analysis, focus two perspectives. In the initial will be on whether conservation tillage is expected to be more profitable than conventional tillage when a new conservation tillage system is compared with a new conventional tillage system. i3, a minimum size machinery complement is developed for each the total machinery is case and system is optimized taking into consideration the implications for all crops in the cropping tillage That sequence. If conservation economically superior under those conditions, the next step will be to assess whether it is economically feasible to make the adjustments from the existing conventional tillage system, taking expli­ cit account of the cost of adjustment. alternative framework, Or, to put the question in an at what point in time should the shift between tillage systems take place? Here, the age in existing equipment and the projected rate of interest in field prices become important variables. 50 Economic analysis will include focus on the dynamics of the adjust­ ment from conventional to conservation tillage, including an accounting of the additional managerial requirements costs that are incurred in the transition. and the "learning-by-doing" Part of the rationale behind the research and extension out-reach project is to better define the condition for success and to minimize managerial and "learning-by-doing" costs associated with the adoption of conservation tillage. 4.3 Agronomic Practices The agronomic measure of primary interest is yield. conservation tilled If yields fields were less than those on conventional tilled fields, it would be important to know if the difference was due to tors that could be corrected. erage. fac­ Also, a factor such as residue cover may have no impact on yield up to a additional on threshold level; beyond this level, residue may reduce yield in proportion to the extent of cov­ Thus, a series of measurements was taken to improve our under­ standing of the factors that potentially influence yield. The measurements were not as comprehensive as those typically in intensive experimental plot studies, but were consistent with the available budget and isolation of factors expected to be important a review of the literature. tilling; fertil­ program; pesticide program; plant variety; date of planting; seed­ ing rate; date of plant emergence; insect from They include: crop history; crop residue cover; soil type, management group, and the extent of izer made and percent germination; row spacing; weed populations; disease incidence; stages of growth; soil moisture on selected farms; yield; and grain moisture at harvest time. 51 The crops considered include c o m grain, navy beans, and sugar beets. These are the dominant crops in the wathershed. Measurements were carried out for crop population, crop residue cover, growth stages, soil moisture, soil analysis, grain moisture, and yield. 4.3.1. Crop population (after full emergence). 4.3.1.1. C o m . The number of c o m plants per17 foot (5.2 meters) of row were counted in three Tandom locations of thefield. The average of the three was multiplied by 2500. Example: If the numbers were 19, 20, and 21, then U 9 + 20 + 21)/3 = 20 20 x 2500 = 50,000 plants/hectare 4.3.1.2. Dry beans and soybeans* The number of bean plants per 10 foot (3 meters) of row in 10 random locations was counted. The numbers were totalled and divided by 100 to get the number of plants perfoot of row. The row width in feet was divided into 43560 and multipliedby the number of plants per foot of row to get plants/hectare. Example: (Total for 10 locations)/10 = 8.16 plants/foot of row 43,560 4 2.5 (30" rows expressed in feet) = 17,425 17,425 x 8.16 = 14,218 plants/acre. This value was multiplied by 2.5 to convert to plants per hectare. 142188 x 2.5 = 355470 plants/hectare Method proposed by Dr. Zane Helsel, Formerly of the Crop and Soil Sciences Department, Michigan State University. 52 4.3.2. Crop Residue and Soil Cover. Crop residue was collected at three different times: after tillage; in the spring after the crop was planted. in fall before any tillage had been done, and The "collect, dry and weigh"* method was 2 Crop residue contained within the bounds of a one square yard (0.8m ) used. frame was collected form three random locations, air dried, The combined dry residue weight in determine ounces the weight of residue per acre. was multiplied order to measure and weighed. by 100 to This value was multipled by a factor of 1.14 to arrive at the weight in In the kilograms per hectare. soil cover, crop residue was collected using the line point sampling technique.* A 50 or a 100 foot (15 or 30 meters) tape or line was laid on the ground diagonal to the rows. Crop residue touching the foot mark (or one-half foot mark for the 50 foot (15 meters) tape) were counted. Each point represents a percent. were counted the field would have a 52 percent cover. Thus if 52 points This procedure was done at three random locations in each field and the percentages reported were averaged. 4.3.3. Growth St ages. Measurements were conducted to determine how fast or slow the crops were developing. Table 4.1 and Figures 4.3, 4.4, and 4.5 show numeri­ cally and graphically growth stages for the crops, namely, com, oats, sugar beets, and beans. wheat, Crops in the field were compared weekly to these figures to determine the stage of growth. ^Method is based on the USDA/SCS Technical notes (Agronomy #16), March 1980. 53 Table 4.1 Coded Crop Growth Stages General life stages - Numberical stages may vary with crop, e.g., com, small grains, sugar beets, etc. Stage 0.1 Stage 5.5 Stage 0.5 Stage 6 Stage 1 Stage 7 Stage 2 Stage 8 Stage 3 Stage 9 Stage 4 Stage 10 Stage 5 Stage 10.1 Stage 10.5 Vegetative and reproductive stages (dry beans and soybeans) VE, VC - Emergence, sotyledon R1 - Beginning bloom VI - First node R2 - Full bloom V2 - Second node R3 - Beginning pod V3 - Third node R4 - Full pod V4 - Fourth node R5 - Beginning Seed V5 - Fifth node R6 - Full seed V6 - Sixth node R7 - Beginning maturity V7 - Eighth node V9 - Ninth node VI0- Tenth node 54 Figure 4,3 GROWTH STAGES OF COSH Stage or Numberical Diagnostic Character Growth Type Pra-eoergenee Emergence Two-leaved Early whorl Mid-whorl Lae* whorl Tasael Silk Maturity Seed planted Coleoptlle above soil 2 leaves fully open 4 to 6 leaves fully emerged 8 to 10 leaves fully emerged 12 to 14 leaves fully emerged 16 leave* fully emerged Silks emerging, pollen shedding Plant pollinated; silks green to brown Brown silk, cob full sized, blister stage Kernel* In "soft dough" Far kernel* with dents, embryos developing All kernels with dent* 'Crain mature and drying GROWTH STAGES OF CORN A B C D E Approximate Time After Emergency 0 0.1 0.5 1 2 3 4 5.0 5.5 6 7 8 9 10 0 1 week 2 to 3 week* 4 to 5 weeks 6 to 7 weeks 8 week* 66 days 12 24 36 48 60 days days days day* days after silking after silking after, allking sfter silking after silking ADAPTED FROM HARWAT, J.J. 1977 SPECIAL REPORT 48 (REVISED) “ “ “ * “ Modal Roots Seminal Root* Primary Root*, Radicle Collar Tassel F “ Silk G - Cob or Ear STAGE 5.0-3,5 STAGE STAGE 0.1 m KJtuntT b r l v Vb»rl U t« Vharl Tnw l TV* U l* 4« CoUlf «t 4tb ColUt tf Bcfc Lilt C«|Ur *f HthTIf *f TuMlVO>lllki £**rtlnf*l. PciUft SM IIlfll L a it YUt b U t Vialbt4t Flrit «*4 U s l V U U M t VlalbU « f P ir m lu t^ C o lM p ttU 3 , W M « t r a l llM tW * Uul In (« }fti lu«t« H>)r M l UlV U lM C m > t« MtilffUl pu4 AWv* Kilr optn 7. Iron Stlk M rail SJtit IlfAAfU it l l u t l l lc « |* M ro a lt In "S oft Domfcb" f*s* b m l i vlth “Omti" All M rnaU fully bratil Mi 2- ) L a r n i 4*4 L u t t l •vnnitm* 4-10 13-14 l a m * nt/alolafU Miiurtcy 14 l u m pvocucnn- A — ^Tvim 55 •Beading- -atM Eit.tuloivFigure 4.4 Stage GROWTH STAGES IN WHEAT, OATS, BARLEY, RYE .R ip e n In I Stage Stage io,5 11 10.1 aiowtrln, Stage 10 Stege 9 _ l.M («*—*» ttaa B*t*a) la %aot' Ufala if Stage L,„ u,r -Till.rlac- s tNT|MU j» * t V liih la la*t U t f J u t V l.iH * Stage 01 3 t « Stage 2 St.ge 0 St“ &C * Yllltn TtlUrUi Sbaatha b«|(6w Mllu fl A r>fi GROUTS STAGS! HI USHAT, OATS, SAIOXI A1TO IT1 (Modlflad from dnvln|a by I.C. Laria, (1754), Plant Pathol, 3tUS-U»> 0 1 I 3 9 10 J ?f*a|MmBM Od * apvout (Mua6«r of l i m i can b« addad) -'Cralrdlot* Baglnnlnt of tlU»rlat Tillara ionad* laavta oftan tviacad aplvally* la i o m variation of vlntar vhoatn plaoto pay bo •brnoping" or proatrata. Patlnnlnp of th* aractloo of tha piaudo~ata«, loaf ohaacha bailnflini to latigthofie Paaudo^ata* (faraad by ahaatha of laavaa) atroogly •rtctid. Flvat nodo of atoa vlalbl* at baa* of abaot. Sacood M d i of aeon f o m d t a«xt»to*Uae loaf juat vlalbla. Laat loaf vlalbla* but atill rolled up, air b*|lftalB| to tw«ll» Ligult of laat loaf Juat vlaiblo* Shaath of laat loaf couplataly trowa out* aar avollaa but not yat vialbl*. IQ. I flvat aata Juat vlalbla {Aim* Juat atarlag in barlay, tar aaeapiaf through a p U t of chaath In whaat or oata)* 1 0 .7 Ona-guartar of heading procaaa C0*pl*t*4* Ona-h*l£ of heading pracvta «o*pltt*d« 10*3 10.4 Ihitfegturtiri offloading proetaa coaplotad. 1 0 .5 All aara out of afcaath* 1 0 .5 .1 bagttinlng of flovarlnf (vbtac)* 1 0 .5 .7 flovtrlog tonplata to top of ear. 1 0 .3 .1 Novating ovar at baa* of aat. 1 0 .5 .4 Novating ovcr« k « n « l uatarp tip*. Milky rlpo 1 1 .1 11.7 Manly rlpa* eontenta of kttnal aoft but dty. K a m a I hard (difficult to dlvida bp thuob-oail). 11*4 Pip* for cutting. Straw daad* U.l .nunao STZH 'cznnioi KEAftCn njouatio (WRIAT) pirotve 56 Figure 4.5 GROWTH STAGES OF THE SUGAR BEET ROOT CROP Stas* se«s* 4 le a f S le a f stage a r o s e tte of leav es, not y e t t tln g those or neighboring p la n ts . Meeting tn th e rows sta root sw elling and appearanee above ground level About 11 leaves. n i m i t a ta g e leaves atill being produced, but many now daad. Axillary leave* present Hypocotyl and top of root clear of aoll 57 4.3.4. Soil Moi3ture. To study the effect that surface residue may have on conserving soil water soil moisture, two -types of testing were performed: (a) The relationship between soil moisture and was determined tension through laboratory analysis for the surface and subsurface horizons. These tests determined the percent moisture in the soil below which plants cannot use. (b) Soil moisture content in the root zone of each tored during the growing season. was moni­ Sampling began after the soil was saturated by rainfall (evidenced by and continued at two day intervals. plot subsurface tile flow) Moisture content was deter­ mined by gravimetric1 methods. 4.3.5. Soil Analysis. Once a year, after harvest (normally fall) was soil. applied, soil samples were and before fertilizer taken from the top 25 centimeters of About 15 cores were taken per treatment. The soil wa3 mixed and one sample was taken and used for mechanical and chemical analysis, done at the Crops and Soils Laboratory, MSU. The gravimetric method consists of weighing the soil samples in their field condition, then oven drying and weighing the samples again. Moisture percentage was then determined by dividing the amount of water by the dried soil weight and multipling by one hundred. 58 4.3.6. Crop Moisture. Crop moisture was measured using moisture meters harvest. at the time of Several samples were taken at random from the bin and then the average percent moisture were reported. 4.3.7. Crop Yield. Several pre-raeasured areas of the field were harvested and weighed. The weights were interpolated to weight per hectare. The average weight per hectare was reported. Accurate assessment of the impact of alternative tillage treatments required comparison on an equivalent or w3ide-by-sidetl basis. budget did not permit standard experimental design procedures with domization and replication; nevertheless, equivalence Also, and as selection of small fields permitted them to be farmed as they would in standard practice. equivalence, ran­ was required. Contiguous fields were selected that were judged to be a3 comparable possible. The 2) farming Thus, two objectives were met: 1) methods reflective of standard practice. When applicable paired "T" tests were run on the "side-by-side" data s' obtained. 4.4 Machinery Selection 4.4.1. Model Requirement. Field machinery was a major subsystem of the farm system. constraints plement. sequence; Several in the farm system affect the selection of a machinery com­ Such constraints include: b) the area to a) type of crop and the cropping be farmed and field size and shape; c) the 59 predominant soil type on the farm; d) geographic conditions; e) location and weather implements and machines that already exist on the farm; f) storage and grain drying facilities; g) labor availability for peak season demands, and h) field operations to be done by the farm's crew or through the custom hire. will not In case of custom hiring an operation, there be a need to purchase the implement needed for such an opera­ tion. In this particular study the following parameters were dealt 1) crop sequences U3ed in the study area (Table 4.2). sequences were chosen because they are the more common with: These crop ones used by farmers and represent the seven most commonly grown crops in the project area. 2) soil types, namely: fine textured, medium textured and textured; 3) tillage systems, namely: conventional or the commonly used methods versus conservation tillage types and plow systems; 4) availability of that date of suitable periods the In these tables the the chisel for certain beginning and periods suitable for a field operation to be per­ formed on a specific crop are reported. farmer specific could best be performed given the location and weather of the project, Tables 4.3 and 4.4. ending in suitable day3 for field operations (go-no go days, see Appendix D); and 5) operations coarse For example in Table 4.3 a should harvest corn between the ninth of October (10/09) and the thirteenth of November (11/13). In order to exhaustively study a system one mu3t rely simulation. on computer With this technique, one can vary the level of one or more components, and observe the impact. Such methods of experimentation are less expensive, less risky and faster than experimentation of the actual 60 Table 4.2 Cropping Sequences Considered in the Model Corn-Navy Bean Com-Soybean Com-Com-Soybean Com-Navy Bean-Sugar Beet Com-Soybean-Sugar Beet Com-Navy Bean-Wheat-Sugar Beet Com-Soybean-Navy Bean-Sugar Beet Com-Navy Bean-Navy Bean-Sugar Beet Com-Com-Navy Bean-Wheat Navy Bean-Com-Soybean Com-Corn-Navy Bean-Sugar Beet Oat-Navy Bean-Sugar Beet Crops Operation Com Navies Soys Beets Wheat Oats Harvest 1009/1113 828/1002 925/1023 925/1113 717/807 724/814 Fertilizer A/1127 w/plant w/plant A/1127 A/1016 A/1127 Fall Disk1 1009/1127 1009/1127 1009/1127 1009/1127 A/1016 1009/1127 Plow A/515 A/619 A/605 A/1127 410/619 515/619 410/605 Spring.. Disk A/1127 410/588 410/515 Field cult. 424/515 522/619 515/605 410/515 925/1016 410/508 Plant 424/515 522/619 515/605 410/515 925/1016 410/508 Spray 424/515 515/619 515/605 Row Cultivate 529/619 619/710 612/703 NH3 529/619 501/508 522/619 612/703 605/626 Bale Straw Fall disking only If preceding crop is corn except for wheat which is always disked. MOTE: A beginning date (A) equals previous crop's harvest date A (w/plant) implies that this operation can be done with planting. These numbers show month and date of operations: for example: 1009/1113 = Begin operation on Oct. 9 and end same operation on Nov. 13. *Adapted from Wolak, (1981). 724/821 62 TABLE 4.4 Calendar Days Within Which Field Operations Can Be Performed Using Chiael Plow Alternative* CROPS Operation Corn Navies SoyB Harvest 1009/1113 828/1002 925/1023 925/1113 717/807 724/314 Fertilizer A/1127 w/plant v/plant A/1127 A/1016 320/424 A/1127 Chisel Plow A/51S 515/619 A/619 A/605 410/515 A/1127 A/1016 A/1127 Field Cult. 501/515 529/619 522/605 417/515 925/1016 417/508 Plant 501/515 529/619 522/605 417/515 925/1016 417/508 Spray 501/515 522/619 522/605 Field Cult. 529/619 619/710 612/703 NH3 605/619 Beets Wheat Oats 501/508 522/619 605/626 Bale Straw NOTE; A beginning date (A) equals previous crap's harvest date. A (w/plant) implies that this operation can be done with planting. These numbers show month and date of operations; for example; 1009/1113 ■ Begin operation on-'October 9 and end same operation on Nov. 13. •Adapted from Wolak, (1931), 63 farm. Simulation is a very useful tool that saves time and in determin­ ing the optimum or "best” solution. had A3 was mentioned earlier, the several and collect data farms to monitor projectareawas96,800 hectares. We on. Therefore given the logistics of the problem, and the fact that machinery data wa3 to be based on properly matched sizes, we could not rely on what farmers owned (See Section 6.1 for details). approach was to simulate Therefore existing we farming decided that conditions the best and generate machinery complements needed for such specific situations. When the decision was made to use some means of computerized niques, different tech­ machinery selection models were checked and based on what the project needs were, a new model had to be designed. In the sections that follow criteria for raodel3, previous investigation and our approach will be discussed. 4.4,2. Machinery Selection Model Development Criteria. No model will generate trustworthy output if and the data and parameters U3ed the algorithms are not reliable. used Therefore, the foremost criterion to look for in any computer simulation model is the procedure it follows to generate its output and the data base that sup­ ports it. In this respect one be in a model. can list a series ofqualities desired to It should: a. Permit estimation of cost differentials among various died. systems b. Let the deduced complement be the be3t economic comparison as close to reality as possible. stu­ and stay 64 c. Be flexible enough to fit the farm. d. Be adequate for applied research where one can assess differences in machinery requirement. e. Be useful as an instruction tool for students as and f. Be transferable to microcomputers. 4.4.3. well as farmers, Previous Investigation Several approaches have been developed requirements and associated costs. to help select machinery These are divided into four distinct categories outlined by Wolak (1981): a. enterprise budgets and custom hire rates; b. whole farm, profit maximizing linear programming models; c. least cost models which seek a minimum cost machinery complement for a given management structure; and d. heuristic models for selecting multiple enterprise machinery sets. Each of these four categories have its advantages and drawbacks, as discussed below. 4.4.3.1. provide a Custom rates useful approximation of capturing cost differences for labor and machinery. estimate. Enterprise Budgets and Custom Hire Rates. This is a very low cost and fast method of providing They an provide quick co3t-benefit trade-off figures for broad screening economics (Black, 1982). However custom rates have some draw­ backs . a. No farmer will go out and custom hire all of the farm work. Because of that the costs given for a custom performed operation are not a true reflection of the actual cost Incurred by owning and using a machine to perform the same operation. 65 b. When U3ing custom rates one assumes that the field operation can be performed immediately or when needed. This is not a valid assump­ tion. Farmers that do custom hire know that they have to accept an early or late job often when the custom operator can only make it at that time, or because of weather uncertainties. c. Custom rates do not reflect timeliness costs incurred by the farmer due to early or delayed field operations. Therefore custom rates are not sufficient to determine total true co3t incurred. 4.4.3.2. used to Linear Programming Models. maximize net profit toavailable resources and are useful for organizing the cropping sequence divided into Linear programming models are with that end in mind. They two classes: a) the first is a user specified class where they need to have a machinery complement in order to give the best mix and are 3how where the machinery crop complement is not adequate or too large. They also help show how to improve the situation (example is the Purdue 'Top Crop' models.) The second several alternate farm model, and Michigan State University TELPLAN class is the mixed integer linear programming machinery components are stored in data blocks. model will search for the best set to match the best crop i3 the Forage Mixed Integer Model), in the enormity instructions of that where input a data mix The (example These have one drawback that lies required and subsequent complicated farmer has to go through, and another in the cost incurred running such models. 4.4.3.3. are such that Least Cost Minimization Models. The Least Cost the minimum cost combination of machinery is calculated for specific situations (Hunt, 1977; Hughes and Holtman, 1973). ness cost Models Timeli­ of operations is considered as a penalty so that profits are Increased by minimizing costs. In this respect timeliness cost and its interaction with weather is the most popular specific item dealt with in 66 conjunction with machinery selection problems. However, such models do not profit maximize enterprise combination (Brown, 1981). Other models developed with cost minimization timeliness in mind deal with cost and determine the least cost machinery set but are lim­ ited to one or two crops. Thi3 makes the crop variety and sequence used in the project area difficult to represent in such models. 4.4.3.4. State Heuristic Models. Heuristic models developed at Michigan University approach: field periods. Machine (Singh, 1979, operations Wolak, must productivity be is done matched scheduled calendar periods such that all time (Wolak, 1981). 1981), take within to the specific following calendar available time during operations are completed on These models have the following restrictions: a. timeliness of operations was considered as a constraint and not as a penalty b. only one type of soil was considered c. area of the farm was restricted from 80-400 hectares d. the farmer was restrained to buy new machines e. the farmer could not use custom hire A revised heuristic model was overcame the above restrictions. presented in the next chapter. developed for this project which A detailed description of the model is No restrictions were placed number of implements and/or power units (tractor or combines). on the CHAPTER 5 MACHINERY SELECTION PROGRAM 5.1. Model Description Constraints influencing machinery selection for a farm include: a) types of crops and the cropping sequence; b) area to be farmed and field size and shape; c) predominant soil type; d) available days suitable for field work; e) labor availability for peak demands; f) implements and machines that already exist on the farm; g) grain drying facilities storage; and and h) field operations to be done by the farm crew or through custom hire. In case of custom hiring an operation, there will not be a need to purchase an implement for the operation. The model developed, "MACHSEL", was designed with these features in mind. This model wa3 developed for the analysis of the impact of til­ lage systems and crop sequence required. on the size and number of machines It is a heuristic model that gives the user the most economic machinery complement that is not necessarily profit maximizing or cost minimizing but close enough to be a "ball park" optimum. This machinery selection model (MACHSEL) was designed as a tool help systems analysts, instructors, extension agents improve on some farm management aspects, or simply, select complement needed or farmers to a hire. and machinery for a grain farm with a specified cropping sequence. The model can take into account equipment that is already owned farmer, to operations by the that the farmer prefers to have done by custom The model can also select implements based upon three different 67 68 type3 of soil, two types of tillage systems (conventional and tion), and three levels of risk the user conserva­ is willing to take with weather. The farmer's fear of risk has been an important reason adoption delayed of conservation tillage systems on fine textured soils. ers typically start planting conservation tilled spring in fields later Farm­ in than they do conventionally tilled fields (Klocke, 1979)* the It is for this reason that the model uses suitable work day probabilities gen­ erated from actual weather data (Rosenberg et al, 1982). These proba­ bilities provide estimates of how many suitable day3 a farm manager can expect for performing field operations. The model matches machine productivity to available time. productivity depends upon machine sizes, allowable operational speeds, implement draft for the soil type under consideration, cies Machine field efficien­ and scheduling and efficiencies related to size and shape. able time is determined by work day length, availability Avail­ of good weather, scheduled periods for operations, and soil type. The model selects the most economical machinery set that can finish all farming operations specified within given time constraints. ness and machinery costs determined. are computed as machinery Timeli­ complements The complement that proves to be the least cost complement given timeliness, labor, ownership, and operation cost is selected. machine are The sizes available within the model are actual implement and trac­ tor 3izes found on the market. 69 The machinery selection process involves several steps. model selects the smallest machinery field operation within the specified harvesting, seedbed First the complement that can finish the time boundaries. This includes preparation, planting and other needed operations. The model then chooses the minimum number of suitable tractors that match the implements chosen. The model determines the total cost of cost the machinery Thi3 includes the timeliness cost incurred for harvesting, planting and tillage operations. the A second machinery set is selected by increasing capacities of the selected implements which cause a timeliness cost by one increment of size. ties Tractors that properly match the new capaci­ are then selected; field operations get rescheduled; and the total cost of the new complement is determined. plements are then compared. The total costs of both the However, set chosen last is less expensive than the first set it is tem­ porarily chosen and another incrementation of size costs com­ If the set first chosen proves to be less expensive or the same cost a3 the second set, it is selected. if set. is done. and calculation of Thi3 process continues until such time when the total cost of the new set i3 equal to or more expensive than the previous set. At this point the last one is chosen. incrementation is stopped and the set prior to the 70 The cropping sequences1 used in the model are *1.2. depicted in Table After discussions with county extension agents concerning the more widely used management practices, primary focus was placed upon sequence 1 (corn-navy bean), sequence 4 (corn-navy bean-sugar beet), sequence 6 (corn-navy bean- wheat-sugar beets), bean-sugar beet). The economic and sequence 11 assessment presented (corn-corn-navy in focuses on these four sequences because they are mo3t commonly Chapter 8 used in the project area. A flow chart of the model algorithm and detailed description of the model follows. A user’s guide, the model code, and definition of vari­ ables is included in Appendices A and G. 5.1.1. Program: MACHSEL The body of the main program MACHSEL (Appendix B) is very small and is made up mainly specific task. 3imple and in the main of call statements that summon subroutines to do a The algorithm was designed to provide a model that was easy tofollow and understand (Fig.5.1). Comment statements program and the subroutines act as guide posts to advise the user of what will be happening next. The total program is briefly As used here, the term cropping sequence refers in which crops are grown. For example, a 240 bean farm (C-NB) would find: — 120 hectares of corn following navy beans — 120 hectares of navy beans following corn Similarly, a corn-corn-navy bean-sugar beet farm — 60 hectares of corn following sugar beets — 60 hectares of corn following corn — 60 hectares of navy bean following corn — 60 hectares of sugar beets following navy to the sequence hectare corn-navy (C-C-NB-SB) beans START Determine Min. Capacities Needed for a Timely Job Determine Min. Number of Tractors To Match Capacities Select a Machinery Complement Based on Capacities G No. of Tractors Determine Schedule of Operation Increment Tractor Number No Yes Determine Total Operating Cost of Farm mprove in Cost? y 7 _ ? Increment All Capacities Output Figure 5.1 FLOW CHART OF "MACHSEL" PROGRAM 72 described here and detailed descriptions of the subroutines can be found in the subsections which follow. The first thing the model does interacts with is call subroutine After which the user and checks what parameters need to be entered. It prompts the user to re-enter data, If needs arise, others. READIN the U3er is and to validate through entering the input for the farm, MACHSEL will call subroutine INIT which processes the Input and initial­ izes the farm constants. INIT contains most of the relevant data needed throughout the selection process. because it No flow chart was presented for INIT is a very simple subroutine to arrange the input data. next subroutine to be called is MINCAP which determines minimum completing all field tasks machinery complements capable of the The sizes of within the total number of hours available a3 specified by the U3er. MACHSEL then call3 subroutine MINTRAC. the minimum number machinery complement. of tractors This subroutine determines needed to be assigned to the current Subroutine IMPSEL Is called next to select a new machinery complement and compares the new set to the one selected previ­ ously, with It also determines the number of tractors this complement. that are associated Subroutine SCHED is then called in order to test the machinery complement and check if it can be scheduled to do a satis­ factory job on all the operations required. If SCHED is unable to do a full schedule, program MACHSEL will call subroutines AND PLNTINC, which HARVINC, TILLINC, will increment the combine, tillage implements and the planters respectively to a larger size. When subroutine SCHED is satisfied with operations, the scheduling of field subroutine TOTCOST is called to determine the total cost of 73 the machinery complement that can do a satisfactory task. in turn call subroutine taxes, amount Sub­ of implement requires to do thetask assigned to it. HARVINC, TILLINC AND PLNTINC are called machinery complement one more time. pared with the costs of which shelter, In order to determine the cost of fuel used. routine ALCOST calls subroutine FUELFIG which determines the fuel each will ALCOST which computes cost3 of the machinery complement including capital, interest, repair, labor, insurance, and fuel. TOTCOST the again to increment the Costs are determined again and com­ previous selection. SETSEL subroutine, always updates the machinery complements, is called and the least cost complement is decided upon. Subroutine OUTPUT is then called to organize the data generated to 3end it to the printer as a final output. 5.1.2. Subroutine READIN This subroutine i3 the channel through which with the is wrong, right user (User's Guide, AppendixA). that section. It The model If the user enters READIN will point out the error and promptthe entry. farm. the user interacts data that for the user will also have a chance to validate and change It also totals the area in each section (parcel) of the arranges the operations that are to be custom hired, and the implements that are owned by the farmer. 5.1.3. Subroutine INIT This subroutine processes all the data read in It initializes subroutine READIN. all the farm constants defined in Sections 5.2-5.A. It ■74 contains data pertaining to soil resistance (draft), available hours (based on chance constraints), soil types, speeds, efficiencies, timeli­ ness costs, and sizes of the equipment and related prices, 5 . 1 . Subroutine MINCAP In this subroutine the minimum machinery complement capable of com­ pleting all tasks (Figure 5.2). tion, the required in the maximum time available is developed In this respect if three weeks are assigned for an opera­ total number of hours suitable for work in these three weeks is determined. The size of the first machinery complement is built around the maximum number of hours available for each operation. MINCAP determines which weeks are used for each operation based on user's input. It determines which week is used for each operation based on the crops farmed (seven possible) and the beginning each operation (20 operations total). to crops and weeks, the used the to acronym and ending dates for When the operations are assigned ACOPDAT (Hectares/Operation/Week) determine the number of hours available for each operation. is A Do Loop going through the whole year (52 weeks) determines the number of hours for each operation in each week. The first fifteen weeks have no hours available for tillage operations due to frozen soil. Then based on the formula: Field Capacity (Hectares/hr) = Speed (kph) * Width (ra) * EFF/10 A minimum width is determined as Width (m) = Hectares * 10/hours * Speed (kph) * Efficiency. This width obtained is not the size of an implement yet; the total it width of an implement needed to perform the task. is simply It can be 75 Start DO 100, J“l,7 Determine Which Weeks are Used for Eact Operation DO 400, K> ACOPDAT, Set Weeks for Ea. Operation DO J-1,52 Determine Hours Per Each Operation Determine Minimum Capacity Needed per Qpei Return Figure 5.2 Flowchart of Subroutine MINCAP 76 equivalent to three units of one implement, or any multiple of imple­ ments, The model translates this total width to the most suitable number of units. Based on this total width, proper sizing and Implement numbers are chosen in subroutine IMPSEL. 5.1.5. Subroutine MINTRAC This subroutine initializes a minimum of two tractors (one and one utility) for each farm. no need for two tractors. cuts down on tillage This choice is revoked if the farm has This step of choosing two tractors 3imply computer time and iteration otherwise required to select the number of tractors needed. No flow chart was presented for MINTRAC because it is very small and straightforward. 5.1.6. Subroutine IMPSEL The first thing dealt with in which the farmer owns. this subroutine is the machinery IMPSEL determines whether power available (if the farmer own3 tractors) is sufficient or what 3ize tractor needs to be selected if available power i3 insufficient (Figure 5.3). thing that IMPSEL does is to select a machinery complement The next given the total widths chosen in MINCAP. The smallest number of each implement type 3izes available on the market. is selected width and the width chosen. NH3 applicator have to of imple­ The model then makes sure that row implements are properly matched; row planter, and on Power is then selected based on the power requirements of implements given power needed per unit ment based combine, match each other. row cultivator This means that If an eight row combine is needed, and a twelve row planter Is required for a 77 Start DO 100, 1-1,1 Determine Power Needed for Imp. and Subseq. Tillage and Utility ______ Tractors______ DO 300, J-1,7, Determine Smallest No. Needed of each Imple­ ment DO 400, J-1,1 Equalize New Row Equipment Sizes Update Tractor Power Given Row Equipment Changes Update Chosen Power Based on Available Sizes ^ Return ^ Figure 5.3 Flowchart of Subroutine IMPSEL 78 timely job, the model will select a twelve row planter and a twelve row combine. The increase in cost will be offset by reduced operating costs on the combine. IMPSEL will then update the power requirement, now that row equip­ ment are matched, to sizes available on the market. 5.1.7. Subroutine SCHED This subroutine (Figure 5.1!), checks to determine if the complement of implements required. by the and tractors chosen can do a timely job of all the tasks It schedules operations to be done within the time frame user. The first operation done is harvesting. mines the hours available for each tractor. hours This will It then deter­ be the number of power. SCHED then goe3 through all the operations that need to be formed by is order of priority. called to schedule schedule owned equipment which mine the hours required. to operations, per­ The next step i3 to start with the first custom At hired this work. point subroutine SCHED will then fit the desired operations, and deter­ NEXTWK is then called to assess the area left to be done for that operation. implements total available for work since implements need tractors for week and the first crop available for work. CUSTOM set it When SCHED is through will scheduling owned schedule implements that are pur­ chased, determine the number of hour3 spent, and the time left for the next operation. 5.1.8. Subroutine NEXTWK. Subroutine NEXTWK (Figure 5.5), is called and CUSTOM to check through from subroutines SCHED the crops planted to determine the area 79 ^ Start ^ Do Weeks = Start to End of Fiscal Year Do Implement = 1, 18 If Possible, Add to Average Avail­ able For This Operation Due to Maturation of Crop Perform Any Custom Versisons of This Operation X Perform Current Operation Using Owned Equipment Perform Current Operation Using Purchased Equipment c Return Figure 5.4 FLOW CHART OF SCHEDULING SUBROUTINE Start Check for Acres Ready for Next Operation Check Next Implement for Ready Crop and Acreage Update Hours Used and Hours Remaining for Work Determine Area Left ^ Return ^ Figure 5.5 Flowchart of Subroutine NEXTWK 81 ready for the next operation. ready, goes through the operation was performed. done It checks on the crop3 and area that are hours available for work left after the last It checks the number of hectares which can be given these hours and determines the number of hectares left to be done to finish that operation, 5.1.9. Subroutine CUSTOM This subroutine (Figure 5.6), checks through the crops planted determines the operations to be done through custom hire. and It calculates a price for such operations given the area and the cost per unit area of custom hiring that operation. It checks the area to be custom hired and calls subroutine NEXTWK in order to determine how many hectares are left undone to be scheduled for the next week. Final custom cost is deter­ mined by inflating and discounting the cost over ten years in real terms to bring it to present value dollars. 5.1.10. Subroutine T0TC0ST This subroutine (Figure 5.7), determines the total by owning costs Incurred the machinery complement for the farm described by the user. It calls subroutine ALCOST several times in each of five distinct possi­ ble cost groups being (1) costs of owned implements; (2) costs of owned tractors; (3) costs of newly selected implements; selected tractors; plement. TOTCOST is made up of do loops implements. (4) costs of newly and (5) timeliness costs incurred by using the com­ that cycle It calls ALCOST to determine costs of: tal, taxes, insurance, and shelter for each implement. through sets of labor, fuel, capi­ 82 Start DO 100,1-1,7' Check Area to Be Custom Hired Call NEXTWK Custom Cost - Area * Price 10 * CRF Determine Total Custom Cost Return Figure 5.6 Flowchart of Subroutine CUSTOM Start Check fot Owned Equip. .Cheek How Kany of Each , I»p. . • Check for Owned Tractors Call AtCOST Determine Imp. Cost Total Owned lop. Cos Call ALCOST Determine ed Tractors Costs Total Owned Equip. Costs Check for Purchased Equip. Call ALCOST Determine Till Tractor Cost Call ALCOST Total Purchased tmfi. Costs Call ALCOST Determine Utility Tractor Cost Determine Cost of. Utility Tractor Determine Utility of Equip, end Tractor DO 900. C-l Determine Total Costs figure S.7 Flowchart of Subroutine TOTCOST 84 5.1.11 Subroutine FUELFIG This subroutine (Figure 5.8), determines the fuel different operations. operations implements. of FUELFIG sorts through the operations performed by the tillage tractors using through requirements owned performed by equipment, if any. It then goes the tillage tractors U3ing purchased FUELFIG then does the same search to check for operations performed by owned and purchased utility equipment and tractors. When the power ratio for each operation is developed a fuel efficiency to ALCOST. constant for (Liter/Kw*Hr) is determined. This factor is multiplied by the power of the tractor and the number that operation. factor of hours spent performing Fuel Is totaled for all operations and then transferred If the implement happens to be a combine FUELFIG will use multiplier (Liter/hectare) adapted from Helsel (1981). a This value is multiplied by the area harvested and transferred to ALCOST. 5.2. Model Equations The mathematical relationships used In the model are based on rela­ tionships outlined in the ASAE Yearbook (1981) Section D230. The major equations used can be grouped under machine productivity, timeliness and fuel consumption. 5.2.1. Machinery Productivity Parameters The Effective Field Capacity (EFC) of a machine, or the measure of how many hectares it covers In one hour was determined using the follow­ ing equation: _ S x W x EFF EFC = c----- S5 'ill3go\N 200,1-2,1 Imp? jr Owned" Tillage ^ VlMP?. No [DO 300,I-2,1BJ Return ‘alculate Tillage Fuel Total Tillage Fuel Utility \J|tpT j DO 400,3-1 Calculate Utility Fuel ( Calculate Fuel * j 1 ■— Total Fuel Total Utility . Flowchart of Subroutine FUELFIG 86 where S = Implement speed (kilometers/hr) Vf s Implement width (m) EFC = Effective field capacity (hectares/hr) EFF = Field efficiency (decimal) C = 10.0 5.2.2. Timeliness Cost Timeliness cost was based on a linear simplification of the cost incurred for not doing a timely job. actual An operation influencing crop yield was given a period of time in which it was not charged any timeli­ ness costs. Any time used before or after that period to finish the operation was charged a cost per day. farm expenses. This cost was then added to other This implies that timeliness, as viewed here, is based on the farmer and hi3 allocation of the dates assigned to do operation. As depicted a certain in Table 5.10, planting and harvesting opera­ tions have been assigned a timeliness cost for certain periods based agronomist's recommendations, market price of the crop. in such periods will average production per The number of hectares planted be charged. unit or on area and harvested For example a hectare of corn not planted by May 15 will be charged $44.0 per hectare If the farmer wishes the minimum timeliness cost he should schedule his operation as much as possible within the periods specified in Tables 4.3 and 4.4 5.2.3. Fuel Consumption Fuel consumption was based on the method outlined in Section of the ASAE yearbook, (1981) as modified by Fontana, (1981). D230 Prediction 87 of fuel consumption for a particular operation required determination of the total tractor power for that operation. was then divided by the rated minimum to get engine. The equivalent PTO power a percent load for the The fuel consumption at that load wa3 obtained from: Diesel (Liter/Kw h) = 2.64 X + 3.91 » 0.2 J 738 X + 173 where X is the ratio of equivalent PTO power required by an operation to the maximum available from the PTO. In order to determine the amount of fuel consumed the following equation was U3ed: Fuel (liter) s Diesel (liter/kw*h) * PTO power 5.3* (kw) * use (h) Machinery Parameters and Their Sources Required machinery parameters of the model include: a. Implement power requirement (Table 5.1) b. Field efficiency of implements (Table 5*2). c. Allowable operating speeds (Table 5.3) d. Sizes of machines available on the market (Table 5.4). e. Service life and repair data of all implements (Table 5.5) f. Available work (go-no-go) hour3 or days (Table 5.6) g. Purchase prices of implements (Table 5.7) h. Data constraints for conventional and conservation tillage systems (Tables 4.3 and 4.4) i. Implements considered in the model (Table 5.8) J. Custom rates in Michigan (Table 5.9) k. Timeliness cost for planting and harvesting (Table 1. Average yields and market price per bushel (Table 5.11). 5.10) 88 Table 5.1 Power Requirement for Implement in kw/meter - Soil Texture - Coarse Inclement Medium Fine Combine 0 0 0 Bean Puller 7.31 7.31 7.31 Beet Topper 9.8 9.8 9.8 Beet Lifter 39.1 39.1 39.1 Soil Saver 18.3 24.4 33.6 V-Ripper 19.6 26.9 36.7 3.7 3.7 3.7 Chisel Plow 18.3 24.4 33.6 MB Plow 16.0 27.9 37.0 Disk Harrow (Tandem) 12.2 14.2 16.0 Disk Harrow (Offset) 17.1 24.4 29.3 Fert. Spreader Field Cultivator 7.3 8.55 9.8 Grain Drill 3.2 4.9 6.4 Row Planter 7.3 8. 8 10.3 N.T. Planter 7.3 8.3 9.3 Sprayer 3.6 3.6 3.6 Row Cultivator 4.9 7.2 7.2 19.6 24.4 28.5 NHj App. Modified from: Hunt, 1977; White, 1978. 89 Table 5.2 Field Efficiency of Implements Used <_ 160 Hectares Farm Size Farm Size > 1 6 0 Hectares Combine .55 .70 Bean Puller .65 .75 Beet Topper .60 .70 Beet Lifter .60 .70 Soil Saver .74 .88 V-Ripper .74 .88 Fert. Spreader .65 .80 Chisel Plow .75 .90 MB Plow .74 .88 Disk Harrow [Tandem) .77 .90 Disk Harrow (Offset) .77 .90 Field Cultivator .75 .90 Grain Drill ,65 .76 Row Planter .60 .76 N.T. Planter .60 ,65 Sprayer .55 .90 Row Cultivator .68 .90 NH3 App. .55 .65 Implement Source: ASAE Yearbook, 1981; White, 1978. 90 Table 5.3 Average Allowable Operating Speeds for Implements Implement Average Speed (kph) Combine 4.8 Bean Puller 5.6 Beet Topper 4.8 Beet Lifter 4.8 Soil Saver 7.2 V-Ripper 4.8 Fert. Spreader 8.1 Chisel Plow 7.2 MB Plow 7.2 Disk Harrow (Tandem) 8.1 Disk Harrow (Offset) 8.1 Field Cultivator 7.2 Grain Drill 6.4 Row Planter 8.1 N.T. Planter 4.8 Sprayer 8.1 Row Cultivator 4.8 NH^ App. 5.6 Modified from ASAE Yearbook, 1981; Hunt, 1977; White, 1978. 91 Table 5.4 Size Increments of Power Units and Implements Available On tho Market In Michigan Implement Market in Michigan Tillage Tractor (KW) 48.5 59.7 74.6 89.5 96.9 Utility Tractor (KW) 37.3 48.5 59.7 74.6 89.5 Combine (Row) 4 6 Bean Puller (Row) 4 6 Beet Topper (Row) 3 4 Beet Lifter (Row) 3 4 Soil Saver (Meter) 2.0 2.7 3,4 V-Ripper (Shank) 3 5 7 Fert. Spreader (Meter) 8 119.3 141.7 12 4.2 5.0 5.7 6.5 12.2 18.3 Chisel Plow (Meter) 2.4 3.1 3.4 4.0 4.6 5.2 5.8 MB Plow . (bottom) 3 4 5 6 7 8 9 Disk Harrow (Tandem) 3.5 4.4 5.2 6.6 7.8 9.1 11.0 Disk Harrow (Offset) 3.5 4.4 5.2 6.6 7.8 9.1 11.0 Field Cultivator (Meter) 3.8 4.7 5.6 6.6 7.8 8.7 10.5 Grain Drill (Meter) 4.0 4.0 Row Planter (Row) 4.0 6.0 8.:0 12.0 N.T. Planter (Row) 4.0 6.0 8.0 12.0 Sprayer (ft) 3.1 4.6 6.1 9.1 Row Cultivator (Row) 4.0 6.0 8.0 12.0 NH3 App. (Row) 4.0 6.0 8.0 12.0 ^Bottom width is 0.4 meters. Modified from: (1981). Blue Book, 1981; Hunt, 1977, Personal Communication with Dealers 92 Table 5.5 Remaining and Repair Values for Power Units and Implements rs: Tractor .025 1.6 .75 .87 Combine .144 1.8 .75 .88 Bean Puller .23 1.8 .70 .90 Beet Topper .26 1.6 .70 .90 Beet Lifter .41 1.3 .70 .90 Soil Saver .23 1.8 .70 .90 V-Ripper .23 1.8 .70 .90 Fert. Spreader CM • 1.3 .70 .90 Chisel Plow .23 1.8 .70 .90 MB Plow .61 1.3 .70 .90 Disk Harrow (Tandem) .23 1.8 .70 .90 Disk Harrow (Offset) .23 1.8 .70 .90 Field Cultivator .23 1.8 .70 .90 Grain Drill .208 1.6 .70 .90 Row Planter .67 1.6 .70 .90 N.T. Planter .67 1.6 .70 .90 Sprayer .71 1.4 .70 .90 Row Cultivator .23 1.8 .70 .90 NH3 App. .23 1.8 .70 .90 Source: Hunt, 1977; Hotz, 1981. CM RCX Implement RV1 rv2 93 Table 5.6 Available Suitable Hours for Field Work Per Week For Three Levels of Risk and Three Types of Soil Confidence Level of Available Hours Per Week (Percent) ----- 50....... ............. 30..... ... Fine -Soil Texture- - Medium Coarse - - -Soil Texture- - Medium Coarse Fine ------- 20... ..... - - -Soil Texture- - Medium Coarse Fine 1-: 0.0 0.0 0,0 0.0 0.0 0.0 0.0 0.0 0.0 16 8.0 12.0 13.0 7.0 6.0 12.0 7.0 5.0 11.0 17 30.0 42.0 44.0 27.0 27.0 41.0 24.0 25.0 38.0 18 50.0 56.0 58.0 42.0 36.0 52.0 37.0 31.0 49.0 19 58.0 56.0 60.0 48.0 36.0 55.0 41.0 31.0 49,0 20 64.0 54.0 60.0 54.0 36.0 55.0 44.0 31.0 47.0 21 63.0 70.0 63.0 62.0 40.0 58.0 56.0 45.0 56.0 22 60.0 67.0 65.0 62.0 57.0 61.0 58.0 50.0 59.0 23 67.0 67.0 68.0 62.0 61.0 65.0 60.0 57.0 62.0 24 67.0 64.0 68.0 59.0 56.0 62.0 54.0 52.0 58.0 51.0 44.0 53.0 40.0 38.0 50.0 25 67.0 56.0 65.0 26 67.0 58.0 69.0 54.0 47.0 56.0 43.0 41.0 53.0 27 70.0 69,0 69.0 68.0 65.0 67.0 67.0 62.0 63.0 28 70.0 69.0 69.0 68.0 65.0 67.0 67.0 62.0 63.0 67.0 65.0 69.0 63.0 60.0 65.0 29 71.0 30 71.0 70.0 75.0 67,0 65.0 . 69,0 63.0 60.0 65.0 31 69.0 70.0 71.0 66.0 65.0 66.0 61.0 60.0 65.0 32 69.0 70.0 69.0 66.0 65.0 64.0 61.0 60.0 64.0 33 69.0 70.0 69.0 66.0 65.0 62.0 58.0 58.0 62.0 34 69.0 69.0 69.0 67.0 62.0 . 62.0 58.0 58.0 62.0 35 69.0 69.0 67.0 67.0 60.0 62.0 58.0 58.0 60.0 70.0 75.0 36 67.0 67.0 64,0 67.0 52.0 62.0 62.0 58.0 60.0 37 67.0 67.0 64.0 65.0 52.0 62.0 60.0 58.0 60.0 38 61.0 59.0 63.0 53.0 53.0 60.0 49.0 50.0 55.0 39 61.0 59.0 63.0 53.0 53.0 60.0 49,0 50.0 55.0 40 60.0 62.0 63.0 51.0 53.0 60.0 46.0 51.0 55.0 41 60.0 62.0 63,0 51.0 53.0 60.0 46.0 51.0 55.0 42 58.0 62.0 63.0 51.0 52.0 60.0 50.0 48.0 58.0 60.0 * 50.0 48.0 58.0 43 58.0 60.0 63.0 51.0 52.0 44 55,0 60.0 62.0 51.0 52.0 60.0 49.0 47.0 58.0 45 51.0 58.0 60.0 51.0 52.0 60.0 49.0 47.0 57,0 37.0 58.0 60.0 32.0 32.0 60.0 29.0 22.0 57.0 19.0 25.0 29.0 77.0 4.0 25.0 4.0 4.0 22.0 14.0 14.0 21.0 5.0 3.0 18.0 3,0 3.0 16.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 94 Table 5.7 Purchase Price of Implements and Power Units (in dollars per meter of width) Implement Tractor (per kw) Combine 224.00 35,000.00 base plus $2,297.00 per meter of header Bean Puller 492.00 Beet Topper 3,281,00 Beet Lifter 7,218.00 Soil Saver 2,707.00 V-Ripper 2,707.00 Fert. Spreader 328.00 Chisel Plow 1,312.00 MB Plow 2,707.00 Disk Harrow (Tandem) 1,477.00 Disk Harrow (Offset) 1,477.00 Field Cultivator 656.00 Grain Drill 328.00 Row Planter 1,969.00 N.T. Planter 2,625.00 Sprayer 2,000.00 base price plus $66,00 per meter Row Cultivator 984.00 NH3 App. 820.00 Modified from Tractor Blue Book, 1981, and local machinery dealers. 95 Table 5.8 Implements Used in MACHSEL and Their Corresponding Code Implement Code Combine 1 Bean Puller 2 Beet Topper 3 Beet Lifter 4 Coulter Chisel (soilsaver) 5 Sufcsoiler (v-ripper) 6 Fertilizer Spreader 7 Chisel Plow 8 Mold Board Plow 9 Disk Harrow (offset) 10 Disk Harrow (tandem) 11 Field Cultivator 12 Grain Drill 13 Row Planter 14 No-till Planter 15 Sprayer 16 Row Crop Cultivator 17 Ammonia Applicator 18 Spring Fertilizer Spreader 19 Second Row Cultivation 20 96 Table S.9 Custom Rate in Michigan Implement Custom Rate $/hectare Combine 40,00 Bean Puller 17.50 Beet Topper 37.50 Beet Lifter 61.25 Soil Saver 20.63 V-Ripper 25.00 Fert. Spreader 6.25 Chisel Plow 20.63 MB Plow 23.88 Disk Harrow (Tandem] 11.50 Disk Harrow (Offset) 11.50 Field Cultivator 9.38 Grain Drill 12.00 Row Planter 16.38 N.T. Planter 16.38 Sprayer 7.SO Row Cultivator 9.38 NH3 App. 8.50 Modified from Schwab, 1980. 97 Table 5.10 Timeliness Costs for Planting and Harvesting Operations - - - -Penalty- - $/week $/week Crop Planting Harvesting Planting Harvesting Com ^ 1 percent per day after May 15 1 percent per day after Nov. 15 17.5 17.5 Wheat* 1 percent per day after Sept. 30 0.5 percent per day after July 30 12.4 12.4 Oats** 2.4 percent per day after April 20 0.5 percent per day after Aug. 23 17.4 4.0 Rye*** --- --- Soybean + 1 percent per day after May 20 1 percent per day before Oct. 1 and after Oct. 15 14.0 14.0 Navy bean .7 percent per day before June 10 and after June 20 .7 percent per day before Sept. 1 and after Sept. 10. 14.2 14.2 Sugarbeet**** 1 percent after May 3 percent after May per day 4 per day 10 28.2 and 35.2 * Connor, et al, 1967. ** Personal communication with Dr. Copland, Crop and Soil Dept. MSU, 1982. *** Rye was not penalized for timeliness because it is assumed to be act winter crop. **** Personal communication and unpublished data from Dr. Don Christenson, Crop and Soil Dept., MSU, 1982. + Data Source: Lehrmann, 1976, as adapted by Rosenberg, 1982. t+Data Source: Drs. L. Robertson and M. Erdman, Crop 8 Soil Dept., MSU 98 Table 5.11 Average Yields Reported for the Project Area And Market Price of the Seven Crops Studied (1981)* Average Yield (Tonnes/hectare) Price ($/Tonne) Com 6.80 97.53 Wheat 3.13 138.23 Oats 4,08 63.07 Crop Rye** — — Soybean 2.11 243.10 Navybean 1.59 453.20 Sugarbeets 50 25.20 *Source of Data: Modified from USDA - Michigan Agri­ culture Statistics, Michigan Crop Reporting Service. **Rye was not penalized for timeliness because it is assumed to be a winter course crop. 99 In order to obtain values for such data, field relevant available sources of data were used. (Chapter 6, Section 6.1.1). for (1977). (1981) some specialized Data on power requirement and machine efficiency were taken from the ASAE Yearbook capacity, or Actual field measurement was done to determine draft and fuel consumption implements experimentation (1981), Machinery power requirements and speeds were from White (1978) and Hunt Suitable hours for field and work were obtained from Rosenberg remaining and repair values for Machinery came from Rotz et al., (1981). 5.4. Model Assumptions The following assumptions and limitations were used in the model so as to maintain a manageable and realistic output. They are divided into three broad categories: 5.4.1 Management Assumptions a. A range of 80 to 20000 hectare farm size. b. The minimum number of full time* laborers was chosen. This i3 based on the selection of the minimum oo3t complement which implies the minimum number of tractors operating in the field at the same time. The farmer would have to judge how many part-time laborers would be needed based on total hours of field work. c. Three textures of 3oils in the Saginaw Bay were fine, medium, and coarse. drainage watershed d. Table 5.12 depicts the number of hours of work allocated per day for each operation. These are based on observation of actual farming operations and on agronomist recommendations. *A full-time operator works at least 40 hours a week. A 1/2 full-time operator works at least 20 hours. This implies that there are times when an operator works more hours than that, depending on the crop and time of year. 100 Table 5.12 Number of Working Hours Assigned to Field Operation* Operation Number of Hours Per Workday Fertilizer Spreading 12 Spraying 12 Tillage 12 Planting 12 Cultivator 12 Ammonia Application 12 Soybean Harvesting 8 Wheat Harvesting 8 Alfalfa Harvesting 9 Field Bean Harvesting 6 C o m Harvesting 10 Oats Harvesting 7 Sugarbeet Harvesting ^Adapted from Wolak (1981). 11 101 e. Table 5.13 depicts calendar dates assignment to model weeks. f. Costs for crop transport, drying and/or processing were not included in the main program but were accounted for when the total farm cost was determined. g. The design probability was determined by the available work day data set. A work day data set at the 80 percent level implies that the given weekly available field work time would occur or be exceeded eight out of ten years. The machinery set developed for the 80 percent workday data set has a design pro­ bability of 80 percent. The farmer has a range of three deci­ sion probabilities from which to choose. h. Purchase price of the power units and implements actual market figures (Blue Book, 1981). i. 5.4.2. a. is based on Annual use cost is based on a cash flow with interest, discount and inflation rate3 to reflect the present economic environment (Rotz, 1981). Agronomic Assumptions Crops handled are: corn, soybeans, navy sugar beets, and rye. beans, oats, wheat, b. Twelve cropping systems (Table 4.2) commonly found in the Saginaw Valley area are used. The model is general enough to handle all these sequences under different tillage systems. c. Based on experimental data collected from the project site, personal communication with farmers practicing conservation tillage, and relevant literature available, date constraints for conservation tillage are set differently from those for conventional tillage because: 1. The soil i3 generally wetter and cooler in spring, indi­ cating a later start than conventionally tilled soils. 2. The soil will "ready" faster, i.e. permits earlier access to the soil, after rain3. This allows farmers to have more suitable days to work the conservation tilled fields. 3. One will be able to harvest sooner after rains because the crop residue on the soil gives good support for combines. 102 Table 5.13 Calendar Dates Assigned To Week Codes Week 1 Corresponding Date Week Corresponding Date 1-7 27 2 8-14 28 9-15 3 15-21 29 16-22 4 22-28 30 23-29 Jan. Jul. 2-8 5 Jan. 29-31/Feb. 1-4 31 Jul. 30-31/Aug. 1-5 6 Feb. 5-11 32 Aug. 6-12 7 12-18 33 13-19 S 19-25 34 20-26 9 Feb. 26-28/Mar. 1-4 35 Aug. 27-31/Sept. 1-2 10 Mar. 5-11 36 Sept. 3-9 11 12-18 37 10-16 12 19-25 38 17-23 24-30 13 Mar. 16-31/Apr. 1 39 14 Apr. 2-8 40 15 9-15 41 8-14 16 16-22 42 15-21 17 23-29 43 22-28 Oct. ■ 1-7 18 Apr. 30/May 1-6 44 Oct. 29-31/Nov. 1-4 19 May 7-13 45 Nov. 5-11 20 14-20 46 12-18 21 21-27 47 19-25 22 May 28-31/June 1-3 48 Nov. 26-30/Dec. 1-2 23 June 4-10 49 Dec. 3-9 24 11-17 50 10-16 25 18-24 51 17-23 25-30/July 1 52 24-31 26 June AGR. CH0UCALS 2,5 r2.4 2.3 - 2.2 - AGR. 2.1 - MACHINERY 2.0 - 1.0 - RAT 1.7 RJEL 1.6 L5 L4 L3 - 1.2 : 1.0 YEAR Figure 9.5 Selected Price Trends For Economic Parameters Over The Past Twenty Years Adjusted For Inflation (Agricultural Prices, Statistical Reporting Service, USDA, 1982). 104 5.4.3. Machinery Assumptions a. Assignment of operations to tractors (Table 5.14) b. No upper limit on numbers of combines, tillage tractors, ity tractors, or implements c. Self-propelled combines are used and tractors are considered, if necessary. d. Maximum power of tillage tractor is 209 kw (centrally articu­ lated four wheel drive). This limit was imposed based on the upper bounds of tractor sizes found in the project area. e. Maximum power of utility tractor is 89 kw. This tractor can be used for tillage operations on smaller farms requiring only one or two tractors. f. Maximum 3ize of combine is twelve rows for corn. g. Row spacing for all row crop equipment i3 fixed at0.75 meters. 4-wheel h. Area to be sprayed in one week must be equal area planted that same week. i. or drive less util­ tillage to the Row crop cultivators and ammonia applicator sizes have to match the planter size. Even though ammonia applicators do not necessarily match the planter size, especially if ammonia is applied in fall ahead of planting, this decision was made to cut down on computer time and model iterations. j. Power requirement for implements under recommended speeds and efficiencies for given soils were predetermined from relevant research and literature. Therefore it is not calculated inter­ nally. This decision was made for logistic reasons. 5.4.4. Economic Assumptions Real figures for inflation, interest and discount rates were on price indices. based Table 5.15 depicts numerically and Figure 5.9 graphi­ cally how such figures compare to the Consumer Price Index (CPI). Based 105 Table 5.14 Operation Assignment to Power Source Power Source Operat ion/Implement Tillage Tractor Moldboard Plow X Disk Harrow X Disk Plow X Chisel Plow X Utility Tractor Field Cultivator X Sugar Beet Topper X Sugar Beet Lifter X No-till Planter X NH3 Applicator X Grain Drill X . X Row Cultivator Row Planter Combine X Fertilizer Spreader X Sprayer X Navy Bean Puller X C o m Head X Wheat Harvester X Soybean Harvester X Navy Bean Harvester X Oat Harvester X Table 5.15 Selected Input Prices Adjusted for Inflation Year 1960 1961 1962 1963 1964 1965 1966 1967** 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 CPI 88.7 89.6 90.6 91.7 92.9 94.5 97.2 100.0 104.2 109.8 116.3 121.3 125.3 133.1 147.7 161.2 170.51 181.5 195.3 217.7 247.0 Ag. Chem. Ag/CPI Ag. Mach. AM/CPI 104 103 102 103 99 98 99 100 101 100 98 100 103 105 119 160 174 157 147 150 160 1.17 1.15 1.13 1.12 1.07 1.09 1.02 1.00 0.97 0.91 0.84 0.82 0.82 0.79 0.81 0.99 1.02 0.87 1.08 0.69 0.65 84 86 88 89 92 93 96 100 104 110 116 122 130 139 159 197 225 246 266 293 326 0.95 0.96 0.97 0.97 0.99 0.98 0.99 1.00 1.00 1.00 1.00 1.01 1.04 1.04 1.08 1.22 1.32 1.36 1.36 1.35 1.32 Wate 74 76 78 80 82 86 93 100 108 119 128 134 152 155 178 192 210 226 242 265 286 W/CPI Fuel F/CPI 0.83 0.85 0.87 0.87 0.88 0.91 0.96 1.00 1.04 1.08 1.10 1.10 1.13 1.16 1.21 1.19 1.32 1.36 1.47 1.22 1.16 94.1 100.0 94.1 94.1 94.1 100.0 100.0 100.0 100.0 105.9 105.9 111.8 111.8 135.3 217.7 229.4 241.2 264.7 270.6 400.0 582.4 1.06 1.12 1.04 1.03 1.01 1.06 1.03 1.00 0.96 0.96 0.91 0.92 0.89 1.02 1.47 1.42 1.41 1.47 1.49 1.84 2.36 **1976 was considered as 100. Source: Agricultural Prices, Statistical ReportingService, USDA, CPI - Consumer Price Index AG - Agricultural Chemicals AM - Agricultural Machinery W - Wages F - Fuel Price (1) - ratio of oneindex over the CPI 1982. 107 on these values and the projections adopted for the few coming years the following rates were assumed (Black, 1982). a. General inflation = 8% b. Machinery price inflation = 8% c. Labor inflation = 8% d. Fuel inflation = 12% e. Discount rate = 13% CHAPTER 6 MACHINERY PERFORMANCE AND MODEL VALIDATION The focus of this chapter is to present estimates of some of the machinery parameters found on farms in the project area and to present a method used to validate the speed, model discussed in Chapter 5. Tractor tillage implement depth, tillage implement width, required trac­ tor horsepower required, soil temperature, and soil type for the conventional and conservation tilled fields. were recorded The data presented were recorded during the 1980 and 1981 growing seasons. Fuel consump­ tion, draft and slippage were measured on selected primary and secondary tillage implements. 6.1 Machinery Performance Table 6.1 describes how farmers managed their newly introduced con­ servation tillage implements and their perception of the conditions of their fields after the use of these implements. fields were The conservation tilled perceived to be more cloddy and wetter. crop residue made planting operations difficult. ever, was done They believed the Their field work, how­ with minor difficulties and few adjustments. This was reflected by the good germination rate obtained in 1980 and 1981 3eason3 (Section 7.3). Implements were not matched to the tractors used. power available to the cultivator varied from 16.63 kw/m for farmer number 20 to 24.5 kw/ra for farmer number 4, were For example, the even though their speeds almost the same, 10.0 and 10.2 kph respectively (Table 6.2). 20% of the difference, 3.42 kw/m can be attributed to the difference tillage depth. Similarly, the 108 Only in power available to the moldboard plow Table 6.1 Observations on Selected Field Operations Performed in Spring (1980) Farmer Date Soil Type 12 Spring 1980 Tappan loam Implement Depth (cm) Speed (KPH) Cons. Field Cult. 16.2 12.0 15.2 12.0 Tillage System Comments 12 If If Conv. M B Plow 12 II II Cons. Row Planter 6.4 10.4 12 tl If Conv, Row Planter 6.4 9.6 Tappan loam Conv. Row Planter 3.8 5.6 Cons.* Row Planter 3.8 5.6 A little wet in places Guelph Cons. Field Cult. 8.9 8.0 Field was wetter than conventional ti Conv. Field Cult. 8.9 8.0 Tappan loam Conv. Field Cult. 12.7 8.8 ii Cons. Field Cult. 10.2 8.8 16 16 4 4 20 20 Spring 1980 M Spring 1980 If Spring 1980 If II Rough seed bed - lots of stalks Excellent seed bed *This farmer had one strip subsoiled, and one no-till planted under the conservation tillage category. Table 6.Z Fall 1980 Field Operations Finn J 4 Soil Type Tillage System 11/6/80 Guelph Cons, 11/6/80 Guelph Conv. 11/5/80 Essexville loamy sand Cons« 11/5/80 Essexville loamy sand Conv, 11/4/80 Tappan loam Cons. 11/4/80 Kiloanagh loam Conv. 11/7/80 Kilmansgh loam Cons. 11/7/80 Cilmaaagh loam Conv. 11/7/80 Guelph Cons. 11/7/80 Guelph Conv. 9/12/80 Broofcston Cons. 9/12/80 Date 11 Residue (kg/hectare) 1,648 Implement Depth (cm) Speed (kph) Tractor Size (kw) kw/meter of width Soil Saver 4.5 15.2 6.4 168 36.7 MB Plow 3.7 10.2 7.7 261 71.4 4.9 8.9 11,0 97 19.8 Tandem Disk 4.9 8.9 11.0 97 19.8 Soil Saver 3.1 20.3 7.7 75 24.5 22.9 5.9 110 52.1 2,428 3,125 Implement Width (meters) 20 KB Plow Offset disk with chisel 2.3 27.9 6.2 101 41.3 MB Plow 2.3 20.3 6.9 101 44.0 Chisel plow 7.6 17.8 8.0 231 30.3 KB Plow 4.11 25.4 8.0 231 56.3 1,032 Soil Saver 3.7 15.2 4.8 97 26.4 Cons. 4,216 Subsoil 5.7 34.2 5.4 131 23.0 9/12/80 Conv. 1,909 Disk 6.1 11.4 7.7 186 30.6 9/17/80 Conv. KB Plow 3.3 22.9 4.9 231 70.9 3,040 21 1,364 C d Extension Demonstration Plots Ill -t varied from 36,0 kw/m for farmer number 14 depth to 71.0 kw/m for the tillage <23 cm) and speed (6.4 kph) were similar. The power available to the moldboard plow was 56 kw/m for farmer number 5 compared to for farmer number 4; they were operating at 7.7 kph. Farmer kw/m. 21 power match them to the tractors they have or that the and they tilled fields not purchased new larger tractors. (Table for 6.4). the In operated slightly faster specifically data for farmer number 4. the do original implement pur­ Harvesting speeds differ between conventionally tilled and vation units used 33 kw/m of planter while farmer 14 used only 11 This suggests that when farmers purchase implements they chase was matched kw/m Table 6.3 also shows that planting equipment were not well matched to the used. 71 all conservation raining when the farmer was combining. The cases, conser­ the combine was tilled fields. Note soil was wet andit was There was noticeable wheel slip­ page with 17% more time consumed on the conventionally tilled field than on the conservation tilled field. This implies that 17% more be covered in the same amount of time. area can Absence of crop residue was per­ ceived to be the cause since other factors (slope, crop, length of run and engine rpm) were constant. 6.1.1, Fuel Consumption Measurement of fuel consumption was not carried out on tractors the cooperating farmers. Tests were done, however, at the Michigan 1The Cooperative Extension Service conservation tillage demonstra­ tion plots. of Table 6.3 Spring Field Operations (1981) Soil Temperature Width (ft) Speed (kph) Depth (cm) M B plow 2.3 6.4 22.9 82 36.0 20.0 Row planting 6,1 3.4 5.1 67 11.0 852 28.2 F. cult. 6.4 6.2 12.7 112 17.4 568 23.3 Row planting 6.1 6.1 5.1 67 11.0 18.9 Row planting 3.1 7.4 5.1 101 33.0 2,443 20.0 F. cult. 5.5 7.8 12.7 101 16.3 2,102 20.0 Row planting 5.5 12.5 5.1 101 18.3 Conv. IS.6 F. cult. 9.5 10.2 12.7 321 24.5 Conv. 15.6 Row planting 9.1 8,6 7.6 104 11.5 Tillage System Cover Residue (kg/hectare) Farmer* Date Soil Type 14 S/8/81 Condo Sandy loam Conv. 20.0 14 S/8/81 11 Conv. 14 4/21/81 11 Cons. 14 S/4/81 II Cons. 21 4/21/81 JCilmanagh Conv, 21 4/21/81 ii Cons. 21 4/2/81 ii Cons. 4 5/4/81 Guelph 4 5/4/81 11 .20 5/4/81 5/4/81 Tappan loams II Operation Power (kw) kw/meter of width Cons. 1,534 21.1 F. cult. 6.7 10.1 10.2 112 16.6 Cons. 1,534 21.1 Row planting 9.1 7.8 2.5 112 12.2 *Farmer 11 decided he was not able to go ahead with the project, so no spring data were collected on his farm. Farmer 8 planted his field without notifying the project, so spring data were not collected. Table 6.4 C o m Harvesting Operations Fall (1980) Tillage System Residue (kg/hectare) Speed (kph) Percent Moisture Essexville loamy sand Cons. 2,537 5.1 19.7 7.0 Essexville loamy sand Conv. — 5.0 20.0 7.2 11/3/80 Tappan loam Cons. 2,394 5.1 25.2 8.6 II Tappan loam Conv. — 4.8 26.8 8.4 Date Soil Type 11/5/80 11 II 13 13 Farmer Yield (tonnes/hectare) 12 11/13/80 Tappan loam Cons. 350a 4.8 30.0 6.2 12 11/13/80 Tappan loam Conv. — 4.5 30.0 6.3 4 10/16/80 Guelph Cons. 1,653 7.8 30.2 10.1 Guelph Conv. — 6.4 29.6 10.2 4 II g Previous crop was cucumber. 114 State University research farms on selected primary and lage operations. of 1981. tillage equipment. introduced Averages of collected was obtained and reported in Table 6.5. lected for different implements literature, where was available, compared with that for similar conditions. the results obtained. reported estimated based in The data on the accuracy The data collected from this test was used in the computer model described in Chapter 5. be all Data col­ moldboard plow and the disk harrow were u3ed as a test for the of con­ The first phase was conducted in the Fall Each field operation was repeated ten times. parameters til­ The purpose for conducting the study was to determine values of parameters needed for the selection of newly servation secondary upon tillage Reductions in fuel use can operations conducted by cooperating farmer's and on estimates of fuel disappearance per hectare according to standards of the American Society of Agricultural Engineers. consumption was estimated to of diesel fuel per Total fuel be less under conservation tillage than under conventional tillage, with a range in savings liters o hectare. of 18.75 to 32.5 This is primarily the result of farmers reducing their trips over the field. One farmer, for example, pulled an anhydrous ammonia tank behind his 4.6 m. modified chisel plow. The power needed to pull the chisel plow and pull the ammonia the tank was same as that needed to pull a 7-40 cm. bottom, moldboard plow. The saving was in the reduction of one trip over the field. Estimates must be regarded as very preliminary since standards are tentative assessments for many of the new tillage tools. Indeed, most have not been analyzed for the soil types in the study area. Table 6.5 Draft and Fuel Consumption of Selected Implements on a Sandy Clay Loam Av. Speed (kph) Depth (cm) Implement Width (meter) Slippage Moldboard plow 5.9 25.4 .81 16.3 15.84 * * Moldboard plow 6.7 20.3 .81 8.6 8.75 * * Coulter/chisel1 6.6 20.3 2.42 333 6.36 23.9 Disk harrow (tandem) 7.° 12.7 3.4 13.0 2.14 19.3 11.45 Field cultivator after M.B. plow 6.2 12.7 3.4 13.5 2.92 16.7 11.4 Field cultivator after soil saver 6.6 12.7 3.4 13.4 3.68 18.2 11.4 Field cultivator after disk harrow 6.7 12.7 3.4 8.0 3.44 17.0 10.4 2nd Field cultivator after M.B. plow 6.4 12.7 3.4 11.5 3.50 17.0 11.4 2nd Field cultivator after soil saver 6.7 12.7 3.4 8.5 3.44 19.3 12.3 2nd Field cultivator 6.6 12.7 3.4 10.6 3.57 17.8 11.4 Operation t%) Fuel Consumption Draft (kw/meter) (liter/hr) (liter/hectare) 4 20.8 115 *The fuel consumption in the case of the mold board plow could not be accurately determined. The method of measuring the plow draft required pulling a second tractor with the mounted plow, thus the fuel consumption was not accurate. ^Glencoe soil saver. 2 Tool bar width is 3.4 meters, however 2 shanks had to be removed so that the tractor could pull the soil saver. The 8 foot is the width of the 5 shanks used. Even with the two shanks removed the tractor still had a hard time pulling the soil saver. 4Since slippage was excessive, this fuel value is higher than would be normally expected. 3These values are rounded up to the nearest one decimal; calculation was done before rounding up. 116 6.1,2. Field Labor There was a reduction in labor required as a result of conservation tillage proportional to the reduction in the number of trips made over the field with various tillage instruments. increase There was (and will be) in time required initially, particularly managerial time, when conservation tillage is introduced because of the need to better stand an under­ crop growth, disease and weed incidence under a new system. farmer will need to develop the best set of cultural practices Each for the soil type and micro-climates on his farm. 6.1.3* Field Entry Data Based on soil temperature, field entry should have been delayed two to by four days under conservation tillage, depending on soil texture and residue levels. However, because of practical considerations, pro­ ject farmers worked conventional and conservation areas at the same time in spring, both cultivating and planting. Conservation tilled fields were wetter at planting. 6.1.4. Tractability and Ease of Operations There was better traction on plots that had crop residue (conserva­ tion 3y3tems) than on conventionally tilled plots. The machinery was better supported and time consumed to do certain operations was noticeably. For example, it reduced took a combine an average of 5 units of time working on a conservation tilled field while it took the same bine 6 units of com­ time to harvest an equal area of similar growth in a conventionally tilled field. 117 6.2. Model Validation Validation of 'MACHSEL' wa3 done in two entailed namely: took testing the stages, first stage sensitivity of the model to changing situations, soil type, area, and risk involving weather. into the The second stage account comparisons between farm machinery complements owned by some farmers in Tuscola County and a simulated complement for the model to same farms. 6.2.1. Sensitivity Analysis. The sensitivity analysis studied changing parameters. the reaction of the The analysis took into consideration: (1) timeli­ ness costs as a cost for not doing a timely job; (2) sizing and ing implements and power units as the confidence level ^ of available working hours changed from 50 to 80 percent; (3) power soil types changed select­ requirements as under the two above mentioned and changing parame­ ters. While doing the tests, only one parameter was changed at one time. This permitted easy recognition of what happened as a result and tied it directly to that parameter. 6.2.1.1. the model selects Sensitivity to Timeliness Costs. the As stated previously least cost machinery complement that will do a ^A 50 percent confidence level a3 understood in the model context means the percent probability that will give the farmer the needed number of suitable hours to finish his field work at least five years out of ten. An 80 percent confidence level means the farmer will finish his work eight or more years out of ten with the selected machinery set. 118 timely Job within the assigned period. a week or more that bears tagged on areas that end up iterated and If the period assigned includes a timeliness cost, then a charge will be being done in that period. The model increased the 3ize of the implements until the least cost complement is obtained. If the assigned period fall3 within the timeli­ ness bounds then no extra costs were included. The iterations and the least cost machinery chosen were based on the complement number of hours available for a job. number of available hours was the bound around which the ment was designed. eventually first This comple­ Accordingly, the least cost complement was influ­ enced by this underlying value. So if, for example, one was dealing with a farm under two different weather confidence levels like 80 and 50 percent probabilities, one would find an appreciably complement for the 50 percent probability. smaller machinery This was due to the larger number of hours available for the higher probability level of 50 per­ cent. An example farm was studied and timeliness costs were monitored land area changed. Special print statements were included in the model in order to show how the model operations as that bear a costs dealt when with such costs and performed outside the timeliness periods. The farm studied was a continuous corn farm (one of commonly practiced crop sequences scheduled the more in the project area), managed with conservation tillage techniques, i.e., no moldboard plowing, and all the chisel plowing was done in the fall. In the spring one field cultiva­ tion was done followed by planting and spraying. The soil was fine tex- tured and the probability of available good weather was 80 percent. The 119 farmer desired to buy all new equipment and hired operations. The 40 to 520 hectares, on 200 and same farm did not want any custom was studied withthe area ranging from in increments of 40 hectares. Emphasis was placed 400 hectares, which represent average and large farm sizes respectively, and 600 hectares, which represents very large farm 3izes not commonly found in the project area. In order to observe how the model selected the least cost machinery complements methods. with for a farm, the same farm was simulated in two different In the first case there were no any operation. timeliness costs associated In the second run timeliness costs were used where they were required. The field operations performed, area farmed, the period chosen to do the work were identical for both farms. ing detailed output ofthese two situations shows how the model and tochoose the least cost set. how it tries and Compar­ behaves Special commands were used in the model to print the data depicted in Table 6.6. Table 6.6 depicts machinery complement the wa3 iterations selected of the model as the for two situations studied for the same farm; one with and the other without timeline33 costs. machinery cost per hectare for costs were The It must be noted incurred after the second iteration. that no This came about because the second set selected was les3 costly than the one timeliness costs. total the 200 hectare farm with timeliness costs shifted from $105.45 down to $97.60. timeliness proper with Therefore, the model avoided that size which caused a timeliness cost whenever possible. The cost per hectare for the same farm without timeliness cost changed from $100.78 after the first itera­ tion to $97.60 in the la3t one. Table 6.6 Example of How "HACHSEI." Iterates and Chsnjes Sizes Until The Least Cost Is Arrived At— Three Artis are Shorn ZOO 400 600 Iapltnent Till Tractor (In ) Util Tractor (In) Combine (m) Fer. Spr. (a) Soil Saver (■) Field Cult, (a) N.T. Planter (■) Sprayer (a) Tiallness Cost (f) Machinery Cost (I) Cost/Hectare (!) Till Tractor ( W ) Util Tractor (kw) Combine (a) Fer. Spr. (a) Soil Saver (a) Field Cult, (fl) N.T. Planter (a) Sprayer (a) Holiness Cost (!) Machinery Cost (j) Cost/Hectare (() Till Tractor (kw) Util Tractor (kw) Combine (a) Fer. Spr. (a) Soil Saver (a) Field Cult, (a) N.T. Planter (a) Sprayer (a) Holiness Cost (!) Machinery Cost (!) Cost/Hectare (J) Ho Penal. 119 4S 6.1 12.2 3.4 3.8 6.1 12.2 — 20154 100.78 209 89 6.1 12.2 6.5 7.8 6.1 12.2 — 46350 115.88 171 75 9.1 12.2 5.0 5.6 9.1 18.3 . — 49625 82.70 W/Penal, Iteration 3 Iteration 2 No Penal, 119 48 6.1 12.2 3.4 3.8 6.1 12.2 934 20154 105.45 119 75 9.1 12.2 3.4 3.8 9.1 18.3 209 89 6.1 12.2 6.5 7.8 6.1 12.2 1869 46354 120.55 1 209 89 9.1 12.2 6.5 7.8 9.1 18.3 171 75 9.1 12.2 5.0 5.6 9.1 18,3 2804 49625 87.38 171 75 9.1 12.2 5.0 5.6 9.1 18.3 — 19518 97.60 — 40514 101.2B 52322 87.20 N/Penal. 119 75 9,1 12.2 3.4 3.8 9.1 18.3 — No Penal. 119 75 9.1 12.2 3.4 3.8 9.1 18.3 — Iteration 4 N/Pcnal, No Penal. K/Penal. 119 75 9.1 12.2 3.4 3.8 9.1 18.3 119 75 9.1 12.2 3.4 3.8 9.1 18.3 — 20650 103.25 119 75 9.1 12,2 3.4 3.8 9.1 18.3 — 19518 97.60 19518 97.60 19518 97.60 209 89 9.1 12.2 6.5 7.8 9.1 18.3 9145 40514 124.15 142 89 9.1 12.2 4.2 10.5 9.1 12.2 142 89 9.1 12.2 4.2 10.5 6.0 18.3 1869 33708 88.95 209 89 9.1 12.2 5.7 6.6 9.1 18.3 171 75 9.1 12.2 5.0 5.6 9,1 18.3 2804 52322 91.88 209 89 9.1 12.2 6.5 7.8 9.1 18.3 209 89 9.1 12.2 6.5 7.8 9.1 18.3 2804 48385 80.33 209 89 9.1 12.2 5.7 10.5 9.1 18.3 32175 80.45 — 48385 80.65 40454 101.13 48428 80.73 — Iteration S (Set Selected) No Penal.. 11/Penal. 119 75 9.1 12.2 3.4 3.B 9.1 18.3 -- 119 75 9.1 12.2 3.4 3.8 9.1 18.3 — 20650 103.25 19578 97.60 19S78 97.60 209 89 9.1 12.2 5.7 6.6 9.1 18.3 I860 41639 108.78 142 89 9.1 12.2 5.7 10.5 6.1 12.2 142 89 9.1 12.2 5.7 10.5 9.1 18.3 1B69 33708 63.95 209 89 9.1 12.2 5.7 10.5 9.1 18.3 2804 48428 85.35 209 B9 9.1 12.2 . 6.5 7.8 9.1 18.3 .. 32175 80.45 — 48385 80.65 171 89 9.1 12.2 6.5 7,8 9.1 18.3 2804 48385 85.33 Oil Iteration 1 Area (hectare) 121 The period assigned for planting was 3 weeks long. weeks (May 1-May 1*1) bore no timeliness cost. The first two The weeks starting with May 15 had a timeliness cost of $43.75 per hectare. The model tried to schedule planting operations that bore timeliness cost3 within the first two weeks when such costs were not incurred. ments and moving operations around within the time frame assigned, the model dropped after By selecting larger imple­ the costs from $120.55after the first iteration to $63.95 the last one for the 400 hectare farm with timeliness costs. total machinery costs per hectare ranged from $115.88 to similar farm with the same time $80.45 The for a frame but with no timeliness costs assigned to operations. The 600 hectares farm costs ranged from $91.88 to $85.33 tare for the farm with timeliness costs. The costs for the hec­ In this case 36 hectares were left to be planted in the third week and were costs. per therefore charged extra 600 hectare farm with no timeliness costs ranged from $87.20 to $81.05 per hectare. The machinery complements finalized for the farms with and timeliness costs were identical in the case of the 200 hectare farm. The timeliness costs, when incurred, were the difference the case causing in the total machinery cost per hectare. tion I, 200 hectares). of the without factor for (Table 6.6, Itera­ The same wa3 true for the 240 hectare farm. 400 hectare planter and combine sizes. In the farm, case the In the two complements differed In where timeliness costs were charged, the model found that a smaller set was the least cost given the costs incurred for late performed jobs. more and therefore A larger size would cost a the cost/benefit effect would not be realized. lot In 122 the case where no timeliness costs were incurred the model found that larger set was less expensive. a In this case the labor and other costs reduced by spending lesser time in the field made up for the increased cost due to a larger machine. 6.2.1.2. weather Sensitivity to Changing Soil Types. this test confidence level wa3 maintained at 80 percent probability while the area changed from 200 to 600 hectares with 200 The In soil was also changed from fine hectare textured to coarse textured. Because of soil types and the underlying assumption of naturally tively well drained soils, drying rates were not the same. that there were a different number of hours available in for a certain operation to be performed. increments. rela­ This implied any one soil In other words, time available for operations in the field were not the same for all three soils. Tables 6.7, 6.8 and 6.9 depict the sizes chosen for the 200, and 600 hectare farm respectively for the three types of soil. 6.7 the cost per hectare for the loamy soil was $98.63 for the fine This occurs because according to the soils (Rosenberg, 1981), available of the year than for hectare higher. and trend hours for such medium textured 3oil is depicted in Table6.8. per hectare is less. therefore pushes the Table 6.7 also depicts, as expected, a small machinery complement for the farm with the coarse same was This forces the complement initially selected to be larger and therefore the final set slightly larger per it therewill be more available hours for fine textured soils in few weeks cost while In Table textured soil and $91.50 for the coarse textured soil. (Table 5.6). $105.87 400 Thisis expected soil. The In this case, however, the cost because textured a3 area increases, the 123 Table 6.7 Machinery Selected for a 200 Hectare Farm Under Three Types of Soil Fine Textured Size Annual Use Hrs 8 98 12.2 Soil Saver (m) Medium Textured Coarse Textured Annual Use Hrs Size Annual Use Hrs 12 66 6 131 26 12.2 26 12.2 26 2.7 119 5.0 64 1.9 167 F. Cult, (m) 3.8 82 5.6 55 3.8 82 N.T. Pint, (rows) 8 91 12 60 6 Sprayer Cm) 12.2 32 18.3 21 9.1 Till. Trac. Ow) 97 210 152 124 48 287 Util. Trac. Chw) 48 139 75 102 48 150 Implement Combine (rows) Fer, Sp. (m) Size 121 42 Tim. Cost 684 -- 2313 Mach. Cost 19043 21176 16018 Cost/Hectare 98.63 105,88 91.50 124 Table 6.8 Machinery Selected for a 400 Hectare Farm Under Three Different Soils Fine Textured Medium Textured Coarse Textured Size Annual Use Hrs Size Annual Use Hrs Combine (rows) 2*8 131 2*12 66 12 Fer. Sp. (m) 12.2 52 12.2 52 12.2 52 Soil Saver (m) 2*4.2 76 2*4.2 75 4.2 152 7.8 80 6.6 47 7.8 80 Implement F. Cult, (m) Size Annual Use Hrs 131 N.T. Pint, (rows) 2*8 60 2*12 60 12 Sprayer (m) 18.3 42 18.3 42 18.3 Till. Trac. (kw) 142 136 142 136 89 272 Util. Trac. (kw) 89 174 89 94 75 174 121 42 Tim. Cost 1368 -- 4625 Mach. Cost 36265 34882 24286 Cost/Hectare 94,00 87.20 72.28 The number preceding the asterisk C*) is the number of units needed of the implement size specified after the (*). 125 Table 6.9 Machinery Selected for a 600 Hectare Farm With Three Different Soils Fine Textured Medium Textured Coarse Textured Size Annual Use Use Size Annual Use Use Size Annual Use Use Combine (rows) 2*12 98 2*12 98 2*12 98 Fer. Sp. (nO 2*15.2 31 12.2 77 12.2 77 Soil Saver (m) 2*6.5 74 2*5.0 114 2*4.2 114 F. Cult, (m) 2*8.7 54 2*7.8 89 2*7.8 60 N.T. Pint, (rows) 2*12 91 2*12 91 2*12 91 Sprayer (m) 18.3 64 18.3 64 12.2 64 Till. Trac. (kw) 2*209 164 3*119 204 2*89 204 Util. Trac. (kw) 2*89 116 3*89 115 2*75 130 Implement Tim. Cost 2052 -- -- Mach. Cost 50260 49461 37566 Cost/Hectare 87.18 82.43 62.60 The number preceding the asterisk (*) is the number of units needed of the implement size specified after the (*). 126 machinery ized. efficiency tends to increase and machinery was better util­ The increased costs of implements wa3 spread over larger area and therefore costs per hectare will be lower. Thi3 trend goes in cycles. in sizes available Since the model selects only machinery on the market, there are times when the complement finally selected, even though it is least in cost for the situation, is 3lightly oversized. Then as area increases, the machinery is more efficiently utilized until another complement of a larger 3ize needed. This trend increases will In Table 6.10, the influence tares for the same farm. 600 hectares of coarse textured soil, respectively. to be noted that 200 hectare farm. It even though the cost for the 100 hectares is higher than that of the 400 hectare farm it is still lower than that the 200, The same trend is observed for the farms with medium and fine textured soils. ought hec­ Tables 6.7, 6,8, and 6.9 depict costs per hec­ tare change from $91.5 to $75.75 and to $84.25 per hectare for the and of and machine 3ize interaction on the cost per hectare is seen as area increases in increments of 40 hectares from 40 to 520 400 be can be clearly seen when Tables 6.7, 6.8, and 6.9 are compared, and Table 6.10 studied. area it of This implies that around the 360 hectare mark, machinery tend to be well utilized. As area increases, a need for larger and therefore initially oversized machinery i3 obtained. 6.2.1.3. test the soil Sensitivity to Changing suitable Probability. type wa3 maintained as a fine textured soil. tested were again 200, 400 and 600 hectares. having Weather The areas The probability levels hours for field work were 80, 70 and 50 percent. implies that 2, 3 and 5 years out of ten the complement In selected of This will this 127 Table 6.10 Influence of Area on Machinery Utilization and Efficiency Area (hectare) Cost Per Hectare 40 331.18 80 209.45 160 181.5 200 152.05 240 137.10 280 163.95 320 151.03 360 145.15 400 149.35 440 150.08 480 150.20 520 140.10 560 139.93 128 not finish the job required on tirae3 and that 8, 7 and 5 years out of ten the job will be done on time. hours This also implies that the number of available for work increase as the probability level changes from 80 to 50 percent. Tables 6.11, 6.12 and 6.13 depict the complements selected for three farm sizes at different levels of risk. the In all three cases costs per hectare decrease as probability level changes from 80 to 50 percent. Costs per hectare change from $98.63 to $89.20 for the 200 hectare farm; from $9*1.08 to $85.75 for the *100 hectare farm; and from $87.10 to $8*1.75 for the 600 hectare farm. In general size of machinery and tractor power decrease dence level cent. This i3 true in all three cases. requirement same drops for In the 200 hectare given farm power from 97 kw for the 80 percent level to 75 kw for the The sizes of implements other than tillage tools the three levels. are One must note however that timeliness cost is nonexistent for the 70 and 50 percent that confi­ of available suitable work hours change from 80 to 50 per­ 50 percent level. the as levels. This indicates the fewer number of available hours at the 80 percent level forces the farmer to work in a period where there is a timeliness cost. In the 400 and 600 hectare farms the differences in sizes were more pronounced. One twelve row planter for the 50 percent level rather than two eight row planters for the 80 percent level. soil saver wa3 Also only one 5.0 m required for the 50 percent level while two 4.2 m soil savers were required for the 80 percent level. also drops from two 142 kw to only one 209 kw. The power requirement 129 Table 6.11 Machinery Selected for a 200 Hectare Farm Under Three Weather Confidence Levels SO percent Implement Size 50 percent 70 percent Annual Use Hrs Size Annual Use Hrs Size Annual Use Hrs Combine Crows) 8 98 8 98 8 98 Fer. Spr. (m) 12.2 26 12.2 26 12.2 26 Soil Saver (m) 2.7 119 3.4 93 1.9 168 F. Cult, (m) 3.8 83 3.8 82 3.8 82 N.T. Pint, (rows) 8 91 8 91 8 91 Sprayer (m) 12.2 32 12.2 32 12.2 32 Till. Trac. (kw) 97 210 11.9 183 75 257 Util. Trac. (kw) 48 139 48 139 48 139 Tim. Cost 684 -- -- Mach. Cost 19043 18829 17838 Cost/Hectare 98,63 94.15 89.25 130 Table 6.12 Machinery Selected for a 400 Hectare Farm Under Three Weather Confidence Levels 80 percent Implement Size 50 percent 70 percent Annual Use Hrs Size Annual Use Hrs Size Annual Use Hrs Combine (rows) 2*8 131 2*12 121 Fer, Sp. (m] 12.2 52 12.2 52 12.2 52 Soil Saver (m) 4.2 76 4.2 76 5.0 128 F. Cult, (m) 7.8 80 6.6 95 4.7 131 N.T. Pint. Crows] 2*8 60 12 Sprayer (m) 12.2 42 12.2 Till. Trac. (kw) 2*142 166 Util. Trac. (kw) 89 174 121 121 12 121 12 42 12.2 42 2*142 136 171 249 75 189 75 225 368 3498 -- Mach. Cost 36265 36225 34195 Cost/Hectare 94.08 87,20 85.50 Tim. Cost The number preceding the asterisk (*) is the number of units needed of the implement size specified after the (*). 131 Table 6,13 Machinery Selected for a 600 Hectare Farm With Three Weather Confidence Levels 80 percent Implement Size 70 percent Annual Use Hrs Size 50 percent Annual Use Hrs Size Annual Use Hrs Combine (rows) 2*12 98 2*12 98 2*8 Fer. Sp. (m) 2*15.2 77 12.2 77 12.2 77 Soil Saver (m) 2*6.5 83 2*3.4 139 2*5.2 114 F. Cult. (m) 2*8.7 60 6.6 142 6.6 142 N,T. Pint. Crows} 2*12 91 2*12 91 2*8 136 Sprayer (m) 18.3 64 18.3 64 18,3 95 Till. Trac. (to#} 2*209 164 2*119 229 2*119 249 Util. Trac. (to#) 2*89 116 75 142 75 157 Tim, Cost 2052 147 -- 4812 Mach. Cost 50260 42023 45956 Cost/Hectare 47,10 70.05 84.75 The number preceding the asterisk (*) is the number of units needed of the implement size specified after the (*). 132 In the case of the 600 hectare farms a similar trend Planter observed. sizes drop from 12 to 8 rows and the soil saver size drops from 6.5 m to 4.2 m as the confidence level changes from 80 Also is power requirement is reduced from interest to note that there is a timeliness 50 percent. 209 kw to 142 kw. It is of cost to associated with the machinery complement selected for the 50 percent confidence level. This implies that the investment cost in a large complement much will have higher costs than that of the selected set (timeliness cost included). 6.2.2. Simulated V3. Heal Farms Three representative real farms in Tuscola and studied. County were simulated One was a 100 hectare farm growing mainly corn, one was a 400 hectare farm with a corn-corn-navy bean-wheat rotation and the third was a 360 hectare farm with a corn-corn-navy bean-sugar beets rotation. The cropping sequences practiced on these farms is typical of the county and the farmers are cooperators in the project. It was assumed, based on the algorithm followed, that the model produced the most economic set for the farm under study. The aim of this comparison was to study how farmers' sets compared with the deduced sets. In order to fit the real farms to a simulation, area was made. some Wheat area was from 64 to 80 hectares while bean area was reduced from 94 to 80 hectares and corn was increased from changes, of For instance in farm number two the actual area farmed was 380 hectares while in the model it was 400 hectares. increased rounding while 222 to 240 hectares. These they change the farm slightly, were needed to match the farms to the model input. Similar changes were made to the other farms. 133 The results show that in several instances farmers tend to oversize their implements. an Thi3 i3 due to weather uncertainties. implement was substituted by replaced by a soil saver). only uses it occasionally. another (Example: In a few cases A moldboard plow The farmer still has the moldboard plow and Size comparisons in cases like this were not made. In general, comparisons were quite close a3 can be seen from Tables 6.14, 6.15 and 6.16. There are times where the simulated number of tractors are less than the real (owned) tractors. The reason is farmers do not replace their tractors asoften as is assumed of this computer model. This is clear from Tables 6.14, 6.15 and 6.16. In all thee farms studied, (except for in the development farm machinery owned by the farmer tillage equipment) was close in size to those simulated in most case3. For the 100 hectare farm only the field cultivator wa3 not close to the simulated one, where the owned size was 7.3 m and the simu­ lated one was 3.8 m. In the case of the 400 hectare owned soil saver corn-corn-navybean-wheat farm the was 5.3 m while the simulated one was 4.1 m, and the owned disk harrow was 5.6 m wide while the simulated one wa3 3-5 m wide. The same trend can be seen for the 360 hectare corn-corn-navy bean-sugar beet farm where the owned disk harrow was 5.6 m wide lated one while the simu­ wa3 3.5 m wide and the owned soil saver was 5.3 n wide while the simulated one was 2.7 m wide. This oversizing of tillage implements implies larger power require­ ments which is also clear from the same comparisons. It is clear from 134 Table 6.14 Comparison of Simulated and Real Machinery for A 100 Hectare Continuous C o m Farm Implement Simulated Actual Age (yrs) Tractor 1 89 kw 97 kw 8 Tractor 2 37 hp 4S kw 10 Tractor 3 — 67 kw 15 Tractor 4 — 52 kw 15 Disk Harrow 3,5 ni 4.4 m Field Cultivator 3.8 m 7, 3 m Row Planter 6 row 6 row Chisel Plow 3.1 m 3.8 m 135 Table 6.15 Comparison of Simulated and Actual Equipment For a 400 Hectare Com-Com-Navy Bean-Wheat Farm Implement Simulated Qctual Age (yrs) Tractor 1 142 kw 231 kw 2 Tractor 2 142 kw 108 kw 10 Tractor 3 60 kw 56 kw 15 Tractor 4 60 kw 52 kw 25 34 kw 29 Tractor 5 Combine 8 row 6 row Bean Puller 8 row 6 row Soil Saver 4.1m 5.3 m Disk Harrow 3,5 m 5.6 m Field Cultivator 3.8 m 9.1 m Grain Drill 4.0 m 4.0 m Row Planter 8 row 6 row Row Cultivator 2*8 row 12 row 136 Table 6.16 Comparison of Simulated and Actual Equipment for a 360 Hectare Com-Com-Navy Bean-Sugar Beet Farm Implement Age (fa's) Simulted Actual Tractor 1 89 kw 171 kw 4 Tractor 2 89 kw 134 kw 1 Tractor 3 60 kw 97 kw 6 Tractor 4 60 kw 67 kw 10 Tractor 5 67 kw 12 Tractor 6 45 kw 20 Combine 6 row 8 row Bean Puller 6 row 8 row Beet Topper 6 row 8 row Beet Lifter 3 row 4 row Soil Saver 2.7 m 5.3 m V Ripper 1.8 m not reported Disk Harrow 3.5 m 5.6 m Field Cultivator 3.8 m not reported Row Planter 6 row 8 row Sprayer 2*9.lm 2*0.lm Row Cultivator 2*6 row 2*8 row 137 this comparison and from personal communications with farmers in the project area that farmers like to get their land "ready" as soon as pos­ sible so they can plant on time. This here, however, required. is not justified oversizing due in the extent seen to the high capital investment CHAPTER 7 AGRONOMIC RESULTS (1979-80 AND 1980-81 CROP YEARS) The results of the "side-by-side" field comparisons of conventional vs. conservation tillage are summarized in this chapter. farm field data are presented in Appendix B. this presented in chapter are based upon seven^ farms that participated in the 1979— 1980 and 16 in the 1980-1981 crop year. in The results Individual 1980/81 had corn. bean3 as well as corn, soils were not farms participating One farm had two corn fields, while two had navy and another had sugar beets. Nineteen farmers started to 1980-1981 season. All seven participate in the project in the Two dropped out and one had to be disregarded because comparable on conservation and conventional tillage plots. Paired "t" tests were used to compare tillage systems germination, grown. percent grain moisture for percent and yield forcorn and dry beans A three way analysis of variance was used to determine if the moisture availability in the conservation tilled soils was statistically different from the conventional tilled soil3. A null hypothesis for all "t" tests done wa3 that the mean of a parameter observed under conserva­ tion tillage was equal to the mean of the same parameter under conven­ tional tillage. Due to fire, one farmer lost most of his records; yield data, however were retained. Yields for another farm were measured by the farmer when, as a result of harvest scheduling difficulties, project personnel could not be notified on time. 138 139 7.1. Crop Residue Quantities of crop residue are reported in Table 7.1. The preplant measurement,^ in kilograms/heotare were made in the 1981, are higher than average spring of the measurement made in the fall of 1980. For farms where the fall measurement (made after tillage) was less than the 3pring measurement, in most cases a modified chisel plow covered some of was used which the residue in the top 5-8 centimeters of soil. result, this residue does not get counted in the fall. spring, p As a However, in the a field cultivator working at a depth of ten or twelve centime­ ters will bring this covered residue up to the surface. Up to 25% residue in spring is normal^, especially in a sandy soil where more covering action is very rapid. The size of the plots exceeded two hectares in made the ever, that observed, sampling of crop residue difficult. there only were four sampling cases biases, many case3. This This does not mean, how­ because needed explanation. out of 25 fields Of all the others, 10 farm3 had a slightly higher count in the spring than in the fall and the remaining 11 had what would be normal trend, i.e., highest count in fall, slightly less in spring before planting, and still less for planting in spring. 1The methodology used follows Soil Conservation Service guidelines and is outlined in Section 2 of Chapter 2. 2 A modified chisel plow, for example "Glencoe” soil saver, does partial soil inversion. 3 Personal communication with Jerry Service Specialist, Saginaw, HI, Lemunyon, Soil Conservation post Table 7.1 Crop Residue Cover Fall 1980 Farmer 1 2 3 4 S 6 7 8 9 10 n a 12 13 14 15 16 17 IB 19 20 21 Date tonncs/hectare 11/12/80 2.04 -- 12/1/80 11/13/80 11/7/80 11/17/80 11/17/80 11/17/80 11/11/80 11/11/80 11/8/80 11/19/80 11/19/81 11/17/81 11/18/80 — 11/11/80 12/1/80 11/18/80 11/10/80 11/7/81 — 3.40 1.93 1.36 2.66 2.29 2.83 3.06 2.28 2.52 0.35 1.81 1.33 2.15 — 3.15 3.69 3.01 3.12 3.03 Spring 1981 % Cover* 51 — 30 50 40 60 55 63 67 55 60 15 45 35 51 — 67 71 64 67 66 Date tonnes/hectare Spring 1981 Post Planting 4 Cover 4/6/81 2.72 4/1/81 3.06 4/6/81 2.04 3/31/81 3.52 4/3/81 3.86 4/2/81 2.89 4/3/81 2.66 4/2/81 2.86 4/2/81 4.17 4/2/81 2.00 Farmer Terminated Participation 4/10/81 0.68 4/10/81 2.49 4/B/80 0.88 4/3/81 2.61 4/6/81 2.66 3/31/81 3.35 4/6/81 2.81 Farmer Terminated Participation 3/31/81 3.37 3/31/81 2.44 60 67 51 70 72 63 60 63 76 SO 4/6/81 4/6/81 4/6/81 4/6/81 Date tonnes/hectare 5/7/81 1.25 ... 6/23/81 5/7/81 -- 6/19/81 5/7/81 6/25/81 4/27/81 — 5/22/81 t Cover — 0.85 2.44 — 2.21 2.64 1.56 1.81 — 0.74 35 — 25 59 — 52 50 42 45 — 22 35 60 25 SO 60 70 63 6/8/81 1,84 49 70 60 5/6/81 4/27/81 1.532.10 42 51 15 28 80 49 5/18/81 5/18/81 5/18/81 5/18/81 4.99 0.77 0.92 0.70 2B 24 28 22 — 5/8/81 5/18/81 5/18/81 -- — 0.57 1.36 4.22 — — 32 40 80 — Demonstration Plots Fall Planted Rye Cover Crop Soil Saved V- Ripped Disked 0.62 1.03 4.23 1.91 ‘Where percent cover was not measured, percent cover was estimated using the USDA Chart for estimating percentage of conopy and mulch covers, USDA, Agriculture Handbook Number 537, December 1978, page 50. aPrevious crop was cucumber. 141 7.2. Plant Population and Early Season Growth Rates Corn in conservation tilled plots grew more slowly in the spring of the 1980 season and were 8 to 13 centimeters shorter four weeks after emergence than corn recovered in conventionally as the season progressed. tilled However, early season nutrient The number of times when a difference was noted in the rate of plant growth between conservation and conventional tillage in season was very few (Table 7*2). the of conservation grown corn by one half growth stage, cent the growth stage. farms week number twelve. both ahead of conventional grown corn by one full systems No difference in growth stages was recorded after In the case of navybeans growth rates were the same throughout the season. Tables 7.2 and 7.3 depicts stages of growth by week for corn and navy bean respectively during 1980-1981 per­ In all cases the difference was gone by the time the next observation was made. under was ahead conservation grown On the other hand in week 11 the conservation corn on eight of 1981 In week numbers eight and ten respec­ tively, 17 percent of the farms had the conventionally grown corn corn. they Poor plant appearance was probably due to cool, wet soil conditions which decrease uptake. plots. season. the Stages reported are the average of several observa­ tions made in each field. 7.3. Plant Population Target seeding rates were held seeding constant rate was not an experimental variable. across tillage systems; In such instances where plant population was reduced by conservation tillage, it was found that Table 7.2 of Growth for C o m Grown In 19S1 Season Sta Farmer Week 1 7 4 CO CT CO CT B 12 10 CO CT CO CT CT CO CT - - - .5 CO 14 ‘15 CO CT .5 - - - 1 1 CT CO CT CT CO 20 CO CT CO CT Meek .5 .5 1 1 - - - _ 3 1 1 2 2 2 2 2 2 3 2 2 2 2 2 2 4 2 2 2 2 2 2 1 2 2 3 3 3 3 2 3 3 3 3 3 4 4 3 5 4 4 4 5 5 5.5 5.5 4 5.5 5.5 5.5 5 5 5 5 5.5 5.5 5 6 6 5.5 5.5 6 5.5 6 6 6 6 1 1 1 .5 .5 1 1 1 1 _ .. 1 1 _ _ 1 1 1 1 1 1 . 3 2 2 2 2 2 2 - - 1 1 2 2 2 2 1 1 2 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 3 3 2 2 3 3 3 3 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 4 4 4 4 4 4 4 4 3 3 3 3 3 3 4 4 3 4 5 5 5 5 5.5 5 5.5 5.5 4 4 5.5 5.5 4 4 5.5 5.5 5 5.S 5.5 5.5 5.5 5.5 5.5 3.5 5.5 5 5 5.5 5,5 5.5 5.5 5.5 1 3.5 5.5 6 6 6 6 6 6 3.5 3.5 6 3.5 5.5 5.5 1 20 16 .5 .5 1 June July 3 5.5 6 6 6 6 6 6 5.3 5.5 6 6 5.5 5.5 6 7 7 7 6 6 7 7 6 6 2 7 7 7 7 8 7 7 7 6 6 7 7 7 7 8 8 7 7 8 8 7 7 7 7 3 4 7 7 0 8 0 8 S 8 7 7 8 8 a a B 8 7 7 a 8 a 8 8 8 4 1 7 7 0 8 9 9 9 9 8 B 9 8 a 8 8 8 8 8 9 9 9 9 9 8 1 2 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 2 3 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 3 4 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 4 ■9 9 •Too noddy to gat Into the field Please refer to Figure 4.3 for explanation of these stages. , „ , Decree Days Units free Average Planting Date (May 16) to Average Fall Safe - fro>s - frost date ■ 2200 units (USDA Agr. Statistics, 19G0) and fitchigan Department of Agriculture, Hlchlgan Agriculture Statistics, 1981). CO > Conventional Tillage CT • Conservation Tillage (-) • Not monitored that week 142 5.5 August 3 143 Table 7.3 Stages of Growth for Dry Beans Grown in 1981 Season Farmer 13 Week CO CT 17 CO CT CO CT CO CT V1 71 V3 73 71 71 V3 V3 74 74 V3 V3 73 V3 1 2 June 3 A 1 2 July 3 4 5 1 2 September 3 4 1 2 3 V1 V1 V2 72 V3 V3 V7 V7 V7 V7 *1 R2 Rj *2 E3 - *1 V6 V6 *1 h h *1 “l *1 *2 "2 R3 R3 R1 *1 R3 R3 R3 R3 R3 R3 R4 R4 R4 R4 R3 R4 R4 - - R6 R6 R6 R6 R3 r3 R5 R5 R6 R6 R6 R6 R6 R6 R6 R6 *7 *7 *7 "7 R6 R6 “7 *7 *8 R8 R8 R7 *7 R8 R8 Harvested Harvested Harvested Harvested 4 Average Growing Degree Units accumulation ■ 1600 (Michigan Department of Agr., Michigan Agr. Statistics, 1981) VE, VC— Emergence, cotyledon VI— First, node V2— Second node V3— Third node V4— Fourth node VS— Fifth node V6— Sixth node V7— Eighth node V9— Ninth node V10— Tenth node CO— Conventional tillage CT— Conservation tillage (-)— Not monitored that week R1— R2— R3— R4— R5— R6— R7— Beginning bloom Full bloom Beginning pod Full pod Beginning seed Full seed beginning maturity 144 the planter did not properly place the seed into because of residue interference. slippage and excessive Other reasons included Percent germination primarily planter wheel for conservation conventional tilled cornwas 84.0 and 83.6 percent respectively for 1980 and 80.7 and 87.2 percent for 1981. was soil\ packing from planter packing wheels because of higher soil moisture conditions. and the 86.7 and 88.5 percent respectively in 1981. Percent germination forbean3 for conservation and conventional tillage Table 7.4 depicts the seeding rate and percentage germination for farms growing corn in the spring of 1980 and 1981. The paired nt" tests reveal that the hypothesis which that the means tion in the conservation tilled corn in 1981 can be verified at least 95 tirae3 test out are equal is rejected. states In other words, the percent germina­ of 100 to be higher than conventional tillage. this difference was observed to be hypothesis is not rejected seven rate and percent spring of 1981 season. In 1980 the and there was not enough evidence to show statistical difference in germination rate. ing percent. For our 1981 Table 7.5 depicts the seed­ germination for farms growing navy beans in the Here too, there was not enough evidence to show statistical difference in dry and navy bean germination rates. 7.4. Fertilizer Rate Target rates and types of fertilizers were held constant tillage systems; rates and types were not experimental variables. ^In most cases farmers did not have access to no-till planters, and had to use regular rowcrop planters. This caused problems with penetration, seed depth, and covering. In case3 where farm­ ers did some alteration to their regular row planters, seed place­ ment and depth were improved. across Table Table 7.4 Seeding Rate and Percent Germination for Farms Growing C o m Spring (1980 and 1981) 1980 1981 Tillage Method Farm Number Seeding Rate (seeds/hectare) Tillage Method CO CT Percent Germination Seeding Rate (seeds/hectare) CO CT Percent Germination 1 (field 1) (field 2) 4 - 68,750 - 62b - 78 55,000 68 80 55,000 85 88 68,950 87 76 7 - - - 60,000 99 98 8 - - - 75,000 82 93 9 - - 75,000 87 96 10 - - 75,000 76 80 c c 75,000 84 96 - 11 67,500 88 78 12 67,500 95 91 c 14 - - - 65,000 78 83 15 - - - 75,000 81 91 65,000 60 82 16 62,500 89 89 20 - - - 61,250 86 76 21 - - - 75,000 76 94 80.7 87.2 (P Average 83.6 84 (P>, 2) aThe seeding rate was the same for conventional and conservation tillage. This farmer had a problem with his 12 row planter. He had uneven depth and poor coverage. 1981 crop was planted with a newly purchased, 12 row maxemerge planter. cDid not cooperate this year. The 146 Table 7.5 Seeding Rate and Percent Germination for Farms Growing Beans (Spring 1981) Tillage Method Farmer Variety Seeding Ratea Kilograms/hectare CT CO Percent Germination 3 Navy bean (seafarer) 47.6 89.1 94.4 5 Black turtle 48.6 70.7 72.9 425000c 97.1 86.7 47.6 97.1 86.7 88.5 86.7 13 Soybean 17 Black turtle Average Seeding rate was the same on both systems. It was assumed that there are 5,500 seeds per kilogram of navy beans. c Narrow planted soybeans (seeds/hectare) 147 7.6 and 7.7 show fertilizer rates and kind3 applied beans respectively and the yield obtained on corn and navy in I960 and 1981 at the cooperating farms. 7.5. Weed Control and Herbicide Rates Rates of application and types the conservation of herbicides were equivalent for and conventionally tilled plots; rates and type3 were not experimental variables. There were no differences in attributable to tillage method weed control for the corn or navy bean plots. The cooperators were very good farm managers. They were very care­ ful when it came to proper pest control, and in particular weed control. The fields were in general very clean and weed free. There were, how­ ever, isolated cases of annual or perennial grasses that occurred on the conventional a3 well plots tended as conservation grasses and observed quack weeds were broad leaves. The most One farm had a weed problem on both tillage systems a cover crop after fall moldboard plowing. rye with a Tables commonly due to In spring the farmer sprayed contact killer and planted with a no-till planter. 7.8 the Another farmer used rye as spraying was not well timed and as a result the rye wa3 not trolled. tillage grass, nut sedge, Canadian thi3tle and pig farmer's sickness for a period of two weeks. the Conservation to have more perennial grasses while conventional tillage plots had more annual weed. tillage. fully The con­ and 7.9 depict the rates and kind3 of herbicides used and crop yields obtained over the 1980 and 1981 season for corn and navy beans. 148 Table 7.6 Target Rates and Kinds of Fertilizers for C o m Used and Yields Obtained Rates and Kinds Mere the Sane for Conventional and Conservation Tillage Systeas 1980 Faroer Fertilizer Rate kg/hectare 1 4 342 *2° 6-41-0 NH3 1981 Yield tonnes/hectare CO CT — -- 10.2 10.1 — B — -- 9 6-18-6 30-0-8, Chicken Manure 22B 179 28,300 12 *2° 137 6.9 7.4 7.0 6.6 6.3 6.2 B.4 8,6 342 7.4 7.2 5-14-13 365 9.9 B.9b 228 465 57 8.6 8.0 13-35-3 Actual N 262 154 10.9 10.7 r2° 7-40-10 NH3 342 10.6 9.7 450 C 9-32-20, It In, 2»fc — 9.8 280 160 Not Given 21 9.S ^Average of 2 fields (B.S and 9.0 hectares/acre) cDld not cooperate this season ^Tonnes/hectare 'Planted beans this season. -- — — B.6 8.4 456 114 3.2 285 12. Sd 235 9.5 9.9 9.5 9.6 11-54-0, 2tiki, It In Actual N 125 200 9.5 9.6 10-20-20 NH3 365 160 9..0 8.7 14-35-3 K}0 (Potash) 143 228 11.3 11.2 Hog Manure *Llters per hectare 684 — — 0-0-60 Line 9-37-7, 2t Zn. 22B — — — IS *2° 7-30-15 NH3 326 8.4 14 20 23 0-0-60 8-40-5, 2tian, It S, 281 N 1/It FE 4-11-44 10-34-0, 2t In 2Bt N 16 Yield tonnes/hectare CO CT 10-26-26- with 2t In Nitrogen 6-18-36, Itrtj. 2tZn KH3 10 13 Rate kg/hectare 8-25-3 125 7 11 Field 1 Fertilizer 47,318 149 Table 7.7 Target Rates and Kinds of Fertilizers Used for Beans and Yields Obtained in 1980-1981 Season. Rates and Kinds Were The Same for Conventional and Conservation Tillage Yield Tonnes/hectare Fertilizer Rate Kilogram/hectare C0a CTb Urea 6-22-22, 2% Zn, 2% Mn 56.8 272.2 1.8 1.5 5 10-34-0 170.1 2.8 2.9 13 0-0-60 12-34-14 113.4 226.8 3.3 3.1 10-20-20, 2% Zn, 2% Mn 311.8 3.0 3.1 Farmer 3C 17 £ Conventional tillage ^Conservation tillage Planted navy beans 150 Table 7.8 Target Rates and Kinds of Herbicides Used on C a m and Yields Obtained Rates and Kinds Here the S u e for Conventional and Conservation Tillage Systems Yield (kg/hectare) Farmer Herbicide Rate Per Hectare CO CT Yield (tonnes/hectare) Herbicide Rate Per Hectare CO CT Atraiine Banvol 5.8 liters 1.8 liters 7.4 7.2 Atraiine Lasso Bladex 2.3 kg 4.B liters 3.8 liters 9,9 8.9 7 Atrailne Sutan Bladex .6 kg 2.3 kg 1.8 kg 8.6 8.0 a Dual Dscaaine 2.5 liters .S liters 10.9 10.7 9 Banvel Esterone 1.0 liters 1.0 liters 10.6 9.7 Banvel Formula 40 1.0 liter 1.0 liter -- 1 4 Atrailne Lasso 2.2 kg 4,8 titers 10.2 10,1 10 — a u Field 1 Lasso Bladex 4,8 liters 418 liters 6.9 7.4 -- * — — Field 2 Lasso Bladex 4.B liters 4.8 liters 7.0 6.6 -- — -- — 12 Lasso Banuel Atraiine 2.5 llteTS .6 kg 1.3 kg 6.3 6.1 Bannel Atraiine Lasso 1.0 liter .6 kg 2.5 liters 8.6 8.4 13 Bladex Atrailne Lasso Rougue w/ Basagran 2.3 kg .6 kg 4.8 liters 8.4 S.6 14 Atraiine Formula 50 Banvel 1.3 liters 1.3 liters ,6 titers 8,4 8.2 IS Atrailne Lasso 2.6 kg 4.8 liters 9.5 9.9 Id Bladex Lasso Atrailne 4,8 liters 2.B liters .6 kg 9.8 9.S Lasso 4.8 liters Atrailne 1.0 kg Roundup (spot application) 9.5 9.6 20 Sutan Atraiine 4.8 liters 2.4 liters — -- Sutan Atrailne 4.8 llteTS 2.4 liters 9.0 9.6 Dual Banvel 99 Concentrate 3.0 liters 1.3 liters 3.1 liters 11.3 11.2 21 *Did not cooperate. 151 Table 7.9 Target Rates and Kinds of Herbicides Used on Dry Beans and Yields Obtained in 1980-81 Season. Rates and Kinds Were The Same for Conventional and Conservation Tillage Farmer 3 5 Herbicide Rate Per Hectare Eptam 2.5 liters Amiben 9.0 kg.a Treflan 1.5 liters Eptam 2.5 liters Treflan 1.3 liters Amiben 3.1 liters Amiben 9.5 liters Lasso 5.0 liters Basagram with oil 1.9 liters Hoelon1 3.3 liters Amiben 4.4 liters Yield tonnes/hectare CO CT 1.8 1.5 2.8 2.9 3.3 3.1 3.0 3.1 13 17 aKilogram *Used on conservation tillage only. 152 7.6. Insect Populations Increased armyworm and corn borer populations were observed for the conservation tillage treatment in some corn fields (however, the popula­ tions were not high enough to have an important impact on yield). Armyworm populations were present where small grain cover crops were not effectively killed by spring fields, and on bean and herbicide beet applications. fields, insect In depict the corn populations were not increased where residues were left on the soil surface. 7.11 other Tables 7.10 and kinds and rates of insecticides used in 1980 and 1981 seasons and yields obtained. 7.7. Crop Diseases No crop diseases were observed that could be attributed to ences in tillage systems. tions of the field. side differ­ One farm had eye spot on corn on both sec­ It was noticed first and was not sprayed early. on the conservation tilled Yield in the conservation plot was one tonne/hectare les3 than the conventional plot. The eye spot might have caused some of this difference. 7.8. Crop Yield Differences in yield per hectare for corn grown under vs. conventional tillage tonnes/hectare for tonnes/hectare for corn were grown small; under conservation "fields" for the 1980 season. the average conventional tillage when While they were 9.5 conservation yields were 8.12 tillage vs. 8.07 across all tonnes/hectare and averaged 9.1 tonne3/hectare for conventional and cn3ervation systems respectively for the 1981 season (Table 7.12), tonnes/hectare respectively, Average yields were 8.4 and 8.3 for conventional and conservation tillage Tablle 7.10 Target Rates and Kinds of Insecticides Used on C o m and Yields Obtained. Rates and Kinds Were the Same on Conventional and Conservation Tillage. Yield tonnes/hectare Farmer Insecticide Rate Per Hectare CO CT 1 4 Yield tonnes/hectare Insecticide None 10.2 10.1 — CO CT 7.4 7,2 Dyfonate 7.9 kga 9.9 8.9 7 Dyfonate 3.1 liters 8.6 8.0 8 Counter 9.1 kg 10.9 10.7 9 Lorsban 7.9 kg 10.6 9.7 None — Rate Per Hectare 10 None — 11 Field 1 6.9 7.4 Field 2 7.0 6.6 Lorsban 2.5 liter 8.7 8.3 14 Counter 9.1 kg 8.4 8.2 15 Dyfonate 7.9 kg 9.5 9.9 Counter 9.1 kg 9.5 9.6 20 Tursban 7.4 kg 9.0 8.7 21 Counter 5.1 kg 11.3 11.2 12 Lorsban 7.9 kg 6.3 6.2 13 Dyfonate 7.9 kg 8.4 8.6 16 Kilogram Counter 9.1 kg 9.8 9.5 154 Table 7.11 Target Rates of Insecticides Used on Beans and Yields Obtained in 1980-1981 Season. Rates and Kinds Were the Same For Conventional and Conservation Tillage Farmer Insecticide 3 None 5 CyGon Rate Per Acre Yield Tonnes/hectare CO CT 1.8 1.5 2.8 2.9 3.3 3.1 3.0 3.1 Average Across Fields 2,7 2.6 Average Across Farms 2.7 2.6 13 None 17 CyGon — Not Reported - - Not Reported 155 Table 7.12 Comparative Corn Yields on Conservation Vs. Conventional Tillage for 1980 and 1981 Seasons (Tonnes/Hectare) 1980 Farmer 1981 CO CT 7.4 9.3 7.2 9.5 9.9 9.9 8.8 9.0 7 8.6 8.0 8 10.9 10.7 9 10.6 9.7 8.6 8.4 14 8.4 8.2 15 9.5 9.9 9.5 9.6 20 9.0 8.7 21 11.3 11.2 CO CT 1 Field 1 Field 2 4 Field 1 Field 2 11 Field 1 Field 2 10,2 10.1 6.9 7.0 7.4 6.6 12 6.3 6.1 13 8.4 8.6 16 9.8 9.5 Average Across Fields 8.1 P>. 2 8.1 Average Across Farms 8.4 8.3 9.5 P=0.035 9.5 9.1 9.2 156 when averaged across farms for the 1980 season. season they were 9.5 1981 The null hypothesis which states that yield of the two tillage treatments are assumed to be equal cannot be rejected for 1980 corn because there (Table 7.12). cannot be is a yield. discernible However difference As for bean yields for the rejected for 1981 1981 it is rejected in yield at the 5% level season the hypothesis because even at 20{ level there was no discernible difference between yield due to tillage treatments. yield the and 9.2 tonnes/hectare respectively for conven­ tional and conservation tillage. mean However, for Average navy bean for 1981 season across all fields was 2.7 tonnes/hectare for con­ ventional and 2,6 tonnes/hectare for conservation tillage. Average yields across the farms (total number of cooperating farms) was the same as that across the fields (total number of fields from which collected) The rain pattern for both tillage treatments (table 7.13). for spring of 1981 wa3 abnormal in distribution as This is believed to well as data was intensity. be the cause for such a difference in yield. In cold, very moist soil conservation planted will have a date start and if these conditions persist then they will not grow normally. This finding is consistent with reports cited in Section 3.2.3- During the 1980 season navy bean and 3ugar beet yield data were not sufficient to draw any conclusions. conservation tillage was practiced, but Sugar beet Navy bean yield was lower where weed control was a problem. yield was slightly higher under conservation tillage. More work is needed to define the optimal set of cultural practices for navy bean and sugar beets. However, in the 1981 season there was no statisti­ cally discernible difference in dry bean yields between both tillage 1S7 Table 7.13 Bean Yield on Conservation Vs. Conventional Tillage for 1981 (Tonnes/Hectare) Parmer Variety CO CT Navy beans Sea farer 1.8 1.5 Black Turtle Beans 2.8 2.9 Soybeans GHL 150 3.3 3.1 Black Turtle Beans 3.0 3.1 Average Across Fields 2.7 2.6 Average Across Fields 2.7 2.6 3 13 17 P>. 2 158 systems. This was due to better management practices on the part of the farmers than was practiced in the 1980 season. 7.9. Grain Moisture at Harvest The average moisture content of corn at harvest was essentially the same for both tillage treatments in 1980; 24.7? for conventional tillage V3, 25.0? for conservation tillage when averaged across farms. 24.8? and It was 25.8? for conventional and conservation tillage respectively in the 1981 season. Table 7.14 and 7.15 depicts the average percentage moisture of corn and navy bean for 1980 and 198 1 seasons respectively. Based on the results of the statistical test3 carried out, the null hypothesis of the means being equal will not be rejected for 1980 corn moisture (Table 7.14). cernible difference This implies that there was a statistically dis­ in moisture content due to tillage practices. On the other hand the hypothesis cannot be rejected for moisture content in beans at harvest time (Table 7.15). This implies no statistically sig­ nificant difference in moisture content due to tillage practices. 7.10. Soil Moisture There was a statistically discernible difference between effects (depth the main and days) but not tillage, as is evident in Table 7.16. Also, there was a discernible difference due to the interaction between depth and tillage practice (Table 7.17). Since main effects of a certain variable "should be individually interpreted only if there is no evidence that the variable Interacts 159 Table 7.14 Corn Moisture Content at Harvest for 1980 and 1981 Seasons 1981 1980 Fanner CO CT CT CO Moisture- - 1 Field 1 Field 2 25.9 22.3 31.5 25.0 4 Field 1 Field 2 24.8 24.8 24.6 25.1 7 29.6 30.9 8 23.7 24.8 9 23.4 25.3 10 20.6 21.4 29.6 30.2 11 Field 1 Field 2 20.0 20.0 19.3 20.0 12 30.0 30.0 28.6 29.7 13 25.1 25.2 — — 14 23.1 22.0 15 28.7 28.2 - - — 16 23 23.5 subsoiled 25.9 subsoiled 26.0 no tilled 22.2 23.7 20 25.0 25.0 24.7 25.0 24.6 25.0 21 Average Across 24.7 Fields 25.0 P=0.117 24.8 25.9 P=.05 Average Across Farms 25.5 25.8 24.8 25.8 160 Table 7.15 Dry Bean Moisture Content of Harvest (1981 Season) Farmer CO CT 3 20.70 19.90 5 16.00 16.00 13 16.10 16.30 17 15.40 16.40 Average Across Fields 17.11 17.20 Average Across Farms 17.11 17.2 P > .2 161 Table 7.16 Analysis of Variance of Interaction of Depth, Drying Days, and Tillage System on Moisture in Clay Soil Source of Variation Sum of Squares Mean Square F Significance of F Main Effects VI = Day V2 = Tillage V3 = Depth 2805.757 2283,616 5.586 516.555 7 4 1 2 400.822 570.904 5.586 258.278 59.525 84.783 .830 38.356 .001 .001 .365 .001 120.741 14 8.624 1.281 .235 28.940 53.147 38.654 4 8 2 7.235 6.643 19.327 1.074 .987 2.870 ,374 .452 .062 3-Way Interactions 34.927 8 4.366 .648 .735 VI X V2 X V3 34.927 8 4.366 .648 .735 Explained 2961.426 29 102.118 15.165 .001 Residual 606.031 90 6.734 3567.457 119 29.979 2-Way Interactions VI X V2 VI X V3 V2 X V3 Total VI V2 V3 Day Tillage Depth DF 162 Table 7.17 Average Values of Moisture Content in Clay Soil at 15.2 and 76.2 cm Deep, Sampled Every Day Two Days After Soil Saturation 15. 2 cm Jay CT CO - - 76.2 cm - CT CO - Percent Moisture - - - - 1 32.1 31.1 26.8 28.1 3 26.8 30.6 24.6 25.1 5 27.1 29.5 23.2 24.8 7 27.9 29.3 22.4 25.1 9 16.9 17.8 14.7 13.90 163 with other variables11 , the only significant (duration of the experiment). is the day Therefore based on this the hypothesis which assumes equal means should be rejected at the 5% level. reject it. effect The interaction between depth and tillage makes it imperative to consider them jointly. interaction main However, at the 10% level there would be no reason to In other words this interaction was not significant at the 5 percent level, but was at the 6 percent level which means that these results can be repeated 91* times out of a 100 when the same test is run. The Analysis of Variance for the interaction between tillage system, depth, and drying days and their effect on soil moisture i3 presented in Table 7.16. The reason there was not a more pronounced difference in moisture content between both tillage systems is the unusually high precipitation experienced through out the 1981 growing season. Had the rainfall pat­ tern been altered to give low precipitation, a statistically discernible difference in soil moisture content would be expected. Box, G.E., W.G. Hunter and J.S. Hunter, 1978. Statistics for Experiments— An Introduction to Design. Data Analysis and Model Building, pp. 317-318. J. Wiley and Sons, Inc., N.Y. CHAPTER 8 ECONOMIC COMPARISON OF CONSERVATION AND CONVENTIONAL TILLAGE The focus of this chapter is upon the estimation of conservation in profitability tillage relative to conventional tillage for the major crop sequences grown Watershed of on Tu3oola the and lake plain Huron Counties. soils in the Saginaw Bay The analysis wa3 conducted from a whole farm perspective with the least cost machinery for The analysis was con­ each tillage system given the crop sequence. complement ducted for farms of 1 6 0 , 2 4 0 and 32 0 hectares, which are common sizes in the study area. As described in Section 2 . 1 , our method of analysis was to construct a hypothetical "representative" farms based on coefficients developed from the field comparisons and complementary Michigan State University experiments. Also, consideration was only given to those aspects of the farm business that differ as a result of the tillage sys­ tem U3ed. The economic results were generated for the above particular, in areas mentioned and for other farms ranging from 120 to 4 8 0 hec­ tares, covering most of the area3 of farms in the project area. The assumption was made in the economic comparison that the conser­ vation tillage implement used for primary tillage was a chisel plow or a modified chisel plow (soil saver), while for the implement used was a moldboard plow. the was based upon input/output tillage. relationships literature, modified by the results of the comparisons vs. conventional tillage in the 164 tillage A field cultivator rather than a disc-harrow was assumed to be used for secondary parison conventional The com­ derived from the of conservation project when there were differences 165 between the literature and the field comparisons a3 discussed in Chapter 7 and Section 8.2. 8.1. The sequence of field selected Machinery Complements operations and the machinery complements for the corn-navy bean (C-NB) corn-navy bean-sugar beet (C-NB- SB), corn-corn navy bean-sugar beet wheat-sugar beet through 8.4. (C-NB-WT-SB) (C-C-NB-SB), sequences Field operations are listed in are the and corn-navy bean- presented in Tables 8.1 sequence with which they are performed. The chisel plow replaced the moldboard tillage plow in system and the disk harrow as was not used. the conservation One field cultiva­ tion was performed for the conservation tilled farms while disk ing and field cultivation were done more than once on the conventional tilled areas. vation lage. harrow­ Also, only one row cultivation was done under the conser­ tillage compared to two row cultivations under conventional til­ All chisel plowing was finished by November 27 for all fields. The harvesting operations were the same for the conventional and conser­ vation systems. methodology, The machinery complements chosen, as indicated achieved the in the timeliness constraints set in Table 4.3 and 4.4 (Section 4.4) in eight years out of ten. The conservation tillage machinery complements were assumption based on the that corn planting must be completed by the same date as for conventional tillage. Thi3 implies that there are fewer days for spring tillage and planting to be completed. 166 The field operation sequence and the ments for resultant machinery comple­ each cropping sequence for conservation and for conventional tillage follow. For the C-NB sequence (Table 8.1) the tractors and implements vary in size with different areas and the combine is two rows larger for the conventional tillage system for This occurs because a four row 160 and 240 hectares. planter and a four row combine do a timely job for the conservation tilled 160 and 240 hectares. On the conventionally tilled 160 and 240 hectares, a six row planter was needed due to the number of operations taking place in spring. combine This forces the size to be larger in order to match the planter size. Combines selected for the 320 hectare C-NB farms were the same size.All tractors needed for the 320 hectare farms under both systems were equal in size. Power needed for conventional tillage under 160 larger because of the large There were few changes in other must match. 240 hectares was implements like the disk harrows chosen. implement sizes since all row equipment As long as the combine or planter sizes did not change from one area to another, the sprayer, row retained and the same size. cultivator, and NH3 applicator Costs of conventional tillage per hectare for the three farm sizes considered was always higher than that for the con­ servation tillage. The conservation tillage was $62.88, $40.58 and $21.2/hectare lower for the 160, 240 and 320 hectares respectively. In the C-NB-SB sequence (Table 8.2) the combine is two row3 for the conventionally tilled 160 and 240 hectares for the same reason stated above, while it is two rows smaller for The larger increase in the 320 hectare farm. area by 80 hectares with chisel plowing to be done in fall for conservation tillage demands more time a larger combine will take less for plowing. Therefore time harvesting and leave more time that T a b U B.l Cooparison of C o m and Machinery Sizes far a Corn-Navy Bean Far* at 160, 240, and 320 Hectares Soil is Fine and Confidence Level is 80V Conventional Annual Use lepleeent Size Combine (Row) 6 Bean Pull, (Row) 6 Fer. Spr. (M) H.B. Plow (Bottoa) 12.2 Hours 240 hectares Conservation Annual Use Size Hours Conventional Annual Use Size 67 4 100 6 73 4 73 6 13 12.2 3 243 Soil Saver (M) - - 1.9 Disk Harr. (H) 3.S 37 - - 13 159 - 12.2 3 Hours 79 94 16 313 320 hectares Conservation Annual Use Size 4 118 4 94 12.2 Hours 105 6 105 126 6 126 21 12.2 21 - - - 3 417 200 - - 1.9 3.5 64 - 267 - - 1.9 3.5 48 - - 3.5 64 98 3.8 130 3.8 130 6 116 6 193 3.5 37 - - 3.5 48 3.B 78 12.5 78 3.8 98 3.8 Row Pint. (Row) 6 73 4 183 6 87 4 Sprayer (10 9.1 20 6.1 9.1 25 6.1 Row Cult. (Row) 6 216 4 86 6 NH3 App. (Row) 6 57 4 86 6 Till. Trae. (Kw) 2*142 263 2*90 250 2*142 331 Util. Trae. (Kw) 48 327 48 283 48 383 Cost/Ha. 6 Size 16 F. Cult. (H) Kach, Cost (1) 6 Hours - Tanden Harr. (M) Tin. Cost (J) Size Conservation Annual Use 12.2 - 30 Hours Conventional Annual Use 73 73 4 217 38 ' 9.1 34 9.1 6 - 34 163 183 2*6 163 109 6 197 6 2*90 310 2*142 441 2*142 341 2*48 167 2*48 255 2*4B 175 4 - 14SO - 2707 2339 312B 38329 26818 44289 32014 54677 47104 239.55 176.68 184.S3 143.95 178.18 156.98 97 291 160 hectares Tibia 8.2 Co^iarison at Costs per Hectare for a Com-Navy Bean-Sugar Beet Fara at 160, 240 and 310 Hectares Soli Is Fine and Confidence Level Is 803 160 hectares Conventional lapleaent Site Annual Use Hours 51 ae Coablne (Row) 6 44 4 Bean Pull. (Ro m ] 6 48 4 Beet Topp. (Row) 3 81 Beet Lift. (Ro m ) 3 81 For. Spr. (IQ 12.2 17 247 240 hectares Conservation Annual Use Hours Conventional Site Annual Use Hours 320 hectares Conservation Site Annual Use Hours Conventional Size Annual Use Hours Conservation Site Annual Use Hours 67 8 39 6 52 6 70 8 52 48 8 42 6 63 6 84 8 56 3 81 3 105 3 105 3 140 4 tos 3 81 3 105 3 105 3 140 4 105 12.2 17 12.2 21 12.2 - - 3 - 3 1S8 - 21 12.2 26 417 12.2 26 - - M.B. PIo m (Bottoa) 3 Soil SaveT (I') - - 1.9 - 2.7 Disk flair. (H) 3.5 25 - - 3.5 32 - - Tandem Disk (H) 3.S SO - - 3.5 64 - - F. Cult. (11) 3.8 78 78 3. B 98 3.8 98 Rov Pint. (Row) 6 73 4 183 8 65 6 30 27 20 40 40 25 30 34 Row Cult. (Row) 6 215 4 161 8 73 6 97 KHJ App. (Row) 6 76 4 114 8 73 6 97 2*6 6S 8 97 Till. Tree. (Kw) 142 600 2*90 292 172 432 142 552 2*142 507 172 524 Util. Trae. (Kw) 48 418 48 377 48 432 48 379 2*48 335 75 365 Tie. Cost (*) Hach. Cost (I) Cost/lla. (J) - 202 39106 30904 I4S.03 194.95 - 143 145 - - 4.2 3.5 43 - - 3.5 85 - - 12.5 131 6.6 116 8 45 40 34 163 8 122 6 30 2*6 6060 - - 504 SO 37160 60320 45118 ' 210.2 154.83 207.18 140.83 121 76 14S - • 168 Sprayer (H) 12.5 315 169 could be allocated for plowing. and Since all row equipment should match, since there was a large competition for time in spring, the planter needed to be larger in order to finish the job in a timely therefore the manner, and need arose for a larger set for conventional tillage for the 160 and 2 4 0 hectares. When area was increased to 3 2 0 hectares there was enough Increase in area to cause competition in fall and the conser­ vation tillage required a larger combine for a faster harvest leaving more time for tillage ta3ks. and thus The combine and planter chosen for the 2 4 0 hectare conventional farm was only 3ix rows. The reason was that two sets of the row cultivators and the NH3 applicator were chosen. This implies that the row cultivation and the NH3 application were earlier and therefore the planter had more hours to plant the navy bean crop with a six row planter. the number done Again for the conservation tillage farms, of tractors selected a3 well as total power requirement was generally leS3. The 3 2 0 hectare farm required only one 172 kw 75 while the conventional tillage farm required two 142 kw kw tractor and two 48 kw tractors. $ 5 5 .3 8 and $ 6 6 .3 5 Here again the cost lower per hectare and was one $ 5 0 .0 8 , for conservation tillage on 1 6 0 , 2 4 0 and 320 hectares respectively. The C-C-NB-SB (Table 8.3) rotation required a six row all three areas planter tested, except for the 3 2 0 hectares conservation til­ lage. In the case of the 160 hectare farm a four row planter was given the farms as was evident from the cost per hectare of the 1 6 0 , 2 4 0 and 3 2 0 hectare farms. row small complexity of the rotation. The six row planter was slightly oversized for this area and therefore fit the 2 4 0 and 3 2 0 hectare better for combine For the conservation tilled 3 2 0 hectare farm, row was selected. an eight This is due to the larger area chiseled Table 8.3 Caparison Costs Per Hectare and Machinery Sites for a Cotn-Ccm-Navy Bean-Sugar Beet F a n at 160, 240 and 320 Hectares Soil la Fine and Confidence Level Is 801 160 hectares lapleneat Conventional Annual Use Hours Size 240 hectares Conservation Annual Use Hours Size Conventional Annual Use Hours Site 320 hectares Conservation Annual Use Site Hours Site Conventional Annual Use Hours Conservation Annual Use Size Hours Coablne (Row) 6 67 6 67 6 79 6 79 6 105 8 79 Bean Pull. (Row) 6 36 6 36 6 47 6 47 6 63 8 42 Beet Topp. (Row) 3 61 3 61 3 79 3 79 3 105 3 105 Beet Lift. (Row) 3 61 3 61 3 79 3 79 3 105 3 105 12.2 19 23 12.2 23 31 12.2 315 - - Fer. Spr. (H) 12.2 12.2 19 H.B. Plow (Bottoa) 3 248 Soil Saver (H) - - 1.9 - - 1.9 Disk Harr. (M) 3.S 18.6 - - 3.5 24 - - Tanden Disk (H) 3.S 56 - - 3.5 72 - F. Cult. (H) 3.8 78 3.8 98 3.8 98 3.8 - 3 159 ■ 313 - 4 200 - - 3.4 3.5 32 - — 3.5 96 - - 98 3.8 130 5.6 88 Row Pint. (Row) 6 55 6 9.1 20 9.1 Row Cult. (Row) 6 189 6 108 6 124 6 122 NHJ App. (Row) 6 86 6 86 6 109 6 109 Till. Trae. (Kw) 142 560 142 473 142 619 142 579 Util. Trae. (Kw) 48 377 48 282 65 4S2 347 Tin. Cost ($) - 20 6 65 9.1 38 65 9.1 48 - 2209 - 145 25 14 B - 6 70 8 9.1 51 12.2 51 6 143 8 122 6 73 8 109 2*142 421 172 548 2*48 301 60 371 1961 Hach. Cost (3) 37132 32752 47178 39041 51144 44177 Cost/Jla. 232.08 204.7 205.78 1S3.S 165.95 138.05 (3) 31 - Sprayer (H) 122 12.2 145 171 in the fall after harvesting the navy bean, lifting the sugar beets harvesting the corn. As stated earlier, a larger combine would allow more time for the chisel plow, especially if labor was power requirement was smaller conventional tilled farms. hectares and limited. Again for the conservation than that for the Costs per hectare for the 160, 240 and 320 in conservation tillage were $27.38, $47.28 and $22.90 respec­ tively lower than conventional tillage. All conventionally tilled C-NB-WT-SB , (Table 8.4) larger combines than the conservation tilled farms. farms required In this case spring activities for conventional tillage competed for time. This required a larger planter which in turn required a larger combine. As shown conser­ vation tilled farm3 required smaller row equipment tilled farms. Here than conventionally again conservation tillage costs less per hectare than conventional tillage. Savings were $11.38; $52.60 and $38.10/hec­ tare for the 160, 240 and 320 hectares respectively. In order to monitor how machinery complements change in number size and for such cropping sequences as farm area3 got considerably larger, one sequence which i3 commonly practiced in the project area wa3 selected. Two hundred and forty, 480 800 and 2000 hectare farms for this study (Table 8.5). 2000 hectares are chosen The 240 and 480 hectares represent an aver­ age and a larger than average farm size in the project and were area. The 800 not common sizes in the project area, however, they do indicate how costs behave when areas farmed become very large. Table 8.4 Caparison Coats per Hectare and Machinery Sites for a Coro-Navy Bean-Kheat-Sugar Beet Fan at 160, 240 and 320 Hectares Soil is Clay and Confidence Level Is 80\ 160 hectares Conventional 240 hectares Conservation Conventional 6 100 4 100 6 79 4 118 8 98 6 Bean Full. (Row) 6 24 4 36 6 60 4 47 8 31 6 48 Beet Topp. (Row) 3 61 3 61 3 79 3 79 3 105 3 105 3 61 3 61 3 61 3 79 3 105 3 105 23 12.2 23 12.2 12.2 31 - - 3 - 200 Beet Lift. (Row) 12.2 19 H.B. Plow (Bottoa) 3 186 Soil Saver ( H) - - Disk Harr. (K ) 3.5 56 * Tandea Disk (It) 3.5 17 - Fer. Spr. (11 ) 12.2 1.9 19 - 12.2 Size - 3 234 159 - - 1.9 - 3.5 72 - - - 3.5 24 - - Size Annual Use Hours Conservation Conbine (Row] Size Annual Use Hours Conventional Site Size Annual Use Hours Conservation Annual Use Hours Iq>lesent Annual Use Hours 320 hectares 31 313 Size 6.3 3.5 32 - - 3.5 32 - - 3.4 78 3.8 78 3.8 9B 3.8 98 3.8 130 3.8 4.0 24 4.0 24 4.0 26 4.0 26 4.0 35 4.0 Row Pint. (Row) 6 5S 4 6 65 4 8 65 6 6.1 9.1 30 Row Cult. (Row) 6 162 NH3 App. (Row) 6 57 4 2*190 Sprayer 0 0 Till. Trae. (Kw) 142 458 Util. Trae. (Kw) 48 375 4 46 45 9.1 3B 6 183 86 6 73 249 2*142 121 349 48 6.1 4 50 138 12.2 8 38 183 9.1 2*6 267 130 35 145 ’ 51 61 4 109 8 73 2*6 61 2*90 311 2*97 357 2*142 254 48 210 48 261 48 10224 Ti*. Cost (1) 2764 5235 9627 7276 12479 (tidi. Cost ($) 341S5 29063 44909 34391 59692 49751 Cost/Ha. 230.75 219.38 227.23 173.63 225.53 187.43 ■ CS) - - F. Cult. ( H ) 163 105 - Gr. Drill ( M ) 138 Annual Use Hours 237 Table 8.5 Influence of Area on Machinery Number and Sizes in a Com-Com-Navy Bean-Sugar Beet Farm 240 Hectares 480 Hectares 800 Hectares 2000 Hectares CO CT CO CT CO CT CO CT 6 6 3 3 12.2 3 6 6 3 3 12.2 8 8 4 4 12.2 8 12 12 4 4 12.2 2*8 8 2*4 2*4 12.2 2*6 2*12 12 2*3 2*3 12.2 4*12 2*12 4*4 4*4 18.3 4*9 9.1 10,5 4*12 3*60 4*12 4*12 209 89 3*12 2*12 4*4 4*4 15.2 --6.5 Implement £ Combine (rows) Bean Puller (rows) Beet Topper (rows) Beet Lifter frows) Fertilizer Spreader (m). Moldboard Plow (bottom) Soil Saver (m) Disk Harrow (m) Field Cultivator (m) Row Planter (row) Sprayer (m) Row Cultivator (row) NH3 Applicator (row) Till. Tractor (hp) Util. Tractor (hp) Tim. Cost ($) Mach. Cost ($) Cost/Hectare ($) - 3.5 3.8 6 30 6 6 142 48 2209 47178 205.78 - 1.9 - 3.8 6 30 6 6 142 48 - 39041 158.50 Row width was maintained at 0.75 m. ^Bottom width was maintained at 0.41 m. - 3.5 6.6 2*8 2*40 2*8 2*8 172 75 7107 65190 150.63 - 5.7 - 8.7 12 24 12 12 209 89 - 57725 120.25 - 4.4 10.5 3*8 3*8 3*8 3*8 172 89 8960 96536 131.90 - 5.7 - 8.7 2*12 40 2*12 2*12 209 89 - 10.5 4*12 4*60 4*12 4*12 209 89 - - 83475 104.35 154948 158528 77.48 79.28 - 174 Multiples of implements and power units were selected where needed. Two and four twelve-row combines and planters were needed for the con­ servation tilled 800 and 2000 hectares respectively. Three eight row and four twelve row planters were needed for the conventional tilled 800 and 2000 hectares respectively. and number in both systems. Tillage implements also changed in size The mold board plow changed in size from one three bottom to four nine bottoms, while the soil saver changed from one 1.9 m to four 6.5 m as area changed from 240 to 2000 hectares, Cost3 per hectare for both systems decreased a3 area increased. However, the advantage of conservation tillage systems over conventional tillage in c03t per hectare decreased a3 area increased. changed This advantage from $47.28 to $30.38 to $27.55 and to $1.8 per hectare as area changed from 240 to 480, to 800 and finally to 2000 hectares. due to This is the increasing difficulty of selecting machinery complements as areas increase considerably. 8.2. Assumptions for Economic Analysis Data collected during the first and second years in the Saginaw Bay Watershed Area revealed the following: 1. Planting population and percent germination were comparable were comparable between tillage systems. 2. Rates of crop growth throughout the season. under both systems This i3 in contrast to results reported in the literature which typically show a reduction season growth rates under conservation tillage. in early 175 3. Rate3 of fertilizer applied were the same for both tillage sys­ tems. Interviews with soil conservation farmers farmers, agents practicing county extension agents and throughout Michigan revealed that conservation tillage do not apply any more fertilizer on their conservation plots than on their conven­ tionally tilled plots. H. Rates of pesticides applied were the same for both tillage sys­ tems. Interviews with farmers and county extension agents and soil conservation farmers agents throughout Michigan revealed that practicing the chisel plow conservation tillage system use the same kinds and application rates of herbicides, insec­ ticides, and fungicides for both tillage systems. 5. Average corn yields across the farms in 1980 were comparable on both tillage systems (Table 7.12). When isolating farms by soil type, corn grown on coarse textured soil3 on conventional tilled ones by out-yielded seven percent. out-yielded corn grown On fine textured soils, conservation conventional conservation tillage by three percent. across percent. lage the average farms favored conventional tillage by four Corn grown on coarse textured soils conservation til­ out-yielded percent. tillage In 1981, a year with abnormally high rainfall in July and August, yields tilled corn grown On fine textured on conventional tillage by five soils, conventional tillage out- yielded conservation tillage by ten percent, while there was no statistically discernible difference tillage systems in 1981 (Table 7.13). in bean yields between 176 Rainfall patterns during the for growing season (June-Septeraber) 1980 and 1981 in Caro, Michigan (Tuscola County) were com­ pared with the 30 year summary. cipitation at least as Probabilities of rates of pre­ large as the amounts in each of the years are reported in Table 8.6. normally with July and 40 percent; of more rainfall being 69 percent. more But, probabilities of 8.1 Corn is in an observed August percent rainfall and 40 percent, and September were very wet, with and 1.1 percent respectively. active physiological growing and seed setting stage during the period of late July to early September. during this respectively). This August tilled soil they were practically eliminated. environment, especially if prolonged due to high rainfall, and caused the air spaces in the conventional tilled soil to be reduced, while in the tion Soils period in 1981 were constantly wet (76 mm and 127 ram of precipitation above the 30 year means during September, than In 1981, June and July had normal precipitation; probabilities were 60 percent respectively. than in contrast, June and August were slightly dryer than normal with the probability of observed relatively September being slightly wetter than normal with the probability being The year 1980 was poor conserva­ Such an drainage or is unfavorable for the root and plant develop­ ment and yields suffer. 177 Table 8.(5 Probability of Recurrence of the 1980 and 1981 Years Over a 30 Year Period1 - -1980- - - -1981- - Month (mm) PPT (mm) Mean (mm) June 83.06 56.90 40 63.75 56.90 60 July 133.60 72.64 19 78.23 72.64 40 Aug. 52.07 57.66 69 132.84 57.66 8 134.34 64.01 10 195.58 64.01 1.1 Sept. Prob. % PPT (mm) Mean (mm) Prob. % The probability distribution function used for this table was the gamma distribution (Winkler, 1972. Introduction to Bayesian Inference and Decision, HRH Inc., pp. 180-181). 1 Source: Dr. Fred Numburger, (1982), Michigan Department of Agriculture, Michigan State University. PPT a precipitation Prob. = Percent chance at which certain amount of precipitation within a specific month can be expected. For example June of 1980 will have a chance of 40% or less of having 83.06 mm again. 178 8.2.1. Synthesis of Information from Literature and Field Research The parameters used in the economic analysis comparing conventional tillage with the chisel plow variant conservation tillage system are presented in this section. 8.2.1.1. The estimate are: Yield. Projected yield differentials between conserva­ tion and conventional tillage for fine, medium and coarse textured soils under dry, average and wet growing season moisture regimes are presented in Table 8.7. A dry season is defined for our purposes as a season in which the probability of more rainfall than actually observed is 90 per­ cent. A wet season is defined as a season in which the probability of more rainfall than observed is only 10 soils, under conservation tillage percent. For coarse textured corn yields are projected to be 10 percent and 5 percent higher, respectively, than under conventional til­ lage in years that have significantly below average and average rainfall during July and August. For significantly higher than average rainfall, only a slight increase in yield under conservation tillage is projected. For medium textured soils, yields under both systems are estimated to be the same regardless of rainfall patterns. parable yields are projected for both rainfall regimes. For fine textured soils, com­ systems under dry and average However for years in which significantly higher than average rainfall occurs, yields under conservation tillage are estimated to be reduced by five percent. Sugar beet yields are assumed to be similar under both tillage sys­ tems. This assumption consistent cooperating farmers results Robertson, et al., (1979). of but is conservative with the relative comparisons to the of the experimental The number of observations from 179 Table 8,7 Estimated Influence of Moisture and Soil Type on C o m Yield For Conservation Tillage Compared to Conventional Tillage for Saginaw Valley, Michigan (Based on Literature Studied and Field Research) Soil Texture Dry - - -Moisture- - - ■ Average Wet Coarse 10% increase in yield 5% increase in yield Slight increase in yield Medium No change to slight increase in yield No change No change to slight increase in yield Fine No change No change to slight decrease in yield 5% decrease in yield 180 cooperating farmers is too small to be meaningful except to note none suffered performance losses as the result of using conservation tillage. Navy bean yields under both tillage systems are assumed to be based on (1979). the same field results, but conservative relative to Robertson et al., Sugar beet and navy bean yield differentials between tillage systems were not varied according to either soil type or rainfall. This is primarily the result of a lack of information and therefore should be regarded should be regarded as very provisional. 8.2.1.2. lage Pesticide Costs. Projected to be the same for both til­ systems during the first four years, after which some weed species like perennial grasses or insect species may become more abundant and therefore may require more chemical application. 8.2.1.3. Fertilizer Co3t3. Projected to be the same for both til­ lage systems. 8.2.1.4. Labor Requirements. Projected to be less tion tillage because of fewer hours spent in the field. all conservation tillage systems. The costs used are for conserva­ This i3 true of based upon farm results in Section 8.2. 8.2.1.5. logistic and Annual Capital and Operating Costs of Machinery. other Due to factors (Muhtar et al., 1982) the annual machinery use costs presented are not based on the machinery complements cooperat­ ing farmers owned. Machinery complements for three sizes of "represen­ tative" farms,(Section 8.1), based on a whole farm concept, were lated using complementary field calcu­ measurements of cooperating farmers, ASAE yearbook, experiments, and values reported in the literature. 181 Annual machinery tillage. use The costs are based on results in Section 8.2. 8.2.1.6. tion costs are projected to be lower for conservation Corn Drying Costs. Projected to be higher for conserva­ tillage only on fine soils under significantly higher than average moisture conditions. The cost of removing one extra percentage point is added as a cost. 8.2.1.7 CommodityPrices. The following commodity prices were used in the analysis: Corn Drying: $2.5/ha for each percentage point of moisture. Corn Grain: $106.88/tonne Wheat Grain: $141.38/tonne Navy Bean: $463.5/tonne Sugar Beet: $28.0/tonne 8.2.1.8 Selected Input Prices. Fuel: $0.30 per liter and inflating at "real" rate of 4 percent per year. Labor: $4.50 per hour Interest (Discount Rate): A "real” rate of 5 percent per year. 8.3. 8.3.1. Comparative Economic Analysis Methodology The analysis focuses returns a that only on those item3 affecting were found to he influenced by tillage system. costs and All costs and returns are computed on an annual basis; thus, the machinery costs 182 reflect the initial investment on a new cost basis, multiplied by a cap­ ital recovery factor (Rotz, et al., 1981). five percent was U3ed in the stated in 1982 dollars. calculations, The "real” interest rate of and therefore, costs are The capital recovery charge is a measure of the accounting literature concepts of depreciation and interest. The impact of year to year variation in growing season rainfall is dealt with by calculating weighted average gros3 returns across all pos­ sible weather events as depicted in the following formula: WAA = 3 4 ^ p ( £ w.g. J i=l 1 j=l 3 13 Where: WAA (in dollars) is the weighted average advantage to tillage; Pj is the probability of the wet); fch i moisture event conservation (dry, average, b L w, is the proportion of area in the j beets and navy beans); and crop (corn, wheat, sugar gjj is the gain in gross returns per hectare, in dollars, from con­ servation tillage relative to the conventional tillage system given the it" weather event and the jth crop (e.g., on a coarse textured soil in a dry year corn yields are projected to be 9 percent higher under conservation tillage than under conventional tillage; thus based on $106.88/tonne corn,(Table 5.11), gross return is projected to be $59.90/hectare more than under conventional tillage.) The cost advantage due to conservation tillage i3 given by: CA = WAA + LCS - ACDC Where: MCS = Machinery cost saving; LCS = Labor cost saving; ACDC = Added corn drying cost. No differences were projected in any of the other costs. Long term gains in productivity that result from reducing wind and water 183 erosion are not taken into account. Also, no credit was given to the reduction in replanting of sugar beets that occurs sometimes under conventional tillage as a result of blowing soil from sand ridges. Thus, the cost advantages stated are lower bounds. 8.3.2. Projected Impact on Annual Machinery and Labor Costs. Crops commonly used in the project area are corn, navy beans, sugar beets, wheat and oat3. the farm's soil type. 1982) corn-navy bean Cropping sequences are practiced depending on Of the more (C-NB), common corn-navy corn-corn-navy bean-sugar beets (C-C-NB-SB) sequences (Muhtar bean-sugar and et al., beets (C-NB-SB), corn-navy bean-wheat1 sugar beet3 (C-NB-WT-SB) will be discussed in the economic analysis . The differences in machinery and labor cost conventional and the chisel differentials between plow variant of conservation tillage for 160, 240 and 320 hectare "representative1* farms are presented below. The economic advantage, in all cases, i3 for conservation tillage. savings for the corn-navy bean crop sequence (C-NB) are $62.88, Cost $40.58 and $22.60/ha for the 160, 240 and 320 hectare farms, respectively. The saving in co3t for the C-NB-SB farm was $50.08, $55.38 for 160, 240 and 320 hectares respectively. $66.35 The C-C-NB-SB had a cost saving of $27.38, $53.60 and $27.90/ha for 160, 240 and tively. and 320 hectares respec­ As for the C-NB-WT-SB farm the cost savings were $16.38, $47.28 and $38.10/ha for the 160, 240 and 320 hectare farm respectively. ^The cropping sequences outlined are appropriate for fine textured and, in most instances, medium textured 3oils bub some of the se­ quences are le3s appropriate for coarse textured soils. Neverthe­ less, in this preliminary analysis, all of the cropping sequences have been maintained for completeness and illustrative purposes. Also there would be a different size of machinery complement for each soil type because of the impact of soil type on go-no go days and draft. 184 8.3-3. Projected Impact on All Cost3 The economic advantage (coat reduction) that 13 estimated to result from the adoption of conservation tillage is discussed in this section. The calculations require two steps. for each crop and for each of the soil textures by moisture condition case outlined in Table 8.7 weighted First, the estimated cost advantage is estimated. In the second step, the average for all crop3 across the dry, average and wet moisture regimes is calculated for each soil type. There are, in principle, nine soil texture by moisture condition combinations; that i3, three 3oil textures by three moisture conditions. However, the nine combinations can be reduced to three since the impact of conservation tillage on yield is similar for the medium and fine tex­ tured soils under the dry regime, the medium under the average and fine or decrease in yield. change to before it conditions yields under conservation tillage. is decrease 5 percent where it is estimated estimated that The impact of conservation tillage on cost for the four cropping sequences 8.8. slight yields increase 10 percent due to conservation tillage and for the fine textured soil under wet under a However, tables must be worked out for the coarse textured soil under dry conditions that soils moisture regime, and the coarse textured soil under the wet moisture regime; namely, that there is no increase textured considered the cases where there is no change in yield are outlined in Table For the corn-navy bean sequence, the cost advantage i3 $62.88 per hectare for the 160 hectare farm and falls to $22.60 per hectare for the 320 hectare farm. sequences by farm Similar calculations are outlined for size. the remaining The case of the fine textured soil under wet 185 Table 8.Q Estimates of Cost Advantage for Conservation Tillage Over Conventional Tillage for Pour Rotations and Three Farm Sizes for a Medium Textured Soil Under All Conditions, for Fine Textured Soils Under Dry and Average Conditions and for Coarse Textured Soil Under Wet Conditions 160 (ha) S/ha 240 (ha) $/ha 320 (ha) 5/ha C-NB 62.88 40.58 22.60 C-NB-SB 50.08 55.38 66.35 C-C-NB-SB 27.38 53.60 27.90 C-NB-WT-SB 16.38 47.28 38.10 Rotation Table 8.9 Estimates of Cost Advantage for Conservation Tillage Over Conventional Tillage for Fine Textured Soil Under Wet Conditions Rotation 160 (ha) $/ha 240* (ha) $/ha 320 (ha) $/ha C-NB 58.70 36.40 19.53 C-NB-SB 46.48 51.78 59.78 C-C-NB-SB 22.15 49.43 23.50 C-NB-WT-SB 13.05 44.78 34.78 Table 9.10 Estimates of Cost Advantage for Conservation Tillage Over Conventional Tillage For Coarse Textured Soils Under Dry Conditions Rotation 160 (ha) $/ha 240 (ha) S/ha 320 (ha) $/ha C-NB 66.03 42.60 24.88 C-NB-SB 51.75 57.23 68.55 C-C-NB-SB 28.75 56.28 29.30 C-NB-WT-SB 16.78 48.45 39.05 186 conditions is outlined in Table 8.9. navy bean sequence The cost advantage for is $58.70 per hectare on the 160 falls to $19.53 per hectare on the 320 hectare farm. advantage the corn- hectare farm and The estimated cost for the coarse textured soil in dry conditions is depicted in Table 8.10. corn-navy The economic advantage for the conservation tillage for the bean sequence rotation is $66.03 for the 160 hectare farm and falls to $2*1.88 per hectare for the 320 hectare farm. The estimated weighted average (across weather events) cost tage for the adoption of conservation tillage for coarse, medium and fine textured soils is depicted in Tables 8.11, 8.12 and tively. Also, the 8.13, respec­ these tables depict the percentage yield reduction that could occur, relative to before advan­ the profitability conventional tillage. projected conservation tillage yields, would be equivalent between conservation and Conservation tillage, in cost advantage over conventional tillage. all instances, has a The biggest gains are for the corn-navy bean crop sequence and the corn-navy bean-sugar beet sequence. Typically, gains are smaller for the corn-navy bean-wheat-sugar beet crop sequence with the corn-corn-navy intermediate. There are interactions advantage to conservation tillage is corn-navy bean-sugar beet sequence in the sense that the economic estimated to decline in some instances such a3 and the larger farm sizes, a sequence. the corn-corn-navy bean-sugar beet sequence cost is higher for the intermediate small under bean sequence as size of farm increases, whereas it increases 33 size of farm increases under the corn-navy bean-sugar beet Also, being farm size than for the result due in part to the fact that machinery complements were budgeted for existing machines on the market. Existing machines match the requirements of large or small farms better 187 Table 8.n Estimated Cost Reduction That Would Result from the Adoption of Conservation Tillage on Coarse Textured Soils and the Percentage Reduction in Conservation Tillage Yields, Relative to Those Projected, That Could Occur Under Conservation Tillage Before Profitability Would be Equal Between Conservation and Conventional Tillage 160 [ha) Rotation C-NB C-NB-SB C-C-NB-SB C-NB-WT-SB $/ha % yld. 64.46 50.92 28.07 16.58 9.51 8.17 3.45 2.17 240 (ha) $/ha 41.59 56.32 58.94 47.87 320 (ha) % yld. $/ha % yld. 6.13 0.94 6.80 6.29 23.97 67.43 28.60 38.58 3.54 10.86 3.51 5.07 C = C o m ; NB = Navy Beans; SB - Sugar Beets; WT = Wheat Table 8.12 Estimated Cost Reduction That Would Result From The Adoption of Conservation Tillage on Medium Textured Soils and the Percentage Reduction in Conservation Tillage Yields, Relative to Those Projected, That Could occur Under Conservation Tillage Before Profitability Would be Equal Between Conservation and Conventional Tillage 240 (ha) 160 (ha) Rotation C-NB C-NB-SB C-C-NB-SB C-NB-WT-SB 320 (ha) $/ha \ yld. S/ha * yld. $/ha % yld. 62.88 50.08 27.38 16.38 9.07 5.17 3.31 2.13 40.58 55.38 53.60 47.28 5.B5 6.28 6.48 6.14 22.60 66.35 27,90 38.10 3.48 7.52 3,37 4.91 C » C o m ; NB » Navy Beans; SB o Sugar Beets; WT » Wheat Table 8.13 Estimated Cost Reduction That Would Result From the Adoption of Conservation Tillage on Fine Textured Soils and the Percentage Reduction in Conservation Tillage Yields, Relative to Those Projected, That Could Occur Under Conservation Tillage Before Profitability Would be Equal Between Conservation and Conventional Tillage 240 (ha) 160 (ha) Rotation C-NB C-NB-SB C-C-NB-SB C-NB-WT-SB $/ha 59.96 47.77 24.36 13.5S % yld. 8.80 5.02 3.06 1.90 5/ha 37.66 52.52 50.68 44.53 320 (ha) % yld. $/ha % yld. 5.57 6.12 6.25 6.05 19.79 63.19 24,97 35.27 3.12 7.45 3.19 4.78 C * C o m ; Navy Beans; SB = Sugar Beets; WT ■ Wheat 188 than those of increase the probably intermediate overstates farm the for true this cost sequence. The cost disadvantage due to the intermediate size farms. The results indicate that advantages are sensitive to both size farm and crop sequence. of This illustrates the importance of defining the system's boundaries for the economic and mechanization analysis as the conservation and whole farm, a3 contrasted to the individual enterprise. The decreases in yields that could occur conventional tillage would before be equivalent in profits per hectare range from 9-10 percent for the 160 hectare corn-navy bean farms to cent for 2-3 per­ the corn-navy bean-sugar beet sequence for 160 hectare farms. These estimates provide farmers with a perspective on how much conserva­ tion tillage yields could fall relative to conventional tillage yields before the economic advantage to conservation tillage would be wiped out. 8.3.4. Risk Farmers view the risk associated with the adoption of tillage from two perspectives. conservation First, they analyze the risk that if they adopted conservation tillage their results would be less favorable, for whatever reason, than implied by our analysis and by the experience of their neighbors. obtained by An estimate of grower's perspective of risk can examining the variations in yield observed in our sample, for they provide the grower with insight into the worse be prospects for doing than average, and the worst that perhaps could possibly happen if he were to have adopted conservation tillage. The worst case in 1981 189 for the use of conservation tillage on corn was a disadvantage of 1.1 tons per hectare, a 10 percent decrease; while the highest advantage was 1.0 ton3. The second type of risk is that which is the result of year to year variation, primarily due to weather, for a given farmer outlined in Table 6. a3 was If there are significant differences in risk which are not adequately compensated for by increased farmers will prefer the conventional system. earnings, risk averse Thus, it becomes important to understand the risk-return trade-offs between systems. 8 .k Summary Conservation tillage costs significantly less per hectare than con­ ventional tillage for well drained 3oils in the Southeast Saginaw Bay Watershed based on an analysis of results reported in the literature and on field research. lage on fine estimated to There is no economic advantage to conservation til­ textured poorly drained soils since corn be 3% less than under conventional tillage. results indicate that advantages to conservation tillage to both size of farm and crop sequence. yields The economic are sensitive Thus, the systems boundaries for the economic and mechanization analysis must be the whole contrasted to the individual enterprise. are farm, as CHAPTER 9 SUMMARY AND CONCLUSIONS 9.1. The coastal selected by the drainage ba3in Agricultural Summary of the southeast Stabilization Saginaw authorized (ACP) of ASCS. and was and Conservation Service (ASCS) as an agricultural water pollution control project. was Bay This project funded under the Agricultural Conservation Program The project area wa3 slightly over 96,800 hectares, of which 87,200 were devoted to intensive agricultural use3. The adoption of conservation tillage systems which reduce erosion rates to less than one-half of that which can be tolerated for maintain­ ing soil productivity are being encouraged in this project area the use of cost-share incentive payments by ACP. The technical cri­ terion for evaluating whether "conservation tillage" has is through been achieved the residual plant matter remaining on the surface of the soil after planting. cal Based upon the predominant soil type3 of the basin, the typi­ technical standard for conservation tillage is a requirement that 1.7 tonne3/hectare. of plant residue remain on the soil after subject to modification depending upon site-specific planting, soil types. Specific tillage implements and methods are not a condition of the con­ servation tillage system, The results of the first two years (Phase I and II) of a three-year study to compare the economics of conservation versus conventional til­ lage in the watershed are reported. Farmers were selected who had fields which met the ASCS/SCS definition of conservation tillage and had 190 191 a contiguous field of comparable soil types farmed using conventional, rnoldboard participated in the first year, while and slope that plow techniques. twenty-one would be Seven farmers participating during the second year.^ Preliminary results include: 1. Conservation tillage resulted in a lower total production hectare, cost per while maintaining yield per hectare in the medium and fine textured soils under normal rainfall of the southeast watershed The annual cost savings in Tuscola and Huron counties. over $50.08 $16.38 per hectare for the corn-navy bean, $27.38 and tillage were Bay for conservation tillage and conventional Saginaw $62.88, corn-navy bean-sugar beets, and corn-corn-navy bean-sugar beet3 corn-navy bean-wheat-sugar beets cropping sequences, respectively, for a 160 hectare farm representative of the area. savings for and The annual cost 240 hectare farra3 for the same rotations were: $40.58, $55.38, $53.60 and $47.78/hectare, respectively, and for the 320 hectare farms they were: $23.63, $66.35, $27.90 and $38.10, respec­ tively. Viewed showed from an alternative that as labor becomes more perspective, these results scarce relative to land area, the economic advantage of conservation tillage increases,because fewer field labor hours are spent relative to conventional tillage. On fine expected in textured one year soils wet (high rainfall) conditions, out of ten, the savings per hectare are reduced because yield for conservation 1 under tillage is depressed by around The small number of farmers participating during the first year was due to project initiation sufficiently late in the fall of 1979 to preclude meeting the contiguous field (side-by-side re­ quirement) and in part, the need to shakedown project methodology. five 192 percent. However, for the corn-navy bean rotation savings per hectare 4 are $61.20, $38.90 and $22.03 for 160, 240 and 320 hectares respectively and for the corn-navy bean-sugar beet farm the $54.28 same areas respectively. and $65.25/hectare for corn-corn-navy bean-sugar beet $26.00/hectare and for the the the savings savings were were $25-70, $48.98, As for $51.93 and corn-navy bean-wheat-sugar beet they were $15.55, $47.28 and $37.28 per hectare for the 160, 240 and 320 hectare farms respectively. On coarse textured soil3 under dry (low rainfall) savings 10*. and per conditions, the hectare increased because yield is expected to increase by Thus for a 160 hectare farm savings will be $66.03, $51.75, $78.75 $16.78/hectare for the corn-navy bean, corn-navy bean-sugar beets, corn-corn-navy beans-sugar beets and corn-navy respectively. As for the 240 and bean, wheat-sugar beet 320 hectares the 3aving3 will be $67.60, $57.23, $56.28, and $24.88, $68.55, $29.30 and $39.05/hectare for the same crop sequences respectively. 2. A sensitivity analysis was carried out to determine how much lower yields under conservation tillage could be, on medium textured soils relative to those advantage of conventional tillage, before the economic to conservation tillage broke even with conservation til­ lage. For the 160 hectare representative farm, yields can fall 9.1*, 5.17* and 3.31* and 2.13* for corn-navy bean; corn-navy bean- sugar beet; corn-corn-navy bean-sugar beet; and navy by bean-wheat- sugar beet sequences respectively, before the economic advantage is lost. As for the 240 and 320 hectare farms, average all crops yields across can drop by 5.85*, 6.28*, 6.48* and 6.14*, and by 3.48*, 193 7.52%, 3.37% and 4.91% for all same sensitivity soils. test was four rotations respectively. conducted for coarse and fine textured The estimated field reduction that would have to conservation The result tillage relative to conventional tillage before profi­ tability would be equal between both tillage systems on coarse tured 6.13%, 6.94%, the respectively same 160 hectares. sequence 3*54%, 10,86%, 3.51%, and 5.07% respectively under 320 hectares. soils the reduction in yield would have to be: 1.9%; on 6.80% and 6.29% respectively for the C-NB-SB for 240 hectares respectively. and tex­ soils is as follows: 9.51%, 8.17%, 3.45%, and 2.17% for C-NB, C-NB-SB, C-C-NB-SB, and C-NB-WT-SB for on 5.57%, As for fine textured 8.8%, 5.02%, 3.06% 6.12%, 6.25% and 6.05%; and 3.12%, 7.45%, 3.19%, and 4.78% respectively for C-NB, C-NB-SB, C-C-NB-SB, and C-NB-WT-SB on 160, 240 and 320 hectares, 3. All farmers had corn in their conservation versus conventional lage comparison. Average corn yields were equivalent. farmer had sugar beet comparison plots and yields Two were Also, one equivalent. farmers had navy bean comparisons, however, only one comparison could be used, as a result of reporting difficulties. conservation lage. til­ tillage. assumption the same. under tillage were inferior to those under conventional til­ However, literature and preliminary 1981 results difference Yields suggest no when cultural practices are appropriate for conservation The economic that analysis reported was conducted on the the yields of navy beans for both systems would be 194 4. There was no measurable difference in the average of corn moisture content at harvest time between conservation and conventional til­ lage for the 1980 season. However, there was a statistically dis­ cernible difference in the 1981 season, 5. There was no difference in the incidence of pests between conserva­ tion and conventional tillage. 6. Corn plants under conservation caught up tillage started more slowly, but to conventional tilled plants before the end of the sea­ son. 7. Based upon a review of the literature and farmers in the previous experiences project area prior to project inauguration, a good understanding of the cultural practices, appropriate tion tillage is necessary Several farmers use of unsuitable were for conserva­ for success with conservation tillage. Proper implements specific for conservation tillage failures of must be used. had poor stands, or suffered reduced yields due to implements in the project area. Similarly, reported due to poor planter design in complimentary projects in the conservation tillage research area. 8. Some of the cooperating farmers did not expect the extent lems (as of prob­ perceived by themselves) that resulted from the amount of residue on the surface that they believed made field operations dif­ ficult and the fields made to look unclean. In general farmers like to see nicely plowed fields with no weeds or uncovered residue, for some of them it was not easy to make a quick switch to accept so 195 crop surface residue. servation However, the increased adoption rate of tillage in the project area and the willingness of 18 new cooperators to participate in two of the studies show tion of con­ the recogni­ the maintenance of yields and reduction of costs will lead to adoption if these results are maintained. 9. Risk analysis is important to the adoption of conservation tillage. Determination of the properties of conservation tillage under alter­ native weather regimes, particularly adverse wet harvest conditions, will be critical in determination of economic viability and farmer adoption. 9.2. Conclusions Even though literature in general paints prospects of a dim picture for the conservation tillage, our field experience at the Saginaw Bay Watershed points to the following positive aspects: 1. Soil temperature will be cooler and its moisture under conservation tillage. higher in spring However this does not create an insur­ mountable problem because new cold tolerant varieties of corn are being introduced. 2. Planting in crop residue was not a problem when farmers their equipment and used proper management techniques. adjusted Farmers have even row cultivated conservation tilled dry beans grown after corn. 3. Pests in general have not been a problem tillage. specific to conservation Pesticide costs were the same throughout the project area under both tillage systems. 196 4. There has been no evidence that more fertilizers were needed or applied under conservation tillage systems. 5. Fanners in the project area planted their conservation tillage fields at the same date they did the conventional ones. 6. Crop residue on improved the surface helped provide better surface traction for harvesting machinery in the fall. and Crop resi­ due also helped cut down on 3oil water 103s during the growing sea­ son. 7. Yields obtained on coarse and medium textured soils under tion conserva­ tilled fields in the project areas were as good or better than those produced under conventional tillage, regardless of soil ture mois­ content; yields were also as good on dry or average moist fine textured soils, but were lower when these fine soils were wet. 8. Overall production costs per unit area were always lower for conser­ vation tillage systems when compared with conventional systems. 9. A multi-crop machinery selection model model is has been developed. user oriented and has a large potential for U3e by exten­ sion agents as they advise farmers on machinery problems. dles This It han­ farms of various crop3, soil types, tillage systems, levels of risk the farmer wishes to tackle and permits custom hire operations and farmer owned equipment. 9.2.1, Scope and Limitations The results outlined are illustrative of a methodology explicit account of the whole farm nature of the that takes comparison of 197 conservation and conventional tillage. izations the Several refinements and general­ will be needed before a definitive assessment can be made, but methodology include: more outlined appears fruitful. Generalizations must explicit consideration of interactions between cropping sequence and yield and input requirement differences due to tillage sys­ tem; study of the problems of the transition from existing to conserva­ tion tillage systems and finding economically desirable time the adjustment; more paths explicit consideration of the role risk makes in farmers choices between systems; explicit account of the economic to the farmer of reducing of tillage practices. limited rates of Clearly, off-site impacts must be con­ sidered in cost-benefit analyses from society's point of have value wind and water erosion as a result of it3 impact on the soil productivity; and attitudes that influence adoption for view, but we our scope to the conditions necessary for voluntary adop­ tion by farm families. The crop sequences considered in the analysis are not all appropri­ ate for each of the soil types. In subsequent studies, the appropriate crop sequences must be more carefully matched to the soil types. Also, the size of machinery complements are influenced by soil type in as much as the ability of the machines to perform field operations i3 upon the tractability of the soil, hence soil type. developed is capable of handling these beyond issues, the scope of this exploratory study. but dependent The methodology they were deemed The framework outlined for dealing with weather events needs to be generalized to permit simulation of the impact on yield differences due to the tillage system of alterna­ tive weather patterns. Thus, empirical probability distributions of yield differentials and cost advantages could be deduced, which would be 198 conservation and conventional tillage. izations the Several refinements and general­ will be needed before a definitive assessment can be made, but methodology include: more outlined appears fruitful. Generalizations must explicit consideration of interactions between cropping sequence and yield and input requirement differences due to tillage sys­ tem; study of the problems of the transition from existing to conserva­ tion tillage systems and finding economically desirable time the adjustment; more paths explicit consideration of the role risk makes in farmers choices between systems; explicit account of the economic to the farmer of reducing of tillage practices. limited rates of Clearly, off-site impacts must be con­ sidered in cost-benefit analyses from society's point of have value wind and water erosion as a result of its impact on the soil productivity; and attitudes that influence adoption for view, but we our scope to the conditions necessary for voluntary adop­ tion by farm families. The crop sequences considered in the analysis are not all appropri­ ate for each of the soil types. In subsequent studies, the appropriate crop sequences must be more carefully matched to the soil types. Also, the size of machinery complements are influenced by soil type in as much a3 the ability of the machines to perform field operations is upon the tractability of the soil, hence soil type. developed is capable of handling these beyond issues, the scope of this exploratory study. but dependent The methodology they were deemed The framework outlined for dealing with weather events needs to be generalized to permit simulation of the impact on yield differences due to the tillage system of alterna­ tive weather patterns. Thus, empirical probability distributions of yield differentials and cost advantages could be deduced, which would be 199 a generalization of the methodology used in this study. Thi3 would per­ mit more accurate assessment of performance differentials. 9.2.2. Future Research Needs The author believes that further attention and field research is needed in the following areas: 1. Since very little is known about sugar beet and dry beans in conservation tillage more agronomic study is needed on these two crops, 2. There are other conservation tillage systems on the market that may prove to be beneficial and need to be tried. Examples are ridge-till and strip tillage systems. 3. More machinery management data i3 needed. In specific: fuel consumption, draft, speeds, slippage, etc. under various soils and tillage systems need to be collected. h. We need to understand more clearly why yields are depressed conservation tilled fine soils during ”wet years” . in 5. The question of machinery rotation needs to be tackled. 13 there a need for multiple machinery systems on one farm? Is there really a need to bring back the moldboard plow after few years? 6. We need to know more definitely how and when to make the tran­ sition from the present farm system to a newly proposed one. The economics of this question need to be resolved. REFERENCES REFERENCES American Society of Agricultural Engineers. 1981. 1981-1985 tural Engineers Yearbook. St. Joseph, HI, pp. 227-238. Agricul­ Allen, R.R., J.T. MUsick, and A.F. Weise. 1976. '’Limited Tillage of Furrow Irrigated Winter Wheat". American Society of Agricultural Engineers Transactions C195:23^—236, 241. Allmars, R.R., W.C. Burrows, and W.E. Larson. 1964. "Early Growth of Corn as Affected by Soil Temperature". Soil Science Society of Amer­ ica Proce. 28:271-275. Amemiya, M. 1977. "Conservation Tillage in the Western Journal of Soil and Water Conservation. 320):29-36. Corn Belt". Bennet, O.L., E.L. Mathias, and (3.B. Sperow. 1976. "Double Cropping for Hay and No-till Corn Production as Affected by Sod Species with Rates of Atrazine and Nitrogen". Agronomy Journal. 68(2):250-254. Bennet, O.L, 1977. 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Forster, D.L., and G.S. Becker. "Cost and Income Effects of Alterna­ tive Erosion Control Strategies: The Honey Creek Watershed". North Central Journal of Agricultural Economics, forthcoming. Forster, D.L., N. Ra3k, S.W. Bone, and Schurle. 1976. "Reduced Tillage Systems for Conservation and Profitability". ESS 532, Department of Agricultural Economics and Rural Sociology, Ohio State University. Glere, J.P., K.M. Johnson, and J.H. Perkins. 1980. "A Closer No-till Farming". Environment. 2(6):15-20, 37-41. Look at .202 Griffith, D.R., J.V. Mannering, H.M. Galloway, S.P. Parsons, and C.B, Richey, 1973. "Effect of Eight Tillage-Planting Systems on Soil Temperature, Percent, Stand, Plant Growth, and Yield of Corn on Five Indiana Soils". Agronomy Journal. 65:321-326. Griffith, D.R., J.V. Mannering and W.C. Moldenhauer. 1977. "Conserva­ tion Tillage in the Eastern Corn Belt". Journal of Soil and Water Conservation. Vol, 32. Harsh, S., et. al., 1971. Lease Cost Dairy Ration— A Telplan Program. 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Soil Conserva­ Ketchsenson, J.W. 1970. "Effects of Heating and Insulating Soil Corn Growth". Canadian Journal of Soil Science. 50:379-384. Kepner, R.A., R. Balner and E.L. Barger. 1972. Machinery. AVI Publishing Co., Westport, CT. Principles of on Farm Klocke, N.G. 1979. "Spring Planting of Row Crops in Wheat Stubble". American Society of Agricultural Engineers Paper No. 79-1517. St. Joseph, MI. Knoll, H.A., D.J. Lathwell, and N.C. Brady. 1964. "Effect of Root Zone Temperature of Various Stages of Growing Periods on the Growth of Corn". Agronomy Journal. 56:143-145. .203 Knoblauch, W. et al. 1978. "An Economic Analysis of New York Dairy Farm Enterprises". AEHS 78-1. Department of Agricultural Economics, Cor­ nell University. Laflen, J.M., J.L. Baker, R.D. Hartwig, W.F. Buchele, and N.P. Johnson. 1978. "Soil and Water Loss from Conservation Tillage Systems". Transactions of the American Society of Agricultural Engineers, 21:881-855. Larson, D.L., W.W. Hinz, J.F. Armstrong and D.D. Fangmeir. "Reduced Tillage In and Water Use in Irrigated Cotton Production", American Society of Agricultural Engineers Paper No. 79-1521. St. Joseph, HI. Le Clerg, E.L., W.H. Leonard, and A.G. Clark. 1966. Field nique. Burgess Publishing Company, Minnesota, 2nd ed. Hannering, J.R. and R.E. Burwell. 1968. Runoff and Erosion in the Corn Belt". letin 330 USDA-ARS. Plot Tech­ "Tillage Methods to Reduce Agricultural Information Bul­ Mannering, J.R. and D.R. Griffith. "Value of Crop Rotation under Vari­ ous Tillage Systems". Purdue University Extension Service Agronomy Guide 4Y-230. Mannering, J.R., D.R. Griffith and C.B. Reichy. 1975. "Tillage for Moisture". Conservation Paper No. 75-2523, ASAE. St. Joseph, MI. Medreski, H.J., and J.B. Jone3, Jr. 1963. on Corn Plant Development and Yield: SCSA Proc. 27:186-189. "Effect of Soil Temperature Studies with a Corn Hybrid". Miller, E.L. and W.D. Shrader. 1976. "Moisture Conservation Potential with Conservation Tillage Treatments in the Thick Loes3 Area of Western Iowa". Agronomy Journal. 68:374-378. Mokma and Robertson. 1976. "Soil Management Groups - A Tool for municating Soils Information". Journal of Agronomic Education. Com­ Moldenhauer, W.C. and Amemiya, M. 1969. "Tillage Practices for Con­ trolling Cropland Erosion". Journal of Soil and Water Conservation, Vol. 24. National Market Report Inc. Blue Book. 1981 National Farm Tractor and Implement National Soil Erosion - Soil Productivity Research Planning Committee. 1981. "Soil Erosion Effects on Soil Productivity: A Research Per­ spective". Journal of Soil and Water Conservation. Nelson, L.V., L.S. Robertson, M.H. Erdmann, R.G. White, and D. Quisenberry. 1976. "No-Till Corn: 1 - Guidelines". Michigan State University Extension Bulletin E-904. 204 Nott, S., G. Schwab, S, Harsh J. Black and M. Kelsey. 1977. "Enter­ prise Budgets, Michigan". 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"Farmer and Researcher Oriented Farm Management Model for Addressing Energy Concerns on Cash Crop Farms in Michigan". MsC Thesis, Department of Agricultural Economics, Michigan State University, East Lansing. 205 Rotz, C.A., J.R. Black and P. Savoie. 1981. "A Machinery Cost Model Which Deals with Inflation”, American Society of Agricultural Engineers Paper No. 81-1513. St. Joseph, MI. Schultz, G.E., W.F. Meggitt, and R.W. Chase. 1979. "No-Till Corn: 4 Weed Control". Michigan State University Extension Bulletin E-907. Schwab, G. 1980. "Custom Prices in Michigan". Extension No. £-458. Michigan State University, East Lansing. Publication Shaw, R.H. 1975. "Growing Degree Units for Corn in the North Central Region". North Central Regional Research Publication No. 229. Ame3, Iowa. Shuller, R.T. 1979. "Reduced Tillage Studies in Potatoes Following Corn". American Society of Agricultural Engineers Paper No. 79-152*1. St. Joseph, Michigan. Siemens, J.L. 1979. "Tillage Systems for Illinois". 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APPENDICES APPENDIX A MACHINERY SELECTION PROGRAM USER'S GUIDE A -l A-2 MACHINERY SELECTION MODEL USER GUIDE Introduction This machinery selection model (MACHSEL) is a tool that helps tems analysts, extension agents or farmers to improve on some farm management aspects, or select a machinery complement needed for a farm with a specified cropping sequence. account equipment that is already owned by the that the farmer prefers The grain model can take into farmer, and to have done by custom hire. takes into consideration three different type3 of tillage sys­ soil, operations The model also two types of systems, and various levels of risk the user is willing to take with weather. The model proceeds to choose the most economical machinery set that can finish all the farming operations specified within the given time. Timeliness and other costs are, therefore, computed as machinery comple­ ments are being selected and the complement that proves to be the least cost complement will be chosen. The sizes that are available within the model are actual implement and tractor sizes found on the market. How to U3e MACHSEL MACHSEL can be used interactively where the user inputs with is prompted for a chance to change the data if necessary, or by the use of computer card3. In either case the user needs to have some familiarity wit the limitations of the model. MACHSEL was designed for actual farming situations, therefore, user needs to be careful the that the crop sequence and farm operations simulated are carefully and realistically chosen. For instance, one A-3 cannot harvest more area than was planted, nor can a crop be planted in an area that has not yet been harvested. prepare the input data This i3 why the user needs to ahead oftime to be 3ure it represents a real farm. To help the user in this respect several tables of informative data have been attached. For instances, Tables 1 and 2 will help the user specify when farm operations are done in Michigan conservation tillage systems. for certain conventional Table 4 depicts used in east the operations, while Table 5 lists the crops that the model can use and Table 6 li3ts suggested cropping sequences tions) or Table 3 will help the user change actual dates to week codes which the model will accept. implements for central Michigan. (rota­ Finally, Table 7 provides an example of input data for farming operations of a rotation which will The model permits the user to enter up to seven crops for one farm help the user get a better feel for preparing the input data. if they are arranged in a rotation form. than one crop to be grown on the farm, themodel indicate the area that a crop will occupy location(called Parcel). 3ix In cases where there is more hundred acres expects the U3er to and the previous crop on that For example, assume that the user's farm and it follows a corn, navy bean rotation. In this case a three hundred acre parcel i3planted with beans and three hundred acre parcel with navy beans following corn. Assume on the other hand that the user ha3 hundred another acres) and the corn is the follows a same area navy (six 3ame two crops, but he would like to have two hundred acre of beans and four hundred acres of corn. farm following In this case the corn, corn, navy bean rotation and the farm is divided A-4 into three parcels each, totaling two hundred acres of case land. In this the parcels would be two hundred acres of corn following corn, two hundred acres of corn following navy beans and two hundred acre3 of navy beans following corn. The model was designed to be as foolproof as possible. all data form. required for That i3 why input will be free formated, and is in integer The farm operations inputed into the model must follow the nological sequence with which they occur in real life. chro­ Take time to look at the example farm in Table 7 before you proceed. Login Commands for Interactive Users: ATTACH, LGO, USERMACHSEL, MR=1. CONNECT, INPUT, OUTPUT, TAPE1, TAPE2. PROMPT=ON. % RMARGIN, 140. LGO. Two sample inputs will be used. the farmer The first one depicts a farm where owns some of the implements. where no implements are owned. (Return) (Return) (Return) (Return) (Return) The second one depicts a case The sample outputs shown here i3 for the second case. Sample Input #1 This farm is 150 acres with a corn-soybean rotation. ture is The soil tex­ heavy, and the farmer wants to own a machinery complement that can finish the farming operations at least eight years out of owns a combine, a moldboard plow anda chisel plow. hire any work. The model prompts the ten. He He does not custom u3er for basicdetails. A-5 ENTER USAGE MODE,1^INTERACTIVE,2=BATCH *1 ENTER SOIL TYPE, 1=LIGHT,2=MEDIUM,3=HEAVY ■3 ENTER CONFIDENCE LEVEL FOR WEATHER,1=30,2=70,3=50 *1 IF SOME EQUIPMENT IS OWNED,ENTER 1 IF NO EQUIPMENT IS OWNED,ENTER 0 ■1 Then it will check if the fanner owns any machinery. ple we show a farm with owned equipment. The user is prompted to enter specific data needed for each owned implement. ten units of each kind of implement. unequal sizes. In this exam­ The user can own up to These ten units can be of equal or Here the user owns a combine, a moldboard disk harrow. IF SOME EQUIPMENT IS OWNED,ENTER 1 IF NO EQUIPMENT IS OWNED,ENTER 0 *1 FOR EACH IMPLEMENT, INPUT THE FOLLOWING QUANTITIES; SIZE (METERS OR FEET) PURCHASE PRICE (DOLLARS) AGE (YEARS) CURRENT TOTAL USAGE (HOURS) TERMINATE LISTS WITH ALL 0'S COMBINE * 10,1)0000 ,3,200 *0,0,0,0 BEAN PULLER * 0 ,0 ,0,0 BEET TOPPER *0,0,0,0 BEET LIFTER *0 ,0,0,0 SOIL SAVER *0,0,0,0 FERTILIZER SPREADER *0,0,0,0 CHISEL PLOW *0 ,0 ,0,0 MOLDBOARD PLOW *10,1)000,4,300 *0 ,0 ,0,0 DISK HARROW *16,450003,150 *0 ,0,0,0 DISK PLOW plow, and a A-6 •0,0,0,0 FIELD CULTIVATOR •0,0,0,0 GRAIN DRILL *0 ,0 ,0,0 ROW PLANTER ♦0,0,0,0 NO TILL PLANTER *0 ,0 ,00,0 SPRAYER • 0 ,0 ,0,0 ROW CULTIVATOR *0 ,0 ,0,0 NH3 APPLICATOR *0,0,0,0 When the user is done entering owned units of one kind of or machine, does not own any of the implements the user is asked about, the fol­ lowing must be entered: 0 ,0 ,0,0 This let3 the model know that the use is ready for the next stage. all When implements are entered in, the model will prompt the user for trac­ tor sizes. Again a maximum of ten tractors can be owned. In this case the user owns two tractors. INPUT INDIVIDUAL OWNED TRACTOR QUANTITIES AS FOLLOWS: POWER RATING (KW OR HP) PURCHASE PRICE (DOLLARS) AGE (YEARS) CURRENT TOTAL USAGE (HOURS) TERMINATE LIST WITH ALL 0'S *150,45000,3,600 *75,23000,6,450 *0 ,0 ,0,0 When all implements are entered the model asks for crops and opera­ tions as i3 shown in sample 2. Example Input if2 In this farm the crops are corn and soybeans and the hundred acres of fine textured soil. land is six The farmer owns no equipment or A-7 tractors and does not want to custom hire any job (i.e., he wants to own all the equipment needed). He would like to have as little risk as pos­ sible. The model will then start prompting the user: ENTER USAGE MODEL,1=INTERACTIVE,2=BATCH *1 ENTER SOIL TYPE,HLIGHT,2-MEDIUM,3=HEAVY *3 ENTER CONFIDENCE LEVEL FOR WEATHER,1=30,2=70,3=50 *1 ENTER CHOICE OF UNITS,1=ENGLISH,2=SI *1 IF SOME EQUIPMENT IS OWNED,ENTER 1 IF NO EQUIPMENT IS OWNED,ENTER 0 *0 Since no equipment are owned the model skips the machinery list and prompts the user for area, crops and operations. FOR EACH FARM PARCEL, INPUT NUMBER OF ACRES TO BE FARMED ON THE PARCEL, ALONG WITH HARVEST CROP INDEX AND PLANTED CROP INDEX, THEN INPUT OPERATION SCHEDULE AS INSTRUCTED. PARCEL NO. 1 ACREAGE,HARVEST CROP, PLANTED CROP? *300,1,5 INPUT OPERA­ TIONS AS FOLLOWS : OPERATION INDEX INITIAL WEEK OF OPERATION FINAL WEEK OF OPERATION CUSTOM OPERATION,1=CUST0M,2=N0 CUSTOM BEGIN WITH HARVEST OPERATIONS, END WITH ALL 0'S *1,41,45,2 *8,41,48,2 *8,42,17,2 *12,18,22,2 *14,18,22,2 *17,25,27,2 *0,0,0,0 At this point the user will be asked to check the data entered the parcel and confirm its correctness. for A-8 PARCEL NUMBER A ACREAGE 300 HARVEST CROP CORN PLANTED CROP SOYBEANS OPERATION COMPLETION DATES OCT. COMBINE 8 TO NOV. 5 CHISEL PLOW OCT. 8 TO NOV. 26 APRIL 30 TO MAY 28 FIELD CULTIVATOR APRIL 30 TO MAY 28 ROW PLANTER JUNE 18 TO JULY 2 ROW CULTIVATOR IF THIS IS CORRECT,ENTER 1 IF THIS IS INCORRECT,ENTER 0 *1 If the answer was (0), the user would have been asked data for the parcel in question. to re-enter But since the answer was (1), the model proceeds on: PARCEL NO. 2 ACREAGE,HARVEST CROP, PLANTED CROP? *300,5,1 INPUT OPERATIONS AS FOLLOWS : OPERATION INDEX INITIAL WEEK OF OPERATION FINAL WEEK OF OPERATION CUSTOM OPTION,1=CUST0M,2=N0 CUSTOM BEGIN WITH HARVEST OPERATIONS, END WITH ALL 0'S *1,40,45,2 *10,40,48,2 *12,17,20,2 *14.17,20,2 *17,25,27,2 *18,2 5,2 8 ,2 *0 ,0 ,0,0 PARCEL NUMBER 2 ACREAGE 300 HARVEST CROP SOYBEANS PLANTED CROP CORN OPERATION COMPLETION DATES COMBINE OCT. 1 TO NOV. 5 OCT. DISK HARROW 1 TO NOV. 26 FIELD CULTIVATOR APRIL 23 TO 14 MAY ROW PLANTER APRIL 23 TO 14 MAY ROW CULTIVATOR JUNE JULY TO 18 2 NH3 APPLICATOR JUNE TO JULY 18 9 IF THIS IS CORRECT,ENTER 1 IF THIS IS INCORRECT,ENTER 0 *1 The user will be prompted for another get of data. data has been supplied the Since all the user should enter zero (0) for all three variables required. PARCEL NO. 3 ACREAGE.HARVEST CROP. PLANTED CROP? •0,0,0 At this point the model will start giving out the farm and operating parameters. Operating Parameters TOTAL FARM AREA SOIL TEXTURE WEATHER CONFIDENCE LEVEL 600 ACRES HEAVY 80 PERCENT FIELD OPERATION SCHEDULE PARCEL NUMBER 1 ACREAGE 300 HARVEST CROP CORN PLANTED CROP SOYBEANS DATES OPERATION COMPLETION NOV. COMBINE OCT. 8 TO 5 TO 26 CHISEL PLOW OCT. 8 NOV. FIELD CULTIVATOR APRIL 30 MAY 28 TO MAY ROW PLANTER APRIL 30 TO 28 2 JULY JUNE 18 TO ROW CULTIVATOR PARCEL NUMBER 2 ACREAGE 300 CORN HARVEST CROP SOYBEANS PLANTED CROP COMPLETION OPERATION DATES COMBINE OCT. 1 NOV. TO OCT. 1 NOV. DISK HARROW TO MAY FIELD CULTIVATOR APRIL 23 TO MAY ROW PLANTER TO APRIL 23 JULY ROW CULTIVATOR TO JUNE 18 JULY TO NH3 APPLICATOR JUNE 18 5 26 14 14 2 9 statistics A-10 Operating Statistics PURCHASED IMPLEMENTS IMPLEMENT SIZE COMBINE 4.0 ROWS CHISEL PLOW 8.0 FEET DISK HARROW 11.5 FEET FIELD CULTIVATOR 12.5 FEET ROW PLANTER 4.0 ROWS ROW CULTIVATOR 4.0 ROWS NH3 APPLICATOR 4.0 ROWS NEW TILLAGE TRACTORS SIZECHP) 120.0 NEW UTILITY LTRACTORS SIZE (HP) 43.8 TOTAL MACHINERY C.OST TOTAL TIMELINESS COST TOTAL OPERATING COST NUMBER HRS/UNIT 1 157.1 1 76.4 1 47*8 1 97.8 1 86.8 1 104.8 1 ' 50.8 COST/UNIT 6637.92 456. 18 497.50 290.25 1398.63 538.83 557.77 NUMBER 1 HRS/TRACTOR 261 .8 COST/TRACTOR 1 1388.69 NUMBER 1 HRS/TRACTOR 202.5 COST/TRACTOR 4170.37 25936.14 83.37 26019.51 At this point the user may repeat a new run for a different farm or "logout" of the interactive system. APPENDIX B MACHINERY SELECTION MODEL: FORTRAN CODE AND DEFINITION OF VARIABLES B-l B-2 110120- 130-C 11t0-C 150-C 160-C 170-C 180-C 190 200 210 220 230 21.0 250 260 270 200-C 290 300 310 320-C 33031*0350360-C 370-C 380 3901*0 0 1*10-C 1.20 1*301*1*0 1*50 1*60 1*70I.80-C A 90 -C 500 5 10-100 520 530 -c 5&0-C 550560 570580 590-C 600-C 610 620630-200 61*0 650 -C 660 -c 670 -c 680 690 700 710 720 - 730- PROGRAM MACHSEL(TAPE 1.TAPE2,1NPUT,OUTPUT) * ft A * PROGRAM TO SELECT A FARM MACHINERY COMPLEMENT IN SOUTHERN MICHIGAN IMPLICIT INTEGER (A-Z) REAL CAPCST REAL TRACCST,COST.TEMPCST,HARVCST,T1LLCST,PLNTCST LOGICAL FLAG OBTAIN INPUT CALL REAOIN PROCESS INPUT AND INITIALIZE FARM CONSTANTS CALL INIT DETERMINE MINIMUM MACHINERY COMPLEMENTS CAPABLE OF COMPLETING ALL TASKS IN MAXIMUM AVAILABLE TIME CALL MINCAP INITIALIZE LOOP FLAGS HARVINO-1 TILLIND-PLNTIND-0 HARVCST-TlLLCST-PLNTCST-0. CAPCST-O. PROVIDE STARTING POINT FOR FURTHER TRACTOR SIZING INCREMENTATION CONTINUE DETERMINE MINIMUM NUMBER OF TRACTORS THAT CAN BE ASSIGNED TO THE CURRENT MACHINERY CAPACITY COMPLEMENT CALL MlNTRAC(MINNUM) TRACNUM-MINNUM PREPARE TO SELECT AND COMPARE A MACHINERY COMPLEMENT FOR THE GIVEN CAPACITY COMPLEMENT AND TRACTOR NUMBER • TRACCST-O. CONTINUE FIND THE NUMBER OF TRACTORS THAT CAN BE ASSOCIATED WITH THE CURRENT MACHINERY SET TO PRODUCE A COMPLETE SCHEDULE IF NO SET CAN BE FOUND INCREASE THE MACHINERY CAPACITIES. CALL IMPSEL (TRACNUM) CALL SCHEO(TRACNUM.FLAG) IF (FLAG) THEN B-3 7U0* 750 760= 770780790800810- TRACNUM-TRACNUM+I IF (TRACNUM.GT.HINNUH*I») THEN CALL HARVINC(1.3) CALL Tt LL INC (1.7) CALL PLNTINC (1.3) TRACNUH-MfNNUM ENDIF 820- 830-C REPEAT SEARCH FOR TRACTOR NUMBER WITH NEW CAPACITIES 8L0- 850860870880 890 - goo- CALL SETSEL (0) GOTO 200 ELSE CALL TOTCOST(COST,TRACNUM) 910 = 920930- IF (TRACCST.EQ.O. .OR. COST.LT.TRACCST) THEN TRACCST-COST 9L0- 950-C CHOOSE HOST ECONOMICAL SET BY TRACTOR NUMBER 960- 970- CALL SETSEL (1) 980- 9901000- ENDIF ENDIF 1010 - 1020-C 1030-C CAPACITY INCREMENTATION AND SELECTION FOR SLIGHTLY REDUCED RUN-TIME IOLO- 1050-C 1060-C 1070-C 1080-C 1090 -C 1100-C lltO-C 1120—C I130-C 11A0-C 11501160117011801 1901200-C 1210—C IF (CAPCST.EQ.O. .OR. TRACCST.LT.CAPCST) THEN CAPCST-TRACCST CALL SETSEL (2) CALL HARVINC (1.3) CALL TILLINC(1.3) CALL PLNTINC (1.3) GOTO 100 ELSE GOTO 500 ENDIF IF (HARVIND .EQ. 1) THEN IF (HARVCST.EQ.O. .OR. TRACCST.LT.HARVCST) THEN UPDATE MOST ECONOMICAL CAPACITY COMPLEMENT AND CONTINUE HARVESTING INCREMENTATION 1220- 123012A01250- HARVCST-TRACCST CALL SETSEL (2) CALL HARVINC (1.3) 1260- 1270- ELSE 1280 - 1290-C 1300 13101320133013L0- BEGIN INCREMENTING TILLAGE EQUIPMENT HARVIND-0 TILLIND-t TlLLCST-HARVCST CALL HARVINC(1/1.3) B-4 1350“ 13601370138013901400= 14101420“ 1430“ 1440-C 1450-C 14601470140014901500 “ 1510= 1520“ 1530-C 1540“ 1550 1560“ 15701580“ 15901600- CALL T 1LLINC 11.3) CALL SETSEL(0) END IF ELSE IF (TlLLIND.EQ.1) THEN IF (THACCST .LT. T1LLCST) THEN UPDATE MOST ECONOMICAL CAPACITY COMPLEMENT AND CONTINUE TILLAGE INCREMENTATION TILLCST-TRACCST CALL SETSEL (2) CALL TILLING (1.3 ) ELSE BEGIN INCREMENTING PLANTING EQUIPHENT TILLIND-0 PLNTIND-1 PLNTCST-TILLCST CALL PLNTINC (1.3) CALL TILLING (1/1.3) CALL SETSEL(0) 161 0 “ 1620- ENDIF 1630 “ 1640- ELSEIF (PLNT1ND.EQ.1) THEN 1650= 1660- IF (TRACCST .LT. PLNTCST) THEN 1670- I6B0-C 16901700“ 171017201730“ 1740“ 1750 1760177017801790“ ISOO-C l8 lO“C 182018301840“ 1850-C i8601870-500 lB80 18901900191019201930-C 19401950- UPDATE MOST ECONOMICAL CAPACITY COMPLEMENT PLNTCST-TRACCST CALL SETSEL (2) CALL PLNTINC(1.3) ELSE GOTO 500 ENDIF ENDIF RE-INITIALI2E TRACTOR INCREMENTATION FOR NEW CAPACITY COMPLEMENT SELECTION GOTO 100 PROVIDE EXIT ACCESS CONTINUE CALL OUTPUT(PLNTCST) END SUBROUTINE MINCAP DETERMINE MINIMUM CAPACITIES NECESSARY TO COMPLETE ALL TASKS IMPLICIT INTEGER (A-2) B-5 1960= 1970= 1980“ 1990* 20002010“ 2020“ 2030“ 20A0“ 2050“ 2060“ 2070“ 2080“ 2090“ 2100“ 2110“ 2120“ 2130“ 2H0“ 2150“ 2160“ 2170“ 2180“ 2190“ 2200“ 2210 “ 2220“ 2230“ 22AO22502260-200 2270 “ 2280-C 2290 “ 2300 “ 23102320“ 2330“ 23AO23502360-AOO 2370“ 23802390“ 2AOO-A50 2A102A202A30-300 2 AA 0 2A50-C 2 A 60 2A702A802A90-500 2500 2510-C 2520-C 2530“ 25AO2550“ 2560“ REAL WKCAPAC,IMPSIZE,IHPCOST,UTILTIM,UTILSIZ,BTUTHRS.UTILCST, +TILLTIM,TILLSIZ.BTTLHRS,Tt LLCST,CUSTCST,TCUSCST,TTIMCST, +ACRSRDY,CPACRDY,OPHRSWK,OPACRWK,OWN IHRS,OWN IAC,OWNTHRS,OWNTAC, +CAPAC,ACRES ,AVALHRS,SPEED.EFF,MAX.TOTACR,CROPACR,OWN IMP,OWN IUTM, +OWNTRAC,OWNTCST,OWNTUTM,SIZCST,TRAC INC,TIMCST,CUSTPRC,DRAFT.OWN ICS +T,IMPHRS.OWN!AGE,OWNTAGE.NAMSIZ.WAITING.OWNFUEL.TILFUEL.UTLFUEL,AC +RMULT.TRCMULT,LENHULT REAL TIHPHRS.TEMP (20) DIMENSION WEEKFLG(52) COMMON /WKDATA/IMPNUM(18),UTILNUM,BTUTNUM.TILLNUM.BTTLNUM,WKCAPAC ( +18),IMPSIZE(18),IMPHRS(I8 ) , +IMPC0ST(18),U T 1LTIN,UTILSIZ,BTUTHRS.UTILCST,TILLTIM,TILLS IZ, +BTTLHRS,T1LLCST,CUSTCST(18),TCUSCST.TTIHCST,ACRSROY(20), +CPACRDY(7,20) .OPHRSWK(7,18,52).OPACRWK(7,18,52).OWNIHRS (10,18), +OWNIAC (10,18).OWNTHRS (20),OWNTAC(20),NAMSIZ(l 8 ) .WAITING(7,20) ,OWNF +UEL (20).T1LFUE +L.UTLFUEL COMMON /FRMDATA/ CAPAC (18 ),ACRES (20),AVALHRS(52),ACOPDAT (7,20,A), +SPEED (20) ,EFF (20) ,HAX(l8 ) .OWNIMP (10.18) ,0WNIUTM(I0,18) , +OWNTRAC(20).OWNTCST(20).OWNTAGE(20).OWNTUTM(20) .NEXTOP(7.20), +SIZCST(18),TRACINC,TfMCST(7,18.52),CUSTPRC(18) ,SO IL,CONLEV, +CROPACR(7),TOTACR,OWNED,OWNEOT,STARTTM.OWNTOT(18),HARVCRP(7), +PLNTCRP (7).DRAFT (18),OWN EA G E (10,18),0WN1CST(10,18).UNITIND,ACRMULT +.TRCMULT,LENMULT DATA TEMP,WKCAPAC /ZO^O.,lB*0./ DO 100 1-1,20 TtMPHRS-O. 00 200 J“ 1,52 WEEKFLG (J)“0 CONTINUE DETERMINE WHICH WEEKS ARE USED FOR EACH OPERATION DO 300 J-1,7 IF (ACOPDAT (J, 1,1).GT.O .AND. ACOPDAT (J,I,A),NE.1) THEN END-ACOPDAT(J,I,3) IF (ACOPDAT (J.1,3).LE.ACOPDAT (J,I,2)}END-52 DO AOO K“ACOPOAT(J,I,2).END WEEKFLG(K)“ l CONTINUE IF (END.NE.ACOPDAT(J, I,3))THEN DO A50 K“ 1.ACOPDAT(J,1,3) WEEKFLG (K)-l CONTINUE ENDIF ENDIF CONTINUE DETERMINE TOTAL HOURS AVAILABLE FOR EACH OPERATION DO 500 J“ l,52 IF (WEEKFLG (J) .NE, 0) TIMPHRS“TIHPHRS+AVALHRS(J) CONTINUE BASED ON TOTAL ACREAGE FOR EACH OPERATION,DETERMINED DETERMINE NECESSARY CAPACITIES TO COMPLETE TASKS IF (TIMPHRS.GT.O.) THEN TEHP (I)“ACRES(I)»8 .25/(TIMPHRS*SPEED(I) *EFF (I)) ENDIF IN IN IT, Lii CQ 0 z < u 1— < X h U1 X o H o ■< cc H t/> w u z o Ul X cc ul Ul _1 CD CL X Ai 3 O z O O N o% Q_ x Q_ uj i, bLkJ a K *"■* a r-H .»---*.p— r-- ■'w~ CL Cl. X X 4-U Lkl )— V— to I CQ ul 2 Z •— JX O u - r^ r** "*^’ Cl 0X X Ul Ul t- h ■ • #—■» H h LD O * * CO CL jH—% H *— x cn o p Ul *“ 1*■—' a B UJ a. a. — CD sc x Ul Ul o Z H h o U — VD < t(L z u* u. 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H*.5.17., 2 1 .5 ,2 5 .5,3 0 ..36.. 3570+ 12 .5 ,15-5.18.5.21.5.25-5, 2 8 .5,3 1*.5, 3580 + 13 .,13.,13-,13-,20 ..2 0 .,2 0 ., 3590+10.,10.,15.,15.,20., 20 .,30*, 3600 + 10 ., 10 ., 15 .,15 ..20 .,2 0 .,3 0 .. 3610+20., 20., 30., 30. ,1*0., 1*0., 60., 3620 + 10 .. 10 ., 15 ., 15 .,2 0 .,2 0 .,3 0 ., 363 O+ 10 .. 10 ., 15 ., 15 .,2 0 .,2 0 .,3 0 ./ 361*03650-C IMPLEMENT SIZES IN ROWS OR BOTTOMS 366036703680 3690 370037103720 373037 LO3750- 376037703780- DATA ((SIZES (I,J,2) ,1-1,7) ,J-l.18) / +!*.,!*,,6 . ,6 .,8 .,8 ., 12 ., +!*.,!*.,!*.,!*.,6 .,6 .,6 ., +3 ■ +3.,3-,3*,L.ti*.,l*.,i* . 1 +26*0., + 3 .,1*.,5 . ,6 ..7 . ,8 ..9 ., + 28 *0 ., +l*.,i*.,6 .,6 .,8 ..8 .,12., +i*.,i*. ,6,,6.,8.,8., 12., + 8 ..8 ., 12 ., 12 ., 16 ., 16 .,21*., +i*.,i*.,6. .6.,8. ,8.,12,. B-8 3790- +4.,4., 6 .,6 .,8 .,8 .,12./ 3800 - 3810-C INITIAL!ZE TRACTOR SIZES AND NUMBERS AND SIZE OF ROW EQUIPMENT 3820- 30303840385038603870- UTILSIZ-O. TILLSIZ-O. TILLNUM- (NUMTRAC+1)/2 UTILNUM-NUMTRAC/2 LINKSIZ (1)-LINKSIZ(2)-I 3880- 3890-C 390039103920393039403950“ 396039703980399040004010-200 4020-201 40304040405040&0-C 4070-C 4060-C 4090410041104120413041404 150 416041704100“ 41 go420042104220423042404250-300 426042704280-100 4290430043104320-C 4330434043504360437043804390- CORRECT FOR OWNED EQUIPMENT DO 100 1-1,18 EXTRCAP (I}-CAPAC (l) DO 200 J-l,10 IF (OWNIHP (J,I),LE.O.) GOTO 201 EXTRCAP (I)-EXTRCAP(I) -OWNIMP(J,0 IF ((DRAFT(l)*OWNIMP £J,I) .GT.UTIL5IZ) ,AND.UTILIMP (I)) + UTILSIZ-DRAFT(I)*OWNIMP(J,I) IF ((GRAFT (I)*OWNIMP(J,I) .GT,TILLSIZ) .AND,TILLIMP {I)) + TILLSIZ-DRAFT(I)*0WNIMP (J,I) CONTINUE CONTINUE IF (EXTRCAP(I).GT.O.) THEN FOR EACH IMPLEMENT, FIND THESMALLEST NUMBER OF MACHINES SUFFICIENT TOFULFILL NEEDED CAPACITIES. FOR EQUAL NUMBERS CHOOSE SMALLER 5IZES. 00 300 J-7,1,-1 NUM-(EXTRCAP (I)/SIZES (J,I.l)) + .999 IF (IMPNUH(I).EQ.O .OR. NUM.LE.IMPNUM(I)) THEN IMPNUM(I)-NUM IMPSIZE (I)-SIZES (J.1,1] NAMSIZ (l)-SIZES (J, 1,2) ELSEIF (LINKED (I,LINKIND) .AND. LINKS IZ(LINKING) .EQ.1) THEN LINKSIZ(LINKIND) -J+l ENDIF CONTINUE ENDIF CONTINUE 00 400 1- 1, 18 EQUALIZE SIZES OF ROW EQUIPMENT IF (LINKED(I, UNKIND) .AND. EXTRCAP (!) .GT.O.) THEN IMPSIZE (I)-SIZES (LINKS1Z (LINKIND),1.1) NAMS!Z (I)-SIZES (LINKS IZ (LINKIND),1,2) IMPNUM(I) - (EXTRCAP (I)/IMPSIZE (I))+.99 ENDIF B-9 ^J+00«C UPDATE TRACTOR SIZE NECESSARY TO POWER IMPLEMENTS LL |o«* 1*1*20“ 1*1*1*0“ U50“ 1*1*601*1*70“ 1*1*80“ 1*1*90“ 1*500“ 1*510“ 1*520“ 1*530= 1*51*01*55045&0“ 1*5701*5801,590“500 1*600-501 1*610= 1*620“ 1.630“ 1*61*01*650“ 1*660“ 1*670-600 1*680“601 1*6901*7001*710-1*00 1*720“ 1*7301*71*0= 1*750= 1*760“C L770-C 1*780“C 1*7901.800“ 1*8101*8201*830“ 1*81*0“ 1*850“ 1*8601*870“ 1*880“ 1*8901*900= 1*910“ 1*9201*930“ 1*91*01*9501*9601*9701*9801*9905000- IF (UTILIMP(l)) THEN IF (IHPNUM (0+OWNTOT (1) .GT.UTILNUM) + ' IMPNUM(l)“UTILNUM-OWNTOT{l) IF (DRAFT(I)*IMPS!ZE(I) .GT.UTILSIZ) + UTILSIZ-DRAFT (I) * IMPSIZE (I) ELSE IF (Tl LL IMP (I)) THEN IF (IHPNUM (I)+OWNTOT (I) .GT.T I-LLNUM) + IMPNUM (I)=TILLNUM-OWNTOT(1) IF (DRAFT {I) *1MPS IZE (I) .GT.T ILLS IZ) + TILLSIZ-DRAFT(I)* IAPSIZE (I) ENDIF DO 500 J“ ),9 IF (POSSTIL(J).GE.TILLSIZ .OR. J.EQ.9) THEN TILLSIZ=POSSTIL (J) GOTO 501 ENDIF CONTINUE CONTINUE DO 600 J“ l,5 IF (POSSUTL(J) .GE.UTILSIZ .OR. J-EQ.5) THEN UTI LS IZ-POSSUTL (J) GOTO 601 ENDIF CONTINUE CONTINUE WKCAPAC (I)“ (CAPAC (I}-EXTRCAP (0 )+1MPNUH (I) * 1MPS IZE (I) CONTINUE RETURN END SUBROUTINE SCHED (NUMTRAC, FLAG) ROUTINE TO SCHEDULE FIELD OPERATIONS GIVEN MACHINERY COMPLEMENT.AVAILABLE PRIORITY OF OPERATIONS BASED ON A HOURS AND IMPLICIT INTEGER (A-Z) LOGICAL HARVIMP, FLAG REAL WKCAPAC,IMPS IZE.IMPCOST,UTILTIM.UTILSIZ.BTUTHRS,UT1LCST, +TILLTIM.TILLSIZ,BTTLHRS.TILLCST,CUSTCST,TCU5CST,TTIMCST, +ACRSRDY ,CPACROY,OPHRSWK,OP ACRWK,OWN IHRS,OWN IAC,OWNTHRS.OWNTAC, +CAPAC,ACRES,AVALHRS,SPEED,EFF,HAX.TOTACR.CROPACR.OWNIMP.OWNIUTM, +OWNTRAC,OWNTCST,OWNTUTM,SIZCST,TRAC INC,TIMCST.CUSTPRC,DRAFT,OWN ICS +T,IHPHRS,OWNIAGE,OWNTAGE.NAHSIZ,WAITING,OWNFUEL,TILFUEL,UTLFUEL.AC +RMULT.TRCMULT,LENMULT REAL ACRSDN,WKACRE,WKHRS,HRS,FDCAPAC.COMHRS REAL TILLHRS.UTILHRS, IHPT1M(18) COMMON /WKDATA/IHPNUM (18),UTILNUM.BTUTNUM.TILLNUM,BTTLNUM,WKCAPAC( +18) .IMPSIZE (18) , IMPHRS (18) , +IMPCOST (18) ,UTI LTIM.UTI LSI Z.BTUTHRS, UTI LCST.T1 LLTIM.TI LLS IZ, +BTTLHRS,TILLCST,CUSTCST(I8) ,TCUSCST,TTIMCST,ACRSRDY(20), +CPACRDY(7,20).OPHRCWK (7.18,52).OPACRWK(7,18,52).OWNIHRS(10.18), +OWNI AC (10, 18) .OWNTHRS (20) .OWNTAC (20) .NAMSIZ(lS) .WAITING (7.20) ,OWNF +UEL (20) ,TI LFUE +L.UTLFUEL COMMON /FRMDAT A/ CAPAC (18) .ACRES (20) .AVALHRS (52) .ACOPDAT (7,20,1*) , B-10 501050205030501*0“ 5050“ 506050705080- +SPEED(20),EFF(20),MA X (18).OWNIMP (10,18),OWNIUTM(IO,18), +0WNTRAC(20).OWNTCST (20),OWNTAGE(20).OWNTUTM(20),HEXTOP (7,20), +SIZCST(18),TBACINC,TIMCST(7*18,52) .CUSTPRC(lB),SOIL.COHLEV, +CROPACR(7),TOTACR,OWNED,OWNEDT,STARTTM,OWNTOT(18).HARVCRP (7), +PLNTCRP (7) ,DRAFT(lS) ,OWN IAGE (10,18) ,OWNICST(10, 18) ,UNITIND,ACRMULT +.TRCMULT,LENMULT LOGICAL TlLLIMP,UTILIMP DATA COMBINE/]/ 5 0 g 0» 5100-C 5110-C SCHEDULE OPERATIONS ONE WEEK AT A TIME, BEGINNING WITH FIRST POSSIBLE WEEK OF HARVESTING OPERATIONS 5120“ 513051*05150“ FLAG-.FALSE. Ti LLTIM-O. UTILTIM-O. 5160“ 00 100 1- 1 ,52 5«70“ 5180“ 51905200“ 5210- IF (t.LE.(52- (STARTTM-1))) THEN WEEK-l+STARTTM-1 ' ELSE WEEK-1-(52-(STARTTM-1)) ENDIF 5220 “ 5230“C 52LO-C 5250“ 5260- FOR ALL EXCEPT COMBINE OPERATIONS, HOURS DEPENO ON TRACTOR AVAILABILITY AND CAN BE SUBDIVIDED IN ANY MANNER UTILHRS-UTILNUMftAVALHRS(WEEK) Tl LLHRS-TILLNUM*AVALHRS(WEEK) 5270“ 528052905300531053205330-C 53*05350“ DO 200 J-1,20 IMP-J IF (IMP.EQ.ig) IMP-7 IF (IMP.EQ.20) IMP-17 UPDATE ACRES AVAILABLE THIS WEEK DUE TO CROP MATURATION DO 150 M-I,7 53605370“ 5380“ IF (HARVIHP(J) .AND.ACOPDAT(M,J,2).EQ.WEEK) CPACRDY(M,J)-ACOPDAT(M,J,1) ACRSRDY(J)-ACRSRDY(J)+ACOPDAT(M,J,1) 5390- ENDIF 5*005*105*205*305**05*50-150 THEN IF (WAITING(M.J).NE.O. .AND.ACOPDAT(M.J,2).EQ.WEEK) THEN CPACRDY(N,J)-CPACRDY(M,J)+WAITING (M.J) ACRSRDY (J)-ACRSRDY(J)+WA1T|NG (M.J) WAITING(M.J)-O. ENDIF CONTINUE 5 *60- 5*705*80“ 5*90“C 550055105520- IF (AVALHRS(WEEK).GT.O.) THEN AN IMPLEMENT CAN ONLY BE SCHEDULED FOR THE AVAILABLE NO. OF HOURS IF (J.LT.19) IMPTIK(J)-AVALHRS(WEEK) ACRSDN-O. 5530- 55*0“C 5550“ 556055705580-C 5590-C ALLOW FOR THE POSSIBILITY OF CUSTOM OPERATIONS FIRST CALL CUSTOM (J.WEEK) ONLY ATTEMPT TO SCHEDULE AN OPERATION IF THERE IS ACREAGE CURRENTLY AVAILABLE TO PERFORM THE OPERATION 5600- 5610“ IF (ACRSRDY (J).LE. .1 ) ACRSRDY (J)-0.‘ B-ll 5620= 5630“ IF (ACRSRDY (J).GT.O.) THEN WKACRE“ACR5R0Y(J) 56140= 5&50-C SCHEDULE PRESENTLY OWNED MACHINERY FIRST 5660“ 5670“ DO 300 K=l,10 5680“ IF (WKACRE.LE.0. .OR. OWNIMP (K,IMP) .LE.O.) GOTO 600 5690“ 5700“ HRS“WKACRE*8.25/(OWN IMP (K,IMP)*EFF (IMP)*SPEEO (IMP)) 5710“ 5720“ IF (HRS.GT.AVALHRS(WEEK)) HRS-AVALHRS (WEEK) 5730“ IF (HRS.GT.TlLLHRS .AND. TlLLIMP (IMP)) HRS“TILLHRS 5760“ IF (HRS.GT.UTILHRS .AND. UTILIMP (IMP)) HR5-UTILHRS 5750“ 5760ACRSDN“HRS*SPEED(IMP)*EFF(IMP)*OWNIMP(K,IMP)/B.25 5770“ CALL NEXTWK(ACRSDN,WEEK,J,OWNIMP(K,IMP).FLAG) 5780“ IF (FLAG) RETURN 5790“ HRS-ACRS0N*8.25/ (OWNIMP(K,IMP)*EFF (IMP)*SPEED (IMP)) 5800“ OWN IHRS(K.IHP) “OWN IHRS(K.IMP)+HRS 5810“ 5820“C UPDATE TRACTOR HOURS 5830“ 5860“ IF (UTILIMP(IHP)) THEN 5850“ UTILTIH“UTILT tH+HRS 5860“ UTILHRS“UTILKRS-HR5 5870“ ELSE IF (TlLLIMP (IMP)) THEN 5880“ TlLLTIM“TILLTIM+HRS 5890= TlLLHRS“T1LLHRS-HRS 5900“ END IF 5910“ 5920“C UPDATE NUMBER OF ACRES REMAINING AND AVAILABLE FOR NEXT 5930“t OPERATION 5960“ 5950“ WKACRE-WKACRE-ACRSDN 5960“300 CONTINUE 5970=600 CONTINUE 5980“ 5990-C REPEAT FOR TOTAL CAPACITY OF PURCHASED MACHINERY 6000FDCAPAC“ IMPS IZE(IMP)* IHPNUH (IMP) 6010“ IF (FDCAPAC.GT.O. .ANO. WKACRE.GT.O.) THEN 6020“ HRS-WKACRE*8.25/(FDCAPAC*EFF(IMP)*SPEED(IMP)) 6030“ IF (HRS.GT.TlLLHRS/IMPNUM(IMP) .AND. TlLLIMP(IMP)) HRS-TILLHRS/IMP 6060“ +HUM(I HP) 6050“ IF (HRS.GT.UTILHRS/IMPNUM(IMP) .AND. UTILIMP(IMP) ) HRS-UTILHRS/IHP 6060“ +NUM(IMP) 6070“ IF (HRS.GT.IMPTIM(IMP)) HRS“ IMPTIM (IMP) 6080ACRSDN-FDCAPACiVEFF(IMP)+SPEED (IMP)*HRS/8.25 6090“ CALL NEXTWK(ACRSDN,WEEK,J,FDCAPAC,FLAG) 6)00IF (FLAG) RETURN 6110IF (ACRSDN.GT.O.) THEN 6120HRS“ACRSON*8.25/(FOCAPAC*EFF(IMP)*SPEED{IHP)) 6130IMPHRS(IMP)“ 1MPHRS (IMP)+HRS 6160“ IMPTIM(IMP)-IMPTIM(IMP)-HRS 61506160“ IF (TlLLIMP (IMP)) THEN 6170TlLLTIM“TILLTIM+HRSrtIMPNUM(IMP) 6180TILLHRS-TILLHRS-HRS*IMPNUM(IMP) 6190ELSE IF (UTILIMP(IMP)) THEN 6200UTILTIM“UTIIT IM+HRS* tMPNUM(IMP) 6210“ UTILHRS“UTILHRS-HRS*IMPNUM (IMP) 6220ENDIF to u u< ca h to lO Cl x a «x CL u < x CN I I— < *o u i - z u o a S h I h I— I— j~ to m ki CL - U UJ □ * ■s ^ I— O K O ul \ c o * LO L> H H H ^ Lij z id * < U * 1 -3 I \ — ac X : Z Z 2 3 U Z U. 3; — L. w O — X © «—O ~ ™ o h a a vJ 2 j u *o < Nl tO — —3 X V) u ^ X X O 3 J h O *a H * h' H to H 3 to O Ul - u Cl o x h H O u. — IO *z a: H ID m • u _» LJ < h • _) LO -J H- K I- u —I n to i/)c ► lj * u o: - u ^ uiarzzofL. £C - h 5 CL-I — to • U I3 2 c a * < *d a . 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IN CO r*'. p^. a 3 to - •— h f N O h Ul -I L — N O Ia _ h—tO >• ■-' ^-a 3 CMd X CL •J x X tO X a o o tu z < H Cl X 3 o — O X of < h- ^ tii lO — « Q !3 — H d to a LJ — X. to + to 0 u J < O -J < o * —|~ 3 3 * COZ Z w i o - - w h tlh h 3 to O 2 z z U < O O— — — U Uh z z z ? ^ o o o o X -Z;* 3 o I— a u to O -HO x ^ 2 CL ^ ^ | o o UJ z X X UJ lLLI o O X U. ^ .- o R K to O Cl II ui bi 3 o X Of 0 1 a o o o o o o o tn o IieRj'flNHLJlJiafltliabBUIINnMRlBEHiaitRinOQOlaiaKMHBIIBaRllOlVTTnVuB o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o l OJ iftsil l-^tO r^CO JVO^rilioau>\0 NCO O O — N iOJ UWD p^flO (TJO « r* J tf\tOrsCQ 0>0 ^ N A ^ lA'fl NOD o\o ^ N ra CMCMCMCMCMIM CM ^ A K \iO f*MO ^ 4 4 -T -}• Lf j - -J -J J tf\ tA LA LA tj\ LA tA LA LA LAtO t£> \0 vO LO tO LO vO vQ sO h*rNr^r^f^fN.f-srsfN.n»COCOCOCO L0 vD tO vO tO O vO O sj> vO tO vO t£) tO \D O \£>sO O tO vO O vD O tO \D O \0O O O \0\0 tO tO O tO O vp \D VO O s£> O O O O o U) tO \fl O B-13 6840- GOODUTL-O 68506860= 6870 = 68806890= 6900= 6950= 6920= 6930= 69AO6950 = 6960= DO 600 1=1,20 IF (OWNTRAC(I).L E .0) GOTO 650 TRAC IND-A IF (OWNTRAC(I).GE.TILLSIZ .AND, GQODTIL.LT.TILLNUH) THEN TRACIND-19 GOODTIL=GOODTIL+1 OWNTHRS (I)“TlLLT1H/TILLNUM ELSE IF (OWNTRAC(I).GE.UTILSIZ.AND.GOODUTL.LT.UTILNUH) THEN TRAC IND-20 OWNTHRS (I)=UT1LTIM/UTILNUM GOODUTL-GOODUTL+l END IF 6970= 6980= NCOST-OWNTRAC (t)*TRACINC 6990= CALL ALCQST{OWNTHRS(1).OWNTAGE (I) ,NCOST.TRACIND.OWNTUTH (I), 7000+OWNTCST(I).OWNFUEL(I) ) 70)0IF (UNITING.EQ.2)OWNFUEL(1)-OWNFUEL(I) *3•75 7020OWNT A C (I)-TCOST 7030COST-COST+TCOST 70AO-600 CONTINUE 7050-650 CONTINUE 7060= 7070-C DETERMINE PURCHASED IMPLEMENT COSTS 7080= 7090= DO 700 1=1,18 7100IF (IHPHRS(I).GT.O.) THEN 7UOIHPCST- (IMPSIZE (I)*SIZCST (I)) +F IXED (1) 7120= CALL ALCOST(IMPHRS(I),0.,IMPOST,I,0..0..DUMFUEL) 7130COST-COST+TCOST*!MPNUM(I) 71 AOIMPC05T {I)-TCOST 7150END IF 7160-700 CONTINUE 71707 180=C DETERMINE PURCHASED TRACTOR COSTS 7190= 7200BTTLNUM-TILLNUM-GOODTIL 7210= 7220IF (BTTLNUM.GT.O .AND. TlLLTIM.GT.O.) THEN 7230BTTLHRS-TILLTIM/TILLNUH 72AOBTTLCST-TILLSIZ*TRACINC 7250CALL ALC0ST(BTTLHRSI0.,BTTtCST,19,0. 10.,TILFUELj 7260IF {UN1TIND.EQ.2} TlLFUEL-TILFUEL*3-75 7270TlLLCST-TCOST 7280COST-COST+TILLCST*BTTLNUM 7290ENOIF 7300= 7310BTUTNUM-UTILNUM-GOODUTL 7320= 7330IF {BTUTNUH.GT.O .AND. UTILTIM.GT.O.) THEN 73AOBTUTHRS-UTIIT IH/UTILNUM 7350BTUTCST-UTILSIZ*TRACINC 7360CALL ALCOST(BTUTKRS.O,,BTUTCST,20,0.,0.,UTLFUEL) 7370IF (UNITIN0 .EQ.2 ) UTLFUEL-UTLFUEL*3*75 7380UTILCST-TCOST 7390COST-COST+UTILCST*BTUTNUM 7A00ENDIF 7A 107A20-C 7A307AA0- DETERMINE TOTAL TIMELINESS COST TTIMCST-O. B-14 71,50- DO 800 1-1. 18 7A60DO 900 J-1,52 71*70= DO 1000 K=l,7 7^71“ IF(OPACRWK{K,I,J),GT.O.)THEN 71*80= TTIMCST-TT1MC5T+0PACRWK (K, I,J) ATIMCST {K, I,J) 7500= PRINT ft,'WK,IMP.AC.TCOST ‘,J,I.OPACRWK(K, I,J).TIMCST(K,I,J) 7501" PRINT*,'IMPSIZE.UITLSIZE.TILLSIZE’,IMPS JZE (I).TILLS IZ,UTILS IZ 7502PRINT*,'COST ',COST 7520= END IF 7530-1000 CONTINUE 7550-900 CONTINUE 7560-800 CONTINUE 7561= PRINTft.TTIMCST 7562= PRINTft.CRF 7570", TTIMCST-TTtMCST*10*CRF 7571* PRINT*,TTIrtCST 75BOCOST-COST+TTIMCST 7590= COST-COST+TCUSCST 7600RETURN 7610END 7620SUBROUTINE NEXTWK(ACRSDN.WEEK,OP,FDCAPAC,FLAG) 7630IMPLICIT INTEGER (A-Z) 761*0= LOGICAL FLAG 7650REAL WKCAPAC.IMPS IZE,IMPCOST.UTILTIM,UTtLSIZ,BTUTHR5,UTILCST, 7660= +T1LLTIH.TILLSIZ.BTTLHRS,TILLCST,CU5TCST.TCUSCST,TTIMCST, 7670+ACRSRDY,CPACRDY,OPHRSWK,OPACRWK.OWN IHRS,OWN IAC,OWNTHRS,OWHTAC, 7680+CAPAC,ACRES.AVALHRS,SPEED,EFF,MAX,TOTACR,CROPACR,OWN IMP,OWN IUTM, 7690= +OWNTRAC,OWNTC5T,OWNTUTM,SIZCST,TRAC INC,TIMCST,CUSTPRC,DRAFT,OWN ICS 7700+T.IMPHRS.OWN IAGE,OWNTAGE,NAMSIZ,WAITING.QWNFUEL,TILFUEL,UTLFUEL,AC 7710+RHULT.TRCMULT,LENMULT 7720REAL ACRSDN,CP IHACR,ACRE IMP,HRS,FDCAPAC 7730COMMON /WK0ATA/IMPNUM(l8) ,UTILNUM,BTUTNUM.TILLNUM,BTTLNUM,WKCAPAC ( 771*0+18) , IMPSIZE (18) .IMPHRS (18), 7750+IMPCQST08) ,UTILTIM,UTILS tZ, BTUTHRS,UT ILCST,TI LLT IH,T ILLS IZ, 7760+BTTLHRS,TILLCST,CUSTCST(18).TCUSCST.TTIHCST,ACRSRDY (20), 7770= +CPACRDY(7,20).OPHRSWK(7,18,52).OPACRWK (7.18,52).OWNIHRS(10,18), 7780+0WNI A C (10,18).OWNTHRS(20),OWNTAC{20),NAMSIZ(lB).WAITING(7,20),OWNF 7790“ +UEL(20) .TILFUE 7800+L,UTLFUEL 7810COMMON /FRMDATA/ CAPAC (18) .ACRES (20) .AVALHRS (52) ,ACOPDAT (7,20,1*) , 7820+SPEED (20) ,EFF (20) ,MAX (18) .OWNIMP (10,18) ,OWIIIUTM(10,18) , 7830+OWNTRAC (20),0WNTCST(20).OWNTAGE(20) ,0WNTUTM(20).NEXTOP(7,20), 781*0+SJZCST08) .TRACING,TIMCST (7,18,52) ,CUSTPRC(18) ,S0 IL, CONLEV, 7850+CROPACR(7),TOTACR,OWNED,OWNEDT,STARTTM,OWNTOT( 18) ,HARVCRP(7), 7860+PLNTCRP(7).DRAFT(18) .OWN IA G E (10,18),OWNICST(IO,18).UNITIND,ACRMULT 7870+.TRCMULT,LENMULT 7880" 7890-C INITIALIZE ACRES COMPLETED BUT NOT YET MATURE FOR NEXT OPERATION 7900= 7910DATA WAITING /lAOftQ./ 79207930IHP-OP 791*0IF (OP.EQ. 19> IHP-7 7950IF (OP.EQ.20) IMP-17 79&0FLAG-.FALSE. 7970ACREIMP-ACRSDN 79807990DO 100 1-1,7 8000= 8010CP IHACR=CPACRDY (I,OP) 8020IF (CPIMACR.GT.O .AND. ACRE IMP.GT.O) THEN B-1S 8030801*080508060-C 8070" 8080= 8090“ 8100“ 8110“ 8120“ 8130“ 811*0“ 8150“ 8l60- IF (CP IMACR.GT.ACRE IHP) CP IMACR-ACREI MP UPDATE NEXT OPERATION INFORMATION IN THE APPROPRIATE MANNER IF (NEXTOP (1,0P).HE.O) THEN NEXT!MP-NEXTOP(I.OP) IF ((WEEK.GE.STARTTM .AND. WEEK.GE.ACOPDAT(I.NEXTIMP,2) + -AND. ACOPDAT(I.NEXTIMP,2).GE.STARTTM) .OR. (WEEK,LT. + STARTTM.AND, ACOPOAT(1.NEXTIMP,2),L£.WEEK)) THEN CPACRDYO.NEXTIMP)-CPACRDY(I,NEXTIMP)+CPIMACR ACRSRDY (NEXTIMP)-ACRSROY (NEXTlMPj+CPIMACR ELSE 8170 “ 8l80" 8190“ 820082108220“ 8230B2A08250“ 82fiO“C 8270“ 8280- WAITING(I.NEXTIMP)“WAITING(I.NEXTIMP) +CPIMACR END IF END1F ACREIMP“ACREIMP-CPIMACR CPACRDY (I,0P)“CPACRDY(I,0P)-CPIMACR ACRSRDY(OP)-ACRSRDY(OP) -CP IMACR UPDATE ACREAGE AND HOURS MATRICES IF (CPIMACR.GT. .1) THEN 8290- 8300“ OPACRWK (I,IMP,WEEK)“OPACRWK(I,IMP,WEEK)+CPIMACR 8310“ 83208330“ 831*0“ 8350B 38OB3708380839081*0081*1081*2081*30-100 81*4081*5061*8081*70“ 81*8081*9085008510“ 85208530851*0855085&08570858O8590’ 8600861086208630- IF (TIMCST (I.IMP.WEEK).GT.l.E+50) THEN FLAG-.TRUE. END IF IF (FDCAPAC.GT.-O.) THEN HR5“CPIMACR*8.25/(EFF(IHP)*SPEE0(IMP)*FDCAPAC) OPHRSWK(I,IMP,WEEK) “OPHRSWK(I,IMP,WEEK)+HRS*IMPNUM(IMP) END IF ENOIF END IF CONTINUE ACRSDN-ACRSDN-ACREIMP RETURN END SUBROUTINE CUSTOM (IMP,WEEK) IMPLICIT INTEGER (A-Z) REAL WKCAPAC,IMPSIZE,IMPCOST,UTtLTIM,UTILSIZ,BTUTHRS,UTILCST, +TILLTIM,TILLSIZ,BTTLHRS,TILLCST,CUSTCST,TCUSCST,TTIMCST, +ACRSRDY,CPACRDY,OPHRSWK,OPACRWK,OWN IHRS,OWN IAC,OWNTHRS,OWNTAC, +CAPAC.ACRES,AVALHRS,SPEED,EFF,MAX,TOTACR,CROPACR,OWN IMP,OWN IUTM, +OWNTRAC,OWNTCST.OWNTUTM,SIZCST,TRAC INC.TIHCST,CUSTPRC,DRAFT,OWN ICS +T,IMPHRS,OWN IAGE,OWNTAGE,NAHSIZ.WAITINC,OWNFUEL,TILFUEL.UTLFUEL,AC +RMULT.TRCMULT,LENMULT REAL ACRSDN,CUSDOL LOGICAL FLAG COMMON /WKDATA/IMPNUH08) ,UTILNUM,BTUTNUM.TILLNUH,BTTLNUM,WKCAPAC ( +18).IMPSIZE (18), IMPHRS(18), +IMPCOST08) ,UT ILT IM, UTI LS IZ, BTUTHRS ,UTI LCST, Tl LLT IM.TI LLSIZ, +BTTLHRS.TILLCST.CUSTCST(18).TCUSCST.TTIMCST,ACRSROY(20), +CPACRDY (7.20).OPHRSWK(7,18,52).OPACRWK(7,18,52).OWNIHRS(10,18), +0WNIAC(10,18).OWNTHRS(20).OWNTAC(20),NAMSIZ(l8),WA fTlMG(7,20) ,OWNF B-16 86140+UEL (20) .TILFUE 8650+L,UTLFUEL 8660COMMON /FRMOATA/ CAPAC (18) ,ACRES (20) ,AVAl.HRS (52) ,ACOPDAT (7,20,A) , 8670+SPEED (20),EFF (20).MAX 0 8 ) .OWN IMP(10,IB),OWN IUTH(10,)8) , 8680+OWNTRAC(20),0WNTCST(2O).OWNTAGE(20),OWNTUTM(20),NEXTOP(7.20), 8690+51ZCST (18).TRACING.TIMCST(7.18,52) .CUSTPRC(18) .SO IL.CONLEV, 8700+CROPACR(7),TOTACR.OWNED,OWNEDT,STARTTM,OWNTOT(18),HARVCRP(7). 8710+PLNTCRP(7).DRAFT CIS).OWN IAGE(10,18) ,0WN1CST(10,18).UNITING,ACRMULT 8720+.TRCMULT,LENMULT 873087*40DO 100 1-1,7 B7508760IF (CPACRDY(I,IMP).LE.O) GOTO 200 87708780IF (ACOPDAT (I,IHP, A).EQ.J) THEN 87908800ACRSDN-ACOPDAT(I,IHP,1) 8810CALL NEXTWK (ACRSDN,WEEK,IMP,0.,FLAG) 8820IF (FLAG) RETURN 8B30CUSDOL-ACRSON*CUSTPRC(IMP)* 10*(.12*(1.+.12) **10)/ ((1.+ .12)**10-1.) 88AOCUSTCST(IMP)-CUSTCST(IMP)+CUSDOL 8850TCUSCST-TCUSCST+CUSDOL 88608870ENDIF 88808890-200 CONTINUE 8900-100 CONTINUE 8910RETURN 8920END 893089A0SUBROUTINE READ IN 8950IMPLICIT INTEGER (A-Z) 896OLOGICAL OWNED,OWNEOT,HARVIMP 8970REAL AREA 8980REAL WKCAPAC,IHPSIZE,IMPCOST,UTILTIM,UTILSIZ,BTUTHRS,UTILCST, 8990. +TILLT!M,TILLSIZ,BTTLHRS,TILLCST,CUSTCST,TCUSCST.TTtHCST, 9000+ACRSROY.CPACRDY,OPHRSWK,OPACRWK,OWN IHRS,OWN IAC,OWNTHRS,OWNTAC, 9010+CAPAC,ACRES,AVALHRS,SPEED,EFF,MAX,TOTACR.CROPACR,OWN IHP,OWN IUTM, 9020+0WNTRAC,0WNTC5T,0WNTUTM,SIZCST,TRAC INC,TIMCST,CUSTPRC,DRAFT,OWN ICS 9030+T,IMPHRS,OWN IAGE,OWNTAGE,NAMSIZ.WAITING.OWHFUEL,TILFUEL,UTLFUEL,AC 90A0+RMULT.TRCMULT,LENMULT 9050DIMENSION CR0PNAM(7),AREANAM(2) ,CUSTNAM{2) ,0PNAH(2,20) 9060COHHON /WKDATA/IMPNUM08),UTILNUM,BTUTNUM.TILLNUM.BTTLNUM,WKCAPAC ( 9070+18),IMPSIZE (|8) ,IMPHRS(18) , 9080+IMPC0ST(l8),UTILTIH,UTILSIZ.BTUTHRS.UTILCST,TILLTIM,TILLS IZ, 9090+BTTLHRS,TILLCST,CUSTCST (18).TCUSCST.TTIHCST,ACRSRDY (20), 9100+CPACRDY(7,20).OPHRSWK(7.18,52).OPACRWK(7,18,52),OWN1HRS (10,18) , 9110+0WN1AC(10,18).OWNTHRS (20).OWNTAC(20) ,NAMSIZ(l8) ,WAITING(7.20),OWHF 9)20+UEL(20),TILFUE 9130+L,UTLFUEL 9H0COMMON /FRMDATA/ CAPAC (18) ,ACRES (20) ,AVALHRS (52) ,ACOPDAT (7,20, A) , 9150+SPEED (20) ,EFF (20) ,MAX (18) .OWNIMPdO, 18) ,0WNIUTM(10, 18) . 9160+OWNTRAC(20).OWNTCST (20).OWNTAGE(20) ,0WNTUTM(20),NEXTOP(7.20), 9170+SIZCST (18) ,TRACINC,TIMCST(7,18,52) ,CUSTPRC(l8) .SOIL,CONLEV, 9180+CROPACR(7),TOTACR,OWNED,OWNEDT,STARTTM.OWNTOT(18) ,HARVCRP(7), 9190+PLNTCRP(7).DRAFT (18),OWN IAGE(10,18),OWNICST(IO,18).UNIT IND,ACRMULT 9200+.TRCMULT,LENMULT 9210COMMON /CROPNAM/ CROPNAM 9220COMMON /1HPNAM/0PNAH 9230COMMON /DATNAH/ DAINAM(52) 92AODATA AREANAM/10H ACRES,10H HECTARES/ B-17 9250- 92609270- DATA CUSTNAM /10H CUSTOM,10H NO CUSTOM / DATA CROPNAM/1*HCORN,1*HOATS,5HWHEAT,3HRYE,8HSOYBEANS, +10HNAVY BEANS,10HSUCAR BEET/ 92809290-C 9300-C 9310=C 9320=0 9330-c OWN IMP OWNTRAC OWN 1ACE OWNTAGE OWNTCST OWNED IMPLEMENT SIZE OWNED TRACTOR SIZE OWNED IMPLEMENT AGE OWNED TRACTOR AGE OWNED TRACTOR COST 931*0= 9350= 936093709380= 9390=C 91*00= 91.1091*20= 91*30= 91*1*091*50= 91*6091*7091,80= 91*90- 9500=0 9510" 95209530= 951*0= 95509560= 9570" 9580= 95909600= 96IO" 96209630961*0965096609670= 968096909700= 97 ID97209730971*09750= 976097709780979Q9800981098209830= 981*09850= DATA OWN IMP,OWNTRAC,OWN 1AGE ,OWNTAGE .0WNTCST/1*20*0./ DATA OWNED.OWNEDT/2*.FALSE./ DATA TOTACR.CROPACR /8*0/ OWNTOT TOTAL NUMBER OWNED OF EACH IMPLEMENT DATA OWNTOT/18*0/ PRINT *,'ENTER SOIL TYPE, l-L1GHT.2-MED1UM,3-HEAVY ' READ *,501L PRINT * , 'ENTER CONFIDENCE LEVEL FOR WEATHER,1-80,2-70,3=50' READ *,CONLEV PRINT * . 'ENTER CHOICE Of UNITS.1-ENGLISH,2-SI ' READ *,UNITIND SET UP CORRECTION FACTORS FOR METRIC CONVERSIONS IF (UNI TlND.EQ.2) THEN ACRMULT=2.1*71 TRCMULT-1.333 LENMULT-3.3 ELSE UNIT1N0-I ACRMULT-TRCMULT-LENMULT-l. END IF PRINT '1F SOME EQUIPMENT IS OWNED,ENTER 1' PRINT * , 'IF NO EQUIPMENT IS OWNED,ENTER O' READ *,OWNIND IF (OWNIND.EQ.I) THEN PRINT PRINT PRINT PRINT PRINT PRINT *,1FOR EACH IMPLEMENT, ft.'SIZE * , 'PURCHASE PRICE ft.'AGE * , 'CURRENT TOTAL USAGE *, 'TERMINATE LISTS WITH INPUT THE FOLLOWING QUANTITIES (METERS OR FEET) ' (DOLLARS) ' (YEARS) ' (HOURS) ' ALL 0 " S ' DO 300 1-1,18 WRITE (2,2000) 0PNAM(1,1) ,0PHAH(2,1) 00 1*00 J-l. 10 READ *,OWN 1MP(J,1) ,QWNICST(J,l),0WNIAGE(J,1),OWN 1UTM (J,1) OWN 1M P (J,1) -OWN 1M P (J ,1) *LENMULT IF (OWN 1MP (J,1) .EQ.O) GOTO 299 OWNED-.TRUE. OWNTOT(1)-OWNTOT(l)+l B-18 9860-LOO 9870-299 98B0»300 9890990099109920993099^099509960997099809990100001001010020=500 10030JOOAO10050-999 10060= 10D7010080100901010010110101201013010H010150-601 10160= 1017010180= 1019010200= 102101022010230102A010250" 1026010270= 1028010290= 10300= 1031010320103301O3AO-602 10350= 10360103701038010390IOAOOT0L1010L 2010L30lOLltO10A5010L60- CONTINUE CONTINUE CONTINUE PRINT ft.'INPUTINDIVIDUAL OWNED TRACTOR QUANTITIES AS FOLLOWS! ' PRINT ft,1POWER RATING {KW OR HP) ' PRINT ft,'PURCHASE PRICE (DOLLARS) ’ PRINT ft,'AGE .(YEARS) PRINT ft,'CURRENT TOTAL USAGE(HOURS) ' PRINT *,'TERMINATE LISTWITH ALL 0 " S ' 00 500 1-1,18 READ ft.OWNTRAC(I) .OWNTCST{I).OWNTAGE(I) ,OWNTUTM(I) OWNTRAC (I)-OWNTRAC(I)*TRCHULT IF (OWNTRAC(I) .EQ. 0)GOTO999 OWNEDT-.TRUE. CONTINUE END IF CONTINUE STARTTH-52 PRINT ft,'FOR EACH FARM PARCEL. INPUT AREA (ACRES OR HECTARES) TO ' PRINT ft,'BE FARMED ON THE PARCEL. ALONG WITH HARVEST * PRINT ft,'CROP INDEX AND PLANTED CROP INDEX. THEN INPUT ’ PRINT ft,'OPERATION SCHEDULE AS INSTRUCTED. ' DO 600 PARCEL-1,7 LASTOP-O CONTINUE WRITE (2,2010) PARCEL READ ft.ACREAGE,HARVCRP(PARCEL).PLNTCRP(PARCEL) IF ((ACREAGE.NE.O. .AND. (HARVCRP (PARCEL).LT.1 .OR. +HARVCRP (PARCEL) .GT.7 .OR. PLNTCRP(PARCEL).LT.1 .OR. +PLNTCRP (PARCEL).GT.7)).OR. (ACREAGE.EQ.O. .AND. PARCEL.EQ.1)) THEN PRINT ft,'INVALID INPUT, PLEASE TRY AGAIN' GOTO 601 ENDIF ACREAGE-ACREAGEftACRMULT IF (ACREAGE.EQ.O) GOTO 900 CROPACR (PARCEL)-ACREAGE PRINT *,‘INPUT OPERATIONS AS FOLLOWS PRINT ft,'OPERATION INDEX' PRINT * , 'INITIAL WEEK OF OPERATION' PRINT *,'FINAL WEEK OF OPERATION 1 PRINT CUSTOM OPTION,1-CUSTOM,2-NO CUSTOM' PRINT *, 'BEGIN WITH HARVEST OPERATIONS, END WITH ALL 0 " S ' CONTINUE READ ft,OP,BEGIN,END,CUSTOM PRINT ft,' ' IF (OP.EQ.O) GOTO 699 IF (OP.EQ.LASTOP) THEN ACOPDAT(PARCEL.OP,1)-ACOPDAT(PARCEL,OP,1)+ACREAGE ELSE ACOPDAT (PARCEL,OP, D-ACREAGE ACOPDAT (PARCEL,OP,2)-BEGIN B-19 101*70= 101*80= 101*90= 10500= 10510= 10520= 10530= 1051*010550= 10560=699 10570= 10580= 1059010600= IF (HARVIMP(OP).AND.BEGIH.LT.STARTTM) STARTTM-BEGIN ACOPDAT (PARCEL,OP,3) “END IF (CUSTOM.NE.l) CUSTOM-2 ACOPDAT (PARCEL,OP,1*) “CU5T0H IF (LASTOP.NE.O) NEXTOP (PARCEL,LASTOP)“OP LASTOP-OP END 1F GOTO 602 CONTINUE AREA-CROPACR(PARCEL) /ACRHULT WRITE (2,1080) PARCEL,AREA,AREAHAM(UNITINO) WRITE (2, 1085) CROPNAM (HARVCRP (PARCEL)),CROPNAM (PLNTCRP (PARCEL)) WRITE (2,1090) 10610= 1062010630* IF (ACOPDAT (PARCEL,1,I).CT.O) THEN 1061 * 0 = START-1 10650= 10660= ELSE IF (ACOPDAT(PARCEL,2,1).GT.O) THEN 106 70 = 10680= 10690 = 10700 = START-2 ELSE IF (ACOPDAT(PARCEL,3.0 .GT. 0) THEN 10710= 10720= 10730= 1071*0= 10750= 10760= 10770=1 10780 = 10790* 10800 = 10810= 10820= IO8301081*0 = 10850 = 10860 = 10870= 10880= 10890* 10900= 1091010920 “ 10930=800 1091*010950= 10960= 10970= 109B010990= 11000 = START-3 END IF NEXT-START CONTINUE WRITE (2,1100) OPNAM(l.NEXT) ,0PNAH{2,NEXT) ,DATHAM(ACOPDAT (PARCEL,N +EXT.2 +)) . +DATNAM(ACOPDAT (PARCEL,NEXT,3)).CUSTNAM(ACOPDAT (PARCEL.NEXT,!*)) NEXT-NEXTOP (PARCEL,NEXT) IF (NEXT.NE.O) GOTO 1 PRINT *,' IF THIS IS CORRECT,ENTER I 1 PRINT *, *IF THIS IS INCORRECT,ENTER O' READ ft,VALID IF (VALID.NE.l) THEN 00 800 1= 1,20 ACOPDAT (PARCEL,I,1)-ACOPDAT(PARCEL,I,2)-ACOPDAT (PARCEL,I,3)“ +ACOPDAT(PARCEL,I,A)-NEXTOP(PARCEL,I)-0 CONTINUE GOTO 601 ELSE TOTACR-TOTACR+CROPACR(PARCEL) END IF 11 0 1 0 = 11020=600 11030=1080 1101*0=1085 11050=1090 11060- 1100 11070-2000 CONTINUE FORMAT (16X,'PARCEL NUMBER ',11,' AREA '.F5.0.A10) FORMAT (16X,'HARVEST CROP ',A10,' PLANTED CROP '.AIO) FORMAT (16X.'OPERATION',I5X,'COMPLETION OATES') FORMAT (6X,2AIO,1*X,AIO, 1 TO \2A10) FORMAT (' ',2A10) B-20 1080-2010 FORMAT ('OPARCEL NO. ',11,' AREA,HARVEST CROP, 1090" +'PLANTED CROP?1) 1100-900 CONTINUE 1110RETURN 1120END 1130SUBROUTINE OUTPUT(COST) 11 AOIMPLICIT INTEGER (A-Z) 1150LOGICAL OWNED.OWNEDT 1160REAL WKCAPAC,IMPS IZE,IHPCOST.UTILTIM.UT1LSIZ,BTUTHRS,UTILCST, 1170+TILLTIH.TILLSIZ,BTTLHRS.TILLCST,CUSTCST,TCUSCST,TTIMCST, 1180+ACRSRDY.CPACRDY,OPHRSWK.OPACRWK,OWN IHRS,OWN IAC.OWNTHRS.OWNTAC, 1lJO+CAPAC,ACRES.AVALHRS.SPEED,EFF,MAX,TOTACR,CROPACR,OWN IMP.OWN IUTH, 1200= +OWNTRAC.OWNTCST.OWNTUTH,SIZCST,TRAC INC,TIMCST,CUSTPRC,DRAFT,OWN ICS 1210+T,IHPHRS,OWN IAGE,OWNTAGE,NAMSIZ,WAITING.OWNFUEL.TILFUEL,UTLFUEL, AC 1220+RMULT.TRCHULT,LENMULT 1230REAL COST,FUELL,TMCHCST,ACRCST 121*0DIMENSION OPNAM(2,20) ,AREANAM(2) ,DATNAM(52).SOILNAM(2.3) ,PWRNAH(2) 12S0+,S 1260 = +IZNAM(l8,2) 127012801290130013101320= 1330131 »01350136013701380139011*001A 1011*2011*3011*1*011*5011*6012*701A8011*90150015101520153015AO15501560157015801590160016101620- +,C0NNAM(3),FUELNAM(2) COMMON /FINAL/DUMMY(13966) ,1MPNUM(I8) .UTILNUM.BTUTNUM, +Tt LLNUM,BTTLNUM,WKCAPAC(IB),IMPSIZE(l8),IMPHRS(I8) , +1MPCOST(18),UTILTIM,UTILSIZ,BTUTHRS,UTILCST,TlLLTIK.TILLSIZ, +BTTLHRS.TILLCST,CUSTCST(18).TCUSCST.TTIMCST,ACRSRDY (20), +CPACROY (7.20).OPHRSWK (7,18,52).OPACRWK(7.18,52).OWHIHRS (10,18), +QWNIAC(10,18).OWNTHRS(20).OWNTAC(20),NAMSIZ(18).WAITING(7.20),OWNF +UEL (20) .TILFUE +L.UTLFUEL COMMON /FRMDATA/ CAPAC (18) .ACRES(20).AVALHRS(52).ACOPDAT (7.20,A ) . +SPEEO(20),EFF (20).MAX(18).OWNIMP(10.18),OWNIUTM(10,18), +OWNTRAC(20),OWNTCST(20).OWNTAGE(20),0WNTUTM(2O),NEXTOP (7,20), +SIZCST(18).TRACING,TIMCST(7.18.52) .CUSTPRC(tS) ,SOIL,CONLEV, +CROPACR(7).TOTACR,OWNED,OWNEDT,STARTTM,OWNTOT(IS),HARVCRP(7), +PLNTCRP(7) .DRAFT(18).OWN IAGE(10,18) .OWN IC5T(10,18) ,UNITIND,ACRMULT +.TRCMULT,LENMULT COMHON /CROPNAM/CROPNAM (7) COMMON /IMPNAM/ OPNAM COMHON /DATNAM/ DATNAM 1630161*01650= 166016701680- DATA AREANAM/7H ACRES. 10H HECTARES/ DATA FUELNAH/10H GALLONS,10H LITERS/ DATA OPNAM / +IOH , 10H COMBINE,10H B,10HEAN PULLER, +10H B,IOHEET TOPPER,10H B,10HEET LIFTER, +IOH , 10HSOI'. SAVER, 10H , IOH V RIPPER, +10H FERTILIZE,10HR SPREADER,IOH C,10HHISEL PLOW, +10H HOLD,IOHBOARD PLOW,JOH 0,1 OHISK HARROW, +10H ,IOH DISK PLOW,IOH FIELD ,10HCULTIVATOR, +10H G,10HRAIN DRILL,IOH R,10H0W PLANTER, +10H NO T l ,10HLL PLANTER,IOH , IOH SPRAYER, +10H ROW ,10HCULTIVATOR,IOH NH3 , IOHAPPLICATOR, +10H FERTILIZE,10HR SPREADER,IOH ROW , IOHCULTIVATOR/ DATA SO ILNAM/ 10HC0ARSE (SA.ltHNDY) ,IOH HEDIUM (L,1*K0AM) , + IOH FINE 330) (1, 1360) (1,1350) (1 .1360) (M370) (1, 1380) 0.1390) (1,1600) (1,1610) (1, 1620) (1. 1630) (1,1660) (1, 1650) (1,1660) (1, 1670) (1, 1680) (1. 1690) DO 800 K-1,52 DO SOI J - 1,18 OPACRWK(1,J,K)-OPACRWK(1,J,K) /ACRMULT CONTINUE WRITE (1,1500) DATNAM(K),(OPACRWK (1,J,K),J-l,l8),K CONTINUE WRITE 1,1510) 1.CROPNAH(HARVCRP( 0 ) , CROPNAM(PLNTC WRITE 1,2000) WRITE 1,1310} WRITE 1. 1320) WRITE M 330) 1, 1360) WRITE WRITE 1,1350) WRITE 1. 1360) WRITE 1.1370) WRITE 1, 1380) WRITE 1.1390) WRITE 1, 1600) WRITE 1,1610) WRITE 1,1620) 1. 1630) WRITE 1,1660} WRITE WRITE 1, 1650) WRITE 1, 1660) WRITE 1.1670) WRITE 1, 1680) WRITE 1.1690) DO 900 K-1,52 WRITE (1,1500) DATNAM(K) . (OPHRSWK(1,J,K) ,J-1,|8),K CONTINUE ENDIF CONTINUE FORMAT ('T'.IOX.'F A R M M A C H I N E R Y SE + ,'0 N M O D E L FOR E A S T E R N MIC FORMAT (26X,'OPERATING PARAMETERS') FORMAT (26X,1..................... ') FORMAT (16X,'TOTAL FARM AREA',15X.F5.0.2X.A10) FORMAT (16X ,’SO 1L TEXTURE*,18X.2A10) FORMAT (16X,'WEATHER CONFIDENCE LEVEL',10X.A10) O < v — . Z Z It => \ ul — S N * *ui < X U- - — <*££<£ X •uo CJ cr co - < ^ - - tc r4 X o CJ * A* Ul O —• e c UJ 4 <5 X n u. 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R 1*760+W T E R R .') 1*770- 11*50 FORMAT{' .',15X.'. « E 1*780+. 0 R * A i*790-H»6o FORMAT(* . 1,15X,1. * 1*800+. . , R , * * * * 4 / » 1*810- 11*70 FORMAT(' .',15X,'. 0 1*820+. « * » * « ■ * ) 1*830-11*80 FORMAT{' .1,15X,'. * E 1*BI*0• +. * * • * ■ 1*850-11*90 FORMAT(' .1,15X,'. R A 86O+. . » • • > 1*870»1500 FORMAT (' ’,A10,2X, 18F6.1,11*) 1*880-1510 FORMAT C T HDURS SPENT, WEEKLY TOTAL BY IMPLEMENT, PARCEL N 1,890+ ',11,' HARVEST CROP '.A 10,' PLANTED CROP 1.A10) 1*900-2000 FORMAT (' ') 1*9)0RETURN 1*920END 1*930SUBROUTINE 1NIT l*gi*0IMPLICIT INTEGER (A-Z) 1*950REAL SOILDFT.TAVLHRS.HAXACR,5 IZEFF(20,2) .TMWKCST.TIMINC, CPMTCST(7, 1*960+ 18,52) 1*970REAL WKCAPAC,IMPSIZE,IMPCOST,UTILTIM,UTILSIZ,BTUTHRS,UTtLCST, 1*980+TILLTIM.TILLSIZ,BTTLHRS,TILLCST,CUSTCST.TCUSCST.TTIMCST, 1*990+ACRSRDY,CPACRDY,OPHRSWK,OPACRWK,OWNI HRS,OWN IAC,OWNTHRS.OWNTAC, 5000+CAPAC,ACRES,AVALHRS,SPEED,EFF.HAX,TOTACR,CROPACR,OWN IMP,OWN IUTM, 5010+OWNTRAC,OWNTCST,OWNTUTH,SIZCST,TRAC INC,TIHCST,CUSTPRC.DRAFT,OWN ICS 50205030+T,IMPHRS,OWNIAGE,OWNTAGE,NAMSIZ,WAIT ING,OWNFUEL,TILFUEL,UTLFUEL,AC 501*0+RHULT,TRCHULT,LENMULT 5050LOGICAL HARVIMP 50600 1MENS ION SOILDFT (18,3) .TAVLHRS(52,3*3) 5070COMHON /WKDATA7lMPNUM(l8) ,UTILNUM,BTUTNUft.TILLNUM,BTTLNUM,WKCAPAC ( 5080+18).IMPSIZE (18),IMPHRS(18) , 5090+IMPCOST (18),UTILTIft,UTILS IZ.BTUTHRS,UTILCST,TILLTIM,TILLS IZ, 5100+BTTLHRS.TILLCST,CUSTCST(18).TCUSCST.TTIMCST,ACRSRDY(20) , 5110+CPACRDY (7.20).OPHRSWK(7,18,52) .OPACRWK(7,18.52).OWNIHRS(10,18), 5120+0WNIAC (10,IB).OWNTHRS(20) .OWNTAC(20) ,NAMSIZ(l8).WAITING(7,20),OWNF 5130+UEL(20).TILFUE 5IA0+L,UTLFUEL 5150COMMON /FRMDATA/ CAPAC (IB) ,ACRES (20) ,AVALHRS (52) ,ACOPDAT (7.20,1*) , 5160+SPEEO (20),EFF (20).MAX(18).OWNIHP(10,18),OWNIUTH{10,18), 5170+OWNTRAC (20).OWNTCST(20).OWNTAGE(20) ,0WNTUTM(20).NEXTOP(7,20), 5180+SIZCST (18),TRAC INC,TIftCST(7,18,52).CUSTPRC(lB),SO IL.CONLEV, 5190+CROPACR (7).TOTACR,OWNED.OWNEDT,STARTTM,OWNTOT(18).HARVCRP(7), 5200+PLNTCRP (7).DRAFT (18).OWN IA G E (10,18),OWNICST(lO,18).UNITIND,ACRMULT 5210+.TRCMULT,LENMULT 5220DATA TAVLHRS 5230-C DATA FOR SANDY SOIL (RELATIVELY WELL DRAINED ) 52A0-C DATA FOR SANOY SOIL AT 80 PERCENT (HOURS PER WEEK— 52WEEKS TOTAL) 5250+/15ft0-. 11. .38,.2*1*9., 1*7. ,56.,59. ,62.,58. ,50.,53.,2*63.,3*65.. 5260+61*., 2*62., 3*60.,1 **55-.3*58.,2*57.,22.. 16. ,1 **0., 5270-C DATA FOR SANDY SOIL AT 70 (HOURS PER WEEK— 52 WEEKS TOTAL) 5280+15*0.,12.,Al.,52.,2*55..58.,61.,65**62.,53.,56..2*67*.2*69., 5290+66.,61*. ,5*62.,9*60.,25., 18.,l»*0., 5300-C DATA FOR SANOY SOIL AT 50 PERCENT (HOURS PER WEEK— 52 WEEKS TOTAL) 5310+ 15*0., 13.,1*1*..58., 2*60., 63., 65.. 2*68.. 65., 3*69*. 2*75. ,71 ..3*69., 5320+67-,2*61*., 6*63.,62.,2*60.,29.,21.,A*0., 5330-C DATA fOR SANOY LOAM SOIL ( RELATIVELY WELL DRAINED) 53^0-C DATA FOR LOAHY SOIL AT 00 PERCENT (HOURS PER WEEK--52WEEKS TOTAL) B-27 15350= 15360= 15370-C 153801539015400-C 151*1015li20= 151*30=0 151*1*0=0 151*50= 151*60= 151*70=C 151*80= 151*90= 15500=0 15510= 15520= 15530= + 15*0. ,5. ,25. ,3*31..A5. .50. ,57- ,52.,38.,i*1 .,2*62.,l**60. ,5*58., +2*50.,2*51. ,2*1*8..2*47.,22.,A.,3. ,4*0., DATA FOR LOAMY SOIL AT ?0 PERCENT (HOURS PER WEEK— 52 WEEKS TOTAL) + 15*0.,6.,27.,3* 36.,40.,57-.61.,56..44. ,47.,7*65.,62.,60.,2*52., +***53- .1**52., 32., A ., 3., 4*0., DATA FOR LOAMY SOIL AT 50 PERCENT (HOURS PER WEEK-- 52 WEEKS TOTAL) +15*0., 12., 1*2.. 2*56., S'*.. 70.. 2*67., 6i*.,56.. 58., 2*69. ,5*70., 2*69., +2*67. ,2*59. ,3*62.,2*60. ,2*58. ,25., 11*. ,1**0., DATA FOR CLAY LOAM SOIL ( RELATIVELY WELL DRAINED) DATA FOR CLAY SOIL AT 80 PERCENT (HOURS PER WEEK— 52 WEEKS TOTAL) +15*0. ,7 -, 21*. ,37 • i1*1•, 1*1*-.58., 58. ,60. ,51.., 1 *0., 1 *3., 2*67.,2* 63,, + 2* 61. ,3*58.,62.,60.,2*1 *9.,2*1 *6.,2*50..2* 1 *9.,29. ,1*.,3. .1 **0., DATA FOR CLAY SOIL AT 70 PERCENT (HOURS PER WEEK— 52 WEEKS TOTAL) +15*0.,7. ,27.,A2., 1*8.,51*.,3*62. ,59*.51 .,51*. ,2*68.,2*67. ,3*66., +3*67., 65., 2*53-. 6*51 ., 32.. 7.. 5*. !**0., DATA FOR CLAY SOIL AT 50 PERCENT (HOURS PER WEEK— 52 WEEKS TOTAL) + 15*0.,8.,30.,50.,58.,61*.,63.,60.,A*67.,2*70.,2*71.,5*69.,2*67-, +2*61.,2*60.,2*58.,55. .51 -.37.. 19-. 11*.,1**0. +/ )55!<0= 15550=C EFFICIENCIES FOR IMPLEMENTS BY SIZE OF FARM,UNDER LOO ACRES/ 15560-C OVER 1*00 ACRES 15570= 15580DATA SIZEFF/ 15590= +.55.*65,2*.6,.71*,.7**..65, .75..71*,2*.77, .75.*65. .6,.6,.55,.68,.55.15600= + 65,.68, 15610+.7..75,.7..7,.88,.88,.8,.9,.88,.9,.9,.9,.9,.76..76,.65,.9,.65,.8, 15620= +.9/ 15630DATA SPEED/ 1561*0= +3*. 3-5.3., 3*. i*.5.3-.5.. i*.5,1*.5,5-.5.. <*.5,!*..5., 3*.5.. 3.. 15650= +3.5,5..3-/ 15660 = 15670-C DRAFTS FOR IMPLEMENTS IN HP/FOOT BY SOIL TYPE SANDY/LOAM/CLAY 15680= 15690DATA SOILDFT/ 15700-C DATA FOR POWER REQUIREMENT ON SANOY SOIL— (18 IPIEMENTS IN ORDER) 15710= +0..3.,A.,16., 7.5.5-1*.1-5.7-5.6.5.5-,7..3..1.3,2*3.,1.5,2..8.. 15720-C DATA FOR POWER REQUIREMENT ON LOAMY SAND SOIL— (18 IPLEMENTS IN ORDER) 15730= +0..3..1 *., 16. ,10., 11.. 1.5,9., 11.A,5.8,10.. 3.5,2>. 3-6,3.1*, 1571*0= + 1.5,3-,10.. 15750-C DATA FOR POWER REQUIREMENT ON CLAY SOIL— (18 IPLEMENTS IN ORDER) 15760= +0., 3.,1*,,16.,13.8,15..1.5,13.8,15.1,6.6,12.,A.,2.6,1*.2, 15770= +3-8,I.5,3-.11-5/ 15780I5790=C HAXI HUM IMPLEMENT SIZES IN FEET 15800 = 15810= DATA MAX/ 15820= +30.,20., 2*10., 21.3, 11*..60., 19.,12.,2*36., 31*.5.20., 2*30., 60., 2* 30./ 15830= 158A0-C IMPLEMENT SIZE-BASED COSTS IN DOLLARS/FOOT 15850= 15860= DATA SIZCST/ 15870= +700., 150 ., 1000., 2200., 825., 825 -. 100., 1*00.,825•, 1*50., 1*50.. 200., 15880= +, 1589015900= 15910-C 15920= 15930= 159L015950-C +600.,800,,20.,300.,250./ TRACTOR COSTS IN DOLLARS/HP OATA TRACINC/300./ TIMELINESS COSTS IN DOLLARS/WEEK/ACRE/CROP 100. B-28 5960= 59705980= 5950" 6000-C 6010" 60206030= 6 0 A0 - DATA TIMCST/ +6552*1.0E+100/ CUSTOM COSTS IN DOLLARS/ACRE--(18 OPERATIONS IN ORDER) DATA CUSTPRC/ + 1 6 . , 0 . , 3 9 - 5 . 8 . 2 5 . 1 0 . , 2 . 5 . 8 . 2 5 , 9 . 3 5 . 2 * 1* . 6 , 3 . 7 5 . ^ - 8 , + 2 * 6 .5 5 ,3 ..3 .7 5 .3 ^ / 6050- 6060=C 6070-C 608O** 60906100“ 6 1 IO“C 6120-C 613O-C 6U0-C 6150-C 6160-C 6170-C 6180-C 6190"C 6200-C 6Z10-C 6220-C ACRES— ACREAGE TOTAL FOR EACH IMPLEMENT NEXTOP— LINKED LIST OF OPERATIONS DATA ACRES,NEXTOP/20*0.,|I*0*0/ TCUSCST TOTAL CUSTOM COST ACRSRDY ACRES READY FOR EACH OPERATION CPACRDY ACREST READY FOR EACHOPERATION BY PARCEL IHP HRS HOURS PER IMPLEMENT OWN IHRS HOURS FOR EACH OWNED IMPLEMENT OWNTHRS HOURS FOR EACH OWNED TRACTOR OPACRWK ACRES/OPERATION/WEEK OWN ICST OWNED IMPLEMENT PURCHASE PRICE TTIMCST TOTAL TIMELINESS COST IMPSIZEIMPLEMENT SIZE IN FEET IHPNUM NUMBER OF EACH IMPLEMETNT CUSTCST CUSTOM COST BY IMPLEMENT 6230“ 621*0“ 6250- DATA TCUSCST,ACRSRDY,CPACRDY,IMPHRS,OWNIHRS,OWNTHRS, +OPACRWK,OWNICST,TTIMCST,IMPSIZE,IMPNUM,CUSTCST/ 6260- +0. ,20*0,, 11,0*0., 18*0., 180*0.,20*0., 6270“ +6552*0.,180*0.,0.,18*0.,18*0,18*0./ 6280 “ 6290“C 6300-C 6310-C 6320-C 6330-C 63AO"C 6350-C 6360"C 6370-C 6380“ 6390- TIMELINESS COSTS IN DOLLARS/ACRE/WEEK TIMELINESS COST IS STORED IN THE FOLLOWING ORDER*. GROUPS OF SEVEN REPRESENTING CROPS ; IN CROUPS OF EIGHTEEN REPRESENTING THE IMPLEMENTS. THE FIRST SEVEN VALUES ARE FOR HARVESTING BY COMBINE ALL SEVEN CROPS. WHERE THE CROP IS NOT HARVESTED BY COMBINE. THE VALUE IS ZERO. EACH IMPLEMENT WILL HAVE SEVEN SLOTS ONE FOR EACH CROP. TIMELINESS COSTS FOR COMBINE OPERATIONS FOR SEVEN CROPS DATA ( (CPMTCST {J,1,l),1-1,52),J“ I,7)/ 6 A 0 0 - C CORN HARVEST T I ME L I N ES S 6LI0+1*0*0.,2*17.5,1**0.,6*7.5. 61*20“C 61*306A1*0“C 61*506A60-C 6A 706A8O-C 61*906500-C 65106520-C 6530“ 651*0655C-C 6560“ WHEAT HARVEST TIMELINESS +27*0.,2*12.A,2*0,,21*6., OATS HARVEST TIMELINESS + 28* 0., 2*1*., 2*0., 20*1*., RYE HARVEST TIMELINESS +52*0.. NAVYBEAN HARVEST TIMELINESS ' +37*0.,2*11*.,2*0., l l * H . t SOYBEAN HARVEST TIMELINESS +32*0.,2*9.8,2*0.,16*9.8, SUGAR BEET COMBINE TIMELINESS +52*0./ TIMELINESS COSTS FOR NAVYBEAN PULLER B-29 16570I658O-C 1659016600-C 16610= 16620-C 16630= 16660= 16650-C 16660= 16670= 16680= l6690=C 16700= 16710= 1672016730-C 16760= 167501676016770-C 1678016790= 16800= 16810-C 1682016830= 16860= 16850-C 16860= 16870= 1688016B90-C 16900= 16910= I692016930-C 1696016950* 16960= 16970-C 16980= 16990-* 17000= 17010-C 1702017030= 17060= 17050-C 17060= 1707017080= 1709017I0Q-C 171101712017130= 171601715017160-C 17170- DATA((CPMTCST(J,2,I) ,1-1,52),J=1,7)/ FOR CORN, WHEAT, OATS, RYE, SOYBEAN +52*0.,52*0.,52*0.,52*0.,52* 0., FOR NAVYBEAN + 37*0..2* 7.,2*0.,11* 7,, FOR SUGAR BEETS + 52*0./ TIMELINESS COST FOR SUGAR BEET TOPPER 0ATA ({CPMTCST {J ,3.I),1 = 1,52),J"1.7)/ +52*0.,52*0..52*0.,52*0..52*0.,52*0.,52*0./ TIMELINESS COST FOR SUGAR BEET LIFTER DATA {(CPHTCST(J,6,I).1-1,52).J-1,7)/ + 52*0.,52*0.,52*0.,52*0.,52*0.,52*0.,52*0./ TIMELINSS COST FOR SOIL SAVER DATA {(CPMTCST[J.5,0.1 = 1,52).J-1,7)/ + 52*0..52*0.,52*0.,52*0.,52*0..52*0.,52*0./ TIMELINESS COST FOR SUBSOILER DATA ((CPHTCST(J.6,I) ,1 = 1.52) ,J=1,7)/ +52*0.,52*0.,52*0.,52*0.,52*0.,52*0.,52*0./ TlMEL INESS COST FOR FERTILIZER SPREADER DATA ((CPMTCST(J.7,0.1 = 1,52),J-1,7)/ + 52*0.,52*0.,52*0..52*0.,52* 0.,52*0.,52* 0./ TIMELINESS COST FOR CHISEL PLOW DATA ((CPMTCST (J,8,I) , I-1,52).J-1,7)/ +52*0.,52*0.,52*0.,52*0.,52*0.,52*0.,52*0./ TIMELINESS COST FOR MOLOBOARD PLOW DATA ((CPMTCST(U,9,I),1 = 1,52),J-1,7)/ +52*0.,52*0.,52*0.,52*0.,52*0.,52*0.,52*0./ TIMELINESS COST FOR OFFSET DISK HARROW DATA ((CPMTCST(J,10,1),1-1,52),J=l,7)/ + 52*0.,52*0 5 2 *0 5 2 *0.,52*052*0.,52*0./ TIMELINESS COST FOR TANDEM DISK HARROW DATA ((CPMTCST(J,11,1),1 = 1,52),J-1,7)/ +52*0.,52*0.,52*0.,52*0.,52*0.,52*0.,52*0./ TIMELINESS COST FOR FIELD CULTIVATOR DATA ((CPMTCST(J,12,1),1 = 1,52),J-1,7)/ +52*0.,52*0.,52*0.,52*0.,52*0.,52*0.,52*0./ TIMELINESS COST FOR GRAIN DRILL DATA ((CPMTCST (J,13.1) , I=1,52),J-l,7)/ +52*0. ,39*0., 13*0., 17*0. ,35*17.31#. +52*0.,52*0,,52*0.,52*0./ TIMELINESS COST FOR ROW CROP PLANTER DATA ((CPMTCST(J,16,I),1 = 1,52),J=1,7)/ +20*0.,32*17.5.52*0.,52*0.,52*0., +21*0.,2*16.2,2*0.,27*16.2,21*0.,31*16.39.15*0.,2*28.22. +2*0.,33*35-2/ TIMELINESS COST fOR HO TILL ROW PLANTER DATA ((CPMTCST (J,15.1),l = lj52),J-1,7)/ B-30 171801719017200- + 20*0..32* 17.5.52*0.,52*0.,52*0.. +21*0.,2*16.2,2*0.,27*16,2,21*0.,31*16.39, >5*0. .2*28.22, +2*0.,33*35.2/ 17210- 17220-C TI MEL INESS COST FOR SPARYER 17230“ DATA((CPMTCST(J,16,I),J-l,52).J-1,7)/ 17260= +52*0.,52*0.,52*0.,52*0.,52*0.,52*0.,52*0./ 1725017260-C TIMELINESS COST FOR ROW CULTIVATOR 17270DATA ((CPMTCST (J,17.I).1-1,52),J-1,7)/ 17280+ 52*0.,52* 0.,52*0.,52*0.,52*0.,52*0..52*0./ 1729017300-C TIMELINESS COST FOR NH3 APPLICATOR 17310DATA ((CPMTCSTU, 18. I) , 1- 1,52),J-l ,7)/ 17320+52*0.,52*0.,52*0.,52*0.,52*0.,52*0.,52*0./ 1733017360-C 17350173601737017380-C 17390-C HOURS/OPERATION/WEEK DATA 0PHRSWK/6552*0./ BTTLHRS BTUTHRS BOUGHT TILLAGE TRACTOR HOURS BOUGHT UTILITY TRACTOR HOURS 17600= 17^10= DATA BTTLHRS,BTUTHRS/2*0./ 17 A2 0 - 17^30-C 17I*I*0-C 17A50-C 17660-C 17l*70=C 17680171*901750017510“ 1752017530-50 1751*017550- OPERATION SCHEDULES 1-ACREAGE 2-BEGIN WEEK 3-6ND WEEK A-CUSTOM INDEX DATA ACOPDAT /560*0/ DO 50 1-1,18 DRAFT (I)-SOILDFT (I,SOIL) CONTINUE DO 100 1-1,20 17560- 00 200 J-1,7 17570“ 175801759017600-200 17610-100 17620176301761*0- IF (ACOPDAT (J, 1.1*) .NE.l) THEN ACRES (I)-ACRES(I)+ACOPDAT(J,I.1) ENDIF CONTINUE CONTINUE MAXACR-O. 17650- DO 1*00 1-1,20 '766017670-1*00 17680= 176901770017710-500 IF (ACRES (I).GT.MAXACR) CONTINUE MAXACR-ACRES (I) DO 500 1-1,52 AVALHRS(I)-TAVLHRS(I,CONLEV,SOIL) CONTINUE 177201773017760177501776017770I778O-6OO SIZIND-1 IF (MAXACR.GT.600.) SIZ1ND-2 DO 600 1-1,20 EFf (I)-SI2EFF (I,SIZIND) CONTINUE B-31 7790= 70007010= 7820= 7830= 781(078507660= 78707890** 79007910** 7920= 7930= 791(07950= 7960= 79707980= 7990= 0010=900 00 700 1= 1,7 DO 800 J=1,20 1HP-J IF flHP.EQ.19) IHP**7 IF (IMP.EQ.20) IHP-17 IF (ACOPDAT(I,J,1).LE.O) GOTO 750 END-ACOPDAT(I,J ,3) IF (ACOPDATfl,J,3).LE.ACOPDATfl.J,2) ) END-52 IF (HARVIMP(IHP)) THEN CROP-HARVCRP (I) ELSE CROP-PLNTCRP (I) END IF DO 900 K=ACOPDAT(l,J,2),END TIHCST (I.IHP,K)-CPHTCST(CROP,IHP.K) CONTINUE 8020 = 8030IF ' (END.NE.ACOPDATfl ,J,3) ) THEN 8040= 8050° DO 1000 K“ 1,ACOPQAT(I,J ,3) 8060TIHCST (I,IHP.K)-CPHTCST(CROP,IHP.K) 8080=1000 CONTINUE 8090= 8100= ENDIF 81108120=750 CONTINUE 0130=800 CONTINUE 81LO-7O0 CONTINUE 8150= RETURN 8160END 8170SUBROUTINE SETSEL(LEVEL) 8180= IMPLICIT INTEGER (A-Z) 8190= REAL WKCAPAC,IMPSIZE,IHPCOST,UTILTIH,UTILSIZ,BTUTHRS,UTtLCST, 8200+TILLTIM.TILLSIZ,BTTLHRS,TILLCST,CUSTCST,TCUSCST,TTIHCST, 8210= +ACRSRDY,CPACRDY,OPHRSWK,OPACRWK,OWN IHRS,OWN IAC,OWNTHRS,OWNTAC, 8220+CAPAC,ACRES,AVALHRS,SPEED,EFF,MAX,TOTACR,CROPACR,OWN IHP,OWN IUTM. 8230= +OWNTRAC,OWNTCST,OWNTUTH,SIZCST,TRAC INC,TIHCST,CUSTPRC,DRAFT,OWN ICS 8240= + T ,IMPHRS,OWN IAGE.OWNTAGE,NAMSIZ.WAITING.OWNFUEL.TILFUEL,UTLFUEL,AC B250+RMULT.TRCMULT,LENMULT 8260= REAL RDUHMY(13966) 8270COMMON /WKOATA/ DUMMY(13966) 8280= EQUIVALENCE (DUMMY,RDUHMY) 8290= COMMON /FRMDATA/ CAPAC(18) .ACRES(20).AVALHRS(52).ACOPDAT (7,20,4), 0300+SPEED(20),EFF(20) ,HAX(l8) ,OWNIHP{10,18),OWNIUTH(10,18), 8310= +OWNTRAC (20).OWNTCST(20).OWNTAGE(20).OWNTUTM(20).NEXTOP (7.20), 8320+SIZCST (18).TRACINC,TIHCST(7>18,52),CUSTPRC <1S ) ,SOIL.CONLEV, 8330= +CROPACR (7) .TOTACR,OWNED,OWNEDT,STARTTM,OWNTOT(18) ,HARVCRP(7) , 8340= +PLNTCRP (7).DRAFT(18),OWN IAGE (10.18},OWNICST(10,18).UNITIND,ACRMULT 8350= +.TRCHULT,LENMULT 8360COMMON /FINAL/ FINAL (13966,2) 8370= 8380= IF (LEVEL.EQ.l) THEN 8390= 840000 100 1-1,13966 8410= FINAL (l.l)=DUHHY(l) 6420=100 CONTINUE B-32 181*30l8l*l*0» 181*50181*60IBL701BL80-200 ELSEIF (LEVEL.GT. 1) THEN DO 200 f-1,13966 FINAL(I,LEVEL)“FINAL(I.LEVEL-1) CONTINUE 181*9 0 - 185001851018520185301851*0-300 18550185601857018580-1*00 18590186001861018620186301861*0186501866018670186801869018700187101872018730“ 1871*0IB7501876018770187BO1879018800188101882018830188A01885018860188701888018890-100 18900189101892018930I89lt0189501B 96O18970189801899019000190101902019030- ENDIF DO 300 1-1,22 DUMMY(t)-0 CONTINUE DO 1*00 1-23,13966 ROUMMY(I)-0, CONTINUE RETURN END SUBROUTINE HARVINC(INC) IMPLICIT INTEGER (A-Z) REAL WKCAPAC,IMPS IZE,IMPCOST, UT ILT IM,UT ILS IZ,BTUTHRS,UT ILCST, +TILLT1M.TILLSIZ,BTTLHRS,TILLCST,CUSTCST,TCUSCST,TTIHCST, +ACRSRDY,CPACRDY.OPHRSWK,OPACRWK,OWN IHRS,OWN IAC,OWNTHRS.OWNTAC, +CAPAC,ACRES,AVALHRS,SPEED,EFF,MAX.TOTACR.CROPACR,OWN IMP,OWN IUTM. +OWNTRAC,OWNTCST,OWNTUTH,SIZCST,TRAC INC,TI MOST,CUSTPRC.DRAFT.OWN ICS +T,IMPHRS,OWN IAGE,OWNTAGE,NAMSIZ,WAIT ING.OWNFUEL.TILFUEL.UTLFUEL,AC +RMULT.TRCMULT,LENMULT COMMON /WKDATA/IMPNUM (1.8) .UTI LNUM, BTUTNUM.T ILLNUM, BTTLNUM,WKCAPAC ( +18),IMPSIZE (18),IMPHRS(18) , +1MPCOST (18) ,UTILTIM,UTILSIZ,BTUTHRS,UTILCST,TILLTIH,TILLS IZ, +BTTLHRS.TILLCST,CUSTCST(18).TCUSCST,TTIMOST,ACRSRDY(20), +CPACRDY(7,20).OPHRSWK(7.18,52) .OPACRWK(7.16,52).OWN IHRS (10.18). +OWNI AC (10,16) .OWNTHRS (20) .OWNTAC (20) .NAMSIZU8) .WAITI NG (7, 20) ,OWNF +'UEL (20) ,TI LFUE +L,UTLFUEL COMMON /FRMDATA/ CAPAC (18).ACRES(20),AVALHRS(52).ACOPDAT(7,20,U ) , +SPEED(20),EFF (20),MAX(l8),0WNIHP(10.18),0WNIUTM(10,18), +OWNTRAC(20).OWNTCST(20).OWNTAGE(20),0WNTUTM(2O).NEXTOP (7,20) , +SIZCST(18).TRAC INC,TIMCST(7,18,52),CUSTPRC(l8>.SOIL.CONLEV, +CROPACR(7).TOTACR,OWNED,OWNEDT,STARTTM.0WNT0T(18).HARVCRP (7), +PLNTCRP(7).DRAFT(18).0WN1AGE(10,18) ,OWNIC5T(tO,18) ,UNITII1D.ACRMULT +.TRCMULT,LENMULT REAL INC DO 100 1-1,1* IF (CAPAC(I) .GT.O.)CAPAC(1)-CAPAC(I) * INC CONTINUE RETURN END SUBROUTINE TILLINC(lNC) IMPLICIT INTEGER (A-Z) REAL WKCAPAC,IMPSIZE,IMPCOST,UTILTIH,UTILSIZ,BTUTHRS,UTILCST, +TILLTIM.TILL5IZ,BTTLHRS,TlLLCST,CUSTCST,TCUSCST.TTIMCST, +ACRSRDY,CPACRDY,OPHRSWK,OPACRWK,OWN IHRS,OWN IAC.OWNTHRS,OWNTAC. +CAPAC,ACRES.AVALHRS,SPEED,EFF,MAX,TOTACR,CROPACR,OWN IMP.OWN IUTM, +OWNTRAC.OWNTCST.OWNTUTM,SIZCST,TRAC INC.TIMCST,CUSTPRC,DRAFT.OWNICS +T,IMPHRS,OWN IAGE,OWNTAGE,NAHSIZ,WAIT ING.OWNFUEL.TILFUEL,UTLFUEL,AC +RHULT.TRCMULT,LENMULT COMMON /WKDATA/IMPNUM(18),UTILNUM.BTUTNUH.TILLNUM,BTTLNUM,WKCAPAC ( +18),IHPSIZE (18) ,IMPHRS (18), B-33 1901*0190501906019070190801909019100191101912019130lgi^O191501916019170191801919019200=100 192101922019230= 1921*019250192601927019280)9290193001931019320= 193301931*01935019360= 19370193801939019LO0I9A10191*20= 191*30191*1*0191*50= igi*60= 191*70191*80191*901950019510-100 19520195301951*01955019560195701958019590196001961019620196301961*0- +IMPCOST(16),UTILT1M,UTIESIZ,BTUTHRS,UTILCST,TlLLTIM.TILLS IZ, +BTTLHRS.TILLCST,CUSTCST(18) .TCUSCST,TTIHCST,ACRSRDY(20) , +CPACRDY(7.20).OPHRSWK(7, 18,52).OPACRWK(7,18,52),OWN!HRS(10,18), +OWNIAC (10,18) .OWNTHRS(20) .OWNTAC (20).NAH5IZ08).WAITING (7,20) ,OWNF +UEL(20).TILfUE +L.UTLFUEL COMHON /FRHDATA/ CAPAC(18) ,ACRES (20),AVALHRS (52) .ACOPDAT(7■20,A), +SPEED (20) ,EFF(20) .MAX(18) .OWNIMP (10,IB),OWNIUTM(10,18) , +OWNTRAC(20) .OWNTCST(20) .OWNTAGE (20),0WNTUTM(20),NEXTOP (7,20), +51ZCST(18),TRAC INC,TIMC5T(7,18,52).CUSTPRC (18) .SOIL,CONLEV, +CROPACR(7).TOTACR,OWNED, OWNEDT,STARTTH,OWNTOT(18).HARVCRP(7) , +PLNTCRP(7).DRAFT(18) ,OWN IA G E (10,18),OWN ICST(10,18).UNITIND,ACRMULT +.TRCMULT,LENMULT REAL INC DO 100 1-5,12 IF (CAPAC (I).GT.O.) CAPAC(I)-CAPAC (I)* INC CONTINUE RETURN END. SUBROUTINE PLNTINC(INC) IMPLICIT INTEGER (A-Z) REAL WKCAPAC,IMPS IZE.IMPCOST,UTILTIM,UTILSIZ,BTUTHRS,UTILCST, +TILLT)M,TILLS IZ,BTTLHRS. TILLCST,CUSTCST.TCUSCST,TTIMCST, +ACRSRDY,CPACRDY,OPHRSWK,OPACRWK,OWN IHRS,OWN IAC,OWNTHRS,OWNTAC, +CAPAC,ACRES,AVALHRS,SPEED,EFF,MAX,TOTACR.CROPACR,OWNIMP,OWNIUTM, +OWNTRAC,OWNTCST,OWNTUTM, SI ZCST,TRAC INC,TIMCST,CUSTPRC,DRAFT,OWN ICS +T,IMPHRS,OWN IAGE,OWNTAGE,NAMSIZ.WAITING.OWNFUEL.TILFUEL,UTLFUEL,AC +RMULT.TRCMULT,LENMULT COMMON /WKDATA/IMPNUM(18) ,UTILNUM,BTUTNUM,TILLNUM,BTTLNUM,WKCAPAC( +18) .IMPSIZE (18) .IMPHRS (18) , +IMPCOST(18),UTILTIM,UTI LStZ,BTUTHRS,UTILCST,TILLTIM,TILLS IZ, +BTTLHRS,TILLCST,CUSTCST(18),TCUSCST,TTIMCST,ACRSRDY (20) , +CPACRDY(7.20).OPHRSWK(7,18,52).OPACRWK(7.18.52).0WNIHR5(10,18), +0WN1AC (10,18),OWNTHRS(20) .OWNTAC(20) .NAMSIZ(lB).WAITING(7,20),OWNF +UEL(20),TILFUE +L,UTLFUEL COMMON /FRHDATA/ CAPAC(18) .ACRES (20),AVALHRS (52).ACOPDAT (7,20.A), +SPEED (20) ,EFF (20) .MAX (18) .OWNIMP (10, 18) ,0WNIUTM(10,18) , +OWNTRAC (20) .OWNTCST (20) .OWNTAGE (20) ,OWNTUTM (20) ,NEXTOP (7 ,20) , +SI ZCST (18) .TRACINC,TIMCST (7,18,52) .CUSTPRC (18) ,SOI L ,CONLEV, +CR0PACR(7).TOTACR,OWNED.OWNEDT,STARTTM,OWNTOT (18).HARVCRP (7). +PLNTCRP(7),DRAFT(18) .OWNI A G E (10,18),OWNICST (10,18).UNITIND,ACRMULT +.TRCMULT. LENMULT REAL INC 00 100 1- 13,18 IF (CAPAC (I).GT.O.) CAPAC(I)-CAPAC (1) *1NC CONTINUE RETURN END LOGICAL FUNCTION HARV IMP (IMPNUM) IMPLICIT INTEGER (A-Z) HARVIHP-.FALSE. IF (I MPNUM.LT.l*) HARVIMP-.TRUE. RETURN END SUBROUTINE ALCOST(USE,AGE.NCOST.T,OLDUSE,PCOST,FUELUSE) COMMON/ECOUT/AOWN,AREP,AFUEL,ALAB,AC,CRF REAL NCOST,INT,LABOR,ACRF(5),IR.CRF INTEGER T DIMENSION RCI (20) ,RC2 (20) ,RV1 (20) ,RV2 (20) B-34 19650=0 19660=0 196701968019690= 19700* 19710= 19720“ 19730197»*0- INITIAL INPUT DATA DATA RCl/. 1l*A,. 23,. 26, .1*1,. 23, .23. -2L, .61, .23, .23, .23, +.23..208,.67,.67..71,.23..23..025..025/ DATA RC2 /1.8,1. 8,1.6,1.3.1.8,1.8,1.3,1-3,1-8,1.8,1.8.1.8 +1.6,1.6,1.6,1.A,1.8,1.8,1.6, 1.6/ DATA RVI/. 75,17**7,2*.75/, RV 2/. 88,17*.9,2*.87/ DATA ACRF/.15,.22,.21,.21,.21/, NN/10/, TR1/.25/ DATA TISR/.01/,G/.08/.A/.13/,B/.12/,C/.08/,DPAY/.2/,NM/5/, DATA FP/. 32/, WAGE/!*.25/ AR = A - G 19750= 19760CRF = **1 TOTAL COST TOTAL - CAP+TIS+REP+FUELL+LABOR-TD PVC = PVC + TOTAL/ (1.+A)**I CONTINUE PRESENT VALUE COSTS AOWN - TQWN*CRF AFUEL - TFUEL*CRF ALAB - TLAB*CRF AC - PVC*CRF RETURN ENO LOGICAL FUNCTION TILLIHP(IHP) IHPLICIT INTEGER(A-Z) 20L1020L2020L3020LLO20A5020A6020L7020ABO201(90= 20500205102052020530“ 205A020550“ 20560205702058020590= 20600206102062020630206L0206502066020670206802069020700207102072020730207A0207502076020770207802079020800208102082020830= 2081(020850- COHHON /WKDATA/IMPNUH{18) ,UT ILNUH.BTUTNUH.TILLNUH,BTTLNUM,WKCAPAC( +18),IMPSIZE (18).IHPHRS (18), +IHPCOST (18),UTILTIM.UTILSIZ,BTUTHRS,UTILCST,TlLLTIH.TILLSIZ, +BTTLHRS,TILLCST,CUSTCST(18) .TCUSCST,TTIHCST,ACRSROY (20), +CPACRDY (7■20).OPHRSWK(7.18.52) .OPACRWK(7.18.52).OWNIHRS (10,18) . +OWNI A C (10,18}.OWNTHRS(20) .OWNTAC(20) .NAMS1Z 0 8 } .WAITING (7,20),OWNF +UEL(20),TILFUE +L.UTLFUEL DI HENS ION TILL!NO(20) DATA TILLIND /0,1,0, 1,1,1,0.1,1, 1,1,0-,0, 1, 1.0,0,1,0,0/ T1LLIHP-.FALSE. IF (TILLIND (IHP).EQ.1 .OR. UTILNUH.EQ.O) TILLI HP-.TRUE. IF (IHP.EQ.l) TILLIHP-.FALSE. RETURN END LOGICAL FUNCTION UTILIHP(IHP) IHPLICIT INTEGER (A-Z) COMMON /WKDATA/IHPNUH(l8) ,UTILNUH,BTUTNUH.TILLNUH,BTTLNUrt,WKCAPAC( +18) , JHPSIZE (18) ,IHPHRS(18), +1HPCOST(18),UTILTIH.UTILSIZ,BTUTHRS,UTILCST.TILLTJH,TILLSIZ, +BTTLHRS.TILLCST,CUSTC5T(18),TCUSCST.TTIHCST,ACRSROY (20), +CPACRDY (7.20).OPHRSWK(7.18,52),OPACRWK(7,18.52),OWNIHRS (10.18), +OWNIAC (10,18).OWNTHRS(20) .OWNTAC(20) .NAMSIZ(18).WAITING(7.20).OWNF +UEL(20),TILFUE +L,UTLFUEL 01 HENS ION UTILIND(20) DATA UTILIND /0,0,1,0,0,0,1,0.0,0,0,1.1,0,0,I,1,0,1,1/ UTILIHP-.FALSE. IF (UTILNUH.EQ.O .OR. IHP.EQ.l) RETURN IF (UTILI NO (IHP).EQ.1)UTILI HP-.TRUE. RETURN END LOGICAL FUNCTION LINKED(IHP,INDEX) IHPLICIT INTEGER (A-Z) OIHENSION LINKING(18,2) DATA LINKIND /0,0,1,1, 1 U 0 , 1 , 1,11*0,1,1, 1,1,1/ L'INKED-.FALSE. DO 100 1-1,2 IF (LINKIND (IHP,1).EQ.l) THEN LINKED-.TRUE. IHDEX-I B-36 208602087020880-100 20890-99 20900209»02092020930209L0209502096020970209802099021000210102102021030210LO210502106021070210802109021100211102112021130= 21140211502116021170211SO2U 9 0 - C 21200-C 21210-C 2121521216-C 2121721220212302121*0212M 212A2-C 2121*3212502125121260212622126521270212802128121282-C 21283212902129121300213022130521310- GOTO 99 ENDIF CONTINUE CONTINUE RETURN END SUBROUTINE FUELFIG(I HP,FUELUSE) IMPLICIT INTEGER (A-Z) LOGICAL TlLLIHP,UTILIMP REAL FUELUSE,IMPFUEL (20) REAL WKCAPAC,IMPSIZE,IttPCOST,UTILTIM,UTILSIZ,BTUTHRS,UTILCST, -+TI LLT1M.TILLSIZ,BTTLHRS,TILLCST,CUSTCST,TCUSCST,TTIMCST, +ACRSRDY.CPACRDY,OPHRSWK,OPACRWK,OWN IHRS,OWN IAC,OWNTHRS,OWNTAC, +CAPAC,ACRES,AVALHRS,SPEED,EFF,MAX.TOTACR, CROPACR,OWN IHP,OWN IUTM, +OWNTRAC,OWNTCST,OWNTUTM,SIZCST,TRAC INC,TIHCST,CUSTPRC,DRAFT,OWN ICS +T,IMPHR5,OWN IAGE,OWNTAGE,NAMSIZ,WAIT! NG.OWNFUEL.TILFUEL,UTLFUEL,AC +RHULT.TRCMULT,LENMULT CDMMON /WKDATA/IMPNUM(18) ,UTILNUM,BTUTNUM.TILLNUH,BTTLNUM,WKCAPAC ( +18),IMPSIZE(18),IHPHRS(18) , + IMPCOST (18).UTILTIH.UTILSIZ,BTUTHRS,UTILCST,TILLTIH.TILLSIZ, +BTTLHRS,TILLCST,CUSTCST5,1.51*,.30.1.36, 1.81,1. 11,. 93..78..56. .57 +,. 68,.33..58.-39..30..58/ IMP >-19 » > TRACTOR IF (IHP.GE.I9) THEN FUELUSE-O. DO 100 1-2,20 IMP-19 » > TILLAGE TRACTOR IF (IMP.EQ.I9) THEN IF (IMPSIZE (I).GT.O.) THEN IF (TILLIHP(I))FUELUSE"FUELUSE+.211*(IMPHRS(I) ) *TILLSIZ* (2.3* (DRAF +T(I)A +IMPSIZE(I) +/TI LLS IZ) +3-l*-0, 171** (738* (DRAFT (I) AIMPSIZE (I)/TILLS IZ) -H-l73)**0.5)/TlLLNUM END IF IMP-20 » > UTILITY TRACTOR ELSE IF (IMP.EQ.20) THEN IF (IMPSIZE (I) .GT.O.) THEN IF (TILL IMP(I) ) FUELUSE-FUELUSE+.211* (IMPHRS(I) ) *UTILSIZ*(2.3*(DRAF +T(I)* +1MPSIZE(I) +/UTILSIZ)+3.A-0. 17L A (738* (DRAFT(I)* 1HP5IZE(i)/UTILSIZ) B-37 2132021321213302131*0-100 ++173)**0.5)/UTILNUH ENDIF ENDIF CONTINUE 213141213^2-C 2 131*32135021360213702138021390= 211*002)1*10- IHP-1 »> COMBINE ELSE IF (IMP.EQ.l) THEN FUELUSE-ACRES(1)/1MPNUM (1)* 1-75 ELSE IF (IMP.HE.1 .AND. IMP.LT.19) THEN FUELU5E-0, ENDIF RETURN END B-38 Variable Definition ACOPDATA Operating Data for each implement: 1= Acres 2= Begin Week 3= End Week 4= Custom = 1/ No Custom = 2 One set for each crop for each implement ACRES Total Acres to be completed for each operation ACRMULT Correction factor for acres/hectares ACRSRDY Acres available due to maturation and previous operation for each crop AVALHRS Hours available of useful time for each week. BTTLNUM Number of purchased tillage tractors BTUTHRS Usage in hours for each utility tractor CAPAC Base total capacity (feet) to be used to determine implement sizes and actual capacity for each implement CONLEV 1=80 percent CPACRDY Acres available due to maturation and previous operation for each operation by parcel CROPACR Acres for each parcel CUSTCST Annual cost for custom operations CUSTPRC Dollars per acre for custom operations DRAFT Horsepower per foot for each implement EFF Percentage of time actually performing operation HARVCRP Harvest crop index 1-7 acreage IMPCOST Annual co3t (dollars) of operating each individual IMP IMPHRS Hours for each individual implement (usage per year) 2=70 percent for each 3=50 percent parcel. Completing new B-39 IMPNUM Number of each implement IMPSIZE Size in feet of each individual implement LENMULT Correction factor for feet/meters MAX Maximum size in feet of each implement NAMSIZ Concurrent size in row or bottoms where applicable NEXTOP Linked 3et of operations for each parcel OPHRSWK Hours per operation per week per parcel OWNED Logical variable for owned implements OWNEDT Logical variable for owned tractors OWNIAC Annual cost for owned implements OWNXCST Purchase price for each implement OWNIHRS Hours of use of each owned implement on the farm OWNFUEL Fuel in gallons for each owned tractor OWNLAGE Age in years for each owned implement OWNXMP Size in feet of owned implements OWNIUTM Previous usage of owned equipment OWNTAGE Age in years of owned tractors OWNTHRS Hour3 of usage of owned tractors OWNTOT Total number of owned implements of each implements OWNTCST Purchase price of owned equipment OWNTRAC Owned tractor size in horsepower PLNTCRP Planted crop index 1-7 for each parcel SIZCST Dollars per foot for each implement SOIL 1 = coarse SPEED Miles per hour STARTTM First harvest date for farm 2 = medium 3 = fine B-40 TILFUEL Fuel in gallons for each individual tillage tractor TXLLNUH Total number of tillage tractors TIHCST Dollars per acre per week operation TOTACR Area of the whole farm TRACINC Dollars per horsepower for tractors TRCMULT Correction factor for hp/kw TTIMCST Total annual timeliness cost UNITIND Indicator of unit choice: UTILCST Annual operating cost of each utility tractor UTLFUEL Fuel in gallons for each utility tractor UTILNUM Number of purchased utility tractors UTILTIM Total utility tractor size UTILSIZ Chosen size WAITING Acres available due to previous operations but crop maturation WKCAPC Total capacity of each implement for timeliness costs for each =English/2=SI of utility tractor in horsepower not due to APPENDIX C MACHINERY SELECTION MODEL: C-l EXAMPLE RUNS C-2 EXAMPLE FARM WITH OWNED EQUIPMENT Example of a 300 acre farm. a 15 Farmer owns; a 12.5 foot chisel plow, foot offset disc harrow, a 6 row planter, and a 6 row cultivator. "MACHSEL" selects around the farmer's machines and incorporates them the selection process. Crop = Corn Area = 300 Acres Soil = Clay Conv. Level = 80? Operations: Combine Chisel plow Disk Field cultivate Row plant Sprayer Row cultivate Apply ammonia in I. '■ operating OPERATION PARCEL NUMBER HARVEST CROP 1 AREA 300. II l I it 1 n n ll ACRES Pl.AHTED CROP COMBINE CHISEL PLOW DISK PLOW FIELD CULTIVATOR ROW PLANTER SPRAYER ROW CULTIVATOR NII3 APPLICATOR OWNED 1 1 f it SCHEDULE 1 OPERATING I. n 3u«. AL’KIS FINE (CLAY) BO PERCENT LEVEL CORN OPERATIOH I -■ l parameters TOTAL FARH AREA SOIL TEXTURE WEATHER COMF1DEUCE FIELD I COMPLETION OCT. NOV. NOV. APRIL APRIL APRIL JUNE JUNE CORN DATES 22 12 12 16 30 30 11 11 TO TO TO TO 10 TO TO TO NOV. APRIL APRIL MAY HAY HAY JUNE JUNE 5 30 30 14 14 14 25 25 STATISTICS IMPLEMENT SIZE CHISEL PLOW DISK PLOW ROU PLANTER ROW CULTIVATOR . OWNED TRACTORS PURCHASED IMPLEHEHT OPERATING USAGEIHRS) 12.5 14. S 15.0 15.0 50.7 37.0 45.9 57.9 COST 1774.03 1920.67 2585.57 1629.73 USABECHRS) SIZE1HP) 130.0 HP 90.0 HP C-3 IMPLEMENTS OPERATING 125.7 0.0 COST FUEL COST 3595.64 685.73 507.00 0.00 IMPLEMENTS COMBINE FIELD CULTIVATOR SPRAYER NU3 APPLICATOR 8.0 12.5 16.0 B.O SIZ E ’ ROWS FEET ROUS ROUS HUMBER 1 1 1 1 HRS/UHIT 75.0 49.0 ID.8 37.6 COST/UNIT 6516.34 241.21 320.49 654.19 HEW TILLAGE TRACTORS POUER 160.0 HP • NUMBER 1 IIRS/TRACTOR ( 179.2 COST/TRACTOR B4B1.97 FUEL USAGE/TRACTOR 975.00 GALLONS NEW UTILITY TRACTORS POWER 50,0 HP NUMBER 1 IIRS/TRACTOR 0,0 COST/TRACTOR 0.00 FUEL USAUfc/TRi'CrOR 0.00 GALLONS TOTAL TOTAL HACNIHERY TIMELINESS COST COST OPERATING COST PER ACRES TOTAL OPERATING COST 20605.57 2311.30 96.15 2(18-13.94 - v.C-4 ii 3 # • * * * • ■ « • —— “ ^ ^5 ^ — — — ^ ^ ^ w ■■ * • * • ■ * • « * * • * * * * * * o o o o o o o o — "i - ^ r;i * «J * ■ ’)“* i n . 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Level = 80S Operations: Combine— custom Chisel plow Disk Field cultivate Row plant Spray— custom Row cultivate Apply NH3— Custom E X E C . .UUH.2 3 . 0 3 . 2 0 . L N T E R lii11L T Y P E . 1 “ LI O U T 1 2 = H E H I U M .3 » H E A V Y *3 E N 1 E R C O N F I D E N C E L E V E L F O R H E A T H E R .1 * H 0 »2=70. 3 C 5 0 *1 ErllER C H O I C E O F U N I T S . 1 = E N U L I S H . 2 » B I XI IF S O M E E Q U I P M E N T I S O U N E D . E N T E R 1 IF H O E U U 1 P H E H T I S O U N E D . E N T E R 0 SO F UR E A C H F A R H PARCEL. INPUT A R E A tACRES O R HECTARES) RE F A RMED UN THE P A R C E L > ALOND UJTH HARVEST C R U P INDEX A N U P L A N T E D C R O P INDEX. T H E N INPUT O P E R A T I O N SCHEDULE A S INSTRUCTED. TO P A R C E L NO. 1 A R E A . H A R V E S T C R O P . P L A N T E D C R O P T *300.1.1 INPUT OPERAT I O N S AS FOLLOUS t O PERA T I O N INDEX INITIAL UEEK OF OPERA T I O N F I N A L U E E K OF O P E R A T I O N • C U S T O h OPT I O N * l e C U S T 0 H . 2 B N Q C U S T O M DEGIN WITH HARVEST OPERATIONS. END U1TH ALL O'S *1.43.45.1 *0.4 5 . IS.2 *11.45.18.2 *12.10.20.2 *14.18.20.2 *16.10.29.1 *17.24.26.2 *10.24.26.1 *0,0.0.0 ACRES PARCEL HUMBER 1 AREA 300. HARVEST CROP CORN P LANTED CROP CORN OPERATION COMPLETION DATES COHBINE N OV. OCT. 22 TO 5 CUSTOH CHISEL PLOU NOV, 5 TO APRIL 30 NO CUSTOH DISK P L O U NOV. 5 TO A P R I L 30 NO CUSTOH FIELD CULTIVATOR APRIL 30 TO HAY 14 NO CUSTOM R O U .PLA N T E R A P R I L 30 TO MAY' 14 NO CUSTOM SPRAYER A P R I L 30 J ULY 16 TO CUSTOH ROU CULTIVATOR JUNE TO JUNE 2S 11 Nl) C U S T O H NH3 APPLICATOR JUNE 25 CUSIUU JUNE 11 TO IF T H I S IS C O R R E C T . E N T E R 1 IF T H I S IS I N C O R R E C T . E N T E R 0 *1 PARCEL *0 .0.0 HO, 2 AREA.HARVEST CROP. PLANTED CROP? f a r h m a c h i n e r y OPERATING OPERATION PARCEL NUMBER I HARVEST CROP* CORN A REA 300. M I C H I G A N ACRES PLANTED CRO P C O M P LETION OCT. NOV. NOV, APRIL APRIL APRIL JUNE JUNE 22 5 5 30 3030 11 11 CORN DATES TO TO TO TO . TO TO TO TO NOV, APRIIAPRIL MAY HAY JULY JUNE JUNE .5 30 30 14 14 16 25 25 C-8 STATISTICS IHPLEHEHTS CHISEL PLOU ' DISK PLO U FIELD CULTIVATOR ROU PLANTER ROU CULTIVATOR 10.0 11.5 12.5 6.0 6.0 SIZE FEET FEET FEET ROUS ROUS NUHUEIt 1 1 1 1 1 HRS/UNIT' 73.3 55.9 50.7 55.0 69.3 COST/UNIT. 559.00 510.27 243.82 1086.55 465.19 NEU TILLAGE TRACTORS POUER 120.0 HP NIJHKER 1 HR 57 TRACTOR 104.2 COST/TRACTOR 7137.54 FU E L USA G E / T R A C T O R 050.00 G A L LONS HEU UTILITY TRACTORS POUER 50.0 HP NUHDER HRS/TRACTOR 1 120.0 C O S T/TRACTOR 3331.47 FUEL USA O E / T R A C T O R 400.00 G A L LONS C U STOH COST IMPLEMENT BY IHPLEHENT COST 3900.00. 750.00 900.00 COMBINE SPRAYER NH3 APPLICATOR TOTAL TOTAL TOTAL AREA E A S T E R N SCHEDULE COMBINE CHISEL PLDU DISK PLOU F IELD CULTIVATOR ROU PLANTER SPRAYER ROU CULTIVATOR NH3 APPLICATOR P U RCHASED IMPLEMENT F O R 300. ACRES FINE ( C L A Y > 00 PERCENT LEVEL OPERATION OPERATING m o d e l PARAMETERS TOTAL FARM ARE A SOIL TEXTURE HEATHER CONFIDENCE FIELD s e l e c t i o n CUSTOH COST HACHINERY COST TIMELINESS COST OPERATING COST FER ACRES TOTAL OPERATING COST COUPLE TEIII 9022.62 13330.83 106.31 77.83 231 17. 76 C-9 EXAMPLE FARM WITH METRIC UNITS Crop = Corn Area = 200 hectares Soil texture = heavy Confidence level = 80? Operations: Combine Chisel plows Disk harrow Field cultivate Row plant Spray Row cultivate Apply NH3 EXEC BEGUN.?2.52.21. enter * s u n . T Y P E » i = i . i G H r » 2 = h r m i J n . 3 =nEAUY ts E N T E R C O N F I D E N C E L E V E L F O R WEA T H ER*l*=SO,2 = 7 0 * 3 - 5 0 *1 ENTER CHOICE OF UNITS*1-EUDLIBH.2-SI *2 IF G O N E E O U I P N E N T IS G U N E D i E N T E R 1 IF N O E O U I P N E N T 19 O W N E D . E N T E R 0 *0 FOR EACH FARH PARCEL* INPUT A REA (ACRES OR HECTARES) TO BE F A R M E D O N TH E PAR C EL * A L O N O W I T H H A R V E S T C R O P I N D E X A N D P L A N T E D C R O P I N D E X. T H E N I N P U T O PERATION SCHEDULE AS INS1RUCTED. P A R C E L NO. *200*1*1 I AREA* H A R V E S T CROP* PLANTED CROP? 'INPUT O P E R A T I O N S AS F O L L O W S I O P E R A T I O N I NDEX INITIAL WEEK OF OPERATION FINAL UEEK OF OPERATION CUSTOM UPTI0N*l=CUSr0Hf2=N0 CUSTOH DEOIN WITH HARVEST OPERATIONS* END WITH ALL O'S *1*43*45*2 *0*45*10*2 I >11*45*10*2 >12*10*20*2 *14*10*20*2 *1B*18,20>2 >17*24*26*2 *10*24*26*2 *0,0,0,0 PARCEL NUMBER 1 AREA 2 00. HECTARES HARVEST CROP CORN PLANTED CROP CORN OPERATION COMPLETION DATES COMBINE OCT. 22 TO NOV. 5 NO CUSTOM CHISEL PLOU NOV. 5 ■ TO APRIL 30 NO CUSTOM NDU. DISK PLOW 5 TO A P R I L 30 NO C U S T O H FIELD CULTIVATOR A P R I L 30 TO MAY 14 HU C1ISTDH ROW PLANTER A P R I L 30 TO HAY 14 NO CUSTDH SPRAYER ‘ A P R I L 30 TU MAY NO C U S T O H 14 ROU CULTIVATOR JUNE 11 TO JUNE 25 NU CUSTOM NH3 APPLICATOR JUNE 11 TO JUNE 25 NO CUSTOH IF T H I S IS C O R R E C T . E N T E R 1 IF T H I S IS I N C O R R E C T * E N T E R 0 *1 PARCEL 10,0 ,0 MO. 2 A R E A*HARVEST CROP* PLANTED CROP? F A R H M A C H I N E R Y OPERATE IIS OPERATION PARCEL NUMBER 1 HARDEST CROP CORN AREA II I 0 A H HECTARES OCT. NOV. NOV. APRIL APRIL APRIL JUNE JUNE 22 TO 5 ro S TO 30 Tn 30 TO 30 ‘ ■ TO TO 11 TO 11 CORN DATES NOV. 5 APRIL 30 APRIL 30 MAY 14 MAY 14 HAY 14 JUNE 25 JUNE 25 C-Ll NEU UTILITY TRACTORS Uli- Hit IMPLEMENTS NEU TILLADE TRACTORS Q.O 2.4 3.5 3.0 0.0 1A .0 a. o 8.0 SIZE ROUS HETERS HETERS HETERS ROUS ROUS ROUS ROUS POUER 120.0 KU POUER 37.5 KU . MACHINERY COST TIMELIMESS COST OPERATING COST PER HECTARES TOTAL OPERATING COST CDMPLETEDi WEEKLY TOTAL GY 1MPLEHEHT. PI USER ABllRT E A S T E R N STATISTICS COHBINE CHISEL PLOU DISK PLOU FIELD CULTIVATOR ROU PLANTER SPRAYER ROU CULTIVATOR NH3 APPLICATOR AREA 200. PLANTED CROP COMBINE CHISEL PLOU ' DISK PLOU FIELD CULTIVATOR ROU PLANTER SPRAYER ROU CULTIVATOR HH3 APPLICATOR TOTAL TOTAL F O R SCHEDULE COMPLETION OPERATING H I) D E L 200, HECTARES FINE (CLAY) 80 PERCENT LEVEL OPERATION PURCHASED IHPLEI1ENT II II PARAMETERS TOTAL FARH AREA SOIL TEXTURE WEATHER COMF1PENCE FIELD S E L E C T ! HUHBER HRS/OMIT 77.0' 125.8 53.8 55.0 36.7 21.4 44.2 42.9 COST/UNIT 7728.27 613.87 506.86 245.84 1264.22 326.21 570,00 663.41 HUHBER IIRS/TRACTOR 129.6 COST/TRACTOR 7791.05 FUEL USAOE/TRACTOR 3010.31 LITERS HUHBER 1 HRS/TRACTOR 120.7 COST/TRACTOR 4451.48 FUEL USAGE/TRACTOR 3130.73 LITERS 31953.05 141.94 160.54 32095.00 APPENDIX D PROBABILITY OF SUITABLE WORKDAYS D-l D-2 Suitable workday probabilities are an important input into the Machinery Component Selection model (Section 3» Chapter 2). suitable workday is a day in which the farmer can For do field A work. example, a calendar month in the spring may have only 11 suit­ able workdays. These probabilities provide estimates of the number of suitable days a farm manager can expect to have to perform field operations. Michigan, Estimates of the probabilities of at project implementation, reflect differences due to region, soil and tillage system. To address were suitable limited texture, day3 for and did not drainage class, these shortcomings, a computer model (FDPGEN) was developed by Rosenberg (1982) reference proposed report in a complementary project funded by Michigan State Univer­ sity. FDPGEN uses historical weather records and information on soil characteristics and suitable workdays. workdays field operations to simulate the incidence of FDPGEN simulates the incidence of suitable by monitoring soil moisture level and tractability condi­ tions. Tractability refers to the ability of a soil to support the weight of a tractor moving across a field without the tractor get­ ting stuck. a good For nonharvest field operations, a day was workday if the soil was dry enough to be tractable. harvest field operations, additional permissible considered precipitation constraints were placed For on to ensure that moisture in the environ­ ment would not cause excessive harvest losses. The number of suitable workdays in each period (e.g.. May 1 to May 7) for each year (e.g., 1973) was estimated for the last 27 D -3 years by the FDPGEN model. ranked for each The number of suitable workdays were period in the year in an ascending order to form empirical cumulative probability distributions.^ trays a hypothetical distribution (after being inate sample roughness). Figure smoothed D.1 por­ to elim­ The X-axis portrays suitable workdays for a week, (i.e., the number of suitable days can be small as zero but cannot exceed seven). The Y-axis portrays the probability that the number of suitable workdays out of seven is 75%. B indicates Similarly, point that the probability of having at least four suitable workdays is 60%. The component of FDPGEN which simulates the incidence of days was validated using observations recorded by 16 farmers for 3 years in Huron County, Michigan. or work not a day suitable for Model predictions as to whether field work was compared to farmer’s observations. Alternatively, the procedure can be thought of as generating a "histogram” describing the probability distribution of suitable workdays. 1001 80% PROBABILITY 60% 20% 0% 1 2 3 4 5 6 DAYS SUITABLE FOR FIELD WORK Figure D-l. Number of days per week In which field work can be conducted. APPENDIX E SAMPLE FORM USED FOR INDIVIDUAL FARM DATA E-l E-2 Thi3 appendix contains forms used to collect data for 1980 and 1981. individual farm Data on cropping sequence, soil types, crop residue, management practices, etc. were collected. The cooperating farmers were selected by personnel in the Soil Conservation Service (SCS) and the Cooperative Extension Service (CES) in Tuscola and Huron Counties. leaves A farmer i3 eligible if he a specified amount of crop residue on the soil surface. If eligible, he.can participate in Agricultural Stabilization and Con­ servation Service CASCS) cost-share program for his conservation tilled acres. Those farmerswho were eligible and fields of comparable soil type were asked to cooperate. Thus, con­ ventional tillage practices were followed on the The had contiguous companion field. project had seven cooperators in 1979-80 and 21 cooperator3 in 1980-81.1 Measurements were taken by project staff stationed in and Huron Counties Tuscola and by campus-based Michigan State University personnel contributing to the project. The large geographic scope of the project area and impossibility of covering every cooperating farm in an exhaustive way forced the- project to into mance, two categories. divide the farms All operations, including machinery perfor­ were monitored in one subset; only agronomic and pest toring were conducted in thesecond subset. i The use of the 1980-81 year means fall tillage occurred in the fall of 1980; the remaining operations were conducted in 1981. moni­ E-3 The criteria used to select farms to be monitored exhaustively were: soil type, crops grown, proximity to other farms, and the level of willingness and cooperation of the farmer. criteria, 7 out of 21 participating Based on these farms were chosen for the 1980-81 crop year for complete monitoring. The farm data sheets do not include the name but a number, to preserve participant privacy. of the farmer, The number assigned was based on an alphabetical order of the farmers' names and is not a scale to judge the level of managerial practices of the farmer. E-4 1980 CO____________________ CT FARMER 4 CROP Com Com CROP HISTORY com - com c o rn - c o rn TYPE Guelph-Londo G uelph-Londo MANAGEMENT GROUP 2 .5 a - 2 .5 b 2 .5 a - 2 .5 b TILING T ile d T ile d 300 l b / a K20 300 l b / a K20 110 l b / a (1 6 -4 1 -0 ) 160 l b / a NH3 110 1 b /a (1 6 -4 1 -0 ) 160 l b / a NH3 A tra z ln e Lasso A tr a z ln e L asso CROP RESIDUE SOIL FERTILIZER PROGRAM PESTICIDE PROGRAM NUTRIENT STATUS (PLANT CHARACTERISTICS) H e rb ic id e E-5 CO ______ CT PLANT 662 (3 9 0 1 ), 332 (3958) 662 (3 9 0 1 ) , 332 (3958) DATE OF PLANTING 5 -1 8 -8 0 5 -1 8 -8 0 SEEDING RATE 2 7 5 0 0 /a c re 2 7 5 0 0 /a c re DATE OF EMERGENCE 1 3 .5 0 " 1 3 .0 " PLANTS/ACRE 17170/a 21 5 0 0 /a 28" 28" TARGET 150 b u /a 150 b u /a ACTUAL 148.1 b u /a 1 4 7 .9 b u /a FALL Plowed 9-10" S o il sa v e d 9“ SPRING 1 fie ld c u ltiv a to r 1 fie ld c u ltiv a to r INSECT POPULATION None None- WEED POPULATION None None DISEASE None None JD 12 row JD 4320 ( p la n tin g ) S te ig e r ( tilla g e ) B ear C at JD 12 row JD 4320 ( p l a n t i n g ) S te ig e r ( t i l l a g e ) B ear C a t VARIETY SEED TREATMENT (6 /7 8 ) HEIGHT AFTER 4 WEEKS ( 6^1 8 > SPACING YIELD OPERATIONS PERFORMED OTHER OBSERVATIONS P la n te r T r a c t o r used E-6 1981 CO CT FARMER 4 .......... CROP Com Corn CROP HISTORY c o m , c o rn c o m , c o rn CROP RESIDUE 1900 l b / a 1900 l b / a TYPE G uelph-Londo G uelph-Londo MANAGEMENT GROUP 2 .5 a - 2 .5 b 2 .5 a - 2 .5 b TILING T ile d T ile d SOIL FERTILIZER PROGRAM F a ll S p rin g 350 l b / a (5 -1 4 -1 3 ) 20 g a l / a (8 -2 5 -3 ) 350 l b / a (5 - 1 4 -1 3 ) 20 g a l / a (8 -2 5 -3 ) PESTICIDE PROGRAM In s e c tic id e H e rb ic id e 7 l b s / a D yfonate 2 l b s / a A tr a z ln e 2 q t / a L asso (20 g a l H20) 7 l b / a D yfo n ate 2 l b s / a A tr a z ln e 2 q t / a L asso (20 g a l H20 ) NUTRIENT STATUS (PLANT CHARACTERISTICS) E-7 CO________________ CT PLANT VARIETY • 3901 P io n e e r 3901 P io n e e r 27850/a 27 8 5 0 /a 30“ 30" 150 b u /a 150 b u /a H oldboard plow S o il sa v e F ie ld c u l t i v a t e F ie l d c u l t i v a t e 12 rows OD 4320 (130 HP) S t e i g e r (225 HP) 12 rows JD 4320 (130 HP) S t e i g e r (225 HP) * SEED TREATMENT DATE OF PLANTING SEEDING RATE DATE OF EMERGENCE PLANTS/ACRE HEIGHT .AFTER A WEEKS SPACING YIELD TARGET ACTUAL OPERATIONS PERFORMED FALL SPRING INSECT POPULATION WEED POPULATION DISEASE OTHER OBSERVATIONS P la n te r P la n tin g t r a c t o r T illa g e tr a c to r APPEN D I X F SOIL MANAGEMENT GROUPS REPRESENTED IN THE PROJECT AREA F-l F-2 Soil Management Groups and Units^ Soil management groups are groups of soil3 (soil series) similar properties and yield potentials. The groups are formed on the ba3is of the dominant texture of the upper profile and with 60 inches of the the natural drainage conditions under which the soils were formed. Numbers are used to identify the dominant texture the (from 0 for fine clay3 to 5 for sands) and lower case profile letters to indicate the natural drainage conditions ("a" drained to "c" for poorly drained). for of well The interrelationships and symbols of soil management groups, as related to corn production in Michigan, are shown in Table D.1. In this table, the dominant tex­ ture of the profile is emphasized— not the texture of soil, as in soil type identifications. the surface Thu3 soil series serve as the basis for groupings. Soil management units are less inclusive than soil management groups in that the unit concept recognizes the slope which is indi­ cated with the capital letters A through F. erosion conditions Severe and very severe are shown by the numbers 3 and 4 respectively. Thus, a 1.5aC3 symbol for a soil management unit whose profiles are dominatey soils clay loam, naturally well drained, have a slope ranging between 6 and eroded. represents 12 percent and are severely Each characteristic is important in evaluating opportuni­ ties for success with alternative tillage systems. Adapted from Robertson, et al (1976). The authors are Michigan State University and Soil Conservation Service Crop and Soils Scientists. F--3 Opportunities and Problems Minimum tillage is defined as "the least tillage necessary for rapid seed germination, tillage method. essential. and a good stand". This definition does not No-till is a minimum state that tillage is It implies that tillage should be done only if there is a good reason. Degree of Slope In general, where average (Slope Class A) other slopes are less than 2 percent minimum tillage methods usually result in fewer production problems, especially those related to soil ture, insects and rodents. Therefore, other methods are recommended over no-till unless struc­ minimum slopes are tillage long and unless wind erosion is a problem, which is likely on the more sandy (3, 3/1. 3/2, 3/5, 4, 4/1, 4/2, 5 or 5/2 groups) and organic soils (M, M/3, M/4 or M/m groups). On steeper slopes, averaging between 2 and Class B) 6 percent soil erosion can be a significant problem. (Slope If soils are in good physical condition, minimum tillage methods can be success­ fully used not only to produce high yields, but to reduce soil ero­ sion. Where soils are compact, other minimum tillage methods involving chisel plows have been more successful. If slopes averaging 6 to 18 percent (Slope Classes are used for corn production, only no-till employed, preferably in combination with other tices, such as strip-cropping. C and D) methods should be conservation prac­ Otherwise, excessive erosion i3 F-4 likely to occur even with other 3lope3 are in excess minimum tillage methods. Where of 18 percent (Slope Classes E and F ) , and especially if they are long, corn should not be grown because of excessive surface water runoff and perpetual erosion problems. Soil Texture The best no-till soils are the naturally well-drained loam soils, 3a, 3/2a and 3/5a management groups. especially those with a fine-textured surface problems 3andy Most other soils, horizon, have real that must be recognized and solved if no-till methods are to be effective. "Good" in Table F.2 suggests that these soils are best to no-till methods. suited Thi3 evaluation is based upon the assumption that the soil3 have a desirable physical condition and that herbi­ cides are effective. The finer-textured soil3 naturally tend to be compact crust. On such and to soils, this is likely to be a problem every year with no-till methods. If field operations occur at high moisture levels, the amount of compaction increases, thus reducing opportun­ ities for success. F-S Natural Drainage In general, the naturally somewhat poorly and the poorly problem soils The high soil mois­ may be intensified where large volumes of crop resi­ dues on the soil surface retard evaporation rates. not "b" drained "c" soils should be tilled and (or) ditch drained before no-till methods are attempted. ture drained No-till should be considered as a substitute for artificial drainage in these groups. Organic Matter Success with minimum till depends upon the herbicides. Successful effective herbicides such the soil. To have been less successful on the poorly and very poorly drained ”c" because of herbicide treatment is closely related to the colloidal content (clay and organic matter) of date, use soils, soils have both mineral relatively and organic, primarily high organic matter levels. Increased rates of herbicide application or different kinds of her­ bicides than normally considered are commonly needed for control of weeds on such 3oils. Covert - Sand (5a) Covert soils are moderately nearly well drained. level or sloping and are The surface layer typically is very dark grayish brown sand about 4 inches thick. light gently The subsurface brownish gray sand about 6 inches thick. layer is The subsoil, about 25 inches thick, is strong brown and brownish yellow loose sand. F-6 The substratum is light yellowish brown sand to a depth of about 60 inches. Bach Silty Loam (2.5c-cs) Bach soils are nearly level and are poorly drained. poorly drained brownish very The surface layer typically is very dark grayish brown calcareous silk loam about 10 inches thick. light or gray very The subsoil i3 fine sandy loam about 20 inches thick. The substratum, to a depth of about 60 inches, is pale brown and brown, stratified. Shebeon Loam (2.5b-d) Minor soils in this map unit are the somewhat poorly drained Shebeon and Avoca soils, the poorly or very poorly drained Bach and Essexville soils, and the poorly drained Kilmanagh soils. tered raised areas are occupied by the Shebeon soils. throughout the unit are occupied by the Avoca soils. stratified Bach Scat­ Sandy ridges Areas of the soils, the noncalcareous Kilmanagh soils, and the sandy Essexville soils are closely intermingled with areas of the Tappan soils. Tappan London (2.4cc-2.5b) Tappan soils are nearly level and are poorly drained. The surface layer typically is very dark grayish brown, calcareous loam about 13 inches thick. The subsoil, about 18 inches thick, light brownish gray to dark yellowish brown loam and 3ilt loam is F-7 and gray loam. loam and The substratum Is yellowish gray loam. brown loam and 3ilt The substratum 13 yellowish brown loam to a depth of 60 inches. Londo soils are nearly level and are somewhat poorly drained. The surface layer typically is very dark grayish brown loam about 9 inches thick. The subsoil is about 11 inches thick and yellowish brown, mottled loam and clay loam. and is brown The substratum is brown, mottled loam till to a depth of 60 inches. Kilmanagh (2.5c) Kilmanagh soils are nearly level and are poorly drained. The surface layer typically is very dark gray loam or cobbly loam about 9 inches thick. and dark The subsoil is about 20 inches thick, and is yellowish brown, mottled loam. gray It is underlain by dark yellowish brown and brown, mottled, friable and very firm loam to a depth of 60 inches. Guelph (2.5a-2.5b) Guelph moderately soils well are or gently undulating well drained. dark brown loam about 9 inches thick. thick, or rolling and are The surface layer typically is The subsoil, about 12 inches is dark brown and dark yellowish brown clay loam. The sub­ stratum is brown and dark brown loam to a depth of 60 inches. F-8 Parkhill (2.5c) Parkhill soils are nearly level and are poorly drained or very poorly drained. The surface layer typically is very dark grayish brown loam about 9 inches thick. thick, is grayish brown The mottled, subsoil, about firable loam. 23 inches The underlying material, to a depth of about 60 inches, is grayish brown, mottled loam. Summary Soils differ in their suitability to no-till methods. The use of the soil management group and unit concept is an aid in predict­ ing where high or low levels of success suited to no-till methods textured and well drained. on naturally are likely. Soils best are those that are sandy loam or loam Production problems are usually greater poorly and very poorly drained soils and those which contain relatively large amounts of clay. APPENDIX G DATA AND GROUPING OF FARMERS ACCORDING TO TILLAGE PRACTICES IN TUSCOLA COUNTY G-l G-2 A survey (Figure G.1) was conducted in the winter of determine to tillage methods commonly used on corn, navy beans, sugar beets and soybeans in farmers 1980 were Tuscola County, Michigan. asked are given in Table G.1. cash crop farmers whose names Extension Service mailing list. were on the The questions Farmers surveyed were Tuscola Cooperative There were 122 valid responses out of 160 received; others were discarded because farmers leased their land or did not fill out the forms properly. The detailed data are presented in the (pages 269-299). Host A plow their land in the fall. of those who grew corn, respectively, that follows farmers did not practice reduced tillage, and none practiced no-till farming. moldboard section navy beans, majority of the farmers Only 20%, 16%, 16% and 21% sugar beets, and soybeans practiced deep tillage in the fall using a subsoiler (Figure 1.3) which penetrated the soil to a depth of 14-26 inches. G-3 Of those who deep till corn fields, 62? followed deep with moldboard spring. plowing; tillage 78% performed in the fall and 22% in the Of those who deep tilled harvested sugar beet fields, followed 64? deep tillage with moldboard plowing; 78? performed in the fall and 22? in the spring. Of those who deep tilled harvested sugar beet fields, 64? followed deep tillage with moldboard plowing with 57? performed in the fall and 43? in that deep tilled the spring. Of those soybeans, 50? moldboard plowed in the fall. those that deep tilled after navy beans, 88? used the Of moldboard plow; 53? plowed in the fall and 47? in the spring. Of those who did not deep till, only 12? used a chisel plow on corn, 9? on navy beans, 21? on sugar beets and 34? on soybeans. the rest, 92? moldboard plowed corn fields in the fall, 52? Of navy beans, 98? sugar beets and 89? soybeans. The raw data were arranged according to tices, and time crop, tillage of year when the tillage practice was performed. Following through an example of the flow charts will give image of Of those deep Eighty-five farmers did subsoiler, 16 not deep used it in the Of those using till; 21 a multiple fall and 2 used it in the spring. . Of the 16 farmers using the multiple the clear tilling, 18 used a multiple shank subsoiler while 3 used a single shank subsoiler. shank a how farmers are grouped. Corn will be used as an example (color code yellow). did. prac­ shank 3ub30iler in fall, ten farmers went over the field once in the fall while 6 went over the field twice. Seven of those 10 farmers subsoiled no deeper than 14 inches, while 3 subsoiled deeper than 14 inches. We now turn to four pages later, following the OFF-THE-PAGE E, to find the CONNECTOR primary and secondary tillage practices of the 7 farmers of those who deep tilled to a depth of less than 14 inches. Three moldboard plowed; all moldboard plowing was conducted in the fall and at a depth in excess of 7 plowed. inches, the remainder chisel These 3 farmers U3ed standard^ row crop planters and cul­ tivated 3 times and harrowed twice. Only one farmer used a row 1 Standard row crop planters are not suitable for conservation tilled or no-till fields because they lack the special corrugated coulter needed in such cases. G-5 crop planter larger than 6 rows; the others used planters that were 6 rows or less. Sana Address Township Section • X Deep Tillage 1. Implement! . a. b. 2. Timet a. b. c. 3. Treatment: a. b. 4. Depth: a. b. c. XI Primary Tillage 1. Implement: a. b. c. d. 2. Depth: a. b. c. 3. Time: a. b. c. d. IXX Secondary Tillage 1. Implement: a. b. c* d. e. f. g. XV Planter 1. Type a. b. 2, Size a. b. c. V Soya Beets Single chisel subsoiler Multiple shank subsoiler — * * ■* “ August - September October - November March - April ee e* * • — - One Two 4* • - - - 10 - 14 inches 14 - IQ inches IS - 22 inches * - - - Moldboard plow Chisel plow Rotary Disc * * - - 4 - 7 inches 7 - 1 0 inches More than 10 inches * March - April May - June September - October November - December - Disc Field cultivator Harrow Mulcher Cultipacker Rotary hoe Two of above used in tandem — “ * m * ** “ - — - ” - — •* - — - • - •* — — * “ — m * * .. «* ♦ Standard No-Till 4 - 6 rows 6 - 1 2 rows 16 or more rows • - “ — * Average number of passes with tractor after primary tillage. (Include pesticide application, incorporation and planting.) a. 1 - 2 b. 3 - 4 c. 5 - 6 d. 7 - 8 e. Mara than 8 * • • - * A * • • Figure G-l. Com Means Questionnaire Used for the Survey *• - — m ** KEY TO FLOWCHARTS O - = terminal (start or end) NP = not practiced STD = standard planter type NT = no-till planter = off-page connector o Secondary Tillage Implements: a = disc b = field cultivator c = harrow d = mulcher e = cultipacker f = rotary hoe g = two of the above used in tandem G-7 = decision CROP WavybeaM DEEP TILLAGE MULTIPLE SHANK SUBSOILER \* V° FA SPRING FA uL G-8 SINGLE CHISEL SUBSOILER SPRING 2 & I----- ONE TREATMENT I f NOT TWO V PRACTICED ) TREATMENTS- - - - - - - - ' |7 r - n £14" >14" 14- <14" PRACTICED >14" <14" >14 TWO TREATMENTS NOT PRACTICED NOT PRACTICED D Navybeaiu (A) PRIMARY TILLAGE -1- - - - i---------MOLDBOARD PLOU CHISEL PLOW |S5 I--------- '--------- 1 SPRING FALL | 44 <7" 44 LTD a FALL >7" i— A <7" I SPRING i >7" r <7" FALL >7" SPRING I i—J L T <7" >7" . I3 , , I53 , I3 , . I‘ , STD NT £t D N T ^TDN? STDNT 7" 16 “I sJC NT NT STD 7" DISC TILLER 5 .NP. 4 5 a 13 a 2 b 41 b 35 b 4 4 c 10 c 2/ c 7 3 d 1 d 5 d e 6 e 9 e f 4 f 4 f f g 8 B 9 g 2 6 <6 ROWS 125 179 (jro) g I i~ — I L" 7 I , 1 ) e I— I <6 >6 _<6 >6<6 >6 <6 >6 ROWS ROWS . ROWS ROWS |3 I 21 13 .7 13 END) (np ) (END) ® / e ND) (ENDJ 6 >6 <6 ROWS ROWS „ ROWS END ROWS ENDIENDL Np)(ENIM NP, r ~i <6 >6 ROWS (END) (NP) (B) Navybean& PRIMARY TILLAGE 1------- I-----MOLDBOARD FLOW il FALL CHISEL PLOW — I li_ I— FALL SPRING i3 DISC TILLER 1 SPRING 1 1 (h e ) >7" <7" X STD NT Xn p ) ( >7" STD NT gb a b 3 c 3 d e f g 1 < m ROWS (H <6 >6 ROWS .END I <5e ) Navy beam 9 PRIMARY TILLAGE 1-----M O L D B O A R D PLOW C H I S E L PL O U l? FALL FALL SPRING U 1 > 7" <7" >7" A LL_ 1 SPRING <7” LL_ I— FA L L >7’ SP RING I— — I <7" >7" 1L_ — <7" 1 >7" 1 1 i TD IjT TD NT ■N p ) STD N NF STD NT — I NT s w 1 II-D _ (— DISC TILLER a a 1 b b J c c f d d e 1 e f f g f <6 g >f! ROWS li <6 ROUS ROWS >6 ROWS i <6 I >6 ROWS 1/ !N0) ^En ^d ) (fig) Navybe.an& PRIMARY TILLAGE 1-------- -------I MOLDBOARD PLOW CHIS E L P L O W i DISC TILLER II d r~ FALL SPRING I L— <7" 1 (NP) i >7" X & T D NT 1 a b J c d e f g >& ROWS (e n d ) © Gib Mavt/beanA PRIMARY TILLAGE A-- I-------MOLDBOARD PLOW , CHISEL PLOW DI S C T I L L E R |3 1 [- FALL FALL SP RING _ 1 L— I— <7" >7" SPRING 1 — i >7" <7" (i) >7" 2 d A * 'm t - © At g L s i !TD W 2 <&£ ! i a b 2 a a b 7 r b 2 c c c d d d e e 7 e f f f S g g _<6 >f ROWS 1 2 17 I If____ I--- 7 <6 ROWS >6 ROWS 17 II Ua.vybean£> 9 PRIMARY TILLAGE 1------ I------- MOLDBOARD PLOW CHISEL PLOW 13 FALL _ l i — I <7" >7" dL I L _ SPRING I— !TD <7M NT 2 FALL (NP) >7" <7" < k >7" &TD NT @ r STD NT 1 <|jP ■ a a a b 7 b c c c d d d e e e f f f g B g b 2 t— <6 >6 ROUS 17 17 END 1 SPRING JL Ji l Ji (§£) DISC TILLER J i. T 11, i 1 i— <6 ROUS 17 END >6 ROUS I NP I 17 NP END NavybzanA PRIMARY TILLAGE _|------ I CHISEL PLOW MOLDBOARD PLOW 2 r~ FALL ( SPRING I DISC TILLER CROP B eet* 'DEEP TILLAGE YES n MULTIPLE SHANK SUBSOILER 11 SINGLE CHISEL SUBSOILER - l l _ FA LL SPRING FA SPRING 3 I f NOT "N TWO V PRACTICED J TREATMENTS- - - - - - - - ' I------ ONE TREATMENT n r - S _<14" >14" <14“ >14" <14“ >14" L_ NOT PRACTICED a<£> $ 1 I------ ONE TREATMENT I4 TWO TREATMENTS \4 <14“ >14“ 3 (E) (F) (£) (1) ( NOT 'N VPRACTICE^ B eet* PRIMARY TILLAGE -A-MOLDBOARD PLOW 14 8 CHISEL PLOW — FALL 14 7 <7" [ \1 13 i— — I i— <7" >7" <7" 1 SPRING FALL 12 >7" DISC TILLER t! L _ I--- SPRING r~ 1 1— i r >7" — ^ <7“ TD NT S^TDfT? S*TD NT. STD >7" NT NH G -17 6 ROWS end )( n ROWS p ~i >2 )v E N D i >6 <6 . ROWS ROUS i I ! >6 <6 ROWS >6 ROWS, Seeti PRIMARY TILLAGE - I -------- I--------------- MOLDBOARD PLOW CHISEL PLOW I7 r~ FALL SPRING Ji <7" >7" 1 X STD NT b 7 c 7 d e f £ 7 <1 >& ROWS 7 i DISC TILLER Bee£& 9 PRIMARY TILLAGE 1------ I------- MOLDBOARD PLOW CHISEL PLOW LL_ 1/ I 1 FALL SPRING i ! <7* (4 1 ■>7" I--- FALL <7" ! jX D >7" J |J NT INP) s W I6 ROWS ENDJ[NP DISC TILLER 1 SPRING J (4 1 (i) PRIMARY TILLAGE . MOLDBOARD PLOW : 1------------- CHISEL PLOW FALL 1 DISC TILLER SPRING G-2Q <6 >6 ROWS J Be&t5 9 PRIMARY TILLAGE 1------ I-------- MOLDBOARD FLOW I CHISEL PLOW 1 I--- 1 FALL SPRING I2 — i < 7" (N|) DISC TILLER 3 > 7" 1! l > 7" <7" STD NT STD NT m ROWS :ows n p j (e n d l e n d . Be.e£i 9 PRIMARY TILLAGE I------- 1 1-------- MOLDBOARD PLOW CHISEL PLOW I--- FALL 1 DISC TILLER / 1 (NP) SPRING I' © <7" >7" 11 STDNT ( © G-22 a b I c d e f g <7" 17 S*TD NT < © G-23 r <6 >6 ROWS 7 Beeii PRIMARY TILLAGE MOLDBOARD PLOW CHISEL PLOW FALL I2 — DISC TILLER 3 12 r~ , 1------ I--------------- 1 r SPRING FALL 1 , SPRING 1 (NP) >7" _<7" >7" 13 (A > NP) s' tD UP nt © G-24 a b 3 c d e f r £6 ROWS I >6 ROWS T 12 I; CROP Cofl-rt < DEEP TILLAGE l» b FA G-25 MULTIPLE SHANK SUBSOILER SINGLE CHISEL SUBSOILER SPRING FA LL SPRING U J------ ONE TREATMENT I2 <14“ TREATMENTS NOT PRACTICED ) TWO TREATMENTS NOT PRACTICED, TREATMENT TWO TREATMENTS \6 r-“ i >14" I 1 I------ ONE TREATMENT <14" >14" <14" >14“ & (£) S (5 NOT PRACTICED NOT PRACTICED Com © PRIMARY TILLAGE 1-----MOLDBOARD PLOW CHISEL PLOW H i_ I73 I FALL SPRING r~ _ i £ Z <7" s rb r~ I >7" NT £ td DISC TILLER SPRING _ lL Hi <7" STD FALL >7" <7" FALL r >7" >7" SPRING i l ~I i— <7" Ll _ I— I5 >7*i <7" <7” >7" NT &TD NT ^TD N'f STD STD NT NF .NP G-26 b 55 6 ROWS >6 < E > 1 ROWS ROWS <6 ROUS >6 <6 ROWS :OWS, ROWS ROWS >6 <6 "rows >6 <6 “ rows >6 <6 “ rows “ ROWS i 7" " &TD NT & a b 1 c J d e f g < m ROWS I DISC TILLER © Com PRIMARY TILLAGE 1-----MOLDBOARD PLOW , U FALL <7" CHISEL PLOW 1 L . SPRING FALL •„ > 7" DISC TILLER — i © SPRING ^ • <^> <7" > 7" Cm) STD NT. i— — i J G -2 S a b 1 c d e f g < m ROWS <6 >6 ROWS ' endY np! Co/m PRIMARY TILLAGE 4MOLDBOARD PLOW 1 CHISEL PLOW I--- FALL DISC TILLER LL_ SPRING (h D <7" >7" I— I'— i STDD N1 1 G-29 a b 7 c d e f S ROWS Conn 9 PRIMARY TILLAGE 1------ I CHISEL PLOW MOLDBOARD PLOW |3 1 FALL SPRING I--- 7" (A) <7" J !TD d T STD"NT © G -3 0 a a b 3 b 5 c 2 c 1 d d e e f f e B < f^ 2 ROWS r <6 "i >6 ROWS \ f j s END. END Com PRIMARY TILLAGE r J 1------ MOLDBOARD PLOW CHISEL PLOW DISC TILLER 12 j-------------------------- , (ffi) FALL I2 _c SPRING FALL ( >7 t« < 7" X 5TD NT i a a b 2 b c c d d e e f f S g <6 >d ROWS 7" <7" . I’ , LTD NT 1 FALL <7" 1 1 STDNT / > 7" Li__ FALL SPRING i JL — i <7" >7’ 7 ST*D NT 1 ROWS <6 >6 ROWS. Ir <6 “1 >6 ROWS G-32 7 © a b (A) i I7 S*TD NT. STD flP i SPRING 1L_ >7" DISC TILLER © Co/til PRIMARY TILLAGE 1-----MOLDBOARD PLOW CHISEL PLOW |3 FALL I-------- 1--------1 1 SPRING FALL 7" <— <7" b DISC TILLER I2 I------------------ SPRING b— (A> X > 1 1 ® >7" JL sr-K W © LTD G-33 a 1 b 1 c d e £ g i— <6 ROUS 17 <6 — l >6 ROWS 12 11 END I? 9 Co fin PRIMARY TILLAGE 1 MOLDBOARD PLOW CHISEL PLOW , DISC TILLER I2 I------------------ FALL 1 GlD (NP) SPRING A >7" G-34 a b I c d e f g <6 >& ROWS CROP S o yb ea n * r DEEP TILLAGE YES IS FA LL I------ ONE TREATMENT 13 <14" (B) ^ >14" FA LL SPRING 4 I ( NOT "\ TWO ^PRACTICED/ TREATMENTS 1 I------ ONE .TREATMENT TWO TREATMENTS 12 <14" L W I4 SPRING 4 ^ PRACTICED >14" G-35 MULTIPLE SHANK SUBSOILER SINGLE CHISEL SUBSOILER I4 <14" >14" $ ( |Z <14" NOT ^ PRACTICED >14" \ (!) J ^ / NOT \ ^PRACTICED/ 1--------------- MOLDBOARD PLOW 1 FALL SPRING <7" i— — i <7" > 7" STD 176 N T £ tD 7 © i 7" 12 >7" JL X ® £ 75 td 7 < 7" 17 14 NT ~NT n i s'tD Wt STD NT 4 z SPRING FALL 177 I 1 I— SPRING FALL >7" 17 2 1 I--- 12 I " DISC TILLER 177 r 1 , CHISEL PLOW 79 I Soybe.an6 7" 7 -i 17 r STD NT STD 7 (NP) 7 NT (tib 92- n a a a 7 a b 2 b 73 b 2 b c c c 7 d d e 7 a b 7 b c c c d d d d e e e e e f f 2 f f f f g S s g g g 7 7 r <6 ROWS >6 <6 >(! <6 >"6 “ ROWS ROWS 17 i n u E N D J C N P A E N D J ^ N P ) ^END, < m ROWS a 3 7 — — 1 (— — i <6 >6 <6 >6 “ rows “ rows fENDj 1 I |— — I <6 >6 <6 >6 ROWS ROWS 17 17 (? Soybean* PRIMARY TILLAGE 1-----MOLDBOARD FLOW CHISEL PLOW r r~ FALL 1 DISC TILLER I1 1 ■ I------ SPRING i SPRING FALL I1 i— — <7" I © >7" © <7" > T (A) STD NT / (t]p) LTD 1 G-37 a b I c d e f g C6 <6 >6 Soybzam 9 PRIMARY TILLAGE 1------------ I------- CHISEL PLOW DISC TILLER ■-------- FALL <—ss) SPRING \ ^ £7" © ’ >7" TD NT J © £D 8£- MOLDBOARD PLOW 1 ROWS S oybzcw i PRIMARY TILLAGE 1------- I------------- MOLDBOARD PLOW CHISEL PLOW ( DISC TILLER I . FALL ^ I* 1 >7" 7 flD I--- ■ SPRING (NP) v— ' FALL SPRING ' <7" ^ ( ED STD NT X ' > 7" >7" (bj?) '— ' 7 G-39 a a b 7 b 1 c c d d e e f f S B 56 ROUS 7 <6 >6 ROWS S oybzam 9 PRIMARY TILLAGE 1------- I------- MOLDBOARD FLOW CHISEL PLOW 1 DISC TILLER 2 1 I— <§P) FALL 1 (A) SPRING 12 i— <7" >7" cA STD NT (A) Ot^D i— '— i a b 2 c d e f g r <6 >6 ROWS n l J END SotjbzcwUi 9 PRIMARY TILLAGE 1 MOLDBOARD PLOW CHISEL PLOW I2 1 j------------------ FALL 1 t-FALL SPRING 2 © I (4 > 7" < 7" >7“ U X (Si) 1 SPRING j r - 1 - . £7" DISC TILLER iro" 12 (i) STD NT , d <6 ROWS ROWS