hMSU RETURNING MATERIAQ: Place in book drop to LJBRARJES remove this checkout from —.‘_. your record. FINES will be charged if book is ~ returned after the date ’rfi‘ “ems. stamped below. “L7 .' , . V' 'H w 'l. 9" x w ' '1', ¥ Us) LONG TERM ECONOMIC COMPARISONS 0F ALTERNATIVE TILLAGE SYSTEMS By Theodore Otto Jenne AN ABSTRACT OF A THESIS Submitted to Michigan State University in partial fulfillment of the require-eats for the degree of MASTER OF SCIENCE Departlent of Agricultural Economics 1985 431.3: "*1. I'- -. he ‘- 17.:1 !’ this in - "H-e.‘ ABSTRACT LONG TERM ECONOMIC COMPARISONS OF ALTERNATIVE TILLAGE SYSTEMS By Theodore Otto Jenne The on and offsite effects of agricultural nonpoint pollution have been a subject of public and private interest. These concerns focus on far. operators who sake short tern cash flow and Long run soil productivity tillage decisons. This study develops soils data and crop budgets by tillage systen for the St. Joseph River Basin in Southwestern Michigan. A discounted net cash flow technique compares the long tern econonic inpacts of crop rotations and tillage practices in the study area. Analysis outcoses are situation specific and dependent on yield and costing assulptions. Reduced or no tillage systens’ nachinery and labor cost advantages are offset by higher crop costs relative to soldboard plow costs. DEDICATION To ny parents, Ker-it and Hanna, for whon agricultural production is reality and not just a special case of theory. 11 ACKNOWLEDGEMENTS I would like to thank the depart-ent of Agricultural Economics at Michigan State for their assistance in my graduate education. Special thanks go to Roy Black, ny najor professor, for guidance and patience. I as sure we both have done sole helling and danning but this "paper” {is finally done. Thanks go to conittee lesbers, Jerry Schwab and Jis [Cells for their consents and suggestions. Appreciation is extended to John Sutton and the crew at 888 for word processing facilites and a good working enviroaent . 111 TABLE OF CONTENTS Chapter 1 INTRODUCTION 1.1 Overview 1.2 Objectives 1.3 Assumptions 1.4 Definitions Chapter 2 PROBLEM STATEMENT 2.1 Justification and Possible Policies 2.2 Erosion Problem 2.3 Soil Characteristics Chapter 3 STUDY AREA 3.1 Ares Description 3.2 Soils Area and Soils Chapter 4 MODEL STRUCTURE 4.1 Methods Used in Other Studies 4.2 General Procedure 4.2.1 Factor Review 4.2.2 Estinator Development 4.3 Discounted Net Cash Flow Formulation Chapter 5 YIELDS 5.1 Literature Review 5.2 Theoretical Assumptions 5.3 Actual Yield Paraneters Used Chapter 6 MACHINERY 6.1 Machinery and Labor Cost Review 6.1.1 Labor 6.2 Theoretical Machinery Cost Assumptions 6.2.1 Machine or Machinery Set Selection Methods 6.2.1.1 Machine or Machinery Set User Costs 6.2.1.2 Machinery Set Annualized Costs H 03th 74 79 .o..-- 5. e o ..’ .1 Table of Contents (continued) Page 6.2.2 Minimization of Machine or Machinery Set 80 Costs over a Selected Analysis Tine Period 6.2.2.1 Matrix Search Procedure 80 6.2.2.2 Minimizing Annualized Cost Procedure 82 6.2.2.3 Estimation of Appreciating Repair 91 Costs and Declining Salvage Values 6.3 Machinery Cost Estimates 96 6.3.1 Explanation and Illustration of the 97 Machine and Machinery Set Optisum Life Cycle Equations 6.3.2 Optinun Machinery Life Cycle Equation 102 Results Chapter 7 PERTILIZERS 110 7.1 Literature Review 110 7.2 Fertilizer Parameters 113 7.2.1 Nitrogen 113 7.2.2.1 Corn Nitrogen Use 117 7.2.1.2 Soybean Nitrogen Use 123 7.2.1.3 Nheat Nitrogen Use 124 7.2.2 Phosphorus and Potassium 126 Chapter 8 HEREICIDES AND INSECTICIDES 133 8.1 Literature Review of Herbicide and 133 Insecticide Use 8.2 Herbicide and Insecticide Theoretical 134 Assumptions 8.3 Herbicide Use 139 8.4 Insecticide and Fungicide Use 141 Chapter 9 PRICES, SEEDING RATES, OTHER COSTS, 144 AND LENGTH OF ANALYSIS 9.1 Literature Review of Pricing and Other 144 Cost Assumptions 9.2 Seeding Costs 145 9.3 Other Costs 146 9.4 Pricing 147 9.5 Costing Assumptions 152 9.6 Discount Rate and Length of Analysis 155 Chapter 10 MODEL RESULTS 158 10.1 Review of Other Studies’ Results 158 10.2 Discounted Net Cash Flow Equation 160 10.3 Discounted Net Cash Flow Equation Explanation 163 10.4 Discounted Net Cash Flow Progran Results 168 V Table of Contents (continued) Chapter 11 SUMMARY 11.1 Model Sunmary and Outcomes 11.2 Model Linitations Appendix A Situational and Soils Data A.1 A.2 A.3 A.4 A.5 Area Information and Tables A1 and A2 St. Joseph Basin Soils Erosion Factors and Calculations of Erosion by Soil and Rotation, Tables A3 - A10, and Figure A1 Using Soil Parameters in the Determination of Soil Productivity Soil Productivity Index Calculations, Tables All - A18 Appendix 8 Model Information ./ \ r. <\ K v“ \ 9E Q03 (VI-lbw N 1 Crop Yield Potentials by Soil, Tables 81 and 82 Crop Operations by Tillage System, Tables 84 - 811 Machine Requirements by Crop Rotation Optimum Replacement of Farm Machinery Non - Optimum Machine or Machinery Set Cycle Considerations Assessing Changes in Tillage Systems Thru Tine Comparison of "Mixed Cycle” and ”Optimum Life” Machinery Sets Machinery Paraneters, Tables 817 - 822 Phosphorus and Potassium Fertilizer Recommendations, Tables 823 - 828 ’ 8.10 Herbicide Use by Crop and Tillage System, Table 829 8.11 Stopping Rule, Tables 830 and 831 and Crop 8udgets, Tables 832 - 835 Appendix C Discounted Net Cash Flow Program 0.1 OPT LIP Program Inputs and Outputs page 172 172 175 177 178 182 191 201 203 221 222 226 232 239 243 248 259 264 273 279 281 291 305 out o '7. I. .. ‘7‘! PD. LIST OF TABLES Yield Estimates by Publication Yield Estinates by Tillage System Yield Estimates by Rotation Yield Estimates by Tillage System Combination and Rotation Calculation of Individual Machine’s Optimun Life and Annuity Value Moldboard Plow Tillage Optimum Life Calculations Chisel Plow Tillage Optimum Life Calculations No - Till Optimum Life Calculations Nutrient Removal by Several Michigan Field Crops Equational Estimates of Corn Yield and Nitrogen Use Relationships The Most Profitable Nitrogen Rate for Corn Based on a Cosputer Model for Predicting Yield and Corn to Nitrogen Price Ratios Nitrogen Fertilizer Recommendations for Short and Stiff Strawed Wheat Varieties Median Phosphorus and Potassium Soil Test in Counties of the St. Joseph River Basin Desired Crop Soil Nutrient and pH Levels Corrective Phosphorus Use by Crop and Yield Level Maintenance Phosphorus and Potassium Use by Crop and Yield Level Insecticide and Pungicide Applications Seeding Rates Commodity Prices Fertilizer and Seed Prices v1.1 Page 61 62 63 64 105 107 108 109 116 118 120 125 128 128 130 131 142 146 149 150 I" LIST OF TABLES (continued) TABLES Chemical Prices Discounted Net Cash Flows(DNCF) for Various Tillage Assunptions and Erosion Rates APPENDIX TABLES A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 Selected Michigan and St. Joseph River Basin Statistics St. Joseph River Basin County Conparisons Soil Erodibility and Soil Loss Tolerance, Water Erosion ”E” and "T" Factors, and T/E Values ”C” Factors for Cropland in Michigan’s Lower Peninsula ”C” Value Matrix for the St. Joseph River Basin "P” Values and Slope Length for Contouring ”P” Values, Maximum Strip Widths, and Slope Length Limits for Contour Stripcropping Soil Loss Ratio Factor ”LS” Soil Parameters and Erosion Calculations for Various Soils in the St. Joseph River Basin Cross Erosion Rates for Various Crop Rotations, Tillage Systems and Soils in the St. Joseph River Basin Nonlimiting, Critical, and Root-limiting Bulk Densities for each Family Texture Class Criteria for Determining Nonlimiting, Critical, and Root-limiting Bulk Densities for each Family Texture Class Coefficients for Equations for each Family Texture Class used in Equation A2 Adjustment Factors for Sufficiency of Bulk Density used in Equation A2 Neighting Factors Soil Erosion/Productivity Relationships v.ii.i Page 151 165 Page 178 180 191 193 195 196 196 197 199 200 206 206 207 207 210 217 fl' . I 0"“ ' f, ..1 _..-..- O - d . . . .. l .. F |. o. LIST OF TABLES (continued) APPENDIX TABLE Bl B2 B3 B4 B5 B6 B7 88 89 810 811 812 Yearly Change in Productivity on a Hillsdale 2 - 6X Slope Soil Characteristics of Various St. Joseph Basin Soils and Calculations of Productivity Indexes at Each Soil Horizon Average Yield Potentials for Crops Grown on Different Soil Management Groups under Good Management with Adequete Drainage but without Irrigation in Areas with a Growing Season of over 140 Frost-Free Days(Southern Michigan) Average Yield Potentials for Crops Grown on Different Soil Managesent Groups under Good Managenent with Adequete Drainage but without Irrigation in Areas with a Growing Season of Less than 140 Frost-Free Days(Northern Michigan) Comparisons of Crop Yields Using Selected Tillage Practices Crop Operations By Tillage System for Wheat after Navy Beans Crop Operations by Tillage System for Oats after ‘Sugar Beets Crop Operations by Tillage System for Alfalfa after Sugar Beets or Alfalfa after Oats Crop Operations by Tillage System for Corn after Navy Beans or Corn after Soybeans Crop Operations by Tillage System for Soybeans after Corn or Soybeans or Soybeans after Navy Beans Crop Operations by Tillage System for Navy Beans after Corn, or Navy Beans after Navy Beans or Navy Beans after Alfalfa Crop Operations by Tillage System for Sugar Beets after Navy Beans or Sugar Beets after Wheat or Sugar Beets after Corn Crop Operations by Tillage System for Corn after Corn or Corn after Wheat or Corn after Sugarbeets or Corn after Alfalfa Field Operations for Various Tillage Combinations on a Corn - Corn - Soybean - Wheat Rotation Page 217 219 222 223 224 226 227 227 228 228 229 230 231 234 o- O ’01.. ."'. II. I '.§ I! u: '5. In 0‘ — LIST OF TABLES (continued) APPENDIX TABLE 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 Field Operations for Various Tillage Combinations on a Corn - Soybean Rotation Field Operations for Various Tillage Combinations on a Alfalfa/Oats - Alfalfa(3 years) - Corn - Corn - Corn Rotation Discounted Net Cash Flows Using Various Machinery Replacement Lives Machinery Cycle Annuity Values and Discounted Net Cash Flows(DNCF) for a 150 Year Analysis under Different Tillage and Change in Soil Productivity Assumptions Diesel Fuel Requirements for Selected Field Operations Fuel Requirements for Various Rotations and Tillage Systems - Tractor and Equipment Parameters used in MACHSEL Machinery Model Parameters Life and Repair Costs of Machines Machinery Variables Annual Phosphorus and Potassium Recommendations for Alfalfa Grown on a Mineral Soil Annual Phosphorus and Potassium Recommendations for Small Grains Annual Phosphorus and Potassium Recommendations for Dry Beans and Soybeans Grown on a Mineral Soil Annual Phosphorus and Potassium Recommendations for Corn Grown on a Mineral Soil Annual Phosphorus and Potassium Recommendations for Potatoes Grown on a Mineral soil Annual Phosphorus and Potassium Recommendations for Sugarbeets Grown on a Mineral Soil Projected Herbicide Use for Alternative Tillage Systems Page 236 237 242 257 265 266 267 269 270 272 273 274 275 276 277 278 279 .u. As ..s .Q LIST OF TABLES (continued) APPENDIX TABLE 830 831 B32 B33 B34 835 Number of Years to Calculate Discounted Net Cash Flows under Different Discount Rate and Error Levels Interest Rates from 1962 thru 1983 First Year Corn Budget Second Year Corn Budget Soybean Crop Cost Budget by Tillage Wheat Crop Cost by Tillage x i Page 283 285 287 288 289 290 TEXT 5.1 5.2 LIST OF FIGURES FIGURES Yield Level Change over Time Using One Tillage System Yield Level Change Over Time Using More than One Tillage System An Integer Matrix Evaluation System for Various Machinery Combinations Machinery Repair Costs over Time Machine Salvage Value over Time Herbicide Use By Tillage System thru Time Insecticide Use by Tillage System thru Time APPENDIX FIGURES A1 A2 A3 81 82 83 B4 85 86 B7 Rainfall Erosion of ”R” Factors Concept of a Sliding Weighting Factor Linear Approximation of Yearly Change in Soil Productivity Net Returns by Tillage System Combinations Short Term Interest Rates OPT LIF Program Flow Chart for OPT LIF Flow Chart for Yearly Machinery Repair Cost in OPT LIF Program Flow Chart for Machinery Set Salvage Value in OPT LIF Program Flow Chart for Yearly Machinery Cost and Annualized Machinery Cost by Cycle year in OPT LIP Program Flow Chart for Discounted Net Cash Flow(DNCF), Annualized DNCF, OPT LIF program and Yearly Cash Flow in xi.i 292 - Page 55 56 81 91 93 137 138 192 211 215 248 286 300 301 302 302 303 304 Chapter 1 Introduction 111-92952195 Increased public awareness of environmental issues, and the long term capacity of U.S. crop acreage to meet domestic and international food demands has renewed focus on soil erosion. Also, increases in energy, irrigation and chemical costs have renewed interest in the preservation of topsoil as a medium for plant growth. Many farmers currently use soil inverting tillage methods to solve insect and weed problems. These practices combined with continuous row cropping were perceived to be more profitab1e[1] but erosive. Many farmers are and society is faced with an soil erosion/profitability tradeoff[2]. Fence row to fence row planting by farmers in response to significantly higher real commodity prices during the 1970s compounded the problem. 1. Profit is used as an accountant’s definition of net returns to management not as an economist’s "pure profits”, the difference between total revenue and total economic costs 2. Tillage practices reducing soil erosion may involve more labor, machine time, or costs per land unit £212. a ’m.. . ..‘.‘ ! n..ef. fi‘. "4.. 6‘.‘.. This study addresses the economic incentive for farmers in selected areas of Michigan to voluntarily adopt erosion reducing tillage methods. The chisel plow as an alternative to the moldboard plow and no till are considered. Off - site impacts, while important, are beyond the scope of this .study. This study is an extension of earlier investigations comparing the effects of moldboard plow and reduced or no tillage systems[3]. The impact of erosion on the long term potential productivity of the soil is considered. An analytical framework will be developed that extends previous studies to consider the economic costs and benefits of alternative tillage systems through time. The actual parameters such as yield levels, crop costs, and the level of soil erosion are hard to derive and accurately enumerate. Sensitivity analyses will be used to estimate the consequences of alternative plausible parameter values[4]. The emphasis is not on the derivation of exact results but on developing decision criteria. Particular focus is placed upon finding the time at which a switch from existing moldboard plow tillage to alternative conservation tillage techniques should occur. The choice criteria used is the expected discounted net cash flow. Study objectives and definitions of terms follow in this chapter. Chapter 2 discusses the nature of the erosion and 3. Hannibal A. Muhtar, 'An Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Drainage Basin’, PhD Dissertation in Agricultural Engineering, Michigan State University, 1982. 4. As data is improved, the results can become more exact ‘n 0'. 5' 1f - a '. s .‘ .. .a ‘ m 9 s .,“- Ii. ..' OJ relevant soil characteristics. Chapter 3 describes St. Joseph River Basin cropping and soil characteristics. An outline of the discounted net cash flow(DNCF) formulation and component development is in chapter 4. Chapter 5 thru 9 develop the yield, machinery, fertilizer, chemical and price component assumptions and estimations. Chapter 10 details the discounted net cash flow equation ans summarizes the results of the various runs. The model outcomes are summarized and model limitations are discussed in chapter 11. I;Z_Qbiss£i!es The objective of this study is to develop an analytical framework to assess the economic consequences of different tillage systems or combinations thereof on long term soil productivity and crop yield levels. A discounted net cash flow (DNCF) formulation is used to estimate long term economic outcomes. The DNCF framework includes yields, prices, machinery costs and crop costs. Specific objectives are: 1. To review the erosion/profitability literature. Results of previous studies shape the assumptions and the direction of this study. 2. Use soils and input/output data from areas in Michigan’s St. Joseph River Basin to estimate some relevant model parameters. ' a ‘s. - ~~ V '~.’ Estimate the influence of choice of tillage system on erosion rates and resultant impacts on soil productivity and on insecticide and herbicide use. Select ”optimal" machinery sets through the use of a model choosing the least cost machinery set given a crop rotation, time constraints, farm size, and soil specifications. This permits an estimation of machinery cost differences between various tillage systems. Estimate the long run discounted costs and returns for the different tillage systems given various crop rotations and parameter set assumptions. Sensitivity analysis will be conducted to assess the effects of the changes in the factors involved. l;§_éesssetiee§ The assumptions define a model and the scope of the analysis. The changes in potential crop yield are primarily due to changes in soil productivity as soil erosion removes topsoil assumed to be more productive than its underlying subsoils. Initial soil characteristics and structure remain constant thru time; plant rooting action does not change the characteristics of soil horizons. I), 3. Offsite social impacts and externality considerations attributable to soil erosion are ignored [5] 4. Crop input ”mixes", and biological and physical technology are assumed to remain constant or change in equal proportions by tillage sytem thru time. 5. Farmers are assumed to operate in a atomistic market in which factor and good markets clear. Farmers are assumed to maximize long run profits. Consumers are assumed to make utility maximizing decisions. These general equilibrium conditions may be tempered somewhat by imperfect information concerning capital goods and input mixes violating some of the simplifying theoretical assumptions. 6. Farm size and labor supply remain constant over time. 7. Economic measures are in real terms with no change in the relative price levels of specific inputs and/or commodities assumed. 8. Real interest rates are used and are constant across the time period. 5. Unless public policy devises mechanisms convey the impact of offsite costs to agricultural firms, they will overproduce since they are equating their own marginal benefits and costs in an effort to maximize utility. As a result, a misallocation of resources and inefficiency can result. 9. There are no tax considerations(income tax or investment credit) included in this analysis. 10. There are no government subsidy or penalty payments assumed though governmental restrictions and policies have considerable impact on the decision making in the agricultural sector. As with the tax question, the consideration of government economic influence on the model complicates calculations and has therefore been excluded. 1.4 Definitions The following definitions concern terminology used in the literature. An understanding of the frame of reference will facilitate in the discussion and critique. 1.4.1 Tillage Systems Moldboard Plowing - involves soil inversion burying most plant residues, weed seeds or seedlings, insect larvae - done with a moldboard plow fully disturbing, loosening and inverting the soil to a depth of 8-10 inches to facilitate plant root development — plowing can be done in the late fall or in the spring[6] 6. Water absorption is increased due to fall plow but soil erosion may be greater due to spring runoff Conservation (chisel, no till, tandem disk, & ridge till) Chisel plowing Tandem No-Till disk plowed surface is worked with disk or disk harrows to produce a relatively smooth and clean planting surface planting is done utilizing commercially available planters with weeding done either mechanically and/or with herbicide application does not totally invert the soil but disturbs the ground leaving decaying plant material and residue on or near the soil's surface in varying degrees and amounts helps reduce soil erosion and increases soil moisture retention during dry weather plant root development may be restricted due to incomplete soil inversion and loosening, may result in cooler spring soil temperatures due to increased levels of surface residue, can encounter increased weed and insect problems or require increased management skill during planting weed control is done by cultivation or through herbicide use (pre and post emergent herbicides) usually done in the fall as deep as done with moldboard plowing (8-10 inches) with the soil being completely disturbed but not inverted thus allowing water penetration while retaining plant residue at or near the surface to help reduce erosion chopping of plant stalks and trash can be done with a disk mounted on the chisel plow or by field cultivator after plowing to reduce surface'roughness involves the use of a tandem disk to disturb the topsoil, chop residue and smooth the surface for the planting of wheat only 2-4 inches of the topsoil are disturbed if a disk is used to chop surface residue if no disking is done, the soil remains undisturbed except for the planting slot which needs to be of sufficient width and depth for seed coverage and soil contact, plant residue remains on the soil surface Ridge Till 1.4.2 Erosion Soil Water Erosion Rate a cover crop may be used between cropping seasons for erosion control herbicides may be used before and after planting to help limit weed populations planting can be done with a slot planter and fertilizers can be surface applied or injected with a knife depending on the fertilizer type and timing desired disease and insect control may be more difficult as fungicide and insecticide effectiveness may be limited by the undisturbed soil and amount of surface residues, erosion is effectively reduced especially on erosion prone soils seedbed preparation and planting are completed in the same operation by scalping the old planting row, pushing the soil and crop residues into the furrow, forming a new ridge and planting allows drier and warmer planting soils in early spring tracking of machinery wheels in the furrows limits the area soil compaction allowing better plant root development in the ridge can slow water and wind erosion due to ridge and furrow residues function: A = R x K x L x S x C x P A - rate of erosion (tons per acre) R - uncontrolled runoff or rainfall factor K - erodibility factor L - slope length factor S - slope gradient factor C - cropping factor P — erosion control practices factor 9 Soil Wind - function: E = f (I, K, C, L, V) Erosion Rate E — soil loss in tons per acre I - soil erodibility (ease of particle detachment, size of particle) K — soil roughness (unevenness of surface traps soil) C - climatic factor (degree of soil moisture and evenness of climate) L - length of field (number of times particles hit ground disturbing others) V - quantity of vegetative cover (amount of residue) Soil Erosion — the detachment by raindrop impact, splash, flowing water, or blowing wind of mineral particles and organic material of the soil Sediment Delivery - the movement of eroded soil from its [7] original resting place to the stream system Non-point - the entrance of soil sediment in streams Pollution due to wind or water erosion on cropland and/or other sources. No singular point source Sediment Control - programs utilizing cropland management Policy[8] which can reduce sediment erosion and improve water quality Soil Tolerance - function of topsoil depth, soil (T value)[9] productivity, and rate of soil regeneration. This is the maximum rate of annual soil erosion which permits the current level of soil productivity indefinitely 7. James C. Wade and Earl O. Heady, 'Controlling Nonpoint Sediment Sources with Cropland Management: A National Economic Assessment’, American Journal of Agricultural Economics, Feb 1977. 8. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, and W.E. Larson, 'Productivity of Soils; Assessing Long Term Changes due to Erosion’, Journal of Soil and Water Conservation Vol. 38, Jan — Feb 1983, pp. 39 - 44. 9. F.J. Pierce, R.R. Dowdy, W;A.P. Graham, and W.E. Larson, 'Productivity of Soils; Assessing Long Term Changes due to Erosion’, Journal of Soil and Water Conservation Vol. 38, Jan - Feb 1983, pp. 39 - 44. 10 Soil Productivity the capacity of a soil to produce a [10] specified plant or sequence of plants under physically defined sets of management practices - soils high is organic matter and nutrients, of medium texture, in good tilth and 60 inches or more are recognized as being highly productive Soil Texture - sandy; coarse textured Classes[1l] a. Sands (coarse sand, sand, fine sand, very fine sand) b. Loamy sands (loamy coarse sand, loamy sand, loamy fine sand, loamy very fine sand) - loamy; moderately coarse textured (coarse sandy loam, sandy loam, fine sandy loam) medium textured (very fine sandy loam, loam, silt loam, silt) moderately fine textured (clay loam, sandy clay loam, silty clay loam) - clayey; fine textured (sandy clay, silty clay, clay) 10. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, and W.E. Larson, ‘Productivity of Soils; Assessing Long Term Changes due to Erosion’, Journal of Soil and Water Conservation Vol. 38, Jan - Feb 1983, pp. 39 - 44. 11. R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, 'Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 11 1.4.3 Terminology Expected[12] - the mean value of a random variable Appreciate[l3] - raise in value or price User Cost[l4] - the nominal value of imputed rental of capital services used in each period. The components of this value are the interest rate or opportunity cost of the capital good(interest rate times the market price of the good) plus the economic depreciation of the capital good during the current time period plus any capital gains or losses of the good during the time period used. 12. William Morris-editor, ’The American Heritage Dictionary of the English Language’, American Heritage Publishing Co. Inc. and Houghton Mifflin Company, 1969. 13. Ibid 14. William H. Branson, 'Macroeconomics Theory and Policy, Harper and Row , Publishers, 1979, p. 231 Chapter 2 PROBLEM STATEMENT Control of soil erosion on U.S. farmland issues is reviewed in this chapter. Offsite and onsite effects of soil erosion on soil characteristics will be examined. 2.1 Justification and Possible Policies The rate of erosion on U.S. farmland is an important long-term issue confronting U.S. agriculture. More extensive and intensive soil cultivation activities in the early 20th century combined with the drought in the 1930s led to serious erosion problems(the ”Dust Bowl”). This resulted in the establishment of the Soil Conservation Service in 1935. Subsequent legislative action in 1936[1] had dual goals of increasing farm income(via price incentives and more efficient input use) and the reduction of gross soil erosion[2][3]. l. The Soil Conservation and Domestic Allotment act 2. Through the encouragement of the use of conservation practices such as terracing, contouring, and stripcropping 3. W.G. Boggess and 8.0. Heady, ‘A Sector Analysis of Alternative Income Support and Soil Conservation Policies’, American Journal of Agricultural Economics, Nov. 1981, pp. 618-628. 1 2 1 3 The simultaneous goals of meeting food and fiber demands, domestic and foreign, and meeting rising public demands for a quality environment have forced change on the structure of the agricultural sector. A basic issue is that topsoil with its rich organic matter can be a better medium for plant growth than its underlying subsoil. There are opportunity costs[4] in monetary measures(foregone cash flows) or in terms of labor requirements to preserve this topsoil. Narrowing per commodity unit net returns and technological change have stimulated the use of larger machinery and higher yields per acre making the use of erosion control paractices such as contouring, strip cropping, and terracing less attractive[5]. These factors combined with relatively high short run net returns[6] of erosive row crops such as corn and soybeans serve to counter government and private efforts to promote widespread adaptation of soil conservation techniques which facilitate the coevolutionary process of technology and the enviroment. Thus the farmer is faced with a basic decision of increasing the returns to land use in the short run or maintaining long run land 4. The preservation of topsoil may involve additional crop costs or lower net returns 5. Iowa State University, 'The Economics of Soil and Water Conservation Practices; Results and Discussion’, Center for Agricultural and Rural Development, Card Report 109 SWAPII, Feb 1983. 6. High commodity prices induce more intensive use of available fixed resources as the marginal return to a factor of production has risen thus stimulating a willingness to pay a higher factor cost for the use of a resource. s-', 223‘. 8“. a s ‘1 l 4 productivity by using conservation practices[7]. Despite recent increased societal awareness of the effect of agricultural erosion on environmental quality, economic incentives such as increases in the demand for agricultural commodities during the 1970s led to the opening of marginal cropland and soil eroding cropping practices. Continued legislative efforts such as Soil Bank(1956) and various other similar programs repeatedly have run up against these same contrary economic motives as perceived and experienced by farmers. Policy questions to be raised are what practices are available, effective and efficient in the control of soil erosion? What policies can be implemented to promote conservation practices and to what extent will farmer short run cash flows and long run economic viability be affected if soil erosion is held to acceptable or tolerable levels[8]? In the formulation of soil erosion control policies, several considerations are necessary to deal with the problem properly[9]. 7. Iowa State University, “Short and Long Term Analysis of the Impacts of Several Soil Loss Control Measures on Agriculture’, Center for Agricultural and Rural Development, Card Report 93, June 1980. 8. Iowa State University, ‘The Economics of Soil and Water Conservation Practices; Results and Discussion’, Center for Agricultural and Rural Development, Card Report 109 SWAPII, Feb 1983. 9. James J. Jacobs and John F. Timmons, 'An Economic Analysis of Agricultural Land Use Practices to Control Water Quality’, American Journal of Agricultual Economics, pp.791—797, Nov 1974. 1 5 1. The physical relationships between alternative agricultural production methods and soil erosion levels need to be identified and quantified. 2. Costs for each alternative production practice need to be determined. 3. Benefits associated with soil erosion control and water quality improvements need to be estimated. These data need to be defined and estimated in monetary or nonmonetary measures to make rational policy decisions. Policies which significantly reduce soil erosion with little or no reduction in net returns to management and factors of production are good candidates for public promotion. Public policy makers can seek to maximize the amount of soil saved given their available funding with public intervention justified on the grounds of market failure[10] and intergenerational equity[11]. Reduction of sediment runoff into streams helps reduce flood danger, navigation problems, and water pollution and is preceived as a benefit to society[12] but questions of assessing who 10. Masked market signals, institutional constraints, externalities, underestimation of soil erosion by farmers 11. Dan E. Eugler, 'Variable Cost-Sharing, Implications for Kentucky’s Jackson Purchase Area; An Economic Study of Cash Grain Production Considering Soil Depletion’, Type III Seminar, Dec 1983. 12. The valuation of flood damage, water pollution and navigation problem reductions is difficult to circumstantiate or to test empirically and may be only possible to rank in ordinal nonmonetary terms. l 6 benefits and loses due to offsite impacts arise[13][l4]. The following is a short summary of some of the policies recommended to help control erosion[15][16][l7]: 1. Direct Regulation 1. Banning preplant tillage on crops following soybeans 2. Banning the use of moldboard plowing on slopes greater than 2% 3. One pass till plant or slot plant on slopes greater than 5: 4. Only farms using reduced or no tillage can participate in commodity programs 5. Limit on the rate of erosion allowed — difficult to measure, results in large reductions of soil erosion 13. Francis M. Epplin, Thomas F. Tice, Alan E. Baquet, and Steven J. Handke, ‘Impacts of Reduced Tillage on Operating Inputs and Machinery Requirements’ 14. Iowa State University, 'Potential Effects of Policy Alternatives on Regional and National Soil Loss’, Center for Agricultural and Rural Development, Card Report 90, June 1980. 15. W.G. Boggess and E.O. Heady, 'A Sector Analysis of Alternative Income Support and Soil Conservation Policies’, American Journal of Agricultural Economics, Nov. 1981, pp. 618-628. 16. Iowa State University, ’Potential Effects of Policy Alternatives on Regional and National Soil Loss’, Center for Agricultural and Rural Development, Card Report 90, June 1980. 17. Dan E. Eugler, 'Variable Cost-Sharing, Implications for Kentucky’s Jackson Purchase Area; An Economic Study of Cash Grain Production Considering Soil Depletion’, Type III Seminar, Dec 1983. I.‘ n. a. ‘ol M'- a. a: 1 7 6. Farmland retirement with most erosive land retired. 7. Economic(Taxing[18] soil erosion - has measurement problems but is found to be the least cost method by Taylor and Frohberg[19], the T-value or maximum soil loss method was found to be slightly more costly. 2. Subsidies per acre of reduced or no till 3. Tax incentives for the use of reduced or no tillage machinery 4. Cost sharing in terrace building — helps cover lost income due to land acreage adjustments to terracing requirements The suggested policies have varying impacts on farmers’ incomes, soil erosion rates and net effects on social welfare. These policies all involve public decision making affecting the allocation of resources in the agricultural sector, changing social welfare and affecting efficiency. A major reason cited for the slow adaptation of reduced or no till is the uncertainty as to which reduced or no till methods are the most effective in erosion control. Not all means of erosion control are cost effective[20]. This analysis is 18. Progressive or equal tax per ton 19. Robert C. Taylor and Klaus K. Frohberg, ’Welfare Effects of Erosion Controls on Banning Pesticides and Limiting Fertilizer Application in the Corn Belt’, American Journal of Agricultural Economics, Feb 1977, pp. 25 -35. 20. Iowa State University, 'The Economics of Terracing in Iowa’, Card Report 123, Jan 1984. 1. .. ‘:.a.'I..-s' 22123;!!! mm I 1 8 compounded by observations that profit maximizing behavior is not the only obstacle to the adaptation of erosion control measures. The risk of reduced yields, lack of management skills, lack of information or the aesthetic look of a clean field dampen enthusiasm and slow adaptation of reduced or no till measures[21]. 2.2 Erosion Problem ihopsoil has been transformed by modern technologies from gat’imarily a stock resource to a largely renewable resource[22]. {Elle rate at which the topsoil depth changes is a function of the amount of soil erosion and the rate of soil regeneration. Rates (mf' erosion greater than the rate of regeneration result in the using up of the soil "stock" and subsequently affects soil productivity. If erosion rates are equal to or less than rates <>f regeneration, the topsoil can maintain itself at current PFOdHCtion levels indefinitely(the motivation behind T - value °°°¢ept). —__ -‘~~ —-— 21. Con; Isawa State University, 'The Economics of Soil and Water Agr' rVation Practices; Results and Discussion’, Center for lcultural and Rural Development, Card Report 109 SWAPII, Feb silo”. Burt, 'Farm Level Economics of Soil Conservation in the Econu'fF Area of the Northwest’, American Journal of Agricultural “1 cs, 63: 83-92, 1982. §3a.Ja‘-£es C. Wade and Earl O. Heady, 'Controlling Nonpoint e 1'e‘11: Sources with Cropland Management: A National Economic t;;§"‘ltent’, American Journal of Agricultural Economics, Feb 1 9 Erosion can be identified by source[23] which are from cropland used for agricultural production (the major source of soil erosion), from cropland not used in production, and non-cropland sources such as gully, streambed, channel erosion. Agricultural pollutants entering the surface ground waters are soil, plant nutrients, herbicides, insecticides, and animal wastes[24]. Soil erosion has become a serious problem as it reduces land g>roductivity thru the reduction of soil water holding capacity, the degrading of the soil structure through surface sealing, <:rusting, and non-uniform soil removal resulting in a surface of nonuniform soil characteristics[25]. Other effects of soil eerwosion which have social and private external impacts are as follows[26]: 1. Deposition of low quality sediment on floodplains 2. Reduced storage capacity of reservoirs 3- Clogged ditches and culverts -— -~-~‘ ~—- 3;,Ja-es J. Jacobs and John F. Timmons, 'An Economic Analysis Of 1c“ltural Land Use Practices to Control Water Quality’, Alerican Journal of Agricultual Economics, pp.791-797, Nov 1974. (225' Tatianal Soil Erosion-Soil Productivity Research Planning ommitt Pers (Be, 'Soil Erosion Effects on Soil Productivity; A Research Mar_:e‘=‘:ive’, Journal of Soils and Water Conservation, USDA, PF 1981, pp.82-90. 26. J. lient Mitchell, John C. Brach, and Earl R. Swanson, ’Costs and Be Imefits of Terraces for Erosion Control’, Journal of Soil and “‘ter Conservation, Sept - Oct 1980, pp.233-236. II. '1 2 O 4. Increased cost of controlling water turbidity in public water supplies 5. Degradation of drainage channels 6. Destroys aquatic habitat 7. Loss of recreation capacity and aesthetic value of streams 8. Soil runoff can carry chemical pollutants Erosion effects on soil productivity are difficult to detect as lnnductivity losses are masked by improved technologies and iIProved plant seed materials which affect yields. Soil loss may also only involve millimeters of surface soil removal per ywar[T7]. The unevenness of soil removal and the rate of erosion are complicated by varying soil horizon characteristics, differing tillage practices and crop rotations. Crop management Practices involving differing amounts of surface residue and a Variety of tillage methods affect the rate of erosion[28][29]. Erosion is a function of slope length and field length which affects the transport capacity of water runoff(dependent on water velocity). The removal of surface residue by fall plowing and 27' A 8““1881 and nonlinear process 28. Iowa St a IlPacts of te Center for University, 'Short and Long Term Analysis of the Several Soil Loss Control Measures on Agriculture’, Agricultural and Rural Development, Card Report 93, June 1980. 29. - c°.-ir::t1°n?1 Soil Erosion-Soil Productivity Research Planning 9 e?’ 3011 Erosion Effects on Soil Productivity; A Research ersPeCLIVe’ Ma _ , Journal of Soils and Water Conservation, USDA, ’ ‘9' 1981. pp.82-90. 2 1 straight row cropping without regard to slope or contours can result in the greatest amount of erosion whereas reduced tillage methods that maintain surface residue year around combined with contouring or terracing can slow erosion rates greatly. Relative soil losses are affected less by crop canopy or kind of residue and more by soil type, amount of residue and the slope of the soil[30]. The USDA comparison of tillage practices[31] and their effects are shown in appendix table 83. In experimental results, small amounts of residue in crop fiuTows (3X slope) slowed erosion to .4 metric tons per hectare, unacceptable level. Meyer, Wischmeier, and Foster[32] indicated soils having 2.24 metric tons of residue per hectare had erosion 0! 11.5 metric tons per hectare' while soils having 4.48 tons of residue per hectare reduced erosion to 2.5 metric tons per hectare on land with a 15X slope. Conservation practices are not costless requiring terracing, contouring, idling land or may involve accepting lower yields or necessitate the use of crops with lower net returns per acre. u-- —--—--- gg- iNational Soil Erosion-Soil Productivity Research Planning .' ttee, 'Sodl Erosion Effects on Soil Productivity; A Research :er8pective” Journal of Soils and Water Conservation, USDA, "“P' 1981. pp.82-90. 3 s . . . . . . TillLee A“ chr‘istiansen and Patrice E. Norris, A Comparison of age Syste. for Reducing Soil Erosion and Water Pollution’, A I Uggmultural Economics Report 499, Economic Research Service, A: May 1983. 32. R.E. P ° 0 g . “Nearch; R hillips, G.W. Thomas and R.L. Blev1ns, No Tillage esearch Reports and Reviews’, University of Kentucky 22 Some research work on the reduction of soil erosion to tolerable levels indicated that costs were three times as great as benefitsf33]. 2.3 Soil Characteristics Soil available water capacity, bulk, density, , permeability, aeretion, pH, electrical conductivity all influence soil productivity[34]. These characteristics differ by soil and by soil horizon and are affected by cropping practices and tillage 'YBtems. Long-term soil productivity is affected by topsoil thickness, plant rooting depth, water capacity and the soil depth with the maximum clay content in the soil profile [35]. The relative productivity of soils and the rate of change in soil Productivity due to erosion depends on the favorable or unfavorable characteristics in the soil profile[36]. 2:. Paul Rosenberry, Russel Knutson and Lacy Harmon, 'Predicting ‘6: Effects of Soil Loss Depletion from Erosion’, Journal of Soil an Water Conservation, May - June 1980, pp. 131 —- 134. 333.153.}. Pierce, R.H. DOWdy, W.A.P. Graham, and W.E. Larson, ErrogucfiV1ty of Soils; Assessing Long Term Changes due to F as on , Journal of Soil and Water Conservation Vol. 38, Jan — °b 1933. pp. 39 - 44. 35. Gerald A, Miller and Minoru Ameniya, 'Soil Erosion and the lIowa Soil 2000 Program’, Iowa State University Extension Bulletin ' 1°55» Ans 1982. 3 s ' 'groth. Pfer‘ce, R.H. Dowdy, W.A.P. Graham, and W.E. Larson, Ctivlty of Soils; Assessing Long Term Changes due to E s Frgnon.' JOur-nal of Soil and Water Conservation Vol. 38. Jan ‘ e 1983. pp. 39 - 44. V-lam w. J-..‘ .- 2 3 The least favorable soil characteristics limit crop production 1evels[37] so conservation efforts and soil rehabilitation should be directed at the most restricting factor, raising soil productivity the most. Plow layers created by moldboard plowing are broken up‘by subsoiling raising yields by increasing water infiltration[38] especially in the case of irrigated land. Aggregate soil particle size also affects soil erosion and productivity levels. Research indicates that there is a better distribution of soil Particles and a better distribution of nitrates under minimum “1188c methods[39]. Soil aggregate size’s are positively correlated to surface residue level decreases was noted by Muhtar[40] implying that the increase in surface crop residue helps ce-ent soil particles together increasing aggregate soil Particle lsize(thereby limiting water and wind soil erosion). 37. W.E. Larson, F.J. Pierce and R.H. Dowdy, 'Threat of Soil Erosion to Long Term Crop Production’, Science Vol.219,‘ Feb 1983, PP.448-465. 3?“ 0.1!. Martin, n.x. Cassel, and E.J. Kamproth, ’Irrigation and Ca :ge Effects of Soybean Yield in a Coastal Plain 8011’, North 1'0 ins State University, Sept 1978, pp.592-594. 22110.". Glenn and A.D. DotZenko, 'Minimum ' vs. Conventional Vol 3:; 111 Commerical Sugarbeet Production’, Agronomy Journal cOl-orado State Universit Ex eriment Station Mar - Apr 12;! Eggnlbal 4. Muhtar, 'An Economic Comparison of Conventional Drains 'ervation Tillage Systems in the Southeast Saginaw Bay Michi 8" Basin”. PhD Dissertation in Agricultural Engineering, gen State University, 1982. 2 4 There is some degree of soil disturbance required or a minimum amount of shallow tillage to allow moisture to penetrate the soil[4l]. Surface crop residue retains soil moisture but untilled soils have difficulty absorbing available moisture. Lower spring soil temperatures and high soil moisture levels when using minimum or no tillage methods[42] result in delayed plant growth and root development. Muhtar’s[43] experimental results alluded to a positive correlation between reduced and no till Yields and soil drainage. Yield differences between tillage methods indicated that no or minimum till methods are more suited to well drained soils. Sh ll ___________ , ‘Soil Moisture and Temperature Response to Sc? °" Tillage in Early Spring’, Canadian Journal of Soil ”We: May 1981, pp. 455 — 460. :gf B;R. Swanson and C.E. Harshburger, 'Economic Analysis of Conic ' f’f Soil Loss on Crop Yields’, Journal of Soil and Water "V“mn. Sept - Oct 1964, pp.183 - 186. 2:5 gzzglbal A. Muhtar, 'An Economic Comparison of Conventional Draina ervation Tillage Systems in the Southeast Saginaw Bay Michi 3° Basin’, PhD Dissertation in Agricultural Engineering, 330 State University, 1982. 'L 'l '- ..‘ Chapter 3 STUDY AREA The general agricultural activities of the St. Joseph River Basin are delineated in this chapter. The soils in the study area are discussed in terms of their characteristics, susceptibility to erosion, and productivity. 3.1 Area Description The area chosen for study is selected for its predominant soil tYpes and present cropping activities. A census of agricultural activities by the U.S. Department of Commerce for 1982 and 1973[1] is selectively summarized for Michigan and for the representative counties for the St. Joseph Basin(8ranch and St. J°sePh counties) in appendix tables A1 and A2. Farming activities are rePorted on the basis of all farms which includes many small far" and 5!? farms having sales over $10,000 or more. Inspection 0f the data shows that farms with sales of $10,000 or more comprise only about 503 of all operations counted as farms but nuflude about 95% of the harvested crop land. Inclusion of small :ér:::::“ 0f Census, U.S. Department of Commerce, '1982 Census of Hillsd lure: Preliminary Report’, Branch, Berrien, Huron, Stat a e:.Kalamazoo, St Joseph, and Tuscola Counties and the 9 0f Mlchigan, Jan. 1984. 2 5 on. m- ~.~ 2 6 farms causes the average farm size and cropping pattern data to be underestimated or misleading so the statistics for farms having sales of $10,000 or more will be used to evaluate the county differences. Corn is the dominate crop in the St. Joseph Basin both in terms of percentage of farms growing corn(90-958) and average crop size (about 60% of the acreage of the average farm acreage). Wheat is grown on about 503 of the farms but it is a minor crop[2]. Soybeans .and wheat are grown as supplementary crop to corn. Alfalfa is grown on about 503 of the farms, as part of a dairy or beef operations, Very few drybeans or sugarbeet‘s are grown in the St. Joseph area. Average farm acreage for farms having sales over $10,000 has increased by about 10% in the time period from 1978 to 1982. humeasea in the number of farms growing soybeans and wheat were observed due to increases in both the absolute and relative Prices of these crops. The average soybean and wheat acreage per farm Has increased. Changes in corn acreage were negligible. Crop rotations in the St. Joseph area are limited primarily to Corn, sOybeans and wheat with minor amounts of oats and alfalfa. --_ ~—_ —-- — 2- Wheat acreage is only about one-tenth the average farm acreage 2 7 The following is a listing of crop rotations grown in the St. Joseph Basin[3][4][5]. 1. Continuous corn 2. Corn - soybean 3. Corn - corn - soybeans 4. Corn - corn - soybeans - wheat 5. Corn - corn - oats - 5 years of alfalfa A representative crop rotation is selected for use in the analysis of various tillage systems on a particular soil. A rotation involving several crops is the corn - corn - soybean ~ wheat rotation. Various machinery combinations and tillage systems are assumed for this rotation as machinery selection is affected by soil characteristics[G] and the crop rotation selected. Yields are affected by the tillage method used and crop rotation assumed. ()ther crop rotations considered are the corn - soybean or continuous corn rotations. The oats and alfalfa crop rotations 3. Northeastern Research Program Group, ‘Branch County: Example of A"erage Yearly Budget for Three Soil Productivity Groups’, Economic; Research Service, USDA, Jan 1984. 4’ Northeastern Research Group, ‘Branch County: Net Returns by EFOP. Soil and Tillage Practice, Economics Research Service, SDA'July 1984. g; tN°Ptheastern Research Group, ‘Branch County: Current Erosion 3 ' aDdReturns by Soil’, Economics Research Service, USDA, July 1984. (sir $9118 with higher bulk densities such as clay have higher a t requirements than lighter soils like sands 2 8 are not used in this analysis as there are some difficult costing assumptions involved. This targeting of crop rotations indicates which rotations can be selected for the analysis and comparison. between tillage systems. Once a crop rotation is chosen for analysis, then various tillage assumptions, yield estimates and input use are assumed for evaluation purposes. 3.2 Soils Area and Soils As indicated earlier, relative yield levels and soil erosion rates are affected by farm soils’ characteristics and tillage systems used. The predominate soils in the St. Joseph Basin[7] are well drained sandy loams or loamy sands with some to little slope. The topsoil is susceptible to water and wind erosion and rests on clay loam and/or loamy sand subsoils. Slowing soil [erosion and reducing droughtiness on these soils are accomplished thrwough the use of reduced tillage making it an attractive economic alternative to moldboard plow tillage. Yields obtained thrwough the use of reduced tillage are generally higher than those on noldboard plow tilled soils. The major soi1s[8] considered are listed below (more complete descriptions are lIPPendix A): 1~ Hillsdale loamy sand(2 - 6* slope) -—_ '-—-- ‘— ;; Berrien, Cass, Van Buren, Kalamazoo, St. Joseph, Calhoun, 'anch and Hillsdale counties 3; Soil Conservation Service, USDA, 'Soil Survey of St. Joseph “My. Michigan’, July 1983. The 2 9 Riddles sandy loam(2 — 6% slope) Spinks loamy sand(0 - 6X slope) Oshtemo sandy loam(O - 6% slope) Oshtemo sandy loam(6 - 12% slope) Oshtemo sandy loam(12 - 25% slope) primary reasons for selecting these representative soils are: To obtain contrasting yield responses due to the use of different tillage systems on the soils To assess differences in yield levels at various points in time as soil productivity is affected by tillage systems The selected soils are some of the more predominate soils in their particular area in terms of acreage The soils are "typical" of the type of soils in the area with other soils being closely related by properties and characteristics 3 0 Once these representative soils are selected, then the rates of erosion[9][10] and soil productivity indexes[ll][12][l3][l4] are calculated from the individual soil profiles as developed by the Soils Conservation Service[15]. The soil erosion rates and soil productivity indexes for the soils listed earlier in this chapter have been calculated and are enumerated in Appendix Tables A9 and A10. An examination of these tables indicates that soil horizon productivity indexes generally decline as soils erode due to the lower soil horizons being less productive than the upper horizons. There are slight differences between soils when assessing the rate of decline in productivity indexes when moving down the soil profile[l6]. 9. Computed by using the USLE soil erosion index 10. Soil Conservation Servcie, USDA, ‘Predicting Soil Loss thru the Use of the Universal Soil Loss Equation’,Lexington, Kentucky, TP-4, May 1978. ll. W.E. Larson, F.J. Pierce and R.H. Dowdy, ‘Threat of Soil Erwosion to Long Term Crop Production’, Science Vol.219, Feb 1983, PP . 448-465 . 12. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, ‘Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, APr 1984. 13. F.J. Pierce, R.H. Dowdy, W.E. Larson, and programmed by R. Healich, ‘Method of Soil Loss Depletion Estimates’, Economic Research Service, USDA. t4- FKJ} Pierce, R.H. Dowdy, W.A.P. Graham, and W.E. Larson, BPPOGHCtivity of Soils; Assessing Long Term Changes due to rollon’, Journal of Soil and Water Conservation Vol. 38, Jan - Feb 1983, pp. 39 - 44. 15; sOil Conservation Service, USDA, Michigan, ‘Soils Rederpetation Record’, Soils: Oshtemo, Spinks, Hillsdale, 1 d193. Schoolcraft, Kalamazoo. 16.. Rates of soil productivity decline are different but are 31‘]. la!- 3 1 The major limiting factor in the St. Joseph area soils is water availibility in the soil. Assumed rooting depths(100 cm.) extend through three horizons in most of the soils so unfavorable characteristics in the subsoils severely affect productivity especially if erosion removes the upper horizons and the major portion of the rooting moves down into the subsoil. The major differences between the soils is the rate.of erosion with some near 1evel(0—SX slope) soils having erosion rates near the tolerance levels(T-value) while the soils with steeper slopes have soil erosion greater than the tolerance levels when using moldboard plow tillage and row cropping(see Appendix Table A10). On soils with less slope, soil productivity may decline slowly while steeper soils erode down through the horizons more quickly causing soil productivity to drop more rapidly per unit of time, but this depends on changes in soil productivity as erosion removes soil horizons. 'The gross erosion rates for the St. Joseph area soils are esdzinated by the Soil Conservation Service[l7][18] by tillage BYSten and crop rotation for Branch County. These rates are shown in .Appendix Table A10 to illustrate the effectiveness of reduced tillaEe methods on erosion rate reduction and to estimate the erosion rates for different soils and situations. The soils’ parameters in conjunction with the calculated ‘- ---- :7' Northeastern Research Group, ‘Branch County: Net Returns by “top, Soil and Tillage Practice, Economics Research Service, SDA’July 1984. 18' N°rtheastern Research Group, ‘Branch County: Current Erosion co't' and Returns by Soil’, Economics Research Service, USDA, July 1984. 32 erosion rates and productivity indexes are used to estimate changes in soil productivity over time and help approximate yield changes thru time. The discounted net cash flow equation uses the calculated changes in yield[19] for one soil to illustrate the workings of the model. A range of changes in soil productivity is considered in chapter 10 to demonstrate the effects of differing assumptions concerning changes in productivity over time. -_-—‘_ -‘ ~— 19. As affected by the change in soil productivity I no.5! 55.1,.._~ :Lsczungpd ‘ . .czaleata l: “Ethos a!" ham R1 0? C0: In} "03 Chapter 4 MODEL STRUCTURE This chapter deals with the structure of the discounted net cash flow(DNCF) formulation used to evaluate long run economic effects of various tillage systems. Economic evaluations used by other studies are reviewed and provide guidelines in model development. The development and derivation of the basic discounted net cash flow structure and factor estimation plus the assumptions involved in setting up the model are discussed. The discounted net cash flow equation structure is documented and its fundamental variables defined. 4.21 Methods Used in Other Studies There have been a variety of methods used to estimate the effects and/0r costs of differing tillage systems and their influence on soil erosion and/or yields through time. 33 31+ Work at Iowa State[l][2][3] used a linear programming model which maximized discounted net crop revenues for a combination of cropping systems. Aggregate gross soil losses were bounded at a specific limiting value. The model allowed increases in yield through time due to technological improvements. Three soils(clay loam, silt loam, loam) with varying slopes, 15 crop rotations(inc1uding crops such as corn, soybeans, oats, meadow, and pasture), five tillage systems (moldboard plow, fall chisel plow, spring disk, fall plant and slot plant) and three supporting practices(contouring, stripcropping, terracing) were considered. The planning horizon selected was 36 years. Yields and net return estimates were a function of soil depth, soil bulk density and soil erosion loss tolerance(T-value). Soil horizon changes and profile depth was related to tillage practice or cropping system by utilizing the USLE index to approximate soil erosion rates. Machinery costing is done by defining operations ,per'crop rotation, calculating field hours required, and costing machinery on a per hour and acre basis assuming machines are chivisible rather than costing machinery sets on a whole farm basis. Machinery costs are adjusted for soil type, conservation Practices and yield changes. 1° Iowa State University, ‘The Economics of Soil and Water Confervation Practices; Results and Discussion’, Center for :gggoultural and Rural Development, Card Report 109 SWAPII, Feb 2. Iowa State University, ‘A Dynamic Analysis of the Economics of (3011 Conservation: An Application of Optimal Control Theory’, Sfigter for Agricultural and Rural Development, Card Report 110 "II . Aug 1982. 3' I°"&.State University, ‘The Economics of Terracing in Iowa’, ard Report 123, Jan 1984. 3 5 Experimental designs in Indiana[4][5] compared yield levels[6] for six tillage systems(moldboard plow, fall chisel plow, spring plow, spring disk, till plant-ridge, and no-till), two crop rotations(continuous corn, corn-soybean), and three soil types(silty clay loam, light silt loam, sandy loam). One major soil type per farm was chosen and a 750 acre farm size used to efficiently utilize labor and selected machinery. The crop operations’ timeliness periods and length were assumed to be the same for different tillage systems and soil types. Machinery is assumed to be replaced periodically but at different machinery life lengths depending on the machine. The same equipment was assumed to be used on different tillage systems rotated on the farm. A 12 year study in Ohio[7][8], compared no till and conventional tillage for three crop rotations: continuous corn, corn - soybeans, and corn — oats - meadow. Two soils were used; a better drained silt loam and a naturally poorly drained silty 40 Deas Griffith, Sena PEFSODB, TaTs Ball-8n, CsRs Edwards, Dons Scx>tt, and F.T. Turpin, ‘A Guide to No-Tillage Planting after Car!) or Soybeans’, Purdue University Extension Bulletin ID-154. 5- D.R. Dorster, D.R. Griffith, J. V. Mannering and S. D. Parsons, ‘Economic Returns for Alternative Corn and Soybean Tillage Systems in Indiana’, Journal of Soil and Water Conservation. 6. "Oldboard plowing in the fall formed the base yield level or :?uolled 100% with other tillage system yield levels being lgher, lower or equal to the base yield in terms of percent. Z' G- Triplett and D. Van Doren, ‘An Overview of the Ohio onservation Tillage Data’ In F. Ditri, Systems Approach to onaervation Tillage, Michigan State University, Feb 1984. fi‘f D-M. Van Doren, G.B. Triplett and J.E. Henry, ‘Long Term Rn luonce of Tillage, Rotation and Soil on Corn Yield.’ Ohio eport, 80 — 82, 1975. 3 6 clay loam. Winter wheat tillage studies in Oklahoma[9] used a least cost integer machinery selection procedure[10]. Five tillage systems(plow, two till, one till, zero till, plow/one till) are compared on one crop (winter wheat) on a 1240 acre farm. All machinery(tractors and implements) are assumed to be purchased simultaneously and have a lO-year life with switching to an alternative tillage system done completely rather than gradually. Yields did not vary by tillage method used and the effects of farm practices on soil erosion are not accounted for. Environmental and offsite impacts of the various tillage systems were not addressed. Work done at Michigan State[ll], which is a forerunner to this analysis, involved the selection of least cost machinery sets for a whole farm meeting operations’ timeliness requirements and assigns a cash penalty for nontimely operations. Initial model assumptions involved a continuous corn farm using fall chisel plowing. Machinery sets were selected and their costs compared for differing farm sizes(200, 400, 600 hectares), different soil types(fine, medium and coarse textured),and changing weather probabilities(50, 70, 80 percent of suitable field work hours 9. Francis M. Epplin, Thomas F. Tice, Alan E. Baquet, and Steven J. Handke, ‘Impacts of Reduced Tillage on Operating Inputs and Machinery Requirements’ 10. The least cost machinery complement which will complete the specified operations in the alloted time 11. Hannibal A. Muhtar, ‘An Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Drainage Basin’, PhD Dissertation in Agricultural Engineering, Michigan State University, 1982. 37 available). The study includes experimental on—farm results of yields, moisture levels and weed and disease incidence on side by side moldboard plow and reduced or no till plots. The machinery selection model was expanded to simulate the machinery set costs of various tillage systems on four crop rotations(corn - corn ~ navybean, corn - navybean - soybean, corn - corn — navybean - soybean, corn - navybean - wheat - soybean) and three farm sizes(160, 240, 320 hectares) plus the previously assumed soil types and weather. probabilities. Yields were assumed to be comparable across tillage systems though experimental corn yields were found to differ slightly by soil type. This was a comparitive static comparison, so changing yield levels .through time and factors affecting soil erosion were not included. Muhtar[12] also lists categories or methods of machinery selection as developed by Wolak (1981). These are: 1. Enterprise budgets and custom hire rates[13] 2. Whole farm profit maximizing linear programming models[l4] 3. Least cost models which seek a minimum cost machinery complement for a given management structure[15] 12. Ibid. 13. Realistically, not all farm work is custom hired but this is a quick approximation method 14. Must specify the machinery complement or use a mixed integer model 15. Searchs for minimum cost machinery set and uses timeliness costs but does not necessarily maximize on-farm profits 3 8 4. Heuristic models for selecting multiple enterprise machinery sets[16] McConnel[l7] developed an analytical structure to estimate the optimal private and social utilization of the soil "stock". The following assumptions are made in the analysis: 1. Farmers maximize the net present value of net cash flow streams through time including the resale or capitalized value of the land and farm. 2. Farmers deplete the soil at rates at which the marginal value[18] equals the marginal cost of foregone marginal discounted future profits[19] 3. Farmers’ planning horizons influence their investment decisions and selection of tillage practices which in turn affect the soil erosion rates. Farmers with a short-term planning horizons use more erosive practices while operators with longer range plans tend to use less erosive tillage methods. The model also observed that expectations of high commodity prices and low discount rates discouraged excessive soil loss. 16. Using timeliness, soil, and farm area restrictions 17. Kenneth E. McConnell, ‘An Economic Model of Soil Conservation’, American Journal of Agricultural Economics, Vol. 65, Feb 1983, pp. 84-89. 18. The marginal profit from an erosive cropping practice relative to a reduced or no till practice 19. Future marginal profits lost due to the decline in soil productivity as affected by the soil erosion rate of the erosive practice 39 An erosion damage function developed by Walker[20] compares the private profitability of choosing an erosive tillage practice over a less erosive practice in the current year as the choice of 'cropping practice affects immediate and long-term profit levels. Key variables involved are changes in soil depth as related to yield levels, costs of various tillage practices and the number of years in the analysis. The equations capture the consequences of tillage choice in current and future income determination. The decision rule for switching from an erosive to a less erosive practice equates marginal value and marginal cost of using an erosive practice. Soil slope, discount rate used, rate of yield level changes due to tillage practice, production costs and commodity price assumptions affect the outcomes and caused the results to be highly sensitive to small changes in the parameters. The evaluation methods used in the literature cited above indicate some of the common and differing assumptions used in the estimation of economic outcomes due to the use of various tillage systems. 20. David J. Walker, ‘A Damage Function” to Evaluate Erosion Control Economics’, American Journal of Agricultural Economics, Nov 1982, pp. 690 - 697. a O The assumptions made are related to the breadth and depth of the model being developed. The Iowa studies[21][22][23] include many variables and factors while the Oklahoma[24] work studied machinery cost differences. This work will try to relax many simplifying assumptions and move beyond a one-year static comparison of tillage systems. 4.2 General Procedure 4.2.1 Factor Review The fundamental purpose of this work is to develop a framework or model consisting of different cost and return components, in order to assess the long term economic impacts of various tillage systems or practices. To accomplish this, various components of the model must be examined and assumptions made concerning their influence and role in the model. To evaluate these factors systematically for development and use in the model framework, the development of the individual components will be divided into 21. Iowa State University, ‘The Economics of Soil and Water Conservation Practices; Results and Discussion’, Center for Agricultural and Rural Development, Card Report 109 SWAPII, Feb 1983. 22. Iowa State University, ‘A Dynamic Analysis of the Economics of Soil Conservation: An Application of Optimal Control Theory’, Center for Agricultural and Rural Development, Card Report 110 SWCPIII, Aug 1982. 23. Iowa State University, ‘The Economics of Terracing in Iowa’, Card Report 123, Jan 1984. 24. Francis M. Epplin, Thomas F. Tice, Alan E. Baquet, and Steven J. Handke, ‘Impacts of Reduced Tillage on Operating Inputs and Machinery Requirements’ , '..tc 3 Ike: 1’. Cu . . :a:: ",3: \ 21.-“.9 .‘g mafizo: "391 ll ( . ‘ JOS‘Qn l leiblcide h I basically three sections: 1. Literature 2. Theoretical Assumptions 3. Use of Actual Parameters in the Model Each major component including the model itself will be reviewed in the light of previous work. This defines the component and aids in its theoretical development by narrowing down the number of the possible assumptions made[25]. Definite component assumptions are drawn and theoretical assumptions developed for the model factors[26]. Once the theory is developed and component use relationships between tillage systems established, estimates of the actual parameters are made. This involves the use of available data and estimates in DNCF calculations[27]. Through the use of actual parameter estimates, the model generates results for various tillage and change in soil productivity assumptions which are compared. In summary, the model is composed of various factors or components(yield levels, erosion rates, machinery types and costs, fertilizer and herbicide use and costs) which are developed for use in the model. The factor is shaped by the literature, clarified through 25. For example, the literature suggests herbicide use is higher on acreage that was reduced or no tilled relative to herbicide use on moldboard plow tillage 26. For example, make herbicide a function of surface residue levels 27. For example, crop scientists estimate the use of Lasso herbicide on corn is 2.0 lbs. a.i. per acre for moldboard plow tilled corn but is increased to 2.5 lbs. a.i. per acre for chisel plowed corn 1+2 the theoretical assumptions and estimated for actual use in the model. By putting the factors together, the model emerges as a discounted net cash flow (DNCF) formulation utilizing the components in its calculation. If different assumptions about the model framework or the various components are made, the model is adjusted accordingly affecting the final results. This particular development of long-term analysis is one way but not necessarily "the way" of dealing with the particular erosion/profitability problem concerned. 4.2.2 Estimator Development Once the literature review of model components is complete and component assumptions developed theoretically, it is appropriate to estimate the values needed in the discounted net cash flow formulation. The initial task is to develop crop budgets by tillage method and soil type or soil management group. A soil is selected which is representative of the area studied. By using soils’ characteristics information[28], the relevant soil erosion rate data and change in soil productivity indexes through time are calculated. Crop yield levels are assumed for a soil type to develop fertilizer, seed, and other input use levels to be adjusted by tillage system as necessary. A crop rotation with adjusted yields is selected with a farm size in the selection of machinery sets. 28. Soil Conservation Service, USDA, Michigan, ‘Soils Interpetation Record’, Soils: Oshtemo, Spinks, Hillsdale, Riddles, Schoolcraft, Kalamazoo. 255.! #3 Various tillage assumptions are developed ‘for the crop rotation[29]. These assumptions dictate machinery type and operations used in a machinery selection model which chooses the least cost machinery set meeting the crop rotation and tillage assumptions. The resulting machinery costs by selected rotation and tillage systems are added to the crop budgets[30]. Once the machinery and crop budget costs by tillage systems are calculated and gross returns per tillage assumption are determined, the net returns by tillage system are compared to find the tillage combination for the crop rotation with the largest net returns. This tillage combination is a good initial candidate to optimize DNCFs in an evaluation of tillage systems through time. An assumption considered is changes in yield holding input and machinery costs constant. There is an inverse relationship between the surface residue levels and yield decline through time[3l]. The implication of low or little surface crop residue allowing greater soil sheet and wind erosion lowers yields through time faster[32] than tillage methods leaving higher amounts of surface residue. If costs remain the same while yield levels decline, net _returns of a tillage system decline through time. This implies a tillage system with the 29. i.e., An all moldboard plow tilled corn-corn—soybean rotation or chisel plowed corn and moldboard plowed soybeans in the corn—corn-soybean rotation 30. Chisel plowed corn - use the chisel plowed corn budgets, moldboard plowed soybeans - use the moldboard plowed soybean budgets 31. Higher surface residue levels - slower rate of yield level decline 32. Assuming soil productivity declines when moving down the soil horizon I. 1" I. :0 “I C 'O'D .2...-5 ZZCIFS 3:: f 3182. Iain;, \ “‘ - 41+ highest net returns initially may no longer have the highest net returns relative to other tillage combinations after erosion occurs and soil productivity declines. The final step is to look at the use of tillage systems over time. Which tillage system or combination of tillage systems maximizes the sum of discounted net cash flows (DNCF) or the net present value (NPV)[33]? A conceptualization of the solution to the maximization problem is to. use the tillage system with the highest net returns(dropping due to erosion affecting soil productivity) until its net returns decline to the net return leve1[34] of the tillage system with the next highest net return level (but has less of a change in soil productivity thru time due to soil erosion). At this point a switch can be made to the alternative machinery system. This is a simplified way of identifying the mix of tillage combinations most likely to maximize the DNCF calculations. A more exhaustive method to calculate the discounted net cash flow of different mixes of tillage combinations at each possible tillage switching year is to find the tillage combination mix with the lowest discounted net machinery cost. A summary of the estimator development is as follows: 33. For example: On a corn—corn-soybean rotation grown on a coarse textured soil with a yield potentials of 113 bu. of first year corn, 103 bu. of second year corn, 40 bu. of soybeans had the highest net present value when using moldboard. plow tillage on the corn—corn-soybean rotation until year 30 at which time a switch was made to chisel plowing the corn and moldboard plowing the soybeans. 34. Though the net return of this option will be less due to drop in soil productivity caused by the first tillage system 45 Select soil type or soil management group Select a crop rotation Calculate crop returns and costs by crop and tillage system Develop various tillage assumptions for the crop rotation Select farm size and determine crop operations Find the least cost machinery set by tillage system using a machinery selection model Calculate soil productivity changes and its effect on yield levels as a function of tillage practices used and soil characteristics Find the mix of tillage combination with the highest discounted net cash flow through time. h 6 4.3 Discounted Net Cash Flow Formulation Once the general situation is portrayed, the discounted net cash flow formulation is used to evaluate different tillage methods used over time. The discounted cash flow equation(equation 4.1) consists of components examined in the light of previous work, whose theoretical assumptions are developed and data are estimated for use in the calculation of net returns by bringing the various estimators together in one equation. The DNCF equation is as follows: (4.1) DNCF II A I I I I I I V where DNCF The net discounted cash flows from year 1 to year q n - Analysis year q - Last year of the analysis NR(n) - Net returns of the agricultural activity in year n r - Interest or discount rate The annual net returns are‘ a function of prices, yields, machinery costs, and crop costs such as fertilizer, pesticide and other crop costs. These variables or components of the net returns are included in the discounted net cash flow equation separately as illustrated below[35]: 35. The expanded DNCF equation is in chapter 10 (4.2) where 1»? Z+\—“( ------ > = n n=k+l n (1+r) (1+r) - The net discounted cash flows from year 1 to year q - Analysis year - Tillage switching year - Last year of the analysis - Net returns of the agricultural activity in year n - Interest or discount rate Chapter 5 YIELDS Yield differences due to the use of moldboard plowing and reduced or no-till tillage methods are reviewed. Theoretical assumptions about changes in yields as a function of tillage practices are developed. Yields are estimated for a soil by tillage method and crop rotation. 5.1 Literature Review The final measure of the influence of input use, tillage systems, soils and other cropping factors is the yield level of crops grown. It is difficult to isolate the influence of one variable on yields so researchers generalize as to the effects of various factors on yields. USDA work[1] used partial budgeting finding reduced till soybean farmers[2] had significantly lower per acre soybean yields (75-908 of moldboard plow yields) which offset lower per acre reduced till production costs. Comparisons of moldboard 1. Michael Duffy and Michael Hanthorn, ‘Returns to Corn and Soybean Tillage Practices’ Agricultural Economics Report 508, Economic Research Service, USDA, Jan 1984. 2. Except in the Southeast U.S. “9 5 O plowed and reduced till corn farms indicated no significant difference in the 1980 average corn yields. The USDA studies observed that weather, pest infestations, and proper input use influenced yields more than tillage practices indicating that experimentors should carefully account for or control the relevant variables before drawing conclusions about the influence of tillage systems on yield levels. USDA[3] also compiled results from many different experiments[4] summarizing that yield differences between tillage systems depended on the particular circumstances of the experiment. One study from Iowa (1982) concluded that tillage system differences did not significantly influence yields in continuous corn and soybean rotations. Other sources cited by USDA indicated no-till yield variation was correlated to soil types and drainage differences[5]. Management levels are higher on reduced or no-till tillage systems to maintain comparable yield levels. Work done in Kentucky[6] established that no-till corn yields on various soils were 12% higher than moldboard plowed corn yields but no-till yield levels appeared to be closely related to more efficient use of soil water. There was a question 3. Lee A. Christiansen and Patrice E. Norris, ‘A Comparison of Tillage System for Reducing Soil Erosion and Water Pollution’, Agricultural Economics Report 499, Economic Research Service, USDA, May 1983. 4. Shown in appendix table 83 5. Yields were high on coarse sandy loam, yields were lower on poorly drained clay based soils 6. R.E. Phillips, G.W. Thomas and R.L. Blevins, ‘No Tillage Research: Research Reports and Reviews’, University of Kentucky .‘ht-a'fl F ':a.'v and nine y:e.. . 1:218. eeeee .-‘— ‘8 : Iowa '°“°?Vati 1983. 3' M. 8Com, Corn 0r SC 9- M. “Sens, Hale Qflservati I ’u .4 l {—3 5 1 concerning the adaptability of no-till to poorly drained soils. Yields of corn following soybeans or meadow in Iowa[7] were adjusted upward by 7x as compared to corn following corn. In Indiana[8][9], no-till corn following no-till soybeans had high yields relative to other tillage systems but no-till soybeans following no-till corn had relatively low yields. The yields for moldboard plowing were highest on poorly drained clay loam soils while yields for no-till were highest on well drained sandy loam .soils. 7. Iowa State University, ‘The Economics of Soil and Water Conservation Practices; Results and Discussion’, Center for Agricultural and Rural Development, Card Report 109 SWAPII, Feb 1983. 8. D.R. Griffith, S.D. Parsons, T.T. Bauman, C.R. Edwards, D.H. Scott, and F.T. Turpin, ‘A Guide to No-Tillage Planting after Corn or Soybeans’, Purdue University Extension Bulletin ID—l54. 9. D.R. Dorster, D.R. Griffith, J. V. Mannering and S. D. Parsons, ‘Economic Returns for Alternative Corn and Soybean Tillage Systems in Indiana’, Journal of Soil and Water Conservation. 'D'm - A‘- .d... 3..) 9| I A 65 ~55- . Your .2 it!!! 0 .52 Triplett and Van Doren’s[10][1l] experiments in Ohio support the previous findings, indicating that no till yields were higher than moldboard plowed yields on a better drained silt loam soil but no till yields were less than the moldboard plow yields on poorly drained silty clay loam soil for both continous corn and corn - soybean rotations. They note that no till performs better than moldboard plow tillage during years of drought stress than in years of ample rainfall. The work at Michigan 'State by Muhtar[12] supported these previous findings. Reduced till corn yields were about 1.5x less than moldboard plow yields on well drained and coarse soils. Moldboard plow yields were about 10: higher on poorly drained soils. Soybean yields did not differ significantly by tillage system used. Miller and Shroder (1976) found that soil moisture levels at 100% of available soil water capacity had little effect on yield differences due to tillage system but at average or below average spring moisture levels[13], reduced or no tillage had higher yields than those obtained with moldboard plow tillage implying soils with limited available soil water are good candidates for 10. G. Triplett and D. Van Doren, ‘An Overview of the Ohio Conservation Tillage Data’ In F. Ditri, Systems Approach to Conservation Tillage, Michigan State University, Feb 1984. ll. D.M. Van Doren, G.B. Triplett and J.E. Henry, ‘Long Term Influence of Tillage, Rotation and Soil on Corn Yield.’ Ohio 12. Hannibal A. Muhtar, ‘An Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Drainage Basin’, PhD Dissertation in Agricultural Engineering, Michigan State University, 1982. 13. Less than 100% of available soil water capacity 5 3 reduced or no tillage. On the other hand, soil water saturation limited soil air capacities causing difficulties for the use of reduced tillage which does not disturb the soil. The USDA listing[l4] of yield comparisons gives some rough indications of past results and how they have relevance to this analysis. Soil selection and crop rotations chosen are important determinants of yield differences between tillage systems so some assumptions or rules of thumb can be drawn and tested in the sensitivity analysis. 5.2 Theoretical Assumptions One of the assumptions made in this analysis is that yields are a cumulative result of many different factors and variables. It is hard even under the strictest of laboratory conditions to reproduce identical yield levels through the control of the differing variables The best experimental trials can do is control or account for as many significant or impacting variables as possible to help reduce the amount of unexplained variance in estimates of dependent variables and to narrow estimator confidence intervals. Simplifying assumptions such as allowing no genetic changes in seed material through time or holding cropping practices and operational timing constant for tillage systems 'may be unrealistic but help define the model and control some of “-—‘———-- 14. In appendix table 83 nuatLe (A, d 6’! nut: is .9215. music assaufi. "Vi‘ub ‘ I151 181 15. e. I] I efo' H.) 1“Crew 51: variables. Experimental design such as using side by side plots for differing tillage systems fixs or controls the influence of variables such as weather, soil type, moisture levels and input levels. An estimation of major influencing components allows an assessment of the effects differing tillage systems have on crop production levels through time. The crop production elements most influenced by tillage systems and cropping procedures are soil erosion rates, depth of fertile soil layers and soil fertility or productivity[l5]. The reviewed literature suggests that moldboard plowing methods lead to more soil erosion and reduces soil productivity faster than reduced or no tillage. The change[16] in yield when using one tillage system through time is graphically illustrated as follows: 15. e.g., A soil with a deep productive "A" horizon may not have significantly declining yield levels despite suffering from excessive erosion due to moldboard plow tillage while a soil eroded to or near a less productive "B" horizon may have an increasing decline in yield levels when using moldboard plow methods with excessive erosion 16. Holding genetic potentials constant 55 Reduced or no tillage Moldboard plow tillage Yield Yv\ Ys ‘ ‘ l I l I I I I l n I . I 0 n q Time Figure 5.1: System Moldboard plow tillage equation: Yield Level Change over Time Using One Tillage v v (04i x n) (5.1a) Yv(n) = va) - ’(o(e ) Reduced till equation: s .3 (”<1 x n) (5.1b) Ys(n) = Ys(q) - 040(e ) where n - Analysis year q - Last year of analysis Yv(0) - Moldboard plow yield level in year 0 YV(D) _ n n n n n n 11 Ys(O) - Reduced till yield level in year 0 Ys(n) _ n n n w n n n Ys(q) __ w n n n n n q v v a Reduced till - n(3) I ' : l ' ' Moldboard plow l I I ' ' I l l . 1 0 n(l) n(2) n(3) Time Figure 5.2: Yield Level Change over Time using more than One Tillage System 1:.:::ard nested t:- (I .4 I. s 5“ “ ‘ ~“ 5 7 Moldboard plow tillage equation(year 0 - k): v X: v ( 1 x n) (5.1a) Yv(n) = Yv(O) - 9 Smmm +2 ( ------------ > — M(S)] J=1 J (1+r)J where MA(S) - Machinery annualized cost in year S 8* - Optimum machinery cycle length in years r - Interest or discount rate M(O) - New machinery costs or aquistion value M(S) - Machinery salvage value ' - Year of machine life cycle R(j) - Machine repair costs in year J C(j) - Other machinery costs in year j Both methods(matrix, minimized annualized cost) are discounted net cost minimization procedures but differ in the exactness of the length of machine or machinery set cycle chosen and the search process involved. The matrix(figure 6.1) sets the length of the machine cycle in integer terms comparing the machine DNCs whereas the optimum replacement cyc1e(equation 6.2) calculates the optimum cycle for the machine or machinery set. The optimum minimized annualized cost can be calculated using equation 6.2. These estimated annualized costs can be used to calculate the DNCs over time for each of the original and alternative tillage machinery sets at a switching year[4l]. Equation 6.2 is composed of two parts, an annuity and a present value calculation. These two components are presented seperately and decomposed into calculations by original and alternative tillage machinery sets. The equational forms calculating present 41. This assumes costs do not change if switching to the alternative machine occurs before the optimum original machinery cycle is complete. If the original machinery set has an optimum cycle of 10 years and the switch to the alternative machine is in year 28, the 8th year of the third original machinery cycle, costs change slightly due to salvaging the third original machinery set out in year eight instead of year ten 85 values by tillage system over its calculated optimum life(as determined in equation 6.2) are as follows; Present value of the original machine for its calculated optimum cycle length(So*)[42]. 80* ((Rot) + (Cot)) (6.3a) PV(So*) = M(Oot) +Z ( --------------- ) - M(.So*) J=1 J Present value of the alternative machine for its calculated optimum cycle length(Sa*)[43] Sa* ((Ra*) + (Ca*)) (6.3b) PV(Sa*) = M(Oa*) + ( --------------- ) - M(Sa*) J= J The next step is to annualize these present values for use in a discounted net cost formulation to facilitate calcula— tions and aid in the comparisons of various machine switching options and possible optimum machine cycles. 42. Equation notations are after equation 6.5 43. Equation notations are after equation 6.5 8 6 Annualized cost of an original machine with an optimum life cycle So*[44][45] , -So* (6.4a) MA(So*) = (r/(l-(l+r) ))(PV(So*)) Annualized cost of an alternative machine with an optimum life cycle Sa*[46][47] -Sa* (6.4b) MA(Sa*) = (r/(l-(l+r) ))(PV(Sa*)) The total net discounted cost(DNC) for the original and alternative machinery sets given a time frame[48] can then be represented as follows[49]; 44. Set is calculated using equation 6.2 45. Equation notations are after equation 6.5 46. Sat is calculated using equation 6.2 47. Equation notations are after equation 6.5 48. Switching from the original to alternative machinery set at time k - 49. The minimized annualized costs can also be calculated using equation 6.2 for the original and alternative tillage system assumptions (6.5) where DNC* DNC* PV(So¥) PV(Sa*) MA(Sot) MA(Sat) 80* Sat M(Oot) M(Oat) Ro*(J) Ra*(J) 00*(J) Ca*(J) M(Sot) M(Sat) M(S) 8 7 -k [(MA(SO*)/r)(1-(l+r) )] -q -(k+1) + [(MA(Sa*)/r)[(l-(1+r) ) - (l-(1+r) )] q - (MS/(1+r) 1 Discounted net machinery costs over the analysis period q using the optimum machine cycle calculation method(using equation 6.2) for both the original and alternative machines or machinery sets optimum life cycle year calculations. Present value of the costs of one original machine or machinery set of cycle life So: calculated using equation 6.3a Present value of the costs of one alternative machine or machinery set of cycle life Sat calculated using equation 6.3b Annualized cost of an original machine or machinery set of life cycle 80* using equation 6.4a, also can be found using equation 6.2. Annualized cost of an alternative machine or machinery set of life cycle Sat using equation 6.4b, also can be found using equation 6.2. Optimum original machine or machinery set cycle length in years as estimated using equation 6.2 Optimum alternative machine or machinery set cycle length in years as estimated using equation 6.2 Year in machine or machinery set cycle life Interest or discount rate New original machinery set cost New alternative machinery set cost Repair costs of an original machine or machinery set in cycle year j ' Repair costs of an alternative machine or machinery set in cycle year j Other machinery costs of an original machine or machinery set in cycle year j Other machinery costs of an alternative machine or machinery set in cycle year j Salvage value of an original machine or machinery set in cycle year So* Salvage value of an alternative machine or machinery set in cycle year Sax Salvage value of the last alternative machinery set in the last year q Switching year from original type of machine to an alternative type of machine Last year of discounted net cost calculations 8 8 For example let; r = .05 80* = 7.5 years Rot = 50 Sat = 9.0 years Rat = 60 M(Oot) = $500 Co* = 0 M(Oa*) = 750 Cat = 0 M(So*) = 100 k = 20 years M(Sat) = 150 q = 50 years After calculations[50]; PV(So*) = $ 733.14 PV(Sa*) = 1,029.77 MA(So*) = 119.72 MA(Sat) = 144.89 DNC* = 2,279.39 These equations estimate machine or machinery set costs thru time and allowing comparisons of a switch from one type of machinery to another. Three major possibilites or outcomes can occur with the use of the matrix or the minimized annualized cost methods to determine of machinery set combinations with the lowest discounted net costs. 1. If no switch from original to alternative tillage takes place, the lowest discounted net costs occur at the last possible switching year(year q) and the original machine or machinery set is used the entire time period. 2. If the lowest discounted net costs take place in the first switching year(year 1), then the alternative tillage is used thru the entire analysis time period. 50. Using equations 6.3a, 6.3b, 6.4a, 6.4b, 6.5 or just 6.2 and 6.5 to derive DNCt 8 9 3. If the lowest discounted net cost occurs at a year between year 1 and q, then the original tillage is used up to the switching year k and the alternative tillage used thereafter. This discounted net cost(DNC*) search can be expanded to include tillage possibilities beyond just strictly moldboard and reduced or no till methods to a consideration of modified moldboard plow, various types of reduced or no till or even the possibility of switching more than once in the time period assumed[51] but a tradeoff must be made between model flexibility and workability. A longer more accurate version using salvage values or machines or machinery sets sold before the optimum replacement cycle is complete[52] is as follows; 51. e.g. moldboard plow to chisel plow to no till 52. If the switching year k occurs before the end of the machinery cycle,S* (6.6) where 9 0 So* 2So* DNC* = [PV(So*)+PV(So*)/(1+r) +PV(So*)/(1+r) +... (co)So* _k_ -n +(<—M(0o*)/(1+r> >- 2_ am am + n=(co)So*+l -k k-(co)So* k (1+r) M(S ))l + [PV(Sa*)/(1+r) + k+Sa¥ k+(ca)Sa* PV(Sa*)/(l+r) +...+((-M(0a*)/(l+r) - {L. -t q-k+(ca)Sa* . q Q (In) 9(a) + ms >/<1+r) >1 n=k+(ca)Sa* DNC* - See equation 6.5 notations PV(SO*) _ n n n n Pv(sa*) _ N n " H M(OO*) _ n n n n M(08*) _ n n n n 80* _ u n n n 88* _ n n n n j __ n n n n r _ n n n n k _ n n n n q __ n w n n R(n) - Machinery costs in year n n - Analysis year co - Number of original machines or machinery sets used before the switching year(k) minus one ca - Number of alternative machines or machinery sets used before the final analysis year(q) minus one ' k-(co)So* M(S ) - Original machine or machinery set salvage value in the switching year k minus the number of previous original machinery machinery cycles(co) times the optimum number of original machinery cycle years(So*) q-k+(ca)Sa* M(S ) - Alternative machine or machinery set salvage value in the final analysis year(q) mi plus the n machinery number of years(Sa*) nus the switching year(k) umber of previous alternative cycles(ca) times the optimum alternative machinery cycle 9 1 6.2.2.3 Estimation of Appreciating Repair Costs and Declining Salvage Values The DNC calculations can be expanded to reflect appreciating repair costs or a schedule of declining salvage values to more accurately reflect machinery cost schedules. The appreciating machinery repair costs 0 can be approximated by the figure below and estimated with the accompanying function. Repair Costs R(J) R(0) 0 J Years of machine life Figure 6.2: Machinery Repair costs over time J (6.7a) n(j) R(0)(l+g) or s x J (6.7b) R(J) = R(0)(e ) Where RCJ) - Repair costs in machine life cycle year j R(0) - Repair costs in machine life cycle year 0 or base machine repair costs g — Rate of yearly repair cost appreciation j - Year in machine life cycle This formulation estimates increasing repair costs if the machine rePair parameters are not known with some degree of accuracy. In this analysis, the engineering department at Michigan State ‘92 University has developed repair cost parameters by type of machine based on per hour of use. These equations are as follows: Cumulative repair cost[53]: B (6.8a) CR(j) = A x (hours used/1000) Repair cost in time j; Bnl (6.8b) R(j) = A x B x (hours used/1000) where CR(j) - The cumulative repair costs in year J as a percent of the new machine costs R(j) - Machine repair costs in year 3 as a percent of the new machine costs A - Repair coefficient(taken from appendix table 819) B - Repair coefficient(taken from appendix table 819) j — Year in machine life cycle - .—-ma--—-—-- 53. As a cumulative percent of original investment cost 93 Declining machine salvage costs over time can also be approxi- mated by the graph below and estimated by the accompanying equations: Salvage value as a percent of aquisition price M(O) M(S) . O 0 J Years of machine life Figure 6.3. Machine Salvage Value over Time —d x j (6.9) M(S(J)) = M(O) (6 ) where M(S(j)) Machine slavage value in year j as a percent of new machine cost M(O) - New machine cost d - Rate of yearly machine value depreciation j — Year in machine life This procedure approximates salvage value thru time if specific values are not known. Salvage value equations are available in the American Society of Agricultural Engineers annual publication but these parameters are based on the time the machine is kept rather than on hours of use(as done with repair cost estimates). The calculation of salvage values based on time rather than on' hours of use can lead to some unrealistic salvage values. The actual yearly machine use hours can deviate greatly from the "average" hours (see appendix table 821) of yearly use as assumed in equational estimates of machine salvage values. Salvage value 91+ based on time as a fraction of the original machine cost is calculated as follows[54]: J (6.10) M(S(j)) = As x (83 ) where M(S(j)) Machine salvage value as a percent of new in year j As - Salvage value coefficient(taken from appendix table 820) Bs - Salvage value coefficient(taken from appendix table 820) j - Year in machinery life cycle Different functions or values can be used to estimate the repair or salvage costs to in the DNC equation(equation 6.2). The repair and salvage funtional forms and costs assist in estimating the optimum replacement cycle for both original and alternative machinery sets or single machines and make the machinery cost calculations more realistic. An expanded version of equation 6.3 would take the following form: 54. R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, ’Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 95 8 x J S R(O)(e ) -d x S (6.11) PV(S) = M(O) + Z ———————————— -M(O)(e . ) J=l ' (1 + r) where PV(S) - Present value of the costs of a machine M(O) - New machine cost or aquisition value S - Machine life cycle length in years j - Machinery cycle year r — Interest rate R(O) - Base repair costs in the first year of the machinery cycle g - Rate of yearly repair cost appreciation d - Rate of yearly machine value depreciation This expanded equation can then be annualized as done in equation 6.4a or 6.4b or 6.2. The optimum machine life cycle annualized costs are determined and can be combined with other costs and revenues to derive the discounted net cash flows for each of the possible switching years. This determines the best combination of machinery sets for the crop rotations and situation over time. S96 6.3 Machinery Cost Estimates The tillage systems used for each crop rotation can involve a variety of machinery from moldboard plow to no-till. Crop rotations can be tilled by one type of tillage system for all the crops in the rotation[55] or by a combination of tillage systems[56]. Variables affected by choice of tillage system are erosion rates, long-term soil productivity, yields and machinery costs. Tillage systems considered for use on crop rotations are: l. Moldboard plowing (fall or spring) 2. Chisel plowing (fall or spring) 3. Ridge till 4. No~till The crop rotations can be evaluated under different tillage systems[57] to determine the optimum machinery set for use on a soil and crop rotation. Appendix tables 84-810 list the crops, the crop operations and machines required for each crop operation. 55. e.g., chisel plow all the crops in a corn~corn—soybean rotation 56. e.g., moldboard plow first year corn, chisel plow second year corn, moldboard plow soybeans 57. This may involve the use of one or more tillage methods on the crop rotation 9'7 6.3.1 Explanation and Illustration of the Machine and Machinery Set Optimum Life Cycle Equations Once a least cost machinery set is selected[58] for a soil texture, crop rotation, and tillage system, the next step is finding the optimum life of the machinery set[59]. A more correct but exhaustive method calculates the optimum life and annualized machinery cost for all the machinery sets the machinery selection model(MACHSEL - see appendix 819) did not select but in the interest of time and space, only the machinery sets chosen by the machinery selection model are used. To find the optimum life of a machinery set, annualized machinery cost are minimized using equation 6.2. The machinery sizes, types and yearly use hours calculated in the machinery selection model[60] are combined with appendix tables 819, 820(listing the machinery repair, salvage and costing parameters) to compute new machinery costs, salvage values and repair costs of the machinery set. These costs, use hours, and salvage and repair coefficients are used in equation 6.12b to calculate the annualized machinery set cost. Also included in the equation are fuel, labor, insurance and housing costs which do not change the optimum life calculations but reflect differences in annualized machinery costs between tillage systems and/or machinery sets. m———————-- 58. Hannibal A. Muhtar, 'An Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Drainage Basin’, PhD Dissertation in Agricultural Engineering, Michigan State University, 1982. 59. The machinery selection model assumes a fixed ten—year life 60. As enumerated in appendix table 822 9 8 The variables are entered in the machinery set optimum life equation 6.12b and calculations performed for each year starting at year one until the year with the lowest annualized machinery costs is found. This is the optimum period of time to keep the machinery set. As indicated earlier in the section 6.2, there are two changing cost components in the machinery cycle equation influencing the optimum machinery cycle length. First, the equations calculating salvage value result in large decreases in salvage value during the first few years with the decreases getting smaller through time(the change in salvage value declines through time[61]). Secondly, the yearly repair costs increase with time[62] so changes in repair values increase with time[63]. The summation of the machines’ yearly salvage values’ decline and the repair costs’ increase result in an aggregate cost curve which declines initially, bottoms out and increases eventually[64] causing the annualized machinery costs to be minimized during the analysis time frame. The derived optimum machinery life is termed the machinery optimum life cycle or length of time to keep the machinery before trading for an identical replacement machinery set at the end of the machinery cycle period. The original set is sold for a salvage 61. dSV‘t’/dt is less than dSV't-1’/dt 62. Total hours are assumed to increase linearly per year 63. dR't’/dt is greater than dR't—l’/dt 64. Fits economic theoretical expectations of the shape of cost curves 99 S* value calculated by M(O)(i)As(i)(Bs(i) )in year S* (the i=1 last year of the machinery cycle) and the new identical machinery .1” is purchased at a cost of ‘;_ M(O)(i) at the end of year 8* or at i l the beginning of the first year of the next machinery cycle. The machinery set optimum life cycle calculations minimizes the cost of the machinery in the determination of total discounted net costs(DNC). To test this proposition,. machinery cycles of different "non-optimum" lengths can be tried to find a discounted net cost with a lower value than the discounted net cost calculated for the optimum machinery cycle(see appendix B disCussion of "non-optimum" machinery cycles). This proposition is tested in the discussion of the optimum replacement equations in appendix 8. The fully developed machine and machinery set optimum life cycle equations are now illustrated[65] as follows(notations follow equation 6.12b): 65. Salvage value equation 6. 10 is used and repair equation 6.8 is in equations 6.12a and 6.12b 1()0 Machine Optimum Life Cycle Equation Minimize the value of the following expression; -S (6.128) MA(S) = (r/(l-(l+r) )) annuity —S [(M(0) - M(O)(ASHBS ) Initial Salvage value cost 8 1000 1000 +2: ( ---------------------------------- > J=1 J (1 + r) Sum of repair values thru year 8 S (liters)(1.15)($.32) + (labor)(l.1)($7.00) + :Z: ( ——————————————————————————————————————— - ------ J=1 J (1 + 1') Fuel Labor + (.01)(M(0) ---------- )1 Insurance & housing 1()1 Machinery Set Optimum Life Cycle Equation Minimize the value of the following expression; -8 (6.12b) MA(S) = (r/(l-(l+r) )) annuity -S [ Z (M(O)(i) + M(O)(i)As(i)(Bs(i) > i=1 “'5 Initial Salvage value cost jxhrs.(i) B(i) (j-l)xhrs.(i) B(i) A(i)(( --------- ) - ( ------------- ) ) Fuel Labor + (.01)(M(0)(i))] Insurance & housing 1()2 where MA(S) Machinery annualized cost in year S S Machinery cycle length in years r Interest or discount rate 1 Machine i f Number of machines in machinery set M(O) New machine costs or aquisition value As Salvage value coefficient(taken from appendix table 820) ' Bs Salvage value coefficient(taken from appendix table 820) j Year of machine life cycle A Repair coefficient(taken from appendix table 819) 8 Repair coefficient(taken from appendix table 819) M(O)(i) New machinery set costs or aquisition value of the ith machine As(i) Salvage value coefficient of ith machine (taken from appendix table 820) Bs(i) Salvage value coefficient of ith machine (taken from appendix table 820) j Year of machinery set life cycle A(i) Repair coefficient of ith machine(taken from appendix table 819) 8(i) Repair coefficient of ith machine(taken from appendix table 819) hrs(i) Hours of annual use of ith machine hrs Hours of annual use of machinery liters Liters of fuel used per year for machinery set labor Number of tractor and combine labor hours annually 6.3.2 Optimum Machinery Life Cycle Equation Results The optimum machinery life cycle equation(6.12a or 6.12b) can be used to calculate the individual machine’s or a tillage set’s optimal life respectively. When calculating the individual machine replacement cycle(using equation 6.12a), machine yearly use hours are a major influence on the length of the machine life. As can be seen in Table 6.1, hours of use by machine as selected in the machinery selection model[66] result in optimum lives calculated which range from five years(moldboard plow - 66. See appendix table 822 1 0 3 high hours of use) to lOO-plus years(sprayer - low use hours). The reasons for these differences is are repair coefficients[67] based on an estimated hours of total use or estimated wear—out life by engineering specialists as illustrated in appendix table B21. Engineers use expected and/or actual data to estimate repair costs at various phases in a machine’s life and develop a function approximating the relationship between repair costs and cumulative hours of use. The reasoning behind these estimates is if a machine is used. a number of cumulative hours, its useful life is at an end(a rough approximation of what the optimum life cycle does) or that the marginal costs of the_"old" machine are greater than the annualized costs of the new machine. By using these techniques,. engineers estimate the total hours of machine use before it is salvaged. The next step is the multiple of annual hours of machine use and the length of machine life in years equalling the total hours of useful life. This combination of annual hours of use and years of use can result in low annual use hours and a long machine life or a high annual hours of use and a short machine life. For example: The grain drill in Table'6.l has low hours of use(26.5 hours per year) determined by the machinery selection model implying that its optimum life is 76 years. This is unrealistic as technological change or aging of parts can prompt the salvaging of this machine earlier, shortening the cumulative hours of use based on 80 hours of use annually and a life of 15 years. If machines have greater or lesser annual hours of actual use than the estimated annual hours of use, the optimum life changes depending on yearly use ~--------- 67. From appendix table 819 1C)“ hours leading to the low and high yearly use hours shown in Table 6.1. This gives a rough range of optimum life calculations for various hours of yearly use. As illustrated in the calculation of actual optimum life estimates, yearly machine hours of use vary widely(due to the nature of the farm and machinery size and crop rotation) from the ”averages" of hours of yearly use as approximated in appendix table 821. Consequently, the machine optimum life calculations can differ greatly from the estimated "average" machine lives. The optimum life cycle equation 6.12b can be used in the calculation of the optimum life of machinery sets(Tab1es 6.2, 6.3, 6.4). The machines’ repair costs and salvage values are summed yearly as shown in equation 6.12b until a minimum annualized value is found for the optimum life of the machinery set. Using the machines and hours of their yearly use as calculated by the machinery selection model(MACHSEL), the optimum machinery set life is calculated to be 16 years for the moldboard plow machinery set, 18 years for the chisel till machinery set and 17 years for the no-till machinery sets[68]. Also shown in Tables 6.2, 6.3, 6.4 are the yearly cash flows using the discounted net cash flow equation 10.1, the yearly machinery costs[69] and the machinery salvage value. 68. Tables 6.2, 6.3, 6.4 69. Repair, fuel, labor and insurance/housing 1(15 Table 6.1. Calculation of Individual Machine’s Optimum Life and Annuity Value! _———————-—-—————-—-————————————-——--—-——-—————————--———n————-——._—_—— Repair Salvage Actual Coeff.# Coeff.# estimated values Initial** --------------------------------------- Machinett Cost-M(O) A B As Bs Hrs. Year Annuity## Tillage $19,500 .012 2.0 .75 .87 413.8 24.0 $ 2,154.14 tractor Utility 17,900 .012 2.0 .75 .87 249.1 52.0 1,398.76 tractor Combine 88,884 .12 2.1 .78 .88 165.7 13.0 11,716.54 Moldboard 4,228 .43 1.8 .70 .90 196.6 5.0 913.68 plow Field 713 .30 1.4 .70 .90 147.8 39.0 94.47 cultivator Grain 6,550 .54 2.1 .70 .90 26.5 76.0 429.64 drill Row 7,482 .54 2.1 .70 .90 85.2 11.0 1,050.00 planter Sprayer 2,242 .41 1.3 .70 .90 13.0 100+ 51.10 Row 1,632 .22 2.2 .70 .90 105.3 14.0 191.13 cultivator NH3 4,440 .38 1.4 .70 .90 88.4 78.0 456.15 applicator * To illustrate the optimum life range given different yearly hours of use *tFrom the moldboard plow tillage coarse soil ”least cost" machinery set shown in appendix table 822. t From appendix tables B19 and 820. ##Annualized yearly machine cost to the farm operation Table 6.1 (cont’d). 1(36 ——————-—-——~———-—-—————_——-—————————-_-——————--—-—-—-‘—~——————”. ———.-- Initialti ———-—-—--———-—————-— Machine** Cost-M(O) Hours Tillage $19, tractor Utility 17, tractor Combine 88, Moldboard 4, plow Field cultivator Grain 6, drill Row 7, planter Sprayer 2, Row 1, cultivator NH3 4, applicator 500 900 884 228 713 550 482 242 632 440 200.0 200.0 75.0 50.0 .75.0 50.0 50.0 100.0 50.0 150.0 47.0 56.0 100+ 27.0 27.0 100+ 6,944. 358. 36. 619. 707. 51. 120. 10 47 69 60 76 800.0 800.0 250.0 125.0 300.0 150.0 150.0 300.0 200.0 8.0 10.0 10.0 15,483 666 164 1,258 1,437 590 286 * To illustrate the optimum life range given different yearly hours of use ##From the moldboard plow tillage coarse soil machinery set shown in appendix table 822. # Arbitrary number of hours of annual use chosen. ##Annualized yearly machinery cost to the farm operation "least cost" 1(17 Table 6.2. Moldboard Plow Tillage Optimum Life Ca1culations* Cycle Machinery Yearlyt Yearly## Salvage Year(j) Annuitytt DNCF*** cash flow mach. cost value##¢ 1 $66,623 -$ 89,581 -$92,268 $ 169,922 $100,290 2 46,216 — 28,201 65,118 12,535 88,354 3 39,309 30,610 64,265 13,388 77,848 4 35,808 86,941 63,400 14,253 68,600 5 33,691 140,874 62,523 15,130 60,458 6 32,282 191,484 60,430 16,016 53,288 7 31,290 239,892 59,536 16,911 46,975 8 30,566 286,178 58,633 17,813 41,415 9 30,029 330,419 57,724 18,722 36,517 10 29,629 372,690 56,808 19,638 ~32,203 11 29,331 413,064 55,887 20,560 28,402 12 29,115 451,162 54,959 21,487 25,053 13 28,964 488,402 54,072 22,419 22,101 14 28,866 523,501 53,090 23,356 19,500 15 28,812 556,974 52,149 24,297 17,208 16 28,796!! 588,882 51,203 25,243 15,187 17 28,812 647,515 -68,373 26,193 13,405 18 28,855 652,117 63,911 27,147 11,833 * Using a a 32 discount rate, The optimum life represents the number of years of machine use which minimizes the annuity value for the machinery set *3 The annualized cost is calculated using equation 6.2 # ##tMachinery set salvage value after cycle years(j) of use !' assuming the new machinery cost is offset by the salvage value of the previous machinery set *ttSum of discounted net cash flows from the use of equation 10.] Yearly farm cash flow ## Yearly machinery purchase, housing/insurance costs repair, labor, fuel, and The lowest annualized cost and years of optimum machinery life cycle 1(18 Table 6.3. Chisel Plow Tillage Optimum Life Calculations: Cycle Machinery Yearly# Yearly## Salvage Year(j) Annuity** DNCFttt cash flow mach. cost value### 1 $72,795 -$107,756 -$109,513 $ 188,945 $112,766 2 49,688 - 43,255 67,367 11,619 99,316 3 41,817 17,655 66,559 12,427 87,481 4 37,791 76,060 65,734 13,251 77,067 5 35,328 132,040 64,896 14,090 67,902 6 33,665 184,505 62,646 14,941 59,835 7 32,474 234,741 61,784 15,803 52,734 8 31,588 282,826 60,912 16,674 46,482 9 30,913 328,837 60,032 17,555 40,977 10 30,394 372,846 59,144 18,443 36,129 11 29,992 414,926 58,249 19,338 31,859 12 29,683 455,149 57,347 20,240 28,098 13 29,448 493,581 56,439 21,148 24,784 14 29,274 530,290 55,524 22,062 21,865 15 29,151 565,339 54,605 22,982 19,292 16 29,072 598,790 53,680 23,907 17,024 17 29,029 630,752 52,750 24,837 15,026 18 29,018!! 661,142 51,815 25,722 13,263 19 29,035 610,900 -88,099 26,711 11,710 20 29,075 642,754 65,968 27,654 10,340 * Using a a 38 discount rate, The optimum life represents the it 0 ###Machinery set salvage value after cycle years(j) of use 1! number of years of machine use which minimizes the annuity value for the machinery set The annualized cost is calculated using equation 6.2 assuming the new machinery cost is offset by the salvage value of the previous machinery set *ttSum of discounted net cash flows from the use of equation 10.1 Yearly farm cash flow ## Yearly machinery purchase, housing/insurance costs repair, labor, fuel, and The lowest annualized cost and years of optimum machinery life cycle 1 0 9 Table 6 4 No - Till Optimum Life Ca1culations* Cycle Machinery Yearlyt Yearly## Salvage Year(j) Annuitytt DNCF*** cash flow mach. cost value### 1 3 61,458 -$ 89,189 -$ 91,865 $ 164,711 $100,244 2 40,982 27,362 65,592 7,253 88,331 3 34,024 31,966 64,831 8,014 77,843 4 30,479 88,871 64,046 8,798 68,609 5 28,322 143,427 63,244 9,601 60,477 6 26,876 194,246 60,681 10,417 53,316 7 25,848 242,911 59,851 11,247 47,008 8 25,092 289,496 59,011 12,087 41,452 9 24,524 334,072 58,161 12,937 36,558 10 24,094 376,710 57,302 13,796 32,245 11 23,768 417,481 56,436 14,662 28,445 12 23,525 456,451 55,562 15,537 25,096 13 23,347 493,686 54,681 16,417 22,144 14 23,224 529,250 53,793 17,305 19,542 15 23,146 563,205 52,900 18,198 17,248 16 23,106 595,611 52,001 19,097 15,225 17 23,099!! 626,526 51,097 20,001 13,442 18 23,120 584,981 -70,727 20,910 11,869 19 23,165 621,391 63,845 21,824 10,481 * Using a a 3% discount rate, The optimum life represents the *1! 8 ###Machinery set salvage value after cycle years(j) of use number of years of machine use which minimizes the annuity value for the machinery set The annualized cost is calculated using equation 6.2 assuming the new machinery cost is offset by the salvage value of the previous machinery set #*#Sum of discounted net cash flows from the use of equation 10.1 Yearly farm cash flow ## Yearly machinery purchase, housing/insurance costs' repair, labor, fuel, and The lowest annualized cost and years of optimum machinery life cycle Chapter 7 FERTILIZERS Literature on fertilizer use by tillage method is reviewed in this chapter. Fertilizer use by crop for alternative yields is estimated. These estimates are used in the crop budgets in appendix B. 7.1 Literature Review The reviewed literature presented a range of impressions concerning the rate and types of fertilizer use by tillage system. Models developed in Iowa use the same fertilizer costs for both moldboard plow and reduced or no till practices in a static comparisons[l]. Pesticide and fertilizer expenditures were projected to increase as surface residue rises[2]. This 'implies an assumption that reduced or no till and moldboard plow pest activity and nutrients are at similar levels. 1. Iowa State University, ’The Economics of Soil and Water Conservation Practices; Results and Discussion’, Center for Agricultural and Rural Development, Card Report 109 SWAPII, Feb 1983. 2. Iowa State University, 'Short and Long Term Analysis of the Impacts of Several Soil Loss Control Measures on Agriculture’, Center for Agricultural and Rural Development, Card Report 93, June 1980. ' - 11 0 1 1 1 Indiana[3] investigators assumed anhydrous nitrogen is knifed in for all tillage systems except for ridge and no till on which nitrogen was surface applied and at 208 higher application rates due to additional surface residue assumed. USDA[4][5] investigations demonstrated that fertilizer requirements were higher for no ti11(25-3OX higher) and reduced ti11(10-l78 higher) than for moldboard plow tillage. The average soybean fertilizer requirements did not differ significantly between tillage systems but there were a wider range of fertilizer recommendations for soybeans than for corn. The inverse relationship between fertilizer application rates and the degree of field preparation was attributed to the higher surface residue on reduced or no till fields. Residue ties up nitrogen while higher soil moisture levels and lower soil temperatures at planting decrease mineralization and increases denitrification. Residual fertilizer and/or chemical concentrations in the top few inches of the soil profile may increase over time if non-soil inverting tillage is used implying that complete soil inversion every few years may be desired to mix the soil. 3. D.R. Griffith, S.D. Parsons, T.T. Bauman, C.E. Edwards, D.R. Scott, and F.T. Turpin, ‘A Guide to No-Tillage Planting after Corn or Soybeans’, Purdue University Extension Bulletin ID-l54. 4. Michael Duffy and Michael Hanthorn, 'Returns to Corn and Soybean Tillage Practices’ Agricultural .Economics Report 508, Economic Research Service, USDA, Jan 1984. 5. Lee A. Christiansen and Patrica E. Norris, ‘A Comparison of Tillage System for Reducing Soil Erosion and Water Pollution’, Agricultural Economics Report 499, Economic Research Service, USDA, May 1983. 11.2 Muhtar[6] raises the question of whether fertilizer levels chosen are from farmers’ perspective or from soil scientists and agronomists views and asks that production functions relating fertilizer(input) and yie1d(output) of different tillage systems be defined or estimated for a clearer understanding of the relationships involved. Higher levels of surface residue were alleged to tie up fertilizer applications and lower soil pH[7]. There may be'a problem in Michigan if nitrogen fertilizer is surface applied and no till is used as soil pH is lowered in the top few inches of the soil layer thru time, especially on sandy soi1s[8]. This affects seed germinations rates and relative effectiveness of herbicides so liming may .be needed to control soil acidity. 6. Hannibal A. Muhtar, 'An Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Drainage Basin’, PhD Dissertation in Agricultural Engineering, Michigan State University, 1982. 7. M.L. Vitosh and D.D. Warnke, 'No Till Corn: 2 - Fertilizer and Liming Practice’, Michigan State University Extension Bulletin E - 905, Nov 1981. 8. V.W. Meintz and L.S. Robertson, ‘Soil Sampling for No - Till and Conservation Tillage Crops’, Michigan State University Extension Bulletin E - 1616, Jan 1983. 1 1 3 7.2 Fertilizer Parameters 7.2.1 Nitrogen Nitrogen is the major nutrient affected by surface residue levels as nitrogen use levels and application methods are critical factors. A few questions arise. Is more nitrogen required as residue levels rise or are the levels of application are the same across tillage systems? Would the use of surface applied nitrogen on no till lower soil pH thru time and require additional liming of fields in the future? Do different tillage systems use of different fertilizer mixes? Do previous crop rotations affect nitrogen use on the present crop? Answers to some of the previous questions are in the literature reviewed and in conversations with scientists and deal with the timing, placement, and type of nitrogen fertilizer used. 11 4 Bulletin E - 802[9] published by the Cooperative Extension Service has the following observations: 1. Spring applications of ammonium nitrogen are more effectively used than fall nitrogen application[10]. Fall nitrogen applications should be used on fine textured soils and after soil temperatures are less than 50 degrees Fahrenheit. 2. Sidedressed nitrogen is more effectively used than plowed under nitrogen[ll]. Side dressing of nitrogen is done when corn is six to twelve inches high. 3. Nitrogen should be incorporated into the soil since surface applied nitrogen[12] suffers from volatization losses if residues prevent the nitrogen from coming in contact with the soil or if soil pH is high or no rain occurs several weeks after application. Christenson[l3] and reviewed "‘~me\“" .2 ‘ _ 1 7 literature suggest that 10 - (15% more nitrogen is required ww— M on reduced or no tilled than on moldboard tilled soils if the nitrogen is surface applied. 9. M.L. Vitosh and R.J. Black, 'Effect of Nitrogen Fertilizer on Corn Yield’, Michigan State University Extension Publication E - 802, Feb 1979. 10. Fall nitrogen application losses are 5-10% on fine and medium textured soils and 10-308 on coarse textured soils 11. Which can be leached deep by heavy rains 12. Urea, 283 nitrogen 13. D.R. Christenson, Personal Communication, Department of Crop and Soil Sciences, Michigan State University, 1984. 1 1 5 4. Most nitrogen fertilizers leave an acidic residue so limestone is required to neutralize the soil[l4] pH at the following rates: 1. 1.8 pounds of limestone for each pound of ammonia nitrate, urea, or anhydrous ammonia 2. 5.5 pounds of limestone for each pound of ammonia sulphate 5. Crops following soybeans benefit from about 0 to 40 pounds of nitrogen left by the soybeans. Verbal estimates[15] put this at about 30 pounds. A full stand of alfalfa leaves up to 70 pounds for the next crop in the rotation. These assumptions help guide some of the decisions made about nitrogen rates[16] for various crops. Exact fertilizer rates given a yield level vary from author and/or scientist for another. A standard reference point for many fertilizer recommendations is the following table which uses removal of nutrients by crop to make maintenance fertilizer recommendations. 14. Raising the soil pH if the soil is acidic 15. D.R. Christenson, Personal Communication, Department of Crop and Soil Sciences, Michigan State University, 1984. 16. Especially about nitrogen use on corn 11 6 Table 7.1. Nutrient Removal by Several Michigan Field Crops Lbs./acre Lbs./unit yield Crop Yields Nit. Phos. Pot. Nit. Phos. Pot. Corn Grain 150 bu. 135 53 40 .9 .35 . 27 Stover 4.5 tons 100 37 145 22.2 8.2 32.2 Silage 25 tons 235 90 195 9.4 3.6 7 8 Soybeant 40 bu. 150 35 55 3.8 .9 1.4 Wheat Grain 40 bu. 50 25 15 .12 .62 .38 Straw 1.5 tons 20 5 35 13.3 3.3 23.3 Oats Grain 80 bu. 50 20 15 .62 .25 .19 Straw 2 tons 25 15 80 12.5 7.5 40.0 Rye Grain 30 bu. 35 10 10 1.17 .33 .33 Straw 1.5 tons 15 8 25 10.0 5.3 16.7 Sorghum Grain 60 bu. 50 25 15 .83 .42 .25 Stover 3 tons 65 20 95 21.7 6.7 31.7 Sugarbeets 15 tons 60 20 50 4.0 1.3 3.3 Potatoes 500 cwt. 167 67 316 .33 .13 .63 Field Beans 18 cwt. 75 25 25 2.5 .83 .83 Alfalfa 5 tons 225 50 225 . 45.0 10.0 45.0 Bromegrass 3 tons 90 18 140 30.0 6.0 47.0 t - Legumes get most of their nitrogen from the air Source: USDA Miscellaneous Publication No. 369 and "Feeds and Feeding” by Morrison. 22nd Edition. M. L. Vitosh and D.D. Warnke, "Phosphorus and Potassium Maintenance Fertilizer Recommendations.", Michigan State University Extension Bulletin E - 1342, Oct 1979. 11.7 This table selects representative yields_ for various crops and W". -——~.......,._.,l___i _,_.._.._.-___ 1.- estimategflfithefl amount of nitrogen, phosphorus and potassium removed from~ the -soil by (the harvest of the crop. Nutrient va* _.—7« . removal[l7] can converted into pounds of nutrient removed per unit of yield. This approximates of acre nutrient removal at differing yields. But pounds of fertilizer used per unit of yield is a linear relationship implying the first bushel grown requires fertilizer. This is unrealistic as crops can produce at 35 - 508 of their potential yields without fertilizer applications depending on initial soil fertility levels. On the opposite end of the spectrum, very high yields require more fertilizer per additional unit of yield than lower yields[18]. This type of linear nutrient estimation procedure can be used for a limited range of yields deviating around the mean yields selected in table 7.1. 7.2.1.1 Corn Nitrogen Use Nitrogen recommendations for corn yields in various publications indicates researchers assume an increase in nitrogen use per bushel of yield at the higher production levels or in other terms, the marginal physical product(MPP) declined as yields rose. This is illustrated by the estimates in table 7.2 below calculated from the nitrogen recommendations for various yields as found in table 2 of the Cooperative Extension Service Bulletin E - 550. 17. In pounds per acre 18. The marginal physical product is declining implying the change in yield decreases for every additional unit of fertilizer input Table 7.2. 11 8 Equational Estimates of Corn Yield and Nitrogen ------“---~—------—--—--—n—-——~——-—--———-—--—-—~-——m---———-—————-~-— Median Yield (bu./acre) Relationships Nitrogen Recommen- Nitrogen Use dations as Predicted (lbs./acre by Equation 70 lbs. 67.4( 66.4)1bs. 100 lbs. 106.5(106.4)1bs. 150 lbs. 149.9(149.9)lbs. 200 lbs. 196.9(197.1)1bs. 250 lbs. 247.3(247.8)1bs. 300 lbs. 300.4(302.l)1bs. Change in Nitrogen Use/ Change in Yield! .22(1.27)1bs./bu. .38(l.39)lbs./bu. .51(1.51)1bs./bu. .62(l.63)1bs./bu. .72(l.75)1bs./bu. .82(1.87)lbs./bu. —-——_-----—-—----—----———-—--———-—--—-—_———_——-——-—-—-—--———- -—-——--~—— *This is the inverse of the marginal physical product so an inverse of column figures will approximate the marginal physical product of the nitrogen use(the per bushel change in corn yield due to the use of one additional pound of fertilizer). Source: D.D.Warnke, Vegetable and Field Crops in Michigan’, Extension Bulletin E - 550, Dec 1981. ’Fertilizer Recommendations for Michigan State University Equations used to estimate the yield and nitrogen relationship are; (7.1a) Power - N = .1896(Y ), (7.1b) Polynomial( ) - N = 17.9 + .976(Y) + where N Y 1.3604 Nitrogen use Yield level 2 R = .996 2 .00198(Y ), 2 R = .998 These equations are used to estimate nitrogen use given an expected yield and illustrate that increasing amounts of nitrogen are needed for an additional unit of yield. and what The choice of method levels of nitrogen use to assume depends on data 11.9 availibility and assumptions made. It must be clarified whether the nitrogen and yield relationships are for one soil management group, management level or tillage method and if the data reflects differing soils groups and yield potentials. Another question is whether nitrogen use for various yields is the most profitable nitrogen use rate[l9] or if nitrogen is used to achieve a level of corn yield[20]. Part of the problem in deciding how much nitrogen to use is related to the net return maximization question. The Cooperative Extension Bulletin~ E - 802[21] has addressed this issue calculating the net return maximizing rate of nitrogen use on corn for various soil types with differing yield potentials. First the corn/nitrogen price ratio is calculated[22]. The net return maximizing nitrogen use rate is found for a yield potential and different corn/nitrogen price ratios in the following table: 19. The marginal cost of nitrogen is just equal to the marginal benefit of additional yield, the marginal value product - MVP equals the marginal factor cost - MFC 20. The level of nitrogen use may not result in the highest net returns per acre but may be used to attain a yield level 21. M.L. Vitosh and R.J. Black, 'Effect of Nitrogen Fertilizer on Corn Yield’, Michigan State University Extension Publication E - 802, Feb 1979. 22. For example — $2.00/bu. corn and $.20/1b. nitrogen results in a 10:1 ratio 1210 Table 7.3. The Most Profitable Nitrogen Rate for Corn based on a Computer Model for Predicting Yield and Corn to Nitrogen Price Ratios ----—-----—--————-_‘—--------——--—--—-----—----——_—-———-u--“——‘-. Corn/Nitrogen Yield Potential - Bu/acre Price Ratio 85 100 115 130 145 160 175 190 -- most profitable nitrogen rate - lbs. N/acre -- 5 : 1 80 90 100 110 130 140 150 170 10 : 1 90 110 130 140 160 180 190 210 15 : 1 100 120 140 160 180 200 220 240 20 : 1 110 130 150 170 190 210 230 250 25 1 120 140 160 180 200 220 240 260 —_—_—-———-o--——-—_—-—-—--—-———--—---——————---—--—--——————————_—~—. Source; M.L. Vitosh and R.J. Black, 'Effect of Nitrogen Fertilizer on Corn Yield’, Michigan State University Extension Publication E - 802, Feb 1979. By selecting a yield potential and a price ratio, the most profitable nitrogen use rate can be found. This however does not give the expected corn yield level[23]. The yield potential is the maximum corn yield [24] obtained while the expected yield is less[25]. The expected corn yield can be estimated using the following equation[26]; 23. The expected corn yield level will be less than but close to the yield potential depending on the nitrogen use rate. 24. At 1003 yield potential, the top of the production curve, the marginal physical product is zero 25. The expected yield is less than 1003 of the potential yield -and the marginal physical product of the expected yield is greater than zero 26. Developed from table 4 in the Extension Bulletin E - 802 1 2 1 (7.2) Y = .4625(Yield Potential) + .845(N) - .0018(Yo/Yp)(N) where Y Expected yield Y0 = Highest yield potentia1[27] Ypl= Yield potential N = Most profitable nitrogen use rate[28] e.g. Y = .4625(115 bu.) + .845(142 lbs.) — .0018(195 bu./115 bu.)(l42 lbs.) Y = 111.7 bu. which is 97% of yield potential As is seen in table 7.3 and calculated from equation 7.2, the higher the most profitable nitrogen use rate, the closer to the potential yield, the expected yield is. 27. From table 7.3 28. From table 7.3 for the example used using a 16.25 price ratio at the 115 bushels yield potential 12.2 When estimating corn nitrogen use, these assumptions, table 7.3, equation 7.2, and estimated corn and nitrogen prices can be utilized in this analysis since the data is available[29]. Yield potentials by soil management groups have been approximated[30] for nitrogen use on specific soil groups. Once the nitrogen levels are calculated for corn on moldboard plow tilled soils, are adjusted upward 10 - 15% for reduced or no tillage if nitrogen is surface applied. 29. For example; If the estimated corn/nitrogen price ratio is 16.6 - $2.65/s.16,an extrapolation down the 100 bushel yield potential column in table 7.3 and projecting to a 16.6 price ratio would indicate that about 123.2 pounds of nitrogen per acre is the most profitable nitrogen use rate. Then by using the 100 bushel yield potential and 123.2 pounds of nitrogen per acre in the expected corn yield equation - equation 7.2, the expected corn yield can be calculated as 98.44 bushels. Y = .4625 x 100 bu. yield potential plus .845 x 123.2 lbs. of nitrogen minus 190/100 x .0018 x 123.2. 80 Y = 98.44 bushels or 98.44x of yield potential. The expected yield is 98.448 of potential yield so this figure can be used to approximate the yield potential and the most profitable nitrogen use rate for an expected yield of 103 bushels per acre which is the base corn yield used in the crop budgets. The 103 bushels, the expected yield, is divided by .9844 to approximate the potential yield which is calculated to be 104.6 bushels per acre. Next an extrapolation along a 16.6 price ratio row and a 104.6 bushel yield potential column indicates that about 129.3 pounds of nitrogen per acre is the most profitable nitrogen use rate and will be used in the corn budget - table B32 and B33 in the appendix. 30. Yield potential estimates by soil group are found in tables Bl and 82 in the appendix 1223 7.2.1.2 Soybean Nitrogen Use Recommended nitrogen use on soybeans varies by publication and is dependent on assumptions. The nutrient removal tab1e[3l] indicates that 150 pounds of nitrogen are removed from the soil by 40 bushels of soybeans but suggests most nitrogen comes from the air. The Fertilizer Recommendation Bulletin[32] lists 40 pounds of nitrogen needed for soybeans if no legumes are used or manures are applied but only 10 pounds of nitrogen are used if legumes are planted in areas north of Lansing[33]. The Soil Conservation Service[34] indicates that 8 pounds of nitrogen are used for soybeans assuming a soybean yield of 30 - 50 bushels per acre. In contrast, the Estimated Crop and Livestock Budgets for Michigan[35] estimate 10 pounds of nitrogen is used for 21 or 30 bushels of soybeans and 20 pounds of nitrogen are used for 38 bushels of soybeans. 31. Table 7.1 32. D.D.Warnke, 'Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin E - 550, Dec 1981. 33. Bulletin E - 550 indicates no nitrogen is used on soybeans south of Lansing 34. Northeastern Research Program Group, 'Branch County: Example of Average Yearly Budget for Three Soil Productivity Groups’, Economics Research Service, USDA, Jan 1984. 35. Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, ’Estimated Crop and Livestock Budgets for Michigan — 1984’, Agricultural Economics Report 446, Michigan State University, 1984. 12 4 Field or navy beans use 25, 30, and 40 pounds of nitrogen for 10, 13, and 17 hundredweight of field or navy beans[36]. Given these estimates for nitrogen use on soybeans and related. navybeans, this analysis uses Soil Conservation Service estimates of 8 lbs. of nitrogen per acre for soybeans grown in the St. Joseph area since the estimates are specific to the area. 7.2.1.3 Wheat Nitrogen Use Nitrogen recommendations for wheat are in the Fertilization of Wheat Bulletin E - 1067[37]. The wheat nitrogen recommendation chart below is based on soil type and percentage of organic matter in the soil plus the wheat yield goal[38]. 36. Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, 'Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. 37. M.L. Vitosh and D.D. Warnke, 'Fertilization of Wheat’, Michigan State University Extension Bulletin E - 1067, Apr 1977. 38. This is not potential yield but similar to expected yield as defined in the corn nitrogen footnote earlier in this chapter 12.5 Table 7.4. Nitrogen Fertilizer Recommendations for Short and Stiff Strawed Wheat Varieties Percent Wheat yield goal(bu/acre) organic ---------------------------------- Soil Type matter 30 40 50 60 70 80 ------- pounds nitrogen/acre--—---— Sandy soils 0 — 2x 45 so 75 90 —- —- Medium-fine 2 - 4x 30 40 50 60 7O 80 textured soils Dark colored 4 - 6% 20 25 30 35 40 45 mineral soils Notes; For varieties susceptible to lodging, do not use more than 60 pounds of nitrogen per acre on sandy soils, 40 pounds nitrogen per acre on medium and fine textured soils and 30 pounds nitrogen per acre on dark colored mineral soils. Do not use topdress nitrogen where the previous crop was a legume or where more than 20 tons of manure per acre has been applied recently. For a smaller application of manure, reduce the nitrogen recommendations by 4 pounds for each ton of manure applied. Wheat is not recommended for organic soils(>63 organic matter), if it is grown on these organic soils do not topdress with nitrogen. Source: M.L. Vitosh and D.D. Warnke, 'Fertilization of Wheat’, Michigan State University Extension Bulletin E - 1067, Apr 1977. 126 St.Joseph soils are sandy loam soils with low organic matter having an expected yield of 43 bushels of wheat per acre[39]. The nitrogen application rate for wheat is 64 pounds of nitrogen per acre[40]. If wheat follows soybeans, nitrogen use is about 30 pounds less[4l]. 7.2.2 Phosphorus and Potassium Unlike nitrogen which is released to plants relatively quickly, phosphorus and potassium fertilizers are released slower over a period of years. depending on soil type and other enviromental conditions. Due to this year to year carryover effect, soil and crop specialists recommend a soil test for potassium and phosphorus yearly to determine soil nutrient levels and their ability to supply the planned crops with adequate phosphorus and potassium.. If nutrient levels are sufficient, there is no need to apply potassium or phosphorus in the current crop year. If levels are inadequate, then additions to the soil are desireable at the following rates[42]: 39. From table 5.2 40. From table 7.4 41. By previous assumptions 42. D.D.Warnke, 'Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin E - 550, Dec 1981. 12.7 1. 5 - 11 pounds of phosphorus to raise the phosphorus soil test by one pound in sandy and loamy sand soils. 2. 12 - 16 pounds of phosphorus to raise the phosphorus soil test by one pound in loans and clay loans 3. 2 - 6 pounds of potassium to raise the potassium soil test by one pound[43] These rates are used if an immediate nutrient buildup is desired but data developed in Bulletin E - 550[44][45] recommends potassium and phosphorus application rates for different soils, desired yields and by crop if a slower five year nutrient buildup is desired. The following table lists some of the counties in the studied area and their median potassium and phosphorus soil tests as determined by the soils department at Michigan State University from samples sent in. 43. This is dependent on soil clay content as less potassium is required in soils with a high clay content 44. The potassium and phosphorus application rates are shown by crop in tables B23 - B28 in the appendix 45. D.D.Warnke, 'Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin E - 550, Dec 1981. 1218 Table 7.5. Median Phosphorus and Potassium Soil Test in Counties of the St. Joseph Basin —~-——————————--——-——-——-—-—-—----—-——————————-—-————————-——-——H-.——— County Phosphorus test Potassium test Van Buren 101 - 125 lbs. 126 - 150 lbs. Kalamazoo 76 - 100 lbs. 151 - 175 lbs. Calhoun 76 - 100 lbs. 151 - 175 lbs. Cass 76 - 100 lbs. 151 - 175 lbs. St. Joseph 76 - 100 lbs. 126 - 150 lbs. Branch 76 - 100 lbs. . 151 - 175 lbs. Source; D.D. Warnke, “Average Soil Test Levels in Michigan’, Soils Division, File 32.82, Michigan State University. The following table indicates some of the desired potassium, phosphorus and pH levels in Michigan for various crops[46]. Table 7.6. Desired Crop Soil Nutrient and pH Levels Crop Yield pH Phosphorus Potassium Alfalfa 6 Tons 6 5 — 7 0 60 lbs. 270 lbs Corn 125 bu. 6 0 - 7 0 60 lbs 225 lbs Wheat 60 bu. 6.0 - 7 0 75 lbs. 225 lbs. Soybeans 45 bu. 6 0 - 7 0 50 lbs. 200 lbs Field Beans 26 bu. 6 0 - 7 0 40 lbs. 200 lbs. Potato 400 cwt. 5 6 - 7 0 180 lbs 325 lbs Source; D.D. Warnke, 'Average Soil Test Levels in Michigan’, Soils Division, File 32.82, Michigan State University. A comparison of desired and actual potassium and phosphorus soil 46. If the soil test results are at these levels, no phosphorus and potassium applications are required 1 2 9 tests illustrated in tables 7.5 and 7.6 indicates most St. Joseph River Basin counties are at or near desired phosphorus levels but are below desired potassium levels for most crops. .There is a choice of raising nutrient levels all at once or spreading the increase over five years. In this analysis, the five year choice is used. Calculations of fertilizer rates[47] result in the following potassium and phosphorus application rates which are used in the appropriate crop budgets(appendix tables B32 - B35) for the first five years of the analysis. Once the soils nutrient levels are corrected to desired levels, then fertilizer maintenance rates in table 7.8 are used for year 6 and onward. 47. From the tables 823 - 828 in the appendix 1 3 0 Table 7.7. Corrective Phosphorus and Potassium Use by Crop and Yield Levelt Corn Yield levels(bu./acre) Nutrient 105 bu 135 bu 165 bu. 195 bu. 225 bu. Phosphorus 25 lbs. 25 lbs. 25 lbs. 25 lbs. 75 lbs. Potassium 75 lbs. 100 lbs. 150 lbs. 200 lbs. 225 lbs. Soybeans Yield level(bu./acre) Nutrient 20 - 40 bu. 40 - 60 bu Phosphorus -- 0 -- -- 0 -- Potassium 25 lbs. 50 lbs. Wheat ~-——- Nutrient 25 - 39 bu. 40 — 64 bu. 65 — 90 bu Phosphorus -- 0 -- -- 0 -- 25 lbs. Potassium 25 lbs. 50 lbs. 75 lbs. -—-——---—--—--————————-‘—-——_—---—-—-_-—-—————————--—-_ —‘-—--------—-———-———-—_—---—--——--—--—-—---_--—-—‘-—-—_-———-———«- *For years 1-5 Source; Computed from tables in Fertilizer Recommendations for Vegetable and Field Crops in Michigan, Michigan State University Extension Bulletin E - 550, Dec 1981, given the characteristics of the actual and desired soil nutrient levels in tables 7.5 and 7.6. 1 3 1 Table 7.8. Maintenance Phosphorus and Potassium Use by Crop and Yield Levelt ——-———-——————_—————.—__——-——-————_——-——————_-———————-———————————_—~-—— Corn Yield levels(bu./acre) Nutrient 105 bu 135 bu. 165 bu 195 bu 225 bu Phosphorus 37 lbs. 47 lbs. 58 lbs. 68 lbs. 79 lbs. Potassium 28 lbs. 36 lbs. 45 lbs. 53 lbs. 61 lbs. Soybeans Yield levels(bu./acre) Nutrient 20 bu 30 bu 40 bu 50 bu 60 bu Phosphorus 18 lbs. 27 lbs. 36 lbs. 45 lbs. 54 lbs. Potassium 28 lbs. 42 lbs. 56 lbs. 70 lbs. 84 lbs. Wheat Nutrient 30 lbs. 50 lbs. 70 lbs. 90 lbs. Phosphorus 19 lbs. 31 lbs. 43 lbs. 56 lbs. Potassium 11 lbs. 17 lbs. 27 lbs. 34 lbs. ._-—-——-—v-——-—--——----—-—---——_——-—-—-—~-—---~————————— *To replace soil nutrients removed by various crops Source; Computed from table 7.1 1j32 These rates are linear projections of nutrient removal rates and may have to be adjusted at the high and low yields to compensate for nonlinear effects. To obtain exact potassium and phosphorus use rates, crop production curves and relationships need to be calculated with appropriate adjustments[48]. There is no significant difference in potassium and phosphorus use by tillage practice[49]. Minor minerals such as boron, magnesium, sulfer, calcium, and manganese should be tested for and corrections made for if there is. a deficiency of these elements but detailed use estimates are not done in this analysis. 48. Clayey soils utilize potassium better 49. An exception may be the use of continuous no till as moldboard plowing or a form of complete soil inversion should be done every three or four years to mix the fertilizer as the soil or the soil nutrient concentrations by rooting zone may vary Chapter 8 HERBICIDES AND INSECTICIDES This chapter reviews some of literature and makes assumptions about the use of herbicides and insecticides on crops tilled by different systems. Herbicide and insecticide quantities used are estimated for crops and tillage assumptions. 8.1 Literature Review of Herbicide and Insecticide Use General impressions of the literature review are increased weed control problems when reduced or no tillage is practiced due to larger weed populations. Herbicides are tied up in surface crop residue and more conducive weed growing conditions are establishedfl]. l. Hannibal A. Muhtar, 'An Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Drainage Basin’, PhD Dissertation in Agricultural Engineering, Michigan State University, 1982. 133 1:34 Herbicide costs by tillage system in the literature[2][3][4][5]vary greatly as reduced and no till herbicide costs are estimated to be 50 to 300% greater than moldboard plowing tillage herbicide costs depending on soil type, cropping patterns, and other factors. Kells[6] estimates reduced or no tillage uses about 25% more herbicide than moldboard plow tillage so herbicide programs by tillage system are worked out using this appraisal. Muhtar[7] notes that pre—emergent herbicides may have effectiveness problems when applied on a rough surface. Post emergent herbicide weed control can be effective on reduced till or no till systems but runs into problems if crop growth is slowed by lower soil planting season temperature or if height differentials between weeds and crop plants are not significant. 2. Lee A. Christiansen and Patrica E. Norris, 'A Comparison of Tillage System for Reducing Soil Erosion and Water Pollution’, Agricultural Economics Report 499, Economic Research Service, USDA, May 1983. 3. Michael Duffy and Michael Hanthorn, ‘Returns to Corn and Soybean Tillage Practices’ Agricultural Economics Report 508, Economic Research Service, USDA, Jan 1984. 4. Francis M. Epplin, Thomas F. Tice, Alan E. Baquet, and Steven J. Handke, 'Impacts of Reduced Tillage on Operating Inputs and Machinery Requirements’ 5. Myron P. Kelsey, ‘Michigan Farm Business Analysis Summary-All Types of Farms; 1982 Telefarm Data’, Agricultual Economics Report 441, Michigan State University, 1983. 6. Jim Kells, Personal Communication, Department of Crop and Soils Sciences, Michigan State University, East Lansing, Michigan, 1984. 7. Hannibal A. Muhtar, 'An Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Drainage Basin’, PhD Dissertation in Agricultural Engineering, Michigan State University, 1982. 135 Mechanical cultivation of reduced or no till fields is complicated by surface residue[8] requiring higher levels of management. Soil pH levels need to be controlled for effective herbicide use. Reviews of the literature[9][10][ll][12][13] supports the axiom that increased insecticide and fungicide applications are needed tillage levels are reduced. The USDA survey[l4] indicated no-till corn farms used twice as much insecticide per acre as moldboard plowed farms. Estimates of insecticide use on soybean farms had a greater variance than corn insecticide use. Average soybean insecticide use by tillage demonstrated no significant differences between tillage systems in some areas though there was higher insecticide use in reduced and no till systems in 8. Rolling cultivation is effective and buries a fraction of the residue 9. Lee A. Christiansen and Patrica E. Norris, ‘A Comparison of Tillage System for Reducing Soil Erosion and Water Pollution’, Agricultural Economics Report 499, Economic Research Service, USDA, May 1983. 10. Michael Duffy and Michael Hanthorn, ‘Returns to Corn and Soybean Tillage Practices’ Agricultural Economics Report 508, Economic Research Service, USDA, Jan 1984. 11. Iowa State University, ‘Short and Long Term Analysis of the Impacts of Several Soil Loss Control Measures on Agriculture’, Center for Agricultural and Rural Development, Card Report 93, June 1980. 12. Iowa State University, ‘The Economics of Soil and Water Conservation Practices; Results and Discussion’, Center for Agricultural and Rural Development, Card Report 109 SWAPII, Feb 1983. ' l3. R.E. Phillips, G.W. Thomas and R.L. Blevins, ‘No Tillage Research: Research Reports and Reviews’, University of Kentucky 14. Michael Duffy and Michael Hanthorn, ‘Returns to Corn and Soybean Tillage Practices’ Agricultural Economics Report 508, Economic Research Service, USDA, Jan 1984. 136 other areas. Work in Indiana[15] indicated higher pesticide costs on no~till and ridge till than for moldboard plowing and listed lower pesticide costs for a corn-soybean rotation as compared to a continuous corn rotation. The causes cited for increased pest activity in reduced or no tillage are the amounts of surface residue[16] and increased soil moisture levels. Application methods and rates plus the selection of insecticides become more crucial as reduced or no tillage is practiced. Insecticide rates are tailored to crops and tillage methods used in the analysis. 8.2 Herbicide and Insecticide Theoretical Assumptions There is a change in herbicide and insecticide use rates assumed when reduced or no tillage is utilized. Operators increase their herbicide and insecticide application levels in response to higher weed populations and insect levels. The increases in pesticide use are estimated by the graphs below: 15. D.R. Griffith, S.D. Parsons, T.T. Bauman, C.R. Edwards, D.H. Scott, and F.T. Turpin, ‘A Guide to No-Tillage Planting after Corn or Soybeans’, Purdue University Extension Bulletin ID-154. 16. Provides a host for insect eggs, larvae, and shelter from insecticides 137 Herbicide Use . RT(H) I I Ho I MET?) A 1 0 n q Time Figure 8.1: Herbicide use by tillage system thru time Moldboard plow tillage herbicide use equation; (8.1a) Ho(n) = Ho Reduced or no tillage herbicide use equation; (-Zp x n) (8.1b) Hs(n) = Hs(q) - Zo(e ) where MB(H) - Moldboard plow tillage herbicde use RT(H) - Reduced or no till herbicide use n - Analysis year Ho - Moldboard plow herbicide use in year 0 Ho(n) - Moldboard plow tillage herbicide use in year n Hs(n) - Reduced or no tillage herbicide use in year n Hs(q) - Reduced or no tillage herbicide use in year q, the last year of the analysis Zo,Zp - Adjustment coefficients for herbicide use when using reduced or no tillage 1:38 Insecticide Use am) . Io MB(I) — C - —.§ .- Time Figure 8.2: Insecticide use by tillage system thru time Moldboard plow tillage insecticide use equation; (8.13) Io(n) = Io Reduced or no tillage insecticide use equation; (-Zr x n) (8.1b) Is(n) = Is(q) - Zq(e ) where MB(I) Moldboard plow tillage insecticide use NT(I) - Reduced or no tillage insecticide use n - Analysis year Io - Moldboard plow insecticide use in year 0 Io(n) — Moldboard plow tillage insecticide use in year n Is(n) - Reduced or no tillage insecticide use in year n Is(q) - Reduced or no tillage insecticide use in year q, the last year of the analysis Zq,Zr - Adjustment coefficients for insecticide use when using reduced or no tillage 139 These equational estimates can be in the crop cost component of the discounted net cash flow equation(10.l). Increases in herbicide or insecticide use due to reduced or no tillage is immediate and may gradually increase through time due to rising weed or insect tolerances to herbicide or insecticide. The initial use levels are the herbicide and insecticide types and usage rates recommended by Michigan State scientists[l7][18] by tillage systems and crop rotations. If subsequent adjustments are necessary, crop costs can be altered, but as a first run, the scientist’s recommendations will be used. 8.3 Herbicide Use The amount and type of herbicides used differs from publication to publication and from scientist to scientist depending on their frames of reference and past experiences. Several herbicide use estimates are available in publications but only one[19] specifically addressed the amount and types of herbicides to use by different crops and tillage systems. A MSU scientist[20] helped modify and develop some of the figures used in this 17. Jim Hells, Personal Communication, Department of Crop and Soils Sciences, Michigan State University, East Lansing, Michigan, 1984. ‘ 18. Dale Mutch, Personal Communication, Cooperative Extension Service, Michigan State University, East Lansing, Michigan, 1984. 19. William F. Meggitt and Jim Hells, ‘1984 Weed Control Guide for Field Crops’, Michigan State University Extension Bulletin E- 434. 20. Jim Hells, Personal Communication, Department of Crop and Soils Sciences, Michigan State University, East Lansing, Michigan, 1984. 1110 analysis(in appendix table B29). These application rates are used in the crop budgets(tables B32 - B35) and are comparable to the rates and types used in other publications such as the 1984 Weed Control Guide for Field Crops - Cooperative Extension Bulletin E - 434[21](lists herbicide use by crop and the types of weeds to be controlled), the Estimated Crop and Livestock Budgets for Michigan[22] (lists herbicide use by crop in dollar terms giving specific herbicide use examples by crop) and the Hoskins thesis[23] (lists herbicide use by crop). Herbicide used differs by soil type as soil texture[24] and soil organic matter influences the effectiveness of soil applied herbicides[25]. Soils with a high clay and organic matter content require greater herbicide rates for weed control while sandy soils require less herbicides and necessitate more careful herbicide placement and selection to avoid crop injury. Herbicide rates increase for soils with high clay content. -——e——mn——.a-—-- 21. William F. Meggitt and Jim Kells, ‘1984 Weed Control Guide for Field Crops’, Michigan State University Extension Bulletin E~ 434. 22. Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, ‘Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. 23. Roger L. Hoskins, ‘An Economic Comparison of Alternative Saginaw Valley Crop Rotations - An Application of Stochastic Dominance Theory’, Thesis PhD, 1981. ' 24. Relative amounts of sand, clay, silt and clay in the soil 25. D.R. Griffith, S.D. Parsons, T.T. Bauman, C.R. Edwards, D.H. Scott, and F.T. Turpin, ‘A Guide to No-Tillage Planting after Corn or Soybeans’, Purdue University Extension Bulletin ID—154. 1 4 1 8.4 Insecticide and Fungicide Use The inclusion of insecticide or fungicide costs in crop budgets and in the calculation of discounted net cash flows is a subject of debate and arbitrary allocations. Unlike fertilizer or herbicide applications which are applied with regular frequency, insecticide and/or fungicides are applied as insect and/or disease incidence approach or exceed tolerance thresholds. These applications are preventative if pests are expected to be a problem or pesticides are used when there is excessive pest outbreak or disease infestation. Rough estimates via conservations with a crop specialist[26] indicate that the frequency of applications range from 1 - 5% [27] to 100% of the acreage being covered[28]. The following is a listing of some of field crops in Michigan and rough estimates of the probability of some common pest and disease problems plus treatment frequencies or percentage of total acreage to be treated. 26. Dale Mutch, Personal Communication, Cooperative Extension Service, Michigan State University, East Lansing, Michigan, 1984. 27. To treat occasional corn armyworm or stalk borer outbreaks 28. For white mold on navy beans if there is a history of the disease - more a preventative measure 1112 Table 8.1. Insecticide and Fungicide Applications Application Crop frequency* Pest Control Corn 1 - 5% Armyworm Sevin(l.5 lb./acre) (first year) Rootworm Sevin(l lb./acre) Stalkborer Sevin(l.5 lb./acre) Corn 25 - 35% Rootworm Furadan(36 fl. oz. (second year) (estimate) (larvae) at planting) Nemotode Dyfonate(36 fl. oz. at planting) Soybeans/ 10 - 100%** Potato leaf Sevin(l lb./acre) Navybeans White mold Benelate ———-———————-——---—--—-—--—————-—--———————-——-—-—-—-———.‘—————_——— * Percent of farm acreage treated or percent of years in a 10 year period the whole farm is treated *t20% of the time or acreage if spot treatment 100% of the time or acreage if there is a historical problem with these pests These are currently observed problems as most disease and insect \ problems are dealt with through genetics and the development of resistant varieties. This situation is dynamic as insects and/or diseases become immune to control measures. The literature review and conservations with various Michigan State scientists support the contention that the use of minimum or no-till practices increases[29] the probability of insect or disease infestations requiring control applications. An interesting observation, not addressed, is that early plantings may suffer more insect attacks than do later plantings[30]. Evidently the insects have a better chance to 29. Roughly doubling application rates for no-till 30. Earlier plantings are taller than later plantings 1 4 3 establish themselves in earlier plantings since they are the only plants available initially. The budgeting approach taken is to cost soil insecticide use for second year corn[31] and prorated control on soybeans[32]. This method differs from Hoskin’s work [33][34]. Brand names used are for costing purposes only and substitute brands can be used if desired. A detailed list of insect control measures for various Michigan field and forage crops are. found in, the Cooperative Extension Service Bulletin No. E - 1582[35]. 31. Yearly application of herbicide and fungicide 32. Fungicide and insecticide every few years 33. Roger L. Hoskins, ‘An Economic Comparison of Alternative Saginaw Valley Crop Rotations - An Application of Stochastic Dominance Theory’, Thesis PhD, 1981. 34. Only .75 1b. of Furadan per acre was used for second year corn or continuous corn 35. Robert F. Ruppel and George W. Bird, ‘1982 Chemical Control of Insects and Nematodes in Field and Forage Crops’, Michigan State University Extension Bulletin E - 1582, Dec 1981. Chapter 9 PRICES, SEEDING RATES,OTHER COSTS, AND LENGTH OF ANALYSIS Input and commodity prices are reviewed and estimated for use in the crop budgets and in the discounted net cash flow(DNCF) formulation. Seeding rates by tillage method and minor costs of cropping are surveyed and estimates developed for each crop. A discussion of the discount rate and length of analysis is performed. 9.1 Literature Review of Pricing and Other Cost Assumptions The Iowa studies[l][2] constructed crop budgets adjusted to the combination of crap rotation, tillage system assumed, soil type used, supporting practices and expected yields. 1. Iowa State University, ‘Short and Long Term Analysis of the Impacts of Several Soil Loss Control Measures on Agriculture’, Center for Agricultural and Rural Development, Card Report 93, June 1980. 2. Iowa State University, ‘The Economics of Soil and Water Conservation Practices; Results and Discussion’, Center for Agricultural and Rural Development, Card Report 109 SWAPII, Feb 1983. ' 1 41+ 1:15 Application rates for herbicide, insecticide and fertilizer were based on recommendations of specialists to maintain soil fertility and productivity [3]. The prices in the Iowa studies reflect the general price level in 1980 with output and input prices held constant[4]. USDA calculations[5] varied input use by tillage system with field preparation costs related to the number of passes required to make the seedbed ready. Accuracy of costs is important when comparing tillage systems as aggregate costs and benefits influence outcomes and decision making directions. 9.2 Seeding Rates Seeding rates vary by tillage method[6]. A 10-15% increase in seeding rates for no-till corn is used relative to rates used on moldboard plow tillage as no-till surface trash can interfere with planting depth and seed placement. This analysis will use a similar assumption for other crops. The seeding rates in lbs. per acre are from crop budgets developed by the Soil Conservation Service (SCS) for Branch County, Michigan. These rates are close" 3. Input use varied by tillage system 4. W.G. Boggess and E.O. Heady, ‘A Sector Analysis of Alternative Income Support and Soil Conservation Policies’, American Journal of Agricultural Economics, Nov. 1981, pp. 618-628. 5. Michael Duffy and Michael Hanthorn, ‘Returns to Corn and Soybean Tillage Practices’ Agricultural Economics Report 508, Economic Research Service, USDA, Jan 1984. 6. L.V. Nelson, L.S. Robertson, M.H.Erdmann, R.G. White and D. Quisenberry, ‘No Till Corn: l-Guidelines’, Michigan State University Extension Bulletin E-904, Mar 1976. 1116 to the rates in the Estimated Crop and Livestock Budgets for Michigan - 1984[7]. Seeding rates by tillage system are as follows: Table 9.1: Seeding Rates Seed per acre by tillage system Crop Yield Seeding ratet Moldboard Reduced till No till Corn 103 bu. 8.5 bu./lb. 12.0 lbs. 13.0 lbs. 14.0 lbs. Soybean 37 bu. .615 bu./1b. 60.0 lbs. 64.0 lbs. 68.0 lbs. Wheat 43 bu 18.0 bu./lb 2.4 bu 2.5 bu 2.7 bu t Per pound of seed Source: Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, ‘Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. 9.3 Other Costs In addition to seed, fertilizer, pest control, machinery, labor and fuel costs, of field crops. not detailed in 7. Sherrill B. Hilker and Allen E. Budgets for Michigan - Michigan State University, there are other costs involved in the production These costs are marketing and overhead and are this analysis. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Shapley, ‘Estimated Crop and Livestock 1984’, Agricultural Economics Report 446, 1984. 1117 An approximation of these costs is selected from the Estimated Crop and Livestock Budgets for Michigan[8] and are used in the crop budgets. Hauling, marketing and utilities/phone costs are functions of yield(cost per unit of yield) while building repairs are coated per acre. Accounting procedures are arbitrary but adequate for these minor costs. 9.4 Pricing There are several methods of price selection. A simple method selects a point in time using prices for commodities at an instant in time. This can lead to some misleading results as agricultural commodity prices fluctuate constantly and "snapshot" prices may not reflect long run averages or trends. A more common method is to develop a model identifing long term price trends for commodities and/or price relationships between commodities. Hoskins[9] developed equations reflecting commodity price relationships for 28 years from 1950 to 1978 using corn price as a base for other crop prices[10]. An examination of the equations and expected cash prices casts doubt on the numbers used for the independent variable time. If the enumeration can 8. Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, ‘Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. 9. Roger L. Hoskins, ‘An Economic Comparison of Alternative Saginaw Valley Crop Rotations - An Application of Stochastic Dominance Theory’, Thesis PhD, 1981. 10. Corn prices are relatively inelastic to local supply and demand conditions 11+8 be clarified, this method can be used to arrive at price relationships. One source of prices is the Estimated Crop and Livestock Budgets for Michigan[ll] listing estimated prices for budget calculations in the publication. Prices are adjusted by past trends and the judgment of the authors. A pricing method related to this analysis is work being done for the Saginaw Bay Project[12] which uses ten year historical price relationships to project current and future prices. Corn is used as a numerare’ or base price similar to that used by Hoskins[l3]. Corn price’s historical trend is estimated and current and future corn prices projected. Other commodity prices are estimated as a ratio of corn price. 'Trends of these ratios are approximated and the current and future prices for the other commodities estimated. These commodity values will be used in this work. No inflation or price changes are assumed in this analysis. The commodity prices used are as follows: -——-—_-‘—— 11. Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, ‘Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. 12. R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, ‘Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 13. Roger L. Hoskins, ‘An Economic Comparison of Alternative Saginaw Valley Crop Rotations - An Application of Stochastic Dominance Theory’, Thesis PhD, 1981. 1119 Table 9.2. Commodity Prices .——-——--——-.-——---————————-—————————-——-———-—————————u——————*——n —--——— Commodity Price Corn $ 2.65/bu. Soybeans 7.14/bu. Wheat 3.74/bu. Navybeans 24.91/cwt. Sugarbeets 31.80/ton Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, ‘Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. USDA forecasts prices estimating national and regional prices adjusted to area situations. This price information is available in USDA documentation and can be used for pricing purposes. Prices of inputs remain relatively stable through time and are a function of general price inflation, technological effects, and raw material[l4] prices. Seed and fertilizer prices used in this analysis are from the Estimated Crop and Livestock Budgets for Michigan and are as follows: '14. Mostly petroleum for chemical inputs 1550 Table 9.3. Fertilizer and Seed Prices Fertilizer Price Seed Price Nitrogen(dry) s .24/lb. 'Corn 3 1.25/1b. Nitrogen(NH3) .16/lb. Soybeans .28/lb. Phosphorus .21/lb. Wheat .15/lb. Potassium .12/lb. Navybeans .50/1h. Limestone 12.00/ton Sugarbeets 10.00/lb. ————-- -——---—~————-——————-—-—-——-——--——---——M——-——~——-———-—_————— - Source: Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, ‘Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. _The pesticide price sources are numerous and varied so the price estimation can be difficult. A wholesale price is used as a basis for most of the costs. Other available estimates are used for the remaining costs as follows: 1 5 1 Table 9.4. Chemical Prices ——————-—-—————-——-—————-—--—a——————n—--——-———————_————————--—————---——_———A —-..————————————————-—-—l——-—c———————.—..._———————_— Chemica1(a.i)* No 446** Survey*** Whlsale# Adj Whls## Atrazine(4 lb/gal) $ 1.45/lb $ 2.37/1b $ 1.81/lb S 2.08/1b+ Lasso(4 lb/gal) 5.00/lb 5.02/lb 4.66/lb 5.36/1b+ Lorox(4 lb/gal) 10.00/lb 9.40/lb 10.81/1b+ Bladex(4 1b/gal) 3.95/15 3.17/lb 3.65/lb4 Roundup(3 1b/ga1) 22.58/1b 25.97/lb+ Treflan(4 lb/gal) 7.00/1b 7.00/lb 6.20/1b 7.13/1b+ Lexone(4 lb/gal) 23.90/lb 21.03/lb 24.18/1b+ Basagram(4 lb/gal) ' 21.05/lb 17.90/lb 20.59/lb+ Blazer(2 lb/gal) 37.50/1b 32.42/lb 37.29/1b+ Crop Oil 4.00/ga1 4.60/gal+ Eptam(7 lb/gal) 3.00/1b 2.92/lb 2.88/lb 3.31/lb+ Amiben(2 lb/gal) 8.00/lb 8.00/lb 7.07/lb 8.13/1b+ Dual(8 lb/gal) 5.96/lb 5.57/lb 6.41/lb+ 2-4,D 8.00/gal 13.21/gal 15.19/gal+ Sevin 80% WP 3.27/lb 3.76/1b+ Dyfonate 4E 31.75/gal 36.51/gal+ Benelate 11.00/lb 12.65/lb+ Furadan 4F 42.05/gal 48.35/gal+ Pyromin 80% WP 13.25/lb+ Antor(4 lb/gal) 8.51/lb+ 273(3 lb/gal) 9.74/lb+ Betamix(l.3 lb/gal) 44.47/lb+ * Active ingredient per gallon of chemical 1* Source: Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, ‘Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. *ttSource: Jerry Schwab, Herbicide Price Estimates for Agri. Sales, Agri. Co., Sohigro, Sloan and Joers, Department of Agricultural Economics, Michigan State University, East Lansing, Michigan. # Wholesalers price list - March 1984 ## Wholesale price list adjusted upward by about 1.15 to approximate retail sales + Prices used in this analysis in the crOp budgets 1.52 9.5 Costing Assumptions An assumption made in the calculation of DNCFs is related to the concept that farmers combine inputs to achieve the highest net returns possible[l5]. This approach does not lead to the operators attaining the highest yield per acre. Farmers may use fertilizer, machinery, chemicals and land available in a combination[16] resulting in the highest expected net returns[l7]. This assumption causes farmers to change input combinations as variables(soil productivity, price levels, or technology) change. Approximating these changes[18] without knowing exact production functions is difficult so some simplifying assumptions are needed: 15. Assuming profit maximization by rational farmers 16. Best input/output mix - profit maximization by solving the first order equations 17. By equating marginal factor costs with the marginal value products - MFC‘i’ = MVP‘i’ for the ith input 18. Using partial derivative analysis 153 l. The "observed"[19] input usage is a good estimate of the "best mix" of inputs for various yield and price levels. This allows available estimates[20][21][22][23] to serve as an approximation of some of the costs. 2. Herbicide and insecticide use holds pest infestations at "tolerable" or threshold levels and are near "best" use rates. 3. Yields are a function of historical price and input relationships representing expected profit maximizing yield levels. This approach may not be theoretically exact helps in the initial estimation of the parameters. Fertilizer, herbicide, insecticide, marketing and other costs can be estimated for use in the development of crop budgets. Once initial input/output combinations are estimated, different variables can change holding other variables constant to approximate the studied 19. Telefarm data, various crop budget estimates 20. L. Brown and Myron Kelsey, ‘Business Analysis Summary for Saginaw Valley Cash Crop Farms: Telfarm Data’, Agricultural Economics Report 435. Michigan State University, 1983. 21. Myron P. Kelsey, ‘Michigan Farm Business Analysis Summary~A11 Types of Farms; 1982 Telefarm Data’, Agricultual Economics Report 441, Michigan State University, 1983. 22. Northeastern Research Program Group, ‘Branch County: Example of Average Yearly Budget for Three Soil Productivity Groups’, Economics Research Service, USDA, Jan 1984. 23. Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, ‘Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. and various extension publications 1:54 variable’s effect[24]. A variable studied in this analysis is the change in soil productivity[25] and its effects on yield and net return levels holding all the other variables[26] constant. This is the basis for earlier assumption of declining yield levels through time[27]. Erosion or soil depletion rates are influenced by tillage system, soil types and the types of crops grown. This analysis assumes a multiple crop farm with crops systematically rotated on the farm. Erosion rates and soil productivity changes are a net effect of all crops in the rotation rather than a sum of individual crop influences. Commodity and input prices held constant. The commodity prices are based on historical price trends. The price relationships between commodities use corn as a base commodity in the approximation of the other commodities’ prices. Input prices are approximated from available price data and a "mean" value is estimated for budget use. Prices or yields can be changed for the individual commodities and/or for inputs to conduct a sensitivity analysis examining how changes' in net return affect the optimum DNCF combinations. 24. Partial derivative with respect to time 25. As affected by the choice of tillage systems 26. Prices, input use and costs, technology and genetics 27. Assuming a decline in soil productivity causes yield levels to drop - chapter 5 155 9.6 Discount Rate and Length of Analysis A factor influencing DNCF results is the discount rate. The appropriate rate is the real interest rate(nominal interest rate minus the inflation rate). This rate fluctuates through time influenced by changing inflation rates, monetary and fiscal policies, business cycles and many other factors. The real interest rate is approximated by examining data[28] prepared by the Federal Reserve Bank. A rough "eyeball" approximation of a real interest rate graph[29] indicates that a 3% real interest rate is near a ”mean" historical value[30] This rate can be used as the discount rate for the initial analysis. Using other real interest rates, higher and lower than 3%, provides a sensitivity test of the discount rate on the final results.‘ Another consideration in the DNCF calculation is the length of analysis. Theoretically, the analysis should be run to infinity capturing all net returns especially if identical replacement machinery set cycles(can be 15 - 20 years per machinery set cycle) are assumed in the DNCF calculations. Comparing DNCFs 28. Using the three month Treasury Bill interest rate or long-term Government Bonds for the nominal interest rate, and using the Consumer Price Index - all commodities for the inflation rate. These rates are illustrated in appendix table B31. 29. Appendix figure B2 30. The 3% discount rate used is slightly higher than a 2~2.5% historical rate due to the recent, 1979-1982, jump in real interest rates. The exact figures are found in appendix table B31 156 across tillage systems with differing optimum life machinery cycles can take three to four machinery set replacement cycles(45 to 80 years) to dampen oscillating effects of varying cash flows[3l] until one tillage system emerges as a higher cost system. The following formulation has been developed by the author to compute the DNCF analysis years calculating discounted net cash flow streams within a certain percent of total DNCF[32] possible if cash flows were calculated to infinity: log x (9.1) - ---------- = n log(l + r) where x - Error level[33](percent difference from total possible discounted net cash flows if the analysis is run infinity) r - Discount rate n - Number of years to run analysis to achieve discounted cash flows within a percent or within a selected error level of the total possible DNCFs 31. Changing repair costs in different machinery set cycles cause the oscillations 32. Yearly net returns divided by the discount rate,‘NR/r’ where NR are even yearly net returns and r is the discount rate 33. Interpeted as the DNCF calculated to year n is in error by x percent of the total possible DNCF 157 This rule works for returns at a point in time[34]and the sum of returns after a point in time. For example: To calculate the analysis length(using a 3% discount rate) to capture 99% of the possible DNCF(or a 1% error), equation 9.1 works as follows: - ------------ = 155.8 years(years to run the log(l + .03) analysis) The expanded explanation of this "stopping rule" is in appendix 34. The net returns in a point at time n add to the DNCF only B. percentage of the amount they would have added in time period 1. i.e. Using a 3% discount rate, a net return of $1 in time period n would only add 1% of the amount a net return of $1 in period one would add to the DNCF Chapter 10 MODEL RESULTS . The results of similar studies are briefly reviewed. Yield, income estimates, and machinery, fertilizer insecticide and herbicide pricing and other cost parameters are entered in the discounted net cash flow equation. The DNCF outcomes are listed by tillage system assumption. 10.1 Review of Other Studies’ Results High net returns of current moldboard plow practices, increasing farmer indebtedness, and uncertainty as to the most effective and efficient means of'erosion control[l][2], makes it difficult to predict changes in agricultural sector tillage practices. Economic and physical factors affecting tillage practices and technologies make the "best" erosion control methods subject to change. Changes in factor and input costs have influenced the adaptation of reduced or no till practices. 1. Iowa State University, ‘The Economics of Soil and Water Conservation Practices in Iowa: Model and Data Documentation’, Center for Agricultural and Rural Development, Card Report 108 SWCP I, Aug 1982. 2. Iowa State University, ‘The Economics of Terracing in Iowa’, Card Report 123, Jan 1984. 1.58 159 USDA[3] reports U.S. corn acreage in ten major producing states as 54% conventional till, 42% reduced till, and 4% no—till. Soybean plantings were reported as 63% conventional till, 33% reduced till and 3% no-till with about a 16% change in acreage from conventional to reduced till during the period 1980 to 1982. About 5% of corn acreage was switched from conventional to reduced or no-till practices in the same time_period. Iowa studies[4] found the corn-soybean crop rotation to to have the highest net returns in Iowa. The alfalfa, hay, oats, and pasture rotation had the highest net returns on highly erosive and unproductive soils. Till-plant was the most cost effective tillage system and reduced or no tillage with contour planting the most economically viable means of reducing soil erosion. USDA[5] found that farmers adapting reduced or no tillage strategies changed their input combinations by reducing labor, fuel, repair and machinery input use. As field preparation lessened, herbicide usage increased or was mixed differently to control weeds substituting for mechanical control. Increased pesticide and seed was used. This reduced farmer dependence on energy and machinery eased planting constraints as a function of crops grown, farm size, and practices used. 9 3. Michael Duffy and Michael Hanthorn, ‘Returns to Corn and Soybean Tillage Practices’ Agricultural Economics Report 508, Economic Research Service, USDA, Jan 1984. 4. Iowa State University, ‘The Economics of Soil and Water Conservation Practices; Results and Discussion’, Center for Agricultural and Rural Development, Card Report 109 SWAPII, Feb 1983. 5. Michael Duffy and Michael Hanthorn, ‘Returns to Corn and Soybean Tillage Practices’ Agricultural Economics Report 508, Economic Research Service, USDA, Jan 1984. 1(50 10.2 Discounted Net Cash Flow Equation After the theoretical treatment and estimation of parameters is completed, it appropriate to combine all the component values in a discounted net cash flow formulation as follows[6]: 6. The text explaining the discounted_ net cash flow equation follows equation 10.1 (10.1) DNCF = P ->:. 1 n \ 1651 5* M(O)(i) M(O)(AS)(Bs) Sum of machinery costs and salvage values jxhrs. (i) B(i) (j—l)hrs.(i) B(i) AX(( --------- ( ) V Sum of machinery fuel, labor and..., + (.01)(M(0) .insurance & housing q [P(m,n)(Y(m,l)).01(100-n(w)) - OC(m,n)][ac.] H 5 Sum of crop x yield x erosion minus crop prices levels rate costs uher Note where Note; S* r M(O)(i) As -When n 1652 Discounted net cash flows Machine i Number of machines in machinery set Machinery cycle(c) Number of machinery cycles in analysis Optimum machine cycle length in years Interest or discount rate New machinery set costs or aquisition value Salvage value coefficient(taken from appendix table B20) Salvage value coefficient(taken from appendix table 820) Year of machine life cycle Year of analysis Last year of analysis Repair coefficient of the ith machine(taken from appendix table 319) Repair coefficient of the ith machine(taken from appendix table B19) Hours of annual use of the ith machine Liters of fuel used per year for the machinery set Number of annual tractor and combine labor hours commodity m Number of commodites in the crop rotation Price of mth commodity in year n Yield of mth commodity in year 1 of analysis Yearly decline in soil productivity(in percent - %) Crop costs of commodity m in year n Number of acres per crop Cycle and cycle year rules 5* + 1, then c(l) = c(0) + l and j = 1 Once the cycle life is exceeded(n = 8* + 1), then the next machinery cycle starts(c(l) = c(0) + l and j = 1) This continues when n = c x 8* + 1 until year q, the final year of the analysis. line equ cos and ass pr fir the the PU: PU: 1e: na Cy Co th C0 CU Pr ma ‘1 He ma 163 10.3 Discounted Net Cash Flow Equation Explanation When the optimum machinery life cycle is determined(using equation 6.2) for a particular machinery set, then machinery costs are included the DNCF(equation 10.1) for the crop rotation and tillage system assumed. Machinery purchase costs can be assumed as the cost of the new machinery with no trade-in of previous machinery set or a trade—in value can be assumed. In this analysis, no trade-in value will offset the cost of the first machinery set but the trade-in value of machinery sets at the end of each machinery cycle is assumed to offset the cost of the subsequent machinery set. When a new machinery set is purchased, the cycle year(j) starts at year one (j=l)[7]. After the first machinery set is salvaged and new machinery is purchased, the cycle year(j) used is 8* years or one cycle length(S* years) less than the DNCF years (n). After the second machinery set purchase, the cycle year(j) is 28* years or two cycle length years(28* years) less than the DNCF year(n). This continues with the cycle years(j) being the cycle number(c) times the cycle length(S*) years less than the DNCF years(n). Repair costs are calculated by subtracting the current cycle year cumulative repair costs from the cumulative repair costs of the previous year equalling the change in cumulative repair costs or marginal repair costs. The yearly repair costs are discounted 7. This allows the program to calculate the salvage value of the new machinery set at the end of its life cycle and compute yearly machinery set repair costs 1614 by(l+r)En and summed yearly in the DNCF. Machinery purchase costs(M(O)(i)) are incurred at the end of the final cycle year (8*) or equivalently at the beginning of the next machinery set’s first cycle year (j=l). The first machinery set’s purchase costs are not discounted. The second set’s costs(M(O)(i) are decreased by the first machinery set’s salvage value (M(O)(i) - M(O)(i)C(i)D(i)ES*) and discounted by (l+r)ES*. This process continues every 8* years and the results summed in the DNCF calculations. The fuel(liters), labor (hours) and insurance/housing(.01(M(O)(i)) costs are discounted yearly by (1+r)En and summed in the DNCF calculations. A component is the crop budget net returns[8]. Yields are adjusted by soil productivity changes or commodity prices changes affecting gross returns. Productivity changes can be linear or nonlinear. This analysis uses a linear change in soil productivity as detailed in appendix A. Crop costs (OC(m,n)) are crop costs by crop and year. Net returns (gross returns minus crop costs) are discounted by (l+r)En and summed in the DNCF. The DNCFs of various tillage assumptions are illustrated in the following table[9]: 8. Gross returns from the sales of crop yields minus the crop production costs 9. The DNCF results are discussed in the text following the table Tab Mac anr Sal val Ne] cos F0 1 6 5 Table 10.1. Discounted Net Cash Flows(DNCF) for Various Tillage Assumptions and Erosion Rates* —-—-—.————_—-—--.-——--——--—-—-—---——--———-——o-—— Moldboard plow++ Yearly returns** $ 1,048.37 Yearly cost**,# 414.46 Yearly cost**,## . 424.31 Cycle year~ 16 Machine $ 28,796 annuity~ Salvage 15,187 value~~ Net machinery 144,820 cost“ Cash flow - 68,373 in year 8+1‘“ Average decline 0 in cash flows+ Discounted net cash flows Year -—50 1,202,116 75 1,415,805 100 1,505,688 125 1,553,535 150 1,574,004 $ Chisel till++ 1,084.48 439.69 451.11 18 29,018 13,263 165,686 - 88,099 1,247,542 1,434,280 1,537,798 1,584,528 1,604,964 No ti11++ $ 1,139.54 544.88 559.14 17 23,009 13,442 141,826 - 70,727 1,237,412 1,425,082 1,523,243 1,566,036 1,588,620 Footnotes are at end of table 166 Table 10.1 (cont’d). —-~-—_—_—_————--———_—-————————-——-_—---———-——————————-———_—~_—-_-‘—~——.-——- _——__-———-———-————-.——-——-——_————_——_-————___._—_.——. _——--— D E F. G Chisel Till No Till Chisel Till No Till crop costs crop costs returns & returns & & MB return & MB return MB cost MB cost —-—.-—_—--.—o—-m-— —--—-—-———- -.-———--.-0. ——_.———————~ —-———————-—-— Yearly returns** $ 1048.37 $ 1048.37 $ 1084.48 $ 1139.54 Yearly cost**,# 439.69 544.88 414.46 414.46 Yearly cost**,## 451.11 559.14 424.31 424.31 Cycle year“ l8 17 18 17 Machine $ 29,018 $ 23,099 $ 29,018 $ 23,099 annuity” Salvage 13,263 13,442 13,263 13,442 value~~ Net machinery 165,686 141,826 165,686 165,686 cost“ Cash flow - 92,522 - 81,895 - 84,816 — 54,211 in year S+l““ Average decline 0 0 0 0 in cash flows+ Discounted net cash flows Year __50 $1,133,727 $ 950,053 $1,331,132 $1,659,908 75 1,302,895 1,093,362 1,530,911 1,913,183 100 1,398,021 1,170,384 1,640,656 2,042,726 125 1,440,768 1,203,010 1,690,361 2,100,436 150 1,459,262 1,219,061 1,712,218 2,130,167 Footnotes are at end of table Year Year Year Cycl Mack annL Salx vaIL Net C051 Casi inj he Dis 1657 Table 10.1 (cont’d). ——-—-——_——-.—--——--——_————-——--————-—————————-—-—_———-———-——_—*——hue..-~— Tillage Assumption ———_——-—-—————-—-—-———-——-——————-————-—————.-—-_———--n.--..——._. ‘ -- Moldboard Plow(Year1y change in soil productivity) _——————-——--————-——..—-~-—————_—_————----._.—-————--————--a .0584%? .13 .15% .25% _—_————————-— —————— ————— ——'.—.—— —-—-——-—— Yearly returns** $ 1048.37 $ 1048.37 $ 1048.37 $ 1048.37 Yearly cost**,# 414.46 414.46 414.46 414.46 Yearly cost**,## 424.31 424.31 424.31 424.31 Cycle year~ 16 16 16 16 Machine $ 28,796 $ 28,796 $ 28,796 $ 28,796 annuity~ Salvage 15,187 15,187 15,187 15,187 value~~ Net machinery 144,820 144,820 144,820 144,820 cost“ Cash flow - 69,648 - 70,556 - 71,648 - 73,831 in year S+l““ Average decline 72.23 128.12 192.13 320.31 in cash flows+ Discounted net cash flows -——-————.——--s—_ Year . fi—50 $1,164,375 $1,114,032 $1,105,178 $1,040,055 75 1,323,461 1,319,820 1,271,828 1,175,843 100 1,437,329 1,389,426 1,330,108 1,213,054 125 1,477,600 1,423,510 1,358,497 1,228,472 150 1,493,640 1,436,394 1,367,354 1,229,979 -———------——-—-—-—---—-———--——-——--—-———-—--———-—-—_————-————-——_—»u-— Footnotes on next page 1638 * For a 200 hectare corn - corn - soybean - wheat farm on a Hillsdale - Riddles 2 - 6X slope soil(using a 3% discount rate) *tPer "acre" equivalant(sum of corn, corn, soybean and wheat costs) # Years 1 thru 5 ##Years 6 to end of analysis ” As calculated by optimum life cycle equation 6.2 ~~As the end of year 8*, the last year of the optimum machinery cycle “ Cost of new set minus the salvage value of the previous set “‘When net machinery cost is incurred + Yearly decline in cash flows due to change in soil productivity ++No change in soil productivity assumed ! Change in soil productivity due to 13.5 tons of annual soil erosion on a Hillsdale 2-6X slope soil, appendix table A17 10.4 Discounted Net Cash Flow Program Results The analysis extends for 150 years capturing about 99% of the possible DNCF[lO] when using a 3% discount rate[ll]. Discounted net cash flow results in year 150 indicate the following: 1. The moldboard plow till(A) and chisel till(B) cash flows are close (within 2% of each other’s total DNCF) after 150 years with each tillage system using its own crop budgets’ returns and crop and machinery costs. Chisel till(B) has a slightly higher machinery cost[12] but has higher yearly net crop returnsIlS]. The no-ti11(C) option has higher gross crop returns and lower machinery costs than the chisel(B) and moldboard plow(A) methods. The higher no 10. If run to infinity 11. See length of analysis text in chapter 9 for calculations 12. $29,018 chisel plow machinery annuity vs. $28,796 moldboard plow machinery annuity 13. Chisel - $633.37 per "acre" vs. moldboard plow — $622.06 per "acre" using the net returns from year six and onward 1 6 9 till yearly crop costs offset its machinery cost advantages resulting in similar DNCFs(within 1* of the other tillages’ DNCFs). Changes in costing or soil productivity assumptions (tillage options(D-L)), change the results dramatically. 2. If chisel tillage(D) has yields equal to moldboard plow tillage yields[14], the DNCF at year 150 of chisel tillage(D) is about 8% less than the moldboard plow method(A). If the no-till method(E) has the same yields[15] as moldboard plow tillage but incurs its own crop costs, the no~till DNCF at year 150 is about 23% less than the moldboard plow(A) DNCF. This implies chisel and no-till tillage methods incurring their own machinery and crop costs on a Hillsdale - Riddles soil, must have yields at least 3.6X(chise1 plow) and 7X(no-ti11) higher than moldboard plow tillage yields to have similar DNCFs at the end of the analysis. 3. A comparison of DNCFs is performed assuming a decline in soil productivity on moldboard plow tilled soils. If chisel tilled crops(D) have yields(with chisel tilled crop costs) equal to moldboard plow yields, the chisel plow DNCF in year 150 is similar to moldboard plow tillage DNCFs(R or I) with about a .073 loss in soil productivity per year. This is equivalent to erosion of about 10 tons per acre per year under the soil productivity assumptions in the appendix table A16. This implies that moldboard plow 14. A 3.4% drop from original chisel till yield levels or gross returns 15. A 7% drop from original no till yield levels 1770 tillage with less than 10 tons of net erosion per year has a greater DNCF by year 150 than chisel plowed crops with yields equal to that of moldboard plow tillage. This scenario is more severe in the case of no-till crops(E) having yields equal to that of moldboard plow tillage. The DNCFs at 150 years of the no=till(E) and moldboard plow tillage(K) with a .258 loss in soil productivity yearly would be similar suggesting erosion losses due to moldboard plow'tillage of less than 35 tons per acre per year(from appendix table A16) would favor the use of moldboard plow tillage rather than no-till[16].. 4. If higher yields are obtained on conservation tillage (chisel and ,no—till) but at crop costs equal or similar to that of moldboard plow tillage[l7] then both the chisel till(F) and no-till(G) have DNCFs in year 150 about 8.8% and 35% higher respectively moldboard plow tillage(A). This implies that if crop costs of reduced or no tillage are lowered by changing' input combinations, there may be some economic advantages to the adaptation of reduced or no ~ tillage. 16. Initial yields are equal for both tillage methods 17. Herbicide costs are the main cause of tillage cost differentials 171 In summary, the model is sensitive to tillage and costing assumptions. Changes in gross returns[18] or cost calculations influences the net returns of a particular tillage system. Tillage system crop yield and cost differentials have a major influence on short and long run farm decision making. The main question arising at the farm level would whether lower reduced or no tillage machinery costs(mainly labor, fuel and some capital costs-no till) and/or yield advantages offset additional reduced or no till crop costs.. The analysis seems to suggest an "indifference" point with the A, B, and C tillage assumptions[lQ] resulting in "equal" or similar DNCFs in year 150. Movement away from this "indifference point" (moldboard plow tillage with erosion and losses in soil productivity, lower chisel plow or no-till returns or costs) can cause one tillage alternative to be more attractive than the others in terms of long-term cash flow maximization. 18. Changes in soil productivity or tillage yield assumptions 19. With chisel gross returns being 3.5% greater and no-till gross returns 73 greater than moldboard plow gross returns Chapter 11 SUMMARY The results of the discounted net cash flow evaluation are situation and assumption specific. Policy makers need to be aware of the role situational differences have on farm level incentives for voluntary adoption of tillage systems that reduce off-site pollution. 11.1 Model Summary and Outcomes This study examines the economic impact of various crop components on long term tillage choices. A specific area of study (St. Joseph River Basin), a particular soil type (Hillsdale Z-SX slope) and a crop rotation (corn-corn~soybean—wheat) were chosen as the given situation.' A least cost machinery selection model was used to develop moldboard plow, chisel plow and no-till machinery complements for a 200 hectare farm. The discounted net cash flow method of analysis is used over a 150 year planning horizon. Machinery sets were replaced periodically using the criterion of minimizing long-run machinery set costs[l]. 1. This ”cycle" period over which tillage machinery sets are used varies: 16 years - moldboard plow tillage, 18 years - chisel tillage, 17 years - no-till 17'2 173 Base run assumptions (Runs A, B, and C in table 10.1) involved the following: 1. The no—till and chisel tilled crop rotation[2] yields were 7% and 3.5% higher respectively than moldboard plow tillage yields. 2. The no—till and chisel tillage were designated as having higher crop[3] costs. No change in soil productivity due to the choice of tillage. The DNCFs of the base run tillage assumptions were within 2% of each other. There are essentially no economic differences. The second set of runs (D,E,F, and G) relaxed tillage yield and cost differences. These outcomes were compared to the results of moldboard plow tillage with declining soil productivity (third set of runs - H, I, J, K). These results indicate some "breakeven" or decison points in the model[4]. Comparisons of these runs are as follows: 2. Rotational gross returns assuming the same price for all commodities. In this case, the corn, soybean and wheat gross returns by tillage system. This can be thought of as the "weighted" return or yield of a "rotational" acre 3. Herbicide, fertilizer and nitrogen 4. If a farm decison maker using moldboard plow tillage has soil erosion of more than 10 tons per acre yearly, it may behoove the farmer to switch to chisel tillage if chisel tillage expected yield levels are equal to and crop costs are higher than the current moldboard plow tillage yield levels and costs. 17'4 1. If chisel and moldboard plow tillage have initial equal yields and chisel tillage has higher crop costs(run D), this roughly equivalent to moldboard plow tillage causing a yearly decline in soil productivity of .13 yearly(run I). 2. If no-till and moldboard plow tillage result in equal yields and if no till has higher crop costs (run E), the DNCF is comparable to moldboard plow tillage resulting in a yearly decline in soil productivity of about .252 yearly(run K). 3. If no-till and chisel tillage have higher yields but incur moldboard plow tillage crop costs(runs F and G), the resultant DNCFs are about 8% (chisel) and 35% (no-till) higher than moldboard plow tillage causing no decline in soil productivity over time(run A). An objective is an evaluation of switching tillage systems across time. The theoretical aspects were addressed and results derived in appendix B. The possibility of using machinery sets with differing replacement lives to minimize long run machinery costs was also addressed and detailed in appendix B. Discounted net cash flow calculations were done on a program developed on a Hewlett-Packard 4l-CX involving about 700 program stepsf5]. Another smaller program(about 150 steps) was developed to calculate the number of years to keep an individual machine to obtain the lowest annualized cost over time. These programs can 5. Mainly to calculate the costs of various machines in a machinery set through time 175 easily be adapted for micro computer use and extended to include crop budget information. Data needs are detailed in appendix C. Machinery and crop costs, changes in soil productivity, prices and crop yields are required. 11.2 Model Limitations This study’s subject material is broad involving a large number of dependent variables. Modelling an agricultural production activity is a complex undertaking requiring both breadth and depth. This work covers the major factors involved in crop production examining each factor in the light of readily available information without going into comprehensive detail. Variable estimates cover the salient points but may not be comprehensive. The tradeoff involved was between model development(identifying decision points) and the accuracy of the data used[6]. If considerable uncertainity exists about the estimated values available, equational forms were developed to approximate the desired values. The model structure is designed to be flexible enough to accomodate differing assumptions and information, but its general nature renders outcomes highly subjective to user assumptions. This is true for the exclusion of tax and govermental policy influences and the hard to quantify social _——-————_- 6. An indifference point of chisel till with 3% higher yields than moldboard plow plow tillage may be calculated. Due to data variability and limited sample sizes, this 38 yield difference may range from 1 to 5x in a 95% confidence interval 176 costs and benefits. Given the informational requirements of estimating the various factors involved, the cost of gathering the information can exceed the benefits of improved decision making(narrow confidence intervals). Outcomes are situation specific aiding individual problems or situations but causes difficulties in general policy recommendations. APPENDICES SL 31. 8C tt [‘8 La SL 111‘ Appendix A Situational and Soils Data Summary statistics on economic data of the St. Joseph River Basin area are presented in this appendix (tables Al and A2) as a supplement to the text description in chapter 3. Selected area soils are described as detailed by the Soil Conservation Service. Representative soils are selected. and USLE factors affecting the soil erosion rates are listed in tables A3 - A8. Erosion rates by soil and rotation are presented in tables A9 - A10. Last, a method calculating changes in soil productivity is summarized as text and intables A11 - A18. 1777 1778 A.1 Area Information and Tables A1 and A2 Appendix Table A1. Selected Michigan and St. Joseph River Basin Statistics St. Joseph Branch Parameters Michigan County County All Crops Farms with sales 1982- 28,443 592 638 over $10,000 1978- 27,341 564 597 Total farm 1982- 8,714,805 194,357 208,397 acres 1978- 8,192,175 183,697 196,090 Farms with har— 1982- 27,640 573 629 vested cropland 1978- 26,700 547 591 Harvested crop- 1982- 6,427,443 149,728 150,785 land acreage 1978- 5,644,737 132,463 133,723 Corn Farms with sales 1982- 20,241 516 597 over $10,000 1978- 19,609 514 561 Total corn 1982- 2,386,679 85,991 86,104 acres 1978- 2,052,150 76,092 78,919 Total corn 1982— 243M 9,607,216 8,598,886 bushels 1978- 167M 7,310,546 6,400,252 (continued) 17’9 Appendix Table A1 (cont’d). St. Joseph Branch Parameters Michigan County County Soybeans Farms with sales 1982- 9,972 365 414 over $10,000 1978- 8,250 276 347 Total soybean 1982- 1,001,445 33,793 35,985 acres 1978- 739,034 23,231 26,688 Total soybean 1982- 31M 1,044,958 1,112,932 bushels 1978- IBM 566,772 615,458 Wheat Farms with sales 1982- 11,149 249 295 over $10,000 1978- 8,975 216 ' 254 Total wheat 1982- 453,369 9,848 10,434 acres 1978- 303,907 8,308 7,653 Total wheat 1982- IBM 333,079 405,297 bushels 1978- 12M 266,448 251,467 Source: Bureau of Census, U.S. Department of Commerce, '1982 Census of Agriculture: Preliminary Report’, Branch, Berrien, Huron, Hillsdale, Kalamazoo, St Joseph, and Tuscola Counties and the State of Michigan, Jan. 1984. Pe gr Av pe Av ac Appendix Table 2. St. 1530 Joseph River Basin County Comparisons Percent of all farms in Michigan Average harvested crop- land acres per farm Corn Percent of county farms growing corn Average acres of corn per farm Average corn yield per acre Soybeans Percent of county farms growing soybeans Average acres of soybeans per farm Average soybean yield per acre Footnotes at end of table 1982- 1978- 1982- 1978- 1982- 1978- 1982- 1978- 1982- 1978- 1982- 1978- 1982- 1978- 1982- 1978- St.Joseph County 2.078 2.058 261 acres 242 acres 908 948 166.6 ac.(648) 148.0 ac.(618) 111.7 bu. 96.1 bu. 63.78 50.58 92.6 ac.(368) 84.0 ac.(358) 30.9 bu. 24.4 bu. 2.278 2.218 239 acres 226 acres 958 958 144.2 ac.(608) 140.6 ac.(628) 99.8 bu. 81.1 bu. 65.88 58.78 87.0 ac.(368) 76.9 ac.(348) 30.9 bu. 23.0 bu. (continued) Pei grc Ave pet Ave ac: 301 Be: COL 1E31 Appendix Table 2 (cont’d). St. Joseph Branch Parameters County County Wheat Percent of county farms 1982- 43.58 46.98 growing wheat 1978- 39.58 42.98 Average acres of wheat 1982- 39.5 ac.(158) 35.4 ac.(158) per farm 1978- 38.5 ac.(168) 30.0 ac.(138) Average wheat yield per 1982- 33.8 bu. 38.8 bu. acre 1978- 32.1 bu. 32.9 bu. ( ) - Percent of average harvested cropland acres in the listed crap Source: Bureau of Census, U.S. Department of Commerce, ‘1982 Census of Agriculture: Preliminary Report’, Branch, Berrien, Huron, Hillsdale, Kalamazoo, St Joseph, and Tuscola Counties and the State of Michigan, Jan. 1984. 1E32 A.2 St. Joseph Basin Soils [1] 4B - Oshtemo sandy loam (0 to 6 percent slopes) This is a nearly level to undulating, well drained soil on upland flats, side slopes, and foot slopes. There are short steep slopes and escarpments adjacent to waterways, lakes, and depressions. The areas are irregular in shape and range from 5 to 1,800 acres in size. Typically, the surface layer is dark grayish brown sandy loam about 9 inches thick. The subsurface layer is brown sandy loam about 5 inches thick. The subsoil is about 46 inches thick. The upper part is dark reddish brown and dark brown, friable saney loam; the lower part is dark brown, loose loamy sand. The substratum to a depth of about 66 inches is grayish brown, loose, calcareous gravelly sand. In some places, there is more than 20 inches of sand and loamy send over the sandy loam. In some places, calcareous gravelly sand is at a depth of less than 40 inches. Included with this soil in mapping are small areas of well drained Spinks soils, moderately well drained Bronson soils, and somewhat poorly drained Brady soils. Spinks soils are more droughty than Oshtemo soils and are in similar positions on the landscape. Bronson soils are in lower flat areas and in slight depressions. Brady soils are in depressions. The included soils make up 5 to 20 percent of the map unit. Permeability is moderately rapid. The available water capacity is moderate, and runoff is slow or medium. The surface layer is very friable and is easily tilled within a fairly wide range of moisture content. This soil is used mainly as cropland. It is suited to crops such as corn, soybeans, winter wheat, and alfalfa hay. Soil blowing and droughtiness are the major concerns in management. Field windbreaks and conservation tillage that does not- invert the soil and that leaves all or part of the crop residue on the surface help reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. Irrigation can increase the crop yield if soil moisture levels are low. If this soil is irrigated, the water application rate should be regulated, and equipment lanes should be seeded to control erosion. This soil is well suited to use as woodland. There are no major management concerns. 1. Soil Conservation Service, USDA. 'Soil Survey of St. Joseph County, Michigan.’, July 1983. gr l8 8!" 4D 1E33 The capability subclass is IIIs. The Michigan soil management group is 3a. 40 - Oshtemo sandy loam (6 to 12 percent slopes) This is a gently rolling, well drained soil on side slopes, knolls, and ridges. There are short steep slopes and escarpments adjacent to waterways, lakes, and depressions. The areas are irregular in shape and range from 5 to 600 acres in size. Typically, the surface layer is dark brown sandy loam about 6 inches thick. The subsoil is about 44 inches thick. The upper part is dark brown, friable sandy loam; the middle part is yellowish brown, very friable loam sand; and the lower part is yellow, loose loamy sand and dark yellowish brown, friable sandy loam. In some places, there is more than 20 inches of sand and loamy sand over the sandy loam. In some places, calcareous gravelly sand is at a depth of less than 40 inches. Included with this soil in mapping are small areas of well drained Spinks soils, moderately well drained Bronson soils, and somewhat poorly drained Brady soils. Spinks soils are more droughty than Oshtemo soils; they are on ridgetops or on side slopes. Bronson soils are on foot slopes, and Brady soils are in depressions and along drainageways. The included soils make up 5 to 15 percent of the map unit. Permeability is moderately rapid. The available water capacity is moderate, and runoff is medium. The surface layer is very friable and is easily tilled within a fairly wide range of moisture content. This soil is suited to crops such as corn, soybeans, winter wheat, and alfalfa hay. Hater erosion, soil blowing, and droughtiness are the major concerns. Grassed waterways and conservation tillage that does not invert the soil and that leaves all or part of the crop residue on the surface help reduce soil loss. A cropping system that includes hay and small grains in the rotation also helps reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. Irrigation can increase crop yields if soil moisture levels are low. If this soil is irrigated, the water application rate should be regulated to control erosion, and equipment lanes should be seeded. This soil is well suited to use as woodland. There are no major management concerns. The capability subclass is IIIe. The Michigan soil management group is 3a. 40 - Oshtemo sandy loam (12 to 18 percent slopes) 1£54 This is a rolling, well drained soil on side slopes and ridges. There are escarpments adjacent to waterways, lakes, and depressions. The areas are irregular in shape and range from 5 to 160 acres in size. Typically, the surface layer is very dark grayish brown sandy loam about 5 inches thick. The subsurface layer is yellowish brown, very friable loamy sand about 5 inches thick. The subsoil is about 50 inches thick. The upper part is strong brown, friable sandy loam. The lower part is loose sand that has bands of dark yellowish brown, friable sandy loam. In some places, calcareous gravelly sand is at a depth of less than 40 inches. In some places, there is more than 20 inches of sand and loamy sand over the sandy loam. Included with this soil in mapping are small areas of well drained Spinks soils and somewhat poorly drained Brady soils. Spinks soils are on side slopes; they are more droughty than Oshtemo soils. Brady soils are in depressions and along drainageways. The included soils make up 5 to 15 percent of the map unit. Permeability is moderately rapid. The available water capacity is moderate. Runoff is rapid. Areas of this soil are mainly idle grassland or are used as , woodland. This soil is suited to crops such as corn, soybeans, winter wheat, and alfalfa hay. Water erosion, soil blowing, and droughtiness are the major concerns in management. Grassed waterways and conservation tillage that does not invert the soil and that leaves all or part of the crop residue on the surface help reduce soil loss. Cropping systems that include hay and small grains in the rotation also help reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. This soil is well suited to use as woodland. There are no major management concerns. The capability subclass is IVe. The Michigan soil management group is 3a. 5B - Spinks loamy sand (0 to 6 percent slopes) This is a nearly level to undulating, well drained soil in flat areas and on side slopes and foot slopes. There are short steep slopes and escarpments adjacent to waterways, lakes, and depressions. The areas are irregular in shape and range from 50 to 1,800 acres in size. Typically, the surface layer is dark brown loamy sand about 10 inches thick. The subsurface layer is yellowish brown loamy sand about 16 inches thick. The subsoil to a depth of about 60 inches is yellowish brown, loose sand that has bands of dark brown, very I8 us F8 81‘ SC es ar Si 1 8 5 friable loamy sand. In some places the surface layer is darker and thicker than is typical. In some places the bands are below a depth of 40 inches. In some places the subsoil is continuous loamy sand. Included with this soil in mapping are small areas of well drained Oshtemo soils. Oshtemo soils and Spinks soils are in similar positions on the landscape. Oshtemo soils are not so droughty as Spinks soils. The included soils make up 2 to 5 percent of the map unit. Permeability is moderately rapid. The available water capacity is low, and runoff is very slow. The surface layer is friable and is easily tilled within a wide range of moisture content. This soil is used mainly as cropland. It is suited to crops such as corn, soybeans, winter wheat, and alfalfa hay. It is suited to some specialty crops, such as asparagus and gladiolus. Soil blowing and droughtiness are the major concerns in management. Field windbreaks, cover crops, and conservation tillage that does not invert the soil and that leaves all or part of the crop residue on the surface help reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. Irrigation can increase crop yields if soil moisture levels are low. If this soil is irrigated, water application rates should be regulated to control erosion, and equipment lanes should be seeded. This soil is well suited to use as woodland. There are few major management concerns. Seedling mortality is moderate. The use of containerized stock may be necessary for higher survival rates. The capability subclass is IIIs. The Michigan soil management group is 4a. 50 - Spinks loamy sand (6 to 12 percent slopes) This is a gently rolling, well drained soil on side slopes and escarpments adjacent to waterways, lakes, and depressions. The areas are irregular in shape and range from 5 to 600 acres in size. Typically, the surface layer is dark brown loamy sand about 9 inches thick. The subsurface layer is brownish yellow loamy sand about 14 inches thick. The subsoil to a depth of 60 inches is yellowish brown, very friable sand that has bands of dark yellowish brown, very friable loamy sand. In some places the bands are below a depth of 40 inches. In some places the subsoil is loamy sand. Included with this soil in mapping are small areas of well drained Oshtemo soils and somewhat poorly drained Brady soils. Oshtemo soils and Spinks soils are in similar positions on the landscape. Oshtemo soils are less droughty than Spinks soils. Brady soils are in depressions and drainageways. The included whe drc set not re: the rec re: 1111‘ ca; inc 8PF equ Iaj lil his SIC 5D rid ac: inc PM is dep ban Pla the dra 03)) Inn The inc PI is 1 1E36 soils make up 5 to 15 percent of the map unit. Permeability is moderately rapid. The available water capacity is low, and runoff is medium. The surface layer is friable and is easily tilled within a wide range of moisture content. This soil is used mainly as cropland, but many areas are idle grassland or conifer plantations. This soil is suited to crops such as corn, soybeans, winter wheat, and alfalfa hay. Soil blowing, water erosion, and droughtiness are major concerns in management. Grassed waterways, field windbreaks, and conservation tillage that does not invert the soil and that leaves all or part of the crop residue on the surface help reduce soil loss. Cropping systems that include hay and small grains in the rotation also help reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. If soil moisture levels are low, irrigation can increase crop yields. If this soil is irrigated, water application rates should be regulated to control erosion, and equipment lanes should be seeded. This soil is well suited to use as woodland. There are few major management concerns. Seedling mortality is a moderate limitation. The use of containerized stock may be necessary for higher survival rates. The capability subclass is IIIe. The Michigan soil management group is 4a. 5D - Spinks loamy sand (12 to 18 percent slopes) This is a rolling, well drained soil on side slopes and ridges. The areas are irregular in shape and range from 5 to 160 acres in size. Typically, the surface layer is dark brown loamy sand about 9 inches thick. The subsoil is about 51 inches thick. The upper part is yellowish brown, very friable loamy sand; the middle part is yellowish brown, loose loamy sand; and the lower part to a depth of about 60 inches is brownish yellow, loose sand that has bands of dark yellowish brown, friable sandy loam. In some places the bands are below a depth of 40 inches. In some places the subsoil is continuous loamy sand. Included with this soil in mapping are small areas of well drained Oshtemo soils and somewhat poorly drained Brady soils. Oshtemo soils and Spinks soils are 'in similar positions on the landscape. Oshtemo soils are less droughty than Spinks soils. The Brady soils are in depressions and drainageways. The included soils make up 5 to 15 percent of the map unit. Permeability is moderately rapid. The available water capacity is low, and runoff is medium. 1137 In most areas this soil is idle grassland or is used as woodland.- This soil is suited to crops such as winter wheat and alfalfa hay. Soil blowing, water erosion, and droughtiness are the major concerns in managing the soil. Grassed waterways and conservation tillage that does not invert the soil and that leaves all or part of the crop residue on the surface help reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve soil structure, maintain fertility, and increase the available water capacity. This soil is well suited to use as woodland. There are few major management concerns. Seedling mortality is moderate. The use of containerized stock may be necessary for higher survival rates. The capability subclass is IVe. The Michigan soil management group is 4a. 108 - Billsdale sandy loam (2 to'6 percent slopes) This is an undulating, well drained soil on slight knolls, side slopes, and ridges. The areas are irregular in shape and range from 5 to 600 acres in size. Typically, the surface layer is dark brown sandy loam about 10 inches thick. The subsoil is yellowish brown sandy loam about 35 inches thick. The upper part of the subsoil is friable, and the lower part is firm. The substratum to a depth of about 60 inches is yellowish brown, friable sandy loam. In some places the substratum is stratified. In some places there is 20 to 40 inches of sand or loamy send over the sandy loam subsoil. Included with this soil in mapping are small areas of moderately well drained Blmdale soils and somewhat poorly drained Teasdale soils. Blmdale soils are in lower flat areas, in slight depressions, and on foot slopes. Teasdale soils are in depressions and along drainageways. The included soils make up 5 to 20 percent of the map unit. Permeability is moderate. The available water capacity is moderate, and runoff is slow or medium. The surface layer is friable and is easily tilled within a fairly wide range of moisture content. This soil is used mainly as cropland. It is well suited to crops such as corn, soybeans, winter wheat, and alfalfa hay. water erosion is the major hazard. Winter cover crops, grassed waterways, and conservation tillage that does not invert the soil and that leaves all or part of the crop residue on the surface help control soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. If this is irrigated, the water application rate should be regulated to control water erosion, and equipment lanes should be seeded. 1138 This soil is well suited to use as woodland. There are no major management concerns. The capability subclass is IIe. The Michigan soil management group is 3a. 10C - Hillsdale sandy loam (6 to 12 percent slopes) This is a gently rolling, well drained soil on side slopes, knolls, and ridges. The areas are irregular in shape and range from 5 to 400 acres in size. Typically, the surface layer is very dark gray sandy loam about 8 inches thick. The subsoil is about 49 inches thick. The upper part is yellowish brown, friable sandy loam; the middle part is brown, friable sandy clay loam; and the lower part is yellowish brown, friable sandy loam. The substratum to a depth of about 60 inches is yellowish brown, friable sandy loam. In some places the substratum is stratified. In some places there is 20 to 40 inches of sand or loamy send over the sandy loam subsoil. Included with this soil in mapping are small areas of well drained Spinks soils, moderately well drained Blmdale soils, and somewhat poorly drained Teasdale soils. Spinks soils are on side slopes and on ridgetops. They are more droughty than Billsdale soils. Blmdale soils are on foot slopes and in slight depressions. Teasdale soils are in depressions and along drainageways. The included soils make up 5 to 20 percent of the map unit. Permeability is moderate. The available water capacity is moderate, and runoff is medium. The surface layer is friable and is easily tilled within a fairly wide range of moisture content. In most areas this soil is used as cropland, but there are many areas of woodland. This soil is suited to crops such as corn, soybeans, winter wheat, and alfalfa hay. Water erosion is the major hazard. Grassed waterways and conservation tillage that does not invert the soil and that leaves all or part of the crop residue on the surface help reduce soil loss. A cropping system that includes hay and small grains also helps reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. If this soil is irrigated, the water application rate needs to be regulated to control erosion, and equipment lanes should be seeded. This soil is well suited to use as woodland. There are no major management concerns. The capability subclass is IIIe. The Michigan soil management group is 3a. 100 - Hillsdale sandy loam (12 to 18 percent slopes) 189 This is a rolling, well drained soil on side slopes and ridges. The areas are irregular in shape and range from 20 to 600 acres in size. Typically, the surface layer is very dark gray sandy loam about 6 inches thick. The subsurface layer is yellowish brown sandy loam about 6 inches. The subsoil is yellowish brown and is about 49 inches thick. The upper part of the subsoil is yellowish brown, friable sandy loam; the middle part is dark yellowish brown, friable loam; and the lower part is dark brown, firm sandy loam. The substratum to a depth of 60 inches in yellowish brown, friable sandy loam. In some places the substratum is stratified. Included with this soil in mapping are small areas of well drained Spinks soils, moderately well drained Blmdale soils, and somewhat poorly drained Teasdale soils. Spinks soils are on side slopes and on ridgetops. They are more droughty than Billsdale soils. Blmdale soils are on foot slopes and in slight depressions. Teasdale soils are in depressions and along drainageways. The included soils make up 5 to 15 percent of the map unit. Permeability is moderate. The available water capacity is moderate. Runoff is rapid. This soil is used mainly as cropland and woodland. It is suited to crops such as corn, winter wheat, and alfalfa hay. Hater erosion is the major hazard. Grassed waterways and conservation tillage that does not invert the soil and that leaves all or part of the crop residue on the surface help reduce soil loss. A cropping system that includes hay and small grains in the rotation also helps reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. This soil is well suited to use as woodland. There are no major management concerns. The capability subclass is IVe. The Michigan mangement group is 3a. 28B - Riddles sandy loam (2 to 6 percent slopes) This is a nearly level to undulating, well drained soil in slightly convex areas and on ridges. The areas are irregular in shape and range from 3 to 150 acres in size. Typically, the surface layer is dark brown sandy loam about 9 inches thick. The subsoil is about 46 inches thick. The upper part is dark brown, firm sandy clay loam; the middle part is yellowish brown, friable sandy loam; and the lower part is dark yellowish brown, friable sandy loam. The substratum to a depth of 60 inches is yellowish brown, very friable loamy sand. Run gn 28( CO! in pa} br: brc sut vel drt soi thz Ru: Kn 1570 Permeability and the available water capacity are moderate. Runoff is medium. This soil is well suited to use as woodland. There are no major management concerns. The capability subclass is IIe. The Michigan soil management group is 2.5a. 280 — Riddles sandy loam (6 to 18 percent slopes) This is a gently rolling to rolling, well drained soil in convex areas and on knolls and ridges. The areas are irregular in shape and range from 3 to 1,000 acres in size. Typically, the surface layer is brown, friable sandy loam about 9 inches thick. The subsoil is about 46 inches thick. The upper part is brown, firm clay loam; the middle part is yellowish brown, friable sandy loay loam; and the lower part is yellowish brown or dark yellowish brown, friable sandy loam. The substratum to a depth of about 60 inches is dark yellowish brown, very friable loamy sand. Included with 'this soil in mapping are small areas of well drained Spinks soils on side slopes and foot slopes. Spinks soils are more droughty than Riddles soils. The make up less than 10 percent of the map unit. Permeability and the available water capacity are moderate. Runoff is rapid. This soil is used mainly as cropland or woodland. It is suited to crops such as corn, soybeans, winter wheat, and alfalfa hay. Water erosion is a hazard. Grassed waterways and conservation tillage that does not invert the soil and leaves all or part of the crop residue on the surface help reduce soil loss. A cropping system that includes hay and small grains in the rotation also helps reduce soil loss. Returning crop residue to the soil and regularly adding other organic matter helps improve fertility, maintain soil structure, and increase the available water capacity. This soil is well suited to use as woodland. There are no major management concerns. The capability subclass is IVe. The Michigan soil management group is 2.5a. 1591 A.3 Erosion Factors and Calculations of Erosion by Soil and Rotation, Tables A3 - A10, and Figure Al Appendix Table A3. Soil Erodibility and Soil Loss Tolerance, water Erosion "K" and "T" Factors, and T/K Values Soil Depth Soil Series Horizons (in.) K T T/K 886 LS 0-15 .17 5 29 2 SL,SCL,L,LS 15-70 .24 — -- - Oshtemo SL,FSL 0-14 .24 5 21 3 LS,LFS 0-14 .17 5 29 2 SL,SCL,GR-SL 14-35 .24 - -- - LS,SL 35-60 .17 - -- - SR-COS-G 60—99 .10 - -- - Riddles L,SIL 0-14 .32 5 16 5 SL,FSL 0-14 .24 5 21 3 SCL,CL 0-14 .32 4 l3 5 SCL,CL,L,SL 14—72 032 - —_ — Spinks LS,LFS 0-10 1 .17 5 29 2 S,FS 0-10 .15 5 33 l LS,SR-FS-LFS, 10-85 .17 - -- - FS ' SL - sandy loam FSL - fine sandy loam L - loam LS - loamy sand SCL - sandy clay loam S - sand CL - clay loam LFS - loamy fine sand FS - fine sand GR-SL - gravelly sandy loam SR-COS-G - stratified coarse gravel SIL - silty loam SR-FS-CFS - stratified fine sand to loamy fine sand K - soil erodibility factor 886 - wind erodibility group T - maximum yearly soil loss in tons to sustain soil productivity indefinitely Source; Soil Conservation Service, USDA, Michigan, 'R Factors, Soil Erodibility and Soil Loss Tolerance, Water Erosion, K and T and T/K Values, Soil Loss Ratio’, Technical Guide, Section I - C, Statewide, Hater 7-14. 3-1. to ’I6‘. I I I I 1' b . ~ :3 I’ ‘ \\ \ \ I V \ ' 1' I \ Must” I,” I \ fl salsa non-om ak'lmm I u may .0 I 1 ‘ ‘ml‘ . sum also“ \ Allyn \ or \ "'5" ‘ mica - . mus: . ‘0" all T 1 0 - H's-u IBNIA l \\ aunt uni 125 "“l‘~ 150 E ,' 0”. Figure A1: Rainfall Erosion or "R" Factors Source: Soil Conservation Service, USDA, Michigan, 'R Factors, Soil Erodibility and Soil Loss Tolerance, Water Erosion, K and T and T/K Values, Soil Loss Ratio', Technical Guide, Section I - C, Statewide, Hater 7—14. AP! 1593 Appendix Table A4. ”C” Factors for Cropland in Michigan’s Lower Peninsula Moldboard Plow Till Fall Spring Plowed Plowed Plow ------------ Plant Residue Residue Residue Residue Wheel Rotation Removed Left Removed Left Track Cont. Soybean88 .56 .51 .52 .46 8 Cont. Corn .50 .41 .47 .38 .26 R880 .32 .25 .26 .22 .16 Rx .40 .31 .33 .26 .18 RRROM .30 .24 .25 .17 .13 RRROMM .25 .19 .20 .16 .11 RROM .18 .13 .16 .12 .10 RxROM . 8 8 .15 .ll 8 RROMMM .12 .095 .ll .08 .07 RxROMMM 8 '8 .10 .076 8 ROM .10 .06 .06 .06 .06 ROMM .08 .06 .07 .05 .05 ROMMM .07 .05 .06 .05 .04 OMMM .05 .03 .02 .01 .01 Yields - 75 Bu. corn(for 100 bushels corn decrease ”C" by 208) 3 Ton hay . 8 - ”C” values are not worked out at this time. 88 - Reduce ”C” by 258 for narrow row soybeans R - Row crop Rx - Row crop with a cover crop O - Oats M - Meadow AP! m 3U 30):? SOL Fac Sec Appendix 1€94 Table A4 (cont’d). Rotation Cont. Soybean88 Cont. corn RROM Rx RRROM RRROMM RROM RxROM RRROMMM RxROMMM ROM ROMM ROMMM OMMM Yields Chisel-Disk-Till Plant No Till 1000 2000 3000 4000 6000 1000 2000 3000 4000 6000 lbs lbs lbs. lbs. lbs. lbs lbs lbs lbs lbs .45 .41 36 .30 .27 .40 24 17 09 07 .31 24 .19 .13 08 28 19 13 .07 05 .19 .14 .11 .09 .06 .16 .12 .09 05 04 .22 .18 .15 .ll .07 .18 .14 .10 06 04 15 .ll .09 .07 .05 .12 .09 .07 .04 .03 .13 .09 .07 .06 .04 .10 .07 .06 .04 .03 .09 .07 .06 .05 .04 .08 .06 .05 .04 .03 8 8 8 8 8 8 8 8 8 .8 .06 .05 .04 .04 .03 .06 .05 .04 .03 .02 8 8 8 8 8 8 8 8 8 .8 .05 .04 .03 8 8 8 .04 .025 8 .8 .04 .03 .025 8 8 8 .025 .02 8 .8 .03 .025 .02 8 8 8 .02 .015 8 .8 8 8 8 8 8 8 8 8 8 .8 75 Bu. corn(for 100 bushels corn decrease "C" by 208) 3 Ton hay "C" values are not worked out at this time 88 888 R Rx 0 M Source; Factors, 8 and T and TI! Values, Section I Reduce "C" by 258 for narrow row soybeans . Corn residue, 1 pound of small grain or soybean residue is equal to 2 pounds of corn or sorghum residue Row Crop Row crop with a cover crop Oats Meadow 'R Hater Erosion, Technical Guide, Soil Conservation Service, USDA, Michigan, Soil Erodibility and Soil Loss Tolerance, Soil Loss Ratio’, - C, Statewide, Water 7-14. PM Appendix Table A5. 1595 "C” Value Matrix for the St. Joseph River Basin Chisel Chisel Ridge Ridge No Disk Disk Till 1000-2000 2000-3000 4000+ 25 .19 06 .29 .25 .16 18 .14 .05 29 .22 .10 .30 .26 12 32 .30 15 22 .17 09 23 17 09 21 .16 , 08 .21 16 08 20 15 07 .20 .15 .07 .15 12 04 13 .10 03 .10 .08 03 12 .09 03 12 .09 03 .08 .06 02 .06 .05 oz 06 .05 02 --- --- .01 -—- —-- .27 Moldboard Spring Plow Plow- Tillage Residue Left Plant Residue - 0 0 Rotations CCC .30 .21 CS-C-C .32 26 CxCxCx .21 --- CCCB .34 --- CCB .37 30. CB .38 --- 00088 28 --- 00080 .29 --— 008" .26 19 CCBWx .26 19 CBW .23 --- CBWx .23 --- CCCOHx 18 14 CCCA3 13 --- CBWA3 .10 --- C-C-CS-W-A4 .14 --- 05A5 .15 --- CCA5 .09 --- CCOA5 .09 .06 C-CS-H-AG .07 --- 0A5 .01 --- A5 .01 --- Asparagus .55 --~ Tomatoes .55 --- Yields - 100 bushel corn C - Corn for grain CS - Corn Silage Cx - Corn with cover crop B - Soybeans planted R - Wheat - ”heat with cover crop - Oats - Oats with cover crop - Alfalfa baled for hay Source; Northeastern Research Program Group, ‘Branch County: Example of Average Yearly Budget for Three Soil Productivity Groups’, Economics Research Service, USDA, Jan 1984. 196 Appendix Table A6. "P" Values and Slope Length for Contouring Land Slope(8) Up and Down Hill Contouring Maximum Length 1 to 2 1.0 .60 400 feet 3 to 5 1.0 .50 300 feet 6 to 8 1.0 .50 200 feet 9 to 12 1.0 .60 120 feet 13 to 16 1.0 .70 80 feet 17 to 20 l 0 .80 60 feet 21 to 25 l 0 .90 50 feet Source: Northeastern Research Program Group, 'Branch County: Example of Average Yearly Budget for Three Soil Productivity Groups’, Economics Research Service, USDA, Jan 1984. Appendix Table 7. ”P” Values, Maximum Strip Widths, and Slope Length Limits for Contour Stripcropping Land Slope(8) A8 B8 C8 Strip width Maximum length 1 to 2 .30 .45 .60 130 feet 800 feet 3 to 5 .25 .38 .50 100 feet 600 feet 6 to 8 .25 .38 .50 100 feet 400 feet 9 to 12 .30 .45 .60 80 feet 240 feet 13 to 16 .35 .52 .70 80 feet 160 feet 17 to 20 .40 .60 .80 60 feet 120 feet 21 to 25 .45 .68 .90 50 feet 100 feet A8 - For a 4 year rotation of row crop, small grain with meadow seeding,and 2 years of meadow. A second row crop can replace the small grain if the meadow is established in it B8 - For 4 year rotation of 2 years row crop, winter grain with meadow seeding, and one year meadow C8 - For alternate strips of row crop and winter small grain Source; Northeastern Research Program Group, ‘Branch County: Example of Average Yearly Budget for Three Soil Productivity Groups’, Economics Research Service, USDA, Jan 1984. 1597 Appendix Table A8. Soil Loss Ratio Factor "LS" Length Percent of Slope(8) of ---------------------------------------------------- Slope(L) .2 .3 .4 .5 1.0 2.0 3.0 4.0 5.0 6.0 8.0 20 .05 .05 .06 .08 .12 .18 .21 .24 .24 .30 .44 40 .06 .07 .07 .08 .10 .15 .22 .28 .34 .43 .63 60 .07 .08 .08 .08 .11 .17 .25 .33 .41 .52 .77 80 .08 .08 .09 .09 .12 .19 .27 .37 .48 .60 .89 100 .08 .09 .09 .10 .13 .20 .29 .40 .54 .67 .99 110 .08 .09 .10 .10 .13 .21 .30 .42 .56 .71 1.0 120 .09 .09 .10 .10 .14 .21 .30 .43 .59 .74 1.0 130 .09 .09 .10 .ll .14 .22 .31 .44 .61 .77 1.2 140 .09 .10 .10 .11 .14 .22 .32 .46 .63 .80 1.2 150 .09 .10 .11 .ll .15 .23 .32 .47 66 .82 1.2 160 .09 .10 .11 .ll .15 .23 .33 .48 .68 85 1.2 180 .10 .10 .11 .12 .15 .24 .34 .51 .72 .90 1.4 200 .10 .11 .11 .12 .16 .25 .35 .53 .76 .95 1.4 300 .ll .12 .13 .14 .18 .28 .40 .62 .93 1.2 1.8 400 .12 .13 .14 .15 .20 .31 .44 .70 1.0 1.4 2.0 500 .13 .14 .15 .16 .21 .33 .47 .76 1.2 1.6 2.2 600 .14 .15 .16 .17 .22 .34 .49 .82 1.4 1.6 2.4 700 .15 .16 .17 .18 .23 .36 .52 .87 1.4 1.8 2.6 800 .15 .16 .17 .18 .24 .38 .54 .92 1.6 2.0 2.8 900 .16 .17 .18 .19 .25 .39 .56 .96 1.6 2.0 3.0 1000 .16 .18 .19 .20 .26 .40 .57 1.0 1.6 2.2 3.0 1100 .17 .18 .19 .20 .27 .41 .59 1.0 1.8 2.2 3.5 1200 .17 .18 .20 .21 .27 .42 .61 1.0 1.8 2.4 3.5 1300 .18 .19 .20 .21 .28 .43 .62 1.2 2.0 2.4 3.5 1400 .18 .19 .21 .22 .29 .44 .63 1.2 2.0 2.6 3.5 1500 .19 .20 .21 .22 29 45 .65 1.2 2.0 2.6 4.0 1600 .19 .20 .21 .23 .30 .46 66 1.2 2.2 2.6 4.0 1700 .19 .21 .22 .23 30 .47 .67 1.2 2.2 2.8 4.0 2000 20 22 23 .24 .32 .49 .71 1.4 2.4 3.0 4.5 Source: Soil Conservation Service, USDA, Michigan, 'R Factors, Soil Erodibility and Soil Loss Tolerance, Water Erosion, K and T and T/K Values, Soil Loss Ratio’, Technical Guide, Section I - C, Statewide, Water 7-14. 1598 Appendix Table A8 (cont’d). 20 .61 .81 1.0 1.2 1.6 1.8 2.6 3.5 5.5 8.0 10 40 .87 1.2 1.4 1.8 2.2 2.6 3.5 5.0 8.0 ll 15 60 1.0 1.4 1.8 2.2 2.6 3.0 4.5 6.0 10 14 18 80 1.2 1.6 2.0 2.6 3.0 3.5 5.5 7.0 11 16 21 100 1.4 1.8 2.2 2.8 3.5 4.0 6.0 8.0 13 18 23 110 1.4 1.8 2.4 3.0 3.5 4.5 6.0 8.0 13 19 24 120 1.6 2.0 2.6 3.0 4.0 4.5 6.0 9.0 14 20 25 130 1.6 2.0 2.6 3.0 4.0 4.5 7.0 9.0 14 20 26 140 1.6 2.2 2.8 3.5 4.0 5.0 7.0 9.0 15 21 27 150 1.6 2.2 2.8 3.5 4.0 5.0 7.0 10 15 22 28 160 1.8 2.2 3.0 3.5 4.5 5.0 7.0 10 16 23 29 180 1.8 2.4 3.0 4.0 4.5 5.5 8.0 ll 17 24 31 200 2.0 2.6 3.0 4.0 5.0 6.0 8.0 11 18 25 33 300 2.4 3.0 4.0 5.0 6.0 7.0 10 14 22 31 40 400 2.8 3.5 4.5 5.5 7.0 8.0 12 16 25 36 46 500 3.0 4.0 5.0 6.0 8.0 9.0 13 18 28 40 52 600 3.5 4.5 5.5 7.0 8.0 10 14 19 31 44 57 700 3.5 5.0 6.0 8.0 9.0 11 16 21 33 47 61 800 4.0 5.0 6.0 8.0 10 12 17 22 36 50 65 900 4.0 5.5 7.0 9.0 10 12 18 24 38 53 69 1000 4.5 5.5 7.0 9.0 11 13 19 25 40 56 73 1100 4.5 6.0 8.0 9.0 ll 14 20 26 42 59 77 1200 4.5 6.0 8.0 10 12 14 20 28 44 62 80 1300 5.0 7.0 8.0 10 12 15 21 29 46 64 83 1400 5.0 7.0 9.0 ll 13 15 22 30 47 67 87 1500 5.5 7.0 9.0 11 13 16 23 31 49 69 90 1600 5.5 7.0 9.0 ll 14 16 24 32 51 71 93 1700 5.5 7.0 9.0 12 l4 17 24 33 52 73 95 2000 6.0 8.0 10 13 15 18 26 36 57 80 104 Note: - Contour limits - 28 slope(400 feet), 88 slope(lOO feet), 148 slope(60 feet) The effectiveness of contouring beyond these limits is speculative - When the length of slope exceeds 400 feet and/or percent of slope exceeds 24 percent, soil loss estimates are speculative as these values are beyond the range of research data Source: Soil Conservation Service, USDA, Michigan, 'R Factors, Soil Erodibility and Soil Loss Tolerance, Water Erosion, K and T and T/E Values, Soil Loss Ratio’, Technical Guide, Section I - C, Statewide, Water 7-14. APP Su' Rx Kt LS Cx Pt Gr. so W1: To "1 H: 199 Appendix Table A9. Soil Parameters and Erosion Calculations for Various Soils in the St. Joseph River Basin Hills- Riddles Spinks Oshtemo Oshtemo Oshtemo dale—LS SL LS SL SL SL 2-68 2—68 0-68 0-68 6-128 12-188 Parameters slope slope slope slope slope slope Soil Mgmt. 3a 3a 4a 3a 3a 3a Group Subclass IIe IIe IIIs 111s 111s 111s R8 125 125 125 125 125 125 E88 .28 .28 .17 .24 .24 .24 LS888 .76 .76 4 .28 1.4 3.43 C8888 .3 .3 .3 .3 .3 .3 P88888 1.0 1.0 1.0 1 0 1.0 1 0 Gross water 7.9 7.9 2 5 2.5 12.6 30.9 soil eros.8 Wind eros. 5.6 5.6 8 4 5.3 4 7 4 4 Total eros. 13 5 13.5 10.4 7.8 17 3 35 3 T value88 5.0 5.0 5.0 5.0 5.0 5 0 Note: The cropping is assumed to be continous corn moldboard plowed(spring plow) leaving no surface residue 8 - Rainfall factor from appendix figure 1 88 - Soil erodibility from appendix table A3 888 - Slope length and gradient factor from Northeastern Research Group, 'Branch County: Current Erosion Costs and Returns by Soil’, Economics Research Service, USDA, July 1984. LS factor is also listed in appendix table A8. 8888 - Crop rotation and tillage factor from appendix table A5 88888 - Erosion control practice factor from appendix table A6 or A7 8 - Gross water soil erosion is calculated by multiplying the R, 8, LS, C, and P factors APP 2(30 Appendix Table A10. Gross Erosion Rates8 for Various Crop Rotations, Tillage Systems and Soils in the St. Joseph River Basin . Soils Hillsdale Crop Riddles Spinks Oshtemo Oshtemo Oshtemo Rotation 2 - 68 0 - 68 0 - 68 6 - 128 12 - 258 0-0-8 MB plow 15.4(5.6) ll.4(8.4) 8.4(5.3) 20.3(4.2) 42.5(4.4) Chisel88 7.9 2.5 2.5 12.6 30.9 No till888 3.2 1.0 1.0 5.0 12.4 C-C-C MB plow l3.5(5.6) 10.9(8.4) 7.8(5.3) 17.3(4.7) 35.3(4.4) Chisel88 6.6 2.1 2.1 10.5 25.8 No till888 1.6 .5 .5 2.5 6.2 C-C-B-W MB plow ll.l(4.2) 8.5(6.3) 6.2(4.0) 14.5(3.5) 30.1(3.3) Chisel88 5.6 1.8 1.8 8.8 21.6 No till888 2.1 .7 .7 3.4 8.2 C-C-0-5A MB plow 3.8(l.4) 2.9(2.1) 2.1(1.3) 5.0(1.2) 10.4(1.l) Chisel88 1.6 .5 .5 2.5 6.2 8 - Tons per acre including wind erosion C - Corn 88 — 1000 - 2000 lbs. residue B - Soybeans 888 - 4000+ lbs. residue W - Wheat ( ) - Amount of wind erosion A - Alfalfa Source; Northeastern Research Group, ‘Branch County: Current Erosion Costs and Returns by Soil’, Economics Research Service, USDA, July 1984. 82 P1 11' 2()l A.4 Using Soil Parameters in the Determination of Soil Productivity Basic soil characteristics are needed to calculate soil productivity. Soil interpretation records (i.e. soils 5 data)[2] list the bulk density BD(I), permeability P(I), available water AW(I), soil reaction (pH), erodibility factors (K and T values) plus the wind erodibility group (WEG). To estimate soil erosion rates, variables in the USLE sheet or water erosion equation[3] must be derived. The soil erosion cropping factor (C) has been developed by the Soil Conservation Service for the lower part of Michigan and for the St. Joseph Basin in appendix tables A4 and A5. The erosion control practices factor (P) and slope-length factor (LS) developed by the Soil Conservation Service, are available in appendix tables A6, A7, and A8 respectively. The L8 factor reflects thejoint impacts on erosion of slope and length of the slope. Estimates of slope are found in the individual county soil surveys, while the field length can be estimated from farm soil’s descriptions. 2. Soil Conservation Service, USDA, Michigan, 'Soils Interpetation Record’, Soils: Oshtemo, Spinks, Hillsdale, Riddles, Schoolcraft, Kalamazoo. 3. R x K x C x P x LS = tons of soil erosion per acre per year 2(32 LS factors are developed for soils in Branch County[4] and are used in the soil erosion and productivity calculations[5]. The rainfall factor (R)[6] can be found on the map (appendix figure A1) and can be estimated for each county by examination of the map. The wind erosion factors such as soil erodibility (1), soil ridge roughness (K), climatic (C), field length (L) and vegetative cover (V) are not available to calculate wind erosion. .The soil interpretation data[7] lists a wind erodibility group estimate for each horizon, but doesn’t list the amount of wind erosion by soil class. Wind erosion figures are estimated for the St. Joseph Basin soils and are approximately eight tons per acre for a class two soil and five tons for a class three soil. These estimates are used to provide a rough estimate of wind erosion losses for other wind erodibility groups (W86). 4. Northeastern Research Group, 'Branch County: Current Erosion Costs and Returns by Soil’, Economics Research Service, USDA, July 1984. 5. Appendix Tables A9 and A10 6. Soil Conservation Service, USDA, Michigan, ’R Factors, Soil Erodibility and Soil Loss Tolerance, Water Erosion, K and T and T/K Values, Soil Loss Ratio’, Technical Guide, Section I - C, Statewide, Water 7-14. 7. Soil Conservation Service, USDA, Michigan, 'Soils Interpetation Record’, Soils: Oshtemo, Spinks, Hillsdale, Riddles, Schoolcraft, Kalamazoo. 203 A.5 Soil Productivity Index Calculations, Tables All-A18 The Larson Pierce,Dowdy,and Graham soil loss depletion estimates[8][9][10][11][12] employ a number of equations to convert soil parameters into calculations of productivity indexes at each soil horizon. This determines the soil productivity by soil horizon and estimates the changes in crop yields. The rate of soil loss is a function of type of tillage system used. This rate of soil loss is used to calculate the number of years to erode a soil horison. This assumes that soil horizon characteristics do not change thru time[l3]. The equations used in the calculations of the soil horizon’s productivity are as —‘-—_—_--— 8. W.E. Larson, F.J. Pierce and R.H. Dowdy, 'Threat of Soil Erosion to Long Term Crop Production’, Science Vol.219, Feb 1983, pp.448-465. 9. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, ’Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. 10. F.J. Pierce, R.H. Dowdy, W.E. Larson, and programmed by R. Hemlich, ‘Method of Soil Loss Depletion Estimates’, Economic Research Service, USDA. ll. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, and W.E. Larson, ’Productivity of Soils; Assessing Long Term Changes due to Erosion’, Journal of Soil and Water Conservation Vol. 38, Jan - Feb 1983, pp. 39 - 44. 12. The soil productivity index calculation estimates by Larson, Pierce, Dowdy, and Graham will be used in this analysis and its procedure is summarized in this section 13. The soil horizons can change and move up or down as soil is depleted or added. This assumption holds constant the fact that plant rooting and/or the elements can change the characteristics of the soil horizon. 2011 follows; 1. Sufficiency of available water(AS)[l4] (Al) A8 = 5.0(A.W.) - if A.W. is greater than or equal to .20, then AS = 1.0 - if A.W. is less than or equal to .03, then AS = 0 where AS - Sufficiency of available water A.W. - Available water at each horizon(in./in.) 2. Sufficiency of bulk density(BS)[l5] (A2) BS = (l - adj.) + (adj. x C) - if BD(I) is less than the critical BD(I)[16], then C = (slope 1 x BD(I)) + intercept 1[l7] - if BD(I) is greater than the critical BD(I)[lB], then C = (slope 2 x BD(I)) + intercept 2(19] 14. Has a value from zero to one 15. Has a value from zero to one 16. See appendix table All 17. From appendix table A13, use coefficients for slope intercept under "low” heading dependent on soil texture class 18. See appendix table All 19. From appendix table A13, use coefficients for slope intercept under ”high" heading dependent on soil texture class and and 2 0 5 - BS is greater than 1.0, then BS equals 1.0 - if BS is less than zero, then let BS equal zero where BS - Sufficiency of bulk density BD(I) - Bulk density at each horizon(grams/cmS), from soils profile data[20] C - Bulk density coefficient adj. - Adjustment éoefficient from appendix table A14 by permeability and texture class 3. Sufficiency of soil reaction(PS)[21] (A3a) P8 = .75 - if PH(I) is greater or equal to 8.0 (A3b) P8 = 2.086 - .167(PH(I)) - if PH(I) is less than 8.0 but greater than 6.5 (A3c) PS = 1.0 - if PH(I) is less than or equal to 6.5 but greater than 5.5 (A3d) P3 = .12 +(.16 x PH(I)) - if PH(I) is less than or equal to or greater than 5.0 (A3e) PS = (.446 x PH)I)).- 1.31 - if PH(I) is less than or equal to 5.0 but greater than 2.9 (A3f) P8 = 0 - If PH(I) is less than or equal to 2.9 where PS - Sufficiency of soil reaction PH(I) - Soil reaction at each horizon(pH) 20. Soil Conservation Service, USDA, Michigan, 'Soils Interpetation Record’, Soils: Oshtemo, Spinks, Hillsdale, Riddles, Schoolcraft, Kalamazoo. 21. Has a value from zero to one 2(36 Appendix Table All. Nonlimiting, Critical, and Root-limiting Bulk Densities for each Family Texture Class Family texture Nonlimiting Critical Root-limiting class bulk density bulk density bulk density --------------- g/cm3---------------- Sandy 1.60 1.69 1.85 Coarse loamy 1.50 1.63 1.80 Fine loamy 1.46 1.67 1.78 Coarse loamy 1.43 1.67 1.79 Fine silty 1.34 1.54 1.65 Clayey: 35-458 1.40 l 49 1.58 > 458 1.30 l 39 1.47 Source: F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, 'Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. Appendix Table A12. Criteria for Determining Nonlimiting, Critical, and Root-limiting Bulk Densities for each Family Texture Class Family textural AWC8 ---------------------- class (in./in.) Clay(8) Nonlimiting Critical Limiting Fine loamy .10 25 20 10 5 Coarse silty .20 10 20 10 5 Fine silty .15 25 20 10 5 Clay: 35-45 .10 40 15 10 5 > 45 .10 50 15 10 5 8Available water-holding capacity Source; F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, 'Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. 2 0'? Appendix Table A13. Coefficients of Equations for each Family Texture Class used in Equation A2 Low High Family Texture ------------------------------------ Class Slope Intercept Slope Intercept Sandy -l.933 4.093 -5.163 9.551 Coarse loamy -l.160 2.717 -4.859 8.746 Fine loamy -0.829 2.210 -7.509 13.366 Coarse silty -0.725 2.037 —6.883 12.321 Fine silty -0.870 2.166 —7.509 12.389 Clay:35-45 -l.933 3.706 -9.l78 14.500 > 45 -l.933 3.513 -10.325 15.178 Source F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, ‘Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. Appendix Table A14. Adjustment Factors for Sufficiency of Bulk Density used in Equation A2 Family Texture ____________________________________________ Class < .06 .06 - .2 .2 - .6 .6 - 2 > 2 Fine loamy 1 Coarse silty 1. Fine silty 1. Clay: 35-60 1 ) 60 1 Source; F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, 'Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. 2(18 Soil properties such as available water, bulk density, permeability, pH, and depth of soil horizons are found in the soil interpetation record developed for each soil series by various state soil conservation offices. Using the soil properties in the earlier equations helps estimate the soil parameters for each soil horizon. Weighting equations are used to calculate productivity indexes for each horizon in the soil profile. The initial weighting equation [22] takes the following form; 5 (A4a) wr<1> WF(i)+(.35-(.l52)log(D(I)+(D(I)xD(I)+6.45)o ) or 100 5 (.35-(.152)log(D(I)+(D(I)xD(I)+6.45)O ) I: (A4b) WF(i) (A4c) then WF(I) = WF(i)/WF(IOO) where WF(I) Weighting factor at depth I WF(i) - Weighting factor coefficient at depth 1 WF(J) - Weighting factor coefficient at depth I + l WF(lOO) - Weighting factor coefficient at depth 100 centimeters ' 0(1) - Depth from soil surface in centimeters 22. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, 'Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. 2 O 9 The weighting factor is calculated for 100 centimeters of soil depth and is a cumulative index[23]. The weighting factors[24] are calculated[25] in the following tab1e(in one centimeter increments). 23. Increases at a decreasing rate, f’x is greater than zero, f”x is less than zero 24. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, 'Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. 25. Soil productivity for the soil surface to the soil depth in centimeters up top 100 centimeters - the assumed plant rooting depth. This may be thought of as the percent of plant roots to the soil depth considered with all 1008 of the plant roots in the first 100 centimeters of the soil. Appendix Table A15. 21.0 Factors 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Weighting WF Cm .353 41 365 42 377 43 389 44 401 45 .413 46 424 47 435 48 .446 49 .457 50 .468 51 .479 52 .489 53 .499 54 .509 55 .520 56 .530 57 .539 58 .549 59 .559 60 .569 .578 .588 .597 .606 .615 .624 .633 .642 .651 .659 .668 .676 .685 .693 .701 .709 .717 .725 .733 61 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 .741 .749 .756 .764 .772 .779 .786 .794 .801 .808 .816 .823 .829 .837 .844 .851 .857 .864 .871 .877 81 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 .884 .891 .897 .904 .909 .916 .923 .929 .935 .941 .947 .953 .959 .965 .971 .977 .983 .988 .994 1.000 Cm WF 0 .000 1 .044 2 .064 3 .083 4 .102 5 .119 6 .137 7 .154 8 .171 9 .187 10 .202 11 .217 12 .232 13 .247 14 .261 15 .275 16 .288 17 .302 18 315 19 .328 20 .340 Source; Larson, F.J. Pierce, and Winkelman, R.H. Dowdy, W.A.P. Graham, 'Computer Model GRAPHI, "Us. A Program to Display the Effects of Erosion on Soil Productivity’, USDA, Soil Science Department, Agricultural Research Service, University of Minnesota, Apr 1984. 2 11 Graphically the weighting factor[26] appears as follows; WEWHUMGF“C“U§ DEPTH (cm) I I! ”cease-ans: n(mmm) Figure A2. Concept of the Sliding Weighting Factor. Note: 26. W.E. Larson, F.J. Pierce and R.H. Dowdy, 'Threat of Soil Erosion to Long Term Crop Production’, Science Vol.219, Feb 1983, As erosion occurs, the curve shifts down the soil profile. The productivity index drops if the subsoil has characteristics less favorable than the soil above it. If a limiting layer is encountered, that portion of the curve below the limiting layer is lost and the productivity index declines. pp.448-465. 2‘12 To determine the weighting factor for a horizon, the difference in the weighting factors for the upper and lower boundaries of a horizon is used in the calculation as follows;. (A5) WF = low depth weight - high depth weight where WF - Horizon weighting factor Low depth - Bottom boundary of the soil horison in centimeters from the soil surface High depth - Top boundary of the soil horizon in centimeters from the soil surface For example: . Depth Weight[27] Low depth 60 cm. .733 High depth 25 cm. .401 Horizon 35 cm. Horizon .332 depth weight‘ The horizon weight can be illustrated as follows; Horizon Horizon Horizon Cumulative depth low depth weight[28] horizon weights 25 cm. 25 cm. .4012 .4012 35 cm. 60 cm .3319 7331 40 cm. 100 cm. .2669 1.0 The horizon weighting factor is combined with other soil parameters as determined by equations Al - A4 to derive the horizon’a productivity as shown below; 27. From appendix table A15 28. Using equation A5 2 1 3 (A6) PI(I) = A8 x BS x PS x W.F. where PI(I) Horizon productivity AS - Sufficiency of available water BS - Sufficiency of bulk density PS - Sufficiency of soil reaction W.F. - Horizon weighting factor This weights horizons by depth and distance from the surface assuming that soil properties near the surface affect plant growth more than the deeper soils’ properties[29]. Next, sum the productivity by horizon to derive the productivity index for the soil profile(rooting depth of 100 centimeters). This summation gives the productivity index for the initial soil profile with no assumed erosion. The productivity index for 100 centimeters of soil may involve rooting thru two or three horizons. The model developed by Pierce[30][3l] develops centimeter by centimeter erosion equations to determine the productivity index. This analysis does not carry out calculations that far. Instead the productivity index for 100 centimeters of soil in this analysis is calculated beginning at the upper boundary of each soil horizon for the top two or three soil horizons. This assumes the erosion of an entire horizon so the roooting zone moves down to begin at the top of the next horizon. The 29. Possibly due to more rooting per centimeter of soil depth in the top of the profile as compared to the rooting near the bottom of the profile 30. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, 'Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. 31. F.J. Pierce, R.H. Dowdy, W.E. Larson, and programmed by R. Hemlich, 'Method of Soil Loss Depletion Estimates’, Economic Research Service, USDA. 2 11+ productivity index is determined(lOO centimeters deep) for the second horizon and below.~ Erosion of the second horizon is assumed and the productivity index is calculated for the third horizon. This gives three productivity index values at different points in the soil profile combined with the number of years it takes to erode a particular horizon, the rate of change(slope or f’(x)) in productivity can be calculated. The difference in productivity indexes of the two horizons divided by the number of years to erode from one horizon to another gives the yearly change in productivity. This erosion period(year l to j) is a function of soil type and tillage practices used. The equation is of the following form: (1171:) PI’(I) ((PIU) - PI(I))/(yr(j) - yr(1))) where PI’(I) Yearly change in soil productivity PI(j) - Productivity index of the second horizon PI(l) - Productivity index of the first horizon yr(j) - Number of years to erode the first horizon yr(l) - Year one of analysis. Yields can be assigned to the productivity indexes and changes in productivity are used to approximate yearly change in yield levels. Changes in productivity can be represented graphically as follows: 2 1 5 Pindex PI(I) PI’(I)1 PI(j) - ............. PI(t)- ............... 1::::> PI(k) - --------------- .--_-1 j t Time Figure A3: Linear Approximation of Yearly Change in Soil Productivity where (A7b) PI’(I)1 = (PI(J) - PI(I))/(yr(J) - yr(1)) (A7C) PI’(I)2 = (PI(k) - PI(J))/(yr(k) - yr(J)) (A7d) PI(t) = PI(J) - (t - J)((P1(k) - PI(J)/(yr(k) - yr(J))) where PI’(I)1 - Yearly change in soil productivity of the first soil horizon PI’(I)2 - Yearly change in soil productivity of the second soil horizon PI(t) - Soil productivity level at time t PI(I) - Productivity index of the first horizon PI(j) - Productivity index of the second horizon yr(l) - Year one of the analysis yr(j) - Number of years to erode the first horizon yr(k) - Number of years to erode the second horizon t - Time t The next step for soil erosion is to approximate the number of years it takes to cause an entire soil horizon to disappear. 2:16 (A8) Years = (D(I) x BD(I) x 102.8)/Erosion rate where Years - Number of years for soil horizon to be eroded away D(I) - Depth of horizon in inches BD(I) - Horizon’s bulk density[32] Erosion - Tons of soil loss per year rate Note: The value of 102.8 converts the centimeter soil depth measure into inches. The model developed by Pierce[33] uses a coefficient of 9.42 when measuring soil loss in centimeters but a recalculation casts doubt on this figure. Gm./cm.E3 converts into 1000kg./meter83 which equals 10kg./meter82 (one cm. thick)r Conversion to hectares equals 100 metric ton/hectare(one cm. thick) which equals 40.4 metric ton/acre(one cm. thick) or 102.8 metric tons/acre(one inch thick) which should be the coefficient. The 102.8 metric ton per acre coefficient will be used in this analysis unless the 9.42 figure is shown to be correct. Using this formulation, the number of years needed to erode an inch of soil for various erosion rates can be calculated in the following table[34]: 32. From Soil Conservation Service, USDA, Michigan, 'Soils Interpetation Record’, Soils: Oshtemo, Spinks, Hillsdale, Riddles, Schoolcraft, Kalamazoo. 33. F.J. Pierce, R.H. Dowdy, W.A.P. Graham, W.E. Larson, and Winkelman, 'Computer Model GRAPHI, A Program to Display the Effects of Erosion on Soil Productivity’, Agricultural Research Service, USDA, Soil Science Department, University of Minnesota, Apr 1984. 34. Assuming a soil bulk density of 1.35 2:17 Appendix table A16. Soil Erosion/Productivity Relationships Yearly Number of years to Yearly change8 erosion rate erode one inch of soil in productivity 5 tons 27.8 years .0368 10 tons 13.9 years .0728 15 tons 9.3 years .1088 20 tons 6.9 years .1458 25 tons 5.6 years .1798 30 tons 4.6 years .2178 40 tons 3.5 years .2868 50 tons 2.8 years .3588 8 Assuming a loss of one inch of soil reduces soil productivity by one percent which is a reasonable estimate when examining data from appendix table A18. These calculations combined with the horizons’ productivity indexes are used to determine the yearly rate of change in productivity. An example using the Hillsdale(2 - 68 slope) soil parameters(from appendix table A18) can result in the following: Appendix table A17. Yearly Change in Productivity on a Hillsdale 2 - 68 Slope Soil Years Adjusted to erode Yearly change Horizon Depth Pindex Pindex horizon8 in productivity88 1 15 in. .81 1008 154 .05848 2 48 in. .74 918 493 ------ 3 7 in. 72 8 Assuming 13.5 tons of erosion per year(from appendix table A9) and soil bulk density of l.35(from appendix table A18). 8898 divided by 154 years This example estimates the yearly changes in productivity using parameters from a soil profile. A more exact method is the use Of. a weighted productivity index changing centimeter by centimeter This involves some computer programming to determine 21.8 weighting factors and productivity indexes changing as soil erosion moves the rooting zone down the soil horizon. This analysis uses the Hillsdale soil example of yearly changes in productivity DNCF calculations. This analysis uses range of yearly productivity reductions of .108, .158, and .258 to evaluate the effects of changes in yield thru time. This range of change in soil productivity estimates some of the results obtained if using actual parameters(soils, erosion rates). The methodology is laid out so interested parties can develop their own soils information. 2 1‘9 Characteristics of Various St. Joseph Basin Soils and Calculations of Productivity Indexes at Each Soil Horizon Appendix Table A18. Hillsdale SL Riddles SL 2 - 68 slope 2 - 68 slope Characteristics 1 2 3 l 2 3 4 Depth Inches 15 48 7 14 14 36 8 Centimeters 38.1 121.9 17.8 35.6 35.6 91.4 20.3 Bulk density-BD(I)8 1.35 1.47 1.62 1.40 1.50 1.50 1.50 Permeability-P(I)8 3.3 3.3 4.0 1.3 1.3 1.3 1.3 Sufficiency of bulk 1.0 1.0 .92 1.0 .98 .98 .98 density-BS8 Available water- .175 .15 .105 .14 .17 .17 .12 AW(I)8 Sufficiency of .875 .75 .525 .7 .85 .85 .60 available water-A88 Soil reaction-PH(I)8 5.8 5.45 8.25 6.7 6.2 6.2 6.5 Sufficiency of soil 1.0 .99 .75 .97 1.0 1.0 .83 reaction—P88 Unweighted .875 .74 .362 .68 .83 .83 .49 productivitytt Weighting factor88 .539 .46 .52 .296 .184 Weighted .47 .34 .35 .245 .153 productivity888 Productivity .81 .74 .75 .83 .811 indexttt 8 From Soil Conservation Service, USDA, Michigan, 'Soils Interpetation Record’, Soils: Oshtemo, Spinks, Hillsdale, Riddles, Schoolcraft, Kalamazoo. 88 From appendix table A15 888 Unweighted productivity times the weighting factor 8 Calculated using equations Al, A2, and A3 #8 Calculated using equation A6 888 Horizon productivity index(calcu1ated for 100 centimeters down from the top of each horizon). For example; The first horizon of the Hillsdale Riddles soil has a productivity index of .81 which is 38.1 cm.(.47) + 61.9cm.(.34). 2,2 0 Appendix Table A18 (cont’d). Hillsdale SL Riddles SL Characteristics 2 - 68 slope 2 - 68 slope "TEES” 1 """ E ‘‘‘‘‘ 3 """ 4 ""1 """ E """ 5 """ 3 Depth ----------------------------------------------- ‘-188hes 10 12 63 14 14 11 25 39 Centimeters 25. 30.5 160.0 35.6 35.6 27. Permeability-P(I)8 13. 11.0 4.0 13.0 4.0 4. 20.0+ Sufficiency of bulk l. 1.0 1.0 1.0 1.0 1. density-888 4 9 5 Bulk density-BD(I)8 1.4 1.4 1.35 1.35 1.4 1.4 1.4 1.35 0 0 0 0 0 0 Available water— .09 .075 .06 .05 .125 .155 '.08 0.03 AW(I)8 Sufficiency of .45 .375 .30 .25 .625 .775 .40 0.15 available water-A88 Soil reactionPH(I)8 6.2 6.45 6.7 7.5 5.8 5.8 6.2 7.9 Sufficiency of soil 1.0 1.0 .97 .83 1.0 1.0 1.0 0.77 reaction-P88 Unweighted .45 .375 .291 .207 .625 .775 .4 0.12 productivitytt Weighting factors88 .401 .30 .299 .52 .242 .238 Weighted .181 .113 .087 .325 .187 .095 productivity888 Productivity .38 .33 .291 .607 .548 .333 indexttt 8 From Soil Conservation Service, USDA, Michigan, 'Soils Interpetation Record’, Soils: Oshtemo, Spinks, Hillsdale, Riddles, Schoolcraft, Kalamazoo. 88 From appendix table A15 888 Unweighted productivity times the weighting factor 8 Calculated using equations Al, A2, and A3 ‘88 Calculated using equation A6 ' 888 Horizon productivity index(calcu1ated for 100 centimeters down from the top of each horizon). For example; The first horizon of the Hillsdale Riddles soil has a productivity index of .81 which is 38.1 cm.(.47) + 61.9cm.(.34). Appendix B MODEL INFORMATION This appendix includes yield potential information and USDA experimental results. Machinery parameters such as machinery requirements by operation and rotation are included. A discussion of optimum machinery cycle derivation, non-optimum machinery cycles, and changes in tillage systems thru time is included. Machinery parameters, fertilizer use rates, herbicide use, the stopping rule, and real interest rates are detailed. Finally crop budgets are developed. 2221 2122 0.1 Crop Yield Potentials by Soil, tables Bl and 02 Appendix Table Bl. Average Yield potentials for Crops Grown on Different Soil Management Groups under Good Management with Adequete Drainage but without Irrigation in Areas with a Growing Season of over 140 Frost-Free Days(Southern Michigan) Soil Management Corn Group Corn silage --bu. tons Loams 2.5a 110 17 2.5b 120 18 2.5c 130 20 Sandy loams8 3/2a 105 17 3/1b or 115 18 3/2b 3/1c or 120 18 3/2c Sandy loams 3a 95 16 3b 105 17 3c 110 17 3/Ra 85 14 80ver clay or loams Source: D.D.Warnke, 'Fertilizer Recommendations Winter wheat Oats bu. 90 100 110 90 95 100 Field beans Alfalfa Soybeans 3 cuts bu. tons-- 35 4.8 40 5.0 45 5.5 35 4.5 40 4.8 40 5.0 30 4.0 33 4.5 35 4.8 28 3.8 for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin 8 - 550, Dec 1981. 223 Appendix Table 82. Average Yield potentials for Crops Grown on Different Soil Management Groups under Good Management with Adequete Drainage but without Irrigation in Areas with a Growing Season of less than 140 Frost-Free Days(Northern Michigan) Soil Management Corn Winter Field beans Alfalfa Group Corn silage wheat Oats Soybeans 3 cuts --bu. tons bu. bu. bu. tons-- Loams 2.5a 90 15 45 80 35 4.0 2.5b 90 15 48 85 40 4.0 2.5c 95 16 50 90 45 4.5 Sandy loams8 3/2a 80 13 40 80 35 4 0 3/lb or 85 14 45 85 37 4 O 3/2b 3/1c or 90 15 50 85 40 4 0 3/2c Sandy loams 3a 75 12 35 75 25 3.5 3b 80 13 35 80 30 3.5 3c 85 14 40 85 35 3.5 3/Ra 70 ll 30 70 25 3.0 80ver clay or loams Source: D.D.Warnke, ’Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin E - 550, Dec 1981. .2 12 1+ vascuuccu 0:02 0:02 0:02 «co: 0:0: 0:0: 0:0: use: see: ecoz one: 0:0: 0:02 one: see: 0:02 0:02 0:02 .ocoz 0:02 one: was: 0:0: one: sequence mod—_sv sen. usages: sus— vaucemm names asucena 0:02 0:02 0:02 0:03 a. o. a-u an- o~- 2.- so- he- as- —~ N—I «.1 «flu n—I e— mm— on. pm N. acouusm c.4m o.mc_ 0.00 6.00— o.no_ o.~o_ o.n__ o.mm_ o.On_ o.e~_ o.mn_ o.nN_ 6.00 0.5” o._o o.mn c.00— O.no_ ~.nn ~._a n.mn e.nn o.~o o.no— o.e_o\n o.oo o.~n c.oo_ annexan Biz H12 +buz +huz +s-z +huz o.en o.¢o_ o.no o.no o.no o.na c.0n— o.vn— o.cn_ o.-_ o.~«_ c.-_ 0.00. o.oo~ c.oc~ c.00— 0.0a— c.oo_ ~.a~ «.00 c.oe c.0e c.o~_ ¢.o~_ c.0sn~n\~ o.-\m a.os\m o.on\~ sausxsm esoo usuu< accesses usuu< esoscuucou eceemxom cusp» uses) ones0u uses) can can can as» noza< hOuu< usuu< hfluu‘ h0uu< .o..< usage eceonsom euou euou enou case case euou case case case case euoo cueu suoo enou euou saou seen esssmaoa euou sessmaom acssmxom case case case eceemaom ass-mac» eassouuesm sszusom Issuuom choc smsusas ensues- ensues- smaus>s oneness oneness oneness oneness ensues- oneness eueus>e emeus>s accuses ensues- eneus>s ensues- smessae oneness smsus>s ussaIN .o—meu no use us souoeu00u «om nulQOO— a In n~-coo_ " -- nsnooa~ " 11 u ussou nausea“ “ seem nuns muouvsm nunsoe— u soon unu- ensues: nnusoe_ a seed unu- assume. a Ashesusomv use-«ve— nsusoe. u seed avenues:- nmnnoa_ u Iso— avesxecsm nsusoa_ u Icon ave-secs: nauseo— u mesa nau- aoeuh nausoo_ « lead unu- aoeuh nauseo— “ seed anus aosuh u Aussseuuozv "ac-«veu nauseo— « seen undo huduu closes nausea. u see“ ands hauas causes nunsoo~ « lean asuu humus ensues. nauseo— ” seed yuan unsoun nsunoo_ u need suns assen- nnn~o¢_ “ seed unu- assen- . aeususeuv .eesuveu use. a seed uuua sauna: o~a_ ” seed nau- sense: nua- u need asno Audu- euaa>u>o= “so. a lead usuu undue augususez use. ” Iced anus humus squasuxoz 559. u need undo augu- suuu>uuoz amass:- uees can ensues. use» «In muo— usa— one. opo— seesqveu "mqocud—u sequence ”I‘uhO—L 8038mm .832. 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Crop Operations by Tillage System for Wheat after Navy Bean Moldboard plow Reduced till No till 1. Field cultivator 1. Field cultivator 1. Grain drill 2. Grain drill 2. Grain drill 2. Fertilizer 3. Fertilizer 3. Fertilizer Spreader spreader8 spreader8 3. Field sprayer 4. Field sprayer88 4. Field sprayer88 4. Combine 5. Combine 5. Combine 8 Top dress in the spring 88Apply herbicide Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, ’Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 2i2 7 Appendix Table B5. Crop Operations by Tillage System for Oats after Sugar Beets Moldboard plow Reduced till No till 1. Fall plow l. Chisel plow 1. Grain drill 2. Field cultivator 2. Field cultivator 2. Field sprayer8 3. Grain drill 3. Grain drill 3. Combine 4. Field sprayer8 4. Field sprayer 5. Combine 5. Combine 8Apply herbicide Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, ’Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. Appendix Table B6. Crop Operations by Tillage System for Alfalfa after Sugar Beets or Alfalfa after Oats Moldboard plow Reduced till No till 1. Fall plow 1. Chisel plow 1. Field sprayer8 2. Disk 2. Disk 2. Grain drill 3. Field cultivator 3. Field cultivator 3. Harvest88 4. Field cultivator8 4. Field cultivator! 4. Field sprayer88 5. Grain drill 5. Grain drill 6. Harvest88 6. Harvest88 7. Field sprayer88 8 Apply herbicide 88Alfa1fa to be clear seeded and a three year stand 8 Incorporate herbicide 88Insecticide application after the first harvest Source: R.J. Black, Alan Rots, Donald Christenson, Hannibal Muhtar, and John Posselius, 'Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 2228 Appendix Table B7. Crop Operations by Tillage System for Corn after Navy Beans or Corn after Soybeans Moldboard plow Reduced till No till 1. Fertilizer spreader 1. Fertilizer spreader l. Fertilizer spreader 2. Fall plow8 2. Chisel plow8 2. Field sprayer88 3. Field cultivator888 3. Field cultivator888 3. Row p1anter8 4. Row planter 4. Row planter8 4. Field sprayer88 5. Row cultivator 5. Field sprayer88 5. Ammonia applicator 6. Ammonia applicator 6 Ammonia applicator 6. Combine 7. Combine 7. Combine 8 Some farm operations may eliminate the plowing sequence 88 Apply herbicide 888Incorporate herbicide 8 Minimum till . 88 Post planting application Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, 'Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. Appendix Table 08. Crop Operations by Tillage System for Soybeans after Corn or Soybeans after Navy Beans Moldboard plow Reduced till No till 1. Fall Plow 1. Chisel plow 1. Row planter 2. Field cultivator8 2. Field cultivator8 2. Field sprayer 3. Field cultivator88 3. Row planter 3. Field sprayer8 4. Row planter 4. Field sprayer 4. Combine 5. Row cultivator 5. Field sprayer8 6. Combine 6. Combine 8 After navy beans, some farming operations would eliminate the field cultivator on one pass 88Incorporate herbicide 8 Post planting application Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, 'Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 212 9 Appendix Table 09. Crop Operations by Tillage System for Navy Beans after Corn or Navy Beans after Navy Beans or Navy Beans after Alfalfa Moldboard plow Reduced till No till 1. Fall plow 1. Fall chisel plow 1. Row planter8 2 Field cultivator 2. Field cultivator 2. Field sprayer 3. Field cultivator88 3. Row planter 3. Field sprayer8 4. Row planter 4. Field sprayer 4. Bean puller & Windrower88 5 Row cultivator 5. Field sprayer8 5. Combine 6 Bean puller & 6. Bean puller & Windrower88 Windrower88 7. Combine 7. Combine 8 Minimum till 88Incorporate herbicide 8 Post planting application 880a some farming operations, windrowing might be a completely added step as farmers may not have a windrower connected in conjunction with their puller Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, 'Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 2:30 Appendix Table 010. Crop Operations by Tillage System for Sugar Beets after Navy Beans or Sugar Beets after Wheat, or Sugar Beets after Corn No till Moldboard plow Reduced till 1. Fall plow 1. Fall Chisel 1. Apply 288 2. Field Cultivator 2. Field cultivator nitrogen & apply 288 nitrogen & apply 288 nitrogen 2. Row planter8 3. Row planter88 3. Row planter8 3. Field sprayer8 4. Field Sprayer88 4. Field sprayer8 4. Beat topper 5. Row cultivator(2) 5. Beet topper 5. Best lifter 6 Beet topper 6. Beet lifter 7 Beet lifter 8 Minimum till with banned herbicide 88Banned herbicide 8 Post planting application 88Post planting application 508 of the time Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, 'Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 231 Appendix Table 011. Crop Operations by Tillage System for Corn after Corn or Corn after Wheat or Corn after Sugar Beets or Corn after Alfalfa Moldboard plow Reduced till No till l. Fertilizer spreader8 l. Fertilizer spreader 1. Fertilizer spreader 2. Fall plow 2. Chisel plow 2. Row planter88 3. Field cultivator 3. Field cultivator 3. Field sprayer888 4. Field cultivator8 4. Row planter88 4. Field sprayer88 5. Row planter 5. Field sprayer888 5. Ammonia 6. Row cultivator 6. Field sprayer88 applicator 7. Ammonia applicator 7. Ammonia applicator 6. Combine 8. Combine 8. Combine 8 Fall 88 Minimum till 888Pre-emergence application 8 Incorporate herbicide 88 Post planting application Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, ‘Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 232 B.3 Machine Requirements by Crop Rotation The preceding appendix tables(B4 - 011) list individual crops and crop operations by tillage method. To determine machinery requirements for a crop rotation, each crop, the tillage methods used are combined to determine the total rotational machine requirements. Some operations are done on all crops[l] and some operations are done on one crop in the rotation[2]. It is important to determine on how many crops a particular machine or crop operation is used. The operational timing and crop acreage determines the size of machine needed meeting whole farm timeliness requirements[3]. The following appendix tables(BlZ - 014) lists different crop rotations using a mix of tillage systems within each crop rotation. Crop operations are listed crop rotation for the assumed tillage method mix. For example: 1. Such as moldboard plowing on corn and soybeans 2. Ammonia application only on corn 3. A moldboard plow used on three crops in a rotation may need to be larger than a moldboard plow only used for one crop of a rotation - assuming each crop is on equally sized parcels 233 - The first table(012) is a corn-soybean-wheat rotation with four possible tillage system mixes. a. Columns 1 thru 4 - all crops are moldboard plowed b. Columns 5 thru 8 - moldboard plow corn and soybeans and tandem disked wheat c. Columns 9 thru 12 - moldboard plowed first year corn, soybeans and wheat and chisel plowed second year corn d. Columns 13 thru 16 - moldboard plowed soybeans, chisel plowed corn and tandem disked wheat - In each column are the possible crop operations for each crop[4] are marked with an x. This results in a listing of the operations and machine use needed in the crop rotation. The crop rotations shown indicate a variety of tillage system combination. The tillage "mixes" chosen are the most likely to be used on these crop rotations. 4. In columns 1 - 3 only corn uses a fertilizer spreader but all crops,‘corn, navybeans, and beets’, are fall moldboard plowed and so on down the crop operation list 2 31+ Appendix Table 812. Field Operations for Various Tillage Combinations on Corn - Corn - Soybean - Wheat Rotation. a. b. MB MB MB MB MB MB MD TD Field operations Co Co Bn Wt Co Co Bn Wt Spread fertilizer x(f) x(f) x(T) x(f) x(f) x(T) Fall plow x x x x x x Tandem disk x Field cultivator x x x x x x x Field cultivator8 x x x x x x Row planter x x x x x x Grain drill x x Field sprayer(pre) x x Row cultivator x x x x x x Ammonia applicator x x x x Combine(corn) x x x x Combine(bean) x x Combine(wheat) x x MB - Moldboard plowed Co - Corn TD - Tandem disk Bn - Soybeans Wh - Wheat 8Incorporate herbicide (f) - Fall (T) - Top dress in the spring Corn - corn - soybean - wheat tillage combinations; a. - All crops are moldboard plowed b. - Moldboard plowed corn and soybeans, tandem disked wheat 2235 Appendix Table 012 (cont’d). MB Ch MB MD Ch Field operations Co Co Bn Wh Co Spread fertilizer x(f) x x(T) x Fall plow x x x Chisel plow x(f) x(f) Tandem disk Field cultivator x x x x x Field cultivator8 x x Row planter x x Row planter x x (minimum till) Grain drill x Field sprayer(pre) x x x Field sprayer(post) x x Row cultivator x x Ammonia applicator x x x Combine(corn) x x x Combine(bean) x Combine(wheat) x MB - Moldboard plowed Co - Corn Ch - Chisel plowed Bn - Soybeans NT - No till Wh - Wheat 8Incorporate herbicide (f) - Fall (T) - Top dress in the spring Ch MB TD Co Bn Wh x x(T) x X(f) X X X X X x X X X X X X x X X Corn — corn - soybean - wheat - tillage combinations; c. - Moldboard plow first year corn, Chisel plow second year corn d. - Chisel plow corn, Moldboard plowed soybeans, Tandem disked wheat soybeans and wheat, 236 Appendix Table 013. Field Operations for Various Tillage Combinations on Corn - Soybean Rotation. a. b. c. d. MB MD Ch MB Ch Ch NT Ch Field operations Co ' Bn Co Bn Co Bn Co Bn Spread fertilizer x x x x Fall plow x x x Chisel plow x x x x Field cultivator x x x x Field cultivator8 x x x x x Row planter x x x Row planter x x x x x (minimum till) Field sprayer(pre) x x x Field sprayer(post) x x x x x Row cultivator x x x Ammonia applicator x x x x Combine(corn) x x x x Combine(bean) x x x x MB - Moldboard plowed Co - Corn Ch - Chisel plowed Bn — Soybeans NT - No till 8Incorporate herbicide Corn - soybean rotation tillage combination; a. - Moldboard plowed corn and soybeans b. — Chisel plowed corn, Moldboard plowed soybeans c. - Chisel plowed corn and soybeans d. - No till corn, Chisel plowed soybeans 237 Appendix Table 814. Field Operations for Various Tillage Combinations on a Alfalfa/Oats - Alfalfa(3 years) - Corn - Corn - Corn Rotation. a. b. MB MD MB MB MB MD Ch Ch Field operations O/A Co Co Co O/A Co Co Co Spread fertilizer x(f) x(f) x(f) x(f) x x Fall plow x x x x x x Chisel plow x(f) x(f) Field cultivator x x x x x x x x Field cultivator8 x x x x Row planter x x x x Row planter x x (minimum till) Grain drill x x Field sprayer(pre) x x x x Field sprayer(post) x x Row cultivator x x x x Ammonia applicator x x x x x x Combine(corn) x x x x x x Combine(oats) x x Cut/Condition x(A) x(A) Bale x(A) x(A) MB - Moldboard plowed Co - Corn Ch - Chisel plowed O/A - Oats/Alfalfa 8Incorporate herbicide (f) - Fall (A) - Operations in the three years of alfalfa crop Oats/alfalfa - alfalfa(3 years) - corn - corn - corn tillage combination; a. - All crops are moldboard plowed b. - Moldboard plowed oats and first year corn, Chisel plowed second and third year corn Appendix Table 014 (cont’d). 2:38 c. d MB Ch Ch Ch Ch Ch NT NT Field operations O/A Co Co Co O/A Co Co Co Spread fertilizer x x x x x x Fall plow x Chisel plow x(f) x(f) x(f) x x(f) Field cultivator x x x x x x Row planter x x x x x x (minimum till) Grain drill x x Field sprayer(pre) x x x x x x x x Field sprayer(post) x x x x x x Ammonia applicator x x x x x x Combine(corn) x x x x x x Combine(oats) x x Cut/Condition x(A) x(A) Bale x(A) x(A) MB - Moldboard plowed C - Corn Ch - Chisel plowed O/A - Oats/Alfalfa NT - No till 8Incorporate herbicide (f) - Fall (A) - Operations in the three years of alfalfa crop Oats/alfalfa - alfalfa(3 years) - corn - corn - corn tillage combination; c. - Moldboard plowed oats, d. - Chisel plowed oats and first year corn, No till second and third year corn Chisel plowed corn 2239 8.4 Optimum Replacement of Farm Machinery This author has a question concerning the determination of the optimum replacement age of farm machinery(assuming an infinite series of identical challengers). The literature reviewed[5][6] uses the following equation[7] which finds the years of machine use which minimizes the value of the following expression: -1 R(t) M(S) (Bl) PV(S) = (1-(1+r) )[ M(0)+ : ( ------ ) - ------ ] t=l t t (1+r) (1+r) where PV(S) Present value at year S PV( ) - Present value at inifinite time DNC8 - Discounted net costs of machine costs run out to infinity MA(S) - Annualized machine costs for 8 cycle years r - Interest or discount rate M(O) - New cost or aquisition value M(S) - Salvage value in year 8 R(t) - Repair and other noncapital costs in year t j - Cycle year c - Number of cycles S - Cycle length This formulation appears to assume the new costs(M(O)) are incurred in time 0 or in the beginning of the first year with the set being salvaged in time S. It is not clear if the machinery 5. Garnett Bradford and Donald Reid, ‘Theoretical and Emperical Problems in Modeling Optimal Replacement of Farm Machines’, Southern Journal of Agricultural Economics, July 1982, pp.109-116. 6. Lindon Robison and Michael H. Abkin, ‘Theoretical and Practical Models for Investment and Disinvestment Decison Making under Uncertainity in the Energy Supply Industry’ Agricultural Economics Report 390, Michigan State University, Mar. 1981. 7. Capital asset replacement 21:0 set’s salvage value in year S is used to offset the initial new cost(M(O)) of an identical replacement set or not. The derivation of the discounted net cash flows to infinity is not specified. A calculation of the discounted net costs[8] did not minimumize DNCs using equation B2. The expression used when new machinery costs are offset by the salvage value of the previous set takes the following form(notations follow equation Bl): d3 R(ch+j) -M(O)+M(S) M(S) (32) 01408: M(O) +§ EH ---------- + ---------------------- ] j=l c=l ch+j ch S( +1) (1+r) (1+r) (1+r) The expression is the new machinery cost (M(O)) in the beginning of year one plus the sum of the discounted present values(PV) of machine costs in a cycle every 8 years plus a salvage term of the last machinery set in year SGfi+ l). The last salvage value goes to zero when discounted out an infinite or large number of years so this term can be dropped. The discounted present value of the cycles takes the following form(notations follow equation Bl): 1 if: 3(3) (:33) PV( ) = < ———————— )[ m0) - M(S) + < ------ ) ch j=l j (1+r) (1+r) 8. Assuming the salvage value in year S is used to offset the new cost of a replacement set 2 4 1 This equation states that the net purchase cost[9] plus the discounted machinery costs(R(j)) equal the present value of the machinery set discounted every S(length of machinery cycle) times C(number of machinery cycles) years. Using the machinery cycle present value formulation times an annuity factor, the optimum life[10] of the machinery set is calculated as illustrated in the following equation(notations follow equation Bl); —s < am (34) MA(S) = (r/(l-(1-) 1): 11(0) - ms) + Z( ------ )1 4... ll H Using this equation results in a machinery optimum replacement age which minimizes the discounted net costs assuming the machine’s salvage value of the present cycle is used to offset the new machine cost in the next cycle. Perhaps the difference in the optimal replacement methods is the assumption of the capital asset salvage value. If the asset’s salvage value does not offset the cost of the new asset, then the first formulation(02) is correct. If the salvage value is used to minimize the new asset cost, than formulation B5 is correct. The following compares discounted net costs when asset salvage values help minimize new asset costs: 9. Initial cost of the next machinery set minus the salvage value of the present set 10. Machinery set life which minimizes the absolute value of the yearly annualized cost of the machine 21+2 Appendix Table B15. Discounted Net Cash Flows Using Various Machinery Replacement Lives8 Machine replacement life Year of Analysis 22 2488 26888 50 40,506 40,830 39,510 100 42,862 42,870 42,823 150 43,120 43,067 43,125 200 43,130 43,081 43,134 8 Using a 58 discount rate and a tractor($19,500 new cost) and 413.8 hours of annual use. 88 The optimum replacement age as calculated by equation B4 (salvage value of the previous tractor is used to offset of the new tractor) 888The optimum replacement age as calculated by equation Bl as developed by Bradford. An examination of the DNCs of different machinery replacement lives indicates replacement of the tractor every 24 years minimizes the long run discounted costs. Not shown but calculated are machinery lives greater than and less than the listed lives but all resulted in values greater than the shown values. Calculations of the discounted net costs assumes salvage values are not used to offset new tractor cost and equation 04 finds the optimum machinery life which minimizes long run machinery cost. 2 4 3 B.5 Non - Optimum Machine or Machinery Set Cycle Considerations Another aspect in machinery calculations complicating equations is the concept that whole machinery sets are not aquired and/or salvaged out at the same time. In practice, machines are bought and sold or retired at different times in addition to having differing costs and repair schedules[11]. A drawback in calculating the optimum life cycle for the whole machinery set is that the optimum life is calculated for the aggregate costs of the various machines. This implies some machines are used beyond their optimum life(see table 6.1) and others have not reached their optimum or useful life. But a tradeoff must be made between model simplicity and the precision of the calculations. An option is to trade the entire machinery set when a machine in the machinery set with the shortest calculated optimum life reaches its optimum life year. Other machines in the machinery set do not reach their optimum life as a result of this approach. A problem is that comparisons between types of machinery sets is difficu1t(hours of annual machine use and other factors differ between tillage systems. An even more complex but relatively exacting method (including graduated repair and salvage costs) calculates annual costs for each machine size by annual use hours. The optimal replacement 11. A tractor may last twenty years while a corn planter may last only ten years but the corn planter may have double the repair cost of a tractor - as a percent of purchase price 2 41: life and annualized cost is calculated by machine and machine size. A machinery selection model matches power and capacities selecting the ”least cost" machinery set given the minimum annual cost for each machine. The machine size selected has the lowest annual cost of the different size machines but meets power and matching criteria and does a timely job. The selected machinery set is then incorporated as part of the DNCF calculations with each machine being replaced at the and of its optimum life[12] rather than replacing the whole set at one time. A tillage switching point may stretch out over a few years as the machinery selection model may gradually select reduced or no-till machinery to replace moldboard plowing machinery to be modeled, run and the results examined. The machinery set chosen may change over time since it is selected machine by machine. The adaptation of a machinery selection model evaluating machinery by type, size or age facilitates the changeover from an existing machinery set to the gradual adaptation of new machinery either identical to the original or different to the original machines. A standard machinery replacement rule can be used to develop machinery switching strategies. If the marginal cost of keeping the original machine one more year is greater than the annualized value[l3] of the new machine, then the operator should switch to the new machine. This results in different switching ‘ patterns than switching at the end of a machine’s optimum life(as calculated by the matrix or optimum replacement method) as the marginal cost greater than the annualized cost rule may cause a 12. More like the piece-to-piece approach 13. Annuity times present value of future net returns 2 4,5 machine to be traded before or after its optimum length life. The concept of replacing machinery during a ”non-optimum" period of time may be a viable option in the minimization of machinery costs over time if the switching year k falls on a ”non-optimum” year of the analysis. For example: If the optimum life cycle for a moldboard plow machinery set is 16 years and the switching year k being considered is at year 68 of the analysis, 4 moldboard plow machinery cycles are used until year 64. The fifth moldboard machinery set is salvaged out after four years in year 68 [14]. These moldboard plow discounted costs up to year 68 can be compared with the discounted cash flows of replacing the moldboard plow machinery set every 17 years[l5]. If switching year k is year 64 (four 16-year optimum life cycles) or year 80 (five 16-year optimum life cycles), the 16-year optimum life cycle replacement rule minimizes machinery cost from year 0 to k, but if the switching year is between year 64 and year 80, "non-optimum” length life cycles are candidates to minimise machinery costs from year 0 to year k. The following expression aids in a search for the minimized DNC of non-optimum life cycles of equal length dividing with no remainder into k years[16]. 14. The discounted costs calculated by using the first part of equation 6.2 15. a non-optimum machinery life cycle, 4 cycles of moldboard plow machinery traded every 17 years up to the switching year 68 16. e.g., four 17-year cycles divides equally into 68 years - the switching year T’l“ (85) DNCF where DNCF Discounted net cash flow PV(c) - Present value of machinery cycle c cash flows i - Cycle year h - Number of machinery cycles minus one to k years S - Cycle length r - Interest rate Another method to minimize discounted machinery costs are "mixed” machinery cycles of differing lengths[l7]. Under this "mixed cycle" method, the machinery cycle with the lowest annualized cost is used in the first part of the DNC calculations[18][19]. The use of ”non-optimum” machinery cycles and "mixed cycles" to calculate discounted machinery costs up to the switching year k complicates the computation of the DNCs beyond the use of optimum length machinery cycles to calculate machinery costs. The machinery cost parameters developed by tillage system(Table 6.2, 6.3, 6.4 ) can be used compute costs of non-optimum machinery sets. The machinery annuity costs (column 2 - Table 6.2, 6.3, 6.4) 17. e.g., For a tillage method switching year after 66 years, 2 moldboard plow optimum machinery cycles of 16 years long each and 2 ”non-optimum” moldboard plow machinery cycles of 17 years long each minimizes the moldboard plow machinery costs over 66 years, the switching year k 18. Effects of the higher annualized cost machinery cycles used toward the last part of the analysis are minimized by the discounting factor 19. e.g., If the switching year k is after 66 years, the costs of the two l6-year moldboard plow machinery sets, the calculated optimum life cycle, are calculated for the first 32 years of the analysis and the cost of two 17-year moldboard plow machinery sets, non-optimum moldboard plow machinery life cycles, are used in year 33-66 of the analysis 247 can be converted to discounted machinery costs by multiplying the annualized costs by the inverse of the annuity factor of the particular cycle year chosen[20]. These discounted machinery costs are the net machinery set costs once the initial costs of the machinery set are offset by the salvage value of the previous machinery set[21]. These discounted costs are the full costs[22] of the particular machinery set up to the cycle year selected. If the new machinery costs are offset by the salvage value of the previous machinery set, this salvage value(column 6 - Table 6.2, 6.3, 6.4) is subtracted from discounted machinery costs to equal net discounted machinery costs and used in the first part of equation 6.6. This calculates total discounted machinery costs of a tillage method up to the switching year allowing a comparison between ”optimum," "non-optimum,” and ”mixed cycle” machinery sets up to the tillage switching year k. 20. This is an inverse annuity 21. Assumed to be identical to present set so the machinery set salvage values of the cycle year chosen must be added to the discounted costs to equal the actual full discounted cost of the machinery set 22. No salvage value offsetting the first machinery set new costs assumed 2168 0.6 Assessing Changes in Tillage Systems Through Time The discounted net cash flows(0NCF) for one tillage system, allows comparisons between the DNCFs of single tillage practices(shown in Table 016). An objective of this study is to develop a framework to assess switching of tillage systems during the analysis time horizon. This may result in higher DNCFs than the use of one tillage system over time. This can be illustrated graphically as follows: Net returns (NR) NR(MB plow)o NR(Chisel)o NR(Chisel)l .—.—-—-_...v_.. _-- . _..._.., ~---.- . ,3..-“ ..-. .mm. _-. y. into—*— —-.-.—.-..-.n -—-..2-.-. - .6. — Mu',~—.s W—- v . __.._.. h-“ -_— *—- Time(n) Figure Bl. Net Returns by Tillage System Combinations thru Time 2,4 9 where —————; - Net returns using moldboard plowing "' thru time [II:[] - Net returns using chisel plowing thru time l: :'j - Net returns using moldboard plowing to year k at which time a switch is made to chisel plow tillage which is used to year q NR(MB plow)o Net returns using moldboard plow tillage in year zero Net returns using chisel plow tillage in year zero NR(Chisel)1 - Net returns using chisel plow tillage in year k after moldboard plow tillage has been used up to year k NR(Chisel)o Assumptions; 1. 2. Curve g-d represents the level of net returns using moldboard plow tillage from year zero to year q. Curve h-f represents the level of net returns using chisel plow tillage from year zero to year q. Curve g—a and curve b-e represent the level of net returns using moldboard plowing up to year k at which time there is a switch in tillage systems to chisel plowing which is used up to year q. In the switching year k, the moldboard plow net returns(NR(MB plow)o are greater than the chisel net returns(NR(Chisel)l). The net returns drop from point a to point b with the switching of tillage systems from moldboard plowing to chisel plowing in year k. The distance g - b. As a percent of distance 0 - g is the same proportion as distance h - i is a percent of distance 0 - h. The use of moldboard plow tillage causes a non linear drop in productivity over time. The discounted moldboard plow net returns(NR(MB plow)) from year zero to q are equal to the discounted chisel net returns(NR(Chisel)o) from year zero to q. This can be expressed mathematically as follows; 9 q f NR(MB plow)o / NR(Chisel)o —-- dn = ----------- dn n n 1 (l + r) l (l + r) 250 7. If the discounted net cash flows(0NCF) of the switching option which are the moldboard plow returns(NR(MB plow)o) discounted from year 0 to k plus the discounted chisel plow net returns(NR(chisel)l) from year k to q are greater than the discounted moldboard plow net returns(NR(MB plow)o) from time zero to q or the discounted chisel plow net returns(NR(Chise1)0) from year zero to q, then the triangle abc’s discounted net returns are less than the discounted net returns fo the triangle cde. The mathematical expressions illustrating these conditions are as follows; k 4 NR((MB plow)o NR(Chisel)1 if ------------- dn + ----------- dn is greater f n f 11 than 1 (1 + r) k (l + r) 9 <1 NR(MB plow)o NR(MB plow)o ,{0 ------------ dn or ------------ dn n n 1 (l + r) l (l + r) T NR(MB plow)o - NR(Chisel)1 then 'f' -------------------------- dn is less than n k (1 + r) triangle abc 9 df' NR(chisel)l - NR(MB plow)o T triangle cde Figure B1 implies that moldboard and chisel plow tillage DNCFs are equal and the switching option DNCF using moldboard plow tillage from year 0 to year k and chisel tillage from year k to q is greater than individual tillage DNCFs[23]. Triangle abc is the 23. The discounted net returns of triangle abc is less than the discounted net returns of triangle cde 2‘51 net return from moldboard plow tillage minus the switched chisel returns from time k to T. Triangle abc is a net loss in revenue due to the switching from moldboard plow tillage to chisel tillage in years k to T. This loss in net returns is offset by the gain in net returns[24]. Triangle cde is the gain in net returns using chisel tillage[25] while moldboard plow tillage net returns decline through time. If the discounted net returns of triangle abc are greater than the discounted net returns of triangle of cde, then the switching option from moldboard plow to chisel DNCF is less than the DNCF of single tillage (moldboard plow or chisel). If the opposite occurs, the switching option has a higher DNCF than the moldboard plow or chisel option. If the equality assumption between the DNCFs of the moldboard plow and ‘chisel tillage systems individually through time is relaxed and the chisel tillage DNCF is greater than the moldboard plow tillage DNCF, the switching option may still be considered as its DNCF may be greater than the chisel tillage DNCF. If initial chisel tillage net returns equal to moldboard plow tillage net returns (violating the first assumption), then the chisel tillage option has the highest DNCF of the options considered. 24. The difference between the switched chisel returns and the net returns from moldboard plow tillage from time T to q 25. Net returns remain constant by assumption 2:52 The switching year k may not occur when moldboard plow net returns equal the chisel net returns (point a in Figure B1). The switching year k can occur at any point in time as a drop in net returns is assumed when switching to chisel tillage in year k[26]. If the discounted net return of triangle cde(gain) is greater than the discounted net returns of triangle abc(loss), the next step finds the switching year k maximizing the difference between the triangles’ discounted net returns, maximizing the switching option’s DNCF. This can be done exhaustively year by year[27] resulting in moldboard plow machinery sets being salvaged out before their optimum life is reached in most of the possible switching years. When this happens, the maximum DNCF possible for the switching option is not realized when switching at an non-optimum machinery life year especially in the first 3 to 5 machinery cycles[28]. The empirical results depicting this behavior were run and inspected but are not illustrated in this work. The use of a machinery set for the number of calculated optimum machinery cycle years may minimize the cost of the machinery set over time thus increasing or maximizing DNCF. As indicated previously, this was true for the first 3 to 5 cycles[29] but this changes in the 26. Due to higher chisel tillage costs with gross return levels the same or slightly higher than that of moldboard plow tillage in year k 27. Using equation 6.6 in the section 6.2 28. The highest DNCF converted to annualized returns occur at the optimum life cycle year which is calculated by equation 6.12b or very near the optimum life year 29. 48-80 analysis years using a 16-year moldboard plow machinery optimum life cycle 253 later years of the analysis (year 80 onward) as the erosion and productivity effects cause the net returns to decline affecting the maximized DNCF switching year[30]. Following this argument, a simplified search is done examining the DNCF results of switching to chisel tillage at the end of each moldboard plow machinery cycle[3l]. This narrows the search for the switching year with the highest DNCF as illustrated in Table 016. Once the switching year with the highest DNCF (beginning of year 49 or the end of year 48) is found, then the ”non-optimum” years (years 45-47, 50-52) on either side of the "optimum” year with the highest DNCF (year 48 in Table 016) can be searched and their DNCFs compared to find the highest DNCF[32] if early in the analysis years. The cycle year of the last chisel machinery cycle in which the last year of the analysis occurs has some influence, though greatly discounted, on the total DNCF. For example, if in the last analysis year[33], a new chisel machinery set is just purchased[34] then there is a negative cash flow effect on the DNCF as opposed to a positive cash flow for the other years of the machinery cycle. 30. May occur in the middle or beginning of a later machinery cycle 31. At years 16, 32, 48, 64, etc. assuming a moldboard plow machinery cycle optimum life of 16 years 32. Most likely to be the optimum life cycle year 33. Year 150 in this work 34. Cycle year 1 2 54} These different annualized net returns due to the analysis ending in different years in the machinery cycle[35] can be seen in Table 016 at the end of each annualized return column. The example illustrated in Table 016 carries out the computations to only an examination of the DNCFs when switching at the end of moldboard plow machinery optimum life cycle year which may be sufficient by earlier assumptions but due to the limited capacity and capabilities of the handheld calculator used, the year-to-year search was not done. An appraisal of Table 016 results illustrate that neither the single use of moldboard plow tillage(DNCF = $1,513,278) or chisel tillage(DNCF = $1,504,714) have higher DNCFs than a combination of the two used through time switching from moldboard plow tillage to chisel tillage, which supports earlier theoretical assertions. Part of the assumptions behind this result is that the DNCFs of the single use of moldboard plow or chisel tillage are fairly close or the switching option cannot result in the highest DNCF. In Table 016, the switching option after 48 years of moldboard plow tillage use has the highest DNCF of all the switching options shown and the single moldboard plow and chisel tillage options. The annualized net return figures shown for each machinery cycle are the net DNCFs over that particular time period multiplied by an annuity factor to give an equivalent yearly net return cash flow over that time period. These can then be converted to present values for use in equation 6.5 for a 35. Years 1—18 for the chisel tillage example 255 calculation of the net present value or DNCF over the time of the analysis. An examination of these annualized cash flows shows that they decline at an increasing rate in the moldboard plow tillage option with a non-linear drop in productivity(due to erosion effects). The chisel till option’s annualized yearly cash flows remain constant with no assumed change in productivity due to the use of chisel tillage but the initial level of net returns is lower than that of moldboard plow tillage(by assumption). The switching options use moldboard plow tillage initially with declining productivity until the switch to chisel tillage at which time there is an initial drop in net returns from moldboard plow net returns at the switching year but no change in productivity in the chisel tillage in the years after the switch. The difference in annualized cash flows between the first and second moldboard plow machinery cycle is due to the full cost of the moldboard plow machinery set incurred in the first cycle (denote this "full cost” as assumption A) while the salvage value of the first moldboard plow machinery set offsets the full cost of the second moldboard plow machinery set in the second machinery cycle[36]. The difference in the annualized cash flows of the chisel tillage once the switch is made from moldboard plow tillage is made, is due to the salvage value of the moldboard plow machinery set offsetting the new cost of the chisel machinery set in the first chisel cycle year after 36. This is also true of the first and second cycle of the single use of chisel tillage machinery through time 2 5GB switching(denote this ”net cost" as assumption 0) while the salvage value of the first chisel tillage machinery set offsets the new cost of the second chisel machinery set in the second cycle of switching. This is illustrated by using the switching option of after 80 years of moldboard plow tillage use. The difference between the annualized cash flows in the first($46,806) and second($47,440) moldboard plow machinery cycles is due to assumption A and declining productivity while the difference between the first($39,674) and second($40,212) chisel machinery cycles is due to assumption 0. Also of note, the difference in annualized cash flows between identical tillage cycles under different switching assumptions is due to the effect of the length of time the moldboard plow tillage is used. The longer the moldboard plow tillage is used, the lower the soil productivity becomes, thus lowering the initial net returns of the chisel tillage once a machinery switch is made. This basic procedure can be used to assess the DNCFs of different tillage and productivity assumptions allowing comparisons between tillage practices. The range and complexity of possible tillage combinations are large and the few examples illustrated in this analysis merely scratch the surface but hopefully provide some guidelines for future considerations. 2 5'? Machinery Cycle Annuity Values and Discounted Net Cash Flows(DNCF) for a 150 Year Analysis under Different Tillage and Change in Soil Productivity Assumptions Table 016. Annualized moldboard plow and chisel plow cash flows for different changes in soil productivity Moldboard plow Chisel Mach- ------------------------------------------- inery Linear-088 Non- No No Cycle Year per year8 linear88 change Year change888 1 l- 16 $46,355 846,806 $47,120 1- 18 $45,026 2 17- 32 46,216 47,440 48,663 19- 36 46,204 3 33- 48 44,577 46,368 48,663 37- 54 46,204 4 49- 64 42,937 45,084 48,663 55- 72 46,204 5 65- 80 41,297 43,590 48,663 73- 90 46,204 6 81- 96 39,657 41,887 48,663 91-108 46,204 7 97-112 38,017 39,973 48,663 109-126 46,204 8 113-128 36,378 37,849 48,663 127-144 46,204 9 129-144 34,738 35,516 48,663 145-150 34,002 10 145-150 24,000 24,155 39,108 8 Assuming a yearly drop in soil productivity of .088. 88 Assuming a nonlinear drop in soil productivity equivalent to a linear drop of -088 per year after 150 years. equation used to estimate the yearly net returns is; Net = returns (year n) (gross returns in year 1) minus (costs(n))] [(1.000002438 - .0003200594(n) - .0000032(n )) x 888Assuming chisel till costs and (1.015) moldboard plow returns, and no change in yearly soil productivity) 2 523 Table 016 (cont’d). Annualized Cash Flows of Switching Tillage Systems Switching at year 328 cycle Mach. cycle Mach. MB plow Chisel Year Cash flow 1 1- 16 $46,806 1 2 17- 32 47,440 2 l 33- 50 43,940 3 2 51- 68 44,477 3 69- 86 44,477 4 87-104 44,477 5 105-122 44,477 6 123-140 44,477 7 141-150 40,836 DNCF - $1,514,054 year Table 016 (cont’d). 0:0:wa.- Switching at year 488 1- 16 17- 32 33- 48 49- 66 67- 84 85-102 103-120 121-138 139-150 MB plow Chisel Year Cash flow $46,806 47,440 46,368 42,710 43,248 43,248 43,248 43,248 41,351 $1,515,104 8Switching at the end of the year or at the beginning of the next Mach. cycle Annualized Cash Flows of Switching Tillage Systems M0 plow Chisel Year Cash flow DNCF - OI-bWNo-I l- 16 17- 32 33- 48 49- 64 65- 82 83-100 101-118 119-136 137-150 $46,806 47,440 46,368 45,084 41,299 41,836 41,836 41,836 40,966 $1,514,966 Switching at year 808 Mach. cycle M0 plow Chisel Year Cash flow (II-boots):— l- 16 17- 32 33- 48 45- 64 65- 80 81- 98 99-116 117-134 135-150 $46,806 47,440 46,368 45,084 43,590 39,674 40,212 40,212 39,923 $1,514,369 8Switching at the end of the year or at year the beginning of the next 259 0.7 Comparison of ”Mixed Cycle” and ”Optimum Life” Machinery Sets If the switching year k falls on a ”non-optimum” year, non-optimum machinery cycles can minimize discounted machinery costs of a tillage system up to the switching year k. The following contrasts discounted costs using a "mixed cycle” and an "optimum life” machinery replacement combination. Inverse Annuity[37]; n -1 (86) ((1 - ((1 + r)) )/r) where r - Interest or discount rate n - Year of analysis Inverse annuity value for cycle year 2 using a 38 interest rate 2 -l ((1 - ((1 + .03) ) /.03) = 1.91347 Inverse annuity value for cycle year 16 using a 38 interest rate 16 -1 ((l - ((1 + .03) ) /.03) = 12.5611 37. Opposite of an annuity value in that an inverse annuity converts annualized values to a present value equivalent 2 6() Inverse annuity value for cycle year 17 using a 38 interest rate 17 -1 ((1 - ((1 + .03) ) /.03) = 13.1661 ”Mixed Cycle” Discounted Net Returns) Use two - 16 year[38] machinery sets and two - 17 year[39] machinery sets to calculate the discounted machinery costs for the first 66 years of the analysis before switching to chisel tillage. The calculations are as follows[40]; Discounted machinery set cycle costs; Discounted moldboard plow machinery set costs after 16 years ($28,7968 x 12.5611) + $15,18788 = $376,896 Discounted moldboard plow machinery set costs after 17 years ($28,81288 x 13.1661) + $13,405“ = $392,747 38. Optimum moldboard plow machinery life cycle as determined in table 6.2 39. Non-optimum moldboard plow machinery sets as determined in table 6.3 40. Notations such as 8, 8, !, A are defined at the end of this section 2(51 Net discounted costs by machinery set cycle; First moldboard plow machinery set8 net discounted costs(present value) after 16 years of use $376,896 - 0 = $376,896 Second moldboard plow machinery set8 net discounted costs(present value) after 16 years of use $376,896 - 15,18788’! = $361,709 Third moldboard plow tillage machinery set8 net discounted costs(net present value) after 17 years of use $392,747 - 15,18788’!! = $377,560 Fourth moldboard plow tillage machinery set8 net discounted costs(net present value) after 17 years of use $392,747 - l3,405"“ = $379,342 Total discounted moldboard tillage costs to switching year 66; $361,709 $377,560 $379,342 $13,405 $836,141 = $376,896 + -------- + -------- + ---------------- 16 32 49 (1+.03) (1+.03) (1+.03) (1+.03) ”Optimum Life Cycle" Discounted Net Returns Use four - 16 year[4l] machinery sets and one - 2 year[42] machinery set to calculate the discounted machinery costs for the first 66 years of the analysis before switching to chisel tillage. 41. Optimum moldboard plow machinery life cycle as determined in table 6.2 42. Non-optimum moldboard plow machinery sets as determined in table 6.2 88 2152 The calculations are as follows[43]: Discounted machinery set cycle costs: Discounted moldboard plow machinery set costs after 16 years ($28,7968 x 12.5611) + $15,18788 = $376,896 Discounted moldboard plow machinery set costs after 2 years ($46,2168! x 1.9347) + $88,3548A = $176,787 Net discounted costs by machinery set cycle: First moldboard plow machinery set8 net discounted costs(present value) after 16 years of use $376,896 - 0 = $376,896 Second, third and fourth moldboard plow machinery set8 net discounted costs(present value) after 16 years of use $376,896 - 15,18788’8! = $361,709 Fifth machinery set8 net discounted costs(net present value) after 2 years of use $176,787 - 15,18788’8A = $161,600 Total discounted moldboard tillage costs to switching year 66; $361,709 $361,709 $361,709 $161,600 ‘842, 110 = $376,896 + -------- + -------- + -------- + ......... 16 32 48 64 (1+.03) (1+.03) (1+.03) (1+.03) $88,354!“ 66 (1+.03) 43. Notations such as 8, 8, !, 2 are defined at the end of this section 2 6,3 8 From cycle year 16, column 2 in table 6.2(annua1ized costs over 16 years minus the slavage value of an identical 16 year machinery set used previously so this salvage value must be added onto the current sets’s costs to obtain the full cost of the set 88From cycle year 17, column 6 in table 6.2 8 Assuming the two - 16 year machinery cycles are used in the first part of the analysis and the two - 17 year machinery cycles are used in the later part of the analysis 88From cycle year 16, column 6 in table 6.2 ! Salvage value from the first 16 year machinery set !!Salvage value from the second 16 year machinery set ‘ From cycle year 17, column 6 in table 6.2 M‘Salvage value from the first 17 year machinery set 88Salvage value of the fourth machinery set - a 17 year machinery cycle 8!From cycle year 2, column 2 in table 6.2 8‘From cycle year 2, column 6 in table 6.2 8!Salvage value from the first, second, and third 16 year machinery sets ~ 8“Sa1vage value from the fourth 16 year machinery set !‘Salvage value from the fifth machinery set - a 2 year machinery cycle These two examples of "mixed cycle” machinery sets(discounted costs - $836,141) and ”optimum cycle" machinery sets(discounted cost - $842,110) used up to the switching year k(year 66) indicates that the ”mixed cycle” has the lowest machinery costs showing that the use of no-optimum machinery cycles are a viable alternative in the minimization of machinery costs up to the switching year k. The work in this analysis will not carry out computations to the point of considering "non-optimum” or ”mixed cycle” for each switching year year k as it is a complex undertaking to to include in the overall analysis but the concept is shown here to indicate how it could be considered. 2 61+ 0.8 Machinery Parameters, Tables 017 - 022 The following section lists the machinery variables used in the model. Parmaters such as fuel use, power requirements, salvage value and repair coefficients are detailed by machine. Estimated annual hours of use for expected machinery life along with MACHSEL least cost machinery sets by tillage system are listed. Appendix Table 017. Diesel Fuel Requirements for Selected Field 265 Operations8 Shredding cornstalks Subsoil chiseling (35.6 cm.) Moldboard plowing (20.3 cm.) Chiseling(20.3 cm.) Offset disking Field cultivating, plowed ground Tandem disking, plowed ground Tandem disking, second time Tandem disking,crnstlks Forming ridges, fall Harrowing, springtooth Harrowing, spike tooth N03 application, no till ground N03 application, plowed ground Field cultivating and planting Strip rotary till and plantar Planting, wheel track Planting, conventional Planting, till Planting, no till Cultivating, disk hiller Cultivating, sweeps Cultivating, rolling tines Rotary hoeing Spraying fertilizers Spraying pesticides Footnotes next page .45 .40 .40 .35 .35 .65 .60 .95 .85 .60 .40 .40 .40 .35 .30 .30 .25 .20 .15 Moderate High Gallons/acre---- — 0.75 0.75 2.10 2.95 1.85 2.60 1.25/‘ 1.75 .95 1.35 .60/' .65 .55 .60 .50 .55 .45 .50 .45 .50 .40 .45 .35 .35 1.05~/ 1.45 .70 J .80 1.05v 1.15 .95 1.05 .65 .70 .50 .50 .50/ .60 .50 .60 .40 .45 .35 .40 .35 .40 .25 .25 .20 .20 .15 '/ .15 266 8 To convert diesel to gasoline equivalent,multip1y by 1.4 88Fuel requirements are averages of tests conducted over a wide range of soils. The actual fuel requirements for a particular field operation in a particular soil type may vary as much as 25 percent or more from the values given. Soil types associated with the draft ratings include: 1ow(sands and sandy loams), moderate(loams and silt loams), and high(c1ay loams and clays). Source: __________ and S.D. Parsons, ‘Energy Requirements for Tillage Planting Systems’, Crop Production with Conservation in the 1980s, Proceedings of the American Society of Agricultural Engineers Conference on Crop Production with Conservation in the 1980s, Dec 1980. Appendix Table 018. Fuel Requirements for Various Rotations and Tillage Systems Light Medium Heavy Rotation soil soi18 soil -------- Gallons/acre88------— Continuous corn, moldboard, fall 6.28 7.15 8.40 Corn-beans, moldboard, fall 4.57 5.54 5.95 Continuous corn, chisel, fall 5.96 6.95 7.10 Corn-soybeans, chisel, fall 5.89 5.69” 5.98 Continuous corn, chisel, spring 5.96 6.95 7.10 Corn-beans, disk, spring 4.46 5.04 4.88 Corn-beans, double chisel, fall 4.61 5.44 6.06 Corn-meadow, moldboard, fall 2.70 3.11 3.65 Corn-meadow, chisel, fall 2.99 3.38 3.79 Corn-meadow, chisel, spring 2.99 3.38 3.79 Continuous corn, no till 3.00 3.35 3.61 Corn-beans, no till 2.44 2.79 3.05 8 These figures are for a central Iowa loam soil. The ”light" and ”heavy" column entries represent adjustments to these basic figures to reflect changes in fuel consumption. For contour tillage, these figures are inflated by 5 percent. Source: George E. Ayers, ”Fuel Requirements for Field Operations” , USDA, Extension Service, Iowa State University, Ames. November 1976. 267 Appendix Table 019. Tractor and Equipment Parameters used in MACHSEL8 Field Field Power requirements Efficiency speed Coarse Medium Fine Combine .55 .70 3.5 --- --- --- Bean puller .65 .75 3.2 2.1 2.5 2.9 Alfalfa .75 .80 4.5 3.7 3.7 3.7 harvester Beet topper .75 .80 4.5 4.2 4.2 4.2 Beet lifter .60 .70 4.5 8.3 9.8 9.8 Subsoiler .74 .88 3.0 9.0 11.0 13.7 Fertilizer .65 .80 5.0 1.0 1.0 1.0 spreader Chisel plow .75 .88 4.4 9.4 10.0 10.6 Moldboard .74 .88 4.4 8.3 11.5 12.6 plow Disk harrow .77 .85 5.0 3.5 4.1 4.4 Heavy .77 .85 4.5 7.4 8.0 8.6 disk Field .75 .85 5.1 4.6 5.0 5.5 cultivator Grain drill .65 .85 4.5 1.4 1.8 1.8 Row planter .60 .76 4.7 2.7 2.9 3.2 Min-till .60 .76 4.0 2.9 3.1 3.4 planter Sprayer .55 .65 6.0 1.2 1.2 1.2 Row .68 .85 3.4 1.6 1.9 2.2 cultivator ' Ammonia .55 .65 4 5 4.1 4.5 5.0 applicator Tractor --- --- --- --- --- --- 8A machinery selection model 268 Appendix Table 019 (cont’d). Tractor ---- ------------------- Implement assignment A($) 0($/ft.) A 0 Combine --- 59,936 2,969 .12 2.1 Bean puller u 4,367 420 .20 1.6 Alfalfa u 4,000 500 .26 1.6 harvester Beet topper n 5,500 900 .23 1.4 Beet lifter t 14,000 1400 .19 1.4 Subsoiler t 1,794 375 .38 1.4 Fertilizer u 0 0 .95 1.3 spreader Chisel plow t - 1,606 835 .38 1.4 Moldboard t - 2,128 1,589 .43 1.8 plow Disk t — 3,741 821 .18 1.7 barrow Heavy t - 1,906 793 .18 1.7 disk Field t - 3,491 446 .30 1.4 cultivator Grain drill u 1,236 545 .54 2.1 Row planter u - 4,520 1,231 .54 2.1 Min-till u - 2,704 1,233 .54 2.1 planter Sprayer u 604 84 .41 1.3 Row u - 1,634 335 .22 2.2 cultivator Ammonia t 0 0 .38 1.4 applicator Tractor - 0 373/hp .012 2 0 8A machinery selection model Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, ‘Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. 269 Appendix Table 020. Machinery Model Parameters8 Parameter Value A88 088 Insurance a shelter rate 18 of new cost Fuel price $ .32/liter Labor price 7.00/hour Machine salvage values Combine .75 .88 Tractor .75 .87 Implements .70 .90 8 Income tax and financing considerations are ignored in the analysis 88Salvage value coefficients used in equation ? Source: R.J. Black, Alan Rotz, Donald Christenson, Hannibal Muhtar, and John Posselius, ‘Results of an Economic Comparison of Conventional and Conservation Tillage Systems in the Southeast Saginaw Bay Coastal Drainage Basin’, East Central Michigan Planning and Development Region’s Southeast Saginaw Bay Monitoring and Evaluation Project. Appendix Table 021. Machine Cultivator Disk harrow Disk plow Moldboard plow Spike-tooth harrow Springtooth barrow Seeder Grain drill Row crop planter Harvesting equipment Rale wagon, automatic Combine, self- propelled Corn picker Cotton picker Cotton stripper Field chopper,pull Hay baler,aux. engine Hay baler,PTO Mower Rake, side Sugarbeet, harvester Windrower, propelled Footnotes at end of self- 2 7‘) Life and Repair Costs of Machines table 2500 2500 2500 2500 2500 2000 1200 1200 2000 2000 2000 2000 2000 2000 2000 2000 2000 2500 2500 2500 Hours per year for wear-out life to equal Average Obsolescence life8 208 167 167 167 167 133 80 80 200 200 200 200 200 200 200 200 200 250 250 250 Repair costs, 8 of new cost Total during per wear-out hour life .048 120 .048 120 .048 120 .080 200 .040 100 .060 120 .075 90 .075 90 .040 80 .025 50 .03888 75 .03588 70 .03088 60 .040 80 .030 60 .040 80 .150 300 .060 150 .030 75 .040 100 Continued 2711 Appendix Table 021 (cont’d). Repair costs, Hours per year 8 of new cost Years Wear- for wear-out --------------------- until out life to equal Average Total during obso- life8 Obsolescence per wear-out Machine lete hours 1ife8 hour life Tractor Wheel type 10 10000 1000 .010 100 Tractor type 15 15000 1000 .007 105 Miscellaneous Fertilizer equip. 8 1200 150 .100 120 Forage blower 12 2000 167 .030 60 Wagon(rubber tire) 15 5000 333 .018 90 8 When average annual use exceeds this number of hours, machine will wear out before it becomes obsolete. 88If a machine is a mounted type, add total of 18 of new cost for each time the machine is mounted and dismounted(normally once a year). 8 Differs from wearout life hours assumed in the determination of the repair coefficients in table 019 but are similar Adapted from: Kepner, R.A., R.0ainer, and R.L. Berger, 1978, ‘Principles of Farm Machinery’, Third Edition, AVI Publishing Company Inc. Westport, Conn. Appendix Table 022. Fuel Use888 Fuel Cost8 Labor Cost88 2772 Machinery Variables8 Moldboard plow $ 4,359.69 Implement Cost Hours Cost Hours Cost Tillage tractor $19,550 413.8 $33,200 276.4 $18,650 Utility tractor 17,900 249.1 17,900 223.8 17,900 Combine 88,884 165.7 88,884 165.7 88,884 Fertilizer spread. 4,440 88.4 4,440 88.4 4,440 0 N03 applicator Moldboard plow 4,228 196.6 Chisel plow 4,093 112.3 Disk harrow 5,598 20.8 5,598A Field cultivator 713 147.8 713 73.9 ___4§>Grain drill 6,550 26.5 6,550 26.5 6,550 Row planter 7,482 85.2 No till planter 9,317 100.1 9,317 Sprayer 2,242 13.0 2,242 78.1 2,242 Row cultivator 1,632 105.3 Tota188 $153,621 662.9 $172,936 500.2 $153,581 11,862 liters $ 4,365.22 8 20.8A 5,348 liters 1,957.24 $ 5,104.33 $ 3,851.50 $ 2,417.00 8 For a 200 hectare corn-corn-soybean-wheat farm with a (Hillsdale sandy loam with a 2-68 slope) soil. 88 Total hours are total tractor hours which equal implement hours. 888From computer printout of machinery selection mode1(MACHSEL), a least cost machinery set selection model as detailed in Muhtar(see references). 8 Total liters times 1.1(oil, .32 per liter. lubrication adjustment) times 8 88 Total tractor hours times 1.1(adjustment factor) times $7.00 per hour. Tandem disked wheat on chisel and no till options coarse 2 7 3 0.9 Phosphorus and Potassium Fertilizer Recommendations, Tables 023 - 028 Appendix Table 023. Annual Phosphorus(P205) and Potassium(K20) Recommendations for Alfalfa Grown on a Mineral Soil Yield, ton/acre 3 - 4 5 - 6 7 - 8 Test Levels ---------- Phosphorus(P) recommendation(lb./acre) 0 19 lb. P/acre 75 100 125 20 - 39 50 75 100 40 - 59 25 50 75 60 - 79 0 25 50 80 - 99 0 0 25 100+ 0 0 0 Potassium(E) recommendations(lb./acre) on sandy loans and loamy sands 0 - 49 lb. K/acre 300 350 400 50 - 99 250 300 350 100 - 149 200 250 300 150 - 199 150 200 250 200 - 249 100 150 200 250 - 299 50 100 150 300 - 349 0 50 100 350 - 399 0 0 50 400+ ‘ 0 0 0 Potassium(E) recommendations(lb.lacre) on loams, clay loams, and clays 0 - 49 lb. K/acre 300 400 500 50 - 99 200 300 400 100 - 149 150 200 300 150 - 199 100 150 200 200 - 249 50 100 150 250 - 299 0 50 100 300 - 349 0 0 50 350+ 0 0 0 Source: D.D.Warnke, ‘Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin E - 550, Dec 1981. 272+ Appendix Table 024. Annual Phosphorus(P205) and Potassium(K20) Recommendations for Small Grains Wheat, bu./acre 25 - 39 40 - 64 65 - 90 Barley, bu./acre 40 - 69 70 - 100 Oats, bu./acre 50 - 79 Test Levels ---------- Phosphorus(P) recommendation(lb./acre) 0 - 19 lb. P/acre 508 75 100 125 20 - 39 25 50 75 100 40 - 59 0 25 50 75 60 - 79 0 0 25 50 80 - 119 0 0 0 25 120+ 0 0 0 0 Potassium(K) recommendations(lb./acre) on sandy lease and loamy sands 0 - 49 lb. K/acre 1008 100 150 150 50 - 99 75 75 100 150 100 - 149 50 50 75 100 150 - 199 25 25 50 75 200 - 249 0 25 25 50 250 - 299 0 0 0 25 300+ 0 0 0 0 Potassium(K) recommendations(lb./acre) on loams, clay loams, and clays 0 - 49 1b. E/acre 1508 150 200 250 50 - 99 100 100 150 200 100 - 149 50 50 100 150 150 - 199 25 25 50 100 200 - 249 0 0 25 50 250 - 299 0 0 0 25 300+ 0 0 0 0 8Recommendations in this column apply to rye, buckwheat, millet, and grass pastures Source: D.D.Warnke, ‘Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin 0 - 550, Dec 1981. 275 Appendix Table 025. Annual Phosphorus(P205) and Potassium(KZO) Recommendations for Dry Beans and Soybeans Grown on a Mineral Soil . Field Beans, cwt./acre 10 - 19 20 - 30 Kidney Beans, cwt./acre 10 - 19 20 - 30 Soybeans, bu./acre 20 - 40 40 - 60 Test Levels 0 - 19 1b. P/acre 50 75 20 - 39 25 50 4o - 59 0 25 60+ 0 0 Potassium(K) recommendations(lb./acre) on sandy loams and loamy sands 0 - 49 lb. K/acre 100 150 50 - 99 75 100 100 - 149 50 75 150 - 199 25 50 200 - 249 0 25 250+ 0 0 Potassium(K) recommendations(lb.lacre) on loams, clay loams, and clays 0 - 49 1b. K/acre 100 150 50 - 99 50 100 100 - 149 25 50 150 - 199 0 25 200+ 0 0 Source: D.D.Warnke, ‘Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin 0 - 550, Dec 1981. _I-I'. _ 2176 Appendix Table 026. Annual Phosphorus(P205) and Potassium(K20) Recommendations for Corn Grown on a Mineral Soil Yield Grain, bu./acre 90-119 120-149 150-179 180-209 210-240 Silage, tons/acre 10-14 15-19 20-24 25-30 0 - 19 1b. P/acre 75 100 125 150 175 20 - 39 50 75 100 125 150 40 - 59 25 5O 75 100 125 60 - 79 25 25 50 75 100 80 - 99 25 25 25 50 75 100 - 119 25 25 25 25 50 120 - 139 0 0 0 25 25 140+ 0 0 0 0 0 Potassium(K) recommendations(lb./acre) on sandy loams and loamy sands 0 - 99 1b. K/acre 150 200 250 300 300 100 - 149 100 150 200 250 275 150 - 199 75 100 150 200 225 200 - 249 50 50 100 150 175 250 - 299 0 25 50 100 125 300 - 349 0 0 0 50 75 350+ 0 0 0 0 0 Potassium(K) recommendations(lb.lacre) on loams, clay loams, and clays 0 - 99 1b. K/acre 150 200 300 400 400 100 - 149 100 150 200 300 350 150 - 199 50 100 150 200 250 200 - 249 0 50 100 150 200 250 - 274 0 0 50 100 150 275 - 299 0 0 0 50 100 300 - 324 0 0 0 0 50 325+ 0 O 0 0 0 Source: D.D.Warnke, ‘Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin E - 550, Dec 1981. 277 Appendix Table 027. Annual Phosphorus(P205) and Potassium(K20) Recommendations for Potatoes Grown on a Mineral Soil Yield, cwt/acre 200 - 349 350 - 449 450 - 550 ---------- Phosphorus(P) recommendation(lb./acre) 0 - 39 lb. P/acre 150 200 250 40 - 79 125 150 200 80 - 119 100 125 175 120 - 169 75 100 150 170 - 219 50 75 125 220 - 269 25 50 100 270 - 319 0 25 75 320 - 399 O 0 50 440+ 0 0 0 Potassium(K) recommendations(lb./acre) on sandy loams and loamy sands 0 - 49 lb. K/acre 300 350 350 50 - 99 250 300 350 100 - 149 200 250 300 150 - 199 150 200 250 200 - 249 100 150 200 250 - 299 50 100 150 300 - 349 0 50 100 350 - 399 0 0 50 400+ 0 0 0 Potassium(K) recommendations(1b./acre) on loams, clay loams, and clays 0 - 49 1b. K/acre 300 400 400 50 - 99 200 300 400 100 - 149 150 200 300 150 - 199 100 150 200 200 - 249 50 100 150 250 - 299 0 50 100 300 - 349 0 0 50 350+ 0 0 0 Source: D.D.Warnke, ‘Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin 8 - 550, Dec 1981. 217 8 Appendix Table 028. Annual Phosphorus(P205) and Potassium(K20) Recommendations for Sugarbeets Grown on a Mineral Soil Yield, ton/acre 18 - 23 24 - 28 0 - 19 1b P/acre 150 200 20 - 39 - 125 150 40 - 59 100 125 60 - 79 75 100 80 - 99 50 75 100 - 119 25 50 120 - 160 0 25 160+ 0 0 Potassium(K) recommendations(lb./acre) on sandy loams and loamy sands 0 - 49 lb. K/acre 200 250 50 - 99 150 200 100 - 149 125 150 150 - 199 100 125 200 - 249 75 100 250 - 299 50 75 300 - 349 0 50 350+ 0 0 Potassium(K) recommendations(lb./acre) on loams, clay loams, and clays 0 — 49 1b. K/acre 200 300 50 - 99 150 200 100 - 149 100 150 150 - 199 75 100 200 - 249 50 75 250 - 299 0 50 300+ 0 0 Source: D.D.Warnke, ‘Fertilizer Recommendations for Vegetable and Field Crops in Michigan’, Michigan State University Extension Bulletin 0 - 550, Dec 1981. 27'9 0.10 Herbicide Use by Crop and Tillage System, Table 029 Appendix Table 029. Projected Herbicide Use for Alternative Tillage Systems Tillage system Crop Moldboard plow Chisel plow No till Corn Pre-emer- Lasso(2 lbs.) Lasso(2.5 lbs.) Lasso(2.5 lbs.) gence8 Atrazine(.5 1b.) Atrazine(.75 lb.) Atrazine(.75 lb.) Bladex(l 1b.) Bladex(l.5 lbs.) Bladex(l.5 lbs.) Paraquat(.5 lb.)8 or Roundup(1.125 lbs.) Pre-emer- Lasso(2 lbs.) Lasso(2.5 lbs.) Lasso(2.5 lbs.) gence88 Bladex(l.5 lbs.) Bladex(2 lbs.) Bladex(2 lbs.) Paraquat(.5 lb.)8 qr Roundup(1.125 lbs.) Soybeans Pre—emer- Lasso(2 lbs.) Lasso(2.5 lbs.) Lasso(2.5 lbs.) gence Lorox(.75 lbs.) Lorox(.75 lbs.) Lorox(.75 lbs.) Paraquat(.5 lb.)8 or Roundup(1.125 lbs.) Post-emer- Basagran(l.5 pt.) Basagran(l.5 pt.) Basagran(l.5 pt.) Blazer(l pt.) Blazer(l pt.) Blazer(l pt.) Crop Oil Conc- Crop Oil Conc- Crop Oil Conc- trate(l pt.) trate(1 pt.) trate(l pt.) Oats 2,4-D(.5 lb.) 2,4-D(.5 lb.) 2,4-D(.5 lb.) Alfalfa Eptam(3.5 pts.) Eptam(3.5 pts.) Eptam(3.5 pts.) Footnotes are on next page (continued) 218 0 Appendix Table 029 (cont’d). Crop Moldboard plow Chisel plow No till Sugarbeets Pre-emer- Pyramin(3 qts.) Pyramin(3 qts.) Pyramin(3 qts.) gence Nortron(2 lbs.) Nortron(2 lbs.) Nortron(2 lbs.) Antor(2 lbs.) Antor(2 lbs.) Antor(2 lbs.) Paraquat(.5 lb.) or Roundup(1.125 lbs.) Post-emer- H-273(.5 1b.) H-273(.5 lb.) H-273(.5 lb.) gence 0etamix(l lb.) 0etamix(l lb.) Betamix(l lb.) Navy Beans Preplant Eptam(2.25 lbs.) Eptam(2.25 lbs.) Dual(2 lbs.) Incorporate Amiben(2 lbs.) Amiben(2 lbs.) Amiben(2 lbs.) or Treflan(.5 1b.) Treflan(.5 lb.) Paraquat(.5 lb.) Pre-emer- (PPI only) (PPI only)88 or gence Roundup(1.125 lbs.) Post-emer- Basagran(l.5 pt.) Basagran(l.5 pt.) Basagran(l.5 pt.) gence Crop Oil Conc- Crop Oil Conc- Crop Oil Conc- trate(l qt.) trate(1 qt.) trate(1 qt.) 8 Preceding all but sugarbeets 88Preceding sugarbeets 8 These rates depict long-run averages. Actual use in a particular year will be dependent upon the specific weed problems and IPM management strategy. 88This treatment requires incorporation to be effective. Although more surface crop residue isusually present following chisel plowing, uniform incorporation should be possible if done properly. This may require a two-pass incorporation program. Source: Dr. James Kells, Department of Crop and Soil Science, Michigan State University, Feb. 1984. 2£31 8.11 Stopping Rule, Tables B30 and B31 The DNCF can be computed out to infinity but this may be costly in time and computer cost. A "stopping rule” can be used to determine the number of time periods to calculate a discounted net cash flow capturing most of the discounted net cash flow possible[44]. Because of the discounting effects, discounted net returns summed after a distant point in time become insignificant in terms' of the total discounted net cash flow. A level of significance or error[45] can arbitrarily selected to determine the number of periods a discounted net cash flow is estimated for a given discount rate. For example: The sum of discounted returns after tine n are only 5x[46] of total discounted net cash flows. Time period n is selected as the terminal DNCF year. DNCF calculations with constant or equal net returns are easy to determine a stopping rule for. The number of time periods for discounted net cash flows with unequal net annual. returns are hard to estimate with the stopping rule and can be estimated by arbitrary methods or an inspection of the net returns. The algebraic derivation of the stopping rule is as follows: 44. Calculations are run out to infinity 45. The calculated discounted net cash flow after period n is in error by a certain percentage of the total possible discounted net cash flow. . 46. The discounted net cash flow calculated for year 1 to n is in error by about 5: of the total possible discounted net cash flows or the calculated discounted net cash flow up to year n captures at least 95s of the total possible discounted net cash flows. 2E32 -n x = ((1 + r) ) n l = x(l + r) log 1 = log x + (n)log(l + r) (n)10¢(1 + r) log 1 - log x Stopping rule equation: (87) n = -(log x)/(log(l + r)) where x r II Error level(percent of a net return equal to one) Discount rate ' Number of years of the discounted net cash flow calculations Using this stopping rule, it is possible to calculate the number of years to compute the discounted net cash flows for various discount rates and error levels as illustrated in the following table; 2 8‘3 Appendix Table B30. Number of Years to Calculate Discounted Net Cash Flows under Different Discount Rate and Error! Levels Discount rate 108 58 18 .58 .018 158 Total DNCFtt 6.66‘ 6.66 6.66 6.66 6.66 Years(n)*tt 16.5 21.4 32.9 37.9 65.9 DNCF(n)# 5.99 6.36 6.60 6.63 6.659 8 of total 908 958 998 99.58 99.998 DNCFtt 108 Total DNCF88 10.0 10.0 10.0 10.0 10.0 Years(n)8tt 24.2 31.4 48.3 55.6 96.6 DNCF(n)# 9.0 9.5 9.9 9.95 9.999 8 of total 908 958 998 99.58 99.998 DNCF?! 58 Total DNCFtt 20.0 20.0 20.0 20.0 20.0 Years(n)*8¥ 47.2 61.4 94.4 108.6 188.8 DNCF(n)# 18.0 19.0 19.8 19.9 19.998 8 of total 908 958 998 99.58 99.998 DNCF!‘ l8 ‘ Total DNCFtt 100.0 100.0 100.0 100.0 100.0 Years(n)txx 231.4 301.1 462.8 532.5 925.6 DNCF(n)# 90.0 95.0 99.0 99.5 99.99 8 of total 908 958 998 99.58 99.998 DNCF?! 8 The amount of error in the estimated discounted cash flows computed for n years relative to the true discounted net cash flow value if computed for an infinite time horizon. 8* Net present value of a $1 yearly return calculated out to infinity using the indicated discount rate(r). The formulation used to calculate the value of this infinite cash flow stream is l/r. 2 81+ *ttYears to run the discounted net cash flow calculations to capture the discounted net cash flows as a percentage of the total possible discounted net cash flows within the error level indicated. These years are calulated using the stopping rule developed in equation B7. 0 Sum of discounted net returns or cash flows up to the nth year assuming an undiscounted yearly net return of $1. ## Sun of discounted returns as a percent(8) of total possible returns(DNCF(n) divided by the total DNCF) As can be seen in appendix table B30, the use of high error levels(108, 58) and discount rates(.15, .10) result in relatively low nunbers of time periods to calculate the discounted net cash flows. In contrast, low error levels(.58, .018) and discount rates(.Ol, .05) result in high numbers of time periods to calculated the discounted net cash flows. Further calculations using a .05 error level(appendix table B30, column 2) result in the number of time periods estimating the discounted net cash flows which estimate(assuming equal returns) about 958(rows 4, 8, 12, 16) of the total DNCFs[47] illustrating that the calculated discounted net cash flows are within 58 of the total possible discounted net cash flows. A 18 error level calculates the number of years to estimate about 998 of the total possible discounted net cash flows. The error levels used are up to the user who trades off between calculation costs and error of the discounted net cash flows. 47. Yearly net returns divided by the interest rate if run to infinity 2 8.5 Table 331: Interest Rates from 1962 thru 1983 U.S. treasury bonds & notes“ Real interest rate+ Treasury Long Treasury Year bills! 5 year term# CPI” bills 5 year CPI 1983 8.61 10.80 10.84 2.90 5.71 7.90 7.94 1982 10.61 13.01 12.23 4.20 6.41 8.81 8.03 1981 14.03 14.24 12.87 10.20 3.83 4.04 2.67 1980 11.43 11.48 10.81 12.60 -1.17 -1.12 -1.79 1979 10.07 9.52 8.74 12.10 -2.03 -2.58 -3.36 1978 7.19 8.32 7.89 8.90 -l.71 - .58 -1.01 1977 5.27 6.99 7.06 6.50 -1.23 .49 .56 1976 4.98 7.18 6.78 5.30 -.32 1.88 1.48 1975 5.80 7.77 6.98 9.14 -3.34 -1.37 -2.16 1974 7.84 7.80 6.99 10.97 -3.13 -3.17 -3.98 1973 7.03 6.92 6.30 6.23 .80 .69 .07 1972 4.07 5.85 5.63 3.38 .69 2.47 2.25 1971 4.33 5.77 5.74 4.21 .12 1.56 1.53 1970 6.39 7.37 6.59 5.92 .47 1.45 .67 1969 6.67 6.85 6.10 5.37 1.30 1.48 .73 1968 5.34 5.59 5.25 4.20 1.14 1.39 1.05 1967 4.29 5.07 4.85 2.88 1.41 2.19 1.97 1966 4.86 5.16 4.66 2.86 2.00 2.30 1.80 1965 3.95 4.22 4.21 1.72 2.23 2.50 2.49 1964 3.54 4.06 4.15 1.31 2.23 2.75 2.84 1963 3.16 3.72 4.00 1.21 1.95 2.51 2.79 1962 2.77 3.57 3.95 1.12 1.65 2.45 2.83 * U.S. treasury bills(3 month secondary market). Average of all calender days - market yield. Bank discount basis rather than investment yield Yields adjusted to constant maturities by U.S. treasury based on recently traded securities 4 Composite long term. Unweighted averages of bonds not due on calleable in less than 10 years Consumer price index(all items) + Bill, note or bond rate minus the consumer price index. ~ Source: Brain Motley, 'Eeal Interest Rates, Money, . Government Deficit’ Economic Review. No. 3, Federal Reserve Ban of San Francisco, Sumner 1983, pp. 31 - 45. 2E36 Percent 20 r- 15- 10 .— 91-day Treasury Bills. . - Jami . w 0 . . :"vqw -10. 1953 1955 1960 1965 1970 1975 1900 1982 Figure 82: Short Term Real Interest Rates Source: Federal Reserve Bulletin, Vols. 57 - 70, U.S. Federal Reserve, 1970 - 1984. 2&3? Appendix Table 832: First Year Corn Crop Cost Budget by Tillage Moldbrd. Plow Chisel Plow No - Till Unit — — Parmaters Price Qty. Amounts Qty. Amounts Qty. Amounts Yield(bu.) $ 2.65 113 $299.45 119 $315.35 124 $328.60 Expenses Seed(lb.) 1.25 12 15.00 13 16.25 14 17.50 NE3(1b.) .16 129.3” 20.69 129.3~ 20.69 129.3~ 20.69 Phosphorus .21 25.0 5.25 25.0 5.25 25.0 5.25 (1b.) 39.6“ 8.32“ 41.6“ 8.73“ 43.4“ 9.11“ Potassium .12 75.0 9.00 75.0 9.00 75.0 9.00 (1b.) 30.5“ 3.66“ 32.1“ 3.85“ 33.5“ 4.02“ Lime8(ton) 12.00 .11 1.27 .11 1.27 .11 1.27 Lasso(lb.) 5.36 2.0 10.27 2.5 13.40 2.5 13.40 Atrazine(lb.) 2.08 .5 1.04 .75 1.56 .75 1.56 Bladex(lb.) 3.65 1.0 3.65 1.5 5.48 1.5 5.48 Roundup 25.97 1.18 30.51 (1b.) Raulingtt .20 113.0 22.60 119.0 23.80 124.0 24.80 (bu.) Dryingtt .30 113.0 33.90 119.0 35.70 124.0 37.20 (bu.) Building 2.50 1.0 2.50 1.0 2.50 1.0 2.50 repairstt (acre) Utilities/ .025 113.0 2.77 119.0 2.92 124.0 3.04 phonett (bu.) Marketing** .015 113.0 1.70 119.0 1.79 124.0 1.86 Totals $130.09 $139.61 8174.06 127.82“ 137.94“ 172.94“ See NR3 use rate calculations in chapter 7 Costs from year 6 and onward 8 Limestone is supplied at the rate of 1.8 lbs. of limestone per 1b. of nitrogen by assumption in chapter 7 *8 From Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James E. Hilker and Allen B. Shapley, “Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. 2:8 8 Appendix Table B33. Second Year Corn Crop Cost Budget by Tillage Moldbrd. Plow Chisel Plow No - Till Unit --------------------------------------- Parmaters Price Qty. Amounts Qty. Amounts Qty. Amounts Income Yie1d(bu.) $ 2.65 106 $280.90 111 $294.15 116 $307.40 Expenses Seed(lb.) 1.25 12 15.00 13 16.25 14 17.50 NE3(1b.) .16 129.3” 20.69 129.3” 20.69 129.3~ 20.69 Phosphorus .21 25.0 5.25 25.0 5.25 25.0 5.25 (1b.) 33.1“ 7.79“ 38.8“ 8.14“ 40.6“ 8.53“ Potassium .12 75.0 9.00 75.0 9.00 75.0 9.00 (1b.) 28.6“ 3.43“ 30.0“ 3.60“ 31.3“ 3.76“ Limet(ton) 12.00 .11 1.27 .11 1.27 .11 1.27 Lasso(lb.) 5.36 2.0 10.27 2.5 13.40 2.5 13.40 Atrazine(lb.) 2.08 .5 1.04 .75 1.56 .75 1.56 Bladex(lb.) 3.65 1.0 3.65 1.5 5.48 1.5 5.48 Roundup 25.97 1.18 30.51 (1b.) Furadant 48.35 .093 4.53 .14 6.77 .186 8.99 (881-) Baulingtt .20 106 21.20 111 2.20 116 23.20 (bu.) Dryingtt .30 106 21.20 111 33.20 116 34.80 (bu.) Building 2.50 1 2.50 1 2.50 l 2.50 repairstt (acre) . Utilities/ .025 106 2.60 111 2.72 116 2.84 phonett (bu.) Marketingtt .015 106 1.59 111 1.67 116 1.74 Totals $130.84 $142.06 $178.73 127.81“ 139.55“ 176.77“ See NR3 use rate calculations in chapter 7 Costs fron year 6 and onward 8 Limestone is supplied at the rate of 1.8 lbs. lb. of nitrogen by assumption in chapter 7 S 36 fl. oz. every three years on second year corn(rates are doubled for no - till and 1.5 times for chisel plow it From Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, 'Estimated Crop and Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. ~ A of limestone per 2 8 9 Appendix Table B34. Soybean Crop Cost Budget by Tillage Moldbrd. Plow Chisel Plow No - Till Unit - Parmaters Price Qty. Amounts Qty. Amounts Qty. Amounts Income Yie1d(bu.) $ 7.14 43.0 $307.20 44.0 $314.16 48.0 $342.72 Expenses Seed(lb.) .28 60.0 16.80 64.0 17.92 68.0 19.04 Nitrogen(1b.) .24 8.0 1.92 8.0 1.92 8.0 1.92 Phosphorus .16 - - - - - - (1b.) 38.7“ 6.19“ 39.6“ 6.34“ 43.2“ 6.91“ Potassium .21 25.0 5.25 25.0 5.25 25.0 5.25 (1b.) 60.2“ 12.64“ 61.6“ 12.94“ 67.2“ 14.11“ Lime! 12.00 .006 .08 .006 .08 .006 .08 (ton) Lorox 10.00 .75 7.50 .75 7.50 .75 7.50 Basagram 10.30 1.5 15.45 1.5 15.45 1.5 15.45 (pt) Blazer(pt.) 9.32 1.0 9.32 1.0 9.32 1.0 9.32 Crop Oil .50 1.0 .50 1.0 .50 1.0 .50 (pt) Lasso(lb.) 5.36 2.0 11.72 2.5 13.40 2.5 13.40 Roundup(1b.) 25.97 1.125 29.22 Eaulingtt .20 43 8.60 44 8.80 48.0 9.60 (bu.) . Building 1.50 1.0 1.50 1.0 1.50 1.0 1.50 repairstt (acre) Utilities/ .27 43 11.47 44 11.73 48 12.80 phonett (bu.) Marketing** .015 43 .65 44 .66 48 .72 (bu.) Totals $ 90.44 $ 94.03 $126.30 104.02“ 108.06“ 142.07“ Costs from year 6 and onward # One lb. applied every four years for infestations , rates are increased by 1.5 for chisel plow and doubled for no - till 8 Limestone is supplied at the rate of 1.8 lbs. of limestone per 1b. of nitrogen by assumption in chapter 7 88 From Sherrill B. Nott, Gerald D. Schwab, Myron P. Kelsey, James H. Hilker and Allen E. Shapley, 'Estimated Crop and Livestock Budgets for Michigan - 1984’, Report 446, Michigan State University, Agricultural Economics 1984. Appendix Table B35. To Costs from year 6 and onward Limestone is supplied at the rate of 1.8 lbs. *8 Yie1d(bu.) penses Seed(bu.) Nitrogen (1b.) Phosphorus (1b.) Potassium (1b.) Limet (ton) 2,4-D(1b.) Baulingtt (bu.) Building repairstt (acre) Utilities/ phonext (bu.) Marketingtt tals 9.00 .24 .21 .12 12.00 15.19 .20 1.50 .27 .015 2 9‘) Moldbrd. Plow Qty. Amounts 43 $160.82 2.4 64 26.7“ 50.0 16.3“ .052 .5 43.0 1.0 21.60 15.36 5.61“ 6.00 1.96“ .63 7.60 8.60 1.50 Wheat Crop Cost Budget by Tillage Qty Amounts 43 $160.82 2.4 21.60 64 15.36 26.7“ 5.61“ 50.0 6.00 16.3“ 1.96“ .052 .63 .5 7.60 43.0 8.60 1.0 1.50 43.0 1.15 43.0 .65 $63.99 65.56“ No - Till Qty. Amounts 43 $160.82 2.4 21.60 64 15.36 26.7“ 5.61“ 50.0 6.00 16.3“ 1.96“ .052 .63 .5 7.60 43.0 8.60 1.0 1.50 43.0 1.15 43.0 .65 $65.79 67.36“ lb. of nitrogen by assumption in chapter 7 From Sherrill B. Nott, James R. Hilker and Allen E. Shapley, Livestock Budgets for Michigan - 1984’, Agricultural Economics Report 446, Michigan State University, 1984. of limestone per Gerald D. Schwab, Myron P. Kelsey, 'Estimated Crop and Appendix C Discounted Net Cash Flow Program calculation flow charts This appendix lists the OPT LIF program, of OPT LIF, and the input/output requirements of OPT LIF. 2'91 l'l 2 9 2 OPT LIF ngggag as Used on a HP‘hl - CX Calculator Bros Co 8 ? PROMPT STO 68 Cost A I ? PROMPT STO 74 Set sachinery cycle to -1 Interest rate Analysis year Errious user r Salvage value of previous aachinery set Gross returns per acre squivilant Erosion coefficient Crop costs year 1 - 5 Figure BB: OPT LIF Pregran 5% 3 I'" I 0 AA cost 8 ? TT cost a ? STO.28 UT cost a ? PROMPT STO O9 prompted init w cos Coabine new cost Chisel plow new cost Moldboard plow new cost Disk harrow new cost Field Cultivator new cost Grain drill new cost Row Cultivator new cost Aamonia Applicator new cost Tillage Tractor new cost Utility tractor new cost b E 33539 §§3§§ 8 ll '0 II ‘0 §se§s msgaaggaggag §§s§§$ 3333 8%58 293 reanae same ‘User prompted or): hours of Coabine hours Qiisel plow hours Moldboard plow hours Diskharrow hours Field cultivator hours Grain drill hours Row planter hours Minimtill pluxterhom-s Sprayerhours Row Cultivator hours A-onia Appli- cator hours Tillage tractor hours Utility tractor hours salvage repair 301. 10 RCL 17 + RCL 18 RCL 19 + RCL 21 RC1. 22 RCL 23 + RCL 24 Descri ion omputation of sach- set newand 122E Descrigtion m Descri ion 4. RCL 25 Salvage value of + previous nadiinery set RCL 26 .1. Calculation of various 301' 27 discounting, repair RCL 28 anth factors + RCL 09 + f yx $10 67 2‘ M(O)(i) 1/x n -1 .01 1:1 s10 60 ((1+r) ) x 1 STO 71 Insurance cost RCL 60 “Calculation of - Jim W 17 RCL 01 set cost in " ‘3' $2: 33 “I 3"” :3; 32 r/(1-((1+r)")'1> 1 5 + RCL 90 3m 05 1 + r y" RCL 02 1/x _1 1 s10 61 ((1+r)°) RCL 05 RCL 61 x i y - RCL 01 x (UHF-1Y1 x’ly m 67 / 62 /< (< fl“) 1: STO r 1- 1+1- sro 69 gum“) air. 60 1 x (1+r)”’1 s10 20 (1+r)n R61. 56 RCL 90 1000 - / RCL 75 S10 61+ j/iOOO x RCL 90 RCL 05 x’i'y . x 1000 1 3411‘. 78 $10 65 __ 3-1/1000 x RCL 69 xry ' j f -1 (t 1) 3m 70 _ Zn(o)(1)/(1+r)n - SV(n-1)/(1+r)° ' i=1 RCL 6h RC1. 80 X RC1. 30 STO 11 R01. 65 RCL 80 X RCL 30 RCL 11 x >. y RCL 29 X RCL 10 X R01. 60 ST+63 RCL 6h RC1. 87 801. 35 STO 13 30L 65 PCL 87 x RCL 35 RCL 13 xty RC1. 37 x RCL 17 x RCL 6O ST+63 Description Ealcul'ation of _yearly coabine Mi— alculation of arly chisel low repair 295 m RCL 64+ RCL 88 x RCL 41 x 1 310 14 ROI. 65 R61. 88 X HOT. 41 X 301. 14 x’I-y RC1. 40 X RC1. 18 x RC1. 60 X W63 RCL 64 R01. 89 x R01. 43 ’x STO 15 RCL 65 301. 89 X RCL 43 ’x RCL 15 x ’1 y R01. £12 X RC1. 19 1 RC]. 60 x SIN-63 Descri ion alculation of yearly 1 ldboard plow repair ‘ mcuhtion of yearly I 296 35.222 Description m Descrifiion RCL 6h alculation of RCL 6h _glculstion of yearly RCL 91 arly field RCL 93 w planter repair co 1: cultivator re x RCL 35 cost RCL 30 x x y 7 STO & 8'10 89 RCL 65 RCL 65 RCL 91 RCL 93 x x RCL 35 RC1. y ya: ’x RCL 82 RCL 811 x>4y x", RCL an RCL £15 1: x RCL 21 RC1. 23 x x RCL 6o RCL 60 x 1: M3 -- 31163 J. RCL 611 Calculation of RCL 64 Calculation of yearly RCL % yearly grain RCL 91+ siniaus till planter x x RCL 30 RCL 30 ’x ’x STO 83 STO 85 RCL 65 RCL 65 RCL % RC1. 9* x x RCL 30 RC1. 30 yr RCL 83 RCL 85 xiy 3‘! RCL as RCL 115 x x RCL 22 RCL 24 x x RCL 6o RCL 60 x x W63 .. S's-63 _L £2522 RCL 611 RCL 95 X RCL 39 X y STO 31 RC1. 65 RCL 95 X RCL 39 yX RCL 31 1: >2 y RCL 1+6 X RCL 25 X RCL 60 x SW63 RCL 6h RCL 96 X RCL 118 X 3m 32 RCL 65 RCL 96 X RCL 48 Y RCL 32 xty RCL 1.7 X RCL 26 X RCL 6o 5M3 rue-em Calculation of Calculation of yearly row cult- ivator repair cost 97 m RCL 69 RCL 97 X RCL 35 X STO 33 RCL 65 RCL 97 X RC1. 35 yX RCL 33 x’cy RCL 3? X RCL 27 X RCL 60 X SIN-63 RCL 6h RCL 98 x RCL 50 X STO 3b RCL 65 RCL 98 x RCL 50 I RCL 34 x l y RCL 49 1: Hal 28 x RCL 60 x ST+63 Descrifiion Calculation of yearly onia applicator Calculation of yearly u-m '1 1 +_v tractor repair cost 122522 RCL 61+ RCL 99 X RCLSO X y ST036 RCL 65 RCL 99 x RCL 50 x 7 RCL 36 x’:y RCL 49 X RCL 28 X RCL 60 X STO-63 O STO 77 RCL 55 RCL 90 x ! RCL 9 X 9m 66 RCL 53 RCL 90 yX RCL 51 1: RC1. 10 1: ST??? RCL 5h RCL 90 x y RCL 51 X RCL 28 1: ST??? Description Calculation of 3 arly tillage tractor repair Calculation of yearly sal 298 m RCL 59 RCL 90 ’X RCL 51 x RCL 09 x ST??? RCL 17 RCL 18 + RCL l9 4. RCL 21 + RCL 22 + RCL 23 4. SCI. 24 + RCL 25 + RCL 26 + RCL 27 4. RCL66 X ST”? RCL 71 RCL 06 q. RCL 07 4. RC1. 60 X SIN-72 RCL (2 RCL 68 x 100 X! .01 X STO 79 Description costs .. .01(100-n(w)) LBL9l+ 222mm 8 return calculation l P(n,n)Y(n,1) Net return i ’ calculation 299 25228.1- RCL 67 RCL 00 81976 1.31. 95 RCL 7O LBL 96 RCL 76 RCL 63 + RCL 72 STO 59 RCL 86 xay DNCF ARCL X AVIEH STO 57 RCL 57 RCL 12 ABC]. AVIEU RCL 57 RCL 58 17y? GT0 55 GTO56 LBL 55 RCL 57 'RCL58 RCL 20 CASHF'LOH ARCL X AVIEU Descrifiion e or cash ow m RCL 61 $1916 RCL 16 9C1. 62 .MMLX AWE” 1.81.56 RCLS? SH358 RCL90 Fq? Gfl357 IBL57 RCL 59 RCL O3 RCL 20 tracmsr .MMLX ANDW IBL58 RCL 59 SNDO3 FwLCR x31? GTOBH STO 7o RCL 02 3N1“! RCL 90 STO90 RCL 56 x£y? CTO 22 CTO 20 Ibmnfi ion Mmmflium¢adur cum2now 'hnfib'umhhnny ant 3()O IBLZZ 8T016 s10 90 RCL 77 s'ro 78 RCL 75 31075 (3023 IELBH lnmmmnmuzcadur unhnmsyune mm mfcnfle Duumufinnt $fltflg¢amhayuu' F‘haomaaniimunmuflhm: nachanmflmrluwmn 3 0 0 r #Ainter Inputs into Fragram AJ [ caIEElate Year - Y:.(n7 } ‘ n .ear 1 Calculate Yearly Lachinery Repair Costs - Re;.(n) :1 Calculate Machinery Set Salvage n=CS*r.' ,lrint Salvage Value in Year j - 3V(j) Yes Value I C Calculate Crap Returns Inci- uding Changes in 5011 Prod- T uctivity - CR(n) 1 Calculate Yearly fiachinery Insurance Costs - Ins.(n) Calculate Yearly Iiscounted Net Print D13? I * Cash Flow in Year a - DNCF(n) l . calculate Annualized D13: in _ Print Anlhli Year n - AnDIJCI (n, . no Calculate Nachinery Annuity In l_1§51§t Ln\;) Year j " 1214:]; 1 . "-'_________,.- 51 n s l Calculate Yearly Lachinery Irint LC 5 Cost - EC(n) _47.F L-i Yearly Cash Flow - CF(n) Print c; C[ , n'$ 1:3? year of analysis n, = next year of analysis [:3- Commari j; = machinery cycle year <37- Decisicn 51 = next machinery cycle yea: co = machinery cycle - 4’ = Figure 39. Flow Chart for on LIF D Pun“ °1 "9’“ “Chine” cycle © - Terminate 8* = last year of machinery cycle 302 n res' v | ours' _ osts - M(O)(i) - f - 01‘ epair Coefficients - 1 B 1) m f A(l)((i! Toohgflo‘lz)3(1) _ ((3:12 xoggs.‘12)3(1)) Rep-(11)}= <2 ( ) i=1 (1 +' r)” Figure 135: Flow Chart for Yearly Machinery Repair Costs in OPT LIF Progra- Individual Machinery vageValue Geoff-‘5] le Cycle Year - 3 ' Costs - N 0)(i) icients - C(i), i) iNachineg Set Sal-is?!r Vane - 87”! f lsv<1>= : maximum)J _ 151 FigureB6: Flow alert for Machinery Set Salvage Value in OPT LIF Program Salvage Value at of Cycle - SV(S*) Yearly Miner-y Re- —‘1 Costs - Rep(n) N r a Z " ° 143.1211.)I1 . ”C(S ). i=1 :(uu) ”1 (in-)3”) Year Cost - m n 4“ Is(n) . m(ss)2) ., Ho(n)” ,. in (MN (1 + r)“ W' ring“; - Ev”; ,, 2‘0 'CycT' Repair Cost in 5) 7.1.2341 1“” 1' “PK-l) J Year COR -iA‘ ) 5)) 14 um) - (1 > (2: - <1>+ —f3—+ 553%) g l 1 7.1:: a - (é(-(m‘ln + 181.1! + .01‘H‘02‘1>))))):‘ i=1 (1 + r) I FigureB'a FloleartforYearlyNaohinsryCostandAnnlalissdHacl-lineryCost byCycleYearinOPTLIl-‘program 1) Calculated in figure for 8* years(length of machinery cycle) 2) I w 3) ‘33-? " n " (yearof:.nalysis) "J : (yearofsechinerycycle) 3oz. Crop Cross ' Erosion Coeff- You-1y m- Eq' 'ui'vT' Returns - icient - w ineryfiost - ilants - P (.111)!th ) _ . ”C(11) 8 yes "'1 1 Year Discounted Net Cash Flow - DNCF n mayo-1): NC(n)1)+ {p( P n n Y a 1 1-.01 100-n w - 00 a n acres )J , ’1 (1 + r)n W Yearly Camila" (____._.r 1 ) (DNCF(R)) = AnDNCF CF 3 (1 + 1.)!1 (WF(D) _ DNCF(n-1)) 1- (1..)5 FigureBB: Flow Chart for Discounted Net Cash flow(DNCF), Annualised DNCF, and YearlyCashFlowinOPTIfl‘prcgru 1) Calculated in figure B7 for n years(year of amlysis) 305 C.l OPT LIF Program Inputs and Outputs 1. Inputs OPT LIF Prompt A. Starting year of analysis - n -----YR-;-;--- B. Starting year of machinery cycle - 3 CYL YR = ? C. Machinery set cycle length - S CY LNTE = ? D. Interest rate - r R = ? E. Machinery set fuel cost (liters x 1.1 x FUEL = ? $.32/1iter - FC(n) F. Machinery set labor cost (total tractor LABOR = ? hours x 1.15 x $7.00) - LC(n) G. Salvage price of previous machinery set SV MACB = ? R. Initial machinery costs - M(O)(i) 1. Coabine CO M0 = ? 2. Moldboard Plow MP M0 = ? 3. Chisel Plow CP M0 = ? 4. Disk Barrow DB M0 = ? 5. Field Cultivator FC M0 = ? 6. Grain Drill GD M0 = ? 7. Row Planter RP M0 = ? 8. Minimum Till Planter MTP M0 = ? 9. Sprayer SP M0 = ? 10. Row Cultivator RC M0 = ? ll. Ammonia Applicator AA M0 = ? 12. Tillage Tractor TT M0 = ? 13. Utility Tractor UT M0 = ? 306 1. Machine yearly use hours - hrs. (1) OPT LIF Prompt 1. Combine CO HRS = ? 2. Moldboard Plow MP HRS = ? 3. Chisel Plow CP HRS = ? 4. Disk Harrow DH HRS = ? 5. Field Cultivator FC HRS = ? 6. Grain Drill CD HRS = ? 7. Row Planter RP HRS = ? 8. Minimum Till Planter MTP HRS = ? 9. Sprayer SP HRS = ? 10. Row Cultivator RC HRS = ? ll. Ammonia Applicator AA HRS = ? 12. Tillage Tractor TT HRS = ? 13. Utility Tractor J. Crop Cross Returns (Yield level x Crop CRY A = ? price for a corn-corn-soybean-wheat rotation, sum of P(m,n)Y(m,1) K. Crop costs (year 1-5) - OC(m,n) OC A = ? L. Crop costs (year 6 to infinity - OC(m,n) 00 B = ? M. Acre equivalents (total farm acres divided ACRES = ? by number of crops in the rotation) N. Erosion coefficient (change in soil YLD CFA = ? productivity per year ) - w Outputs A. Year of analysis - n YEAR = B. Salvage value of the machinery set in SY MCB = each year of the machinery cycle - SV(j) C. Yearly machinery cost - MC(n) MCH CST = D. Annualized machinery cost by cycle AN MACH = year - MA(J) E. Discounted net cash flow - DNCF(n) DNCF = F. Annualized discounted net cash flow - An DNCF = AnDNCF(n) - G. Yearly cash flow - CF(n) CSH FLO B IBLIOGRAPHY BIBLIOGRAPHY American Society of Agricultural Engineers. 1982-1983 Agricultural Engineers Yearbook. St. Joseph, Michigan, pp. 209-221. 1982. Aarstad, J.S. and D.E. Miller. "Effects of Small Amounts of Residue on Furrow Erosion.” American Soil Science Society Journal Vol. 34. 1980. Alberta, E.E., R.H. Neibling, and 8.0. 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