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FINES will be charged if book —is returned after the date stamped below. l tl/#;7 :Ti>vé m 11,8?”fl| .wnf? g? §I195730ffir7 1300 [1183 EUL 0 916.111! ‘ 21.0 ‘(d'tW‘IJ ~J DEVELOPMENT OF A FIELD MACHINERY SELECTION MODEL BY Francis J. Wolak A DISSERTATION Submitted to Michigan State university in partial fulfillment of the requirements for the degree of ' DOCTOR 0F PHILOSOPIIY Department of Agricultural Engineering 1981 ABSTRACT DEVELOPMENT OF A FIELD MACHINERY SELECTION MODEL BY Francis J. Welak Michigan's Saginaw Valley Farmers face problems of non-increasing navy bean yields and soil erosion. Many solutions have been proposed 1 to these problems. A.need exists for analysis of management strategies in production agriculture. A thorough analysis will hasten the adoption of socially and individually attractive production practices. A major component of the farm is the maehinery complement require- ment. A computer model was developed which selects field machinery capable of satisfying the requirement. The model considers seven crops: alfalfa, corn, navy beans, oats, soybeans, sugar beets, and wheat. Labor supply can vary between 1 and 2 full time operators. Machineryh technology includes self-propelled combines and fourdwheel drive I tillage tractors. ri Model input includes: farm size, crap rotation, field operation 7 date constraints, labor supply, and available workday data. The number and size of machines to complete all field work within prescribed date constraints is determined. The average annual cost of individual machines and total complement is determined. Average annual cost Francis J. Wolak includes aspects of: depreciation, interest, repairs, shelter, insurance, and fuel use. The deterministic model uses standard engineering techniques to match machine productivity to available time. Available time is a function of available workday data, workday length, and labor supply. Machine productivity depends upon: assumed available machine sizes, allowable operating speeds, implement draft, and machine efficiencies. The model increments machine complement productivity. For each productivity step a weekly work schedule is developed. The smallest machinery complement which produces a satisfactory work schedule is selected as the required machinery set. Once a complete weekly work schedule is developed a second machinery complement is determined by oversizing the combine if possible. The harvester oversizing may allow more time for fall tillage to take place and thus allow a relatively smaller tractor—implement set to be chosen. The tractor-implement combination is resized. The O smallest total cost determines which of the two developed machinery ? , 1 sets is designated as the required machinery set. The model is unique in its measure of the risk associated with lgselected machinery complements. Using actual yearly available workday data, the model selects a machinery set for each year of data. The selected machinery sets are ranked on a cost per hectare basis. Risk issues can be addressed by analyzing the relative rankings of the selected machinery complements. The model can, if desired, consider one available workday data set. Results show the use of one available workday data set, a Francis J. Wolak statistical grouping of several yearly data sets, to be inferior in its measure of risk when compared to generation of machinery sets for each year of actual data. Model results show rotations containing alfalfa and/or sugar beets to exhibit the highest machinery costs. A whole farm analysis of crop rotation shows the C-NB-NB-BT (corn-navy bean-navy bean—sugar beet) rotation to have the highest net return to land, for considered rotations. Model results were sensitive to several model parameters and inputs. The deletion of fall cornstalk disking reduced required machinery cost $60.00 per hectare on a 162 hectare O/ArA-AeC-C-NB-C (oats/a1falfa-alfalfa-alfalfa-corn-corn-navy bean-corn) farm. Two extra spring field cultivations increased machinery cost $46.00 per hectare on a 162 hectare C-C-NB-W (corn-corn-navy bean-wheat) farm. Varying the available workday data set resulted in a $60.00 per hectare difference between data set extremes on a 162 hectare NB-C-SB (navy bean—corn-soybean) farm. Approved by: jor Pro essor ACKNOWLEDGMENTS The author wishes to express his sincere gratitude to the following: Dr. T. H. Burkhardt, the author's major professor and committee chairman, for providing encouragement and guidance. Dr. J. R. Black who served on the authoris guidance committee, for his poignant comments and unyielding encouragement. Dr. D. R. Christenson, Dr. L. J. Connor, and Dr. B. A. Stout who also served on the author's guidance committee, for their helpful suggestions and continued interest. Dr. J. B. Holtman for providing the author with his initial spark. ii TABLE OF CONTENTS Page LIST OF TABLES O 0 O O I O O O O 000000000000 O O O O O O O O O O O O O O O O 0 O O O O O O O 0 O O 0 v LIST OF FIGURES O O O O O O O O O O O O O O 0 O O O 0 O O O O O O O I O O O O O O O O O O O O O O O O O O O O O O V11 LIST OF SMOLS ......OOOOOOO.....OOOOOOOOOO00.0.0000.........OOOViii Chapter 1. IMRODUQION OOOIOOOOOOOOOO......OOOOOOOO......OOOOOOOOOOO l 1 Problem ............................................ 2 Conceptual Paradigm .3 Machinery‘as a Subsystem 4 Previous Investigation 5 Problem Statement .................... .............. G‘s-‘NHH N tr: 1:; I." U MAGIINERY SELEQIONMODEL ......OOIOOOOOOOOOOOO ..... 8 FMSM Program ............ ...... ...... ............... 12 Subroutine TIME ........... ..... ....... . ........... . 16 Power Unit Sizing .................................. 19 Subroutine CAPCITY ................................. 24 Subroutine CMBSCHD 27 Subroutine WRKSCHD ................................. 32 subroutine PREVOP .................... ..... .. ..... .. 37 Subroutine ALCATE ..... . 39 Subroutine SIZING .................................. 42 Subroutine FUEL .................................... 44 Subroutine COST . 46 Model Hierarchy ...... ............ .................. 49 HHI—IQQNO‘Mkle-l NHO NNNNNNNNNNNN 3. mDEL PAWTER AND INPUT DEWLOMM . O. O O. CO. C ........ O O 52 3.1 MaChj-nery Paramters ......OOOOOOOOOOOO ...... 0...... 53 3.1.1 Machine Productivity Parameters ............. 53 3.1.2 Machinery Economic Data ........ ............ . 60 Available Workdays .. ..... .............. ...... ...... 64 Farm Identification ................. . .............. 73 WW DON iii Chapter Page 1., ANALYSIS OF DESIGN PROBABILITY CALCULATION METHODS . . . .. . . 76 5. RESIILTS 0.0.0.0..........OOOOOOOOOOO.....OOOOOOOOOOOOOOOO. 89 5.1 mt ktums to Land 0.0..........OOOOOOOOOOOOOOO.... 89 5.2 Crap Rotation Impact on Machinery Requirements 94 5.3 Labor Supply, Farm Size and Design Probability mlysis I...0..........OOOOOOOCOOO0.00.00.00.00... 96 5.4 semitiflty malys18 ................OOOOOOOOOOOOOOO 103 5.5 le use ........O.......OOOOOOOOOOOOOOOOO0.0.0.0... 110 6. SUMMARY AND RECOMMENDATIONS FOR FUTURE WORK ...... .. 116 APPENDICES A. FIELD MACHINERY SELECTION MODEL FORTRAN LISTING .......... 120 Be INPIH MIA FORMAT 0.000000000000000...0.000000000000000... 15“ 0. MODEL OUTPUT FOR.A C-NBéw-BT FARM; USING 28 YEARS or AVAILABLE woaany DATA, SUMMARY STATISTICS ONLY ......... 156 D. MODEL OUTPUT FOR A C-NB FARM; USING 28 YEARS OF AVAILABLE WORKDAY DATA, SUMMARY STATISTICS ONLY . . . . . . . . . 162 E. MODEL OUTPUT FOR A C-NB FARM; USING THE NINTH YEAR OF AVAILABLE WORKDAY DATA, COMPLETE SET OF STATISTICS .. . . .. 168 LIST OF REFERENCES ..... . ........ . ........... . ................. .. 178 iv Table 2.1 2.2 2.3 3.1 3.2 3.3 3.4’ 3.5 3.6 3'. 7 3.8 3.9 3.10 4.1 4.2 4.3 4.4 LIST OF TABLES Effect of Utility Tractor Maximum Power Level Upon MaChinery Set Cost ......IOOOCOOOOO...-00.0.00... ...... Operation Assignment to Power Source ...... . ............. Fuel Use of Combine Operations ..................... ..... Base Point Match of Tractor Power Level to Implement Speed andw1dth ......QOOIIOOOOOOOOOOOOO... ....... 0.... Assumed Draft and Tractive Efficiencies ................. Assumed Implement Operating Speeds and Field Eff1c1enc1es ...... O ..... ......OQOOCOOO ...... O ......... Assumed Harvest Capacities, Field Efficiencies, and speed0.0.......OOOOOOOOOOO00.00.... ..... I. ..... O ...... Assumed Repair and Service Life Data ..... ............... Assumed Machinery Sizes and Associated Prices ........... Operations Schedule for a C—NB Farm ...... ............... Machinery Sets for a C-NB Farm Under Four Workday Data sets ......... 0...... ..... O 0000000000 O 000000000000 Estimated Workday Lengths ............................... Assumed Timing of Field Operations ..... ....... . ......... Operations Schedule for a C-NB-W-BT Farm ................ Operations Schedule for a C-NB-NB-BT Farm ............... Required Machinery Complement Cost for Twenty Eight Years of Available WOrkday Data ....................... Model Results for Assumed Available WOrkday Data Distributions and Probability Distributions ........... Page 26 46 55 57 59 79 80 81 83 Table 4.5 4.6 4.7 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 Page Available Harvest Hours at 80 Percent Level ............. 85 Required Machinery Complement Cost for Twenty Eight Years of Available Workday Data ................. ..... . 87 Mbdel Results for Assumed Available Workday Data Distributions and Probability Distributions Under a Modified Operations Schedule ................. ....... 88 Expected Yields Under Alternate Rotations ....... ........ 90 Estimted crop Prices .........OOOOOODOOOOOOOOO .......... 91 Income-Expense Summary for 162 Hectare, 1-Operator Farm 0.0.0.0.... 000000000000 O ..... 0.0.0.0.... .......... 92 Summary of Machinery Complements at a 0.69 Design Probability .. ............ ...... ........ . .............. 95 Selected Machinery Sets for a 0.79 Design Probability ... 97 Selected Machinery Sets for a 0.69 Design Probability ... 98 Selected Machinery Sets for a 0.59 Design Probability ... 99 Selected Machinery Sets for a 0.48 Design Probability ... 100 Sensitivity Analysis at a 0.79 Design Probability ....... 104 Sensitivity Analysis at a 0.69 Design Probability ...... . 105 Sensitivity Analysis at a 0.59 Design Probability . ..... . 106 Sensitivity Analysis at a 0.48 Design Probability ....... 107 Model Output for 28 Years of Available Workday Data 011 a C-C-NB-BT Farm coococo00000000000000...ooooooooooo 111 Average Diesel Fuel Use of Field Operations ............. 112 Diesel Fuel Use Sensitivity Analysis .... ................ 114 Input Data Format .... ..................... . ..... . ....... 154 vi Figure 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 LIST OF FIGURES Page FMSM flow diagram ....................................... 13 Flow diagram for subroutine TIME ......... ............... 17 Flow diagram for subroutine PWRSIZE ..... . .. ........... 21 Flow diagram for subroutine CAPCITY .................. . 25 Flow diagram for subroutine CMBSCHD .. ................... 28 Flow diagram for subroutine WRKSCHD . ........ . ......... . 33 Flow diagram for subroutine PREVOP ...................... 38 Flow diagram for subroutine ALCATE ....... ........ . ..... . 40 Flow diagram for subroutine SIZING .............. . ..... 43 Flow diagram for subroutine FUEL .. ...... ............ . 45 Flow diagram for subroutine COST ........ ....... . ..... . 48 Field machinery selection model hierarchy ............... 50 Supply (harvested crap land) - Demand (first field operation) Matrix ........................... .......... 155 vii FLOW CHART SYMBOL O U . o C) Crop Rotation Symbol LIST OF SYMBOLS DESCRIPTION Decision - Branching along alternate paths Off-Page Connector On-Page Connector Terminal - The beginning or end of a subroutine Processing of Data Information Flow Loop Control Description Alfalfa Sugar beets Corn Navy beans Oats Soybeans Wheat Example: O/AeArNB-BT; 4 year rotation where 25 percent of the total crop area is assigned to oats seeded with alfalfa, alfalfa, navy beans, and sugar beets, respectively. viii 1. INTRODUCTION 1.1 Problem The Saginaw Valley is one of Michigan's most productive agri- cultural regions. But several agronomic problems have surfaced in the area. Navy bean yields have remained fairly constant in spite of introduction of superior navy bean varieties and continued yield increases of competing crops. In addition, environmentalists report declining water quality for Saginaw Bay and the surrounding lakes and streams. This decline is due partly to agricultural non-point source pollution. Finally, improved cultural practices are needed as energy supplies dwindle. Many solutions to these problems have been prOposed, including the use of different crop rotations and alternate tillage practices. 1.2 Conceptual Paradigm The ability to advise farm operators in management activities lies with the advisor's skill in synthesizing the impact of proposed actions on farm operation. Farm operators will be more receptive to new ideas when accompanied by a thorough analysis of their impact on farm operation. Beneficial solutions may be offset by their unfavorable impact on other farm areas. A new crop rotation could improve yields, but may require increased labor, machinery, and management skills. An alternate tillage practice designed to reduce soil and nutrient erosion could alter the material flow of the farm. Any farm management solution requires analysis within the entire farm framework. To do this, it helps to think of the farm as a system comprised of interacting components. Each component can then be studied individually. The researcher should focus on one area. When sufficiently understood the component can be listed in an inventory for use in future whole farm analysis. The systems approach requires expertise in several areas. Agronomic issues require input from crop and soil scientists. The physical relationship between machinery, energy, and labor necessitates agricultural engineering skills. Agricultural economists are needed to analyze price and risk issues. A group of members from the above fields has been formed at Michigan State University. Crop rotation impact on farm operation was studied by the Michigan State University group. The objective of the investigators was to assess the component's inventory and develop a framework for presenting results to the farm community. 1.3 Machinery as a Subsystem Field machinery, as a major component of the farm system, is selected to meet a set of interacting constraints, including: 1) weather conditions; 2) labor supply; 3) productive land area; 4) soil type; 5) available machinery; 6) crops to be grown; and 7) field operations. This dissertation focuses on the analysis and development of a field machinery system. To analyze the impact of crop rotation on a particular farm, necessary field machinery and its cost must be determined. Further analysis can be undertaken for various management strategies. The absolute level of machinery cost is useful to show the farmer model results are "in the ballpark". A Naturally, differences will occur between the farmer's machinery complement and that generated in the model. These arise because management alternatives differ with each operation. When probable differences in actual and generated machinery complements are rationalized, the difference in generated machinery complements can be studied. All generated complements are based on the same assumptions. A percentage difference in machinery cost for two generated systems can provide information to the farm operator after his specific management practices are accounted for. In summary, the generated machinery Complement must closely resemble actual machinery complements to give the farm operator confidence in further analysis. More importantly, accurate relation- ships between generated machinery complements must be established before the relative merits of different management strategies can be assessed. 1.4 Previous Investigation Several approaches to machinery requirements and associated costs have been employed: 1) enterprise budgets and custom hire rates; 2) whole farm, profit maximizing linear programming models; 3) cost minimization models which seek a least cost machinery complement for a given management structure; and 4) heuristic models for selecting multiple enterprise machinery sets. The enterprise budget and custom hire approach is limited by failure to adequately_address the inter- action of labor supply, timeliness, weather patterns, operations schedule, and crop rotation. Models which seek to maximize returns are generally linear programming models (International Harvester ProeAg, 1977). Models in this category account for the interaction of timeliness, labor constraints, crop rotation, and weather patterns. Linear programming models are useful for organizing the enter- prise mix to maximize returns given a current machinery complement. .—.____“~—‘_.‘ ~--, i,- The models point out where bottlenecks occur in the present machinery complement and how much should be spent to alleviate them. Linear programming models are less effective for application to search strategies. Use of these models for machinery selection requires . considerable user input and this is unacceptable for this current multidisciplinary study. Models which seek minimum cost machinery sets for a given farm organization employ various selection criteria. Singh (1978) reviewed models in this category. The economic minimization models select a minimum cost machinery set for a given farm size and crop rotation. The objective function includes machinery cost, labor cost, and timeliness cost terms. These models account for field operation schedules and weather conditions. Only a limited number of crop enterprises can be considered using cost minimization models. Current models of this type consider one or two crops at most. It is the author's opinion that expansion of cost minimization models to include the seven major crop enterprises of the Saginaw valley would not be feasible for an easily understood multi- disciplinary model. The heuristic machinery models developed at Michigan State university view timeliness as a constraint, rather than a penalty. This results in a less complicated model structure. Field operations& I must be completed within specific calendar periods. Thus, machine 11 productivity is matched to available time during scheduled calendar periods such that all operations are completed. The Singh (1978) machinery selection model was recently tested in the Saginaw Valley region by this author. The following problems were noted during its application and operation: 1) The number of crop enterprises considered required expansion to include sugar beets. 2) The unlimited labor supply assumption produced results unacceptable to Saginaw Valley farmers in light of their relatively limited and more discrete labor supply. 3) Model technology did not include 4-wheel drive tractors. A separate type of tractor, an alfalfa harvest tractor, was not well received by farm operators. 4) Initial results indicated a potential problem in the accounting for available workday data. (This problem is not limited to the Singh (1978) model, but is relevant to most machinery models developed to date.) 5) The model reflected soil and available workday conditions of central lower Michigan. The soil and workday parameters would need adjustment for studies of the Saginaw Valley. This represents a problem in application only. 1.5 Problem Statement The problems associated with the Singh (1978) model were great enough to warrant development of a new model. The objectives associ- ated with the new model were to: 1) Develop a multidisciplinary farm decision framework machinery '3 selection model. V 2) Develop a machinery selection model for both physical and economic prediction purposes. The model should consider operator labor constraints and the seven major Saginaw Valley field crops. 3) Compare model operation using actual workday_data and employ- ing chance-constraint techniques outlined in previous machinery selection models. 4) Develop a method of validating machinery and available workday generator parameters. Machinery involved in crop transport, drying and/or processing is not considered by the model. The new model boundaries include: 1) A 80 to 400 hectare farm size. 2) A 1 to 2 full time operator range. 3) Fine-textured lake plain soils of the Saginaw Valley. 4) Cash crop enterprises only. 5) Maximum of 1 combine, 1 tillage tractor, and 1 utility // tractor. 6) Self-propelled combines and the availability of 4dwheel drive J tillage tractors. The objective of this dissertation is to develop a machinery selection model. The model must be designed such that nonengineers will be comfortable with its use and results. The developed model will be used by extension and research faculty for analysis purposes. The developed model may also play a role in classroom instruction. The model is outlined in Chapter 2. Input data and methodology to determine their validity are presented in Chapter 3. Chapter 4 compares the use of the actual to probabilistic workday data. The crop rotation impact on machinery requirements and on the entire farm are presented in Chapter 5. A program listing, input data structure, and example output is contained in Appendicies ArE. 2. FIELD MACHINERY SELECTION MODEL The machinery selection model developed at Michigan State University served as a starting point for developing the new machinery selection model. It is useful to review the heuristic model of Singh (1978) and contrast the new model with it. As stated previously, the Singh (1978) model matches machine productivity to available time. The smallest set of machinery, which enables required time to be less than or equal to available time, is selected as the required machinery complement. An iterative search technique is employed. The search proceeds in five steps: 1) combine sizing, 2) tillage tractor selection, 3) utility tractor selection, 4) alfalfa harvest tractor selection, and 5) tractor-drawn implement sizing. The selected machinery complement is qualified with a design probability. This is the percentage of years for which the selected machinery complement could complete all field operations within the user specified field operation date constraints. The design probability is determined by the available workday data set. This is a statistical grouping of many individual yearly workday records and is used as an indicator of the weather, soil, and crop condition impact on scheduling field work. The workday data of Singh (1978) are assigned a probability level. A workday data set at the 80 percent level implies that the weekly available field work time described there in would occur or be exceeded 8 out of 10 years. The machinery set developed for the 80 percent workday data set would, in theory, have a design probability of 80 percent. Singh (1978) calculated a single machinery complement for a single workday data set. Correlations exist between the weekly values of available workdays. Singh (1978) attempted to capture the cor- relations by combining weekly means and standard deviations of available workdays over an operation's scheduled period and using areas under the normal curve to arrive at weekly available workday values. The new model uses actual yearly workday records. Each year of data is used to develop a machinery complement. The collection of yearly machinery complements is inspected to arrive at any user specified design probability. This method of selecting the design probability is the major difference between the new model and that of Singh (1978). Another change concerns the labor supply assumptions. Singh (1978) assumes an unlimited labor supply. This does not offer an accurate portrayal of management options available to Saginaw Valley farmers. The assumption is probably more realistic for large acreage farms where labor hire decisions may be more routine. A.wide range of farm sizes can be considered simply by allowing selection of multiple machinery units. The new model is developed for a limited labor supply. The maximum number of combines, tillage tractors, and utility tractors allowed is one each. Because of operation assignment to the combines and tractors, labor supply is restricted to a maximum of two operators. Seldom does a week occur when all three power units can be utilized. V1 u When this does happen, one man can generally do all work assigned to 10 the combine and utility tractor. Because the labor supply is restricted the current model considers only a limited farm size. Heuristic models cannot be judged absolutely correct or incorrect. The drawback associated with farm size restrictions due to the limited labor supply assumption is offset by superior accounting of labor and machinery interaction and the closer approximation of the real world decision making process. Hopefully, problems associated with the limited labor supply assumption can be resolved with future reSearch. The search procedure is modified in the new model. The Singh (1978) iterative search considers combines, tillage, utility, and alfalfa harvest tractors, in order. The implied field operation priority is the same as the order of power unit consideration. Within each power unit category, field operation priority is assigned by operation ending date constraints. A priority scheduling of this type is possible because no competition occurs between power units for drivers. The new search procedure allocates machine productivity to available work time and operator time for each week during the year. Competition of power units for labor must be considered. In a limited labor mode the model cannot simply schedule tillage tractor operations and then utility tractor operations. The tillage tractor could utilize all available labor leaving the utility tractor unable to accomplish any work during the week. The new iterative search is a two-step process. Combine size and sugar beet harvest capacity are determined first. Tractor size is determined second. Harvest operations receive priority during this 11 process. Conceivably, all operator time in a week could be allocated to harvest operations. Only two categories of tractors, tillage and utility, are considered. For tractor operations priority is based on ending date constraints only. In each week the model attempts to accomplish the maximum work for the priority operation regardless of which tractor does the field work. The work accomplished for lower priority oper- ations must account for the tractor and labor assignments of operations already scheduled during the week. The new model incorporates several other changes. It has the capacity to consider 4dwhee1 drive tillage tractors. Oats and sugar beets were added to the existing alfalfa, corn, navy bean, soybean, and wheat crops list. The model output can be given in English or SI units. An attempt is made to select two satisfactory machinery complements for a given year. The lower cost complement is selected as the required machinery complement. In summary, for each year of available workday data a machinery complement is selected. The selection occurs in two steps. Combine and sugar beet harvest operations are considered first. Harvest capacity is increased stepwise until all operations are completed within their specified date constraints. Tractor sizes are selected after harvest capacity is selected. Tractor power level is increased step- wise until all field operations are completed within specified date constraints. A weekly scheduling process determines the appropriateness of each step in harvest capacity and tractor size. If an operationis unfinished in the last week to schedule it, the current size is judged too small 12 and the next step in size is considered. If work is completed or no date constraint occurs in a week, scheduling continues to the next week. Once a combine and tractor size are selected to satisfy the weekly work schedule, the model is used to select implement size. Fuel use and cost of the selected machinery complement are predicted. If possible, a second machinery complement based on an oversized combine is determined. The oversized combine should complete its assigned operation sooner and therefore, allow more time for fall tillage. Increased time for fall tillage would enable use of a smaller tractor. The less expensive of the two possible machinery complements is designated the required machinery complement for the year. The required machinery complements for each year of workday data are ranked according to cost per hectare. A detailed presentation of the model follows. 2.1 FMSM Program The field Machinery Selection Model (FMSM) of Figure 2.1 directs model information flow. Model input includes 28 years of weekly work- day data. For each year, workday data were collected in four cat- egories: 1) corn harvest; 2) alfalfa harvest; 3) tillage; and 4) combine operations (Section 3.2). Farm input data subject to analysis include: 1) crop rotation; 2) farm size; 3) percentage of farm assigned to each crop; 4) labor supply; 5) alfalfa yield; and 6) operation date constraints. Model operation parameters and print control variables are listed at the end of the farm data input file. (Appendix B). 13 Read workday data Read farm data at NYR and END NYRPNYR+1 ITT-O Set base combine size Set base beet harvest capacity level» Rank yearly machine sets by cost» EECE} Print machine set rankin;s et tractor power level base CALL TIME L PWRSIZE , Increase combine or A beet harvester size omplet- schedule lITT-ITT+1I Increase combine size Select minimum cost machinery— set for the year constraint violated Print summary data END - Last year ITT - Counter for machinery sets selected per year (1-2) NYR . Counter for year (1-28) Figure 2.1. FMSM flow diagram. 14 The model can generate four output levels. These vary from summary statistics to detailed weekly work schedules and machinery sizing information. A machinery set is generated using available workday data for one year. Because 28 years of available workday data exist, the model will generate 28 machinery sets for the input farm. Each machinery set reflects the unique workday availabilities of the year for which it was developed. The 28 selected machinery sets are used to calculate individual design probabilities. The model can be run for less than 28 years of workday data. It is envisioned that 28-year runs have summary outputs only. Detailed output could be obtained for selected years if further analysis of a 28-year run is desired. A detailed output for only 1 year out of a possible 28 will lessen computing expense. NYR and END variables control the number of years analyzed. Combine, sugar beet harvester, and tractor size.are set at the start of each year. The arbitrarily chosen base settings are 2-row, level 1 (0.81 ha/h, 2 A/h) and 60 PTOkW (80 PTOHP), respectively. The model operates in three major parts. A TIME routine is used to calculate available time for field operations. Using another routine, PWRSIZE, power and capacity needs are determined. A SIZING routine is then used to obtain the tractor-drawn machine size, fuel use and cost factors. The FMSM program contains two sizing aspects. When a complete schedule cannot be found by PWRSIZE, FMSM.attempts to utilize possible harvester-tractor’trade-offs. Insufficient harvester capacity or 15 inadequate tractor size for a given harvest capacity results in an incomplete schedule. Generation of the first machinery set for each year may involve selection of combine and sugar beet harvest capacities greater than the minimum capacities required. When an incomplete schedule is detected an attempt is made to resize the harvest capacities. If inadequate harvester capacity is the cause of the incomplete schedule, the model moves on to the sizing of implements and the incomplete schedule is noted in the output file. If the incomplete schedule is due to insufficient tractor size, the over-sizing of harvest capacity may free up time for fall tillage and allow adequate tractor selection. After SIZING is called, an attempt is made to generate a second machinery set. Sugar beet harvest capacity is held constant. Combine size is increased and a new tractor-implement set is selected. The year end results could show 0, l or 2 satisfactory machinery sets. The least expensive machinery set is chosen. Sugar beet harvest capacity is held constant for the second machinery set for several reasons. Larger combine and sugar beet harvest capacities may entail increases in truck size or number. The model does not include economic aspects of transport vehicles. The higher cost of the larger combine is included in the economic choice of machinery sets for the year. Although increased sugar beet harvest capacity will not necessarily require larger machinery, higher equipment operation speeds may be needed. In this case, little or no economic penalty is assigned to a system with an oversized 16 beet harvest capacity; therefore, an oversized sugar beet harvest capacity is not considered when selecting the second machinery set. The model ranks the generated machinery sets based on average annual cost per hectare. The yearly cost and summary statistics of each machine set are recorded. The machine set corresponding to any design probability can be obtained directly from.the machinery set rankings. ' 2.2 Subroutine TIME The TIME subroutine (Figure 2.2) calculates the weekly hours available for each operation. For weeks when an operation cannot be scheduled because of time constraints, the available hours are set to 0.0. The available operation hours for a scheduled week depend on workday length (hr/day), work week length (days), and the weekly work- day data (percent of week available for work). Harvest operations are considered first for each week. Tillage operations are scheduled once available harvester time is exhausted or harvest operations are completed. A problem in model operation may occur when a scheduled tillage or planting operation overlaps a harvest operation assigned a lower priority (later ending date constraint). The problem relates to input data priority. By scheduling harvest operations first, the model implicitly gives priority to harvesting over tillage or planting, regardless of ending date constraint. The model will assign available time to harvest operations in the beginning of each week. As a result, the remaining time may be 0.0. Since l7 €EEEEI> DO I-l,52 DO Io-l, number of field operations, THOURS I IO IIO.O Can 10 be scheduled in week I No [THOURS(I,IO)-Available workday data * HRSDA(IO) *_§] Wheat and Sugar beets in rotation 'es and Io-beet harvest -For: I-first week of beet harvest THOURS(I,IO)-THOURS(I,IO)l2 NO | For: I-weeks of spraying, Io-spraying and IP-planting following THOURS(I, 10)- (THOURS(I, IO)-THOURS(I, IP))/2 planting es +(THOURS((I+1), IO)-THOURS((I+1, IP))/2 . 1 For: I-May 22 and Io-baling» in week of Yes THOURS(I,IO)-THOURS(I,IO/2 May 22 I No 1 HRSDA(IO) - Assumed workday length of operation IO THOURS(I,IO) - Available field workhours for operation IO in week I Figure 2.2. Flow diagram for subroutine TIME. 18 a tractor cannot accomplish any work when there is no time, the model generates an incomplete schedule. This problem occurs twice in the current field operation schedules for rotations containing wheat and sugar beets (see Table 3.10). It could occur whenever a tillage or planting operation overlaps harvest- ing in the fall and the harvest operation has a lower priority (later ending date constraint). Because the problem solution would involve major rewriting of the machinery selection model, a simplifying assumption was made. Sugar beet harvest beginning date must be delayed 1.5 weeks when wheat is in the rotation (Christenson, 1979). One week of this delay can be accounted for by changing the starting date for sugar beet harvest. The rest of the 1.5 week delay is made up by reducing available sugar beet harvest hours by one-half in their first scheduled week. Wheat can then be planted for one-half of the week in question. Cases may exist where wheat planting is completed before sugar beet harvesting begins. Generally, the weekly scheduling process would schedule some activity in this time slot. However, it is possible that half of the first sugar beet harvest week may go unused. Any herbicide applied to crops after planting is considered a pre-emergence spray. Spraying must occur within several days of planting. Hours available for spraying always equal or exceed those available for planting since they were generated using a less restrictive C3 value (Tulu moisture criteria, Section 3.2). The available spraying hours in a week are adjusted to reflect the necessity of waiting until the crop is planted and the deadline of spraying before emergence. The adjustment incorporates the 19 difference in planting and spraying hours. The available spraying hours in week I are one-half the difference in spraying and planting hours in weeks I and 1+1. Thus, time available is that which occurs after planting is finished in week I and before it is scheduled in week I+1 for each week I spraying is scheduled. Available alfalfa harvest time during the week of May 22 is reduced by one-half. The reduction simulates the desired May 25 starting date for harvesting (Helsel, 1979), since the model will schedule only one-half the maximum time available during May 22. 2.3 Power Unit Sizing An iterative search procedure is used to determine power unit sizes. The model cannot consider multiple numbers of any individual power unit type, therefore power units are limited to a maximum of 1 7‘ combine, 1 tillage tractor and 1 utility tractor. The search begins with a 2-row combine and 60 PTOkW (80 PTOHP) tillage tractor. A 2-row combine is the smallest size considered in the model. The 60 PTOkW tractor is used as a starting point only, since the model will select a tractor smaller than 60 PTOkW if J necessary. Power unit size is increased until all date constraints are met. If the maximum combine size of 8-rows or tillage tractor size of 201 PTOkW (270 PTOHP) is exceeded, selection stops and the inability to v// complete all operations is noted in the output file. The maximum power unit sizes were felt to be the maximum sizes generally found 20 in the Saginaw Valley. The maximum values can be changed to reflect the views of the person using the model. Figure 2.3 is a flow chart for determining power unit size. Harvest operations, except alfalfa, are considered first. Current combine capacity is determined using the CAPCITY subroutine. Sub- routine CMBSCHD is used to calculate the amount of work accomplished per week. The CMBSCHD algorithm.is also used to select the sugar beet harvest capacity. A harvest schedule completion check follows. If any harvest operation is not completed by its end date, the schedule is incomplete and thus a larger combine is needed. When all harvest operations can be finished the schedule is complete. Tractor sizing begins when the harvest schedule is complete. First, energy requirements (kW—h/ha, HP-hr/A) for tractor- powered operations are determined using the ENERGY subroutine. The base power sizing increment is 30 kilowatts (40 HP). The utility tractor size is the minimum of current tillage tractor size or the maximum available utility tractor size of 104 PTOkW (140 PTOHP). The utility tractor size assumption is made for several reasons. The 104 PTOkW level is close to the breakpoint PTOkW of two- and four- wheel drive tractors (Implement and Tractor Red Book, 1979). Most tractors above this level are 4-wheel drive. It is assumed only a twoewheel drive unit can perform the row crop operations assigned to the utility tractor. Current ground speed assumptions prevent further power increases past the 104 PTOkW utility tractor level from increasing field capacity. 21 [Select all harvest operations except alfalfa] [Combine-2 rowel Determine tractor-powered operations CALL ENERGY Tillage tractor kW-60 INCREMENT-30 .3flfitility kWiMIN(104,tillage tractor kW) I CALL CAPCITY CALL WRKSCHD . .kW-kW+INCREMENT or kW-270 kW-kW-6.7 INCREMENT-0.75 ICMPLT - Indicator of work schedule completeness (0-unfinished schedule, 1-finished schedule) INCREMENT a Tractor power level step size kW - Tillage tractor size in killowatts NN - Counter used in tractor sizing SIZE ' Combine size in rows Figure 2.3. Flow diagram for subroutine PWRSIZE. 22 A trade-off between tillage and utility tractor size may exist. The optimum size relationship between the two may depend on farm size, available labor, crops grown, yearly weather patterns, field operations needed for each crop rotation, and possibly other factors. The experimentation required to determine the quantitative effects of these factors was not undertaken. A quick check of utility tractor maximum size was made. Maximum utility tractor sizes ranging from 52 PTOkW (70 PTOHP) to 112 PTOkW (150 PTOHP) were analyzed for a 162 hectare corn-navy bean farm. Table 2.1 gives results for four design probabilities on the example farm. The results show an equal or smaller cost when the maximum utility tractor size is increased from 75 PTOkW. At the 25-year completion level, the 52 to 75 PTOkW utility tractors were unable to complete all field work. It appears, based upon Table 2.1 results, that oversizing the utility tractor has less effect on system cost than undersizing. Equating utility tractor size to tillage tractor size could, in some cases, oversize the utility tractor and its associated equipment. But this effect is cancelled somewhat by the lower purchase price assigned to the utility tractor due to its increased age. For all the above reasons utility tractor size is equated to tillage tractor size until it's maximum productivity level of 104 PTOkW is reached. The capacity of each tractorepowered operation is determined by CAPCITY. A weekly schedule is assigned using the WRKSCHD subroutine. Tractor power is increased by the initial 30 PTOkW increment until a 23 vouuwooam manna: xuos Ham vouoagaou use announces sows: now mums» no access musomoueou wawxamm «a .muswmuumaoo sumo ouwwauoam canoes maowumuono macaw Ham unmanaoo on Canon: «a . OUGflMHUgOO ”Hg .anmm mZIU honeymoona .mumuoo: No" R mud ohm he" nag nag nn~ nna mud hum nu mm" mm“ mow ooN com can bow bow wow mm «a emu mmN man can Nan dfim “LN Haw mm «a «a «a «a anm ecu wmu can wnm mu «m co up mm mm mm mm «cg Naa macaw .Ho>oH mason uouomuu huwawu: assess: .m£\w .umoo use season: «as “mm we use mumohv waaxdmu uom magnum: a.umoo uom huoausomx son: Ho>on uosom auauxmx uouomua huaawua mo uoowum «.N cases i 24 satisfactory schedule is developed. The power increment is then reset until the lowest satisfactory tractor size to the nearest 0.75 kilo- watt is found. Weekly field operation schedules can be printed in the output file if desired. 2.4 Subroutine CAPCITY The CAPCITY subroutine (Figure 2.4) is used to determine effective field capacity (EFC) fer various field operations. Field operation, implement required, and power source must be determined for each component in the figure. QEE£E_E;3,118t8 the power source assignment for implements. Fer example, the moldboard plow is powered by the tillage tractor. The EFC of sugar beet toppers and lifters can be calculated using the CMBSCHD subroutine. Then CAPCITY can be used to check the required utility and tillage tractor kilowatt levels against current levels. To find the required kilowatts the equations and parameters of section 3.1.1 are employed. If necessary, tractor kilowatt levels are increased and the routine is rerun from the beginning. Based on data statements, model input also includes the capacities of the four navy bean combine sizes. The EFC of corn, wheat, soybean, and oat harvest can be calculated using Equation 3.2. Speed, width, and field efficiency of eagh crop are fixed for combine size; thus, M *"""' 839.18 fixed for crop and combine size. - ~——.——--- —.._ r...-_—¢ By incorporating yield, cutting, and machine capacity data, baler EFC can be determined. The yields for first, second, and third 25 9 @1333 [DO LOOP for array containing field operationsLE:___N [Field operation - element I of array] ugar beet Yes ec. kW kW Yes harvest level sufficien , No No (D adjust kW (9 EFC fixed for crop] I and combine size I Yes EFC calculated for a Mg/hr capacity basi [EFC calculated for full utilization of kg] Cultivato EFC adjusted for planter size or NH3 applicator Yes and speed constraints Operation Yes 5, EFC adjusted for size and speed constraints No Yes 8%; 0 N0 Row planter Yes ‘ Determine size needed]__9Q) operation to match chosen EFC EFC = Effective field capacity kW - Tractor size (kilowatt) Figure 2.4. Flow diagram for subroutine CAPCITY. 26 Table 2.2 Operation Assignment to Power Source. Power Unit Tillage Utility ,Qperation/Implement Tractor Tractor Combine Moldboard plow Moldboard plow & leveler Disk harrow* Disk** Chisel plow Field cultivator 1** Field cultivator 2" Sugar beet topper Sugar beet lifter No-till planter NH3 applicator Grain drill Row cultivator Rotary hoe * Mower-conditioner Row planter Baler Rotary cutter Fertilizer spreader Rake* Sprayer Windrower Navy bean puller Corn head Wheat harvest Soybean harvest Navy bean harvest Oat harvest >4 xxxxxxx xxxxxxxxxxxxxx >4 NNNNN * , May be assigned to tillage tractor on two-operator farms. ** Similar implements may have different draft force requirements due to different operating depths. 27 cuttings are assumed to be 50, 30, and 20 percent of the total alfalfa yield, respectively. Oats and/or wheat straw yield is assumed to be 3.4 Mg/ha (1.5 T/A). The EFC is simply the baler capacity (Mg/hr) divided by crop yield (Mg/ha). For non-harvest operations, EFC is first calculated to fully utilize all tractor power. The EFC is then adjusted, if necessary, to fit in the range of allowable EFC for a particular implement. The allowable EFC range is determined by the maximum and minimum EFC boundaries. Maximum and minimum EFC are calculated using fastest and slowest ground speed and largest and smallest machine widths in Equation 3.2. If EFC is out of range on the high side, EFC is equated to the maximum allowable. If EFC is out of range on the low side, tractor power levels are adjusted to the required horsepower for the minimum EFC. The EFC of cultivators and ammonia applicators is constrained not only by speed limits but also by planter size. Presumably, cultivator and ammonia applicator sizes match planter size exactly. The ammonia applicator is generally used only after planting. Use of the ammonia applicator for plow down or disk incorporation would not be possible unless model parameters were changed to analyze machinery designed specifically for that purpose. 2.5 Subroutine CMBSCHD The CMBSCHD subroutine is used to determine the capacity of all harvest operations except alfalfa (Figure 2.5). Completion of all combine operations can be checked using the counter N mechanism. When 28 Yes N>No. Combine operation Sugar beets in rotation REHR-AREA(N) IEFC Date constraint violation [REHR-REHR—THOURS (I .111] B AReset Thours array to reflect only the scheduled hours for her fat AREA(N) 8 Land area assigned to Nth combine operation JC - Completeness of work schedule (JO-1 satisfactory schedule, JC=2 unsatisfactory schedule) K - Counter of beet harvest capacity N - Counter for combine operations REHR = Required hours for completion of Nth combine operation THOURS(I,N) - Maximum available hours in week I for operation N Figure 2.5. Flow diagram for subroutine CMBSCHD. 29 Wit-Base harvest capacity level-fl fl Beet harvest Yes complet- No Determine available harvest hours ‘Schedule available time Beet harvest Yes complete 0 Date constraint violation 1‘" No No arm in rotation Yes N0 an corn be scheduled Yes Determine available harvest hoursf Schedule available time Yes |:te constraint violation Figure 2.5. (continued). 30 all combine operations are completed, the subroutine sizes the beet harvester or proceeds directly to the summary section. To determine the required field time for the Nth combine operation, the land area to harvest is divided by current combine capacity. The calendar date is incremented in 1 week units. During each week the required field time will be reduced by the available hours in the week. If at the end of the week required hours are still positive, the date constraint is checked. If the ending date constraint is not violated the next week is considered. An ending date constraint violation implies a larger combine is needed. When required harvest hours become negative, the operation is completed and operation N+1 is considered. The user-specified date constraints for sugar beet harvest may give it priority over corn harvest. Available work time previously allocated to corn harvest may now be given to sugar beet harvest. Two machines, a topper and a lifter are required for sugar beet harvest. The potential harvest capacity is determined by machine size and labor supply. The machinery size which enables two-man operations to harvest at 1 megagram per hour would allow a one-man operation to harvest at one-half megagram per hour. The single operator must divide time equally between topping and lifting, thus reducing the capacity attainable for a given size of machinery. With this model, sugar beet harvest capacity is based on the lifting operation only. Available work hours are adjusted to reflect the labor supply constraint on capacity as follows: 31 BTHR - THOURS (1+PCTHRD), (2.1) 2 where: BTHR - weekly available sugar beet lifting hours THOURS - weekly available hours for both topping and lifting PCTHRD - extra labor available over the onedman minimum (0.0 to 1.0 operators) On a two-man farm (PCTHRD - 1.0) hours for lifting would equal the maximum available. On a one-man farm.(PCTHRD . 0.0), one-half the maximum time would be available for lifting. -After sugar beet harvest capacity is determined, time spent topping is equated to time spent lifting to facilitate full labor accounting. The available hours adjustment simplifies the model since only one operation is considered. The adjustment also ensures the correct allocation of labor and available work time to both sugar beet harvest operations, since the amount of sugar beets harvested equals the amount topped. Part A in Figure 2.5 can be summarized as follows: Sugar beet harvest is given priority in each week. Any time remaining in each week is allocated to corn combining for farms with corn in rotations. When all time in a week is allocated, the next week is considered until operations are completed. If a date constraint for corn or sugar beets is violated, larger sugar beet harvest capacity is selected. This procedure does not investigate possible tradeoffs between sugar beet and corn harvest capacity. The smallest possible sugar beet harvest capacity is selected for the current combine size. 32 When a satisfactory harvest set is determined, available harvest hours are adjusted. The available hours array for harvest is equated to the exact time needed in each week for each harvest operation. Unfinished operations in their last scheduled week indicate an unsatisfactory power level. If the unfinished operation is not alfalfa harvest, a return to PWRSIZE with JC=2 is made. The model considers one size each of a baler, rake, and mower— conditioner. If alfalfa harvest cannot be completed within its date constraints, the integer multiple of required extra alfalfa harvest machinery is determined. The labor required to operate the extra machinery units is not included in model results. When the extra alfalfa harvest machinery sets are determined, weekly scheduling continues. 2.6 Subroutine WRKSCHD The WRKSCHD subroutine (Figure 2.6) develops a weekly work schedule based on the tractor power level supplied to it by the PWRSIZE subroutine (Section 2.3). Field work is scheduled week-by- week until all operations are finished or a date constraint is violated. The main information WRKSCHD supplies to PWRSIZE is the status of the developed work schedule, either satisfactory or unsatisfactory. An unsatisfactory work schedule indicates to the PWRSIZE sub- routine that the tractor power level is too small. A satisfactory work schedule implies that the current tractor power level supplied by the PWRSIZE subroutine is adequate. WRKSCHD does not evaluate the possibility of a smaller tractor power level also being adequate. 33 [I-First week for operations~I] Week(I)zEnding date of last o-eratio lBIGTMFSMLTMFSMTRI-COBHRPTRMAXFWMAXFACREWbO.0 J Select operation which can be scheduled in week(I rim LOOP for operations to be scheduled in weeknfl Yes ACREW‘THOURS(I,IO)*EFC COBHR9COBHR+THOURS(I,IO Sugar beet harvest ACREW-THOURS(I,IO)*EFC] Yes Sugar beet lifting BIGTM-BIGTM+ r_______________ THOURS(I,IO) SMLTM-SMLTM+THOURS(I,Ioil ICOUNT-O [IOTHRs-BIGTM+Slfl.TM-l-COBHRk————[ICOUNT-ICOUNTj(-——' [ASsIgn harvestedTIEnd to first operation of crop] £9 Figure 2.6. Flow diagram for subroutine WRKSCHD. r1DO LOOP for operations Select other equal priority operations I scheduled in week(I) WMAX=THOURS(I,IO)*(1+PCTHRD) I 17’ TRMAX=THOURS(I,IO) |[ACRET(IO)=ACRET(IO)+ACREW(IO)] CALL PREVOP 34 [:}"‘9[From week(I) operations, find priority operation] RMAX=MAX((WMAX—TOTHRS),0.0) CALL ALCATE No praying before olanting Area planted must equal area sprayed [JC=1, Determine extra alfalfa I 1) SMTR1=EXTRA TIME harvest machinery sets required 2) SMLTM=unscheduled time 3) Reduce area planted "““E‘E @e—J ACRET(IO) - Total area finished for operation IO ACREW(IO) - Area of operation finished in week(I) BIGTM = Allocated tillage tractor time COBHR = Time allocated to combine ICOUNT - Loop counter JC = Schedule completion indicator (JC=1, satisfactory schedule; JC=2, unsatisfactory schedule) PCTHRD = Level of labor available from second worker (0.0 - 1.0) RMAX = Remaining time in week, accounting for time previously allocated SACRE(IO) - Maximum area to schedule for operation I0 SMLTM - Allocated utility tractor time SMTRl = Extra spraying time allocated to utility tractor THOURS(I,IO) - Available hours for operation IO in week(I) TOTHRS - Sum of labor time assigned to all power units TRMAX - Maximum time for any power unit in week(I) WMAX - Maximum hours available in week Figure 2.6. (continued). 35 PWRSIZE, through an iterative process, supplies WRKSCHD with tractor power levels until the smallest tractor power level is found. At the beginning of week I, a set of operations which can be completed during the week is developed. Weekly indicators of allocated time are set to 0.0 (Figure 2.6). If the set of operations considered in week I contains combining or sugar beet harvest, labor and land area associated with them must be determined. Since combining and sugar beet harvest scheduling occurs previously (Subroutine CMBSCHD), available hours for these operations are the exact amount needed in each week. Weekly land area harvested for these crops is simply the EFC times the available hours. Power unit time is the available time. The area worked each week is constrained by the area available. For example, the area planted in a week can be less than or equal to the area prepared for planting. For the second through Nth operations of a crop, the available land is that area finished for the preceeding operation. The available land for the first crop operation must come from the harvested land area of the preceeding crop. PREVOP determines the available area. WRKSCHD updates information used in PREVOP. After the first crop operation is scheduled in WRKSCHD, the harvested area it used is made unavailable for further use by any other crop. The first operations for two or more crops may compete for a parcel of harvested ground. An example rotation is 0/ArAeArC-C-NB-C. After corn is harvested, the first operation for navy beans (NB) or corn (C) may be scheduled on the harvested land. The model matches supply (harvested ground) to demand (first crop operation). Priority 36 is based on ending date constraint. Once a supply is used to satisfy a demand, it cannot be used to satisfy any other demand. When the first operation for a crop can be scheduled WRKSCHD attempts to find harvested land which is used only for the crop in question. If no exclusive supplier of land is found or more land area is required, harvested land is matched to demand on a "first come, first serve" basis in accordance with the demand operations ending date constraint. 4 After supply and demand accounting, or for the first pass in the week I (ICOUNT-0, Figure 2.6), WRKSCHD searches the workable operations set for week I. The highest priority operation is selected. Priority is based on ending date constraint. If the priority operation has already been scheduled in week I, or if the operation is completed, the next highest priority operation is selected. A subset of priority operations is then developed. Operations with the same ending date constraint and for the same crop as the above priority operation are included in the subset. An operation assigned to a different workday data set is not included in the subset. Subset operations are flagged such that they are considered only once in the current week. The weekly maximum hours available are multiplied by the available labor supply. The maximum time a single tractor can use is the time available for the operations subset. PREVOP is called to determine the area which can be scheduled. If land area is available, the remaining time is adjusted by the time already allocated in the week. ALCATE determines the land 37 area worked in the week and assigns the remaining time to the subset field operations. When the last operation of the subset is planting, a check is made to see if the following operation is spraying. Hectares planted in a week must equal the hectares sprayed in the week. Spraying belongs to a different workday data set than planting; thus, it has a lower priority than planting. Spraying workday availability is based on a 63 value of 1.00 (Section 3.2). Spraying hours will always equal or exceed planting hours (C3-0'980: Section 3.2). Area sprayed in the week is equated to area planted by first assigning the extra available spraying time. If the extra spraying hours are insufficient to complete spraying, any unused hours for planting are scheduled. Finally, if necessary, area of planted ground is reduced to allow more time for spraying. When all operations for the week are considered or time is expired, end-of-week accounting occurs. The operations set for week I is inspected. Land area of each operation is totaled. If week I is the last week for scheduling the operation, a completion check is made. 2.7 Subroutine PREVOP The PREVOP subroutine (Figure 2.7) determines which land areas can be scheduled at the beginning of the week. An operations subset from WRKSCHD is considered. All subset operations are for one crop (WRKSCHD). Once available land area for the first operation is determined, remaining subset operation areas can be identified. 38 First farm operation _LEss-area of operationl minus area completedé] Yes First crop operation [Find harvested land source] Find previous operation on same crop] Subtract harvested land ‘ LEss-Difference in scheduled area] ' assigned to other crops LEss-Available harvested area minus area already finished rFor other subset operations 7 LEss-f(subset operations onl ) m LESS - Land area that can be scheduled for operation in the beginning of each week Figure 2.7. Flow diagram for subroutine PREVOP. 39 The first farm operation can be scheduled in any quantity within its assigned area. The first crop operation is constrained by the harvested land area available. The land area available for second or later crop operations depends on the previous operation. 2.8 Subroutine ALCATE The purpose Of the ALCATE subroutine (Figure 2.8) is to assign tractor and labor time to field work and maximize hectares completed within constraints. ALCATE is called by WRKSCHD for subsets of operations with equal ending dates for one crop. The subsets may contain one or more field operations. Each Operation is identified with a land area that could be scheduled in the beginning of the week. The initial queue is calculated using the PREVOP subroutine. When the subset contains two or more operations the last Operation is assumed to have the highest priority. As much of the last Operation as possible will be scheduled first. It is possible with this assumption to plant previously tilled ground before all remaining tillage is completed. Delaying some tillage to allow early planting is believed to be a realistic farming practice. Often, with the exception of spring planting, the set contains only one field Opera- tion, and this assumption has little bearing on farm work assignment. The land area available for the final operation is scheduled first if time permits. When all available land for the final operation is scheduled, the next-to-last Operation is considered. Tasks scheduled for the hectares of the next-to-last Operation could be simultaneously completed with the final operation Of the subset. Thus, 4O Yes Alfalfa harvest and >1 labor suppl Calculate EFC of baling component; assign cutting and raking to tillage tractor; baling to utility tractor Time constraint V1018t1-1 Readjust time such that baler time is maximized TEFc-EFC of IQSUB operations II to last operation Required hours-Schedule hours/TEFC Manhours-MIN(required hours, available hours) Area worked-Manhours*TEFC Reset time available such that violation is corrected Violation flag=1 [Allocate time to tractors] Figure 2.8. Flow diagram for subroutine ALCATE. 41 the last two Operations are then considered one and as available time permits the hectares will be scheduled equally between the two. The allocation is expanded to include the final three operations and so on until all work Operations in the subset are completed or available time is exhausted. On farms with more than one Operator, drivers may compete for power units. In cases of fixed tractor assignment, all available labor may not be used. As an example, the utility tractor cannot be used for plowing nor can the tillage tractor be used for row cultivation. Thus, a tractor Operator may be idle because the model does not allow his tractor to switch from plowing to cultivation or vice versa. An exception is on farms where more than one man is available to harvest alfalfa. Once the combined capacity of the alfalfa harvest system is determined, mowing-conditioning and raking operations are assigned to the tillage tractor while baling is assigned to the utility tractor. In some situations, an unusually large tractor may be designated for cutting or raking hay. On an actual farm, an older tractor would likely be assigned this task; since the model considers only two tractor sizes, this is not reflected in the output. From the stand- point of machinery requirements, the absence of an older tractor for cutting or raking would be the only model error. Tillage and utility tractor sizes would not be affected since a rake does not determine tractor size directly, but only consumes Operator time. Tillage tractor fuel use and expected life may be affected slightly. The addition of an older tractor would not affect average cost significantly; thus, it is reasonable to assume that the tillage tractor can perform cutting and raking tasks. 42 2.9 Subroutine SIZING Some sizing occurs before the SIZING subroutine is called. Row crop planter size is determined in the CAPCITY subroutine. Combine size is selected in the CMBSCHD subroutine. The SIZING subroutine (Figure 2.9) finalizes the selection of width and speed for the remaining machinery. The EFC of each operation is known (see CAPCITY, Section 2.4). Mbdel input also includes allowable operating speeds (see Section 3.1.1). Equation 3.2 is used to relate EFC to implement speed and width. Each field Operation is analyzed separately. Cultivators and ammonia applicator sizes are adjusted to correspond with planter size. The width of land into one windrow of the baler will depend on the crop grown or the alfalfa cutting. The navy bean combine must operate on a windrow that will facilitate bean puller and planter size matching. Combine speed and width for Operations other than navy bean harvest are specified in the input data. The remaining implements are incremented through available sizes. The speed is calculated using methods outlined in Section 3.1.1. The minimum machine size that will produce an allowable Operating speed is selected. The tendency is to select operating speeds relatively close to the maximum allowable. For each operation, the annual implement use hours and fuel usage are determined. A bean puller must be selected for navy bean harvest. However, the puller is not scheduled for weekly use like other operations because combine operations are scheduled before tractors are considered. Instead, land area combined is considered pulled the same day. Labor and fuel 43 am.) l---{;{DO LOOP for all field operationgl NH3 applicator Yes Set width to planter siEEL Determine speed NO Yes Straw o Width-2*swath widyjl ‘O balin; . Yes Navy bean Yes [Width-2*combine width combine No First cuttin; Match windrow width to combine and planter size» known from combine size requirement .Match harvestor size» to planter size Determine annual use hours CALL FUEL Increment implement size until , the speed constraints are just me avy bean harvest Select width and speed of navy bean puller Figure 2.9. Flow diagram for subroutine SIZING. 44 use for navy bean pulling is considered in the summary. Planter width and combine capacity are used to arrive at a correct puller width. Given appropriate field patterns 4, 6, 8, and 12-row windrows are possible. 2.10 Subroutine FUEL Diesel fuel usage can be calculated using the FUEL subroutine (Figure 2.10). FUEL is called by SIZING for each crop operation. The formula outlined in the ASAE YEARBOOK (1978) is used to calculate the fuel consumption of tractor-powered Operations. Fueleff-2.64*RAT + 3.91 - 0.20 *(738*RAT +1173)15 (2.2) Where: Fueleff - Fuel efficiency (L/kWhr) Rat - Ratio of equivalent PTO power required to maximum available PTO power Fuel consumption should be reported in litres per hectare units. The required conversion is: Fueluse - Fueleff * EN/EFC Where: Fueluse - Diesel fuel usage (L/ha) EN - Required power for operation (PTOkW) EFC - Effective field capacity (ha/hr) The use of these equations was spot-checked against data in the literature (White, 1974). Medal results were average to slightly higher than average. Implement drafts are higher than average for Saginaw valley soils, thus a higher than average fuel use is expected. 45 Combine operation Yes iDetermine PTOkW required for operation Determine ratio of required to available PTOkW Fuel use-f(tractor loading rate) Convert L/kW-hr to L/ha Combine fuel use is ' constant for particular crop Calculate fuel use total for each implement Calculate fuel use total for farm m Figure 2.10. Flow diagram for subroutine FUEL. 46 Combine Operation fuel usage is constant for each crop harvested. The usage figures taken from Singh (1978) are given in Table 2.3. The fuel subroutine also totals fuel usage for field Operations and for all farm operations. Table 2.3 Fuel USe Of Combine Operations Operation Fuel USe,L/ha (gal/acre) Corn harvest 15.0 (1.6) Wheat harvest . 13.6 (1.5) Oat harvest 13.6 (1.5) Soybean harvest 10.3 (1.1) Navy bean harvest 13.6 (1.5) 2.11 Subroutine COST The average yearly cost per hectare is computed for each selected machinery set. Depreciation, interest, repairs, insurance, shelter, and fuel cost are determined. Machinery cost is based on an 8-year service life for equipment. Techniques outlined by Bowers (1975) are used to calculate machinery cost. Depreciation based on a 10 percent salvage value is determined using a straight line formula. Average interest is found by multiplying the interest rate by one-half the sum Of purchase and salvage prices. (A machine with a service life shorter than 8 years should be adjusted for purchase price, such that interest will be calculated for the 8-year period.) The interest rate used for this study was 0.13 (Black, 1979). 47 Repairs are calculated on an annual use basis. The average repair cost per machine per year is the purchase price multiplied by the total repairs factor over the service life of the machine and prorated to the annual machine usage. Insurance and shelter costs are fixed percentages of the purchase price. (Insurance cost must be adjusted, if machine life is less than 8 years, to a 8-year basis). Shelter costs are calculated only on the initial price because a replacement machine would occupy the same shelter space as did the worn-out machine. An algorithm for machinery cost calculation is presented in Figure 2.11. The routine contains a list of machines and widths required to complete all field Operations. The annual use of each machine is assigned to the appropriate power source. Some field Operations use the same machine but operate at dissimilar drafts, and thus require different sizes for the same implement (see Table 2.2). An attempt is made to adjust speed and width for the heavy draft Operation using the COST subroutine. The purpose of the adjustment is to equate machine size to that of the lighter draft Operation without violating speed constraints. The resizing technique will depend on the way machine sizes were initially chosen. Implement sizes are selected based on the minimum width that will satisfy the speed constraints of the machine (Section 2.9). Machine speeds tend toward the upper speed limit since that speed will allow the smallest implement width. A larger machine operated at a slower speed (but still within the prescribed speed range) could be chosen, but a smaller machine at a higher speed could never be used in the resizing procedure. 48 ‘Determine hours Of use for combine, I tilla tractor and utility tractor Operation using same machine at different drafts Calculate speed of heavy draft Operation using width of light draft operation Yes Speed constraint violated F§L_______{Only one machine is required v , iDetermine tillage tractor drive wheel number Calculate costs including: depreciation, interest, insurance, repairs, shelter and fuel Sum costs for machines and farm mm Figure 2.11. Flow diagram.for subroutine COST. 49 If width is increased for the heavy draft operation machine, speed is necessarily lowered. If the width can be made equal to that of the lighter draft operation without violating speed constraints, then only one machine Of the larger size will be required. If speed constraints are violated, two units of the same implement must be included in the cost computations. Once the final list of machines and sizes is formulated, the purchase price of each machine is found from the data (Section 3.1.2). Costs are calculated as outlined previously. The results are recorded for the second through fourth output levels (Section 2.1). 2.12 Madel.Hierarchy The model hierarchy is presented in Figure 2.12. The model begins by determining available worktime in TIME. Power unit size is determined in PWRSIZE. PWRSIZE first determines harvester requirements by incrementing harvester size. Capacity of each increment is calculated in CAPCITY. Available time is matched to machine capacity in CMBSCHD. The second task of PWRSIZE is to determine tractor power levels. ENERGY is called to determine the kW-h/ha levels of each tractor- powered field Operation. The EFC for each tractor size increment is calculated. WRRSCHD matches available tractor time to EFC. PREVOP tells WRKSCHD how much work can be scheduled in the beginning of each week. ALCATE calculates field work completed and time used. PWRSIZE |_SIZING | 1 l’ 2 1 2 1 3 ENERGY WRKSCHD | COST 1 2 PREVOP ALCATE Figure 2.12. Field machinery selection model hierarchy. 51 Finally, FMSM.calls SIZING to match implement speed and width to calculated EFC. Fuel use for each field Operation is calculated in FUEL. The average annual cost of individual and total implements is calculated in the COST subroutine. 3. MODEL PARAMETER AND INPUT DEVELOPMENT Parameter and input data fall into one of three categories: 1) machinery related; 2) available time related; and 3) farm identi- fication. Machinery data determine the productivity of each step in machinery size during the selection process. These data are also used to size tractor-drawn implements and to compute costs. Available time data defines the portion of each week which can be scheduled for field operations. Farm data pertain to Operation scheduling dates and work- day length constraints. Collection Of input data can be accomplished by searching the literature or making field Observations. Both sources are utilized in this study. The data collected become model input. Results using the collected data must be verified. Validation is an important and time consuming process. The validation of the data used in the current study was accomplished by utilizing a "backwards" approach. In reality the validation is carried out first. MOdel parameters are then adjusted such that model results match those found in the validation. In developing the model parameters published reports were used as bench marks whenever possible. The "backwards" approach is useful because it forces the researcher to get first-hand field experience and it serves as a valuable tool in presenting model results. Listing validation results will quickly demonstrate to the audience the physical relationships Of the model. Since all model results will be based on the relations listed, a degree of confidence is immediately given to the model results. 52 53 The "backwards" approach is used to determine the machinery draft-speeddwidth relations and the available workday parameters. Data collection is outlined in the following sections. 3.1 Machinery Parameters Required machinery parameters of the newly developed model include: a) draft b) field efficiency c) tractive efficiency d) allowable operating speeds e) theoretical harvest capacities f) reliability 3) sizes available and list prices h) service life and repair data The collection of machinery data is discussed in two parts: 1) productivity parameters, and 2) parameters for determining machinery set fixed and variable costs. 3.1.1 Machine Productivity Parameters' Productivity data are required by two model equations developed from the ASAE YEARBOOK (1978): PTOkW 3 D * S * W * RE. LF * TE * CONV * CI (3.1) EFC = S * W * Eff (3.2) OZ 54 where: PTOkW - Tractor power takeoff power (kW) D - Implement draft (N/m) 8 - Implement speed (kmdh) W - Implement width (m0 LF - Tractor load factor (decimal) TE - Tractive efficiency (decimal) CONV - Tractor PTO to axle power ratio (decimal) Cl - Dimensionality constant (Cl - 3600) EFC . Effective field capacity (ha/h) Eff - Field efficiency (decimal) C2 - Dimensionality constant (C2 - 10) RE - Implement reliability (decimal) Equation 3.1 provides a means of matching reported data to actual field conditions. A method to determine the data validity for Saginaw Valley conditions follows. Table 3.1 lists the probable base point match of tractor power to implement speed and width. Data in Table 3.1 are synthesized from published reports (Benson, 1978); spot checks Of Saginaw Valley farms; and conversations with tractor dealers, extension agents, and farm industry groups. variables in Equation 3.1 are selected to meet the performance figures of Table 3.1. When data are substituted from Table 3.1 into Equation 3.1, five unknowns remain. Load factor (LP) is the tractor design loading rate. It reduces tractor wear while providing extra power for difficult field conditions briefly encountered. LF is designated 0.80 (White, 1977). 55 Table 3.1. Base Point Match of Tractor Power Level to Implement Speed and Width. Tractor Implement Implement Size Width ,Speed Implement PTOkW PTOHP 111 ft km/ h mph Maldboard plow 97.0 130.0 2.0 6.7 6.4 4.0 Disk corn stalksk./ 97.0 130.0 7.3 24.0 7.2 4.5 Disk barrow ‘ 97.0 130.0 6.3 20.7 6.4 4.0 Chisel plow /' 97.0 130.0 3.2 10.4 6.4 4.0 Field cultivate! 97.0 130.0 5.9+. 19.4+ 8.9 5.5 Sugar beet topper“ 82.0 110.0 4.0+’ 4.0+ 8.1 5.0 Sugar beet lifter/ 97.0 130.0 4.0... 4.0... 8.1 5.0 Row planter / 75.0 100.0 12.0+ 12.0+ 7.2 4.5 Ammonia applicator” 75.0 100.0 12.0+ 12.0+ 7.0 4.3 Row cultivator 2‘ 75.0 100.0 12.0+_ 12.0... 7.4 4.6 Rotary hoe 75.0 100.0 12.0 12.0 10.1 6.3 ~rGrain drill" 44.0 59.0 3.7 12.0 9.7 6.0 Rotary cutter 47.0 63.0 3.7 12.0 9.7 6.0 Fertilizer spreadeTI' 58.0 78.0 18.3 60.0 8.1 5.0 Sprayer: 22.0 30.0 9.1. 30.0 8.1 5.0 Navy bean puller/ +’ + windrower 46.0 62.0 6.0 6.0 8.1 5.0 MOwer-conditioner 47.0 63.0 3.7 12.0 9.7 6.0 Rake 16.0 21.0 3.7 12.0 8.1 5.0 Baler 49.0 66.0 5.4* 6.0* ** * +Width in rows (0.76 m, 30 in.) *Width in Mg/hr (T/hr) ** Ground speed is not a direct factor in power requirement 56 The PTO to axle power conversion (CONV) is 0.96 (ASAE YEARBOOR, 1978). For the sake of simplicity, reliability (RE) is assumed to be 1.0 for all machines considered. The remaining unknowns are draft (D) and tractive efficiency (TE). Domier and Friesen (1969) report a slight tractive efficiency advantage for four-wheel drive tractors in tests on a clay stubble field. Osborne (1971) reported little difference between tractors when operated on firm soil, but a greater advantage for four—wheel drive tractors when operated in soft soil. Dwyer and Pearson (1976) reported a 142 advantage for four-wheel drive tractors over a range of surface conditions. For simplicity of model operation, let us assume that tractive efficiencies are constant between two- and fourdwheel drive units. Plowing requires more energy than other field operations and thus is a major factor in tillage tractor selection. Tractive efficiency is nearly equal for two- and four-wheel drive tractors when operated on firm soil, thus specification of different tractive efficiencies become less important when sizing tractor power levels. Equation 3.1 cannot be solved until one of the remaining unknown variables, tractive efficiency or draft, is specified. Tractive efficiencies were selected to match reported data (Bowers, 1975, and White, 1977). Any error present in the tractive efficiency values (Table 3.2) will be Offset when draft is calculated. Substitution of power speed, and width data from Table 3.1; tractive efficiency data from Table 3.2; and the LF, RE and CONV parameters from above; into equation 3.1 results in one equation with one unknown. The unknown, draft, can be solved for each field 57 Table 3.2. Assumed Draft and Tractive Efficiencies. Draft Tractive Efficiency Implement N/m lb/ft Z Maldboard plow 16,272 1,115 80 Moldboard plow/leveler 17,513 1,200 80 Disk harrow 4,962 340 75 Disk 3,794 2601 75 Chisel plow 10,508 720 80 Field cultivator 1 3,794 260 75 Field cultivator 2 3,036 208 60 Sugar beet topper 5,284* 1,188* 75 Sugar beet lifter 6,245* 1,404* 75 No-till planter 2,402* 540* 72 Ammonia applicator 1,779* 400* 72 Grain drill 2,481 170 72 Row cultivator 1,668: 375 72 Rotary hoe 1,223 275* 72 MOwer-conditioner 2,919 200 80 Row planter 1,713* 385* 72 Baler 5.5+ 6.7+ 80 Rotary cutter 2,919 200 80 Fertilizer spreader 876 60 80 Rake 1,168 80 80 Sprayer 584 40 72 Navy bean puller/windrower 2,113* ' 475* 80 *Draft per row (0.76 m, 30 in.) +Drawbar kW/Mg (hp/T of alfalfa) 58 operation. The calculated drafts are listed in Table 3.2. The drafts resemble data reported by White (1977) except that: l) moldboard plow draft is slightly higher; 2) calculated sugar beet topping draft is higher; and 3) sugar beet lifting draft is lower. The draft discrepency of moldboard plowing may only be a function of soil type. White (1977) lists plowing drafts for sandy, loam, and clay loam soils. The soil associated with the calculated draft may contain more clay than the clay loam White (1977) lists. Discrepancy in sugar beet lifting draft may be a function of soil moisture content. A.wetter soil tends to cling to the sugar beet root, increasing the work expended to uproot the plant and the weight of material lifted. No justification could be found for the higher sugar beet topping draft. It is speculated that plant moisture differences may be a cause. The draft figures reported in the validation were assumed in the current model. Table 3.3 outlines field efficiency and speed data (White, 1978). Operating speed ranges were adjusted to allow full use of tillage tractor horsepower. The drawbar power requirements of a given machine operated at its fastest speed overlap those of the next larger machine operated at its slowest speed. This adjustment eliminates the possibility of a tractor, at a given power level, to be too small for a particular size machine operating at its slowest speed and yet over- powered for the next smaller size machine operating at its fastest speed. Harvest capacities cannot be determined entirely using machine productivity data. Material handling capability may play a larger 59 Table 3.3. Assumed Implement Operating Speeds and Field Efficiencies Operatingspeed, km/h (mph) Field Efficiency Implement Minimum Maximum 1 Moldboard plow 5.5 (3.4) 7.2 (4.5) 80 Moldboard plow/leveler 5.5 (3.4) 7.2 (4.5) 80 Disk harrow 4.8 (3.0) 8.1 (5.0) 80 Disk 4.8 (3.0) 8.1 (5.0) 85 Chisel plow 6.4 (4.0) 9.7 (6.0) 85 Field cultivator 1 6.4 (4.0) 9.7 (6.0) 85 Field cultivator 2 6.4 (4.0) 9.7 (6.0) 85 Sugar beet topper 3.2 (2.0) 8.9 (5.5) 70 Sugar beet lifter 3.2 (2.0) 8.9 (5.5). 70 No-till planter 4.8 (3.0) 9.7 (6.0) 55 Ammonia applicator 4.8 (3.0) 9.7 (6.0) 65 Grain drill 4.0 (2.5) 9.7 (6.0) 70 Row cultivator 4.0 (2.5) 8.1 (5.0) 80 Rotary hoe 8.1 (5.0) 6.1 (10.0) 80 Mbwer-conditioner 4.8 (3.0) 9.7 (6.0) 75 Row planter 4.8 (3.0) 9.7 (6.0) 55 Baler 4.0 (2.5) 8.1 (5.0) 60 Rotary cutter 4.8 (3.0) 9.7 (6.0) 70 Fertilizer spreader 3.2 (2.0) 8.1 (5.0) 70 Rake 6.4 (4.0) 8.1 (5.0) 80 Sprayer 3.2 (2.0) 8.1 (5.0) 65 Navy bean puller/ windrower 3.2 (2.0) 8.1 (5.0) 80 60 role than harvester capacity. The current machinery model does not account for movement of harvested material. However, limiting effects of material handling are partially incorporated by assuming discreet step sizes in harvest capacity. Harvest capacities are listed in Table 3.4. Presumably, the crop is loaded into transport vehicles in the field. Combine capacities for corn, navy beans, soybeans, and wheat were obtained (Singh, 1978). It is assumed that oat harvest capacity will likely equal wheat harvest capacity. Wheat and oat plants have relatively similar characteristics although lodging would be a larger problem for oats. Five sugar beet harvest capacities were developed with help from a sugar beet industry official (Brimhall, 1979). The capacities range between 0.8 to 1.9 hectares per hour for a two-operator farm. A 1.9 hectare per hour capacity would require a four-row lifter operating at 8.9 kilometer per hour. Alfalfa baler capacity is approximately 5.4 Mg/h (Rider, 1976). Table 3.4 lists mower-conditioner, raking, and baling operations for a 4.5 megagram per hectare crap. The alfalfa harvest system capacity (cut, rake, bale) is also given. 3.1.2 Machinery Economic Data Service life and repair data are obtained from.Bowers (1975) and listed in Table 3.5. Available machinery sizes are from the National Farm and Power Equipment Dealer's Association (Fall 1979) and the Deere and Company Agricultural Sales Manual (1979). 61 Table 3.4. Assumed Harvest Capacities, Field Efficiencies, and Speed Crop Harvester Speed, Field Efficiency, Field Capacity, Size km/h (mph) Z ha/hr (A/hr) Corn 2-row 5.3 (3.3) 70 0.6 1.4 4-row 4.8 (3.0) 65 1.0 2.4 6-row 4.5 (2.8) 63 1.3 3.2 8-row 4.5 (2.8) 60 1.6 4.0 Wheat 2-row 4.5 (2.8) 75 1.0 2.5 4-row 4.8 (3.0) 75 1.5 3.6 6-row 5.6 (3.5) 75 1.7 4.1 8-row 5.6 (3.5) 70 1.9 4.8 Oats 2-row 4.5 (2.8) 75 1.0 2.5 4-row 4.8 (3.0) 75 1.5 3.6 6-row 5.6 (3.5) 75' 1.7 4.1 8-row 5.6 (3.5) 70 1.9 4.8 Soybeans 2-row 4.0 (2.5) 70 0.8 2.1 4-row 4.5 (2.8) 70 1.3 3.1 6-row 5.3 (3.3) 70 1.5 3.6 8-row 5.3 (3.3) 65 1.7 4.1 Navy beans 2-row 8.0 (5.0) 70 1.7 4.3 4-row 8.0 (5.0) 67 2.1 5.3 6-row 8.0 (5.0) 65 2.5 6.3 8-row 8.0 (5.0) 63 3.1 7.7 Sugar beets 1* * * 70 0.8 2.0 2 * * 70 1.1 2.8 3 * * 70 1.4 3.5» 4 * * 70 1.7 4.3 5 * * 70 1.9 4.7 Alfalfa ** ** ** ** 0.6 1.5 * Sugar beet harvest capacity is set at 5 levels. Speed ranges between 3.2 to 8.9 km/h (2.0 to 5.5 mph) depending upon harvester width. ** Alfalfa harvest consists of mower-condition, rake, and bale operations. Capacity listed is for a 3.7 m (12 ft) swath, 5.4 Mg/hr baler, and 4.5 Mg/ha crop. Speed and efficiency values are in Table 3.3. 62 Table 3.5. Assumed Repair and Service Life Data Total Repairs Service Life Implement Z of Purchase Price hrs Moldboard plow 80.0 2,000 Disk 65.0 2,000 Chisel plow 65.0 2,000 Field cultivator 65.0 2,000 Sugar beet topper 60.0 2,000 Sugar beet lifter 80.0 2,000 No-till planter 75.0 1,000 Ammonia applicator 75.0 2,000 Grain drill 75.0 1,000 Row cultivator 65.0 2,000 Rotary hoe 65.0 2,000 Mower-conditioner 180.0 1,000 Row planter 75.0 1,000 Baler 80.0 2,000 Rotary cutter 60.0 2,000 Spin spreader 75.0 1,200 Rake 75.0 2,000 Sprayer 75.0 1,200 Navy bean puller 75.0 2,000 Corn head 33.0 2,000 Grain platform 33.0 2,000 Windrow pickup/platform 33.0 2,000 Combine 33.0 2,000 2-wheel drive tillage tractor 90.0 10,000 2-wheel drive utility tractor 90.0 4,800 4-wheel drive tillage tractor 90.0 10,000 63 Tractor and combine prices were obtained from the National Farm and Power Equipment Dealer's Association (Fall 1979). Prices were adjusted to August 15, 1979, levels using information compiled by the Statistical Reporting Service (1979). Prices per PTOkW were determined for three tractor categories: four—wheel drive tillage; two~wheel drive tillage; and two-wheel drive utility. A linear regression of paired data (i.e., tractor size, PTOkW and price) was used. Where tractors were rated in drawbar or engine kilowatt, power takeoff kilowatt levels were calculated using data from the ASAE YEARBOOK (1978). Utility tractors were determined to be 4 years old (Singh, 1978). Utility tractor purchase price is assumed to be 48 percent of the new price (Hunt, 1977). The developed equations are: Two-wheel drive purchase price . 302.51 * PTOkW: r28 0.94 Four-wheel drive purchase price - 346.49 * PTOkW: r2- 0.85 Where r2 - coefficient of determination Combine prices were ranked from lowest to highest. The lowest and highest cost machines were $22,175 and $66,283, respectively. The price list was divided into four categories: $20,000; $30,000; $40,000 and $50,000 and above. The number of machines in each category were 5, 7, 7, and 6 respectively. The average price of each category was used as the purchase price for 2, 4, 6, and 8-row capacity machines, respectively. Corn head prices were average for all manufactures (National Farm Power and Equipment Dealer's Association, Fall 1979). Prices of grain and pickup platforms and for tillage and planting equipment were obtained from Deere and Company (1979). Bean puller, bean windrower, 64 ammonia applicator, and fertilizer spreader prices were obtained in dealer interviews and from previous studies (Benson, 1978 and Singh, 1978). Prices and sizes of equipment are given in Table 3.6. 3.2 Available Workdays The literature reports two categories of workday data: 1) observed date for a location and year (e.g., Link, 1968), and 2) generated data using weather and soil parameter inputs (e.g., Kish and Privette, 1974). Singh (1978) reviewed the literature in this area. No observed workday records exist for the Saginaw valley, thus a workday generator (Tulu, 1973) is used in the present study. The work- day simulator predicts daily work, nOdwork day sequences for tillage, crop maintenance, and corn combining operations. WOrkday sequences for harvest of crops other than corn are not explicitly developed in the simulation, thus a new method is developed to generate harvest workday sequences. The generation of harvest workday sequences will be discussed later in this section. The Tulu (1973) simulation is based on soil moisture budgets. His model includes five weather and two soil parameters. The weather data are: 1) maximum daily temperature (OF); 2) minimum daily temperature (OF); 3) daily precipitation (hundredths of an inch); 4) daily open pan evaporation (hundredths of an inch); and 5) snow condition (0 8 no snow, 1 8 snow). 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Hoodoo noon Human ocq.¢ 55m.w 555.5 mmm.o Nm~.¢ ~O5.m oao.~ ¢5~.~ w an.- mm.oH 5e.» 55.5 mm.o co.m N5.q Hm.m Nev nuofia woum>wuflao vaofim -~.5 o~0.5 mum.m mem.m qu.m Nwo.m aw~.~ 5mm.~ m cc.o 5m.m m~.m 5m.¢ cm.m mm.m no.m <<.N 58“ some: scan Homfisu amo.- «05.m~ ~m5.eu c~o.- Ham.m moe.w mom.q ~5m.< m a~.- 5m.o~ 5H.m ow.5 om.o -.m cm.¢ w¢.m Nav coves xmfia mmn.o~ woo.m~ ~o~.NH Nm¢.o~ m05.m can.w m~¢.5 mmm.~ w ca m m 5 o m c m New coeds soda vuoonvaoz ouaum\on«m masons: uouaoHQEH «mo own: moowum voumfiooom< paw moufim enoawsumz ooaamm< .e.m manna 66 .un\mz_l o .uuuua n.a .30“ Add omV ao o5 u u .aouuon Add oHV so do I a « me.osm axoem\m ~o~ as «as sycam, \ m~.ams ssoem m sod as me axoam \ om.~om saoam\w ~H_ as me sacs» «No.5m Hso.ne mh~.on mam.s~ a m o s N no“ nova: man.” man.“ mms.~ a mm.s sa.n mo.m Nae sues: «Ng.o oo~.n oom.m a ww.s om.n mo.n New asses «~H.s~ oa5.~s “no.a ws~.s a m o s N No“ nova: oao.aa can.“ nn~.s a m s a Nam nun“: oss.~ a s~.¢ Nae guess sme.s w os.m New sue“: oom.~ a m.m~ Nay nuvfi3 acuowuu owmaaau Hooaalc Houuwuu 5ufiaau: Hoogalw Houomwu owoaawu Hmosalw uawnaoo osxoao a showuoam auomuoan awouu moo: ouoo soaaso soon 5>oz Hohouom axon outdone» swam ouaum\ouwm masons: no use: .g uauaoaoaH A6133 66 ~33. 67 The soil moisture criterion describes the available water in a soil layer as a percentage of the maximum available water in the layer. Maximum available moisture is defined as field capacity minus wilting point. Tulu (1973) divided the soil profile into three layers. The first layer occupies the 0.0-to 1.2-inch region. Second and third layers are at the 1.2-to 3.0-and 3.0-to 6.0-inch depths, respectively. Each layer is assigned a moisture criterion (Ci) - The top two layers are assigned a value of 0.985 (Cl - C - 0.985). The third layer 2 criterion will depend on soil type, drainage conditions, and field operation performed (Tulu, et al., 1974). A brief summary of the Tulu model follows . Heat units are accumulated from.the beginning of the year to determine the date of soil thaw. At spring thaw, the soil presumably is at the saturation point. The date of spring thaw triggers the moisture budget. If, on any day, the budget indicates a moisture level lower than the criterion set for each level, then a workday is said to have occurred. Conversly, if the budget indicates a higher moisture level than the criterion for at least one level, then a nondworkday is recorded. The model continues in this fashion until the soil freezes in the fall. The model results produce a daily workday record for the tillage, crop maintenance, and corn harvest operation categories. Tulu (1973) ran the model for northern Indiana where actual work— day data existed on three farms for 1 year each. The model gave good results for spring tillage and fall corn harvest using the C3 values of 0.985 and 1.0, respectively. No validation was undertaken for fall tillage operations. 68 A technique to determine workday availability for harvest of all crops was needed. Two conditions must exist before harvest can begin: 1) the ability to adequately propel a harvester across the field, and 2) crop moisture should be at an acceptable level. Von Bargen (1966) describes an open haying day as a day for which hay-harvesting operations can take place. Three factors: rainfall on the date, percentage of possible sunshine on the date, and rainfall on the previous date, determine whether an open haying date occurs. Using the Tulu (1973) model and the idea of an open haying day (Von Bargen, 1966) a procedure was developed to predict workday sequences for soybean, navy bean, wheat, oat and alfalfa harvest. Tulu (1973) model results for C - 1.00 predict the ability to propel 3 a machine over a field. With the help of extension agronomists (Helsel, 1979) rainfall levels conducive to harvest were hypothesized. It is hypothesized harvest workdays can be predicted using Tulu (1973) model results and rainfall data. A summary of the hypothesized procedure follows. An alfalfa harvest workday would occur on any day that: 1) is part of a 3-day workday sequence for C - 1.00; 2) it does 3 not rain; and 3) less than 0.20 inch rain was recorded for the other days in the C - 1.0 workday sequence. 3 A harvest day for soybeans, navy beans, wheat and oat harvest is established provided: 1) there is no rain that day; 2) less than 0.20 inch rain was recorded the previous day; and 3) it is a workday according to the data generated using C3 = 1.0. The number and distribution of harvest workdays for sugar beets are equated to the usable days available for tillage during the same 69 period. This assumption was drawn after conversations with several Michigan sugar beet farmers. It is hoped that the hypothesized method of determining harvest workdays will be investigated in future studies. As previously mentioned, the Tulu (1973) model was validated for Nbrthern Indiana. A new validation was needed for the Saginaw valley area. The validation would confirm the correctness of the C value 3 selected. The selection of the C parameter for the Saginaw valley 3 and the validation were carried out hand in hand. While farmers and extension personnel cannot produce workday records, they can identify a reasonable machinery complement for a given area. This is the key point to this validation procedure. Using a water—holding capacity of 1.9 inches per foot (Meints, 1979), several work/no-work day tillage sequences are generated for different C3 values. The Bad Axe weather station provided weather data for the years 1949 to 1977. Open pan evaporation for these years was obtained from the East Lansing station. Actual data for each of the 28 years are used as input by the machinery selection model (Chapter 2). The model selects the machinery complement which accomplishes all work in each of the 28 years for the given farm and operation date constraints. The selected machinery sets are ranked from highest to lowest for cost per hectare. A 162 hectare C-NB farm.was selected for study. The operations schedule for the farm is presented in Table 3.7. Four machinery sequences were developed for 03 parameters of 0.970, 0.975, 0.980, and 0.985. Harvest workdays as developed previously are identical for all comparisons. Results are given in Table 3.8. 70 Table 3.7. Operations Schedule for a C-NB Farmf Starting Finishing Field Work Operation Crop Date Date hrs/day ** Harvest Navy beans 8/28 10/01 6 Harvest Corn 10/09 11/12 10 Disking Navy beans 10/09 11/26 12 Fertilizer spreading Corn 8/28 11/26 12 Moldboard plowing Corn 8/28 5/14 12 Field Cultivation 1 Corn 4/24 5/14 12 Planting Corn 4/24 5/14 12 Spraying Corn 4/24 5/14 12 Moldboard plowing Navy beans 10/09 6/18 12 Disk harrowing Navy beans 4/10 6/18 12 Field cultivation 1 Navy beans 5/15 6/18 12 Field cultivation 2 Navy beans 5/22 6/18 12 Planting Navy beans 5/22 6/18 12 Cultivation Corn 5/29 6/18 12 NH3 application Corn 5/29 6/18 12 Cultivation Navy beans 6/19 7/09 12 * 162 hectare, 1-operator ** Mbnth/day 71 N no M5~ N no OOH N am cod N me On_ O a On Nam a me O5~ N on «ON N me onH m a OO own a «a O5H N on «ON N Oe Onu ON N no «ON a on NO" N mm «OH N me On“ NH N 5O MON N OO and N mm «OH N ma On~ NN e O5 OHN N mm NON N am OON N Om NON Nu a ma ONN O OO MON a ac O5H N Om NON «a O OO OMN N mm «ON N we «OH N gm NON nu O O5 nnN N 50 mON e 5n and N Na meg OH N MNN NON a <5 ONN a NO 50~ N am «On 5H N mNn NeN N am ONN a «O NON N on nOH ON O OO mnN a NO HNN N 5O mON a On and OH N OMN mnN c 5O eNN a mo an a an NON ON a a a O mm OMN a O5 mHN e mm «mm HN « a a N MN“ HON N ma OHN N cm can NN a a a O OO an e O5 ONN a on «ma MN « a a O Onu OON N «Na ficN N on naH «N x a a a a a O OO NON N mm NON mN a a a s a a a a a N 5O nON ON a a a a a a a a a a a « 5N « a a a a a a a a a a « ON msom zaoam o£\w atom 33095 on\m neon axOHm un\m msom axosm mn\w mama odqnaoo uouooua umoo osanaoo uououus umoo ooanaoo nououus umoo oaanaou uouomwa umoo O50.0 M50.0 OO0.0 mm0.0 Anowuoufiuo ousumwoa sasavmo +muom moon hooxuo3 upon some: sham OZIO o How oumm haoafinomz .m.m manna 72 ousaouumooo came Oowwwoqu casuaa ans: oaoam Nam ouoaasou ou wannabe HauwquOIH .ououoonINO~+ N ac Omfi N ac on“ N ac on“ N ac Oma H N ac On" N ac Omg N ac Ona N ac On“ N N ac Ona N ac Oma N ac can N ac OMH m N on Nod N ac Ona N ac omH N ac ona c N 5m mod N Na mod N ac ona N ac OmH m N no mod N 5m mo— N ac OmH N ac OmH c c ac O5H N OO mod N am Nca N ac Oma 5 030m 3309a on\w msom 3308a wax» meow 3308a mn\m msom 3308a wnxm scum onwnaoo Houowuh umoo onweaoo uouomua umoo oaanaou uouooua umou mownaou uouomua umoo O5a.O m5a.O ONa.O mma.O Aaoauouwuo ounumfioa oasav no A.v.ucouv .m.m macaw 73 The appropriate moisture criterion for tillage is selected by identifying the sequence of machine sets most likely to appear for the given farm and study area. The C3 parameter of 0.980 was selected because it generated the sequence of machinery sets most likely to be found on the example farm as judged by extension specialists at Michigan State university. The selection criterion was based on the relative ranking of machinery set costs for the four C3 parameters and personal observations of farms. 3.3 Farm Identification Data concerning the farm pertain to crop rotation, length of workday, and scheduled field operations. Crop rotations with alfalfa, corn, navy beans, oats, soybeans, sugar beets, and wheat are common. Probable workday lengths for field operations are given in Table 3.9. Field operation sequence and scheduled periods are obtained from Christenson et al. (1980) and reported in Table 3.10. 74 Table 3.9. Estimated WOrkday Lengths Operation Field Werk, hr/day Corn harvest 10.0 Soybean harvest 8.0 ’ Alfalfa harvest 9.0 Navy bean harvest 6.0/ Wheat harvest 8.0 Oat harvest 7.0 Sugar beet harvest 11.0 ’ Tillage 12.0 75 .aaoo uuoga ouomon no ohoo wound waaxufiv Hawk « .aouo msoa>oua on» no unopuon noumo unauo oaaoa socuauoao ownu momma < mo sumo wdasoawon_<%+ .mn uuaameoz ovum can a uoaouoo ouuwum umo>won nuoo umnu nouooapca mH\~H:a~\Ofl+ mono H NBucN: 33...... 33 .ouaaao ON\oInO\O a~\OIaN\m nmz MO\5IN~\O a~\OINN\n mO\5INN\o O~\5IaH\O aH\OIaN\n oum>auaao mO\nIHO\n nO\OIn~\m aN\OINN\m m~\mtcN\c wouaouam OO\mIO~\c OO\mIOH\c OH\O~InN\a nH\nION\c mO\oIO~\c a~\OINN\m m~\mIcN\c woaunaflm OO\mION\c OO\nIO~\c O~\O~InN\a m~\m1O~\c mO\OIm~\m aN\OINN\n n~\mIcN\c .uaso OHon 2 3-2 3 S Sumac n 33c OO\nIO~\c OO\mION\c mO\OIO~\c a~\OIOH\c wocuam 3: 74 3:24 3: 74 834 234 23% mars: 5O\~NIaO\O~ 5N\-|aO\O~ O~\O~L< 5N\H~IaO\O~ 5N\-IaO\OH 5N\~HIaO\O~ 5N\~HIaO\O~ «snap Adam cN\cION\m 3:73.. 3: Tc 2 3.33 2 \S .< 3333 2333 3333 .152: ..c 3:33..” NfixanN\n cO\aI~N\O caxwtcN\5 cH\OIcN\5 cN\5IOH\5 5O\OI5H\5 n~\-InN\a MN\ONImN\a NO\OHION\O +mH\H~IaO\ON uoo>uom umauuaoz awoo nowuwuoao mooauowoao macaw mo mafiafia ooaaou< .O~.n MHOMH 4. ANALYSIS OF DESIGN PROBABILITY CALCULATION METHODS Selected machinery complements must be qualified with a design probability. The design probability indicates the number of years out of 10 for which the selected machinery set could complete all field work. A design probability of 0.70 would indicate that for 7 out of 10 years, the selected machinery complement would complete all field work for the given farm, within the specified date constraints. The design probability allows unbiased comparison of machinery requirements for different rotations. An unequal design probability would undersize one machinery set relative to the other, without accounting for lower productivity of the smaller set. Equal design probabilities assure that the selected machinery sets are equal in productive ability. The best way to determine the design level is to develop machinery complements for each year's weather data. This is the same procedure used to determine the workday simulator parameters. From an economic standpoint, inspection of Table 3.8 shows that a 78 PTOkW tractor and a 4-row combine would complete all field work on the C-NB farm 23 years out of the sampled 28, for 03-0.980. The physical design probability may not be that machinery complement corresponding to cost number 23. The twenty-third largest tillage tractor is 87 PTOkW. The twenty-third largest combine is 4—rows. Because the model investigates combine-tractor trade-offs (Section 2.1), a 4-row combine and less than 87 PTOkW tractor may have been adequate in year 19 but may have cost more than set No. 19 in Table 3.8. 76 77 The physical design probability must account for tractor size, combine size, and sugar beet and alfalfa harvest capacity requirements. Because the model prints only the lowest cost machinery set for each year and not all satisfactory machine sets, the economic design probability is used in the following discussion. The development of machinery sets for each year of available work- day data, while giving good results, has a drawback. Analysis of labor vs. machinery, and number of operations vs. machinery requirements, would necessitate generation of many yearly machinery complements for the same farm. Attempts have been made to statistically group workday data. Burrows and Siemens (1974) determined the 80 percent level of time available in each work period; these data are then used in their machinery model. Tulu (1973) developed a unique distribution to describe generated workday data. Using this distribution function, he inputted the probability of workdays in each period for his machinery model. Singh (1978) considered weekly workday data to be normally distributed and independent from week to week. By grouping weekly means and standard deviations together over an operation's scheduled period, and using areas under the normal curve, a percentage of time available for work on the given operation can be found. The weekly number of hours available is a proportion of weekly mean to the sum of means over the period. For each method reviewed, the choice of a probability level for each period translates to an equal design probability level for the machinery set chosen. Fulton et a1. (1976) point out that a probability 78 level assigned to periods does not necessarily imply that the given probability level will be correct for the entire season. Correlations may exist between periods during a season. The computational advantages of using statistically grouped data are great enough that an unbiased estimation technique was sought. For comparison, model performance using actual data is determined first. Three 162 hectare farms, C-NB, C-NB-W-BT, and C-NB-NB-BT were selected V/ for study. Operations scheduled for the farms are shown in Tables 3.7, 4.1 and 4.2. Machinery complements for the three farms were developed for the 28 years of daily workday data. Cost per hectare for generated machinery sets are given in Table 4.3. The machinery set cost figure is used as an indication of the machinery set field capacity. Field capacity is a function of tractor; implement, combine, alfalfa baler, and sugar beet harvester size. An analysis utilizing the physical capacity of each implement using 28 machinery sets would be unwieldy. Using the machinery complement cost simplifies the analysis procedure, while still giving the reader a feel for the relative field capacities involved. A C-NB farm (Table 4.3) would require a machinery set costing 248 $/ha to complete all field work within specified data constraints 25 out of 28 years. For three of the 28 years no machinery set could be found to complete the field work within the specified data constraints. The inability to complete the field work for the 3 years out of 28 may be due to the 1 man labor supply, maximum tractor size of 201 PTOkW or maximum combine size of 8 rows (Section 2.3). Analysis of crop rotation is made by inspecting machinery set cost for a single ranking and for the highest rank obtainable. A probability 79 Table 4.1. Operations Schedule for a C—NBAWaBT Farm+ Starting Finishing Field Work Operation Crop Date Date hrs/day * Harvest Wheat 7/17 8/06 8 Harvest Navy beans 8/28 10/01 6 Fertilize Wheat 8/28 10/08 12 c/g5Disk harrow Wheat 8/28 10/08 12 Field cult. Wheat 9/25 10/08 12 Plant Wheat‘ 9/25 10/08 12 Topping Sugar beets 10/02 11/12 11 Lifting Sugar beets 10/02 11/12 11 Harvest Corn 10/09 11/12 10 Fertilize Sugar beets 8/28 11/26 12 Flow Sugar beets 8/28 11/26 12 Disk Navy beans 10/09 11/26 12 Fertilize Corn 9/25 11/26 12 Fertilize Wheat 3/20 4/23 12 Spray Wheat 5/01 5/07 12 Field cult. 1 Sugar beets 4/10 5/14 12 Field cult. 2 Sugar beets 4/10 5/14 12 Plant Sugar beets 4/10 5/14 12 Spray Sugar beets 4/10 5/14 12 Plow Corn 9/25 5/14 12 Field cult. Corn 4/24 5/14 12 Plant Corn 4/24 5/14 12 Spray Corn 4/24 5/14 12 Plow Navy beans 10/09 6/18 12 Disk harrow Navy beans 4/10 6/18 12 Field cult. Navy beans 5/15 6/18 12 Field cult. 2 Navy beans 5/22 6/18 12 Plant Navy beans 5/22 6/18 12 Cultivate Sugar beets 5/22 6/18 12 NH3 applic. Corn 5/29 6/18 12 Cultivate Corn 5/29 6/18 12 NH3 applic. Sugar beets 6/05 6/25 12 Cultivate Sugar beets 6/12 7/02 12 Cultivate Navy beans 6/19 7/09 12 +162 hectare, 1-operator * Mbnth/day Table 4.2. Operations Schedule for a C-NB-NB-BT Farmf Starting Finishing Field WOrk Operation Crop Date Date hrs/day Harvest Navy beans 8/28 10/01 6 Topping Sugar beets 9/25 11/12 11 Lifting Sugar beets 9/25 11/12 11 Harvest Corn 10/09 11/12 10 Fertilize Sugar beets 8/28 11/26 12 Flow Sugar beets 8/28 11/26 12 Disk Navy beans 1 10/09 11/26 12 Fertilize Corn 9/25 11/26 12 Field cult. 1 Sugar beets 4/10 5/14 12 Field cult. 2 Sugar beets 4/10 5/14 12 Plant Sugar beets 4/10 5/14 12 Spray Sugar beets 4/10 5/14 12 Plow Corn 9/25 5/14 12 Field cult. 1 Corn 4/24 5/14 12 Plant Corn 4/24 5/14 12 Spray Corn 4/24 5/14 12 Plow Navy beans 2 8/28 6/18 12 Disk harrow Navy beans 2 4/10 6/18 12 Field cult. 1 Navy beans 2 5/15 6/18 12 Field cult. 2 Navy beans 2 5/22 6/18 12 Plant Navy beans 2 5/22 6/18 12 Flow Navy beans 1 10/09 6/18 12 Disk harrow Navy beans 1 4/10 6/18 12 Field cult. 1 Navy beans 1 5/15 6/18 12 Field cult. 2 Navy beans 1 5/22 6/18 12 Plant Navy beans 1 5/22 6/18 12 Cultivate Sugar beets 5/22 6/18 12 NH3 applic. Corn 5/29 6/18 12 Cultivate Corn 5/29 6/18 12 NH3 applic. Sugar beets 6/05 6/25 12 Cultivate Sugar beets 6/12 7/02 12 Cultivate Navy beans 1 6/19 7/09 12 Cultivate Navy beans 2 6/19 7/09 12 +162 hectare, 1—operator * Month/day 81 Table 4.3. Required Machinery Complement Cost for Twenty Eight Years of Available Workday Data Machinery complement cost. $/ha Ranking+ c-un C-NB-W-BT C-NB-NB-BT 2 8 * * * 2 7 * * * 26 * * 319 25 248 * 282 24 241 A * 277 23 219 326 243 22 216 299 232 21 215 284 231 20 214 264 231 19 205 257 229 18 192 252 228 17 187 251 220 16 184 251 216 15 184 236 198 14 170 227 197 13 166 218 197 12 164 207 196 11 164 206 196 10 164 205 196 9 164 201 195 8 164 190 184 7 162 190 184 6 150 190 184 5 150 190 184 4 150 190 184 3 150 190 184 2 150 190 184 1 150 190 184 +Ranking is based on cost per hectare *Unable to select a machinery complement capable of completing all field operations within specified date constraints **l62 hectare, one-operator 82 of 0.69 corresponding to the 20th year out of 28 (20/28+1-0.69, Thom 1966) would require machinery sets with costs of 214, 264 and 231 $/ha for C—NB, C-NB-W-BT, and C-NB-NB-BT farms respectively. Model performance using the Singh (1978) method of available time determination is compared to performance using actual data. Weekly means and standard deviations were calculated from.the daily workday records. The means and standard deviations were combined over each field operation's scheduled calendar period. using area under the normal curve and an 80 percent level (i.e., for 8 out of 10 years at least the calculated amount or more worktime occurs), the total time for each field operation was determined. Weekly available time is the ratio of weekly mean to total of means over the scheduled period. Available hours were inputted to the machinery model. Cost per hectare for the selected machinery sets are given in Table 4.4 under the normal distribution heading. The table ranks the cost per hectare in terms of the Table 4.3 results using actual yearly data. Note the unequal nature of the rankings. Using the Singh (1978) method an equal 0.80 design probability level is assumed for each of the three farms. Comparison to Table 4.3 results show the design probabilities are actually 0.62, 0.52 and 0.59 for the C-NB, C-NBAW-BT, and C-NB-NB-BT farms respectively. The normal distribution method of Singh (1978) overstates the design probability for the three example farms. Further, the normal distribution method misleads one to believe the three farms have an equal design probability when in reality these design probabilities vary. Using the normal distribution would bias results in favor of the 83 Table 4.4. Model Results for Assumed Available Workday Data Distributions and Probability Distributions" Farm size-162 hectares, one operator labor supply WOrkday _ _ _ _ _ probability C NB + C NB W BT + C-NB NB BT + level $lha rank $lha rank $lha rank Normal Distribution 0.800 192 18 242 15 221 17 Beta Distribution 0.525 185 16 276 20 235 22 0.550 203 18 308 22 253 23 0.575 215 21 344 23 285 25 0.600 219 23 341 23 315 25 * Field operations for each crop are given in Tables 3.7, 4.1, 4.2. +Rank is in relation to Table 4.3 results, No. out of 28. C-NB-W-BT rotation since its machinery set costs ranking (actual design probability) indicates a performance rating lower than the C-NB or C-NB-NB-BT farms for the same normal curve parameters. Because of the shortcomings associated with the normal distri- bution method a second probability distribution was tested. The weekly mean and standard deviations for workday data were fit to the beta distribution parameters k and k2 (Naylor et al., 1966). Areas 1 under the beta curve were calculated using the MDBETI subroutine of the International Mathematical and Statistical Library (1979). The 80 percent design level was sought. Specifying the 80 percent workday probability level for each week's data is more restrictive than specifying that level for an operation's scheduled period. The beta distribution technique was used in a trial and error setting to discover a beta percentage level for individual weeks which would develop an 80 percent design probability for the entire farm. The selection of beta parameters first focused on harvest operations to be followed by tillage and planting operations. From inspection of the actual available harvest workday data, the minimum hours available 8 out of 10 years were selected (Table 4.5). An iterative search yielded the weekly beta parameters which recreate the actual data of Table 4.5. After the weekly harvest time was found, noneharvest operation time availability was determined. Since non-harvest operations can occur over relatively long periods of time and since various non- harvest field operations compete for the same time, the procedure used for selection of harvest beta parameters is not applicable to finding non-harvest beta parameters. Through trial and error four weekly beta percentages were selected to be presented in this section. The beta percentages are 52.5, 55.0, 57.5 and 60.0. A beta percentage of 55.0 denotes the value of the random variable which occurs or is exceeded 55 percent of the time. It was desired to select the weekly beta percentage such that the chosen machinery set exhibited an 80 percent design probability. Failing the first test it was hoped that the design probability would be equal among example farms, when comparison was made to design probabilities generated by selection of machinery sets for each of the 28 years of available workday data. The work time available for harvest was combined with each non- harvest available workday data set. The combined time set was Table 4.5. Available Harvest Hours at 80 Percent Level** 85 Harvest Beginning Ending Work Hours Total Hours Operation Date Date Per Day in Period Navy beans 8/28* 10/01 6.0 82.3 Soybeans 9/25 10/22 8.0 75.4 Wheat 7/14 8/06 8.0 61.7 Oats 7/24 8/13 7.0 60.0 Sugar beets 9/25 11/12 11.0 132.0 Corn 10/09 11/12 10.0 162.9 Alfalfa first 5/22+ 6/11 9.0 88. 7 Alfalfa second 7/10 7/23 9.0 54.0 Alfalfa third 8/21 9/03 9.0 61.8 * Month/day +Usable hours during week 5/22 are reduced by one-half to approximate a starting date of 5/25. at 8 out of 10 years at least the listed harvest hours would occur, based upon the 28 year available workday data set developed in Section 3.2. processed as input for the machinery model. Results appear in Table 4.4 under the beta distribution heading. It is hypothesized that a beta percentage can be found to recreate any design probability. The 52.5 to 60.0 beta percentage range produced actual design probabilities of 0.55 to 0.79 for the C-NB farm. A beta percentage of 52.5 recreates the 55 percent design level for a C-NB farm. However, the 52.5 percent beta level does not produce equal design probabilities for all farms in Table 4.4. Thus the beta distribution technique can be used, in all likely- hood, to find an 80 percent design probability for a particular farm. The beta percentages selected for a particular crop rotation could not be used to analyze other crop rotations, since the results in 86 Table 4.4 show unequal design probabilities between crop rotations for any given beta percentage level. One final test of the beta and normal distribution methods was made. Since the beta and normal distribution methods could be used to find a particular design probability on a single given crop rotation, they were tested for the case where the field operation schedule was modified. Deletion of one operation was arbitrarily selected for the test . The results shown for the three farms listed in Tables 3.7, 4.1, and 4.2 were adjusted by deleting fall disking. Model results for actual data use are given in Table 4.6, probability distributions are presented in Table 4.7. The deletion of fall corn stalk disking lowered the machinery set cost associated with a given design probability Table 4.6 vs. Table 4.3. Focusing on the 0.69 design probability (rank-20), cost reductions were 48, 5, and 11 $lha for the C-NB, C-NBeW-BT, and C-NB-NB-BT farms respectively. The reduction in cost is expected as the workload is reduced on the farm. Inspection of Tables 4.7 and 4.4 show the model results for both normal and beta distribution methods to have changed. The beta distribution method utilizing a 52.5 percentage yielded a C-NB farm with a rank of 16. Deletion of fall disking changed the rank on the C-NB farm to 19 for the same 52.5 beta percentage. As in the previous discussion a given probability level generated unequal design prob- abilities between the example farms. Thus, work with statistically 87 Table 4.6. Required Machinery Complement Cost for Twenty Eight Years of Available Werkday Data++ Machinery complement cost, $lha mum? am ammar 04mmar 28 * * * 27 233 * * 26 224 * 317 25 210 * 262 24 202 * 241 23 193 289 235 22 186 280 226 21 167 259 223 20 166 259 220 19 164 246 200 18 164 242 199 17 163 240 196 16 162 231 195 15 162 217 195 14 161 208 195 13 161 206 195 12 160 205 195 11 160 204 194 10 160 189 194 9 149 189 183 8 149 189 183 7 149 189 183 6 149 189 183 5 149 189 183 4 149 189 183 3 149 189 183 2 149 189 183 1 149 189 183 +Ranking is based on cost per hectare of each machinery complement. *Unable to select a machinery complement capable of completing all field operations within specified date constraints. ++162 hectare, one-operator farm, and deletion of corn stalk disking operation. 88 Table 4.7. Model Results for Assumed Available Workday Data Distributions and Probability Distributions Under a Modified Operations Schedule Workda Farm size-162 hectare, one operator labor supply y C-NB C-NB-W-BT C-NB-NB-BT probability + + + level $lha rank $lha rank $lha rank Normal Distribution 0.800 167 21 231 16 201 19 Beta Distribution 0.525 165 19 257 19 221 20 0.550 184 21 290 23 ' 242 24 0.575 190 22 323 23 266 25 0.600 193 23 316 23 298 25 * Field Operations are given in Tables 3.7, 4.1, 4.2, except fall disking of corn stalks is excluded. +Rank is in relation to Table 4.6 results, No. out of 28. grouped data was halted. Design probabilities are found by generating machinery sets for each year and selecting the appropriate set from the ranking. In summary, design probabilities must be determined by using actual yearly data in the machinery model. Statistically grouped data, for normal and beta distributions, were unsatisfactory for use in design probability determinations. Comparison of machinery sets using statistically grouped data may lead to biased conclusions because machine set productivity may not be equal between complements. 5. RESULTS Results for farm size, labor supply, and.design probability are discussed. A sensitivity analysis of machinery cost and fuel use is provided. Results for net return to land, including crop receipts, operating costs, machinery and labor costs are also provided. 5.1 Net Returns to Land Sixteen crop rotations are considered for analysis. For each rotation, 28 machinery complements corresponding to each year of workday data were developed. For illustrative purposes the machinery set with the 20th highest cost (0.69 design probability) was used in the comparison. Equally valid net return to land comparisons could be made for design probabilities other than 0.69. Labor supply was restricted to one full-time operator. Farm size was set at 162 hectares. Machinery expenditure is only one aspect of the farm's total cost. The gross income, operating expenses, crap hauling and drying costs, and labor expenses must be known. Expected-yield relationships among crops for the Saginaw Valley region are shown in Table 5.1 (Christenson et al., 1980). Crop prices, seed, fertilizer, herbicides, insecti- cides, crop hauling and drying, and labor costs are obtained from Christenson et al. (1980). Crop prices are listed in Table 5.2. Table 5.3 summarizes income and expenses for each farm. Substan- tial variations in net returns per hectare to land exist. The 89 90 .msmonaom no mason apes mafiaoaaom choc . « .ooowuomua Housuaoo oooouaaooou moo .owoawmuo commune moo oaau oumaaoom .maaoo cacao mama wouauxounooam ao>au+ a.a m.O~ N.N aONN aonfi ONN5 , OHN5 5oc5 Olmznulo a 4443 ac 50nm nmNH HNINZIO m.O~ N.N ac aowN na5H Hm1m21<1<\o 5c aonH coao coao HQINZIOIO nan cmmN OHN5 annulmz mnoc aona coao coao 31NZIOIO a.a O.a a.a n.ON N.N aowN can OHN5 5oc5 Olouul< 1mz ouowuouom oumsuoua< woos: moaoaw vouooaxm .~.n macaw + 91 Table 5.2. Estimated Crop Prices Crop Price, $ Corn 0.09/kg Navy beans 0.44/kg Soybeans 0.23/kg Sugar beets 25.90/Mg Wheat 0.12/kg Oats 0.10/kg Alfalfa 60.63/Mg Source: Christenson et al. (1980) 92 .umoa Hon OOO.m~m no such sou um mama ow Hououooo moo an an“ .oooauaabomo swam show one Momma oo>aw onu now canoowouuo Ho>oH smegma: was mo3.mwnu ocean mm.O ma huaaaeonouo nuance «canomzaa A .Oouoaooo you one mono» auuoaoua “memos confide: maafia «Hosea moouo % .36 3 53329:. 83% meanest... .onxm no couscous ooaHo> mauowouoo some ea oooaaooa ow woaumuoao so uoououoH+ mm.51 ua.OOm ~m.OO cc.NO am.am~ 5H.mmm 0101014 1<1<1<1<\O Nn.nH N5.5aN Hm.OO N5.~O cN.NcH Nn.mam 01mm10 101<1¢1<\O a5.00 cn.onN Hm.OO a~.aO mm.wn~ ~c.nOO mm1010 cN.N5 cc.O~m am.OO ON.~c c0.0cH ~M.O5o 01mz:0 101ww onu you manoofiouuo Ho>oH umonwwn onu ms: was» ocean .auauanmnoua swwooo mm.O a as aw show Hm131mm1oa .ouwooou moo .Hoaw .umououow .ooaumaoowaoo "movaaoou++ .shom uoumuooo1ooo .ououoo: NOfl+ 95 Na.a5H 5.05 O O0.0 c an ac mm101mz ca.mHN N.N» O O0.0 c Ammo we m210 nO.HNN N.m5 O O0.0 c Oa 0a nm10 m0.0mN a.am O Oc.O N c5 c5 am1m21m210 O0.0NN n.~5 O O0.0 c ma ma 31mz1010 cn.onN a.O5 O O0.0 O Oa Oa Om1010 NO.MON c.a5 O O0.0 N 5a 5a 9&131NZ10 cO.N5N m.OO O Om.O O 5O 5O HOIOZ10 Oc.NaN 5.00 O om.O O M5 N5 Hm1m21010 “5.5aN m.OO O O0.0 O cog Nu“ 01mm10 10L<1<1<\O c5.5aN 5.Na a Oc.O N aa aa an1n21<1<\o O0.00m N.ca O oc.O N cOH and Hmlmzlo «OO.mOn O.c5 O O0.0 O 5a 5a an131mm10 Na.won N.O5 a O0.0 m mm mm 0101014 1<1<1<1<\O cc.ONm O.Nw O O0.0 w cOH HAN 01m210 101<1<1<\O -.Onm 5.5m O ~5.0 O cOH 5nd Hm1mm10 mac” sacs erouuaaaum ouom us\oa seam geese 33sec newsroom ++umoo Noam Newman season: uno>uom .aufiooamO uno>uom .uaNcEOO uouomwa uouomue mono massaged: muHoma< muuxm uoom spasm auNHNuO oonHNH _+asaaaooooem omaaoa ae.o a so ausuauaaaoo messages: we auoaaam .c.m oases 96 because the harvest date is earlier and the planting date is later for the navy beans on the C-NB farm. Harvest begins a month earlier for navy beans, thus bean harvest competition with corn is less and more time is available for fall tillage. The later planting date for navy beans increases the time for spring tillage. Machinery costs for the NB-C-SB farm are less than for either C-SB or C-NB farms because of the advantage of the earlier harvest and later planting dates for navy beans, and in any week the required work is less, so the work load is more evenly distributed. On equal-sized farms less work is required during corn planting on the NB-C-SB farm than on a C-NB or C-SB farm because corn acreages are larger on those farms. 5.3 Labor Supply, Farm.Size, and Design Probability Analysis Labor supply, farm size, and design probability analyses are outlined in Tables 5.5 through 5.8. Three farm sizes (182, 283, and 404 hectare), three labor supplies (1.0, 1.5, and 2.0), and four design probability levels are studied. Design probability ranges between 0.48 and 0.79. Table 5.5 gives results for the 0.79 design probability. A satisfactory machine set was selected for fewer than one-third of the farm size/labor supply combinations. While one could expect a machinery set for a one-operator, 162 hectare farm to be satisfactory for a two-man, 283 hectare farm, this is not always the case (Table 5.5). 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Comparison to the base run shows a range in cost decreases from $5.00 per hectare for C-NB-WeBT to $59.00 per hectare for O/AeArArC-C-NB-C rotations. The large cost decrease for the O/ArArArC-C-NB-C farm is due to the reduced competition between alfalfa harvest and navy bean planting. The expanded spring planting period had a relatively smaller impact on crop rotations whose planting is spread more uniformly throughout the year. One could expect the expanded spring planting periods to have a greater impact as farm size is increased. For the O/ArArArC-C-NB-C rotation the cost of the yield reduction for the delayed planting on the whole farm must be less than $59.00 per hectare to make the expanded planting period profitable. Only the portion of the crop planted in the extra week would be included in the timeliness penalty. In reality a yield reduction greater than $59/hectare would be acceptable for the crap planted in the extra week since the portion of the crOp planted within the original date constraints would have no relative yield reduction. Fall disking of corn stalks is excluded in model run No. 4. Cost differences ranged from $5.00 to $60.00 per hectare for the C-NB‘W-BT and O/ArA-A-C-C-NB-C rotations, respectively. Three factors account for the cost decreases associated with the elimination of fall disking. Fewer field operations mean less tractor power is needed on the farm as a whole. Removal of a fall tillage operation allows more fall plowing to take place. As more plowing is accomplished in the fall, less tillage tractor power is needed since the workload is spread more evenly throughout the year. Finally, 1/ 109 the elimination of fall disking could free up enough labor time such that a smaller combine, operated for a longer period, might be feasible. It is not known whether disking corn stalks will effect yields of the following crops. using 46 centimeter plow bottoms or keeping a 40-centimeter plow adjusted such that fall disking is not required could return as much as $60.00 per hectare for the O/AeArArC-C-NB-C rotation. One and two extra spring field cultivations for each crop are considered in model run No's. 5 and 6, respectively. The cost increase ranged from $2.00 per hectare for the O/AeAeAeC-C-NB-C farm to $22.00 per hectare for the C-NBeW-BT farm in run No. 5. Cost increases for two extra field cultivations ranged between $4.00 per hectare for the O/ArAvAeC-C-NB-C farm to $46.00 per hectare for the C-C-NB-W rotation. Several items account for cost increases associated with extra field cultivations. The crop rotation in question plays a significant role. Wheat and alfalfa require no spring field cultivations. Crops with relatively early planting dates (i.e. corn, sugar beets, and oats) find less good weather per week for planting than crops with later planting dates (i.e. navy beans and soybeans). Finally, the ability to shift plowing to the fall is a factor. As tractor size is increased not only will the field cultivation capacity increase but the time available might also rise. The extra time would occur because the increased tractor size allowed more fall plowing, thus freeing up previously allocated time in the spring for field cultivation. The results for model run No's. 7 through 9 are not applicable to the farmers case directly. The tests are designed to analyze 110 different available workday data sets. The person putting the model into practice is cautioned to study the results to get a feel for the impact available workday data sets have on model performance. The base run is for C3 (Tulu moisture criterion) - 0.980. Cost reductions for the less restrictive C3 value of 0.985 ranged from $18.00 per hectare for NB-C-SB to $69.00 per hectare for the O/ArArArC-C-NB-C rotation. At the more restrictive C3 values of 0.970 and 0.975, cost increases ranged from $21.00 to $60.00 per hectare for NB-C-SB in tests 7 and 8. For the case of C3 - 0.970 no satisfactory machinery set could be found on any farm except for the NB-C-SB rotation. The results point out the care which must be taken when selecting the available workday data set. The requirement of available workday data validation is obvious after looking at the results. 5.5 Fuel use Diesel fuel usage is calculated for each selected machinery set (Table 5.14). Inspection of the C-C-NB-BT farm (Table 5.13) shows fuel use varies for each of the 23 selected machinery sets. One could expect machinery sets in Table 5.13 to consume equal fuel amounts, since each accomplishes the same work. However, fuel use per hectare generally increases with tractor size. Minimum fuel use occurs with a tractor loading rate of 0.82 (Equation 2.2). As tractor size increases, loading rate decreases because of the speed constraints for many implements. When the loading rate moves away from 0.82, fuel efficiency decreases. 111 .9358, O Hoe—5o How..— .Hcaao H551O5 0cm 5O1OO cocci N Hood H mucosa: xccu you 3339:. .ooHucHoHB uchuucsoo mace Ocaoaco mono vac ccHucuooos woucuoaoH .oucuoon NOH+ HOaH Osaka» on 83 NH3 mo was.» mono Hcauoc snail. 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HOUOMHH HOB «NHO? some HHaam 633 +auoe anumzuono a so soon Nausea: oHoaHHoa< we seem» NN soc eaeoao Hove: .nH.n oHooa Table 5.14. Average Diesel Fuel Use of Field Operations+ Crap Rotation Diesel Fuel Use, L/ha C-NB-NB-BT C-NB-BT C-NB‘W—BT 0/ArArNB-BT C-C-NB-BT NB-C-SB C-SB-BT C-NB O-NB-BT C-SB-W-BT c-ss aamm 0/A-A-ArC-C-NB-C c-c-ss 0/ANAeA-c-c-ss-c 0/AsANANANAec-c-c 88.0 90.9 78.3 83.8 88.7 77.1 88.7 80.8 87.7 77.1 70.2 70.2 75.9 76.4 73.9 72.4 +162 hectare, 1-operator farms. Does not include transport. 113 To eliminate variability between individual machinery sets, fuel use was averaged over all satisfactory machinery 33t3_§2£fl¢§£§,, V// rotatigpg Fuel consumption results are shown in Table 5.14. r.___ Rotations described in Table 5.14 are listed in the same order as the net returns to land analysis offered in Table 5.3. It was noted previously that crop rotations with sugar beets have the highest net returns to land; they also use the most diesel fuel. Usage ranges between 70.2 liters and 90.9 liters for C-C-NBOW and C-NB-BT farms, respectively. The 20.7 liter difference is significant from a physical standpoint. The economic significance of this difference decreases when the cost of the 20.7 liters (20.7 liters X 0.20 $/liter - $4.14) is compared to the net returns differential of $140.65 per hectare between the two farms (Table 5.3). Only diesel fuel usage is analyzed. Direct energy for fertilizer, crop transport, insecticides, herbicides, and crop drying is not included in the research study. A complete energy analysis would also require measures of indirect energy use for each rotation. Fuel use sensitivity is investigated in Table 5.15. The same model runs are used as in Section 5.4. In general only slight variations occurred between runs. The major changes occurred when the amount of field work changed. The most significant changes occurred for model run No's. 4 through 6. Run No. 4 (deletion of fall corn stalk disking) caused a slight reduction in fuel use. One or two extra field cultivations added enough work to make a noticeable difference in fuel use per hectare. 114 .cONH cc oouuoaou mouaOHO was Hmam .aucm uoucuooo1H .oucuoon NOHc OOa.O I AcOHwouHuo ouaucaoa aHsHv O0 c you Ooucuocow coco acoxuoz I a O5a.O I Acoauouwuo ouauaHoa aaaav O0 c you Ooucnocow coco acvxuoz I O O5a.O I HsOHuouHuu ouaucaoa afloav O0 c you ooucuooow coco havens: I 5 .aouo coco you mcoauc>HuH=o ONOHO OcHuOm cuuxo 039 I O .Oouo Homo pom moouuc>audao OHOHO Oofiuoc cuuxo ocO I O .cxacum cuoo mo OcmeHo NHcm oz I c .oouo coco now Home coo an Oooccaxo mooauoa OoHuocHa Ooauom I O .asaaxca sou1O ou ocuHaHH nuvfia wouocHa sex I N .O usuacc0 we came unocH mean: can soon I H "mesa Hoooa mo acquaauucmp+ N.5O 0.00 N.aO H.OOH 0.0a a.OO O.5O O.Ha 5.00 HO1OO10 0.05 0.05 c.O5 N.HO 5.05 0.00 0.05 5.05 N.O5 31OZ1010 a.O5 O.55 c.55 H.Ha O.cO 5.c5 a.O5 H.55 O.55 Om101mz H.O5 O.55 O.55 c.OO O.HO a.H5 N.a5 N.O5 a.O5 01OZ10101<1<1<\O a.55 O.a5 0.05 H.Oa N.cO H.O5 5.55 c.a5 0.05 HO131O210 a m N a m c n N +H coHuouom coco eoHoNHasc NuHeHoHosom so: Hose HoooHa .mH.n oHooN 115 For all other model runs the fuel use variations are due to the loading rate of the tractors as discussed previously in this section. The amount of field work accomplished for sensitivity tests 2,3,7,8, and 9 does not vary and thus the fuel use does not vary except due to the tractor loading rate influence. 6. SUMMARY AND RECOMMENDATIONS FOR FUTURE WORK The following conclusions are made: 1) The machinery selection model is useful in a farm decision framework because: a) The model predicts the number and size of the machinery required for a given enterprise organization. b) The model provides an estimate of annual machinery cost. c) The model points out relative differences in management strategies. While an absolute answer from a model is always open to debate, the relative difference between management strategies is believed accurate. d) Model logic is simple; therefore, it has relatively more value in an interdisciplinary study. The straight forward logic is also easily understood by audiences unfamiliar with computer modeling. e) The model can easily be part of an experimental design. Alternative management strategies can be studied by operating the model for each alternative and analyzing the output. f) Tillage methods employed in the current model can be easily modified. g) Custom hire of non-harvest field operations can be simulated by excluding the operation in the input data list. 116 117 2) The results show: a) b) e) d) Actual workday data use is superior to probabilistic workday data use. Probabilistic workday data cannot capture the correlations between calendar periods in a single year. use of actual workday data does not pose a computing expense problem when only yearly sumary statistics are printed in the output file. Crop rotation has a significant impact on machinery complement requirements. The results show a $247.75 per hectare difference between the extreme machinery comple- ment costs for C-NB-NB-BT and O/ArArArArArC-C-C farms (Table 5.3). Crop rotation affects field machine fuel usage. The difference between the C-NB-BT and C—C-NBOW farms was 20.7 liters per hectare (Table 5.14). The fuel use figure, while being significant physically, accounts for only 3 percent of the net returns differential between the same rotation (fuel cost - 0.20 $/L, net returns differential - $140.65/ha (Table 5.3)). Field machinery fuel usage is generally highest in crop rotations containing sugar beets. The number of tillage operations effects both fuel use and machinery complement size requirements. This result could easily be presented to farm operators in a farmer workshop setting. The addition of two extra spring field e) f) 118 cultivations would necessitate their being worth $46.00 per hectare in increased yields for a 162 hectare, one- operator C—C—NBOW farm (Table 5.10). IModel results can be sensitive to specification of field operation date constraints. An expansion of spring planting period by one week decreased cost significantly. The increased planting periods reduced machinery cost $59.00 per hectare for a O/ArArArC-C-NB-C farm (Table 5.10). The model results were sensitive to changes in available workday generator parameters. A $60.00 per hectare difference was found for the extreme workday data generator parameters on a NB-C-SB farm (Table 5.10). This result points out the necessity of collecting available workday data in the field for validation purposes. 3) The "backwards" approach to parameter validation (Chapter 4) 5) 3.1.1) is a quick and effective method. The model and use of the "backwards" approach should be useful to check current data bases. The model results should be helpful to farm operators. In particular the model addresses: machinery cOst associated with design probabilities; choice of date constraints; impact of enterprise mix; and effect of tillage equipment and practice. Research should proceed in the following areas: 1) Set up stations to check the workday generator parameters. 2) 3) 4) 5) 6) 7) 119 Include a crop transport, drying and storage component in the model. Combine and fourdwheel drive tractor selection parameters may need more study. Combine technology is dynamic; capacity rates may be different from those currently predicted. The literature points out a tractive efficiency advantage for fourdwheel drive tractors; this may require incorporation into the present model. The model should be modified to allow excess labor to do the same field work as the primary labor (i.e. both operators could row cultivate or disk at the same time). At present the fixed tractor assignment to operations could lower productivity by idling one tractor and laborer in multi- operator farms. Using an experimental design procedure, an investigation should be made to determine the optimum planting and harvest periods. The effect and cost of timeliness could be a study itself. The model should be expanded to include labor supplies greater than two operators. This change would involve use of multiple combines; tillage and utility tractors. The change would allow consideration of larger hectare production units. The model, as part of a whole farm analysis, should be presented to farm operators in a workshop setting. APPENDICES APPENDIX A FIELD MACHINERY SELECTION 100111. FORTRAN LISTING 120 APPENDIX A FIELD MAmINERY SELECTION MODEL FORTRAN LISTING 19.38567 H90123Q 567 F9 .UIZIYQSbIIr 9.0125Q557F9 CI2385LIP901951567 590123Q567L90I?3Q5C7 OCOCOUQOILII IUIIIIIIIn/N. pen/.2916?(223353siuhe33rufi ‘Q h “O“O‘CSCJSLHKJSOJCJRHRHG6666 DIN. O6677777777 o 0000000411010. .UrJOOoJOCJafi 0n. .vC..U.Un1L~U unocoafo. .U u: UnsueJrJ "OUJOr 0 0.1. .11 .10 0501130301.. WO. 0000051000 AAIIAAIIAAILAAIIIIAI. AIIIIA. Aisle-51.111.111.111]AAIIAIIIIAIII. 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Data is read from Tape 1 input file. T: Z of week available for tillage, planting or sugar beet harvest B: Z of week available for combine operations except corn harvest A: Z of week available for alfalfa harvest operations C: 2 of week available for corn harvest, fertilization and spraying Farm input data. RA1,RA2: Crop rotation name ISIU: Unit Indicator (O-English, l-SI) TACRE: Total crop area considered I,ACRE(I): Percentage of total area in each crop I (see BLKDATA subroutine) I,J,ORDER(I,J): Supply—Demand matrix for harvested ground (see section 2.6, BLKDATA subroutine, and Figure A.1) PCTHRD: Percentage of extra laborer available ALFYLD: Alfalfa yield (t/acre or Mg/ha) ISTART: Starting date of field operation (month/day) IEND: Ending date of field operation (month/day) IOPER: Field operation number (see BLKDATA subroutine) ICROP: Crop number (see BLKDATA subroutine) IENDZ: Date for which field work must be halted in the fall NPRINT: Output print control variable (l—summary statistics, 2-#1 plus, machinery set and cost tables; 3-#2 plus, weekly work schedule; 4-#3 plus, combine and tractor sizing information NFF: Output print control variable (0-28 year run, selecting 0-2 machinery sets; l-less than 28 year run, selecting 0-2 machinery sets; 2-less than 28 year run, testing a specific machinery set IBEGIN: IFINSH: ICMPLT: IZCOMB: IZBEET: TRHPBIG: Starting year of weather data to be considered by model Ending year of weather data to be considered by model O-selection of machinery set, l-testing of specific machinery set Combine size (rows) Sugar beet harvest capacity (level 1-5, see Table 3.4) Tillage tractor power level 155 Figure A.1 Supply (harvested crop land) - Demand (first field operation) Matrix+ Supply(i) Demand(j) Corn N U § 0‘ ‘1 Wheat Oats Navy beans Sugar beets U' Soybeans Alfalfa Corn 1 Corn 2 Wheat 1 Wheat 2 Oats l Oats 2 Navy beans 1 Navy beans 2 Sugar beets l 10 Sugar beets 2 11 Soybeans 1 12 Soybeans 2 13 Alfalfa 1 14 Alfalfa 2 +Used to determine values for data variable ORDER(I,J), see Table A.l I-Supply number (1-7), J-Demand number (1—14), ORDER(I,J) = value in matrix cell (1,1), (0.0-1.0). of the total crop acres. value in each cell represents a percent APPENDIX C MODEL OUTPUT FOR A C-NB-W-BT FARM; USING 28 YEARS OF AVAILABLE WORKDAY DATA, SUMMARY STATISTICS ONLY 156 APPENDIX C MODEL OUTPUT FOR A C-NB-WeBT FARM: USING 28 YEARS OF AVAILABLE WORKDAY DATA, SUMMARY STATISTICS ONLY 1127 1127 68.633 690.59 971.3399 991111777770. 1199.7 11 111 .b HostficuomtbnOIJQUnuin/afu? 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Z. 2. 2. 2 2. 2 ? 2 2 p—h—p—ooo6.0000006060000000. ézzchnhnmcmhcma‘c J‘C o\0~c‘-U~c a~a ummoawmmmammmcccccetcacdc (((H—l czmx hhh mmm 2:2 000 uuuocoooooooooo coo Q'thr-Hfic'mhtmfl‘o‘ahhfl‘a' “mumooummommm¢e ¢ ¢ Pbbfln <«4 can Noah XLD‘ D'fiOtfiflJ‘O‘OU-U-U-O O~O‘0~O‘U"J‘O' O hmmnmaczmhmmns‘ommmwoomm00mm coo00000000000000.0000... ccmm—nmnmcnmmommmomason-00mm- ccmmmnwooomma¢awocuacocccrmaLu-x nnNNOINNNNO‘H—iHHo-AHHHHHHHHHH \DO-HP’QWNIrONHNxOHF3tmNDF3 O¢WY~WMDF «Josho-oohhnohmmmnnmmmmoaw«asks IUIAIAUIOIIIIIIIIIIAIIIIIIII WQONFNHN)O‘HOHP~O‘GN'fitfl‘rQIoO‘VOO0000 Qflfif‘ vDsb 0h~000t~m¢mmmuurammm~o~casks ll‘xbm N VON—£60 xfiomtnmuoo-mhmm chair-c NND.‘ mar ¢ APPENDIX D MODEL OUTPUT FOR A C-NB FARM; USING 28 YEARS OF AVAILABLE WORKDAY DATA, SUMMARY STATISTICS ONLY 162 AUTENUUKD MODEL OUTPUT FOR A C-NB FARM; USING 28 YEARS OF AVAILABLE WORKDAY DATA, SUMMARY STATISTICS ONLY _ 1127 1127 85711111777.771.17 1.1 5,340.16 62 1367 631.3 22 2 12 1.111 D 0 «(37755550999990 0 7 55 0.122111111111111. 80000 0 0 01.111555566665667 .5555 00 1111. I o o a 0 60000 089988O4490:4220199 51 7100?002222201122221 N 0080088“4004555556 - 1.7580140 11 1. CI 11.0 0 =C'NB ROTATION E FOR I00 0F 0) SY‘SATEG Fl~3§?é~‘ CROP OPFRATION IIZTTO°Q09 1127T0-909 oewumoocumnoecwnaoc 000.000.0000.... 6°NNNNNNNN~NNNNN .nnaunu-unnuuuwnuuwu «NOOOOOOQODQQODO °u«WN—unnuuuuuunn—unc auunnnnflOOOOOOON «nun: ”OGQQOOQWflmNNOOD NGONNNNNOuuflwflNd ogeoooocgcnnnnno on all "No-Add CIA muuuuuvw’mmmuum 883818228'2222 OOOOOODDDKJEOOD ZFZZZ?2>>>>>23> K>U¢¢¢K>>>>>CW> O‘OOODO““COOC UIUUUUUZZZ72UUI NAVY BEANS >2 \Dfl ijnfii U >3 ()2 D- Z i- O" <0 cub: ~¢hh2h4h 3C.L¢C4OHJ 03-! 03.23.1313 D—h-H ...H Int-.1 an iQUd‘J main-a ‘Al >>M£ 9‘: (00033703 ¢¢~u34oao~44aoru troutuxmtouucrtr uwnonohoo-o—nwncna HHHHHHa—O 16109 0.00 TOTAL CROP HECTARES= PERCENTAGE OF EATAA LABOR AVAILABLE ”ER DECK P2“ “DOING 150.32 SIHA 49-50 COST YEAR: 163 1. 1. I 81. IO 33°UO¢~ KKKIKOJ O'cuaoenn bans-name 50:51 COST216Q.O6 SIHA YIAR P30 maun 183.65 SIHA COST: YEAR: 51-52 CAPACITY hncsman COST:165.85 SIHA 52:53 YEAR: . .JHAJuibLJ ~h&u4un~¢ h3¢nmvmu 16A.JE S/HA COST : 53-54 YEAF C O "O". Q C . IQ." a. . 0 HM O O 0 "d a” O O H" (D O O 0"." 0 . . d“ O . . "I" Q U I ‘ ... I U I l.) \ $ id ‘0‘ 3 KW ‘ UK Mbm’hl NNOODDU. NNGOOOO . O O O O O O C Q-NDOOA‘. CO H gr Id 0' m II .J U' (9 h Is. F. K N U (H2é II I. “HA-1‘ C-XNVM: 4.1 EC ~I~hl£k n—t-«J’b—u fl 0-0- (3 LIP xt- PWUUIW h'n SIHA 3150.32 COST : Sfi-bS YEA? 164 o o o O o o o 0 A O 0 O o o O A o o and and "—0 .104 word OCH '1'. H“ «.4 DO 0.!) CG 0C flow 0” 36 CD 13 . o o o o o o o o o o o o . o . o a... FOO-A r..-a r..-a o-u-c .4.- .-.r4 «.4 «a an: can mm mm cm can we" cur a J" o o o o o o o o o o o o o . . . . 0 H74 '1'! 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U C ~ N D F o I-_ U .— C” U U (J A- .4 '- x" U I4 CURL“ ll" uuw« hat Dd-fl'u...‘ 2.58 SIHA C(STZI4 = 71-72 YEA. RS SIHECTARE ‘1‘ G‘b—M’Id [1'1 IUQJ 0090630 #3063005 0 o o o o o o HO‘OONO-fi 3.4 H n Noni H '_c w '1 FR II D b .9 I— ‘4 H a“ I. U '4 «(NSC IIN Guam-.1 90‘:a UU‘A— 1.. ddeU J—J \lu “Lama-o v—‘at urvv I.I. \ 72-73 CC:T::3*."2 S/HA YEA} O. '0'. mm 0.1: m3 670 EC QG‘ HECTAHL J‘I\ cooaocun ...-000003. 0 0 o o 0 . 0 DOOOOJON #1 NF .-0 II C. In G". .- II J )- (I h- I.) .— .xII U :4 (IIZID IIII CLUu‘ r.. INZU LI. '4— VI I—I—I. unit) I- fiumn'mm 2I0.59 TINA LJfT . 0 HF. Q C 0 ”ii :56" O O ”H T! O O "H ’1‘!) O C “d .5 O O ‘0‘ J... O C 0". a C O "— LID W -, ‘ b ‘ U I U \ T U (’1‘ x ‘W . Na MbM)I.I tu1~b REQUIRED E: chmm44 ...x-‘Ulu‘k O-I—M>A-IL-) KZ-JIL ur-IIJ‘C‘ Ur—II4 4 (04H Q‘Iu 44..”- KIA ..l—IU30‘U “fit-014$!" O-fiUIIIU'IIf‘ 150006 61H. CUET 74-75 YEAR «.4 C 0 0 0401 mm 0 0 .-.-. In 0 0 '00-. (DO 0 0 ...-1 0 0 0 .4— cm 0 0 .-..-I U 0 0 ~— 00 '4 C Q .- 8 U I I4 \ I I4 In\ ¢ Cw C U8 mbm>u IUD—it- ‘Totdfioyu. 2812804 C‘IQD‘IOOO ...-.90000 0 0 0 0 0 0 0 mmwooo~ Int!" '0 II n I.) 8 CI! n 3 D- O V- U .-. KI! U I4 1 ”2.: II II QUUC I“ CNZU Ck) U" In I—I—II VIC: ~30 IHIG’IJO 75-76 CO£Y=1h~.35 31H: YEAR on!“ o 00 Mai mm .0 0-00-0 0* O 0 010-! mm .0 ~— 0 .0 ~01 mo .0 HOH c0 0 0 F001 mm I4 a C .- E U U \ t U mx x am d be Mbm>u )UJNP anamor~ grezxo4 nIvoooon «Nooooo O O O O I 6 O DONooom 0' 0 u D All a II. n D > o p u H an U I.) dnzo nu aun¢ a: (qu 0'4 U.- V) ...—II (0.3 UUuh & <bU3 I-II-(JLIIJXN O-DUF'HVIULJ 150.32 $er C331 AR: 76-77 YE 166 FAR! HECTARE C-hb OPERATOR 161.0 1.0 RAVRED HICHIHIRY SLYS FOR A «momma-Iaomnmuocoommwmn.nann onwnmawomhoonmmnnmooo.000 0.000000000.0000.00.0.000 NflNnMflNNNNNNNNNNNNNNNNNN nnnnmnnnnnmnnnmnnnnnnn 00000000000 000000000.000 OOODOOOOUOOOOCIODOOUDOOUuO D I. (Cooaooooooooooooocoo troooo CW... 82oceanooooaooo04ooooooooono 0000.0..0..0.........0. 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APPENDIX E MODEL OUTPUT FOR A C-NB FARM; USING THE NINTH YEAR OF AVAILABLE WORKDAY DATA, COMPLETE SET OF STATISTICS 167 1127 1127 9571111177777117 11 8590166715676313 2? 2 12 1111 00 2577555599009G90 7 .35 0.122111111111111 80000 0. 0111555566666667 05555 00 1111 10000 60000 08998844490522999 31 71002002222201122221 N 008008894404555556 . 17580140 11 1 Cl 1.10 0 APPENDIX E MODEL OUTPUT FOR A C-NB FARM; USING THE NINTH YEAR OF AVAILABLE WORKDAY DATA, COMPLETE SET OF STATISTICS =C-NB ROTATION STARTING DATE CROP OPERATION 168 1127T0-409 1127T0-009 O 5000 OOO 050° 000° 0000000000.00000 0 ONNN NNNNNNNNNNN dedHHHHHHflddHfl «N000 ccchQaimcnmcoo‘ OHNNFIFIHHHv-Iv-IHMHHD o—nnnmmmosooomomh HHHH QU'O‘QQJCCQO DIDNNO fl-U‘ NOONNtvNNOdHNNNNv-I WDOQUCCQ OOIDJ'HDIDIDU NH 0-0 H Mdv-Io-Io-I H ANS WUUBIUMJWWWWWUUW 222.?ET2ZL'ZdL’222 ¢'()OC) DOJJO'JQQQZOOC‘ bi >2>22222>>>>>2?> >cz>u a azq>>>>>xm> cocoooco-..J ”nan-u) {0(0an «av >>mm 01a: VHJJJJ‘I’), zmHLI¢J~Z~QLHJJOOIp III—Jutucrmxlfiu u.o’n’?a' amncmohxa‘ caHNKNNnO 161.9 TOTAL CROP HECTARES 0.00 [EV RCEHTAGE OF EXTRA LABOR AVAILAELL OER H ... 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DODJ—‘DOO—Idorzocoa o n 90 00000000000000... - QH ax 2b4223m44 wunaaamdmxzqm mnazoawauouo~h SouzzaummUQUhD OF COMBINE CAPACITY SELECTION la. ‘1' ROM COMBI’ 50 OF TRACTOR SIZE SELECTION FOR CROP L32: 8905 SECOND TRACTOR KILOHATT SECOND TRACTOR KIL ‘5. 890 TILLAGE TRACTOR KILCHATT NAVY BN8 1 BY SEC N) 2 AV FOR CROP E TRACTOR KILOUATT= 670] NISH OPERATION = DISK T LL 6 3 I I A NOT AbLL T‘ F 174 3073 7‘06 ECTARES LEFT i HATT 120 ILO 1 SECOND TRACTOR KILOUATT K BY 0 N FOR CROP 8200 8200 TILLABE TRACTOR KILOUATT 2019 FOR CROP NOT ABLE FOR CROP : 06“ 7706 1120 fitcranzs LEFT COND TRACTOR KILOUATT NAVY BUS 1 BY SECOND TRACTOR KILOUATT FOR CROP TRACTOR KILOHATT 7803 SE 7803 A53 TILL TRACTOR KILCUATT: 7803 MSINE: 6000 175 UORK SCHEDULE TILLAGE TRACTOR: 3008 HECTARETOT TIME HECTARES/HR 2602 10029 2 55 2602 2055 2602 50080 TILLAGE TRACTOR: TIRE 2%046 03 5600 3R09 HECTARETOT 8009 3009 TILLAGE TRACTOR: TILLASE TRACTOR: 1003 TIME HECTARES/HR 10029 085 HECTARETOT 6407 TILLAGE TRACTOR: HECTARSTOT TIME 800 19003 TILLAGE TRACTOR: TILLAGE TRACTO HECTARETOT T 7605 60 «306 12 TILLAGE TRACTOR: HECTARETOT TIME 3009 30.9 TILLAGE TRACTOR: TILLRGE TRACTOR: TILLAGE TRACTOR: HECTARETOT TIME 8009 10025 TILLRGE TRRCTOR: TILLAGE TRACTOR: TILLRGE TRRCT3R= 3009 HECTARETOT TIHE HECTARES/HR 1908 23028 085 1908 7058 TILLAGE TRACTOR: TIME HECTARETOT HECTARES/HR 2 55 HECTARFS/HR 085 HECTARES/HR 1027 HECTARES/HR 1027 HECTARESIHR 306“ 000 UTILITY HECTARES/HR SECOND TRACTOR RILOUATT: 7903 UTILITY TRACTOR: 205 MAXIFUPZHOURS 7 7 @000 havoc hung TRRCTORZ 503 HAXI'UF HOURS 21096 61071 61071 TRACTOR: 000 TRACTOR: 000 MAXIHuM HCURS 10029 TRACTOR: 000 ‘OXIHUH HOURS 61071 TRACTCR: 000 TRACTOR: 000 RAXIFUP HOURS 60000 72000 TRACTOR: O00 MAXIMUM HOURS 3009 TRACTOR: 000 TRACTOR: 000 TRACTOR: 000 RAXIMUM HOURS 10029 10029 TR‘CT093 000 TRACTOR: 000 TRACTOR: 000 ”AXIFU“ HOJRS 30 6 30086 TRACTOR: 000 RAXIHUH HOURS CROP NAVY 94$ HEEK HEEK CHBIN£= 000 HEC;AREUK :fimounxq OF 508 CCHBIHE= OPERATIC HECTAREUK "3 PL 603 DIS" SRRH 603 000 ...-0“ ORBINE= 000 HECTAREVK I 8009 COHEINE= 000 COMFINE= 000 OF 619 COMBINE: OPERATION HECEA 000 ARE UK 1 R00 CULT 9 RF 626 COMBINE: 000 OF 703 COMBINE: 009 176 390 7 2 390 7 TILLAGE TRACTOR: 1508 HECTARETOT TIRE 0 1080 70 1 006 5705 12001 TILLAGE TRACTOR: 6009 HECTARETOT TIME 800 60.6 800 9 6016 8009 0091 7R07 91011 7907 13037 TILLAGE TRACTOR: HECTARETOT TIME 8009 7035 6009 2039 TILLAGE TRACTOR: 2203 HECTARETOT gIHE 8009 2 02-; 3029 7057 085 2062 UTILITY HECTARES/HR 3 R 3081 R078 UTILITY HCCERRES/HR 3081 “078 085 2062 90 7 UTILITY HECTARLS’HR 2062 UTILITY HECTARLS’HR 306R TILLAGE TRACTOR: 2203 UTILITY HEchRETOT TIH TILLAGE TRACTOR: HECTARETOT TIRE 80019021 8009 13065 TILLAOE TRACTOR: TILLAGE TRACTOR: TILLAGE TRACTOR: HECTARETOT TIME 8009 10021 TILLAGL TRACTOR: TILLAGE TRACTOR: HECTARES/HR 30 69 3081 000 UTILITY HECTARESIHP 50 70 9032 000 UTILITY 000 UTILITY 000 UTILITY HECTARES/HR 5070 000 UTILITY 000 UTILITY 30086 30086 TRACTOR: 27.1 "AXIHUF HOURS 0086 0086 R6029 TRACTOR: 1101 RAXIHUH HOURS 7 0 12000 '7200 0 0 'NN MN) 1300 TRRCTOR= 000 MAXIMUM HOURS 61071 61071 TRACTOR: 000 RAXIMUF ROJRS 61071 TRACTOR: 2102 RAXIHUH HOURS 72000 72000 TRACTOR: 320 9 HAXIPUH HOURS 41014 TRACTOR: 000 TRACTOR: 000 TRACTOR: 14.2 ”AXIFUH HOURS «1 1a TRACTOR: O00 TRACTOR: O00 T 7803 SECOND TRACTOR KILOUATT IACHINERY S 3 60 7803 COPBINE SIZE TILL‘GF TRACTOR KILObATTz OPERATION TR EFF 1 DRAFT FLO EFF SIZE OCOQHhOfluhomcnhnh omenomsonomshohnh 001000 00010000410000 nwom dNNMuflNNNnNN u n WCNCOdeQMNCQflOOO “WNOnQOQFOOOOQFnN 000000000000000 NNan nnc wnnnm:m 0‘ cmncmmoonnnoommoo NOCWOflOOOWfiOQflFmF 00000 000000 00000 CE'OQOEFDOFOOFFNF noanonwwommamwwm 1m1r~m¢mu~h¢wu~ohmu~h 0 00000000000000 unocmnmunaouunmcnno :smmmomwmmmoom 0000010000010000 I 33)) C000 ltl‘flllflkfuaufi \\\\\\\\\\\\\\ 2’72222712’272 0000100000<00000 OIHVQONNUHQUNVQOWD Ohhw~¢ho¢mnwba hdmhhmmmbcfiofiw n on— Ocnnnunn fl 0000 2112091 NIPOU CCKObuHDMCHgDunuwCIHJO ractlmlutntliauxa ooooaamccocmwoooo DOOmNOFOflanFDOOO 00000000000000000 N60¢200N00000NNNN noinn ha a n «a u (do 0 ~ Inad (r h crhh I-Jh 13w B‘ACCEQJJKJQJ ax: mcanuuujzzxauznxb JUP Uuh u‘U a 322:»a 2:11 l rdxmm Qua wQDdJflI UUQNHLJJQmHJJJCIW 3&UQKZKRWIQumuazu HACHINERY COST AVALYSIS FUEL COST 020 SILITER 8 YEAR OEPRECIATION SCHEDULE EST RATE: 013 IVTER PRICE DEPRCTN INTEREST INSIHOUS REPAIRS EL TOTAL USE HRS I'PLE'ENT QUANTITY 177 um 38052002 Onflmflflflfihfl$a° CONNfl$NOOOCGO 0000000000000 anowomwnwvhoo COhOCGflQOfln wwn ~N HNN 2612031 COOGNmMNOOCONfl onnnmnwaccuwow 00000000000000 CQQNnONFNnAFON oom~0~nmcn0~oo n mg a ha 3505009 Inne¢u~0¢umoalhmnr «docunocmcncmo 00000000000000 cnowhcnomscoon thnONNNhNNmnn N d Nd 1690007 OCOOthOdHnnov aonmmnhhhmonnm 00000000000000 OONQFQfldeHnnN mmm~nowm~noom~ monmnonnmommow u n 1 11755013 nonoamnonmwn nQOOONNOnNOO 00N000~F$nfi0 3 6 0 6 3 2 8 9 9 3 8 3 2660060 1279001 monnmn~~mco~ — N H 0 18095083 OOOOODOOOOOO ODOOOQOOOOOO mmoohmocoonn Nfi0cfltocc¢fl¢ R 5 6 2 7 0 S 6 9 7 8 6 23685038 11Mm09n 160907036 FONnCNNNFNFn N d C «somwcowowmcco qflfiOCtlwnOFNOF 00000000000000 anomcmhohn~ma OflOdNO "NOWCH: H ”fl 01010-0 “'1 flflflflqd—‘F‘F‘ «a Q 00 "—0 m: 00 «a m 00 dd 00 00 an 0 00 d— m 00 nu $0 00 an m u I i p C U I a \ T U W\ 8 cm ‘ UC «Anusw Jui~h J)OMO®W‘ ‘ZKKIMHDJ 0000000 nncumaom 00000 0 DDCONOGI hh cu n h D m r 0-0 II D > n h 61 fl 8" U u (n2: HM mumc (a «uwxu Op tau 0- bhu (0‘: uuuhvu. ‘CNMCJJ aunuwcu hpfl’bu: FDUUWMH 51-58 COSI:21°.00 S/HA YEAR FARM HECTARE C-NF 100 OFERATOR 16109 SETS FOR A RANKLP MACHIHIRY ALFALFA HEN (EXTRA) Y CAPACITY (HA/HR) T A0) L YEA“ CUST LIPbF 1’46 TFACTLR RJM 0000 00 52096 790 780 .0 120 219090 57-58 20 LIST OF REFERENCES LIST OF REFERENCES ASAE EP391. 1978. Agricultural machinery management. Agricultural Engineer's Yearbook. ASAE, St. Joseph, Michigan, p 271-274. Benson, F. J. 1978. Minnesota farm.machinery economic cost estimates for 1978. Agri. Ext. Serv., university of Minnesota, St. Paul, Minnesota. Black, J. R. 1979. Personal communication. Agr. Econ. Dept., Michigan State University, East Lansing, Michigan. Bowers, wendall. 1975. Fundamentals of machine operation; machinery management. Deere and Company, Moline, Illinois. Brimhall, P. 1979. Personal communication. Monitor Sugar Company, Bay City, Michigan. Burrows, W. C. and J. C. Siemens. 1974. Determination of optimum machinery for corn—soybean farms. Transactions of the ASAE, St. Joseph, Michigan. Christenson, D. R. 1979. Personal communication. Crop and Soil Science Dept., Michigan State University, East Lansing, Michigan. /{ “Christenson, D. R., Z. Helsel, V Meints, R. Black, R. Hoskin, F. Wolak, and T. Burkhardt. 1980. Agronomics and economics of some cropping systems for fine-textured soils. Michigan Dry Bean Digest 4 (2):6-8. Deere and Company. 1979. Agricultural sales manual. Moline, Illinois. Deere and Company. 1979. Agricultural whole goods price list. Moline, Illinois. Domier, K. W. and 0. H. Friesen. 1969. Performance parameters of tractors equipped with singles, duals, and four-wheel drive. Can. Agr. Eng. 11(1):16-19. Dwyer, M. J. and G. Pearson. 1976. A field comparison of the tractive performance of two-and four-wheel drive tractors. J. Agr. Eng. Res. (1976)21:77-85. 178 179 Fulton, C. V., G. E. Ayres, E. O. Heady. 1976. Expected nunber of days suitable for field work in Iowa. Transactions of the ASAE, 19(6):1045-1047. Helsel, Z. 1979. Personal communication. Crop and Soil Science Dept., Michigan State university, East Lansing, Michigan. Hoskin, R. 1981. An economic analysis of alternative Saginaw Valley crop rotations -- an application of stochastic dominance theory. Unpublished Ph.D. dissertation. Agr. Econ. Dept., Michigan. Hunt, D. 1977. Farm Power and Machinery Management. Iowa State university Press, Ames, Iowa. Implement and Tractor Redbook. 1979. Intertec Publishing Corp., Overland Park, Kansas. International Harvester Company. 1977. Pro-Ag program. Albany, New York. International Mathematical and Statistical Libraries, Inc. 1979. The IMSL, Volume 2., Houston, Texas. Kish, A. J. and C. V. Privette. 1974. Number of field working days available for tillage in South Carolina. ASAE Paper No. 74-1019. ASAE, St. Joseph, Michigan. Link, D. A. 1968. Research needs for farm machinery scheduling. Computers and Farm Machinery Management Seminar Proceedings. ASAE Publication FROG-468 p. 28-32. Meints, V. 1979. Personal communication. Crop and Soil Science Dept., Michigan State university, East Lansing, Michigan. National Farm and Power Equipment Dealers Association. Fall, 1979. Offical Guide to Tractors and Farm Equipment, St. Louis, Missouri. Naylor, T. H., J. L. Balintfy, D. S. Burdick, K. Chu. 1966. Computer Simulation Techniques. John Wiley & Sons, Inc., New York, New York. Osborne, L. E. 1971. A field comparison of the performance of two- and four-wheel drive and tracklaying tractors. J. Agr. Eng. Res. Rider, A. L. and S. D. Barr. 1976. Fundamentals of machine operation, Hay and Forage Harvesting. Deere and Company, Moline, Illinois. Singh, D. 1978. Field machinery system modeling and requirements for selected Michigan cash crop production systems. Unpublished Ph.D. dissertation. Agr. Eng. Dept., Michigan State University, East Lansing, Michigan. 180 Statistical Reporting Service. 1979. Agricultural Prices. USDA, Washington, DC. Thom H. C. S. 1966. Some methods of climatological analysis. Technical note NO. 81, World Meteorological Organization, Geneva, Switzerland. Tulu, M. Y. 1973. Simulation of timeliness and tractibility conditions for corn production systems. Unpublished Ph.D. dissertation. Agr. Eng. Dept., Michigan State University, East Lansing, Michigan. Tulu, M. Y., J. B. Holtman, R. B. Fridley, S. D. Parsons. 1974. Timeliness costs and available working days —- shelled corn. Transactions of the ASAE (17)5:798-800,804. Von Bargen, K. 1966. Systems analysis in hay harvesting. Transactions of ASAE (9)4:768-770,773. White, R. G. 1974. Fuel requirements for selected farming operations. Ext. Bul. E-780. Coop. Ext. Ser., Michigan State University, East Lansing, Michigan. White, R. G. 1977. Matching tractor horsepower and farm implement size. Ext. Bul. E-llSZ, SF-ll. Coop. Ext. Ser., Michigan State University, East Lansing, Michigan. White, R. G. 1978. Determining capacities of farm machines. Ext. Bul. E-1216, SF-lé. Coop. Ext. Ser., Michigan State University, East Lansing, Michigan.