AN ECONQMIC ANALYSIS OF ALTERNATIVE MEANS OF ACQUIRING FARM MACHINERY SERVICES FOR SOUTHERN MICHIGAN CASHr GRAIN FARMS Thesis for the Degree of M. S.“ MICHIGAN. STATE UNIVERSITY GARY LEE BENJAMIN 1968 TH ESIS ' r‘ 9'. ' ‘ ”J ‘xfi'amc am. 14. m .0 1'1 ,. { 1' amomc 3y HUAG & SONS' BOOK BINDEIIY INC. LIBRARY BINDERS 3P.IUGPO!F. 539132231 Ah—m ABSTRACT AN ECONOMIC ANALYSIS OF ALTERNATIVE MEANS OF ACQUIRING FARM MACHINERY SERVICES FOR SOUTHERN MICHIGAN CASH-GRAIN FARMS By Gary Lee Benjamin Although current per farm machinery investments only represent 12 percent of total per farm investments, certain unique characteristics make the investment difficult to man— age. Some of these characteristics are, (1) rapid technolOg- ical developments which render machinery to be obsolete long before it is physically depreciated, (2) high initial costs and relatively low disposal values, and (3) the changing farm structure which emphasizes large items of machinery that cannot be passed from "first line" to "second line" equipment. The major objectives of the study were, (1) to des- cribe alternative methods of acquiring farm machinery services, (2) to determine the relationship between farm size and per acre power, machinery and labor costs for selected farm ma- chinery systems, (3) to determine acreage levels at which total costs and revenues are equal for selected systems of farm machinery, and (4) to determine an Optimum farm Size which would achieve minimum costs per dollar of revenue for selected farm machinery systems. Gary Lee Benjamin A snythetic one-man farming operation was utilized to represent a southern Michigan cash—grain farm. Farm size was varied in 40-acre increments with a constant typical acre consisting of 36 percent corn, 15 percent soybeans, 19 percent navy beans, 15 percent wheat, 12 percent diverted and 3 per- cent idle. Since primary emphasis was placed on analyzing farm machinery as a system, the following systems were identi- fied: (a) 4-row system with complete ownership, (b) 4-row system with a combination of ownership and custom hiring, (c) 6-row system with complete ownership, (d) 6-row system with a combination of ownership and custom hiring, and (e) complete custom hiring. Because the results of a mailed questionnaire to farm machinery dealers in Michigan showed little evidence of machinery rental and leasing and relatively high rates, rental and leasing were not included in the machinery systems analyzed. The analysis procedure employed two budgeting models. Budgeting Model I derived average total machinery and labor costs per acre. Budgeting Model II included the concept of timeliness of Operations in developing cost: revenue ratios. Revenues were based entirely on cash sales of crops produced, while costs included labor, machinery, seed, fertilizer, herb- icide, custom hauling and an Opportunity cost on land. Although the alternative of complete custom hiring gave lowest per acre costs for farms up to 322 tillable acres, the results of Budgeting Model I showed costs per acre de- creasing rapidly in this acreage range for the other four Gary Lee Benjamin machinery systems. Between 323 and 343 tillable acres, the 4-row system with a combination of ownership and custom hir- ing resulted in the lowest costs per acre, while the 4-row system of complete ownership showed lowest costs per acre for farms of 344 to 597 tillable acres. For farms with more than 597 tillable acres, the 6-row system of complete ownership gave the lowest per acre costs. In terms of breakeven analysis, the results of Bud— geting Model II indicated that the 4-row machinery system with a combination Of ownership and custom hiring required a minimum of 89 tillable acres before revenues would equal costs. Other breakeven acreage levels Of 107, 123, and 152 tillable acres were noted for the 6-row with a combination of ownership and custom hiring, the 4-row system of complete ownership, and the 6-row system of complete ownership, respectively. Although all the machinery systems studied showed a range in acreage for relative efficiencies, the greatest economies of size occurred with a farm of 760 tillable acres using a 6-row machinery system of complete ownership. By defining constant costs as those cost: revenue ratios falling with five percent of the most efficient point, the acreage ranges for constant costs were 300, 304, 374, and 470 tillable acres for the 4-row system of complete ownership, the 4—row system with a combination of ownership and custom hiring, the 6-row system of complete ownership, and the 6-row system with a combination of ownership and custom hiring, respectively. Gary Lee Benjamin The primary implications of the analysis indicated the following: (1) the importance and benefits of analyzing farm machinery as a system to fulfill the overall needs of a farm; (2) the possibility of an increased demand for the services provided by custom operators; (3) the possibility of an increased supply of custom Operators who hold excess machinery capacity and desire to market their labor and capi- tal through custom services; and (4) the potential for a farm machinery dealer to obtain a return on his inventory of used machinery by Offering short-term machinery rental to farmers. The results of this study were limited somewhat by the lack of data in the areas of general farm labor require- ments, crop losses due to untimely Operations, and the affects of inclement weather on available field work time. The limit— ing data in these areas indicate relevant needs for future research. AN ECONOMIC ANALYSIS OF ALTERNATIVE MEANS OF ACQUIRING FARM MACHINERY SERVICES FOR SOUTHERN MICHIGAN CASH-GRAIN FARMS BY Gary Lee Benjamin A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Department of Agricultural Economics 1968 ACKNOWLEDGMENTS The author wishes to express his appreciation to the many people who have contributed so much to the com- pletion of this thesis. Dr. Larry J. Connor deserves much credit and the author sincerely appreciates his contribution. As major professor, he continually provided encouragement, stim— ulated ideas, and Offered helpful suggestions as the study progressed from its beginning to completion. Thanks are due Dr. L. L. Boger and the Department of Agricultural Economics for the financial assistance and the facilities which have made this segment of my educa- tion possible. Much credit is also due Drs. Ralph E. Hepp, Karl T. Wright, and Alden C. Olson who read earlier drafts and made worthwhile suggestions for improvement. Appreciation is also extended to Dr. Robert G. White and Lauri Ahti who helped considerably in the early planning stages of this project. Special recognition is extended to Karen who, dur- ing the initial phases of this project, became the author's wife. Her cooperation and unselfish attitude made the final phases both possible and bearable. TABLE OF CONTENTS Page ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . ii LIST OF TABLES . . . . . . . . . . . . . . . . . . . V LIST OF FIGURES . . . . . . . . . . . . . . . . . . . vi LIST OF APPENDIX TABLES . . . . . . . . . . . . . . . Vii CHAPTER I INTRODUCTION AND SETTING . . . . . . . . . . . l The Changing Agriculture . . . . . . . . . l The Problem Setting . . . . . . . . . . . . 4 A Problem Statement . . . . . . . . . . . . ll Objectives of the Study . . . . . . . . . . 11 The Thesis Format . . . . . . . . . . . . . 12 References . . . . . . . . . . . . . . . . 13 II THEORETICAL FRAMEWORK AND SUBJECT REVIEW . . . 14 Theoretical Framework . . . . . . . . . . . 14 Problems in Applying Theory to Actual Data 0 I I C O O O O O I O O O O O O O O 19 Length of Run and Fixed Versus Variable Inputs 0 O O O O O O O O O O O O O O 19 Handling Discrete Resources . . . . . . 20 The Role of Management . . . . . . . . . 22 Profit and Residual Claimant . . . . . . 24 A Review of Other Studies . . . . . . . . . 26 Oklahoma Study . . . . . . . . . . . . . 27 Iowa Study . . . . . . . . . . . . . . . 29 References . . . . . . . . . . . . . . . . 32 III METHODOLOGY AND INFORMATIONAL SOURCES . . . . 34 Selection of a Farm and Its Characteristics 34 General Data Sources . . . . . . . . . . . 36 Survey on Renting and Leasing . . . . . . . 37 Short-Term Renting and Leasing . . . . . 38 Long-Term Renting and Leasing . . . . . 41 The Selection and Description of Various Farm Machinery Systems . . . . . . . . . 44 iii Table of CHAPTER Contents (Continued) General Assumptions . . . . . The Analysis Procedure . . . Budgeting Model I . . . . Budgeting Model II . . . . References . . . . . . . . . IV THE ANALYSIS 0 O O O O O O O O O Budgeting Model I . . . . . . Budgeting Model II . . . . . References . . . . . . . . . V SUMMARY AND CONCLUSIONS . . . . APPENDIX Problem Review and Analysis Results Implications of Study . . . . Limitations of Study . . . . Indications for Further Study References . . . . . . . . . TABLES O O O O O O O O O O O O BIBLIOGRAPHY . . . . . . . . . . . . . iv Page 48 50 51 53 57 59 59 62 69 7O 71 74 79 85 88 89 152 LIST OF TABLES TABLE Page 1 Farm numbers, farm size, and crop production index for the United States and Michigan . . 3 2 Value of productive assets per farm used in agriculture for Michigan and the United States 0' 0 o o o o o o o o o o o o o o o o o 6 3 Michigan commercial farms by type; number and percent of total, 1959-64 and 1980 prOjeCtionS O O O O O I O O O O O O O O O 0 lo 4 Summary of questionnaires returned, number of dealers renting or leasing, and short term contractual responsibilities . . . . . . . . 40 5 Summary of results for budgeting model I . . . 60 6 Summary of results for budgeting model II . . . 66 7. Ranges in farm size exhibiting relatively constant costs of production for various definitions of constant costs and variOus machinery systems . . . . . . . . . 68 LIST OF FIGURES Figure Page 1 A diagram of short-run and long-run cost curves 0 O O O O O O O O O O O O O O O O O 0 l6 2 Costs per acre for various farm sizes and machinery systems . . . . . . . . . . . . . 61 3 Cost:revenue ratios for various farm sizes 64 and machinery systems . . . . . vi LIST OF APPENDIX TABLES APPENDIX TABLE Page 1 Estimated typical acre for a cash-grain farm in southern Michigan . . . . . . . . . . . 89 2 Crop yields, fertilizer and herbicide require- ments, and other productive practices for the synthetic cash-grain farm . . . . . . 90 3 Assumed prices paid and received . . . . . . 92 4 Items, capacity, new cost, ownership costs, and expected life of farm machinery for the 4-row and 6-row machinery systems . . 93 5 Schedule of annual ownership costs by farm size with a 4—row system of complete ownership . . . . . . . . . . . . . . . . 96 6 Schedule of annual ownership costs by farm size with a 4-row system using a combina- tion of ownership and custom hiring . . . 97 7 Schedule of annual ownership costs by farm size with a 6—row system of complete owner-Ship O O O O O O O O O O O O O O O O 9 8 8 Schedule of annual ownership costs by farm size with a 6-row system using a combina- tion Of ownership and custom hiring . . . 100 9 Operating costs per hour of use for selected farm machinery items . . . . . . . . . . . 101 10 Factors used to estimate machine, power, and labor requirements for specific field operations in southern Michigan . . . . . 104 ll Machinery, power, and labor operating costs per acre by enterprise and Operation using a 4-row system with complete ownership . . . . . . . . . . . . . . . . 106 vii List of Appendix Tables (Continued) APPENDIX TABLE 12 Machinery, power, and labor Operating costs per acre by enterprise and Operation using a 4-row system with a combination of ownership and custom hiring . . . . . 13 Machinery, power, and labor Operating costs per acre by enterprise and Operation using a 6-row system with complete ownership . . . . . . . . . . . . . . . 14 Machinery, power, and labor Operating costs per acre by enterprise and Operation using a 6-row system with a combination of ownership and custom hiring . . . . . 15 Custom rates per acre and per forty-acre increments by enterprise and Operation . 16 Computation of variable cost per acre by enterprise and machinery systems . . . . . 17 Copy of rental and leasing questionnaire . 18 Items, capacity, number of units, time period of contract, rates charged, and delivered sales price as reported by 18 farm machinery dealers holding short- term rental or lease contracts in 1967 . 19 Budget for computing machinery, power, and labor costs per acre using a 4-row system with complete ownership . . . . . 20 Budget for computing machinery, power, and labor costs per acre using a 4-row system with a combination of ownership and custom hiring . . . . . . . . . . . 21 Budget for computing machinery, power, and labor costs per acre using a 6-row system with complete ownership . . . . . 22 Budget for computing machinery, power, and labor costs per acre using a 6-row system with a combination of ownership and custom hiring . . . . . . . . . . . 23 Budget for computing cost per acre for complete custom hiring . . . . . . . . . viii Page 108 110 112 114 115 116 120 122 123 124 126 128 List of APPENDIX TABLE 24 25 26 27 28 29 30 31 32 33 Appendix Tables (Continued) Estimated number of days lost in a 6—day work week due to inclement weather . . . . . . Relationship between inches of precipitation and field work days lost . . . . . . . . . Critical planting and harvesting periods and losses in yield resulting from late planting and harvesting . . . . . . . . . Summary of late planting and harvesting operations by farm size and 4-row machinery systems . . . . . . . . . . . . Summary of late planting and harvesting operations by farm size and 6-row machinery systems . . . . . . . . . . . . Budget for computing cost:revenue ratios for a 4-row system with complete ownership . . . . . . . . . . . . . . . . Budget for computing cost:revenue ratios for a 4-row system with a combination of ownership and custom hiring . . . . . . Budget for computing cost:revenue ratios for a 6—row system with complete ‘ ownership . . . . . . . . . . . . . . . . Budget for computing cost:revenue ratios for a 6-row system with a combination of ownership and custom hiring . . . . . . Budget for computing cost:revenue ratios for complete custom hiring . . . . . . . . ix Page 129 131 132 133 138 143 145 147 149 151 CHAPTER I INTRODUCTION AND SETTING The Changinnggriculture Much has been said and written about the changing scene in agriculture. The agricultural industry, once mainly confined within the boundaries of many self-support- ing individual farm units, has grown into a truly big in- dustry in every sense of the word. As an industry, it is now one of the biggest employers of human labor and other vital resources such as electricity, steel and oil. As an industry, agriculture also supports and promotes one of the finest research efforts of any industry. This changing agriculture has also had its effect on the individual farm unit. The magnitude and rapidity of this change is well recorded in countless volumes of statistical data and records. A review of these statis- tical sources will reveal two distinct trends centered by a crucial turning point in the 1910—20 decade. Prior to that time, history records increasing farm numbers, in- creasing crOpland acres, farm output increasing (but at a decreasing rate), and increasing farm employment. With the beginning of the 1920's, a second trend started to l prevail and has lasted to present day. During this second trend farm employment decreased, farm output increased at an increasing rate, and crOpland acreage fluctuated with increases and decreases. Although there are many debates and studies [l]* as to what specifically caused this increased output (in the wake of decreasing farm numbers and employment), the fact still remains that farming has taken on new dimen- sions. Farming has continued to grow since the 1920's, but this second growth has resulted from new capital for- mation, adoption of new practices and techniques, and im- proved farm management abilities. Consequently, from 1920 to 1964, the number of farms in the United States has de- creased by 51.1 percent while the average farm size in- creased in acreage by 136.8 percent. During the same period of time, the crop production index (1957-59=100) for the United States has increased from 76 to 109. Fig- ures for the State of Michigan show a 52.4 percent decrease in farm numbers and a 50.1 percent increase in farm size for the same 1920-64 range. Table 1 gives further details on how rapidly the farm structure has changed. In spite of the past record of change in farming and agriculture, the future is certain to reveal more of the same, and most likely at an accelerated rate. Cochrane *All references and footnotes appear at the end Of each chapter. TABLE 1.--Farm numbers, farm size, and crop production index for United States and Michigan. United States Michigan Per- Average Crop Per- Average Farm cent Farm Prod.2 Farm cent Farm Year Numbers Change Size Index Numbers Change Size (acres) (acres) 1850i 1,449,073 202.6 NA 1860l 2,044,077 41.1 199.2 NA 62,422 113.0 18701 2,659,985 30.1 153.3 NA 98,786 58.3 101.0 18801 4,008,907 50.7 133.7 NA 154,008 55.9 90.0 1890 4,564,641 13.9 136.5 NA 172,344 11.9 86.0 1900 5,739,657 25.7 146.6 NA 203,261 17.9 86.4 1910 6,366,044 10.9 138.5 63 206,960 1 8 91.5 19201 6,453,991 1.4 148.5 76 196,447 5.1 96.9 1925 6,371,640 - 1.2 145.1 72 192,327 2.1 93.8 19301 6,295,103 - 1.3 157.3 69 169,372 11.9 101.1 1935 6,812,350 8.3 154.8 70 196,517 16.0 93.9 19401 6,102,417 —10.5 174.5 78 187,589 4.5 96.2 1945 5,859,169 — 3.9 194.8 85 175,268 6.6 104.9 19501 5,388,437 — 8.1 215.8 89 155,589 -11.2 111.0 1954 4,782,416 -11.1 242.2 93 138,922 -10.7 118.5 1959 3,710,503 -22.6 302.8 103 111,817 -19.5 132.2 1964 3,157,857 -14.9 351.6 109 93,504 -l6.4 145.4 1Data for Alaska and Hawaii not included in U.S. figures. 2Includes feed grains (corn for grain, oats, barley, sorghum grain), food grains (all wheat, rye, buckwheat and rice), hay and forage (all hay, sorghum forage, corn silage and for 1939 to date, sorghum silage), vegetables (potatoes, sweet potatoes, dry edible beans, dry field peas, truck crOps for processing, and truck crops for fresh market having value), fruits and nuts (fruits, berries, and tree nuts having value), sugar crops (sugar beets, sugarcane for sugarcane syrup, and maple syrup), cotton (cotton lint and cotton seed), tobacco, and oil crops (soybeans, peanuts picked and threshed, peanuts hogged, flaxseed and for 1939 to date, tungnuts), farm gardens, hay seeds, pasture seeds and cover-crop seeds and some miscellaneous crop production. Index 1957-59 = 100 Source: U.S. Department of Commerce, Bureau of the Census, United States Census of Agriculture (by years), Washington. [2] has done some star gazing into the future for what he labels as "probable and possible" developments up to the year of 2000. Although, some of his ideas appear to be far fetched at present, we can rest assured that they are, indeed, within the realm of possibilities. Farming is now built on a solid foundation of scientific research, rapid technological development, expanding managerial abilities, and superior means of communications. Hence farming, and the hungry farmers starved by the cost-price squeeze, will adopt new developments at a faster rate, in the hopes of lowering per unit costs and increasing net returns. The big question does remain, however, as to whether or not individual farmers will be prepared to meet these rapid changes. The farmer of today who has hopes of still being a farmer in 1980 will be forced to make rapid economic decisions as new developments occur. Unless he has prepared himself knowledgeably and financially, the farmer of today will find it impossible to salvage his economic existence in the future. The Problem Setting As indicated above, the future of farming will call for increasing changes. One of the most dynamic aspects of this change will be in the area of farm investments. The emphasis of this study is to analyze one segment of the farm investment structure as it applies to a given type of farm. More specifically, the analysis pertains to farm power and machinery requirements as related to a Southern Michigan cash-grain farm. As Table 2 shows, farm power and machinery invest- ments make up about 10 to 13 percent of the total farm in- vestment which at first, may appear to be a minor part of the total investment program. However, investment in farm power and machinery carries certain other unique character— istics which do not apply to the other segments making up the total farm investment. Foremost among these unique characteristics is the fact that farm power and machinery are continuously subjected to improvements. Engineers are trying to develop new and better machines to replace old and oftened outdated methods of operation. Witness for example, the surge of new fruit harvesting machines which are gradually replacing the need for hand labor and revo- lutionizing the fruit industry. Another good example is the trend that is emerging for self-propelled equipment and the "uni-system" which provides one source of power for several field Operations. It should be pointed out, however, that such devel- 0pments are not limited to the type which revolutionize an industry. In fact, farm equipment manufacturers operate much the same as do automobile manufacturers. Each year brings new models and new improvements to almost every existing item of equipment; new tractors emerge with higher .mmm .mammumbmtb oumum cmmmnomz .mOHEOQoom HOHSDHSOHHmd mo usmfibummmo .Amnmmm mnv puomom mmwcflmsm Show COMAQOHE .OADDHDOHHmm mo ucmfignmmmo mmumpm UmuHcD .moa>umm nonmmmmm oHEocoom .oz cflumaasm COHDMEMOMCH OHSHHOOHMO¢ .hmma .OHDDHSOHHmd mo umonm mosmHmm One “mousom nll..mmusmAm .m.s npAg magomuflv Ownmmfioo on uoc UHSOQm ovum: mam mgmum may now mommum>m uoa mum mwusmflm .wgflm IHO>HGD mumpm savanna: on mOHoomH ca Ooawmfi .Eumm so comma mfl :mmHQOHz How upmom .mfinmm Ham How mmmnm>¢a h.mH Hom.ma Hem.ma omm.ma Ham.ooa mnm.mva m.oa Hmm.m mma.m mmm.m mmh.mm mmm.nm coma H.NH mma.ma mna.HH nmm.va moo.am mmm.mma m.oa vwm.w mao.m mmm.v Hom.hv mmm.am mmma w.HH www.ma mon.oa mmo.hH Hma.mm moa.ama m.oa mm>.m moo.m Hmm.w w¢m.mv mmm.mm voma h.NH mmm.ma nmo.m woa.ma www.mm mav.mm m.oa mmv.m www.a vmm.v mav.mm wmm.am mmma h.NH mmm.HH mmv.b mmH.¢H wmm.mm th.Hm h.oa Hma.m Nob.a mev.v mnm.mm www.5v moma m.mH Hmm.oa ova.o mmm.aa mah.mm wmm.am H.HH mom.¢ mmn.a nmo.w Hmm.mm mmm.¢v Hmma o.ma mom.m om>.m mmv.oa mah.mv omm.mw v.HH omm.v mmh.a mvm.m mom.am www.mv owma N.mH mmm.m 5mm.m Hmo.m www.mv mmm.mm N.HH ovm.w wmm.a Hmm.w mmb.mm oov.ov mmma w.ma mvm.m mmm.v mmh.h www.mm mmm.mm h.HH voa.v www.a wom.m mom.mm vma.mm mmma «.ma omm.m mmm.a mmv.m wm>.mm mom.am nmma v.om mom.m omn.m omm.m aam.mm www.0v m.NH hmosm mmm.a mmm.m vom.om mmv.mm mmma H.Hm Amm.A Ava.v mmw.m mmo.om VAA.Am o.ma ~o¢.m NAA.H Amm.m AHA.AH msa.mm mmma m.om vwm.m mvo.m mmm.m mmm.ma ava.mm v.HH mmm.a mmH.H mma.m moo.ma mnm.na omma m.¢a omm.m hom.a th.m mow.oa wmm.ha ¢.oa an.H Hmm mvm.a mmh.h va.HH mvma m.HH wwm.a who.a mvm.a mva.m mmw.MH m.m Ham Hmm mom mom.v mma.m ovma HODOB HODOB AoA A A A A A AoA A A A A .A humcflsomz mmouu MOOHm mamumm Hmuoa mmHOH£m> HOQDO Moogm manpmm HODOB Hmmw qu Hmzom cam IO>HA Hmmm Houoz UGO Im>wq Hmwm comm mumcflnomz Show NammAAOAz ammumum smuAcp mwumum OODHCD map can cmmflzowz How musuHSOAHmm cw poms Enmm mom mgmmmm O>HDOSOOHQ mo ODHO>|I.N mqmfia horsepower ratings, the plow adds an additional bottom, six-row planters gradually replace four—row planters, and tillage tools are developed to handle the once-over Opera— tion. As with the automobile industry, it has become al- most impossible to keep up with complete line of makes and models representing the various farm equipment companies. The continuous developments in the farm equipment industry presents another unique characteristic of farm machinery investment. This is the disposal problem. As W. H. M. Morris points out, the increasing capacity Of farm machinery has ". . . made individual machines more expensive; and it is beginning to create a problem in the disposal of used machinery of large capacity. A depression in the price of these machines (used machinery) is to be expected. These two effects combine to make the owner- ship cost more expensive. Increase in size of tractors . . . tends to make the utilization of a machine more uniform throughout its life on a farm. It used to be practiced to demote the first line tractor to the second and even ultimate- ly the third line. It does not seem conceivable that a 125 hp tractor could be used in this way. So when it fails to fulfill the needs as a 'first line' tractor it will have to be traded for a new first line unit. There may be relatively small demand for such a used machine. This also leads to the second line unit being purchased as such"[3]. Aside from the fact that there is little market for a 125 horsepower tractor, the problem of rapid initial deprecia- tion still exists. The continuous developments in farm machinery render a machine to be technically obsolete far sooner than it is physically Obsolete. Commercial farmers are perplexed as to whether they should take a loss by trading in their four year Old tractor for a new and better model, or continue to struggle along with the older model till it becomes worn out and market values again approx- imate depreciation. As can be seen by the above arguments, the costs associated with the farm machinery investment are high. New costs of the larger items of equipment run into several thousand dollars. Financing such an investment requires considerable knowledge of sources of capital and debt re- payment abilities. Coupling this to the problem of finan- cing the remaining farm investments, it becomes easy to understand the problem of competition that exists between alternative uses of limited capital and credit. The whole method of establishing investment priorities becomes very important when the entire investment picture is visualized. Another important characteristic of farm power and machinery is that the services from such equipment can be acquired in several ways. Quite logically, the most common method is through equity ownership of the entire system. However, the range of choice also includes complete custom hiring and combinations of ownership with custom hiring, short-term rental or lease, and long-term rental or lease. Under various situations and circumstances, each alterna- tive would most likely prove feasible, since each Offers different costs, different responsibilities Of management, varying probabilities of crop completion, and varying prob— abilities of service acquisition. The problem remains how- ever, as to exactly what situations make these alternatives feasible. A final important characteristic of the farm power and machinery investment is that it can be analyzed as an entire system. Regardless of what individual machinery units are required to perform the entire sequence of farm operations, the final decision as to how the machinery ought to be acquired, should be based on an analysis of the entire system. Farming is made up of several Opera- tions, and usually made up of several enterprises. Because of this, any attempt to reorganize a single portion of the farm structure, should first be evaluated on the basis of what affects the reorganization will have on the entire farm. In the case of farm machinery, this requires that the selection process should be based on an analysis of machinery as a system. As mentioned above, this study is an analysis of farm machinery selection on a cash-grain farm. The reason for selecting this type of farm is also based on predicted changes in the structure of farming. Based on 1964 data, the number of cash-grain farms made up 25.6 percent of all commercial farms in Michigan. This was second only to the dairy farm which accounted for 33.6 percent of all commer- cial farms. Projections to the year Of 1980 indicate that, 10 even with a reduction in commercial farm numbers of about 57 percent, the number of cash—grain farms in Michigan will comprise some 35.1 percent of all commercial farms and thus become the most prominent farm type in Michigan. Table 3 indicates the projected numbers Of all farm types in Mich- igan by 1980. TABLE 3.--Michigan Commercial Farms by Type: Number and Percent of Total, 1959-64 and 1980 Projections Number of farms Percent of all farms 1980 1980 pro- pro— Type of Farm 1959 1964 jection 1959 1964 jection Dairy 24,663 20,230 8,000 37.9 33.6 21.6 Poultry 2,079 1,734 400 3.2 2.9 1.1 Other livestock 9,849 8,725 8,000 15.1 14.5 21.6 Cash-grain 14,262 15,418 13,000 21.9 25.6 35.1 Other field crops 1,235 1,027 800 1.9 1.7 2.2 Fruit 4,135 4,181 2,000 6.4 7.0 5.4 Vegetable 1,304 1,335 1,000 2.0 2.2 2.7 General 6,197 5,287 2,300 9.5 8.8 6.2 Miscellaneous 1,318 2,250 1,500 2.1 3.7 4.1 Total 65,042 60,187 37,000 100.0 100.0 100.0 Source: Research Report 47 "Project '80 Rural Michigan Now and in 1980," Agricultural Experiment Station and COOpera- tive Extension Service, Michigan State University, 1964, p. 20. 11 A Problem Statement With the understanding of the amount of investment a farmer ties up in farm power and machinery, and with the realization of the swiftness in machinery turnover due to technology, it is not hard to see why farmers have diffi- culty managing this portion of their total farm investment. A farmer is faced with several alternatives, ranging from ownership, custom hiring, renting, and leasing, when he attempts to acquire farm machinery services. An economic- ally feasible selection process requires knowledge Of op- erating costs, ownership costs, expected years of life, salvage values, efficiency schedules, machine capacities, custom rates, rental rates, and leasing rates. It is the intent of this study to analyze the costs associated with the alternative methods available to the farmer in acquir- ing the services of the complete farm machinery system. Objectives of the Study The main Objectives of this study are as follows: 1. To describe various alternatives of acquiring the services of selected farm machinery systems. 2. To determine the relationship between farm size and per acre total farm power and machinery costs for selected farm machinery systems on a Southern Michigan cash-grain farm. 3. To examine the effects of inclement weather on the timeliness of field Operations for Southern Michigan cash-grain farms using alternative farm machinery systems. 12 4. To determine the breakeven points between total costs and revenues for a Southern Michigan cash-grain farm using alternative farm machin- ery systems. 5. To determine the optimum farm size which would achieve minimum acre production costs for each of the various farm machinery systems selected for a Southern Michigan cash-grain farm. The Thesis Format The remainder of this thesis is broken down into four chapters. Chapter II contains a discussion of the theoretical framework for economies of size studies and presents some of the problems in relating the theory to empirical research. Such things as defining length of run, resource divisibility, residual claimant, and risk and uncertainty are described in detail. Chapter III explains the research methodology used in this study. Discussion centers on the selection of a farm for analysis, the selection and description of various farm machinery systems, and the analysis procedure. A de— tailed summary Of a survey on current farm machinery rental and lease programs in Michigan is also included in Chapter III. Chapter IV follows with the results of the analysis as applied to a southern Michigan cash—grain farm. Chapter V contains a brief summary on the conclusions and implica- tions of the study, and the needs for future research. The appendix at the end of the text includes most of the tables of supporting data. 13 References 1. See Dale E. Hathaway's comments in Government and Agri- culture: Economic Policy in a Democratic Society, the Macmillan Company, New York, 1963. p. 93. 2. Willard W. Cochrane, The Citngan's Guide to the Farm Problem, McGraw-Hill Book Company, New York, 1966. pp. 32-42. 3. Farm Machinery Replacement, Unpublished manuscript by W. H. M. Morris, Purdue University. Parenthesis mine. CHAPTER II THEORETICAL FRAMEWORK AND SUBJECT REVIEW The groundwork for any type of research rests on the theories of the supporting discipline. In economies of size studies [1], the supporting discipline is econom- ics. The purpose of this chapter is to portray the theory and some of the associated difficulties of applying the theory to real life problems. The latter part Of the chap- ter contains a review of two studies already completed in the area of farm machinery selection. Theoretical Framework The theory of production is expressed in terms of short-run and long-run planning horizons. Explanation of the lnegth of run is dependent upon the knowledge of which factors of production are fixed to the firm and which are variable. A fixed resource is defined as one which is worth more in its present use than any other entity will pay for it, but not worth enough in its present use to justify getting more of it. The marginal value of the fixed resource is less than the purchase price of an addi- tional unit of the resource and more than the salvage price obtainable by selling the unit [2]. l4 15 A variable resource is defined as having a marginal value which exceeds its purchase price or is less than its salvage value. Hence, the amount of a variable resource used by the firm is flexible. More, or less of it can be used at increased profits to the firm. The length of the planning horizon depends on how the factors of production are viewed. In general, economic literature defines four distinct time periods as the very short-run, the short-run, the long-run, and the very long- run. The very short-run is a time period so short that a firm cannot change its output, while the short-run time period is sufficiently long enough to allow the firm to expand output but not capacity. Hence, the latter time period allows some, but not all, factors of production to vary. The long-run refers to a time period sufficiently long enough to allow all the firms factors of production to vary. This is distinguished from the very long-run where all factors of the firm, the industry, and the econ- omy, are allowed to change. To better understand this theory, a diagram is presented on the following page. This diagram shows five different short-run planning horizons which are identified by the five SAC (short-run average cost) curves. Each SAC curve shows the relative position of the firm facing a 16 FIGURE 1.--A Diagram of the Short-Run and Long- ’ Run Cost Curves SAC Cost:Revenue SAC1 Ratio ' SAgz ‘ SAC4 \ SAC3 Dollars / I D --------------—-- 17 time period so short that part of the resources are fixed. Each SAC represents a different level of fixed resources. The optimum (or most efficient) point of production for each Of these planning horizons is at the bottom of the respective SAC curves. This is the point where marginal cost is equal to average cost; hence, per unit of costs of production are lowest. However, the theory says a firm will produce in the short-run at the point where marginal cost is equated with marginal revenue as long as total revenue at least covers the total variable costs and con- tributes to the fixed cost. Each SAC curve shows the three general segments of decreasing, constant, and increasing costs. In the down- ward sloping portion Of decreasing costs, more units of the variable resources are added to the existing level of fixed resources, thus resulting in greater output. Total average costs are decreasing because the fixed costs are spread over more units of output. However, a point is eventually reached whereby increasing proportions of the variable resources must be added to the fixed resources in order to increase output. Fixed costs are still being spread over more units of output, but the total variable cost becomes much higher per unit of product. Consequent- ly, total average costs increase. 18 The long-run planning horizon is shown by the LAC (long-run average cost) curve which is drawn tangent to the SAC curves. This curve reveals the least possible cost per unit of product for various levels of output when all factors of production are variable. Hence, it is an indication of the long-run economies of size available to the firm for assumed levels of technology and price rela- tionships. It presents the least-cost resource combination where the marginal physical products per dollar of resource are equal for all factors of production. In this long-run situation, the firm will continue to produce if revenues are sufficient to cover all costs. The most efficient out- put level is OQ as indicated by the diagram since, at this point, the per unit costs of production are lowest. If the assumption of perfect competition is added to the model, this will, indeed, be the ultimate output of the firm since price levels tend to adjust to the point where resources gain a return sufficient enough to retain the employment of the inputs being used in production, but not sufficient enough to lure additional units of the inputs into the pro- duction function. In other words, there would be no econ- omic profits. This price level is shown as RP on the diagram. 19 Problems in Applying Theory to Actual Data The problem in the application of economic theory to real life situations, is the necessary relaxation for some of the assumptions upon which the theory is built. For instance, what happens if the decision maker lacks perfect knowledge? Or, what happens if resources are not perfectly divisible? Or again, what determines the length of run and the fixity of resources? These are some of the questions involved in understanding research on economies of size. Such questions do not negate the true value of economic theory; they just make the application of the theory more challenging. Madden [3] has summarized the research pertaining to farm economies of size and much of what follows is a review of the points which he raises concerning empirical economies of size studies. Length of Run and Fixed Versus Variable Inputs Whenever studying problems related to economies of size, some mention must be made of the length of run. Eco- nomies which occur from firm adjustment are the result of different motives depending on whether the short-run or long-run is advocated. Madden points out that, "Short—run economies are viewed as resulting from fuller utilization of a fixed plant, long-run economies as resulting from ef- ficiencies obtained by changing plant size, presumably 20 involving a longer time period" [4]. However, the problem still unanswered is: where does the short-run end and where does the long—run begin? Or stated another way: when does a fixed resource become a variable resource? Farm inputs come in various classes with differing life spans. Durable farm resources have useful life spans ranging from two to forty years, and can reasonably be con- sidered fixed to the firm for this period of time. The issue is complicated more by the fact that there is no predetermined order for which fixed resources become var- iable. In light of this, length-Of-run becomes a fictional time period which cannot be specified by a calendar. In- stead, a series of progressively longer lengths-of-run evolve as the planning horizon lengthens and more inputs are considered variable [5]. The length of run and the fixity of resources thus become relative terms, depending solely on the entrepreneur's (or researchers) frame of mind. Handling Discrete Resources Economies of size studies must also deal with the problem of discrete and divisible resources. A discrete resource is defined as an input which is available to the firm in the form of specific sizes or in counted quantities. It may be a single item, or it may be increments of set sizes. A divisible resource, on the other hand, is one which is available to a firm in measured quantities. 21 The discrepancy between these two types of re- sources occurs in the utilization of the inputs. Divisible resources are usually fully utilized. Such things as gas- oline, fertilizer, electricity, herbicides, seeds, etc., can be obtained in the exact amount required for production. If the divisible resource is storable, it may be saved for future use or returned to the seller. Discrete resources, however, are often underuti- lized. Farm machinery invariably falls into this trap for the simple reason that the various machinery items within the system have different capacities. As a result, some items of machinery may be underutilized, while others may be overutilized at the same acreage level. The solutions to the problem of discrete resources hinge on two alternatives. In the first place, the service of a discrete resource may be available to the firm in divisible quantities. Applying this to the farm situation, it is easy to see that such things as the hiring of part- time help, the use of custom hiring, or machinery rental and lease, are all alternatives to the discrete resource hangup. A second alternative is presented by Madden [6], who argues that the firm can come closer to full utilization of discrete resources if it uses smaller increments of the resource relative to the total quantity of the resource used by the firm. Applying this to the situation of dis- crete machinery inputs, it appears reasonable that, if a 22 farm acquires tractors in discrete units of 100 horsepower for every 200 acres of land, a farm of 300 acres would re- quire two such tractors. However, if tractors were purchased in units of 50 horsepower; and if the assumption that the smaller the tractor--the lower the cost, is valid (and it would be in this case), then a farm of 300 acres would most likely be handled at a lower per unit cost if one, 100 horsepower, and one, 50 horsepower tractor were used. It perhaps would be wise to point out that the prOper emphasis of full utilization of resources should be handled with some caution in economic studies. In general, it is true that the full utilization of resources results in a reduction of average costs; however, full utilization to lower average costs is only one method of increasing profit [7]. Consequently the profit motive may in itself dictate that the underutilization of some discrete re- sources will lead tO increased revenues. Underutilization, or excess capacity, may also be argued as an ace in the hole against risk and uncertainty. Take for example, the accumulation of excess machinery capacity as a guard against unfavorable weather and late field operations. The Role of Management The term "farm management" is an often used phrase which covers a considerable area of relatively unknown boundaries. Numerous attempts have been made to define 23 management, but as yet, no one really knows what management is, how it operates, or what capacities it offers. There are even dissenting views as to whether or not management is a factor of production or something in addition which helps explain and describe the production function. About the only concrete aspect of management that is ascertain— able, is the apparent results of managerial activities, and in some cases this has to be interpreted with caution. In general however, farm management is the decision making process for a farm. Not only does this include the day in--day out type of decision required for normal ac- tivity, it also pertains to the process of formulating major decisions which often change the structure of a farm- ing operation. The activities of a farm manager are usually described in terms of supervision, coordination, and entre— preneurship. The first two involve decision making to handle daily operations and coordinate daily Operations into a smooth and efficient production cycle. Entrepreneur- ship pertains to the process of making major decisions and accepting the risk and uncertainty associated with the suc- cess or failure possibilities after the decision is made [8]. The problem of handling farm management in studies on farm structure and behavior, is that it represents in- tangible attributes. Farm management per se, cannot be perceived in any form of the senses, and hence cannot be quantified. As a result, the only alternative for defining 24 farm management rests in the ability to qualify the term in some identifiable manner relative to present levels of technology and knowledge. In economies of size studies, once this is done all other resources are added to the production function to develop cost statistics for various levels of output. The real problem occurs when it becomes Obvious that to advantageously add resources (other than those with an assumed level), requires excess managerial capacity on the part of the farmer. When farm complexity and size is increased, the chances of financial success or failure become greater; as do the problems of supervision and coordination. Defining the managerial capacity at some level may not be sufficient to cover the entire range stud- ied and, hence, may seriously limit the size or extent of the farm to something below that indicated as most effi- cient by an assumed managerial level. Profit and Residual Claimant Economic studies which include costs and profits are often misinterpreted, especially when comparing one study with another. As Madden points out, the problem is likely to be a lack of specification for the residual claimant; or that set of resources which absorb the profit [9]. The definitions of profit and residual claimant vary, depending on the extent to which the factors of pro- ductions have received a fair return from gross income. 25 For instance, two popular concepts of profit and residual claimant [10] in farm management studies are "net farm income" and "operator management income." Under the first concept, all cash costs and depreciation are subtracted from gross receipts to arrive at the net farm income figure. Such a figure however, fails to recognize the opportunity cost on the equity portion of farm investments, and it fails to recognize an Opportunity cost on the farm oper- ator's labor and managerial abilities. The second concept is a further refinement of re- sidual in that "Operator management income" also subtracts a return for interest on investment and a return for Oper— ator labor. The amount of receipts which still remain, represent the return to the operator for his managerial services. The importance of the above two definitions can readily be seen in a hypothetical farming situation. As- sume for the moment that a farmer held full equity in his operation and received a positive net farm income but a negative Operator management income. Based on this assump- tion, a farmer in such a position could continue Operating indefinitely since, even though he is not receiving a fair market return on his investment, and his ability as a la- borer and manager, all depreciation and cash costs would be covered by the receipts. However, for a young indivi— dual considering the long-run consequences of starting a 26 similar farm, the revenues would not be sufficient to en- tice him into the same farm type since alternative invest- ment and employment would offer greater returns. The above points out that for proper interpretation of farm management and economies of size studies, it is imperative that the individual understand how the residual claimant is defined and how other resources are priced. Unless this is done, a cost statistic, a profit statistic, or a cost:revenue statistic has no real meaning within itself nor in comparison with similar statistics from other studies. A Review of Other Studies There recently has been a number of economies of size studies for various types of farms throughout the United States. Some of these studies have emphasized beef- lot economies of size [11, 12], while about an equal number of studies have directed primary emphasis toward economies within a dairy farm [13, 14]. In the area of crop produc— tion, size efficiency studies have emphasized such things as Optimizing fruit harvesting [15] and least-cost enter- prise combinations [16]. The discussion which follows is a review of two prior studies in the area of selecting farm machinery. The first article refers to a study which treated farm machinery in preharvest and harvesting systems, while the latter analyzes cost for entire systems. 27 Oklahoma Study In 1964, Walker published a bulletin entitled Machinery Combinations for Oklahoma Panhandle Grain Farms [17]. In his study, Walker attempted to isolate average cost statistics for alternative preharvest farm machinery systems and for alternative harvesting methods. Machinery performance and cost data in his study was Obtained from a 1960 survey of 57 farmers and 10 machinery dealers in the Oklahoma Panhandle. Walker's cost statistics are straightforward average total preharvest machinery costs per acre for the alterna- tive machinery combinations. Thus, the cost curves are continuously downward sloping to a point of limited machin— ery capacity. These capacities for the alternative systems were calculated on the basis of calendar time periods for all critical jobs, the corresponding probabilities of 10 hour work days available, and machinery performance rates. The cost curves presented in Walker's study do not include receipts from products sold, and consequently, there are no cost:revenue ratios. His technique is to limit the analysis of cost only to the range of acreage for which a given machinery system is capable of handling field Operations within critical time periods. At the acreage level for which the capacity of the preharvest machinery combination limits timely operations, the cost curves abruptly stop. 28 The preharvest machinery combinations analyzed were; (a) two, 4-plow tractors and equipment, (b) one, 4- plow tractor and equipment, (c) one, 3-plow tractor and equipment, (d) two, 5-plow tractors and equipment, (e) one 5-plow tractor and equipment, and (f) complete custom hir— ing. The analysis showed that in 50 percent of the years, farmers could cover the critical Operations at a minimum cost with the following systems and acreage ranges: 1. custom hiring——O to 300 acres of crOpland 2. one, 3-plow tractor and equipment--300 to 400 acres of cropland 3. one, 4-plow tractor and equipment--400 to 900 acres of crOpland 4. one, 5-plow tractor and equipment—-900 to 1400 acres of cropland 5. two, 4—plow tractors and equipment--l400 to 2000 acres of crOpland The latter part of Walker's article is devoted to an analysis of harvesting operations using the alternatives of ownership of 12, 14, and 16 foot self-propelled combines versus custom hiring. The author shows that the breakeven acreage levels between ownership and custom hiring at 3 dollars per acre, are 360, 385, and 445 acres for the 12, 14, and 16 foot machines respectively. He goes on to de- velop five different harvesting "strategies" based on var- ious assumptions as to the availability of custom operators 29 and a fixed number of days available for harvesting Opera- tions. For each strategy he develops per acre harvesting and insurance costs for completing harvesting Operations on time. The conclusions that Walker draws from his study are mainly that the number of "tractors and the use of custom rather than owned machinery may have substantial effects on total machinery costs" [18]. The availability of custom operators would reduce costs on farms of up to 300 acres. The decision as to the size of tractor appeared to have little relevance in the 600 to 1000 acre crOpland farms, however, the maintenance and purchase of a second tractor added approximately $600 to annual machinery costs on the same size farm. In regards to harvesting methods, Walker concluded that the larger machines ". . . provide lower cost services when days to combine are fixed. Smaller machines allow lower per acre costs when a restriction is not placed on harvest days" [19]. Iowa Study In 1964, Ihnen and Heady published a study called Cost Functions in Relation to Farm Size and Machinernyech— nology in Southern Iowa [20]. The study was directed to- wards farms in nine southern Iowa counties and used synthetic-firm budgeting models. The farms were divided 30 into three different classes of topography--hilly, upland, and average. The objective was to develop least-cost machinery combinations for various size farms in each of the three classes. The machinery systems considered in the Ihnen and Heady publication were identified by the size of the mold- board plow and were as follows; (a) 2-plow, (b) 3-plow, (c) 2-plow, 2-plow, (d) 2-plow, 3-plow, and (e) 3-plow, 3- plow. The machinery, excepting for one case of custom Operations, was fully owned. The analysis used by Ihnen and Heady was based on two budgeting models. The first model assumed costs and revenues for cr0p enterprises while the second model in— cluded both crOp and a beef-cow enterprise. The results of changing from the first model to the second model showed "relatively little effect upon the basic budgeting results or cost relationships" [21]. The authors included in their analysis a schedule for cr0p losses due to untimely field Operations and treated these losses as a cost rather than reductions in revenue. Other costs included depreciation, interest, taxes, housing and insurance, seed, fertilizer, insecticides, fuel, oil, repairs, land, and labor. The results of the analysis showed that "substan- tial reduction in average total cost per dollar of crOp product can be Obtained by using larger machinery 31 combinations on larger crop acreages when custom operations are not considered" [22]. Minimum unit costs were found to exist at about 320 crop acres on each farm. The range of constant costs [23] ran from 196 to 232 crOp acres for a 2-man, 2-tractor machinery combination when custom opera- tions were not considered. For smaller acreages, the smaller machinery systems resulted in the lowest unit costs, but these costs were high relative to the minimum unit costs. Also, the smaller machinery systems resulted in more yield and revenue losses due to untimely field Operations when acreage increased. In the one isolated example of custom operations, Ihnen and Heady found that "custom operations increase the relative efficiency of the l-man, l-tractor machinery com- bination and makes these small machinery combinations as efficient on small acreages as the larger machinery com- binations on the larger acreages" [24]. 32 References l. The expressions "economies Of size" and "economies of 10. 11. scale" are often used interchangeably in studies examining the relationship between average costs and levels of production. However, since the word "scale" usually implies constant proportions, much confusion has resulted from the improper specifi- cation Of these two concepts. In general, since firms do not expand output by increasing resources and products in exactly the same proportions, "eco- nomies of size" has evolved as the more prOper expression. At any rate, the term economies of size is adopted for this study, and defined as reductions in total cost per unit of production resulting from changes in the quantity of resources employed by the firm or in the firms output. Further refinement of the fixed versus variable clas- sification can be made. For instance, Bradford and Johnson talk of resources which are fixed to the firm, but variable between enterprises. See, Lawrence A. Bradford and Glenn L. Johnson, Farm Management Analysis, John Wiley & Sons, Inc., New York, 1963, p. 168. J. Patrick Madden, Economies of Size in Farming, Agri- cultural Economic Report NO. 107, Economic Research Service, U. S. Department of Agriculture, February 1967. Ibid., p. 3. Ibid., p. 5. Ibid., p. 7. Ibid., p. 8. Ibid., p. 13. For a definition and listing of other alternatives see, J. Patrick Madden, Ibid., p. 14. John A. Hopkin, "Economies of Size in the Cattle Feeding Industry of California," Journal of Farm Economics, Vol. 40, No. 2, May 1958, pp. 417—429. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 33 Elmer C. Hunter and J. Patrick Madden, Economies of Size for Specialized Beef Feedlots in Colorado, Agricultural Economics Report No. 91, U. S. Depart- ment of Agriculture, May 1966. William E. Martin and James S. Hill, Cost-Size Rela- tionships for Central Arizona Dairies, Technical Bulletin No. 149, AriZOna Agricultural Experiment Station, September 1962. Boyd M. Buxton, Economies of Size in Dairy Farming, Farm Business Notes 467, University of Minnesota, November 1964. Gerald W. Dean and Harold 0. Carter, Economies of Scale in California Cling Peach Production, California Agricultural Experiment Station Bulletin No. 793, February 1963. J. Patrick Madden and Bob Davis, Economies of Size on Irrigated Cotton Farms of the Texas High Plains, Texas Agricultural Experiment Station, Bulletin B-1037, June 1965. Odell L. Walker, Machinery Combinations for Oklahoma Panhandle Grain Farms, Experiment Station Bulletin B-630, Oklahoma State University, November 1964. Ibid., p. 22. Ibid., p. 23. Loren Ihnen and Earl O. Heady, Cost Functions in Relation to Farm Size and Machinery Technology in Southern Iowa. Agricultural and Home Economics Experiment Station, Research Bulletin 527, Iowa State Univer- sity, May 1964. Ibid., p. 125. Ibid., p. 125. Constant costs were defined as those costs within five percent of the minimum. Ihnen and Heady, loc. cit., p. 125. CHAPTER III METHODOLOGY AND INFORMATIONAL SOURCES There are, depending on the motives and situations, a number of methods for analyzing economies of size studies [1]. However, it is generally recognized that the synthet- ic firm approach offers the best method for isolating dif— ferences in average costs per unit of output which are attributable to differences in size of the firm. Since this represents the main interest of this study, the "syn- thetic firm" approach was adopted in the analysis procedure. Selection of a Farm and Its Characteristics The location of the synthetic farm was placed in the southern half of lower Michigan; exclusive of the Sag- inaw Valley area. The soils of the hypothetical firm were assumed to consist entirely of the adequately well drained clay to clay loam series, which closely correspond with the majority of actual soil types located in this part of Michigan. The selection of the product mix was based on the average number of enterprise acres reported by the forty- three Michigan cash—grain farms enrolled in the Michigan 34 35 Telfarm project in 1966 [2]. From this data, a typical acre was calculated and defined as the percentage composi- tion of each crop or productive use made of an acre of tillable land. The percentages are based on the reported average number of tillable acres minus the acreage desig- nated as "other crOps." For simplicity, and because the oat enterprise is similar in most respects to the wheat enterprise, the relatively small acreage reported as oats was combined with wheat acreage and called the wheat enter- prise. As Appendix Table 1 shows, the typical acre was found to consist of 36 percent corn, 15 percent soybeans, 19 percent navy beans, 15 percent wheat, 12 percent diverted, and 3 percent idle. For purposes of analysis, this study only consid- ered the costs and revenues associated with the productive crOps of corn, soybeans, navy beans, and wheat. Because of the wide range of alternative uses and practices for idle and diverted acres, the costs (machinery and labor) and the possible revenues, attributable to these acreages, were ignored. The level of management (and corresponding produc- tion practices and inputs) assumed for the synthetic farm was above average and defined as the level ". . . required to obtain yields intermediate between present average yields and highest yields presently being attained 36 experimentally and by some producers" [3]. The associated crop yields and productive practices are listed in Appendix Table 2. General Data Sources Much of the supporting data for this study was obtained from other sources. The majority of the machinery data on new costs, Operating costs, and ownerships costs came from an earlier publication by Connor [4] and his supporting unpublished data. Whenever necessary, his data was supplemented by information Obtained from the Agricul- tural Engineering Department at Michigan State University and from farm equipment manufacturers offices located in the Lansing area. Information on current rental and leas— ing practices in Michigan was obtained by the use of direct mail questionnaires sent out to 375 Michigan farm machinery dealers. The data for yield losses due to untimely Opera- tions, was provided by the efforts of the CrOp Science Department at Michigan State University. The United States Weather Bureau and the Agricultural Engineering Department at Michigan State University were helpful in providing data on inclement weather and resulting lost field work time. USDA, Experiment Station, and Departmental publi- cations were also used, but are too numerous to mention 37 individually. However, an attempt has been made to iden- tify; either in the text or in the supporting Appendix Tables; all relevant informational sources. Survey on Renting and Leasing The practice of renting and leasing farm equipment is not new to farm machinery dealers. Trade journals and farm equipment representative association publications have recently explored this practice and found it has been fair- ly successful for a few dealers located in the Midwest [5, 6, 7]. However, even though most farm equipment manufac- turers provide their dealers with apprOpriate guidelines, the practice of renting and leasing farm machinery is rel- atively unknown in Michigan. Therefore, in an attempt to learn the exact nature and extent of farm machinery rental and leasing as it applies to Michigan, a mailed survey questionnaire was sent to 375 farm equipment dealers lo- cated throughout the state. The following discussion sum- marizes the results of that questionnaire. A copy of the actual questionnaire used appears in Appendix Table 17. A total of 375 questionnaires were mailed to the major farm equipment dealers who held membership in the Michigan Farm Power and Equipment Association. Of the 163 questionnaires returned (43 percent) only twenty—six deal- ers indicated they had programs to rent or lease to farmers. 38 Eighteen additional dealers reported they had intentions of starting such a program within the next two years while six others indicated they were undecided about starting a rental or lease program. Short-Term Renting or Leasing Out of the twenty-six dealers who had rental or lease provisions, only eighteen rented or leased farm machinery on a short-term basis in 1967. Six of these eighteen provided the service on both new and used equip- ment; ten dealers rented or leased only used equipment, and two dealers rented or leased only new equipment. Of the dealers responding to the question on wheth- er or not the farmer is required to pay the short-term payments when use of the machine is delayed by inclement weather, 71 percent indicated the farmer did, in fact, have to bear the risk of bad weather by meeting payment obligations whether he did, or did not use the machine. Responses to other short-term responsibilities indicated that a majority of the dealers considered the farmer obli- gated for the following items; liabilities, operating costs, and transportation costs. Dealers themselves assumed the costs for insurance, taxes, and normal wear and tear. The maintenance responsibility was about evenly divided with 59 percent of the dealers indicating the farmer paid this 39 cost while two other dealers reported that maintenance was handled on a fifty-fifty basis. A summary of the responsi- bilities appears in Table 4. The results to the question on the extent and na- ture of the short-term rental or lease programs in 1967 showed considerable variation in all respects. Although the tractor and tractor-plow combination appeared to be the most pOpular items placed under short-term contracts, the different items of machinery reported, ranged from manure Spreaders to post hole diggers. Sizes or capacity of the major items also showed considerable variation. For instance, the size of tractors varied from 30 horsepower to 124 horsepower. Considerable differences were also noted in the time period, or calendar period, Of the short-term contracts. Although eight weeks was the longest period reported, the majority of responses fell within the one week or less category. Rates charged for the tillage and planting equip- ment were, for the most part, fairly comparable. However, the number of these items reported was very limited. Rates charged for tractors and/or plows showed wide variation depending on the size of the tractor and the number of plow bottoms. Generally rates were quoted on a per day, per acre, or per hour basis with a couple of reports charging on a combination of hours and days. The range ran from 3 dollars per hour to 7 dollars per hour plus 5 dollars per day. 40 TABLE 4.--Summary Of questionnaires returned, number of dealers renting or leasing, and short-term con- tractual responsibilities. Number of questionnaires mailed 375 Number of questionnaires returned 163 Percent returned 43 Number of dealers with rental or lease programs 26 Number of dealers holding short-term contracts in 1967 18 Number of dealers renting or leasing new and used equipment in 1967 on short-term basis 6 Number of dealers renting or leasing used equip- ment only in 1967 on short-term basis 10 Number of dealers renting or leasing new equip- ment only in 1967 on short-term basis 2 Farmer responsible for short-term payments when inclement weather prohibits use of the machine1 YES 15 NO_§ Responsibilitiesl Number of Dealers Reporting the Farmer was Dealer was Responsible Responsible Insurance 3 16 Taxes 5 12 Liabilities l6 3 Maintenance2 10 7 Normal Wear and Tear 4 15 Operating Cost 19 0 Transportation 6 12 1Although only 18 dealers had short-term rental or lease programs in 1967, other dealers indicated the provi- sions within their contracts, by responding to these ques- tions. Hence the number of responses will in some cases exceed 18. 2Two dealers indicated this was handled on a 50-50 basis. 41 Since the results of the extent and nature of short term renting and leasing showed such wide variation, no attempt was made to compute average values. However, Ap- pendix Table 18 gives the complete breakdown of items, sizes, lengths of contract, and rates charged as reported by the eighteen dealers. Long-Term Renting and Leasing Of the twenty-six dealers who reportedly had provi- sions for renting or leasing farm machinery to farmers, only two dealers indicated they actually held farm machin- ery under long-term contracts [8] in 1967. In addition to this, eleven other dealers indicated that, although they had no machinery placed under long-term arrangements in 1967, they might have facilities for doing so, by answering questions which pertained only to the long-term portion of the questionnaire. Since this led to some doubt [9] as to the validity of the responses, the following discussion summarizes the reports of the two dealers separately from the eleven other dealers. The two dealers holding long-term contracts in 1967 reported that the agreements applied only to new farm ma- chinery. Both dealers required the farmer to bear 100 per- cent of the responsibilities listed. Responses to the question on investment credit revealed that one dealer passes this tax benefit on to the farmer while the other 42 makes no provisions. Both dealers were likewise split on the question of a purchase option; one dealer reporting his contracts did not contain the purchase Option and the other indicating that such an Option was a part of the contract. The items and sizes of machinery reported as pres- ently under long-term contracts were as follows; one tractor of 130 horsepower; one lZ-row planter; one 5 foot stalk chopper; one 13 foot combine; and one chopper. The re- ported length Of the contracts were for three years with one dealer listing an annual rate of 28.7 percent of deliv- ered sales price and other dealer reporting an undeterm- inable rate. From the additional information obtained from the other eleven dealers, it appeared that the problem of handling investment credit under long-term lease contracts, posed the least continuity among dealers. The responses to this question were about evenly divided between the dealer taking the investment credit himself, the dealer passing it on to the farmer, and no provisions made. These same dealers were, however, almost unanimous in reporting that their contracts for long-term leasing contain a pur- chase option. Their responses were fairly even, but slightly in favor of a specific purchase option price, and slightly against applying a percentage of past lease pay— ments to the purchase Option price. 43 The above presents some idea of the current devel— Opment of farm machinery rental and leasing as it is pres- ently known for Michigan. It appears that this type of a program is in a beginning stage and, aside from the few dealers currently renting and leasing, most dealers have no immediate intentions of expanding this service into their overall program. Dealers and farmers alike are still floundering with the problem of how contracts can be for- malized for the mutual benefit of both parties. As more knowledge and experience is obtained in this area, and as the structure of farming changes, the renting and leasing of farm machinery may become a common practice. In the meantime, it will remain a limited means of acquiring machinery services. For the farmer, renting and leasing of equipment is an expensive alternative and from this standpoint, it can not compete with other alternatives. Current rates severly limit the practicability of renting or leasing those items of equipment which are used extensively in the farming operation. The unpredictability of weather alone places some doubt as to the feasibility of short-term agreements since no farmer wants to pay rent on a tractor for three days only to have it sit idle due to inclement weather. Also, aside from some initial "down payment" benefits, long-run costs, under the long-term contracts, often considerably exceed other alternatives. 44 Since the results of the farm machinery rental and lease survey showed relatively little of this practice currently being done in Michigan, and because the costs of such an alternative are not comparable to other means of acquiring farm machinery services, the analysis of this study does not include renting and leasing within the machinery systems. Major emphasis is instead, placed more on the current machinery systems used by cash-grain farmers. Chapter V does, however, contain some possible economic implications of the renting and leasing alternative. The Selection and Description of Various Farm Machinery Systems The commercial cash-grain farmer of today must have answers to certain questions before he can wisely select a machinery system to till, plant, and harvest his crOps. First and foremost, he must determine what crop enterprises are most profitable for his farm business. Secondly, based on the knowledge of the farm organization and recommended technological practices, the farmer should determine what types of machinery are needed to accomplish the recommended practices. Thirdly, in order to determine the number and sizes of machines needed, a comparison should be made be- tween machinery efficiency (or capacity) schedules and the number of acres which have to be covered in the limited time available. The fourth step requires knowledge of 45 alternative means of acquiring machinery services, while step number five requires some knowledge of alternative uses for capital and credit [10]. The over-riding criteria, however, is the impor- tance of analyzing farm machinery needs in terms of a systems approach rather than by the needs for one machine or the machinery needs for one enterprise. The production cycle of a cash-grain farm is typically made up of several separate and distinct field operations which are performed by the same piece of machinery, and which must be performed within certain time periods. The unfortunate problem is that in many cases these separate and distinct Operations must be done within the same time period with only one piece of machinery. For instance, a similar problem arose in this study where the Optimum harvesting dates for soy— beans was October 1-10. This period was overlapped by the October 5-15 Optimum harvesting dates for corn. Another example occurs in the early season of field work where optimum dates for planting corn interfer with normal til- lage for the soybeans and navy beans. When such problems as these occur, the analysis procedure which concentrates only on the machinery needs for a particular enterprise would most likely have recom- mended a combine too small in the first example because it would fail to acknowledge the needed capacity to complete both harvesting Operations on time. In the second example, 46 if the analysis procedure were based solely on the needs of a tractor, the results again would under-estimate actual requirements because Of a failure to account for the com- peting time element. In both cases, a far better method of approximating machinery needs to fulfill the require- ments of the entire farm situation, rests with the use of a systems approach. The commercial cash-grain farmer of today is faced with several alternatives for selecting the services of farm machinery systems. His range of choice includes com- plete ownership, complete custom hiring, a combination of ownership with custom hiring, and a combination of owner— ship with renting or leasing. As previously indicated, the emphasis of this study is placed on the current machin- ery systems used by Michigan farmers. Consequently only the first three alternatives above are utilized in the analysis. Five basic machinery systems were identified in this study. These systems were: (a) 4-row system with complete ownership, (b) 4-row system with a combination of ownership and custom hiring, (c) 6-row system with complete ownership, (d) 6-row system with a combination of ownership and custom hiring, and (e) complete custom hiring [11]. Appendix Table 4 lists the items Of machinery included in the 4-row and 6-row systems. 47 Although the method of complete custom hiring is not currently widely used in Michigan, there are, in some areas of the state, reports that such a practice has been fairly successful [12]. However, the main reason this system was included in the analysis was because of the potential that such a method of acquiring farm machinery services might have in the future. For example, complete custom hiring could be beneficial to the elderly farmer who is nearing retirement or it could be an alternative to the farmer with a limited labor supply or a limited level of technical skills which are required for owning and op— erating the necessary equipment. AdOption of the complete custom hiring alternative allows farmers to concentrate entirely on the management functions with primary respon- sibilities devoted to selecting custom Operators who have the prOper equipment for doing the job right, and schedul- ing their services into the proper sequence of production practices. The 4-row and 6—row systems with complete ownership represent the majority of machinery systems currently used on Michigan cash-grain farms. Under these two systems, the farmer owns and Operates all of the machinery required to complete the production cycle for all enterprises. Hence he becomes directly responsible for all Operating and ownership costs for the entire machinery system. 48 For the 4—row and 6-row systems, with a combination of ownership and custom hiring, it is assumed that all harvesting Operations and the bulk spreading of fertilizer, are completed by custom Operators. The farm owner only maintains equity in the power (tractor), tillage, and planting equipment. As a manager, he is responsible for scheduling his own Operations and those of the custom Op- erators into a smooth and efficient productive cycle. General Assumptions As in any analytical study, several simplifying assumptions must be made in order to designate a workable problem. The assumptions listed below pertain to the bud- geting models used in this study. 1. The farm owner was assumed to be the manager and Operator for all field work, except for the cases cal- ling for custom operations, in which case the farm owner became strictly a manager. 2. Labor requirements fulfilled by the farm owner were charged at the rate of $1.50 per hour. In the case of custom hiring, all labor was supplied by the custom Op- erators who included the labor charge within the custom rates. 3. The farm, regardless of size, consisted of only one soil type, namely the adequately well drained, clay to clay loam. 49 4. The cropping plan remained the same as farm size varied. This crOpping plan allowed for the following typical productive crop acre: 36 percent corn, 19 percent navy beans, 15 percent soybeans, and 15 percent wheat. The remaining 15 percent consisted of idle and diverted acres. 5. The level of management was assumed to be above average, with yields (and corresponding production prac- tices) intermediate between current average yields and the highest yields being recorded experimentally and by tOp producers. 6. All field operations had to be completed in the prOper sequence of normal operations. Thus late com- pletion of one Operation delayed the starting time Of the next operation. 7. The five machinery systems included in the analysis remained fixed as farm acreage increased. 8. The number of hours available for field Opera- tions by the farmer was limited to 10 hours per day and 6 days per week (i.e. 60 hours per week). 9. All owned farm machinery was assumed to be pur- chased as new equipment, and depreciated on a basis of the number of years of normal life or the number of years until physically worn out; whichever was shorter. 10. All prices paid for inputs were assumed to ap- proximate current market prices. Prices received were the approximate 1965-66 season average prices received in southern lower Michigan. 50 11. Custom operators were assumed to be available in sufficient numbers to complete all Operations on time, or as soon as biologically possible. Hence, if the corn, navy beans, and soybeans crops were planted on time, they would also be harvested on time. If 10 percent of any of these crOps were planted one week late, then 10 percent of the same crop was harvested one week late. (The above does not apply to late planting of wheat because after surviving the dormant winter stage, all wheat will mature at about the same time, regardless of the various planting dates.) 12. All crops were assumed to be transported from the point of harvest to public storage facilities by custom hauling. Hence, the farm machinery systems did not include facilities for crop hauling nor did the labor requirements include time spent for crop storage. 13. Harvesting and planting Operations were not permitted to commence before the Optimum time periods for maximum possible yields. 14. A constant state of the arts was assumed. The Analysis Procedure In an effort to meet the objectives for this study, two budgeting models were utilized in the analysis procedure. The first model was referred to as Budgeting Model I, and the second; Budgeting Model II. 51 Budgetinngodel I The intent of Budgeting Model I was to relate farm size with power, machinery, and labor costs for the five machinery systems analyzed. To better understand this relationship, budgets and graphs were developed which por- trayed these costs on a per acre basis. The procedure assumed that the five machinery systems remained fixed as farm size ranged from 0 to 1000 tillable acres by 40 acre increments. Machinery costs included both the operating and ownership costs [13]. Operating costs included repairs, fuel, lubrication, oil, and oil filters. Repairs for all items of machinery were based on a percentage of new costs. Lubrication charges for tractors and self-propelled items were estimated as a percentage of new costs while lubrica- tion costs for other items of machinery were computed at 5 cents per hour of use. Fuel rates for tractors were based on an average fuel consumption of .065 gallon of diesel fuel per rated drawbar horsepower [l4]. Combine fuel rates were based on an average fuel consumption of .3 gallon per hour per foot of cut. Oil and oil filter costs were based on a rate of 15 percent of fuel costs. These Operating costs for selected items of machinery are shown in more detail in Appendix Table 9. 52 The ownership costs of a machinery system included depreciation, interest, and insurance. Depreciation was calculated on the straight line method over the expected normal life of the machine until technological obsolescence required replacement. In the event that the number of til- lable acres at a given farm size required excessive use of any machinery item (i.e., it became worn out before reach- ing technological Obsolescence), the shorter number of years of life which resulted, was used as the base. In all cases, salvage values were estimated to be 10 percent of new costs. Annual interest and insurance rates were based on the average investment value for each item of machinery within a system. Rates of 6 percent and .7 percent, which approximate current rates, were chosen for the interest and insurance charges, respectively. More complete details of annual machinery ownership costs and expected years of life until technological Obsolescence are given in Appendix Table 4. Appendix Tables 5—8 present the annual ownership costs schedules by farm size for excessive machinery use. For Budgeting Model I, data obtained from farm machinery and labor efficiency tables gave results leading to "direct" [15] labor and machinery hours required per acre. Since all machinery Operating costs were based on hourly rates, and labor was charged at the rate of $1.50 per hour [16], these results were easily converted to 53 dollars of Operating and labor costs for any given size of farm. Total costs were computed by adding annual ownership costs to the Operating and labor costs. Finally, by assum- ing the enterprise mix and the machinery systems were fixed, an average total cost per acre was calculated by dividing tillable acres into the total costs for each 40 acre incre— mental increase in farm size. Appendix Tables 10-14 relate the efficiency schedules and the per acre Operating costs for selected items of machinery. Budgeting Model II Budgeting Model II is a refinement of its counter- part in that this model included two importantly related problems; the problem of inclement weather and the problem of completing Operations on time to avoid losses in crop yields. The ultimate Objective of Budgeting Model II was to obtain cost:revenue curves showing the traditional seg- ments of decreasing, constant, and increasing costs of production for each of the machinery systems studied. By generating these costs curves, further insights were pos- sible into such areas as break-even analysis, and basic size-efficiency relationships for southern Michigan cash- grain farmers. The ability to complete field Operations on time is important to a cash-grain farmer for two reasons. In the first place, losses in crop yields could reasonably 54 be expected to occur from planting and harvesting crOps too early. For instance, a late frost in the spring can stunt or delay the growth of crops planted too early, while a lack of complete maturing or high moisture contents are deterents to early fall harvesting. A second reason for completing Operations on time is to avoid late planting and harvesting operations which result in shortened growing seasons and increase the risks of early killing frosts in the fall. From a practical standpoint, early Operations pre- sent a small problem relative to late operations. For the most part, crops are planted in the spring as soon as weather permits and harvested in the fall when the crop ripens. Consequently, this study ignored early planting and harvesting by assuming these two Operations could not begin before the apprOpriate calendar dates for maximum possible yields. Late planting and harvesting operations were, how- ever, considered an important part of Budgeting Model II. As farm acreage increased in 40 acre increments, each of the fixed machinery systems ultimately reached a point of limited capacity; above which late planting and harvesting Operation resulted in reduced yields per acre. This study treated the lower yields as reductions in revenue in inter- preting the effects of late Operations on size-efficiency 55 relationships. The summary for critical planting and har- vesting dates and losses in yields resulting from late Operations is found in Appendix Table 26. Complicating the problem of timeliness of Operation was the problem of inclement weather and the corresponding lost time available for field work. Because of a lack of data, incorporating this problem into the budgets posed one of the biggest headaches of this study. The method finally adopted was based on a straight line regression between inches of precipitation and field work days lost [17]. For analytical purposes, the field working season from April 1 to November 30, was divided into 35 "climatic weeks," and based on local climatological data, an average weekly precipitation value was calculated for each of the climatic weeks. The corresponding number of work days lost within each week was obtained directly from the regression line. Appendix Table 24 lists the average amounts of pre- cipitation (and corresponding work days lost) by weeks. Appendix Table 25 shows the plotted regression equation. The end result Of Budgeting Model II was to plot total dollar costs per total dollar receipts (i.e., cost: revenue ratio) against farm size [18]. Total receipts consisted entirely of cash sales from the corn, soybean, navy bean, and wheat enterprises. Costs included machin- ery, labor, custom crOp hauling, seed, fertilizer, herbi- cides, and an Opportunity cost of six percent on the 56 land investment. A listing of the prices paid (other than machinery) and received are found in Appendix Table 3. l. 2. 10. 57 References For a listing and description of the more common alter- natives, see; J. Patrick Madden, Economies of Size in Farming, Agricultural Economic Report No. 107, Economic Research Service, U. S. Department of Agriculture, pp. 24-34. The averages of the 43 farms as a group, were not pub- lished, although the information is available from the Department of Agricultural Economics, Michigan State University. For some idea of the averages as published by size of farm investment, see; Cash Grain Farming Today: What It Costs, How It Pays, A. BC. 68, Cooperative Extension Service, and De- partment of Agricultural Economics, Michigan State University, 1967. Larry J. Connor, Costs and Returns for Major Cash Crops in Southern Michigan, Agricultural Economics Report No. 87, Department of Agricultural Economics, Mich— igan State University, 1967. Ibid. "Rental Opens the Door to Increased Sales," Farm and Power Equipment, March 1967, p. 25. Warren Smith, "They've Made Leasing Work," Implement and Tractor, March 21, 1965, p. 20. Bill Fogarty, "A New Enterprise for Leasing Farm Equip- ment," Implement and Tractor, May 15, 1962, p. 38. A short-term contract was defined as the rental or lease agreement that is binding for less than one year. Long-term contracts were defined as binding for one year or more. The question of doubt was primarily that these 11 dealers might have confused questions on long—term contracts with short-term contracts. Larry J. Connor, Guidelines for Selectinngachinery Systems, unpublished speech given at Farmer's Week, Michigan State University, February 1968. 11. 12. 13. 14. 15. 58 Although some farmers currently Operate with basically a two-row system, the number of farmers doing so represent a small percent of the commercial cash- grain farmers in Michigan. This same reasoning applies to the few isolated farmers who operate what could be termed as an 8-row system. Since the extent of these two systems are limited, they are not included in the analysis. Based on an interview with a professional farm manager. These are sometimes referred to as variable and fixed costs. Rated drawbar horsepower is 75 percent of the maximum. Direct hours are defined as the number of actual hours that machinery and labor are utilized while in the field. This includes normal in field repair and adjustments for all equipment. It does not, how- ever, account for the time a farmer spends in trav- eling from one field to another or for the time spent going from the tool shed to the field. Also, it fails to account for the time required to con- vert one machine to an alternative operation (i.e., replacing the grain platofrm on a self-propelled combine with the corn head). 16. A rate of $1.50 per hour for labor approximates the average hourly wage rate paid to farm labor. 17. Unpublished data, U. S. Weather Bureau and Agricultural Engineering Department, Michigan State University. 18. Since this study uses a constant enterprise mix, farm size is an acceptable measure of output levels. CHAPTER IV THE ANALYSIS The analysis was completed by using the two budget- ing models described in Chapter III. The analysis results for both models are described in the following text. Sup- porting material is presented in the Appendix. Budgeting Model I The intent of Budgeting Model I was to show the Inelationship between costs and farm size for the five ma- cflninery systems analyzed. More specifically, the Objective vmas to show how farm machinery and labor costs per acre decrease as farm acreage increases. The data supporting Budgeting Model I are presented 111 the following Appendix Tables: Appendix Tables 5-8 Exresent the annual ownership cost schedule, Appendix Tables III—14 compute the Operating costs per acre by enterprise ft>r the various machinery systems involving ownership, Appendix Table 15 shows custom rates by enterprise for the Complete custom hire system. The budgets, which derived 'tllea cost per acre statistics for Budgeting Model I, were 59 60 computed from the above data. These budgets are located in Appendix Tables 19 through 23. The results of the bud- gets are presented in graphical form in Figure 2 [1]. The results of Budgeting Model I indicated that, from a cost standpoint, complete custom hiring Offered the cheapest means of acquiring farm machinery services for farms with 322 tillable or less. Between 323 and 343 til- lable acres, the 4-row system with a combination of owner- ship and custom hiring resulted in the least dollar labor and machinery costs per acre. From 344 to 597 tillable acres, the system of complete ownership of a 4-row system was found to give the lowest per acre costs; while above 597 acres the 6-row system of complete ownership presented the lowest per acre costs for labor and machinery. These findings are summarized in Table 5 below. TABLE 5.-—Summary of Results for Budgeting Model I Costs Per Acre at The Range in Acreage Lower Higher Machinery Exhibiting Acreage Acreage System Minimum Cost Level Level (acres) ($) 791 (Zomplete custom hiring 0 - 322 16.68 16.68 Combination ownership & custom hire, 4-row 323 - 343 16.67 16.27 Complete ownership 4—row 344 - 597 16.24 12.57 Complete ownership 6~xow 598 - 1000 12.56 10.66 Cost Per Acre 61 FIGURE 2.--Costs Per Acre for Various Farm Sizes and Machinery Systems 6-Row Complete Ownership 4-Row Complete Ownership 6—Row Combination Owner- ship and Custom Hire 4-Row Combination Owner- ship and Custom Hire Complete Custom Hiring 15‘ .lO I l I l L l l l I I I I I | l I ' I I . 1 I I 80 160 240 320 460 480 560 640 720 800 880 960 Tillable Acres . .. ~r- 62 Budgeting Model II The second budgeting model was used in an attempt to portray the entire picture of both costs and revenues. It incorporated the timeliness of operations into the anal- ysis and charged lost revenues from late operations as a deduction in total revenue. The intent of Budgeting Model II was to derive breakeven points between costs and rev- enues, and to analyze the size-efficiency relationships for a southern Michigan cash-grain farm. The costs of this second model included those of Budgeting Model I plus seed, fertilizer, herbicide, an Opportunity cost for land investment, and custom hauling. Appendix Table 3 lists the prices paid as used in this study. Appendix Table 16 aggregates the total variable costs per acre by machinery system and enterprise. In all cases, receipts were based on sales of har- vested crop acreage. For each 40 acre increment, the figure for total receipts is based on a time table of Operations whereby each field Operation must be completed in a given sequence. Although, no critical time periods were placed on the tillage operations, late tillage resulted in late plantings and harvesting for the larger acreages analyzed. These latter operations were bounded by critical time per- iods and if late plantings or harvesting occurred, the yields were reduced accordingly. The derivation of the 63 total revenue was based on the rates of reductions in yields for late planting and harvesting (see Appendix Table 26), the required man hours to complete all opera- tions, and the loss of field work days due to inclement weather. Appendix Tables 27 and 28 lists the number Of acres planted or harvested late by farm size for the 4—row and 6-row systems, respectively. The budgets used in Budgeting Model II were calcu- lated from the additional data on costs and timeliness of Operations, and appear in Appendix Tables 28 through 31. Figure 3 shows the cost:revenue ratios in graphical form. In terms of breakeven analysis, the results of Budgeting Model II indicated that a minimum of 89 tillable acres were required before costs would equal revenues for any system other than complete custom hiring. More speci- fically, this breakeven acreage level was obtained by the use of a 4-row system with a combination of ownership and custom hiring. Other breakeven acreage levels noted were 107, 123, and 152 tillable acres under a 6-row system with a combination of ownership and custom hiring, a 4-row system of complete ownership, and a 6-row system of complete own- ership, respectively. In terms of short-run efficient machinery systems, the alternative of complete custom hiring resulted in the most efficient machinery system for both the smaller (below 326) and the larger (above 937 tillable acres) farm sizes [2]. 64 Amuoe manAAHAa AMA com com ems ovm 66A 6AA cos 6mm csu oma om . . . u . b . . L p 1 . . . . . q . A _ d . -xmm. \.\ ..o>. \ \ mcauam .\ Ecumau muwameou \sI \ Jimh. 110m. 11mm. Tom. . muwm Ecumso new 11mm. magnumczo cowumcflneou 30min magnumcso mumamfioo 3omnv .1 III JIOO.H mud“. Ecumso can. I I I I I I manmuwczo cowumcwnfioo 30m|w macmumczo cowumcflnaoo 3om|m Irmo.a msmummm humcflcomz can mmuwm Emma mcowum> uom mowumm maco>mmuumOUIl.m mmDon orqeu enueAsu:qsoo 65 Between the levels of 326 and 349 tillable acres, cost:rev- enue statistics were lowest for the 4-row system with a combination of ownership and custom hiring. From 350 to 537 tillable acres, the 4-row system of complete ownership resulted in the most effieient machinery system, while the 6-row system of complete ownership showed greatest effi- ciencies for farms of 538 to 822 tillable acres. Between 823 and 937 tillable acres, the 6—row system with a com— bination of ownership and custom hiring gave the lowest cost:revenue statistics. The results of Budgeting Model II are summarized in the Table 6. As can be seen from the table, the 6-row complete ownership system resulted in greatest economies of size among the systems studied. Although all of the systems indicated some range in tillable acres for which they were more efficient than the other systems, the costs within these ranges were relatively higher than the costs within the efficient range of the 6-row system of complete owner- ship. The added capacity of this system (and hence, the added revenues) more than offset the increased costs of this system over either Of the 4-row alternatives. Also, at the larger acreage levels, the annual ownership costs of the 6-row system with complete ownership, were spread thinner allowing this system to be relatively more effi- cient than its counterpart with a combination of ownership and custom hiring. 66 .Hm manna xAOcmmmm 0mm .mmuom mHQmHHHu com um mnsooo ucflom uflmoum swan ms» Ammuom mannaaflu own um Empmhm humcflnome pcmfloammm umoE may ca OmuHSmmH mAcmMmcso mumamfioo mo EmumMm BOHIA may nmsocuam v .mep :0 mGOHumum 1&0 AAA muOHmEoo ou mnmnfidc ucmfloammsm CA mHQMHHm>m mum muoumnmmo Soumso pmnp COADQESmmm map mo mmsmomn oflumn mdcm>muuumoo ucmumcoo m CH mUHSmmH mafiuwn EOAmso mumamfiou mumoo Hmuou soaps Mom muflm SHAH mo Hm>ma was“ mm cacammp ma wmmmuom cm>®xmmum m .mmmmnocfl Op unmum mm>Hno mun .mocm: Amumoo Hmcflmnmfi swan mmma mum mmscm>mu Hmcflmumfi .mam>mH mmmmuom 0mmga m>on¢ N .mscm>mn Hmuou Op Hmsqm mum H AAA. AAA. AAAAIAAA AAA. .1. In- Amnflm soumso mpmamsoo mmA. mom. Amm Immm Amm. omA Aoa Guam Eopmdo cam magnumcao coflumcflnfioo .3oulm AAA. AAA. AAA IAAA AAAA. AAA AAA AAAmuocso mnmAmsoo .3ouuA vmm. mmA. Amm nomm vmw. owm mma mflnmumczo mumHmEOO .Bonlw mmn. mmA. mvm Immm Aon. omm mm muflm Eoumzo Ocm mflcmnmc3o coflumcflnfiou .Boulw AAA. AAA. AAA no AAA. nu- In- Amman scamso mumamsoo Amv hwy nmwuomv hwy Ammuvmv Ibmmnomyr mmcmn mmcmu “COAOmem ucfiom Hm>wq ucflom Emummm mocmfiowmmm mocmAOAmmm umoz pawaoammm o mmuod Hcm>m mo paw swam mo Ocm 3oq mmcmm um owumm quHOHmum Mmmum mmmmnofi mscm>mm pmoz um "UmOU mOHOAN pm owumm m9cm>mmuumou HH proz mcfiummpsm How muacmmm mo mumEEdmul.m mamfia 67 It is also interesting to point out the fact that even at the most efficient point for each of the machinery systems studied, some losses were apparent because of late operations. For instance, at the 560 tillable acre level, where the 4-row system of complete ownership resulted in its most efficient short—run economies of size, the follow- ing late Operations occurred (see Appendix Table 27): 71 acres of corn were planted one week late; 74.3 acres of corn were harvested one week late and 43.0 acres of corn were harvested two weeks late, 2.2 acres of navy beans were planted and harvested one week late, and 3.2 acres of wheat were planted one week late. Similar results also occurred at the most efficient points for the other machin- ery systems studied (excluding complete custom hiring). The important point, however, is that the marginal revenue from expanding to the points of most efficient production for each system still exceeded the marginal cost. Conse- quently, the cost:revenue ratio at these acreages did not increase. A second interesting feature of the cost curves presented in Budgeting Model II was the apparently large acreage ranges exhibiting relatively constant costs for each of the machinery systems studied. Table 7 summarizes these ranges in acreage for different definitions of con- stant costs. .Emummm mnmcflnoma cm>flm A How Aofiumn mccm>muuumoo ummBOHV ucflom ucmAOAmmm “woe mcu mo mmmn m Eoum Umcflmmp mum mumoo ucmumcoo .mmmmo Ham cHH 68 AAAIAAN AAAIAAA AAAIAAA AAAIAAA AAAA soumso AAA macmuwc3o cowpmcHQEov 3oulm AAAIAAA HAAIAAA AAAIAAA AAAIAAA AAAAHmaao mumamsoo 3onuA AAAIAAA AAAIAAN AAAIANA AAAIAAA mAAm sepmso cam mflsmumc3o coflumcflnfiou Bonlw AAAIAAA AAAINAA AAAIAAN AAAIAAA AAAAumc3o mumamsoo soAIA wmmuomv Ammuomv Ammuomv Ammuomv mscm>mn HMHHOU mscm>mn HMHHOU woa cfinpfiz wm manna: Ewummm me umoo mo OOH cAcqu Ham umoo mo om cflcuflz muwcflcomz mm Umcwmmo umoo ucmumcou How mmuom cw mmcmm mamummm wumcflnomz msowum> paw Hmumou unapmcoo mo mcowuflcflmma mDOAHm> “Om cofluospoum mo mumou ucmumcou >Hm>wumHmm mcfiuwnwsxm muflm Enmm CH mmmcmmll.A mam¢9 69 References 1. Close examination of the budgets and cost curves will show that the cost per acre statistics do not re— sult in a smooth function. The explanation for this is based on the increased depreciation charges which occur at the higher acreage levels when a given item of machinery is used more intensively than under normal conditions. This increased cost, at a given 40 acre incremental level, had the ten- dency to shift the average cost curve in a nonuniform manner. Such a tendency can most easily be seen at the 800 and 1000 acre level for the 4-row and 6-row complete ownership systems, respectively. The re— sults at these points showed increasing cost per acre statistics, rather than the expected contin- uously decreasing values. However, if the analysis were extended to the next 40 incremental level, the cost statistic would most likely decrease rather than increase. Since only a few isolated points deviated from the function, the cost curves are pre- sented in a smooth function. 2. Although complete custom hiring appeared to give the best efficiencies for farms with more than 937 tillable acres, such efficiencies would only be Obtained if the number of custom Operators avail- able were sufficient to complete all operations on time. 2 CHAPTER V SUMMARY AND CONCLUSIONS Problem Review and Analysis Results This study started by recognizing the problems which plague farmers attempting to select the services of farm machinery. Although the current magnitude of per farm machinery investments includes only about 12 percent of total farm investments, there are certain unique charac— teristics associated with the machinery investment which render it difficult to manage. Some of these character— istics recognized in this study, were (a) rapid technolog- ical developments which result in machines becoming obsolete long before it is physically depreciated, (b) the initial high cost of farm machinery and the relatively low disposal value, (c) the changing farm structure which emphasizes large items of machinery that cannot be passed from "first line" machinery to a "second" line, and (d) the relative rapidity of farm machinery turnover. In an attempt to analyze the problems surrounding methods of acquiring farm machinery services, the following objectives were cited: 70 71 1. To describe alternative means of acquiring farm machinery services. 2. To determine the relationship between farm size and per acre power, machinery, and labor costs for selected systems of farm machinery. 3. To examine the effects of inclement weather and untimely field Operations on size-efficiency relationships. 4. To determine acreage levels at which total costs and revenues are equal for selected farm machinery systems. 5. To determine an optimum farm size which would achieve minimum costs per dollar of revenue for selected systems of farm machinery. To meet these objectives, a synthetic one-man farm- ing operation was selected to represent a typical southern Michigan commercial cash-grain farm. For the analysis, farm size varied in 40 acre increments from 0 to 1000 til- lable acres. In all cases, the product mix (typical acre), consisting of 36 percent corn, 15 percent soybeans, 19 per- cent navy beans, 15 percent wheat, 12 percent diverted, and 3 percent idle, was considered fixed. Five alternative farm machinery systems were de- fined and assumed to remain fixed as farm size expanded. These five systems were identified as (a) complete custom hiring, (b) 4-row system with complete ownership, (c) 4—row system with a combination of ownership and custom hiring, 72 (d) 6-row system with complete ownership, and (e) 6-row system with a combination of ownership and custom hiring [1]. Machinery rental and leasing was not included in the anal- ysis because of its apparent lack of pOpularity in Michigan and because of the relatively high rates. In order to meet the objectives listed above, two budgeting models were employed in the analysis. Budgeting Model I derived average total power, machinery, and labor costs per acre, while Budgeting Model II included both costs and revenues to derive cost:revenue ratios. The costs of the second model included those of Budgeting Model I, plus seed, fertilizer, herbicide, custom hauling, and an opportunity cost on land investment. Revenues were based entirely on sales from crOp production. The results of Budgeting Model I showed that com- plete custom hiring offered the lowest costs per acre for farms of less than 323 tillable acres. Although the costs per acre statistics for the other four systems drOpped considerably within this range (see graph, page 61) these costs were higher relatively to the costs Offered by com- plete custom hiring. From 323 to 343 tillable acres, the 4-row system with a combination of ownership and custom hiring resulted in the lower costs per acre, while the 4- row system of complete ownership showed the lower costs per acre for a farm of 344 to 597 tillable acres. Above 597 acres the 6—row system with complete ownership gave the lowest costs per acre Of all systems studied. 73 An interesting result of Budgeting Model I was the relatively small variation in per acre costs exhibited at the higher acreage levels studied. For instance, on farm sizes of 400 to 720 tillable acres, the costs per acre varied by less than $1.50 among the farm machinery systems involving some form of ownership. From 760 to 1000 tillable acres, these costs per acre variations were within the range of $1.50 to $2.00. The results of Budgeting Model II indicated that the greatest economies of size occurred on a farm of 760 tillable acres with a 6—row machinery system of complete ownership. Although each machinery system analyzed showed a range in acreage at which that particular system was more efficient than others (see graph on page 64 and Table 6 on page 66), the cost:revenue ratios at these acreage ranges were relatively higher than at the most efficient point. In terms of breakeven analysis, Budgeting Model II indicated that the 4-row system with a combination of ow- nership and custom hiring required a minimum of 89 tillable acres before revenues would equal costs. Other breakeven acreage levels of 107, 123, and 152 tillable acres were noted for the 6-row system with a combination of ownership and custom hiring, the 4-row system of complete ownership, and the 6-row system of complete ownership, respectively. 74 Depending on the definition used, the cost:revenue curve showed fairly substantial ranges in acreages for which relatively constant costs were observed (see Table 7, page 68). FOr instance, the 6- and 4-row systems of com- plete ownership showed a range of 374 and 340 acres respect- ively, for constant costs defined as cost:revenue ratios falling within 5 percent of the most efficient point. Implications of the Study Regardless of whether or not the results of this study are judged as good or bad, several interesting as- pects have emerged from the analysis. Primary among these interesting aspects is the degree Of importance placed on analyzing farm machinery as a system. Machinery costs are quite substantial on the commercial farms emerging today and it is only natural that farmers are looking for ways of reducing these costs. Research has generally approached this problem in two ways; either it analyzes machinery in separate units, or it analyzes machinery in terms of the needs for one particular enterprise. In either case, the very nature of farming limits the usefulness of the above approaches. Farms, for the most part, are multi-product firms with different productive cycles for each product. In the case of a cash-grain farm, these productive cycles overlap and often conflict with one another for the limited time 75 and machinery available. To the extent that the limited time and machinery can be used in all enterprises, the conflicting overlaps compete for the same time and same machinery. Consequently, analysis procedures which con- centrate on individual machinery items; or on machinery needs for a particular enterprise; often ignore the effects of delayed operations in competing enterprises. Because such late Operations result in yield and revenue losses, these two approaches overlook sizeable cost reductions which, in fact, are available. The only alternative to such oversights is the method used in this study which treats farm machinery as a system capable of fulfilling the needs of an entire farm. The usefulness of the systems approach to machinery analysis can be readily adapted to the individual farmer about to select a machinery system. Primary steps require that the farmer know the kinds (items) of machinery needed, alternative ways for service acquisition, and alternative uses for capital. Based on this knowledge, the final re- quirements for an efficient machinery system are (a) recom- mended operations for specific enterprises must be performed, (b) these operations must be completed on time, and (c) a and b must be accomplished in the least-cost manner. Ful- fillment of these last requirements necessitate the systems approach in order to determine a "most" efficient machinery system for a given farm. However, as Connor points out, 76 changing levels of technology requires ". . . continuous planning in order to maintain an efficient machinery system for any given farm" [2]. Another important implication of this study per- tains to the use of custom hiring as a means of acquiring farm machinery services. The results of Budgeting Model II showed that complete custom hiring was the most economical means of acquiring farm machinery services on farms of 325 tillable acres or less; while the 4-row system with a com- bination Of ownership and custom harvesting expanded the efficiency range up to 350 tillable acres. The significance of custom hiring on these rather large acreage levels was more pronounced by the fact that the average number of tillable acres for smaller cash-grain farms enrolled in the Telfarm project in 1966 was only 291 acres [3]. Al- though average farm size is expected to increase, certain elements will prohibit many farms from expanding acreages to any significant degree within the relatively near future. Consequently, if custom operators are available, the pos- sibilities are reasonably good that elderly farmers and farmers with a limited supply of capital and labor will find custom hiring to be the most economical short-run means of acquiring farm machinery services. There are also some implications from this study which indicate that custom operators may, in fact, be available in the future; and in greater numbers. The 77 results indicated that rather large farms are a requirement for economic justification of complete farm machinery ow- nership. For the individual farmer who has already acquired equity ownership in some of the large and costly items of machinery, there may be no immediate possibilities for ex- panding farm size. Therefore, the farmer in this situation may decide that the only short-run solution to reducing per acre costs (spreading fixed costs over more acres), rests in his willingness to market the excess capacity by per- forming the services of a custom operator. Although the results of the questionnaire on cur- rent farm machinery rental and leasing indicated very little Of this practice being done in Michigan, there are condi- tions whereby such alternatives may, indeed, be feasible [4]. For the most part, renting and leasing of farm machinery would appeal to the farmer who suddenly found himself in a pinch for time. For instance, an unseasonably late spring could reduce the normal amount of time for spring field work and, hence, delay planting dates. In such a case, a farmer is faced with the decision as to whether or not it is worth the cost to rent an extra tractor for two weeks in order to catch up with the field work and assure himself that crOps will be planted on time. If the added revenues from planting on time to Obtain maximum yields exceed the cost of renting the tractor (and the necessary labor to run the tractor) then renting is a feasible solution [5]. 78 There is also one aspect of short—term renting and leasing which should render it feasible to farm machinery um£ H mpw>HuHso mm >mZImH mmz H wmumm .mNHHHpHmm QOQHEO .hcmam .QA H AAIAAIAA AA. 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H wmnmm Emumm cam omHO .mQH m H BOHQ mm mammm m>mz I-lr-I HO] OH mcsblH mash Amwpmov Amu06\.mnHv AmHOM\.mQHV Amnow>suv AmHOM\.:QV mmOHOHw mHQHmmom Hm>o coHumHmmo mucme NONMImOlez mucmE OHme mouo ESEmez How UOHHmm mmEHB ImHqumm mpcmEmHqumm ImHqumm OHQHmmom H mEHB HMOHDHHO OUHOHQHmm HmNHHHuHmm pmmm Omfidmmm EDEmez mmOHuomum m>HuOSOOAm umnuo .UOSGHHCOUII.N WHm¢B XHQmem¢ 92 .APPENDIX TABLE 3.—-Assumed prices paid and received.1 Item Unit Price2 PRICES PAID (d01lars) SEED: corn for grain bu. 13.50 wheat bu. 3.25 soybeans bu. 4.50 navy beans bu. 5.50 FUEL AND LUBRICANTS: gasoline gal. .174 diesel gal. .154 motor oil gal. .90 lubricant lb. .22 FERTILIZER (bulk): nitrogen lb. .105 phosphate lb. .087 potash lb. .043 FERTILIZER (bag): nitrogen lb. .113 phosphate 1b. .092 potash 1b. .046 CHEMICALS: atrazine lb. 2.90 amiben lb. 5.00 eptam lb. 2.83 HAULING: corn, soybeans, and navy beans bu. .06 wheat bu. .05 LAND: acre 300. annual opportunity cost at 6% acre 18. LABOR: hour 1.50 EZIEICES_RECEIVED3 Corn7F bu. l . 20 Soybeans bu. 2.60 Navy beans bu. 3 . 75 PVheat bu. 1.55 \ lMachinery prices are located in Appendix Table 4. 2These price assumptions are not to be interpreted as predictions or prospective prices. 3Approximate 1965-66 season average price. 4In the analysis a discount of 13 cents per bushel was as sumed for drying. Hence the net return per bushel was $1.07. SC31:1rce: Larry J. 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AA. =AHIA 30Hm ummnz AA.NH mno< Hmm umou mcflpmnmmo Hmuoe AAA.H AN.H AA.H Hoan HAA. AA. III NH.H mm AA Humzom HAA. AA. III AN.H mm AA HHGBOQ AAA.A III III III AEogmsov umm>umn AAA.N III III III “Eoymsov Bouvcfl3 can HHsm HAH. AA. AA. AA. 30H A AmmEHu NV mum>AuHso AAH. AH. NN. AA. 30H A mNAHAAHmA mam pcMHm AAA. AA. AA. AA. .uA AA Amumm Acm omAA NAN. AA. AA. AA. =AHIA son mammm >>mz AAA Amusogv Amusogv AAA umoo wuawEmHstmm muQmEmuflsvmm usom Hmm mNAA coaumummo mafipmmmmo Hsom mcflnomz monmq pmoo ucmfimflswm Ucm mmflumumgcm maflumnmmo .UMDCHfiGOUII.AH mqmdB XHozmmm¢ 114 APPENDIX TABLE 15.-—Custom rates per acre and per forty-acre increments by enterprise and Operation. Custom Rates Per Forty- Enterprise and Custom Rates Acre Operation Per Acre Increments ($) ($) Corn spread fertilizer 1.05 15.12 plow 5.50 79.20 plant and fertilize 2.30 36.00 spray 1.50 21.60 cultivate 2.00 28.80 harvest 7.00 100.80 Total for Corn 19.55 281.52 Soybeans plow 5.50 33.00 harrow 1.50 9.00 plant, fert. and spray 2.50 15.00 cultivate 2.00 12.00 harvest 6.00 36.00 Total for Soybeans 17.50 105.00 Navy Beans plow 5.50 41.80 disc and spray 2.50 19.00 plant and fertilize 2.50 19.00 cultivate (2 times) 4.00 30.40 pull and windrow 2.00 15.20 harvest 7.00 53.20 Total for Navy Beans 23.50 178.60 Wheat plow 5.50 33.00 disc 2.00 12.00 harrow 1.50 9.00 drill and fertilize 2.00 12.00 harvest 6.00 36.00 Total for Wheat 17.00 102.00 lForty—acre increments are made up of 36 percent corn, 15 percent soybeans, 19 percent navy beans, and 15 percent wheat. Sources: Doane Agricultural Service, Inc., 1967 Machinery Custom Rates, Vol. 30, No. 7-8, March, 1967. Rates for Custom Work in Michigan, Extension Bul— letin E-485. Cooperative Extension Service, Mich— igan State University. 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Confidential: For Research Purposes Only Department of Agricultural Economics Michigan State University Questionnaire On Farm Machinery Rental and Leasing 1. Do you as a farm machinery dealer have a program whereby you rent or lease farm machinery to farmers? Yes No la. If you presently are not renting or leasing farm machinery, do you have any plans or intentions of doing so within the next two years? Yes No If you checked "No" in question 1, disregard the remainder of this questionnaire and return it in the enclosed envelope. SHORT-TERM: Questions 2 through 6 pertain to short-term (less than one year) rental or lease agreements. 2. Have you rented or leased new farm machinery on short- term arrangements since January 1, 1967? Yes NO 3. Have you rented or leased used farm machinery on short— term arrangements since January 1, 1967? Yes No 4. Under your short—term rental or lease agreements, is the farmer required to make rental or lease payments when the item of machinery sets idle due to inclement weather? Yes No 5. Under your short—term rental or lease arrangements, who is responsible for the following and what is the per- centage of responsibility between the dealer and the farmer? Responsibility Dealer Farmer (Percent) (Percent) Insurance (fire, wind, theft) Taxes (where applicable). Liabilities (personal injury) 117 APPENDIX TABLE l7.--C0ntinued. Responsibility Dealer Farmer (Percent) (Percent) Maintenance and Repairs "Normal" wear and tear Operating Costs (fuel, oil, lubrication) Transportation Costs (between farmer and dealer) Other 6. Please indicate below the extent and nature of your short- term rental or lease program which you have carried on since January 1, 1967: a. The items of farm machinery which you have or leased on a short-term basis. rented b. The "average" or typical capacity of each item rented or leased. c. The number of units of each item. d. The "average" or typical time period of rental or lease for each item. e. The rate (in dollars) charged per time period or unit measure, i.e., acre, ton, bu., hour, day, week, month, etc. f. The delivered sales price of the item rented or leased. (a) (b) (c) (d) (e) (f) Rate Per Average Time Time Period Period of or Unit ($ Delivered Number Contract per acre, Sales Item of Average of (hrs., days, hr., week, Price Equipment Capacity Units week, mo.) mo.) ($) Farm Trac- tors HP Planters rows Drills ft. Balers Combines (small grain) ft. Combines (corn) rows Corn Pick- ers rows 118 APPENDIX TABLE l7.-—Continued. (a) (b) (C) (d) (e) (f) Rate Per Average Time Time Period Period of or Unit ($ Delivered Number Contract per acre, Sales Item of Average of (hrs., days, hr., week, Price Equipment Capacity Units week, mo.) mo.) ($) Choppers rows Plows bottoms Discs ft. Harrows ft. Manure Spreaders bu. Cultiva- tors rows Sprayers ft. Fertilizer Spreaders ft. Dryers bu. Other LONG-TERM: Questions 7 through 12 pertain to long-term (one year or more) leasing arrangements. If you have no long-term lease arrangements, please disregard the remainder of this questionnaire and return it in the enclosed envelope. 7. Are you presently leasing new farm machinery on long-term arrangements? Yes No 8. Are you presently leasing used farm machinery on long- term arrangements? Yes No 9. Of the following alternatives, which best describes your policy of handling the investment credit tax benefit ap- plicable to new machinery? Take It Myself Pass On To The Farmer No Provisions _—- __ Made__ 10. Do your long-term lease contracts contain a purchase option? N0" Yes 119 APPENDIX TABLE l7.--Continued. 10a. Does the purchase option identify a specific pur- chase Option price? Yes No 10b. Does the purchase option specify a percentage of past lease payments that will apply if the item is purchased? Yes No 11. Under your long-term lease arrangements, who is respon- sible for the following and what is the percentage of responsibility between the dealer and the farmer? Responsibility Dealer Farmer (Percent) (Percent) Insurance (fire, wind, theft) Taxes (where applicable) Liabilities (personal injury) Maintenance and Repairs "Normal" wear and tear Operating Costs (fuel, oil, lubrication) Transportation Costs (between farmer and dealer) Other 12. Please indicate below the extent and nature of your long-term leasing program applicable to the present: a. The items of farm machinery you are currently leas- ing on long-term arrangements. b. The "average" or typical capacity of each item. c. The number ofunits of each item. d. The formula for establishing the amount of lease payment, i.e., a percent of retail value, etc. e. The required frequency of lease payments. (a) (b) (C) (d) (e) Item of Average Number Formula Frequency Equipment Capacity of Units for Rates of Payments Additional Comments: Please return the questionnaire in the self-addressed envelope. 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NN NN-NH ummm mm.H vH.N mH m.H mm. «N mHIm ummm ANNNNV Amusosv Amusozv Ammmwv Anoch mxmmz Muoz wmo Cmm xmmz Hmm mxmmz umm COHumuHmHomum .oz UOHHmm HmemHmo amolem m CH umoq mHCom umOH musom umoq m%ma H mmmum>¢ umoq mwmo mo nmnfisz mo HmCECz “Com CmB ummw CoB xmmz UHCMEHHU m0 HmCECz mmmum>d mo Hmnfisz .UmsCHuCOUI:.wm mqmHCD opmum CmmHCOHE .mOCmHom mono mo “Cmsuummoa rmumc ooCmHHQCmCD "condom ON N H N NCHumm>umr .On NH NH NN NH N H N mchcmHm ON-OH NHsO NN-NH .ummm pawns NN ON N.HH N.N N.N N.H NcHumw>umc NH .ummm .59 NN ON NH N N H NcHucmHm -NN .NON OHIH NOOO mammn >>Nz O.NH N.HH N.N N.N N.H NaHumm>umz .sn NN NH N N H maHpamHm OHuH .uoo NN-NH mm: mammnmom NN ON NH OH O N N N H mcHumm>umn .sn NN HH OH N OchcmHm NHuN .uoo OHuH mm: cHoo 1.5nvA.OnOA.OnO1.5nvi.snvi.snv1.5nOA.OnOA.Onv N N N N N H N N H NHmHN cH NcHumm>umm NcHuqum mHmHN mGOHHOSUmm ESEHXMZ GUHMQ mun COH#MH$QQ mxmmz MO HGQESZ OCHmDMU UHOHW CBEHXME GHMUHQO UCM QOHU mCoHumuwmo on UOHHmm HmoHHHHU mCOHuosomm UHmHM mumq .mCHpmm>HMC UCm mCHuCMHm mHMH Eoum mCHUHsmmH UHmHm CH mommoH oCm mUOHHmm mCHumm>HMC UCw mCHpCMHm HMUHuHHUII.oN mqmde xHozmmmC 133 N o.ov H 0.0m z m.wN N o.Ho H o.Hm m.hm H o.Hw Z m.hm H m.hm m.hm H m.hm m w.mw m m.Nh N m.vNH H N.HNH m.vh H m.vNH 0 000 H N.m 3 N.N H N.N N.N H N.N Z o.mw N o.Hh H o.Hh m.vh H o.Hb 0 com w.mN N o.mH H o.mH N.HN H o.mH U 0Nm H.mH N m.vn H U omv h. N m.vh H U ovw m.om H U oov N.ov H 0 com N.Hm H U 0Nm ¢.hH H U omN o.m H U vovN Amuomv AxmmBV .Amnomv Amuomv Axmmzv Amuomv mumH mmumq mumq mung mmHMH mumq Nmouu Amonom woumm>umm mxmmz mo omquHm Umpmm>umm mxmmz mo omuCMHm mHCmHHHBV mmuufl CmCECz mmuofl monofl HmQECz mmuod mNHm Such mHHm Eoumso UCm mHCmHmCBO CoHHMCHQEOU mHCmHmCSO mumHmEOU ow oNHm Eumm mg mCOHpmHmmo wcfipmo>um£ Ucm H.NENNNNN NCNOHNONE NOC-H NCHNCNHN NHNH mo Numeesmuu.nm mqmqe xHszNNN 134 v m o.Hh 3 «.NH m H.mm N H.mm m.mv N H.mm m.mv H m.mw m.bm H m.mv z m.mN m H.5N N H.5N H.Nh N «.NN o.Hn H N.Hn H N.HN m H.mm m o.on H H.HN m m.mm N m.mm o.ov N m.mm H.mmH H H.@MH H H.mMH 0 0mm m o.Hh N o.mN 3 N.ow m H.mm N H.mm m.m¢ N H.mm m.Nm H m.Nm m.nm H m.Nm z H.H N H.H m.mm N H.H m.mm H m.mm H.mm H m.mm m H.mH m o.on v H.Hh m N.HH N N.HH m.N> N N.HH H.mmH H H.mmH H H.mmH u owe Amuomv AxmmBV Amuomv Amuomv Axmmav Amuomv mumq mmumq mumq mpmq mmumq oumq Nmono Ammuom Umumo>umm mxmmz mo UmuCMHm Umummpnmm mxmmz mo UmuCMHm mHCmHHHBV mound HmCECz mmuod mmuofi HmCECz mmuom mNHm Eumm mHHm 60pmso UCm mHCmHmCBO COHHMCHQEOU mHCmHmC3O mumHmEOU .UNBCHucoouu.NN mHmms xHozmmmC 135 H.Hw N H.Hm N H.Hm m N.mm h N.HN N 0.05 m 0.0m H m.0> m m.0h H.H m m.0h N.NHH N N.NHH N N.hHH 0.mm H m.mm H 0.mm 0 00h m o.m H 0.00H H.00H H 3 H.mH H m.mm m m.mm H.mH m m.0m 0.00H N 0.00H m.MH N 0.00H z H.mN H w.mm m 0.mm N 0.mm m.H N 0.m0 0.0H H 0.0H H 0.0H m N.HH h N.Hh 0 0.05 m 0.05 H 0.0m m m.mmH N m.mMH N m.mMH H.mHH H H.mHH H H.mHH U 0N5 Amuomv AxmeV Amuomv Amnomv Axmwzv Amhomv mpmq mmpmq mumH mumq mmpmq ogwq NQOHU Ammhom Umpmm>umm mxmmz mo UmquHm Umgmm>nmm mxmmz mo UmHCMHm GHQMHHHBV mmuofi HmQECz monom mmuom HmQEdz mound mNHm finch mHHm EOHmCU UCm mHCmHmCSO COHHMCHQEOU mHCmHmCBO mumHmEOU .UmCCHuCOUII.NN MHmmh NHszmm¢ 136 m.mH m N.Hm H m.Hm m.05 H N.Hm m.0m m m.0m m m.0m m.5m N m.5m N m.5m m 5.m m 0.50 m 0.50 5 m.H5 o 0.05 m o.H H N.HOH m o.HoH m m.HOH N.5HH N N.5HH N N.5HH m.mm H m.mm H m.mm 0 00m m N.HH H N.am m.m m m.NOH N N.H H 3 H.mH m 0.m H 0.m 0.mH H 0.m N.m0H m N.00H H.mH m N.o0H N.mN N N.mN N.mN N N.mN z N.NN H A.pcoov m.mH m o.mH H.5m m m.mH 0N5 Amuomv Axmmzv Amuomv Amuomv Axmmzv Amuomv mgmq mmumq mpmq mumq mmHMH oumq Nmonu Ammuom omumm>umm mxmmz mo omquHm Umumm>nmm mxmmz mo omuCMHm mHCMHHHBV mmuofi umnECz mmuod mmuom HmCECz mmuofi mNHm Eumm muHm Eoumso UCM mHCmHmCBO COHHMCHQEOU QHCmHmCBO mumHmEOU .U®DCHHGOUII.5N mqm¢9 XHQmemd 137 .mHCp CMCC HmHHcEm mEHMM Co Umuusooo mmCHpmo>HMC Ho mmCHuCMHm mymH ozH .mmCHumm>HMC UCm mmCHuCMHm mpmH Cuon 0p Cmmmu CECHOO mHCH CH mHmQECZM .pmmCB n z umCmoQ >>MC n z umCmeNOm n m “Cuoo n UN .mEHu Co mCOHumummo HHm muoHQEoo ou mHMQECC pCmHonmCm CH GHQMHHm>m mum muoamummo EOCNCU HMCH COHUQECmmm mCu mo mmsmomn UmwsHoCH 90C mH OCHHHC EOHmCo mHmHmEoo mo Emumwm one H 0 H.mm m o.Hm N.MH H w.moH m 3 m.m 0 H.0H m H.0H 0.0m m H.0H m.5m H m.5m 0.mH H m.5m H.HCOUV 5.mH m 5.mH 5.MH m 5.mH 2 00m Amnomv Axmmzv Amnomv Ammomv Axomzv Amuomv mumq mmumq mgmq mung mmgmq mpmq mouu Ammuom omumm>umm mxmmz mo UmHCMHm Umumm>umm mxmmz mo UmHCMHm N mHCmHHHBV mmuod HmCECz meUN mmuod HmCECz mmuo< mNHm Emmm wHHm Eoymso UCm mHCmHoCBO mumHmEOU mHCmHmCBO CoHuMCHnEOU .605CHHCOUII.5N mqm¢8 XHQZMAQfl 138 H 5.00 3 0.5H H 0.5H 0.0m H 0.5H z 0.0m N 5.05 H 5.05 0.HOH H 5.05 U 000 H 0.00 3 N.MN H z 0.00 N m.mH H m.mH 0.HOH H m.mH U 005 H 0.0 3 0.mH H z N.5m N 0.HOH H U 0N5 0.0 H z m.mH N 0.HOH H U 000 H. H 2 H. N 0.HOH H U 0H0 0.00 H U 000 0.50 H 0 00m N.5H H o 0N0 0.0N H U 00H H.0H H U H0HH “whomv AxmmEV Amuomv Amuomv Axmm3v Amuomv mHMH mmumq mpmq mama mmumq mumq Nmouu Amwnom Umumm>nmm mxmmz mo UmuCMHm ompmm>umm mxmmz mo UmpCmHm mHCMHHHBV mmuo4 HmQECz monod mound Confidz mmuod mNHm Each mHHm EOHmCU UCm mHCmHmCBO wpmHmEOU QHCmHmCBO COHHMCHCEOU .mEmumwm mnmCHComE 30u|0 H NCHHCNHC mumH No NumeEsmun.NN CHCNN xHCZNNCN UCM mNHm Eucm mg mCOHHMHmmO mCHumm>Hmn UCm 139 H 0.5 m H.HNH 3 H.5 H H.0H m 0.H N 0.H m.MH N 0.H N.HmH H N.HMH 0.50 H N.HmH Z 0.0N N 0.Hm H m.Hm H.00H H m.Hm m 0.05 m 0.00 H 0.00H m 0.NH N 0.00H H 0.00H H m.m0H U 000 m o.H N 0.NNH 3 0.0 m m.mH N 0.H0 H 0.H0 m.5m H 0.H0 Z 5.HH N 0.0H H 0.0H H.HOH H m.mH m H.0H H 0.00H m 0.NOH N H.5NH H H.5NH 0.00 H H.5NH U 0H0 Hmuomv AxmmBV Amuomv Amuomv Axmmzv Amuomv mgmq mmumq mumq mpmq mmumq mumq Nmouo Ammnom ompmm>wmm mxmmz mo UmquHm Umumm>umm mxmmz mo UmHCch oHQmHHHBV mmuo< HmQECZ monofl mmmod HmQECZ mmuofl mNHm Enmm mHHm Eoumso UCm mHCmHmCBO COHHMCHQEOU mHCmHmC3O mumHmEou .GGDCHHCOUII.0N mqmda xHozmmmd 140 0.0NH H 0.0NH .. H 0.0NH m 0.Hm 5 0.HOH 0 0.00 m 0.00 H 0.HH m N.50 N N.50 N N.50 N.HON H N.HON H N.HON o 000 H 0.0NH m H.5H 3 0.0H H H.0H m N.mm N N.mm 0.0H N N.mm 0.mHH H 0.0HH 0.50 H 0.0HH Z 0.0 H H.HOH m m.HN N 0.00 H 0.00 H 0.00 m N.m5 0 0.00 m 0.00 H 0.00 m 5.0H N 5.0H N 5.0H N.HON H N.HON H N.HON U 0N0 Amuomv Axmmzv Hmuomv Amuomv AxmwBV Amnomv muMH mpmq mumq mHMH mmumq mumq mono Ammuom Umumm>umm mxmmz mo UmuCMHm Umumm>umm mxmmz mo UmuCMHm N mHCmHHHBV mmuofl HmnECZ mmuofi mmuofi HmQECZ monofl mNHm Eumm mHHm Eoumsu UCm mHCmHmCBO COHHMCHCEOU mHCmHmC3O mumHmEOU IIIHII/ 1 1!! ill .UmCCHuCOUII .NN mamwe NHCZMCCC 141 H.mm m H.00 H.0H m H.00 0.00 N 0.00 0.0H N 0.00 Z H.Hm m 0.00 H 0.00 N 0.00 N 0.00 0.00 H 0.00 H 0.m0 m 0.0H 0 0.Hm 5 0.HOH 0 0.00 m 0.0H H 0.00H N 0.00H N 0.00H N.HON H N.HON H N.HON U 000H 0 H.mm H 0.HOH 0.0 H 3 5.0H m 0.0H H H.0H m H.HmH N H.HmH 0.0H N H.HmH N.HN H 0.Hm 0.Hm H 0.Hm Z 0.00 H 5.m0 m H.HCOUV 0.0H N 0.0H N 0.0H m 000 Amuomv Axmm3v Amuomv Amuomv Axomzv Amnomv mumq mwumq mumq mHMH mch mpMH mono Ammuom Umpmm>umm mxmmz mo omuCMHm Umumm>umm mxmmz mo UquMHm N mHQMHHHBV monod HmCECZ mmuom mound Confidz mmuofi mNHm Eumm mHHm Eoumsu UCm mHCmHmC3O meHmEOU mHCmHoCBO CoHHMCHCEou .stCHuCoouu.NN mHmma xHozmmm4 142 .mHCp CMCu HmHHmEm mECmm Co Umuusooo mCHumm>HMC Ho mCHpCmHm mHMH ozH .mmCHumm>HMC UCm mmCHpCMHm mHMH anon on Hmmmn CECHoo mHCp CH mHmQECZm .ummCB u 3 NmCme >>MC u z umCmmCNOm n m “CHOU u UN .maHu Co mCOHHMHmmo HHm mumHmEoo Op meCECC HCmHOHmma CH mHCMHHm>m mum mnogmummo EoumCo HMCH CoHumfismmm may mo mmsmomn wmcsHoCH HOC mH mCHHHC EOHmCo mpmHmEoo mo Emummm mCBH 0 N.mN m 0.0NH N.HNH H 3 0.NH N H.HCOUV 0.NH H z 000H Amuomv AxmmBV Amuomv Amnomv AmeBV Amnomv mgmq mmumq mama mumq moumq opmq mouo Ammuom UmHmm>Hmm mxmmz mo omuCMHm woumm>nmm mxmmz mo UmpCMHm N mHCMHHHBV mmC04 umCECz mwuod mmuod HmCECz monod mNHm Eumm ouHm EoumCU oCm mHCmHmCBO wumeEoo QHCmHmCBO COHHMCHQEOU UmCCHuCOUII.0N mqmde xHozmmmd 143 HNN. HNN. NNN. NON. HHN. NNN. NHN. umHmomm HNHH0O\umoo umHHoo HONNN NHHNN HNNNN NHNON OHHNN HNNNN NNNNN mumHmomN ngoa ONNNN NHNNN NNNNN NNNHN NNOON NNHNH ONNNH Hmoo Hmuoa NNNN NNNN NONN NNNN HNNN HNNN HNNN umoo NHamumczo Hmscc< msHm NNHNN HNHHN NNNNH NNHNH HNNNH NNNHH NHNNH “moo mHanum> Hmsch Hmpoe NNON.HH HNNN NNHN NNHN NNNN ONNN HNNN NNHN NNHN.N NN.HH Hams: HONH HNNH HONH HNNN HONN HNHN OONN HHNN.N NO.NH mammn N>mz NNNN HNNN NNHN NNNN HHNN ONNN NNON NNNN.N HN.NH mammnNom HNNOH NNOOH HONN NNNN NNNN NNNN NONN HNNN.NH HN.NN :uoo 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 . . . . . . . ouofi muofi Com mmHumHmqu HONN NONN NONH «OHH «OOH NONN NONN HmonNe umoo Hmm #mOU GHQMHHMNV NmHGmeHOZH mH04 OH me wflmOU GHQMHHM> GHQMHHM> NNN. NNN. NNN. NON. NOO.H NHN.H NHN.H pmHmomN umHHooxumoo HNHHOQ NNNNH ONNNH NOHHH NNNHH HNHN HHNN ONNN NumHmomm Hmuoa NNHNH NNHNH NNN H HNHOH NHNN ‘ NNNN NHNN Hmoo Hmuoe HNNN HNNN HNNN HNNN HNNN HNNN HNNN umoo NHgmumazo Hmsccm msHm NNNHH HHNN NNNN OHNN NNNH NONN NNNH “moo mHanum> Hmsan Hmuoa NNON.HH NNNH NNNH ONNH HNOH NNN NNN NNN NNHN.N NN.HH Hmong ONHN OOHN ONNH OOHH ONOH OON ONN HHNN.N NO.NH mammn N>mz NNNH NNNH NONH HHOH NNN NNN HNN NNNN.N HN.NH mammnNom NNHN NNNH NNNN HOHN NNNN HNNH NNN HNNN.NH HN.NN cuoo 1N0 ANV 1N0 1N0 ANV 1N0 1N0 ANN 1N0 . . . . . . . oHoC muo¢ Com mmHHmHmqu NONN HOHN NOON NONH NONH NON «OH HMOHNNN umoo Hmm #mOU GHQMHHM> NmflflmEmHOGH who/N OH Hmm mprU GHQMHHM> QHQMHHM> CpHB Eoummm BOHIH m How NOHHMH oCCm>mH .mHCmHmCBO mumHmEoo "umoo NcHusmsoo now ummwsmlu.NN NHNNN NHQZNNNN 144 N>MC “Cmoumm NH .mCmeNOm quoumm 0H 1CHOU quonm 00 .mCHwCCOH on $50 com pOC mmE mCECHoo CH mmHCmHm N .HmmCB quoumm mH UCm .mCmmn mo mumHmCoo whom HMUHmmp NH HON.H NOH.H NHN. NNN. NNN. HNN. mumHmomm NNHHOO\pmoo NNHHoo NONNN HNNNN HHNNN NNONN NHNNN HONNN manmomm Hmpoa NHNNN NNNNN NNONN NNNNm ONHON NHNNN umoo Hmuoa HONH NNNH ONNH NHNH NNOH NNOH umoo NHsmumazo Hmsqu msHm NHONN NNNHN NHNNN HNONN NNHNN NNNHN umoo mHanum> Hmsaaa Hmuoe NNON.HH ONNN HNON NNNH NNNH NNNH ONNN NNHN.N NN.HH ummnz HOON HNNN HONN HNNN HONN HNNN HHNN.N NO.NH mammn N>mz HNNN ONNH NNNH NNHH NNHH NHNN NNNN.N HN.NH NcmmnNoN NONNH HNNHH NNNNH ONHNH NOHNH NNNHH HNNN.NH HN.NN cuoo 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 . . . . . . mnofl muod Mom meHmHmucm NOON NONN NONN NONN HOHN NOON HmonNN pmoo Hmm umoo GHQMHHC> NmuCGEmHUCH wHO¢ OH. .Hmm mvaU mHQMHHm> QHQMHHM> .UmCCHpCOUII.mN mqmde xHQmem¢ 145 NON. NON. NON. HHN. HNN. NNN. OHN. umHmomN NNHHom\umoo umHHoO OHONN ONNNN NHNNN NNOHN NONNN HNNNN HNNNN mpmHmomm Hmuoa HNNNN bwmmm mpbwm HONNN NNmOm OHNNH NNNNH umoo Hmuoa NNNN NHNN NNNN NONN NNHN NNHN NNHN umoo NHsmumczo Hmsaq¢ msHN NOHNN HNNNN NNNHN HNNNH NHHNH NNNNH NHNHH “moo mHnNHHN> Hmsch Hmuoa NNNN.NH NOHH NHNN NHNN NNNN NNNN OHNN NHNN ONNN.N NN.NH puma: NNHN NOHN OHNH NHNH NNNN NNNN OHHN NNHN.N NN.HN mammn N>Nz HNNN NONN HNHN NNHN NNNN NNNN NNNN NNNH.N NN.NH mammnNoN OHNHH NNNOH NNHOH NNNN NNHN NNNN NHNN NNNO.HN NN.NN cuoo 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 . . . muofi muofi Hmm mmHHmumqu NONN NONN NONH .NOHH .NOOH .NONN .NONN HmoHNNN umoo Com pmoo mHCMHHm> NmHCQEGHOCH QHUHN OH Hmm mHmOU GHQMHHMNV GHQMHHM> HNN. NNN. NNN. NNN. HON. NNO.H NHH.H umHmomm NNHHoo\umoo “NHHOO NHNNH NNNNH NOHHH NNNHH HNHN HHNN ONNN mpmHmomN Hmuoa HNN H MMbMH HNNHH NNHN NNNN NONN NNNN umoo Hmyoe NNHN NNHN NNHN NNHN NNHN NNHN NNHN umoo mHnmumczo Hmsch NsHN m05mn mmmon m5pm mmm5 NNHN mm0m mHOH “moo mHCMHHm> HMCCCN Hmuoa NNNN.NH NNON ONNH NNHH NNHH ONN NNN NNN ONNN.N NN.NH among NHNN NNNN NNNH ONNH NNHH NNN NNN NNHN.N NN.HN mammn N>mz NNNH NHNH NNHH HHHH NNN HNN NNN NNNH.N NN.NH mammnmom NONN HNON NHNH HNNN HNNN NNNH HHN NNNO.HN NN.NN cuoo 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 . . . . . . . ouofi muo< mom mmHHmHCUCm NONN NOHN NOON NONH NONH NON «OH HMoHNNB umoo Mom umou mHCmHHm> NmHCGEQHOCH wuufl OH Hmm mflmOU MHQMHHM> mHQMHHm> m Cqu Emummm BOHIH c How moHuwH mCCm>mH .mCHHHC Ecumso UCm mHCmHmCBO mo COHHMCHQEOO "umoo NCHusCEOU Com ummnzmnu.ON NHNNN xHQZNNNH 146 >>MC “Cmoumm NH .mCmeNOm quouom 0H .Cuoo quoumm 00 mo mumHmCoo mHom HMOHmmu N mCHGCCOH ou $50 wow HOC mos mCECHoo CH mmHCmHm N .pmmCB quonm 0H oCm .mCmmC H OHO.H HNN. NON. NNN. ONN. NNN. mpmHmomm HNHH0O\umoo umHHoO NNHNN HNNHH NNNNH ONNNH NNNHH NNNOH mumeomN ngoa ONNNN NNNNN HHONN NNHNN NNNHN NHNNN umoo Hmuoe NNNN NNNN ONNN NHNN NHNN NNNN umoo mHnmumczo HmscaN msHm NNNNN NNHHN HNNNN NHNON NNONN ONNNN umoo mHanHm> HmscaN Hmuoa NNN .NH NONN NNNN NNNN NNNH NNNH NNNH ONNN.N NN.NH pmmnz HNNN NNHN NNON NNNN HNNN NNNN NNHN.N NN.HN mammn N>Nz NONN HNHN NNHN ONNH NNNH ONNH NNNH.N NN.NH mammnNON HNNNH NNONH HNHNH OHNHH NNHNH NNNNH NNNO.HN NN.NN . auoo 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 . . . . . . muod muofi mom mmHHmuoqu NOON NONN NONN NONN NOHN NOON HMOHNNN umoo Hmm #mOU mHQMHHm> NmpCmEmHUCH muofi 0H mom mumou mHCMHHm> mHQMHHm> .UODCHHCOUII.0M mqm Hmsch Hmuoa NNOH.OH NNHH HNNN ONNN NNNN NNHN NNNN HHNN NNNN NNNN.N NN.NH Hams: NNHN HNHN NNNH NHHH NOHH NNNN HNHN NNON NHNN.N HO.NH mammn N>mz NNOH NNNN NNNN NHNN NNON HONN NHNN HNNN ONNN.N NH.NH mammnmom HNHNH NNNHH NONOH ONNN NNON NNNN NNNN ONNN NNHN.NH NN.NN auoo 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 . . . . . . . . Hmuom whom mom mmHHmHmqu NOHN HOON NONN NONN NONH NOHH NOOH NONN HNOHNNN “moo “mm pmoo mHannm> NmuHflmfimhuaH GHOAN OH Hmm mflmOU mHQMHHm> QHQMHHM> NNN. HON. NNN. NNN. NNN. NOH.H NNN.H HNH.N mumHmomm NNHHoo\umoo HNHHoo HNNNN NHNNH NNNNH NOHHH NNNHH HNHN HHNN ONNN mpmHmomm Hmuoa HNHNH NHNNH NONHH NNNNH NNNOH NNNN NNNN HNHN pmoo Hmpoa NONH NONH NONH NONH NONH NONH NONH NONH “moo NHNNHmazo Hmsccm msHm NNNNH NHNHH NNNN HNON HNHN NHNH HNNN NHNH umoo mHanum> Hmsaam Haney mmpwwpm HNON ONNH NNNH NONH NHOH HNN NNN HNN NNNN.N NN.NH puma: NNNN NNNN NNON OHNH NNNH NNOH HNN NHN NHNN.N HO.NH mammn N>Nz NNON HNNH NNNH HNNH ONOH NNN OHN NNN ONNN.N NH.NH mammnNom NNON HONN NHNH NNNN HNON NNNN NHNH NNN NNHN.NH NN.NN cuoo ANV 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 1N0 . . . . . . . . GHQ/w QHOAN .Hmm mmHHQHmHGW NONN NONN NOHN NOON NONH NONH NON «OH HMUHNNN umoo mom umoo mHQmHHm> NmquEmHOCH muom 0H Mom mumou mHCMHHm> mHCMHHm> CHHB Emummm 30HI0 m Com moHumn mCCm>mH .mHCmHmCzo oumHmEoo "umoo NcHusmeoo Com umNHsmun.HN NHNNN xHozmmmN 148 >>CC quonm 0H .0CHUCCOH OH 050 mom .mCmmflmom quoumm 0H .CHOU quonmm 00 mo mumHmCoo wnom HMUHmwu H uOC >08 mCECHoo CH mmusmHm N .ummCB quonm 0H UCm thme H 00H.H 0H0. 000. 005. 0H5. 050. 050. 550. 550. umHmomm HmHHoo\mpmoo HMHHOQ 0N000 0000H H0500 00NHO 50H00 0HH00 000N0 05H00 0055H mpmHmomm HMHOB 0mNON mmmwh mmme moohw MMhbh mmw5m 5H00m N5n00 Nmmmm mpmoo Hmuoe 5500 N000 HOH0 HOH0 5HHO HHHO HHHO H000 000H umoo AHHCmMmCEO HmsCC< Hmuoe mowom 0050M 05H5m Nmm00 m0000 mNmmm m05pm 00omN N5N5N “moo mHCMHHm> HMCCCN Hmuoa 0NOH.0H 0000 H5N0 0H00 H050 00H0 0NNO 500H 005H HHHH 0000.0 50.0H pmmCB N000 0HNO 0005 0N05 HOH5 NH00 00H0 50H0 0H00 0H00.0 H0.0H mCme m>mz N500 5HHO N000 5000 N000 0000 0HOH 000H 000H 0N50.0 0H.NH mCmonmom 0HO0H 00HOH 0NH5H 0500H NHO0H 00HOH 500HH 0000H HO0NH NOH0.0H N0.N0 CHOU $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 . . . . . . . . . muofi muo< Mom mmHHmHmHCm 0000H 0000 ¢0N0 «000 ¢0H0 «000 N005 ¢0N5 «000 HMUHQNB pmou Hmm umou mHCMHHm> ngCoEmHOCH muofi 0H mom mumoo mHCMHHm> mHQMHHm> .UGSQHHCOUII.H0 mqmdfi XHmzmmm< 149 H00. 000. N05. 505. 0H5. 0N5. 0N5. 005. mHmHOOOm HMHHOQ\HmOU HmHHOQ 0NHOH 500NH 50H00 N0000 0H000 0NOH0 00NON H000N mumHmOOm HMHOB NNMHm HH00m 0H55m 0m00N mmnmm 0mmmN 5Nm0w 0050" HmOU Hmuoa 0H5N 000N NO0N NO0N 050N 050N 050N 050N HmOU mHCmHOCBO HNCCCH mCHm H000N 0HO0N 0NO0N HHNON 00HHN 0000H 5505H 0000H umOU OHCMHHm> HMCCCC Hmuoa 0m00.nm 0HOH 000H 000H 0550 00H0 HOH0 HO0N HHON 000N.5 0H.0H ummCB 0HHO H050 N000 050H 500H 00NH 0N00 HHHO 0500.0 00.00 mCmmC N>mz 05HH 00HH 0H00 0000 5000 5500 505N 5HON 0000.0 N0.0H mCmmCNom 0000H 000NH 000HH 0000H HNO0H 00H0 0000 0H05 0000.0N H0.00 CHOU HNV ANV ANV ANV ANV ANV ANV H00 ANV ANV . . . . . . . . mHom .wHod Hmm mmHHmHmHCm NOHN NOON NONN NONN NONH NOHH «OOH NONN HMOHNNN “moo Hmm umOU mHCMHHm> NmHCOEOHOCH mHod 0H Hmm mHNOU OHCMHHm> OHCMHHm> N05. 005. N05. 0N0. 050. 0H0. 50H.H H00.H mHmHmomm HMHHOQ\umOU HmHHOC HO0NN 0H50H 0NO0H NOHHH NONHH HOH0 HH00 0NON mHmHmomm HOHOH N500H HOHOH 0000H 000HH HN00 0 00 0 N0 0000 HNOU HOHOH 050N 050N 050N 050N 050N 050N 050N 050N HmOU QHCmHmCBO HMCCCd mCHm Nommn mnmNn wN5Dn mm0m Hmnh m0mm 050m mmbH umOU mHQMHHM> HMSCCN HOHOH 0000.H 0NON 000N NH5H NOHH NOHH H50 H00 00N 000N.5 0H.0H HMOCB 0000 050N HONN HHOH 0NOH 5HHH 005 N00 0500.0 00.00 mCmmC m>mz 00NN 000H 050H 000H 0HHH 000 000 00N 0000.0 N0.0H mCmonmom 0000 5H00 NH00 55HH HH00 000N H50H 000 0000.0N H0.00 CHOU A00 ANV ANV ANV HNV ANV HNV HNV Amy ANV . . . . . . . . OH0¢ mHod Hmm mmHHmHmpCm «ONN NONN NOHN HOON NONH NONH NON NOH HMOHNNH “moo Hmm umOU OHCMHHm> NmquEOHOCH mHofl 0H Hmm mumOU OHCMHHm> OHCMHHM> .mCHHHC EOHmCO UCM mHCmHmCBO HO COHHMCHQEOO m CHHB Empmmm BOH|0 c How mOHHMH OCCO>OH "Hwoo 0CHHCQEOO How um0©5mll.N0 MHmNB xHozmmmd 150 .mCHUCCOH OH $50 000 HOC Noe mCECHOO CH mOHsmHmN .ummgz HCOOHOQ 0H UCm .mCme >>MC HCOOHOQ 0H .mCmeMOm HCOOHOQ 0H .CHoo HCOOHOQ 00 mo mpmHmCOO mHom HMOHQNH «H NNN. HNN. NNN. NHN. NON. NNN. NNN. NNN. HNN. umHmomm HNHH0O\pmoo HNHHOO H0500 00500 05000 00H00 H0050 00500 00H00 00500 0H05H mumeomm HMHOB 0005H 0NO0H H0000 00HNH N5000 0H000 00500 0HOH0 0 H 0 umOU HMHOB HHON HHON 000N 000N 0NON H05N H05N H05N HH5N umOU mHCmHmCBO HMCCCN mCHm HO0HH 000NH 0HHHH 00000 0H050 00500 50000 05HNO N0000 HmOU OHCmHHm> HMCCC4 HMHOB 0000.Hw 00N5 0500 0500 0000 0000 0000 0H00 5NNO 500H 000N.5 0H.0H HmmCz 5000 05H0 N050 0HHO 0N00 0H05 00N5 H000 00H0 0500.0 00.00 mCmmn >>mz 0000 0H50 HOH0 HOH0 H500 H000 0H00 0000 005H 0000.0 N0.0H mCmeNom H000N 0HO0N 0HNOH 0500H NH05H 5050H N500H 0000H HONHH 0000.0N H0.00 CHOU $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 . . . . . . . . . 0H04 mH04 Hmm mmHHmHmHCm «OOOH NONN NONN NONN NOHN NOON NONN NONN NONN HMUHNHN umoo Hmm umOU OHQmHHm> NmquEmHOCH mHoé 0H Hom mumOU OHCmHHm> OHCMHHM> .UOCCHHCOUII.N0 mHmfia xHQmem¢ 151 m>MC HCOOHOQ 0H .mCmemOm HCOOHOQ 0H .CHOO HCOOHOQ 00 mo mHmHmCOO mHom HMOHQNH N .HMOCB HCOOHOQ 0H UCw .mCmmg H 005. mHmHoowm HOHHOQ\HmOU HMHHOC 0H.0N0N mumHmoom HMHOB HON.NNON HOHH.NN umoo Hmuoa ONH.NNN ONHH.N NO.NN ANNHV ummaz NHN.NNH NNNN.HH HN.ON ANNHO mammn N>mz OOO.NNN OONH.N ON.HN ANNHO mammnNom NON.NNN NNNN.NN NN.NN ANNNO cuoo OH0< 0H Hmm 0H04 HMOHQNB Hmm mHo¢ Hmm HmOU mmHHmHmqu #mOU mHQMHHm> HUmOU MHQMHHM> GHQMHHM> .0CHHHC EOHmCO OHOHQEOO How mOHHMH OCCO>OH "umoo 0CHHCQEOO How um0050||.00 mqmme xHozmmHC BIBLIOGRAPHY Books Bradford, Lawerence A., and Johnson, Glenn L., Farm Manage- ment Analysis. New York: John Wiley & Sons, Inc., 1963. Cochrane, Willard W. The City Man's Guide to the Farm Prob- lem. 'New York: McGraw Hill Book Company, 1963. Hathaway, Dale E. Government and Agriculture. New York: Macmillan Company, 1963. Articles and Bulletins Buxton, Boyd M. Economics of Size in Dairy Farming. Farm Business Notes 467, University of Minnesota, 1964. Cash Grain Farming Today: What It Costs, How It Pays. A. BC. 68, Cooperative Extension Service and Department of Agricultural Economics, Michigan State University, 1967. Connor, Larry J. Costs and Returns for Major Cash CrOps in Southern Michigan. Agricultural Economics Report No. 87. Department of Agricultural Economics, Mich- igan State University, 1967. Dean, Gerald W., and Carter, Harold 0. Economics of Scale in California Cling Peach Production. Bulletin No. 793, California Agricultural Experiment Station, University of California, (Davis), 1963. Fogarty, Bill. "A New Enterprise for Leasing Farm Equipment." 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Research Report 47, Agricultural Experiment Stat1on, and COOp- erative Extension Service, Michigan State University, 1964. "Rental Opens the Door to Increased Sales." Farm and Power Equipment, March, 1967, p. 25. Smith, Warren. "They've Made Leasing Work." Implement and Tractor, March 21, 1965, p. 20. Van Arsdall, Roy M. Labor Requirements, Machinery_Invest- ments, and Annual Costs for the Production of Selected Field CrOps in Illinois, 1965. AE-4112, Illinois Agri- cultural Experiment Station, University of Illinois, 1965. Walker, Odell L. Machinery Combinations for Oklahoma Pan- handle Grain Farms. Bulletin B-630, Experiment Station, Oklahoma State University, 1964. Government Publications Hunter, Elmer C., and Madden, J. Patrick. Economies of Size for Specialized Beef Feedlots in Colorado. Agricul- tural Economics Report No. 91, ERS, U.S. Department of Agriculture, Washington, D.C., U.S. Government Printing Office, 1966. Madden, J. Patrick. Economies of Size in Farming. Agricul- tural Economics Report No. 107, ERS, U.S. Department of Agriculture, Washington, D.C., U.S. Government Printing Office, 1967. U.S. Department of Agriculture. The Balance Sheet of Agri— culture. Agriculture Information BulletIn No. 329, Wash1ngton, D.C., U.S. Government Printing Office, 1967. 154 U.S. Department of Commerce, Bureau of the Census, United States Census of Agriculture (by years), Washington, D.C., U.S. Government Printing Office. Unpublished Sources Connor, Larry J. Guidelines for Selecting Machinery Sys- tems. Unpublished speech given at FarmerTs Week, Michigan State University, February, 1968. Morris, W.H.M. Farm Machinery Replacement. Unpublished manuscript, Purdue University. I M'11111111171111)!flfljlfliflfijflwijfljlflfifu)“