AND CONCLUSIONS 197 BIBLIOGRAPHY 205 ACKNOWLEDGMENTS 211 APPENDIX A 212 APPENDIX B 219 APPENDIX C 241 APPENDIX D 244 APPENDIX E 248 profit-maximizing farm organizations. Specifically the objectives are: 1. To determine characteristics of present farm organizations of farms in Northeast Iowa. 2. To derive profit-maximizing farm organizations by using linear programming techniques on representative farm types of Northeast Iowa for alternative hog and milk prices. 3. To derive normative supply

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... tions and cross-supply functions which are aggregated to represent all of Northeast Iowa. 4. To provide data for regional aggregation and comparison of supply schedules for the Lake States to analyze the comparative advantage of states, areas and types of farms in producing milk, hogs, beef, corn and other products. 5. To investigate the feasibility of individual farm planning in regional adjustment problems. 6. To determine some of the characteristics which facilitate and impede farm adjustments in Northeastern Iowa and to investigate the nature and rate of present adjustments. ANALYTICAL FRAMEWORK FOR THE DAIRY ADJUSTMENT STUDY The farm commodity with which the Lake States dairy adjustment study (of which this study is a part) is mainly concerned, is milk. Therefore, in this chapter the general supply-demand situation for milk is briefly discussed as background for the study. Then, some aspects of the theoretical framework upon which this study is based are presented. Following that, the use of linear programming in deriving aggregate supply functions is discussed. The Dairy Problem Developments occurring in markets for agricultural products and the changing structure of production costs on farms are forcing changes in the organization of farms. Present and potential farmers need to be able to accurately evaluate the adjustment alternatives they face. Dairy farmers currently receive relatively low returns for their labor and man agement because of low prices for dairy products at the farai level and low productivity of resources on dairy farms. For daily farmers, particularly, the change in the form in which dairy products are used is an important consideration. The decline in demand for milk-fat is forcing many dairy farmers, particularly those not equipped to sell grade A milk, to re-evaluate their farm organizations. In addition, a surplus of milk production (at a price farmers consider to be satisfactory) has been prevalent since 1953. Since 1953» the U. S. Department of Agriculture has spent an average of approximately $320 million dollars per year for dairy price support programs. The prospects are for costs in the fiscal year of 1961-62 to be the highest ever at $532 million dollars (73, p. 21). Purchases by the U. S. Department of Agriculture have amounted to about 5 percent of production since 1953 (73, p. 31). Thus, some substantial readjustments, either in price or in production are needed to bring production more nearly into balance with consumption. In spite of a very substantial decline in number of dairy cows in the United States the production of milk continues to increase as shown in figure 1. Even though there has been a 30 percent decrease in cow numbers since World War H, milk production has increased by about 7 per cent. This increase in total production has resulted from the great in crease in production per cow because of substantial improvement in the inherent ability of cows and better care and feeding. Population expansion would have absorbed the slight overall increase in milk production, except that per capita consumption has fallen. Per capita consumption of milk used in fluid items reached a high in 1945 at 335 pounds. After the World War II, fluid milk use declined to around 300 pounds per year. Since 1956, per capita use has been falling by an average of about 5 pounds per year. By I960, civilian per capita con sumption of fluid milk was 287 pounds. Cream consumption has fallen even more rapidly. There has been a steady decrease in per capita consumption from 13.6 pounds in 1946 to 9.3 pounds in i960 (71, p. 5). The decline in demand for these products partly reflects consumer reaction to real or imagined benefits from reducing intake of certain fats contained in milk. It also represents decreased consumption on farms where fewer farmers are 160 140 PRODUCTION PER COW <0 * * m 120 u. O TOTAL MILK PRODUCTION 15 techniques for estimating firm supply curves. The adaptation of variable pricing modifications of linear programming provides a convenient method for deriving optimum farm plans in response to product price changes. There have been several articles describing research which used the same general methodology as used in this study. One of the earliest studies where firm supply functions were derived using linear programming was made by Easley (13). He derived optimum supply functions for milk for a particular farm under various resource restrictions and for several types of dairy enterprises. The stepped functions derived by Easley are reported in Ladd and Easley (33), with smoothed curves and supply elasticities. Other studies which have used similar techniques are Krenz et al. (31), Heady et al. (19) and Toussaint (57). Plaxico (38), McKee and Loftsgard (35) and Krenz et al. (32) discuss and show examples of optimum firm supply schedules and the aggregation of these firm supply schedules. Barker and Heady (5) in a somewhat different type of study, derived cost curves for various types of milking facilities. In the programming for this study, enterprises which were feasible over the next few years were used. Beef-fattening, beef cows, as well as cropping activities were alternatives to the hog and dairy enterprises. These alternative activities represent opportunity costs to production of milk or hogs in the programming solutions. The procedures of linear programming have been treated in many articles. The use of linear programming to derive supply curves, however, will be discussed briefly. Cochrane ( 10) has distinguished between supply functions and response 16 functions. The supply relations derived in this study are of the type which Cochrane describes as "supply" functions. Optimum quantities of production are specified where price of only the one product in question is varied. All other prices, resource restrictions and production coef ficients are held constant. The procedure used in this study is to vary prices of one product over the relevant range of interest to determine the price ranges over which a particular combination of enterprises is optimum. This provides a normative supply curve, indicating the amounts of products which should be produced at each price level, if profits are maximized. The procedure is then repeated, holding a price or resource at another level and vary ing one of the prices. Thus, a group of ceteris paribus supply curves are produced. The supply function so derived is of a stepped nature be cause of the linear nature of the production data, and the limited number of production alternatives and resource restrictions. Thus, supply curves derived by linear programming differ from classical, smooth supply curves. The stepped supply functions have horizontal ranges, extending until a particular resource restriction is encountered. They then have a vertical segment which defines the price range over which there is no change in the plan. The assumption of profit-maximization used in programming optimum farm plans restricts the nature of the supply function to a normative schedule, or what "should exist" if producers made decisions aimed at maximizing net income based on perfect knowledge. In this study the programming matrix was formulated as described by Heady and Candler (20, pp. 265-30?) to obtain linear programming solutions 17 for varied prices. For variable pricing of milk, all dairy activities contribute milk production to a special milk row rather than crediting each alternative dairy cow activity with the proceeds from milk sales. A separate activity is used to sell milk. The price on this single activity is then varied, with this activity "using up" the milk contributed into the milk row by the dairy activities. Solution of a program begins at prices below which any milk production is profitable, then proceeding to higher prices. In the solution of a program, when a new plan is ob tained prior to any milk production the Zj -C. value of the milk selling v J activity is observed. If the Zj -Cj is greater than zero, the price on the dairy selling activity is increased until the Zj -Cj is equal to zero. If the Zj -Cj on milk selling or any other activity is less than zero, an optimum plan has not yet been obtained. When the Zj -Cj on milk selling is zero, this activity may be introduced without any change in Zj -Cj's of other activities or change in profit. When the Zj -Cj of the milk selling activity is a positive value, there is an opportunity cost to selling milk. The increase in milk selling price is to remove the opportunity cost. The next problem is to find the minimum price change to make another plan optimum. The milk selling price for the new plan is equal to the price in the previous plan plus the minimum price change necessary to drive one of the Zj -Cj's to zero. The procedure is thus repeated until all plans have been derived over the range of prices of interest. The stepped supply functions show quantities that "should be" produced in different price ranges given the normative assumptions. Although traditional supply concepts assume a continuous supply 20-21 satisfactory linear programming solution is not so readily obtained. In this case the most productive activities also come in first in the ordin ary programming solution. But, the most productive activities, represented by the segment EF in figure 3» are not valid without being preceded by the activities represented by the segments 0D and DE. In this study it was found that labor requirements per dairy cow decrease as cow numbers increase because of certain set-up time require ments for equipment and for cleaning and other fixed or semi-fixed labor uses. These labor requirements are not perfectly divisible. Capital in puts also tend to be indivisible since the milking parlor and pipeline milking equipment come as a unit. Therefore, two programming solutions were compared: (l) A plan which holds input of labor and capital used in dairy activities to zero, and (2) a solution which requires use of activities represented by the segment 0G, and chooses among activities represented by the segment GN in figure 3» The segment GN represents activities for which labor and capital requirements are only marginal re quirements. In this study, all fixed labor times and capital costs which were not perfectly divisible over all the ranges of inputs are charged against the first five cows. Then, only none or any number more than five cows, are allowable in program solutions. Thus, the complete use of the segment 0G in figure 3 (which represents the first five cows in this study) is required in order to be on the segment GN. Program solutions were compared, i.e., net revenue associated with the optimum plan with no cows was compared with net revenue from plans with more than 5 cows. Since milk was being variable priced, cow numbers, as well as net revenue 22 were increasing as each iteration of the program solution proceeded. The exact milk price and number of cows at which it became more profitable to have more than five cows than to have no cows was computed. This method of handling increasing returns was only used in connection with the dairy enterprise, since it is an expensive device to use in computing. Also, compared to other types of enterprises, there is more evidence of increas ing returns to the variable factors in dairying. 23 REPRESENTATIVE FARM SITUATIONS FOR NORTHEASTERN IOWA Northeastern Iowa was selected for the area of this study because of its contiguity with the states of Minnesota, Wisconsin and Illinois. These states are also participating in the Lake States Dairy Adjustment Study. In addition, northeastern Iowa has more dairy farms than do other parts of Iowa. In order to study present farm characteristics and derive optimum farm plans for representative farm situations in northeastern Iowa, a sampling procedure was devised. The following section describes pro cedures used in conducting the farm survey. Following that, some area and farm characteristics are presented. Sample of Farms In selecting representative farm situations for the 17 county area of northeastern Iowa, the region was first divided into two major soil areas as shown in figure 4. These major soil areas, divided on county lines, are based on the principal soil association areas as shown in Shrader et al. (4l, p. 9) and Stritzel (55. p. 2). Area I is generally the Carrington-Clyde area (more recently called the Kenyon soil area) and Area II is generally the Fayette soil area. This delineation of major soil areas was used to make production areas as homogeneous as possible. A sample of farms was taken from each of these two areas separately. The sample was drawn on the area segment basis using the Master Sample of Agriculture. One hundred farms in each major soil area were set as an approximate goal in order to have an adequate sampling rate. Using the HOWARD eriMNEOWl WORTH I MlTOHCLL LVON CMMCT ARE HANCOCK OttO flOTOO PALO ALTO CLAY FUOYP PCCAHONTA» MVH»OLOT PLYMOUTH CHCPOKte AR pclawakc Dw&vqwt OtACX HWK DUCHANAIt HAMILTON VOODIUAV 8AC TAMA MOHONA IOWA FOLK FCT TAWATTA MIC union oes DAVIS TAYLOR LCC. Figure 4. Major soil area county groups for northeastern Iowa 195^ Census of Agriculture (69), the average number of farms per area sampling segment was obtained. From this, the number of segments necessary to obtain the expected sample of 100 farms was determined. A secondary sample of segments, one-half as large as the primary sample, was drawn. This secondary sample was to be used in case of need for re placements to compensate for refusals and the decline in farm numbers which has occurred since the 195^ census. A two stage sampling procedure was used to reduce travel costs in enumerating. Townships were first identified. Then, a sample of town ships was drawn at random from each major soil area. Sample segments were then drawn at random with the condition that two primary segments and one secondary segment would be drawn in each of the townships se lected. In making the farm survey in June, 1959, attempts were made to con tact each of the farmers whose farmstead was located in a primary sampling segment. Secondary sampling segments were used as needed to get the ap proximate number of desired fara schedules. It happened by chance that 103 usable farm schedules were obtained from each area, making a total of 206 schedules for both areas. The data obtained in the farm survey includes information on: location of the farm, tenure of the operator, farm size, crop production, sales and expenditures, fertilizer use, machinery and buildings and equip ment available, livestock enterprise descriptions, recent farm changes, family composition and labor available for farming, expectations for future disposition of the farm, expected future occupations of operator le Atkinson, Jacob and Hardin, Lowell. Raising, buying, selling feeder pigs. Indiana Agr. Expt. Sta. Bui. 587. 1953. 2. Aune, H. J. and Day, L. M. Determining the effect of size of herd and equipment on dairy chore labor. J. Farm Econ. 41:569-583» 1959. 3« Bailey, R. A. and Sitterly, J. N. Man labor on the commercial hog enterprise. Qhio Agr. Expt. Sta. Res. Bui. 792. 1937. 4. Barker, Randolph. The response of milk production to price: a re gional analysis. 54. Strain, J. Robert, Fincham, Robert C., Wright, Earl 0. and Roth, Fred W. Dairy enterprise alternative evaluation sheet. Iowa State Univ. of Soi. and Tech. Cooperative Ext. Serv. in Agr. and H. Ec. Publication No. M-913 (Rev.). 1959. 55' Stritzel, J. A. Take a good soil sample. Iowa State Univ. of Sci. and Tech. Cooperative Ext. Serv. in Agr. and H. Ec. Pamphlet 287. 1962.