The Determinants of Wheat Profitability in Kansas Michael R. Langemeier, Michael W. Haley and Fredrick D. DeLano The determinants of winter wheat profitability in Kansas were examined using data from the Kansas Farm Management Associations.Though the majority of farms exhibited highly variable profit per acre over time, approximately 20 percent of the farms were in the high one-third profit category in four or more of the six years. Results of regression analysis indicated that per-acre wheat profit was highly dependent on output price, yield, cost of production, specialization and size. Many of the wheat farms in the sample could benefit from increases in size and diversification. Introduction Production costs and profitability vary considerably among wheat farms. For example, wheat producers in northwest Kansas had an average total cost per acre of $129.32 from 1991 through 1994 while producers in the high-cost group had production costs of $179.29 per acre. Net returns for the same geographic region and cost groups were $15.28 for the lowcost producers and a net loss of $11.23 for the highcost producers (DeLano and Langemeier). The profitability of farm enterprises has been examined thoroughly in past research (Fox, Bergen and Dickson). Numerous studies have focused on the magnitude, determinants and causal relationships of farm enterprise profitability. However, few if any of these studies have narrowed their focus to hard red winter wheat. The 1996 Farm Bill will likely have a large impact on the farm-level production decisions of winter wheat producers. Under this legislation, annual acreage idling provisions are eliminated, and farmers are given flexibility to plant any crop, except fruits and vegetables, on program acres. Under the previous Farm Bill, farmers had to plant wheat to maintain a wheat base. A high-cost or low-profit wheat producer will now have to become more competitive or switch to another crop.

The objective of this paper is to examine the important determinants of wheat enterprise profits. Specifically, several important factors that affect wheat enterprise profitability will be identified; the relative importance of each factor will be determined; and the number of farms that are consistently high- or low-profit will be investigated. The information from this paper can be used to focus cost-cutting efforts and to identify bottlenecks to profitability. Empirical Models Enterprise data are used to categorize each farm into top one-third, middle one-third and bottom one-third cost and profit (return to land and management) categories. The top one-third refers to low-cost or highprofit producers while the bottom one-third refers to high-cost or low-profit producers. Using this information, the number of years that each farm falls into the top and bottom cost categories and the top and bottom profit categories is determined. Standardized partial regression coefficients are used to examine the effect of yield, wheat income and various cost items on wheat profit (return to land and management). The standardized partial regression coefficients are computed using the following equation (Edwards, van der Sluis and Stevermer): (1) SPRCi = (Σ xi2 / Σ yi2)0.5, where SPRCi is the standardized partial regression

Michael R. Langem

Michael W. Hal

Michael R. Langemeier is assistant professor of agricultural economics at Kansas State University. He has a Ph.D. in agricultural economics from Purdue University. His research interests include technical and economic efficiency, and economies of size. Michael W. Haley has an M.S. degree in agricultural economics from Kansas State University. Fredrick D. DeLano is administrator for the Kansas Farm Management Association (KFMA) program at Kansas State University. He has an M.S. degree in agricultural economics from the University of Missouri. His research and extension interests include studies using data from the KFMA records to determine more profitable farming systems, with special emphasis on variability in enterprise costs and profitability.

Fredrick D. DeLa

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coefficient for the ith independent variable, xi is the deviation from the mean of the ith independent variable for each farm and yi is the deviation from the mean of the dependent variable for each farm. The numerator of equation (1) represents the sum of the squares of the deviations from the mean for the ith independent variable. The denominator of equation (1) represents the sum of the squares of the deviations from the mean for the dependent variable. The dependent variable in this case is wheat profit. Independent variables include wheat yield, wheat income, operator labor, hired labor, machinery and equipment, interest, utilities and fuel, seed, fertilizer, herbicide and insecticide, conservation expense and miscellaneous expense.All variables are expressed on a per-acre basis. Each standardized partial regression coefficient represents the fraction that the profit standard deviation would change given a one standard deviation change in the respective independent variable. If the standardized partial regression coefficient is relatively large for a particular independent variable, this variable has a relatively large influence on the dependent variable. Pooled cross-sectional regression analysis (Kmenta) was used to investigate the importance of size, specialization, yield, price, cost efficiency, age of operator, land tenure and economies of scope in explaining differences in wheat profit per acre among farms. Specifically, the following relationship was specified: (2) PROFIT = f(WACRE, SPECA, YRATIO, PRATIO, COSTM, GRATIO, HRATIO, AGE, TENURE, BEEFXW, SWINEXW, DAIRYXW, CORNXW, SOYXW, SORGXW, SUNFXW,YR91,YR92,YR93,YR94,YR95), where PROFIT represents the return to land and management;WACRE represents the number of wheat acres operated; SPECA represents the number of wheat acres divided by crop acres;YRATIO represents an individual farm’s wheat yield divided by the county average wheat yield; PRATIO represents an individual farm’s price received for wheat divided by the average price in the crop reporting district; COSTM represents an individual farm’s cost divided by the average cost for all farms; GRATIO represents the percent of gross farm income derived from government payments; HRATIO represents an individual farm’s hired labor expense divided by total labor expense; AGE represents the age of the operator;TENURE represents the percent land owned; BEEFXW represents the percentage of income from beef multiplied by the percentage of income from wheat multiplied by four; SWINEXW represents the percentage of income from swine multiplied by the percentage of income from wheat multiplied by four; DAIRYXW represents the

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percentage of income from dairy multiplied by the percentage of income from wheat multiplied by four; CORNXW represents the percentage of acres in corn production multiplied by the percentage of acres in wheat production multiplied by four; SOYXW represents the percentage of acres in soybean production multiplied by the percentage of acres in wheat multiplied by four; SORGXW represents the percentage of acres in sorghum multiplied by the percentage of acres in wheat multiplied by four; SUNFXW represents the percentage of acres in sunflowers multiplied by the percentage of acres in wheat multiplied by four; and YR91 through YR95 represent annual dummy variables. All percentage variables were expressed in decimal form. The BEEFXW, SWINEXW, DAIRYXW, CORNXW, SOYXW, SORGXW and SUNFXW variables were multiplied by four to facilitate interpretation of the mean values.An index close to 1 would indicate that the farm produced approximately 50 percent wheat and 50 percent of the other enterprise in question. The number of wheat acres (WACRE) is used to investigate the importance of economies of size. If the average cost curve displays the typical u-shaped pattern, profits will increase until the scale-efficient point is reached and then will decrease. This suggests a nonlinear relationship between profit and enterprise size. Wheat acres is hypothesized to be positively related to profitability, and wheat acres squared is hypothesized to be negatively related to profitability. SPECA is used to measure the level of specialization. Increased industrialization typically leads to an increase in specialization. Firms specialize in an effort to increase economic efficiency. Improvements in economic efficiency lowers costs and improves profits.Thus, the specialization variable is expected to be positively related to profit per acre. Five management variables are included in the model: yield ratio (YRATIO), price ratio (PRATIO), cost ratio (COSTM), hired labor ratio (HRATIO) and the percent of gross farm income derived from government payments (GRATIO). The YRATIO relates to soil quality and an operator’s ability to manage production. The PRATIO variable relates to how well an operator markets his or her crop.The expected sign of the coefficients on the YRATIO and PRATIO variables is positive. Better marketing and production practices will result in an increase in profit.The expected sign of the COSTM variable is negative. Lower costs or improved cost management should increase profit. A negative sign on the coefficient of the hired labor ratio would indicate that operator labor is more efficient or productive than hired labor. A positive sign on this variable would indicate that operator labor is less pro-

1998-99 Journal of the ASFMRA

ductive.The GRATIO variable captures the difference in profits between those producers that participate and those that do not participate in the government program for wheat. The operator age variable captures differences in efficiency among age groups and the importance of experience. Based on a study by Tauer, a nonlinear relationship between operator age and profitability is hypothesized. Specifically, profit is hypothesized to be positively related to operator age and negatively related to operator age squared. Based on previous research by Ellinger and Barry, the expected sign of the land tenure variable (TENURE) is negative. Increases in owned land result in a decrease in profit. The BEEFXW, SWINEXW, DAIRYXW, CORNXW, SOYXW, SORGXW and SUNFXW variables are used to measure scope economies or the decrease in costs associated with producing two or more enterprises together. If economies of scope are present, the coefficients on the livestock and crop variables will be significant and positive.A negative value on these coefficients would indicate that diseconomies of scope exist. Annual dummy variables are included to capture variation in profit among years in the study period.Yearto-year variations in rainfall, length of the growing season and growing degree days would contribute to the variation in year-to-year profits. The relationship between profit and each yearly dummy variable will be relative to the base year of 1990. Significance of these variables will be contingent on differences between the dummy variable year and the base year. Elasticities are computed for each independent variable using the regression coefficients and variable means. These elasticities measure the sensitivity of profit to each independent variable. Data Crop rotation practices in eastern and western Kansas are markedly different due to the amount of rainfall received during the crop season. For example, Haskell County in southwest Kansas received 14.75 inches of precipitation in 1995 while Crawford County in southeast Kansas received 49.05 inches of precipitation the same year (Kansas Agricultural Statistical Service). The difference in precipitation forces wheat farming practices to differ considerably from east to west. The prevalent crop rotations in western Kansas are wheat fallow and wheat-sorghum fallow. In eastern Kansas, wheat is grown continuously, often in various crop rotations. Because cropping Resource File

practices differ in western and eastern Kansas, a separate analysis is conducted for each region. Data was obtained from the Kansas Farm Management Associations. The data set contains six years of continuous financial and production data for 98 wheat enterprises in Kansas: 46 continuously cropped wheat enterprises from eastern Kansas and 52 wheat fallow enterprises from western Kansas. Descriptive statistics for the continuously cropped and wheat fallow enterprises are presented in Tables 1 and 2.The income and cost variables are presented on a per-acre basis. All income and cost variables are converted to real 1995 dollars using the implicit price deflator for personal consumption expenditures (U.S. Department of Commerce). The average return to land and management (profit) for the continuous wheat group is $41.39 per acre with a standard deviation of $48.57.Approximately 17 percent of the 276 observations (46 farms x 6 years) had a profit per acre of less than $0 while another 10 percent had a profit per acre of more than $100. For the wheat fallow group, the average return to land and management (profit) is $63.09 per acre with a standard deviation of $42.62. Approximately 6 percent of the 312 observations (52 farms x 6 years) had a profit per acre of less than $0.Approximately 18 percent of the observations for the wheat fallow group had a profit per acre of more than $100.Thus, profit per acre is highly variable for both groups of producers. The average wheat acreage for the continuous wheat enterprises is 701 acres with a standard deviation of 497 acres. The number of wheat acres per farm ranged from 67 to 2,764. On average, approximately 56 percent of the crop acres are devoted to wheat. Wheat is commonly produced with beef, sorghum and soybeans. The three largest costs are machinery and equipment, operator labor and fertilizer. For the wheat fallow enterprises, the average wheat acreage is 671 acres.The number of wheat acres per farm ranged from 25 to 3,201. On average, approximately 43 percent of the crop acres are devoted to wheat. Crop acres for the wheat fallow group include fallow acres, so the percentage of planted acres in wheat would be substantially higher. Wheat is commonly produced with beef and sorghum. The three largest costs for the wheat fallow enterprises are machinery and equipment, operator labor and interest. Results The number of farms in the top one-third and bottom one-third cost and profit categories are listed in Table 65

3.The year column signifies the number of years that a farm was included in either the high/low profit or cost category.Within the continuous wheat enterprise group, 72 percent of the farms were in the top onethird cost category for at least one of the six years, and 74 percent were in the high-profit category for at least one of the six years.The corresponding percentages for the wheat fallow group were 73 percent and 79 percent. The results from Table 3 indicate that movement within and across categories, be it cost or profit, occurs at a relatively high frequency. However, a number of farms were consistently high profit and/or low cost. For example, 26 percent of the continuous wheat farms and 19 percent of the wheat fallow farms were in the top one-third cost category for

Table 1. Descriptive Statistics for Continuously Cropped Wheat Enterprises Variable

Unit

Mean

Standard Deviation

157.55 21.34 4.71

52.23 17.46 6.08

34.83 12.92 9.68 6.16 20.50

21.69 7.85 4.18 6.13 10.20

3.41 0.10 2.50

4.10 0.78 2.41

41.39

48.57

acres acres percent index index index

1209.80 700.69 56.41 1.08 1.00 1.00

612.99 496.78 20.58 0.25 0.14 0.39

percent percent years percent inches

18.43 20.48 50.52 26.41 33.05

11.59 24.40 10.74 25.95 9.81

Enterprise Interaction Variables Beef/Wheat index Swine/Wheat index Dairy/Wheat index Corn/Wheat index Sorghum/Wheat index Soybean/Wheat index

0.147 0.034 0.013 0.025 0.352 0.115

0.220 0.113 0.079 0.069 0.281 0.221

Income and Cost Variables Gross Income $/acre Operator Labor $/acre Hired Labor $/acre Machinery and Equipment $/acre Interest $/acre Utilities and Fuel $/acre Seed $/acre Fertilizer $/acre Herbicide and Insecticide $/acre Conservation $/acre Miscellaneous $/acre Return to Land and Management $/acre Farm Characteristics Dryland Crop Acres Wheat Acres Specialization Yield Ratio Price Ratio Cost Ratio Income from Govern. Payments Percent Hired Labor Age of Operator Percent Land Owned Rainfall

Source: Kansas Farm Management Associations.

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four or more years. Similarly, 20 percent of the continuous wheat farms and 21 percent of the wheat fallow farms were in the top one-third profit category for four or more years. A number of farms were consistently poor performers. From 15 percent to 26 percent of the farms were in the bottom one-third cost and profit categories for four or more years. The standardized partial regression coefficients for wheat income, yield and costs per acre are presented in Table 4. The larger the coefficient, the more influence a particular variable had on profit per acre. Wheat income had the largest influence on profit per acre for both the continuous wheat and wheat fallow groups. Yield had more influence on profit per acre

Table 2. Descriptive Statistics for Wheat Fallow Enterprises Variable

Unit

Mean

Standard Deviation

166.35 16.97 4.68

42.90 12.08 7.54

34.61 12.90 10.38 6.02 10.62

13.46 9.20 3.80 4.13 7.99

2.65 0.19 4.24

4.14 0.90 3.98

63.09

42.62

acres acres percent index index index

1535.60 671.15 42.80 1.00 1.01 1.00

1078.60 513.47 11.74 0.26 0.14 0.34

percent percent years percent inches

20.35 20.62 49.46 34.85 22.70

11.86 24.55 11.86 30.13 4.18

0.319 0.017 0.025 0.083 0.022

0.304 0.059 0.076 0.135 0.068

Income and Cost Variables Gross Income $/acre Operator Labor $/acre Hired Labor $/acre Machinery and Equipment $/acre Interest $/acre Utilities and Fuel $/acre Seed $/acre Fertilizer $/acre Herbicide and Insecticide $/acre Conservation $/acre Miscellaneous $/acre Return to Land and Management $/acre Farm Characteristics Dryland Crop Acres Wheat Acres Specialization Yield Ratio Price Ratio Cost Ratio Income from Govern. Payments Percent Hired Labor Age of Operator Percent Land Owned Rainfall

Enterprise Interaction Variables Beef/Wheat index Swine/Wheat index Corn/Wheat index Sorghum/Wheat index Sunflower/Wheat index

Source: Kansas Farm Management Associations.

1998-99 Journal of the ASFMRA

Table 5 contains the coefficients and standard errors for the pooled cross-sectional regressions, examining the relationship between profit per acre and farm characteristics. All of the variables except for the dummy variable for 1993 were significant for at least one of the two regressions.The discussion below will be focused on the variables that were significant. Elasticities for each variable are reported in Table 6.

and the quadratic terms were negative, suggesting that profit per acre increases at a decreasing rate in response to changes in wheat enterprise size. This result also suggests a u-shaped average cost curve.The coefficients on the size variables can be used to compute the optimal enterprise size. The optimal wheat enterprise size for the continuous wheat group was 1,188 acres, or approximately 69 percent larger than the average wheat enterprise size. For the wheat fallow group, the optimal enterprise size was 1,421 acres, or approximately 112 percent larger than the average wheat enterprise size. Using the information in Table 6, a one standard deviation increase in enterprise size would increase profit per acre by 21 percent for the continuous wheat group and 12 percent for the wheat fallow group.

Size had a significant impact on profit per acre for both groups of farms.The linear terms were positive,

Specialization was inversely related to profit per acre. Thus, farms that were more specialized (less diversi-

for the wheat fallow group than for the continuous wheat group, indicating that weather was a more important factor in the western part of the state. Operator labor and machinery and equipment had a larger influence on profit per acre than the other costs. Thus, it is relatively important to control these two costs.

Table 3. Number of Farms in Top One-Third and Bottom One-Third Cost and Profit Categoriesa

# of Years

Continuous Wheat Top One-Third Bottom One-Third

Wheat-Fallow Top One-Third Bottom One-Third

Cost Per Acre 0 1 2 3 4 5 6

13 11 4 7 7 3 2

9 13 7 8 4 4 2

14 12 10 6 3 4 3

12 14 8 10 4 1 3

Profit Per Acre 0 1 2 3 4 5 6

12 9 7 10 6 3 0

10 12 8 5 9 1 2

11 16 7 7 6 4 1

9 16 11 5 7 3 1

a Top

one-third refers to low-cost or high-profit producers while bottom one-third refers to high-cost or low-profit producers.

Table 4. Standardized Partial Regression Coefficients for Wheat Income, Yield and Costs Per Acre Variable Yield Wheat Income Operator Labor Hired Labor Machinery and Equipment Interest Utilities and Fuel Seed Fertilizer Herbicide and Insecticide Conservation Miscellaneous

Resource File

Continuous

Wheat

0.2366 0.7959 0.3595 0.1252 0.4466 0.1615 0.0860 0.1263 0.2099 0.0844 0.0160 0.0496

0.3339 1.2444 0.2835 0.1770 0.3157 0.2160 0.0891 0.1056 0.2164 0.2111 0.0615 0.1718

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Table 5. Pooled Cross-Sectional Regression Analysis Examining the Relationship Between Profit-Per-Acre and Farm Characteristics Variable

Continuous Wheat

Intercept

-189.310*** (33.170) Wheat Acres 0.042*** (0.012) Wheat Acres2 -1.762E-03*** (4.924E-04) Specialization -0.193* (0.110) Yield Ratio 89.092*** (7.284) Price Ratio 87.891*** (10.350) Cost Ratio -56.363*** (5.759) Income from Gov. 0.508*** Payments (0.186) Percent Hired Labor 0.149** (0.071) Age of Operator 2.333** (0.944) Age of Operator2 -0.022** (0.009) Percent Land Owned -0.187** (0.074) Beef/Wheat 28.018*** (6.642) Swine/Wheat -1.026 (13.590) Dairy/Wheat 120.400*** (16.350) Corn/Wheat -68.149** (29.950) Sorghum/Wheat 31.969*** (6.385) Soybean/Wheat 17.843* (9.625) Sunflower/Wheat 1991 1992 1993 1994 1995 Buse R2

25.400*** (4.791) 15.168*** (4.914) 2.735 (4.658) 26.790*** (5.632) -2.022 (6.396) 0.760

Wheat Fallow -15.790 (20.820) 0.027*** (0.009) -9.442E-04*** (3.138E-04) -0.452*** (0.151) 79.411*** (6.449) 73.306*** (13.110) -53.679*** (5.794 0.172 (0.245) 0.027 (0.069) -0.304* (0.161)

-0.110* (0.066) 2.176 (5.453) 37.609* (22.390)

-8.503 (20.000) 26.671* (13.840)

78.129*** (24.370) -5.725 (5.347) -28.116*** (5.748) -1.837 (5.449) 0.260 (6.271) 15.861* (8.407) 0.594

Notes: Numbers in parentheses are standard errors. Single, double and triple asterisks (*, ** and ***) denote significance at the 10%, 5% and 1% levels, respectively.

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fied) tended to have a relatively lower profit per acre. This result stresses the importance of diversification. The interaction variables between various livestock and crop enterprises can be used to determine the enterprise combinations contributing to increased wheat profitability. For the continuous wheat group, farmers would benefit from diversifying into beef, dairy, sorghum or soybeans. For this group, diversifying into corn actually reduced wheat profit per acre. Farmers in the wheat fallow group would benefit from diversifying into swine, sorghum or sunflower production. The yield ratio, price ratio and cost ratio were significantly related to profit per acre for both groups of farms. Using the information in Table 6, a one standard deviation increase in the yield ratio would increase wheat profit per acre by 54 percent for the continuous wheat group and 33 percent for the wheat fallow group.Wheat profits were also sensitive to changes in the price ratio and cost ratio.A one standard deviation increase in the price ratio would increase profit per acre by 30 percent for the continuous wheat group and 18 percent for the wheat fallow group.A standard deviation increase of one in the cost ratio would reduce profit per acre by 53 percent for the continuous wheat group and by 29 percent for the wheat fallow group. Given the wide variability in these three ratios among the wheat farms in both regions of the state, there is a lot of room for improvement. Age was positively related to profit per acre for the continuous wheat group and negatively related to profit per acre for the wheat fallow group.The quadratic term for age was not significant for the wheat fallow group, so it was dropped from the model reported in Table 5. Using the coefficients on age of operator for the continuous wheat group, the optimal age was 53 years. This was higher than the optimal age found by Tauer. Using the information in Table 6, a standard deviation increase of one in operator age would increase profit by 3 percent for the continuous wheat group and would decrease profit by 6 percent for the wheat fallow group. The percentage of gross farm income from government payments and the percentage of hired labor variables were significant and positively related to profit per acre for the continuous wheat group but insignificantly related to profit per acre for the wheat fallow group. Thus, for the continuous wheat group, farms—which received a relatively higher percent of their gross farm income from government payments or which hired relatively more labor—had a higher profit per acre. As expected, percentage of land owned was negatively related to profit per acre. Farms that leased a 1998-99 Journal of the ASFMRA

higher percent of their land were more profitable. The yearly dummy variables indicate that profit varied among the years. For the continuous wheat group, 1991, 1992 and 1994 were more profitable than 1990. For the wheat fallow group, 1995 was more profitable than 1990 while 1992 was less profitable than 1990. Conclusions and Implications This study examined winter wheat enterprise profitability in Kansas. Data from 46 continuous wheat enterprises from eastern Kansas and 52 wheat fallow enterprises in western Kansas from 1990 through 1995 were used in the analysis. Several of the farms were consistently high performers in terms of low cost or high profit during the study period. From 19 percent to 26 percent of the farms, depending on the category and region of the state, were in the top one-third cost and profit categories for four or more of the six years. Some farms were also consistently poor performers. From 15 percent to 26 percent of the farms were in the bottom one-third cost and profit categories for four or more of the six years.

References DeLano, F. and L.N. Langemeier. 1996, “Dryland Wheat CostReturn Analysis.” Kansas Cooperative Extension Farm and Financial Management Newsletter, No. 5. Edwards, W.M., G.T. van der Sluis, and E.J. Stevermer. 1989. “Determinants of Profitability in Farrow-to-Finish Swine Production.” North Central Journal of Agricultural Economics 11(January):17–25. Ellinger, P. and P. Barry. 1989. “The Effects of Tenure Position on Farm Profitability and Solvency.” Agricultural Finance Review 47:106–118. Fox, G., P.A. Bergen, and E. Dickson. 1993. “Why Are Some Farms More Successful Than Others? A Review,” in Size, Structure, and the Changing Face of American Agriculture, A. Hallam, ed. Boulder, CO: Westview Press. Kansas Agricultural Statistical Service. 1996. Kansas Farm Facts. Kmenta, J. 1971. Elements of Econometrics. New York, NY: Macmillan Publishing. Tauer, L. 1995. “Age and Farmer Productivity.” Review of Agricultural Economics 17(January):63–69. U.S. Department of Commerce. Various issues, 1990-1996. Survey of Current Business.

Wheat income, machinery and equipment, and operator labor had a large influence on profit per acre.This result emphasizes the importance of marketing and production practices, controlling machinery and equipment, and operator labor costs. Several farm characteristics had an important impact on profit per acre. Profit per acre was positively related to the number of wheat acres, yield and price, and was negatively related to specialization, cost of production and the percent of land owned. There are two important implications of this study. First, optimal wheat enterprise size was significantly higher than the average enterprise size for both the continuous wheat group and the wheat fallow group. Thus, significant economic gains are possible if the size of the wheat enterprise is increased. Second, diversified operations were relatively more profitable than specialized operations were. For the continuous wheat group, benefits from diversifying into beef, sorghum or soybeans are evident. For the wheat fallow group, benefits accrue to those that diversify into swine, sorghum or sunflowers. The two implications discussed above suggest that there are strong incentives for farms to increase in size and scope of operation.

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Table 6. Profit-Per-Acre Elasticities Variable

Wheat Acres Specialization Yield Ratio Price Ratio Cost Ratio Percent of Income from Government Payments Percent Hired Labor Age of Operator Percent Acres Owned Beef/Wheat Swine/Wheat Dairy/Wheat Corn/Wheat Sorghum/Wheat Soybean/Wheat Sunflower/Wheat

Continuous Wheat

Wheat Fallow

0.291 -0.263 2.328 2.128 -1.362

0.151 -0.307 1.253 1.179 -0.851

0.226 0.073 2.794 -0.119 0.099 -0.001 0.037 -0.042 0.272 0.050

0.055 0.009 -0.238 -0.061 0.011 0.010 -0.003 0.035 0.028

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