Farm Characteristics and Business Risk in Production Agriculture

Agricultural & Applied Economics Association

Farm Characteristics and Business Risk in Production AgricultureAuthor(s): Bryan Schurle and Mike TholstrupSource: North Central Journal of Agricultural Economics, Vol. 11, No. 2 (Jul., 1989), pp. 183-188Published by: Oxford University Press on behalf of Agricultural & Applied Economics AssociationStable URL: http://www.jstor.org/stable/1349106 .

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FARM CHARACTERISTICS AND BUSINESS RISK IN PRODUCTION AGRICULTURE

Bryan Schurle and Mike Tholstrup

This article uses historical farm level data to investigate relationships between business risk and farm characteristics. Farm characteristics examined are the enterprise mix, farm size, location of the farm, age of the operator, financial obligation of the farm, machinery investment, government program payments and a measure of returns to the operation. Many of these variables are significantly related to business risk, which suggests several possibili- ties for future research.

The risk associated with Great Plains agricultural production has been widely recognized for many years. The low average and highly variable rainfall has contributed to this perception. Barber and Thair even argued that yield uncertainty on Great Plains farms was such a dominant force that agricultural policy for the area deserved separate consideration from policies for other parts of the country. These perceptions lead to a substantial research effort beginning in the late 1940s. Many of these studies examined yield variability or used budgets to compare incomes from different enterprise combinations, spatial diversification or yields (Castle, Bailey, Schickele).

These early efforts have given way to modeling efforts to explore the risk associated with different farm organizations. In particular, quadratic programming, MOTAD and Target MOTAD have been used to study risk- return trade-offs for different farm organizations (Mapp and Helmers; Musser, Mapp and Barry; Watts; Held and Helmers). The data requirements of risk models are substantial. Measures of yield and price variability are combined to generate measures of income variability for the total farm. The data sources are frequently criticized, particularly if they are aggregated data. Aggregated data under-

states farm level variability (Eisgruber and Schuman).

The uniqueness of this study is that it uses farm records to examine actual income variability over time for farms. Little research has been done using this approach, largely because of the requirements for data collected over several years. This study concentrates on business risk, which refers to variations in net income from yield, price and cost variability (Lee et al.). The study looks at the enterprise mix on the farm, as well as other characteristics that probably have an impact on income variability. Farm characteristics examined are farm size, location, age of the operator, financial obligation of the farm, machinery investment, government program payments and a measure of return to the operation.

A model was developed to estimate the relationships between enterprise mix and variance of income. Net income for a farm can be calculated as

Net = 2SiTNi, where i

Si = share of assets in enterprise i T = total assets

Ni = net income per dollar of assets in enterprise i.

Assuming Si and T are constant,

V(Net) = 2 ST2V(Ni) + 2; SiSjT2C(Ni, Nj) i ij for i4 j,

where V is variance and C is covariance.

Dividing each component by T2 gives

V(Net) = 2 Sj V(Ni) + 2; SiSjC(Ni, Nj) T2 i ij

for i j,

Thus, the ratio of variance of net farm income to assets squared depends on the shares

Professor and Graduate Student, Department of Ag- ricultural Economics, Kansas State University.

The authors acknowledge the helpful suggestions of Jeff Williams, Allen Featherstone, Larry Langemeier and three anonymous reviewers. Thanks also to Ron Fleming for helping with the data analysis.



184 NORTH CENTRAL JOURNAL OF AGRICULTURAL ECONOMICS, Vol. 11, No. 2, July 1989

of the farm in enterprise i (squared) times the variance of the net return per dollar of asset in each enterprise (SV(Ni)) and the shares of the enterprises i and j multiplied by the covariances of the net returns between enterprises i and j (SiSjC(Ni, Nj)). The covariance terms measure the impacts of diversification, which has long been recognized as a risk-reducing strategy (Heady).

In addition to the basic model, several other variables believed to have an impact on income variability were used in the analysis. These variables included a measure of size of the farm, government payments as a proportion of gross farm income, interest payments as a proportion of gross farm income, age of the primary operator, machinery investment per crop acre as a proxy for timeliness of operation, net farm income as a proportion of capital managed as a measure of return to the operation and location of the farm.

Empirical Evidence

Data from the Kansas Farm Management associations for 686 farms from 1973 to 1985 were used to estimate these relationships. All financial variables were deflated using an im- plicit price deflator for gross national product. This was done to adjust all financial variables to constant dollars so that variability measures would reflect variability in constant dollars. Gross farm income was calculated on an ac- crual basis as total sales plus government payments plus miscellaneous income and inventory changes. Expenses were calculated as cash

operating expenses plus depreciation minus interest expense. Depreciation from the records reflects depreciation used for tax pur- poses. Interest was not included in expenses to remove the impact of financial leverage on net incomes, as business risk was the main emphasis of this study. Net farm income was calculated as gross farm income minus expenses as defined above.

Because each farm had 13 years of data, means and variances were calculated for the relevant variables. Table 1 shows the averages for all farms of several key variables to provide a perspective on the characteristics of the farms. Standard deviation of net farm income was calculated as a measure of the income variability over the period for the farm. The average standard deviation of net farm income shows the great variability in income that farms experienced in that time period.

A model similar to that developed earlier in the paper was then estimated. Each farm was represented in the data by one observa- tion. T in the model was measured by capital managed in the operation. The dependent variable for the analysis was the ratio of the variance of net farm income to capital managed squared. This ratio represents variability of net farm income adjusted for size of the operation.

Five enterprise variables were used to represent beef, swine, wheat, feed grains and soybeans. The enterprise shares (Si) were estimated to represent the share of total capital devoted to each of five enterprises. The SiSj,

Table 1. Descriptive Statistics (in 1972 dollars) for 686 Farms Each Having Data from 1973 Through 1985

Variable Mean Min. Max.

Gross Farm Income $82,724 8,477 695,625 Expense 60,136 8,215 614,855 Net Farm Income 22,588 -4,157 106,990 Standard Deviation of Net Farm Income 21,969 2,631 137,545 Capital Managed 503,931 96,125 2,505,129 Acres Operated 1,449 160 7,542 Acres Owned 614 0 5,672 Crop Acres 885 13 7,300 Machinery Investment/Acre 32 4 139



FARM CHARACTERISTICS AND BUSINESS RISK IN PRODUCTION AGRICULTURE Schurle, Tholstrup 185

where i 4j, were the products of the shares of the enterprises. For the livestock enterprises, data on sales and turnover ratios from budgets were used to estimate assets in the enterprise. For the crop enterprises, data on production and average yearly prices were used to estimate sales. The turnover ratios from budgets then were used to estimate assets in the enterprise.

Several other variables representing characteristics of the farm or farm operator were used. These variables were selected because they represent characteristics that were believed to have an impact on income variability. The reciprocal of capital managed was included in the analysis to test the hypothesis that relative variability of income decreases as size increases. The age of the primary operator was used as the variable to represent the experience of the operator. Interest payments as a proportion of gross was used as the variable representing the relationship between financial obligation and the relative variability of net farm income. Government payments received by the farm divided by gross were used to show the relationship of government payments to relative variability of net farm income. (Both the financial obligation variable and the government payment variable were divided by gross farm income to remove size impacts.) Crop machinery investment per acre was used to represent timeliness of operations, on the assumption that the more investment in machinery and equipment, the greater the likelihood of timely cropping operations. The ratio of net farm income to capital managed was used as a measure of return to the operation, as one would expect higher return strategies to be associated with greater variability of income. Dummy variables representing the Kansas Farm Manage- ment associations were used to represent location of farm. The relative variability of net farm income was expected to be smaller for farms in the eastern part of the state than for farms in the western part.

Results of estimating the model are in Table 2. The model had an adjusted R2 of .46, which is reasonable given the wide range of income variability one would expect from farm data.

The reciprocal of capital managed was significant at the .0001 level, with a coefficient of 239.9. This result suggests that relative variability of net income decreases as size increases.

Government payments as a proportion of gross was significant at the .0003 level, with a coefficient of -0.014. The results indicate that as government payments increase as a proportion of gross, the relative variability of net farm income decreases. This implies that government payments have a stabilizing impact on business risk in production agriculture.

The financial obligation variable (interest payments as a proportion of gross) was significant at the .07 level, with a coefficient of 0.0018. This suggests that as the financial obligation of the farm increases, the relative variability of net farm income increases. In some instances, operators may not have total control over their marketing practices and are required to sell crops at harvest time rather than spread sales throughout the year. This could result in greater variability of income.

The age of the primary operator was significant at the .0001 level, with a coefficient of 0.000032. The results suggest that as the operator's age increases, the relative variability of net farm income also increases. Several explanations for the positive relationship can be posited. It is possible that the operator's experience was overshadowed by inability or unwillingness to extend their labor efforts. Second, the older operator may be less flexible in adjusting to unusual circumstances. Third, older operators may not keep pace with technological advances. Finally, as the operator gets older, his wealth position may increase, so he may not be as risk averse. Thus, he may not be so willing to sacrifice to reduce income variability.

The measure of return (ratio of net farm income to capital managed) was significant at the .0001 level, with a coefficient of 0.016. The result indicates that as the return to the operation increases, the relative variability of net farm income also increases. This confirms the usually assumed trade-off between return and risk. Higher return strategies generally




Table 2. Regression Coefficients and T Values with the Ratio of the Variance of Net Farm Income to Capital Managed Squared as the Dependent Variable and Farm Characteristics as the Independent Variables

Equation Independent Variable Coefficient T Value

Reciprocal of Capital Managed 239.9** 4.44

Government Payments/Gross Farm Income -0.014** -3.68

Interest Payments/Gross Farm Income 0.0018* 1.79

Age of Operator 0.000032** 3.99

Machinery Investment per acre 0.0000032 0.67

Net Farm Income/Capital Managed 0.016** 4.57

Enterprise Shares Squared Beef 0.0068** 10.02 Swine 0.0074** 5.24 Wheat 0.0020** 3.34 Feed Grains 0.00074 0.43 Soybeans -0.00043 -0.08

Enterprise Pairs Beef-Swine -0.0027 -0.41 Beef-Wheat -0.011** -4.39 Beef-Feed Grains 0.014** 4.36 Beef-Soybeans 0.0074 0.66 Swine-Wheat -0.0093* -1.76 Swine-Feed Grains 0.0023 0.46 Swine-Soybeans 0.011 0.78 Feed Grains-Wheat -0.0017 -0.70 Feed Grains-Soybeans 0.0031 0.52 Wheat-Soybeans -0.0011 -0.17

Locations North Central 0.0010** 3.70 South Central 0.00032 1.255 South West 0.0016** 5.79 North East 0.000097 0.48 North West 0.00072** 2.19

Intercept -0.0013** -2.42

Adjusted R2 0.46 *The variable is significant at the .10 level.

**The variable is significant at the .01 level.



FARM CHARACTERISTICS AND BUSINESS RISK IN PRODUCTION AGRICULTURE Schurle, Tholstrup 187

result in higher variability of income. This could be evident particularly in marketing, where attempting to sell at the time of peak price might result in higher income but also greater variability from year to year than would be obtained by a strategy of marketing throughout the year.

The coefficients on the enterprise variables can be interpreted as the variance of net returns for the enterprise (i.e., V(Ni)). Several of these coefficients were significant. The greater the coefficient, the greater the variability associated with the net return for the enterprise.

The SiSj's coefficients can be interpreted as the covariance of net returns between enterprises i and j (i.e., C(Ni, Nj). Three of these coefficients were significant. Negative coefficients suggest the greatest potential for diversification to reduce variability of net income. In this case, beef and wheat, which is a com- mon enterprise combination, would be a good diversification strategy to reduce variability of income.

Three of the dummy variables representing location of the farm were significant. The coefficients of the location dummy variables were interpreted as the deviation from the Southeast Association. The two Western and the North Central associations had significant coefficients. The results indicated that West- ern and North Central Association farms have greater income variability. This relationship is most likely explained by weather because rainfall is substantially lower in the Western As- sociation areas.

Conclusions

This study examines income variability for farms over a 13-year period. Income variability for the farms appears to be substantial as measured by an average standard deviation of net farm income of $21,969, compared to an average net farm income only slightly greater than that ($22,588).

This work also suggests that several farm characteristics may be related to variability of net income. Of most interest may be the

indication that the relative variability of net income decreases as size of operations increases. This relationship needs to be studied extensively, given its potential implications, such as another advantage for large farms that has not been recognized previously.

Study results suggest that the financial obligation of a farm is positively related to the relative variability of net farm income. This suggests the possibility of a positive relationship between financial risk and business risk. If an increase in financial risk tends to increase business risk, financial risk should be an additional major focal point of future research.

The results show that farms with a greater proportion of their gross income from government payments have a lower relative variability of net income. This suggests that government programs during this period were a positive force in reducing income variability.

As the age of the primary operator increases, the relative variability of net farm income increases. This relationship may be due to the risk preference of operators, who may be wealthier as they grow older, or it may be due to the inability or unwillingness of operators to extend their labor efforts or keep up with technological advances.

Results indicate that there was a positive relationship between return and relative variability of net income. This implies that operations that have higher returns have greater relative variability of income and confirms the often-assumed positive relationship between return and business risk.

Location of the farm was related to relative variability of income. The results sug- gested that the relative variability of income is greater for farms in the western and north central part of the state. Rainfall patterns are probably responsible for this relationship as average rainfall is substantially lower in the western part of the state.

Three of the five enterprise variable coefficients were significant. The coefficients can be interpreted as the variance of net returns for the enterprise. The enterprise pair coef-




ficients can be interpreted as the covariance of returns between two enterprises. These coefficients show the enterprise pairs that provide the greatest opportunity of reducing income variability through diversification. Enterprise pairs with negative covariances reduce income variability more than enterprise pairs with positive covariances. Beef-wheat and swine- wheat were the only pairs with significant covariances. Beef-feed grains had a significant positive coefficient. None of the other coefficients were significant.

Additional research needs to be directed to areas examining the risk associated with production agriculture. In particular, the relationship between financial and business risk and their relationship to size should be studied.

These data provide an indication of the net income variability that farms have experienced in the 1973-1985 period. This was an interesting period including both high prices and costs and lower prices and costs. There also was some growth and contraction of some farms during this period. The impacts of these changes in size on variability of income have not been accounted for by the analysis. In addition, the farms studied may not be a representative sample of Great Plains farms. Even with these limitations, the research suggests interesting relationships that need fur- ther study.

References

Bailey, Warren R. "Organizing and Operating Dryland Farms in the Great Plains." Or- ganizing and Operating Dryland Farms in the Great Plains, Summary of Region 1 Research Project GP-2, ERS-301, 1967.

Barber, Lloyd E. and Philip Thair. "Institu- tional Methods of Meeting Weather Un-

certainty in the Great Plains." Journal of Farm Economics 62(1950): 391-410.

Castle, Emery N. "Adapting Western Kansas Farms to Uncertain Prices and Yields." Technical Bulletin 75, Agricultural Exper- iment Station, Kansas State University, February 1954.

Eisgruber, L.M. and L.S. Schuman. "The Use- fulness of Aggregated Data in the Analysis of Farm Income Variability and Resource Allocation." Journal of Farm Economics 45(1963): 587-591.

Heady, Earl O. "Diversification in Resource Allocation and Minimization of Income Var- iability." Journal of Farm Economics 34(1952): 482-96.

Lee, Warren F., Michael D. Boehlje, Aaron G. Nelson and William G. Murray. Agri- cultural Finance, Seventh Edition. Iowa State University Press, Ames, Iowa, 1980.

Mapp, Harry P. Jr. and Glenn A. Helmers. "Methods of Risk Analysis for Farm Firms." Risk Management in Agriculture, ed. Peter J. Barry, pp. 129-147. Ames, IA: The Iowa State University, 1984.

Musser, Wesley N., Harry P. Mapp, Jr. and Peter J. Barry. "Applications I: Risk Pro- gramming." Risk Management in Agricul- ture, ed. Peter J. Barry, pp. 129-147. Ames, IA: The Iowa State University Press, 1984.

Schickele, Rainer. "Farm Business Survival Under Extreme Weather Risks." Journal of Farm Economics 61(1949): 931-43.

Watts, M.J., L.J. Held and G.A. Helmers. "A Comparison of Target MOTAD to MO- TAD." Canadian Journal of Agricultural Economics 32(1984): 175-85.



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Farm Characteristics and Business Risk in Production Agriculture