Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-29T00:51:48.005Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of Alternative Risk Specifications in Farm Programming Models

Published online by Cambridge University Press:  15 September 2016

Stephen A. Ford ,
Beth Pride Ford  and
Thomas H. Spreen
Stephen A. Ford
Affiliation:
Department of Agricultural Economics and Rural Sociology, The Pennsylvania State University
Beth Pride Ford
Affiliation:
Department of Agricultural Economics and Rural Sociology, The Pennsylvania State University
Thomas H. Spreen
Affiliation:
Food and Resource Economics Department, University of Florida
Get access

Abstract

The use of alternative probability density functions to specify risk in farm programming models is explored and compared to a traditional specification using historical data. A method is described that compares risk efficient crop mixes using stochastic dominance techniques to examine impacts of different risk specifications on farm plans. Results indicate that a traditional method using historical farm data is as efficient for risk averse producers as two other methods of incorporating risk in farm programming models when evaluated using second degree stochastic dominance. Stochastic dominance with respect to a function further discriminates among the distributions, indicating that a density function based on the historic forecasting accuracy of the futures market results in a more risk-efficient crop mix for highly risk averse producers. Results also illustrate the need to validate alternative risk specifications perceived as improvements to traditional methods.

Type
Articles
Information
Agricultural and Resource Economics Review , Volume 24 , Issue 1 , April 1995 , pp. 25 - 35
Copyright
Copyright © 1995 Northeastern Agricultural and Resource Economics Association 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, R.M., Menkhaus, D.J., and Woolery, B.A. “Alternative Parameter Specification in E,V Analysis: Implications for Farm Level Decision Making.” Western Journal of Agricultural Economics 5 (1980): 13–20.Google Scholar
Anderson, J.R., Dillon, J.L., and Hardaker, J.B. Agricultural Decision Analysis. Ames: Iowa State Univ. Press. 1977.Google Scholar
Black, F. “The Pricing of Commodity Contracts.” Journal of Financial Economics 3 (1976): 167–79.Google Scholar
Boggess, W.G., and Ritchie, J.T. “Economic and Risk Analysis of Irrigation Decisions in Humid Regions.” Journal of Production Agriculture 1 (1988): 116–22.CrossRefGoogle Scholar
Boisvert, R.N., and McCarl, B. “Agricultural Risk Modeling Using Mathematical Programming.” Southern Cooperative Series, Bulletin No. 356, 1990.Google Scholar
Bosch, D., and Johnson, C. “An Evaluation of Risk Management Strategies for Dairy Farms.” Southern Journal of Agricultural Economics 24 (1992): 173–82.Google Scholar
Buccola, S. “Minimizing Mean Absolute Deviations to Exactly Solve Expected Utility Problems: Comment.” American Journal of Agricultural Economics 64 (1982): 789–91.Google Scholar
Ford, S., and Hewitt, T. “1989 Feed Grains Program.” Farm Management Newsletter. Food and Resource Economics Department, University of Florida, 1989.Google Scholar
Gardner, B.L. “Commodity Options for Agriculture.” American Journal of Agricultural Economics 59 (1977): 986–92.Google Scholar
Hazell, P.B.R. “A Linear Alternative to Quadratic and Semivariance Programming for Farm Planning under Uncertainty.” American Journal of Agricultural Economics 53 (1971): 384–90.Google Scholar
Hewitt, T. “1989 Crop Budgets for North Florida.” Extension Mimeograph. Food and Resource Economics Department, University of Florida, 1989.Google Scholar
Johnson, D., and Boehlje, M. “Minimizing Mean Absolute Deviations to Exactly Solve Expected Utility Problems.” American Journal of Agricultural Economics 63 (1981): 728–9.Google Scholar
Johnson, D., and Boehlje, M. “Minimizing Mean Absolute Deviations to Exactly Solve Expected Utility Problems: Reply.” American Journal of Agricultural Economics 64 (1982): 791–3.Google Scholar
King, R. “Operational Techniques for Applied Decision Analysis under Uncertainty.” Ph.D. dissertation, Michigan State University, 1979.Google Scholar
King, R.P., Benson, F.J., Black, J.R., and Held, P.A. “Agricultural Risk Management Simulator.” Minnesota Extension Service AG-CS-257, University of Minnesota. 1989.Google Scholar
King, R.P., and Fackler, P.L. “Probabilistic Price Forecasts Based on Commodity Option Values.” Department of Agricultural Economics P85-28, University of Minnesota. 1985.Google Scholar
King, R.P., and Robison, L.J. “An Interval Approach to Measuring Decision Maker Preferences.” American Journal of Agricultural Economics 63 (1981): 510–20.CrossRefGoogle Scholar
King, R.P., and Robison, L.J. “Risk Efficiency Models.” Risk Management in Agriculture. Barry, P.J., ed., pp. 68–81. Ames: Iowa State University Press, 1984.Google Scholar
Lindgren, B.W. Statistical Theory. New York: MacMillan Publishing Co., Inc., 1976.Google Scholar
Lins, D., Gabriel, S., and Sonka, S. “An Analysis of the Risk Aversion of Farm Operators: An Asset Portfolio Approach.” Western Journal of Agricultural Economics 6 (1981): 15–30.Google Scholar
Mapp, H.P., and Helmers, G.A. “Methods of Risk Analysis for Farm Firms.” Risk Management in Agriculture. Barry, P.J., ed., pp. 116–128. Ames: Iowa State University Press, 1984.Google Scholar
McCarl, B.A., and Apland, J. “Validation of Linear Programming Models.” Southern Journal of Agricultural Economics 18 (1986): 155–64.Google Scholar
McCarl, B., Knight, T., Wilson, J., and Hastie, J. “Stochastic Dominance over Potential Portfolios: Caution Regarding Covariance.” American Journal of Agricultural Economics 69 (1987): 804–12.Google Scholar
Moss, C.B., Ford, S.A., and Castejon, M. “Effect of Debt Position on the Choice of Marketing Strategies for Florida Orange Growers: A Risk Efficiency Approach.” Southern Journal of Agricultural Economics 23 (1991): 103–12.Google Scholar
Musser, W.N., Mapp, H.P. Jr., and Barry, P.J. “Applications 1: Risk Programming.” Risk Management in Agriculture. Barry, P.J., ed., pp. 129–147. Ames: Iowa State University Press, 1984.Google Scholar
Meyer, J. “Second-Degree Stochastic Dominance with Respect to a Function.” International Economic Review 13 (1977): 477–87.Google Scholar
Meyer, J. “Two Moment Decision Models and Expected Utility Maximization.” American Economic Review 77 (1987): 421–30.Google Scholar
Meyer, J., and Rasche, R. “Sufficient Conditions for Expected Utility to Imply Mean-Standard Deviation Rankings: Empirical Evidence Concerning the Location and Scale Condition.” Economic Journal 102(January 1992): 91–106.Google Scholar
Robison, L. “Expanding the Set of Expected Utility and Mean Standard Deviation Consistent Models” Risk Modeling in Agriculture: Retrospective and Prospective. Program proceedings for the annual meeting of the Technical Committee of S-232. Iowa State University, August 1994.Google Scholar
Robison, L., and Lev, L. “Distinguishing Between Indirect and Direct Outcome Variables to Predict Choices Under Risk or Why Woody Chip Went to the Air.” North Central Journal of Agricultural Economics 8(January 1986): 59–67.CrossRefGoogle Scholar
Schurle, B., and Erven, B. “Sensitivity of Efficient Frontiers Developed for Farm Enterprise Choice Decisions.” American Journal of Agricultural Economics 61(August 1979): 506–11.Google Scholar
Thomson, K., and Hazell, P. “Reliability of Using the Mean Absolute Deviation to Derive Efficient E-V Farm Plans.” American Journal of Agricultural Economics 54(August 1972): 503–6.Google Scholar
Young, D.L. “Evaluating Procedures for Computing Objective Risk from Historical Time Series.” In Risk Analysis in Agriculture: Research and Educational Developments. Department of Agricultural Economics AE-4492, University of Illinois. 1980.Google Scholar
Young, D.L. “Risk Concepts and Measures for Decision Analysis.” Risk Management in Agriculture. Barry, P.J., ed., pp. 31–42. Ames: Iowa State University Press. 1984.Google Scholar