Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-19T06:26:58.314Z Has data issue: false hasContentIssue false
  • English
  • Français

Microeconomic Effects Of Reduced Yield Variability Cultivars Of Soybeans And Wheat

Published online by Cambridge University Press:  09 September 2016

Carl R. Dillon
Carl R. Dillon*
Affiliation:
Department of Agricultural Economics and Rural Sociology, University of Arkansas, Fayetteville
Get access Rights & Permissions [Opens in a new window]

Abstract

Economic analysis was conducted on hypothetical agronomic research on new crop cultivars for Arkansas dryland soybean and wheat producers. In relation to farmers' attitudes toward risk, the microeconomic effects and level of adoption of yield variability reducing cultivars were analyzed utilizing a production management decision-making model formulated with mathematical programming techniques. The study indicated that negative covariance between crops continues to be an effective means of reducing production risk associated with yield variability. However, under varying circumstances, agronomic research on the breeding of new soybean and wheat cultivars with reduced yield variability is worthwhile if there is only slight concurrent reduction in expected yields.

Keywords

farm managementrisk analysisyield variabilitymathematical programming
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 24 , Issue 1 , July 1992 , pp. 121 - 133
Copyright
Copyright © Southern Agricultural Economics Association 1992

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

Anderson, J.R., Dillon, J.L. and Hardaker, J.B.. Agricultural Decision Analysis. Ames, Iowa: Iowa State University Press, 1977.Google Scholar
Arkansas Agricultural Statistics Service. Arkansas Agricultural Statistics 1989. Arkansas Agricultural Experiment Station, Report Series 316, University of Arkansas, July 1990.Google Scholar
Boisvert, R.N. and McCarl, B.A.. Agricultural Risk Modeling using Mathematical Programming. Southern Cooperative Series Bulletin 356. Cornell University Agr. Exp. Sta., July 1990.Google Scholar
Brill, W.J. “Genetic Engineering Applied to Agriculture: Opportunities and Concerns.” Am. J. Agr. Econ., 68(1986):1081–1087.CrossRefGoogle Scholar
Brink, L. and McCarl, B.A.. “The Trade Off Between Expected Return and Risk Among Cornbelt Crop Farmers.” Am. J. Agr. Econ., 60(1978):259–263.CrossRefGoogle Scholar
Cooperative Extension Service. Estimating 1990 Production Costs in Arkansas. Ext. Tech. Bull. Nos. 72 and 76, University of Arkansas, 1989.Google Scholar
Dillon, C.R., Mjelde, J.W. and McCarl, B.A.. “Biophysical Simulation in Support of Crop Production Decisions: A Case Study in the Blacklands Region of Texas.” So. J. Agr. Econ,. 21(1989):73–86.Google Scholar
Dillon, J.L. “Applications of Game Theory in Agricultural Economics: Review and Requiem.” Australian J. Agr. Econ., 6(1963):20–35.Google Scholar
Hess, C.E. “The Emerging Agricultural Research Agenda.” Am. J. Agr. Econ., 71(1989):1112–1116.CrossRefGoogle Scholar
Holloway, G.J. “Distribution of Research Gains in Multi-Stage Production Systems: Further Results.” Am. J. Agr. Econ., 71(1989):338–343.CrossRefGoogle Scholar
Johnson, S.R. “A Re-Examination of the Farm Diversification Problem.” J. Farm Econ., 49(1967):610–621.CrossRefGoogle Scholar
Kennedy, J.O.S. “Applications of Dynamic Programming to Agriculture, Forestry and Fisheries: Review and Prognosis.” Rev. Marketing and Agr. Econ,. 49(1981):141–173.Google Scholar
Lin, W., Dean, G.W., and Moore, C.V.. “An Empirical Test of Utility vs. Profit Maximization in Agricultural Production.” Am. J. Agr. Econ., 56(1974):497–508.CrossRefGoogle Scholar
Martinez, S., and Norton, G.W.. “Evaluating Privately Funded Public Research: an Example from Poultry and Eggs.” So. J. Agr. Econ., 18(1986): 129–140.Google Scholar
McCarl, B.A. “Model Validation: An Overview with Some Emphasis on Risk Models.” Rev. Marketing and Agr. Econ., 52(1984): 153–174.Google Scholar
McCarl, B.A. and Bessler, D.. “Estimating an Upper Bound on the Pratt Risk Aversion Coefficient When the Utility Function is Unknown.” Australian J. Agr. Econ., 33(1989):56–63.Google Scholar
Meyer, J. “Two-Moment Decision Models and Expected Utility Maximization.” Am. Econ. Rev., 77(1987):421–430.Google Scholar
Norton, G.W. and Davis, J.S.. “Evaluating Returns to Agricultural Research: A Review”. Am. J. Agr. Econ., 63(1981):685–699.CrossRefGoogle Scholar
Pardey, P.G. and Craig, B.. “Causal Relationships Between Public Sector Agricultural Research Expenditures and Output.” Am. J. Agr. Econ., 71(1989):9–19.CrossRefGoogle Scholar
Pope, A. and Shumway, R.E.. “Management of Intensive Forage Beef Production Under Yield Uncertainty.” So J. Agr. Econ., 16(1984):37–44 Google Scholar
Rasmussen, W.D. “Public Experimentation in Innovation: An Effective Past but Uncertain Future.” Am. J. Agr. Econ., 69(1987):890–899.CrossRefGoogle Scholar
Ritchie, J.T. and Otter, S.. “Description and Performance of CERES—Wheat: A User Oriented Wheat Yield Model,” pp 159–175. ARS Wheat Yield Project, Willis, W.O., ed. Washington, D.C.:USDA ARS-38, June 1985.Google Scholar
Teague, P. W. and Lee, J.G.. “Risk Efficient Perennial Crop Selection: A Motad Approach to Citrus Production.” So. J. Agr. Econ., 20(1988): 145–152..Google Scholar
Trice, K.L. “The Economics of Double Cropping Wheat and Soybeans: A Simulation Analysis Using Wheatsoy.” Master's thesis, University of Arkansas, 1986.Google Scholar