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The Effect of Spatial Variability of Irrigation Applications on Risk-Efficient Strategies

Published online by Cambridge University Press:  28 April 2015

Daniel J. Bernardo
Daniel J. Bernardo*
Affiliation:
Department of Agricultural Economics, Oklahoma State University
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Abstract

The effect of irrigation system uniformity on the selection of risk-efficient irrigation strategies is evaluated using crop simulation and stochastic dominance procedures. Alternative strategies are evaluated under assumptions of both uniform and non-uniform application. Results indicate that the variability of net returns resulting from the employment of a specified schedule increases when irrigation uniformity is explicitly represented. Solutions derived using economic efficiency and stochastic dominance criteria indicate that the uniformity with which irrigations are applied contributes to the application of water-intensive irrigation schedules.

Keywords

irrigation uniformitycrop simulationgrain sorghumirrigation strategiesstochastic dominance

Information

Type
Submitted Articles
Information
Journal of Agricultural and Applied Economics , Volume 20 , Issue 1 , July 1988 , pp. 77 - 86
Copyright
Copyright © Southern Agricultural Economics Association 1988

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References

Arkin, G.F., Vanderlip, R.L., and Ritchie, J.J.. “A Dynamic Grain Sorghum Growth Model.” Trans. Amer. Society Agr. Engineers, 19 (1976):622–626.CrossRefGoogle Scholar
Boggess, W.G., Lynne, G.D., Jones, J.W., and Swaney, D.P.. “Risk-Return Assessment of Irrigation Decisions in Humid Regions.” So. J. Agr. Econ., 15 (1983):135–143.Google Scholar
Chambers, R.G. “Risk in Agricultural Production: Discussion.” Amer. J. Agr. Econ., 65 (1983):1114–1115.CrossRefGoogle Scholar
Cochran, M., Robison, L.J., and Lodwick, W.. “Improving the Efficiency of Stochastic Dominance Techniques Using Convex Set Stochastic Dominance.” Amer. J. Agr. Econ., 67 (1985):289–295.CrossRefGoogle Scholar
Fishburn, P.C.Decision and Value Theory. New York: John Wiley and Sons, 1964.Google Scholar
Hadar, J., and Russell, W.. “Rules for Ordering Uncertain Prospects.” Amer. Econ. Rev., 59 (1969):23–24.Google Scholar
Hanoch, G., and Levy, H.. “Efficiency Analysis of Choices Involving Risk.” Rev. Econ. Studies, 36 (1969):335–346.CrossRefGoogle Scholar
Harris, T.R., and Mapp, H.P.. “Stochastic Dominance Comparison of Water-Conserving Strategies.” Amer. J. Agr. Econ., 68 (1986):298–305.CrossRefGoogle Scholar
Heerman, D.F., and Hein, P.R.. “Performance Characteristics of Self-Propelled Center-Pivot Sprinkler Irrigation Systems.” Trans. Amer. Society Agr. Engineers, 11 (1968):11–15.Google Scholar
Hornbaker, R.H. A Near-Optimal Dynamic Programming Model for On-Farm Irrigation Scheduling of Grain Sorghum in the Oklahoma Panhandle. Unpublished Ph.D. Dissertation, Dept. of Agricultural Economics, Oklahoma State University, December, 1985.Google Scholar
King, R.P., and Robison, L.J.. Implementation of the Interval Approach to the Measurement of Decision Maker Preference. Research Report No. 418, Michigan State University, East Lansing, Michigan, 1981.Google Scholar
Kletke, D.D., Harris, T.R., and Mapp., H.P. Irrigation Cost Program User's Reference Manual. Oklahoma State University Experiment Station Report P-770, May, 1978.Google Scholar
Maas, S.J., and Arkin, G.F.. “Sensitivity Analysis of SORGF a Grain Sorghum Model.” Trans. Amer. Society Agr. Engineers, 23 (1980):671–675.CrossRefGoogle Scholar
Maap, H.P., Eidman, V.R.Stone, J.R., and Davidson, J.M.. Simulating Soil Water and Atmospheric Stress—Crop Yield Relationships for Economic Analysis. Oklahoma Agr. Exp. Sta. Bull. T-140, Feb., 1975.Google Scholar
Merriam, J.L., Keller, J., and Alfaro, J.F.. Irrigation System Evaluation and Improvement. Utah State University Water Resources Laboratory, 1973.Google Scholar
National Weather Service. Climatological Data for Oklahoma. Dept. of Commerce, Washington D.C., 1961-1980.Google Scholar
Oklahoma State University Cooperative Extension Service. Oklahoma Crop and Livestock Budgets. Dept. of Agr. Economics, Oklahoma State University, 1985.Google Scholar
Pair, C.H. “Water Distribution Under Sprinkler Irrigation.” Trans. Amer. Society Agr. Engineers, 11 (1968):648–651.CrossRefGoogle Scholar
Prickett, M., Cochran, M., Scott, D., and Holder, S.. “Irrigation Scheduling for Arkansas Soybean Production: A Simulation Study.” Paper presented at annual meetings of the Western Ag. Econ. Assoc., Saskatoon, Sask., July, 1985.Google Scholar
Quirk, J., and Saposnik, R.. “Admissibility and Measurable Utility Function.” Rev. Econ. Studies, 29 (1962):140–146.CrossRefGoogle Scholar
Raskin, R., and Cochran, M.J.. “Interpretations and Transformations of Scale for the Pratt-Arrow Absolute Risk Aversion Coefficient: Implications for Generalized Stochastic Dominance.” West J. Agr. Econ., 11 (1986):204–210.Google Scholar
Ring, L., and Heerman, D.C.. “Determining Center-Pivot Uniformities.” Paper No. 78-2001, presented at summer meetings of the Amer. Soc. of Agr. Engineers, Logan, Utah, June 27-30, 1978.Google Scholar