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Production Risk And Crop Insurance In Malting Barley: A Stochastic Dominance Analysis

  • Wilson, William W.
  • Gustafson, Cole R.
  • Dahl, Bruce L.

Malt barley is an important specialty crop in the Northern Plains and growers mitigate risk with federally subsidized crop insurance and production contracts. However, growers face considerable risk due to "coverage gaps" in crop insurance that result in uncertain indemnity payments due to uncertainty of their crop meeting contract specifications. A stochastic dominance model is developed to evaluate alternative risk efficient strategies for growers with differing risk attitudes and production practices (irrigation vs. dryland). Results show that efficient choices are highly dependent on risk attitudes for dryland growers, but not irrigated growers. Sensitivities with respect to acceptance risk and level of crop insurance subsidization are presented. Increased specialization of agricultural crops with greater emphasis on quality characteristics will limit dryland producer interest in federal crop insurance.

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Paper provided by North Dakota State University, Department of Agribusiness and Applied Economics in its series Agribusiness & Applied Economics Report with number 23561.

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Date of creation: 2006
Date of revision:
Handle: RePEc:ags:nddaae:23561
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  1. Bruce A. McCarl & David A. Bessler, 1989. "Estimating An Upper Bound On The Pratt Risk A Version Coefficient When The Utility Function Is Unknown," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 33(1), pages 56-63, 04.
  2. Babcock, Bruce A. & Hennessy, David A., 1996. "Input Demand Under Yield and Revenue Insurance," Staff General Research Papers 794, Iowa State University, Department of Economics.
  3. J. Brian Hardaker & James W. Richardson & Gudbrand Lien & Keith D. Schumann, 2004. "Stochastic efficiency analysis with risk aversion bounds: a simplified approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 48(2), pages 253-270, 06.
  4. Racine, Jeffrey S. & Ker, Alan P., 2006. "Rating Crop Insurance Policies with Efficient Nonparametric Estimators that Admit Mixed Data Types," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 31(01), April.
  5. Roderick M. Rejesus & Keith H. Coble & Thomas O. Knight & Yufei Jin, 2006. "Developing Experience-Based Premium Rate Discounts in Crop Insurance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(2), pages 409-419.
  6. Richard E. Just & Linda Calvin & John Quiggin, 1999. "Adverse Selection in Crop Insurance: Actuarial and Asymmetric Information Incentives," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 834-849.
  7. Seo, Sangtaek & Mitchell, Paul D. & Leatham, David J., 2005. "Effects of Federal Risk Management Programs on Optimal Acreage Allocation and Nitrogen Use in a Texas Cotton-Sorghum System," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 37(03), December.
  8. Gustafson, Cole R. & Wilson, William W. & Dahl, Bruce L., 2006. "Malt Barley Risk Management Strategies," Western Economics Forum, Western Agricultural Economics Association, vol. 5(02).
  9. Nganje, William E. & Kaitibie, Simeon & Wilson, William W. & Leistritz, F. Larry & Bangsund, Dean A., 2004. "Economic Impacts Of Fusarium Head Blight In Wheat And Barley: 1993-2001," Agribusiness & Applied Economics Report 23627, North Dakota State University, Department of Agribusiness and Applied Economics.
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