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Uncertain Yields In Sectoral Welfare Analysis: An Application To Global Warming

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Author Info

  • Lambert, David K.
  • McCarl, Bruce A.
  • He, Quifen
  • Kaylen, Michael S.
  • Rosenthal, Wesley
  • Chang, Ching-Cheng
  • Nayda, W.I.

Abstract

Agriculture operates in an uncertain environment. Yields, prices, and resource usage can change dramatically from year to year. However, most analyses of the agricultural sector, at least those using mathematical programming methods, assume decision making is based on average yields, ignoring yield variability. This study examines how explicit consideration of stochastic yield outcomes influence a sector analysis. We develop a model that can be used for stochastic sector analysis. We extend the risk framework developed by Hazell and others to incorporate discrete yield outcomes as well as consumption activities dependent upon yield outcomes. An empirical application addresses a comparison between sector analysis with and without considerations of the economic effects of yield variability in a global warming context.

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File URL: http://purl.umn.edu/15257
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Bibliographic Info

Article provided by Southern Agricultural Economics Association in its journal Journal of Agricultural and Applied Economics.

Volume (Year): 27 (1995)
Issue (Month): 02 (December)
Pages:

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Handle: RePEc:ags:joaaec:15257

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Web page: http://www.saea.org/jaae/jaae.htm
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Related research

Keywords: Agricultural sector analysis; Global warming; Partial equilibrium models; Stochastic programming; Environmental Economics and Policy; Risk and Uncertainty;

References

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  1. Spreen, Thomas H., 2006. "Price Endogenous Mathematical Programming Models and Trade Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(02), August.
  2. Tobey, James A. & Reilly, John M. & Kane, Sally, 1992. "Economic Implications Of Global Climate Change For World Agriculture," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 17(01), July.
  3. Apland, Jeffrey & Hauer, Grant, 1993. "Discrete Stochastic Programming: Concepts, Examples And A Review Of Empirical Applications," Staff Papers 13793, University of Minnesota, Department of Applied Economics.
  4. McCarl, Bruce A. & Parandvash, Gholam Hossein, 1988. "Irrigation Development Versus Hydroelectric Generation: Can Interruptible Irrigation Play A Role?," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 13(02), December.
  5. Beach, Robert H. & Thomson, Allison M. & McCarl, Bruce A., 2010. "Climate Change Impacts On Us Agriculture," Proceedings Issues, 2010: Climate Change in World Agriculture: Mitigation, Adaptation, Trade and Food Security, June 2010, Stuttgart- Hohenheim, Germany 91393, International Agricultural Trade Research Consortium.
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Cited by:
  1. Butt, Tanveer A. & Mccarl, Bruce A., 2005. "An analytical framework for making long -term projections of undernourishment: A case study for agriculture in Mali," Food Policy, Elsevier, vol. 30(4), pages 434-451, August.
  2. Chen, Chi-Chung & McCarl, Bruce A., 2000. "The Value Of Enso Information To Agriculture: Consideration Of Event Strength And Trade," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(02), December.
  3. Guan, Z. & Philpott, A.B., 2011. "A multistage stochastic programming model for the New Zealand dairy industry," International Journal of Production Economics, Elsevier, vol. 134(2), pages 289-299, December.

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