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

Author

Listed:
  • 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.

Suggested Citation

  • Lambert, David K. & McCarl, Bruce A. & He, Quifen & Kaylen, Michael S. & Rosenthal, Wesley & Chang, Ching-Cheng & Nayda, W.I., 1995. "Uncertain Yields In Sectoral Welfare Analysis: An Application To Global Warming," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 27(2), pages 1-14, December.
    • Handle: RePEc:ags:joaaec:15257
    DOI: 10.22004/ag.econ.15257
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    References listed on IDEAS

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    Cited by:

    1. Jones, Jason P.H. & McCarl, Bruce A., 2016. "Impacts of U.S. Production-Dependent Ethanol Policy on Agricultural Markets," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236258, Agricultural and Applied Economics Association.
    2. Choi, Hyung Sik & Schneider, Uwe A. & Rasche, Livia & Cui, Junbo & Schmid, Erwin & Held, Hermann, 2015. "Potential effects of perfect seasonal climate forecasting on agricultural markets, welfare and land use: A case study of Spain," Agricultural Systems, Elsevier, vol. 133(C), pages 177-189.
    3. Chengcheng J. Fei & Bruce A. McCarl & Yingqian Yang & Essayas Kaba Ayana & Raghavan Srinivasan & Yuhong Lei & Lingyi Li & Bingru Sheng & Xinxin Fan, 2022. "Impacts of climate change on water management," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(3), pages 1448-1464, September.
    4. 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(2), pages 1-18, December.
    5. 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.
    6. Kung, Chih-Chun & Cao, Xiaoyong & Choi, Yongrok & Kung, Shan-Shan, 2019. "A stochastic analysis of cropland utilization and resource allocation under climate change," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    7. Kung, Chih-Chun, 2019. "A stochastic evaluation of economic and environmental effects of Taiwan's biofuel development under climate change," Energy, Elsevier, vol. 167(C), pages 1051-1064.
    8. Kung, Chih-Chun & Zhang, Ning & Choi, Yongrok & Xiong, Kai & Yu, Jiangli, 2019. "Effectiveness of crop residuals in ethanol and pyrolysis-based electricity production: A stochastic analysis under uncertain climate impacts," Energy Policy, Elsevier, vol. 125(C), pages 267-276.
    9. 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.

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