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Uncertain Yields in Sectoral Welfare Analysis: An Application to Global Warming

Published online by Cambridge University Press:  28 April 2015

D.K. Lambert
Affiliation:
Departments of Agricultural Economics at theUniversity of Nevada, Reno
B.A. McCarl
Affiliation:
Texas A&M University
Q. He
Affiliation:
Texas A&M University
M.S. Kaylen
Affiliation:
University of Missouri, Columbia
W. Rosenthal
Affiliation:
Blackland Research Center of Texas Agricultural Experiment Station, Temple, Texas
C.C. Chang
Affiliation:
The Institute of Economics, Nankang, Taipei, Taiwan
W.I. Nayda
Affiliation:
Texas A&M University

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.

Type
Articles
Copyright
Copyright © Southern Agricultural Economics Association 1995

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References

Adams, R.M., Glyer, J.D., and McCarl, B.A.. “The Economic Effects of Climate Change on U.S. Agriculture: A Preliminary Assessment.Report to Congress on the Effects of Global Climate Change. Washington DC: U.S. Environmental Protection Agency, 1988.Google Scholar
Adams, R.M., Rosenzweig, C., Peart, R.M., Ritchie, J.T., McCarl, B.A., Glyer, J.D., Curry, R.B., Jones, J.W., Boote, K.J., and Allen, L.H.. “Global Climate Change and U.S. AgricultureNature. 345(1990): 219224CrossRefGoogle Scholar
Apland, J., and Hauer, G.Discrete Stochastic Programming: Concepts, Examples and a Review of Empirical Applications.” Staff Paper 93-21, Department of Agricultural Economics, University of Minnesota, September, 1993.Google Scholar
Boisvert, R.N. and McCarl, B.A.. “Agricultural Risk Modeling Using Mathematical Programming.Regional Research Bulletin No. 356. Southern Cooperative Series. (1990): 1103.Google Scholar
Chang, C.C., McCarl, B.A., Mjelde, J.W., and Richardson, J.W.. “Sectoral Implication of Farm Program Modifications.Amer. J. Agr. Econ. 74(1992):3949.CrossRefGoogle Scholar
Cocks, K.Discrete Stochastic Programming.Mgmt. Sci. 15(1968):7279.CrossRefGoogle Scholar
Dantzig, G.Linear Programming Under Uncertainty.Mgmt. Sci. 1(1955): 197206.CrossRefGoogle Scholar
Hazell, P.B.R. and Pomareda, C.. “Evaluating Price Stabilization Schemes with Mathematical Programming.Amer. J. Agr. Econ. 63(1981): 550-56.CrossRefGoogle Scholar
Hazell, P.B.R. and Scandizzo, P.L.. “Competitive Demand Structures under Risk in Agricultural Linear Programming Models.Amer. J. Agr. Econ. 56(1974):235-44.CrossRefGoogle Scholar
Hazell, P.B.R. and Scandizzo, P.L.. “Farmers' Expectations, Risk Aversion, and Market Equilibrium under Risk.Amer. J. Agr. Econ. 59(1977): 204-19CrossRefGoogle Scholar
Hazell, P.B.R. and Scandizzo, P.L.. “Market Intervention Policies when Production is Risky.Amer. J. Agr. Econ. 57(1975):641-49.CrossRefGoogle Scholar
He, Quifen. “The Effect of Global Warming on U.S. Agriculture and its Variability.Masters Thesis, Texas A&M University, December 1992.Google Scholar
Kaiser, H.M.Climate Change and Agriculture.N. E. J. Agr. Res. Econ., 20(1991): 151-63.Google Scholar
Kokoski, Mary F. and Smith, V. Kerry. “A General Equilibrium Analysis of Partial Equilibrium Welfare Measures: The Case of Climate Change.Amer. Econ. Rev. 77(1987):331341.Google Scholar
Lambert, D.K. and McCarl, B.A.. “Risk Modeling Using Direct Solution of the Expected Utility Function.Amer. J. Agr. Econ. 67(1985):846-52.CrossRefGoogle Scholar
Lambert, D.K. and McCarl, B.A.. “Sequential Modeling of White Wheat Marketing Strategies.N.C.J. Agr. Econ. 11(1989): 105-15.Google Scholar
Martin, LJ.Quadratic Single and Multicommodity Models of Spatial Equilibrium: A Simplified Exposition.Cdn. J. Agr. Econ. 29(1981): 2148.CrossRefGoogle Scholar
McCarl, B.A. and Parandvash, G.L.. “Irrigation Development versus Hydroelectric Generation: Can Interruptible Irrigation Play a Role?W. J. Agr. Econ. 13(1988):267276.Google Scholar
McCarl, B.A. and Spreen, T.H.. “Price Endogenous Mathematical Programming As a Tool for Sector Analysis.Amer. J. Agr. Econ. 62(1980):87102.CrossRefGoogle Scholar
Norton, R.D. and Schiefer, G.W.. “Agricultural Sector Programming Models: A Review.Eur. Rev. Agr. Econ. 7(1980):229-64.CrossRefGoogle Scholar
Onal, H. and Fang, Y.. “The Effects of Future Climate Change upon the Agriculture of Illinois.World Resource Review. 3(1991):259275.Google Scholar
Pomareda, C. and Samayoa, O.. “Area and Yield Response to Price Policy: A Case Study in Guatemala, CA.Amer. J. Agr. Econ. 61(1979):683686.CrossRefGoogle Scholar
Rae, Allen N.Stochastic Programming, Utility, and Sequential Decision Problems in Farm Management.Amer. J. Agr. Econ. 53(1971a): 448460.CrossRefGoogle Scholar
Rae, Allen N.An Empirical Application and Evaluation of Discrete Stochastic Programming in Farm Management.Amer. J. Agr. Econ. 53(1971b): 625638.CrossRefGoogle Scholar
Reilly, John and Hohman, Neil. “Climate Change and Agriculture: The role of International Trade.Amer. Econ. Rev. 83(1993):306312.Google Scholar
Rosenberg, N.J., and Crosson, P.R.. “Processes for Identifying Regional Influences of and Responses to Increasing Atmospheric C02 and Climate Change ~ The MINK Project: An Overview.” Prepared for the United States Department of Energy. Resource for the Future, Washington D.C, August 1991.Google Scholar
Rosenzweig, C, Parry, M., Fischer, G., and Frohberg, K.. “Climate Change and World Food Supply: A Preliminary Report.” Manuscript, Environmental Change Unit, University of Oxford, Oxford, UK. May 1992.Google Scholar
Samuelson, P.A.Spatial Price Equilibrium and Linear Programming.Amer. Econ. Rev. 42(1952):283303.Google Scholar
Sherony, K.R., Knowles, G.J., and Boyd, R.. “The Economic Impact of Crop Losses: A Computable General Equilibrium Approach.W. J. Agr. Econ. 16(1991): 144155.Google Scholar
Simmons, R.L. and Pomareda, C.. “Equilibrium Quantities and Timing of Mexican Vegetable Exports.Amer. J. Agr. Econ. 57(1975):472-79.CrossRefGoogle Scholar
Takayama, T. and Judge, G.G.. “Spatial Equilibrium and Quadratic Programming.J.Farm Econ. 46(1964):6793.CrossRefGoogle Scholar
Thaysen, K.An Analysis of Agricultural Risk Implications of United States Policy Changes.” Ph.D dissertation in process, Department of Agricultural Economics Texas A&M University, personal communication, 1994.Google Scholar
Tobey, James, Reilly, John, and Kane, Sally. “Economic Implications of Global Climate Change for World Agriculture.J. Agr. Res. Econ. 17(1992): 195204.Google Scholar
U.S. Department of Agriculture. Agricultural Statistics. Washington D.C. 1987, 1990.Google Scholar
Weimar, M.R. and Hallam, A.. “Risk, Diversification, and Vegetables as an Alternative Crop for Midwestern Agriculture.N. C.J. Agr. Econ. 10(1988):7589.Google Scholar
Willett, K.D.Single and Multi Commodity Models of Spatial Equilibrium in a Linear Programming Framework.Cdn. J. Agr. Econ. 31(1983):205-21.CrossRefGoogle Scholar