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Acreage Response Under Farm Programs for Major Southeastern Field Crops

Published online by Cambridge University Press:  28 April 2015

Patricia A. Duffy ,
Kasazi Shalishali  and
Henry W. Kinnucan
Patricia A. Duffy
Affiliation:
Department of Agricultural Economics and Rural Sociology, Auburn University and Alabama Agricultural Experiment Station
Kasazi Shalishali
Affiliation:
Department of Business, Tuskegee University
Henry W. Kinnucan
Affiliation:
Department of Agricultural Economics and Rural Sociology, Auburn University and Alabama Agricultural Experiment Station
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Abstract

An expected utility model that includes output price and yield uncertainty was used to estimate cotton, com, and soybean acreage response equations for the Southeast. The model appeared to fit the soybean and corn data well, resulting in own-price elasticity estimates of 0.317 for com and 0.727 for soybeans. When applied to cotton acreage, however, the model did not yield satisfactory results. When elasticity was allowed to change over time, however, statistical results for the cotton equation improved, yielding an own-price elasticity of 0.915 at data means.

Keywords

Government ProgramsAcreage ResponseExpected UtilityTime-Varying Parameters
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 26 , Issue 2 , December 1994 , pp. 367 - 378
Copyright
Copyright © Southern Agricultural Economics Association 1994

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References

Bailey, K.W., and Womack, A.W.. “Wheat Acreage Response: A Regional Econometric Investigation.” S. J. Agr. Econ. 17,2 (1985): 171–80.Google Scholar
Belsley, D.A., Kuh, E., Welsch, R.E.. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: Wiley, 1980.CrossRefGoogle Scholar
Chavas, J.P. “Constrained Choices Under Risk.” S. Econ. J. 53(1987):662–76.Google Scholar
Chavas, J.P., and Holt, M.T.. “Acreage Decisions Under Risk: The Case of Corn and Soybeans.” Amer. J. of Agr. Econ. 72, 3 (1990): 529–38.CrossRefGoogle Scholar
Duffy, P., Wolhgenant, M.K., and Richardson, J.W.. “Regional Cotton Acreage Response.” S. J. of Agr. Econ. 19,2 (1987): 99–108.Google Scholar
Gallagher, P. and Green, R.G.. A Cropland Use Model: Theory and Suggestions for Estimating Planted Acreage Response. NED, ERS, USDA. ERS Staff Report No. AGES 840410, November, 1984.Google Scholar
Glesjer, H. “A New Test for Heteroscedasticity.” Journal of the American Statistical Association. 64 (1969): 316–23Google Scholar
Greene, W.H.Econometric Analysis. New York: Macmillan Publishing Company. 1990.Google Scholar
Houck, J.P., and Subotnik, A.. “The U.S. Supply of Soybeans: Regional Acreage Functions.” Agr. Econ. Research. 21 (1969): 99–108.Google Scholar
Johnson, N.L. and Kotz, S.. Distributions in Statistics: Continuous Multivariate Distributions. New York: John Wiley and Sons, 1972.Google Scholar
Lee, D.R. and Helmberger, P.. “Estimating Supply Response in the Presence of Farm Programs.” Amer. J. Agr. Econ. 67, 2 (1985): 193–202.CrossRefGoogle Scholar
Madadala, G.S. “Methods of Estimation for Models of Markets with Bounded Price Variation.” Int. Econ. Rev. 24 (1983): 361–87.CrossRefGoogle Scholar
Mims, A.M., Duffy, P.A., and Young, G.. “Effects of Alternative Acreage Restriction Provisions on Alabama Cotton Farms.” S. J. of Agr. Econ. 21, 2 (1989): 85:94.Google Scholar
Morzuch, B.J., Weaver, R.D., and Helmberger, P.G.. “Wheat Acreage Supply Response under Changing Farm Programs.” Amer. J. Agr. Econ. 62, 1 (1980): 29–37.CrossRefGoogle Scholar
Romain, R.F. “A Commodity Specific Model for U.S. Agriculture.” College Station, Texas: Ph.D. Dissertation. Texas A&M University. December, 1980.Google Scholar
Sandmo, A. “On the Theory of the Competitive Firm Under Price Uncertainty.” Amer. Econ. Rev. 61 (1971): 65–73.Google Scholar
Singh, B., Nagar, A.L., Choudry, N.K., and Ray, B.. “On the Estimation of Structural Change: A Generalization of the Random Coefficient Model.” International Econ. Rev. 17(1976):340–61.CrossRefGoogle Scholar
Shumway, C.R. “Supply, Demand, and Technology in a Multiproduct Industry: Texas Field Crops.” Amer. J. Agr. Econ. 65 (1983):748–60.CrossRefGoogle Scholar
Stone, R.Model for Demand Projections. University of Cambridge Reprint Series, No. 236. Department of Applied Economics, University of Cambridge, 1965.Google Scholar
Taylor, C. R.A Description of Agsim. an Econometric-Simulation Model of Regional Crop and National Livestock Production in the United States. Auburn University, Dept. of Agr. Econ. and Rur. Soc., Series, ES89-1, January, 1989.Google Scholar
Tomek, W.G. and Robinson, K.L.. “Agricultural Price Analysis and Outlook.” In A Survey of Agricultural Economics Literature. Volume 1. Martin, Lee R. (ed.). Minneapolis: University of Minnesota Press, 1977.Google Scholar
U.S. Department of Agriculture. Agricultural Statistics. 1960-1988Google Scholar
U.S. Department of Agriculture, Economic Research Service. “Economic Indicators of the Farm Sector, Income and Balance Sheet Statistic,” Various Issues.Google Scholar