Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-24T10:33:14.699Z Has data issue: false hasContentIssue false
  • English
  • Français

Long-Term Planning of a Livestock-Crop Farm Under Government Programs

Published online by Cambridge University Press:  28 April 2015

Pierre-Justin Kouka ,
Patricia A. Duffy  and
C. Robert Taylor
Pierre-Justin Kouka
Affiliation:
University of Arkansas at Pine Bluff
Patricia A. Duffy
Affiliation:
Department of Agricultural Economics and Rural Sociology at Auburn University, Alabama
C. Robert Taylor
Affiliation:
Department of Agricultural Economics and Rural Sociology at Auburn University, Alabama
Get access Rights & Permissions [Opens in a new window]

Abstract

Optimal crop and livestock mix was determined for a representative Alabama farm using a dynamic programming model. Results indicate that decisions concerning livestock production are highly influenced by the amount of cotton base available on the farm. In most cases, increasing cotton base results in less cattle production. The triple base provisions of the 1990 Farm Bill, however, may give some cotton farmers an incentive to produce more stocker cattle during the winter months. Research results also indicate that the availability of farm programs can alter the optimal enterprise mix on a farm with no beginning base in cotton.

Keywords

farm programsdynamic programminglivestock
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 26 , Issue 1 , July 1994 , pp. 275 - 286
Copyright
Copyright © Southern Agricultural Economics Association 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alabama Crop and Livestock Reporting Service. Alabama Ag. Stat. Montgomery, Al. Various Issues. Alabama Farm Analysis Association. Various Reports.Google Scholar
Bellman, R.Dynamic Programming. Princeton, NJ: The Princeton University Press. 1957.Google ScholarPubMed
Bransby, David. Private Communications. Dept. of Agronomy and Soils. Auburn University, Alabama. 1992.Google Scholar
Burden, R.L. and Faires, J.D.. 1985. Numerical Analysis Third Edition. Boston: Prindle, Weber, and Schmidt, pp. 153–198.Google Scholar
Burt, O.R. “Decision Rules for the Dynamic Animal Feeding Problem.” Working Paper. University of California, Davis. 1991.Google Scholar
Burt, O.R. and Allison, J.R.. “Farm Management Decisions with Dynamic Programming.” J. Farm Econ. 45(1963): 121–37.CrossRefGoogle Scholar
Cain, D.L. “Acreage and Farm Program Participation Decisions Made on Alabama Cotton Farms under Provisions of the 1990 Farm Bill.” Unpublished Master's Thesis. Auburn University, Al., 1992.Google Scholar
Dreyfus, S.E. and Law, A.M.. The Art and Theory of Dynamic Programming. New York: Academic Press Inc. 1977.Google Scholar
Duffy, P.A. and Taylor, C.R.. “The Effects of Uncertainty about the Future of the Farm Programs on a Corn-Soybean Farm.” Amer. J. Agr. Econ. 76(February, 1994). Forthcoming.CrossRefGoogle Scholar