Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T18:33:15.399Z Has data issue: false hasContentIssue false

A Mathematical Programming Model for Vegetable Rotations

Published online by Cambridge University Press:  28 April 2015

Wesley N. Musser
Affiliation:
Department of Agricultural and Resource Economics, Oregon State University University of Georgia
Vickie J. Alexander
Affiliation:
Department of Agricultural Economics, University of Georgia
Bernard V. Tew
Affiliation:
Department of Agricultural Economics, University of Kentucky University of Georgia
Doyle A. Smittle
Affiliation:
Department of Horticulture, Coastal Plains Experiment Station, University of Georgia, Tifton

Abstract

Rotations have historically been used to alleviate pest problems in crop production. This paper considers methods of modeling rotations in linear programming models for Southeastern vegetable production. In such models, entering each possible crop rotation as a separate activity can be burdensome because of the large numbers of possible rotational alternatives. Conventional methodology for double crop rotations reduces the number of activities but must be adapted to accommodate triple crop rotational requirements in vegetable production. This paper demonstrates these methods both for a simple example and an empirical problem with numerous rotation alternatives. While the methods presented in this paper may have computational disadvantages compared to entering each rotation as a separate activity, they do have advantages in model design and data management.

Type
Submitted Articles
Copyright
Copyright © Southern Agricultural Economics Association 1985

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

Danok, A. B., McCarl, B. A., and T. K. White. “Machinery Selection Modeling: Incorporation of Weather Variability.Amer. J. Agr. Econ., 62(1980):700708.CrossRefGoogle Scholar
El-Nazer, Talaat and McCarl, Bruce A.. “The Choice of Crop Rotation: A Modeling Approach and Case Study.” Unpublished Paper, Department of Agr. and Res. Econ., Oregon State University, 1984.Google Scholar
Hildreth, C. and Reiter, S.. “On the Choice of Crop Rotation.Analysis of Production and Allocation, Koopmans, T. (ed.), New York: John Wiley and Sons, 1951, p. 177188.Google Scholar
Kletke, Darrell D.Operations Manual for the Oklahoma State University Enterprise Budget Generator, Research Report P1979, Agricultural Experiment Station, Oklahoma State University; August, 1979.Google Scholar
Kletke, D. D., Harris, T. R., and Mapp, H. P. JrIrrigation Cost Program Users Reference Manual, Research Report P-770, Agricultural Experiment Station, Oklahoma State University; May, 1978.Google Scholar
Laughlin, David H.A Microcomputer Linear Programming Package: An Alternative to Mainframes.So. J. Agr. Econ., 16,1 (1984):183186.Google Scholar
McCarl, Bruce A., Candler, Wilfred V., Doster, D. Howard, and Robbins, Paul R.. “Experiences with Farmer Oriented Programming for Crop Planning.Canadian J. Agr. Econ., 25(1977):1730.CrossRefGoogle Scholar
McCarl, Bruce A. and Tice, Thomas. “Should Quadratic Programming Problems be Approximated.Amer. J. Agr. Econ., 64(1982):585589.CrossRefGoogle Scholar
Tew, B. V., Musser, W. N., and Robertson, J. D.. Economic Potential of Irrigated Multiple-Crop Production in the Coastal Plain of Georgia, Research Report 412, Georgia Experiment Station, University of Georgia, 1983.Google Scholar