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Choice of Optimal Planting and Marketing Decisions for Fresh Vegetable Producers: A Mathematical Programming Approach

Author

Listed:
  • Vassalos, Michael
  • Dillon, Carl R.
  • Coolong, Tim

Abstract

This study combines whole farm economic analysis with biophysical simulation techniques in order to achieve a twofold objective. First, the study seeks to develop a multiple enterprise vegetable farm model with a production and marketing decision interface and, second, to determine optimal production practices for Kentucky vegetable growers. Three vegetable crops are examined: tomatoes, bell peppers and sweet corn. The findings indicate that the risk associated with vegetable production can be significantly mitigated with diversification of production mix and with a greater number of transplanting dates. However, this reduction in risk comes at a high cost in terms of expected net returns.

Suggested Citation

  • Vassalos, Michael & Dillon, Carl R. & Coolong, Tim, 2012. "Choice of Optimal Planting and Marketing Decisions for Fresh Vegetable Producers: A Mathematical Programming Approach," 2012 Annual Meeting, February 4-7, 2012, Birmingham, Alabama 120016, Southern Agricultural Economics Association.
  • Handle: RePEc:ags:saea12:120016
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    File URL: http://purl.umn.edu/120016
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    References listed on IDEAS

    as
    1. Dillon, Carl R., 1999. "Production Practice Alternatives for Income and Suitable Field Day Risk Management," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 31(02), pages 247-261, August.
    2. Timothy J. Lowe & Paul V. Preckel, 2004. "Decision Technologies for Agribusiness Problems: A Brief Review of Selected Literature and a Call for Research," Manufacturing & Service Operations Management, INFORMS, vol. 6(3), pages 201-208.
    3. McCarl, Bruce A. & Bessler, David A., 1989. "Estimating An Upper Bound On The Pratt Risk A Version Coefficient When The Utility Function Is Unknown," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 33(01), April.
    4. Musser, Wesley N. & Tew, Bernard V., 1984. "Use Of Biophysical Simulation In Production Economics," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 16(01), July.
    5. Deng, Xiaohui & Barnett, Barry J. & Hoogenboom, Gerrit & Yu, Yingzhuo & Garcia, Axel Garcia y, 2008. "Alternative Crop Insurance Indexes," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(01), pages 223-237, April.
    6. Robert K. Kaufmann & Seth E. Snell, 1997. "A Biophysical Model of Corn Yield: Integrating Climatic and Social Determinants," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 178-190.
    7. Brent Hueth & Ethan Ligon, 1999. "Producer Price Risk and Quality Measurement," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 512-524.
    8. Mario F. Crisostomo & Robert O. Burton & Allen M. Featherstone & Kenneth W. Kelley, 1993. "A Risk Programming Analysis of Crop Rotations Including Double-Cropping," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 15(3), pages 443-461.
    9. Dillon, Carl R., 1999. "Production Practice Alternatives For Income And Suitable Field Day Risk Management," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 31(02), August.
    10. Brent Hueth & Ethan Ligon, 1999. "Agricultural Supply Response Under Contract," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 610-615.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    vegetable production; mean-variance; biophysical simulation; farm management; Farm Management; C61; C63; D81;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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