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Decision Rules for the Dynamic Animal Feeding Problem

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  • Oscar R. Burt

Abstract

Under a criterion of maximum expected present value, dynamic programming is used to derive sequential decision rules that give feed rations as a function of animal weight and indicate the critical weight at which to sell a batch of animals. These rules are adaptable to short-term random changes in prices of inputs and outputs, and typically require solution of a single equation at a given weight. A new stochastic model is introduced that encompasses both the infinite sequence and single batch models found in the literature; the infinite chain of batches is randomly terminated after each batch is marketed.

Suggested Citation

  • Oscar R. Burt, 1993. "Decision Rules for the Dynamic Animal Feeding Problem," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(1), pages 190-202.
    • Handle: RePEc:oup:ajagec:v:75:y:1993:i:1:p:190-202.
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    File URL: http://hdl.handle.net/10.2307/1242967
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    Cited by:

    1. Chen, Gang & Roberts, Matthew C., 2004. "A Dynamic Programming Framework For Using Weather Derivatives To Manage Dairy Profit Risk," 2004 Annual meeting, August 1-4, Denver, CO 20171, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Blank, Steven C. & Orloff, Steve B. & Putnam, Daniel H., 2001. "Sequential Stochastic Production Decisions For A Perennial Crop: The Yield/Quality Tradeoff For Alfalfa Hay," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 26(1), pages 1-17, July.
    3. Chao, Hui-Ping, 1998. "Regime Switching In Us Livestock Cycles," 1998 Annual meeting, August 2-5, Salt Lake City, UT 20824, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Roemen, J.H.J. & de Klein, J., 1998. "An optimal delivery strategy for porkers with heterogeneity and dependent prices," Research Memorandum FEW 763, Tilburg University, School of Economics and Management.
    5. Roemen, J.H.J. & de Klein, J., 1998. "An optimal delivery strategy for porkers with heterogeneity and dependent prices," Other publications TiSEM 4775b203-6880-43c8-b1c7-3, Tilburg University, School of Economics and Management.
    6. Robert, Marion & Bergez, Jacques-Eric & Thomas, Alban, 2018. "A stochastic dynamic programming approach to analyze adaptation to climate change – Application to groundwater irrigation in India," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1033-1045.
    7. Niemi, Jarkko K., 2012. "Designing coordination contracts to support efficient flow-scheduling in pork chain," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 125208, Agricultural and Applied Economics Association.
    8. Mariano J. Gonzalez-Alcorta & Jeffrey H. Dorfman & Gene M. Pesti, 1994. "Maximizing profit in broiler production as prices change: A simple approximation with practical value," Agribusiness, John Wiley & Sons, Ltd., vol. 10(5), pages 389-399.

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