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Chance Constrained Programming Models for Risk-Based Economic and Policy Analysis of Soil Conservation

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  • Zhu, Minkang
  • Taylor, Daniel B.
  • Sarin, Subhash C.
  • Kramer, Randall A.

Abstract

The random nature of soil loss under alternative land-use practices should be an important consideration of soil conservation planning and analysis under risk. Chance constrained programming models can provide information on the trade-offs among pre-determined tolerance levels of soil loss, probability levels of satisfying the tolerance levels, and economic profits or losses resulting from soil conservation to soil conservation policy makers. When using chance constrained programming models, the distribution of factors being constrained must be evaluated. If random variables follow a log-normal distribution, the normality assumption, which is generally used in the chance constrained programming models, can bias the results.

Suggested Citation

  • Zhu, Minkang & Taylor, Daniel B. & Sarin, Subhash C. & Kramer, Randall A., 1994. "Chance Constrained Programming Models for Risk-Based Economic and Policy Analysis of Soil Conservation," Agricultural and Resource Economics Review, Cambridge University Press, vol. 23(1), pages 58-65, April.
    • Handle: RePEc:cup:agrerw:v:23:y:1994:i:01:p:58-65_00
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    References listed on IDEAS

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    1. Segarra, Eduardo & Kramer, Randall A. & Taylor, Daniel B., 1985. "A Stochastic Programming Analysis Of The Farm Level Implications Of Soil Erosion Control," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 17(2), pages 1-8, December.
    2. Andrew J. Hogan & James G. Morris & Howard E. Thompson, 1981. "Decision Problems Under Risk and Chance Constrained Programming: Dilemmas in the Transition," Management Science, INFORMS, vol. 27(6), pages 698-716, June.
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    1. Peterson, Jeffrey M. & Boisvert, Richard N., 1998. "Optimal Voluntary "Green" Payment Programs To Limit Nitrate Contamination Under Price and Yield Risk," Research Bulletins 122687, Cornell University, Department of Applied Economics and Management.
    2. Kampas, Athanasios & White, Ben, 2003. "Probabilistic programming for nitrate pollution control: Comparing different probabilistic constraint approximations," European Journal of Operational Research, Elsevier, vol. 147(1), pages 217-228, May.
    3. Aftab, Ashar & Hanley, Nick & Baiocchi, Giovanni, 2017. "Transferability of Policies to Control Agricultural Nonpoint Pollution in Relatively Similar Catchments," Ecological Economics, Elsevier, vol. 134(C), pages 11-21.
    4. Doole, Graeme & Pannell, David J., 2011. "Evaluating environmental policies under uncertainty through application of robust nonlinear programming," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(4), pages 1-18.
    5. Gianpiero Canessa & Julian A. Gallego & Lewis Ntaimo & Bernardo K. Pagnoncelli, 2019. "An algorithm for binary linear chance-constrained problems using IIS," Computational Optimization and Applications, Springer, vol. 72(3), pages 589-608, April.
    6. Lixia H. Lambert & Eric A. DeVuyst & Burton C. English & Rodney Holcomb, 2021. "Analyzing the Trade-Offs between Meeting Biorefinery Production Capacity and Feedstock Supply Cost: A Chance Constrained Approach," Energies, MDPI, vol. 14(16), pages 1-13, August.

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