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

Published online by Cambridge University Press:  15 September 2016

Minkang Zhu ,
Daniel B. Taylor ,
Subhash C. Sarin  and
Randall A. Kramer
Minkang Zhu
Affiliation:
Department of Agricultural Economics, Virginia Polytechnic Institute and State University
Daniel B. Taylor
Affiliation:
Department of Agricultural Economics, Virginia Polytechnic Institute and State University
Subhash C. Sarin
Affiliation:
Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
Randall A. Kramer
Affiliation:
Center for Resource and Environmental Policy Research, Duke University, Durham, NC 27706
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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.

Type
Agricultural, Resource, and Environmental Policies in the 1990s
Information
Agricultural and Resource Economics Review , Volume 23 , Issue 1 , April 1994 , pp. 58 - 65
Copyright
Copyright © 1994 Northeastern Agricultural and Resource Economics Association 

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