Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-27T08:49:53.128Z Has data issue: false hasContentIssue false
  • English
  • Français

A Stochastic Programming Analysis of the Farm Level Implications of Soil Erosion Control

Published online by Cambridge University Press:  28 April 2015

Eduardo Segarra ,
Randall A. Kramer  and
Daniel B. Taylor
Eduardo Segarra
Affiliation:
Department of Agricultural Economics, Virginia Polytechnic Institute and State University
Randall A. Kramer
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
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper analyzes the effects of uncertain soil loss in farm planning models. A disaggregated approach was used because of an interest in examining the impact of probabilistic soil loss constraints on farm level decisionmaking. A stochastic programming model was used to consider different levels of probability of soil loss. Traditional methods of analysis are shown to consistently overestimate net returns.

Keywords

soil conservationstochastic programminguncertainty
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 17 , Issue 2 , December 1985 , pp. 147 - 154
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

Anderson, Glenn B.Conservation Agronomist, Soil Conservation Service; Richmond, Virginia, Personal Communication; February, 1985.Google Scholar
Forbes, Peter O. and Marshall, J. Paxton. “Flue-cured Tobacco Quota Ownership in Nine Counties in Virginia in 1983“ Department of Agricultural Economics, Virginia Polytechnic Institute and State University, M.B. 316; December, 1983.Google Scholar
Googins, Richard L.State Soil Scientist, Soil Conservation Service; Richmond, Virginia, Personal Communication; February, 1985.Google Scholar
Kramer, Randall A., McSweeney, William T., and Stavros, Robert W.. “Soil Conservation with Uncertain Revenues and Input Supplies.” Amer. J. Agr. Econ., 65 (1983):694–702.CrossRefGoogle Scholar
Nott, H. and Combs, G. F.. “Data Processing of Ingredient Composition Data.” Feedstuff's, 39(1967):21–24.Google Scholar
Rahman, Sabir A. and Bender, F. E.. “Linear Programming Approximation of Least Cost Feed Mixes with Probability Restrictions.” Amer. J. Agr. Econ., 53(1971):612–619.CrossRefGoogle Scholar
Smith, David N.Agricultural Economist, Soil Conservation Service; Richmond, Virginia, Personal Communication; February, 1985.Google Scholar
Soil Conservation Service. Crop Budget System Users Guide. Economics Division, USDA; Washington, DC, 1977.Google Scholar
Soil Conservation Service. Piedmont Bright Leaf Erosion Control Area. USDA, SCS; Richmond, Virginia; 1983.Google Scholar
U. S. Department of Commerce, Bureau of the Census. 1982 Census of Agriculture. Preliminary Report. United States Government Printing Office; Washington, D.C.; 1983.Google Scholar
Wade, James D. and Heady, Earl O.. “Controlling Non-Point Sediment Sources with Cropland Management: A National Economic Assessment.” Amer. J. Agr. Econ., 59(1977):13–24.CrossRefGoogle Scholar
Walker, David J. and Timmons, John F.. “Costs of Alternative Policies for Controlling Agricultural Soil Loss and Associated Stream Sedimentation.” J. oil and Water Cons., 35(1980):177–182.Google Scholar
Wishcmeier, W. H. and Smith, D. D.. Predicting Rainfall Erosion Losses—A Guide to Conservation Planning. USDA, Agriculture Handbook No. 537, 1978.Google Scholar