Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-26T03:00:48.219Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of Biophysical Simulation in Production Economics

Published online by Cambridge University Press:  28 April 2015

Wesley N. Musser  and
Bernard V. Tew
Wesley N. Musser
Affiliation:
Department of Agricultural Economics, University of Georgia
Bernard V. Tew
Affiliation:
Department of Agricultural and Natural Resource Economics, Colorado State University
Get access Rights & Permissions [Opens in a new window]

Extract

Simulation has become a standard methodology in agricultural economics with models being used in all aspects of the profession. Johnson and Rausser identify two major types of production simulation models in their recent survey of the topic—firm and process models. Firm models, especially those concerned with growth, are most prominent in the agricultural economics literature. However, Johnson and Rausser also review some application of process models, which emphasize specific types of firm decisions. Biophysical simulation models are a specific form of these models concerned with the interaction of weather, soil, and/or biological processes in agricultural production and/or environmental loadings. In the recent agricultural economics literature, these models often are identified as bio-economic simulators. However, similar models are being utilized to evaluate erosion. Since erosion is largely a physical process, biophysical simulation seems more appropriate for the general classification of models considered in this paper.

Type
Invited Papers and Discussions
Information
Journal of Agricultural and Applied Economics , Volume 16 , Issue 1 , July 1984 , pp. 77 - 86
Copyright
Copyright © Southern Agricultural Economics Association 1983

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

Adams, R. M., Farris, P. J., and Menkhaus, D. J.. “Response Functions and the Value of Soil Test Information: The Case of Sugar Beets.” No. Cen.J. Agr. Econ. 5(2)(1983):77–82.Google Scholar
Anderson, Jock R., Dillon, John J. and Hardaker, Brian. Agricultural Decision Analysis. Ames, Iowa: Iowa State University Press, 1977.Google Scholar
Bessler, David A. “Subjective Probability.” Risk Management in Agriculture, Barry, Peter J., editor. Ames: Iowa State University Press, forthcoming.Google Scholar
Boggess, W. G., and Amerling, C. B.. “A Bioeconomic Simulation Analysis of Irrigation Investments.” So.J. Agr. Econ. 15(2)(1983):85–92.Google Scholar
Boggess, W. G., Lynne, G. D., Jones, J. W., and Swaney, D. P.. “Risk-Return Assessment of Irrigation Decisions in Humid Regions.” So.J. Agr. Econ. 15(1)(1983):135–144.Google Scholar
Bradford, Garnett L. “Optimizing Models: Some Observations and Opinions on the State of Progress.” Modeling Farm Decisions for Policy Analysis, Baum, Kenneth H. and Schertz, Lyle P., editors. Boulder: Westview Press, 1983, pp. 358–363.Google Scholar
Brorsen, W. Wade, Walker, Odell L., Horn, Gerald W., and Nelson, Ted R.. “A Stocker Cattle Growth Simulation Model.” So.J. Agr. Econ. 15(1)(1983):97–102.Google Scholar
Carter, H. O., and Dean, G. W.. “Income, Price and Yield Variability for Principal California Crops and Cropping Systems.” Hilgardia 30(1960):175–218.CrossRefGoogle Scholar
Debertin, David L. “Discussion: Value Judgments and Efficiency in Publicly Supported Research.” So. J. Agr. Econ. 15(1)(1983):9–10.Google Scholar
Dillon, John L. The Analysis of Response in Crop and Livestock Production, Second edition. Elmsford, N.Y.: Pergamon International Press, 1977.Google Scholar
Feddes, R. A., Kowalik, P. J., and Zaradny, H.. Simulation of Field Water Use and Crop Yield. Wageningen, Netherlands: Pudoc Publishing, 1978.Google Scholar
Friedman, Milton. Essays in Positive Economics. Chicago: University of Chicago Press, 1953.Google Scholar
Groenewegen, John R., and Clayton, Kenneth C.. “Agricultural Price Supports and Cost of Production: Reply.” Am. J. Agr. Econ. 65(1983):626–628.CrossRefGoogle Scholar
Hall, Harry H. “Economic Evaluation of Crop Response to Lime.” Amer.J. Agr. Econ. 65(1983):811–817.CrossRefGoogle Scholar
Hanson, Gregory D., and Eidman, Vernon R.. “Farm Size Evaluation in the El Paso Valley: Comment.” Amer.J. Agr. Econ. 65(1983):340–343.CrossRefGoogle Scholar
Harris, Thomas R., and Mapp, Harry P. Jr.. “A Control Theory Approach to Optimal Irrigation Scheduling in the Oklahoma Panhandle.” So.J. Agr. Econ. 12(1)(1983):165–172.Google Scholar
Heady, Earl O. “Elementary Models in Farm Production Economics Research.” J. Farm Econ. 30(1948):201–225.CrossRefGoogle Scholar
Intriligator, Michael D. Mathematical Optimization and Economic Theory. Englewood Cliffs: Prentice-Hall, Inc., 1971.Google Scholar
Johnson, Glenn L. “Agro-Ethics: Extension, Research and Teaching.” So. J. Agr. Econ. 14(1)(1982):1–10.Google Scholar
Johnson, S. R., and Rausser, G. C.. “Systems Analysis and Simulation: A Survey of Applications in Agricultural and Resource Economics.” A Survey of Agricultural Economics Literature, Vol. 2, Judge, G. G. et al., editors. Minneapolis: University of Minnesota Press, 1977, pp. 157–301.Google Scholar
Lacewell, Ronald D., and McGrann, James M.. “Research and Extension Issues in Production Economics.” So.J. Agr. Econ. l4(1)(1982):65–74.Google Scholar
Ladd, George W. “Value Judgments and Efficiency in Publicly Supported Research.” So.J. Agr. Econ. 15(1)(1983):1–8.Google Scholar
Mapp, Harry P. Jr., and Eidman, Vernon R.. “A Bioeconomic Simulation Analysis of Regulating Groundwater Irrigation.” Amer.J. Agr. Econ. 58(1976):391–402.CrossRefGoogle Scholar
Mapp, Harry P. Jr., and Eidman, Vernon R.. “Simulation of Soil Water-Crop Yield Systems: The Potential for Economic Analysis.” So.J. Agr. Econ. 7(1975):47–53.Google Scholar
McCarl, Bruce A., and Nelson, A. Gene. “Model Validation: An Overview With Some Emphasis on Risk Models.” Risk Management Strategies for Agricultural Production Firms: Perspectives and Research issues. Okla. St. Univ., Dept. of Agr. Econ. AE-8350, May 1983, pp. 80–106.Google Scholar
McGuckin, Tom. “Alfalfa Management Strategies for a Wisconsin Dairy Farm: An Application of Stochastic Dominance.” No. Cen. J. Agr. Econ. 5(1983):43–50.Google Scholar
Miller, Thomas A. “Understanding and Human Capital as Outputs of the Modeling Process.” Modeling Farm Decisions for Policy Analysis, Baum, Kenneth H. and Schertz, Lyle P., editors. Boulder: Westview Press, 1983, p. 91–95.)Google Scholar
Miranowski, John A. “Micromodeling: A Systems Approach at the National Level.” Modeling Farm Decisions for Policy Analysis, Baum, Kenneth H. and Schertz, Lyle P., editors. Boulder: Westview Press, 1983, p. 286–288.Google Scholar
Musser, Wesley N., Mapp, Harry P. Jr., and Barry, Peter J.. “Applications II: Risk Programming.” Risk Management in Agriculture, Barry, Peter J., editor. Ames: Iowa State University Press, forthcoming.Google Scholar
Musser, Wesley N., Martin, Neil R. Jr., and Reid, Donald W.. “A Polyperiodic Firm Model of Swine Enterprises.” Modeling Farm Decisions for Policy Analysis, Baum, Kenneth H. and Schertz, Lyle P., editors. Boulder: Westview Press, 1983, p. 289–309.Google Scholar
Musser, Wesley N., and Musser, Lynn Mather. “Psychological Perspectives on Risk Analysis.” Risk Management in Agriculture, Barry, Peter J., editor. Ames: Iowa State University Press, forthcoming.Google Scholar
Parvin, D. W. Jr., and Tyner, Fred H.. “The Systems Approach—Research or Research Management?” So.J. Agr. Econ. 6(1974):57–66.Google Scholar
Patrick, G. F., and Kliebenstein, J. B.. Multiple Goals in Farm Firm Decision-Making: A Social Science Perspective. Purdue Ag. Exp. Sta. Bui. 306, Dec, 1980.Google Scholar
Perrin, R. K. “The Value of Information and the Value of Theoretical Models in Crop Response Research.” Amer.J. Agr. Econ. 58(1976):54–61.CrossRefGoogle Scholar
Reichelderfer, Katherine H. “Positive Farm Models: A Comment on Aesthetic Versus Functional Value.” Modeling Farm Decisions for Policy Analysis, Baum, Kenneth H. and Schertz, Lyle P., editors. Boulder: Westview Press, 1983, p. 281–285.Google Scholar
Reichelderfer, Katherine H., and Bender, Filmore E.. “Application of a Simulative Approach to Evaluating Alternative Methods for the Control of Agricultural Pests.” Amer. J. Agr. Econ. 61(1979):258–267.CrossRefGoogle Scholar
Simon, Herbert A. Models of Man: Social and Rational. New York: John Wiley & Sons, Inc., 1957.Google Scholar
Taylor, C. Robert. “Complimentarities Between Micro- and Macro-Systems Simulation and Analysis.” Modeling Farm Decisions for Policy Analysis, Baum, Kenneth H. and Schertz, Lyle P., editors. Boulder: Westview Press, 1983, p. 63–72.Google Scholar
Tew, Bernard V. “An Expected Value-Variance Analysis of Alternative Production Systems: A Study of Irrigation Schedules in the Georgia Coastal Plain.” University of Georgia, Unpublished Ph.D. dissertation, In process.Google Scholar
Tew, Bernard V., and Musser, Wesley N.. A Survey of Use of Biophysical Simulation in Agricultural Economics. University of Georgia, Dept. of Agr. Econ. Faculty Ser., In process.Google Scholar
Woodworth, Roger C. “Agricultural Production Function Studies.” A Survey of Agricultural Economics Literature, Vol. 2, Judge, George G., et al., editors. Minneapolis: University of Minnesota Press, 1977, p. 128–154.Google Scholar
Young, Douglas L. “Risk Concepts and Measures for Decision Analysis.” Risk Management in Agriculture, Barry, Peter J., editor. Ames: Iowa State University Press, forthcoming.Google Scholar