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Markov Intertemporal Land Use Simulation Model

Published online by Cambridge University Press:  28 April 2015

Bruce O. Burnham
Bruce O. Burnham*
Affiliation:
National Resource Economic Division, Economic Research Service, USDA
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Extract

The simulation model discussed in this paper evolved from problems encountered in estimating future United States cropland availability as part of the OBERS agricultural projection system. Available literature describing land use changes indicate that land resource economists have not been concerned with projecting future patterns of land use implied by historic observations.

Some research has involved selection of optimum cropping patterns for agricultural cropland subject to alternative flood plain management policies. However, the broader application of such models between sectors (agriculture, industrial, urban, etc.), in the main, has been ignored. Because of “historical bias” there has not been a concerted effort to develop analytical capabilities for use in evaluating the future implications of alternative regional and/or national policies designed to alter trends in land use shifts.

Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 5 , Issue 1 , July 1973 , pp. 253 - 258
Copyright
Copyright © Southern Agricultural Economics Association 1973

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References

[1]Adelman, Irman G., “A Stochastic Analysis of the Size Distribution of Firms,” American Statistical Association Journal, Dec. 1958, pp. 893–904.Google Scholar
[2]Dill, Henry W. Jr., and Otte, Robert C., “Urbanization of Land in the Western United States,” UJS. Department of Agriculture, Economic Research Service, ERS-428, Jan. 1970.Google Scholar
[3]Dill, Henry W. Jr., and Otte, Robert C., “Urbanization of Land in the Northeastern United States,” U.S. Department of Agriculture, Economic Research Service, ERS-485, Aug. 1971.Google Scholar
[4]Frey, H. Thomas and Dill, Henry W. Jr., “Land Use Change in the Southern Mississippi Alluvial Valley, 1950-69 - An Analysis Based on Remote Sensing.” U.S. Department of Agriculture, Economic Research Service, Agricultural Economic Report No. 215, Oct. 1971.Google Scholar
[5]Hallberg, M. C., “Projecting the Size Distribution of Agricultural Firms - An Application of Markov Process With Non-Stationary Transition Probabilities,” American Journal of Agricultural Economics, May, 1969, pp. 289–302.Google Scholar
[6]Krenz, Ronald D., “Projection of Farm Numbers for North Dakota with Markov Chains,” Agricultural Economics Research, Vol. 16, July, 1964, pp. 77–83.Google Scholar
[7]Lacewell, Ronald P., and Eidman, Vernon R., “A General Model for Evaluating Agricultural Flood Plains,” American Journal of Agricultural Economics, Feb. 1972, pp. 92–101.Google Scholar
[8]Lee, Judge, and Takayama, , “On Estimating the Transition Probabilities of a Markov Process,” Journal of Farm Economics, Aug. 1965, pp. 742–762.CrossRefGoogle Scholar
[9]Padberg, Daniel I., “The Use of Markov Processes in Measuring Changes in Market Structure,” Journal of Farm Economics, Feb. 1962, pp. 189–199.Google Scholar
[10]Solow, R., “Some Long-Run Aspects of the Distribution of Wage Incomes,” Econometrica, July, 1951, pp. 333–334.Google Scholar