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Use of Markov Processes in Predicting Dairy Farm Size Distribution Changes

Published online by Cambridge University Press:  10 May 2017

John W. Wysong  and
Mahmood Y. Seyala
John W. Wysong
Affiliation:
Department of Agricultural and Resource Economics, University of Maryland
Mahmood Y. Seyala
Affiliation:
Department of Agricultural and Resource Economics, University of Maryland
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Extract

During the past several decades, a number of research publications have used Markov chain processes to predict changes in number of farm firms, the average size of farms and labor resource productivity in farm production (Conneman, Harris and Wilson, Judge and Swanson, Kottke, Seyala, Willett and Saupe, Wysong and Seyala). The Markov method assumes that the pattern of change exhibited in the past will continue into the future. This method provides information on historical changes by frequency distribution categories as well as for the whole cohort, and allows for projection of these changes into the future. Previous studies have used mainly short-run periods of up to 5 years as the primary data base. This study has considered the use of longer-run base periods of 10 to 20 years combined with periodic updating of sample data in projecting long-term trends in numbers and sizes of dairy farms and revising such projections through time.

Type
Contributed Papers
Information
Journal of the Northeastern Agricultural Economics Council , Volume 8 , Issue 2 , October 1979 , pp. 59 - 72
Copyright
Copyright © Northeastern Agricultural and Resource Economics Association 

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References

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