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Successful Dairy Farm Management Strategies Identified by Stochastic Dominance Analyses of Farm Records

Published online by Cambridge University Press:  10 May 2017

Jonas B. Kauffman III
Affiliation:
Genesee County, New York
Loren W. Tauer
Affiliation:
Cornell University
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Abstract

First-degree and second-degree stochastic dominance were used to separate a panel of 112 dairy farms with ten annual observations per farm into successful and less successful groups using four different performance measures. Logit regression using 16 independent variables was then used to determine important farm characteristics leading to farm success. High milk production and controlling hired labor and purchased feed expenses were important. The selective adoption of new technologies was also important. Optimal debt-asset ratios varied over the 10-year period.

Type
Articles
Copyright
Copyright © 1986 Northeastern Agricultural and Resource Economics Association 

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Footnotes

The authors thank B.F. Stanton and R.N. Boisvert for their comments and advice in completing this research.

References

Anderson, J.R., Dillon, J.L. and Hardaker, J.B. Agricultural Decision Analysis. Ames: The Iowa State University Press, 1977.Google Scholar
Bratton, C.A.Dairy Management Practices and New York Dairy Farm Incomes,” A.E. Research 84-6, Department of Agricultural Economics, Cornell University, 1982.Google Scholar
Danok, Abdulla B., McCarl, Bruce A., and White, T. Kelley. “Machinery Selection Modeling: Incorporation of Weather Variability,” American Journal of Agricultural Economics, 62 (1980):700708.Google Scholar
Hardaker, J.B., and Tanago, A.G.Assessment of the Output of a Stochastic Decision Model.” The Australian Journal of Agricultural Economics, 17 (1973):170178.Google Scholar
Kramer, Randall A., and Pope, Rulon D.Participation in Farm Commodity Programs: A Stochastic Dominance Analysis.” American Journal of Agricultural Economics, 63 (1981):119128.Google Scholar
McGuckin, Tom. “Alfalfa Management Strategies for a Wisconsin Dairy Farm—An Application of Stochastic Dominance.” North Central Journal of Agricultural Economics, 5 (1983):4349.Google Scholar
Pederson, Glenn D.Selection of Risk-Preferred Rent Strategies: An Application of Simulation and Stochastic Dominance.” North Central Journal of Agricultural Economics, 6 (1984):1727.Google Scholar
Richardson, James W., and Nixon, Clair J.Producer's Preference for a Cotton Farmer Owned Reserve: An Application of Simulation and Stochastic Dominance.” Western Journal of Agricultural Economics, 7 (1982):123132.Google Scholar
Richardson, James W., and Nixon, Clair J.Selecting Among Alternative Depreciation Methods: A Stochastic Dominance Approach.” Paper presented at the A.A.E.A. annual meeting, Ithaca, NY, August 1984.Google Scholar
Schoney, Richard A., and McGuckin, J. Thomas. “Economics of the Wet Fractionation System in Alfalfa Harvesting.” American Journal of Agricultural Economics, 65 (1983):3844.Google Scholar
Schurle, Bryan W., and Williams, Jeffrey R.Application of Stochastic Dominance Criteria to Farm Data.” Kansas Agricultural Experiment Station Contribution Number 42-400-A, Department of Agricultural Economics, Kansas State University, 1982.Google Scholar
Wilson, Paul N., and Eidman, Vernon R.Dominant Enterprises Size in the Swine Production Industry.” American Journal of Agricultural Economics, 67 (1985):279288.Google Scholar
Zacharias, Thomas P., and Grube, Arthur H.An Economic Evaluation of Weed Control Methods Used in Combination with Crop Rotation: A Stochastic Dominance Approach.” North Central Journal of Agricultural Economics, 6 (1984):113120.Google Scholar