Hostname: page-component-848d4c4894-wg55d Total loading time: 0 Render date: 2024-05-09T14:29:43.600Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimal Marketing Decisions for Feeder Cattle under Price and Production Risk

Published online by Cambridge University Press:  19 March 2018

Xuecai Wang ,
Jeffrey H. Dorfman ,
John McKissick  and
Steven C. Turner
Xuecai Wang
Affiliation:
The University of Georgia and is now an at American Express
Jeffrey H. Dorfman
Affiliation:
Department of Agricultural & Applied Economics at The University of Georgia, Athens GA 30602-7509
John McKissick
Affiliation:
Department of Agricultural & Applied Economics at The University of Georgia, Athens GA 30602-7509
Steven C. Turner
Affiliation:
Department of Agricultural & Applied Economics at The University of Georgia, Athens GA 30602-7509
Get access

Abstract

In many parts of the U.S., beef cattle production is a large sector of the agricultural economy, yet few of the cattle are stockered; instead the production is focused on cow-calf operations only. Restricting their Operation to only the first phase of beef production may be limiting the cattle owners’ profit potential. This paper examines the opportunities for Operators to earn additional profit from stockering cattle. Using a representative risk-averse producer, a decision set with seven possible marketing strategies is evaluated for the optimal decision in a Bayesian framework which allows for price and production risk. We find that in many instances retaining the cattle for stockering is a superior decision when done in conjunction with specific hedging strategies utilizing options and futures contracts.

Keywords

cattledecision scienceestimation riskmarketingC6D2Q1
Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 33 , Issue 3 , December 2001 , pp. 431 - 443
Copyright
Copyright © Southern Agricultural Economics Association 2001

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

Babcock, B. A., Choi, E. K., and Feinerman, E.. “Risk and Probability Premiums for CARA Utility Functions.” Journal of Agricultural and Resource Economics 18,1 (1993): 17–24.Google Scholar
Barry, C. B. “Portfolio Analysis Under Uncertain Means, Variance and Covariance.” Journal of Finance 29(1974):515–522.Google Scholar
Barry, P. J. Risk Management in Agricultural. Arnes: Iowa State University Press, 1984.Google Scholar
Bawa, V. And Brown, S.. “Capital Market Equilibrium: Does Estimation Risk Really Matter?” Estimation Risk and Optimal Portfolio Choice, eds. Bawa, V., Brown, S., and Klein, R., Chapter 6. Amsterdam: North-Holland Publishing Co., 1979.Google Scholar
Bawa, V., Brown, S., and Klein, R.. “Estimation Risk: An Introduction.” Estimation Risk and Optimal Portfolio Choice, eds. Bawa, V., Brown, S., and Klein, R., Chapter 1. Amsterdam: North-Holland Publishing Co., 1979.Google Scholar
Berger, J. O., Statistical Decision Theory and Bayesian Analysis, 2nd ed. Springer-Verlag, New York, 1985.Google Scholar
Chalfant, J. A., Collender, R. N., and Subramanian, S., “The Mean and Variance of the Mean-Variance Decision Ruie” American Journal of Agricultural Economics 72,4(1990) :966–974.Google Scholar
Cochran, M. J., and Raskin, R.. “Interpretation and Transformations of Scale for the Pratt-Arrow Absolute Risk Aversion Coefficient: Implications for Generalized Stochastic Dominance.” Western Journal of Agricultural Economics 11,2(1986):204–210.Google Scholar
Collender, R. N. “Estimation Risk in Farm Planning Under Uncertainty.” American Journal of Agricultural Economics 7I,4(1989):996–1002.Google Scholar
DeGroot, M. Ft., Optimal Statistical Decisions. McGraw-Hill, New York, 1970.Google Scholar
Dixon, B. L., and Barry, P. J.. “Portfolio Analysis Considering Estimation Risk and Imperfect Markets.” Western Journal of Agricultural Economics 8,2(1983): 103–111.Google Scholar
Dorfman, J. H. Bayesian Economics Through Numerical Methods Springer, New York, 1997.Google Scholar
Ethridge, D. E., Zhang, P., Dahl, B. E., Ervin, R. T., and Rushemeza, J., “Cattle Ranching Production and Marketing Strategies under Combined Price and Weather Risks.” Western Journal of Agricultural Economics 15,2(1990):175–185.Google Scholar
Geweke, J. “Antithetic Acceleration of Monte Carlo Integration in Bayesian Inference,” Journal of Econometrics 38(1988):73–89.Google Scholar
Geweke, J. “Monte Carlo Simulation and Numeric Integration.” Federal Reserve Bank of Minneapolis Research Dept. Staff Report No. 192 (1995).Google Scholar
Lence, S. H. and Hayes, D. J.. “Parameter-ba.sed Decision Making under Estimation Risk: An Application to Future Trading.“ The Journal of Finance XLIX(1994):345–357.Google Scholar
Lence, S. H. and Hayes, D. J.. “Land Allocation in the Presence of Estimation Risk.” Journal of Agricultural and Resource Economics 20,1(1995):49–63.Google Scholar
McKissick, J. C, An Economic Analysis of Alternative Pricing Method for Alabama Stocker Cattle Producers. Ph.D. dissertation, Auburn University, Auburn Alabama, 1987.Google Scholar
McKissick, J. C, and Ikerd, J.. “Retained Ownership in Cattle Cycles.” Managing for Today's Cattle Market and Beyond, eds. Bastian, Chris and DeeVon Bailey Department of Agricultural Economics, Utah State University, 1996.Google Scholar
McKissick, J. C, and Brown, D. T., Profit Cattle Market, for the Cow-Calf Producer. B-1078, Cooperative Extension Service, The University of Georgia College of Agricultural and Environmental Science, 1996.Google Scholar
Newbery, D. M. G., and Stiglitz, J. E.. The Theory of Commodity Price Stabilization. Oxford: Oxford Univ. Press, 1981.Google Scholar
Pope, R. D., and Ziemer, R. F.. “Stochastic Efficiency, Normality, and Sampling Errors in Agricultural Risk Analysis.” American Journal of Agricultural Economics 66,1 (1984):31-40.Google Scholar
Tanner, M. A. Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, Third Ed. New York: Springer-Verlag, 1996.Google Scholar