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Risk-Efficiency Criteria for Evaluating Economics of Herbicide-Based Weed Management Systems in Corn

Published online by Cambridge University Press:  20 January 2017

Thomas R. Hoverstad ,
Jeffrey L. Gunsolus ,
Gregg A. Johnson  and
Robert P. King
Thomas R. Hoverstad*
Affiliation:
Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108
Jeffrey L. Gunsolus
Affiliation:
Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108
Gregg A. Johnson
Affiliation:
Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108
Robert P. King
Affiliation:
Department of Applied Economics, University of Minnesota, St. Paul, MN 55108
*
Corresponding author's E-mail: [email protected]
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Abstract

Evaluation of economic outcome associated with a given weed management system is an important component in the decision-making process within crop production systems. The objective of this research was to investigate how risk-efficiency criteria could be used to improve herbicide-based weed management decision making, assuming different risk preferences among growers. Data were obtained from existing weed management trials in corn conducted at the University of Minnesota Southern Research and Outreach Center at Waseca. Weed control treatments represented a range of practices including one-pass soil-applied, one-pass postemergence, and sequential combinations of soil and postemergence herbicide application systems. Analysis of risk efficiency across 23 herbicide-based weed control treatments was determined with the mean variance and stochastic dominance techniques. We show how these techniques can result in different outcomes for the decision maker, depending on risk attitudes. For example, mean variance and stochastic dominance techniques are used to evaluate risk associated with one- vs. two-pass herbicide treatments with and without cultivation. Based on these analyses, it appears that a one-pass system is preferred by a risk-averse grower. However, we argue that this may not be the best option considering potential changes in weed emergence patterns, application timing concerns, etc. The techniques for economic analysis of weed control data outlined in this article will help growers match herbicide-based weed management systems to their own production philosophies based on economic risk.

Keywords

Corn, Zea mays L.Analysis of variancemean variance procedurestochastic dominanceCV, coefficient of variationFSD, first-degree stochastic dominancePOST, postemergencePRE, preemergenceSSD, second-degree stochastic dominance
Type
Research
Information
Weed Technology , Volume 18 , Issue 3 , September 2004 , pp. 687 - 697
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Auld, B. A., Menz, K. M., and Tisdell, C. A. 1987. Weed Control Economics. London: Academic. Pp. 76–77, 86.Google Scholar
Boehlje, M. D. and Eidman, V. R. 1984. Farm Management. New York: J. Wiley. Pp. 438–494.Google Scholar
Gebremedhin, B., Schwab, G., Harwood, R., Christenson, D., and Becker, C. 1998. A Stochastic Dominance Analysis of Alternative Sugarbeet and Navy Bean Based Crop Rotations in Michigan. Staff Paper 98-22. East Lansing, MI: Michigan State University.Google Scholar
Gunsolus, J. L. and Buhler, D. D. 1998. A risk management perspective on integrated weed management. J. Crop Prod 2:167–187. Co-published in “Expanding the Context of Weed Management,” Pp. 167-187.Google Scholar
Johnson, W. G., Bradely, P., Hart, S., Buesinger, M., and Massey, R. 2000. Efficacy and economics of weed management in glyphosate-resistant corn. Weed Technol. 14:57–65.Google Scholar
King, R. P. and Robison, L. J. 1984. Risk efficiency models. Chap. 6. in Barry, Peter J., ed. Risk Management in Agriculture. Ames, IA: The Iowa State University Press. Pp. 68–81.Google Scholar
Lazarus, W. F. 2000. Minnesota Farm Machinery Economic Cost Estimates for 2000. FO-6696 2000. St. Paul, MN: University of Minnesota Extension Service.Google Scholar
Lybecker, D. W., Schweizer, E. E., and King, R. P. 1988. Economic analysis of four weed management systems. Weed Sci. 36:846–849.Google Scholar
Olson, K. D. and Eidman, V. R. 1992. A farmer's choice of weed control method and the impacts of policy and risk. Rev. Agric. Econ 18:523–535.Google Scholar
Reddy, K. and Whiting, K. 2000. Weed control and economic comparisons of glyphosate-resistant, sulfonylurea-tolerant, and conventional soybean systems. Weed Technol. 14:204–211.Google Scholar
Swanton, C. J., Shrestha, A., Clements, D. R., Booth, B. D., and Chandler, K. 2002. Evaluation of alternative weed management systems in a modified no-till corn-soybean-winter wheat rotation: weed densities, crop yield, and economics. Weed Sci. 50:504–511.Google Scholar