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Palweed:Wheat: A Bioeconomic Decision Model for Postemergence Weed Management in Winter Wheat (Triticum aestivum)

Published online by Cambridge University Press:  12 June 2017

Tae-Jin Kwon ,
Douglas L. Young ,
Frank L. Young  and
Chris M. Boerboom
Tae-Jin Kwon
Affiliation:
Dep. Agric. Econ., Washington State Univ., Pullman, WA 99164
Douglas L. Young
Affiliation:
Dep. Agric. Econ., Washington State Univ., Pullman, WA 99164
Frank L. Young
Affiliation:
U.S. Dep. Agric., Washington State Univ., Pullman, WA 99164
Chris M. Boerboom
Affiliation:
Crop and Soil Sci., Washington State Univ., Pullman, WA 99164
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Abstract

Based on six years of data from a field experiment near Pullman, WA, a bioeconomic decision model was developed to annually estimate the optimal post-emergence herbicide types and rates to control multiple weed species in winter wheat under various tillage systems and crop rotations. The model name, PALWEED:WHEAT, signifies a Washington-Idaho Palouse region weed management model for winter wheat The model consists of linear preharvest weed density functions, a nonlinear yield response function, and a profit function. Preharvest weed density functions were estimated for four weed groups: summer annual grasses, winter annual grasses, summer annual broadleaves, and winter annual broadleaves. A single aggregated weed competition index was developed from the four density functions for use functions for use in the yield model. A yield model containing a logistic damage function performed better than a model containing a rectangular hyperbolic damage function. Herbicides were grouped into three categories: preplant nonselective, postemergence broadleaf, and postemergence grass. PALWEED:WHEAT was applied to average conditions of the 6-yr experiment to predict herbicide treatments that maximized profit. In comparison to average treatment rates in the 6-yr experiment, the bioeconomic decision model recommended less postemergence herbicide. The weed management recommendations of PALWEED:WHEAT behaved as expected by agronomic and economic theory in response to changes in assumed weed populations, herbicide costs, crop prices, and possible restrictions on herbicide application rates.

Keywords

Winter wheat, Triticum aestivum L. ‘Daws' and ‘Hill 81.’Multi-speciesweed seedling countweed biomassyield response functionoptimization
Type
Weed Biology and Ecology
Information
Weed Science , Volume 43 , Issue 4 , December 1995 , pp. 595 - 603
Copyright
Copyright © 1995 by the Weed Science Society of America 

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References

LITERATURE CITED

1. Beattie, Bruce R. and Robert Taylor, C. 1992. The economics of production. Krieger, Melbourne, FL.Google Scholar
2. Boerboom, C. M., Young, F. L., Kwon, T., and Feldick, T. 1993. IPM research project for inland Pacific-Northwest wheat production. Wash. State Univ. Agric. Exp. Stn. Res. Bull. XB 1029. 46 pp.Google Scholar
3. Brooke, A., Kendrick, D., and Meeraus, A. 1992. GAMS. Release 2.25. The Scientific Press, South San Francisco.Google Scholar
4. Burrill, L. C., William, R. D., Parker, R., Boerboom, C., Callihan, R. H., Eberlein, C., and Morishita, D. W. 1992. Pacific Northwest weed control handbook. Oregon State Univ. Press. 326 pp.Google Scholar
5. Cousens, R. D. 1985. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513–521.Google Scholar
6. Cousens, R. D., Doyle, C. J., Wilson, B. J., and Cussans, G. W. 1986. Modelling the economics of controlling Avena fatua in winter wheat. Pestic. Sci. 17:1–12.Google Scholar
7. Davidson, Russel and Mackinnon, James G. 1981. Several tests for model specification in the presence of alternative hypotheses. Econometrica 49:781–793.Google Scholar
8. Doyle, C. J., Cousens, R., and Moss, S. R. 1986. A model of the economics of controlling Alopecurus myosuroides huds. in winter wheat. Crop Prot. 5:143–150.Google Scholar
9. Ethridge, D. E., Ervin, R. T., Hamilton, C. M., Keeling, J. W., and Abernathy, J. R. 1990. Economic weed control in high plains cotton. J. Prod. Agric. 3:246–252.Google Scholar
10. Judge, G. G., Hill, R. C., Griffiths, W. E., Lutkeplhl, H., and Lee, T. C. 1985. Introduction to the theory and practice of econometrics. 2nd ed. John Wiley and Sons, New York.Google Scholar
11. King, R. P., Lybecker, D. W., Schweizer, E. E., and Zimdahl, R. L. 1986. Bioeconomic modeling to simulate weed control strategies for continuous corn (Zea mays). Weed Sci. 34:972–979.Google Scholar
12. Kwon, T. J. 1993. Bioeconomic decision models for weed management in wheat, barley, and peas: an econometric approach. Unpublished Ph.D. dissertation, Dep. of Agric. Econ., Wash. State Univ., Pullman. 335 pp.Google Scholar
13. Lichtenberg, E. and Zilberman, D. 1986. The econometrics of damage control: why specification matters. Am. J. Agric. Econ. 68:261–273.Google Scholar
14. Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions in corn based on bioeconomic modeling. Weed Sci. 31:124–129.Google Scholar
15. Marra, M. C. and Carlson, G. A. 1983. An economic threshold model for soybeans (Glycine max). Weed Sci. 31:604–609.Google Scholar
16. Marra, M. C., Gould, T. L., and Porter, G. A. 1989. A computable economic threshold model for weeds in field crops with multiple pests, quality effects and uncertain spraying period length. Northeast J. Agric. Res. Econ. 18:12–17.Google Scholar
17. Painter, K., Hinman, H., Miller, B., and Bums, J. 1992. 1991 crop enterprise budgets: eastern Whitman County, Washington State. Wash. State Univ. Coop. Ext. Bull. EB 1437. 45 pp.Google Scholar
18. Pannell, D. J. 1990. Responses to risk in weed control decisions under expected profit maximization. J. Agric. Econ. 41:391–403.Google Scholar
19. SAS Institution, Inc. 1988. SAS/ETS User's Guide, Version 6 Ed. Cary, NC.Google Scholar
20. Shribbs, J. M., Lybecker, D. W., and Schweizer, E. E. 1990. Bioeconomic weed management models for sugarbeet (Beta vulgaris) production. Weed Sci. 38:436–444.Google Scholar
21. Swinton, S. M. and King, R. P. 1990. WEEDSIM, a bioeconomic model of weed management in corn. Univ. Minn., St. Paul, Inst. of Agric., Forestry and Home Econ., Staff Paper P90-71.Google Scholar
22. Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J. 83:413–417.Google Scholar
23. Young, D.L., Kwon, T.J., and Young, F.L. 1994. Profit and risk for integrated conservation farming systems in the Palouse. J. Soil and Water Conserv. 49:581–586.Google Scholar
24. Young, F. L., Ogg, A. G. Jr., Papendick, R. I., Thill, D. C., and Alldredge, J. R. 1994. Tillage and weed management affect winter wheat yield in an integrated pest management system. Agron. J. 86:147–154.Google Scholar