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PALWEED: WHEAT II: revision of a weed management decision model in response to field testing

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:
Department of Agricultural Economics, Washington State University, Pullman, WA 99164
Frank L. Young
Affiliation:
USDA-ARS, Washington State University, Pullman, WA 99164
Chris M. Boerboom
Affiliation:
Department of Agronomy, University of Wisconsin, Madison, WI 53706
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Abstract

PALWEED:WHEAT is a bioeconomic decision model for determining profit-maximizing postemergence herbicide treatments for winter wheat in the Washington–Idaho Palouse region. PALWEED:WHEAT performed relatively well economically in 2 yr of on-farm field tests. However, the model was less sensitive than desired in prescribing postemergence broadleaved herbicides in the presence of high densities of broadleaved weed seedlings. Therefore, PALWEED:WHEAT was revised in response to the field testing. This paper compares the revised model's agronomic and economic performance to the original model in computer simulations. The revised model, PALWEED:WHEAT II, differs from the original model in several respects: (1) exponential functions replace linear functions in predicting weed survival, (2) preplant application of a nonselective herbicide is entered as an exogenous binary variable, (3) separate indices of broadleaved and grass competition are substituted for an aggregate weed competition index in the wheat yield function, (4) hyperbolic replaces logistic functional representation of weed damage to wheat yield, and (5) separate models are estimated for winter wheat after spring dry pea and for winter wheat in all examined crop rotation positions. In simulations including a variety of agronomic and economic conditions, PALWEED:WHEAT II recommended postemergence herbicide types and rates that consistently complied with agronomic and economic theory. Furthermore, the revised model, especially when estimated from the relevant wheat after pea data set, was markedly more balanced in recommending both broadleaved and grass herbicides in response to observed densities of both weed groups. The substantial change in herbicide recommendations in response to changes in model functional specifications following field testing confirms the importance of field testing and revision of bioeconomic decision models.

Keywords

Winter wheat, Triticum aestivum L. ‘Daws’ and ‘Hill 81.’Bioeconomic modelmultispeciesprofit maximizationweed biomassweed seedling countyield response function
Type
Weed Management
Information
Weed Science , Volume 46 , Issue 2 , April 1998 , pp. 205 - 213
Copyright
Copyright © 1998 by the Weed Science Society of America 

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Footnotes

current address: Korea Rural Economic Institute, Seoul, Korea

References

Literature Cited

Beattie, B. R. and Taylor, C. R. 1993. The Economics of Production. Melbourne, FL: Krieger. 258 p.Google Scholar
Black, I. D. and Dyson, C. B. 1993. An economic threshold model for spraying herbicides in cereals. Weed Res. 33: 279–290.CrossRefGoogle Scholar
Boerboom, C. M. 1993. Using bioeconomic modeling to identify profitable and environmentally safe weed management systems. Pages 103–106 in 1993 STEEP II Progress Report. University of Idaho, Oregon State University, Washington State University, and USDA-ARS. Pullman, WA: Land Management Unit, USDA-ARS, Washington State University.Google Scholar
Boerboom, C. M., Young, F. L., Kwon, T., and Feldick, T. 1993. IPM Research Project for Inland Pacific Northwest Wheat Production. Pullman, WA: Washington State University Agricultural Experiment Station Research Bull. XB1029. 46 p.Google Scholar
Brooke, A., Kendrick, D., and Meeraus, A. 1996. GAMS. Release 2.25. A User's Guide. Washington, DC: GAMS Development Corporation. 171 p.Google Scholar
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
Davidson, R. and Mackinnon, J. G. 1981. Several tests for model specification in the presence of alternative hypotheses. Econometrica 49: 781–793.Google Scholar
Etheridge, D. E., Ervin, R. T., Hamilton, C. M., Keeling, J. W., and Abemathy, J. R. 1990. Economic weed control in high plains cotton. J. Prod. Agric. 3: 246–252.Google Scholar
Feder, G. 1979. Pesticides, information and pest management under uncertainty. Am. J. Agric. Econ. 77: 33–39.Google Scholar
Fox, G. and Weersink, A. 1995. Damage control and increasing returns. Am. J. Agric. Econ. 77: 33–39.Google Scholar
Hall, M. J. 1995. Economics of Weed Management and Farming Systems in the Palouse Region. . Washington State University, Pullman, WA. 217 p.Google Scholar
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. New York: J. Wiley. p. 456.Google Scholar
King, R. P., Lybecker, D. W., Regmi, A., and Swinton, S. M. 1993. Bioeconomic models of crop production systems: design, development, and use. Rev. Agric. Econ. 15: 389–401.Google Scholar
King, R. P., Lybecker, D. W., Schweizer, E. E., and Zimdahl, R. L. 1986. Bioeconomic modeling to simulate weed control for continuous corn. Weed Sci. 34: 972–979.Google Scholar
Kwon, T. J., Young, D. L., Young, F. L., and Boerboom, C. M. 1995. PALWEED:WHEAT: a bioeconomic decision model for postemergence weed management in winter wheat (Triticum aestivum). Weed Sci. 43: 595–603.CrossRefGoogle Scholar
Lichtenberg, E. and Zilberman, D. 1986. The econometrics of damage control: why specification matters. Am. J. Agric. Econ. 68: 261–273.CrossRefGoogle Scholar
Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions in corn based on bioeconomic modeling. Weed Sci. 39: 124–129.CrossRefGoogle Scholar
Marra, M. C. and Carlson, G. A. 1983. An economic threshold model for weeds in soybeans (Glycine max). Weed Sci. 31: 604–609.CrossRefGoogle Scholar
Moffit, L. J., Hall, D. C., and Osteen, C. D. 1984. Economic thresholds under uncertainty with application to corn nematode management. South. J. Agric. Econ. 16: 151–157.Google Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5: 445–452.Google Scholar
Painter, K., Hinman, H., Miller, B., and Burns, J. 1992. 1991 Crop Enterprise Budgets: Eastern Whitman County, Washington State. Pullman, WA: Washington State University Cooperative Extension Bull. EB 1437. 45 p.Google Scholar
Painter, K. M. and Young, D. L. 1991. Market Price Projections of Major U.S. Crops by Farm Policy Scenario: Results of a Survey of Economists. AE 91–1. Pullman, WA: Washington State University. 21 p.Google Scholar
[SAS] Statistical Analysis Systems. 1988. SAS/ETS User's Guide. Version 6. Cary, NC: Statistical Analysis Systems Institute. 221 p.Google Scholar
Taylor, C. R. and Burt, O. R. 1984. Near-optimal management strategies for controlling wild oats in spring wheat. Am. J. Agric. Econ. 66: 50–60.CrossRefGoogle Scholar
Weersink, A., Deen, W., and Weaver, S. 1992. Evaluation of alternative decision rules for postemergence herbicide treatments in soybean. J. Prod. Agric. 5: 298–303.Google Scholar
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