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On-Farm Comparison of Three Postemergence Weed Management Decision Aids in Michigan

Published online by Cambridge University Press:  20 January 2017

Scott M. Swinton ,
Karen A. Renner  and
James J. Kells
Scott M. Swinton*
Affiliation:
Department of Agricultural Economics, Michigan State University, East Lansing, MI 48824
Karen A. Renner
Affiliation:
Department of Crop and Soil Sciences, Michigan State University, East Lansing, MI 48824
James J. Kells
Affiliation:
Department of Crop and Soil Sciences, Michigan State University, East Lansing, MI 48824
*
Corresponding author's E-mail: swintons@msu.edu
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Abstract

Weed management decision aids have proliferated in recent years, but none of them have been rigorously compared with actual farmer weed management on farm fields. This research compares the Michigan WEEDSIM/GWM bioeconomic model and the CORNHERB and SOYHERB herbicide selection models with farmer weed management in Michigan. In 19 site-years of research in corn and soybean during 1996 to 1997, we found that crop yield, weed control costs, and gross margin over weed control costs (profitability) with the computerized decision aids were not statistically superior to the farmer treatments, even at a one-sided threshold of P = 0.10. In corn the gross margin of the farmer treatment ranked highest in both years. In soybean the gross margin of the SOYHERB treatment ranked highest in 1996 and that of the WEEDSIM/GWM treatment was highest in 1997. Overall, the farmer treatment had the highest gross margin 5½ times, the CORNHERB–SOYHERB treatment 6 times, and the WEEDSIM/GWM treatment 7½ times (where ties were divided equally to give 1/2 to each). However, none of these rank differences corresponded to a statistically significant gross margin gain over the farmer treatment.

Keywords

Corn, Zea mays L.soybean, Glycine maxBioeconomic modelingCORNHERBdecision modelintegrated weed managementon-farm trialsparticipatory researchSOYHERBtechnology adoptionweed controlweed management modelWEEDSIM
Type
Commentary
Information
Weed Technology , Volume 16 , Issue 3 , September 2002 , pp. 691 - 698
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Buhler, D. D., King, R. P., Swinton, S. M., Gunsolus, J. L., and Forcella, F. 1996. Field evaluation of a bioeconomic model for weed management in corn (Zea mays). Weed Sci. 44: 915–923.Google Scholar
Buhler, D. D., King, R. P., Swinton, S. M., Gunsolus, J. L., and Forcella, F. 1997. Field evaluation of a bioeconomic model for weed management in soybean (Glycine max). Weed Sci. 45: 158–165.Google Scholar
CIMMYT. 1988. From Agronomic Data to Farmer Recommendations: An Economics Training Manual. (Completely revised edition.) CIMMYT Economics Program. Mexico, D.F.: International Maize and Wheat Improvement Center (CIMMYT). 79 p.Google Scholar
Forcella, F., King, R. P., Swinton, S. M., Buhler, D. D., and Gunsolus, J. L. 1996. Multi-year validation of a decision aid for integrated weed management in row crops. Weed Sci. 44: 650–661.Google Scholar
Forcella, F., Wilson, R. G., Renner, K. A., Dekker, J., Harvey, R. G., Alm, D. A., Buhler, D. D., and Cardina, J. A. 1992. Weed seedbanks of the U.S. corn belt: magnitude, variation, emergence, and application. Weed Sci. 42: 636–644.Google Scholar
Hoffman, M. L., Buhler, D. D., and Owen, M. D. K. 1999. Weed population and crop yield response to recommendations from a weed control decision aid. Agron. J. 91: 386–392.Google Scholar
Kells, J. J. and Black, J. R. 1991. CORNHERB—Herbicide Options Program for Weed Control in Corn: An Integrated Decision Support Computer Program—Version 2.0. East Lansing, MI: Michigan Agricultural Experiment Station, Michigan State University.Google Scholar
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
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenck, R., Kells, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)–velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci. 44: 309–313.Google Scholar
Lindquist, J. L., Mortensen, D. A., and Westra, P. et al. 1999. Stability of corn (Zea mays)–foxtail (Setaria spp.) interference relationships. Weed Sci. 47: 195–200.Google 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.Google Scholar
Monks, C. D., Bridges, D. C., Woodruff, J. W., Murphy, T. R., and Berry, D. J. 1995. Expert system evaluation and implementation for soybean (Glycine max) weed management. Weed Technol. 9: 535–540.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
Renner, K. A. and Black, J. R. 1991. SOYHERB: a computer program for soybean herbicide decision making. Agron. J. 83: 921–925.Google Scholar
Renner, K. A., Swinton, S. M., and Kells, J. J. 1999. Adaptation and evaluation of the WEEDSIM weed management model for Michigan. Weed Sci. 47: 338–348.CrossRefGoogle Scholar
[SAS] Statistical Analysis Systems. 2000. The SAS System for Windows. Version 8e. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Swinton, S. M. and King, R. P. 1994a. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44: 313–335.Google Scholar
Swinton, S. M. and King, R. P. 1994b. The value of pest information in a dynamic setting: the case of weed control. Am. J. Agric. Econ. 76: 36–46.Google Scholar
Swinton, S., Sterns, J., Renner, K., and Kells, J. 1994. Estimating Weed–Crop Interference Parameters for Weed Management Models. Research Rep. 538. East Lansing, MI: Michigan Agricultural Experiment Station, Michigan State University.Google Scholar
Vangessel, M. J., Schweizer, E. E., Lybecker, D. W., and Westra, P. 1996. Integrated weed management systems for irrigated corn (Zea mays) in Colorado: a case study. Weed Sci. 44: 423–428.Google Scholar
Wiles, L., King, R., Schweizer, E., Lybecker, D., and Swinton, S. 1996. GWM: general weed management model. Agric. Syst. 50: 355–376.Google Scholar