Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T12:06:58.581Z Has data issue: false hasContentIssue false

Adaptation and evaluation of the WEEDSIM weed management model for Michigan

Published online by Cambridge University Press:  12 June 2017

Scott M. Swinton
Affiliation:
Department of Agricultural Economics, Michigan State University, E. Lansing, MI 48824
James J. Kells
Affiliation:
Department of Crop and Soil Sciences, Michigan State University, E. Lansing, MI 48824

Extract

The WEEDSIM bioeconomic model was developed in Minnesota and was designed to support weed management decisions for both soil-applied and postemergence weed control programs in Zea mays and Glycine max. In this research, we adapted the WEEDSIM weed management model to Michigan by modifying the crop yield loss functions and herbicide efficacy ratings. We then validated the components of the model and determined whether WEEDSIM led to more profitable weed management than recommendations from SOYHERB or CORNHERB, computer decision aids based solely on herbicide efficacy and cost. The crop year significantly influenced the weed-free yield in Z. mays and G. max, but the weed—crop interference function did not change each year. Total weed seed increased in the untreated compared with the weed-free control over the 3-yr period. Weed seed did not increase significantly in WEEDSIM preemergence/postemergence (PRE/POST), WEEDSIM postemergence, or CORNHERB or SOYHERB treatments compared with the weed-free control, although annual grass seedling density at the time of postemergence herbicide application had increased by 1995 in the WEEDSIM postemergence treatment in G. max because of a 2,4-D amine application only in Z. mays in 1994. WEEDSIM PRE/POST and CORNHERB provided excellent weed control in all three years, and WEEDSIM PRE/POST resulted in gross margins over weed control costs equal to or greater than CORNHERB recommendations. In G. max, Chenopodium album and annual grass control was excellent in all three years for WEEDSIM PRE/POST, WEEDSIM postemergence, and SOYHERB treatments. The highest average gross margin for the 3-yr study was from mechanical weed control in 76-cm-wide rows of G. max ($806 ha−1) and from SOYHERB in 38- and 19-cm-wide rows of G. max ($776 and $808 ha−1, respectively). WEEDSIM recommendations controlled weeds and maintained crop yield in both Z. mays and G. max.

Type
Weed Management
Copyright
Copyright © 1999 by the Weed Science Society of America 

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

Literature Cited

Bates, D. M. and Watts, D. G. 1988. Nonlinear Regression Analysis and Its Application. New York: Wiley and Sons.Google Scholar
Berti, A. and Zanin, G. 1997. GENSTINF: a decision model for postemergence-emergence weed management in soybean [Glycine max (L.) Merr.]. Crop Prot. 16:109116.Google Scholar
Buhler, D. D. 1996. Development of alternative weed management strategies. J. Prod. Agric. 9:501505.Google Scholar
Buhler, D. D., Gunsolus, J. L., and Ralston, D. F. 1992. Integrated weed management techniques reduce herbicide inputs in soybean. Agron. J. 84:973978.Google Scholar
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. 44:915923.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. 45:158165.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.Google Scholar
Crook, T. M. and Renner, K. A. 1990. Common lambsquarters (Chenopodium album competition and time of removal in soybean (Glycine max). Weed Sci. 38:358364.CrossRefGoogle Scholar
Dieleman, A., Hamill, A. S., Wiese, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (Amaranthus retroflexus) interference in soybean (Glycine max). Weed Sci. 43:612618.Google Scholar
Forcella, F. 1992. Prediction of weed seedling densities from buried seed reserves. Weed Res. 32:2938.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. 44:650661.Google Scholar
Forcella, F., Wilson, R. G., Dekker, J., et al. 1997. Weed seed bank emergence across the corn belt. Weed Sci. 45:6776.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 seed banks of the U. S. corn belt: magnitude, variation, emergence, and application. Weed Sci. 42:636644.Google Scholar
Fuller, E., Lazarus, B., Carrigan, L., and Green, G. 1992. Minnesota Farm Machinery Economic Costs Estimates for 1990. St. Paul, MN: University of Minnesota Cooperative Extension Service Publication AG-FO-2308-c.Google Scholar
Gross, K. A. and Renner, K. A. 1989. A new method for estimating seed numbers in the soil. Weed Sci. 37:836839.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 State University Agricultural Experiment Station.Google Scholar
Kidder, D., Posner, B., and Miller, D. 1989. WEEDIR: weed control directory, Version 3.0. St. Paul, MN: University of Minnesota Extension Service AG-CS-2163.Google Scholar
Knezevic, S. Z., Wiese, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568573.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. 9:535540.Google Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5:445452.Google Scholar
Nott, S. B., Schwab, G. D., Jones, J. D., Hilker, J. H., and Copeland, L. O. 1995. Crops and Livestock Budgets Estimates for Michigan. East Lansing, MI: Michigan State University Department of Agricultural Economics, Agricultural Economics Report No. 581.Google Scholar
Renner, K. A. and Black, J. R. 1991. SOYHERB: a computer program for soybean herbicide decision making. Agron. J. 83:921925.Google Scholar
Renner, K. A. and Woods, J. J. 1999. Influence of cultural practices on weed management in soybean (Glycine max). J. Prod. Agric. 12:4853.CrossRefGoogle Scholar
Swinton, S. M., Buhler, D. D., Forcella, F., Gunsolus, J. L., and King, R. P. 1994a. Estimation of crop yield loss due to interference by multiple weed species. Weed Sci. 42:103109.Google Scholar
Swinton, S. M. and King, R. P. 1994a. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44:313345.Google Scholar
Swinton, S. M. and King, R. P. 1994b. The value of weed population information in a dynamic setting: the case of weed control. Am. J. Agric. Econ. 75:3646.CrossRefGoogle Scholar
Swinton, S. M., Sterns, J., Renner, K., and Kells, J. 1994b. Estimating weed-crop interference parameters for weed management models. East Lansing, MI: Michigan Agricultural Experiment Station Research Report 538.Google Scholar
[USDA] U.S. Department of Agriculture. 1991. U.S. average costs of production for major field crops. Washington, DC: Economic Research Service, Agriculture Information Bulletin No. 639.Google Scholar
[USDA] U.S. Department of Agriculture. 1993. Agricultural resources: inputs situation and outlook. Washington, DC: Economic Research Service, Resources and Technology Division Report AR-29.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J. 83:413417.Google Scholar
Woods, J. J. 1992. Reduced herbicide inputs for corn and soybean production. M.S. thesis. Michigan State University, East Lansing, MI. 90 p.Google Scholar