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WeedSOFT: Effects of Corn-Row Spacing for Predicting Herbicide Efficacy on Selected Weed Species

Published online by Cambridge University Press:  20 January 2017

Shawn M. Hock ,
Stevan Z. Knezevic ,
William G. Johnson ,
Christy Sprague  and
Alex R. Martin
Shawn M. Hock
Affiliation:
University of Nebraska, Lincoln NE 68583
Stevan Z. Knezevic*
Affiliation:
University of Nebraska, Concord, NE 68728
William G. Johnson
Affiliation:
Purdue University, West Lafayette, IN 47905
Christy Sprague
Affiliation:
Michigan State University, East Lansing, MI 48824
Alex R. Martin
Affiliation:
University of Nebraska, Lincoln NE 68583
*
Corresponding author's E-mail: [email protected]
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Abstract

The ability to accurately estimate herbicide efficacy is critical for any decision-support system used in weed management. Recent efforts by weed scientists in the North Central United States to adopt WeedSOFT across a broad region have resulted in a number of regional research projects designed to assess and improve the predictive capability of WeedSOFT. Field studies were conducted from 2000 to 2002 in Nebraska, Missouri, and Illinois to evaluate herbicide-efficacy predictions made by WeedSOFT in two corn-row spacings. Following crop and weed emergence, input variables, such as weed densities and heights, were entered into WeedSOFT to generate a list of treatments ranked by predicted crop yields. The five treatments evaluated included those predicting highest crop-yield potential (recommended control treatment 1), a 10% yield reduction, a 20% yield reduction, a 10% yield reduction plus cultivation, and cultivation alone. These treatments were applied to corn grown in 38- and 76-cm rows. Generally, treatments applied in 38-cm rows had more accurate herbicide-efficacy predictions compared with 76-cm rows. WeedSOFT provided better control predictions for broadleaf than grass species. WeedSOFT provided excellent herbicide-efficacy predictions for the highest crop-yield potential, which indicates a good potential for practical use of this software for herbicide recommendations.

Keywords

Pennsylvania smartweed, Polygonum pensylvanicum L. POLPYCorn, Zea mays L.Decision-support systemherbicide decision aid
Type
Research
Information
Weed Technology , Volume 21 , Issue 1 , March 2007 , pp. 219 - 224
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

van, AckerR. C., Weise, S. F., and Swanton, C. J. 1993. Influence of interference from a mixed weed species stand on soybean [Glycine max (L.) Merr.] growth. Can. J. Plant Sci. 73:1293–1304.Google Scholar
Hock, S. M., Knezevic, S. Z., Martin, A. R., and Lindquist, J. L. 2006. Performance of WeedSOFT for predicting soybean yield loss. Weed Technol. 20:478–484.CrossRefGoogle Scholar
Knezevic, S. Z., Evans, S. P., and Mainz, M. 2003. Row spacing influences the critical timing for weed removal in soybean (Glycine max). Weed Technol. 17:666–673.CrossRefGoogle Scholar
Lindquist, J. L. 2001. Performance of INTERCOM for predicting corn-velvetleaf interference across north-central United States. Weed Sci. 49:195–201.Google Scholar
Mitchell, P. L. and Sheehy, J. E. 1997. Comparison of predictions and observations to assess model performance: a method of empirical validation. in Kropff, M.J., Teng, P.S., Aggarwad, P.K., Bouma, J., Bouman, B.A.M., Jones, J.W., van Laar, H.H., eds. Applications of Systems Approaches at the Field Level. London Kluwer Academic. 437–450.Google Scholar
Mulugeta, D. and Boerboom, C. M. 2000. Critical time of weed removal in glyphosate-resistant Glycine max . Weed Sci. 48:35–42.Google Scholar
Neeser, C., Dille, J. A., Krishnan, G., Mortensen, D. A., Rawlinson, J. T., Martin, A. R., and Bills, L. B. 2004. WeedSOFT: a weed management decision support system. Weed Sci. 52:115–122.Google Scholar
Schmidt, A. A. and Johnson, W. G. 2004. Influence of early season yield loss predictions from WeedSOFT® and soybean row spacing on weed seed production from a mixed weed community. Weed Technol. 18:412–418.Google Scholar
Shaw, D. R., Rankins, A. Jr, Ruscoe, J. T., and Byrd, J. D. Jr. 1998. Field validation of weed control recommendations from HERB and SWC herbicide recommendation models. Weed Technol. 12:78–87.CrossRefGoogle Scholar
Walker, R. H. and Buchanan, G. A. 1982. Crop manipulation in integrated weed management systems. Weed Sci. 30:17–24.CrossRefGoogle Scholar
Wiley, G. L. 1986. Velvetleaf (Abutilon theophrasti) interference in soybeans (Glycine max) as influenced by row width, tillage system and disease. Diss. Abstr. Int. B Sci. Eng. 46:2149B. [Abstract].Google Scholar