Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-24T01:16:00.794Z Has data issue: false hasContentIssue false
  • English
  • Français

Weed Community Emergence Time Affects Accuracy of Predicted Corn Yield Loss by WeedSOFT

Published online by Cambridge University Press:  20 January 2017

Mark R. Jeschke ,
David E. Stoltenberg ,
George O. Kegode ,
J. Anita Dille  and
Gregg A. Johnson
Mark R. Jeschke
Affiliation:
Department of Agronomy, 1575 Linden Drive, University of Wisconsin, Madison, WI 53706
David E. Stoltenberg*
Affiliation:
Department of Agronomy, 1575 Linden Drive, University of Wisconsin, Madison, WI 53706
George O. Kegode
Affiliation:
Department of Plant Sciences, North Dakota State University, Fargo, ND 58105
J. Anita Dille
Affiliation:
Department of Agronomy, 3701 Throckmorton Hall, Kansas State University, Manhattan, KS 66506
Gregg A. Johnson
Affiliation:
Department of Agronomy and Plant Genetics, University of Minnesota, Southern Research and Outreach Center, Waseca, MN 56093
*
Corresponding author's E-mail: destolte@wisc.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

WeedSOFT is a state-of-the-art decision support system for weed management in the north central region of the United States, but its accuracy to predict corn yield loss associated with later-emerging weed communities has not been adequately assessed. We conducted experiments in 2004 and 2005 to compare observed and predicted corn yield related to four establishment times of mixed-species weed communities for validation of competitive index modifier (CIM) values in WeedSOFT. Weed communities were established at VE, V2, V4, and V6 corn (emergence, second-leaf, fourth-leaf, and sixth-leaf stages, respectively), and consisted largely of annual grass and moderately competitive annual broadleaf species. Compared to weed-free corn, yield loss occurred in each of seven site-years for weed communities established at VE corn, but in only one site-year for communities established at V2 corn. No corn yield loss was associated with weed communities established at V4 or V6 corn. For communities established at VE corn, predicted corn yield differed from observed yield in all but one site year, with predicted yield less than observed yield in three site-years, and greater than observed yield in two site-years; however, nonlinear regression analyses of yield data pooled over site-years showed that fitted values were similar between predicted and observed yield. For communities established at V2 and V4 corn, predicted yield was less than observed yield in six and five site-years, respectively. For communities established at V6 corn, predicted yield was less than observed yield in three of six site-years, but was similar to observed yield in three of six site-years. These results indicated that the CIM values in WeedSOFT tended to overestimate the competitiveness of late-emerging weed communities. Corn yield data from a pooled analysis of all site-years were used to generate a revised set of growth stage CIM values, which improved the accuracy of predicted corn yield. These results should improve weed management decisions and reduce the need for herbicide applications to late-emerging weeds.

Keywords

CornZea mays LBioeconomic modelcompetitive indexdecision support systemherbicide timingintegrated weed management
Type
Education/Extension
Information
Weed Technology , Volume 23 , Issue 3 , September 2009 , pp. 477 - 485
Copyright
Copyright © 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

Aldrich, R. J. 1987. Predicting crop yield reductions from weeds. Weed Technol 1:199–206.Google Scholar
Buhler, D. D., Liebman, M., and Obrycki, J. J. 2000. Theoretical and practical challenges to an IPM approach to weed management. Weed Sci 48:274–280.CrossRefGoogle Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol 6:191–195.Google Scholar
Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q 2:13–20.Google Scholar
Crawley, M. J. 2002. Statistical Computing: An Introduction to Data Analysis Using S-Plus. West Sussex, England: John Wiley & Sons Ltd. 305–322.Google Scholar
Deines, S. R., Dille, J. A., Blinka, E. L., Regehr, D. L., and Staggenborg, S. A. 2004. Common sunflower (Helianthus annuus) and shattercane (Sorghum bicolor) interference in corn. Weed Sci 52:976–983.Google Scholar
Hock, S. M., Knezevic, S. Z., Johnson, W. G., Sprague, C., and Martin, A. R. 2007. WeedSOFT: Effects of corn-row spacing for predicting herbicide efficacy on selected weed species. Weed Technol 21:219–224.CrossRefGoogle 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.Google Scholar
Neeser, C., Dille, J. A., Krishman, 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
O'Donovan, J. T. 1996. Weed economic thresholds: useful agronomic tool or pipe dream? Phytoprotection 77:13–28.Google Scholar
Pinheiro, J. C. and Bates, D. M. 2000. Mixed-Effects Models in S and S-Plus. New York: Springer Verlag. 528.CrossRefGoogle Scholar
Ratkowsky, D. A. 1990. Handbook of Nonlinear Regression Models. New York: Marcel Dekker. 241.Google Scholar
Schmidt, A. A., Johnson, W. G., Mortensen, D. A., et al. 2005. Evaluation of corn (Zea mays L.) yield-loss estimations by WeedSOFT® in the north central region. Weed Technol 19:1056–1064.CrossRefGoogle Scholar
Schnitker, D. D., Young, B. G., Johnson, W. G., and Loux, M. M. 2006. Accuracy of WeedSOFT® for predicting early-season competitive loads following residual herbicides in glyphosate-resistant corn. North Cent. Weed Sci. Soc. Abstr 61:37.Google Scholar
Swanton, C. J., Weaver, S., Cowan, P., Van Acker, R., Deen, W., and Shrestha, A. 1999. Weed thresholds: theory and applicability. Pages 9–29. In Buhler, D. D. Expanding the Context of Weed Management. Binghamton, NY: Food Products.Google Scholar