Hostname: page-component-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-06-02T10:33:20.979Z Has data issue: false hasContentIssue false

Evaluation of Corn (Zea mays L.) Yield-loss Estimations by WeedSOFT® in the North Central Region

Published online by Cambridge University Press:  20 January 2017

Andrew A. Schmidt
Affiliation:
Department of Agronomy, University of Missouri, Columbia, MO 65211
William G. Johnson*
Affiliation:
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1155
David A. Mortensen
Affiliation:
Department of Crop and Soil Sciences, Penn State University, University Park, PA 16802
Alex R. Martin
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583-0915
Anita Dille
Affiliation:
Department of Agronomy, Kansas State University, Manhattan, KS 66506
Dallas E. Peterson
Affiliation:
Department of Agronomy, Kansas State University, Manhattan, KS 66506
Corey Guza
Affiliation:
Department of Crop and Soil Sciences, Michigan State University, East Lansing, MI 48824-1325
James J. Kells
Affiliation:
Department of Crop and Soil Sciences, Michigan State University, East Lansing, MI 48824-1325
Ryan D. Lins
Affiliation:
Department of Agronomy, University of Wisconsin, Madison, WI 53706
Chris M. Boerboom
Affiliation:
Department of Agronomy, University of Wisconsin, Madison, WI 53706
Christy L. Sprague
Affiliation:
Department of Crop and Soil Science, University of Illinois, Urbana, IL 61801
Stevan Z. Knezevic
Affiliation:
Department of Agronomy, University of Nebraska, Concorde, NE 68728-2828
Fred W. Roeth
Affiliation:
Department of Agronomy, University of Nebraska, Clay Center, NE 68933
Case R. Medlin
Affiliation:
Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078-6028
Thomas T. Bauman
Affiliation:
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1155
*
Corresponding author's E-mail: wgj@purdue.edu

Abstract

Field studies were conducted in 2000 and 2001 to evaluate corn yield-loss predictions generated by WeedSOFT, a computerized weed management decision aid. Conventional tillage practices were used to produce corn in 76-cm rows in Illinois, Indiana, Kansas, Michigan, Missouri, Nebraska, and Wisconsin. A total of 21 site-years from these seven states were evaluated in this study. At 4 wk after planting, weed densities and size, crop-growth stage, estimated weed-free yield, and environmental conditions at the time of application were entered into WeedSOFT to generate POST treatments ranked by percent maximum yield (PMY). POST treatments were chosen with yield losses ranging from 0 to 20%. Data were subjected to linear regression analysis by state and pooled over all states to determine the relationship between actual and predicted yield loss. A slope value equal to one implies perfect agreement between actual and predicted yield loss. Slope value estimates for Illinois and Missouri were equal to one. Actual yield losses were higher than the software predicted in Kansas and lower than predicted in Michigan, Nebraska, and Wisconsin. Slope value estimate from a data set containing all site years was equal to one. This research demonstrated that variability in yield-loss predictions occurred at sites that contained a high density of a single weed specie (>100/m2) regardless of its competitive index (CI); at sites with a predominant broadleaf weed with a CI greater than five, such as Palmer amaranth, giant ragweed, common sunflower, and common cocklebur; and at sites that experience moderate to severe drought stress.

Type
Extension/Outreach
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

Allen, J. A., Johnson, W. G., Smeda, R. J., and Kremer, R. J. 2000. ALS-resistant Helianthus annuus interference in Glycine max . Weed Sci. 48:461466.CrossRefGoogle 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. 45:159165.CrossRefGoogle Scholar
Fausey, J. C. and Renner, K. A. 1997. Germination, emergence, and growth of giant foxtail (Setaria faberi) and fall panicum (Panicum dichotomiflorum). Weed Sci. 45:423425.CrossRefGoogle 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.CrossRefGoogle Scholar
Massinga, R. A., Currie, R. S., and Horak, M. J. 2001. Interference of Palmer amaranth in corn. Weed Sci. 49:202208.CrossRefGoogle Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control-decision aid software. Weed Technol. 5:445452.CrossRefGoogle Scholar
Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. 1996. Applied Linear Regression. 3rd ed. New York: McGraw-Hill. Pp. 5153.Google Scholar
Renner, K. A., Swinton, S. M., and Kells, J. J. 1999. Adaptation and evaluation of the WEEDSIM weed management model in Michigan. Weed Sci. 47:338348.CrossRefGoogle Scholar
Snedecor, G. W. and Cochran, W. G. 1989. Statistical Methods. 8th edition. Ames, IA: Iowa State University Press. Pp. 6482.Google Scholar
Snipes, C. E., Street, J. E., and Walker, R. H. 1987. Interference periods of common cocklebur (Xanthium strumarium) with cotton (Gossypium hirsutum). Weed Sci. 4:529532.CrossRefGoogle Scholar
Swinton, S. M. and King, R. P. 1994. The value of weed population information in a dynamic setting: the case of weed control. Am. J. Agric. Econ. 75:3646.CrossRefGoogle 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.CrossRefGoogle Scholar