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HERB and MSU-HERB Field Validation for Soybean (Glycine max) Weed Control in Mississippi

Published online by Cambridge University Press:  12 June 2017

Alfred Rankins Jr. ,
David R. Shaw  and
John D. Byrd Jr.
Alfred Rankins Jr.
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
David R. Shaw
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
John D. Byrd Jr.
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
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Abstract

In 1994, herbicide efficacy and competitive index databases were adjusted in the soybean herbicide recommendation program HERB to best reflect data for Mississippi. Field experiments were conducted to compare efficacy and economics of postemergence herbicides recommended by HERB and MSU-HERB. The study was conducted utilizing four locations over 2 yr, which provided different soil types, weed spectra, and environmental conditions with which to evaluate weed control from herbicides recommended by these programs. HERB and MSU-HERB agreed on an herbicide recommendation in 62% of the modeling runs. Herbicides recommended by both software versions were generally effective for controlling the predominant weed species at each location. In instances where there was a significant difference in herbicide efficacy between herbicides recommended by HERB and MSU-HERB, improved weed control resulted from herbicides recommended by MSU-HERB. In 1994, excellent moisture conditions enabled soybean to gain a significant competitive advantage over weeds and, as a result, yield loss predictions after treatment were overestimated in most instances. Conversely, 1995 environmental conditions better represented average Mississippi growing conditions, and yield loss predictions after treatment were more accurate. Yield loss prediction accuracy of HERB versions was related to the length of interference between soybean and weeds. Thus, prediction accuracy of HERB and MSU-HERB was similar. Soybean yield increase and net economic gain following MSU-HERB recommendations was as high or higher than following HERB recommendations.

Keywords

Soybean, Glycine max (L.) MerrCompetitive indicesefficacyprediction accuracynet economic gaincompetitive advantageDAE, days after weed emergenceDAT, days after treatmentPOST, postemergencePPI, preplant incorporatedPRE, preemergence
Type
Research
Information
Weed Technology , Volume 12 , Issue 1 , March 1998 , pp. 88 - 96
Copyright
Copyright © 1997 by the Weed Science Society of America 

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References

Literature Cited

Anonymous. 1994. Weed Control Guidelines for Mississippi. Mississippi Cooperative Extension Series Pub. 1532. Mississippi State, MS: Mississippi State University. 143 p.Google Scholar
Castner, E. P., and Banks, P. A. 1989. Soybean herbicide decision model verification in Georgia. Proc. South. Weed Sci. Soc. 42:324.Google Scholar
Dowler, C. C., 1992. Weed survey—southern states. Proc. South. Weed Sci. Soc. 45:400–404.Google Scholar
Green, J. D., and Martin, J. R. 1992. Field evaluations of computer aids for soybean weed control. Proc. South. Weed Sci. Soc. 45:327.Google Scholar
Holloway, J. C., and Shaw, D. R. 1996. Effect of herbicides on ivyleaf morningglory (<i>Ipomoea hederacea</i>) interference in soybean (<i>Glycine max</i>). Weed Sci. 44:860–864.CrossRefGoogle Scholar
Jones, R. E., and Banks, P. A. 1988. Verification of a herbicide decision model in Georgia. Proc. South. Weed Sci. Soc. 41:314.Google Scholar
Kropff, M. J., Weaver, S. E., and Smits, M. A. 1992. Use of ecophysiological models for crop–weed interference: relations among weed density, relative time of weed emergence, relative leaf area and yield loss. Weed Sci. 40:296–301.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 (<i>Glycine max</i>) 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
Ruscoe, J. T., Shaw, D. R., Byrd, J. D. Jr., and Hall, J. 1994. Economic Evaluation of HERB and SWC for Mississippi soybean weed management Proc. South. Weed Sci. Soc. 47:44.Google Scholar
Wilkerson, G. G., Coble, H. D., and Modena, S. A. 1987. A postemergence herbicide decision model for soybeans. Weed Sci. Soc. Am. Abstr. 27: 253.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J. 83:413–417.Google Scholar