Hostname: page-component-76fb5796d-9pm4c Total loading time: 0 Render date: 2024-04-26T12:28:59.024Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysis of Synergistic and Antagonistic Effects of Herbicides Using Nonlinear Mixed-Model Methodology

Published online by Cambridge University Press:  20 January 2017

David C. Blouin ,
Eric P. Webster  and
Wei Zhang
David C. Blouin
Affiliation:
Department of Experimental Statistics, 45 Ag. Administration Building, Louisiana State University, Baton Rouge, LA 70803
Eric P. Webster*
Affiliation:
Department of Agronomy and Environmental Management, 104 Sturgis Hall, Louisiana State University AgCenter, Baton Rouge, LA 70803
Wei Zhang
Affiliation:
Department of Agronomy and Environmental Management, 104 Sturgis Hall, Louisiana State University AgCenter, Baton Rouge, LA 70803
*
Corresponding author's E-mail: ewebster@agcenter.lsu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

When herbicides are applied in mixture, and infestation by weeds is less than expected compared with when herbicides are applied alone, a synergistic effect is said to exist. The inverse response is described as being antagonistic. However, if the expected response is defined as a multiplicative, nonlinear function of the means for the herbicides when applied alone, then standard linear model methodology for tests of hypotheses does not apply directly. Consequently, nonlinear mixed-model methodology was explored using the nonlinear mixed-model procedure (PROC NLMIXED) of SAS System®. Generality of the methodology is illustrated using data from a randomized block design with repeated measures in time. Nonlinear mixed-model estimates and tests of synergistic and antagonistic effects were more sensitive in detecting significance, and PROC NLMIXED was a versatile tool for implementation.

Keywords

Least significant differencelinear mixed modelsrepeated measurestank mixtureDAT, days after treatmentGH, glufosinate plus mixture herbicideGHD, glufosinate plus mixture herbicide by DATML, maximum likelihoodMSE, mean square errorRep, replication
Type
Education/Extension
Information
Weed Technology , Volume 18 , Issue 2 , June 2004 , pp. 464 - 472
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.)

Footnotes

∗ Publication 03-14-0974 Louisiana Agricultural Experiment Station Journal Series.

References

Literature Cited

Colby, S. R. 1967. Calculating synergistic and antagonistic responses of herbicide combinations. Weeds. 15:20–22.Google Scholar
Flint, J. L., Cornelius, P. L., and Barrett, M. 1988. Analyzing herbicide interactions: a statistical treatment of Colby's method. Weed Technol. 2:304–309.Google Scholar
Gowing, D. P. 1960. Comments on tests of herbicide mixtures. Weeds. 8:379–391.Google Scholar
Hydrick, D. E. and Shaw, D. R. 1994. Effects of tank-mix combinations of nonselective foliar and selective soil-applied herbicides on three weed species. Weed Technol. 8:129–133.Google Scholar
Lanclos, D. Y., Webster, E. P., and Zhang, W. 2002. Glufosinate tank-mix combinations in glufosinate-resistant rice (Oryza sativa). Weed Technol. 16:659–663.CrossRefGoogle Scholar
Littell, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. 1996. SAS System® for Mixed Models. Cary, NC: Statistical Analysis Systems Institute. 633 p.Google Scholar
[SAS] Statistical Analysis Systems. 1999. SAS Online Doc®, Version 8. Cary, NC: Statistical Analysis Systems Institute. Pp. 2419–2504.Google Scholar
Tammes, P. M. L. 1964. Isoboles, a graphic representation of synergism in pesticides. Neth. J. Plant Pathol. 70:73–80.Google Scholar
Webster, E. P. and Shaw, D. R. 1997. Potential Stale Seedbed Herbicide Combinations for Cotton. Mississippi Agricultural and Forestry Experiment Station Technical Bulletin 216. P. 6.Google Scholar
Yvantis, E. A. and Flatman, G. T. 1991. On confidence interval for the product of three means of three normally distributed populations. J. Chemometrics. 5:309–319.Google Scholar