Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-05-08T09:57:22.553Z Has data issue: false hasContentIssue false
  • English
  • Français

On a Method of Analysis for Synergistic and Antagonistic Joint-Action Effects with Fenoxaprop Mixtures in Rice (Oryza sativa)

Published online by Cambridge University Press:  20 January 2017

David C. Blouin ,
Eric P. Webster  and
Jason A. Bond
David C. Blouin*
Affiliation:
Department of Experimental Statistics, 45 Agricultural Administration Building, Louisiana State University Agricultural Center, Baton Rouge, LA 70803
Eric P. Webster
Affiliation:
School of Plant, Environmental, and Soil Sciences, 104 Sturgis Hall, Louisiana State University Agricultural Center, Baton Rouge, LA 70803
Jason A. Bond
Affiliation:
Delta Research and Extension Center, Mississippi State University, Stoneville, MS 38776
*
Corresponding author's E-mail: dblouin@lsu.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Presented and illustrated is an easy-to-implement and flexible methodology for the analysis of synergistic and antagonistic effects when the effects are defined as nonlinear functions of means. The methodology augments standard mixed-model analyses with the Delta method for standard errors of nonlinear functions of means. Explained is why standard ANOVA methods that have been adopted in the literature are not recommended. To illustrate the methodology, the joint-action effects of fenoxaprop with companion herbicides in two-component mixtures for weed control in rice were evaluated. The companion herbicides were halosulfuron, bispyribac-sodium, bensulfuron, penoxsulam, carfentrazone, quinclorac, and imazethapyr. Weeds evaluated were barnyardgrass and broadleaf signalgrass. Experiments in Louisiana and Mississippi in 2009 revealed a preponderance of antagonistic effects. The analysis showed that mixtures with bispyribac-sodium, penoxsulam, quinclorac, and imazethapyr were generally the most antagonistic and provided the least control.

A continuación se presenta e ilustra una metodología flexible y fácil de implementar para el análisis de efectos sinergéticos y antagónicos cuando éstos están definidos como funciones no lineales de los medios. La metodología complementa los análisis estándar de modelo mixto con el método Delta para errores estándar de funciones no lineales de los medios. Se pretende explicar por qué no se recomiendan los métodos estándar de análisis de variancia que han sido adoptados en la literatura. Para ilustrar esta metodología se evaluaron los efectos de acción conjunta del fenoxaprop con otros herbicidas en las mezclas de dos componentes para el control de las malezas en el cultivo de Oryza sativa L. Los herbicidas combinados con fenoxaprop fueron halosulfuron, sodio-bispyribac, bensulfuron, penoxsulam, carfentrazone, quinclorac, e imazethapyr. Las malezas evaluadas fueron Echinocholoa crus-galli L. Beauv. ECHCG y Urochloa platyphylla (Nash) R.D. Webster BRAPP. Los experimentos en Luisiana y Mississipi en 2009 revelaron una preponderancia de efectos antagónicos. El análisis mostró que las mezclas con sodio-bispyribac, penoxsulam, quinclorac, e imazethapyr fueron generalmente los más antagónicos y proporcionaron el menor control.

Keywords

Barnyardgrass, Echinocholoa crus-galli L. Beauv. ECHCGbroadleaf signalgrass, Urochloa platyphylla (Nash) R.D. Webster BRAPPrice, Oryza sativa L.AntagonismDelta methodinteractionnonlinear mixed modelssynergism
Type
Education/Extension
Information
Weed Technology , Volume 24 , Issue 4 , December 2010 , pp. 583 - 589
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

Berenbaum, M. C. 1981. Criteria for analyzing interactions between biologically active agents. Adv. Cancer Res. 35:269–335.Google Scholar
Blanche, B., Harrell, D., and Saichuk, J. 2009. General agronomic guidelines. Pages 3–15. In Saichuk, J. ed. Louisiana Rice Production Handbook. Pub2321. Baton Rouge, LA: Louisiana State University Agricultural Center.Google Scholar
Blouin, D. C., Webster, P. E., and Zhang, W. 2004. Analysis of synergistic and antagonistic effects of herbicides using nonlinear mixed-model methodology. Weed Technol. 18:464–472.Google Scholar
Casella, G. and Berger, R. L. 2002. Statistical Inference. Pacific Grove, CA: Duxbury. 700 p.Google Scholar
Colby, S. R. 1967. Calculating synergistic and antagonistic responses of herbicide combinations. Weeds 15:20–22.Google Scholar
Drury, R. E. 1980. Physiological interaction, its mathematical expression. Weed Sci. 28:573–579.Google Scholar
Ferriera, K. L. and Coble, H. D. 1994. Effect of DPX-PE350 on efficacy of graminicides. Weed Sci. 42:222–226.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
Gennings, C., Carter, W. H. Jr., Campain, J. A., Bae, D., and Yang, R. S. H. 2002. Statistical analysis of interactive cytotoxicity in human epidermal keratinocytes following exposure to a mixture of four metals. J. Agric. Biol. Environ. Stat. 7:58–73.Google Scholar
Hatzios, K. K. and Penner, D. 1985. Interactions of herbicides with other agrochemicals in higher plants. Rev. Weed Sci. 1:1–63.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 weeds. Weed Technol. 8:129–133.Google Scholar
Jordan, D. L. 1995. Interaction of fenoxaprop with bensulfuron and bentazon in dry-seeded rice (Oryza sativa). Weed Technol. 9:724–727.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.Google Scholar
Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., and Schabenberger, O. 2002. SAS for Mixed Models. 2nd ed. Cary, NC: SAS Institute. 814 p.Google Scholar
Morse, P. M. 1978. Some comments on the assessment of joint action in herbicide mixtures. Weed Sci. 26:58–71.Google Scholar
Myers, P. F. and Coble, H. D. 1992. Antagonism of graminicide activity on annual grass species by imazethapyr. Weed Technol. 6:333–338.Google Scholar
Nash, R. G. 1981. Phytotoxic interaction studies—techniques for evaluation and presentation of results. Weed Sci. 29:147–155.Google Scholar
Ramsey, F. L. and Schafer, D. W. 2002. The Statistical Sleuth: A Course in Methods of Data Analysis. 2nd ed. Pacific Grove, CA: Duxbury. 816 p.Google Scholar
Reif, A. E. and Colton, T. 1984. Determination of carcinogenic synergism from the latent period. Carcinogenesis 5:837–840.Google Scholar
Rhodes, G. N. Jr. and Coble, H. D. 1984a. Influence of application variables on antagonism between sethoxydim and bentazon. Weed Sci. 32:436–441.Google Scholar
Rhodes, G. N. Jr. and Coble, H. D. 1984b. Influence of bentazon on absorption and translocation of sethoxydim in goosegrass. Weed Sci. 32:595–597.Google Scholar
SAS Institute Inc 1990. SAS/IML Software: Usage and Reference, Version 6. 1st ed. Cary, NC: SAS Institute.Google Scholar
SAS Institute Inc 1999. SAS/STAT User's Guide, Version 8, Volume 2. Cary, NC: SAS Institute.Google Scholar
Smith, R. J. Jr. 1988. Weed thresholds in southern U.S. rice, Oryza sativa . Weed Technol. 2:232–241.Google Scholar
Snipes, C. E. and Street, J. E. 1987. Rice (Oryza sativa) tolerance to fenoxaprop. Weed Sci. 35:401–406.Google Scholar
Stork, L. G., Gennings, C., Carter, W. H. Jr., Johnson, R. E., Hayes, D. P., Simmons, J. E., Wagner, E. D., and Plewa, M. J. 2007. Testing for additivity in chemical mixtures using a fixed-ration ray design and statistical equivalence testing methods. J. Agric. Biol. Environ. Stat. 12:514–533.Google Scholar
Streibig, J. C., Kudsk, P., and Jensen, J. E. 1998. A general joint action model for herbicide mixtures. Pestic. Sci. 53:21–28.Google Scholar
Webster, E. P. and Shaw, D. R. 1997. Weed control programs in stale seedbed cotton. Miss. Agric. For. Exp. Stn. Tech. Bull. 216.Google Scholar
Zhang, W., Webster, E. P., Blouin, D. C., and Leon, C. T. 2005. Fenoxaprop interactions for barnyardgrass. Weed Technol. 19:293–297.Google Scholar