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Phytotoxic Interaction Studies – Techniques for Evaluation and Presentation of Results

Published online by Cambridge University Press:  12 June 2017

R. G. Nash
R. G. Nash*
Affiliation:
Pestic. Degrad. Lab., Agric. Envir. Qual. Inst., Agric. Res., Sci. Ed. Admin., U.S. Dep. Agric., Beltsville, MD 20705
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Abstract

Two or more pesticides together may produce a growth response in plants that is not predictable by their individual or independent toxicities. This unpredicted (dependent) response results from an interaction, a concept that usually is not easily interpreted. Dependent responses are further complicated by the fact that they can be either synergistic or antagonistic. Several methods exist for identifying and measuring phytotoxic interactions. Nearly all methods have certain shortcomings, however. Additive and multiplicative models (mathematical expressions) are the two basic approaches to determining pesticide interactions. The two-parameter, isobole, and calculus methods axe additive; whereas, Colby and regression estimate are multiplicative models. Regression estimate analysis considers deviations due to experimental errors, and a statistical significance can be attached to the interaction magnitude, thereby overcoming the deficiencies of the Colby method, but both methods seem to be limited to response data in which the combined pesticide concentration is the sum of the individual pesticide concentrations. The two-parameter method seems to be limited to response data in which the combined concentration is equal to the individual pesticide concentration and to response data in which a pesticide concentration necessary to produce a 50% of control value is interpolated rather than extrapolated. The calculus method is a mathematical expression of the growth response, and interaction is measured by derivation of the equation obtained. The calculus method is difficult to interpret and has a major weakness because it depends upon the multiple regression equation of the observed data. The regression estimate method is recommended as a reasonable approach to interpretation of interaction type data, with a SAS language computer program available from the author.

Keywords

Synergismantagonismregression estimate analysiscalculus analysis
Type
Research Article
Information
Weed Science , Volume 29 , Issue 2 , March 1981 , pp. 147 - 155
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

1. Akobundu, I. O., Duke, W. B., Sweet, R. D., and Minotti, P. L. 1975. Basis for synergism of atrazine and alachlor combinations on Japanese millet. Weed Sci. 23:43–48.Google Scholar
2. Akobundu, I. O., Sweet, R. D. and Duke, W. B. 1975. A method of evaluating herbicide combinations and determining herbicide synergism. Weed Sci. 23:20–25.Google Scholar
3. Colby, S. R. 1967. Calculating synergistic and antagonistic responses of herbicide combinations. Weeds 15:20–22.Google Scholar
4. Drury, R. E. 1980. Physiological interaction, its mathematical expression. Weed Sci. 28:573–579.Google Scholar
5. Duncan, D. G. 1955. Multiple range and multiple F test. Biometrics 11:1–42.CrossRefGoogle Scholar
6. Finney, D. J. 1952. Probit Analysis. Cambridge Univ. Press. Cambridge, MS. 318 pp.Google Scholar
7. Gowing, D. P. 1960. Comments on tests of herbicide mixtures. Weeds 8:379–391.Google Scholar
8. Hamill, A. S. and Penner, D. 1973. Chlorbromuron-carbofuran interaction in corn and barley. Weed Sci. 21:335–338.Google Scholar
9. Hayes, R. M., Yeargan, K. V., Witt, W. W., and Raney, H. G. 1979. Interaction of selected insecticide-herbicide combinations on soybeans (Glycine max.) Weed Sci. 27:51–54.Google Scholar
10. Johnson, B. J. 1970. Effects of nitralin and chloroxuron combination on weeds and soybeans. Weed Sci. 18:616–618.Google Scholar
11. Morse, P. M. 1978. Some comments on the assessment of joint action in herbicide mixtures. Weed Sci. 26:58–71.Google Scholar
12. Nash, R. G. 1967. Phytotoxic pesticide interactions in soil. Agron. J. 59:227–230.Google Scholar
13. Nash, R. G. 1968. Synergistic phytotoxicities of herbicide-insecticide combinations in soil. Weed Sci. 16:74–77.Google Scholar
14. Nash, R. G. and Harris, W. G. 1973. Screening for phytotoxic pesticide interactions. J. Environ. Qual. 2:493–497.Google Scholar
15. Nash, R. G. and Jansen, L. L. 1973. Determining phytotoxic pesticide interactions in soil. J. Environ. Qual. 2:503–510.CrossRefGoogle Scholar
16. Putnam, A. R. and Penner, D. 1974. Pesticide interactions in higher plants. Residue Rev. 50:73–113.Google Scholar
17. Rummens, F. H. A. 1975. An improved definition of synergistic and antagonistic effects. Weed Sci. 23:4–6.Google Scholar
18. Rummens, F. H. A., Rummens-Ditters, D. C. M., and Smith, A. E. 1975. The effects of diallate and its isomers on the growth of wild oats. Weed Sci. 23:11–14.CrossRefGoogle Scholar
19. Savage, K. E. and Ivy, H. W. 1973. Fluometuron-disulfoton interactions in cotton as affected by soil properties. Weed Sci. 21:275–278.Google Scholar
20. Smith, R.J. and Tugwell, N. P. 1975. Propanil-carbofuran interaction in rice. Weed Sci. 23:176–178.Google Scholar
21. Stein, J. Ed. 1971. The Random House Dictionary of the English Language. Random House, N.Y. 2059 pp.Google Scholar
22. Tammes, P. M. L. 1964. Isoboles, a graphic representation of synergism in pesticides. Neth. J. Plant Pathol. 70:73–80.Google Scholar