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.
Published Version
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