Predicting the persistence and efficacy of chlorothalonil on peanut leafspot

Leafspot, a fungal disease which infects peanut plants, is commonly controlled with the chemical chlorothalonil. The effectiveness of the fungicide is dependent on several factors including time of application, weather, stage of leafspot development, and growth stage of the peanut plants. Because of the complex factors involved in the decision to spray, a simulation model was developed to assist the producer. Two models of fungicide persistence from the literature were studied; a model in which the fungicide residue decayed exponentially with time, and a model where the amount of fungicide residue was dependent on rainfall and temperature. The fungicide efficacy model was developed by the authors and assumes chlorothalonil slows disease progress by inhibiting germination of spores. A sigmoidal dose-response curve is assumed, with the 50% lethal dose at approximately 0.062 microgram chlorothalonil/cm2 leaf area. Simulations using both persistence models were compared to observed disease levels for 1982 and 1988, the only two years for which data were available. The persistence model which calculated fungicide decay as a function of rainfall and ambient temperature predicted disease levels that were in closer agreement with the observed values than the constant-rate exponential decay persistence model. It has been suggested by other authors that fungicide decay models which are driven only by time since fungicide application are acceptable, however our research suggests that rainfall and temperature are essential components of fungicide persistence models. Other environmental variables were not studied. The weather-driven fungicide persistence model and the sigmoidal dose-response curve efficacy model predicted disease levels and percent yield reductions which agreed well with the observed field data for 1982 and 1988. In 1982, the observed yield from the plots where no fungicide was applied was 46.7% less than the plot where chlorothalonil was applied conventionally, and the model predicted a 48.3% yield reduction. The yield reduction observed when half the recommended amount of fungicide was applied was 4.7%, and the model predicted 2.8% reduction in yield. The potential yield reduction from a predicted leafspot infection is the main driving force behind the decision of whether or not to spray the crop. Since the simulation model accurately predicted yield reductions and leafspot infections resulting from varying fungicide applications, the model could be a valuable tool for exploring alternative chlorothalonil application strategies for the control of leafspot.