Evaluation of models to control potato early blight (Alternaria solani) in Denmark

Abstract

Early blight is an important foliar disease in potato growing areas worldwide. Controlling the disease is dependent mainly on fungicide application. In order to regulate the application of fungicide to control early blight, field experiments were conducted in 2015 and 2016 at Flakkebjerg Research Center, Denmark, to evaluate different models to control early blight. The experiments were carried out with the starch and late maturity cultivar Kardal. The experiments were artificially inoculated with barley grains infested with Alternaria solani and A. alternata on 24th June and 13th June in 2015 and 2016, respectively. The models evaluated in this study included two types of Maturity-based models (I & II) in which fungicide was applied according to the age-related susceptibility of the crop; Modified-TOMCAST model, in which first fungicide was applied at 330 Pdays and subsequent applications at 20 TOMCAST DSV (disease severity value) threshold; TOMCAST + Maturity-based model in which the TOMCAST DSV was combined with the Maturity-based model; and lastly a model which recommends fungicide application from the onset of early blight attack (First Symptoms). Results from this study showed that early blight can be effectively controlled by following models that predict the maturity of the crop, weather-based models (TOMCAST) and starting fungicide application at the onset of the disease without negatively affecting starch yield or net income. A multivariate technique was used to classify the performance of the models. In 2015, the Maturity-based models (I & II) and First Symptoms model were classified as very good models, whereas the Modified-TOMCAST and TOMCAST + Maturity-based models were classified as good models. In 2016, only the Maturity-based model II was classified as a very good model, whereas the Modified-TOMCAST and TOMCAST + Maturity-based model, First Symptoms and Maturity-based model I were classified as good models.