Modification of the tomcast model with aerobiological data for management of potato early blight

Abstract

The present study focuses on establishing thresholds of weather variables for predict early blight in potato crops. For this, the TOMCAST model was adjusted using weather variables and Alternaria conidia levels (mainly A. solani and A. alternata) during six growing seasons in A Limia (Northwest Spain). TOMCAST for the effective management of early blight considers leaf wetness and air temperature to calculate daily severity values (DSVs). Spearman correlations between temperature (minimum and average), mean temperature during leaf wetness period and Alternaria concentration showed the highest positive significant coefficients (0.386, 0.230 and 0.372, respectively; p < 0.01). Specifically, Alternaria levels higher than 50 spores/m3 were found the days with air mean temperature above 18◦ C, more than 7 h of leaf wetness. Leaf wetness was decisive to estimate the concentration of Alternaria, resulting in a significant linear regression model (R2 = 0.41; p < 0.001). TOMCAST was adapted to the area, considering 10◦ C the minimum threshold for the mean value of temperature during the wet period and 10–15 accumulated disease severity values (DSV). Using TOMCAST, it was possible to predict the first Alternaria peak in most of potato growing seasons. Combining aerobiological and meteorological data to control fungal diseases during crops are a useful tool for sustainable agriculture.