Application of prediction models of asian soybean rust in two crop seasons, in Londrina, Pr

Predictive models of Asian soybean rust have been described by researchers to estimate favorable responses to epidemics. The prediction strategies are based on weather data obtained during period when initial symptoms of the disease are observed. Therefore, this study will evaluate the application of two prediction models of Asian soybean rust, and compare the results from two harvest seasons. The experiments were carried out during the 2011/2012 and 2012/2013 seasons in Londrina, PR. “SIGA spore traps” were installed to monitor the presence of Phakopsora pachyrhizi uredospores, and “Electronic trees,” to collect data on weather variables. Following the detection of the first urediniospores, incidence and disease severity were assessed and compared with the predictions made by the models. The model described by Reis et al. (2004) did not indicate conditions favorable for the development of the first rust lesions following the detection of the first urediniospores during the 2011/2012 season. The premonitory symptoms of rust in the first and second harvest seasons were observed only when the model of Reis et al. (2004) indicated SDVPI close to 15 units. The model of Del Ponte et al. (2006b) overestimated the final rust severity during the two seasons.