ABSTRACT
BACKGROUND: Grapevine black rot caused by Guignardia bidwellii is a serious threat in vineyards, especially in areas with cool and humid springs. A mechanistic, weather-driven model was recently developed for the detailed prediction of black rot epidemics. The aim of this work was to evaluate the model by comparison with observed disease development in leaves and clusters in a vineyard in north Italy from 2013 to 2015. RESULTS: The model accurately predicted disease onset. The probability of predicting new infections that did not occur (i.e. unjustified alarms) was ≤0.180, while the probability of missing actual infections was 0.175 for leaves and 0.263 for clusters. In 78% of these false negative predictions, the difference between expected and actual disease onset was ±2 days; therefore, only one infection period was actually missed by the model. The model slightly overestimated disease severity (mainly on leaves) when the observed disease severity was >0.6. CONCLUSION: The model was highly accurate and robust in predicting the infection periods and dynamics of black rot epidemics. The model can be used for scheduling fungicide sprays in vineyards. © 2016 Society of Chemical Industry.
