Dynamic simulation for predicting warning and action thresholds: A novelty for strawberry powdery mildew management

Abstract

Strawberry powdery mildew (SPM), caused by Podosphaera aphanis, is gaining in importance as production of day-neutral strawberry and resistance to fungicide increase. This disease is difficult to predict because of the wide range of weather conditions favorable to its development. We explored dynamic simulation modelling. The model uses equations developed in-house, published algorithms describing sub-processes of P. aphanis life cycle (e.g. sporulation, conidia germination, leaf colonization) and assembled knowledge of the interactions among the pathogen, host, and weather. Weather, disease, and host data collected at three sites in 2006, 2007, 2008, 2015, 2016 and 2018, for a total of nine epidemics were used to validate the model. The results show a good simulation of the trends, shape and amplitude of SPM severity. The relationship between simulated and observed SPM severity was significant at all sites and years. The proportion of linear variations in the observed SPM explained by the variation in simulated disease severity were between 0.60 and 0.80. Based on receiver operating curve analysis, the model accurately predicted both warning and action thresholds of 5% and 15% disease severity. This is the first model that can quantitatively predict SPM severity, warning and action thresholds to tune fungicide application strategies. In addition, the model can be used to assess the impact of different production systems on powdery mildew and hence anticipate risk.