A Bayesian model for control strategy selection against Plasmopara viticola infections - Valleggi, Lorenzo; Carella, Giuseppe; Perria, Rita; Mugnai, Laura; Stefanini, Federico Mattia (Frontiers in Plant Science, 2023)
Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. In this study, we developed a Bayesian model to predict the probability of Plasmopara viticola infection in grapevines, considering various disease management approaches. To aid decision-making, we introduced a multi-attribute utility function that incorporated a sustainability index for each strategy. The data used in this study were derived from trials conducted during the production years 2018- 2020, involving the application of five disease management strategies: conventional Integrated Pest Management (IPM), conventional organic, IPM with substantial fungicide reduction combined with host-defence inducing biostimulants, organic management with biostimulants, and the use of only biostimulants. Two scenarios were considered, one with medium pathogen pressure (Average) and another with high pathogen pressure (Severe). The results indicated that when sustainability indexes were not considered, the conventional IPM strategy provided the most effective disease management in the Average scenario. However, when sustainability indexes were included, the utility values of conventional strategies approached those of reduced fungicide strategies due to their lower environmental impact. In the Severe scenario, the application of only biostimulants emerged as the most effective strategy. This suggests that in situations of high disease pressure, the use of conventional strategies effectively combats the disease but at the expense of a greater environmental impact. The use of Bayesian decision support, incorporating diverse information sources and customizable utility functions, can assist agronomists in selecting the most effective crop protection strategy.