AUTHOR=Valleggi Lorenzo , Carella Giuseppe , Perria Rita , Mugnai Laura , Stefanini Federico Mattia 

TITLE=A Bayesian model for control strategy selection against Plasmopara viticola infections

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1117498

DOI=10.3389/fpls.2023.1117498

ISSN=1664-462X

ABSTRACT=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.