A decision support system based on Bayesian modelling for pest management: Application to wireworm risk assessment in maize fields

Abstract

Protecting crops against pests is a major issue in the current agricultural production system. In particular, assessing the risk to crops can promote integrated pest management (IPM) strategies that encourage natural control mechanisms and advocate the use of pesticides as a last resort. In this study, we focused on wireworms, major soil-dwelling insect pests inflicting severe economic damage on various crops (including maize, potatoes and cereals) across Europe and North America. We have developed an original hierarchical Bayesian model that explicitly accounts for biological knowledge and uncertainty in field observations, rather than relying solely on statistical correlations, to predict the level of wireworm infestation. The model was calibrated and validated using a substantial dataset originating from an agro-environmental survey carried out over three consecutive years (2012–2014) in France, which provides the wireworm abundance in 419 maize fields, together with information on the landscape context, field history, weather conditions, soil characteristics and farming practices associated to each field. Model outcomes show good agreement with current knowledge from literature and field expertise in terms of the effects of variables on wireworm abundance, and provide fairly good predictive capacity. Subsequently, the model was encapsulated as a software (R shiny application) to predict the risk of wireworm infestation in any field of interest, and can be used by farmers or agricultural advisors as a decision support system for the implementation of IPM strategies. The conceptual framework that we implemented can be adapted to a wide range of similar situations involving other crops and pests.