Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction

We have developed a theoretical model that belongs to the family of Dynamic Bayesian Networks in order to compare several PMNs performances. This model links the characteristics of a PMN to treatment decisions and the resulting pest dynamics. Using simulation and inference tools for graphical models, we derived the proportion of impacted fields, the number of pesticide treatments and the overall gross margins for three types of pest with contrasting levels of endocyclism. The presence of purely endocyclic pests at a given time increases the probability of reoccurrence. Conversely, slightly endocyclic pests have a low persistence. The simulation analysis considered ten scenarios: an expected margin-based strategy with a spatial resolution of four PMNs and two memory lengths (one year or eight years), as well as two extreme crop protection strategies (systematic treatments on all fields and systematic no treatment).<br><br>Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction. Cros MJ, Aubertot JN, Gaba S, Reboud X, Sabbadin R, Peyrard N. Manuscrit submitted for publication (2020).Matlab code, numerical results provided on Figshare.