Please use this identifier to cite or link to this item:
Title: Towards pest outbreak predictions: Are models supported by field monitoring the new hope?
Authors: Bono Rosselló, Nicolás
Rossini, Luca 
Speranza, Stefano 
Garone, Emanuele
Issue Date: 2023
Physiologically-based models are the core of Decision Support Systems (DSS) for insect pest and disease control in cultivated fields. However, the large-scale use of DSS remains scarce and limited, despite the continuous update and formulation of new models by the literature. The main reason behind this lack of real-world use relates to the purely descriptive approach of these models, which are usually validated a posteriori. The major limiting factors that preclude the use of these tools for prediction purposes are their dependence on time zero and initial abundance to start the simulations. In this study, we present a theoretical framework that includes field monitoring data as an active part of a pest population density model simulation, which helps to overcome these obstacles. More specifically, we propose the application of an estimator scheme in the form of an Extended Kalman Filter (EKF) to a revised physiologically-based model from the literature. In the paper, we carry out a preliminary test of the theoretical framework applied to the case of Drosophila suzukii. This case study shows that the dependence of the simulations on the initial conditions and time zero is strongly reduced by using the EKF. Overall, the outcome of this research indicates that an estimator scheme is a necessary step to move from description to prediction in the pest population modelling field.
ISSN: 1574-9541
DOI: 10.1016/j.ecoinf.2023.102310
Rights: Attribution 4.0 International
Appears in Collections:A1. Articolo in rivista

Files in This Item:
File Description SizeFormat
1-s2.0-S1574954123003394-main.pdfLavoro completo1.23 MBAdobe PDFView/Open
Show full item record

Page view(s)

checked on Feb 28, 2024


checked on Feb 28, 2024

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons