Articles
Closing the loop: tandem between monitoring and modelling to predict Bactrocera oleae infestations
Article number
1446_36
Pages
265 – 274
Language
English
Abstract
Bactrocera oleae is one of the key pests for olive cultivations.
Every year, this species is responsible for several direct and indirect yield losses in many countries worldwide on both table and oil olives.
In the most infested areas, several control actions are required to maintain B. oleae populations under the damage threshold level, subsequently lowering the level of economic and environmental sustainability of the orchards.
Recently, a mathematical model has been introduced which provided promising results in describing the stage population dynamics of this pest by connecting its biological parameters with environmental conditions.
Even though the model was successfully validated with field data, additional steps are needed to switch the model from purely descriptive to predictive.
This work aims to lay the foundations of this transition, by applying a Kalman Filter estimation scheme to include monitoring data into the physiologically based model describing B. oleae life cycle.
The model estimations are effectively improved by monitoring data, moreover, this scheme overcomes the dependence of the system on the initial conditions, information commonly unknown for wild populations developing in natural environments.
Every year, this species is responsible for several direct and indirect yield losses in many countries worldwide on both table and oil olives.
In the most infested areas, several control actions are required to maintain B. oleae populations under the damage threshold level, subsequently lowering the level of economic and environmental sustainability of the orchards.
Recently, a mathematical model has been introduced which provided promising results in describing the stage population dynamics of this pest by connecting its biological parameters with environmental conditions.
Even though the model was successfully validated with field data, additional steps are needed to switch the model from purely descriptive to predictive.
This work aims to lay the foundations of this transition, by applying a Kalman Filter estimation scheme to include monitoring data into the physiologically based model describing B. oleae life cycle.
The model estimations are effectively improved by monitoring data, moreover, this scheme overcomes the dependence of the system on the initial conditions, information commonly unknown for wild populations developing in natural environments.
Publication
Authors
L. Rossini, M. Contarini, N. Bono-Rosselló, E. Garone, S. Speranza
Keywords
decision support systems, olive fruit fly, population dynamic models, integrated pest management, IPM, Olea europea, olive pest management
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