Parametric interpretability of growth kinetics equations in a process model for the life cycle of Lobesia botrana

• Published in: Ecological modelling 2023-08, Vol.482, p.110407, Article 110407
• Main Authors: Aguirre-Zapata, Estefania, Alvarez, Hernan, Dagatti, Carla Vanina, di Sciascio, Fernando, Amicarelli, Adriana N.
• Format: Article
• Language: English
• Subjects: Growth kinetics; Identifiability; Interpretability; Lobesia botrana; Parametric sensitivity
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2023.110407

Modeling processes span multiple disciplines and aim to generate new knowledge about a given process or system for subsequent analysis or control. Mathematical models are representations of these systems using mathematical equations and parameters, which may or may not be interpretable depending on their application or use. In the specific case of the life cycle of L. botrana, the goal of the mathematical model is to develop a model-based decision support system (MB-DSS) that enables experts to monitor the evolution of the pest using predictive models. Based on these predictions, timely decisions can be made to control and eradicate the pest. The interpretability of model parameters is critical to the design of MB-DSS systems. This paper analyzes the response of the growth model of L. botrana under laboratory conditions and proposes a new structure that considers two limiting factors of growth: temperature and relative humidity. The proposed analysis seeks to determine the best growth kinetics while preserving the compromise between the model’s fit to experimental data, interpretability, identifiability, and sensitivity of the model parameters. The results show an improvement in the fit, descriptive capacity, and number of inputs considered by the model. [Display omitted] •It is desirable that the life cycle model of L. botrana has parameters that are interpretable.•In biotechnological processes, interpretability is as highly valued as accuracy.•Identifiability, sensitivity and interpretability are important in semi-physical models.