Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

Paul-Henry Cournède 1, * Yuting Chen 1 Qiongli Wu 1 Charlotte Baey 1 Benoît Bayol 1
* Corresponding author
1 DIGIPLANTE - Modélisation de la croissance et de l'architecture des plantes
MAS - Mathématiques Appliquées aux Systèmes - EA 4037, Inria Saclay - Ile de France, Ecole Centrale Paris, CIRAD - Centre de Coopération Internationale en Recherche Agronomique pour le Développement
Abstract : Mathematical models of plant growth are generally characterized by a large number of interacting processes, a large number of model parameters and costly experimental data acquisition. Such complexities make model parameterization a difficult process. Moreover, there is a large variety of models that coexist in the literature with generally an absence of benchmarking between the different approaches and insufficient model evaluation. In this context, this paper aims at enhancing good modelling practices in the plant growth modeling community and at increasing model design efficiency. It gives an overview of the different steps in modelling and specify them in the case of plant growth models specifically regarding their above mentioned characteristics. Different methods allowing to perform these steps are implemented in a dedicated platform PYGMALION (Plant Growth Model Analysis, Identification and Optimization). Some of these methods are original. The C++ platform proposes a framework in which stochastic or deterministic discrete dynamic models can be implemented, and several efficient methods for sensitivity analysis, uncertainty analysis, parameter estimation, model selection or data assimilation can be used for model design, evaluation or application. Finally, a new model, the LNAS model for sugar beet growth, is presented and serves to illustrate how the different methods in PYGMALION can be used for its parameterization, its evaluation and its application to yield prediction. The model is evaluated from real data and is shown to have interesting predictive capacities when coupled with data assimilation techniques.
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Paul-Henry Cournède, Yuting Chen, Qiongli Wu, Charlotte Baey, Benoît Bayol. Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform. Mathematical Modelling of Natural Phenomena, EDP Sciences, 2013, 8 (4), pp.112-130. ⟨10.1051/mmnp/20138407 ⟩. ⟨hal-00860902⟩

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