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

Published online by Cambridge University Press:  10 July 2013

P.-H. Cournède ,
Y. Chen ,
Q. Wu ,
C. Baey  and
B. Bayol
P.-H. Cournède*
Affiliation:
Ecole Centrale Paris, Laboratoire MAS, Digiplante - 92290 Châtenay Malabry, France
Y. Chen
Affiliation:
Ecole Centrale Paris, Laboratoire MAS, Digiplante - 92290 Châtenay Malabry, France
Q. Wu
Affiliation:
Ecole Centrale Paris, Laboratoire MAS, Digiplante - 92290 Châtenay Malabry, France
C. Baey
Affiliation:
Ecole Centrale Paris, Laboratoire MAS, Digiplante - 92290 Châtenay Malabry, France
B. Bayol
Affiliation:
Ecole Centrale Paris, Laboratoire MAS, Digiplante - 92290 Châtenay Malabry, France
*
⋆ Corresponding author. E-mail: paul-henry.cournede@ecp.fr
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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 modelling 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.

Keywords

good modelling practicesensitivity analysisuncertainty analysisparameter estimationparticle filteringdata assimilationplant growth modelsLNAS model
Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 8 , Issue 4: Plant growth modelling , 2013 , pp. 112 - 130
Copyright
© EDP Sciences, 2013

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