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Development and Evaluation of Plant Growth Models: Methodologyand 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: [email protected]
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Abstract

Mathematical models of plant growth are generally characterized by a large number ofinteracting processes, a large number of model parameters and costly experimental dataacquisition. Such complexities make model parameterization a difficult process. Moreover,there is a large variety of models that coexist in the literature with generally anabsence of benchmarking between the different approaches and insufficient modelevaluation. In this context, this paper aims at enhancing good modelling practices in theplant growth modelling community and at increasing model design efficiency. It gives anoverview of the different steps in modelling and specify them in the case of plant growthmodels specifically regarding their above mentioned characteristics.

Different methods allowing to perform these steps are implemented in a dedicated platformPYGMALION (Plant Growth Model Analysis, Identification and Optimization). Some of thesemethods are original. The C++ platform proposes a framework in which stochastic ordeterministic discrete dynamic models can be implemented, and several efficient methodsfor sensitivity analysis, uncertainty analysis, parameter estimation, model selection ordata 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 toillustrate 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 realdata and is shown to have interesting predictive capacities when coupled with dataassimilation 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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