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.