Modeling Inter-Individual Variability in Sugar Beet Populations

P. de Reffye 1 Sébastien Lemaire 2 Nitish Srivastava 3 Fabienne Maupas 2 Paul-Henry Cournède 3, 4, *
* Corresponding author
3 DIGIPLANTE - Modélisation de la croissance et de l'architecture des plantes
MAS - Mathématiques Appliquées aux Systèmes - EA 4037, CIRAD - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Inria Saclay - Ile de France, Ecole Centrale Paris
Abstract : Modeling heterogeneity in field crops is a key issue for a better characterization of field production. This paper presents some experimental data on sugar beet illustrating this heterogeneity. Several sources of individual variability within plant populations are identified: namely, initial condition (seed biomass, emergence delay), genetic variability (including phyllochron) and environment (including spacing and competition). A mathematical framework is introduced to integrate the different sources of variability in plant growth models. It is based on the classical method of Taylor Series Expansion, which allows the propagation of uncertainty in the dynamic system of growth and the computation of the approximate means and standard deviations of the model outputs. The method is applied to the GreenLab model of plant growth and more specifically to sugar beet. It opens perspectives in order to assess the different sources of variability in plant populations and estimate their parameters from experimental data. However important issues like optimization of data collection and system identifiability have to be resolved first, since the uncertainty effects may be mixed in an inextricable way or may necessitate a too huge amount of experimental data for their estimation.
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P. de Reffye, Sébastien Lemaire, Nitish Srivastava, Fabienne Maupas, Paul-Henry Cournède. Modeling Inter-Individual Variability in Sugar Beet Populations. Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA09), Nov 2009, Beijing, China. ⟨inria-00537646⟩

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