Analytical study of a stochastic plant growth model: application to the GreenLab model
2008
Kang, M. Z. | Cournède, Paul-Henry | de Reffye, Philippe | Auclair, Daniel | Hu, Bao-Gang | Eco-informatics (LIAMA) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Chinese Academy of Sciences [Changchun Branch] (CAS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institute of Automation - Chinese Academy of Sciences-Centre National de la Recherche Scientifique (CNRS) | Capital Normal University [Beijing] | Mathématiques Appliquées aux Systèmes - EA 4037 (MAS) ; Ecole Centrale Paris | Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Occitanie]) | Modélisation de la croissance et de l'architecture des plantes (DIGIPLANTE) ; Mathématiques Appliquées aux Systèmes - EA 4037 (MAS) ; Ecole Centrale Paris-Ecole Centrale Paris-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre Inria de Saclay ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
International audience
Show more [+] Less [-]English. A stochastic Functional-Structural model simulating plant development and growth is presented. The number of organs (internodes, leaves and fruits) produced by the model is not only a key intermediate variable for biomass production computation, but also an indicator of model complexity. To obtain their mean and variance through simulation is time-consuming and the results are approximate. In this paper, based on the idea of substructure decomposition, the theoretical mean and variance of the number of organs in a plant structure from the model are computed recurrently by applying a compound law of generating functions. This analytical method provides fast and precise results, which facilitates model analysis as well as model calibration and validation with real plants. Furthermore, the mean and variance of the biomass production from the stochastic plant model are of special interest linked to the prediction of yield. In this paper, through differential statistics, their approximate results are computed in an analytical way for any plant age. A case study on sample trees from this Functional-Structural model shows the theoretical moments of the number of organs and the biomass production, as well as the computation efficiency of the analytical method compared to a Monte-Carlo simulation method. The advantages and the drawbacks of this stochastic model for agricultural applications are discussed.
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