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Analytical study of a stochastic plant growth model: application to the GreenLab model

Abstract : 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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https://hal.archives-ouvertes.fr/halsde-00306691
Contributor : Yannick Brohard Connect in order to contact the contributor
Submitted on : Monday, July 28, 2008 - 12:01:41 PM
Last modification on : Tuesday, September 6, 2022 - 4:53:09 PM

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M. Z. Kang, Paul-Henry Cournède, Philippe de Reffye, Daniel Auclair, Bao-Gang Hu. Analytical study of a stochastic plant growth model: application to the GreenLab model. Mathematics and Computers in Simulation, Elsevier, 2008, 78 (1), pp.57-78. ⟨10.1016/j.matcom.2007.06.003⟩. ⟨halsde-00306691⟩

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