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Updated: Dec 14, 2025
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Parametric identification of a functional-structural tree growth model and application to beech trees (Fagus
Véronique Letort1, Paul-Henry Cournède1, Amélie Mathieu1
1Ecole Centrale of Paris, Laboratoire de Mathématiques Appliquées aux Systèmes, F-92295 Châtenay-Malabry cedex, France.
Functional Plant Biology : FPB
|July 22, 2020
Summary
The GreenLab model simplifies tree architecture to simulate growth plasticity and trophic competition in beech trees (Fagus sylvatica L.). This functional-structural model successfully reproduces observed tree growth with global parameter identification.
Area of Science:
- Ecology
- Forestry
- Computational Biology
Background:
- Functional-structural models offer detailed tree growth representations with forestry applications.
- Parametric identification of these complex models is challenging due to intricate tree architecture.
Purpose of the Study:
- To present the GreenLab approach for modeling tree growth, focusing on plasticity and trophic competition.
- To assess if simplified tree architecture representations allow flexible parameterization for diverse growth conditions.
Main Methods:
- Developed the GreenLab model simulating tree growth plasticity, topological development, and cambial growth.
- Employed a species-specific branching pattern description for simplified tree architecture.
- Utilized global parametric identification, estimating all parameters simultaneously.
Main Results:
- The model successfully reproduced the growth of two beech trees (Fagus sylvatica L.) under different conditions using a single parameter set.
- Source-sink dynamics throughout tree development were accurately retrieved.
- Simulated and measured trunk profiles and branch compartment masses showed good agreement.
Conclusions:
- The GreenLab approach, with its simplified architecture, provides a flexible and effective tool for modeling tree growth.
- Global parameter identification enhances the description of sub-process interactions in tree development.
- Further improvements may incorporate topological criteria for even greater model accuracy.
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