Global sensitivity analysis has a key role to play in the design and parameterization of functional-structural plant growth models (FSPM) which combine the description of plant structural development (organogenesis and geometry) and functional growth (biomass accumulation and allocation). Models of this type generally describe many interacting processes, count a large number of parameters, and their computational cost can be important. The general objective of this thesis is to develop a proper methodology for the sensitivity analysis of functional structural plant models and to investigate how sensitivity analysis can help for the design and parameterization of such models as well as providing insights for the understanding of underlying biological processes. Our contribution can be summarized in two parts: from the methodology point of view, we first improved the performance of the existing Sobol's method to compute sensitivity indices in terms of computational efficiency, with a better control of the estimation error for Monte Carlo simulation, and we also designed a proper strategy of analysis for complex biophysical systems; from the application point of view, we implemented our strategy for 3 FSPMs with different levels of complexity, and analyzed the results from different perspectives (model parameterization, model diagnosis).
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00719935 |
| Date | 19 April 2012 |
| Creators | Wu, QiongLi |
| Publisher | Ecole Centrale Paris |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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