Developed recently, Functional Structural Models of Plant Growth (FSPM) aim at describing plant structural development (organogenesis and geometry), functional growth (biomass accumulation and allocation) and the complex interactions between both. They serve as a framework to integrate complex biological and biophysical processes in interaction with the environment, at different scales. The resulting complexity of such models regarding the dimensionalities of the parameter space and state space often makes them difficult to parameterize. There is usually no systematic model identification from experimental data and such models still remain ill-adapted for applicative purposes. The objective of this study is to explore how global sensitivity analysis can help for the parameterization of FSPM, by quantifying the driving forces during plant growth and the relative importance of the described biophysical processes regarding the outputs of interest. The tests are performed on the GreenLab model. Its particularity is that both structural development and functional growth are described mathematically as a dynamical system (Cournède et al., 2006). Its parameterization relies on parameter estimation from experimental data. Sensitivity analysis may help to optimize the trade-off between experimental cost and accuracy. This is crucial to develop a predictive capacity that scales from genotype to phenotype for FSPM.