The development of functional-structural plant models has opened interesting perspectivesfor a better understanding of plant growth as well as for potential applications inbreeding or decision aid in farm management. Parameterization of such models is however adifficult issue due to the complexity of the involved biological processes and theinteractions between these processes. The estimation of parameters from experimental databy inverse methods is thus a crucial step. This paper presents some results anddiscussions as first steps towards the construction of a general framework for theparametric estimation of functional-structural plant models. A general family of models ofCarbon allocation formalized as dynamic systems serves as the basis for our study. Anadaptation of the 2-stage Aitken estimator to this family of model is introduced as wellas its numerical implementation, and applied in two different situations: first amorphogenetic model of sugar beet growth with simple plant structure, multi-stage anddetailed observations, and second a tree growth model characterized by sparse observationsand strong interactions between functioning and organogenesis. The proposed estimationmethod appears robust, easy to adapt to a wide variety of models, and generally provides asatisfactory goodness-of-fit. However, it does not allow a proper evaluation of estimationuncertainty. Finally some perspectives opened by the theory of hidden models arediscussed.