The development of functional-structural plant models has opened interesting perspectives
for a better understanding of plant growth as well as for potential applications in
breeding or decision aid in farm management. Parameterization of such models is however a
difficult issue due to the complexity of the involved biological processes and the
interactions between these processes. The estimation of parameters from experimental data
by inverse methods is thus a crucial step. This paper presents some results and
discussions as first steps towards the construction of a general framework for the
parametric estimation of functional-structural plant models. A general family of models of
Carbon allocation formalized as dynamic systems serves as the basis for our study. An
adaptation of the 2-stage Aitken estimator to this family of model is introduced as well
as its numerical implementation, and applied in two different situations: first a
morphogenetic model of sugar beet growth with simple plant structure, multi-stage and
detailed observations, and second a tree growth model characterized by sparse observations
and strong interactions between functioning and organogenesis. The proposed estimation
method appears robust, easy to adapt to a wide variety of models, and generally provides a
satisfactory goodness-of-fit. However, it does not allow a proper evaluation of estimation
uncertainty. Finally some perspectives opened by the theory of hidden models are
discussed.