By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits--such as cob weight--and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GREENLAB maize model. The paper discusses the potentials of GREENLAB to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand.

An increasing interest is given to the potential benefits of introducing ecophysiological knowledge in breeding programs. Indeed, crop models provide powerful tools to predict phenotypic traits from new genotypes under untested environmental conditions. But, until now, few attempts have been undertaken to bridge the gap from genes to phenotype with a chain of functional processes. In this paper, we propose a framework for simulating plant growth from its genotype. Thus the genetic correlations between the parameters can be taken into consideration when optimization processes are used to define ideotypes based on model parameters. The example of virtual maize growing under constant environmental conditions is presented using the functional-structural model GreenLab.