Optimizing Plant Growth Model Parameters for Genetic Selection Based on QTL Mapping

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.