  A crop can be represented as a biotechnical system in which components are
either chosen (cultivar, management) or given (soil, climate) and whose
combination generates highly variable stress patterns and yield responses.
Here, we used modeling and simulation to predict the crop phenotypic plasticity
resulting from the interaction of plant traits (G), climatic variability (E)
and management actions (M). We designed two in silico experiments that compared
existing and virtual sunflower cultivars (Helianthus annuus L.) in a target
population of cropping environments by simulating a range of indicators of crop
performance. Optimization methods were then used to search for GEM combinations
that matched desired crop specifications. Computational experiments showed that
the fit of particular cultivars in specific environments is gradually
increasing with the knowledge of pedo-climatic conditions. At the regional
scale, tuning the choice of cultivar impacted crop performance the same
magnitude as the effect of yearly genetic progress made by breeding. When
considering virtual genetic material, designed by recombining plant traits,
cultivar choice had a greater positive impact on crop performance and
stability. Results suggested that breeding for key traits conferring plant
plasticity improved cultivar global adaptation capacity whereas increasing
genetic diversity allowed to choose cultivars with distinctive traits that were
more adapted to specific conditions. Consequently, breeding genetic material
that is both plastic and diverse may improve yield stability of agricultural
systems exposed to climatic variability. We argue that process-based modeling
could help enhancing spatial management of cultivated genetic diversity and
could be integrated in functional breeding approaches.
