FACCE ERA-NET+MODCARBOSTRESS

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Climate change accelerates the need for a smarter, more efficient, more secure agriculture. Because climate change is predicted to increase spatial and temporal variability, crop models able to predict the best local allele/phene combinations within a species, in addition to the best management systems (such as, for instance, species choice, rotations, sowing dates...) will be of great value for farmers and breeders worldwide. Aware of these issues and avenues, breeding companies now massively invest in crop and climate modelling.However, current crop models have large uncertainties, in particular under drought and high temperatures that often occur in combination and while their occurrences are likely to increase in several regions of the world. Whereas major environmental drivers of growth such as temperature, light and evaporative demand are now well captured in experiments, in particular following a concerted effort of the community, the availability of these information under various [CO2] is the exception. Our project will aim at delivering to simple, low cost, principles and solutions for manipulating combined stresses, including elevated CO2, in experimental set-ups. We will start to apply these principles to the different platforms that are part of the current project.