FACCE ERA-NET+MODCARBOSTRESS

气候变化加速了对更智能、更高效、更安全的农业的需求。由于预计气候变化会增加空间和时间变异，因此能够预测物种内最佳本地等位基因/基因组合的作物模型，以及最佳管理系统（例如，物种选择、轮作、播种日期等），将对全世界的农民和育种者具有重要价值。意识到这些问题和途径，育种公司现在大量投资于作物和气候模型。然而，目前的作物模式具有很大的不确定性，特别是在干旱和高温经常同时发生的情况下，而它们在世界若干区域的发生可能会增加。虽然主要的环境驱动因素，如温度、光照和蒸发需求，现在已经在实验中得到了很好的捕获，特别是在社区的共同努力下，但在各种[CO2]下获得这些信息是例外。我们的项目旨在提供简单、低成本的原理和解决方案，以控制实验装置中的综合应力，包括二氧化碳浓度升高。我们将开始将这些原则应用到当前项目的不同平台上。

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.