Improved process understanding of CO2-induced mechanisms on yield and yield quality of selected field-grown wheat genotypes

二氧化碳(CO2)是由各种人类活动排放的。这会导致大气中二氧化碳的增加，进而导致气候变暖以及降水模式的变化，从而影响农业生态系统。除了对作物产量的影响外，CO2浓度的增加以及与其他环境因素的相互作用也可能对小麦籽粒品质产生不利影响。然而，潜在的作物生理过程还远不清楚。因此，该项目的目的是提供实验数据，以改善对气候变化下作物产量形成和产量质量反应的机理理解。小麦将被用作模式作物，以利用先前的数据，并填补在气候变化下作物生理反应方面的一些仍然存在的空白。为了跟踪养分的获取和分配，两个不同的小麦基因型将在田间的自由空气二氧化碳浓缩(FACE)系统中暴露，并将通过叶片气体交换和实际植物蒸腾测量来评估水分关系和光合作用表现。稳定同位素分析(13C、15N)将有助于跟踪碳和氮的再分配，并获得对水利用效率和水关系的综合估计。除了有针对性的分析外，我们还将使用代谢组学和蛋白质组学方法来确定气候变化对谷物产量质量的微妙影响。我们还将侧重于全面的数据评估(例如，荟萃分析)，包括先前实验所涵盖的气候因素的选定星座的影响，特别是高温、干旱和氮素形态等极端事件，以阐明生理过程。数据还将用于基于对植物内部运输过程的显式模拟来模拟气候变化对作物生长和产量质量的影响。此外，还将对研究地点和“新作物”进行关于作物发展和作物产量的实地评估。数据将提供给整个财团用于模型校准和验证。

Carbon dioxide (CO2) is emitted from various human activities. This causes atmospheric CO2 enrichment and in turn climate warming as well as changes in precipitation patterns, thus affecting agro-ecosystems. Besides effects on crop yield, CO2 enrichment and the interactions with other environmental factors might also have deleterious effects on wheat grain quality. However, the underlying crop physiological processes are far from being clear. The aim of this project is thus to provide experimental data to improve the mechanistic understanding of crop response in terms of yield formation and yield quality under climate change. Wheat will be used as model crop to make use of previous data and to fill some of the still existing gaps with regard to physiological responses of crops under climate change. Two contrasting wheat genotypes will be exposed in a free-air CO2 enrichment (FACE) system in the field in order to follow nutrient acquisition and allocation, and water relations and photosynthetic performance will be assessed by means of leaf gas exchange and actual plant transpiration measurements. Stable isotope analyses (13C, 15N) will aid to track C and N redistribution and to gain integrated estimates of water use efficiency and water relations. Besides targeted analyses, we will use metabolomic and proteomic approaches to identify subtle effects of climate change on grain yield quality. We will also focus on comprehensive data evaluation (e.g., meta-analyses), including the impacts of selected constellations of climate factors covered by previous experiments, in particular extreme events such as high temperature and droughts and nitrogen forms, in order to elucidate the physiological processes. Data will also be used to model impacts of climate change on crop growth and yield quality based on explicit simulation of plant internal transport processes. In addition, field assessments at the research sites and for "new crops" will be performed with regard to crop development and crop yield. Data will be provided to the whole consortium for model calibration and validation.