DROOGHT · Improving cereal yield predictions under drought: root diameter as a predictor of plant water uptake across scales.

As droughts are becoming more frequent and severe, there is a call for adaptation strategies that enhance the crop’s resilience to these climate conditions. One pathway to do so is to select crops whose root system optimize the soil water uptake. However, the identification of adequate ideotypes is compromised by the limited understanding of the structural drivers controlling the water uptake from the root to crop scales. This knowledge gap is attributable to the multiscale and nonlinear nature of the soil-plant interactions. DROOGHT aims to address these gaps by identifying the dominant drivers of the complex below-ground processes in cereal crops. Based on suggestive pieces of evidence, the project builds on the primary hypothesis that the distribution of the root diameter within a cereal root system is an indicator of its structure and functions at the organ and field scales. The value and groundbreaking nature of such an indicator would lie in its simplicity: diameters are one of the easiest root traits to measure in any field set-up and at large scales. I will test this hypothesis and identify the dominant structural root traits controlling plant water uptake dynamics under water-limited conditions using complementary in silico and in vivo approaches. The outputs of this project will be (i) a novel multiscale computational framework that links local root structures to plant and crop functions; (ii) a phenotyping pipeline that links root structure to function; and (iii) the identification of cereal root properties favorable to higher yields across European pedoclimatic conditions and climate change scenarios. More broadly, this project will allow a significant step forward in our understanding of the role of root systems' structural traits on water uptake dynamics. It will provide practical insight for breeders and simpler, more elegant below-ground processes model components to insert into crop models.