SummaryThe positive effects of stay-green quantitative trait loci on grain yield of sorghum under post-anthesis drought are emergent consequences of their effects on water-use patterns, resulting from changes in pre-anthesis canopy size.

SummaryStay-green is an integrated drought adaptation trait characterized by a distinct green leaf phenotype during grain filling under terminal drought. We used sorghum (Sorghum bicolor), a repository of drought adaptation mechanisms, to elucidate the physiological and genetic mechanisms underpinning stay-green.Near-isogenic sorghum lines (cv RTx7000) were characterized in a series of field and managed-environment trials (seven experiments and 14 environments) to determine the influence of four individual stay-green (Stg1-4) quantitative trait loci (QTLs) on canopy development, water use and grain yield under post-anthesis drought.The Stg QTL decreased tillering and the size of upper leaves, which reduced canopy size at anthesis. This reduction in transpirational leaf area conserved soil water before anthesis for use during grain filling. Increased water uptake during grain filling of Stg near-isogenic lines (NILs) relative to RTx7000 resulted in higher post-anthesis biomass production, grain number and yield. Importantly, there was no consistent yield penalty associated with the Stg QTL in the irrigated control.These results establish a link between the role of the Stg QTL in modifying canopy development and the subsequent impact on crop water use patterns and grain yield under terminal drought.

Summary Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high‐throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field‐based plant phenomics can enable next‐generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from ‘Green Revolution’ traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high‐throughput phenotyping could enable the next generation of crop research and breeding is presented.