Evolution of wheat architecture,physiology,and metabolism during domestication and further cultivation:Lessons for crop improvement

In recent decades,genetic advances in yield improvement in the major cereal crops,including wheat,has stagnated or proceeded at a slower rate than is required to meet future global food demand,particularly in the face of climate change.To reverse this situation,and in view of the future climate scenario,there is a need to increase the genetic diversity of wheat to increase its productivity,quality,stability,and adaptation to local agro-environments.The abundant genetic resources and literature are a basis for wheat improvement.However,many species,such as wild relatives,landraces,and old cultivars have not been studied beyond their agronomic characteristics,highlighting the lack of understanding of the physiological and metabolic processes(and their integration) associated with higher productivity and resilience in limiting environments.Retrospective studies using wheat ancestors and modern cultivars may identify novel traits that have not previously been considered,or have been underestimated,during domestication and breeding,but that may contribute to future food security.This review describes existing wheat genetic diversity and changes that occurred during domestication and breeding,and considers whether mining natural variation among wheat ancestors offers an opportunity to enhance wheat agronomic performance,spike architecture,canopy-and organ-level photosynthetic capacity,and responses to abiotic stress,as well as to develop new wheat hybrids.

Crop phenotyping in a context of global change:What to measure and how to do it

High-throughput crop phenotyping,particularly under field conditions,is nowadays perceived as a key factor limiting crop genetic advance.Phenotyping not only facilitates conventional breeding,but it is necessary to fully exploit the capabilities of molecular breeding,and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems.In terms of phenotyping,it is necessary to determined which selection traits are relevant in each situation,and which phenotyping tools/methods are available to assess such traits.Remote sensing methodologies are currently the most popular approaches,even when lab-based analyses are still relevant in many circumstances.On top of that,data processing and automation,together with machine learning/deep learning are contributing to the wide range of applications for phenotyping.This review addresses spectral and red-green-blue sensing as the most popular remote sensing approaches,alongside stable isotope composition as an example of a lab-based tool,and root phenotyping,which represents one of the frontiers for field phenotyping.Further,we consider the two most promising forms of aerial platforms(unmanned aerial vehicle and satellites)and some of the emerging data-processing techniques.The review includes three Boxes that examine specific case studies.

Multiscale assessment of ground,aerial and satellite spectral data for monitoring wheat grain nitrogen content

Wheat grain quality characteristics have experienced increasing attention as a central factor affecting wheat end-use products quality and human health.Nonetheless,in the last decades a reduction in grain quality has been observed.Therefore,it is central to develop efficient quality-related phenotyping tools.In this sense,one of the most relevant wheat features related to grain quality traits is grain nitrogen content,which is directly linked to grain protein content and monitorable with remote sensing approaches.Moreover,the relation between nitrogen fertilization and grain nitrogen content(protein)plays a central role in the sustainability of agriculture.Both aiming to develop efficient phenotyping tools using remote sensing instruments and to advance towards a field-level efficient and sustainable monitoring of grain nitrogen status,this paper studies the efficacy of various sensors,multispectral and visible red-greenblue(RGB),at different scales,ground and unmanned aerial vehicle(UAV),and phenological stages(anthesis and grain filling)to estimate grain nitrogen content.Linear models were calculated using vegetation indices at each sensing level,sensor type and phenological stage.Furthermore,this study explores the up-scalability of the best performing model to satellite level Sentinel-2 equivalent data.We found that models built at the phenological stage of anthesis with UAV-level multispectral cameras using red-edge bands outperformed grain nitrogen content estimation(R2=0.42,RMSE=0.18%)in comparison with those models built with RGB imagery at ground and aerial level,as well as with those built with widely used ground-level multispectral sensors.We also demonstrated the possibility to use UAV-built multispectral linear models at the satellite scale to determine grain nitrogen content effectively(R2=0.40,RMSE=0.29%)at actual wheat fields.