RESUMO
An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature.
RESUMO
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.
