Key message: GWAS on a bread wheat panel with high D genome diversity identified novel alleles and QTLs associated with resilience to combined heat and drought stress under natural field conditions. As heat (H) and drought stresses occur concurrently under field conditions, studying them separately offers limited opportunities for wheat improvement. Here, a wheat diversity panel containing Aegilops tauschii introgressions was evaluated under H and combined heat–drought (HD) stresses to identify quantitative trait loci (QTLs) associated with resilience to the stresses, and to assess the practicability of harnessing Ae. tauschii diversity for breeding for combined stress resilience. Using genome-wide analysis, we identified alleles and QTLs on chromosomes 3D, 5D, and 7A controlling grain yield (GY), kernel number per spike, and thousand-kernel weight, and on 3D (521–549 Mbp) controlling GY alone. A strong marker–trait association (MTA) for GY stability on chromosome 3D (508.3 Mbp) explained 20.3% of the variation. Leaf traits—canopy temperature, vegetation index, and carbon isotope composition—were controlled by five QTLs on 2D (23–96, 511–554, and 606–614 Mbp), 3D (155–171 Mbp), and 5D (407–413 Mbp); some of them were pleiotropic for GY and yield-related traits. Further analysis revealed candidate genes, including GA20ox, regulating GY stability, and CaaX prenyl protease 2, regulating canopy temperature at the flowering stage, under H and HD stresses. As genome-wide association studies under HD in field conditions are scarce, our results provide genomic landmarks for wheat breeding to improve adaptation to H and HD conditions under climate change.

Kernel weight and shape-related traits are inherited stably and increase wheat yield. Narrow genetic diversity limits the progress of wheat breeding. Here, we evaluated kernel weight and shape-related traits and applied genome-wide association analysis to a panel of wheat multiple synthetic derivative (MSD) lines. The MSD lines harbored genomic fragments from Aegilops tauschii. These materials were grown under optimum conditions in Japan, as well as under heat and combined heat–drought conditions in Sudan. We aimed to explore useful QTLs for kernel weight and shape-related traits under stress conditions. These can be useful for enhancing yield under stress conditions. MSD lines possessed remarkable genetic variation for all traits under all conditions, and some lines showed better performance than the background parent Norin 61. We identified 82 marker trait associations (MTAs) under the three conditions; most of them originated from the D genome. All of the favorable alleles originated from Ae. tauschii. For the first time, we identified markers on chromosome 5D associated with a candidate gene encoding a RING-type E3 ubiquitin–protein ligase and expected to have a role in regulating wheat seed size. Our study provides important knowledge for the improvement of wheat yield under optimum and stress conditions. The results emphasize the importance of Ae. tauschii as a gene reservoir for wheat breeding.

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

Kernel hardness influences the milling and baking quality of wheat. Stress environments such as heat and combined heat-drought can produce harder kernels, thereby affecting the overall wheat quality. Beside puroindoline genes that are known to determine hardness, other QTLs contribute to the hardness. These QTLs, especially under stress conditions, need extensive research. Moreover, understanding the modification or stabilization of hardness under stress condition and the relationship with stress tolerance will facilitate the selection of superior lines that maintain both high yield and quality even under the stress environment. Therefore, in the current work, we aimed to identify the genetic loci and marker trait associations (MTAs) that contributes for hardness under optimum conditions in Japan, and heat and combined heat-drought (HD) conditions in Sudan. We used a panel of multiple synthetic derivatives (MSD) having diverse Aegilops tauschii genome segments and investigated the association between hardness stabilization and stress tolerance. Under stress conditions, we observed that less reduction of kernel weight is associated with either low change or stable kernel hardness. We identified 47 markers associated with hardness under all conditions; the D genome was the main contributor. For the first time, we found a significant association with hardness under stress conditions on chromosome 4D. We dissected several candidate genes associated with the change of hardness under stress conditions. Our results will improve the understanding of the genetic factors that affect wheat hardness stability.

Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield‐related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [ Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross‐combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.

With the aim of providing genetic materials for breeding drought-resistant wheat varieties, the physiological and metabolic plasticity of three drought-resistant wheat multiple synthetic derivative lines (MSDLs) and their backcross parent “Norin 61” (N61) were evaluated in response to drought stress. The results indicated that the D-genome introgressions from Aegilops tauschii into the MDSLs improved their drought-adaptive traits. Specifically, MNH5 and MSD345 showed higher photosynthesis rates and triose phosphate utilization than N61 under control conditions, resulting in greater accumulation of glucose and sucrose in the shoots. However, under drought stress, MNH5 and MSD345 had higher intrinsic water use efficiency than MSD53 and N61. The total antioxidant capacity and superoxide dismutase activity increased in all three MSDLs, whereas no significant changes were found in N61 in response to drought stress. Metabolome analysis identified six common drought-induced metabolites in all of the investigated genotypes. However, four metabolites (adenine, gamma aminobutyric acid, histidine, and putrescine) each specifically accumulated in an MSDL in response to drought stress, suggesting that these metabolites are important for MSDL drought resistance. In conclusion, MNH5 and MSD345 showed better adaptive responses to drought stress than MSD53 and N61, suggesting that these two MSDLs could be explored for breeding drought-resistant wheat lines.