Durum wheat (Triticum turgidum L. ssp. durum) production can experience significant yield losses due to crown rot (CR) disease. Losses are usually exacerbated when disease infection coincides with terminal drought. Durum wheat is very susceptible to CR, and resistant germplasm is not currently available in elite breeding pools. We hypothesize that deploying physiological traits for drought adaptation, such as optimal root system architecture to reduce water stress, might minimize losses due to CR infection. This study evaluated a subset of lines from a nested association mapping population for stay-green traits, CR incidence and yield in field experiments as well as root traits under controlled conditions. Weekly measurements of normalized difference vegetative index (NDVI) in the field were used to model canopy senescence and to determine stay-green traits for each genotype. Genome-wide association studies using DArTseq molecular markers identified quantitative trait loci (QTLs) on chromosome 6B (qCR-6B) associated with CR tolerance and stay-green. We explored the value of qCR-6B and a major QTL for root angle QTL qSRA-6A using yield datasets from six rainfed environments, including two environments with high CR disease pressure. In the absence of CR, the favorable allele for qSRA-6A provided an average yield advantage of 0.57 t·ha⁻¹, whereas in the presence of CR, the combination of favorable alleles for both qSRA-6A and qCR-6B resulted in a yield advantage of 0.90 t·ha⁻¹. Results of this study highlight the value of combining above- and belowground physiological traits to enhance yield potential. We anticipate that these insights will assist breeders to design improved durum varieties that mitigate production losses due to water deficit and CR.

Optimal root system architecture (RSA) is critical for efficient resource capture in soils, hence being an interest in crop breeding. Seminal root angle (SRA) at the seedling stage in durum wheat has been suggested to be a good indicator of RSA. However, research on correlating such laboratory‐based seedling root phenotyping to RSA at later phases of plant growth is limited, resulting in the importance of root trait variation seen in seedlings often being overstated. To explore the role of SRA in modifying RSA at later phases of plant growth, we assessed 11 genotypes contrasting in SRA (wide and narrow), grown in a rhizobox designed for phenotyping root systems of plants during late‐tillering. Aboveground traits and root dry mass in different soil depths and across the entire soil volume were measured manually, while root architectural traits were extracted using image analysis and summarised by multiple factor analysis to describe RSA. When comparing the wide and narrow genotypes, no differences were detected for aboveground traits and total root dry mass. However, differences were observed in the allocation of root dry mass at different depths. The wide and narrow genotypes showed distinct RSAs, particularly in the upper soil (0–30 cm). The wide genotypes exhibited a ‘spread‐out’ root system with dense and thin roots, whereas the narrow genotypes had a compact root system with fewer but thicker roots. Our study demonstrated a clear difference in RSA between the wide and narrow genotypes, highlighting the association between SRA on the direction and distribution of root growth in plants at later growth stages.

Multiparent populations are now widespread in crop genetic studies as they capture more genetic diversity and offer high statistical power for detecting quantitative trait loci (QTLs). To confirm the suitability of using a recently developed chickpea ( Cicer arietinum L.) multi‐parent advanced generation intercross (MAGIC) population for genetic studies, we characterized the diversity of the eight founder lines and explored the linkage disequilibrium decay, marker coverage, segregation distortion, allelic variation, and structure of the population. The MAGIC population was genotyped using whole‐genome sequencing; following marker curation, a total of 4255 high‐quality polymorphic single nucleotide polymorphism markers were used for genomic analyses. To demonstrate the effectiveness of the MAGIC population to dissect the genetics of key agronomic traits (days to 50% flowering and plant height), we employed both a genome‐wide mapping approach using fixed and random model circulating probability unification and a haplotype‐based mapping using the local genomic estimated breeding value approach. Our analyses confirmed the role of genomic regions previously reported in the literature and identified several new QTLs for days to 50% flowering and plant height. We also showed the potential for trait improvement through stacking the top 10 haploblocks to develop early flowering chickpea and selection of desirable haplotypes on chromosome 4 to improve plant height. Our results demonstrate the chickpea MAGIC population is a valuable resource for researchers and pre‐breeders to study the genetic architecture of complex traits and allelic variation to accelerate crop improvement in chickpea.

Mungbean is an important sub‐tropical legume crop grown across Asia, Africa, and Australia. Yield improvement is crucial for expanding production, but phenotyping important traits across diverse environments using current approaches is challenging, limiting the scale and complexity of information captured. High‐throughput phenotyping platforms offer a solution by rapidly screening traits at scale. This study deploys an unmanned aerial vehicle (UAV) platform to determine the potential of phenotyping a range of agronomic and physiological traits within a diverse mungbean population evaluated across three field trials. Three predictive data‐driven modeling approaches were undertaken to evaluate performance accuracy in predicting these traits: linear regression, stepwise regression, and partial least squares regression. Results show that using the geometric trait “coverage” as a proxy is most suitable for screening visual traits like early vigor. For functional traits (i.e., aboveground biomass), predictive data‐driven models demonstrate high accuracy during early‐ and mid‐canopy development stages ( R ² 0.79, root mean square error [RMSE] 4.08 and R ² 0.8, RMSE 26.92, respectively), but accuracy declines in late‐canopy development ( R ² 0.33 and RMSE 43.15). Prediction accuracy can be optimized by using different modeling approaches at different stages during the transition from early‐ to mid‐canopy development as well as canopy closure. Similar findings were observed when examining the prediction models for the physiological trait, stomatal conductance ( R ² 0.69 and RMSE 0.10). These approaches are expected to enable breeders and researchers to incorporate UAV‐based phenotyping systems into mungbean improvement programs. Such approaches might be most efficiently used at scale if applied as part of a “real‐time” calibration approach.

Mungbean is an important source of plant protein for consumers and a high-value export crop for growers across Asia, Australia and Africa. However, many commercial cultivars are highly vulnerable to biotic stresses, which rapidly reduce yield within the season. Fusarium oxysporum is a soil-borne pathogen that is a growing concern for mungbean growers globally. This pathogen causes Fusarium wilt by infecting the root system of the plant resulting in devastating yield reductions. To understand the impact of Fusarium on mungbean development and productivity and to identify tolerant genotypes, a panel of 23 diverse accessions was studied. Field trials conducted in 2016 and 2021 in Warwick, Queensland, Australia under rainfed conditions investigated the variation in phenology, canopy and yield component traits under disease and disease-free conditions. Analyses revealed a high degree of genetic variation for all traits. By comparing the performance of these traits across these two environments, we identified key traits that underpin yield under disease and disease-free conditions. Aboveground biomass components at 50 % flowering were identified as significant drivers of yield development under disease-free conditions and when impacted by Fusarium resulted in up to 96 % yield reduction. Additionally, eight genotypes were identified to be tolerant to Fusarium. These genotypes were found to display differing phenological and morphological behaviours, thereby demonstrating the potential to breed tolerant lines with a range of diverse trait variations. The identification of tolerant genotypes that sustain yield under disease pressure may be exploited in crop improvement programs.