BACKGROUND: Climate change induces perturbation in the global water cycle, profoundly impacting water availability for agriculture and therefore global food security. Water stress encompasses both drought (i.e. water scarcity) that causes the drying of soil and subsequent plant desiccation, and flooding, which results in excess soil water and hypoxia for plant roots. Terrestrial plants have evolved diverse mechanisms to cope with soil water stress, with the root system serving as the first line of defense. The responses of roots to water stress can involve both structural and physiological changes, and their plasticity is a vital feature of these adaptations. Genetic methodologies have been extensively employed to identify numerous genetic loci linked to water stress-responsive root traits. This knowledge is immensely important for developing crops with optimal root systems that enhance yield and guarantee food security under water stress conditions.
RESULTS: This review focused on the latest insights into modifications in the root system architecture and anatomical features of legume roots in response to drought and flooding stresses. Special attention was given to recent breakthroughs in understanding the genetic underpinnings of legume root development under water stress. The review also described various root phenotyping techniques and examples of their applications in different legume species. Finally, the prevailing challenges and prospective research avenues in this dynamic field as well as the potential for using root system architecture as a breeding target are discussed.
CONCLUSIONS: This review integrated the latest knowledge of the genetic components governing the adaptability of legume roots to water stress, providing a reference for using root traits as the new crop breeding targets.

Some citrus orchards in China often experience nitrogen (N) deficiency. For the first time, targeted metabolomics was used to examine N-deficient effects on hormones in sweet orange (Citrus sinensis (L.) Osbeck cv. Xuegan) leaves and roots. The purpose was to validate the hypothesis that hormones play a role in N deficiency tolerance by regulating root/shoot dry weight ratio (R/S), root system architecture (RSA), and leaf and root senescence. N deficiency-induced decreases in gibberellins and indole-3-acetic acid (IAA) levels and increases in cis(+)-12-oxophytodienoic acid (OPDA) levels, ethylene production, and salicylic acid (SA) biosynthesis might contribute to reduced growth and accelerated senescence in leaves. The increased ethylene formation in N-deficient leaves might be caused by increased 1-aminocyclopropanecarboxylic acid and OPDA and decreased abscisic acid (ABA). N deficiency increased R/S, altered RSA, and delayed root senescence by lowering cytokinins, jasmonic acid, OPDA, and ABA levels and ethylene and SA biosynthesis, increasing 5-deoxystrigol levels, and maintaining IAA and gibberellin homeostasis. The unchanged IAA concentration in N-deficient roots involved increased leaf-to-root IAA transport. The different responses of leaf and root hormones to N deficiency might be involved in the regulation of R/S, RSA, and leaf and root senescence, thus improving N use efficiency, N remobilization efficiency, and the ability to acquire N, and hence conferring N deficiency tolerance.

UNASSIGNED: Root system architecture (RSA) plays an important role in the plant\'s ability to sustain yield under abiotic stresses such as drought. Preceding crops (precrops) can affect the yield of the proceeding crop, partially by affecting the RSA. This experiment aims to explore the interactions between precrop identity, crop genotype and drought at early growth stages.
UNASSIGNED: Rhizotrons, sized 60 × 80 × 3.5 cm, were used to assess the early root growth of two winter wheat (Triticum aestivum L.) genotypes, using precrop-treated soil around the seedlings and differing water regimes. The rhizotrons were automatically imaged 3 times a week to track root development.
UNASSIGNED: Precrop-treated soil affected the RSA and changes caused by the reduced water treatment (RWT) were different depending on the precrop. Largest of these was the 36% reduction in root depth after wheat, but 44% after OSR. This indicates that effects caused by the precrop can be simulated, at least partially, by transferring precrop-treated soils to controlled environments. The genotypes had differential RSA and reacted differently to the RWT, with Julius maintaining an 8.8-13.1% deeper root system compared to Brons in the RWT. In addition, the combined environmental treatment affected the genotypes differently.
UNASSIGNED: Our results could help explain discrepancies found from using precrops to enhance yield as they indicate differences in the preceding crop effect when experiencing drought stress. Further, these differences are affected by genotypic interactions, which can be used to select and adapt crop genotypes for specific crop rotations, depending on the year. Additionally, we have shown a viable method of stimulating a partial precrop effect at the seedling stage in a controlled greenhouse setting using field soil around the germinated seed.

The root systems of Brassica species are complex. Eight root system architecture (RSA) traits, including total root length, total root surface area, root average diameter, number of tips, total primary root length, total lateral root length, total tertiary root length, and basal link length, were phenotyped across 379 accessions representing six Brassica species (B. napus, B. juncea, B. carinata, B. oleracea, B. nigra, and B. rapa) using a semi-hydroponic system and image analysis software. The results suggest that, among the assessed species, B. napus and B. oleracea had the most intricate and largest root systems, while B. nigra exhibited the smallest roots. The two species B. juncea and B. carinata shared comparable root system complexity and had root systems with larger root diameters. In addition, 313 of the Brassica accessions were genotyped using a 19K Brassica single nucleotide polymorphism (SNP) array. After filtering by TASSEL 5.0, 6,213 SNP markers, comprising 5,103 markers on the A-genome (covering 302,504 kb) and 1,110 markers on the C-genome (covering 452,764 kb), were selected for genome-wide association studies (GWAS). Two general linear models were tested to identify the genomic regions and SNPs associated with the RSA traits. GWAS identified 79 significant SNP markers associated with the eight RSA traits investigated. These markers were distributed across the 18 chromosomes of B. napus, except for chromosome C06. Sixty-five markers were located on the A-genome, and 14 on the C-genome. Furthermore, the major marker-trait associations (MTAs)/quantitative trait loci (QTLs) associated with root traits were located on chromosomes A02, A03, and A06. Brassica accessions with distinct RSA traits were identified, which could hold functional, adaptive, evolutionary, environmental, pathological, and breeding significance.

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Plants modify their root system architecture (RSA) in response to nitrogen (N) deficiency. The plant steroidal hormone, brassinosteroid (BR), plays important roles in root growth and development. This study demonstrates that optimal levels of exogenous BR impact significant increases in lateral root length and numbers in Arabidopsis seedlings under mild N-deficient conditions as compared to untreated seedlings. The impact of BR on RSA was stronger under mild N deficiency than under N-sufficient conditions. The BR effects on RSA were mimicked in dominant mutants of BZR1 and BES1 (bzr1-1D and bes1-D) transcription factors, while the RSA was highly reduced in the BR-insensitive mutant bri1-6, confirming that BR signaling is essential for the development of RSA under both N-sufficient and N-deficient conditions. Exogenous BR and constitutive activity of BZR1 and BES1 in dominant mutants led to enhanced root meristem, meristematic cell number, and cortical cell length. Under mild N deficiency, bzr1-1D displayed higher fresh and dry shoot weights, chlorophyll content, and N levels in the shoot, as compared to the wild type. These results indicate that BR modulates RSA under both N-sufficient and N-deficient conditions via the transcription factors BES1/BZR1 module and confers tolerance to N deficiency.

BACKGROUND: X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes.
RESULTS: VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data.
CONCLUSIONS: A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.

Roots play a pivotal role in the adaption of a plant to its environment, with different root traits adapting the plant to different stresses. The environment affects the Root System Architecture (RSA), but the genetic factors determine to what extent, and whether stress brought about by extreme environmental conditions is detrimental to a specific crop. This study aimed to identify differences in winter wheat RSA caused by cultivation region and practice, in the form of preceding crop (precrop), and to identify if modern cultivars used in Sweden differ in their reaction to these environments. This was undertaken using high-throughput phenotyping to assess the RSA. Clear differences in the RSA were observed between the Swedish cultivation regions, precrop treatments, and interaction of these conditions with each other and the genetics. Julius showed a large difference between cultivars, with 9.3-17.1% fewer and 12-20% narrower seminal roots. Standardized yield decreased when grown after wheat, 23% less compared to oilseed rape (OSR), and when grown in the Southern region, 14% less than the Central region. Additionally, correlations were shown between the root number, angle, and grain yield, with different root types being correlated depending on the precrop. Cultivars on the Swedish market show differences that can be adapted to the region-precrop combinations. The differences in precrop effect on RSA between regions show global implications and a need for further assessment. Correlations between RSA and yield, based on root-type × precrop, indicate different needs of the RSA depending on the management practices and show the potential for improving crop yield through targeting genotypic and environmental conditions in a holistic manner. Understanding this RSA variance, and the mechanisms of conditional response, will allow targeted cultivar breeding for specific environments, increasing plant health and food security.

The increase in soil salinization represents a current challenge for plant productivity, as most plants, including crops, are mainly salt-sensitive species. The identification of molecular traits underpinning salt tolerance represents a primary goal for breeding programs. In this scenario, the study of intraspecific variability represents a valid tool for investigating natural genetic resources evolved by plants in different environmental conditions. As a model system, Arabidopsis thaliana, including over 750 natural accessions, represents a species extensively studied at phenotypic, metabolic, and genomic levels under different environmental conditions. Two haplogroups showing opposite root architecture (shallow or deep roots) in response to auxin flux perturbation were identified and associated with EXO70A3 locus variations. Here, we studied the influence of these genetic backgrounds on plant salt tolerance. Eight accessions belonging to the two haplogroups were tested for salt sensitivity by exposing them to moderate (75 mM NaCl) or severe (150 mM NaCl) salt stress. Salt-tolerant accessions were found in both haplogroups, and all of them showed efficient ROS-scavenging ability. Even if an exclusive relation between salt tolerance and haplogroup membership was not observed, the modulation of root system architecture might also contribute to salt tolerance.

Soil salinization is one of the leading threats to global ecosystems, food security, and crop production. Plant growth-promoting rhizobacteria (PGPRs) are potential bioinoculants that offer an alternative eco-friendly agricultural approach to enhance crop productivity from salt-deteriorating lands. The current work presents bacterial strain CNUC13 from maize rhizosphere soil that exerted several PGPR traits and abiotic stress tolerance. The strain tolerated up to 1000 mM NaCl and 30% polyethylene glycol (PEG) 6000 and showed plant growth-promoting (PGP) traits, including the production of indole-3-acetic acid (IAA) and siderophore as well as phosphate solubilization. Phylogenetic analysis revealed that strain CNUC13 was Microbacterium azadirachtae. Maize plants exposed to high salinity exhibited osmotic and oxidative stresses, inhibition of seed germination, plant growth, and reduction in photosynthetic pigments. However, maize seedlings inoculated with strain CNUC13 resulted in significantly improved germination rates and seedling growth under the salt-stressed condition. Specifically, compared with the untreated control group, CNUC13-treated seedlings exhibited increased biomass, including fresh weight and root system proliferation. CNUC13 treatment also enhanced photosynthetic pigments (chlorophyll and carotenoids), reduced the accumulation of osmotic (proline) and oxidative (hydrogen peroxide and malondialdehyde) stress indicators, and positively influenced the activities of antioxidant enzymes (catalase, superoxide dismutase, and peroxidase). As a result, CNUC13 treatment alleviated oxidative stress and promoted salt tolerance in maize. Overall, this study demonstrates that M. azadirachtae CNUC13 significantly enhances the growth of salt-stressed maize seedlings by improving photosynthetic efficiency, osmotic regulators, oxidative stress resilience, and antioxidant enzyme activity. These findings emphasize the potential of utilizing M. azadirachtae CNUC13 as a bioinoculant to enhance salt stress tolerance in maize, providing an environmentally friendly approach to mitigate the negative effects of salinity and promote sustainable agriculture.