GiA Roots: software for the high throughput analysis of plant root system architecture

Genome-Wide Association study for root system architecture traits in field soybean [Glycine max (L.) Merr.]

Roots play a crucial role in plant development, serving to absorb water and nutrients from the soil while also providing structural stability. However, the impacts of global warming can impede root growth by altering soil conditions that hinder overall plant growth. To address this challenge, there is a need to screen and identify plant genotypes with superior Root System Architecture traits (RSA), that can be used for future breeding efforts in enhancing their resilience to these environmental changes. In this project, 500 mid to late-maturity soybean accessions were grown on blue blotting papers hydroponically with six replicates and assessed seven RSA traits. Genome-Wide Association Studies (GWAS) were carried out with root phenotypic data and SNP data from the SoySNP50K iSelect SNP BeadChip, using both the TASSEL 5.0 and FarmCPU techniques. A total of 26 significant SNP-trait correlations were discovered, with 11 SNPs on chromosome 13. After SNP selection, we identified 14 candidate genes within 100-kb regions flanking the SNPs, which are related to root architecture. Notably, Glyma.17G258700, which exhibited substantial differential expression in root tips and its Arabidopsis homolog, AT4G24190 (GRP94) is involved in the regulation of meristem size and organization. Other candidate genes includes Glyma.03G023000 and Glyma.13G273500 that are also play a key role in lateral root initiation and root meristem growth, respectively. These findings significantly contribute to the discovery of key genes associated with root system architecture, facilitating the breeding of resilient cultivars adaptable to changing climates.

Salt stress memory in tall fescue: Interaction of different stress stages, pollination system and genetic diversity

INTRODUCTION

The effects of salinity memory and its interaction with genetic diversity for drought tolerance and pollination system in terms of morphological, physiological, root characteristics and spectral reflectance indices (SRIs) in tall fescue is still unknown.

METHODS

Four tall fescue genotypes (two drought-sensitive and two drought-tolerant) were manually controlled to produce four selfed (S1) and four open-pollinated (OP) progeny genotypes (finally eight progeny genotypes). Then all genotypes were assessed for two years in greenhouse under five salinity treatments including control treatment (C), twice salinity stress treatment (primary mild salinity stress in two different stages and secondary at the end stage) (S1t1S2 and S1t2S2), once severe salinity stress treatment (secondary only, S2), and foliar spray of salicylic acid (SA) simultaneously with secondary salinity stress (H2S2).

RESULTS

Results indicated that obligate selfing (S1) caused to inbreeding depression in RWC, plant growth, catalase activity, root length and the ratio of root/shoot (R/S). Once salinity stress treatment (S2) led to depression in most measured traits, while pre-exposure to salinity (salinity memory) (S1t1S2 and S1t2S2) improved photosynthetic pigments, proline, antioxidant enzymes and R/S.

CONCLUSION

Salinity memory was more pronounced in drought-sensitive genotypes, while it was more evident in OP than S1 population. Foliar spray of salicylic acid (SA) was almost equally effective in reducing the effects of salinity stress in both populations. The efficacy of application was more pronounced in tolerant genotypes compared to sensitive ones. The possibility of modeling correlated spectral reflectance indices (SRIs) for prediction of different morphological, physiological and root characteristics will be discussed.

Multi-locus genome-wide association study for phosphorus use efficiency in a tropical maize germplasm

Phosphorus (P) is an essential macronutrient for maize (Zea mays L.) growth and development. Therefore, generating cultivars with upgraded P use efficiency (PUE) represents one of the main strategies to reduce the global agriculture dependence on phosphate fertilizers. In this work, genome-wide association studies (GWAS) were performed to detect quantitative trait nucleotide (QTN) and potential PUE-related candidate genes and associated traits in greenhouse and field trials under contrasting P conditions. The PUE and other agronomy traits of 132 maize inbred lines were assessed in low and normal P supply through the greenhouse and field experiments and Multi-locus GWAS was used to map the associated QTNs. Wide genetic variability was observed among the maize inbred lines under low and normal P supply. In addition, we confirm the complex and quantitative nature of PUE. A total of 306 QTNs were associated with the 24 traits evaluated using different multi-locus GWAS methods. A total of 186 potential candidate genes were identified, mainly involved with transcription regulator, transporter, and transference activity. Further studies are still needed to elucidate the functions and relevance of these genes regarding PUE. Nevertheless, pyramiding the favorable alleles pinpointed in the present study can be considered an efficient strategy for molecular improvement to increase maize PUE.

The role of magnesium oxide foliar sprays in enhancing mint (Mentha crispa L.) tolerance to cadmium stress

This study investigates using magnesium foliar spray to enhance mint plants' growth and physiological performance under cadmium toxicity. It examines the effects of foliar application of magnesium oxide (40 mg L-1), in both nano and bulk forms, on mint plants exposed to cadmium stress (60 mg kg-1 soil). Cadmium stress reduced root growth and activity, plant biomass (32%), leaf hydration (19%), chlorophyll levels (27%), magnesium content (51%), and essential oil yield (35%), while increasing oxidative and osmotic stress in leaf tissues. Foliar application of magnesium increased root growth (32%), plant biomass, essential oil production (17%), leaf area (24%), chlorophyll content (10%), soluble sugar synthesis (33%), and antioxidant enzyme activity, and reduced lipid peroxidation and osmotic stress. Although the nano form of magnesium enhanced magnesium absorption, its impact on growth and physiological performance was not significantly different from the bulk form. Therefore, foliar application of both forms improves plants' ability to withstand cadmium toxicity. However, the study is limited by its focus on a single plant species and specific environmental conditions, which may affect the generalizability of the results. The long-term sustainability of such treatments could provide a more comprehensive understanding of magnesium's role in mitigating heavy metal stress in plants.

Effect of solution pH on root architecture of four apple rootstocks grown in an aeroponics nutrient misting system

The pH of the solution in the rhizosphere is an important factor that determines the availability and mobility of nutrients for plant uptake. Solution pH may also affect the root distribution and architecture of apple rootstocks. In this study, we evaluated the effect of solution pH on root system development of apple rootstocks using an aeroponics system designed and developed at Cornell AgriTech Geneva, USA. Four Geneva® apple rootstocks (G.210, G.214, G.41, and G.890) were grown in an aeroponic system under nutrient solution misting featuring continuously adjusted pH levels to three pH treatments (5.5, 6.5, and 8.0). Root development was monitored for 30 days and evaluated regularly for distribution and root mass. Images of the developed roots grown in the aeroponic system were collected at the end of the experiment using a high-resolution camera and analyzed using GiA Roots® software, which generates root architecture parameter values in a semi-automated fashion. The resulting root architecture analysis showed that the Geneva® rootstocks were significantly different for two architecture parameters. The length-to-width ratio analysis represented by two GiA Roots parameters (minor-to-major ellipse ratio and network width-to-depth ratio) showed that G.210 was flatter than G.890, which had a greater tendency to grow downward. Rootstocks G.214 and G.41 displayed similar growth values. The solution pH affected most root architecture parameter measurements where overall root growth was higher at pH 8 than at pH 5.5 and 6.5, which showed similar growth. In general, the average root width tended to decrease at higher pH values. While there were no significant differences in the leaf nutrient concentrations of P, K, Ca, Mg, S, B, Zn, Cu, and Fe within the four rootstocks, the pH level of the solution had a significant effect on P, Ca, and Mn. This study is the first of its kind to investigate the effect of pH on root architecture in a soil-free (aeroponic) environment and may have implications for apple root behavior under field conditions where pH levels are different.

Distinct growth patterns in seedling and tillering wheat plants suggests a developmentally restricted role of HYD2 in salt-stress response

KEY MESSAGE

Mutants lacking functional HYD2 homoeologs showed improved seedling growth, but comparable or increased susceptibility to salt stress in tillering plants, suggesting a developmentally restricted role of HYD2 in salt response. Salinity stress threatens global food security by reducing the yield of staple crops such as wheat (Triticum ssp.). Understanding how wheat responds to salinity stress is crucial for developing climate resilient varieties. In this study, we examined the interplay between carotenoid metabolism and the response to salt (NaCl) stress, a specific form of salinity stress, in tetraploid wheat plants with mutations in carotenoid β-hydroxylase 1 (HYD1) and HYD2. Our investigation encompassed both the vulnerable seedling stage and the more developed tillering stage of wheat plant growth. Mutant combinations lacking functional HYD2 homoeologs, including hyd-A2 hyd-B2, hyd-A1 hyd-A2 hyd-B2, hyd-B1 hyd-A2 hyd-B2, and hyd-A1 hyd-B1 hyd-A2 hyd-B2, had longer first true leaves and slightly enhanced root growth during germination under salt stress compared to the segregate wild-type (control) plants. Interestingly, these mutant seedlings also showed decreased levels of neoxanthin and violaxanthin (xanthophylls derived from β-carotene) and an increase in β-carotene in roots. However, tillering hyd mutant and segregate wild-type plants generally did not differ in their height, tiller count, and biomass production under acute or prolonged salt stress, except for decreases in these parameters observed in the hyd-A1 hyd-B1 hyd-A2 hyd-B2 mutant that indicate its heightened susceptibility to salt stress. Taken together, these findings suggest a significant, yet developmentally restricted role of HYD2 homoeologs in salt-stress response in tetraploid wheat. They also show that hyd-A2 hyd-B2 mutant plants, previously demonstrated for possessing enriched nutritional (β-carotene) content, maintain an unimpaired ability to withstand salt stress.

Fast and Efficient Root Phenotyping via Pose Estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

Quantitative analysis of lateral root development with time-lapse imaging and deep neural network

During lateral root (LR) development, morphological alteration of the developing single LR primordium occurs continuously. Precise observation of this continuous alteration is important for understanding the mechanism involved in single LR development. Recently, we reported that very long-chain fatty acids are important signalling molecules that regulate LR development. In the study, we developed an efficient method to quantify the transition of single LR developmental stages using time-lapse imaging followed by a deep neural network (DNN) analysis. In this 'insight' paper, we discuss our DNN method and the importance of time-lapse imaging in studies on plant development. Integrating DNN analysis and imaging is a powerful technique for the quantification of the timing of the transition of organ morphology; it can become an important method to elucidate spatiotemporal molecular mechanisms in plant development.

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.

Arsenic accumulation pattern in water-soil-rice systems: A study of tolerance mechanisms and associated health risks

Regardless of the daunting challenge of arsenic (As) contamination in Pakistan, literature on tolerance and responsible factors in paddy fields remain elusive. In this regard, we aimed to explore physiochemical factors responsible for As availability in water-soil-rice systems. The study highlighted rice defense mechanisms to mitigate As toxicity on growth and yield. In the present study, basmati rice samples were collected along with irrigation and soil samples from control (<10 μg/L), low (11-25 μg/L), medium (26-100 μg/L), and high (>100 μg/L) contaminated regions. Oxidative stress markers (MDA and H2O2) and antioxidant enzymatic assays (SOD, CAT, POD, APX) were measured by spectrophotometer. The Durov diagram was constructed by using Grapher software to identify prevalent water types in irrigation wells. Total As was measured in water, soil, and rice tissues by hydride generation-atomic absorption spectroscopy (HG-AAS). The Durov diagram showed that the majority of irrigation water was Ca-Mg-Cl type. Furthermore, the FTIR analysis identified different organic compounds, i.e., OH, CC, CI, and CBr, particularly in soil from high regions. The results indicated higher accumulation and translocation of As in the water-soil-rice system from a high region compared to control and other regions. Phenotypic traits, i.e., grain yield, biological yield, chlorophyll, and root parameters were significantly impacted under high As-contaminated region. A concentration-dependent increase was indicated in oxidative stress and antioxidant activities except for APX. Risk assessment indicated a higher hazard quotient (1.09) and carcinogenic risk (5.0 × 10-03) due to grain consumption in high As-contaminated regions. The present study emphasized the need for strict regulations and policies to mitigate As calamity at the local level and protect human health.

Evaluation of functional kompetitive allele-specific PCR (KASP) markers for selection of drought-tolerant wheat (Triticum aestivum) genotypes

Wheat (Triticum aestivum ) is a major crop around the globe and different techniques are being used for its productivity enhancement. Germplasm evaluation to improve crop productivity mainly depends on accurate phenotyping and selection of genotypes with a high frequency of superior alleles related to the trait of interest. Therefore, applying functional kompetitive allele-specific PCR (KASP) markers for drought-related genes is essential to characterise the genotypes for developing future climate-resilient wheat crop. In this study, eight functional KASP markers and nine morphological traits were employed to evaluate the 40 wheat genotypes for drought tolerance. Morphological traits showed significant variation (P ≤0.05) among the genotypes, except tiller count (TC), fresh root weight (FRW) and dry root weight (DRW). PCA biplot showed that 63.3% phenotypic variation was explained by the first two PCs under control treatment, while 70.8% variation was explained under drought treatment. It also indicated that root length (RL) and primary root (PR) have considerable variations among the genotypes under both treatments and are positively associated with each other. Hence, the findings of this study suggested that both these traits could be used as a selection criterion to classify the drought-tolerant wheat genotypes. KASP genotyping accompanied by morphological data revealed that genotypes Markaz, Bhakar Star, China 2, Aas and Chakwal-50 performed better under drought stress. These outperforming genotypes could be used as parents in developing drought-tolerant wheat genotypes. Hence, KASP genotyping assay for functional genes or significant haplotypes and phenotypic evaluation are prerequisites for a modern breeding program.

Constitutive basis of root system architecture: uncovering a promising trait for breeding nutrient- and drought-resilient crops

Root system architecture (RSA) plays a pivotal role in efficient uptake of essential nutrients, such as phosphorous (P), nitrogen (N), and water. In soils with heterogeneous nutrient distribution, root plasticity can optimize acquisition and plant growth. Here, we present evidence that a constitutive RSA can confer benefits for sorghum grown under both sufficient and limiting growth conditions. Our studies, using P efficient SC103 and inefficient BTx635 sorghum cultivars, identified significant differences in root traits, with SC103 developing a larger root system with more and longer lateral roots, and enhanced shoot biomass, under both nutrient sufficient and deficient conditions. In addition to this constitutive attribute, under P deficiency, both cultivars exhibited an initial increase in lateral root development; however, SC103 still maintained the larger root biomass. Although N deficiency and drought stress inhibited both root and shoot growth, for both sorghum cultivars, SC103 again maintained the better performance. These findings reveal that SC103, a P efficient sorghum cultivar, also exhibited enhanced growth performance under N deficiency and drought. Our results provide evidence that this constitutive nature of RSA can provide an avenue for breeding nutrient- and drought-resilient crops.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-023-00112-w.

Dose-dependent toxicity of polyethylene microplastics (PE-MPs) on physiological and biochemical response of blackgram and its associated rhizospheric soil properties

Microplastic contamination in terrestrial ecosystem is emerging as a global threat due to rapid production of plastic waste and its mismanagement. It affects all living organisms including plants. Hence, the current study aims at understanding the effect of polyethylene microplastics (PE-MPs) at different concentrations (0, 0.25, 0.50, 0.75, and 1.00% w/w) on the plant growth and yield attributes. With blackgram as a test crop, results revealed that a maximum reduction in physiological traits like photosynthetic rate; chlorophyll a, b; and total chlorophyll by 5, 14, 10, and 13% at flowering stage; and an increase in biochemical traits like ascorbic acid, malondialdehyde, proline, superoxide dismutase, and catalase by 11, 29.7, 16, 22, and 30% during vegetative stage was observed with 1% PE-MP application. Moreover, a reduction in growth and yield attributes was also observed with increasing concentration of microplastics. Additionally, application of 1% PE-MPs decreased the soil bulk density, available phosphorus, and potassium, whereas the EC, organic carbon, microbial biomass carbon, NO3-N, and NH4-N significantly increased. Moreover, the presence of PE-MPs in soil also had a significant influence on the soil enzyme activities. Metagenomic analysis (16 s) reveals that at genus level, Bacillus (19%) was predominant in control, while in 1% PE-MPs, Rubrobacter (28%) genus was dominant. Microvirga was found exclusively in T5, while the relative abundance of Gemmatimonas declined from T1 to T5. This study thus confirms that microplastics exert a dose-dependent effect on soil and plant characteristics.

Fast and efficient root phenotyping via pose estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models

BACKGROUND

Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset.

RESULTS

The best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives.

CONCLUSIONS

Although correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts-limiting the need for model retraining.

RootBot: High-throughput root stress phenotyping robot

Premise

Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil.

Methods and Results

RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data.

Conclusions

The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.

Dynamic transcriptome analysis unravels key regulatory genes of maize root growth and development in response to potassium deficiency

MAIN CONCLUSION

Integrated root phenotypes and transcriptome analysis have revealed key candidate genes responsible for maize root growth and development in potassium deficiency. Potassium (K) is a vital macronutrient for plant growth, but our understanding of its regulatory mechanisms in maize root system architecture (RSA) and K+ uptake remains limited. To address this, we conducted hydroponic and field trials at different growth stages. K+ deficiency significantly inhibited maize root growth, with metrics like total root length, primary root length, width and maximum root number reduced by 50% to 80% during early seedling stages. In the field, RSA traits exhibited maximum values at the silking stage but continued to decline thereafter. Furthermore, K deprivation had a pronounced negative impact on root morphology and RSA growth and grain yield. RNA-Seq analysis identified 5972 differentially expressed genes (DEGs), including 17 associated with K+ signaling, transcription factors, and transporters. Weighted gene co-expression network analysis revealed 23 co-expressed modules, with enrichment of transcription factors at different developmental stages under K deficiency. Several DEGs and transcription factors were predicted as potential candidate genes responsible for maize root growth and development. Interestingly, some of these genes exhibited homology to well-known regulators of root architecture or development in Arabidopsis, such as Zm00001d014467 (AtRCI3), Zm00001d011237 (AtWRKY9), and Zm00001d030862 (AtAP2/ERF). Identifying these key genes helps to provide a deeper understanding of the molecular mechanisms governing maize root growth and development under nutrient deficient conditions offering potential benefits for enhancing maize production and improving stress resistance through targeted manipulation of RSA traits in modern breeding efforts.

Redesigning crop varieties to win the race between climate change and food security

Climate change poses daunting challenges to agricultural production and food security. Rising temperatures, shifting weather patterns, and more frequent extreme events have already demonstrated their effects on local, regional, and global agricultural systems. Crop varieties that withstand climate-related stresses and are suitable for cultivation in innovative cropping systems will be crucial to maximize risk avoidance, productivity, and profitability under climate-changed environments. We surveyed 588 expert stakeholders to predict current and novel traits that may be essential for future pearl millet, sorghum, maize, groundnut, cowpea, and common bean varieties, particularly in sub-Saharan Africa. We then review the current progress and prospects for breeding three prioritized future-essential traits for each of these crops. Experts predict that most current breeding priorities will remain important, but that rates of genetic gain must increase to keep pace with climate challenges and consumer demands. Importantly, the predicted future-essential traits include innovative breeding targets that must also be prioritized; for example, (1) optimized rhizosphere microbiome, with benefits for P, N, and water use efficiency, (2) optimized performance across or in specific cropping systems, (3) lower nighttime respiration, (4) improved stover quality, and (5) increased early vigor. We further discuss cutting-edge tools and approaches to discover, validate, and incorporate novel genetic diversity from exotic germplasm into breeding populations with unprecedented precision, accuracy, and speed. We conclude that the greatest challenge to developing crop varieties to win the race between climate change and food security might be our innovativeness in defining and boldness to breed for the traits of tomorrow.

Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits

Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots.

Early selection for drought tolerance in popcorn based on gene effects estimated in seedlings

Low rainfall rates are becoming increasingly frequent because of climate change, causing droughts and threatening world food security. For popcorn, drought is the most limiting abiotic factor for plant's growth and development. Thus, the water deficit directly impacts for crop productivity. Based on knowledge of the genetic basis of traits involved in stages of popcorn germination and seedling development under water stress, genotypes with potential for adaptation to adverse growing conditions can be selected early. Therefore, data on genetic effects and combining ability of 10 popcorn parents were compiled to propose breeding strategies for the development of cultivars with greater adaptation to water stress in the early stages. Forty-five diallel hybrids were evaluated under two different water regimes, that is, water stress and full irrigation. This corresponded to a water retention capacity of 25% and 70% of the germination paper. The plants were watered daily as needed for seven days. A range of factors were evaluated, that is, germination traits including the germination speed index and germination on the seventh day; shoot traits including length and dry weight; and root system including length, dry weight, root-to-shoot ratio, maximum root number, root network area, specific and root network length, and root volume. Breeding for drought adaption in the early stages of popcorn development can be successful when hybrids are used, because of the genetic effects of dominance (ϕ_s). These control the traits evaluated at the seedling stage. The combinations L61 x P2 and L71 x P3 were recommended, in view of the more successful performance estimated for traits related to the shoot and root system.

A temporal analysis and response to nitrate availability of 3D root system architecture in diverse pennycress (Thlaspi arvense L.) accessions

Introduction

Roots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand plastic responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity.

Methods

Using a root imaging and analysis pipeline, the 4D architecture of the pennycress root system was characterized under four nitrate regimes, ranging from zero to high nitrate concentrations. These measurements were taken at four time points (days 5, 9, 13, and 17 after sowing).

Results

Significant nitrate condition response and genotype interactions were identified for many root traits, with the greatest impact observed on lateral root traits. In trace nitrate conditions, a greater lateral root count, length, density, and a steeper lateral root angle was observed compared to high nitrate conditions. Additionally, genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density.

Discussion

These findings illustrate root trait variance among pennycress accessions. These traits could serve as targets for breeding programs aimed at developing improved cover crops that are responsive to nitrate, leading to enhanced productivity, resilience, and ecosystem service.

Genome-wide association study in two-row spring barley landraces identifies QTL associated with plantlets root system architecture traits in well-watered and osmotic stress conditions

Water availability is undoubtedly one of the most important environmental factors affecting crop production. Drought causes a gradual deprivation of water in the soil from top to deep layers and can occur at diverse stages of plant development. Roots are the first organs that perceive water deficit in soil and their adaptive development contributes to drought adaptation. Domestication has contributed to a bottleneck in genetic diversity. Wild species or landraces represent a pool of genetic diversity that has not been exploited yet in breeding program. In this study, we used a collection of 230 two-row spring barley landraces to detect phenotypic variation in root system plasticity in response to drought and to identify new quantitative trait loci (QTL) involved in root system architecture under diverse growth conditions. For this purpose, young seedlings grown for 21 days in pouches under control and osmotic-stress conditions were phenotyped and genotyped using the barley 50k iSelect SNP array, and genome-wide association studies (GWAS) were conducted using three different GWAS methods (MLM GAPIT, FarmCPU, and BLINK) to detect genotype/phenotype associations. In total, 276 significant marker-trait associations (MTAs; p-value (FDR)< 0.05) were identified for root (14 and 12 traits under osmotic-stress and control conditions, respectively) and for three shoot traits under both conditions. In total, 52 QTL (multi-trait or identified by at least two different GWAS approaches) were investigated to identify genes representing promising candidates with a role in root development and adaptation to drought stress.

A method of cotton root segmentation based on edge devices

The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.

Biomining Sesuvium portulacastrum for halotolerant PGPR and endophytes for promotion of salt tolerance in Vigna mungo L

Halophytic plants can tolerate a high level of salinity through several morphological and physiological adaptations along with the presence of salt tolerant rhizo-microbiome. These microbes release phytohormones which aid in alleviating salinity stress and improve nutrient availability. The isolation and identification of such halophilic PGPRs can be useful in developing bio-inoculants for improving the salt tolerance and productivity of non-halophytic plants under saline conditions. In this study, salt-tolerant bacteria with multiple plant growth promoting characteristics were isolated from the rhizosphere of a predominant halophyte, Sesuvium portulacastrum grown in the coastal and paper mill effluent irrigated soils. Among the isolates, nine halotolerant rhizobacterial strains that were able to grow profusely at a salinity level of 5% NaCl were screened. These isolates were found to have multiple plant growth promoting (PGP) traits, especially 1-aminocyclopropane-1-carboxylic acid deaminase activity (0.32-1.18  μM of α-ketobutyrate released mg^-1 of protein h^-1) and indole acetic acid (9.4-22.8  μg mL^-1). The halotolerant PGPR inoculation had the potential to improve salt tolerance in Vigna mungo L. which was reflected in significantly (p < 0.05) higher germination percentage (89%) compared to un-inoculated seeds (65%) under 2% NaCl. Similarly, shoot length (8.9-14.6 cm) and vigor index (792-1785) were also higher in inoculated seeds. The strains compatible with each other were used for the preparation of two bioformulations and these microbial consortia were tested for their efficacy in salt stress alleviation of Vigna mungo L. under pot study. The inoculation improved the photosynthetic rate (12%), chlorophyll content (22%), shoot length (5.7%) and grain yield (33%) in Vigna mungo L. The enzymatic activity of catalase and superoxide dismutase were found to be lower (7.0 and 1.5%, respectively) in inoculated plants. These results revealed that halotolerant PGPR isolated from S. portulacastrum can be a cost-effective and ecologically sustainable method to improve crop productivity under high saline conditions.

Phenotypic variation of root-system architecture under high P and low P conditions in potato (Solanum tuberosum L.)

BACKGROUND

Phosphorus (P) is an essential macronutrient required for plant metabolism and growth. Its acquisition by plants depends on the availability of dissolved P in the rhizosphere and on the characteristics of P uptake mechanisms such as root-system architecture (RSA). Compared to other crops, potato (Solanum tuberosum L.) has a relatively poor P acquisition efficiency. This is mainly due to its shallow and sparsely branched root system, resulting in a rather limited exploitable soil volume. Information about potato genotypes with RSA traits suitable to improve adaptation to nutrient scarcity is quite rare. Aim of this study is to assess phenotypic variation of RSA in a potato diversity set and its reactions to P deficiency.

RESULTS

Only one out of 22 RSA-traits showed a significant increase under low-P conditions. This indicates an overall negative effect of P scarcity on potato root growth. Differences among genotypes, however, were statistically significant for 21 traits, revealing a high variability in potato RSA. Using a principal component analysis (PCA), we were able to classify genotypes into three groups with regard to their root-system size. Genotypes with both small and large root systems reacted to low-P conditions by in- or decreasing their relative root-system size to medium, whereas genotypes with an intermediate root system size showed little to no changes.

CONCLUSIONS

We observed a huge variation in both the potato root system itself and its adaptation to P deficiency. This may enable the selection of potato genotypes with an improved root-zone exploitation. Eventually, these could be utilized to develop new cultivars adapted to low-P environments with better resource-use efficiencies.

Brassica napus Roots Use Different Strategies to Respond to Warm Temperatures

Elevated growth temperatures are negatively affecting crop productivity by increasing yield losses. The modulation of root traits associated with improved response to rising temperatures is a promising approach to generate new varieties better suited to face the environmental constraints caused by climate change. In this study, we identified several Brassica napus root traits altered in response to warm ambient temperatures. Different combinations of changes in specific root traits result in an extended and deeper root system. This overall root growth expansion facilitates root response by maximizing root-soil surface interaction and increasing roots' ability to explore extended soil areas. We associated these traits with coordinated cellular events, including changes in cell division and elongation rates that drive root growth increases triggered by warm temperatures. Comparative transcriptomic analysis revealed the main genetic determinants of these root system architecture (RSA) changes and uncovered the necessity of a tight regulation of the heat-shock stress response to adjusting root growth to warm temperatures. Our work provides a phenotypic, cellular, and genetic framework of root response to warming temperatures that will help to harness root response mechanisms for crop yield improvement under the future climatic scenario.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.

The calcium sensor CBL7 is required for Serendipita indica-induced growth stimulation in Arabidopsis thaliana, controlling defense against the endophyte and K+ homoeostasis in the symbiosis

Calcium is an important second messenger in plants. The activation of Ca²⁺ signalling cascades is critical in the activation of adaptive processes in response to environmental stimuli. Root colonization by the growth promoting endophyte Serendipita indica involves the increase of cytosolic Ca²⁺ levels in Arabidopsis thaliana. Here, we investigated transcriptional changes in Arabidopsis roots during symbiosis with S. indica. RNA-seq profiling disclosed the induction of Calcineurin B-like 7 (CBL7) during early and later phases of the interaction. Consistently, reverse genetic evidence highlighted the functional relevance of CBL7 and tested the involvement of a CBL7-CBL-interacting protein kinase 13 signalling pathway. The loss-of-function of CBL7 abolished the growth promoting effect and affected root colonization. The transcriptomics analysis of cbl7 revealed the involvement of this Ca²⁺ sensor in activating plant defense responses. Furthermore, we report on the contribution of CBL7 to potassium transport in Arabidopsis. We analysed K⁺ contents in wild-type and cbl7 plants and observed a significant increase of K⁺ in roots of cbl7 plants, while shoot tissues demonstrated K⁺ depletion. Taken together, our work associates CBL7 with an important role in the mutual interaction between Arabidopsis and S. indica and links CBL7 to K⁺ transport.

Assessing the Storage Root Development of Cassava with a New Analysis Tool

Storage roots of cassava plants crops are one of the main providers of starch in many South American, African, and Asian countries. Finding varieties with high yields is crucial for growing and breeding. This requires a better understanding of the dynamics of storage root formation, which is usually done by repeated manual evaluation of root types, diameters, and their distribution in excavated roots. We introduce a newly developed method that is capable to analyze the distribution of root diameters automatically, even if root systems display strong variations in root widths and clustering in high numbers. An application study was conducted with cassava roots imaged in a video acquisition box. The root diameter distribution was quantified automatically using an iterative ridge detection approach, which can cope with a wide span of root diameters and clustering. The approach was validated with virtual root models of known geometries and then tested with a time-series of excavated root systems. Based on the retrieved diameter classes, we show plausibly that the dynamics of root type formation can be monitored qualitatively and quantitatively. We conclude that this new method reliably determines important phenotypic traits from storage root crop images. The method is fast and robustly analyses complex root systems and thereby applicable in high-throughput phenotyping and future breeding.

Histone methyltransferases SDG33 and SDG34 regulate organ-specific nitrogen responses in tomato

Histone posttranslational modifications shape the chromatin landscape of the plant genome and affect gene expression in response to developmental and environmental cues. To date, the role of histone modifications in regulating plant responses to environmental nutrient availability, especially in agriculturally important species, remains largely unknown. We describe the functions of two histone lysine methyltransferases, SET Domain Group 33 (SDG33) and SDG34, in mediating nitrogen (N) responses of shoots and roots in tomato. By comparing the transcriptomes of CRISPR edited tomato lines sdg33 and sdg34 with wild-type plants under N-supplied and N-starved conditions, we uncovered that SDG33 and SDG34 regulate overlapping yet distinct downstream gene targets. In response to N level changes, both SDG33 and SDG34 mediate gene regulation in an organ-specific manner: in roots, SDG33 and SDG34 regulate a gene network including Nitrate Transporter 1.1 (NRT1.1) and Small Auxin Up-regulated RNA (SAUR) genes. In agreement with this, mutations in sdg33 or sdg34 abolish the root growth response triggered by an N-supply; In shoots, SDG33 and SDG34 affect the expression of photosynthesis genes and photosynthetic parameters in response to N. Our analysis thus revealed that SDG33 and SDG34 regulate N-responsive gene expression and physiological changes in an organ-specific manner, thus presenting previously unknown candidate genes as targets for selection and engineering to improve N uptake and usage in crop plants.

Efficiency of Combining Strains Ag87 (Bacillus megaterium) and Ag94 (Lysinibacillus sp.) as Phosphate Solubilizers and Growth Promoters in Maize

Increasing phosphorus (P) use efficiency in agricultural systems is urgent and essential to significantly reduce the global demand for this nutrient. Applying phosphate-solubilizing and plant growth-promoting bacteria in the rhizosphere represents a strategy worthy of attention. In this context, the present work aimed to select and validate bacterial strains capable of solubilizing phosphorous and promoting maize growth, aiming to develop a microbial inoculant to be used in Brazilian agriculture. Bacterial strains from the maize rhizosphere were evaluated based on their ability to solubilize phosphate and produce indole acetic acid. Based on these characteristics, 24 strains were selected to be further evaluated under laboratory, greenhouse, and field conditions. Among the selected strains, four (I04, I12, I13, and I17) showed a high potential to increase maize root growth and shoot P content. Strains I13 (Ag87) and I17 (Ag94) were identified by genomic sequencing as Bacillus megaterium and Lysinibacillus sp., respectively. These strains presented superior yield increments relative to the control treatment with 30% P. In addition, combining Ag87 and Ag94 resulted in even higher yield gains, indicating a synergistic effect that could be harnessed in a commercial inoculant for Brazilian agriculture.

Recent advances in methods for in situ root phenotyping

Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.

The pho1;2a'-m1.1 allele of Phosphate1 conditions misregulation of the phosphorus starvation response in maize (Zea mays ssp. mays L.)

Plant PHO1 proteins play a central role in the translocation and sensing of inorganic phosphate. The maize (Zea mays ssp. mays) genome encodes two co-orthologs of the Arabidopsis PHO1 gene, designated ZmPho1;2a and ZmPho1;2b. Here, we report the characterization of the transposon footprint allele Zmpho1;2a'-m1.1, which we refer to hereafter as pho1;2a. The pho1;2a allele is a stable derivative formed by excision of an Activator transposable element from the ZmPho1;2a gene. The pho1;2a allele contains an 8-bp insertion at the point of transposon excision that disrupts the reading frame and is predicted to generate a premature translational stop. We show that the pho1;2a allele is linked to a dosage-dependent reduction in Pho1;2a transcript accumulation and a mild reduction in seedling growth. Characterization of shoot and root transcriptomes under full nutrient, low nitrogen, low phosphorus, and combined low nitrogen and low phosphorus conditions identified 1100 differentially expressed genes between wild-type plants and plants carrying the pho1;2a mutation. Of these 1100 genes, 966 were upregulated in plants carrying pho1;2a, indicating the wild-type PHO1;2a to predominantly impact negative gene regulation. Gene set enrichment analysis of the pho1;2a-misregulated genes revealed associations with phytohormone signaling and the phosphate starvation response. In roots, differential expression was broadly consistent across all nutrient conditions. In leaves, differential expression was largely specific to low phosphorus and combined low nitrogen and low phosphorus conditions. Of 276 genes upregulated in the leaves of pho1;2a mutants in the low phosphorus condition, 153 were themselves induced in wild-type plants with respect to the full nutrient condition. Our observations suggest that Pho1;2a functions in the fine-tuning of the transcriptional response to phosphate starvation through maintenance and/or sensing of plant phosphate status.

Comparative transcriptomics of fungal endophytes in co-culture with their moss host Dicranum scoparium reveals fungal trophic lability and moss unchanged to slightly increased growth rates

Mosses harbor fungi whose interactions within their hosts remain largely unexplored. Trophic ranges of fungal endophytes from the moss Dicranum scoparium were hypothesized to encompass saprotrophism. This moss is an ideal host to study fungal trophic lability because of its natural senescence gradient, and because it can be grown axenically. Dicranum scoparium was co-cultured with each of eight endophytic fungi isolated from naturally occurring D. scoparium. Moss growth rates, and gene expression levels (RNA sequencing) of fungi and D. scoparium, were compared between axenic and co-culture treatments. Functional lability of two fungal endophytes was tested by comparing their RNA expression levels when colonizing living vs dead gametophytes. Growth rates of D. scoparium were unchanged, or increased, when in co-culture. One fungal isolate (Hyaloscyphaceae sp.) that promoted moss growth was associated with differential expression of auxin-related genes. When grown with living vs dead gametophytes, Coniochaeta sp. switched from having upregulated carbohydrate transporter activity to upregulated oxidation-based degradation, suggesting an endophytism to saprotrophism transition. However, no such transition was detected for Hyaloscyphaceae sp. Individually, fungal endophytes did not negatively impact growth rates of D. scoparium. Our results support the long-standing hypothesis that some fungal endophytes can switch to saprotrophism.

Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline

Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using "RootPainter." Then, an automated feature extraction from the segments is carried out by "RhizoVision Explorer." To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (r = 0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.

Combined Drought and Heat Stress Influences the Root Water Relation and Determine the Dry Root Rot Disease Development Under Field Conditions: A Study Using Contrasting Chickpea Genotypes

Abiotic stressors such as drought and heat predispose chickpea plants to pathogens of key importance leading to significant crop loss under field conditions. In this study, we have investigated the influence of drought and high temperature on the incidence and severity of dry root rot disease (caused by Macrophomina phaseolina) in chickpea, under extensive on- and off-season field trials and greenhouse conditions. We explored the association between drought tolerance and dry root rot resistance in two chickpea genotypes, ICC 4958 and JG 62, with contrasting resistance to dry root rot. In addition, we extensively analyzed various patho-morphological and root architecture traits altered by combined stresses under field and greenhouse conditions in these genotypes. We further observed the role of edaphic factors in dry root rot incidence under field conditions. Altogether, our results suggest a strong negative correlation between the plant water relations and dry root rot severity in chickpeas, indicating an association between drought tolerance and dry root rot resistance. Additionally, the significant role of heat stress in altering the dynamics of dry root rot and the importance of combinatorial screening of chickpea germplasm for dry root rot resistance, drought, and heat stress have been revealed.

Sorghum root epigenetic landscape during limiting phosphorus conditions

Efficient acquisition and use of available phosphorus from the soil is crucial for plant growth, development, and yield. With an ever-increasing acreage of croplands with suboptimal available soil phosphorus, genetic improvement of sorghum germplasm for enhanced phosphorus acquisition from soil is crucial to increasing agricultural output and reducing inputs, while confronted with a growing world population and uncertain climate. Sorghum bicolor is a globally important commodity for food, fodder, and forage. Known for robust tolerance to heat, drought, and other abiotic stresses, its capacity for optimal phosphorus use efficiency (PUE) is still being investigated for optimized root system architectures (RSA). Whilst a few RSA-influencing genes have been identified in sorghum and other grasses, the epigenetic impact on expression and tissue-specific activation of candidate PUE genes remains elusive. Here, we present transcriptomic, epigenetic, and regulatory network profiling of RSA modulation in the BTx623 sorghum background in response to limiting phosphorus (LP) conditions. We show that during LP, sorghum RSA is remodeled to increase root length and surface area, likely enhancing its ability to acquire P. Global DNA 5-methylcytosine and H3K4 and H3K27 trimethylation levels decrease in response to LP, while H3K4me3 peaks and DNA hypomethylated regions contain recognition motifs of numerous developmental and nutrient responsive transcription factors that display disparate expression patterns between different root tissues (primary root apex, elongation zone, and lateral root apex).

Managing semi-arid oak forests (Quercus brantii Lindl.): Mature oak trees of different dimensions create contrasted microhabitats influencing seedling quality

We investigated the influence of mature of oak trees of various dimensions on soil properties, acorn and oak seedling characteristics in semi-arid forests in western Iran. A total of 24 oak trees were selected in comparable site conditions according to three size categories: small trees (DBH< 20 cm), medium trees (DBH: 20-50 cm) and large trees (DBH> 50 cm). Soil properties, light availability below canopy, acorn dimensions and weight, various below- and above-ground seedling morphological traits were measured. Besides, a seedling quality index (SQI) was also produced as an integrative measure of the seedling response. We found an increasing light availability from small trees to large trees (1512-103 μmol m^-2 s^-1) and soil fertility was largely improved from small trees to large trees: soil organic carbon (1.33-2.2%), available phosphorus (12.9-18.1 ppm) and potassium (301.2-470.4 ppm). However, soil properties did not significantly differ between medium and large trees. In contrast, acorn weight and dimensions as well as many seedling traits, including the aerial and belowground biomass and the SQI, were the highest in the medium tree category. To fully explore the relationships among our large set of variables, we produced a partial least square path model which explained 72% of the variation of SQI across the three tree classes. To conclude, we identified a clear effect by mature trees which provided favourable conditions for seedling establishment, but that effect was mediated by tree size and optimal conditions were found below the canopy of medium trees.

Dataset of above and below ground traits assessed in Durum wheat cultivars grown under Mediterranean environments differing in water and temperature conditions

Ideotypic characteristics of durum wheat associated with higher yield under different water and temperature regimes were studied under Mediterranean conditions. The focus of this paper is to provide raw and supplemental data from the research article entitled "Durum wheat ideotypes in Mediterranean environments differing in water and temperature conditions" [1], which aims to define specific durum wheat ideotypes according to their responses to different agronomic conditions. In this context, six modern (i.e. post green revolution) genotypes with contrasting yield performance (i.e. high vs low yield) were grown during two consecutive years under different treatments: (i) winter planting under support-irrigation conditions, (ii) winter planting under rainfed conditions, (iii) late planting under support-irrigation. Trials were conducted at the INIA station of Colmenar de Oreja (Madrid). Different traits were assessed to inform about water status (canopy temperature at anthesis and stable carbon isotope composition (δ13C) of the flag leaf and mature grains), root performance (root traits and the oxygen isotope composition (δ18O) in the stem base water), phenology (days from sowing to heading), nitrogen status/photosynthetic capacity (nitrogen content and stable isotope composition (δ15N) of the flag leaf and mature grain together with the pigment contents and the nitrogen balance index (NBI) of the flag leaf), crop growth (plant height (PH) and the normalized difference vegetation index (NDVI) at anthesis), grain yield and agronomic yield components. For most of the parameters assessed, data analysis demonstrated significant differences among genotypes within each treatment. The level of significance was determined using the Tukey-b test on independent samples, and ideotypes were modelled from the results of principle component analysis. The present data shed light on traits that help to define specific ideotype characteristics that confer genotypic adaptation to a wide range of agronomic conditions produced by variations in planting date, water conditions and season.

Genetic Variability of Arabidopsis thaliana Mature Root System Architecture and Genome-Wide Association Study

Root system architecture (RSA) has a direct influence on the efficiency of nutrient uptake and plant growth, but the genetics of RSA are often studied only at the seedling stage. To get an insight into the genetic blueprint of a more mature RSA, we exploited natural variation and performed a detailed in vitro study of 241 Arabidopsis thaliana accessions using large petri dishes. A comprehensive analysis of 17 RSA traits showed high variability among the different accessions, unveiling correlations between traits and conditions of the natural habitat of the plants. A sub-selection of these accessions was grown in water-limiting conditions in a rhizotron set-up, which revealed that especially the spatial distribution showed a high consistency between in vitro and ex vitro conditions, while in particular, a large root area in the lower zone favored drought tolerance. The collected RSA phenotype data were used to perform genome-wide association studies (GWAS), which stands out from the previous studies by its exhaustive measurements of RSA traits on more mature Arabidopsis accessions used for GWAS. As a result, we found not only several genes involved in the lateral root (LR) development or auxin signaling pathways to be associated with RSA traits but also new candidate genes that are potentially involved in the adaptation to the natural habitats.

Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel

Mung bean [Vigna radiata (L.) Wilczek] is a drought-tolerant, short-duration crop, and a rich source of protein and other valuable minerals, vitamins, and antioxidants. The main objectives of this research were (1) to study the root traits related with the phenotypic and genetic diversity of 375 mung bean genotypes of the Iowa (IA) diversity panel and (2) to conduct genome-wide association studies of root-related traits using the Automated Root Image Analysis (ARIA) software. We collected over 9,000 digital images at three-time points (days 12, 15, and 18 after germination). A broad sense heritability for days 15 (0.22-0.73) and 18 (0.23-0.87) was higher than that for day 12 (0.24-0.51). We also reported root ideotype classification, i.e., PI425425 (India), PI425045 (Philippines), PI425551 (Korea), PI264686 (Philippines), and PI425085 (Sri Lanka) that emerged as the top five in the topsoil foraging category, while PI425594 (unknown origin), PI425599 (Thailand), PI425610 (Afghanistan), PI425485 (India), and AVMU0201 (Taiwan) were top five in the drought-tolerant and nutrient uptake "steep, cheap, and deep" ideotype. We identified promising genotypes that can help diversify the gene pool of mung bean breeding stocks and will be useful for further field testing. Using association studies, we identified markers showing significant associations with the lateral root angle (LRA) on chromosomes 2, 6, 7, and 11, length distribution (LED) on chromosome 8, and total root length-growth rate (TRL_GR), volume (VOL), and total dry weight (TDW) on chromosomes 3 and 5. We discussed genes that are potential candidates from these regions. We reported beta-galactosidase 3 associated with the LRA, which has previously been implicated in the adventitious root development via transcriptomic studies in mung bean. Results from this work on the phenotypic characterization, root-based ideotype categories, and significant molecular markers associated with important traits will be useful for the marker-assisted selection and mung bean improvement through breeding.

TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging

BACKGROUND

3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture.

RESULTS

We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image.

CONCLUSIONS

TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.

RhizoVision Explorer: open-source software for root image analysis and measurement standardization

Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.

A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements

In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I-VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.

A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements

In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I-VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays)

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks

Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 × Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction (GP) methods. We found GPAs were improved using multitrait (BN) compared to single trait GP in traits with low to moderate heritability. Two groups of individuals were selected based on GPs and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on GPs did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multitrait GP model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

Fully-automated root image analysis (faRIA)

High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.