Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities

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.

From phenotyping to genetic mapping: identifying water-stress adaptations in legume root traits

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.

An advanced three-dimensional phenotypic measurement approach for extracting Ginkgo root structural parameters based on terrestrial laser scanning

The phenotyping of plant roots is essential for improving plant productivity and adaptation. However, traditional techniques for assembling root phenotyping information are limited and often labor-intensive, especially for woody plants. In this study, an advanced approach called accurate and detailed quantitative structure model-based (AdQSM-based) root phenotypic measurement (ARPM) was developed to automatically extract phenotypes from Ginkgo tree root systems. The approach involves three-dimensional (3D) reconstruction of the point cloud obtained from terrestrial laser scanning (TLS) to extract key phenotypic parameters, including root diameter (RD), length, surface area, and volume. To evaluate the proposed method, two approaches [minimum spanning tree (MST)-based and triangulated irregular network (TIN)-based] were used to reconstruct the Ginkgo root systems from point clouds, and the number of lateral roots along with RD were extracted and compared with traditional methods. The results indicated that the RD extracted directly from point clouds [coefficient of determination (R 2) = 0.99, root-mean-square error (RMSE) = 0.41 cm] outperformed the results of 3D models (MST-based: R 2 = 0.71, RMSE = 2.20 cm; TIN-based: R 2 = 0.54, RMSE = 2.80 cm). Additionally, the MST-based model (F1 = 0.81) outperformed the TIN-based model (F1 = 0.80) in detecting the number of first-order and second-order lateral roots. Each phenotyping trait fluctuated with a different cloud parameter (CP), and the CP value of 0.002 (r = 0.94, p < 0.01) was found to be advantageous for better extraction of structural phenotypes. This study has helped with the extraction and quantitative analysis of root phenotypes and enhanced our understanding of the relationship between architectural parameters and corresponding physiological functions of tree roots.

Regulation of root growth and elongation in wheat

Currently, the control of rhizosphere selection on farms has been applied to achieve enhancements in phenotype, extending from improvements in single root characteristics to the dynamic nature of entire crop systems. Several specific signals, regulatory elements, and mechanisms that regulate the initiation, morphogenesis, and growth of new lateral or adventitious root species have been identified, but much more work remains. Today, phenotyping technology drives the development of root traits. Available models for simulation can support all phenotyping decisions (root trait improvement). The detection and use of markers for quantitative trait loci (QTLs) are effective for enhancing selection efficiency and increasing reproductive genetic gains. Furthermore, QTLs may help wheat breeders select the appropriate roots for efficient nutrient acquisition. Single-nucleotide polymorphisms (SNPs) or alignment of sequences can only be helpful when they are associated with phenotypic variation for root development and elongation. Here, we focus on major root development processes and detail important new insights recently generated regarding the wheat genome. The first part of this review paper discusses the root morphology, apical meristem, transcriptional control, auxin distribution, phenotyping of the root system, and simulation models. In the second part, the molecular genetics of the wheat root system, SNPs, TFs, and QTLs related to root development as well as genome editing (GE) techniques for the improvement of root traits in wheat are discussed. Finally, we address the effect of omics strategies on root biomass production and summarize existing knowledge of the main molecular mechanisms involved in wheat root development and elongation.

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/.

Plant microphenotype: from innovative imaging to computational analysis

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.

Temporal phenotypic variation of spinach root traits and its relation to shoot performance

The root system is important for the growth and development of spinach. To reveal the temporal variability of the spinach root system, root traits of 40 spinach accessions were measured at three imaging times (20, 30, and 43 days after transplanting) in this study using a non-destructive and non-invasive root analysis system. Results showed that five root traits were reliably measured by this system (RootViz FS), and two of which were highly correlated with manually measured traits. Root traits had higher variations than shoot traits among spinach accessions, and the trait of mean growth rate of total root length had the largest coefficients of variation across the three imaging times. During the early stage, only tap root length was weakly correlated with shoot traits (plant height, leaf width, and object area (equivalent to plant surface area)), whereas in the third imaging, root fresh weight, total root length, and root area were strongly correlated with shoot biomass-related traits. Five root traits (total root length, tap root length, total root area, root tissue density, and maximal root width) showed high variations with coefficients of variation values (CV ≥ 0.3, except maximal root width) and high heritability (H2 > 0.6) among the three stages. The 40 spinach accessions were classified into five subgroups with different growth dynamics of the primary and lateral roots by cluster analysis. Our results demonstrated the potential of in-situ phenotyping to assess dynamic root growth in spinach and provide new perspectives for biomass breeding based on root system ideotypes.

Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination (R2), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.

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.

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/.

Challenges facing sustainable protein production: Opportunities for cereals

Rising demands for protein worldwide are likely to drive increases in livestock production, as meat provides ∼40% of dietary protein. This will come at a significant environmental cost, and a shift toward plant-based protein sources would therefore provide major benefits. While legumes provide substantial amounts of plant-based protein, cereals are the major constituents of global foods, with wheat alone accounting for 15-20% of the required dietary protein intake. Improvement of protein content in wheat is limited by phenotyping challenges, lack of genetic potential of modern germplasms, negative yield trade-offs, and environmental costs of nitrogen fertilizers. Presenting wheat as a case study, we discuss how increasing protein content in cereals through a revised breeding strategy combined with robust phenotyping could ensure a sustainable protein supply while minimizing the environmental impact of nitrogen fertilizer.

The role of root size and root efficiency in grain production, and water-and nitrogen-use efficiency in wheat

BACKGROUND

The root system is the major plant organ involved in water and nutrient acquisition, influencing plant growth and productivity. However, the relative importance of root size and uptake efficiency remains undetermined. A pot experiment was conducted using two wheat varieties with different root sizes to evaluate their capacity for water and nitrogen (N) uptake and their effects on grain production, water-use efficiency (WUE), and N-use efficiency (NUE) under two water treatments combined with three N levels.

RESULTS

The leaf water potential and root exudates of changhan58 (CH, small root variety) were higher or similar to those of changwu134 (CW, large root variety) under water/N treatment combinations, indicating that small roots can transport enough water to above the ground. The addition of N improved plant growth, photosynthetic traits, and WUE significantly. There were no significant differences in WUE or grain production between the two cultivars under well-watered conditions. However, they were significantly higher in CH than in CW under water deficit stress. Nitrogen uptake per unit root dry weight, glutaminase, and nitrate reductase activities were significantly higher in CH than in CW, regardless of moisture conditions. Root biomass was positively correlated with evapotranspiration, while the root/shoot ratio was negatively correlated with WUE (P < 0.05) but not with NUE.

CONCLUSION

In a pot experiment, water and N uptake were more strongly associated with resource uptake availability than root size. This may provide guidance in wheat breeding programs for drought-prone regions. © 2023 Society of Chemical Industry.

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.

Effect of crop rotational position and nitrogen supply on root development and yield formation of winter wheat

The lower yield of wheat grown after wheat (second wheat) compared with the first wheat after a break crop is frequently attributed to fungal disease occurrence, but has also been found without visible disease infection; thus, other factors might be responsible for the lower yield of the second wheat. The aims of this study were to analyze the effects of growing wheat as first and second wheat after oilseed rape, as well as monoculture in a long-term field experiment over three years on (i) aboveground biomass formation, root development and nutrient acquisition during the growing season, (ii) take-all occurrence, and (iii) grain yield and yield components. Subsoil root length density of winter wheat was significantly higher after oilseed rape as pre-crop than after wheat, which was independent of take-all occurrence. Differences in wheat aboveground biomass occurred at early growth stages and were persistent until harvest. Grain yield loss correlated well with take-all disease severity in a wet year but yield differences among crop rotational positions occurred also in a dry year without visible fungal infection. Thus, an effect of the crop rotational position of wheat beyond take-all disease pressure can be assumed. Overall, wheat root length density might be the key to understand wheat biomass formation and grain yield in different crop rotational positions.

Manifestation of Triploid Heterosis in the Root System after Crossing Diploid and Autotetraploid Energy Willow Plants

Successful use of woody species in reducing climatic and environmental risks of energy shortage and spreading pollution requires deeper understanding of the physiological functions controlling biomass productivity and phytoremediation efficiency. Targets in the breeding of energy willow include the size and the functionality of the root system. For the combination of polyploidy and heterosis, we have generated triploid hybrids (THs) of energy willow by crossing autotetraploid willow plants with leading cultivars (Tordis and Inger). These novel Salix genotypes (TH3/12, TH17/17, TH21/2) have provided a unique experimental material for characterization of Mid-Parent Heterosis (MPH) in various root traits. Using a root phenotyping platform, we detected heterosis (TH3/12: MPH 43.99%; TH21/2: MPH 26.93%) in the size of the root system in soil. Triploid heterosis was also recorded in the fresh root weights, but it was less pronounced (MPH%: 9.63-19.31). In agreement with root growth characteristics in soil, the TH3/12 hybrids showed considerable heterosis (MPH: 70.08%) under in vitro conditions. Confocal microscopy-based imaging and quantitative analysis of root parenchyma cells at the division-elongation transition zone showed increased average cell diameter as a sign of cellular heterosis in plants from TH17/17 and TH21/2 triploid lines. Analysis of the hormonal background revealed that the auxin level was seven times higher than the total cytokinin contents in root tips of parental Tordis plants. In triploid hybrids, the auxin-cytokinin ratios were considerably reduced in TH3/12 and TH17/17 roots. In particular, the contents of cytokinin precursor, such as isopentenyl adenosine monophosphate, were elevated in all three triploid hybrids. Heterosis was also recorded in the amounts of active gibberellin precursor, GA19, in roots of TH3/12 plants. The presented experimental findings highlight the physiological basics of triploid heterosis in energy willow roots.

Integrating genome-wide association study with transcriptomic data to predict candidate genes influencing Brassica napus root and biomass-related traits under low phosphorus conditions

BACKGROUND

Rapeseed (Brassica napus L.) is an essential source of edible oil and livestock feed, as well as a promising source of biofuel. Breeding crops with an ideal root system architecture (RSA) for high phosphorus use efficiency (PUE) is an effective way to reduce the use of phosphate fertilizers. However, the genetic mechanisms that underpin PUE in rapeseed remain elusive. To address this, we conducted a genome-wide association study (GWAS) in 327 rapeseed accessions to elucidate the genetic variability of 13 root and biomass traits under low phosphorus (LP; 0.01 mM P +). Furthermore, RNA-sequencing was performed in root among high/low phosphorus efficient groups (HP1/LP1) and high/low phosphorus stress tolerance groups (HP2/LP2) at two-time points under control and P-stress conditions.

RESULTS

Significant variations were observed in all measured traits, with heritabilities ranging from 0.47 to 0.72, and significant correlations were found between most of the traits. There were 39 significant trait-SNP associations and 31 suggestive associations, which integrated into 11 valid quantitative trait loci (QTL) clusters, explaining 4.24-24.43% of the phenotypic variance observed. In total, RNA-seq identified 692, 1076, 648, and 934 differentially expressed genes (DEGs) specific to HP1/LP1 and HP2/LP2 under P-stress and control conditions, respectively, while 761 and 860 DEGs common for HP1/LP1 and HP2/LP2 under both conditions. An integrated approach of GWAS, weighted co-expression network, and differential expression analysis identified 12 genes associated with root growth and development under LP stress. In this study, six genes (BnaA04g23490D, BnaA09g08440D, BnaA09g04320D, BnaA09g04350D, BnaA09g04930D, BnaA09g09290D) that showed differential expression were identified as promising candidate genes for the target traits.

CONCLUSION

11 QTL clusters and 12 candidate genes associated with root and development under LP stress were identified in this study. Our study's phenotypic and genetic information may be exploited for genetic improvement of root traits to increase PUE in rapeseed.

Integrated phenotyping of root and shoot growth dynamics in maize reveals specific interaction patterns in inbreds and hybrids and in response to drought

In recent years, various automated methods for plant phenotyping addressing roots or shoots have been developed and corresponding platforms have been established to meet the diverse requirements of plant research and breeding. However, most platforms are only either able to phenotype shoots or roots of plants but not both simultaneously. This substantially limits the opportunities offered by a joint assessment of the growth and development dynamics of both organ systems, which are highly interdependent. In order to overcome these limitations, a root phenotyping installation was integrated into an existing automated non-invasive high-throughput shoot phenotyping platform. Thus, the amended platform is now capable of conducting high-throughput phenotyping at the whole-plant level, and it was used to assess the vegetative root and shoot growth dynamics of five maize inbred lines and four hybrids thereof, as well as the responses of five inbred lines to progressive drought stress. The results showed that hybrid vigour (heterosis) occurred simultaneously in roots and shoots and was detectable as early as 4 days after transplanting (4 DAT; i.e., 8 days after seed imbibition) for estimated plant height (EPH), total root length (TRL), and total root volume (TRV). On the other hand, growth dynamics responses to progressive drought were different in roots and shoots. While TRV was significantly reduced 10 days after the onset of the water deficit treatment, the estimated shoot biovolume was significantly reduced about 6 days later, and EPH showed a significant decrease even 2 days later (8 days later than TRV) compared with the control treatment. In contrast to TRV, TRL initially increased in the water deficit period and decreased much later (not earlier than 16 days after the start of the water deficit treatment) compared with the well-watered plants. This may indicate an initial response of the plants to water deficit by forming longer but thinner roots before growth was inhibited by the overall water deficit. The magnitude and the dynamics of the responses were genotype-dependent, as well as under the influence of the water consumption, which was related to plant size.

Hybrid performance evaluation and genome-wide association analysis of root system architecture in a maize association population

KEY MESSAGE

The genetic architecture of RSA traits was dissected by GWAS and coexpression networks analysis in a maize association population. Root system architecture (RSA) is a crucial determinant of water and nutrient uptake efficiency in crops. However, the maize genetic architecture of RSA is still poorly understood due to the challenges in quantifying root traits and the lack of dense molecular markers. Here, an association mapping panel including 356 inbred lines were crossed with a common tester, Zheng58, and the test crosses were phenotyped for 12 RSA traits in three locations. We observed a 1.3 ~ sixfold phenotypic variation for measured RSA in the association panel. The association panel consisted of four subpopulations, non-stiff stalk (NSS) lines, stiff stalk (SS), tropical/subtropical (TST), and mixed. Zheng58 × TST has a 2.1% higher crown root number (CRN) and 8.6% less brace root number (BRN) than Zheng58 × NSS and Zheng58 × SS, respectively. Using a genome-wide association study (GWAS) with 1.25 million SNPs and correction for population structure, 191 significant SNPs were identified for root traits. Ninety (47%) of the significant SNPs showed positive allelic effects, and 101 (53%) showed negative effects. Each locus could explain 0.39% to 11.8% of phenotypic variation. By integrating GWAS results and comparing coexpression networks, 26 high-priority candidate genes were identified. Gene GRMZM2G377215, which belongs to the COBRA-like gene family, affected root growth and development. Gene GRMZM2G468657 encodes the aspartic proteinase nepenthesin-1, related to root development and N-deficient response. Collectively, our research provides progress in the genetic dissection of root system architecture. These findings present the further possibility for the genetic improvement of root traits in maize.

Seven plant capacities to adapt to abiotic stress

Abiotic stresses such as drought and heat continue to impact crop production in a warming world. This review distinguishes seven inherent capacities that enable plants to respond to abiotic stresses and continue growing, although at a reduced rate, to achieve a productive yield. These are the capacities to selectively take up essential resources, store them and supply them to different plant parts, generate the energy required for cellular functions, conduct repairs to maintain plant tissues, communicate between plant parts, manage existing structural assets in the face of changed circumstances, and shape-shift through development to be efficient in different environments. By illustration, we show how all seven plant capacities are important for reproductive success of major crop species during drought, salinity, temperature extremes, flooding, and nutrient stress. Confusion about the term 'oxidative stress' is explained. This allows us to focus on the strategies that enhance plant adaptation by identifying key responses that can be targets for plant breeding.

Combining transcriptomics and metabolomics to identify key response genes for aluminum toxicity in the root system of Brassica napus L. seedlings

By integrating QTL mapping, transcriptomics and metabolomics, 138 hub genes were identified in rapeseed root response to aluminum stress and mainly involved in metabolism of lipids, carbohydrates and secondary metabolites. Aluminum (Al) toxicity has become one of the important abiotic stress factors in areas with acid soil, which hinders the absorption of water and nutrients by roots, and consequently retards the growth of crops. A deeper understanding of the stress-response mechanism of Brassica napus may allow us to identify the tolerance gene(s) and use this information in breeding-resistant crop varieties. In this study, a population of 138 recombinant inbred lines (RILs) was subjected to aluminum stress, and QTL (quantitative trait locus) mapping was used to preliminarily locate quantitative trait loci related to aluminum stress. Root tissues from seedlings of an aluminum-resistant (R) line and an aluminum-sensitive (S) line from the RIL population were harvested for transcriptome sequencing and metabolome determination. By combining the data on quantitative trait genes (QTGs), differentially expressed genes (DEGs), and differentially accumulated metabolites (DAMs), key candidate genes related to aluminum tolerance in rapeseed were determined. The results showed that there were 3186 QTGs in the RIL population, 14,232 DEGs and 457 DAMs in the comparison between R and S lines. Lastly, 138 hub genes were selected to have a strong positive or negative correlation with 30 important metabolites (|R|≥ 0.95). These genes were mainly involved in the metabolism of lipids, carbohydrates and secondary metabolites in response to Al toxicity stress. In summary, this study provides an effective method for screening key genes by combining QTLs, transcriptome sequencing and metabolomic analysis, but also lists key genes for exploring the molecular mechanism of Al tolerance in rapeseed seedling roots.

Response of in situ root phenotypes to potassium stress in cotton

Potassium plays a significant role in the basic functions of plant growth and development. Potassium uptake is closely associated with morphological characteristics of the roots. However, the dynamic characteristics of phenotype and lifespan of cotton (Gossypium hirsutum L.) lateral roots and root hairs under low and high potassium stress remain unclear. In this study, potassium stress experiments (low and high potassium, medium potassium as control) were conducted using RhizoPot (an in situ root observation device) to determine the response characteristics of lateral roots and root hairs in cotton under potassium stress. The plant morphology, photosynthetic characteristics, root phenotypic changes, and lifespan of lateral roots and root hairs were measured. Potassium accumulation, aboveground phenotype, photosynthetic capacity, root length density, root dry weight, root diameter, lateral root lifespan, and root hair lifespan under low potassium stress were significantly decreased compared to medium potassium treatment. However, the root hair length of the former was significantly increased than that of the latter. Potassium accumulation and the lateral root lifespan were significantly increased under high potassium treatment, while root length density, root dry weight, root diameter, root hair length, and root hair lifespan were significantly decreased compared to the medium potassium treatment. Notably, there were no significant differences in aboveground morphology and photosynthetic characters. Principal component analysis revealed that lateral root lifespan, root hair lifespan of the first lateral root, and root hair length significantly correlated with potassium accumulation. The root had similar regularity responses to low and high potassium stress except for lifespan and root hair length. The findings of this study enhance the understanding of the phenotype and lifespan of cotton's lateral roots and root hairs under low and high potassium stress.

A bench-top Dark-Root device built with LEGO® bricks enables a non-invasive plant root development analysis in soil conditions mirroring nature

Roots are the hidden parts of plants, anchoring their above-ground counterparts in the soil. They are responsible for water and nutrient uptake and for interacting with biotic and abiotic factors in the soil. The root system architecture (RSA) and its plasticity are crucial for resource acquisition and consequently correlate with plant performance while being highly dependent on the surrounding environment, such as soil properties and therefore environmental conditions. Thus, especially for crop plants and regarding agricultural challenges, it is essential to perform molecular and phenotypic analyses of the root system under conditions as near as possible to nature (#asnearaspossibletonature). To prevent root illumination during experimental procedures, which would heavily affect root development, Dark-Root (D-Root) devices (DRDs) have been developed. In this article, we describe the construction and different applications of a sustainable, affordable, flexible, and easy to assemble open-hardware bench-top LEGO® DRD, the DRD-BIBLOX (Brick Black Box). The DRD-BIBLOX consists of one or more 3D-printed rhizoboxes, which can be filled with soil while still providing root visibility. The rhizoboxes sit in a scaffold of secondhand LEGO® bricks, which allows root development in the dark and non-invasive root tracking with an infrared (IR) camera and an IR light-emitting diode (LED) cluster. Proteomic analyses confirmed significant effects of root illumination on barley root and shoot proteomes. Additionally, we confirmed the significant effect of root illumination on barley root and shoot phenotypes. Our data therefore reinforces the importance of the application of field conditions in the lab and the value of our novel device, the DRD-BIBLOX. We further provide a DRD-BIBLOX application spectrum, spanning from investigating a variety of plant species and soil conditions and simulating different environmental conditions and stresses, to proteomic and phenotypic analyses, including early root tracking in the dark.

Uncovering genetic control of primary root length variation in Brassica napus using QTL-seq. A commentary on: 'Rapid identification of a major locus qPRL-C06 affecting primary root length in Brassica napus by QTL-seq'

This article comments on:

Wei Wang, Haijiang Liu, Yiwen Xie, Graham John King, Philip John White, Jun Zou, Fangsen Xu, and Lei Shi. Rapid identification of a major locus qPRL-C06 affecting primary root length in Brassica napus by QTL-seq, Annals of Botany, Volume 131, Issue 4, 14 March 2023, Pages 569–583, https://doi.org/10.1093/aob/mcac123

A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.

Rhizosphere engineering for sustainable crop production: entropy-based insights

There is a growing interest in exploring interactions at root-soil interface in natural and agricultural ecosystems, but an entropy-based understanding of these dynamic rhizosphere processes is lacking. We have developed a new conceptual model of rhizosphere regulation by localized nutrient supply using thermodynamic entropy. Increased nutrient-use efficiency is achieved by rhizosphere management based on self-organization and minimized entropy via equilibrium attractors comprising (i) optimized root strategies for nutrient acquisition and (ii) improved information exchange related to root-soil-microbe interactions. The cascading effects through different hierarchical levels amplify the underlying processes in plant-soil system. We propose a strategy for manipulating rhizosphere dynamics and improving nutrient-use efficiency by localized nutrient supply with minimization of entropy to underpin sustainable food/feed/fiber production.

Application of Internet of Things to Agriculture-The LQ-FieldPheno Platform: A High-Throughput Platform for Obtaining Crop Phenotypes in Field

The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation. However, high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology. In view of this, this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno, which was developed by China National Agricultural Information Engineering Technology Research Centre. The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform, which introduces the Internet of Things (IoT) into agricultural breeding. The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment, including an automatic control system, upward field track, intelligent navigation vehicle, and environmental sensors. Furthermore, it combines an RGB camera, a 6-band multispectral camera, a thermal infrared camera, a 3-dimensional laser radar, and a deep camera. Special software is developed to control motions and sensors and to design run lines. Using wireless sensor networks and mobile communication wireless networks of IoT, the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput, automatic, and high time sequence. Moreover, the LQ-FieldPheno has the characteristics of multiple data acquisition, vital timeliness, remarkable expansibility, high-cost performance, and flexible customization. The LQ-FieldPheno has been operated in the 2020 maize growing season, and the collected point cloud data are used to estimate the maize plant height. Compared with the traditional crop phenotypic measurement technology, the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters. The proposed platform could contribute to the research of crop phenotype, remote sensing, agronomy, and related disciplines.

Crop root system plasticity for improved yields in saline soils

Crop yields must increase to meet the demands of a growing world population. Soil salinization is increasing due to the impacts of climate change, reducing the area of arable land for crop production. Plant root systems are plastic, and their architecture can be modulated to (1) acquire nutrients and water for growth, and (2) respond to hostile soil environments. Saline soils inhibit primary root growth and alter root system architecture (RSA) of crop plants. In this review, we explore how crop root systems respond and adapt to salinity, focusing predominately on the staple cereal crops wheat, maize, rice, and barley, that all play a major role in global food security. Cereal crops are classified as glycophytes (salt-sensitive) however salt-tolerance can differ both between species and within a species. In the past, due to the inherent difficulties associated with visualising and measuring root traits, crop breeding strategies have tended to focus on optimising shoot traits. High-resolution phenotyping techniques now make it possible to visualise and measure root traits in soil systems. A steep, deep and cheap root ideotype has been proposed for water and nitrogen capture. Changes in RSA can be an adaptive strategy to avoid saline soils whilst optimising nutrient and water acquisition. In this review we propose a new model for designing crops with a salt-tolerant root ideotype. The proposed root ideotype would exhibit root plasticity to adapt to saline soils, root anatomical changes to conserve energy and restrict sodium (Na⁺) uptake, and transport mechanisms to reduce the amount of Na⁺ transported to leaves. In the future, combining high-resolution root phenotyping with advances in crop genetics will allow us to uncover root traits in complex crop species such as wheat, that can be incorporated into crop breeding programs for yield stability in saline soils.

Crop germplasm: Current challenges, physiological-molecular perspective, and advance strategies towards development of climate-resilient crops

Germplasm is a long-term resource management mission and investment for civilization. An estimated ∼7.4 million accessions are held in 1750 plant germplasm centres around the world; yet, only 2% of these assets have been utilized as plant genetic resources (PGRs). According to recent studies, the current food yield trajectory will be insufficient to feed the world's population in 2050. Additionally, possible negative effects in terms of crop failure because of climate change are already being experienced across the world. Therefore, it is necessary to reconciliation of research advancement and innovation of practices for further exploration of the potential of crop germplasm especially for the complex traits associated with yield such as water- and nitrogen use efficiency. In this review, we tried to address current challenges, research gaps, physiological and molecular aspects of two broad spectrum complex traits such as water- and nitrogen-use efficiency, and advanced integrated strategies that could provide a platform for combined stress management for climate-smart crop development. Additionally, recent development in technologies that are directly related to germplasm characterization was highlighted for further molecular utilization towards the development of elite varieties.

Remote sensing for field pea yield estimation: A study of multi-scale data fusion approaches in phenomics

Introduction

Remote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model.

Methods

The multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points.

Results and discussion

The major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.

Root system architecture of historical spring wheat cultivars is associated with alleles and transcripts of major functional genes

We evaluated root system architecture (RSA) of a set of 58 historical spring wheat cultivars from Pakistan representing 105 years of selection breeding. The evaluations were carried out under control and water-limited conditions using a high-throughput phenotyping system coupled with RhizoVision Explorer software. The cultivars were classified into three groups based on release year as cultivars released pre-1965, released between 1965 and 2000, and cultivars released post-2000. Under water-limited conditions a decline in 20 out of 25 RSA component traits was observed in pre-1965 cultivars group. Whereas cultivars released after the 1965, so-called green revolution period, showed a decline in 17 traits with significant increments in root length, depth, and steep angle frequency which are important root traits for resource-uptake under water-limited conditions. Similarly, cultivars released after 2000 indicated an increase in the number of roots, depth, diameter, surface area, and steep angle frequency. The coefficient of correlation analysis showed a positive correlation between root depth and yield-related traits under water-limited conditions. We also investigated the effects of green-revolution genes (Rht1) and some phenology-related genes such as DRO1, TaMOR, TaLTPs, TaSus-2B on RSA and identified significant associations of these genes with important root traits. There was strong selection pressure on DRO1 gene in cultivated wheat indicating the allele fixed in modern wheat cultivars is different from landraces. The expression of DRO1, and TaMOR were retrieved from an RNAseq experiment, and results were validated using qRT-PCR. The highest expression of DRO1 and TaMOR was found in Chakwal-50, a rainfed cultivar released in 2008, and MaxiPak-65 released in 1965. We conclude that there is a positive historic change in RSA after 1965 that might be attributed to genetic factors associated with favored RSA traits. Furthermore, we suggest root depth and steep angle as promising traits to withstand water-limited environments and may have implications in selection for breeding.

Development specifies, diversifies and empowers root immunity

Roots are a highly organised plant tissue consisting of different cell types with distinct developmental functions defined by cell identity networks. Roots are the target of some of the most devastating diseases and possess a highly effective immune system. The recognition of microbe- or plant-derived molecules released in response to microbial attack is highly important in the activation of complex immunity gene networks. Development and immunity are intertwined, and immunity activation can result in growth inhibition. In turn, by connecting immunity and cell identity regulators, cell types are able to launch a cell type-specific immunity based on the developmental function of each cell type. By this strategy, fundamental developmental processes of each cell type contribute their most basic functions to drive cost-effective but highly diverse and, thus, efficient immune responses. This review highlights the interdependence of root development and immunity and how the developmental age of root cells contributes to positive and negative outcomes of development-immunity cross-talk.

QTL analyses of soybean root system architecture revealed genetic relationships with shoot-related traits

The genetic basis of soybean root system architecture (RSA) and the genetic relationship between shoot and RSA were revealed by integrating data from recombinant inbred population grafting and QTL mapping. Variations in root system architecture (RSA) affect the functions of roots and thus play vital roles in plant adaptations and agricultural productivity. The aim of this study was to unravel the genetic relationship between RSA traits and shoot-related traits in soybean. This study characterized RSA variability at seedling stage in a recombinant inbred population, derived from a cross between cultivated soybean C08 and wild soybean W05, and performed high-resolution quantitative trait locus (QTL) mapping. In total, 34 and 41 QTLs were detected for RSA-related and shoot-related traits, respectively, constituting eight QTL clusters. Significant QTL correspondence was found between shoot biomass and RSA-related traits, consistent with significant correlations between these phenotypes. RSA-related QTLs also overlapped with selection regions in the genome, suggesting the cultivar RSA could be a partial consequence of domestication. Using reciprocal grafting, we confirmed that shoot-derived signals affected root development and the effects were controlled by multiple loci. Meanwhile, RSA-related QTLs were found to co-localize with four soybean flowering-time loci. Consistent with the phenotypes of the parental lines of our RI population, diminishing the function of flowering controlling E1 family through RNA interference (RNAi) led to reduced root growth. This implies that the flowering time-related genes within the RSA-related QTLs are actually contributing to RSA. To conclude, this study identified the QTLs that determine RSA through controlling root growth indirectly via regulating shoot functions, and discovered superior alleles from wild soybean that could be used to improve the root structure in existing soybean cultivars.

Effects of Domestication on Plant-Microbiome Interactions

Through the process of domestication, selection is targeted on a limited number of plant traits that are typically associated with yield. As an unintended consequence, domesticated plants often perform poorly compared to their wild progenitors for a multitude of traits that were not under selection during domestication, including abiotic and biotic stress tolerance. Over the past decade, advances in sequencing technology have allowed for the rigorous characterization of host-associated microbial communities, termed the microbiome. It is now clear that nearly every conceivable plant interaction with the environment is mediated by interactions with the microbiome. For this reason, plant-microbiome interactions are an area of great promise for plant breeding and crop improvement. Here, we review the literature to assess the potential impact that domestication has had on plant-microbiome interactions and the current understanding of the genetic basis of microbiome variation to inform plant breeding efforts. Overall, we find limited evidence that domestication impacts the diversity of microbiomes, but domestication is often associated with shifts in the abundance and composition of microbial communities, including taxa of known functional significance. Moreover, genome-wide association studies and mutant analysis have not revealed a consistent set of core candidate genes or genetic pathways that confer variation in microbiomes across systems. However, such studies do implicate a consistent role for plant immunity, root traits, root and leaf exudates and cell wall integrity as key traits that control microbiome colonization and assembly. Therefore, selection on these key traits may pose the most immediate promise for enhancing plant-microbiome interactions through breeding.

New Insights into the Phosphorus Acquisition Capacity of Chilean Lowland Quinoa Roots Grown under Low Phosphorus Availability

Reducing phosphate fertilizer inputs while increasing food nutritional quality has been posited as a major challenge to decrease human undernourishment and ensure food security. In this context, quinoa has emerged as a promising crop due to its ability to tolerate different stress conditions and grow in marginal soils with low nutrient content, in addition to the exceptional nutritional quality of its grains. However, there is scarce information about the phosphorus acquisition capacity of quinoa roots. This work aimed to provide new insights into P acquisition and functional root traits, such as root biomass, rhizosphere pH, carboxylate exudation, and acid phosphatase activity of thirty quinoa genotypes grown under P limiting conditions (7 mg P kg^-1). Significant genotypic variation was observed among genotypes, with average P accumulation ranging from 1.2 to 11.8 mg. The shoot biomass production varied more than 14 times among genotypes and was correlated with the P accumulation on shoots (r = 0.91). Despite showing high variability in root traits, only root biomass production highly correlated with P acquisition (r = 0.77), suggesting that root growth/morphology rather than the measured biochemical activity possesses a critical role in the P nutrition of quinoa.

Paclobutrazol improves surface water use efficiency by regulating allometric trait behavior in maize

Paclobutrazol (PBZ) role in drought management of maize is least understood. In maize, root traits are linked with surface water management. Over three years, early and terminal deficit irrigation (EDI and TDI) with or without PBZ were imposed on DKC-9144 and PG-2475 maize varieties. Several allometric parameters viz. stem height, stem diameter, leaf area and root traits along with physiological processes were measured. Implication of these parameters in the management of soil surface irrigation in terms of water use efficiency (WUE) was demonstrated in maize. Increased number of lateral roots and root number density in DKC-9144 provided more surface area for water absorption for better management of EDI. Root growth rates showed a similar pattern with root length, root surface areas, and root numbers in EDI. Elevated expressions of ZmRTCL, ZmRTCS and ZmARF34 in EDI and EDI plus PBZ were associated with seminal roots and root laterals initiation. Under TDI alone or in combination with PBZ, root lengths (BRL, CRL, SRL) and root surface areas varied in DKC-9144 and PG-2475 over control. Furthermore, correlation analysis showed that decrease in WUE under TDI was significantly associated with a reduction in stem thickness and leaf surface area. For WUE_N in TDI and PBZ plus TDI, structural equation modelling proposed, brace root surface area (BRSA_N) as a positive contributor, while a negative contributor was seminal root surface area (SRSA_N). Present study explained the importance of specific root traits and their association with other allometric parameters for improving WUE in DKC-9144 variety of maize and the crop in general.

Plant Growth Promotion and Heat Stress Amelioration in Arabidopsis Inoculated with Paraburkholderia phytofirmans PsJN Rhizobacteria Quantified with the GrowScreen-Agar II Phenotyping Platform

High temperatures inhibit plant growth. A proposed strategy for improving plant productivity under elevated temperatures is the use of plant growth-promoting rhizobacteria (PGPR). While the effects of PGPR on plant shoots have been extensively explored, roots-particularly their spatial and temporal dynamics-have been hard to study, due to their below-ground nature. Here, we characterized the time- and tissue-specific morphological changes in bacterized plants using a novel non-invasive high-resolution plant phenotyping and imaging platform-GrowScreen-Agar II. The platform uses custom-made agar plates, which allow air exchange to occur with the agar medium and enable the shoot to grow outside the compartment. The platform provides light protection to the roots, the exposure of it to the shoots, and the non-invasive phenotyping of both organs. Arabidopsis thaliana, co-cultivated with Paraburkholderia phytofirmans PsJN at elevated and ambient temperatures, showed increased lengths, growth rates, and numbers of roots. However, the magnitude and direction of the growth promotion varied depending on root type, timing, and temperature. The root length and distribution per depth and according to time was also influenced by bacterization and the temperature. The shoot biomass increased at the later stages under ambient temperature in the bacterized plants. The study offers insights into the timing of the tissue-specific, PsJN-induced morphological changes and should facilitate future molecular and biochemical studies on plant-microbe-environment interactions.

RhizoPot platform: A high-throughput in situ root phenotyping platform with integrated hardware and software

Quantitative analysis of root development is becoming a preferred option in assessing the function of hidden underground roots, especially in studying resistance to abiotic stresses. It can be enhanced by acquiring non-destructive phenotypic information on roots, such as rhizotrons. However, it is challenging to develop high-throughput phenotyping equipment for acquiring and analyzing in situ root images of root development. In this study, the RhizoPot platform, a high-throughput in situ root phenotyping platform integrating plant culture, automatic in situ root image acquisition, and image segmentation, was proposed for quantitative analysis of root development. Plants (1-5) were grown in each RhizoPot, and the growth time depended on the type of plant and the experimental requirements. For example, the growth time of cotton was about 110 days. The imaging control software (RhizoAuto) could automatically and non-destructively image the roots of RhizoPot-cultured plants based on the set time and resolution (50-4800 dpi) and obtain high-resolution (>1200 dpi) images in batches. The improved DeepLabv3+ tool was used for batch processing of root images. The roots were automatically segmented and extracted from the background for analysis of information on radical features using conventional root software (WinRhizo and RhizoVision Explorer). Root morphology, root growth rate, and lifespan analysis were conducted using in situ root images and segmented images. The platform illustrated the dynamic response characteristics of root phenotypes in cotton. In conclusion, the RhizoPot platform has the characteristics of low cost, high-efficiency, and high-throughput, and thus it can effectively monitor the development of plant roots and realize the quantitative analysis of root phenotypes in situ.

Strategies of tree species to adapt to drought from leaf stomatal regulation and stem embolism resistance to root properties

Considerable evidences highlight the occurrence of increasing widespread tree mortality as a result of global climate change-associated droughts. However, knowledge about the mechanisms underlying divergent strategies of various tree species to adapt to drought has remained remarkably insufficient. Leaf stomatal regulation and embolism resistance of stem xylem serves as two important strategies for tree species to prevent hydraulic failure and carbon starvation, as comprising interconnected physiological mechanisms underlying drought-induced tree mortality. Hence, the physiological and anatomical determinants of leaf stomatal regulation and stems xylem embolism resistance are evaluated and discussed. In addition, root properties related to drought tolerance are also reviewed. Species with greater investment in leaves and stems tend to maintain stomatal opening and resist stem embolism under drought conditions. The coordination between stomatal regulation and stem embolism resistance are summarized and discussed. Previous studies showed that hydraulic safety margin (HSM, the difference between minimum water potential and that causing xylem dysfunction) is a significant predictor of tree species mortality under drought conditions. Compared with HSM, stomatal safety margin (the difference between water potential at stomatal closure and that causing xylem dysfunction) more directly merge stomatal regulation strategies with xylem hydraulic strategies, illustrating a comprehensive framework to characterize plant response to drought. A combination of plant traits reflecting species' response and adaptation to drought should be established in the future, and we propose four specific urgent issues as future research priorities.

Root system architecture for abiotic stress tolerance in potato: Lessons from plants

The root is an important plant organ, which uptakes nutrients and water from the soil, and provides anchorage for the plant. Abiotic stresses like heat, drought, nutrients, salinity, and cold are the major problems of potato cultivation. Substantial research advances have been achieved in cereals and model plants on root system architecture (RSA), and so root ideotype (e.g., maize) have been developed for efficient nutrient capture to enhance nutrient use efficiency along with genes regulating root architecture in plants. However, limited work is available on potatoes, with a few illustrations on root morphology in drought and nitrogen stress. The role of root architecture in potatoes has been investigated to some extent under heat, drought, and nitrogen stresses. Hence, this mini-review aims to update knowledge and prospects of strengthening RSA research by applying multi-disciplinary physiological, biochemical, and molecular approaches to abiotic stress tolerance to potatoes with lessons learned from model plants, cereals, and other plants.

Architectural analysis of root system and phytohormone biosynthetic genes expression in wheat (Triticum aestivum L.) inoculated with Penicillium oxalicum

In this study, a fungal plant growth promoter Penicillium oxalicum T4 isolated from non-rhizosphere soil of Arunachal Pradesh, India, was screened for different plant growth promoting traits in a gnotobiotic study. Though inoculation improved the overall growth of the plants, critical differences were observed in root architecture. Confocal Laser Scanning Microscope, Scanning electron microscope and stereo microscopic study showed that inoculated wheat plants could develop profuse root hairs as compared to control. Root scanning indicated improvement in cumulative root length, root area, root volume, number of forks, links, crossings, and other parameters. Confocal scanning laser microscope indicated signs of endophytic colonization in wheat roots. Gene expression studies revealed that inoculation of T4 modulated the genes affecting root hair development. Significant differences were marked in the expression levels of TaRSL4, TaEXPB1, TaEXPB23, PIN-FORMED protein, kaurene oxidase, lipoxygenase, ACC synthase, ACC oxidase, 9-cis-epoxycarotenoid dioxygenase, and ABA 8'-hydroxylase genes. These genes contribute to early plant development and ultimately to biomass accumulation and yield. The results suggested that P. oxalicum T4 has potential for growth promotion in wheat and perhaps also in other cereals.

The Higher Water Absorption Capacity of Small Root System Improved the Yield and Water Use Efficiency of Maize

The root system in plants absorbs water and minerals. However, the relationship among root size, yield, and water use efficiency (WUE) is controversial. Two pot experiments were conducted to explore these relationships by using two maize varieties with contrasting root sizes and reducing the root-shoot ratio (R/S) through root pruning to eliminate genotypic effects. Maize plants were grown in an open rainout shelter under both water-sufficient and deficient conditions. Yield-related parameters, root hydraulic conductivity (Lpr), and WUE were determined. The results showed that the small root variety (XY) has a higher yield and WUE compared to large root variety (QL) under both soil moisture conditions, likely related to the higher Lpr of XY. XY also had a higher leaf water potential than QL under drought stress, indicating that small root system could provide enough water to the shoot. Further pot experiment showed that both small and large root pruning on QL (cut off about 1/5 roots, RP1; and cut off about 1/3 roots, RP2, respectively) improved WUE and Lpr, and the RP1 yield increased by 12.9% compared to the control under well-watered conditions. Root pruning decreased transpiration and increased photosynthesis. Thus, this study reveals that it is possible to increase water absorption, yield, and WUE by reducing R/S in modern maize varieties, which may be important for the future breeding of new cultivars suitable for arid regions.

Strategic roadmap to assess forest vulnerability under air pollution and climate change

Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long-term monitoring programs.

MYB Transcription Factors Becoming Mainstream in Plant Roots

The function of the root system is crucial for plant survival, such as anchoring plants, absorbing nutrients and water from the soil, and adapting to stress. MYB transcription factors constitute one of the largest transcription factor families in plant genomes with structural and functional diversifications. Members of this superfamily in plant development and cell differentiation, specialized metabolism, and biotic and abiotic stress processes are widely recognized, but their roles in plant roots are still not well characterized. Recent advances in functional studies remind us that MYB genes may have potentially key roles in roots. In this review, the current knowledge about the functions of MYB genes in roots was summarized, including promoting cell differentiation, regulating cell division through cell cycle, response to biotic and abiotic stresses (e.g., drought, salt stress, nutrient stress, light, gravity, and fungi), and mediate phytohormone signals. MYB genes from the same subfamily tend to regulate similar biological processes in roots in redundant but precise ways. Given their increasing known functions and wide expression profiles in roots, MYB genes are proposed as key components of the gene regulatory networks associated with distinct biological processes in roots. Further functional studies of MYB genes will provide an important basis for root regulatory mechanisms, enabling a more inclusive green revolution and sustainable agriculture to face the constant changes in climate and environmental conditions.

Deciphering the Genetic Basis of Root and Biomass Traits in Rapeseed (Brassica napus L.) through the Integration of GWAS and RNA-Seq under Nitrogen Stress

An excellent root system is responsible for crops with high nitrogen-use efficiency (NUE). The current study evaluated the natural variations in 13 root- and biomass-related traits under a low nitrogen (LN) treatment in a rapeseed association panel. The studied traits exhibited significant phenotypic differences with heritabilities ranging from 0.53 to 0.66, and most of the traits showed significant correlations with each other. The genome-wide association study (GWAS) found 51 significant and 30 suggestive trait–SNP associations that integrated into 14 valid quantitative trait loci (QTL) clusters and explained 5.7–21.2% phenotypic variance. In addition, RNA sequencing was performed at two time points to examine the differential expression of genes (DEGs) between high and low NUE lines. In total, 245, 540, and 399 DEGs were identified as LN stress-specific, high nitrogen (HN) condition-specific, and HNLN common DEGs, respectively. An integrated analysis of GWAS, weighted gene co-expression network, and DEGs revealed 16 genes involved in rapeseed root development under LN stress. Previous studies have reported that the homologs of seven out of sixteen potential genes control root growth and NUE. These findings revealed the genetic basis underlying nitrogen stress and provided worthwhile SNPs/genes information for the genetic improvement of NUE in rapeseed.

Building soil sustainability from root-soil interface traits

Great potential exists to harness plant traits at the root-soil interface, mainly rhizodeposition and root hairs, to 'build' soils with better structure that can trap more carbon and resources, resist climate stresses, and promote a healthy microbiome. These traits appear to have been preserved in modern crop varieties, but scope exists to improve them further because they vary considerably between genotypes and respond to environmental conditions. From emerging evidence, rhizodeposition can act as a disperser, aggregator, and/or hydrogel in soil, and root hairs expand rhizosheath size. Future research should explore impacts of selecting these traits on plants and soils concurrently, expanding from model plants to commercial genotypes, and observing whether impacts currently limited to glasshouse studies occur in the field.

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.

Capturing crop adaptation to abiotic stress using image-based technologies

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.

Discovery of Genomic Regions and Candidate Genes Controlling Root Development Using a Recombinant Inbred Line Population in Rapeseed (Brassica napus L.)

Marker-assisted selection enables breeders to quickly select excellent root architectural variations, which play an essential role in plant productivity. Here, ten root-related and shoot biomass traits of a new F₆ recombinant inbred line (RIL) population were investigated under hydroponics and resulted in high heritabilities from 0.61 to 0.83. A high-density linkage map of the RIL population was constructed using a Brassica napus 50k Illumina single nucleotide polymorphism (SNP) array. A total of 86 quantitative trait loci (QTLs) explaining 4.16–14.1% of the phenotypic variances were detected and integrated into eight stable QTL clusters, which were repeatedly detected in different experiments. The codominant markers were developed to be tightly linked with three major QTL clusters, qcA09-2, qcC08-2, and qcC08-3, which controlled both root-related and shoot biomass traits and had phenotypic contributions greater than 10%. Among these, qcA09-2, renamed RT.A09, was further fine-mapped to a 129-kb interval with 19 annotated genes in the B. napus reference genome. By integrating the results of real-time PCR and comparative sequencing, five genes with expression differences and/or amino acid differences were identified as important candidate genes for RT.A09. Our findings laid the foundation for revealing the molecular mechanism of root development and developed valuable markers for root genetic improvement in rapeseed.

Root Pulling Force Across Drought in Maize Reveals Genotype by Environment Interactions and Candidate Genes

High-throughput, field-based characterization of root systems for hundreds of genotypes in thousands of plots is necessary for breeding and identifying loci underlying variation in root traits and their plasticity. We designed a large-scale sampling of root pulling force, the vertical force required to extract the root system from the soil, in a maize diversity panel under differing irrigation levels for two growing seasons. We then characterized the root system architecture of the extracted root crowns. We found consistent patterns of phenotypic plasticity for root pulling force for a subset of genotypes under differential irrigation, suggesting that root plasticity is predictable. Using genome-wide association analysis, we identified 54 SNPs as statistically significant for six independent root pulling force measurements across two irrigation levels and four developmental timepoints. For every significant GWAS SNP for any trait in any treatment and timepoint we conducted post hoc tests for genotype-by-environment interaction, using a mixed model ANOVA. We found that 8 of the 54 SNPs showed significant GxE. Candidate genes underlying variation in root pulling force included those involved in nutrient transport. Although they are often treated separately, variation in the ability of plant roots to sense and respond to variation in environmental resources including water and nutrients may be linked by the genes and pathways underlying this variation. While functional validation of the identified genes is needed, our results expand the current knowledge of root phenotypic plasticity at the whole plant and gene levels, and further elucidate the complex genetic architecture of maize root systems.