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Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category.

A cultivation facility that can assist users in controlling the soil water condition is needed for accurately phenotyping plants under drought stress in an artificial environment. Here we report the Internet of Things-based pot system controlling optional treatment of soil water condition (iPOTs), an automatic irrigation system that mimics the drought condition in a growth chamber. The Wi-Fi-enabled iPOTs system allows water supply from the bottom of the pot, based on the soil water level set by the user, and automatically controls the soil water level at a desired depth. The iPOTs also allows users to monitor environmental parameters, such as soil temperature, air temperature, humidity, and light intensity, in each pot. To verify whether the iPOTs mimics the drought condition, we conducted a drought stress test on rice (Oryza sativa L.) varieties and near-isogenic lines, with diverse root system architecture, using the iPOTs system installed in a growth chamber. Similar to the results of a previous drought stress field trial, the growth of shallow-rooted rice accessions was severely affected by drought stress compared with that of deep-rooted accessions. The microclimate data obtained using the iPOTs system increased the accuracy of plant growth evaluation. Transcriptome analysis revealed that pot positions in the growth chamber had little impact on plant growth. Together, these results suggest that the iPOTs system is a reliable platform for phenotyping plants under drought stress.

The root system architecture (RSA) of crops can affect their production, particularly in abiotic stress conditions, such as with drought, waterlogging, and salinity. Salinity is a growing problem worldwide that negatively impacts on crop productivity, and it is believed that yields could be improved if RSAs that enabled plants to avoid saline conditions were identified. Here, we have demonstrated, through the cloning and characterization of qSOR1 (quantitative trait locus for SOIL SURFACE ROOTING 1), that a shallower root growth angle (RGA) could enhance rice yields in saline paddies. qSOR1 is negatively regulated by auxin, predominantly expressed in root columella cells, and involved in the gravitropic responses of roots. qSOR1 was found to be a homolog of DRO1 (DEEPER ROOTING 1), which is known to control RGA. CRISPR-Cas9 assays revealed that other DRO1 homologs were also involved in RGA. Introgression lines with combinations of gain-of-function and loss-of-function alleles in qSOR1 and DRO1 demonstrated four different RSAs (ultra-shallow, shallow, intermediate, and deep rooting), suggesting that natural alleles of the DRO1 homologs could be utilized to control RSA variations in rice. In saline paddies, near-isogenic lines carrying the qSOR1 loss-of-function allele had soil-surface roots (SOR) that enabled rice to avoid the reducing stresses of saline soils, resulting in increased yields compared to the parental cultivars without SOR. Our findings suggest that DRO1 homologs are valuable targets for RSA breeding and could lead to improved rice production in environments characterized by abiotic stress.

IR64 is a rice variety with high-yield that has been widely cultivated around the world. IR64 has been replaced by modern varieties in most growing areas. Given that modern varieties are mostly progenies or relatives of IR64, genetic analysis of IR64 is valuable for rice functional genomics. However, chromosome-level genome sequences of IR64 have not been available previously. Here, we sequenced the IR64 genome using synthetic long reads obtained by linked-read sequencing and ultra-long reads obtained by nanopore sequencing. We integrated these data and generated the de novo assembly of the IR64 genome of 367 Mb, equivalent to 99% of the estimated size. Continuity of the IR64 genome assembly was improved compared with that of a publicly available IR64 genome assembly generated by short reads only. We annotated 41,458 protein-coding genes, including 657 IR64-specific genes, that are missing in other high-quality rice genome assemblies IRGSP-1.0 of japonica cultivar Nipponbare or R498 of indica cultivar Shuhui498. The IR64 genome assembly will serve as a genome resource for rice functional genomics as well as genomics-driven and/or molecular breeding.

Water and nitrogen availability limit crop productivity globally more than most other environmental factors. Plant availability of macronutrients such as nitrate is, to a large extent, regulated by the amount of water available in the soil, and, during drought episodes, crops can become simultaneously water and nitrogen limited. In this review, we explore the intricate relationship between water and nitrogen transport in plants, from transpiration-driven mass flow in the soil to uptake by roots via membrane transporters and channels and transport to aerial organs. We discuss the roles of root architecture and of suberized hydrophobic root barriers governing apoplastic water and nitrogen movement into the vascular system. We also highlight the need to identify the signalling cascades regulating water and nitrogen transport, as well as the need for targeted physiological analyses of plant traits influencing water and nitrogen uptake. We further advocate for incorporation of new phenotyping technologies, breeding strategies, and agronomic practices to improve crop yield in water- and nitrogen-limited production systems., Given the critical importance and interconnectedness of water and nitrogen in determining crop yield, there is great impetus to understand and optimize their uptake. We review this intersection and provide proposals for improving these critical crop traits.

Root system architecture (RSA) is one of the most important traits determining water and nutrient availability for plants. Modification of RSA is known to be a useful approach for improving root performance of crops. However, for conducting root phenotyping, there are few alternatives for the rapid collection of root samples from a constant soil volume. In this report, we propose a rapid root-sampling method, which uses a steel cylinder known as round monolith and backhoes to reduce the physical effort. The monolith was set on the ground surrounding individual rice plants and vertically driven back by a backhoe. Soil samples with 20 cm width and 25 cm depth were excavated by the monolith, from which root samples were then isolated. This backhoe-assisted monolith method requires at most five minutes to collect root samples from one plant. Using this method, we quantified the root traits of three rice lines, reported to form different types of root system such as shallow-, intermediate-, and deep-roots, using a root image analysis software. The data obtained through this method, which showed the same trend as previously reported, clearly demonstrated that this method is useful for quantitative evaluation of roots in the soil.

Root system architecture affects plant drought resistance and other key agronomic traits such as lodging. However, although phenotypic and genomic variation has been extensively analyzed, few field studies have integrated phenotypic and transcriptomic information, especially for below-ground traits such as root system architecture. Here, we report the phenotypic and transcriptomic landscape of 61 rice (Oryza sativa) accessions with highly diverse below-ground traits grown in an upland field. We found that four principal components explained the phenotypic variation and that accessions could be classified into four subpopulations (indica, aus, japonica, and admixed) based on their tiller numbers and crown root diameters. Transcriptome analysis revealed that differentially expressed genes associated with specific subpopulations were enriched with stress response-related genes, suggesting that subpopulations have distinct stress response mechanisms. Root growth was negatively correlated with auxin-inducible genes, suggesting an association between auxin signaling and upland field conditions. A negative correlation between crown root diameter and stress response-related genes suggested that thicker crown root diameter is associated with resistance to mild drought stress. Finally, co-expression network analysis implemented with DNA affinity purification followed by sequencing (DAP-seq) analysis identified phytohormone signaling networks and key transcription factors negatively regulating crown root diameter. Our datasets provide a useful resource for understanding the genomic and transcriptomic basis of phenotypic variation under upland field conditions.

The development of genotypes and fertilizer management practices that facilitate high phosphorus (P) use efficiency is needed given the depleting phosphorus ore deposits and increasing ecological concerns about its excessive use. Root system architecture (RSA) is important in efficiently capturing immobile P in soils, while agronomically, localized P application near the roots is a potential approach to address this issue. However, the interaction between genetic traits of RSA and localized P application has not been examined. Near-isogenic lines (NILs) and their parent of rice (qsor1-NIL, Dro1-NIL, and IR64, with shallow, deep, and intermediate root growth angles (RGA), respectively) were grown in flooded pots in a uniform and P-sufficient condition (Pinco), and with localized P application by dipping seedling roots into P-enriched slurry at transplanting (P-dipping). The P-dipping created an available P hotspot at the soil surface and substantially improved applied P-use efficiency (equivalent biomass at one fifth of application rate of the Pinco). Further, the qsor1-NIL had significantly greater biomass and P uptake than the other genotypes in the P-dipping. The qsor1-NIL consistently had a greater root biomass and surface area in the 0–3 cm soil layer, despite that there were no genotype differences in total values and that the other genotypes also reduced their RGAs responding to the P hotspot in the P-dipping. The shallow root system of qsor1-NIL facilitated P uptake from the P hotspot. P-use efficiency in crop production can be further increased by combining genetic traits of RSA and localized P application.

Root system architecture affects plant drought tolerance and other key agronomic traits such as lodging. However, although phenotypic and genomic variation has been extensively analyzed, few field studies have integrated phenotypic and transcriptomic information, especially for below-ground traits such as root system architecture. Here, we report the phenotypic and transcriptomic landscape of 61 rice (Oryza sativa) accessions with highly diverse below-ground traits grown in an upland field under mild drought stress. We found that four principal components explained the phenotypic variation and that accessions could be classified into four admixture groups (admixed, aus, indica, and japonica) based on their tiller numbers and crown root diameters. Transcriptome analysis revealed that differentially expressed genes associated with specific admixture groups were enriched with stress response-related genes, suggesting that admixture groups have distinct stress response mechanisms. Root growth was negatively correlated with auxin-inducible genes, suggesting an association between auxin signaling and mild drought stress. A negative correlation between crown root diameter and stress response-related genes suggested that thicker crown root diameter is associated with resistance to mild drought stress. Finally, co-expression network analysis implemented with DNA affinity purification followed by sequencing (DAP-seq) analysis identified phytohormone signaling networks and key transcription factors negatively regulating crown root diameter. Our datasets provide a useful resource for understanding the genomic and transcriptomic basis of phenotypic variation under mild drought stress.ONE-SENTENCE SUMMARYCatalog of the phenomes and transcriptomes of rice cultivars grown in upland fields provides a resource for further studies toward breeding climate-resilient crops.

The development of genotypes and fertilizer management practices that facilitate high phosphorus (P) use efficiency is needed given the depleting phosphorus ore deposits and increasing ecological concerns about its excessive use. Root system architecture (RSA) is important in efficiently capturing immobile P in soils, while agronomically, localized P application near the roots is a potential approach to address this issue. However, the interaction between genetic traits of RSA and localized P application has not been examined. Near-isogenic lines (NILs) and their parent of rice (qsor1-NIL, Dro1-NIL, and IR64, with shallow, deep, and intermediate root growth angles (RGA), respectively) were grown in flooded pots in a uniform and P-sufficient condition (Pinco), and with localized P application by dipping seedling roots into P-enriched slurry at transplanting (P-dipping). The P-dipping created an available P hotspot at the soil surface and substantially improved applied P-use efficiency (equivalent biomass at one fifth of application rate of the Pinco). Further, the qsor1-NIL had significantly greater biomass and P uptake than the other genotypes in the P-dipping. The qsor1-NIL consistently had a greater root biomass and surface area in the 0–3 cm soil layer, despite that there were no genotype differences in total values and that the other genotypes also reduced their RGAs responding to the P hotspot in the P-dipping. The shallow root system of qsor1-NIL facilitated P uptake from the P hotspot. P-use efficiency in crop production can be further increased by combining genetic traits of RSA and localized P application.

Significance Genetically improving the root system architectures of plants is an effective strategy for developing climate-resilient crops. In this study, we revealed that a cloned rice quantitative trait locus associated with root growth angle, qSOR1, is a DRO1 homolog involved in root gravitropic responses. The loss-of-function allele qsor1 resulted in roots that developed on the soil surface and enabled plants to avoid the reducing stress found in saline paddy soils and, consequently, increased yields. We show that the DRO1 homologs could be useful for the controlled breeding of root system architectures that are adapted to the abiotic stress conditions caused by global climate change., The root system architecture (RSA) of crops can affect their production, particularly in abiotic stress conditions, such as with drought, waterlogging, and salinity. Salinity is a growing problem worldwide that negatively impacts on crop productivity, and it is believed that yields could be improved if RSAs that enabled plants to avoid saline conditions were identified. Here, we have demonstrated, through the cloning and characterization of qSOR1 (quantitative trait locus for SOIL SURFACE ROOTING 1), that a shallower root growth angle (RGA) could enhance rice yields in saline paddies. qSOR1 is negatively regulated by auxin, predominantly expressed in root columella cells, and involved in the gravitropic responses of roots. qSOR1 was found to be a homolog of DRO1 (DEEPER ROOTING 1), which is known to control RGA. CRISPR-Cas9 assays revealed that other DRO1 homologs were also involved in RGA. Introgression lines with combinations of gain-of-function and loss-of-function alleles in qSOR1 and DRO1 demonstrated four different RSAs (ultra-shallow, shallow, intermediate, and deep rooting), suggesting that natural alleles of the DRO1 homologs could be utilized to control RSA variations in rice. In saline paddies, near-isogenic lines carrying the qSOR1 loss-of-function allele had soil-surface roots (SOR) that enabled rice to avoid the reducing stresses of saline soils, resulting in increased yields compared to the parental cultivars without SOR. Our findings suggest that DRO1 homologs are valuable targets for RSA breeding and could lead to improved rice production in environments characterized by abiotic stress.

Root distribution in the soil determines plants’ nutrient and water uptake capacity. Therefore, root distribution is one of the most important factors in crop production. The trench profile method is used to observe the root distribution underground by making a rectangular hole close to the crop, providing informative images of the root distribution compared to other root phenotyping methods. However, much effort is required to segment the root area for quantification. In this study, we present a promising approach employing a convolutional neural network for root segmentation in trench profile images. We defined two parameters, Depth50 and Width50, representing the vertical and horizontal centroid of root distribution, respectively. Quantified parameters for root distribution in rice ( Oryza sativa L.) predicted by the trained model were highly correlated with parameters calculated by manual tracing. These results indicated that this approach is useful for rapid quantification of the root distribution from the trench profile images. Using the trained model, we quantified the root distribution parameters among 60 rice accessions, revealing the phenotypic diversity of root distributions. We conclude that employing the trench profile method and a convolutional neural network is reliable for root phenotyping and it will furthermore facilitate the study of crop roots in the field.

Background: Plants adjust their root system architecture (RSA) against changing environments to optimize their growth. Nondestructive phenotyping of roots beneath the soil not only reveal the response of RSA against environmental stimuli but also allow for designing an ideal RSA for crop cultivation. Generally, roots beneath the soil surface are three-dimensionally visualized using X-ray computed tomography (CT). However, root isolation from X-ray CT images involves a longer time; in addition, CT scanning and reconstruction processes require longer periods. For large-scale root phenotyping, a shorter image acquisition time is required. Thus, the objective of this study is to develop a high-throughput pipeline to visualize rice RSA consisting of radicle and crown roots in the soil, from X-ray CT images.Results: We performed the following three processes to develop the pipeline. First, we used calcined clay with uniform soil particle size as the soil substrate. The size of voids between the soil particles was less than the scanning resolution, resulting in a clear root shape in the CT images. Second, we optimized the parameters for rapid X-ray CT scanning. Higher tube voltage and current produced the highest root-to-soil contrast images. Third, we used a 3-D median filter to reduce noise, and an edge detection alogism to isolate the root segments. The detection limits of the root diameters of the pots of diameters 16 cm and 20 cm were 0.2 mm and 0.3 mm, respectively. Because the crown root diameter of rice is generally higher than 0.2 mm, almost all crown roots could be visualized. Our condition allows for simultaneously performing CT scanning and reconstruction by a high-performance computing technology. Consequently, our pipeline visualizes rice RSA in the soil, requiring less than 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing to visualize rice RSA. We scanned the roots of the upland rice (considered in this study) daily, and our pipeline successfully visualized the root development dynamics over three weeks. Conclusions: We developed a rapid three-dimensional visualization method to visualize rice RSA in the soil using X-ray CT and a fully automated-image processing method known as RSAvis3D. Our methodology allows for high-throughput measuring and requires no manual operators in image processing, thereby providing a potentially efficient large-scale root phenotyping.

DEEPER ROOTING 1 (DRO1) of rice controls the gravitropic response of root growth angle. In order to clarify the effects of DRO1 on root growth angle and root length density under different soil resistance to penetration, and to quantify the relationship between root growth angle and root length density, we assessed the root growth of Dro1-NIL (a near-isogenic line homozygous for the Kinandang Patong allele of DRO1 in the IR64 background) under upland Andosol field conditions in Japan in 2013 and 2014. The trial included three levels of soil compaction (none, moderate, and hard). Root length density at a depth of 30 to 60 cm was largest in Kinandang Patong, followed by Dro1-NIL, and was least in IR64 in both years and in all compaction treatments. Root length density at this depth decreased with hard compaction (to 70% of control) and increased with moderate compaction (to 135%). The number of roots with a deep angle (i.e. 45° to 90° from the horizontal) measured by the basket method was similar at maximum tillering and maturity stages, and its value as a proportion of the total number of roots was strongly correlated with the root length density at 30 to 60 cm in both years, which demonstrates the importance of a deep root angle for the development of deep roots. Dro1-NIL had a higher proportion of deep roots than IR64, but the difference was small under hard compaction, with a significant genotype × compaction interaction.

We developed a new method of using seedling trays to evaluate root angle distribution in rice (Oryza sativa. L), and found a wide genetic variation among cultivars. The seedling tray method can be used to evaluate in detail the growth angles of rice crown roots at the seedling stage by allocating nine scores (10° to 90°). Unlike basket methods, it can handle large plant populations over a short growth period (only 14 days). By using the method, we characterized the root angle distributions of 97 accessions into two cluster groups: A and B. The numbers of accessions in group A were limited, and these were categorized as shallow rooting types including soil-surface root. Group B included from shallow to deep rooting types; both included Indica and Japonica Group cultivars, lowland and upland cultivars, and landraces and improved types. No relationship between variation in root vertical angle and total root number was found. The variation in root angle distribution was not related to differentiation between the Japonica and Indica Groups, among ecosystems used for rice cultivation, or among degrees of genetic improvement. The new evaluation method and associated information on genetic variation of rice accessions will be useful in root architecture breeding of rice.

Roots are the only organ system to uptake water and nutrients from the soil. The root system is crucial for plants to survive and adapt to environmental stresses. Therefore, the root system architecture (RSA) is an important breeding target for developing climate-resilient rice. Since the rice genome has been completely sequenced, many genes for root development have been cloned and characterized. In addition, with the advances in technologies related to omics analysis, such as high-throughput sequencing, transcriptome analysis of roots has also progressed. In contrast, high-throughput root phenotyping has not been established not only in rice but also in whole plants because roots are hidden underground. This deficiency represents a bottleneck for utilizing an integrated multi-omics approach for molecular breeding of RSA. We first summarized previous transcriptome analyses for root development under various abiotic stresses such as drought, salinity, and heat, and assessed the current status of root phenotyping technology and modeling in rice. This knowledge allowed us to contemplate the possibility of applying an integrated multi-omics dataset from RSA to molecular breeding of climate-resilient rice.

Heading date 1 (Hd1) is one of the major determinants of flowering time and regional adaptability in rice (Oryza sativa L.), because there is a significant correlation between Hd1 allelic diversity and phenotypic differences in rice cultivars. In this study, we isolated a novel rice mutant with a Tos17 retrotransposon insertion upstream of Hd1. The Tos17 insertion mutant of Hd1 [designated as Tos17(Hd1)] had decreased photoperiod sensitivity and mRNA expression of Hd1, as compared with wild-type Nipponbare. Between Tos17(Hd1) and Nipponbare, expression levels of OsPRR1 and OsGI were similar, whereas those of two florigen genes Hd3a and RFT1, were significantly different under both short and long-day conditions. Tos17(Hd1) showed photoperiod sensitivity intermediate between that of Nipponbare and a near-isogenic line having a non-functional allele of Hd1, indicating that Tos17(Hd1) has an attenuated but functional Hd1 allele. Our results revealed that allele mining of Hd1 may provide good opportunities to develop novel rice cultivars showing different levels of photoperiod sensitivity.

Root system is essential for absorbing water and nutrients as well as anchoring shoots to the ground. Understanding the genetic mechanisms related to the formation of root system architecture is necessary for improving rice productivity. Here, we first describe the potential of genetic improvement using quantitative trait locus (QTL) for root system architecture based on our field experiments using a genetic material of DEEPER ROOTING 1, which is a rice QTL controlling root growth angle. Next, we summarize the accumulated knowledge on the genetic mechanisms of root formation in rice including the development of the radicle, crown roots, lateral roots, and root hairs. We also overview the current status of the genetic dissection of root system architecture in rice, namely, the identification and characterization of natural and artificial alleles. Root traits are rarely chosen as breeding targets because their evaluation in a large number of plants under field conditions is more laborious and time-consuming than evaluation of aboveground traits. The genetic dissection of root system architecture would facilitate the breeding of root traits, eventually improving rice yield irrespective of soil and other environmental conditions.

The volume that the root system can occupy is associated with the efficiency of water and nutrient uptake from soil. Genetic improvement of root length, which is a limiting factor for root distribution, is necessary for increasing crop production. In this report, we describe identification of two quantitative trait loci (QTLs) for maximal root length, QUICK ROOTING 1 (QRO1) on chromosome 2 and QRO2 on chromosome 6, in cultivated rice (Oryza sativa L.). We measured the maximal root length in 26 lines carrying chromosome segments from the long-rooted upland rice cultivar Kinandang Patong in the genetic background of the short-rooted lowland cultivar IR64. Five lines had longer roots than IR64. By rough mapping of the target regions in BC4F2 populations, we detected putative QTLs for maximal root length on chromosomes 2, 6, and 8. To fine-map these QTLs, we used BC4F3 recombinant homozygous lines. QRO1 was mapped between markers RM5651 and RM6107, which delimit a 1.7-Mb interval on chromosome 2, and QRO2 was mapped between markers RM20495 and RM3430-1, which delimit an 884-kb interval on chromosome 6. Both QTLs may be promising gene resources for improving root system architecture in rice.

The root system is an essential organ for taking up water and nutrients and anchoring shoots to the ground. On the other hand, the root system has rarely been regarded as breeding target, possibly because it is more laborious and time-consuming to evaluate roots (which require excavation) in a large number of plants than aboveground tissues. The root growth angle (RGA), which determines the direction of root elongation in the soil, affects the area in which roots capture water and nutrients. In this review, we describe the significance of RGA as a potential trait to improve crop production, and the physiological and molecular mechanisms that regulate RGA. We discuss the prospects for breeding to improve RGA based on current knowledge of quantitative trait loci for RGA in rice.

Abiotic stresses such as, drought, heat, salinity, and flooding threaten global food security. Crop genetic improvement with increased resilience to abiotic stresses is a critical component of crop breeding strategies. Wheat is an important cereal crop and a staple food source globally. Enhanced drought tolerance in wheat is critical for sustainable food production and global food security. Recent advances in drought tolerance research have uncovered many key genes and transcription regulators governing morpho-physiological traits. Genes controlling root architecture and stomatal development play an important role in soil moisture extraction and its retention, and therefore have been targets of molecular breeding strategies for improving drought tolerance. In this systematic review, we have summarized evidence of beneficial contributions of root and stomatal traits to plant adaptation to drought stress. Specifically, we discuss a few key genes such as, DRO1 in rice and ERECTA in Arabidopsis and rice that were identified to be the enhancers of drought tolerance via regulation of root traits and transpiration efficiency. Additionally, we highlight several transcription factor families, such as, ERF (ethylene response factors), DREB (dehydration responsive element binding), ZFP (zinc finger proteins), WRKY, and MYB that were identified to be both positive and negative regulators of drought responses in wheat, rice, maize, and/or Arabidopsis. The overall aim of this review is to provide an overview of candidate genes that have been identified as regulators of drought response in plants. The lack of a reference genome sequence for wheat and non-transgenic approaches for manipulation of gene functions in wheat in the past had impeded high-resolution interrogation of functional elements, including genes and QTLs, and their application in cultivar improvement. The recent developments in wheat genomics and reverse genetics, including the availability of a gold-standard reference genome sequence and advent of genome editing technologies, are expected to aid in deciphering of the functional roles of genes and regulatory networks underlying adaptive phenological traits, and utilizing the outcomes of such studies in developing drought tolerant cultivars.

Three near-isogenic lines (NILs) of Oryza sativa subsp. indica cv. IR64 (Dro1-NIL, Sta1-NIL, Dro1+Sta1-NIL) with DEEPER ROOTING 1 (DRO1), a novel gene for steeper root growth angle, and/or with Stele Transversal Area 1 (Sta1), a QTL for wider stele area, were tested under flooded lowland (FL), alternate wetting and drying lowland (AWD), and rainfed upland (UP) conditions in 2013 and 2014 to compare the effects of DRO1 and Sta1 on yield across different water management regimes. Genotypic variation and water management effects were significant for grain yield, aboveground biomass, and harvest index, as well as their interactions with year, but no significant genotype × water interaction was detected. Dro1-NIL had 14% higher yield than that of IR64 across the three water conditions due to higher harvest index, aboveground biomass, leaf area index, and number of grains. Sta1 tended to reduce the carbon isotope composition (δ13C), leading to a higher harvest index of Sta1-NIL than that of IR64, but grain yield was not increased. Dro1+Sta1-NIL had the highest fraction of intercepted radiation, cumulative radiation interception, and panicle number, with a small but insignificant yield improvement over IR64, but the combination of DRO1 and Sta1 did not surpass the increment from the effects of DRO1 alone. AWD in the more rainy year 2014 attained both higher water productivity and higher biomass, with significant water by year interaction for water productivity. Genotypic variation in water productivity was related with higher leaf area index and fraction interception, with Dro1-NIL larger than in IR64 and Sta1-NIL.

Root is the only organ to uptake water and nutrients from soil. Root system is crucial for plants to survive and/or adapt to environmental stresses, therefore, root system architecture (RSA) is an important breeding target for developing climate-resilient rice. Since the rice genome has been completely sequenced, many genes for root development were cloned and characterized so far. In addition, with the advances in technologies related to omics analysis such as high-throughput sequencer, transcriptome analysis of roots has also been progressed. In contrast, high-throughput root phenotyping has not been established in not only rice but also whole plants because root is hidden underground. This should be a bottleneck for utilizing multi-omics integrated approach for molecular breeding of RSA. We first summarize previous transcriptome analysis for root development under various abiotic stresses such as drought, salinity, heat etc. and overview current status of root phenotyping technology and modelling in rice. These knowledges would allow us to contemplate a possibility of applying of integrated multi-omics data of RSA to molecular breeding of climate-resilient rice.