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The role of antioxidative enzymes as reactive oxygen species-scavengers under water-limited (WL) conditions was studied in five contrasting rice genotypes, including two checks (Sahabhagidhan as a tolerant genotype and IR 64 as a susceptible genotype). The experiment was performed in pots, and the irrigation was withdrawn five days before flowering for 15 days. For stress imposition, stress pots were maintained at 50% field capacity whereas nonstress pots were maintained at 100% field capacity. The antioxidant enzyme activity such as superoxide dismutase (SOD), catalase (CAT), and peroxidase (POX), was significantly increased under the WL conditions. Genotypes Sahabhagidhan and Parijata had the highest SOD, CAT, and POX activity with the lowest production of superoxide radical (O2-) and hydrogen peroxide (H2O2). At the same time, IR 64 and Prasad exhibited the lowest SOD, CAT, and POX activity, suggesting a lower potential to eliminate oxidative stress. The higher scavenging capacity of free radicals in Sahabhagidhan and Parijata was supplemented by the higher level of relative water content (RWC), membrane stability index (MSI), Maximum quantum yield of PSII photochemistry (Fv/Fm), chlorophyll content, and low lipid peroxidation which resulted in higher grain yield accompanied by higher biomass partitioning towards the grain.

Photorespiration, which is prevalent under higher temperature and arid conditions, significantly affects crop productivity by reducing yields up to 50% in C3 crops like rice under severe stress conditions. This is primarily attributed to a reduction in net photosynthetic rate (PN). Rice flag leaf photosynthesis is the primary supplier of sugar to the maturing spikelets after anthesis. This study evaluated the grain quality traits and starch content of the wild type (WT) and transgenic rice generated by introducing Escherichia coli (E. coli) glycolate catabolic pathway bypassed (GCPB) through agrobacterium mediated transformation. Leaf soluble protein, photosynthetic CO2 assimilation rate, leaf non-structural carbohydrate content, grain quality traits such as hulling and milling percentages, head rice recovery, water uptake, volume expansion, alkali spreading value, gel consistency, grain breadth, grain starch content and amylose content were affected to a great extent in GCPB transgenic plants (T4). This study indicates the possible role of photorespiratory bypass mechanism in the regulation of source-sink communication, starch biosynthesis and grain quality in rice.

Rice genes namely TPP7 ( Trehalose Phosphate Phosphatase 7), Sub1A ( Submergence 1a) and their interactions regulate tolerance to submergence at germination and seedling stage, respectively. Sequential stress of initial submergence during the germination and subsequent seedling stage water deficit stresses are not properly addressed in direct seeded rice cultivation. In our initial linkage disequilibrium and meta-QTL analysis, Sub1 locus and TPP7 genes co-segregated for tolerance to submergence at germination and seedling stage and were in linkage disequilibrium only in aus subpopulations of rice. Phenotypically, root traits were positively correlated (correlation coefficient = >0.8) with seedling growth in germination under submergence and also in subsequent water deficit stresses in recombinant inbred lines developed from N22 and Bhalum 2 cross. Further, favourable alleles of Sub1 in N22 and 3’-UTR allele of TPP7 in Bhalum 2 enhanced the root traits (>20 per cent) and seedling survival (>25 per cent), respectively in germination under submergence stress. Additionally, the interaction of N22 alleles of Sub1 and TPP7 significantly enhanced the culm diameter and root dry weight in submergence at germination stress. Interestingly, two QTLs with high additive effects associated with sixteen different traits for submerged germination and water deficit tolerance traits were identified within the genomic regions spanning Sub1 and TPP7 genes (~4 Mbp) in Chr09 indicative of genomic region effects on the trait response rather than Sub1 and TPP7. Further, favourable haplotype within the Sub1 and TPP7 genomic region had an epistatic effect on trait responses and enhanced the crown root number, root dry weight, and specific root area by 11.45%, 15.69%, and 33.15% respectively in flooded germination condition, indicative of haplo-allelic contribution in trait response. A wall-associated kinase 79 ( WAK79) and malectin-like receptor-like kinase 59 ( MRLK59) were identified as candidate genes, that through regulation of cell wall elongation might coordinate the ‘haplogenic model’ of quantitative trait response under flooded germination, recovery, and subsequent water deficit conditions and thus, favourable haplotypes could be employed in direct seeded rice improvement.

Rice is an ideal crop with huge germplasm diversity and post-genomic resources for improvement of nitrogen (N) use efficiency (NUE). There is a paucity of comparative studies on rice genotypes contrasting for NUE, especially with urea, the predominant fertilizer in rice growing countries. In this study, low urea-responsive transcriptomes of contrasting rice genotypes namely Nidhi (low NUE) and Panvel1 (high NUE) were compared. They were based on whole plants grown for 21 days in pots containing nutrient-depleted soil fertilized with normal (15 mM) and low urea (1.5 mM) media. There were 1497 and 2819 differentially expressed genes (DEGs) in Nidhi and Panvel1, respectively, of which 271 were common. Though 1226 DEGs were genotype-specific in Nidhi and 2548 in Panvel1, there was far higher commonality in underlying processes. High NUE is associated with the urea-responsive regulation of other nutrient transporters, miRNAs, transcription factors and better photosynthesis, water use efficiency and post translational modifications. Many of their genes co-localized to NUE QTLs on chromosomes 1, 3 and 9. Field evaluation of the contrasting genotypes under different doses of urea revealed better performance of Panvel1 in different agronomic parameters including grain yield, transport/uptake efficiencies and NUE. Comparison of our urea-based transcriptomes with our previous nitrate-based transcriptomes from the same contrasting rice genotypes revealed many common processes despite large differences in their expression profiles. Our model proposes that differential involvement of transporters and transcription factors among others contributes to better urea uptake, translocation, utilization, flower development and yield for high NUE.SummaryRice genotypes with contrasting urea use efficiency differ in the role of transporters, transcription factors, miRNAs, post-translational modifications, photosynthesis and water use efficiency

Photorespiration accounts for 20–50 % reduction in grain yield in C3 crops. The process is essential to remove 2-phosphoglycolate produced due to the oxygenation activity of the ribulose-1,5-bisphosphate carboxylase oxygenase (RuBisCO) enzyme. Attempts were made to improve photosynthesis through enriched CO2 concentration by installing numerous photorespiratory bypass modules in the chloroplast of several crops. In this study, we have introduced Escherichia coli glycolate catabolic pathway (ECGC) into rice chloroplast to bypass photorespiration partially (PB) or completely (FB). Five genes encoding glyoxylate carboligase (GCL), tartronic semialdehyde reductase (TSR), and three subunits of glycolate dehydrogenase (GDH) were introduced to get FB plants, whereas only the three subunits of GDH were introduced to get PB plants. Southern analysis confirmed stable integration of the transgenes and their expression was confirmed by RT-qPCR analysis in the T3 progenies. Both FB and PB transformed lines exhibited increased photosynthetic efficiency, biomass, and grain yield than wild type (WT) with empty vector control. The introduction of ECGC pathway favoured the carboxylase activity of RuBisCO while decreasing its oxygenase activity fostering the functioning of Calvin-Benson cycle and resulting in an increased carbon-assimilation that was manifested in their superior architecture and harvest index. These findings will support rice and related cereal crop breeding programs to increase yield under elevated temperature and arid conditions.

With the advancement of sequencing technology, rapid progress in genomic studies has been achieved; however, the lack of large-scale accurate estimation of phenotypic data is one of the major bottlenecks slowing down the progress in functional genomics and crop breeding studies. With the advancement of recent high-throughput phenotyping technologies, researchers can explore complex traits that are impossible to measure with conventional phenotyping methods. In this chapter, the major phenotyping tools and techniques used in field conditions and controlled environments have been discussed, along with their advantages and limitations. Some conceptual challenges have been proposed, and our perception to bridge the gap between phenotype and genotype are also discussed. There is no doubt that the recent developments in high-throughput plant phenotyping will accelerate crop genetic improvement and promote next-generation green revolution.

Rice is considered as a model plant to map the complete genome and identify the genes in the genome for various traits. Recent advance in molecular biology and statistical analysis techniques could boost the plant breeder to gain knowledge on rice genome and find out the function and expression of rice genes. Near the genes, several quantitative trait loci (QTLs) have been mapped and identified with genetic markers to find out its location in chromosome. With the popularity of computational approach, it is easy to find out the QTLs position in the genome and introgress these QTLs into the popular rice genotypes to study the expression and its effect with trait used in MAS breeding program. Several models and QTL mapping methods are used to detect the QTL position between marker interval. QTL mapping is a highly efficient way of determining genetic interactions in plant disease resistance. The QTL mapping was carried in F2, RILs, NILs, backcrossing populations, and double haploids (DH) in rice to study the performance of respective QTLs and its genetic basis of expression in grain yield and other traits as well as other parameters of yield like biotic and disease resistance. Various studies have been reported for QTL mapping and its function in phenotypic variation found in rice. This fine mapping and detection of QTL position provide a new way to improve the rice plant against various theft of decreasing yield.

Long non-coding RNA (lncRNA) is the largest non-protein and functional RNA. The majority of lncRNAs are functionally uncharacterized. Thus, researchers are employing both experimental as well as computational approaches to characterize unknown lncRNAs. Recently, the majority of lncRNAs have been characterized using transcriptome sequencing datasets under different conditions. The information on these transcripts was mainly restricted to genomic loci and expression patterns. This discrepancy in lncRNAs’ functional understanding has largely been due to the lack of methods to classify basic genome-scale bio-molecular interactions and resources that systematically archive these interactions. Additionally, experimental methods are time-consuming and cost-effective. To overcome this, various bioinformatics tools have been developed to predict lncRNA. Thus, in this chapter, the authors attempted to understand the underlying principle of various computational approaches to detect lnRNAs. Information obtained reveals that lncRNA may be annotated by employing its coding potential and sequence conservation, folding algorithms, and interactions. Additionally, several databases have also been developed to help researchers detect lncRNAs from the large genomic dataset. Though the result obtained from these tools and databases is useful, a systematic integrative and metadata analysis is also required to understand diverse lncRNA regulatory mechanisms of action at various levels. Further attempts would be made to annotate their features, which is beneficial in understanding the underlying cell biology.

Drought is one of the major abiotic stresses that affect plant growth and survival. It alters morphological, biochemical, and molecular processes in plant cells. The implementation of multi-omics and improved breeding strategies is a dynamic step towards drought tolerance in crop plants. The identification of several drought-responsive genes, differentially expressed transcripts, proteins, metabolites, and miRNAs associated with drought responses has become possible through genomics, transcriptomics, metabolomics, proteomics, micromics, and phenomics, etc. Investigations on the latest omics technologies will provide us with an overall idea of the pathways involved in drought tolerance. Additionally, GWAS and QTL mapping have made an impressive contribution to improving plant responses to water-deficit conditions. This chapter discussed how various omics tools and breeding technologies enable plants to withstand the devastating effects of drought stress.

Over the past 10 years, gene set analysis has been the first option for studying gene expression and gene interaction for gaining insights into the fundamental dynamic biology of disease/traits. It, therefore, reduces the complexity of traditional statistical research and increases the illustrating strength of the outcomes achieved. Although approaches to gene set analysis are commonly utilized in gene expression analytics, the statistical framework and steps generally employed in these methods have not yet been thoroughly explored, restricting their usefulness. Thus, in this chapter, the authors include an outlined statistical framework and steps for the analysis of gene set used for various genome studies, ranging from microarrays, RNA sequencing, and the analysis of genomic widespread association results. The drawbacks of these approaches and strengths have also been addressed depending on their separate components such as their gene score, null hypotheses, and essential evaluation methods. The authors believe that a standardized approach for testing the methods of gene set analysis can also be used for correcting the lack of agreement on the method of preference for a specific experiment. The benchmark expression data sets will reflect actual expression data characteristics and prevent oversimplifying conclusions, such as naturally distributed data with zero or constant gene-gene correlations. In the near future, information present in this chapter will be highly useful for underlying process with any disease or trait in more comprehensive way.

Genome editing is the main goal in improving rice productivity by inducing gene-specific mutations to modify the gene function. Due to the advances in the biotechnology approach, it is possible to make the gene manipulation that could give effective results against various biotic and abiotic stresses and grain quality. Nowadays, population increases day by day; therefore, it is a big challenge for developing countries. However, it is important to release new crop varieties with effective function, and which will minimize the food shortage and food security problem. The existing rice genome sequences have made it possible to identify genetic variations and insert mutagenesis to improve rice traits. Various methods and techniques are available for genome editing to modify the gene through inducing mutagenesis or by gene silencing. Zinc finger nucleases (ZFNs), transcriptional activator-like nucleases (TALENs), and newly identified clustered regularly interspaced repeats (CRISPR)/CRISPR associated protein 9 (Cas9)-based genome editing methods are more widely used in rice breeding programs. Among them CRISPR/Cas9 is the most reliable tool in genome editing due to its simple targeting method for improving grain quality and quantity. Thus, the application of CRISPR/Cas9 system introduces a double-strand break in DNA to edit the gene of interest at a specific position. Additionally, it may be utilized in the near future to develop disease and herbicide resistance, as well as to improve rice production, quality, and quantitative characteristics.

All cellular structures are heterogeneous. Thus, investigating true cell heterogeneity is highly required to further understand cellular connectivity and accountability within a disease or normal conditions. Because of its rapidly decreasing costs, the Next-Generation (NGS) sequence is widely used to analyze various biological data. However, these approaches may fail to provide detailed insight into cells’ true heterogeneity. Recently developed single-cell RNA sequencing (scRNA-seq) technology tries to tackle these bulk NGS issues by linking transcriptomic, epigenomic, proteomic, and molecular sequences to a specific cell. Thus, in this chapter, the author addresses the process involved, relative strengths, possible uses, and limitations of scRNA-seq techniques methods. Information obtained revealed that cell isolation methods may be broadly divided into two categories centered on different principles. The first category centered on physical characteristics, while the second category mainly focuses on cell’ features and primarily involves affinity approaches. After obtaining raw data, the general approach for analyzing ScRNA-Seq data are pre-processing, batch effect correction, normalization, dimensionality reduction, feature selection, cell type identification, differential expression analysis, and rebuilding cell hierarchy. scRNA-seq technologies are continuously being employed to unmask various biological processes ranging from epigenetic regulation to biomarker identification. However, scRNA-seq technologies do have difficulties like cumbersome activity and high detection costs that restrict technology promotion. Hence, there is an urgent requirement to develop more robust tools so that, in the near future, the technology for single-cell sequencing will be streamlined and is more efficient.

Rice is one of the world’s most important agricultural crops and a model plant that is widely studied. The completion of the complete rice (Oryza sativa) genome sequence through high-throughput experimental platforms has resulted in a huge amount of data being generated and specialized databases, and bioinformatic tools for data processing, analysis, efficient organization, and visualization have been developed. In this chapter, we address a set of biological databases that host rice-specific sequence, genetic variation, gene expression, pathways, and gene–interactome data from various genomic and proteomic sources, and aid in data analysis and visualization.

miRNAs, on average comprised of 22-nucleotides with small non-coding RNAs, regulate the gene expression of targeted genes. Thousands of miRNAs have been identified, having biological significance in many pathways. These microRNAs have also been used as biomarkers for diagnostic and agricultural purposes. Thus, this chapter attempts to describe in brief miRNA biogenesis pathways, miRNA profiling methods, and bioinformatics tools of miRNA profiling. Additionally, we will discuss the role of mi RNA and its applications. miRNA biogenesis can be broadly categorized into canonical and non-canonical pathways. There are various sample types and miRNA extraction procedures. miRNA sequences, once extracted, can be subjected to various computational tools that may aid in understanding its structure and functions. However, few researchers have suggested that there is still scope for developing these tools with appropriate algorithms for avoiding false positive results.

The simultaneous genetic change in interacting species as a result of mutually imposed natural selection is known as co-evolution. Many biologists believe that the process of co-evolution between plants and the biota that surrounds them, which includes mammals, viruses, fungus, bacteria, insects, and nematodes, is responsible for most of the earth’s biological variety. While most of the debate around plant co-evolution focuses on one host–one pathogen, two or more hosts–one pathogen, and one host–two contrasting co-occurring pathogens single plant–pathogen interactions, a wide range of many other micro-and macroevolutionary processes occur concurrently in a single plant, posing the issue of whether or not we should even discuss it. In this review, we have discussed the framework of co-evolution theory, as well as the complexities of investigating co-evolution in natural conditions.

The main food of half the world’s population is rice, Oryza sativa L. By 2030, rice production must increase by at least 25% to meet global food demand of ever growing human population. In order to reduce the impact of climate change and arable land loss and ensure stable global food supplies, accelerated genetic gains during rice improvement are highly required. Since this process is complicated, we first need to have detail information regarding the genetic diversity of the oryza sp. gene pool, the correlation of diverse alleles with essential traits of rice, and the systematic use of the rich genetic diversity through employing methods that adopt expertise in rice improvements through breeding strategies. Considering this, in 2014, an international sequencing project of 3000 rice genomes was published. These details information may help us to detect novel alleles associated with important phenotypes of rice by employing various bioinformatics or genetic methods. It will also help us to unmask the O. Sativa genomic diversity more precisely. This project also encouraging the global rice community to employ data present in the 3000 rice genomes project for establishing various global public rice genetic/genomic database, which in turn will promote rice-breeding technology in the future. Thus, in this chapter, authors made an attempt to understand in brief about the various germplasms employed in 3000 genome project and the genetic diversity of O. sativa, which, in the near future, may help us to increase grain yield of rice.

Food security is a significant challenge for emerging and underdeveloped countries all over the world. The world’s population is growing tremendously; therefore, more food will be required to fulfill their needs. As a result, farmers and breeders put more pressure on agricultural land to produce more food grains. Rice is a primary food crop for 1.3 billion people globally, with Asia contributing for 90% of rice production and consumption. In undeveloped nations, the majority of farmers rely on traditional farming methods, which are insufficient to meet rising food grain demand. Their poor farming methods must be wreaking havoc on the land by utilizing more harmful pesticides and chemical fertilizers. As a result, a destructive impact on soil microfauna and flora affects soil nutritional quality, agricultural practices, and the soil becomes barren with the loss of fertility. In the future, agriculture automation is a big concern for all countries to feed and provide food security to their population. This book chapter focuses on evaluating and exploring a variety of published solutions, methods, and perspectives on “artificial intelligence (AI),” “machinelearning”, “big data applications”, and “high-performance computing,” which have opened up new avenues for data-intensive research (ML) in multidisciplinary agro-technology. Thus, there is an immediate need for various automation methods to decode rice and other crop cultivation problems, farm management, agricultural practices, irrigation, fertilization, weeding, harvesting, marketing, food distributions, toxic pesticides, regulated drainage, emissions regulation, and environmental impact. The automation of farming activities will help to improve soil benefits and strengthening soil fertility. Therefore, the advancing practical application and solutions pave the way for a modern data-centric age of agriculture exploration.

The genome-wide association study (GWAS) is one of the potential approaches for identifying QTLs/genes and complex traits associated with target traits quickly using natural variations. With the advancement in genome sequencing technology, it is possible to examine genome-wide genetic variants of agro-morphological, physiological, biochemical, and molecular traits across diverse genetic materials. In nature, natural variants of crops are generated due to spontaneous mutations and manual breeding in the wild progenitors. Traditional landraces are adapted to various environmental conditions, rich sources of alleles, and genes linked with various traits valuable for variety improvement through molecular breeding because of the availability of high-throughput sequencing technologies and a reference genome sequence, accurate re-sequencing of a significant number of crop genomes as possible. It aided in understanding the genetic basis of phenotypic variance and allows for functional studies of evolutionary changes in crops. This rapid development will significantly improve crop design research using genomics-assisted breeding, allowing it to be used in gene recognition, cloning, QTL identification, and crop breeding using marker-assisted selection or genetic engineering. This book chapter presents an overview of the entire process of a typical GWAS, various software applications and, its limitations, and future perspectives.

High-throughput methods are rapidly becoming prevalent in biological sciences and clinical studies. Thus, more rigorous statistical techniques are required to accurately predict the resulting big datasets. In this chapter, the authors attempted to understand how statisticians develop and employ various strategies to investigate and analyze these big datasets. Results obtained revealed that, to date, several statistical methods have been developed for analyzing large-scale biological data, like multiple testing, unsupervised learning and data visualization, clustering, and bootstrapping. However, a larger dataset analysis often faces various challenges, like multiple comparisons, high dimensionality, small n and large p problem, noise, and heterogeneous information. Additionally, while statisticians play an essential role in numerous bioinformatics studies, most of them are only interested in obtained and preprocessed data. Few researchers have proposed that statistician participation in the initial data collection and preprocessing phase will minimize errors and contribute to more critical scientific conclusions.

Microsatellites (MS) or simple sequence repeats (SSRs) is a DNA sequence set comprising of tandemly repeated motifs. SSRs with codominant inheritance, higher amounts, moderately conservative flanking sequences, and rich polymorphism are commonly distributed throughout the plants and animals’ genome. MS has already been employed in several crop plants for determining their seed lots’ genetic integrity and to evaluate the capacity of plant varieties to defend their intellectual property. Thus, the key objective of this chapter is to include a revised and comprehensive overview of the SSR marker and its applications in various biological domains. Additionally, we have discussed genomic occurrence and the advantage/disadvantages of employing microsatellites as genetic markers in agricultural research.

Plant domestication has significantly influenced the growth of human society. The domestication of rice lists amongst the most significant historical breakthroughs. However, the sources and domestication methods are debatable. Thus, in this chapter, authors attempted to understand in brief about genetic diversity in rice, as well as a description of the processes about the domestication of rice began and at which location rice was domesticated. Information retrieved from the published literature to date suggests that the rice genus Oryza is a small genus comprising about 25 species, but it has incredible adaptive capabilities to differing ecological circumstances. Within the genus Oryza, two distinct domestication events have occurred—one in Asia and another in Africa. In Asia, wild rice O. rufipogon is popular, which was cultivated about 9000 years ago. In Africa, the wild rice Oryza, namely O. glaberrima, was independently domesticated around 3000 years ago. During domestication, plant experience decreased nucleotide diversity, enhanced linkage disequilibrium and modified population frequencies of polymorphic nucleotides within the domestication-related genes. In the near future, the information presented in this chapter may aid in enhancing rice’s yield.

Rice production is severely threatened by drought stress in Eastern India. To develop drought tolerant varieties, selection of donors for breeding programme is crucial. Twenty one selected rice genotypes including both tolerant and sensitive to drought were grown under well-watered and drought stress conditions in dry seasons of two successive years of 2017 and 2018. Leaf water potential, relative water content displayed significant difference among the genotypes during vegetative screening. At reproductive stage drought screening, days to 50% flowering was delayed in all genotypes except N22 and Anjali (showed early flowering) however grain yield and other yield related traits decreased significantly compared to well watered condition. Correlation analysis of phenological and yield related traits with grain yield revealed that tiller numbers and panicle numbers are highly correlated with grain yield both under well-watered and water stress conditions and contributes maximum towards grain yield. The dendrogram grouped Mahamaya, Sahabhagidhan, Poornima, IBD 1, Hazaridhan, Samleshwari and Danteshwari into one cluster which performed better under water stress conditions and had grain yield more than 1.69 tha−1. Sahabhagidhan, Poornima, Vandana, and N22 displayed tolerance to drought both under vegetative and reproductive conditions which could be a good selection for the breeders to develop drought tolerant rice cultivars for eastern region of India.

Drought is the most catastrophic abiotic stress that affect plant growth and development. Plant initiates alterations at physiological, biochemical and molecular levels to combat deleterious effects of drought in which abscisic acid, play a pivotal role. The present investigation was conducted to investigate how abscisic acid modulates different biochemical traits under osmotic stress. Twenty-one days old seedlings were exposed to osmotic stress, exogenously applied abscisic acid and the combination of both osmotic stress and abscisic acid to study the influence of abscisic acid on biochemical traits under moisture stress. The results reveals that abscisic acid has no influence on leaf water potential, however, it has active role in increasing osmolytes like proline. Δ1-pyrroline-5-carboxylate reductase and proline dehydrogenase remained unresponsiveness to ABA that indicates ABA-independent regulation of these two enzymes but the activity of Δ1-pyrroline-5-carboxylate synthesase and Ornithine-δ-aminotransferase was enhanced (3 fold) in response to abscisic acid. Antioxidative enzymes increased (catalase-2.5 fold; peroxidase-3 fold and SOD-6 fold) in response to abscisic acid and osmotic stress, gave an insight of the regulation of these traits under the influence of abscisic acid. Aldehyde oxidase remained unresponsive to abscisic acid but showed enhanced expression when both abscisic acid and osmotic stress was applied suggest its ABA-independent regulation. The information provided here has significance in understanding the regulation of different catabolite that play important role in drought tolerance and can be implemented for the development of drought-tolerant varieties.

The experiment was carried out to examine the effect of moisture stress (drought) on different root traits, rooting pattern and to identify important root morphological markers contributing to stress tolerance. Moisture stress was imposed on 21 days old seedlings of selected rice genotypes grown in PVC cylinders. Samplings were done at 10 and 20 days after imposition of stress to examine the effect of stress on root growth and development. Results revealed that rate of assimilate allocation impeded more under 20 days stress (severe) than in 10 days stress (moderate) in terms of enhanced root: shoot ratio, maximum root length to shoot length ratio and root volume. Magnitude of trait variation among genotypes was more pronounced under severe stress than under moderate stress. The root: shoot ratio, maximum root length to shoot length ratio and root volume were observed to be the most crucial morphological markers in determining drought tolerance in rice genotypes analyzed through biplot analysis. Among the thirteen genotypes tested, AC-42994, AC-42997, AC-43020, CR-143-2-2, Ronga Bora and Bora were found to possess desirable root traits and these genotypes can be used in the breeding programme for enhancing drought tolerance in rice.