Browsing by Author "Shravan Kumar Singh"

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Future-Proofing Plants Against Climate Change: A Path to Ensure Sustainable Food Systems
(Springer International Publishing, 2022) Prasanta Kumar Majhi; Basit Raza; Partha Pratim Behera; Shravan Kumar Singh; Aalok Shiv; Suma C. Mogali; Tanmaya Kumar Bhoi; Biswaranjan Patra; Biswaranjan Behera
Climate change has altered the pattern of rainfall, temperature, carbon dioxide (CO2) levels, and emission of greenhouse gases, which result in the frequency and severity of extreme events such as drought, flood, salinity, heavy metal stress, nutrient stress, new diseases, and insect pest. This significantly impacts agriculture production, food security, livelihoods, and nutrition. Worldwide, millions of people are affected due to the consequence of climate change and particularly become the most vulnerable, by increasing the frequency and virulence of extreme meteorological events that cause population displacement and reduction in agricultural productivity. A paradigm shift toward more resilient, productive, and sustainable agriculture and food systems is required. The world must act immediately act on it to put an end to hunger and malnutrition. To ensure rapid and advanced agricultural development in a short period, precision farming practices and smart breeding strategies need to follow; such as machine learning, deep learning, big data analysis, remote sensing, artificial intelligence, system biology study, genomic prediction, speed breeding, and haplotype breeding. These techniques can prove the future plants against climate variability with increased yield potential and improved resilience to achieve the goal of resilient climate agriculture. © Springer Nature Switzerland AG 2023.
Genetic gain and selection of stable genotypes in high zinc rice using AMMI and BLUP based stability methods
(United Arab Emirates University, 2023) Partha Pratim Behera; Shravan Kumar Singh; Prasanta Kumar Majhi; Kasireddy Sivasankarreddy; Bodeddula Jaysankar Reddy; Nayanmoni Borah; Niharika Saharia; Ramendra Nath Sarma
Rice is the staple food of almost half of the world’s population, impacting nutrition especially in children, pregnant women, and nursing mothers. Because the traits were quantitatively inherited, they are affected by changes in location and year. A RBD with three replications was used to identify superior and stable high-zinc rice genotypes in Uttar Pradesh, India. Grain zinc content (GZC) is negatively correlated with grain yield using genetic association study. There was a significant G × E interaction (GEI) and V16 and V21 for GYP and V9, V2 and V10 for GZC were identified as stable based on the AMMI model and bi-plot. V11, V5, V21 for grain yield per plant (GYP) and for GZC, V14, and V10 are found to be stable and common in all AMMI stability parameters. V6, V13 and V5 for GYP and V10, V8 and V2 for GZC were identified as stable based on the mean vs. WAASB bi-plot. V21 for GYP and V4 for GZC was the highest yielder and widely adaptable based on WAASBY scores. V13 for GYP and V1 for GZC were all-time winners. V13 and V1 have the highest predicted mean for GYP and GZC, respectively, based on BLUP. V6, V21and V13 were identified as stable and selected based on the multi-trait stability index (MTSI). These selected genotypes selected through BLUP-based stability methods, MTSI, and strength and weakness plots make it easier to evaluate and select genotypes for varietal recommendations and future Zn-fortified rice breeding studies. © 2023, United Arab Emirates University. All rights reserved.
Genotype × environment interactions for grain iron and zinc content in rice
(John Wiley and Sons Ltd, 2020) Shilpa M Naik; Anitha K Raman; Minnuru Nagamallika; Challa Venkateshwarlu; Suresh Prasad Singh; Santosh Kumar; Shravan Kumar Singh; A. Tomizuddin; Sankar Prasad Das; Krishna Prasad; Tajwar Izhar; Nimmai P Mandal; Nitendra Kumar Singh; Shailesh Yadav; Russell Reinke; Ballagere Prabhu Mallikarjuna Swamy; Parminder Virk; Arvind Kumar
BACKGROUND: Nutrient deficiency in humans, especially in children and lactating women, is a major concern. Increasing the micronutrient concentration in staple crops like rice is one way to overcome this. The micronutrient content in rice, especially the iron (Fe) and zinc (Zn) content, is highly variable. The identification of rice genotypes in which there are naturally high Fe and Zn concentrations across environments is an important target towards the production of biofortified rice. RESULTS: Phenotypic correlations between grain Fe and Zn content were positive and significant in all environments but a significant negative association was observed between grain yield and grain Fe and Zn. Promising breeding lines with higher Zn or Fe content, or both, were: IR 82475-110-2-2-1-2 (Zn: 20.24–37.33 mg kg−1; Fe: 7.47–14.65 mg kg−1); IR 83294-66-2-2-3-2 (Zn: 22–37–41.97 mg kg−1; Fe: 9.43–17.16); IR 83668-35-2-2-2 (Zn: 27.15–42.73 mg kg−1; Fe: 6.01–14.71); IR 68144-2B-2-2-3-1-166 (Zn: 23.53–40.30 mg kg−1; Fe: 10.53–17.80 mg kg−1) and RP Bio 5478-185M7 (Zn: 22.60–40.07 mg kg−1; Fe: 7.64–14.73 mg kg−1). Among these, IR82475-110-2-2-1-2 (Zn: 20.24–37.33 mg kg−1; Fe: 7.47–14.65 mg kg−1) is also high yielding with 3.75 t ha−1. Kelhrie Cha (Zn: 17.76–36.45 mg kg−1; Fe: 7.17–14.77 mg kg−1), Dzuluorhe (Zn: 17.48–39.68 mg kg−1; Fe: 7.89–19.90 mg kg−1), Nedu (Zn: 18.97–43.55 mg kg−1 Fe: 8.01–19.51 mg kg−1), Kuhusoi-Ri-Sareku (Zn: 17.37–44.14 mg kg−1; Fe: 8.99–14.30 mg kg−1) and Mima (Zn: 17.10–45.64 mg kg−1; Fe: 9.97–17.40 mg kg−1) were traditional donor genotypes that possessed both high grain Fe and high Zn content. CONCLUSION: Significant genotype × location (G × L) effects were observed in all traits except Fe. Genetic variance was significant and was considerably larger than the variance of G × L for grain Zn and Fe content traits, except grain yield. The G × L × year variance component was significant in all cases. © 2020 Society of Chemical Industry. © 2020 Society of Chemical Industry
Mapping of QTL conferring resistance to Turcicum Leaf Blight using Microsatellites in Maize (Zea mays L.)
(Crea Journals, 2024) Dan Singh Jakhar; Rajesh Singh; Shravan Kumar Singh
Turcicum Leaf Blight (TLB), caused by Exserohilum turcicum is a foliar disease of maize. This study was conducted to identify quantitative trait loci (QTL) for TLB resistance in maize. A mapping population constituting 185 F2:3 populations was developed by crossing two inbred lines viz., CM 212 (susceptible) and V 336 (resistant), and was evaluated in two environments to generate phenotypic data for QTL mapping. A polymorphic survey of 183 pairs of simple sequence repeat (SSR) or microsatellite primers between the two parents helped in identification of 101 polymorphic markers. Data on four disease severity traits viz., Percent Disease Index (PDI), Area Under Disease Progress Curve based on PDI (AUDPC-PDI), Lesion Area (LA), and Area Under Disease Progress Curve based on LA (AUDPC-LA) were generated for QTL mapping. Eight QTL intervals for resistance to TLB were located on chromosomes 2, 3, 4, 5, 7 and 9. Out of the eight QTL; one QTL was reported for LA on chromosome 4 flanking phi019 and bnlg2162 markers at the low disease-pressure environment (E1), six QTL at high disease-pressure environment (E2) and one QTL across pooled environments. Out of the six QTL identified at high disease-pressure environment, one QTL for AUDPC-PDI was identified on chromosome 9 flanked by markers phi065 and phi016 while the remaining five QTL for LA were identified on chromosomes 2, 3, 5 and 7. One QTL for PDI was identified across environments analysis on chromosome 3 flanked by markers mmc0071 and bnlg1160. For these QTL, the LOD values ranged from 2.70 to 14.84 and corresponding R2 (% variation explained) ranged from 12.96 to 18.98 % in the individual environments. All QTL showed overdominance gene action except QTL 4 (dominance) at their respective chromosome. © 2024, Crea Journals. All rights reserved.
Molecular fingerprinting of highly resistant maize lines to turcicum leaf blight
(Cambridge University Press, 2024) Dan Singh Jakhar; Rajesh Singh; Shravan Kumar Singh
The present study generates information related to the molecular divergence between turcicum leaf blight (TLB)-resistant and -susceptible lines. During molecular diversity studies, a total of 212 alleles were detected at 75 marker loci and ranged from two to five with an average of 2.83 alleles per locus. A direct correlation for the number of alleles and polymorphism information content (PIC) values was ascertained. For instance, marker phi123 produced high number of alleles (5) with PIC values of 0.77. Using the DARwin 6.0 programme, the UPGMA dendrogram grouped 40 maize inbreds into two distinct clusters, cluster-I (36 inbreds) and cluster-II (4 inbreds). Cluster-I contained two subclusters; the first subcluster contained 28 inbreds and the second subcluster contained eight inbreds whereas cluster-II contained four inbreds. This major cluster-II was further classified into two subclusters which contained two inbreds each. Most of the inbred lines except V-25 from cluster-II were highly resistant to TLB disease. These inbred lines can be used in crossing programmes to develop TLB-resistant hybrids by using divergent parents. In this study, allelic diversity and PIC values indicated a good efficiency of markers for studying the polymorphism level available in studied inbred lines. High level of diversity among the inbreds detected with simple sequence repeat markers indicated their suitability for the further breeding programme. Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of National Institute of Agricultural Botany.
Multivariate stability analysis to select elite rice (Oryza sativa L.) genotypes for grain yield, zinc and Iron
(Nature Research, 2025) Akansha K. Singh; Dhirendra Kumar Singh; Shravan Kumar Singh; Vikas Kumar Singh; Arvind Kumar
The present study was conducted to evaluate 30 rice genotypes at three different locations in eastern Uttar Pradesh during the Wet- 2020–21 and determine the impact of GEI on grain yield (tha-1), days to 50% flowering, grain Fe content (PPM), and grain Zn content (PPM). The study also aimed to identify the genotypes that displayed the best performance according to the multi-trait stability index (MTSI), multi-trait genotype-ideotype distance index (MGIDI), and factor analysis and ideotype-design (FAI-BLUP) index. AMMI analysis demonstrated significant variation for environment (E), genotype (G), and genotype-by-environment interaction (GEI) (P < 0.01) for all the studied traits. The AMMI1 biplot showed that PC1 explained the majority of the variation for GY (77.6%), DTF (90.5%), Fe (73.5%), and Zn (86.8%), helping to identify stable and high-performing genotypes. AMMI2 biplot further resolved complex GEI patterns, highlighting genotypes with specific adaptability to individual environments. The GGE biplot revealed clear “which-won-where” patterns for GY, DTF, Fe, and Zn, explaining 94.37%, 99.71%, 83.49%, and 96.93% of GEI variation, respectively. BLUP analysis using a linear mixed model revealed significant GEI effects for GY, DTF, Fe, and Zn across 30 rice genotypes in three environments. Low heritability was observed for Fe (28.2%) and moderate for GY (54.4%) and Zn (56.4%), while DTF showed high heritability with strong genotypic accuracy. Genotype G7 was identified as stable, early, high-yielding, and rich in Fe based on HMGV, RPGV, and HMRPGV indices. The MTSI, MGIDI and FAI-BLUP analysis revealed that BHU-SKS-1 (G15) and IR105696 -1–2-3–1-1–1 -B (G9) were the most stable and best mean performer for high grain yield and high grain Fe & Zn content, while IR 108,195–3-1–1-2 (G7) was the most stable and best mean performer for high grain yield and high grain Fe content with early flowering. © The Author(s) 2025.
Nanotechnology Interventions for Sustainable Plant Nutrition and Biosensing
(Springer Science and Business Media Deutschland GmbH, 2024) Akansha Singh; Priti Upadhyay; Esha Rami; Shravan Kumar Singh
The application of nanotechnology in agriculture is driven by the pressing need to meet the increasing global demand for food production. Nanoparticles, owing to their incredibly small size, bridge the gap between macroscopic materials and atomic or molecular structures, making them ideal for various agricultural applications. They have the potential to revolutionize conventional farming practices by optimizing nutrient utilization, resource management, and environmental sustainability. The impact of nanotechnology on agriculture spans a wide range of areas, including nutrient delivery, pest management, soil fertility improvement, precision farming, water management, post-harvest preservation, environmental sustainability, smart delivery systems, genetic modification, and nanofertilizers (NFs). NFs, in particular, have garnered attention for their ability to improve nutrient delivery and enhance crop development, while minimizing environmental harm and reducing costs compared to traditional fertilizers. These nano-sized nutrients significantly enhance nutrient bioavailability to plants, ultimately promoting crop growth and yield. However, the application of nanomaterials in agriculture also raises concerns regarding their potential impact on soil microbial diversity, which plays a crucial role in maintaining soil health. In addition to NFs, this article discusses the role of carbon nanotubes (CNTs) in agriculture. CNTs possess unique properties that can improve plant growth, root development, and resistance to salinity and disease. Furthermore, the article also deals with nanobiosensors and their application in precision agriculture. Moreover, this article addresses the importance of considering the toxicity, biosafety, and regulatory aspects when implementing nanotechnology in agriculture to maximize its potential benefits while safeguarding natural and environmental resources. © The Author(s) under exclusive licence to Sociedad Chilena de la Ciencia del Suelo 2024.
Proofing direct-seeded rice with better root plasticity and architecture
(MDPI, 2021) Siddharth Panda; Prasanta Kumar Majhi; Annamalai Anandan; Anumalla Mahender; Sumanth Veludandi; Debendranath Bastia; Suresh Babu Guttala; Shravan Kumar Singh; Sanjoy Saha; Jauhar Ali
The underground reserve (root) has been an uncharted research territory with its untapped genetic variation yet to be exploited. Identifying ideal traits and breeding new rice varieties with efficient root system architecture (RSA) has great potential to increase resource-use efficiency and grain yield, especially under direct-seeded rice, by adapting to aerobic soil conditions. In this review, we tried to mine the available research information on the direct-seeded rice (DSR) root system to highlight the requirements of different root traits such as root architecture, length, number, density, thickness, diameter, and angle that play a pivotal role in determining the uptake of nutrients and moisture at different stages of plant growth. RSA also faces several stresses, due to excess or deficiency of moisture and nutrients, low or high temperature, or saline conditions. To counteract these hindrances, adaptation in response to stress becomes essential. Candidate genes such as early root growth enhancer PSTOL1, surface rooting QTL qSOR1, deep rooting gene DRO1, and numerous transporters for their respective nutrients and stress-responsive factors have been identified and validated under different circumstances. Identifying the desired QTLs and transporters underlying these traits and then designing an ideal root architecture can help in developing a suitable DSR cultivar and aid in further advancement in this direction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
QTL mapping reveals different set of candidate genes governing stable and location specific QTLs enhancing zinc and iron content in rice
(Springer Science and Business Media B.V., 2024) Sonali Vijay Habde; Shravan Kumar Singh; Dhirendra Kumar Singh; Arun Kumar Singh; Rameswar Prasad Sah; Mounika Korada; Amrutlal R. Khaire; Prasanta Kumar Majhi; Uma Maheshwar Singh; Vikas Kumar Singh; Arvind Kumar
Rice is a cornerstone of global food security. Addressing contemporary dual challenge of global food and nutritional security, this study focuses on identification of genomic regions/QTLs that control uptake and translocation of micronutrients (Zn and Fe) in rice. Using mapping population derived from a cross between URG 30 (Zn 32.2 ppm and Fe 15.3 ppm) and Rajendra Kasturi (Zn 19.2 ppm and Fe 9.5 ppm), evaluated at three locations, we identified 8 QTLs each for grain Zn and Fe content distributed across 8 chromosomes. Notably 3 major QTLs for grain Zn content (chromosomes 1, 5 and 6) and 1 major QTL for grain Fe content (chromosome 3) were identified with phenotypic variance (PV) ranging from 10.7 to 15.19% for Zn and 12.10% for Fe. Two stable QTLs for grain Zn content (PV 5.89–8.98% and 3.18–13.62%) and one for Fe content (PV 1.84–7.59%) were consistently identified at two locations. Seven transgressive segregants for yield and Zn content are identified at two locations. Correlation analysis uncovered significant positive associations between grain Zn and Fe content. We also interpreted the functional role of 24 candidate genes including key candidates OsZIP1, OsSPR1, OsZIP2, OsPEZ1, OsZIP6, OsNAS1, OsNAS2, OsYSL3 underlying stable and location specific QTLs in the context of mineral uptake strategies utilized by rice. The research supports marker assisted breeding efforts towards substantial nutritional enhancement in rice grain. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.
QTLS MAPPING FOR TURCICUM LEAF BLIGHT RESISTANCE IN MAIZE (ZEA MAYS L.)
(Bangladesh Botanical Society, 2022) Dan Singh Jakhar; Rajesh Singh; Shravan Kumar Singh
Turcicum leaf blight (TLB) is a prevalent maize disease found throughout the world, including India. To identify quantitative trait loci (QTLs) for TLB resistance with the help of appropriate mapping populations, namely F2:3 families and F2:6 families or recombinant inbred lines (RILs) evaluated in two environments were investigated. The cross CM 212 × V 336 was selected to generate the F2:3 families and the established F2:3 mapping population of cross CM 212 x CM 145 was further advanced to generate the F2:6 families for construction of linkage map and mapping of QTLs. The present investigation identified a total of 23 QTLs for TLB resistance in maize. Out of these QTLs, Nine QTLs were found in Linkage Group 4 (LG 4), followed by four QTLs in LG 2, two QTLs in each of LG 1, 3, 5 and 9 and one QTL in each of LG 6 and 7. On the other hand, the LOD values for these QTLs ranged from 2.64 to 14.84 in individual environments and over environments for both mapping populations, while the associated R2 values ranged from 10.80 to 18.98. The majority of the QTLs had over dominance at their respective chromosomes due to gene action. © 2022 Bangladesh Botanical Society. All rights reserved.
SpeedFlower: a comprehensive speed breeding protocol for indica and japonica rice
(John Wiley and Sons Inc, 2024) Pramod Gorakhanath Kabade; Shilpi Dixit; Uma Maheshwar Singh; Shamshad Alam; Sankalp Bhosale; Sanjay Kumar; Shravan Kumar Singh; Jyothi Badri; Nadimpalli Rama Gopala Varma; Sanjay Chetia; Rakesh Singh; Sharat Kumar Pradhan; Shubha Banerjee; Rupesh Deshmukh; Suresh Prasad Singh; Sanjay Kalia; Tilak Raj Sharma; Sudhanshu Singh; Hans Bhardwaj; Ajay Kohli; Arvind Kumar; Pallavi Sinha; Vikas Kumar Singh
To increase rice yields and feed billions of people, it is essential to enhance genetic gains. However, the development of new varieties is hindered by longer generation times and seasonal constraints. To address these limitations, a speed breeding facility has been established and a robust speed breeding protocol, SpeedFlower is developed that allows growing 4–5 generations of indica and/or japonica rice in a year. Our findings reveal that a high red-to-blue (2R > 1B) spectrum ratio, followed by green, yellow and far-red (FR) light, along with a 24-h long day (LD) photoperiod for the initial 15 days of the vegetative phase, facilitated early flowering. This is further enhanced by 10-h short day (SD) photoperiod in the later stage and day and night temperatures of 32/30 °C, along with 65% humidity facilitated early flowering ranging from 52 to 60 days at high light intensity (800 μmol m−2 s−1). Additionally, the use of prematurely harvested seeds and gibberellic acid treatment reduced the maturity duration by 50%. Further, SpeedFlower was validated on a diverse subset of 198 rice accessions from 3K RGP panel encompassing all 12 distinct groups of Oryza sativa L. classes. Our results confirmed that using SpeedFlower one generation can be achieved within 58–71 days resulting in 5.1–6.3 generations per year across the 12 sub-groups. This breakthrough enables us to enhance genetic gain, which could feed half of the world's population dependent on rice. © 2023 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.
Yield attributing traits of high zinc rice (Oryza sativa L.) genotypes with special reference to principal component analysis
(Action for Sustainable Efficacious development and Awareness, 2022) Partha Pratim Behera; Shravan Kumar Singh; Kasireddy Sivasankarreddy; Prasanta Kumar Majhi; Bodeddula Jayasankar Reddy; Dhirendra Kumar Singh
Total 21 high zinc rice genotypes were evaluated under five different locations for 14 different yield attributing traits, including grain yield/plant (gm) to determine the pattern of variation, the relationship among the individuals and their characteristics through Principal Component Analysis (PCA) during the Kharif-2017. PCA was done for all the locations individually as well as pooled analysis for all locations using R software. Out of the 14 PCs, the initial four PCs contributed more to the total variability. The highest cumulative variability of the first four PCs found at Bhikaripur (81.11%) followed by BHU Agriculture research farm-II (79.23%) etc. and Pooled variability was 76.61%. Pooled data analysis indicates PCA biplot or loading plot of first two principal components revealed that days to maturity, days to 1st flowering date and days to 50% flowering loaded more on the first component and number of spikelets per panicles, number of grains/panicles, grain weight per panicle, grain yield/plant accounted more variation in the second component compared to the other parameters. Thus, the pooled analysis of principal component analysis revealed the characters contributing to the variation and genetic variability that exists in these rice genotypes. This is because the genotypes BRRIdhan 72, Sambamahsuri and Swarna were identified in different principle components related to grain yield and grain quality, and were also located farthest away from biplot origin in individual PCA based biplot. So they may be employed to improve yield attributing factors like total effective tiller number. PC1, PC2 and PC3 have days to first flowering and days to 50% flowering, hence their genotypes may be valuable in producing early maturing cultivars. Thus, the results revealed that wide range of variability was shown by different traits of the genotypes which can be utilized in rice improvement programmes. © ASEA.