Browsing by Author "Partha Pratim Behera"

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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.
Genetic Mapping of Valued Genes with Significant Traits in Crop Plants: Basic Principles, Current Practices, and Future Perspectives
(wiley, 2024) Prasanta Kumar Majhi; Akansha Guru; Suma C. Mogali; Prachi Pattnaik; Ritik Digamber Bisane; Lopamudra Singha; Partha Pratim Behera; Prateek Ranjan Behera
The identification of the genetic underpinnings of quantitative traits continues to pose a significant hurdle in the realm of crop enhancement. The combined utilization of linkage mapping and association mapping methodologies in genetic mapping has proven to be effective in dissecting complicated traits in several crop species. The primary distinction between the two techniques is in the utilization of mapping populations to identify marker-trait associations, which subsequently facilitates the identification of quantitative trait loci (QTLs). This differentiation directly impacts the resolution and power of the mapping process. To find QTLs for marker-assisted selection (MAS), gene tagging, and a deeper comprehension of the genetics of complex characteristics, genetically related DNA markers are crucial. In the past, two groups, namely family-based and natural population-based mapping populations, were utilized for QTL mapping. The use of multiparent advanced generation inter-cross and nested association mapping, two cutting-edge mapping populations, for QTL fine mapping is currently widespread in the field of agricultural development. In addition to accelerating computation and detection, these populations are illuminating new study areas including large-scale metagenome analysis for crop genetic mapping. For the effective application of high-throughput genotyping technologies in the age of next-generation sequencing, suitable population design, enhanced statistical techniques, and trustworthy phenotyping have become crucial. © 2024 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.