Genetic Variability, Character Association, Path Coefficient, and Diversity Analysis of Rice ( Oryza sativa L.) Genotypes Based on Agro‐Morphological Traits

• Published in: International journal of agronomy 2024-08, Vol.2024 (1)
• Main Authors: Singh Yadav, Shubh Pravat, Bhandari, Sujan, Ghimire, Netra Prasad, Mehata, Dipesh Kumar, Majhi, Soni Kumari, Bhattarai, Susmita, Shrestha, Samaz, Yadav, Bishnu, Chaudhary, Pratima, Bhujel, Sangita
• Format: Article
• Language: English
• Subjects: Agricultural production; Agricultural research; Cluster analysis; Coefficient of variation; Correlation analysis; Crop yield; Cross-breeding; Cultivars; Flowering; Genetic analysis; Genetic diversity; Genetic engineering; Genetic improvement; Genetic variability; Genotypes; Germplasm; Grain; Heritability; Heterogeneity; Investigations; Morphology; Phenotypic variations; Plant breeding; Principal components analysis; Research facilities; Rice; Seeds; Testing laboratories; Variance analysis
• ISSN: 1687-8159; 1687-8167
• DOI: 10.1155/2024/9946332

Any program aimed at improving rice quality must investigate and comprehend the variety and heterogeneity of germplasm genotypes. Genetic variability analysis aids the breeders in determining the best criteria and selection procedure to be utilized in order to enhance the desired attributes. Twenty genetic materials were used in a two‐year (2021‐2022) field experiment at the G.P. Koirala College of Agriculture and Research Center in Morang, Nepal. This study aimed to determine the genetic diversity by principal component analysis (PCA) and cluster analysis, as well as to assess the path coefficient (PC), genetic deviation, and character association based on grain yield (GY) and other yield‐attributing variables. For all the parameters under study, variance analysis across all genotypes showed substantial differences ( P < 0.001), suggesting a higher level of genetic variability for selection purposes. The higher phenotypic and genotypic coefficient of variation (PCV and GCV) were observed for yield‐related traits, including grains per panicle (GP), straw yield (SY), harvest index (HI), days to flowering (DF), and test weight (TW). Higher genetic advance as a percentage of the mean (GAM) and higher heritability () for each attribute indicated nonadditive gene activity and highlighted that selection would likely be successful. According to the correlation analysis, selection based on days to maturity (DM), TW, HI, tiller per m 2 (TM), and effective tiller per hill (ETH) will be beneficial for raising rice GY. The results of PCA and PC indicated that the direct selection of DM, ETH, TW, and HI would be productive in improving the GY of rice in future breeding projects. The results of the cluster analysis divided the 20 rice genotypes into seven groups, with Cluster VII with genotype Swarna Sub‐1 for GY and HI; Cluster I with genotypes NR2188‐13‐5‐2‐5‐1, Radha‐13, and Bas Dhan for PH and SY; Cluster VI with genotypes NR2187‐33‐1‐2‐1‐1‐1 and Ranjeet for DF and DM; and Cluster V with genotypes NR2182‐33‐3‐2‐1‐1‐1 and NR2192‐16‐1‐1‐1‐1 for TM and PL being chosen as the best ones. Upon confirmation, these genotypes can be recommended for commercial release or used as prospective breeding material in cross‐breeding initiatives aimed at producing cultivars possessing desirable homogeneous features.