Multivariate Analysis to Study Genetic Diversity for Yield and its Attributing Traits in Rice (Oryza sativa L.)
Suraj Kumar
*
Department of Genetics and Plant Breeding, College of Agriculture, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, Uttar Pradesh 224001, India.
S. C. Vimal
Department of Seed science and technology, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, Uttar Pradesh-224001, India.
Rajendra Prasad Meena
Department of Genetics and Plant Breeding, College of Agriculture, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, Uttar Pradesh 224001, India.
Lalu Prasad
Department of Vegetable Science Acharya Narendra Deva University of Agriculture and Technology Kumarganj Ayodhya up, India.
Suraj Luthra
Department of Vegetable Science Acharya Narendra Deva University of Agriculture and Technology Kumarganj Ayodhya up, India.
Banoth Srikanth
Department of Genetics and Plant Breeding, College of Agriculture, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, Uttar Pradesh 224001, India.
Alok Kumar
Department of Entomology, Chandra Shekhar Azad University of Agriculture and Technology Kanpur, 208002, India.
Ramjee Kumar Pal
Department of Genetics and Plant Breeding, College of Agriculture, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, Uttar Pradesh 224001, India.
*Author to whom correspondence should be addressed.
Abstract
The present investigation was carried out with 72 germplasm lines and three checks of rice (Oryza sativa L.) were grown at Crop Research Station, Masodha conducted in the GPB farm, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya (U.P.) during Kharif June 2022- February 2023. Data on 10 characters, including grain yield per plant focused on diversity and PCA analysis. This investigation involved the analysis of 72 rice germplasm lines alongside three checks, showcasing extensive variation in agronomic and morphological traits. The study utilized Non-hierarchical Euclidean cluster analysis to assess genetic diversity. The pseudo F-test determined the optimal grouping of 75 genotypes into six distinct clusters. Cluster distribution revealed varying genotype compositions, with Cluster V comprising the highest entries (20), followed by Clusters I, VI, and II. Intra- and inter-cluster distances illustrated significant variability among clusters, emphasizing genetic diversity. Examining agronomic traits across these clusters revealed noteworthy variations in days to 50% flowering, days to maturity, plant height, and productive tillers. Panicle length, flag leaf area, biological yield, harvest index, 1000-grain weight, and grain yield per plant also exhibited cluster-specific variations. These findings provide valuable insights for rice breeding programs, facilitating targeted enhancements of specific agronomic traits within the rice population, thus contributing to the development of more resilient and productive rice varieties.
Keywords: Diversity, cluster, F test, PCA, euclidean