Genetic Diversity Estimation of Rice Genotypes based on Morphological and Quality Parameters through Principal Component Analysis
Genetic Diversity Estimation of Rice Genotypes based on Morphological and Quality Parameters through Principal Component Analysis
Salma Sharif1, Rana Arsalan Javaid2*, Abid Majeed2, Muhammad Shahzad Ahmed2, Qurat ul Ain Sani2, Faiza Siddique2, Muhammad Arshad3 and Niaz Ali1
ABSTRACT
For more than half of the world’s population, rice (Oryza Sativa L., family=Poaceae, 2n=24) is the second most important food crop after wheat. It is produced in several states all around the world, while primarily being grown in river deltas of Asia and the Southeast Asia. Therefore, it is of major significance to food security in a growing amount of less privileged countries. A study was carried out to assess indigenous rice germplasm using qualitative and quantitative features, as well as to select attractive genotypes for use in breeding programs in the future. A set of 65 accessions were evaluated in the field of Crop Sciences Institute, NARC following factorial design with three replications. highly significant differences were observed for all the quantitative traits as days to heading, days to maturity, plant height, number of tillers, panicle length, flag leaf area, leaf length, culm length, culm diameter, number of grains per panicle, grain length, grain diameter, chlorophyll content, net differential vegetation index, thousand grain weight and grain yield. The overall mean for Grain yield ranged from 1.71 tons/ha to 6.18 tons/ha. Grand mean of all genotypes for Grain yield was 4.1 tons/ha. Maximum Grain yield was observed in GSR 11 followed by GSR 10 and GSR 44 that were followed by 5.88 and 5.74 tons/ha respectively. Significant variation was also found in case of qualitative characters as obvious groups were formed based on visual observations. In PCA, the first two components were found to contribute 33.902% of the total variability so, the biplot was created using the first two components. The results of Principal Component Analysis (PCA) matched those of the cluster analysis quite closely. Breeders may now use these findings to create high-yielding rice varieties as well as novel breeding techniques for rice development.
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