Agro-morphological characterization and morphological based genetic diversity analysis of Rice (Oryza sativa L.) germplasm
Atul Kumar Pachauri, AK Sarawgi, Sandeep Bhandarkar, GC Ojha
Abstract
Atul Kumar Pachauri, AK Sarawgi, Sandeep Bhandarkar, GC Ojha
Abstract
The present study was carried out to characterize one hundred twenty four rice germplasm accessions on the basis of nineteen morphological and eleven agronomical traits. Most of the morphological characters showed variation in different accessions except leaf: ligule and leaf: shape of ligule. A significant amount of variation was displayed for most of the agronomical traits examined. After evaluation of 124 accessions for eleven quantitative characters, on the basis of mean values, top ten accessions were identified for the yield ancillary traits. Principal Component Analysis was used to analyze quantitative data. The 72.48% of the total variation. The PC1 showed first four principal components explained about 37.12%, while PC2, PC3 and PC4 exhibited 13.56, 11.04 and 10.76% variability, respectively results of PCA revealed that the traits such as No. of effective tillers, 100 grain weight were the principal discriminatory traits. Thus the results of principal component analysis used in the study have revealed the high level of genetic variation and the traits contributing for the variation was identified. Hence this population panel can be utilized for trait improvement in breeding programs for the traits contributing for major variation.
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The present study was carried out to characterize one hundred twenty four rice germplasm accessions on the basis of nineteen morphological and eleven agronomical traits. Most of the morphological characters showed variation in different accessions except leaf: ligule and leaf: shape of ligule. A significant amount of variation was displayed for most of the agronomical traits examined. After evaluation of 124 accessions for eleven quantitative characters, on the basis of mean values, top ten accessions were identified for the yield ancillary traits. Principal Component Analysis was used to analyze quantitative data. The 72.48% of the total variation. The PC1 showed first four principal components explained about 37.12%, while PC2, PC3 and PC4 exhibited 13.56, 11.04 and 10.76% variability, respectively results of PCA revealed that the traits such as No. of effective tillers, 100 grain weight were the principal discriminatory traits. Thus the results of principal component analysis used in the study have revealed the high level of genetic variation and the traits contributing for the variation was identified. Hence this population panel can be utilized for trait improvement in breeding programs for the traits contributing for major variation.
Key concepts: Germplasm, Principal component analysis, Biology, Quantitative trait locus, Oryza sativa, Trait, Agronomy, Genetic diversity