Assessing the genetic diversity and association of traits among the rice (Oryza sativa L.) landraces and varieties from Tamil Nadu

Abstract

In this study, the genetic diversity among forty-two rice genotypes were evaluated for fifteen quantitative and eleven qualitative traits using D2 statistics and DUS characterization. The genotypes were grouped into eight clusters of which the cluster VIII was the largest consisting of twelve genotypes. While, the clusters IV and VII were the smallest with a single genotype each. The maximum inter-cluster distance was observed between clusters I and II. Hence, the genotypes from these clusters could be used as parents for rice hybridization programmes in future. Among all traits, days to 50% flowering and apiculus colour were found to be the major contributing quantitative and qualitative traits towards genetic diversity. Beyond this, in this study Cluster II recorded the highest mean value for shoot length, plant height, flag leaf width, panicle length, number of seeds per panicle, 1000 grain weight and single plant yield. However, the cluster VII recorded the highest mean for seedling height, flag leaf length, grain length and grain width. Thus, the genotypes from these clusters which were Aanai Komban, Kichili samba and Karuppu kavuni could be used in improving the yield parameters in rice. Further, the correlation analysis for fifteen traits revealed a significant positive correlation of number of productive tillers, flag leaf length, flag leaf width, number of seeds per panicle, grain length and 1000 grain weight with single plant yield. Further the path analysis portrayed that the from these correlated traits, number of productive tillers, flag leaf width, number of seeds per panicle and grain length were observed to have a positive direct influence on yield. Thereby, it could be observed that these traits could be used as major indicators for selection of desirable genotypes for utilization in hybridization programme to develop elite rice genotypes.

Keywords

• genetic diversity
• rice landraces
• d2 statistics
• clustering by pca
• correlation and path analysis