Genetic variability, association and multivariate analysis for yield and yield parameters in rice (Oryza sativa L.) landraces Gayathridevi G.1, Shanthi P.1,*, Suresh R.2, Manonmani S.2, Geetha S.3, Sathyabama K.4, Geetha P.5 1Department of Genetics and Plant Breeding, CPBG, TNAU, Coimbatore - 641003 2Department of Rice, CPBG, TNAU, Coimbatore - 641003 3Department of Pulses, CPBG, TNAU, Coimbatore - 641003 4Department of Soil Science and Agricultural Chemistry, TNAU, Coimbatore - 641003 5Department of Food Science, Centre for Post-Harvest Technology, TNAU, Coimbatore - 641003 *E-Mail: shanthi_pbg@yahoo.com, shanthipbg@tnau.ac.in
Online Published on 20 October, 2023. Abstract The present experiment was undertaken to study the variability, trait association and principle component analysis among eighteen characters in 53 rice genotypes including landraces and cultivars. Higher estimates of GCV and PCV coupled with high heritability and GAM were observed for a number of filled grains per panicle followed by a number of spikelets per panicle and single plant yield indicated that these traits could be utilised for selection in rice improvement. Study of association of yield attributing traits revealed that the single plant yield had a significant positive association with the total number of spikelets per plant, number of filled grains per plant and number of primary branches per plant and had a significant negative association with the length of the primary branches. Hence, direct selection of positively associated characters can improve the grain yield and increased primary branch length reduces the number of primary and secondary branches that might lead to a reduction in yield. Path coefficient analysis showed that the number of spikelets per panicle, primary branches per plant and length of secondary branches had a positive direct effect on yield. Therefore, giving weightage during selection to the above the mentioned traits could improve the yield and yield attributes. The principal component analysis showed that out of 18 traits studied, only eight principal components (PCs) had Eigen values greater than 1.00 and accounted for approximately 80.18% of the total cumulative variability. Single plant yield, number of fertile spikelets and total number of spikelets per plant and number of tillers showed maximum vector length confirming that the maximum contribution to the total diversity and the genotypes with the above characters could be utilized as donors to improve the yield and its attributing traits in future breeding programme. Top Keywords Rice, Land races, Germplasm, PCV, GCV, Heritability, GAM, Correlation, Path analysis, Principal component analysis. Top |