Genetic Diversity Analysis for Yield Associated and Quality Traits in Promising Rice Varieties of Tamil Nadu

Abstract

An experiment was conducted with 55 rice varieties to assess the genetic diversity by using Mahalanobis D2 Statistical and characterization of genotypes using principal component analysis. All genotypes exhibited a wide and significant variation for 19 traits, by cluster analysis grouped into ten clusters. The maximum genotypes were included in Cluster 6 (16) followed by cluster 4 (10), cluster 3 (8), cluster 2 (7), cluster 5 (5), cluster 8 (4), cluster 1 (2), with 29.09, 18.18, 14.54, 12.72, 9.09, 7.27 and 3.63 proportion respectively, the rest of three clusters had one genotype each. Maximum cluster distance obtained between cluster×constituted by single entry (Pusa Basmati) showed highest inter cluster distance from cluster V (20727.37), VII (18414.79), I (17228.89) and cluster III (17010.24) are having very high inter cluster distance and also by cluster IX from cluster VIII (8852.36), VI (7559.67), I (7444.68) and cluster VII (6666.83) followed by cluster VI from cluster V (6225.95). The lowest inter cluster distance was observed between cluster II and cluster IV III and VI followed by between cluster I and cluster VIII, XI, II, VI and cluster IV. The intra cluster D2 values ranged from Zero (Cluster VII, IX, X) to 2233.91 (Cluster VIII). Contribution of amylose content was highest towards genetic divergence (23.43%) by taking 348 times ranked first followed by days to 50% flowering (23.37%) by 347 times, single plant yield (23.3%) by 346 times. The PCA analysis showed that first eight principal components accounted for about 85.4%.

Divergence Study of Rice (Oryza sativa L) Genotypes for Consumer-preferred Quality Traits

“…The vector of ER meets with EI, GB, KBAC, KB and amylcnt, also, LBBC meets vectors of KL, GL and KLAC almost at 90° indicating non-significant or low negative association with these traits. Similar experimental findings were reported by Akinol TF et al [14], and Naik et al [9].…”

“…The variables included in the first PC which explained 44.98% of total variance thus showed high importance for primary selection in understudied rice breeding lines. Ashok et al [13], Naik et al [9] demonstration of the use of factor analysis for efficient selection criteria in rice breeding mprogram provides strong support for our findings. The LBBC, GL, KL, KB, GB, KLAC, KBAC and ER had higher vector length indicating the presence of large variability, while remaining traits namely, ADV, Amyl.cnt and EI had smaller vector lengths indicating low variability (Fig.…”

“…By combining related units, clustering can also be utilized to communicate and summarize information about the relationships between various things. In the present experiment, "forty genotypes were grouped into six clusters using Tocher's method" [9] based on Mahalanobis D 2 values. Clusters with their genotypes are presented in table 1.…”

“…The maximum amount of desired heterosis will be manifested in cross combinations involving the parents belonging to most divergent clusters for quality characters and is important objective for a plant breeder" [9]. By considering this, genotypes belonging to Cluster II to cluster V, followed by cluster V and cluster III, and cluster II and cluster VI, can be used for making crosses.…”

Genetic Parameters and Divergence Studies for Gall Midge Incidence and Yield Components in Rice (Oryza sativa L.)

“…Several workers viz. Ramanjaneyulu et al (2014), Mohan et al (2015), Srinivas et al (2016) and Naik et al (2021) used D 2 statistics for estimation of genetic divergence in the populations. The crosses among parents with maximum genetic divergence are better responsive in genetic improvement (Govindaraj et al, 2014).…”