Vol. 7, Issue 7 (2018)

Author(s):
Arpna Kumari and Vedna Kumari

Abstract:
In the present study, we investigated the genetic diversity pattern in thirty one Brassica juncea genotypes, using morphological traits. The genotypes were analysed for fifteen morphological traits in randomized complete block design replicated three times in two consecutive years [rabi 2008-09 (Env.I) and rabi 2009-10 (Env.II)]. Field data of two consecutive years were initially subjected to analysis of variance.Highly significant genotypic differences were found in the combined analysis of variance for days to flower initiation, days to 50 per cent flowering, days to 75 per cent maturity, plant height, siliqua length, 1000- seed weight and harvest index, confirming the possibility of improving these traits through selection. Moreover, genotypes responded differently to changes in the environmental conditions at the two locations as G × E interaction mean squares were highly significant (P ≤ 0.01) for all the traits except for days to 50 per cent flowering, days to 75 per cent maturity, plant height, number of primary branches per plant, siliqua length and 1000- seed weight.The phenotypic divergence and relative importance were estimated by multivariate analysis. The cluster analysis based on Tocher’s method classified the genotypes into four major groups of different sizes. Cluster I was the largest with 28 genotypes or constituting closer to 90 % of the total population, while cluster II, III and IV had one genotype each.The intra- cluster distance was comparable for cluster I (D² =1.14), while for clusters II, III and IV, intra- cluster distance was zero as the clusters were constituted by single genotype each.The maximum inter- cluster distance was found between clusters II and III (D² = 1.81). The second most divergent clusters were III and IV (D² = 1.78).Among the traits, 1000- seed weight contributed maximum (17.63 %) to genetic divergence followed by days to flower initiation (16.13 %) and siliqua length (12.26 %).Principal component analyses based on phenotypic data identify six most informative principal components explaining at least 80.92 % of the total variability, in particular PC1 contributing with 28.02 %, PC2 with 17.85 %, PC3 with 12.89 %, PC4 with 8.82 %, PC5 with 7.86 % and PC6 with 5.48 %. The principal component analysis largely confirmed the grouping of the genotypes obtained through cluster analysis.