Genetic Divergence Analysis on some Bread Wheat Genotypes Grown in Ethiopia

Abstract

An investigation was carried out with 49 bread wheat genotypes to assess the genetic diversity for yield and yield related traits. The Unweighted Pair Group Method using Arithmetic Averages (UPGMA) technique was used for the analysis. The genotypes were evaluated for 17 Characters and showed wide variability for the components studied. The cluster analysis grouped the 49 bread wheat genotypes into 22 different clusters. This indicates the presence of wide diversity among the tested genotypes. From cluster mean values, genotypes in cluster 9 and 13 deserve consideration for their direct use as parents in hybridization programs to develop high yielding wheat varieties. The genotypes in cluster 3 and 16 may be used for improvement of protein and gluten contents, early maturity-ness and other desirable characters other than grain yield. The result of the principal components analysis revealed that nine principal components (PC1 to PC9) accounted nearly 80 % of the total variation. It was also noted that differentiation of genotypes into different clusters was because of the small contribution of few character rather than the cumulative effect of a number of characters. The information obtained from this study can be used to plan crosses and maximize the use of genetic diversity and expression of heterosis.

Genetic Divergence Analysis of Agronomic Traits in Bread Wheat (Triticum aestivum L.) Genotypes

“…For genotypes grouped into 7 clusters, mean value for 12 characters given in Table- These findings are similar in agreement with earlier reported by Gupta et al, (1996), Jai Chand Rana et al, (2000, Bergale et al, (2001), Nimbalkar et al, (2002), Yousaf Ali, et al, (2008), Tsegaye et al, (2012), Yagd et al, (2012), Redhu et al, (1995), Walia and Garg (1996) and Hailegiorgis et al, (2011).…”

Multivariate Analyses of Phenotypic Diversity of Bread Wheat (Triticumaestivum L.) in the Highlands of Northern Ethiopia

“…Likewise, days to maturity and thousand-seed weight in the third PCA; biological yield and harvest index in the fourth PCA; number of grains per spike in the fifth PCA, were the major contributing characters for variability to those principal components. Similar findings of grouping bread wheat genotypes by principal component analysis were reported [19][20][21]. It is also evidenced by the PCA plot (Figure 1) that genotypes depicted on the top part of the plot had high values of days to heading, spike length and spike number per spike; and those depicted on the right side of the plane had higher values of grain yield and plant height.…”

“…Thus, crossing of genotypes between members of cluster I with members of cluster III; members of cluster II with members of cluster III, IV and VI may produce high amount of heterotic expression in the F1's and broad spectrum of variability in segregating (F2) populations. Genetic divergence in bread wheat genotypes reported by earlier workers [19][20][21]23].…”

Assessment of Genetic Diversity in Promising Bread Wheat (Triticum aestivum L.) Genotypes

“…Therefore, these clusters may be chosen for transferring the traits having high mean values through hybridization programme. Selection of genotypes based on cluster mean for the better exploitation of genetic potential also reported by Dwivedi and Pawar (2004), Roy et al, (2009), Hailegiorgis et al, (2011 and Desheva and Kyosev (2014). The highest intra cluster distance (Table 4) was observed in cluster VI followed by cluster IV, cluster I, cluster V and cluster II indicating differences in genotypes within cluster.…”