Clustering and principal component analysis of Barley (Hordeum volugare L.) Landraces for major morphological traits from North Western Ethiopia

Abstract

Huge collections of barley landrace genotypes in Ethiopian are not studied for the magnitude of genetic distances from each other. Though knowing the contribution of individual traits is crucial to focus on particular traits in cultivar development; the traits of these genotypes are not yet studied. Hence, this experiment was conducted on 48 barley landrace accessions which were not studied yet and four standard checks to estimate the magnitude of genetic diversity among the genotypes and to identify the major morphological traits contributing for the observed variations. The experiment was laid in Augmented Randomized Complete Block Design in six blocks at Adet in 2016/17 cropping season. The traits used for analysis were days to 50% heading, days to maturity, plant height, total number of tillers/plant, effective number of tillers/plant, number of spikes/plant, spike length, average number of grains/plant, biomass yield/ha, thousand grain weight/gm, grain yield tones/ha and harvest index tones/ha. The 52 genotypes were grouped into six clusters where 65.39% of the genotypes (34) fall in cluster I, III and IV. Early matured genotypes were grouped in cluster III, late matured in cluster VI and high yielding and tall genotypes in cluster IV. The highest intra cluster distance was 22.513 for cluster III and IV. The highest inter cluster distance was 57.00 between cluster V and IV. The fi rst three principal components contributed 74.20% of the total variations observed among the genotypes. Principal component one (Pc1) alone had contributed 49.96%.% of the total variations mainly due to grain yield, biomass yield, thousand grain weight and plant height in their respective order. Principal component two (Pc2) contributed 15.98% of the total variations mainly through total number of tillers per plant, number of effective tillers per plant and number of spikes per plant in their descending order. Principal component three (Pc3) had contributed 8.25% of the total variations through days to maturity, days to 50% heading and number of grains per plant. The result ensures the existence of high genetic divergence among the studied genotypes.

Multivariate analysis of quantitative characters variability in Ethiopian barley (Hordeum vulgare L.) landrace: Based on regions and altitude

“…The results of inter-cluster analysis revealed that the genotypes had wide genetic divergence with each other indicating existence of high probability for recombination. Results of this study from cluster analysis also strengthen the availability of genotypic variability even within clusters similar to previous researches on barley landraces by DERBEW et al, (2013), TAHIR (2016) and ENYEW et al (2019) in which they reported the existence of wide genetic divergence among the landraces expected to manifest maximum heterosis in crossing and wide genetic variability…”

“…Cluster analysis grouped landraces with greater morphological (phenotypic) similarities, however, it did not consider in including all landraces from the same or adjacent regions. Similar phenomenon was reported by (ABEBE et al, 2010;ENYEW et al, 2019;MEKONNON et al, 2015;ZAKOYA and BENKOVA, 2004), who described that clustering of landraces based on the morphological (agronomic) characters showed no distinct regional grouping pattern s in which landraces from same or adjacent regions could appear in different cluster classes. Characterization of landraces and clustering of them on the basis of their morphological and genetic similarity helps in identification and selection of the best parents for hybridization (SOUZA and SORRELLS, 1991).…”

Multivariate Analysis of Malt Barley Genotypes for Different Malt Quality and Agronomic Traits in Ethiopia

“…Scald and net blotch severity contribute largely to these variability (Table 5). Likewise, in Enyew et al (2019) study the first PC alone explained about 50% of the total variance mainly due heading date, plant height and grain yield, biomass yield. However, Abebe et al, (2010) and Enyew et al (2019) reported more contribution of thousand kernel weight for percent variation explained by PC1.…”

“…Similarly, Amabile et al, 2014, group thirteen malt barley genotypes in two similarity group. However, Enyew et al (2019), Gupta et al (2009) and Mekonnon et al (2014), group 48, 207 and 102 barley landrace accessions in six and five distinct clusters, respectively. Generally, the cluster analysis group the genotypes of similar origin in the same cluster and these genotypes assembled in one cluster had similar agronomic performance and malt quality.…”

Morpho-physiological diversity of barley (Hordeum vulgare L.) germplasm for heat tolerance

“…Cluster analysis is one of the most popular methods for analysing multivariate data and for exploring the underlying structure of a given data set. Various techniques used for clustering include principal component analysis (PCA), principal coordinate analysis (PCoA), redundancy analysis (RDA) and UPGMA (Sneath and Sokal, 1973;Anderson 2001;Cha-um et al, 2010;Enyew et al, 2019). All these techniques employ mathematical algorithms and assemble large sample units in various groups and subgroups depending upon similarity or distance matrixes.…”