Multivariate analysis of morphological descriptors for identification of Sesbania Scop. accessions
Keywords:Sesbania Scop.; Numerical data; Agglomerative hierarchical clustering; Principal component analysis; Dissimilarities distance
Based on the 36 quantitative morphological descriptors, agglomerative hierarchical clustering (AHC) and principal component analyses (PCA) were conducted to identify 106 diverse Sesbania accessions. The AHC analysis identified three major clusters with 11 sub-clusters. In PCA, the first and second PCs explain 72.48% and 12.72% of total variations with high Eigen value 9.1 and 1.7, respectively. Sesbania accessions occupied four distinct positions in the PCA graph. Based on multivariate analyses and qualitative descriptors, Sesbania accessions have been identified as S. bispinosa (90 accessions), S. cannabina (9 accessions), S. sesban (6 accessions) and the known S. rostrata. The AHC dendrogram has detected the close similarities between S. rostrata and S. cannabina. However, the PCA has emerged to be better than the AHC as a species identification tool. Among these four species, the highest discriminating distance (23.69%) was observed between S. sesban and S. bispinosa, and the lowest (10.52%) was in S. bispinosa and S. cannabina.
Bangladesh J. Sci. Ind. Res.55(3), 215-220, 2020
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