Multivariate analysis of morphological descriptors for identification of Sesbania Scop. accessions

Authors

  • SC Chanda Laboratory of Plant Systematics, Department of Crop Botany, Bangladesh Agricultural University, Mymensingh, Bangladesh
  • Md A Razzak Laboratory of Plant Systematics, Department of Crop Botany, Bangladesh Agricultural University, Mymensingh, Bangladesh
  • Md E Haque Department of Agriculture Extension, Sirajganj, Bangladesh
  • AKM Golam Sarwar Laboratory of Plant Systematics, Department of Crop Botany, Bangladesh Agricultural University, Mymensingh, Bangladesh

DOI:

https://doi.org/10.3329/bjsir.v55i3.49395

Keywords:

Sesbania Scop.; Numerical data; Agglomerative hierarchical clustering; Principal component analysis; Dissimilarities distance

Abstract

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

Downloads

Download data is not yet available.
Abstract
3
PDF
2

Downloads

Published

2020-09-24

How to Cite

Chanda, S., Razzak, M. A., Haque, M. E., & Sarwar, A. G. (2020). Multivariate analysis of morphological descriptors for identification of Sesbania Scop. accessions. Bangladesh Journal of Scientific and Industrial Research, 55(3), 215–220. https://doi.org/10.3329/bjsir.v55i3.49395

Issue

Section

Articles