CLUSTER ANALYSIS: A COMPARISON OF FOUR METHODS IN RICE BEAN [VIGNA UMBELLATE (THUNB.)OHWI & OHASHI]

Article Id: ARCC1287 | Page : 95 - 101
Citation :- CLUSTER ANALYSIS: A COMPARISON OF FOUR METHODS IN RICE BEAN [VIGNA UMBELLATE (THUNB.)OHWI & OHASHI].Legume Research-An International Journal.2010.(33):95 - 101
R.C. Misra and P. Swain
Address : Orissa University of Agriculture and Technology, Bhubaneswar– 751 003, India

Abstract

Twenty five rice bean elite lines from Bhubaneswar, Ludhiana and Ranchi were evaluated in
RBD for nine yield component traits and seed yield. The genotypes showed wide and highly
significant variations in all ten traits. Cluster analysis of the 25 rice bean genotypes based on the
ten yield and component traits was done in four different methods viz. Genetic Divergence D2,
Canonical analysis, Similarity coefficients and Principal component analysis. In all four methods
the 25 genotypes could be classified into nine different clusters. Though the composition of
clusters varied with methods, there was similarity of cluster composition of D2 analysis with
canonical analysis and of similarity coefficient with principal component analysis. Comparison
of effectiveness of the four methods of clustering in bringing out the diversity among the clusters
of genotypes was done by partitioning total sum of squares among genotypes for each character
into sum of squares for between and within cluster variation. The study revealed that similarity
coefficient and principal component analysis were more effective in maximizing between cluster
differences than D2 and canonical analysis. Moreover, similarity coefficient analysis gave a
hierarchical presentation and would help in further partitioning into sub-clusters. Based on intergenotype
diversity in all four methods and character complementation it can be inferred that
crosses of BRB-20, BRB-6, BRB-14 and BRB-20 with BRB-9 are expected to produce more
transgressive segregants in later generations.

Keywords

Rice bean Cluster analysis D2 Canonical Similarity coefficient Componentanalyses.

References

  1. Chaudhari, G.B. et al. (1999). Indian J. Pulses Res., 12: 251-253.
  2. Das, S. et al. (2008). Agric. Sci. Digest.,28: 298-300.
  3. Dora, D. K. et al. (2003). Orissa J. Hort., 31: 76-79.
  4. Mandal, N. and Dana, I. (1998). Ann. Agric.Res., 19: 18-21.
  5. Mardia, K.V. et al. (1979). Multivariate Aanalysis. Academic press, London.
  6. Rao, C.R. (1952). Advance statistical methods in biometric research. John Wiley and Sons, New York.
  7. Sahu, P.K. et al. (2007). Env. and Eco., 25S(3A) : 808-812.
  8. Singh, G. et al. (1998). Indian J. Genet., 58: 101-105.
  9. Singh, M.R.K. et al. (1999). Indian J. Genet., 59: 221-225.
  10. Sneath, P.H. and Sokal, R.R. (1973): Numerical Taxonomy: The Principles and Practice of Numerical Classification.
  11. W.H. Freeman and Company. San Francisco, USA.
  12. Thaware, B.L. et al. (1997). Legume Res., 20: 91-96.
  13. Tropical Legumes (1979). Resources for future. Report of Advisory Committee on Technology Innovations. National
  14. Research Centre. National Academy of Sciences, Washington D. C., 80-85

Global Footprints