volume 33 issue 2 (june 2010) : 95 - 101
CLUSTER ANALYSIS: A COMPARISON OF FOUR METHODS IN RICE BEAN [VIGNA UMBELLATE (THUNB.)OHWI & OHASHI]
1Orissa 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.
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.
REFERENCES
- Chaudhari, G.B. et al. (1999). Indian J. Pulses Res., 12: 251-253.
- Das, S. et al. (2008). Agric. Sci. Digest.,28: 298-300.
- Dora, D. K. et al. (2003). Orissa J. Hort., 31: 76-79.
- Mandal, N. and Dana, I. (1998). Ann. Agric.Res., 19: 18-21.
- Mardia, K.V. et al. (1979). Multivariate Aanalysis. Academic press, London.
- Rao, C.R. (1952). Advance statistical methods in biometric research. John Wiley and Sons, New York.
- Sahu, P.K. et al. (2007). Env. and Eco., 25S(3A) : 808-812.
- Singh, G. et al. (1998). Indian J. Genet., 58: 101-105.
- Singh, M.R.K. et al. (1999). Indian J. Genet., 59: 221-225.
- Sneath, P.H. and Sokal, R.R. (1973): Numerical Taxonomy: The Principles and Practice of Numerical Classification.
- W.H. Freeman and Company. San Francisco, USA.
- Thaware, B.L. et al. (1997). Legume Res., 20: 91-96.
- Tropical Legumes (1979). Resources for future. Report of Advisory Committee on Technology Innovations. National
- Research Centre. National Academy of Sciences, Washington D. C., 80-85
Disclaimer :
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Copyright :
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Published In
Legume Research
Article Metrics
0
Views
Citations
Reviewed By
In this Article
APC
APC cover the cost of turning a manuscript into a published manuscript through peer-review process, editorial work as well as the cost of hosting, distributing, indexing and promoting the manuscript.
Publish With US
Submit your manuscript through user friendly platform and acquire the maximum impact for your research by publishing with ARCC Journals.
Become a Reviewer/Member
Join our esteemed reviewers panel and become an editorial board member with international experts in the domain of numerous specializations.
Open Access
Filling the gap between research and communication ARCC provide Open Access of all journals which empower research community in all the ways which is accessible to all.
Products and Services
We provide prime quality of services to assist you select right product of your requirement.
Support and Policies
Finest policies are designed to ensure world class support to our authors, members and readers. Our efficient team provides best possible support for you.
