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African
Journal of Agricultural Research Vol. 2(9), pp. 409-411, September, 2007
ISSN 1991- 637X© 2007 Academic Journals
Short Communication
Cluster analysis in rapeseed (Brassica napus L.)
Mabel Jendeka Mahasi and John Waweru Kamundia
National Plant Breeding Research Centre, P. O. Njoro, Kenya.
*Corresponding
author. E-mail:
jendekamabel@yahoo.com
Accepted 26 July, 2007 |
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Many oilseed crops (e.g.
sunflower, soybeans, rapeseed/mustard, sesame, groundnuts etc) are grown
in Kenya. But oilseed rape
is preferred because of its
high yields (1.5 tons – 4.0 tons/ha)
with high oil content of 42 – 46%. It is soft seeded hence oil
extraction is relatively easy. The meal is high in protein and very
useful in livestock feed supplementation. The success of any crop
improvement programme depends on the extent of genetic diversity in the
material. Hence, it is essential to evaluate introductions for
adaptation and study the similarities if any among them. Evaluation
trials were carried out on 17 rapeseed genotypes (nine of Canadian
origin and eight of European origin) at 4 locations in Kenya namely
Endebess, Njoro, Timau and Mau Narok for two years. An analysis of
variance was carried out on seed yields which indicated that the
genotypes were significantly different (LSD, 0.05). Cluster analysis
based on mean seed yields suggested only one major group existed within
the material. In the first year, genotypes 2, 3, 8 and 9 didn’t group
with the rest. Genotype 8 was the only one that did not classify with
the rest of the Canadian genotypes. Three European genotypes (2, 3 and
9) were however not classified with the others. In the second year,
genotypes 10 and 6 didn’t fall in the major cluster. Of these two,
genotype 10 is of Canadian origin. Genotypes were more similar in the
second year than the first year due to favorable weather. It is evident
that genotypes from different geographical areas, that is, Europe and
Canada fell in the same clusters suggesting
that they have genetic similarity. The groupings indicated no
correspondence between geographical diversity and clustering pattern.
Key words: Genetic diversity, cluster analysis, genetic
similarity and cluster pattern. |
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