Cotton Genomics and Genetics 2015, Vol.6, No.1, 1-6
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Table 4 Cluster analysis of CLCuD, earliness and quality traits in various genotypes of cotton
Variable
Cluster-1
Cluster-2
Cluster-3
Cluster-4
Cluster-5
Cluster-6
DFS
40
46
46
63
48
49
DFF
55
63
63
81
79
67
CLCuD (%)
88
44
63
33
96
69
PH (cm)
70
87
38
47
46
121
NTFF
8
6
7
4
8
6
MPP
1
3
0
1
3
5
SPP
17
15
13
8
15
18
BW (g)
2.7
2.8
2.4
2.5
2.2
2.3
GOT (%)
32
35
40
40
32
33
SL(mm)
27
27
28
27
27
27
FF (µg/inch)
4.2
5
4.2
4.9
5.0
4.6
Discussion
The information of association among various traits
facilitates to initiate any breeding programs as it aids
to select genotypes having superior characters (Ali et
al., 2009). In this set of experiment the correlation
analysis results showed significant association among
various traits. The days to 1st square had significant
positive correlation with days to first flowering and
had positive association with nodes to 1st fruiting
branch whilst it showed negative correlation with fiber
fineness. CLCuD exhibited highly significant negative
correlation with plant height. Plant height showed
highly positive significant association with sympodia
per plant and monopodia per plant while it showed
significant negative association with GOT. The
positive association among various studied traits is
important for selection of high yielding genotypes.
Farooq et al. (2013) found significant positive
association among yield and yield contributing traits
and also found negative correlation among CLCuD
and seed cotton yield. The exploitation and
maintenance of genetic resources could be made more
efficient by partitioning of total variance into its
components. Meanwhile it also aids in utilization of
suitable germplasm for improvement of particular
plant character (Pecetti et al., 1996). Principle
component analysis is a powerful technique that
facilitates to get appropriate parental lines thus to
initiate successful breeding program (Akter et al.,
2009). In present set of experiment, PC analysis
grouped total variation into 5 PCs. Saeed et al. 2014
reported significant higher contribution of 1st and
2ndprinciple component towards total variability. In
present experiment 1st PC was elaborated by mainly
due to days to 1st square, days to first flower and
CLCuD. The 2nd PC was mainly explained by the
genotypes having diversity for plant height and
monopodia per plant. PC III contribution towards total
variance was due to diversity among genotypes for
nodes to 1st fruiting branch, staple length and CLCuD.
PC IV elucidated by diversity among genotypes for
staple length, monopodia per plant and boll weight
with positive factor loadings.
Principle component analysis eventually validated
ample amount of variation for the studied traits which
could be exploited for planning a successful breeding
program initiative aimed at refining earliness, CLCuD
and fiber quality traits. Malik et al. 2011 and
Ashokkumar and Ravikesavan 2011 reported that
abundant amount of diversity in colored cotton
genotypes allow opportunities to characterize color
cotton genotypes.
Cluster analysis also assisted to elucidate adequate
amount of diversity in this set of genotypes at
different growth phases. The genotypes in cluster I, II
and III comprised of genotypes with better earliness
and fiber quality traits. Cluster IV was characterized
by genotypes having less attack of CLCuD, higher
