Cotton Genomics and Genetics 2015, Vol.6, No.1, 1-6
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GOT %and somehow good quality of fiber traits.
Cluster VI was represented by genotypes having good
plant height, sympodial branches per plant and fiber
fineness. Genotypes of cluster I, II and III could be
exploited in new breeding programs because of their
better earliness and fiber quality traits. Cluster IV
genotypes are good to utilize for better CLCuD
tolerance and GOT% whilst Cluster VI genotypes are
suitable for plant height, sympodial branches per plant
and fiber fineness.
The existence of inclusive diversity between clusters
is of great genetic values that allow selecting the
genotypes with wider genetic base for better tolerance
against CLCuD and earliness traits. Ayana and Bekele
et al. reported similar kind of results for grouping of
germplasm into various clusters.
Conclusion
In this present set of experiment PC analysis,
correlation coefficient and cluster Analysis was
employed that provide facilitation in grouping of
genotypes and identification of genotypes having
better tolerance against CLCuD, better fiber quality
and earliness trait. The information gathered through
correlation, PC and cluster analysis will be fruitful to
design breeding programs to obtain genotypes
possessing higher fiber quality, CLCuD tolerance and
earliness traits.
Materials and Methods
Plant Material & Site Characteristics
A total of 159 genotypes were evaluated for this study
carried out during the cropping seasons 2012-13 on
19th of June. The experiment was carried out at
Cotton Research Institute, Faisalabad, Punjab, Pakistan.
Experimental Design, Plot Size & Cultural
Practices
For each entry, plot size measured 4.572 m × 1.524 m,
comprising 2 rows set 75 cm apart. Distance between
plants within rows was 30 cm. Normal agronomic and
cultural practices (irrigation, weeding, hoeing, and
fertilizer applications) were adopted as and when
required.
Measurement of the studied traits
For measuring the traits 10 representative, undamaged
plants were selected in each line and marked for
identification. Data were collected for nodes to 1st
fruiting branch counted from zero node (cotyledonary
node) to the node at which first flower had appeared,
number of days to first square and flower, plant
height, monopodia and sympodia per plant, boll
weight, and ginning out turn (GOT). For GOT cleaned
and dry samples of seed cotton were weighed and then
ginned separately with single roller electric ginning
machine. The lint obtained from each sample was
weighed and ginning out turn % was calculated by the
following formula: Ginning outturn (%) = Weight of
lint/Weight of seed cotton × 100, Fiber charac-
teristics such as staple length, fiber fineness of each
guarded plant were measured by using spin lab
HVI-900.
Cotton leaf curl virus disease incidence (%)
methodology
Cotton leaf curl virus disease incidence (%) and
reaction of the cultivars was determined by using
disease scale (Table 5) modified from the scale
described by Akhtar et al (2010). Then percentage of
CLCuD incidence was calculated by using the
following formula.
CLCuD incidence (%) = Sum of all disease ratings/
total number of plants ×25
Statistical Analysis
The average data of both the years were subjected to
basic statistics, correlation analysis, cluster analysis
and principal component analysis (PCA) using
statistical software packages of SPSS version 19 and
STATISTICA version 5.0 (Sneath and Sokal, 1973).
Cluster analysis was performed using K-means
clustering while tree diagram based on elucidation
distances was developed by Ward’s method. First two
principal components were plotted against each other
to find out the patterns of variability among genotypes
and association between different clusters using SPSS
version 19.
