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22 International Journal of Modern Agriculture, Volume 4, No.1, 2015 Copyright © Zohdi Publisher ISSN: 2305‐7246 CLUSTER ANALYSIS, GENOTYPIC AND PHENOTYPIC
CORRELATION AMONG YIELD CONTRIBUTING TRAITS IN BREAD
WHEAT (Triticum aestivum L.) GERMPLASM
Arshad Jamil*1, Shahjahan Khan1, Ubaidullah1, Muhammad Zeeshan1, Muhammad Zulfiqar Ali2.
1
Department of Plant Breeding and Genetics Faculty of Agriculture Gomal University Dera Ismail Khan, Pakistan
Huazhong Agriculture University, China
*Corresponding author (e-mail: [email protected])
2
Abstract
Wheat is the world’s second main source of food energy and nutrition. Sixty different genotypes and two bread
wheat cultivars i.e. Gomal-08 and Hasham-08 were used in this study. Randomized complete block design (RCBD)
with three replications were used. Results showed that Grain yield plant-1 has highly significant positive correlation
with days to maturity, number of tillers plant-1, peduncle length, number of grains spike-1 and 1000grains weight,
while spikelets spike-1 had significant positive correlation with grain yield plant-1 at both phenotypic and genotypic
levels. Significant positive correlation were observed between flag leaf area and grain yield plant-1 at phenotypic
level. Grain yield plant-1 showed significant negative correlation with days to 50 % heading and spike density at
phenotypic level while the same two parameters had highly significant negative association with grain yield plant-1
at genotypic level. The 60 genotypes were separated into six distinct cluster by keeping 10 as linkage distance.
According to this comparison, Cluster-III is important for number of tillers plant-1, plant height, number of spikelets
spike-1, grain yield plant-1 and 1000-grain weight because these characters showed their highest value in this cluster.
Number of grains spike-1 and spike length showed their highest values in cluster-VI and hence can be preferred for
the given parameter. Such study can be helpful in breeding programs for wheat improvement.
Key words: Cluster Analysis, Correlation, Germplasm, Triticum aestivum, Yield traits.
characters are associated (Inamullah et al., 2006;
Abu-Amer et al., 2011).
Direct selection for yield is often misleading in wheat
because wheat yield is controlled by many genes
(Akash and Kang, 2010). For effective utilization of
the genetic stock in crop improvement, information
of mutual association between yield and yield
components is necessary. It is, therefore, necessary to
know the correlation of various component characters
with yield and among themselves. The correlation
coefficients between yield and yield components
usually show a complex chain of interacting
relationship Majumder et al, (2008). But grain yield
is greatly affected by two main factors i.e. genetic
factors as well as environmental alterations. So in
that regards direct and independent selection for yield
could be useless in wheat breeding programs,
therefore for making meaningful selection, it does
Introduction
Wheat is purely a self pollinated crop. It is
considered to be originated in South East Asia. It is
the world’s second main source of food energy and
nutrition. In Pakistan, bread wheat is consumed
widely and is considered as one of the most important
cereal crop of the country. As grain yield is complex
inherited trait, therefore, wheat yield fluctuates on a
wide scale because of its interaction with
environment. Grain yield directly or indirectly
affected by many factors. Grain yield can be
increased by the development of better performing
varieties. Effective selection for grain yield can be
made if required amount of genetic diversity is
present in the parent material. Genotypic and
phenotypic correlations are important in determining
the degree to which various yield contributing
International Journal of Modern Agriculture, Volume 4, No.1, 2015 23 Where
rp =Phenotypic correlation
PCovxy= Phenotypic covariance of x and y character.
PVx=Phenotypic variance of x character.
PVy=Phenotypic variance of y character.
Genotypic correlation
Genotypic correlation refers to the heritable
association between two characters. Formula of the
genetic correlations (rg) between two characters is
depend upon the knowledge about genetic variation
and also the inter-relationship of grain yield with
various morphological traits (Akash et al., 2009;
Ullah et al., 2011).
The purpose of this study was to find out the interrelationship among different yield contributing traits
towards grain yield. This information may help wheat
breeder’s in future breeding program.
Materials and Methods
Sixty different genotypes of bread wheat were kindly
provided by National Agriculture Research Center
(NARC), Islamabad to conduct this research and the
two bread wheat cultivars i.e. Gomal-08 and
Hasham-08 which were used as check, were brought
from Agriculture Research Institute (ARI) Dera
Ismail Khan. The research was carried out
Department of Plant Breeding and Genetics, Faculty
of Agriculture, Gomal University Dera Ismail Khan,
during 2012-2013. The sixty wheat genotypes were
sown in the third week of November, 2012 during
Rabi season. The experiment was conducted using
RCBD having three replications. The size of the
plot was kept 18 m × 5 m with 60 lines per replication,
and each line was kept 5 meter long and 30 cm apart.
Data of different parameters viz., Number of fertile
tillers plant-1, Plant height, Number of Spikelets
spike-1, Number of grains spike-1, Grain yield plant-1
and 1000 grain weight were recorded from five
randomly selected plants
(rg) =
GVx .GVy
Where
rg =Genetic correlations
GCovxy= Genetic covariance between x and y
character.
GVx= Genotypic variance of x character.
GVy= Genotypic variance of y character.
Results and Discussions
Number of tillers plant-1
Many important factors are involved in the
improvement of crop yield and number of fertile
tillers is one of them. Because as the number of
fertile tiller increase, the number of grain increase
which ultimately affect the crop yield, especially in
what it is very important character. It was observed
from the results that number of tillers plant-1
indicated highly significant positive correlation with
length of the spike, number of spikelets spike-1, grain
yield plant-1 and 1000 grains weight at both genotypic
and phenotypic level. Which mean that more the
number of tillers plant-1 will result in more number of
spikelets spike-1 which will ultimately result into
greater grain yield plant-1. Ashraf et al. (2012) also
find the same effects. At genotypic level number of
tillers plant-1 exhibited highly significant positive
correlation with number of grains spike-1 while non
significant positive correlation was seen at
phenotypic level. At genotypic level significant
positive correlation was seen with plant height while
at phenotypic level highly significant positive
correlation was observed (Table 1). These results
lead us to give preference to number of tillers plant-1
during selection program. Ashraf et al. (2012), Khan
et al. (2012) encouraged these findings.
Statistical Analysis
The data recorded were subjected genotypic and
phenotypic correlations among the parameters
through ANOVA and ANCOVA. Cluster analysis
was performed by using Wrad’s method (1963).
Phenotypic and Genotypic correlation:
Gentypic (rg) and phenotypic correlations (rp)
between two characters were estimated according to
Kwon and Torrie (1964).
Phenotypic correlation
Phenotypic correlation (rp) refers to the observable
association between two characters. Formula of the
phenotypic correlation (rp) between two characters is
(rp) =
GCov xy
PCov xy
PVx .PVy
International Journal of Modern Agriculture, Volume 4, No.1, 2015 24 Table 1. Phenotypic correlation and Genotypic correlation for twelve quantitative characters of sixty wheat
genotypes
Parameters
NTP
PH
SL
NSS
NGS
GYP
1000 GW
Number of tillers plant-1
0.69**
0.23*
0.52**
0.97**
0.74**
0.36**
NS
NS
Plant Height
0.08
0.03
0.23**
0.23*
0.30*
-0.66**
Spike Length
Number of Spiklets Spike-1
Number of Grains spike
-1
0.50**
0.46**
0.01 NS
0.01 NS
0.68**
0.20**
-0.01
-0.09
Grain Yield plant
0.58**
-0.54**
0.07 NS
1000-Grain Weight
0.35**
0.24**
-1
0.08NS
0.46**
0.10NS
-0.07NS
-0.02NS
0.48**
0.23*
0.19 NS
0.35**
0.04 NS
0.35**
0.17*
0.09 NS
0.36**
0.08 NS
0.77**
0.78**
observed with plant’s yield for grains and weight of
the
1000-grains
both
genotypically
and
phenotypically (Table 1). Khan et al. (2012) showed
similar inter-relationship. Shah et al. (2007) reported
significant positive inter-relationship between spike
length and 1000-grains weight. These differences
may be either due to the difference in plant material
or due to environmental change.
Number of spikelets spike-1
The number of spikelets spike-1 was found to have
highly significant positive correlation with number of
grains spike-1 at both levels. Number of spikelets
spike-1 showed significant positive correlation with
grain yield plant-1 and non significant positive
correlation with 1000 grains weight at both genotypic
and phenotypic levels (Table 1). The significant and
positive relation between number of spikelets spike-1
and grain yield plant-1 suggests that selection of
wheat genotypes with more spikelets spike-1 will give
more yield as shown by Bangarwa et al. (1987);
Kinyua and Ayiecho (1991) and Akram et al. (2008)
in their studies in wheat. It may be concluded from
the present study that number of spikelets spike-1,
number of grains spike-1, 1000 grains weight and
number of tillers plant-1 contributed to final grain
yield. Therefore, indirect selection for these traits
may be effective in developing high yielding wheat
cultivars.
Number of grains spike-1
It is one of the most important factors which directly
affect the grain yield. It depends upon the number of
spikelet’s and spike length. As the spike length and
number of spikelet’s high, the number of grains will
also high that automatically increase the grain yield.
It was found from the results that number of grains
spike-1 showed highly significant positive genotypic
and phenotypic correlation with grain yield plant-1
Plant height
Plant height showed non-significant positive
correlation with grain yield plant-1 at both genotypic
and phenotypic levels. Plant height possessed non
significant positive genotypic association with spike
length. This insignificant positive inter-relationship
indicated that plant height did not have any
remarkable effect on grain yield. But commonly short
stature plants are considered well for lodging
resistance. Work of Khan et al. (2012) and Asharaf et
al, (2012) revealed that plant height possessed highly
significant negative relation with grain yield plant-1.
Plant height indicated significant positive genotypic
correlation with 1000 grains weight while at
phenotypic level highly significant positive
correlation was noticed. Findings of Akram et al.
(2008) showed similar results for plant height with
1000 grains weight. This parameter possessed
significant positive correlation at genotypic level
with number of spikelets spike-1 while at phenotypic
level non significant positive correlation was
observed between the two. Highly significant
negative correlation with number of grains spike-1
was recorded at both phenotypic and genotypic level
which indicates that higher plant will produce spikes
having less number of grains (Table 1). Ali et al.
(2008) also showed the similar results which
encouraged these findings.
Spike length
Genotypically non significant positive correlation
was seen with spikelets number spike-1 while
phenotypically it correlated positively and highly
significantly. At genotypic level length of the spike
showed non-significant positive inter-relationship
with grains number spike-1 whereas highly significant
positive correlation was observed at phenotypic level.
Non significant and negative association was
International Journal of Modern Agriculture, Volume 4, No.1, 2015 25 accessions were placed into six distinct clusters by
doing analysis of genetic diversity through cluster
analysis on the basis of Euclidian dissimilarity
distance by keeping 10 as linkage distance using
Ward’s (1963) method. (Figure.1)
Cluster analysis divided the sixty accessions into six
distinct clusters. The details of these genotypes in
each cluster are given in (Table 2). Cluster-I was
comprised of 18 genotypes and was divided into two
subgroups out of which one sub group contained
seven accessions while the second sub group
contained eleven accessions (Figure 1). Cluster-II
comprised of total eight accessions which were
further classed into two subgroups one out of two
subgroups consisted on six accessions while the
second one had two accessions (Figure 1). Cluster-III
possessed minimum most number of accessions that
was two (Figure 1). Cluster-IV was also comprised of
eight accessions having two subgroups containing six
accessions in one subgroup while two in the other
(Figure 1). Cluster-V enclosed four accessions which
on further division got two subgroups with two
genotypes each (Figure 1). Cluster-VI was found the
densest group of all the six groups due to the
presence of twenty accessions in it which were
further subdivided into two subgroups one out of two
contained thirteen accessions while the second one
had seven genotypes (Figure 1).
(Table 1). This result matched with the findings of
Ahmad et al. (2010), Akram et al. (2008), Zeeshan et
al. (2013) and Tabbal et al. (2012). Non significant
positive correlation was noticed with 1000 grains
weight at both phenotypic and genotypic levels. Ali
et al. (2008) witnessed the same inter-relationship.
Positive associations suggest that increased grain
yield could be achieved if the selection is based on
grains spike-1.
1000 grain weight
It is very important factor which have direct impact
on yield as well as harvest index. But the effect of
this character is unpredictable due to grain health and
composition. It mainly depends upon the
environmental conditions. However, in this study,
1000-grains weight was reported to have highly
significant positive correlation with grain yield plant-1
at both phenotypic and genotypic levels (Table 1).
1000 grains weight was reported to have strong
significant genotypic inter-relationship with grain
yield plant-1. These effects were encouraged by Khan
et al. (2012).
Cluster analysis
Genetic variation is the main weapon to develop new
varieties and right information about the material is
very important. Cluster analyses play important role
in the selection of right material. In this study, sixty
Table 2. Detail of sixty accessions in six clusters
S. No
Cluster I
Cluster II
Cluster III
Cluster IV
Cluster V
Cluster VI
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
10760
11030
11173
11152
11451
12863
11492
11192
12854
12871
11199
11424
11791
24738
12853
11420
12857
11493
11133
11300
11164
12882
12895
12948
11171
12894
11144
12846
11153
11520
11567
11166
11179
11180
11194
11198
11158
11160
11177
11178
11156
11421
11487
24739
11183
11422
11795
11790
24740
24741
12844
12849
12957
11193
11197
11195
11573
11574
12955
12947
International Journal of Modern Agriculture, Volume 4, No.1, 2015 26 10760
11030
11173
11152
11451
12863
11492
11192
12854
12871
11199
11424
11791
24738
12853
11420
12857
11493
11133
11300
11164
12882
12895
12948
11171
12894
11144
12846
11153
11520
11567
11166
11179
11180
11194
11198
11158
11160
11177
11178
11156
11421
11487
24739
11183
11422
11795
11790
24740
24741
12844
12849
12957
11193
11197
11195
11573
11574
12955
12947
0
5
10
15
20
25
30
Linkage Distance
Figure 1. Cluster analysis of 60 accessions of wheat for 6 morphological characters using Ward’s (1963) method.
International Journal of Modern Agriculture, Volume 4, No.1, 2015 27 maximum value for 1000-grain weight having
cluster-II on second number. After getting results
from cluster analysis mean values of a cluster were
compared with other clusters for all the traits.
According to this comparison it becomes clear
Cluster-III is important for number of tillers plant-1,
plant height, number of spikelets spike-1, grain yield
plant-1 and 1000-grain weight because these
characters showed their highest value in this cluster.
Number of grains spike-1 and spike length showed
their highest values in cluster-VI and hence can be
preferred for the given parameter (Table 3).
Means of different traits for all the clusters are shown
in (Table 3). Numbers of tillers plant-1 were recorded
with maximum value in cluster-III leaving cluster-I
just behind it. Cluster-III was also found having
highest value for plant high followed by cluster-V.
Spike length showed its highest value in cluster-IV
followed by cluster-I. Number of spikelets spike-1
was found maximum in cluster-III while the
accessions of cluster-IV were at number two. ClusterVI had such accessions which witnessed maximum
value for the number of grains spike-1 while cluster-II
was next smaller group. Grains yield plant-1 had
highest value in cluster-III followed by the accessions
of cluster-II. Accessions of cluster-III showed
Table 3. Grouping based on mean of different clusters of sixty wheat genotypes
Parameters
Cluster-I
Cluster-II
Cluster-III
Cluster-IV
Cluster-V
Cluster-VI
NO of Tillers Plant-1
10.56±.43
10.1±0.72
12.1±0.3
10.4±0.29
9.78±1.49
8.78±0.47
Plant Height
124.86±2.87
91.42±1.41
137.4±14.8
120.40±3.95
130±2.97
99.17±2.23
Spike Length
10.99±0.19
8.35±0.23
9.61±0.23
12.51±0.23
10±0.29
12.30±0.23
19.53±0.22
19.93±0.65
24.1±0.1
21.19±0.31
19±0.57
21.19±0.39
No of Grains Spike
48.19±1.62
59.85±2.41
58.2±17.6
41.58±1.76
28.35±1.43
60.76±1.99
-1
Grain Yield Plant
21.13±1.20
23.84±2.11
33.4±0.14
12.77±0.85
6.61±1.35
14.07±1.31
1000-Grain Weight
41.72±1.43
44.34±1.05
64.87±11.93
32.22±1.72
21.63±2.35
29.17±2.32
No of Spiklets Spike-1
-1
is further suggested that grain/ spike and tillers per
plant may be used a criteria for single plant selection
in the early segregating generation derived from the
multiple crosses among the selected genotypes. So,
hybridization between genotypes of divergent cluster
will lead to accumulation of favourable genes in a
single variety and also suggested to create variability
for developing the varieties involving a large number
of different lines instead of closely instead of closely
related ones (Kumar et al., 2009).
For successful breeding program, the proficient
genetic information is very important for the
evaluation and selection of genotypes especially in
wheat to improve the grain yield. But to increase the
breeding efficiency, the more important is the
availability of correct information about the traits on
which the yield of a crop depends (Khodadadi et al.,
2011). The assessment of the genetic diversity of
different wheat lines is very important to get the
useful information about the competent genotype
(White et al., 2008).
Based on cluster means, the cluster have been
identified for selecting parents for future
hybridization programme. The genotypes superior in
the cluster may be involve in a multiple crossing
programme to recover transgressive segregants with
high genetic yield potential. It is observed that grains
per spike and number of tillers per plants are showing
positive significant relationship with grain yield. So it
Conclusion
It was concluded that information about characters
that correlate with the grain yield is very important.
Cluster analyses to select the best group of genotype
are also play important role. The genotypes in the
group III can be used for breeding purpose to further
studies.
International Journal of Modern Agriculture, Volume 4, No.1, 2015 28 component analyses for breeding strategies.
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