Annals of Agri-Bio Research 19 (3) : 447-450, 2014
Correlation and Path Coefficient Analysis for Quantitative Traits in Wheat
(Triticum aestivum L.) under Normal Condition
RAVINDRA KUMAR*, BHARAT BHUSHAN, RISHI PAL AND S. S. GAURAV
Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut (U. P.), India
*(e-mail : godwalravindra@gmail.com)
ABSTRACT
Thirty cultivars of wheat were evaluated in randomised block design (RBD) for yield and yield contributing traits
during rabi 2007-08 to find out genetic variability, character association, direct and indirect effects. Significant genotypic
differences were observed for all the 11 quantitative traits studied, indicating considerable amount of variation among
genotypes. The correlation coefficient analysis showed that most of the traits were positively and significantly
correlated at both the phenotypic and genotypic levels. In case of grain yield/plant, it was highly significant and
positively associated with number of grains/plant and harvest index at both the levels. However, number of tillers/
plant, weight/ear and number of grains/spike were positively correlated with harvest index at genotypic level. Path
coefficient analysis was used to determine the direct and indirect effects of different characters on grain yield. Path
analysis revealed that the direct effects of number of grains/spike, biological yield/plant and harvest index on grain
yield. These characters merit special attention in formulating selection strategy in wheat for developing high yielding
varieties.
Key words : Genetic variability, correlation, direct and indirect effects, wheat
INTRODUCTION
MATERIALS AND METHODS
Wheat (Triticum aestivum L.) is one of the most
important food crops in the world. Among various
food grains, wheat stands next to rice, both in area
and production. The share of wheat in total food grain
production is around 35.5% and share in area is about
21.8% of the total area under food grains. The major
wheat producing states are Uttar Pradesh, Punjab,
Haryana, Madhya Pradesh, Rajasthan, Bihar,
Maharashtra, Gujarat, etc. These states contribute
about 87.5% of total wheat production in the country.
The correlation coefficient gives a measure of the
relationship between traits and provides the degree to
which various characters of a crop are associated with
productivity. Association of character with yield and
among themselves and the extent of environmental
influence on the expression of these characters are
necessary. In such situations, correlation and path
coefficient analysis could be used as an important tool
to bring information about appropriate cause and effects
relationship between yield and yield contributing traits.
Singh and Singh (1998) reported the positive significant
association between number of tillers/plant and spike
length. According to Gupta et al. (1996) the positive
significant correlation was observed between spike
length and grain yield. Correlation and path-coefficient
analysis lead us to a clear understanding of the genetic
association of various plant traits and their contribution
of various yield and yield contributing traits.
The present investigation was conducted to examine
the genetic variability, correlation coefficient and path
analysis for several morphological traits in bread wheat
(Triticum aestivum L.). The experiment was evaluated
at the Research Farm of Department of Genetics and
Plant Breeding, C. C. S. University, Meerut during
rabi 2007-08. The experimental materials consisted
of 30 diverse cultivars of bread wheat viz., PBW-226,
UP-2338, PBW-502, WHS-711, HD-2281, UP-2425,
PBW-154, HD-2932, PBW-343, HD-2687, HD-2329,
HD-1981, HD-2733, HD-2402, HD-2380, HD-2770,
HD-2620, HD-2425, HD-2307, HD-2285, HD-2189,
HD-2643, CPAN-1964, MUW-44, MUW107, WH-196,
CDWR-9526, WH-526, CPAN-1910 and CPAN-4062
grown in the randomized block design (RBD) with
three replications. Seeds were sown in the field with
spacing of 5 cm between row to row. All the
agronomical packages and practices were applied to
raise healthy crop. At the time of maturity, five plants
of each genotype from each replication were randomly
selected. The data were recorded from the randomly
selected plant from the field for various quantitative
characters viz., plant height, number of tillers/plant,
number of spikelets/spike, spike length (cm), weight/
ear (g), number of grains/spike, number of grains/
plant, biological yield/plant, grain yield/plant, test
weight and harvest index. The analysis of variance
and covariance was estimated by Panse and Sukhatme
448
Kumar, Bhushan, Pal and Gaurav
(1967). Correlation coefficient analysis was done by
using formulae developed by Johnson et al. (1955).
The path coefficient analysis was done according to
Dewey and Lu (1959).
RESULTS AND DISCUSSION
The analysis of variance showed highly significant
differences among the genotypes for all the characters
studied (Table 1), thereby suggesting the presence of
considerable amount of variability among the 30
cultivars of wheat evaluated in the present study. Similar
findings were also reported by Narwal et al. (1999)
and Tazeen and Naqvi (2009).
The study of inter-relationship among various
characters in the form of correlation is, in fact, one of
very important aspects in selection programme for
the breeder to make an effective selection based on
the correlated and uncorrelated response. The
phenotypic correlation includes a genotypic and
environmental effect, which provides information
about total association between the observable
characters. The phenotypic correlations were normally
of genetic and environmental interaction which
provided information about the association between
the two characters. Genotypic correlation is provided
a measure of genetic association between the
characters and normally used in selection. While
environmental as well as genetic architecture of a
genotype plays a great role in achieving higher yield
combined with better quality.
The genotypic and phenotypic correlation for grain
yield and its component traits in wheat is presented in
Table 2. The relationship of plant height with spike
length/plant was observed positively significant at
genotypic level negative with biological yield per plant
at both the levels. A negative significant correlation
recorded in number of tillers/plant with weight/ear at
both the levels and positive significant with harvest
index and grain yield/plant at genotypic level. A positive
significant association was observed in case of weight/
ear with number of grains/spike and biological yield/
plant at both the levels and highly significant with test
weight at phenotypic level. The relationship between
number of spikelets/spike and number of grains/spike
was highly significant at both the levels, while number
of spikelets per spike and number of grains per spike
were highly significantly correlated at genotypic level.
A positive significant association was observed
between number of grains/spike to number of grains/
Table 1. The analysis of variance (ANOVA) among 11 quantitative characters in wheat
d. f.
Characters
PH
Replication
Treatment
Error
2
29
58
NT/P
42.875
6.041
134.045** 18.692**
17.331
2.527
W/E
SL/P
NS/S
NG/S
NG/P
BY/P
GY/P
TW
HI
5.761
0.300
4.875
0.288
5.065
41.941
1383.010
22.988
14.835
19.213 123.531
4.276** 5.786** 260.745** 98405.379** 73.590** 60.419** 47.173 685.487**
0.797
1.166
18.524
22489.793
15.656
8.695
11.469 116.110
PH–Plant height, NT/P–Number of tillers/plant, W/E–Weight/ear, SL/P–Spike length/plant, NS/S–Number of spikelets/spike, NG/S–Number of grains/
spike, NG/P–Number of grains/plant, BY/P–Biological yield/plant, TW–Test weight, HI–Harvest index and GY/P–Grain yield/plant.
**Significant at P=0.01 level.
Table 2. Genotypic (above the diagonal) and phenotypic (below the diagonal) correlation coefficient among 11 characters in wheat
Characters
PH
NT/P
W/E
SL/P
NS/S
NG/S
NG/P
BY/P
HI
TW
GY/P
PH
1.00
0.12
0.26
0.20
-0.22
-0.27
-0.06
-0.80**
-0.11
-0.20
0.16
NT/P
W/E
SL/P
NS/S
-0.22
1.00
-0.38*
0.06
0.40
-0.22
0.10
-0.23
0.14
0.16
0.28
-0.29
-0.45*
1.001
0.06
0.06
0.32*
0.24
0.32*
0.28
0.80**
0.08
0.33*
0.09
0.06
1.00
-0.17
-0.23
-0.13
-0.11
0.09
0.14
0.14
-0.27
0.14
-0.12
-0.21
1.00
0.62**
0.13
0.05
0.13
-0.12
0.10
Treatment details are given in Table 1.
*,**Significant at P=0.05 and P=0.01 levels, respectively.
NG/S
NG/P
-0.41* -0.10
0.25
0.25
0.45*
0.24
-0.24
-0.04
0.79** 0.88**
1.00
0.75 **
0.55** 1.00
0.01
0.19
0.29
0.44*
-0.25
-0.27
0.23
0.24
BY/P
-0.51**
-0.26
0.45*
0.07
-0.02
-0.01
0.14
1.00
0.07
-0.18
-0.57**
HI
0.20
0.36*
0.34*
0.04
0.21
0.31*
0.61*
0.05
1.00
-0.33*
0.76**
TW
GY/P
-0.23
-0.09
-0.07
0.29
-0.29
0.38*
-0.42*
-0.31*
-0.43*
1.00
-0.18
0.20
0.46*
0.05
-0.02
0.20
0.26
0.46*
-0.51**
0.81**
-0.14
1.00
Correlation and path analysis in wheat
plant at both the levels and negative significant
correlation was observed with test weight. However,
positive significant correlation was observed in case
of number of grains/plant with harvest index at both
the levels and a negative significant correlation with
test weight at genotypic level and positive significant
association with grain yield/plant. Biological yield/plant
was negatively correlated with test weight and grain
yield/plant at both the levels. A negative association
was recorded between harvest index with test weight
and positively significant with grain yield/plant at both
the levels. The available literature has also identified
number of spikelets/spike and number of grains/spike
as major direct contributors to grain yield (Chaturvedi
and Gupta, 1995; Akanda and Mundt, 1996).
Path coefficient analysis is an important tool for
partitioning the correlation coefficients into the direct
and indirect effects of independent variables on a
dependent variable. With the inclusion of more variables
in correlation study, their indirect association becomes
more complex. Two characters may show correlation,
just because they are correlated with a common third
one. In such circumstances, path coefficient analysis
provides an effective means of a critical examination
of specific forces action to produce a given correlation
and measure the relative importance of each factor.
In this analysis, grain yield was taken as dependent
variable and the rest of the characters were considered
as independable variables. The path coefficient analysis
splits total correlation coefficient of different
characters into direct and indirect effects on grain
yield/plant in such a manner that the sum of direct
and indirect effects is equal to total genotypic
correlation (Table 3). The path analysis revealed that
number of grains/spike showed the highest positive
direct effect on number of grains/plant followed by
biological yield/plant, harvest index, number of tillers/
449
plant, test weight, plant height, number of spikelets/
spike, number of grains/plant, weight/ear and spike
length/plant showed negative direct effects on number
of grains/plant. Whereas indirect effects of plant height
showed positive effect through number of grains/plant
and number of grains/spike negatively. Number of
tillers/plant showed indirect effect through harvest
index positively and biological yield/plant negatively.
Number of spikelets/spike showed through number
of grains/spike positive indirect effect and number of
grains/plant highly indirect effect negatively. Number
of grains/spike showed positively indirect effect
through number of spikelets/spike, harvest index and
spike length/plant and negative with number of grains/
plant, test weight, plant height, number of tillers/plant
and weight/ear. Number of grains/plant showed
positively indirect effect through harvest index and
number of grains/plant, number of spikelets/spike and
number of tillers/plant and negative indirect effect
through weight/ear.
Biological yield/plant did not show indirectly positive
effect, whereas it showed highly negative with harvest
index, number of grains/plant, test weight, number of
tillers/plant and weight/ear. Test weight showed
negative indirect effect through number of grains/
plant, number of grains/spike, number of spikelets/
spike and biological yield/plant, whereas it was positive
with spike length/plant. Harvest index showed highly
indirect positive effect through number of grains/plant,
number of grains/spike and number of tillers/plant,
and highly negative through biological yield/plant and
test weight. Some other workers also indicated the
positive direct effect of plant height on grain yield
(Tazeen and Naqvi, 2009). On the other hand,
Mohammad et al. (2002) and Bharat Bhushan et al.
(2013) pointed out that plant height had negative direct
effect on grain yield. The direct effect of grain
Table 3. Direct and indirect effects of yield component characters on grain yield in wheat
Characters
PH
NT/P
W/E
SL/P
NS/S
NG/S
NG/P
BY/P
HI
TW
GY/P
PH
NT/P
W/E
SL/P
NS/S
NG/S
NG/P
BY/P
TW
HI
2.21
0.49
-0.64
0.74
-0.61
-0.91
-0.22
-0.11
-0.51
0.44
0.72
3.23
-1.47
0.31
0.46
-0.81
0.83
-0.85
-0.29
1.51
0.49
0.77
-1.70
-0.11
0.21
-0.77
-0.42
-0.78
0.12
0.08
-0.01
-0.02
-0.01
0.24
0.52
0.58
0.01
-0.17
-0.07
0.05
-0.36
0.18
-0.16
-0.28
1.31
1.03
1.16
-0.38
-0.38
-0.27
-3.44
-2.09
-3.75
-1.99
6.57
8.31
6.26
-0.09
-3.21
2.23
0.86
-2.20
-2.10
0.35
-7.75
-6.44
-8.55
-1.21
3.62
-3.97
-0.33
-1.67
2.89
0.44
-0.18
-0.07
0.90
6.31
-1.99
-3.27
-0.71
-0.28
-0.22
0.90
-0.90
-1.18
-1.30
-0.96
3.07
-0.57
0.83
1.98
0.02
-0.08
0.87
1.12
1.93
-2.16
-0.77
4.17
0.11
0.14
0.28
0.09
0.13
0.29
0.44*
0.07
-0.33*
0.76**
Bold figures represent direct effects. Residual effects=0.028. *,**Significant at P=0.05 and P=0.01 levels, respectively.
450
Kumar, Bhushan, Pal and Gaurav
numbers/spike on grain yield was positive but small.
Similarly, there are similar reports indicating the positive
effect of grain weight on grain yield (Mohammad et
al., 2002). The indirect effect of number of grains
and test weight was small due to plant height and grain
weight. Grain weight/spike had a positive direct effect
on grain yield. Similar result was also reported by earlier
workers (Ismail, 2001) and Khan and Dar (2010).
The indirect effects of grain weight via all components
were positive. These indirect positive effects were
small via number of grains and 1000-seed weight. The
direct effect of 1000-seed weight on grain yield was
positive and small. In some of the previous studies,
this positive direct effect was found (Ahmed et al.,
2011). The residual effect was found to be moderate
which indicated that there might be some more of its
components that were contributing towards grain yield.
On the basis of results, it can be concluded that
whatever may be the character chosen for increasing
the yield by selecting ideotypes having high number
of grains/spike, biological yield/plant, test weight and
grain yield. These traits showed positive direct effects
along with significantly positive association with grain
yield. Therefore, these traits are likely to be
successfully employed for the selection of high
yielding wheat cultivars.
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