International Journal of Agriculture, Forestry and Fisheries
2014; 2(1): 16-21
Published online March 20, 2014 (http://www.openscienceonline.com/journal/ijaff)
Character association and casual effects of
polygenic traits in spring wheat (Triticum
aestivum L.) genotypes
Muhammad Zeeshan1, *, Waheed Arshad1, Muhammad Imran Khan1, Shiraz Ali1, Muhammad Tariq2
1
2
Barani Agricultural Research Station, Fatehjang, Pakistan
Barani Agricultural Research Institute, Chakwal, Pakistan
Email address
[email protected] (M. Zeeshan)
To cite this article
Muhammad Zeeshan, Waheed Arshad, Muhammad Imran Khan, Shiraz Ali, Muhammad Tariq. Character Association and Casual
Effects of Polygenic Traits in Spring Wheat (Triticum aestivum L.) Genotypes, International Journal of Agriculture, Forestry and
Fisheries. Vol. 2, No. 1, 2014, pp. 16-21
Abstract
The present study was conducted at Barani Agricultural Research Station, Fatehjang, during rabi 2011-12 to evaluate
variability, heritability, genetic advance, character association and their causal effects on grain yield plant-1 in ten wheat
genotypes viz. BARS-09, AARI-11, Punjab-11, Millat-11, Chakwal-50, Lasani-08, NARC-09, Seher-06, Auqab-2000
and 05FJS3074. All the genotypes demonstrated highly significant differences for all the traits. Grain yield plant-1 had
positive and significant correlation at both genotypic and phenotypic levels with 1000-grain weight and harvest index,
number of tillers plant-1 and spike length but non-significant with number of spikelets spike-1 and flag leaf area. 1000grain weight showed maximum direct effect (0.7298) towards grain yield plant-1 followed by number of tillers plant-1
(0.6638), harvest index (0.4021) and spike length (0.2629) while lowest direct effect was contribute by flag leaf area (0.1196). High heritability genetic advance was exhibited by number of tillers plant-1, harvest index and grain yield plant-1
that confirms their additive gene action. Characters like; 1000-grain weight, spike length, number of tillers plant-1 and
harvest index should be considered in selection procedure towards improvement in grain yield plant-1 indirectly.
Keywords
Wheat, Yield Components, Heritability, Genetic Advance, Correlation, Path Coefficient
1. Introduction
Wheat (Triticum aestivum L.) is the staple food in
Pakistan and cultivated at an area of 8.9007 million
hectares with production of 25.2138 million tones with an
average yield of 2833 kg/ha (Government of Pakistan,
2011). Being an agricultural country it is our basic aim to
obtain self sufficiency and to export surplus amount of
wheat to generate revenue. Wheat being the most important
agricultural commodity hence improvement in its yield
must be regularly made with the passage of time. Grain
yield being a complex attribute and governed by several
traits that contribute towards it directly or indirectly.
Character association is the most important and basic
technique to evaluate the behavior and interrelationship of
different plant parameters in combination of each other and
provides us the magnitude to which various characters are
associated towards yield. By obtaining association
information breeders can suggest the selection criteria for
further improvement. Along with traits association other
studies like their heritability and genetic advance confirms
the genetic behavior. Furthermore each character’s effect
on grain yield is studied by path coefficients, which enables
us to perform direct or indirect kind of selection towards
grain yield which is the ultimate objective of every
breeding program. Since path coefficient analysis was
applied first by Dewey and Lu (1959), this statistical
method was followed by many scientists to facilitate
selection and breeding program enhancements.
International Journal of Agriculture, Forestry and Fisheries 2014, 2(1): 16-21
Khaliq et al. (2004) evaluated 5 wheat genotypes and
their 20 hybrids which showed positive and significant
phenotypic and genotypic correlation of plant height, tillers
plant-1, spike length, spikeltes spike-1, number of grains
spike-1 and 1000-grain weight with grain yield plant-1 and
spike length with highest direct effect on grain yield plant-1.
Khan et al. (2005) studies positive significant correlation of
tillers plant-1, spike length and 1000-grain weight with
grain yield while plant height and spikelets spike-1 had
negative correlation with grain yield. Further tillers plant-1
had maximum direct casual effect on grain yield. Joshi et al.
(2008) reported grain yield was positively correlated with
tillers number at genotypic level. They further suggest that
grain filling period is an important factor and number of
tillers and grains spike-1 should also be considered during
selection for getting high yielding genotypes. Mohsin et al.
(2009) concluded that grain yield correlated positively with
major yield contributing traits. Path coefficient analysis
indicated that grain yield had a positive direct effect with
spike length and number of grains spike-1 that can be
considered as a suitable selection criteria for evolving high
yielding elite wheat genotypes. Maximum heritability value
was exhibited by harvest index. Genetic gain was observed
in number of spikelets spike-1 followed by days to heading
and 1000-grain weight. Khan and Dar (2010) reported that
the magnitude of positive direct effect on seed yield was
highest through number of spikelets plant -1, followed by
number of grains spike -1 and 100-seed weight. Khokhar et
al. (2010) observed that days to maturity had highest
correlation value and maximum direct effect on grain yield
plant-1, while plant height showed negative correlation with
grain yield plant-1. El- Mohsen et al. (2012) reported
positive association in between number of tillers plant-1,
number of spikelets spike-1, spike length, number of grains
spike-1 and 1000 grain weight with grain yield plant-1 at
both phenotypic and phenotypic levels. However, days to
50% heading and plant height contributed negatively
towards grain yield at both levels. Path analysis showed
that maximum positive direct effect on grain yield plant-1
was contributed mostly by number of grains spike-1,
followed by number of tillers plant-1 and 1000-grain
weights. Baranwal et al. (2012) revealed that yield plot-1
had high positive and significant correlation with tillers per
m2 and 1000-grain weight. Path coefficient analysis
revealed maximum direct contribution towards yield plot-1
with sheath length followed by grains spike-1. Tsegaye et al.
(2012) found biological yield, tillers plant-1 and 1000kernal weight had positive correlation with grain yield
plant-1. The cause and effect study showed that biological
yield and harvest index are the top positive direct
contributors towards yield. High heritability coupled with
high genetic advance as percent of mean was observed for
thousand grain weight, spike length and number of
spikelets spike-1.
While keeping in mind above citations this study was
carried out to evaluate 10 wheat genotypes for their
interrelationship and path coefficient analysis to observe
17
their heritability, genetic advance, genetic divergence,
correlation and causal effects to discover the best trait
combination towards yield enhancement.
2. Materials and Methods
The studied material consists of ten wheat genotypes
namely BARS-09, AARI-11, Punjab-11, Millat-11,
Chakwal-50, Lasani-08, NARC-09, Seher-06, Auqab-2000
and 05FJS3074. The plant material was sown under rainfed
conditions at the research area of Barani Agricultural
Research Station, Fatehjang, Pakistan during rabi 2011-12
in a triplicate randomized complete block design. The
experimental unit area comprise of 6m2 having 4 lines of
5m length. The row to row and plant to plant distance was
kept at 30 and 20 cm respectively. Randomly ten guarded
plants were selected of the middle two rows to avoid
biasness in the trial. The parameters under study were plant
height, spike length, number of spikelets spike-1, number of
tillers plant-1, flag leaf area, biological yield plant-1, 1000
grain weight, harvest index (%) and grain yield plant-1.
Genetic variability was calculated according to the
formulae provided by Steel et al. (1997). Further data was
analyzed for correlation according to Kown and Torrie
(1964). Path coefficient was calculated according to Dewey
and Lu (1959). Heritability and genetic advance at 5%
selection intensity were computed according to Singh and
Chaudhry (1979).
3. Results and Discussion
3.1. Genetic Variability
The mean squares as presented in table 1 indicating that
all the genotypes showing highly significant differences for
all the traits representing the genetic diversity for further
selection procedures in the experimental material under
study. The high phenotypic and genotypic coefficient of
variability in table 4 for most of the parameters under study
is the indication of the wide range of variation. The PCV
values are close to the GCV values that tells the story of
least influence of the environment on the traits. Our results
are in confirmation with early findings of Subhashchandra
et al. (2009), Abinasa et al. (2011) and Tsegaye et al. 2012.
3.2. Correlation Coefficient
Correlations of both types i.e., genotypic and phenotypic,
provides a digitized association of the traits among
themselves and also the effect of the environment on the
particular trait. As grain yield per plant is an important
parameter in every breeding programme, so ample
importance is given to its studies in different nature of
experiments. Grain yield per plant had a positive, high and
significant correlation with 1000-grain weight (0.9369) and
harvest index (0.7163) at genotypic levels and at
phenotypic level of association 0.9275 and 0.7184
respectively. Similarly if we go through table 2 we can see
18
Zeeshan et al.: Character Association and Casual Effects of Polygenic Traits in Spring Wheat
(Triticum aestivum L.) Genotypes
that grain yield had positive and significant correlation with
number of tillers plant-1 at genotypic level (0.6659) and
phenotypic level (0.5021). Similar kind of relationship
exists among grain yield plant-1 and spike length at both
levels. Similar results were reported by Khaliq et al. (2004),
Khan et al. (2005), El- Mohsen et al. (2012) and Tsegaye et
al. (2012). It can be said that grain yield can directly be
improved by selecting those parameters that had positive
and significant correlation with it. While studying
association of grain yield plant-1 among rest of the traits we
can see that it had positive but non-significant correlation
with number of spikelets spike-1 and flag leaf area. Plant
height and biological yield plant-1 were the traits that had
negative correlation with grain yield plant-1 at the
phenotypic and genotypic levels of correlation coefficient.
Confirmatory findings were reported by El- Mohsen et al.
(2012) for both traits while Mohammad et al. (2005) only
for plant height.
Table 1. Mean Squares of 10 wheat genotypes for all the traits.
SOV
Genotypes
Replication
Error
Degree
of
freedom
9
2
18
Plant
height
Spike
length
Spikelets
/ spike
Flag leaf
area
Tillers /
plant
175.58**
39.43 N.S
30.54
13.85**
0.03 N.S
3.03
8.01**
0.63 N.S
1.04
48.83**
25.24 N.S
19.09
29.64**
0.13 N.S
1.24
Biological
yield /
plant
258.41**
0.35 N.S
1.29
1000grain
weight
52.76**
0.009 N.S
0.076
Harvest
index
229.42**
0.46 N.S
1.98
Grain
yield/
plant
23.60**
0.13 N.S
0.22
**= Significant at 1% level, N.S= Non Significant
Plant height had positive genotypic and phenotypic
correlation with spike length, number of spikelets spike-1
(Zecevic et al., 2004) and harvest index while it had
negative correlation with number of tillers plant-1, 1000grain weight (Khan et al., 2005 and Joshi et al., 2008), flag
leaf area and biological yield per plant. There are vice versa
findings of Mohammad et al. (2005) and Ashraf et al.
(2002) with ours for correlation of plant height with
biological yield plant-1 and flag leaf area respectively.
While viewing table 2 it can be judged that spike length
had positive correlation with number of spikelets spike-1
(Ashraf et al., 2002 and Khan et al., 2005) and harvest
index (Ahmad et al., 2010) at both genotypic and
phenotypic levels while significant negative correlation can
be observed among spike length and number of tillers plant1
(Subhani and Chowdhry, 2000). Among rest of the traits,
number of tillers plant-1 had positive significant
correlation with biological yield plant-1 at both
genotypic and phenotypic levels Tsegaye et al. (2012).
Similarly 1000-grain weight was positively and
significantly associated with harvest index (Subhani and
Chowdhry, 2000). The association among biological
yield plant-1 and harvest index is the only one that is
negative and highly non significant at genotypic and
phenotypic correlation level (Ashraf et al., 2002), while
contradictory results were given by Subhani and
Chowdhry (2000), Ahmed et al. (2003) and Mohammad et
al. (2005).
Table 2. Genotypic (bold) and phenotypic correlations of the 10 wheat genotypes of all polygenic parameters.
Plant height
Spike
length
0.6686 *
0.3908
Spike length
Spikelets /
spike
0.0704
0.0027
0.2858
0.2334
Spikelets / spike
Flag leaf area
Flag leaf
area
-0.0211
0.031
-0.0577
0.2447
0.0865
-0.0076
Tillers /
plant
-0.4563
-0.3025
-0.6667 *
-0.5261
0.2534
0.2270
0.5096
0.2636
Tillers / plant
Biological yield
/ plant
1000-grain
weight
Harvest index
Biological
yield / plant
-0.2434
-0.1617
-0.5829
-0.4428
0.4477
0.3926
0.0745
0.0324
0.8323 **
0.8125 **
1000-grain
weight
-0.2444
-0.1941
0.3913
-0.2365
-0.283
-0.2365
0.0345
0.0203
-0.2326
-0.2139
-0.2036
-0.2005
Harvest
index
0.0912
0.0529
0.7573 *
0.0529
-0.3442
-0.2844
0.2293
-0.5759
-0.5844
-0.5759
-0.7785 **
-0.7729 **
0.6698 *
0.6636 *
Grain yield/
plant
-0.0946
-0.0854
0.5201*
0.357*
0.1695
0.1155
0.2657
0.1292
0.6659**
0.5021*
-0.1522
-0.1477
0.9369 **
0.9275**
0.7163 **
0.7184 **
**= Significant at 1% level, *= Significant at 5% level
3.3. Path Coefficient
Path coefficient studies partitions the genetic correlation
of dependent component, which is grain yield in our study,
further into its direct and indirect effects of rest of the traits.
The analysis also provides a hint to relative importance of
each causal factor and its contribution to the grain yield.
The casual effects are compiled in the table 3. The direct
contributors towards grain yield were 1000-grain weight
(0.7298), number of tillers plant-1 (0.6638) Khan et al.
(2005), harvest index (0.4021), spike length (0.2629), plant
height (0.149) and biological yield plant-1 (0.0722) our
International Journal of Agriculture, Forestry and Fisheries 2014, 2(1): 16-21
results coincide with the results of Subhani and Chowdhry
(2000) except for number of tillers plant-1. Tsegaye et al.
(2012) supported our above results for number of tillers
plant-1, harvest index and biological yield plant-1. Our
finding was also confirmed by Anwar et al. (2009). While
flag leaf area (-0.1196) and number of spikelets spike-1 (0.0749) were among the negative direct contributors.
Similar findings of Khaliq et al. (2004) and Khokhar et al.
(2010) supported our results, while our results were in
disagreement with the conclusions of Ashraf et al. (2002).
As far as indirect effects are concerned maximum
positive indirect effect was scored by harvest index via
1000-grain weight (0.4888), followed by biological yield
plant-1 via number of tillers plant-1 (0.4698), number of
tillers plant-1 via biological yield plant-1 (0.3601), spike
length via harvest index (0.3045), 1000-grain weight via
harvest index (0.2693), flag leaf area via number of tillers
plant-1 (0.2873) and spike length via 1000-grain weight
19
(0.2856). From table 3 it can be seen that maximum
negative indirect effect was posted by spike length via
number of tillers plant-1 (-0.3759), followed by harvest
index via number of tillers plant-1 (-0.3295), biological
yield plant-1 via harvest index (-0.3131) and plant height
via number of tillers plant-1 (-0.2573). From above path
coefficient analysis results it can be suggested that grain
yield plant-1 can be improved by direct selection of 1000grain weight, number of tillers plant-1, harvest index and
spike length, as they shown positive direct contribution
towards grain yield Subhani and Chowdhry (2000) and
Khan et al. (2005) supported our results. 1000-grain weight
and number of tillers per plant’s almost all indirect effects
were negative, yet their positive and significant correlation
with grain yield plant-1 is the indication of major
contribution of direct effect to grain yield plant-1. Findings
of Ahmed et al. (2003), Khokhar et al. (2010) and Tsegaye
et al. (2012) correlate with ours in this respect.
Table 3. Path Coefficients showing Direct (Bold) and indirect effects of 10 wheat genotypes for quantitative traits.
Plant height
Spike length
Spikelets / spike
Flag leaf area
Tillers / plant
Biological yield
/ plant
1000-grain
weight
Harvest index
Plant
height
Spike
length
Spikelets /
spike
Flag leaf
area
Tillers /
plant
Biological
yield / plant
0.149
0.0996
0.0465
-0.0031
-0.068
0.1758
0 .2629
0.1781
-0.0152
-0.0053
-0.0053
-0.0214
-0.0749
-0.0065
-0.019
0.0025
0.0069
-0.0104
-0.1196
-0.061
-0.2573
-0.3759
0.1429
0.2873
0 .6638
-0.0176
-0.0421
0.1323
0.0054
0.3601
1000grain
weight
-0.1783
0.2856
-0.1066
0.0252
-0.0697
-0.0363
-0.1532
-0.0335
-0.0089
0.4693
0 .0722
-0.0364
0.1029
0.0212
-0.0041
-0.1311
0.0136
0.1991
0.0258
-0.0274
-0.3295
Number of spikelets spike-1 and flag leaf area had
negative direct effects but their positive indirect effects
were more than compensated the negative effects that
resulted in positive correlation with grain yield plant-1.
Confirmatory findings of Khaliq et al. (2004), Khokhar et
al. (2010) and El- Mohsen et al. (2012) supported our
results, while Ashraf et al. (2002) found against of ours. On
the other hand plant height and biological yield plant-1 had
negative and non-significant correlation as well as weak
positive direct effect on grain yield. In view of this
selection of those traits will not be beneficial as they shown
negative correlation and weak positive direct effect towards
grain yield plant-1 (Tsegaye et al., 2012).
3.4. Heritability and Genetic Advance
Heritability and genetic advance are important selection
parameters. Heritability estimates along with genetic
advance are normally more helpful in predicting the gain
under selection than heritability alone. However, it is not
necessary that a character showing high heritability will
also show high genetic advance (Johnson et al., 1955).
Estimates of heritability also give some idea about the gene
action involved in the expression of various polygenic traits.
Heritability and genetic advance in percentage of means
are presented in table 4. High heritability coupled with
Harvest
index
Grain yield/
plant
0.0367
0.3045
-0.1384
0.0922
-0.135
-0.0946
0.5201
0.1695
0.2657
0.6659
-0.1486
-0.3131
-0.1522
-0.0147
0 .7298
0.2693
0.9369
-0.0562
0.4888
0 .4021
0.7163
high genetic advance was exhibited by number of tillers
plant-1, biological yield plant-1, harvest index, 1000grain weight and grain yield plant-1 that indicates the
presence of additive gene effects, hence effectiveness of
selection for improvement of these traits (Ashraf et al.,
2002 and Mohsin et al., 2009).
Plant height, spike
length and number of spikelets spike-1 showed moderate
amount of heritability along with low genetic advance
indicating the traits are governed by non additive gene
effects with greater influence of environment. Tsegaye et
al. (2012) supported our results while Ashraf et al. (2002)
and Mohsin et al. (2009) differed with our results for plant
height.
4. Conclusion
The present study exemplified the existence of ample
ranges of genetic diversity for most of the traits and
opportunities of the genetic gain through selection or
hybridization. 1000-grain weight had maximum positive
and significant genotypic correlation along with its
maximum direct effect hence considered to be the most
important traits while in future breeding programs in
improvement of grain yield plant-1. Other parameters like
number of tillers plant-1 and harvest index are also
20
Zeeshan et al.: Character Association and Casual Effects of Polygenic Traits in Spring Wheat
(Triticum aestivum L.) Genotypes
important as they shown additive gene effects and their
Table 4. Mean Performance, GCV, PCV, h
Agronomic traits
Plant height
Spike length
Spikelets / spike
Flag leaf area
Tillers / plant
Biological yield / plant
1000-grain weight
Harvest index
Plant height
Mean
88.967
16.769
19.834
24.789
11.467
30.782
45.619
50.749
15.051
2
b.s
GCV
7.816
11.328
7.69
12.703
26.833
18.242
9.168
28.285
18.547
direct selection is useful.
and GAPM for all the traits of wheat genotypes.
PCV
9.984
15.37
9.251
21.727
28.542
18.379
9.206
28.654
18.818
h2b.s
0.613
0.543
0.691
0.342
0.884
0.985
0.996
0.974
0.971
GAPM
12.60
17.17
13.16
15.29
51.89
61.50
18.87
34.89
37.67
Where GCV= Genotypic Coefficient of Variability, PCV= Phenotypic Coefficient of Variability, h2b.s= Heritability (broad sense), GAPM= Genetic
Advance as Percentage of Mean
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