International Research Journal of Plant Science (ISSN: 2141-5447) Vol. 2(7) pp. 191-200, March, 2011
Available online http://www.interesjournals.org/IRJPS
Copyright © 2011 International Research Journals
Full length Research Paper
Correlation, path-coefficient and genetic diversity in
lentil (Lens culinaris Medik) under rainfed conditions
Sunil Dutt Tyagi and Mudasir Hafiz Khan
Division of Plant Breeding and Genetics KPG College, Ghaziabad (UP)
Accepted 21 March, 2011
An experiment was carried out during winter (rabi) season of 2007 and 2008 to assess the correlation,
path coefficient and genetic diversity in 30 morphological diverse accessions of lentil (Lens culinaris
Medik) under rainfed conditions. Days to 50% flowering, biological yield/plant, seed yield/plant and
100-seed weight showed significant differences and wide variations during both years. Low
differences between phenotypic coefficient of variability and genotypic coefficient of variability were
observed for all the descriptors during both years. Pods/plant, days to 50% flowering, biological
yield/plant, seed yield/plant and 100-seed weight in both the years showed high heritability coupled
with high genetic advance (per cent of mean) signifying the influence of additive gene effects. The
characters viz., biological yield/plant and number of primary branches/plant showed positive and
significant correlations with seed yield/plant and exerted positive and high direct effects on seed
yield/plant for both years. D2 analysis groped accessions into three clusters having 16, 10 and 4
accessions. The highest genetic diversity was observed between cluster I and III. Hence, accessions
belonging to cluster I and III can be used as parents for hybridization programme for the development
of high yielding lentil genotypes under rainfed conditions.
Key words: Correlation, cluster analysis, lentil, path coefficient, variability.
INTRODUCTION
Among pulses, lentil (Lens culinaris Medik) with 2n = 14,
is one the most important legume crops in India. It is one
of the principal crops cultivated in semi arid regions of the
world, particularly in the Indian sub-continent, and the dry
areas of Middle East. Globally, lentil shows only 5-6% of
the total area under pulses. It is predominantly grown in
Asia which accounts for 80 – 95% global area and
production (Malik, 2005), respectively. However, even
now over 2/3rd of the cultivated area is un-irrigated
*Corresponding author Email: kmudasirhafiz@yahoo.com
and productivity in these areas can only be increased by
the development of crops that are well adapted to dry
conditions. Genetic variability is a pre-requisite for any
crop improvement programme. The knowledge of genetic
diversity and association of characters with yield is of
great importance to the breeder for making an
improvement in a complex character like seed yield which
showed little response to direct selection.
Path analysis is used to determine the amount of direct
and indirect effects of the causal components on the
complex component (Guler et al., 2001). The
relationships between yield and plant characters affecting
192 Int. Res. J. Plant Sci.
yield and in between these characters are usually
neglected and often reported as meaningless (Sing et al.,
1973). Ghafoor et al. (1990) found positive direct effect of
harvest index and biological yield on yield. According to
path analysis, there were strong direct effects of the
biological yield, harvest index and number of seeds per
plant on the seed yield (Ciftci et al., 2004). Yadav et al.
(2003) reported that seed yield/per plant showed a
positive and significant association with biological
yield/per plant and harvest index. Biological yield/per
plant had the positive direct effect on seed yield. Kakde
et al. (2005) found that seed yield/plant was positively
correlated with harvest index but it was negatively
correlated with pods number/per plant. Harvest index and
biological yield showed direct relationship with seed yield.
However, days to maturity and pods/per plant had direct
effect on seed yield/per plant. Bicer and Şakar (2008)
reported that total biological yield and number of clusters
and pods per plant had high positive direct effects on
seed yield. Younis et al. (2008) explained that days to
flowering, plant height, number of primary branches,
biological yield, harvest index and hundred seed weigh
had positive direct effects on seed yield. Biological yield,
hundred seed weigh and harvest index also had positive
and highly significant genotypic and phenotypic
correlation with seed yield. Hence, these traits could be
used in breeding for seed yield in lentil.
Cluster analysis helps to understand the genetic
relation among the accessions and also to facilitate the
selection of genetically diverse parents in hybridization
programme resulting in considerable amount of heterosis
and wide range of segregation. Genetic divergence has
been studied in lentil (Kumar et al., 2004; Sirohi et al.,
2007; Solanki et al., 2000 and Sultana et al., 2005)
legumes (Ghafoor and Ahmad, 2005) . Hence, the study
was taken to investigate the extent of genetic diversity,
association of seed yield/plant with other quantitative
characters and to estimate the direct and indirect effects
of various characters on seed yield in lentil under rainfed
conditions.
MATERIALS AND METHODS
Experimental material
Seeds of thirty accessions of lentil viz., P-32225, L-412, L-4661, L4677, L-415, L-3 96, L-4676, L-4618, L-386, L-381, L-4594, L-¸ L4598, L-2147, L-4596, L-414, L-4597, L-416, L-417, L-310, L-307,
L-4595, L-4674, L-308, L-306, L-309, L-4671, L-395, L-4672 and L-
4620 were obtained from National Bureau of Plant Genetic
Resource, New Delhi, India.
Experimental layout
The experimental was laid in a randomized block design during
winter (rabi) 2007 and 2008 under rainfed conditions at
Experimental Farm of Kisan (PG) College, Simbhaoli, Ghaziabad
(UP), India. In each of the four experiments (2 sowing dates x 2
years), each genotype was assigned to a single row/plot of 3 mt
length in each replication. The row to row and plant to plant
distance was kept at 25 and 10 cm, respectively.
During
experiment only pre-sowing irrigation was applied to ensure proper
seed germination. The total rainfall during the growth period was
30-35 cm which was sufficient to maintain moisture stress condition
under field conditions.
Recording of data
The data were recorded from 20 randomly selected plants from
each treatment on eleven distinct morphological characters namely
days to 50% flowering, day to maturity, number of primary
branches, number of secondary branches, pods plant-1, plant
height, seeds pod-1, seed yield (g), biological yield (g), 100-Seed
weight (g) and harvest index (%).
Statistical analysis
Statistical analysis was performed on quantitative characters for
each year and pooled analysis was carried out when the errors
were homogeneous. Phenotypic and genotypic coefficients of
variations were calculated according to Burton (1952). Heritability
(bs) and expected genetic advance were estimated according to
Burton (1952) and Burton and Devane (1953), respectively. The
estimates of direct and indirect contribution of various
characteristics to seed yield were calculated through path
coefficient analysis as suggested by Wright (1921) and elaborated
by Dewey and Lu (1959). The quantitative diversity of genotypes
was grouped into different clusters following Toucher’s Method
(Rao, 1952).
RESULTS
Analysis of variance and coefficient of variability
The analysis of variance for different characters is
presented in Table 1. The critical perusal of table
revealed that highly significant genotypic differences
were observed for all the characters under study. The
genotypic coefficient of variability (GCV) and phenotypic
coefficient of variability (PCV) for various characters
studied are presented in Table 2. It was observed that the
genotypic coefficient of variation for biological yield/plant,
seed yield/plant, 100-seed weight, and days to 50%
Tyagi and Khan 193
Table 1. Pooled (2007 and 2008) analysis of variance for quantitative characters
Source
variation
of
Environment
(E)
Replication
(R)
ExR
Genotypes
(G)
ExG
Error
df
Day to
maturity
3
Days
to
50%
flowering
525.42**
Number
of
Second
5.30**
Pods
plant-1
564.00**
Number
of
Primary
1.26**
Seeds
pod-1
seed
yield (g)
432.33**
Plant
Height
(cm)
156.04**
2
100Seed
weight
2.71*
Harvest
index (%)
245.37**
Biologica
l
yield (g)
27.37**
0.18**
128.75**
4.25
0.05
0.02
10.38
40.97**
0.02
3.95
0.51
0.05
0.72
6
42.33**
8.75*
0.20
0.46
28.58
4.44
0.02
6.39
0.44
0.03
1.99*
29
4049.84**
709.16**
1.67**
5.34*
2445.15**
103.33**
0.29**
270.97**
30.29**
14.37**
4.79**
87
232
14.89
11.99
21.68**
3.84
0.09
0.10
0.26
0.37
32.61
60.81
5.69
7.77
0.01
0.02
5.86
7.69
0.51
0.79
0.10**
0.04
3.23**
0.72
Table 2: Variability, heritability and expected genetic advance for quantitative characters
Source of variation
Days to 50% flowering
Day to maturity
Number of Primary branches
Number of Secondary branches
Pods plant-1
Plant height
Seeds pod-1
Seed yield (g)
Biological yield (g)
100-Seed weight (g)
Harvest index (%)
Yea
r
Range
I
II
I
II
I
II
I
II
I
II
I
II
I
II
I
II
I
II
I
II
I
II
45.50-106.17
44.00-105.00
126.50-151.00
125.00-151.00
3.34-5.05
3.52-5.22
6.20-8.67
6.60-8.92
52.95-400.33
52.32-100.67
26.62-37.19
26.09-38.37
1.30-1.88
1.33-1.89
9.29-30.14
10.10-29.87
3.02-10.15
3.25-9.79
2.74-7.94
2.63-7.84
31.27-34.92
29.37-33.89
Parameters
Mean
GCV %
PCV %
85.38
85.67
143.36
142.47
4.18
4.24
7.47
7.70
81.65
83.30
32.04
32.50
1.62
1.68
20.12
21.63
6.59
6.93
5.04
5.02
32.76
31.99
21.64
21.32
5.46
5.68
7.91
9.04
7.31
8.98
16.19
17.32
7.87
8.96
9.54
8.62
22.50
21.82
22.94
22.59
21.88
21.84
2.86
3.11
22.21
21.50
5.68
5.79
11.69
11.07
11.63
11.15
20.12
18.25
13.14
10.86
12.66
10.62
27.48
23.69
27.55
24.48
22.30
22.07
4.06
3.96
Heritabili
ty
94.90
98.35
92.25
96.25
46.35
67.55
41.50
65.70
65.85
90.20
39.50
67.80
57.70
66.40
68.85
84.90
70.55
85.40
96.30
97.95
51.50
61.40
Expected
advance
43.43
43.18
10.79
11.66
11.15
14.02
9.68
13.92
27.01
30.81
10.46
15.19
15.20
16.38
38.22
38.60
39.59
39.48
44.27
42.68
4.37
5.04
genetic
26.29**
194 Int. Res. J. Plant Sci.
flowering was high during both years , 2007 and 2008
with the values of 22.50 and 21.82; 22.94 and 22.59;
21.88 and 21.84 and 21.64 and 21.32, per cent,
respectively. However, moderate genotypic coefficient of
variation was observed for pods/plant, number of primary
branches, number of secondary branches, seeds/pod
and plant height with the values of 16.19 and 17.32; 7.91
and 9.04; 7.31 and 8.98; 9.54 and 8.62 and 7.87 and
8.96 per cent, respectively. However, the rest of the
characters showed low values of genotypic coefficient of
variation. On the other hand, phenotypic coefficient of
variation also exhibited similar trend of high, moderate
and low variations with slightly higher values.
Heritability and expected genetic advance
The estimates of heritability in broad sense and expected
genetic advance for various characters studied are
presented in Table 2. The estimates of heritability in
broad sense were high for days to 50 per cent flowering
(94.90 and 98.35), days to maturity (92.25 and 96.25),
100-seed weight (96.30 and 97.95), seed yield per plant
(70.55 and 85.40), biological yield per plant (68.85 and
84.90) and pods per plant (65.85 and 90.20). Moderate
heritability estimates were observed for seeds per pod
(57.70 and 66.40), harvest index (51.50 and 61.40),
number of primary branches (46.35 and 67.55) and
number of secondary branches (41.50 and 65.70). Low
heritability was observed for plant height (39.50 and
67.80)
Highest value of expected genetic advance, expressed
as per cent of mean during both years, 2007 and 2008
was obtained for days to 50 per cent flowering (43.43 and
43.18), 100-seed weight (44.27 and 42.68) seed yield per
plant (39.59 and 39.48) and biological yield per plant
(38.22 and 38.60), while the rest of the character showed
moderate to low values of genetic advance as per cent of
mean.
Correlation and path coefficient analysis
The results pertaining to correlation coefficients are
presented in Table 3. The seed yield/plant was positively
and significantly associated with number of primary
branches, secondary branches, pods/plant, plant height,
biological yield/plant and 100-seed weight. Number of
seeds/pod showed negative and significant correlation
with 100-seed weight in both years. Primary branches
and secondary branches/plant showed positive and
significant correlation with pods/plant, plant height and
biological yield/plant during both years of experiment.
Pods/plant showed significant and positive association
with plant height and biological yield/plant.
The results pertaining to path analysis are presented in
Table 3. It can be noticed from the table that out of 11
characters four exhibited positive direct effect on seed
yield/plant during both years, whereas three characters
showed positive direct effect during first year and
negative direct effect during second year. Days to
maturity exhibited negative direct effect during both years
of experiment.
Indirect effects on seed yield/plant were also
estimated and it was found that biological yield/plant
showed maximum indirect effect via number of primary
branches and plant height while the indirect effect of plant
height was also positive via number of primary branches
and biological yield/plant. Similarly, pods/plant showed
positive indirect effect via number of primary branches,
plant height and biological yield, while harvest index
exhibited positive indirect effect via primary branches,
plant height, biological yield and 100-seed yield.
Genetic divergence
2
On the basis of D values, all the 30 genotypes were
grouped in 3 clusters (Table 4). The maximum number of
genotypes (16) was grouped in cluster II followed by
cluster III (10) and cluster I (4). The intra and inter-cluster
distance among the genotypes was of varying magnitude
(Table 5). The maximum intra- cluster distance was
observed in cluster III (2.896) followed by cluster I (2.643)
and cluster II (2.309). The maximum inter-cluster
distance was observed between cluster III and I (5.155)
followed by cluster III and II (3.467) and cluster II and I
(3.233).
The cluster mean for each character is presented in
Table 6. Highest mean value for days to 50% flowering
(94.10) and days to maturity (146.87) was observed in
cluster III, while least mean values for these characters
were observed in cluster I. Cluster I exhibited the highest
mean values for number of primary branches (4.49),
number of secondary branches (8.30), pods/plant
(96.62), plant height (34.94), biological yield/plant (24.29)
and seed yield/plant (8.00).Cluster II consisted of
seeds/pod (1.69), 100-seed weight (5.22) and harvest
index (33.15) genotypes while, least mean values for
these character were observed in
Tyagi and Khan 195
Table 3. Direct (bold) and indirect effects of characters on seed yield/plant and correlation coefficients (in parenthesis) between characters.
Character
s
Year
Days to
50%
flowering
Day to
maturity
Primary
branches
Secondary
branches
Pods
plant-1
Plant
height (cm)
Seeds
pod-1
Biological
yield (g)
100-Seed
weight (g)
Harvest
index
(%)
seed
yield (g)
Days
to
50%
flowering
I
0.020
0.025
Day to
maturity
I
0.014
II
0.017
-0.008
I
-0.006
0.011
-0.073
(-0.296)
-0.112
(-0.322)
0.080
(-0.326*)
-0.121
(-0.348*)
0.246
II
-0.008
0.003
0.348
I
-0.002
0.013
0.211
0.006
(-0.086)
0.010
(-0.103)
0.028
(-0.379*)
0.021
(-0.211)
-0.062
(0.859**)
-0.081
(0.807**)
-0.073
II
-0.003
0.002
0.281
-0.100
I
-0.005
0.013
0.168
--0.053
-0.046
(-0.241)
0.067
(-0.257)
-0.074
(-0.389*)
0.091
(-0.348)
0.130
(0.682**)
-0.188
(0.722**)
0.139
(0.727**)
-0.174
(0.666**)
0.191
II
-0.006
0.003
0.251
-0.067
-0.261
I
-0.005
0.008
0.142
-0.034
0.160
-0.004
(-0.256)
-0.023
(-0.285)
-0.004
(-0.247)
-0.022
(-0.269)
0.009
(0.575**)
0.050
(0.614**)
0.008
(0.462*)
0.046
(0.562**)
0.014
(0.836**)
0.062
(0.755**)
0.017
-0.020
(-0.138)
0.043
(-0.193)
0.015
(0.100)
-0.003
(0.012)
0.049
(0.336*)
-0.059
(0.267)
0.033
(0.229)
-0.036
(0.164)
0.027
(0.183)
-0.071
(0.320)
-0.015
-0.135
(-0.250)
-0.231
(-0.258)
-0.146
(-0.271)
-0.306
(-0.342)
0.521
(0.965**)
0.839
(0.938**)
0.468
(0.867**)
0.674
(0.753**)
0.374
(0.692**)
0.617
(0.690**)
0.222
(0.412*)
0.014
0.055)
-0.021
(0.081)
-0.009
(-0.035)
0.008
(-0.032)
0.101
(0.309*)
-0.061
(0.239)
0.073
(0.282)
-0.040
(0.156)
-0.034
(-0.132)
0.063
(-0.245)
-0.032
(-0.122)
0.006
(-0.114)
0.011
(0.042)
0.011
(-0.218)
0.006
(0.024)
-0.020
(0.400*)
0.090
(0.360)
-0.006
(0.129)
0.133
(0.532**)
-0.008
(0.151)
0.120
(0.480**)
-0.023
(0.459*)
-0.255
II
-0.023
(0.699**)
-0.006
(0.681**)
-0.033
0.258
0.475**
Number of
primary
branches
Number of
secondary
branches
Pods
plant-1
Plant
height
(cm)
Seeds
pod-1
Biological
yield (g)
100-Seed
weight (g)
Harvest
index (%)
(-0.100)
II
-0.007
0.002
0.213
-0.056
-0.197
0.082
I
-0.003
-0.003
0.083
-0.017
0.035
-0.002
-0.073
(0.331)
0.146
II
0.005
0.000
0.093
-0.063
-0.083
0.027
-0.221
I
-0.005
0.009
0.238
-0.063
0.132
0.007
0.025
0.569
(0.636**)
0.093
(0.172)
-0.017
(-0.019)
0.540
II
-0.006
0.003
0.326
-0.075
-0.180
0.052
0.004
0.895
I
0.001
0.001
0.096
-0.020
-0.025
-0.002
-0.075
0.256
0.035
(-0.135)
-0.134
(-0.514**)
0.182
(-0.709**)
0.123
(0.474**)
-0.116
(0.453*)
0.260
II
0.002
0.000
0.083
-0.016
0.064
-0.011
0.157
0.405
-0.257
I
-0.002
0.007
0.099
-0.009
0.029
0.008
0.022
0.098
0.057
0.048
(0.192)
-0.007
(0.148)
0.066
(0.263)
-0.009
(0.350)
0.088
(0.219)
-0.011
(-0.022)
-0.006
(-0.022)
-0.050
II
-0.001
0.000
0.125
-0.053
-0.125
0.16
-0.058
0.313
0.006
0.251
-0.238
-0.028
-0.317
0.980**
0.933**
0.865**
0.783**
0.687**
0.712**
0.441*
0.616**
0.191
0.025
0.997**
0.990**
0.481**
0.422*
196 Int. Res. J. Plant Sci.
Table 4. Distribution of 30 genotypes in various clusters
Cluster I
Number
genotypes
4
Cluster II
16
Cluster III
10
Cluster
of
Genotypes included
P-32225, L-412, L-4661, L-4677
L-415, L-3 96, L-4676, L-4618, L-386, L-381, L-4594, L-¸ L-4598, L-2147, L4596, L-414, L-4597, L-416, L-417, L-310
L-307, L-4595, L-4674, L-308, L-306, L-309, L-4671, L-395, L-4672, L-4620
Table 5. Average inter and intra-cluster (bold values) distances among different cluster in lentil
Cluster
Cluster I
Cluster II
Cluster III
Cluster I
2.643
3.233
5.155
Cluster II
Cluster III
2.309
3.467
2.896
Table 6. Cluster means for different clusters in lentil
Characters
Days to 50% flowering
Day to maturity
Number of Primary branches
Number of Secondary branches
Pods plant-1
Plant height
-1
Seeds pod
Seed yield (g)
Biological yield (g)
100-Seed weight (g)
Harvest index (%)
Cluster I
55.42
128.00
4.49
8.30
96.62
34.94
1.59
24.29
8.00
5.09
32.98
Cluster II
90.71
146.67
4.41
7.85
90.46
34.44
1.69
23.96
7.93
5.22
33.15
Cluster III
94.10
146.87
3.83
7.08
66.46
30.17
1.60
16.05
5.31
5.18
33.07
cluster I. Cluster III was found as the low seed yields
among the three clusters. The low mean values for
pods/plant, plant height and biological yield/plant were
found in cluster III. Overall results indicated that
maximum mean values for most of the yield contributing
characters were found in cluster III.
of lentil. It will provide an effective means to assess the
extent of available variability, which will be useful for
selecting superior genotypes on the basis of their
phenotypic expression so as to use them in breeding
programme to improve the commercially important
characters.
DISCUSSION
Variability, heritability and genetic advance
The aim of the present investigation was to study the
genetic divergence in 30 genotypes on eleven characters
The mean sum of squares due to genotypes was
significant for all the characters indicating that the
Tyagi and Khan 197
variation was genetic. Though variability in population is
an indispensable prerequisite for any improvement, it
cannot be the only criterion for deciding, as to which trait
is showing the highest degree of variability. Phenotypic
and genotypic variances and coefficient of variation can
help in this regard. Maximum variation (phenotypic and
genotypic) was exhibited by biological yield/plant, seed
yield/plant, 100-seed weight, and days to 50% flowering.
Similar results on variability for different characters were
reported by Chakraborty and Haque (2000), Kishore and
Gupta (2002), Rathi et al. (2002), Bicer and Sakar
(2004), Haddad (2004) and Singh et al. (2005). However,
low variability for other yield contributing traits was
reported by Ayaz et al. (2004) and Singh et al. (2004).
The minimum variation was recorded for number of
primary branches per plant, harvest index, days to
maturity and number of seeds per pod. Results on these
aspects were reported by El-Attar (1991) and
Chakraborty and Haque (2000).
Genetic variability is very important for the
improvement of crop plants. The more the variability in
the population, the greater is the chances for producing
desired plant types. Heritability estimates and genetic
advance in a population provides information about the
expected grain in the following generations. The most
important functions of heritability estimates in the genetic
studies of quantitative characters is their predictive role.
Possible advance through selection based on phenotypic
values can be predicted only from knowledge of the
degree of correspondence between phenotypic and
genotypic values.
Thus, it is clear that a character with high GCV and
high heritability will have high genetic advance. It can be
stated that high heritability for a trait does not necessarily
mean that it will also show high genetic gain, unless it is
coupled with high GCV. The heritability, which is a ratio of
genotypic and phenotypic variance, is mainly due to the
additive gene effects in narrow sense, but in the broad
sense it includes both additive as well as non-additive
gene effects. The heritability values estimated in the
present study are expressed in broad sense. Broad
sense heritability, however, gives only a rough estimate.
Moreover, broad sense heritability and narrow sense
heritability
are
generally
negatively
correlated
(Kempthorne, 1957). If heritability was mainly due to
additive effects, it would be associated with high genetic
gain and if it is due to non-additive, genetic gain would be
low (Panse, 1957).
Only six characters namely, days to 50 per cent
flowering, days to maturity, 100-seed weight, seed yield
per plant, biological yield per plant and pods per plant
showed high heritability. The high heritability indicated
that the characters were less influenced by the
environment. Singh (1999), Chakraborty and Haque
(2000), Kishore and Gupta (2002), Rathi et al. (2002),
Bicer and Sakar (2004), Singh et al. (2005) also
estimated high heritability for important morphological
traits. Table 2 reveals that days to maturity showed high
heritability, did not show equally high genetic advance.
Johnson et al. (1955) suggested that characters with high
heritability coupled with high genetic advance would
respond to selection better than those with high
heritability and low genetic advance.
The characters like days to 50% flowering, 100-seed
weight, seed yield/plant, biological yield/plant and
pods/plant showed both high heritability as well as high
genetic advance which could be improved through either
pure line selection or simple mass selection.
Moderate heritability and medium genetic advance
was observed for seeds per pod number of primary
branches and number of secondary branches. It indicates
that these parameters are governed by additive gene
action and could be equally improved through selection.
On the other hand, harvest index exhibited moderate
heritability with low genetic advance indicating that this
character was governed by non-additive genes and
selection would not be effective for this character.
Days to 50% flowering, biological yield per plant, seed
yield/plant and 100-seed weight showed high GCV,
heritability and genetic advance (as percentage of mean).
This indicated that these characters were governed by
additive gene effects and can be improved through
selections effectively. On the other hand, days to maturity
exhibited low GCV and genetic advance with high
heritability indicating non-additive gene effects and for
improving this character heterosis breeding or recurrent
selection should be followed. Genotypes which exhibited
both high variability and heritability along with high
genetic advance for certain characters may be evaluated
in multi-location trials and isolated as donors for these
characters or used as parents in hybrid development
programme.
Correlation and path coefficient analysis
Seed yield is a complex quantitative character governed
by large number of genes and is highly influenced by the
environment. Studies on correlation provide an
198 Int. Res. J. Plant Sci.
opportunity for critically assessing the relationship of
these characters with seed yield. The correlation over the
wide range of environments is likely to give true picture
about the relationship, which will help the breeder to
formulate strategies for indirect selection. Therefore, it is
always possible to bring in improvement by resorting to
indirect selection for one or more of its component
characters. From this point of view, the information on
correlation of seed yield with related traits is the
prerequisite to form an effective selection strategy aimed
at its improvement.
Agarwal et al. (2001) elaborated the fact that positive
correlation with seed yield and cluster per plant was
consistent. This positive association was earlier reported
Chauhan and Singh (2001). Moreover, positive
association of pods per plant, biological yield/plant, plant
height and 100-seed weight was additionally supported
by Hamdi et al. (2003) and Luthra asnd Sharma (1990).
High positive correlation of pods plant-1 with seed yield
may be attributed to the increased sink strength
(Nakaseko, 1984). So these characters may be
considered as important selection criteria for making
significant gain in seed yield. Changes in direction and
magnitude of correlation coefficient observed between
the years may be ascribed to significant genotype x
environment interaction. This is confirmed by treatment x
year interaction (Table 1).
Knowledge of correlation alone is often misleading as
the correlation observed may not be always true. Two
characters may show correlation just because they are
correlated with a common third one. In such cases, it
becomes necessary to study a method which takes into
account the causal relationship between the variables in
addition to the degree of such relationship. Path
coefficient analysis measures the direct influence of one
variable upon the other and permits separation of
correlation coefficients into components of direct and
indirect effects. Partitioning of total correlation into direct
and indirect effects provides actual information on
contribution of characters and thus forms the basis for
selection to improve the yield.
Path coefficient analysis (Table 3) for seed yield
revealed that the traits like biological yield, number of
primary branches and plant height and showed highest
positive direct effect on seed yield. It means a slight
increase in any one of the above traits may directly
contribute towards seed yield. Similar results have also
been reported by Solanki (2006), Yadav et al. (2005) and
Joshi et al. (2005). Positive direct effect of biological yield
and plant height and indirect positive effect via number of
primary branches were the main reason for strong
positive correlation of these characters with seed yield
(0.997** and 0.990** and 0.441** and 0.616**,
respectively). Similar results were reported by Dixit and
Dubey (1984).
The path coefficient analysis revealed that direct and
indirect contribution of biological yield, plant height,
number of primary branches and pods plant-1 was
maximum on seed yield. The above findings revealed
that whatever may be the character chosen for increasing
the seed yield, the improvement could be achieved
mainly through these traits. The residual effect was found
to be moderate which indicates that there may be some
more components that are contributing towards seed
yield.
Genetic Divergence
Genetic divergence (D2) is the basis of variability and
helps to craft the designed genotypes as per the
requirement. The present study aims at analyzing the
genetic divergence of 30 genotypes of indigenous and
exotic origin using D2 statistics. The pattern of clustering
in respect to lentil genotypes depicted very interesting
picture. By and large, there were 3 distinct clusters
encompassing all genotypes. The clustering pattern also
revealed that the groups of genotypes which were
together in a cluster also indirectly proved their stable
performance. Another important observation was the
presence of an exclusively separate cluster of lentil
genotypes,
which
suggested
that
geographical
background also plays an important role in constellation.
The exclusive distinct cluster by the lentil genotypes
included in the studies proves that there is a clear
relationship between clustering pattern and geographical
distribution. Jeena and Singh (2002), Kumar et al. (2002),
Haddad (2004), Sarker et al. (2005), Sirohi (2007) and
Solanki (2007) also studied the genetic divergence in
lentil.
Thus, in view of considerable genetic diversity in lentil
found in the present study, their appearance had
sufficient scope for genotypic improvement through
hybridization between the genotypes from divergent
clusters.
Tyagi and Khan 199
ACKNOWLEDGEMENT
The financial support of this research from the Kisan P.G.
College, Simbhaoli, Ghaziabad, U.P,
India is
appreciated.
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