INTRODUCTION
Ridge gourd [Luffa acutangula Roxb. (L.)] is one

of the most important vegetables grown throughout the year
in all the tropical regions of our country, and, in Asian and
African countries. It is rich in Vitamin A, C and iron (Fe)
(Yawalkar, 1985). Average productivity (kg/ha) in gourds
(9.5t/ha) in India is lower than the world average (12.5t/
ha), falling far below the productivity achieved by developed
countries. The main reasons attributed for this is lack of
availability of improved varieties/hybrids, quality seeds and
improved production technologies. Yield is a complex trait
influenced by genetic factors and their interaction with the
environment. Success in any breeding programme depends
upon the existing genetic variability in base-populations, and,
on the efficiency of selection. For successful selection, it is
necessary to study the nature of association of the trait of
interest with other, relevant traits, as also the genetic
variability available for these. Path coefficient analysis
provides a better index for selection than mere correlation
coefficient, thereby separating correlation coefficient of the
yield and its components into direct and indirect effects.

J. Hortl. Sci.
Vol. 10(2):154-158, 2015

Genetic variability, correlation and path analysis in ridge gourd
[Luffa acutangula (Roxb.) L.]

B. Varalakshmi, M. Pitchaimuthu, E. Sreenivas Rao, K.S. Sanna Manjunath and S.H. Swathi
Division of Vegetable Crops

ICAR-Indian Institute of Horticultural Research
Hesaraghatta Lake Post, Bengaluru-560 089, India

E-mail: bvl@iihr.ernet.in

ABSTRACT
The present investigation was made to determine variability, heritability, genetic advance and correlation of fruit

yield with 10 yield-contributing traits in ridge gourd. A wide variability was observed for days taken to first female-
flower appearance, fruit length, fruit number/plant, fruit weight and fruit yield/ha. Phenotypic coefficient of variation
was higher than genotypic coefficient of variation for all the traits studied, indicating environmental influence on the
expression of these traits. However, high heritability (broad-sense), along with high genetic advance, was recorded in
node number at which first female-flower appeared, number of branches, fruit length, number of fruits/plant and fruit
weight, indicating presence of additive gene effects. Fruit yield/ha was significantly and positively associated with
peduncle length, fruit length, number of fruits/plant (at the phenotypic level), fruit weight and fruit yield/plant. Fruit
weight had the highest direct effect (0.847) on fruit yield/ha, followed by fruit yield/plant (0.793), fruit number
(0.344), peduncle length (0.237) and number of branches (0.216). Therefore, for yield improvement in ridge gourd,
emphasis may be laid on indirect selection using fruit parameters like fruit weight, number of fruits/plant and fruit
yield/plant.

Key words: Ridge gourd, Luffa acutangula (Roxb) L., genetic variability, heritability, correlation path analysis

Therefore, the present study was undertaken to assess the
nature and magnitude of variability, heritability, correlation
coefficient and path analysis for various quantitative
parameters in ridge gourd. Information generated on these
aspects can greatly help formulate appropriate breeding
strategies for genetic upgradation of this crop.

MATERIAL AND METHODS
The experiments were carried out at Vegetable Farm,

ICAR-Indian Institute of Horticultural Research, Bengaluru,
during the rabi-summer season of the years 2011-12, 2012-
13 and 2013-14. The experiments were laid out in
Randomized Block Design, with 51 germplasm lines in two
replications, in all the three years. Ten plants per replication
were raised. Two-week-old seedlings were planted at 150cm
x 50cm spacing. Recommended agronomic practices were
applied to the crop. Observations were recorded on five
randomly-selected plants in each replication, on 10
quantitative traits (node number for first female-flower
appearance, days taken to first female-flower appearance,
vine length (m), number of branches, peduncle length (cm),



155

Genetic variability, correlation and path analysis in ridge gourd

fruit length (cm), fruit girth (cm), number of fruits /plant,
fruit weight (g), fruit yield/plant (kg) or fruit yield/ha (t).

Pooled data for the three years was analyzed as per
Panse and Sukhatme (1984) for Analysis of Variance
(ANOVA). Phenotypic and genotypic coefficient of variation
(PCV and GCV, respectively), heritability in a broad sense,
and genetic advance as per cent of mean, were calculated
as per Burton and De Vane (1953) and Johnson et al (1955).
Correlation co-efficient among all the possible character
combinations at genotypic (rg) and phenotypic (rp) levels
were estimated using the formula of Al-Jibouri et al (1958)
and path coefficient analysis was done as per Dewey and
Lu (1959). GENRES Statistical Software Package
(GENRES, 1994) was employed for analysis of variance
and estimation of correlation among traits.

RESULTS AND DISCUSSION
Mean, range and estimates for various genetic

parameters of 10 traits in 51 germplasm lines of ridge gourd
studied are presented in Table 1. Analysis of Variance
revealed significant difference among germplasm lines for
all the 10 traits studied. A wide range of variation was
observed for most of the traits like days taken to first female-
flower appearance (37.0-66.4), fruit length (10.5-41.3cm),
number of fruits/plant (4.7-33.8), fruit weight (79.8-300.8g),
and fruit yield/ha (8.5-37.9 t). High variability present for
these parameters can form a basis for effective selection
of superior lines in ridge gourd. Such wide variability has
also been reported by Choudhary and Suresh Kumar (2011)
in this crop. The degree of variability seen in various
parameters can be judged by the magnitude of GCV and
PCV. GCV, which indicates the extent of genetic variability

present in a population, ranged from 11.0 (days taken to
first female-flower appearance) to 39.8 (number of fruits /
plant). Similar findings were reported by Varalakshmi et al
(1995), Singh et al (2002) and Choudhary et al (2008) in
ridge gourd. Table 1 shows that a considerable difference
exists between PCV and GCV values for all the traits under
study. This points to the presence of higher environmental
influence on expression of all these parameters, and, selection
may not be effective for improvement of ridge gourd.
Further, GCV values were low in magnitude compared to
PCV values for all the characters studied. This also indicates
that direct selection may not be effective for these traits,
and heterosis breeding may be resorted to for further
improvement.

With help from GCV alone, it is not possible to
determine the extent of variation heritable. Thus, estimates
for heritability indicate the effectiveness with which selection
can be expected for exploiting existing genetic variability.
Broad-sense heritability was high (>60%) for node number
for first female-flower appearance (74.3%), number of
branches (80.8%), fruit length (78.6%), number of fruits/
plant (66.7%) and fruit weight (72.8%). Similar findings were
reported by Varalakshmi et al (1995), Karuppaiah et al
(2002) and Singh et al (2002) in ridge gourd. Moderate
heritability (40-60%) was observed for days taken to first
female-flower appearance (49.2%), vine length (58.2%),
peduncle length (57.4%), fruit yield/plant (57.7%), and fruit
yield/ha (56.8%) (Table 1). Johnson et al (1955) reported
that heritability, along with genetic advance, was more useful
than heritability alone in predicting the effect of selecting
the best individual genotype, as, it suggests presence of
additive gene effects. In the present study, high heritability,

Table 1. Mean, coefficient of variation, heritability and genetic advance for various traits in ridge gourd
Sl. Trait Mean Range Genotypic Phenotypic Genotypic Phenotypic Herita- Genetic GA
No. Variance Variance Coefficient of Coefficient bility Advance as %

(GV) (PV) Variation of Variation (h2) (GA) Mean
(GCV) (PCV)

1 NFF 9.8 5.3- 19.9 8.6 11.5 29.9 34.7 74.3 5.2 53.1
2 DFF 47.7 37.0- 66.4 27.6 56.1 11.0 15.7 49.2 7.6 15.9
3 Vine length (m) 3.9 2.2- 6.5 0.8 1.4 23.6 30.9 58.2 1.4 37.0
4 Number of branches 6.9 2.9- 13.3 7.3 9.0 39.0 43.4 80.8 5.0 72.3
5 Peduncle length (cm) 8.2 4.9- 12.7 3.1 5.3 21.3 28.1 57.4 2.7 33.3
6 Fruit length (cm) 22.5 10.5- 41.3 60.9 77.5 34.7 39.1 78.6 14.2 63.4
7 Fruit girth (cm) 11.8 8.1- 16.9 2.8 8.8 14.1 25.2 31.5 1.9 16.4
8 Number of fruits /plant 13.6 4.7- 33.8 29.2 43.8 39.8 48.8 66.7 9.1 67.0
9 Fruit weight (g) 169.8 79.8-300.8 2915.4 4004.1 31.8 37.3 72.8 94.9 55.9
10 Fruit yield/plant (kg) 1.8 0.6- 2.8 0.2 0.4 26.6 35.0 57.7 0.7 41.7
11 Fruit yield/ha (t) 23.3 8.5- 37.9 37.3 65.8 26.2 34.8 56.8 9.5 40.7
NFF: Node number for first female flower appearance; DFF: Days taken to first female flower appearance

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156

along with a high genetic advance, was recorded in node
number for first female-flower appearance, number of
branches, fruit length, number of fruits/plant and fruit weight,
indicating the presence of additive gene effects. Thus,
selection can be employed for improvement in these
parameters in ridge gourd. Fruit yield/plant and fruit yield/
ha recorded moderate levels of heritability and genetic
advance. This suggests that environmental effects constitute
a major factor for total phenotypic variation, and therefore,
direct selection for these traits would be less effective.
Similar findings were reported by Choudhary and Suresh
Kumar (2011) in ridge gourd.

All possible correlation coefficients between fruit
yield/ha and its component traits were estimated at the
genotypic (G) and phenotypic (P) level (Table 2). From these
associations, it appears that higher fruit yield/ ha was
significantly and positively associated with peduncle length,
fruit length, number of fruits/plant (at the phenotypic level
only), fruit weight and fruit yield/plant. In the present
investigation, interrelations among these parameters were
also seen to be positive and significant. Fruit length exhibited
a positive and significant association with fruit weight and
fruit yield/plant, and, a negative association with fruit girth

and number of fruits/plant. Number of fruits/plant had
significant positive association with fruit yield/plant, and fruit
weight/plant. This implies that indirect selection for all these
traits can help improve fruit yield in ridge gourd. These results
are in conformity with Varalakshmi et al (1995), Rao et al
(2000), Chowdhury and Sharma (2002), Prasanna et al
(2002), Choudhary et al (2008), Hanumegowda et al (2012)
and Rabbani et al (2012) in ridge gourd.

Though correlation analysis can quantify the degree
of association between any two characters, it does not
provide the reasons for such an association. Simple linear
correlation coefficient is designed to detect presence of linear
association between two variables. This does not imply
absence of any functional relationship between the two
variables. Path coefficient analysis resolves this mystery
by breaking the ‘total correlation’ into components of direct
and indirect effects. Thus, path analysis was performed to
assess direct and indirect effects of various characters on
fruit yield/ha (Table 3). Fruit weight had the highest direct
effect (0.847) on fruit yield/ha, followed by fruit yield/plant
(0.793), number of fruits/plant (0.344), peduncle length
(0.237) and number of branches (0.216) (Choudhary et al,
2008). Indirect effects of most other parameters through

Table 2. Genotypic (rg) and phenotypic (rp) correlation coefficient among various traits in ridge gourd
Trait NFF DFF Vine Number Peduncle Fruit Fruit Number Fruit Fruit Fruit

length of length length girth of fruits/ weight yield/ yield/
(m) branches (cm) (cm) (cm) plant (g) plant ha (t)

(kg)
NFF (rg) 1.000 0.790** 0.674** 0.513** 0.461** 0.508** -0.280* -0.580** 0.646** -0.135 -0.179

(rp) 1.000 0.522** 0.478** 0.396** 0.311* 0.439** -0.145 -0.590** 0.470** -0.136 -0.180
DFF (rg) 1.000 0.500** 0.465** 0.381** 0.372** -0.296* -0.600** 0.519** -0.137 -0.181

(rp) 1.000 0.261 0.346* 0.257 0.265 -0.297* -0.610** 0.348* -0.138 -0.182
Vine (rg) 1.000 0.599** 0.717** 0.682** -0.298* -0.620** 0.780** 0.240 0.195
length(m) (rp) 1.000 0.471** 0.471** 0.505** -0.299* -0.600** 0.592** 0.203 0.213
Number of (rg) 1.000 0.450** 0.521** -0.300* -0.640** 0.573** -0.028 0.019
branches (rp) 1.000 0.299* 0.399** -0.301* -0.650** 0.480** 0.043 0.078
Peduncle (rg) 1.000 0.902** -0.302* -0.660** 0.919** 0.529** 0.503**
length (cm) (rp) 1.000 0.755** -0.303* -0.670** 0.682** 0.377** 0.392**
Fruit length (rg) 1.000 -0.304* -0.680** 0.961** 0.401** 0.391**
(cm) (rp) 1.000 -0.305* -0.690** 0.823** 0.277* 0.279*
Fruit girth (rg) 1.000 -0.700** -0.239 -0.085 -0.186
(cm) (rp) 1.000 0.174 -0.201 0.027 -0.187
Number of (rg) 1.000 -0.745** 0.143 0.223
fruits /plant (rp) 1.000 -0.551** 0.282* 0.328*
Fruit (rg) 1.000 0.292* 0.279*
weight (g) (rp) 1.000 0.276* 0.284*
Fruit yield/ (rg) 1.000 0.948**
plant (kg) (rp) 1.000 0.903**
Fruit yield/ (rg) 1.000
ha (t) (rp) 1.000
**Significant at P=0.01, *Significant at P=0.05
NFF- Node number for first female-flower appearance; DFF- Days taken to first female-flower appearance

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157

Table 3. Direct and indirect effects of various traits on fruit yield/plant at the genotypic level in ridge gourd
Trait NFF DFF Vine Number of Peduncle Fruit Fruit Number Fruit Fruit Genotypic

length branches length length girth of fruits/ weight yield/ correlation
(m) (cm) (cm) (cm) plant (g) plant (kg)

NFF -0.144 -0.036 -0.211 0.111 0.110 -0.300 0.051 -0.200 0.547 -0.107 -0.179
DFF -0.114 -0.045 -0.157 0.100 0.091 -0.220 0.053 -0.206 0.439 -0.272 -0.181
Vine -0.097 -0.023 -0.313 0.129 0.170 -0.402 0.049 -0.170 0.661 0.190 0.195
length (m)
Number of -0.074 -0.021 -0.188 0.216 0.107 -0.307 -0.004 -0.172 0.485 -0.022 0.019
branches
Peduncle -0.067 -0.017 -0.225 0.097 0.237 -0.532 0.021 -0.210 0.779 0.419 0.503**
length (cm)
Fruit -0.073 -0.017 -0.214 0.112 0.214 -0.590 0.048 -0.221 0.814 0.317 0.391**
length (cm)
Fruit 0.040 0.013 0.085 0.005 -0.028 0.156 -0.181 -0.008 -0.202 -0.067 -0.186
girth (cm)
Number of 0.084 0.027 0.155 -0.108 -0.145 0.379 0.004 0.344 -0.631 0.114 0.223
fruits /plant
Fruit -0.093 -0.023 -0.245 0.124 0.218 -0.567 0.043 -0.257 0.847 0.232 0.279*
weight (g)
Fruit yield/ 0.019 0.016 -0.075 -0.006 0.126 -0.236 0.015 0.049 0.248 0.793 0.948**
plant (kg)
**Significant at P=0.01, *Significant at P=0.05
Figures on the diagonal in bold font indicate direct effect
NFF- Node number for first female-flower appearance; DFF- Days taken to first female-flower appearance

these stated parameters were also positive. Similarly, Rabbani
et al (2012) from Bangladesh reported fruit weight and
number of fruits as having the maximum direct effect on
fruit yield in ridge gourd. Rest of the parameters such as
node-number for first female-flower appearance, days taken
to first-flower appearance, vine length, fruit length and fruit
girth exhibited a negative direct effect on fruit yield/ha;
indirect effects via these parameters were also negative
for several of the traits. Positive direct and indirect effects
of fruit weight, fruit yield/plant and fruit number lead to the
significant and positive correlation with fruit yield/ha. This
indicates that the positive selection for these parameters is
going to contribute to higher fruit yields in ridge gourd.

Therefore, for yield improvement in ridge gourd,
emphasis is to be laid on indirect selection using fruit
parameters like fruit weight, fruit number and fruit yield/
plant.

REFERENCES
Al-Jibouri, H.H., Miller, P.A. and Robinson, H.F. 1958.

Genotypic and environmental variances and
covariances in upland cotton crosses of interspecific
origin. Agron. J., 50:633-637

Burton, G.W. and De Vane, E.W. 1953. Estimating
heritability in tall fescue (Festuca arundinacea)
from replicated clonal material. Agron. J., 45:478-81

Chowdhury, D. and Sharma, K.C. 2002. Studies on
variability, heritability, genetic advance and correlation
in ridge gourd (Luffa acutangula Roxb.). Hort. J.,
15:53-58

Choudhary, B.R., Pandey, S., Bhardwaj, D.R., Yadav, D.S.
and Rai, M. 2008. Component analysis for quantitative
traits in ridge gourd [Luffa acutangula (Roxb) L.].
Veg. Sci., 35:144-147

Choudhary, B.R. and Suresh Kumar. 2011. Genetic analysis
in ridge gourd [Luffa acutangula (Roxb) L.] under
hot arid conditions. Indian J. Arid Hort., 6:55-58

Dewey, D.R. and Lu, K.H. 1959. A correlation and path
coefficient analysis of components of crested wheat
grass seed production. Agron. J., 51:515-518

GENRES. 1994. Data entry module for genres statistical
software Pascal Int’l. Software Solution, Version 3.11

Hanumegowda, K., Shirol, A.M., Mulge, R., Shantappa, T.
and Prasadkumer. 2012. Correlation coefficient
studies in ridge gourd [Luffa acutangula (Roxb) L.].
Karnatka J. Agril. Sci., 25:160-162

Johnson, H.W., Robinson, H.F. and Comstock, R.E. 1955.
Estimates of genetic and environmental variability in
soybean. Agron. J., 47:314-318

Karuppaiah, P., Kavitha, R. and Senthilkumar, P. 2002.
Studies on variability, heritability and genetic advance
in ridge gourd. Indian J. Hort., 59:307-312

Panse, V.G. and Sukhatme, P.V. 1984. Statistical Methods

Genetic variability, correlation and path analysis in ridge gourd

J. Hortl. Sci.
Vol. 10(2):154-158, 2015



158

for Agricultural Workers. Indian Council of
Agricultural Research, New Delhi, India

Prasanna, S.C., Krishnappa, K.S. and Reddy, N.S. 2002.
Correlation and path coefficient analysis studies in
ridge gourd. Curr. Res., Univ. Agril. Sci., Bengaluru,
31:150-152

Rabbani, M.G., Naher M.J. and Hoque, S. 2012. Variability,
character association and diversity analysis of ridge
gourd (Luffa acutangula Roxb.) genotypes of
Bangladesh. SAARC J. Agri., 10:01-10

Rao, B.N., Rao, P.V. and Reddy, B.M.M. 2000. Correlation
and path analysis in the segregating population of ridge

gourd (Luffa acutangula (Roxb.) L.). Crop Res.,
20:338-342

Singh, R.P., Mohan, J. and Singh, D. 2002. Studies on genetic
variability and heritability in ridge gourd. Agril. Sci.
Digest, 22:279-280

Varalakshmi, B., Rao, P.V. and Reddy, Y.N. 1995. Genetic
variability and heritability in ridge gourd (Luffa
acutangula). Indian J. Agril. Sci., 65:608-610

Yawalkar, K.S. 1985. Vegetable Crops of India (3rd
Edition). Agri. Horticultural Publishing House, Nagpur
- 440010, India, pp. 166-170

(MS Received 05 June 2015, Revised 01 October 2015, Accepted 19 October 2015)

Varalakshmi et al

J. Hortl. Sci.
Vol. 10(2):154-158, 2015