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Genetic Variability, Correlation and PathCoefficient Analysis in TomatoDharminder Kumar a , Rajeev Kumar a , Sandeep Kumar a , M. L.Bhardwaj a , M. C. Thakur a , Ramesh Kumar a , Kuldeep SinghThakur a , Balbir Singh Dogra b , Amit Vikram a , Ashok Thakur c &Prince Kumar aa Department of Vegetable Science , Dr. Y.S. Parmar University ofHorticulture and Forestry, Nauni , Solan , HP , Indiab Regional Horticulture & Forestry Research Station , Bhota ,Hamirpur , Indiac Seed Technology and Production Centre , Dr. Y.S. Parmar Universityof Horticulture and Forestry, Nauni , Solan , HP , IndiaAccepted author version posted online: 02 Apr 2013.

To cite this article: Dharminder Kumar , Rajeev Kumar , Sandeep Kumar , M. L. Bhardwaj , M. C.Thakur , Ramesh Kumar , Kuldeep Singh Thakur , Balbir Singh Dogra , Amit Vikram , Ashok Thakur& Prince Kumar (2013) Genetic Variability, Correlation and Path Coefficient Analysis in Tomato,International Journal of Vegetable Science, 19:4, 313-323, DOI: 10.1080/19315260.2012.726701

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International Journal of Vegetable Science, 19:313–323, 2013Copyright © Taylor & Francis Group, LLCISSN: 1931-5260 print / 1931-5279 onlineDOI: 10.1080/19315260.2012.726701

Genetic Variability, Correlationand Path Coefficient Analysisin Tomato

Dharminder Kumar,1 Rajeev Kumar,1 Sandeep Kumar,1

M. L. Bhardwaj,1 M. C. Thakur,1 Ramesh Kumar,1

Kuldeep Singh Thakur,1 Balbir Singh Dogra,2 Amit Vikram,1

Ashok Thakur,3 and Prince Kumar1

1Department of Vegetable Science, Dr. Y.S. Parmar University of Horticulture andForestry, Nauni, Solan, HP, India2Regional Horticulture & Forestry Research Station, Bhota, Hamirpur, India3Seed Technology and Production Centre, Dr. Y.S. Parmar University of Horticultureand Forestry, Nauni, Solan, HP, India

Tomato (Solanum lycopersicum L.) yield in India is below world averages. To increaseproductivity, it is necessary to develop superior varieties/hybrids. The efficiency ofselection depends upon the nature and magnitude of genetic variability and degreeof transmissibility of desired characters. Because yield is a complex character, itsdirect improvement is difficult. Twenty-six genotypes of tomato were assessed to deter-mine the nature and magnitude of variability, correlation, and path coefficient analysisbetween yield and yield-contributing characters. The analysis of variance (ANOVA)revealed highly significant differences among all genotypes for the characters. Thegenotype ‘EC-357838’ had the highest mean value for number of fruit per plant, totalsoluble solids, and yield per plant. High PC phenotypic coefficient of variability (PCV),genotypic coefficient of variability (GCV), and heritability estimates, coupled with highgenetic gain, were observed for plant height, number of fruit per plant, yield per plant,and fruit weight, indicating that these characters are under additive gene effects andmore reliable for effective selection. Correlation indicated that yield was significantlyand positively associated with number of fruit per plant and per cluster. Path analysisat the genotypic level indicated that fruit weight had the most positive direct effect onyield per plant followed by number of fruit per plant, fruit diameter, and number of fruitper cluster. Direct selection on the basis of fruit weight, number of fruit per plant, fruitdiameter, and number of fruit per cluster is reliable for yield improvement in tomato.

Keywords Solanum lycopersicum, Genetic advance, Heritability, Tomato.

Address correspondence to Dharminder Kumar, Assistant Professor, Department ofVegetable Science, Dr. Y.S. Parmar University of Horticulture & Forestry, Nauni,Solan-173 230 HP, India. E-mail: dharmruder@gmail.com

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314 D. Kumar et al.

Tomato (Solanum lycopersicum L.) is an off-season crop in the NorthernIndian plains. Identification and development of new cultivars are importantto improve production and productivity. Planning and execution of a breedingprogram for improvement of quantitative attributes depends, to a great extent,upon the magnitude of genetic variability and the extent to which desirablecharacters are heritable (Adunga and Labuschangne, 2003). Phenotypic andgenotypic coefficients of variation are useful in detecting amounts of variabil-ity present in genotypes. Heritability and genetic advance help in determiningthe influence of environment in expression of characters and the extent towhich improvement is possible after selection (Robinson et al., 1949). Heritablevariation can be effectively studied in conjunction with genetic advance. Highheritability alone is not enough to make efficient selection in segregatinggeneration and needs to be accompanied by a substantial amount of geneticadvance (Johanson et al., 1955). Correlation studies between fruit weight andits components and their relative contribution to yield are of value in plan-ning a breeding program. Path analysis facilitates the partitioning of thecorrelation coefficient into direct and indirect effects on yield and any otherattributes (Islam et al., 2010). Although it has been used in tomato previ-ously, the results are not clear due to different experimental material used.It is necessary to confirm previous results using new experimental materialunder different environmental conditions so selection can be made for higheryield in tomato. This investigation was undertaken to ascertain the magni-tude and extent of genetic variability, correlation, and path coefficient analysisusing new planting material in tomato.

MATERIALS AND METHODS

Experimental Site and EnvironmentThe investigation was carried out at the Research Farm of the Department

of Vegetable Science, Dr. Y.S. Parmar University of Horticulture and Forestry,Nauni, Solan, in the state of Himachal Pradesh, India. This location is at30◦50′ N latitude and 77◦11′30′′ E longitude and is 1260 m above meansea level and represents the mid-hill zone of Himachal Pradesh. Annualprecipitation is 1000–1300 mm, with most rainfall occurring from June toSeptember.

Experimental Material, Arrangement, and ObservationThe experimental material consisted of the tomato genotypes A-3, AP-

10, EC-110264, EC-12217, EC-141827, EC-21132, EC-357838, FT-5, FT-100,Lo1003, Lo 2410, Lo 5635, Oregon-2, Oregon Pride, UHF-503, UHF-505,

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Genetic Variability in Tomato 315

UHF-506, UHF-512, UHF-517, UHF-519, UHF-521, UHF-562, UHF-571,UHF-659, V-16, and Solan Vajr. The experiment was arranged in a randomizedcomplete block design with three replications of each genotype. The exper-imental field was disked and leveled. About 25 Mt of well-decomposed cowmanure, 100 kg calcium ammonium nitrate, 475 kg single superphosphate, and90 kg muriate of potash·ha−1, respectively, was incorporated at soil prepara-tion. In addition, 100 kg·ha−1 calcium ammonium nitrate was applied at 1 and2 months after transplanting. One-month-old seedlings were transplanted tothe field with a spacing of 90 × 30 cm between and within rows, respectively.Standard cultural practices as recommended in the Package of Practices forVegetable Crops (Anonymous, 2009) were followed. Ten plants were randomlyselected and observations on plant height, number of fruit per cluster, num-ber of fruit per plant, days to first harvest, yield per plant, fruit weight, fruitlength, fruit diameter, pericarp thickness, number of locules per fruit, stem endscar size, harvest duration, number of seed per fruit, hundred seed weight, andtotal soluble solids were determined.

Statistical AnalysisData were subjected to analysis of variance (ANOVA) as per Gomez and

Gomez (1983). The genotypic and phenotypic coefficients of variation were cal-culated using the formulae of Burton and De Vane (1953). Heritability andgenetic advance were calculated according to Allard (1960) and genetic gainwas estimated using the method of Johanson et al. (1955). Genotypic andphenotypic correlations were calculated as per Al-Jibouri et al. (1958) usingan ANOVA and covariance matrix in which total variability was split intoreplications, genotypes, and errors. The genotypic and phenotypic correlationcoefficients were used to determine direct and indirect contribution towardyield per plot. The direct and indirect paths were obtained according to themethod of Dewey and Lu (1959).

RESULTS AND DISCUSSION

Mean PerformanceANOVA indicated significant differences among genotypes for all charac-

ters (Table 1). These differences indicated variability in the germplasm andoffer opportunity for improvement in yield and quality traits of tomato throughselection. The shortest plants were obtained for ‘Oregon Pride’ and the tallestfor ‘EC-110264’. The least number of fruit per cluster was obtained for ‘OregonPride’ and the most for ‘Lo1003’. The most fruit per plant was obtained for‘EC-357838’ and the least for ‘Oregon Pride’.

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Tab

le1:

Ra

ng

e,m

ea

n,a

nd

AN

OV

Afo

rdiff

ere

nt

ch

ara

cte

rsin

tom

ato

.

Rang

e

Cha

rac

ter

Min

imum

Ma

xim

umM

ea

nSE

(m)±

MSS

valu

eC

D(0

.05)

Pla

nt

he

igh

t(c

m)

39.1

7(O

reg

on

Prid

e)

163.

67(E

C-1

1026

4)11

2.90

3.77

4093

.80

18.5

6N

um

be

rof

fru

itp

erc

lust

er

1.27

(Ore

go

nP

ride

)4.

63(L

o10

03)

3.14

00.

172.

540.

81N

um

be

rof

fru

itp

erp

lan

t7.

67(O

reg

on

Prid

e)

57.4

0(E

C-3

5783

8)25

.64

0.62

407.

723.

04D

ays

tofir

sth

arv

est

57.0

0(E

C-1

4182

7)84

.67

(UH

F-50

1)66

.22

0.63

63.4

93.

12Y

ield

pe

rpla

nt

(g)

181.

67(O

reg

on

Prid

e)

1427

.50

(EC

-357

838)

945.

3848

.17

308,

876.

9023

7.19

Fru

itw

eig

ht

(g)

16.5

0(O

reg

on

-2)

72.5

0(F

T-5)

40.5

60.

7883

0.80

3.85

Fru

itle

ng

th(c

m)

2.12

(Ore

go

n-2

)6.

95(U

HF-

562)

3.87

0.07

3.79

0.34

Fru

itd

iam

ete

r(c

m)

2.36

(Ore

go

n-2

)5.

64(V

-16)

3.86

0.13

1.74

0.64

Peric

arp

thic

kne

ss(m

m)

2.35

(EC

-211

32)

6.34

(FT-

5)4.

010.

122.

760.

61N

um

be

rof

loc

ule

sp

erf

ruit

2.27

(Lo

2410

)4.

87(L

o10

03)

3.55

0.13

1.65

0.63

Ste

me

nd

sca

rsiz

e(m

m)

3.52

(UH

F-51

9)10

.33

(Lo

1003

)7.

420.

1610

.69

0.78

Ha

rve

std

ura

tion

(da

ys)

22.3

3(O

reg

on

-2)

39.3

3(U

HF-

505,

521)

35.5

90.

6540

.99

3.22

Nu

mb

ero

fse

ed

pe

rfru

it53

(Org

eo

n-2

)15

6(F

T-5)

101.

011.

7922

55.9

18.

82H

un

dre

dse

ed

we

igh

t(m

g)

190

(So

lan

Va

jr)36

0(U

HF-

503)

249.

055.

4135

47.0

826

.65

Tota

lso

lub

leso

lids

(◦ B)

3.13

(UH

F-51

2)4.

87(E

C-3

5783

8)4.

020.

090.

720.

43

MSS

:Me

an

Sum

of

Squ

are

s;C

D:C

ritic

alD

iffe

ren

ce

.

316

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Genetic Variability in Tomato 317

Days to first harvest were shortest for ‘EC-141827’ and longest for ‘UHF-501’. Yield per plant was least for ‘Oregon Pride’ and highest for ‘EC-357838’.The highest fruit weight was obtained for ‘T-5’ and the least for ‘Oregon-2’.The shortest fruit were obtained for ‘Oregon-2’ and the longest for ‘UHF-562’. The widest fruit diameter was obtained for ‘V-16’ and the narrowestfruit for ‘Oregon-2’. The pericarp was thickest for ‘FT-5’ and thinnest for ‘EC-21132’. The fewest number of locules per fruit was obtained for ‘Lo2410’ andthe most for ‘Lo-1003’. Stem end scar size was smallest for ‘UHF-519’ andlargest for ‘Lo1003’. Harvest duration was shortest for ‘Oregon-2’ and longestfor ‘UHF-521’ and ‘UHF-505’. The lowest number of seed per fruit was obtainedfor ‘Oregon-2’ and the most for ‘FT-5’. The lowest hundred seed weight wasobtained for ‘Solan Vajr’ and the highest for ‘UHF-503’. The highest total sol-uble solids was found for ‘EC-357838’ and the lowest for ‘UHF-512’. Tomato isknown to vary widely in expression of horticultural and quality traits in othercultivars or environments (Haydar et al., 2007; Kamruzzahan et al., 2000;Manna and Paul, 2012; Mohamed et al., 2012; Mohanty, 2003) and this allowsopportunities for improvement.

Parameters of Variability

Coefficients of Variability

There were differences among all of the genotypes for phenotypic andgenotypic coefficients of variability (Table 2). Estimates of phenotypic andgenotypic coefficients of variability indicated the amount of variation in thegermplasm. For all characters studied, phenotypic coefficients of variabilitywere higher in magnitude than genotypic coefficients of variability, though thedifference was small in some cases. The traits were not influenced by environ-mental factors. Selection based on phenotypic performance is more reliable.Coefficients of variability varied in magnitude from character to character (low,moderate, or high), indicating a large amount of diversity.

Because the estimates of phenotypic variability cannot differentiatebetween genetic and environmental effects, the study of genetic variability iseffective in portioning out real genetic differences. The higher the genotypiccoefficients of variability, the better the chances of improvement in thatcharacter.

The genotypic coefficient of variability (GCV) was high for number of fruitper plant, followed by fruit weight, yield per plant, and plant height. ModerateGCV occurred for number of fruit per cluster, fruit length, fruit diameter,pericarp thickness, number of locules per fruit, stem end scar size, and numberof seed per fruit. The GVC was low for days to first harvest, harvest duration,hundred seed weight, and total soluble solids. Mohanty (2003), Haydar et al.(2007), T. P. Kumar and Tewari (1999), and Mohamed et al. (2012) reported

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Tab

le2:

Estim

ate

so

fp

ara

me

ters

of

varia

bili

tyfo

rdiff

ere

nt

ch

ara

cte

rsin

tom

ato

.

Co

effi

cie

nto

fva

riab

ility

(%)

Cha

rac

ter

Me

an

Rang

ePC

VG

CV

He

rita

bili

ty(%

)G

ene

tica

dva

nce

Ge

netic

ga

in(%

)

Pla

nt

he

igh

t(c

m)

112.

9039

.17–

163.

6732

.21

33.7

391

.20

71.5

263

.35

Nu

mb

ero

ffr

uit

pe

rclu

ste

r3.

141.

27–4

.63

27.7

831

.92

75.6

91.

5649

.79

Nu

mb

ero

ffr

uit

pe

rpla

nt

25.6

47.

67–5

7.4

45.2

845

.85

97.5

223

.62

92.1

1D

ays

tofir

sth

arv

est

66.2

257

.00–

84.6

712

.62

12.9

894

.59

16.7

525

.29

Yie

ldp

erp

lan

t(g

)94

5.38

181.

67–1

427.

5033

.09

36.5

182

.13

57.8

4261

.78

Fru

itw

eig

ht

(g)

40.5

616

.50–

72.5

040

.88

41.2

898

.04

33.8

383

.37

Fru

itle

ng

th(c

m)

3.87

2.12

–6.9

528

.89

29.3

896

.74

2.27

58.5

5Fr

uit

dia

me

ter(

cm

)3.

862.

36–5

.64

18.8

621

.47

77.1

61.

3234

.12

Peric

arp

thic

kne

ss(m

m)

4.01

2.35

–6.3

423

.30

25.0

986

.21

1.79

44.5

7N

um

be

rof

loc

ule

sp

erf

ruit

3.55

2.27

–4.8

719

.98

22.6

977

.51

1.28

36.2

4St

em

en

dsc

ars

ize

(mm

)7.

423.

52–1

0.33

25.1

525

.96

93.8

53.

7350

.19

Ha

rve

std

ura

tion

(da

ys)

35.5

922

.33–

39.3

39.

8911

.32

76.2

96.

3317

.79

Nu

mb

ero

fse

ed

pe

rfru

it10

1.01

53–1

5626

.97

27.4

996

.25

55.0

654

.51

Hu

nd

red

see

dw

eig

ht

(mg

)24

9.05

190–

360

13.2

814

.79

80.5

761

.17

24.5

6To

tals

olu

ble

solid

s(◦ B

)4.

023.

13–4

.87

11.5

913

.29

76.0

70.

8420

.83

318

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Genetic Variability in Tomato 319

high genotypic coefficient of variability for number of fruit per plant, fruitweight, yield per plant, and plant height in different genotypes of tomato andclimatic conditions.

Heritability and Genetic Gain

Genotypic coefficients of variation do not estimate the variations thatare heritable, and estimation of heritability becomes necessary. Burton andDe Vane (1953) suggested that genetic coefficients of variability, along withheritability estimates, would provide a reliable indication of expected degreeof improvement through selection.

The estimates of heritability (broad sense) were high for plant height,number of fruit per plant, days to first harvest, yield per plant, fruit weight,fruit length, pericarp thickness, stem end scar size, number of seed perfruit, and hundred seed weight. Number of fruit per cluster, fruit diameter,number of locules per fruit, harvest duration, and total soluble solids hadmoderate heritability estimates (Table 2). T. P. Kumar and Tewari (1999),Mohanty (2003), Haydar et al. (2007), and Mohamed et al. (2012) reportedhigh heritability estimates for plant height, fruit weight, and number of fruitper plant in different genotypes of tomato.

Genetic gain (expressed as a percentage of population mean) was low tohigh in nature for different characters (Table 2). It was high for plant height,number of fruit per plant, yield per plant, fruit weight, fruit length, stem endscar size, and number of seed per fruit. Moderate genetic gain was observedfor number of fruit per cluster, days to first harvest, fruit diameter, pericarpthickness, and number of locules per fruit. Genetic gain was low for harvestduration, hundred seed weight, and total soluble solids. The results agreewith Mohamed et al. (2012), who reported high heritability estimates for plantheight and fruit weight and moderate heritability for number of fruit percluster in different genotypes. Mohanty (2003) and Haydar et al. (2007) alsoreported high heritability estimates for plant height and fruit weight in tomatogenotypes in different environments.

High heritability estimates, coupled with high genetic gain, occurred forplant height, number of fruit per plant, yield per plant, fruit weight, fruitlength, stem end scar size, and number of seed per fruit, indicating that thesecharacters are under additive gene effects and that these characters are morereliable for effective selection (Panse, 1957). High heritability, coupled withmoderate genetic gain, was observed for days to first harvest and pericarpthickness, indicating that these characters are under nonadditive gene effectsand that selection for these characters will be less effective. These traitsare under the influence of the environment and do not respond to selection.High heritability estimates coupled with high genetic gain were reported byMohamed et al. (2012) for plant height and fruit weight; by Mohanty (2003)for plant height, fruit weight, and number of fruit per plant; and by Haydar

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320 D. Kumar et al.

et al. (2007) for number of fruit per plant and fruit weight in different tomatogenotypes.

Correlation StudiesThe correlation coefficients among characters were determined at the

phenotypic and genotypic levels (Table 3). In general, genotypic correlationcoefficients were higher in magnitude than phenotypic correlation coefficients.This can be interpreted as a strong inherent genotypic relationship betweencharacters studied, through their phenotypic expression was impeded by envi-ronmental influence. These results agree with Harer et al. (2002) and Daret al. (2011), who reported that the genotypic correlation coefficient was higherthan the phenotypic correlation for all characters examined in tomato underdifferent environments.

The phenotypic and genotypic correlation coefficients among differentcharacters indicated that yield per plant was significantly and positively asso-ciated with number of fruit per plant at the phenotypic and genotypic levels.Yield per plant was significantly and positively correlated with number of fruitper cluster only at the genotypic level. Number of fruit per plant was signifi-cantly and positively correlated with number of fruit per cluster. Fruit weightwas significantly and positively correlated with fruit length, fruit diameter,pericarp thickness, and stem end scar size and significantly and negatively

Table 3: Phenotypic and genotypic coefficients of correlation among significantcharacters in tomatoa.

Trait NFPC NFPP YPP FW FL FD PT SESS

PH P 0.605∗G 0.719∗∗

NFPC P 0.698∗∗G 0.769∗∗ 0.561∗ −0.525∗

NFPP P 0.534∗ −0.523∗ −0.548∗G 0.599∗ −0.525∗ −0.549∗

DTFP PG 0.516∗

FW P 0.624∗∗ 0.771∗∗ 0.664∗∗ 0.726∗∗G 0.656∗∗ 0.915∗∗ 0.666∗∗ 0.730∗∗

FL P 0.584∗G 0.589∗

FD P 0.648∗∗ 0.690∗∗G 0.761∗∗ 0.827∗∗

PT P 0.512∗G 0.516∗

∗, ∗∗Significant at the 5% and 1% level of significance.aNFPC = number of fruit per cluster, NFPP = number of fruit per plant, YPP = yield per plant (g),FW = fruit weight (g), FL = fruit length (cm), FD = fruit diameter (cm), PT = pericarp thickness(mm), SESS = stem end scar size (mm), PH = plant height (cm), DTFP = days to first harvest, P =phenotypic, G = genotypic.

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Genetic Variability in Tomato 321

correlated with number of fruit per plant at the phenotypic and genotypic lev-els. To improve yield per plant in tomato, selection should be based on thesetraits. Significant positive correlations of yield per plant with other horticul-tural traits in tomato were reported by Mohanty (2003), Rani et al. (2010), Daret al. (2011), and M. Kumar and Dudi (2011). Moreover, a significant nega-tive correlation of fruit weight with number of fruit per plants was reportedby Premalakshmi (2001) and Rani et al. (2010) under different environmentalconditions.

Path Coefficient AnalysisAlthough correlation studies are helpful in determining components of

yield, with the inclusion of more variables in correlation studies, the indirectassociation becomes more complex. Two characters may show a correlationbecause they correlate with a common third one. Under such circumstances,path analysis helps in partitioning of correlation coefficients into direct andindirect effects, permitting a critical examination of the relative importance ofeach trait.

Path analysis (Table 4) at the genotypic level indicated that fruit weighthad the most positive direct effect on yield per plant followed by number of fruitper plant, fruit diameter, and number of fruit per cluster. The most positiveindirect effects of fruit diameter, fruit length, pericarp thickness, and stemend scar size were via fruit weight to affect yield per plant. Mohanty (2003),Bhardwaj and Sharma (2005), Haydar et al. (2007), Rani et al. (2010), and Daret al. (2011) reported the same effects of component traits on yield in differentgenotypes and environments.

The genotype ‘EC-357838’ had the highest mean value for number offruit per plant, total soluble solids, and yield per plant. High PCV, GCV,

Table 4: Estimates of direct and indirect effects of significant characters on yield oftomatoa.

Trait PH NFPC NFPP FW FL FD PT SESS

PHa −0.252b 0.261 0.251 −0.061 0.061 0.020 0.016 0.069NFPC −0.181 0.363 0.530 −0.313 0.019 −0.103 −0.009 0.163NFPP −0.092 0.280 0.690 −0.420 0.079 −0.214 −0.030 0.171FW 0.019 −0.019 −0.362 0.799 −0.236 0.572 0.082 −0.227FL 0.043 −0.060 −0.152 0.524 −0.360 0.368 0.050 −0.080FD −0.008 −0.26 −0.236 0.731 −0.212 0.625 0.094 −0.257PT −0.032 −0.056 −0.164 0.532 −0.146 0.476 0.124 −0.160SESS 0.056 −0.191 −0.379 0.584 −0.092 0.517 0.064 −0.311

aResidual effect: 0.0077. PH = plant height (cm), NFPC = number of fruit per cluster, NFPP =number of fruit per plant, FW = fruit weight (g), FL = fruit length (cm), FD = fruit diameter (cm),PT = pericarp thickness (mm), SESS = stem end scar size (mm). Bold figures represent the directeffect.

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322 D. Kumar et al.

and heritability estimates, coupled with high genetic gain, occurred for plantheight, number of fruit per plant, yield per plant, and fruit weight, indicat-ing that these characters are under additive gene effects and more reliable foreffective selection. Correlation indicated that yield was significantly and posi-tively associated with number of fruit per plant and per cluster. Path analysisat the genotypic level indicated that fruit weight had the most positive directeffect on yield per plant followed by number of fruit per plant, fruit diam-eter, and number of fruit per cluster. It is always necessary for untested ornew genetic material be evaluated so that improvements can be made in qual-ity and yield. To do this, the evaluation is best made with a combination ofcorrelation and path analysis.

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