VARIABILITY, CORRELATION AND PATH COEFFICIENT ANALYSIS OF YIELD ATTRIBUTING TRAITS IN 6 GENOTYPES OF Lentil (Lens culinaris) AT IAAS, PAKLIHAWA, RUPANDEHI, NEPAL. This research had been undertaken as a part of UPA (Undergraduate Practicum Assessment)

1. Welcome to UPA
Presentation 2019
Vegetative growth stage of Lentil UPA researcher team

2. STUDY ON VARIABILITY, CORRELATION AND PATH COEFFICIENT ANALYSIS OF
6 GENOTYPES OF LENTIL (Lens culinaris) AT IAAS, PAKLIHAWA, NEPAL.
Plant height measurementBoard Installation

3. Advisory committee
Asst. Prof. Ganga Ram Kohar
Prof. Laxmeshwor Yadav (PhD)
Department of Agronomy, Plant breeding and Agri statistics
Paklihawa campus, IAAS, Rupandehi, Nepal
Principal Researchers
Aatish Gurung
Archana Paudel
Dinesh Ghimire
Rajendra P. Poudel
Sushmita Kunwar
`

4. Presentation outline
1. Introduction
2. Materials and methods
3. Result and discussion
4. Summary and conclusion
5. Acknowledgement

5. Section 1
INTRODUCTION
Archana Paudel

6. Introduction
• Lentil(Lens culinaris Medikus spp. Culinaris) also known as Musuro or Masuro or Masur in
Nepal.
• A rabi pulse crop growing extensively in temperate countries like Canada, USA, Turkey, Chile
etc. and in tropical countries like India, Australia, Ethiopia, Nepal, Pakistan etc.
• Ancient food crop originated from south-western Asia as early as 7000 B.C. (Dhuppar, Biyan,
Chintapalli, & Rao, 2012)
• The name “lentil” derives from its typical lens-shaped seeds(Watts, 2011).

7. Contd.
• Self-pollinating plant of family Leguminosae having diploid chromosome 2n=14(
Karpech-enko, 1925).
• Nepal ranks on 6th position(0.25 Million MT) in terms of production(Statista,
2017).
• Accounts for 63% of area and 70% production of total grain legume(MOAD,
2016/17).
• Commonly grown varieties:- Khajura-1, Khajura-2, Khajura-3, Black masuro
(Kalo), Simrik, Simal, Shikhar, Sindhur, Shishir, Shital etc.

8. Contd.
• The average productivity of these varieties in Nepal is about 1 ton per
hectare(MOAD, 2017).
• Contains 63.35 % CHO, 24.63 % protein, 1.06 % total fat, 10.7 %
dietary fibre (USDA National Nutrient Database).
• Also known as Poor man’s meat.
• Associated with cholesterol and lipid-lowering effect in human with
reducing the incidence of colon cancer and type-II diabetes (Marcela,
2017).

9. Figure.1. Graphical Representation of Area, Production and productivity of lentil from 2008-2016,
Data Source: MOAD, 2016
0
200
400
600
800
1000
1200
0
50000
100000
150000
200000
250000
300000
Productivity(Kg/ha.)
Area(ha.)and
Production(MT)
Years
Trend of lentil production from 2008-2016
Productivity in kg/hac Area in Hactare Production in MTon
Contd.

10. Introduction to Statistical analysis :
1 Descriptive Statistics:
• Used to summarize and describe given set of data.
• Range, mean, Standard deviation, coefficient of variance.
1.1. Genotypic and phenotypic coefficient of variation:
• Phenotypic variance: Combined effect of both genotypic and environmental variance.
• Genotypic variance: Combined effect of additive genetic variance, dominance variance and
epistatic variance.

11. 1.2 Heritability:
• Statistical concept that describes how much of the variation in a given trait can be attributed
to genetic variation.
H2 = VG/VP & h2 = VA/VP
Where VG = Genotypic variance = VA +VD +VI
VP = Phenotypic variance = VG + VE + VGE
VA= Additive variance
1.3 Genetic advance:
• Improvement in the mean genotypic value of selected plants over the parental population.
Contd.

12. 1.4 Correlation :
• Simply, association between any two variables
• Indicates the extent to which two or more variable fluctuates together.
Positive correlation
Negative correlation
Correlation coefficient:
Used to find out the degree and direction of relationship between two or more variables.
Contd.

13. 1.5 Path coefficient analysis :
• “A method of partitioning the correlation coefficient into direct and indirect effects and
provides the information on actual contribution of a trait on the yield” (Dewedy & Lu, 1959).
• Variable under study is taken as dependent variable (effect) affected by the other characters
called independent variables (causes).
Contd.

14. Problem Statement
• Though the importance of lentil is increasing in both national and international
market, the rate of growth in productivity seems to be nearly flat.
• The main reason behind this is due to the lack of appropriate selection and
breeding programs in lentil.
Contd.

15. Rationale of study
• The research was carried out in order to justify the correlation among the
parameters and further carry out path coefficient analysis to estimate the influence
of each variable upon the resultant variable directly as well as indirectly by
partitioning the genetic correlation coefficients.
• Performance of different lentil genotypes was estimated through correlation and
path coefficient analysis and an elite genotype was recommended in research
domain area.
Contd.

16. Research Objectives:
Broad objective
To identify promising high yielding elite genotype of Lentil to Rupandehi district.

17. Specific objective
• To estimate variability, heritability and genetic advance in various germplasm
lines of lentil.
• To find out the character association of yield and yield attributing traits by means
of simple correlation coefficient.
• To assess the direct and indirect contribution of each character on grain yield
through path coefficient analysis.
Contd.

18. Hypothesis
Null Hypothesis:
Ho : There is no significant difference on performance of six genotypes
of lentil.
Alternate hypothesis:
H1 : There is significant difference on performance of six genotypes of
lentil.

19. Literature Review
• Origin and Distribution
• Taxonomy hierarchy
• Biology of lentil
• Growth stages of lentil
• Variability
• Correlation
• Path coefficient analysis

20. Section 2
MATERIAL AND METHODS
Sushmita kunwar

21. Material and methods
• Experimental site
Agronomy Farm
IAAS, Paklihawa , Rupandehi
27°30’N latitude
83°27’E longitude
Altitude 80 masl

22. Plant Material
• Six genotypes obtained from Regional Agriculture Research Station(RARS)
Khajura, Banke were used as a planting material for the research.
Entry number Genotype Source of origin
L1 Kalo Nepal(Pipeline)
L2 Khajura-3 Nepal
L3 Khajura-1 Nepal
L4 Simrik Nepal
L5 Simal Nepal
L6 Local Nepal
Contd.

23. Experimental
layout
• Randomized complete
block design (RCBD)
• 6 treatments(Lines)
• 4 replications
Contd.

24. Agrometeorological features
Agro-meterological information was collected from Department of Hydrology and
Meterology, Bhairahawa Agri-station as represented in table below:
Months Average Minimum
Temperature
(˚C)
Average Maximum
Temperature
(°C)
November 13.63 29.41
December 9.07 24.84
January 8.4 23.26
February 11.78 24.77
Contd.

25. S.N. Agronomic practices Activities Date
1 Field Preparation Two harrowing followed by levelling using
tractor
17th Nov. 2018
2 Field Layout 19th Nov. 2018
3 Sowing Line Sowing 20th Nov. 2018
4 Fertilizer application
 Urea
 DAP
 MOP
Broadcasting @27:45:33 kg/ ha
• Basal dose (931 gm)
Split dose (648.48 gm)
 Full dose (1137.9 gm)
 Full dose (1552 gm)
• 20th Nov. 2018
• 24th Feb 2019
 20th Nov. 2018
 20th Nov. 2018
Agronomic Practices

26. 5 Irrigation
 Pre-sowing
 Seedling
 Vegetative
 Early bloom
Light sprinkle irrigation
 18th Nov. 2018
 2nd Dec. 2018
 14th Dec. 2018
 2nd Jan. 2019
6 Weeding
 1st
 2nd
 Manual
 Major weeds found:
Vicia sativa, Cynodon dactylon,
Chenopodium album, Anagallis arvensis
 17th Dec. 2018
 26th Jan.. 2019
Contd.

27. 7 Tagging
 Plot tagging
 Sample plant
In plastic sheet
With Red thread
29th Nov, 2018
30th Dec, 2018
8 Harvesting
 Sample plant harvest
 Bulk harvest
Harvest index: Straw colored dried pods
 Manual harvest of each sample plant and kept
in separate plastic bag.
 Each plot harvested with the help of sickle
and placed in a separate bag.
20th , 22nd, 24th
March, 2019
Contd.

28. 9 Threshing  Sun dried
 Placed in plastic sheet
 Threshed on the floor by beating with
stick
28th March, 2019
Contd.

29. S.N. Parameters Variables Date
1. Morphological traits at
vegetative phase
No. of primary branches per plant (NPB)
No. of secondary branches per plant (NSB)
Plant height (PH)
25th Feb, 2019
2. Morphological trait of flower Flower color (FC)
(Fully opened flowers observed visually)
19th Feb, 2019
Observation recorded

30. 3. Morphological traits of
pod
No. of pods per plant (NPP)
No. of grains per pod (NGP)
Pod length (PL)
20th March, 2019
4. Phenological traits Days to 50% flowering
Days to 50% maturity
29th Feb, 2019
15th Feb, 2019
5. Grain Yield 26th March, 2019
6. Test weight (1000 grain
weight)
26th March, 2019
Contd.

31. Statistical analysis and tools used:
Data entry and processing: Ms office(Excel, word)
Data analysis:
IBM SPSS statistics Data Editor V 24
R studio
Indo-stat
Contd.

32. Coefficient of variance(CV)
According to Burton and Devane (1993):
• PCV = (Phenotypic SD/General mean of character)× 100
• GCV= (Genotypic SD/General mean of character) ×100
Some Formulae used:

33. Contd.
Heritability
It was calculated by the formula given by Allard (1960) which is as below:
H2 = σ2
g / σ2
p
Where,
σ2
g = Genotypic variance =
MST – MSE
r
σ2
p = Phenotypic variance = σ2
g + σ2
e (MSE)
Where MST= Treatment mean sum of square
MSE= Error mean sum of square
r= Number of replications

34. Genetic advance (GA)
A/c to Robinson et.al.(1949),
GA = H2 ×σp×I
Where,
H2 = Broad Sense Heritability
σp = Phenotypic standard deviation
I = Efficiency of selection which is 2.06 at 5% selection intensity.
Contd.

35. Correlation coefficient:
The simple phenotypic correlation coefficients among pairs of
characters were calculated according to the formula suggested by Searle
(1961).
Where,
X1 = Character 1
X2 = Character 2
r (X1X2) is the correlation between characters X1 and X2
Cov(X1X2) is the covariance between X1 and X2
V(X1) is the variance of X1
V(X2) is the variance of X2
r(X1X2) =
Cov (X1X2)
)()( 21 XVXV 
Contd.

36. Path coefficient analysis
• The path-coefficients were obtained by solving a set of simultaneous equations of
the form:
Where,
• rX1Y to rX5Y denotes coefficient of correlation between independent characters X1 to X5 and
dependent character Y.
• rX1x2 to rX4X5 denotes coefficient of correlation between all possible combinations of independent
characters.
• PX1Y to PX5Y denotes direct effects of character X1 to X5 on Y.
YXXXYXXXYXXXYXYX 56133122111
Pr.....PPrP  rr
YXXXYXXXYXYXXXYX 56233221122
P.....PPP rrrr 
YXYXXXYXXXYXXXYX 53352251155
.....PPP rrrrr 
.
.
.
Contd.

37. Section-3
RESULT AND DISCUSSION
(Level of significance: 5%)
Dinesh Ghimire

38. Abbreviation used in Result and Discussion
DF- Days to 50% flowering
PH- Plant Height
NPB- No. of 1° branch
NSB- No. of 2° branch
PL- Pod length
NPP- No. of pod per plant
NGP- No. of grains per pod
TW- Test weight
DM- Days to 50% maturity
GY- Grain yield per plot
DMRT- Duncan’s Multiple Range Test

39. Treatment details Grain Yield per plot (gm)
Simrik 281.825b
Simal 329.45b
Khajura-1 374.355ab
Local check 443.525ab
Khajura-3 475.575ab
Kalo 571.175a
Table. 1. Effect of genotypes on grain yield
Contd.
• Treatment means are separated by DMRT.
• Two different alphabetical notations denote significant difference between the
respective means.
• The columns represented by same letter (s) are not-significantly different among each
other.

40. • Grain yield had a wide variation (281.825 (Simrik) to 571.175 (Kalo) kg
ha−1) in tested lentil materials. This result pointed out that grain yield
potential in lentil may be varied from variety to variety.
Similar result was obtained by Yasin, G. 2015.
Contd.

41. Table.2. Performance of Genotypes as per traits
Traits Genotype with lowest value Genotype with highest value
Days of 50% flowering Local Simrik
Plant height Simrik Khajura-3
No. of primary branch Kalo Khajura-1
No. of secondary branch Simrik Kalo
Pod length Local Kalo
No. of pod per plant Simrik Kalo
No. of grain per pod Simrik Khajura-3
Test weight Khajura-1 Kalo
Days of 50% maturity Khajura-3 Kalo & Simrik
Grain yield Simrik Kalo
Contd.

42. Table. 3. Variability parameter for 10 quantitative traits in 6 genotype
of Lentil on 2019
Traits Days to
50%
flowerin
g
Plant
height
(cm)
No. of
primary
branch
No. of
seconda
ry
branch
Pod
length
(cm)
No. of
pod per
plant
No. of
grain
per pod
Test
weight(g
m)
Date of
50%
maturity
Grain
yield per
plot
(gm)
Range 3 5.6935 1.52865 1.907775 0.07768 15.657 0.529175 3.4175 2.25 289.35
Mean 100.125 21.7861 3.6165 3.2827 0.8156 21.2384 1.7747 16.0083 121.4166
67
412.6508
S.E.M 1.10 2.71 0.059 0.115 0.014 0.78 0.038 0.1733 0.1604 9.0521
PCV 0.11749 0.1827 0.2564 0.89028 0.22063 0.5188 0.4861 0.1578 0.0183 0.3654
GCV 0.0838 0.0518 0.1148 0.497475 0 0.2421 0.4475 0.03328 0 0.2017
Heritability 0.53 0.26 0.49 0 0 0 0 0.16 0 0.62
Genetic
advance
GA as % of
mean
1.1928
1.1913
2.1321
9.7865
0.9360
25.88
0
0
0
0
0
0
0
0
0.8328
5.202
0
0
192.631
46.681
Contd.

43. • The highest PCV and GCV were found in number of pods per plants and the
lowest PCV for days to flowering, maturity and pod length.
Similar result was obtained by Ajmal et al., 2009.
• High heritability along with high GCV and genetic advance were noticed for grain
yield.
Similar result was obtained by Firas, M, 2014.
• Low to moderate heritability coupled with low GCV and genetic advance were
observed for days to 50% flowering, Plant height, no. of 2 branch, pod length and
days to 50% maturity.
Similar result was obtained by Firas, M, 2014, Tyagi and khan, 2010.
• High GCV, heritability and genetic advance were obtained in grain yield.
Similar result was obtained by Yadav, et. Al 2003 and Younis, 2008.
Contd.

44. Table. 4. Correlation coefficients among various pairs of characters under study
of 6 genotypes
Contd.
DF PH NPB NSB PL NPP NGP TW DM GY
DF 1 -0.572** -0.238 -0.457* -0.238 -0.212 -0.395 0.222 0.800** -0.333
PH 1 -0.522** 0.443* 0.335 0.333 0.396 -0.158 -0.544** 0.420*
NPB 1 0.250 0.212 0.148 0.343 -0.006 -0.304 -0.093
NSB 1 0.465* 0.755** 0.540** 0.356 -0.420* 0.282
PL 1 -0.540** 0.895** 0.375 -0.224 0.327
NPP 1 0.516** 0.517** -0.095 0.424*
NGP 1 0.300 -0.303 0.290
TW 1 0.243 0.260
DM 1 -0.201
GY 1

45. • Negative significant correlation was obtained with days to 50% maturity and
days to 50% flowering.
Similar result was obtained by Latif et. al., 2010; Kumar et. al., 2014; Kumar et.
al., 2017).
• Seed yield shows positive and highly significant correlation with no. of pods per
plant.
Similar result was obtained by Hamdi et.al, 2003, Lakendra et. al, 1999,
Saleem et. al, 1999, Tripathi, 1998, Tikka et al 1997, Saraf et.al, 1985.
• No. of pods per plant showed high correlation with test weight.
Similar result was obtained by Lakendra et. al. 1999
• Seed yield was positively correlated with plant height.
Similar, result was obtained by Tigist, (2003), Hamdi et.al, 2003 and Vir et.
al., (2001).
• Seed yield was negatively correlated with days to 50% flowering
Contd.

46. Table.5.Path analysis (direct and indirect effects) of various component characters on yield of
lentil
Traits DF PH NPB NSB PL NPP NGP TW DM
DF 0.1621 -0.1123 -0.1049 -0.0926 -0.0035 -0.0295 -0.1003 -0.0035 -0.2952
PH -1.0261 1.4815 1.1225 0.4859 0.1919 0.4612 0.9166 -0.1837 0.4338
NPB 0.9383 -1.0986 -1.4500 -0.4241 0.0561 -0.1999 -0.6085 0.3377 -0.1302
NSB -0.3580 0.2054 0.1832 0.6263 0.1881 0.4544 0.2369 0.3410 0.5646
PL 0.0152 -0.0901 0.0269 -0.2090 -0.6958 -0.3597 -0.6213 -0.0863 0.3010
NPP -0.0424 0.0724 0.0321 0.1688 0.1202 0.2326 0.1004 0.2170 0.8175
NGP -0.4097 0.4095 0.2778 0.2503 0.5910 0.2857 0.6618 0.3450 0.3906
TW -0.0003 -0.0018 -0.0033 0.0077 0.0018 0.0133 0.0013 0.0142 0.7850
DM 0.4258 -0.4323 -0.2146 -0.2486 -0.1487 -0.0405 -0.1949 0.1486 -0.0846
Contd.
N.B. >0.2 = High Magnitude impact, <0.2= Low impact
Plant height exhibited high +ve direct effect on grain yield with in conjuction with its +ve indirect effect via.
NPB and NGP =Resulting in its strong positive association with grain yield.

47. • Plant height produce highest direct effect on yield. The result was similar with that
of Singh, 1977.
• Pod length had a positive correlation with grain yield (0.3) but its direct effect
was negative (-0.69). This may be due to counter-balancing by other positive
indirect effect of PL . So, while selection, such indirect effects are to be considered
simultaneously for selection.
• Days to 50% flowering had a negative correlation with grain yield(-0.2952) but its
direct effect was positive(0.1621). So, a restricted simultaneous selection model is
to be followed to nullify the undesirable indirect effect in order to make use of
direct effect (Singh and kakar, 1977, Firas et.al. 2014 )
• Path coefficient value more than 1 reported by Tahir, M, 2015.
Contd.

48. Section 4
SUMMARY AND CONCLUSION
Aatish Gurung

49. • A wide range was observed for grain yield per plot (289.35), no. of pod per plant
(15.657) and plant height (5.7).
• The highest estimate of phenotypic coefficient of variation was observed for no. of
pod per plant (0.5188), no. of 2° branch (0.49028) and grain yield per plot
(0.3654).
• Genotypic coefficient of variation was highest for no of grain per pod (2.475)
followed by no. of 2 branches (0.80).
• A high estimate of both phenotypic and genotypic coefficient of variation was
observed for grain yield per plot and no. of pod per plant.
• Highest value of broad sense heritability was obtained for grain yield per plot
(0.62).
Contd.

50. • Highest value of GA as % of mean was obtained for grain yield per plot (46.681).
• Positive high correlation coefficient was observed in between no. of pods per plant
and test weight (0.93), pod length and no. of grain per pod (0.89), test weight and
grain yield (0.785), no. of secondary branch and no. of pod per plant (0.72).
• Plant height (1.4815), No. of grains per pod (0.6618), No. of 2° branches (0.6263),
No. of pods per plant (0.2326), Days to 50 % flowering (0.1621) and test weight
(0.0142) showed positive effect on grain yield.
Contd.

51. CONCLUSION
• Plant height, no. of grains per pod, no. of 2° branches and no. of pods
per plant – Main contributors towards association with grain yield
• Selection based on these traits would be effective for improvement in
grain yield.
• Khajura-3 and Kalo – Elite genotypes
Contd.

52. RECOMMENDATION
• Promising varieties: Khajura-3 and Kalo
• Our experiment can be taken as a base for further research activities.
• Research should be carried out at various locations so as to
recommend elite genotypes at each domain.
• Result obtained can be used to make legume policy at National level.

53. ACKNOWLEDGEMENT
Rajendra P. Poudel

54. Acknowledgement
Special Thanks to:
• Advisor Asst. Professor Ganga Ram Kohar, Department of Agronomy, Plant
Breeding and Agri-statistics.
• Member Advisor Prof. Laxmeshwar Yadav (PhD), HOD, Department of
Agronomy, Plant Breeding and Agri-statistics.
• Prof. Dr. Kanhaiya Prasad Singh, Campus chief, IAAS, Paklihawa for
managing the research site with proper security.
• Mr. Tara Prasad Thapaliya, Farm manager and Mr. Gopal Giri, Agronomy
lab boy for their continuous support during our research program.
• Analysis department Hyderabad for providing us with Path Analysis data.
• Seniors Kshitiz Dhakal, Anu Basnet and Renu ojha for their continuous support.
• All the Friends and beloved Juniors for their support from the beginning till the
end of the research program.

55. Some glimpse of Research

56. Sprinkle irrigation

57. Field inspection Flowering stage
1st Weeding

58. Weeding

59. Fertilizer application
Drainage canal preparation

60. Flower color inspection
Data Collection

61. Harvesting

62. Transport Lab Work
Threshing

63. Pod length measurement
Test weight measurement
Data Analysis and Discussion

64. THANK YOU