STUDY OF GENETIC DIVERGENCE IN CORE

GERMPLASM ACCESSIONS OF SOYBEAN

(Glycine max (L.) Merrill)

SOURAV BANIK
B.Sc. Hons(Agriculture)

MASTER OF SCIENCE
IN
AGRICULTURE
(AGRICULTURAL BOTANY)

DEPARTMENT OF AGRICULTURAL BOTANY COLLEGE OF

AGRICULTURE, PARBHANI.
VASANTRAO NAIK MARATHWADA KRISHI VIDYAPEETH
PARBHANI - 431402, MAHARASHTRA, INDIA.

2023
 STUDY OF GENETIC DIVERGENCE IN CORE
GERMPLASM ACCESSIONS OF SOYBEAN
(Glycine max (L.) Merrill)

BY
SOURAV BANIK
B.Sc. Hons(Agriculture)

A thesis submitted to
Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani
in partial fulfilment of the requirement for the degree of

MASTER OF SCIENCE
IN
AGRICULTURE
(AGRICULTURAL BOTANY)

DEPARTMENT OF AGRICULTURAL BOTANY COLLEGE OF

AGRICULTURE, PARBHANI.
VASANTRAO NAIK MARATHWADA KRISHI VIDYAPEETH
PARBHANI - 431402, MAHARASHTRA, INDIA.

2023
 DECLARATION BY THE CANDIDATE

I hereby declare that the thesis entitled, “STUDY OF GENETIC

DIVERGENCE IN CORE GERMPLASM ACCESSIONS OF SOYBEAN
(Glycine max (L.) Merrill)", submitted by me is based on the actual work carried out
by me under the guidance and supervision of Dr. S.P.Mehtre. The extent of
information derived from the existing literature have been duly cited and referenced.
The existing research work or its any part is not submitted anywhere else for the award
of any degree or diploma.
I also hereby declare that no sentence, equation, diagram, table, paragraph or
section has been copied verbatim from previous work unless it is cited and duly
referenced. There is no plagiarism; the work presented is original and own work of the
researcher. No ideas, process, results or words of other have been presented as
researcher’s own work.

Place: Parbhani Sourav Banik

Reg.No:2021A/39M
Date: / / 2023

i
 CERTIFICATE-I

This is to certify that the thesis entitled “STUDY OF GENETIC

DIVERGENCE IN CORE GERMPLASM ACCESSIONS OF SOYBEAN
(Glycine max (L.) Merrill)" submitted by SOURAV BANIK, 2021A39M in partial
fulfilment of the requirement for the award of the degree of MASTER OF SCIENCE
(AGRICULTURE) in the subject of AGRICULTURAL BOTANY (GENETICS
AND PLANT BREEDING) submitted to the Vasantrao Naik Marathwada Krishi
Vidyapeeth, Parbhani is a record of bonafide research work carried out by her under
my guidance and supervision. The thesis or its part has not previously formed the basis
for the award of any degree, diploma or other similar title.

Place: Parbhani Dr. S.P.Mehtre

Research guide & chairman
Date: / / 2023 Advisory committee

ii
 CERTIFICATE-II

This is to certify that the dissertation entitled “STUDY OF GENETIC

DIVERGENCE IN CORE GERMPLASM ACCESSIONS OF SOYBEAN
(Glycine max (L.) Merrill)" submitted by SOURAV BANIK, 2021A39M in partial
fulfilment of the requirement for the award of the degree of MASTER OF SCIENCE
(AGRICULTURE) in the subject of AGRICULTURAL BOTANY (GENETICS
AND PLANT BREEDING) to the Vasantrao Naik Marathwada Krishi Vidyapeeth,
Parbhani has been approved by the Student's Advisory Committee after viva voce
examination in collaboration with the External Examiner.

( ) Dr. S. P. Mehtre

External Examiner Research Guide & Chairman

Advisory Committee

(Dr. H. V. Kalpande)
Member

(Dr. R. R. Dhutmal)
Member

(Dr. R. S. Jadav)
Member

Associate Dean & Principal

College of Agriculture
VNMKV, Parbhani

iii
 PLAGIARISM CLEARANCE CERTIFICATE

This is to certify that the dissertation/thesis entitled “STUDY OF GENETIC

DIVERGENCE IN CORE GERMPLASM ACCESSIONS OF SOYBEAN
(Glycine max (L.) Merrill)" submitted by SOURAV BANIK, 2021A39M has been
properly examined by URKUND: Anti plagiarism software. The percentage of
similarities found in the thesis is 8 %. No sentence, equation, diagram, table,
paragraph or section has been copied verbatim from previous work unless it is duly
cited and referenced. The work presented is original and own work of the researcher
(i.e. there is no plagiarism). No ideas, process, results or words of other have been
presented as researchers own work. The thesis has been checked using URKUND
anti plagiarism software.

Signature
Dr. S. P. Mehtre

(Research Guide)

iv
v
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B- Upgrade (11-40%)

121 A C-Poor (41-60%)

D-Unacceptable (61-100%)

SIMILARITY % MATCHED SOURCES GRADE

LOCATION MATCHED DOMAIN % SOURCE TYPE

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vi
 ACKNOWLEDGEMENT

First and foremost, I praise and thank the Almighty for giving the strength and
encourage for completing this endeavour successfully in time.

I wish to express my deepest gratitude and sincere grateful thanks to my

chairperson Dr. S. P. Mehtre, Soybean breeder and Incharge, AICRP on Soybean,
College of Agricultural, V.N.M.K.V, Parbhani for encouragement, guidance, sustained
interest, suggestion throughout the course of this investigation.

I express my grateful thanks to member of my advisory committee Dr. H.V.

Kalpande, Head, Department of Agricultural Botany, V.N.M.K.V, Parbhani, Dr. R. R.
Dhutmal, Associate Professor, Department of Agricultural Botany, V.N.M.K.V,
Parbhani and Dr. R. S. Jadav, Junior Entomologist, AICRP on Soybean, VNMKV,
Parbhani for timely guidance.

I wish to express my sincere gratitude to Dr. H .V. Kalpande sir, Head

Department of Botany for support, critical and constructive review of the thesis.

I wish to thank the entire staff members Dr. D. K. Zate, Dr. J. D. Deshmukh,
Dr. M. P. Wankhede, Prof. A. W. More, Department of Agricultural Botany, College
of Agricultural, Parbhani for their timely help during my research work and
curriculum.

I am heartily thankful to Dr. Indra Mani Hon. Vice Chancellor, V.N.M.K.V,

Parbhani, Dr. D. N. Gokhale, Director of Instruction and Dean, Faculty of Agriculture,
V.N.M.K.V, Parbhani, Dr. Syed Ismail, Associate Dean and Principal, College of
Agriculture, V.N.M.K.V, Parbhani.
Thanks cannot be expressed just in words and I submit everything at the feet of
my beloved parents Sri. Tapas Banik, Smt. Pratima Banik, Elder brother Pritam
Banik for supporting me through different phases of my life and dedicated efforts in
shaping my career since childhood and bringing out the best of my abilities in each of
my endeavour, without whose affection, support, sacrifice, love and blessings this study
would scarcely have been accomplished.

I would like to express my sincere appreciation to my friends Mayur, Ganesh,

Suyog, Yogesh, Achyut, Anuradha, Kiran, Komal, Ritu, Snehal for their
encouragement and co-operation in completing course credit and research problem.

vii
 I am very much thankful to my seniors Pramod Sir, Gajanan Sir, Sachin Sir
and junior friend Bhaarat for their guidance and moral support.

I also express my special thanks to the technical staff Somnar Sir,

Dhyaneshwar and other staffs of Soybean Research Station, VNMKV, Parbhani.

It is of great pleasure to express my thanks to Vasantrao Naik Marathwada

Krishi Vidyapeeth, Parbhani for providing opportunity to carry over this study.

Finally, Thanks to everyone who have helped me directly and indirectly in my

research works.

Place: Parbhani

Date: / /2023 (SOURAV BANIK)

viii
 LIST OF TABLES
Table Page
No. Title No.
3.1 List of germplasms along with checks included in study 26-
29
4.1 Analysis of variance for morphological and yield and yield 52
contributing characters in Soybean.
4.2 Plant Height (cm) 53
4.3 Leaf shape 53
4.4 Plant growth habit 54
4.5 Leaf colour 54
4.6 Days to 50% flowering 55
4.7 Number of pod clusters per plant 55
4.8 Number of pods per plant 56
4.9 Pod pubescence 56
4.1 Pod pubescence colour 56
4.11 100 seed weight(g) 57
4.12 Days to maturity 57
4.13 Mean performances of Soybean genotypes for morphological, 58-
physiological, yield and yield contributing traits. 65
4.14 Genetic variability parameters for grain yield, it’s related traits in 71
Soybean
4.15 Genotypic Correlation coefficient for yield and it’s attributing 75
characters
4.16 Phenotypic Correlation coefficient for yield and it’s attributing 76
characters
4.17 Direct and indirect effects (genotypic level) of yield components 82
on seed yield in Soybean
4.18 Direct and indirect effects (phenotypic level) of yield components 82
on seed yield in Soybean
4.19 Composition of three hundred and eighteen genotypes of Soybean 87
into different clusters by Tocher’s method.
4.2 Average intra and inter cluster distance D2 values in Soybean. 88
4.21 Cluster means of different characters to genetic diversity in 89
Soybean.

ix
 LIST OF FIGURES
Figure Page
No. Title No.

3.1 Layout of experimental field 34

3.2 Field view of Experiment 35

4.1 Genotypic Correlation coefficient for yield and it’s attributing

characters. 77

4.2 Phenotypic Correlation coefficient for yield and it’s

77
attributing characters.

4.3 Genotypical Path Diagram for seed yield/ plant(g) 83

4.4 Phenotypical Path Diagram for seed yield/ plant(g) 83

4.5 Mahalonobis Eucledean distance using Trocher Method 90

x
 ABBREVATIONS

* - Significant at 5 per cent

** - Significant at 1 per cent
d.f. - Degrees of freedom
et al., - and other co-workers
g - Gram
GCV - Genotypic coefficient of variation
σ2 g - Genotypic Variance
ha - hectare
h2 - Heritability
/ - Per
MSS - Mean sum of squares
No. - Number (s)
PCV - Phenotypic coefficient of variation
σ2 - Variance
σ2 p - Phenotypic Variance
% - per cent
R - Correlation coefficient
viz., - Namely
vs - Versus
cm - Centimeter(s)
GM - General Mean
GA - Genetic advance
GAM - Genetic advance as percent of mean
X - Mean
Σ - Summation
Fig. - Figures
SE+ - Standard error

xi
CV - Coefficient of variation
RBD - Randomized Block Design
b.s - Broad Sense
EMS - Error Mean Sum of Squares
rg - Genotypic correlation
rp - Phenotypic correlation
D2 - D square value
G - Genotypic correlation
P - Phenotypic correlation

xii
 ABSTRACT

1. Title of the “Study of genetic divergence in core germplasm accessions

thesis : of Soybean (Glycine max (L.) merril) ”
2. Name of
student : SOURAV BANIK
3. Name of
Dr. S. P. Mehtre
Research Guide :
4. Department : Department of Botany (Genetics and Plant Breeding)
5. College name : College of Agriculture, VNMKV, Parbhani
6. Degree to be
awarded : Master of Science (Agriculture)

THESIS ABSTRACT
The present investigation entitled “Study of genetic divergence in core
germplasm accessions of Soybean (Glycine max (L.) merril) ” was carried out during
kharif 2021-22 at experimental farm of "All India Coordinated Research Project on
Soybean", Vasantrao Naik Marathwada Agriculture University, Parbhani. Two
hundred ninety eight germplasm lines along with twenty four checks are evaluated to
elicit the information on (i) the genetic divergence in core germplasm of Soybean. (ii)
nature and degree of genetic variability, heritability, genetic advance as percent mean
(iii) character association among yield, yield components (iv) the direct and indirect
effect of component character on yield. Observations were recorded on 8 characters.

Analysis of variance shows that there is considerable difference between the

genotypes for all the traits. High estimates of genotypic and phenotypic coefficient of
variation were observed for plant height, number of pod clusters per plant, number of
pods per plant, seed yield per row, seed yield per plant. Moderate PCV and GCV values
were recorded for days to 50% flowering, 100-seed weight. Lower values were
observed for days to physiological maturity. High heritability coupled with high genetic
advance as per cent mean was observed for plant height, number of pod clusters per
plant, number of pods per plant, 100 seed weight, seed yield per row and seed yield per
plant. High GCV and PCV coupled with high heritability were observed for the traits

xiii
plant height, number of pod clusters per plant, number of pods per plant, seed yield per
row and seed yield per plant.

Correlation studies revealed that the characters number of pod clusters/ plant
(G=0.123, P=0.121), number of pods/ plant (G=0.430, P=0.419), 100 seed weight
(G=0.551, P=0.534), seed yield per row (G=0.621, P=0.567) were significantly and
positively correlated with seed yield per plant at genotypic and phenotypic level. A
significant and negative correlation was observed between seed yield/ plant and days
to 50% maturity (G= -0.191, P= -0.183), days to maturity (G= -0.195, P= -0.180), plant
height (G= -0.127, P= -0.124) at both genotypic and phenotypic level.

Path coefficient analysis revealed that the characteristics like days to 50%
flowering, number of pod clusters/plant, number of pods/plant, 100 seed wt, seed
yield/row had greater importance for indirect selection for seed yield.

On the basis of D2 and Tocher‟s method 318 genotypes were grouped into 20
clusters. The cluster 15 was with the highest number of genotypes (24) followed by
cluster 17 (23), cluster 2 (22), cluster 8 (20), cluster 12 (20), cluster 13 (20), cluster 9
(18), cluster 16 (18), cluster 18 (18), cluster 14 (17), cluster 6 (16), cluster 6 (16),
cluster 11 (16), cluster 1 (15), cluster 7 (15), cluster 3 (13), cluster 12 (10), cluster 19
(7), cluster 5 (5), cluster 20 (5).

The intra cluster distance (D2) range from 0 to 1146.7, whereas inter cluster
distance D2 ranges from 55 to 7442.2. Maximum inter cluster distance (D2 = 7442.2)
was observed between cluster 15 and cluster 18. The minimum inter cluster distance
(D2 = 55) was between clusters 5 and 6.

(Key words: Germplasm Soybean, variability, correlation, path analysis, genetic

diversity, D2 analysis, clusters, seed yield ).

xiv
 CONTENTS
Sr. No. Title Page No.
1 Declaration by the Research Student i
2 Certificate-I ii
3 Certificate-II iii
4 Plagiarism Clearance Certificate iv
5 First Two Pages of Plagiarism Report v-vi
6 Acknowledgement vii-viii
7 List of Tables ix
8 List of Figures x
9 Abbreviations used xi-xii
10 Thesis Abstract xiii-iv
11 Chapter – I : Introduction 1-4
12 Chapter – II : Review of Literature 5-27
13 Chapter – III : Materials and Methods 28-50
14 Chapter – IV : Results and Discussion 51-90
15 Chapter – V : Summary and Conclusions 91-93
16 Literature Cited 94-104
17 Appendix 105-106
18 Curriculum Vitae 107

xv
 Chapter – I

INTRODUCTION
CHAPTER - I

INTRODUCTION
The golden bean or miracle bean, Soybean is a significant oil seed crop.
Because of its ability to fix atmospheric nitrogen through symbiotic interactions with
rhizobium and because it can supply fixed nitrogen to crop rotations, which growing
plants can use to produce amino acids, proteins, and nucleic acids, Soybean has served
as a model species for sustainable agriculture. Because of this, the plant does not
require additional nitrogen, unlike corn (Zea mays), cotton (Gossypium hirsutum L.),
and other non-legume crops. Because Soybean is cholesterol-free and high in protein
(40–42%) and oil (18–22%), it has a high nutritional value.

The genus Glycine, the family Fabaceae, the subfamily Faboidae, and the
order Fabales are all considered to be the home of Soybean. Glycine and Soja are the
two subgenera that make up glycine. While Soja (Moench) contains the domesticated
Soybean, Glycine max (L.) Merrill (2n=40), as well as two annual species, Glycine soja
(Siebold and Zucc), Glycine has 16 perennial species. The cultivated species, G. max,
has a low crossability rate with its perennial cousins but can easily hybridize with its
wild annual relative and most likely progenitor, G. soja (Singh and Hymowitz, 1999).

The three varieties of Soybean are G. ussuriensis wild, G. max (L.) Merr.
domesticated, and G. gracilis intermediate (Salunkhe et al., 1992). It is believed that
sowing of Soybeans began in China or Eastern Asia. It has historically been grown in
the hills in the north and northeast, as well as in sporadic packages throughout the
entirety of central India, and has formed a significant component of the daily food in
these regions. Bhat, Bhut, Gari-kalai, Garrykalya, Bhatman Kalitur, Kulthi, Ramkuthi,
Suntha Kadalai, and Teli Kulth are just a few of the various names for Soybean in
India.

Today, Soybeans are a significant crop in Indian agriculture and have

significantly improved the socio-economic circumstances of farmers, especially in the
country's central region. Since Soybeans have so many uses in food, industry, and the
production of common byproducts like tofu, soy milk, soy sauce, tempeh, and soy
flour, Soybean cultivation is expanding due to its short growing season and superior

1
yield compared to other members of the legume family, such as black gram and green
gram.

A total of 120.50 million ha of Soybeans were grown worldwide in Kharif

2022, producing 355.60 million tons, with a productivity estimate of 2.95 t/ha.
According to the United States Department of Agriculture (USDA), 391.17 million
metric tons of Soybeans will be produced worldwide in 2022–2023. For the years
2020–21, India's Soybean production, area, and productivity were, respectively, 121.2
lakh ha, 135.8 lakh million tons, and 1125 kg/ha. India is fifth globally in terms of
Soybean production, productivity, and area, behind Brazil, the United States,
Argentina, and China (USDA, March 2021).

In the years between 1984 and 1985, Soybean was first planted in the state of
Maharashtra on 5.6 million acres. India's Maharashtra. Maharashtra produced 54.21
lakh million tons of Soybeans during the Kharif 2021–22 season on an area of 46.17
lakh ha, with an average productivity of 1174 kg/ha. During the Kharif 2021–2022,
there would be 22.94 lakh acres in the Marathwada region. With an average
productivity of 990 kg/ha and a total production of 22.02 lakh million tons. In terms of
output, Madhya Pradesh comes in first place, followed by Maharashtra, Rajasthan,
Karnataka, and Gujarat. Because of its short growing season and higher yield compared
to other crops in the legume family like green gram and black gram, Soybean
production is expanding daily.

To increase Soybean yield, oil content, and protein content in general, plant
breeders still have a lot of work to do. This can be accomplished by selection, whose
efficacy is greatly influenced by the kind and level of genetic variety. Phenotypic
selection of genotypes based only on their performance may not always be a practical
strategy because phenotypically superior genotypes frequently result in subpar hybrids
or poor recombinants. As a result, it is imperative to pick parents based on their genetic
merit and heritability, which are both vital for selection. The study of genetic factors,
such as heritability, genetic progress, phenotypic and genotypic coefficient of
variation, is the first step towards a better understanding of the genetic basis of
quantitative inherited traits. It's critical to understand heritability in order to determine
how much genetics contributes to yield.

2
 All breeding initiatives aim to increase yield, yet yield has a low heritability
and is strongly impacted by numerous abiotic and biotic variables. In order to increase
yield, both direct and indirect selections are crucial. biometrical correlation approaches
that shed light on the relative contribution of various constituent features to economic
yield. Heritability is crucial since it aids breeders in figuring out the genetic
composition of their progeny. Specific breeding techniques can be used to achieve the
level of genetic advancement. In light of the fact that the current study tried to evaluate
variance for yield and characteristics that contribute to yield, as well as variance indices
like genotypic coefficient of variation (GCV), phenotypic coefficient of variation
(PCV), heritability in the broad sense (bs), genetic advance (GA), and genetic advance
as a percentage of mean.

It is important to study the diversity of crop which serves as a way for population
to adapt to changing environments with more variation. Without genetic variation, a
population cannot evolve in response to changing environment. The assessment of
genetic diversity using quantitative traits has been of prime importance in many
contexts particularly in differentiating well defined population. Mahalonobis (1936)
introduced the concept of D2 statistic for measuring the divergence among two
populations grouped into clusters. The results which were based on magnitude of
divergence between clusters and it were independent of size of samples. Multivariate
analysis using the concept of statistical distance which is very powerful tool in
revaluating genetic diversity in biological population and has been successfully used
even in conditions where overlapping of characters provide the conventional method
of classification ineffective. Plant Breeder utilizes genetic diversity to create improved
crop varieties with traits such as yield, disease and insect pest resistance and
environment stress. A study on core germplasm accessions of Soybean may provide
new advantages in diversifying Soybean crop by using it in processing sector to
generate additional benefits.

Considering this the present study entitled “Study of genetic divergence in core
germplasm accessions of Soybean” was undertaken to assess the genetic variability
and characters association in Soybean germplasm for grain yield and its related
component and thereby generate information as well as to identify superior germplasm
for further improvement with following objectives.

3
1.To study variability for yield and yield attributing characters in Soybean.

2.To study the correlation and path analysis for genetic characters with seed yield and
quality traits.

3.To identify potential germplasm lines for hybridisation through D2 analysis.

4
 Chapter – II
REVIEW OF LITERATURE
CHAPTER II

REVIEW OF LITERATURE
Soybean is considered as one of the important grain legume, because of high
nutritional value and oil content. In India, it is extensively cultivated during kharif
season. Soybean has witnessed increasing trend in the production and productivity over
the year. So there is need to develop high yielding varieties with proper plant
architecture, which are resistant to biotic and abiotic stresses. An attempt has been
made in the present investigation to understand the variability, correlation and genetic
diversity in the Soybean genotype. The review of literature pertaining to these aspects
in presented in this chapter under the following headings:

2.1. Variability, heritability and genetic advances as per cent of mean

2.2. Correlation and Path analysis

2.3. D2 analysis.

2.1. Variability.

Any crop plant's potential to improve is strongly influenced by the level of

genetic diversity. Genetic variability is the term used to describe a set of genotypes or
a genotype that is responsible for the expression of phenotypic variability in any
species that may be divided into genotypic and phenotypic components.

The genotypic component of total variability is heritable. The breeder's

selection tactics are influenced by its magnitude for yield and its component features.

Fisher (1918) divided the genotypic variance into three component viz,
additive, dominance and interaction component. Hayman and Mather (1955)
partitioned the epistatic component into three types viz., additive x additive, additive
x dominance and dominance x dominance

Jain and Ramgiry (2000) recorded high phenotypic and genotypic coefficient
of variability for seed yield per plant followed by plant height, plant weight and
moderate coefficient of variability for 100 seed weight, seed per pod and days to

5
flowering. High heritability estimates accompanied by high genetic advance as a
percentage of mean were noticed for seed yield, plant height and pod per plant.

Agrawal et al., (2001) studied genetic variability parameter viz. GCV, PCV,
heritability and genetic advance for plant growth character and observed high GCV for
degree of indeterminate growth habit, plant height and branches per plant. Estimate of
GCV were moderate for days to flower initiation, whereas low for days to maturity.
Heritability and genetic advance as percentage of mean where high for all the plant
growth character studied.

Filho et al., (2001) evaluated character viz weight of hundred seed, oil content
and protein content, expressed in % oil content ranged from 12 to 20.37% and protein
content from 35.66 % in the experiment carried out in the field, while in the experiment
carried out in the greenhouse oil content ranged from 12.26 to 21.79 % and protein
content from 32.95 to 41.56 %

Sudaric et al., (2001) calculated phenotypic variability, wide sense heritability,

genetic gain and relative genetic gain from selection for seed yield, protein and oil
content. Result of biometrical analyses indicated that advance in yield and quality had
been achieved through the Soybean breeding programme.

Mimura (2001) analyzed the genetic variability in 131 accessions of edamame

Soybean using phenotypic traits e.g. maturity information, testa colour and 100-seed
weight. The obtained results indicated that Edamame genetic diversity wasgenerally
clustered around maturity groups and testa colour. It was also reported that the genetic
diversity among the Japanese edamame cultivars was narrow, compared to Chinese
maodou; Japanese edamame and Chinese maodou Soybeans may have different
genetic pools.

Dhillon et al. (2005) observed that most of the characters controlled sufficient
genetic variability. High heritability was attended by high genetic advance for seed
yield per plant indicating the occurrence of additive gene action in the expression of
this character. Non additive heritability was observed in the expression of days to 50
per cent flowering, protein content and palmitic acid as these traits had high heritability
coupled with low genetic advance.

6
 Karad et al, (2005) revealed high phenotypic coefficient of variation than
genotypic coefficient of variation for all the characters indicating the role of
environment on expression of characters studied. The estimates of GCV were high for
yield per plot, plant height and number of pods per plant, while they were moderate
for number of seeds per pod, number of branches per plant indicating that these yield
contributing characters have scope for improvement by selection.

Sahay et al., (2005) observed high heritability and genetic advance for plant
height, number of unfilled pods, seed weight per plant, number of pods per plant,
number of pod clusters and 100 seed weight.

Faisal et al., (2006) studied highly significant differences among Genotypes

for all the characters. High heritability was recorded for 100 grain weight, days to
maturity, days to flowering, days to pod initiation, days to 50% flowering, oil content,
Grain yield per plant, plant height and protein content respectively indicating for
source of additive type of gene action. They also reported high heritability for oil
content and other morphological and quantitative characters in Soybean indicating the
role of additive gene action for these traits.

Gohil et al., (2006) studied genetic variability, broad sense heritability and
expected genetic advance for seed yield and its component units. The highest GCV
was observed for number of pod per plant followed by seed yield per plant, plant
height, number of clusters per plant, number of pods par plant and seed yield per plant
had high genetic advance coupled with high heritability suggesting that these four traits
are under the control of additive gene action and can be improved through simple
selection procedures.

Gupta and Punetha (2007) reported genotypic and phenotypic variability,

heritability and genetic advance for 12 quantitative traits including seed vigour and
seed yield per plant. The trait pods per plant showed the highest amount of genetic
variability followed by seed vigour, seeds per pod, seed yield per plot, 100 seed weight
and Pods per plant also stated highest heritability and expected genetic advance.

7
 Sharma et al. (2007) reported the range for Phenotypic coefficient of variation
was 3.19 per cent and genotypic coefficient of variation was minimum for protein
content among traits studied in Soybean.

Sirohi et al. (2007) observed eight quantitative traits in Soybean viz, days to 50
per cent flowering, days to maturity, plant height, number of primary branches, number
of pods per plant, seed weight, seed yield per plant, and harvest index. The estimations
of PCV were ranged from 3.33 for pod width to 38.13 for number of pods per plant.
High estimation of genotypic coefficient of variation were observed for plant height,
number of pods per plant, seed yield per plant, number of clusters per plant and
biological yield per plant.

Karnwal and Singh (2009) studied twenty elite breeding lines of Soybean for
genetic variability for eighteen economically important traits. Six genotypes viz.
PK1272, PK-1274, PK-1281, PK-1283, PK-1284 and PK-1286 were found
significantly superior in yield and other major yield contributing traits. In general,
phenotypic coefficient of variation were higher than the genotypic coefficient of
variation and ECV values for all the traits, suggesting that these traits were relatively
much influenced by the environment.

Aditya et al. (2011) studied highest phenotypic coefficient of variation and

genotypic coefficient of variation for dry matter weight per plant and number of nodes
per plant. Grain yield per plant shown highly significant and positive genetic
correlation with dry matter weight per plant, number of primary branches per plant,
number of pods per plant and harvest index.

Patil et al. (2011) reported the highest GCV and PCV for plant height followed
by seed yield per plant and pods per plant and it was lowermost for days to 50%
flowering, days to maturity and pod length. High heritability and genetic advance were
detected for plant height, seed yield per plant and pods per plant He also detected that
the low values of genotypic and phenotypic coefficient of variances for protein content
in Soybean.

Saleem et al., (2011) conducted to estimate the genetic variability in 20

different Soybean genotypes. The results of analysis revealed that all the characters
like days to flowering, days to pod formation, days to maturity, plant height (cm),

8
branches per plant, pod/plant, pod length (cm), seeds pod, 100 grain wt (g) and yield
(g/plant) were significantly affected due to various Soybean genotypes.

Athoni and Basavaraja (2012) observed the prevalence of significant difference

among the genotypes for all the eleven characters studied. Plant height was the only
character which showed high phenotypic and genotypic co-efficient of variation while
days to maturity, number of nodes per plant and oil content recorded a low phenotypic
and genotypic coefficient of variation and rest of characters recorded moderate
phenotypic and genotypic coefficient of variation.

Mahawar et al. (2013) observed higher value of phenotypic coefficient of

variation than genotypic coefficient of variation for primary branches per plant,
followed by seeds per pod, pods per plant and pod clusters per plant.

Rauji et al. (2013) reported that the GCV was observed higher for the traits like
number of pods per plant. Plant height per plant and number of seeds per pod
representing that phenotypic selection would be effective for genetic improvement in
these characters.

Reni and Rao (2013) evaluated 45 genotypes of Soybean of various origin for
variability. Analysis of variance revealed highly significant differences among the
genotypes for all the characters. High phenotypic coefficient of variation coupled with
high genotypic coefficient of variation, revealed for branches per plant, pods per plant,
biological yield, harvest index and yield per plant show the presence of wider
adaptability for these characters in the genotypes studied, suggested the less effect of
environment in the expression of characters.

Pushpa and Rao (2013) studied highly significant differences among the
genotypes for the all the traits. High phenotypic coefficient of variation coupled with
high genotypic coefficient of variation observed for the characters viz. branches per
plant, pods per plant and seed yield per plant representing the presence of wider
adaptability for these characters in the genotypes studied. High heritability coupled
with high genetic advance as percent of mean was observed for days to 50% flowering,
plant height, branches per plant, pods per plant, 100 seed weight and seed yield per
plant indicating operation of additive gene action and the sufficient scope for
improvement traits through simple selection.

9
 Baraskar et al. (2014) revealed that 61 genotypes of germplasm and estimated
the PCV was higher than GCV, this showed that expression of characters under study
was influence due to environmental factors. The high values of phenotypic coefficient
of variation and genotypic coefficient of variation were observed for number of clusters
per plant, seed yield per plant, biological yield per plant, number of pods per plant and
plant height representing presence of sufficient genetic variability for selection in these
characters. He also observed that high heritability accompanied by high genetic
advance for plant height, number of clusters per plant, number of primary branches per
plant, seed yield per plant, biological yield per plant and number of pods per plant
recommended selection could be effective for these traits. High magnitude of
heritability and low to moderate magnitude of genetic advance was observed for days
to 50% flowering, days to maturity, number of seeds per pod, protein content and oil
content may due to lack of genetic variability.

Barh, et al. (2014) studied 13 characters to be more important for the genetic
diversity. Lower contribution was made by days to maturity (1.29%) and maximum
contribution was made through number of pods per plant (14.703%) in genetic
diversity. The variability studies suggests that character like number of pods per plant
consists of less heritability and genetic advance which can be functioned out for mass
selection in future but yield governing characters like number of primary branches and
number of seed per pod are having very lowest. These results suggests that the
genotypes taken under examination having a most diverse range of pods followed by
variable dry matter per plant, seed yield per plant, harvest index, plant height, 100-
seed weight which contribute most towards diversity.

Sawale et al. (2014) studied highest PCV for seed yield/ha, seed yield per plant
and number of pods per plant and highest GCV for number of pods per plant, seed
yield/ha and seed yield per plant indicating that these traits could be used as selection
for crop enhancement.

Baruah et al., (2015) studied that high GCV as well as PCV for 100-seed weight
followed by seed yield per plant and seed per plant studying a high level of diversity
among the genotypes for these characters.

Jain et al. (2015) revealed that phenotypic coefficient of variation was higher
than the genotypic coefficient of variation values for all the traits i.e. days to 50 %

10
flowering, 100 seed weight and seed yield per plant. The broad sense heritability
estimates were high for all traits. High heritability estimates for days to 50 % flowering,
100 seed weight and seed yield per plant shown that selection for these traits will be
effective.

Mahbub et al. (2015) studied 28 genotypes and found that GCV was the highest
for seed yield per plant (31.45%) followed by number of branches per plant (29.9%)
and plant height (27.42%). Days to maturity (99.93%) had the highest heritability.
Plant height, pod length, number of seeds per pod, number of pods per plant, hundred
seed weight, branches per plant and number of seeds per pod shown significant positive
genotypic and phenotypic correlation with seed yield. Heritability was higher for all
traits. High genetic advance was found for plant height and seed per plant. Genetic
advance was moderate for days to first flowering, days to 50% flowering, days to
maturity and number of pods per plant and low for branches per plant, plant height,
number of seeds per pod, hundred seed weight and seed yield per plant Genetic
Advance (GA) in percent of mean for seed yield per plant branches per plant, plant
height, seeds per pod, number of pods per plant and hundred seed weight were high.

Malek et al. (2015) studied eighteen mutants to perform superiority to their

mothers in respect to seed yield and some morphological traits including yield
attributes. Narrow differences between PCV and GCV for most of the characters
revealed less environmental influence on then expression. High values of heritability
and genetic advance with high genotypic coefficient of variation for branch number,
plant height, pod number, and seed weight can be considered as favourable attributes
for Soybean improvement through phenotypic selection and high probable genetic gain
can be achieved. Pod and seed number and maturity period appeared to be the first
order characters for higher yield and priority should be given in selection due to their
strong relations and high magnitudes of direct effects on yield.

Pagde et al. (2015) revealed that genetic variability in thirty Soybean strains.
Pods per plant showed highest genetic variability, followed by plant height, seed yield
per plant, test weight, seeds per pod. Pods per plant also expressed highest heritability
and genetic advance.

11
 Kumar et al. (2015) estimated that genetic variability as well as direct and
indirect effects of yield related traits on seed yield of twenty-five genotypes of Soybean
a wide range of genetic variation.

Dixit et al. (2015) studied comparatively high estimations of genetic variation

for harvest index, biological yield per plant and seed yield per plant.

Chandrawat et al. (2017) evaluated an experiment to evaluation of genetic

variability present in the 41genotypes and 5 checks (two local + three national checks)
and observations were recorded on various yield and yield contributing characters viz.
days to 50% flowering, days to maturity, plant height, number of branches per plant,
number of pods per plant, 100 seed weight, harvest index and grain yield per plant at
field level while oil content, protein content and trypsin inhibitor content in the
laboratory.

Aliyu et al. (2019) studied results obtained for seed yield and yield components
show that the eleven cowpeas exhibited substantial variability for all plant characters.
However, seed yield and yield components differ significantly across the three
locations, which further emphasize the important role of soil and climatic variables to
cowpea production. In this study, two varieties (IT07 K-299-6 and IT11K-61-82)
reliably combined high seed yield (＞2 tons/ha) with precocity across the three
locations, and could be multiplied for distribution to farmers as short-term involvement
for yield increase.

10.2. Correlation and Path analysis

The correlation coefficient analysis aids in resolving the complicated

relationship between occurrences into basic forms of associations, allowing us to
determine the type and strength of relationship between two measurable characters.
Plant yield is influenced by its physio-morphological characteristics. For the purpose
of creating efficient selection procedures, it is imperative to understand how each of
these characters contributes personally. Each component contributes directly to yield
and indirectly to other factors in ways that can't be distinguished by simple correlation.
Analysis of the path coefficient closes the gap. Wright (1921) invented and explained
genetic analysis, which may be used to determine the indirect and direct impacts of
any collection of factors that are in turn related to one another. Later, Dewey and Lu

12
(1959) employed this method with wheat grass. Since that time, its use has been
extended to numerous other crops.

Rajanna et al. (2000) revealed significant and positive correlation for number
of pods per plant and 100 seed weight with seed yield. plant height, days to maturity
and number of branches per plant exhibited significant and positive correlation with
number of clusters per plant and number of pods per plant.

Jain and Ramgiry (2000) conducted correlation and path analysis for pod and
seed attributes in 56 soyabean genotypes to determine the major yield-contributing
traits. The genotypic correlation coefficients were higher than the phenotypic
correlation coefficients. Seed yield did not exhibit any association with any other traits
except harvest index. Branches/plant exhibited a positive correlation with pods/plant
and plant weight. Height of pod-bearing nodes and seeds/plant had the highest direct
effect on seed yield and appeared to be the major yield-contributing characters in
soyabean.

Raut et al. (2001) reported that correlation and path analysis in Soybean for ten
quantitative traits viz. days to 50 per cent flowering, plant height, number of branches
per plant, days to maturity, number of clusters per plant, 100 seed weight, oil content,
harvest index, number of pods per plant and seed yield per plant. Seed yield showed
positive and significant association with number of pods per plant, 100 seed weight,
oil content and harvest index both at genotypic and phenotypic level.

Rezaizad et al. (2001) found that the correlation analysis and observed that
number of seeds per plant and seed yield per plant needed the highest significant
correlation coefficient. Other significant correlation coefficients were found between
biomass per plant and yield between number of pods and yield.

Bangar et al. (2003) revealed that served positive and significant correlation
coefficient for seed yield with 100 seed weight followed by days to maturity, plant
height and days to 50 % flowering. Days to maturity, plant height, pod number per
plant and 100 seed weight among themselves were positively significant.

Chettri et al. (2003) studied that grain yield was significantly correlated with
days to maturity and number of grains per pod at the genotypic level. Days to maturity
was significantly correlated with plant height and days to 50 % flowering at both the

13
phenotypic and genotypic levels. The trait number of days to 50 % flowering was
positively and significantly correlated with days to 50 % flowering but negatively with
number of pods per plant and 100 seed weight at the genotypic level. The character
100 seed weight did not display any correlation with grain yield.

Iqbal et al. (2003) recorded the interrelationship between yield and its
components by genotypic correlation and path coefficient analysis in ten Soybean
varieties. The results observed that seed yield per plant was positively and significantly
correlated with all parameters studied.

Oneml (2003) studied that plant yield had positive and significant correlation
with number of branches, number of pods, pod length and 1000 grain weight The
correlations between seed yield per plant and lower most pod height were significantly
negative. The number of pods maintained positive and significant correlations with
plant height, number of branches and 100 grain weight while negative correlations with
lower one pod length.

Ganeshamurthy and Seshadri (2004) found that plant height, number of pods,
seed yield, and dry matter production show high GCV. Correlation studies indicated
that seed yield per plant shown significant positive correlation with plant height,
number of branches, dry matter production, days to flowering and days to maturity.

Mukhekar et al. (2004) studied that the seed yield was significantly and
positively associated with number of pods per plant, plant height, days to 50 %
flowering, mean internodal length, days to maturity and number of branches per plant.

Vart et al. (2005) revealed that magnitude of correlation coefficients at the

genotypic level was higher than the corresponding phenotypic level, thus twenty
indicating the strong inherent association between these traits. The overall results
indicated that significant positive correlations with seed yield were due to relationship
between biological yield and pods per plant and hence referred as the more important
component of yield.

Bhairav et al. (2006) studied correlation using 198 germplasm lines of Soybean
for eight component characters including seed yield both at genotypic and phenotypic
level. plant height, Pods per plant, branches per plant and 100 seed weight had positive
and significant correlation with seed yield. They observed that pods per plant, branches

14
per plant, 100 seed weight and plant height, biological yield per plant had positive and
significant correlation with seed yield both at genotypic and phenotypic levels.

Faisal et al. (2006) recorded information on yield correlations is derived from

data on 16 yield-related traits in twenty-five genotypes of Soybean. Analysis of
variance, heritability, correlation coefficient and path analysis were carried out for the
data recorded for various agronomic and yield traits and oil and protein content. Results
revealed that there were highly significant differences among genotypes for all the
characters.

Gohil et al. (2006) found that genotypic correlation coefficient was highest in
magnitude than their corresponding phenotypic correlation coefficients for most of the
traits. Seed yield per plant showed significant and positive association with plant
height, branches per plant, clusters per plant, pods per plants and 100 seed weight and
only negatively correlated with oil content at both levels. Among component traits,
pods per plant showed significant and positive association with plant height, branches
per plant. Days to 50 per cent flowering and days to maturity showed significant and
positive correlation with each other.

Muhammad et al. (2006) studied that grain yield had positive and significant
correlation with days to maturity, number of branches, number of pods and 100 seed
weight.

Ngon et al. (2006) found that the genetic diversity of genotypes of Soybean
based on the yield-related traits and concluded that differences among genotypes for
all characters were highly significant and the grain yield was positively and
significantly correlated with number of pods per plant.

Saharan et al., (2006) studied that seed yield per plant was positively correlated
with number of pods per plant, number of branches per plant, 100 seed weight and
protein content. Oil content did not indicate any correlation with seed yield.

Truong et al., (2006) estimated the correlation analysis and showed that days
to flowering and days to maturity had close association with agronomic traits se well
as yield and yield components. Both days to flowering and days to maturity had
positive correlation with the other characters except one hundred seed weight.

15
 Gaikwad et al. (2007) studied higher values for GCV than PCV. Number of
pods per plant showed a positive and significant correlation with seed yield per plant.

Muhammad Faisal Anwar Malik et al., (2007) conducted assessment of genetic

variability, correlation and path analyses for yield and its components in Soybean
characters exhibiting high degrees of genetic association among traits under
consideration. Correlation coefficient for bean yield was positive with leaf area, first
pod height, days to flowering, days to maturity, plant height and number of branches
per plant. Path coefficient analysis revealed that days to flowering completion had
maximum direct contribution to yield followed by days to pod initiation, chlorophyll
content, number of pods per plant and plant height.

Narjesi et al. (2007) estimated that number of pods per plant number of seeds
per plant and 100 seed weight had higher genotypic correlation with seed yield.
Number of pods/plant, number of seeds/plant and 100-seed weight, all of which are
considered yield components, had the highest genotypic correlation with seed yield.
Harvest index was more important for predicting seed yield compared to other traits
based on standardized ßs. Results of path analysis showed that the harvest index and
protein precentage had the highest and lowest direct and positive effect.

Sirohi et al. (2007) conducted correlation and path analysis in Soybean for yield
and yield related traits and observed that seed yield showed positive and significant
association with days to 50 per cent flowering, number of pods per plant, days to
maturity, plant height and biological yield per plant.

Yadav (2007) studied ten quantitative traits viz. plant height, days to 50 per
cent flowering, days to maturity, pod bearing length, pod number per plant, seed
number per plant, 100 seed weight, oil percentage, protein percentage and genetic
correlation for seed yield. Results indicated that seed yield was significantly correlated
with height, pod bearing length, number of pods per plant and number of seeds per
plant.

Karnwal and Singh (2009) studied that seed yield showed significant positive
correlations with total dry matter weight per plant primary branches per plant, pods per
plant, seed yield efficiency, 100-seed weight and harvest index while, protein and oil
contents shown significant and negative association with each other Therefore, main

16
emphasis should be given on these traits during phenotypic selection for developing
high yielding genotypes of Soybean.

Iqbal et al. (2010) revealed that the grain yield was positively and significantly
associated with all studied traits except plant height, which shown non-significant
association during both years. Oil content shown significant and positive correlation
with grain yield, 100-seed weight, and harvest index while significantly negative
correlation was observed with days to maturity, plant height and number of branches
per plant.

Showkat and Tyagi (2010) studied correlation and path coefficient analysis for
seed yield and its components in 40 genotypes of Soybean. The studies indicated that
out of 16 characters, seed yield showed positive and significant association with days
to maturity, branches per plant, pod filling period, pods per plant, harvest index,
100seed weight and clusters per plant indicating that an intense selection for these
characters improve seed yield in Soybean.

Aditya et al. (2011) conducted correlation and path analysis in Soybean for
yield and yield related traits and found that highly significant and positive genetic
association for grain yield per plant with dry matter weight per plant (rg =0.491),
number of primary branches per plant (rg=0.403), number of pods per plant (0.631)
and harvest index (0.487).

Akram et al., (2011) studied correlation and path analyses between seed
weight/plant and its components being days to flowering, days to maturity, plant
height, number of branches/plant, number of pods/plant and 100-seed weight. Results
showed significant differences among Soybean genotypes for all studied traits. Highly
significant and positive correlation coefficients were detected between seed
weight/plant and each of number of pods/plant, number of branches/plant, plant height
and days to maturity while only significant positive relation was observed between
seed weight/plant and days to flowering. Regarding 100-seed weight, it had negative
and insignificant association with seed weight/plant. Path analysis showed that the
traits of number of pods/plant, number of branches/plant and plant height were the
most directly contributed traits to seed weight/plant. As a result, the three mentioned
traits could be used as selection criteria in the present Soybean breeding program,
where they were the most important traits in determining seed weight/plant.

17
 Machikowa and Laosuwan (2011) studied 8 characters of Soybean and verified
direct and indirect effects for the selection of seed yield. Significantly positive
phenotypic correlation was observed between seed yield and days to flowering. Further
genotypic correlation showed that seed yield was positively correlated with all
characters except 100 seed weight. They observed pods per plant gave the highest
positive direct effect on seed yield, followed by branches per plant. In addition, the
indirect effects of most characters were high through pods per plant.

Patil et al. (2011) examined that the seed yield per plant was positively and
significantly correlated with plant height, pods per plant, and days to 50 % flowering
and days to maturity. They observed pods per plant had highest positive direct effect
on seed yield per plant followed by plant height. The studies advise that selection for
pods per plant, seed yield and plant height to evolve high yielding varieties of Soybean.

Peric et al. (2011) studied correlation between yield and yield components and
determine the components whose selection would lead to enhancement of genetic yield
potential, genetic and phenotypic correlations were considered. Based on obtained
results, in two populations pod number per plant and seed number per plant were
determined as the most reliable and efficient selection criteria in breeding for seed
yield. In third population, seed number per plant and 100 seed mass revealed as the
components with the highest influence on seed yield.

Athoni and Basavaraja (2012) revealed that prevalence of significant difference

among the genotypes for all eleven characters studied. Plant height was the only
character which showed high PCV and GCV while days to maturity, number of nodes
per plant and oil content recorded a low PCV and GCV and rest of traits recorded
moderate phenotypic and genotypic coefficient of variation.

Bekele et al. (2012) observed relatively high broad sense heritability for plant
height (0.42), days to flower (0.6) and days to maturity (0.45) indicating the existence
of possibility for selection of varieties which are early with short height to resist
lodging. Heritability estimates of branches/plant, protein content and seed yield were
low (40%) with high genetic advance (>10%) and genetic advance in percentage of
mean (>20%) was exhibited for only plant height Days to flowering, days to maturity
and oil content revealed high heritability (>40) with low genetic advance (<10).

18
 Sarutayophat (2012) studied that the positive and significant association
between the plant height and number of marketable pods per plant (0.821), plant height
and marketable pod yield (0.520), and number of marketable pods per plant and
marketable pod yield, Indirect effect of the plant height on marketable pod yield
through its association with number of marketable pods per plant was positive and
significant (1.075). The results of this study suggested that the number of marketable
pods per plant, green pod weight and plant height were important traits that should be
taken into account as selection criteria in improving marketable pod yield of the
vegetable Soybean.

Ghodrati (2013) studied a strong positive correlation (r=0.61*) between seed

yield and plant height and determined that simultaneous selection for improving seed
yield through increasing the number of nodes per plant, number of pods per plant and
plant height would be an effective approach to increase seed yield as well as protein
yield. They indicated that the total dry matter gave the greatest direct positive effect
(0.718) on seed yield, followed by harvest index (0.589). He directed that seed yield
had significant correlations with 100 seed weight, number of pods per plant, days to
maturity, harvest index, oil percentage and protein percentage.

Okonkwo and Idahosa (2013) studied significant positive correlation between

grain yield and seeds per pod, 100-seed weight, pods per plant, pod length, days to
flowering and plant height, nodes per plant, seeds per pod and plant height, LAI,
flowering, pod length, 1000-seed weight and flowering, maturity, pods per node and
seeds per pod.

Salimi and Abdola (2013) reported significant difference among the studied
Soybean genotypes in the majority of traits, correlation analysis showed that grain
yield per plant had significant and positive correlation with all studied traits. The
highest positive correlation were observed between day to 50% flowering and day to
maturity (r =0.966**) and between fresh & dry weight of plant (r= 0.939). The results
compare means showed between genotypes significant difference were exists and data
recorded in showed that Grain yield increased across the Soybean genotypes. They
observed that the numbers of seeds per plant have highest positive direct effect on grain
yield under normal and moisture stress condition.

19
 Badkul et al. (2014) studied that highest genetic advance as percentage of mean
were recorded for number of branches per plant followed by seed yield per pod,
biological yield per plant, number of seeds per plant moderate for plant height and 100
seed weight.

Barh et al. (2014) conducted correlation studies in Soybean for yield and yield
related traits and concluded that number of primary branches, number of pods per
plant, number of seed per pod, 100 seed weight, and seed per plant were significantly
correlated with yield of Bhat.

Baraskar et al. (2014) evaluated 61 genotypes of germplasm and estimated the

PCV was higher than GCV, this indicated that expression of traits under study was
influenced due to environmental factors. The high values of genotypic coefficient of
variance and Phenotypic coefficient of variance were observed for seed yield per plant,
biological yield per plant, number of pods per plant number clusters per plant, and
plant height indicating presence of sufficient genetic variability for selection in these
characters. He also observed that high heritability accompanied by high genetic
advance for number of clusters per plant, number of primary branches per plant, seed
yield per plant, plant height, biological yield per plant and number of pods per plant
suggested selection could be effective for these traits. High magnitude of heritability
and low to moderate magnitude of genetic advance was observed for days to maturity,
number of seeds per pod, protein content, days to 50% flowering, and oil content may
due to lack of genetic variability.

Hwang et al. (2014) studied the analysis of variance indicated that the
accessions differed significantly (P < 0.0001) in seed protein content, and no
significant correlation between accessions and locations was noticed. The correlation
coefficient (r) of seed protein concentration between the MD and NE experiments was
quite high, r = 0.98 (P < 26 0.0001). The analysis of variance for seed oil revealed that
accessions was significantly different (P < 0.0001) and as was the case with protein,
there was no significant accession x location interaction for oil. The correlation
between mean seed protein and oil contents in the MD experiment and in the NE,
experiment was r = -0.64 and r = -0.66 respectively.

Osekita and Olorunfemi (2014) revealed that seed yield has significantly &
positively correlated with number of seed per plant (0.43) and seed dry weight (0.936),

20
while negatively associated with days to 50 per cent flowering (-0.786) and days to
maturity (-0.396).

Jain et al. (2015) reported that phenotypic coefficient of variation was higher
than the genotypic coefficient of variance values for all the characters i.e., days to 50
% flowering, 100 seed weight and seed yield per plant. The broad sense heritability
estimates were high for all traits. High heritability estimates for days to 50 % flowering,
100 seed weight and seed yield per plant revealed that selection for these characters
will be effective.

Mahbub et al. (2015) revealed variance for plant height, branches per plant,
number of seeds per pod, seed yield per plant and number of pods per plant controlled
by additive gene action. Plant height, pod length, number of pods per plant, 100 seed
weight, and number of seeds per pod showed significant positive genotypic and
phenotypic correlation with seed yield.

Silva et al. (2015) revealed that negative correlation between seeds per plant
and hundred seed weight (-0.520) but seeds per plant shown positive correlation with
yield (0.74) similarly 100 seed weight has negative correlation with yield (-0.54).

V. V. Baraskar et al. (2015) conducted an experiment to study correlation and

path analysis for seed yield in Soybean [Glycine max

(L.) Merrill]. Seed yield is a complex character governed by several

contributing characters. Hence, character association was studied in the present
investigation to assess the relationship among yield and its components for enhancing
the usefulness of selection criterion to be followed while developing varieties.
Correlation and path analysis were made for nine characters in 61 genotypes of
Soybean.

Chavan et al. (2016) conducted an experiment to study correlation and path

coefficient analysis in thirty genotypes of Soybean for eleven characters. The
correlation analysis revealed that seed yield per plant showed highly significant
positive association with 100 seed weight (0.4996), followed by number of pods per
plant (0.2919) and protein content (0.2589). The characters branches per plant (-
0.0074) and seeds per pod (-0.0013) showed negative and nonsignificant association
with seed yield per plant. Path coefficient analysis showed that 100 seed weight

21
(0.4996), number of pods per plant (0.2919), oil content (0.2176), days to 50%
flowering (0.2068), and days to maturity (0.0531) had high positive direct effect on
seed yield per plant. The plant height (-0.1208) and protein content (-0.0539) had
negative direct effect on seed yield per plant.

Garg et al. (2017) found that the higher GCV PCV was observed for electrical
conductivity test, germination after accelerating ageing (96 hr.), seedling vigour index
II and seedling dry weight. Association analysis showed that standard germination
showed a significant and positive correlation with shoot length, root length, seedling
length, seedling dry weight, seedling vigour index I, seedling vigour index II and
germination after accelerating ageing (48, 72 and 96 hr.).

G. Neelima (2017) reported that hundred and twenty four Germplasm

accessions of Soybean consisting of exotic and indigenous collections along with five
national checks. Correlation and Path analysis were carried out to study the character
association and contribution of various traits like days to initial flowering, days to 50
per cent flowering, days to maturity, plant height, number of branches per plant,
number of nodes per plant, number of pod clusters per plant, number of pods per plant,
100 seed weight, oil content and protein content to seed yield per plant. The character
seed yield per plant was found to be positively correlated to number of nodes per plant,
number of pod clusters per plant, number of pods per plant, 100 seed weight and oil
content at both genotypic and phenotypic level.

M. Pallavi et al. (2018) studied the wide genetic variability with high
heritability and genetic advance was observed for seedling dry weight, SVI I, Seed
Yield per ha and clusters per plant indicating additive gene action with further scope
to enrich variation for these characters. The seed yield per ha has shown significant
positive correlation with days to 50per cent flowering, plant height, clusters per plant,
pods per plant and 100 seed weight. SVII has shown positive association with 100 seed
weight, germination %, seedling length and seedling dry weight while SVI II for 100
seed weight, germination %, Seedling dry weight and SVI I. genotypes GP 13, Basar,
A-3-1 and RKS 18 have shown superior performance in terms of seed longevity and
seed yield.

Thi et al. (2019) revealed that there were consistencies of correlations across
generations and higher direct and indirect effects in F6 than in F7. most direct effects
22
were in contract with correlations, indicating true associations. Significant positive
correlations (r) and highly positive direct effects on grain yield were observed for total
number of pods (r = 0.406-0.928), total number of seeds (r = 0.484-0.939) and 100
seed weight (r = 0.361-0.626) across generations and crosses.

Shrishti mehra et al. (2020) studied that genotypic and phenotypic variability,
correlation coefficient and path analysis were worked out on 46 important Soybean
genotypes for yield and its attributing characters at JNKVV, Jabalpur. The highest per
cent of PCV (37.69), GCV (37.48) and heritability (98.9) were found for number of
pods per plant. The number of pods per plant was significantly correlated with number
of seeds (0.7595) and seed yield per plant (0.6316). Path coefficient analysis also
revealed, substantially higher positive direct effects of number of pods per plant
(0.8364) and number of seeds per pod (0.5177) on seed yield per plant.

M. G. Pawar et al. (2020) of correlation coefficient and path analysis in

different Soybean genotypes based on yield and yield contributing traits was
undertaken to study the interrelation between variables means correlation and path
analysis among 30 genotypes of Soybean. The genotypic correlation coefficients were
high in magnitude than their corresponding PCV for all the traits. The seed yield per
plant showed a highly significant positive correlation with number of pods per plant,
number of seeds per pod, 100 seed weight, SPAD value, plant height, days to 50 %
flowering, oil content, number of primary branches per plant and 50 percent flowering
at genotypic level.

Habte Berhanu (2021) studied a total of 46 Soybean genotypes traits showed

positive correlations among themselves both at phenotypic and genotypic levels. Seed
yield had highly significant and positive genotypic and phenotypic correlation with
primary number of branches per plant, number of pods per plant, number of seeds per
pod and plant height, indicating that simultaneous improvement of grain yields with
the associated traits is favourable. Plant height exerted the highest genotypic (0.74)
and phenotypic (0.54) direct effect on seed yield, and followed by hundred seeds
weight and number of pods per plant showed higher genotypic direct effect on seed
yield. This suggested that attention should be given for these traits mainly for direct
and indirect selection for variety development.

23
10.3. D2 analysis

The genus Glycine contains enormous wealth of genetic diversity. The

assessment of genetic diversity using quantitative traits has been of prime importance
in many contexts particularly in differentiating well defined population. Mahalonobis
(1936) introduced the concept of D2 statistic for measuring the divergence among two
populations. The results which were based on magnitude of divergence and it were
independent of size of samples. Multivariate analysis using the concept of statistical
distance which is very powerful tool in revaluating genetic diversity in biological
population and has been successfully used even in conditions where overlapping of
characters provide the conventional method of classification ineffective.

Veni et al.,(2008) genetic divergence studies in Soybean [Glycine max (L.)

Merrill] using Mahalonobis D2 analysis. Based on the relative magnitude of D2 values,
all the germplasm lines were grouped into eight clusters. Majority of the germplasm
lines were grouped in cluster I (42) followed by cluster II (9) and cluster III (7). Rest
of the clusters viz., V, VI and VII possessed only one genotype each. The pattern of
distribution of genotypes into different clusters was at random. Genotypes belonging
to same geographic origin were included in different clusters suggesting that
geographic diversity does not necessarily represent genetic diversity.

T.V. Shadakshari et al. (2011) studied genetic diversity based on 12

morphological characters of 50 Soybean genotypes it could be inferred that, the traits
seeds per plant and seed yield per plant contributed maximum to the genetic diversity.
The clustering pattern revealed that there was no correlation between the geographical
diversity and genetic diversity.

Bekele et al., (2012) studied gentic diversity among Soybean lines in Ethiopia
based on agronomic traits using D2 analysis. Genotypes were clustered following the
Mahalanobis's D2 distance between genotypes. The clustering of genotypes based on
17 traits revealed the existence of variability among genotypes. The maximum inter
cluster divergence was observed between genotypes under clusters IX and X hence,
the genotypes grouped under these clusters could be used for crossing if yield superior
varieties are planned in the hybrid development program.

24
 S.D. Tyagi, M.H. Khan and V. Tyagi., (2012) evaluated forty indigenous and
exotic genotypes of Soybean for sixteen characters to study genetic divergence by
employing D2 analysis. Based on D2 statistics, 40 genotypes were grouped into six
clusters. The maximum intra cluster distance was registered for cluster VI followed by
cluster V and cluster VI. The genotypes included in these clusters were divergent as
well as higher mean values for important yield component traits.

V.H. Kachhadia et al., (2014) conducted divergence analysis among sixty one
Soybean (Glycine max (L.) Merrill.) genotypes collected from different geographic
sources using Mahalanobis’s D2 statistic. The genotypes were grouped into eleven
different clusters.

Thakur et al. (2015) estimated genetic divergence analysis by using

Mahalanobis D² statistics and the results revealed that 40 genotypes were grouped into
six clusters. The analysis further indicated that the genotypes of common geographical
origin or same location were grouped into different clusters, suggesting a lack of
relationship between genetic and geographical diversity.

Jain et al. (2015) studied genetic diversity in Soybean [Glycine max (L.)
Merrill] using D2 statistics.

Dubey et al. (2018) carried out divergence analysis among 50 genotypes using
Mahalonobis D2 statistic. The genotypes were grouped into ten clusters. Diversity
among the clusters varied from 53.05 to 3181. Genotypes belonging to clusters with
maximum intra-cluster distance are genetically more divergent and hybridization
between divergent clusters is likely to produce wide variability with desirable
segregants. The most important trait causing maximum genetic divergence was
biological yield per plant (78.53%) followed by days to maturity (8.90%), protein
content (5.55%), seed yield per plant (3.10%), days to 50% flowering (2.29%) and oil
(1.14%). Hence, it is advisable to select divergent parents based on these three
characters and attempt crossing between them so as to achieve a broad spectrum of
favourable genetic variability for yield improvement in Soybean.

Mishra et al. (2018) studied genetic divergence in advance breeding lines of

Soybean [Glycine max (L.) Merrill] for yield attributing traits. Mishra et al. (2018)
studied genetic divergence in advance breeding lines of Soybean [Glycine max (L.)

25
Merrill] for yield attributing traits. Mishra et al. (2018) studied genetic divergence in
advance breeding lines of Soybean [Glycine max (L.) Merrill] for yield attributing
traits

Santosh Kumar, Vedna Kumari and Vinod Kumar (2018) assessed the genetic
diversity conducted an experiment with 31 Soybean genotypes grown in randomized
block design with three replications. The data were recorded on ten important
quantitative traits. The genotypes could be grouped into 10 clusters which indicated
the presence of sufficient diversity among the tested genotypes.

Sareo et al. (2018) carried out an experiment to study the extent of genetic
diversity among the 75 genotypes of Soybean [Glycine max (L.) Merr.] under rainfed
condition of Manipur by employing Mahalanobis D² analysis based on 13
morphological characters. The 75 genotypes of Soybean were grouped into 9 clusters.
The character plant height (34.13%) contributed highest to the total divergence of the
genotypes, followed by pods/plant (29.62%) and days to 80% maturity (14.59%). The
highest cluster means values for yield and its component characters were observed in
cluster IX.

Shanti Kumari et al. (2019) analysed genetic diversity of 120 genotypes of

Glycine max (L.) merrill by using D2 analysis. An experiment was conducted in
randomized augmented block design at ICAR-Indian Agricultural Research Institute
(IARI), during kharif-2016-17. The genetic divergence assessed using D2 statistics for
characters enabled grouping of all the genotypes in eleven distinct clusters. Maximum
(17) genotypes were grouped in cluster X and minimum (3) in clusters VI. The inter
cluster distance in matrix ranged between 2.98 to 10.45. The maximum intra-cluster
distance was recorded in cluster V. The maximum intercluster distance was found in
the cluster VII (60.78). The minimum inter - cluster distance was being observed
between cluster IX (8.91) and cluster X (7.02). The highest cluster mean for seed yield
/ row in (g) was recorded in case of cluster V followed by cluster XI whereas minimum
cluster mean was recorded in cluster III.

Goonde et al. (2021) genetic diversity and character association for yield and
yield related traits in Soybean (Glycine max l.) genotypes using D2 statistics. D2

26
statistics showed that the genotypes were clustered in to 10 diverse groups, indicating
further genetic diversity in the genotypes.

Swar et al. (2021) studied genetic diversity in magic population of Soybean

(Glycine max (l.) merrill) based on Mahalanobis D2 distance. All the genotypes were
grouped into 16 clusters by performing Tocher’s clustering method using Mahalanobis
D2 distance. The Soybean MAGIC lines belong to the cluster XI and cluster XV were
found to be the most divergent hence can be utilised in the recombination breeding
programs to exploit maximum heterosis.

Upadhyay et al. (2022) assessed genetic diversity of exotic lines of Soybean

based on D2 and principal component analysis. Fifty Soybean genotypes were grouped
into five clusters. The maximum percentage of contribution towards genetic
divergence was shown by the number of seeds per plant and the minimum contribution
was shown by the number of primary branches per plant.

27
 Chapter – III

MATERIALS AND METHODS

CHAPTER III
MATERIAL AND METHOD
The present studies have been done to know genetic divergence in core
germplasm accessions of Soybean. Each plot and replication had five randomly chosen
plants that were tagged, recorded observations and used the mean of five plants for
statistical analysis. observations on various morphological characteristics of these
plants were made at various stages of crop growth. The present experiment was
conducted during Kharif -2021-22 at experimental farm of AICRP on Soybean. The
details of the materials and techniques adopted during the course of investigation are
described below:

3.1 General Description

The present investigation on “STUDY OF GENETIC DIVERGENCE IN

CORE GERMPLASM ACCESSIONS OF SOYBEAN” was undertaken during Kharif
2021-22. Field work at the experimental farm of “All India Coordinated Research
Project on Soybean” Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani. All
recommended agronomic package of practices and plant protection measures were
followed to raise normal healthy stand.

3.1.1 Experimental material

Two hundred ninety eight germplasm lines along with twenty four checks are
evaluated at experimental farm of "All India Coordinated Research Project on
Soybean", Vasantrao Naik Marathwada Agriculture University, Parbhani. This set
consist of diverse germplasm lines, i.e. indigenous, exotic, specific for different biotic
and abiotic traits as well as high seed yield and associated traits. The list of germplasm
along with checks is presented in Table 3.1.

28
Table 3.1. List of germplasms along with checks included in study
S.N. Code Genotype S.N. Code Genotype
1 GW 3 IC 15089 41 GW 51 JS 20-72
2 GW 4 CAT 47 42 GW 52 MAUS 47
3 GW 6 NRC 12 43 GW 53 RAUS -5
4 GW 7 JS 20-34 44 GW 54 ALANKAR
5 GW 8 KALITUR 45 GW 55 PALAM SOYA
6 GW 9 AGS 153 46 GW 57 IMPROVED PLICA
7 GW 10 EC 528623 47 GW 58 PK 308
8 GW 12 ICS 84/86-85B-41 48 GW 59 ANKUR
9 GW 13 JS 20-38 49 GW 60 CO-SOYA-2
10 GW 14 EC 390977 50 GW 61 GUJARAT SOYBEAN
11 GW 15 NRC -7 51 GW 62 SL-295
12 GW 16 EC 241756 52 GW 63 SHILAJEET
13 GW 17 TGX 854-42 D 53 GW 64 SHIVALIK
14 GW 18 TGX 854-429 54 GW 65 DURGA
15 GW 19 TGX 825-17 E 55 GW 66 PUSA 37
16 GW 20 PI 340900 56 GW 67 MAUS 71
17 GW 22 AMS-MB-5-18 57 GW 68 MAUS-1
18 GW 23 AGS 142 58 GW 69 JS 75-46
19 GW 24 BR 15 59 GW 70 JS 20-98
20 GW 25 EC 291397 60 GW 71 DS 205
21 GW 26 NRC 2 61 GW 73 TGX 860-11 D
22 GW 27 EC 457464. 62 GW 74 TGX 85544 G
23 GW 28 RVS 2001-18 63 GW 75 JS 20-86
24 GW 30 TGX 311-101 F 64 GW 76 NRC 2396
25 GW 31 JS 93-05 65 GW 77 MACS 1034
26 GW 32 B160-3 66 GW 78 MACS 1028
27 GW 34 NRC86 67 GW 79 PUNJAB
28 GW 36 EC457280 68 GW 80 EC 457475
29 GW 37 TGX B 1435 E 69 GW 81 DSB 23
30 GW 38 TGX 780-5A 70 GW 82 JS 90-41
31 GW 39 PK 474 71 GW 83 Kaeri 651-6
32 GW 42 EC 457050 72 GW 84 EC 333859
33 GW 43 BRG 1 73 GW 85 EC 274711
34 GW 44 EC 547464 74 GW 86 EC 93601
35 GW 45 EC 100778 75 GW 87 EC 241995
36 GW 46 EC 95815 76 GW 88 EC 391346
37 GW 47 EC 457052 77 GW 89 EC 389148
38 GW 48 EC 39376 78 GW 90 EC 589400
39 GW 49 EC 396067 79 GW 94 ICAL 122
40 GW 50 JS 20-76 80 GW 95 PS 1024

29
S.N. Code Genotype S.N. Code Genotype
81 GW 96 MACS 57 121 GW 145 JS 94-67
82 GW 97 JS335 122 GW 146 PS 1225
83 GW 99 EC 546882 123 GW 147 LESOY 273
84 GW 100 EC 333901 124 GW 148 MACS 227
85 GW 101 JS 20-29 125 GW 149 PK 262
86 GW 102 EC 287754 126 GW 150 MACS 58
87 GW 103 EC 538807 127 GW 151 CAT 1710
88 GW 105 TCJX 849-309 F 128 GW 152 CAT 1149
89 GW 106 TGX 825-3 D 129 GW 153 CAT 2797
90 GW 107 TGX 317-37 E 130 GW 154 MACS 1520
91 GW 108 JSM 288 131 GW 155 DS 321
92 GW 110 JS(SH) 2001-64 132 GW 157 EC 274713
93 GW 111 JS 20-73 133 GW 158 EC 309543
94 GW 112 PK 1284(MlX) 134 GW 159 EC 589409
95 GW 113 PK 566 135 GW 160 EC 391181
96 GW 114 PS 1029 136 GW 161 EC 542431
97 GW 115 K 53 137 GW 162 TGX 298-7D
98 GW 116 MACS 708 138 GW 163 TGX 803-99 E
99 GW 117 PI 283327 139 GW 164 CAT 660
100 GW 118 EC 456566 140 GW 165 TGX 312-012 E
101 GW 119 AGS 205 141 GW 166 TGX 802-100 F
102 GW 120 EC 343310 142 GW 167 TGX 573-209-23
103 GW 123 MACS 1460 143 GW 168 IC 24532A
104 GW 124 CAT 1706 144 GW 169 EC 357998
105 GW 125 DS 97-12 145 GW 170 MACS 124
106 GW 126 EC 389165 146 GW 171 EC 602288(CAT 3293)
107 GW 128 TGX 825-1 E 147 GW 172 AGS 218
108 GW 129 TGX 1016-19 F 148 GW 173 EC 343312
109 GW 130 TGX 239-43 D 149 GW 174 EC 242003
110 GW 131 TGX 854-4 F 150 GW 175 EC 550828
111 GW 132 JS 79-82 151 GW 176 EC 615187
112 GW 133 HIMSO 175 152 GW 177 EC 572160
113 GW 134 M 1052 153 GW 179 TGX 702-4-8
114 GW 135 PUSA 16 154 GW 180 TGX 855-32 E
115 GW 138 EC 341115 155 GW 181 TGX 8116-21 D
116 GW 139 EC 528622 156 GW 182 TGX 86-24-1 D
117 GW 140 EC 309537 157 GW 183 JS 20-37
118 GW 142 TGX 984-18 E 158 GW 184 JSM 245
119 GW 143 TGX 311-59 E 159 GW 185 KB 249
120 GW 144 JSM 302 160 GW 186 MAUS 41

30
S.N. Code Genotype S.N. Code Genotype
161 GW 187 IC 13051 201 GW 231 SL 142
162 GW 188 YOUNG 202 GW 232 NRC 2007-1-3
163 GW 189 IC 15759 203 GW 233 INDRA SOYA9
164 GW 190 BRAGG 204 GW 234 KHSB 2
165 GW 191 EC 241715 205 GW 235 ADT-1
166 GW 192 EC 39573 206 GW 236 CO- soy-1
167 GW 193 TGX 824-35 E 207 GW 237 EC 456613
168 GW 195 TGX 328-049 208 GW 238 EC 457516
169 GW 196 PLSO 79 209 GW 239 B 252
170 GW 197 TGX 802-265-D 210 GW 242 EC 250577
171 GW 198 MACS 450 211 GW 244 EC 538802
172 GW 199 CAT 1258 212 GW 245 EC 389163
173 GW 200 RSC 10-52 213 GW 247 TGX 849-309 A
174 GW 201 CAT 1341 214 GW 248 TGX 822-10 E
175 GW 202 EC 313974 215 GW 249 TGX 302A-68 D
176 GW 203 EC 291400 216 GW 250 M 1085
177 GW 204 EC 289099 217 GW 251 MAUS 142
178 GW 205 EC 389391 218 GW 252 LEE
179 GW 206 TGX 849-47 F 219 GW 253 EC 456620
180 GW 207 HARDEE 220 GW 254 AGS 128
181 GW 208 GC 12 221 GW 255 EC 287460
182 GW 209 TAMS 38 222 GW 258 EC 103334
183 GW 210 EC 241712 223 GW 259 EC 18593
184 GW 211 EC 581521 224 GW 260 EC 15553
185 GW 212 TGX 330-325 D 225 GW 263 TGX 854-60 A
186 GW 213 PI 200465 226 GW 264 TGX 849-143 D
187 GW 214 MAUS-61-2 227 GW 265 JS 95-52
188 GW 215 AGS 156 228 GW 266 GP 493
189 GW 216 ACC 1026 229 GW 267 AGS 143
190 GW 217 BR 10 230 GW 268 AGS 105
191 GW 218 EC 341822 231 GW 270 EC 2579
192 GW 219 EC 289091 232 GW 272 EC 389178
193 GW 220 EC 251432 233 GW 273 TGX 802-150 D
194 GW 221 EC 250591 234 GW 275 TGX 539-2D-7
195 GW 222 EC 251498 235 GW 276 TGX 885-44 E
196 GW 223 EC 291399 236 GW 278 TGX 814-148 F
197 GW 224 EC 325113 237 GW 279 TGX 713-1 F
198 GW 225 EC 393231 238 GW 280 TGX 573-219 D
199 GW 226 EC 13054 239 GW 281 TGX 297-16 F
200 GW 230 TGX 86-24-6 E 240 GW 282 TGX 297

31
 S.N. Code Genotype S.N. Code Genotype
241 GW 283 TGX 239-6 D 269 GW 312 EC 287457
242 GW 284 MACS 13 (S) 270 GW 313 EC 457305
243 GW 285 AMSS 34 271 GW 314 AGS 110
244 GW 286 B 1667 272 GW 315 B 471
245 GW 287 EC 34060 273 GW 316 EC 39177
246 GW 288 EC 274701 274 GW 317 TGX 560-20D
247 GW 289 EC 100804 275 GW 318 VLS 75
248 GW 290 EC 291453 276 GW 319 EC 333870
249 GW 291 EC 76759 277 GW 358 EC 389154
250 GW 292 EC 389173 278 GW 360 EC 389152
251 GW 293 EC 309529 279 GW 361 EC 467282
252 GW 294 EC 251416 280 GW 362 MACS 250
253 GW 295 TGX 863-26 F 281 GW 365 SQL 5
254 GW 296 JS 98-02 282 GW 368 EC 251514
255 GW 297 T 49 283 GW 369 EC 390979
256 GW 298 WT 150 284 GW 370 EC 390981
257 GW 299 NRC I 285 GW 372 AKSS 143
258 GW 300 AGS 193 286 GW 373 N 298
259 GW 301 DS 366 287 GW 374 NRC 57
260 GW 302 EC 241761 288 GW 375 EC 380522
261 GW 303 EC 251388 289 GW 376 EC 389179
262 GW 304 EC 287464 290 GW 377 SQL 40
263 GW 305 AGS 25 291 GW 378 P 318
264 GW 306 EC 251456 292 GW 379 SQL 98
265 GW 308 TGX 996-5 F 293 GW 382 INDORE-2
266 GW 309 TGX 854-77 D 294 GW 383 NANKING
List of Checks included in study
S.N. Check S.N. Check
1 JS 97-52 (Ch) 13 VLS 59 (Ch)
2 JS 20-69 (Ch) 14 VLS 89 (Ch)
3 PK 472 (Ch) 15 VLS 63 (Ch)
4 NRC 37 (Ch) 16 RSC 10-52 (Ch)
5 JS 71-05 (Ch) 17 PS 26 (Ch)
6 NRC 128 (Ch) 18 NRC 138 (Ch)
7 JS 20-116 (Ch) 19 NRC 130 (Ch)
8 AMS 2014-1 (Ch) 20 KDS 753 (Ch)
9 RSC 11-07 (Ch) 21 DSB 34 (Ch)
10 SL 1074 (Ch) 22 Palam Soya 1 (Ch)
11 RKS 113 (Ch) 23 SL 955 (Ch)
12 MACS 1407 (Ch) 24 RVSM 2011-35 (Ch)

32
3.1.2 Location

The experimental farm of “All India Coordinated Research Project on

Soybean” is located at the campus of V.N.M.K.V., in Parbhani. The location Parbhani
is located at 19.27°N to 76.78°E. 1 has an elevation of 347 meters.

3.1.3 Layout and sowing

Two hundred ninety four germplasm lines along with twenty four checks were
sown in Randomized Block Design with two replication during Kharif, 2022 at
experimental farm of “All India Coordinated Research Project on Soybean”, Vasantrao
Naik Marathwada Agriculture University, Parbhani on 1st July 2022. Plan and layout
is presented in Fig 3.1.

3.1.4 Details of experiment

Design : R.B.D. (Randomized Block Design)

Treatments : 318 (294 germplasm lines + 24 checks)

Replications : Two (2)

Plot size : 1 row, 1.5m long

Spacing : Row to row distance: 45 cm Plant to plant distance: 5 cm

Date of sowing : 01/07/2022

3.1.5 Crop Management

The crop was managed following all the agronomical practices recommended
for Soybean cultivation published by Vasantrao Naik Marathwada Krishi Vidyapeeth,
Parbhani. Two seeds were planted in each hill to facilitate emergence and to provide
uniform stand of plants. Just before sowing, fertilizers were applied at the rate of 30:
60: 20 and 20 kg/ha of N, P, K and S respectively uniformly over the soil and
recommended package of practices were adopted for optimum crop growth and
development with proper plant protection under rainfed condition.

33
 Germplasm Multilocation Evaluation Trial Kharif 2022-23
One Line Border MAUS 162
RI RII
T2-I T2-II T3-I T3-II T4-I T4-II T5-I T5-II
Sr. Sr. Sr. Sr. Sr. Sr. Sr. Sr.
No. Code No. Code No. Code No. Code No. Code No. Code No. Code No. Code
1 GW 3 81 GW 96 161 GW 187 244 GW 283 80 GW 95 160 GW 186 243 GW 282 322 RVSM 2011-35 (Ch)
2 GW 4 82 GW 97 162 GW 188 245 GW 284 79 GW 94 159 GW 185 242 GW 281 321 SL 955 (Ch)
3 GW 6 83 GW 99 163 GW 189 246 GW 285 78 GW 90 158 GW 184 241 GW 280 320 Palam Soya 1 (Ch)
4 GW 7 84 GW 100 164 GW 190 247 GW 286 77 GW 89 157 GW 183 240 GW 279 319 DSB 34 (Ch)
5 GW 8 85 GW 101 165 GW 191 248 GW 287 76 GW 88 156 GW 182 239 GW 278 318 KDS 753 (Ch)
6 GW 9 86 GW 102 166 GW 192 249 GW 288 75 GW 87 155 GW 181 238 GW 276 317 NRC 130 (Ch)
7 GW 10 87 GW 103 167 GW 193 250 GW 289 74 GW 86 154 GW 180 237 GW 275 316 NRC 138 (Ch)
8 GW 12 88 GW 105 168 GW 195 251 GW 290 73 GW 85 153 GW 179 236 GW 273 315 PS 26 (Ch)
9 GW 13 89 GW 106 169 GW 196 252 GW 291 72 GW 84 152 GW 177 235 GW 272 314 RSC 10-52 (Ch)
10 GW 14 90 GW 107 170 GW 197 253 GW 292 71 GW 83 151 GW 176 234 GW 271 313 VLS 63 (Ch)
11 GW 15 91 GW 108 171 GW 198 254 GW 293 70 GW 82 150 GW 175 232 GW 268 312 VLS 89 (Ch)
12 GW 16 92 GW 110 172 GW 199 255 GW 294 69 GW 81 149 GW 174 231 GW 267 311 VLS 59 (Ch)
13 GW 17 93 GW 111 173 GW 200 256 GW 295 68 GW 80 148 GW 173 230 GW 266 310 MACS 1407 (Ch)
14 GW 18 94 GW 112 174 GW 201 257 GW 296 67 GW 79 147 GW 172 229 GW 265 309 RKS 113 (Ch)
15 GW 19 95 GW 113 175 GW 202 258 GW 297 66 GW 78 146 GW 171 228 GW 264 308 SL 1074 (Ch)
16 GW 20 96 GW 114 176 GW 203 259 GW 298 65 GW 77 145 GW 170 227 GW 263 307 RSC 11-07 (Ch)
17 GW 22 97 GW 115 177 GW 204 260 GW 299 64 GW 76 144 GW 169 226 GW 260 306 AMS 2014-1 (Ch)
18 GW 23 98 GW 116 178 GW 205 261 GW 300 63 GW 75 143 GW 168 225 GW 259 305 JS 20-116 (Ch)
19 GW 24 99 GW 117 179 GW 206 262 GW 301 62 GW 74 142 GW 167 224 GW 258 304 NRC 128 (Ch)
20 GW 25 100 GW 118 180 GW 207 263 GW 302 61 GW 73 141 GW 166 222 GW 255 303 JS 71-05 (Ch)
21 GW 26 101 GW 119 181 GW 208 264 GW 303 60 GW 71 140 GW 165 221 GW 254 302 NRC 37 (Ch)
22 GW 27 102 GW 120 182 GW 209 265 GW 304 59 GW 70 139 GW 164 220 GW 253 301 PK 472 (Ch)
23 GW 28 103 GW 123 183 GW 210 266 GW 305 58 GW 69 138 GW 163 219 GW 252 300 JS 20-69 (Ch)
24 GW 30 104 GW 124 184 GW 211 267 GW 306 57 GW 68 137 GW 162 218 GW 251 299 JS 97-52 (Ch)
25 GW 31 105 GW 125 185 GW 212 268 GW 308 56 GW 67 136 GW 161 217 GW 250 298 GW 383
26 GW 32 106 GW 126 186 GW 213 269 GW 309 55 GW 66 135 GW 160 216 GW 249 297 GW 382
27 GW 34 107 GW 128 187 GW 214 270 GW 310 54 GW 65 134 GW 159 215 GW 248 296 GW 379
28 GW 36 108 GW 129 188 GW 215 271 GW 311 53 GW 64 133 GW 158 214 GW 247 295 GW 378
29 GW 37 109 GW 130 189 GW 216 272 GW 312 52 GW 63 132 GW 157 213 GW 245 294 GW 377
30 GW 38 110 GW 131 190 GW 217 273 GW 313 51 GW 62 131 GW 155 212 GW 244 293 GW 376
31 GW 39 111 GW 132 191 GW 218 274 GW 314 50 GW 61 130 GW 154 211 GW 242 292 GW 375
32 GW 42 112 GW 133 192 GW 219 275 GW 315 49 GW 60 129 GW 153 209 GW 239 291 GW 374
33 GW 43 113 GW 134 193 GW 220 276 GW 316 48 GW 59 128 GW 152 208 GW 238 290 GW 373
34 GW 44 114 GW 135 194 GW 221 277 GW 317 47 GW 58 127 GW 151 207 GW 237 289 GW 372
35 GW 45 115 GW 138 195 GW 222 278 GW 318 46 GW 57 126 GW 150 206 GW 236 288 GW 370
36 GW 46 116 GW 139 196 GW 223 279 GW 319 45 GW 55 125 GW 149 205 GW 235 287 GW 369
37 GW 47 117 GW 140 197 GW 224 281 GW 358 44 GW 54 124 GW 148 203 GW 233 286 GW 368
38 GW 48 118 GW 142 198 GW 225 282 GW 360 43 GW 53 122 GW 146 204 GW 234 284 GW 362
39 GW 49 119 GW 143 199 GW 226 283 GW 361 41 GW 51 123 GW 147 202 GW 232 285 GW 365
40 GW 50 120 GW 144 200 GW 230 284 GW 362 42 GW 52 121 GW 145 201 GW 231 283 GW 361
41 GW 51 121 GW 145 201 GW 231 285 GW 365 40 GW 50 120 GW 144 200 GW 230 282 GW 360
42 GW 52 122 GW 146 202 GW 232 286 GW 368 39 GW 49 119 GW 143 199 GW 226 281 GW 358
43 GW 53 123 GW 147 203 GW 233 287 GW 369 38 GW 48 118 GW 142 198 GW 225 279 GW 319
44 GW 54 124 GW 148 204 GW 234 288 GW 370 37 GW 47 117 GW 140 197 GW 224 278 GW 318
45 GW 55 125 GW 149 205 GW 235 289 GW 372 36 GW 46 116 GW 139 196 GW 223 277 GW 317
46 GW 57 126 GW 150 206 GW 236 290 GW 373 35 GW 45 115 GW 138 195 GW 222 276 GW 316
47 GW 58 127 GW 151 207 GW 237 291 GW 374 34 GW 44 114 GW 135 194 GW 221 275 GW 315
48 GW 59 128 GW 152 208 GW 238 292 GW 375 33 GW 43 113 GW 134 193 GW 220 274 GW 314
49 GW 60 129 GW 153 209 GW 239 293 GW 376 32 GW 42 112 GW 133 192 GW 219 273 GW 313
50 GW 61 130 GW 154 211 GW 242 294 GW 377 31 GW 39 111 GW 132 191 GW 218 272 GW 312
51 GW 62 131 GW 155 212 GW 244 295 GW 378 30 GW 38 110 GW 131 190 GW 217 271 GW 311
52 GW 63 132 GW 157 213 GW 245 296 GW 379 29 GW 37 109 GW 130 189 GW 216 270 GW 310
53 GW 64 133 GW 158 214 GW 247 297 GW 382 28 GW 36 108 GW 129 188 GW 215 269 GW 309
54 GW 65 134 GW 159 215 GW 248 298 GW 383 27 GW 34 107 GW 128 187 GW 214 268 GW 308
55 GW 66 135 GW 160 216 GW 249 299 JS 97-52 (Ch) 26 GW 32 106 GW 126 186 GW 213 267 GW 306
56 GW 67 136 GW 161 217 GW 250 300 JS 20-69 (Ch) 25 GW 31 105 GW 125 185 GW 212 266 GW 305
57 GW 68 137 GW 162 218 GW 251 301 PK 472 (Ch) 24 GW 30 104 GW 124 184 GW 211 265 GW 304
58 GW 69 138 GW 163 219 GW 252 302 NRC 37 (Ch) 23 GW 28 103 GW 123 183 GW 210 264 GW 303
59 GW 70 139 GW 164 220 GW 253 303 JS 71-05 (Ch) 22 GW 27 102 GW 120 182 GW 209 263 GW 302
60 GW 71 140 GW 165 221 GW 254 304 NRC 128 (Ch) 21 GW 26 101 GW 119 181 GW 208 262 GW 301
61 GW 73 141 GW 166 222 GW 255 305 JS 20-116 (Ch) 20 GW 25 100 GW 118 180 GW 207 261 GW 300
62 GW 74 142 GW 167 224 GW 258 306 AMS 2014-1 (Ch) 19 GW 24 99 GW 117 179 GW 206 260 GW 299
63 GW 75 143 GW 168 225 GW 259 307 RSC 11-07 (Ch) 18 GW 23 98 GW 116 178 GW 205 259 GW 298
64 GW 76 144 GW 169 226 GW 260 308 SL 1074 (Ch) 17 GW 22 97 GW 115 177 GW 204 258 GW 297
65 GW 77 145 GW 170 227 GW 263 309 RKS 113 (Ch) 16 GW 20 96 GW 114 176 GW 203 257 GW 296
66 GW 78 146 GW 171 228 GW 264 310 MACS 1407 (Ch) 15 GW 19 95 GW 113 175 GW 202 256 GW 295
67 GW 79 147 GW 172 229 GW 265 311 VLS 59 (Ch) 14 GW 18 94 GW 112 174 GW 201 255 GW 294
68 GW 80 148 GW 173 230 GW 266 312 VLS 89 (Ch) 13 GW 17 93 GW 111 173 GW 200 254 GW 293
69 GW 81 149 GW 174 231 GW 267 313 VLS 63 (Ch) 12 GW 16 92 GW 110 172 GW 199 253 GW 292
70 GW 82 150 GW 175 232 GW 268 314 RSC 10-52 (Ch) 11 GW 15 91 GW 108 171 GW 198 252 GW 291
71 GW 83 151 GW 176 234 GW 271 315 PS 26 (Ch) 10 GW 14 90 GW 107 170 GW 197 251 GW 290
72 GW 84 152 GW 177 235 GW 272 316 NRC 138 (Ch) 9 GW 13 89 GW 106 169 GW 196 250 GW 289
73 GW 85 153 GW 179 236 GW 273 317 NRC 130 (Ch) 8 GW 12 88 GW 105 168 GW 195 249 GW 288
74 GW 86 154 GW 180 237 GW 275 318 KDS 753 (Ch) 7 GW 10 87 GW 103 167 GW 193 248 GW 287
75 GW 87 155 GW 181 238 GW 276 319 DSB 34 (Ch) 6 GW 9 86 GW 102 166 GW 192 247 GW 286
76 GW 88 156 GW 182 239 GW 278 320 Palam Soya 1 (Ch) 5 GW 8 85 GW 101 165 GW 191 246 GW 285
77 GW 89 157 GW 183 240 GW 279 321 SL 955 (Ch) 4 GW 7 84 GW 100 164 GW 190 245 GW 284
78 GW 90 158 GW 184 241 GW 280 322 RVSM 2011-35 (Ch) 3 GW 6 83 GW 99 163 GW 189 244 GW 283
79 GW 94 159 GW 185 242 GW 281 MAUS 162 2 GW 4 82 GW 97 162 GW 188 MAUS 162
80 GW 95 160 GW 186 243 GW 282 MAUS 162 1 GW 3 81 GW 96 161 GW 187 MAUS 162
One Line Border MAUS 162

Fig 3.1- Layout of field experiment

34
Fig 3.2- Field view of experiment

35
3.1.6 Collection of Experimental Data

Randomly selected five plants from each treatment and replication were
labelled for recording observations and the mean of five plants was used for statistical
analysis. Observations on different morphological characteristics were recorded on
these plants at different stages of crop growth. The observations on the following yield
and yield components and quality parameters were recorded as per the guidelines of
DUS Soybean.

3.2 Plant morphological characters

3.2.1 Plant height (cm)

The length of the randomly selected five plants were measured in centimetres
from the base of the plant at the ground level to the tip of the main shoot at maturity
and grouped as follows:

Short : 40 cm or less

Medium : 41-60 cm

Tall : > 60 cm

3.3 Flower Morphological characters

3.3.1 Days to 50%flowering

Days required from sowing to flowering of approximately 50 percent plants in

each treatment was recorded and average number of days to 50 percent flowering was
worked out.

Early Flowering : 35 days or less

Medium Flowering : 36-45 days

Late Flowering : > 45 days

36
3.3.2 Flower colour

The flower colour of selected and tagged plants in each treatment at flowering
stage under natural day light condition was recorded and grouped in two categories;

I. Purple
II. White

3.4 Pod morphology

3.4.1 Pod pubescence

The pod pubescence i.e, presence of hair has been observed on plot basis in
each treatment at advanced pod filling stage and grouped in two categories:

I. Present
II. Absent

Pod pubescence colour

The pod pubescence colour has been observed on plot basis in each treatment
at advanced pod filling stage and grouped in two categories:

I. Grey
II. Tawny

3.4.3 Number of pods per plant

The total number of pods obtained from each 5 randomly selected plants were
calculated and then their average is been taken from each treatment

Less : <35.00

Medium flowering : 35.00-40.00

Late flowering : >40.00

3.4.4 Number of pod clusters per plant

The total number of pod clusters observed from each 5 randomly selected plants
were taken and then their average has been taken from each treatment

37
Low : <4

Medium : 4-8

High :>8

3.5 Seed morphological characters

3.5.1 100 seed weight (g)

Weight of the randomly selected 100 grains from a well dried composite sample
of each of the genotype was recorded in grams using an electronic balance and
genotypes were classified as follows:

Light : 10 g or less

Medium : 10.1-13 g

Heavy : > 13 g

3.5.2 Seed yield per plant (g)

Seed yield of five observations was recorded and average was taken as seed
yield per plant.

3.5.3 Seed yield per row (g)

Seed yield of whole row of a particular treatment was recorded and average
was taken as seed yield per row.

3.6 Other characters

3.6.1 Days to maturity

Days required from sowing to maturity of 80 per cent of plants in a plot were
recorded as days to maturity.

Early : 95.00 days or less

Medium : 96.00-105.00 days

Late : > 105.00 days

38
3.6.2 Leaf type

The leaf type i.e., lanceolate, pointed ovate and rounded ovate were recorded
at the stage of pod development for each treatment

3.6.3 Leaf colour

Leaf colour is observed at full flowering stage and classified as light and dark
green.

3.6.4 Plant growth habit

Plant growth habit is observed at the time of maturity. On the basis of that crop
is divided into erect and semi-erect.

3.7 Statistical analysis

The mean values of five random plants selected in each plot were used for
statistical analysis. The data were subjected to following statistical analysis

1. Analysis of variance

2. Estimation of mean and range

3. Estimation of genetic variability parameters

4. Heritability and genetic advance

5. Correlation

6. Path analysis

7. Analysis of divergence

39
3.7.1 Analysis of Variance (ANOVA)

ANOVA was calculated in order to test the significance of differences between

the treatments for all the traits as per the standard procedure suggested by Panse and
Sukhatme (1985).

Yij = m + Gj + By

Where,

Y ij = Observed value of jth genotype in ith replication.

M = average mean
Gj = effect of jth genotypes
Eij = Uncontrolled variation associated with jth genotype in ith replication

The analysis of variance is set out under ANOVA table.

Mean sum of squares Variance
Sr. Source of Table
D.F. ratio „F‟
No. variation Observed Expected „F‟
observed
1. Replication (r-1) RMS σ2 e + σ2 r
2. Treatment (t-1) TRMS Σ2e + rσ2g
3. Error (r-1)(t-1) EMS σ2 e
Total (rt-1)

Where,
TRMSS = Treatment mean sum of squares

EMSS = Error mean sum of squares

R = Number of replication

F test were used to test the mean squares against error variance. For
comparing any two genotypic means, standard error of difference was deliberated by
using the formula

Standard Error (SE) ± = √EMSS/r

Where,
EMSS = error mean sum of squares.

40
 R = number of replications
Critical difference (CD) = SE x √2 x t

Where,

t = Table value of „t‟ as error d.f. at 5 or 1 per cent level of significance

3.7.2 Estimation of mean and range

The Mean values of all the treatments for various traits of the genotypes were
worked out by dividing the total value of the observations by number of observations.

Sum of all observations

Mean = ----------------------------------
Number of observations

OR

X=

Where,

X = mean of character

Xi = ith observation of population

n = number of observations per replication.

The maximum and minimum values or a highest and lowest value of mean of
each trait is nothing but the range.

3.7.3 Estimation of genetic variability parameters

The given below are the different formulae for calculating genetic variability
parameters among the germplasm under investigation.

41
3.7.3.1 Estimation of phenotypic and genotypic variances

The mean square from variance table (Burton 1952) was used to calculate
genotypic and phenotypic variance.

i. Genotypic variance(σ2g)

σ2 g =

ii. Error variance(σ2e)

(σ2e) = EMSS

iii. Phenotypic variance(σ2p)

σ2 p = σ2 g + σ 2 e

Where,

R = number of replications
T = number of treatments
σ2 e = error variance

σ2 g = genotype variance
σ2 r = replication variance

3.7.3.2 Estimation of phenotypic and genotypic coefficient of variation

The calculation of genotypic and phenotypic coefficient of variation (PCV and

GCV) was estimated by the method suggested by the Burton (1952). Following were
the formulae for calculating GCV & PCV:

i. Phenotypic coefficient of variation (PCV)

PCV (%) =

ii. Genotypic coefficient of variation (GCV)

GCV (%) =

42
Where,

σ2g = Genotypic variance

σ2p = Phenotypic variance

X = General or grand mean of character

Sivasubramanian and Menon (1973) suggested the classification of GCV &

PCV.

0-10 % = Low
10-20 % = Moderate

20 % and above = High

3.7.4 Heritability and genetic advance

Allard (1960) suggested the method for calculating the heritability (Broad
sense):

i. Heritability (h2) (b.s.) (%)

Heritability % = × 100

Where,

σ2g= genotypic variance

σ2p = phenotypic variance

The heritability was classified as recommended by Johnson et al., (1955):

0-30% = Low

31-60% = Medium

61% and above = High

ii. Genetic advance

G.A = h2 x K x σ2p

43
 Where,

h2 = Heritability (broad sense)

K = Selection difference at 5 per cent selection intensity

(the value of K = 2.06)

σp = Phenotypic standard deviation

iii. Expected genetic advance (EGA)

The genetic advance (at 5 per cent selection intensity) was calculated for each
trait using the formula suggested by Johnson et al. (1955):
GA
EGA = ------------ x 100
Mean
Where,
EGA = Expected genetic advance.
GA = Genetic advance.
Mean = General or grand mean of character.

Johnson et al. (1955) categorised genetic advance as per cent of mean (EGA)
as
Low = 0-10%
Med = 10-20%
High = >20%

3.7.5 Estimation of correlation coefficient

To understand the degree of relationship between different characters the
genotypic and phenotypic correlation coefficients were carried out from their respective
variances and co-variances. Johnson et al. (1955) suggested the formulae for
calculation of correlation which are given below:
3.7.5.1 Phenotypic correlation coefficient (rgxy)

Cov (gxx gy)

(rgxy) = -------------------
√σ2 gx x σ2gy

44
 Where,

Cov (gx.gy) = Genotypic covariance between character x and y.

σ2gx and σ2gy = Genotypic variance of character x and y respectively.

3.7.5.2 Phenotypic correlation coefficient (rgxy)

Cov (pxx py)

(rgxy) = ------------------
√σ2 px x σ2py
Where,

Cov (px.py) = Phenotypic covariance between character x and y.

σ2px and σ2py = Phenotypic variance of character x and y, respectively.

From the table of Fisher and Yates significance of correlation coefficient at 5

and 1 per cent level of significance was carried out. The “r” values were compared
against (n-2) degrees of freedom.

3.7.6 Path analysis

The genotypic correlation coefficient among yield and its attributes were
classified into direct and indirect effects with the path coefficient analysis as outlined
by Wright (1921) and Dewey and Lu (1959).

The first stage in path analysis is to construct the path diagram from the source
of cause and effect relationship. In the present study, path diagram was arranged by
taking yield (Y) as the effect i.e. the function of various components like X1, X2, X3
and these components and some other indeterminate factors selected as r show
following type of correlation with each other.

45
 X1

P1y r1.3

P2y r1.
X2
Y

P3y r2.3

X3
R

Fig. 3.1: Path Diagram

In the diagram double arrowed lines shows the existence of mutual relationship
between and the yield and attributing traits, while single arrowed lines shows the
undeviating influence of factors on the yield. Correlation coefficient (rij) used to
calculate mutual relationship while path coefficient (Pij) used to calculate direct effect
of traits.

By solving a set of simultaneous equations, direct and indirect effects of

variables on yield can be measured as recommended by Dewey and Lu (1959).
rny = pny + rn2 p2y + rn3 p3y +.............

Where,

rny = Represents correlation coefficient between one component and grain

yield.

rn2 = Represents correlation coefficient between that character and each of

other components.

pny = Represents path coefficient between that characters and grain yield.

46
Matrix- A Matrix- B

r1,y P1 r1,1 r1,2…..r1,n

r2,1 r2,2…..r2,n
r2,y P2

rn,y Pn rn,1

rn,2…..1

Matrix-B was inverted (B-1) and following formula used to calculate path
coefficient (Pij)

(Pij) = A x (B-1)

Where,

P1, P2, …..,Pn are estimates of direct effects of character rijpij are indirect
effects of ith character on grain yield character through jth character

Residual factor which was uncounted by the above associations was calculated
by using the following formula:

Residual factor (Rx) = √l-R2

Here,

R2 = (Ply, r1y + p2y, r2y +..............+ Pny, rny)

Where,

rly, r2y, ....... rny, = Correlation coefficient

Ply, P2y, .........Pny, = Path values

47
3.7.7. D2 statistical analysis

The analysis of divergence was worked out by D2 statistics of Mahalanobis

(1936) as suggested by Rao (1952). ANOVA for individual trait studied was carried
out as per Randomized Block Design to test the significance of differences between the
genotypes as described under. The traits showing significant variability were only used
for further analysis of D2 statistics.

i. Analysis of dispersion and contribution of different traits towards diversity

The v-statistic value at a great degrees of freedom (pq = 12x131). Where, p is

number of characters and q is total number of varieties is calculated. The v statistic
value is classified as X2 and it is tested against the tabulated value at desired level for
the test of significance of difference. On the basis of analysis of dispersion the relative
contribution of different quantitative characters towards genetic diversity is measured.

ii. Computation of D2 values

Inverse the common dispersion matrix used to device a set of equation by which
the original correlated variables (X1-X6) were transformed to an uncorrelated (Y1-Y6)
set of variables. The D2 values between any two variables is the sum of squares of
difference (di2) of their corresponding Y values

D2=

Where,

i = 1 to Y superscripts 1 & 2 over Y are the variables 1 & 2 respectively. For

each combination, the mean deviation i.e. Yi1-Yi2 with i=1,2…P is computed & the
D2 is calculated as SS of these deviations i.e., similarly the D2 values for each
variable D2 is calculated by averaging all D2 for ith variable occurring in n-1 i.e. 101-
1=100 pairs.

iii. Significance of D2 values

Each D2 value is analogous to calculated Y2 at degrees of freedom (p). The D2

value obtained for a pair of population is taken as value of ψ2 and is tested against the

48
tabulated value of ψ2 for p degrees of freedom where p is number of characters
considered.

4. Group Constellation

No specific rules lay down for finding two clusters because a cluster is not a
well defined term. Based on the degree of divergence (D2 value) among any two
genotypes, a logical grouping of genotypes with low D2 values can be done. Since the
range D2 values vary extremely from one set of accessions randomly. The start
clustering the range of the D2 values were found out. Then the appropriate D2 value
was fixed with the lowest D2 value between the genotype numbers the starting group
was determined. Then find out the third variable having the lowest average D2 value
from previous two genotypes and add it to them. Similarly the fourth genotype with the
lowest divergence from the first three and the procedure were continued.

At any stage when there is abrupt increase in the average D2 value of the
genotypes for the cluster, then that genotype was taken outside the former cluster and
was included in the next cluster. This procedure was continued till all the genotypes
were included in one or the other cluster. This is followed by Tocher‟s scheme of
cluster formations.

5. Computation of cluster means

The average inter and intra cluster distance within and between the cluster along
with their means in respect of each character are also calculated. Computation of intra
and inter cluster distances, the intra cluster divergence has been computed for each
cluster. The average intra cluster distance was obtained by using formula.

Average intra cluster distance =

Where,

Di2 = sum of distance between all possible combination of genotypes involved

in a cluster.
The inter cluster divergence is calculated by averaging all possible D2 values
among all genotypes belonging to different clusters concerned, divider being the
number of pairs involved.

49
 Inheritance of quantitative characters is often influenced by other character
which may be due to pleiotropy or genetic linkage (Harland 1939). Hence knowledge
of association between yield and its components obtainable through estimation of
genotypic and phenotypic correlation helps a great deal to formulate selection.
Correlation coefficient (r) measures only the degree (intensity) and nature (direction)
of association between characters.

50
 Chapter – IV

RESULTS AND DISCUSSION

CHAPTER IV

RESULT AND DISCUSSION

The present study was conducted with 298 Soybean germplasm lines collected
from AICRP on Soybean, V.N.M.K.V. Parbhani along with 24 checks. The main
objectives of this study was to estimate variability in morphological traits related to
yield of Soybean germplasm lines and correlation between these yield related traits and
to form clusters based on yield related traits.

Mean data of genotypes was analysed as per standard procedure and are presented
under following sub-headings.

4.1 Analysis of variance

4.2 Mean performance

4.3 Genetic variability

4.4 Correlation

4.5 Path analysis

4.6 D2 analysis

4.1 Analysis of variance

The results of analysis of variances between genotypes showed highly

significant differences for all the characters. The mean sum of squares due to
treatments were highly significant both at 5 and 1 per cent probability level for all 8
characters viz., days to 50% flowering, days to maturity, plant height, number of pod
clusters per plant, number of pods per plant, 100 seed weight, seed yield per row(g),
seed yield per plant(g) are presented in Table 4.1. The variation due to replication was
not significant for all the characters under study both at 5 and 1 per cent probability
level.

51
Table 4.1 Analysis of variance for morphological and yield and yield contributing characters in Soybean.

Sources of d.f Days to Days to Plant No of pod No of 100 Seed Seed

variations 50% maturity height clusters/plant pods/plant seed yield/row(g) yield/plant(g)
flower (cm) wt(g)
Replication 2 29.08 136.83 0.11 17.34 1.20 0.01 132.23 1.02

Genotype 317 38.15** 37.66** 551.38** 50.80** 633.58** 7.49** 5730.89** 7.76**

Error 318 0.26 0.21 0.57 0.28 0.89 0.09 754.04 0.21

*, ** Significance at 5% and 1%, respectively

52
4.2 Mean Performance

The one of the legal recognized methods in our country for varietal
identification and genetic purity practices by seed certification is based on field-plot
grow out test, which include the morphological characteristics of a genotypes and
also helpful to know the genetic variability for the crop (Table 4.13).

4.2.1 Plant morphological characters

4.2.1.1 Plant Height (cm)
The plant height varied among different Soybean genotypes (Table 4.2). The
mean plant height of the genotypes was 65.08 cm (Table 4.13). The highest plant
height was observed in JS 71-05 (115.60cm), while, the lowest plant height was
observed in GW 10 (21.80 cm). Based on the plant height, genotypes were grouped
into three categories as short (<40.00 cm), medium (41.00-60.00 cm) and tall (>60.00
cm). The findings were as follows:
Table 4.2- Plant Height (cm)
Plant height Germplasm lines Checks Total
Short 14 5 19
Medium 92 14 106
Tall 188 5 193
Total 294 24 318

4.2.1.2 Leaf shape

On the basis of the leaf shape, genotypes were divided in four groups’ viz,
lanceolate, triangular, pointed ovate and rounded ovate (Table 4.3). The findings were
as follows:

Table 4.3- Leaf shape

Leaf shape Germplasm lines Checks Total
Lanceolate 8 2 10
Triangular 44 5 49
Pointed ovate 198 14 212
Rounded ovate 44 3 47
Total 294 24 318

53
4.2.1.3 Plant growth habit

All the Soybean genotypes were divided in two categories viz., erect and semi-
erect according to their plant growth habit (Table 4.4). The findings were as follows:
Table 4.4- Plant growth habit
Plant growth type Germplasm lines Checks Total
Erect 217 15 232
Semi erect 77 9 86
Total 294 24 318
4.2.1.4 Leaf colour
Leaf colour is observed at full flowering stage and classified as light and dark green
(Table 4.5). The findings were as follows:

Table 4.5- Leaf colour

Leaf colour Germplasm lines Checks Total
Light green 34 5 39
Dark green 260 19 279
Total 294 24 324

4.2.2 Flower morphological characters

4.2.2.1 Days to 50% flowering

The days to 50 percent flowering varied among the genotypes

(Table 4.13). The average days taken by the genotypes for 50 % flowering
were 43.16 days. Days to 50 percent flowering in germplasm lines ranged
from 33 days (GW 377) to 56 days (GW 2).

The genotypes were grouped in to three categories as early (<36.00

days), medium (36.00-45.00 days) and late (>45.00 days). Early flowering
varieties are the most important attributes that need to be considered in
selecting genotypes for escaping post flowering drought and best suited in
multiple and/or mixed cropping systems in changing scenario of climate
change (Table 4.6). The findings were as follows:

54
Table 4.6- Days to 50% flowering
Days to 50% flowering Germplasm lines Checks Total
Early 4 0 4
Medium 64 15 79
Late 226 9 235
Total 294 24 318

4.2.3 Pod morphology

4.2.3.1 Number of pod clusters per plant

The number of pod clusters per plant varied among different Soybean
genotypes. The mean number of pod clusters per plant of the germplasm lines was
14.60 (Table 4.13). Highest number of pod clusters per plant was observed in GW 119
(31.00) while, the lowest number of pod clusters were observed in GW 377 (6.40)
Based on the number of pod clusters per plant, the genotypes were
grouped into three categories as less (< 4.00), medium (4.00-8.00) and
more (> 8.00) pods per plant (Table 4.7). The findings were as follows:

Table 4.7- Number of pod clusters per plant

No. of pods clusters per plant Germplasm lines Checks Total
Less 0 0 0
Medium 16 14 30
More 278 10 288
Total 294 24 318

4.2.3.2 Number of pods per plant

The number of pods varied among the different Soybean genotypes. The mean
number of pods per plant of the genotypes was 47.50 (Table 4.13). The highest number
of pods was recorded in the germplasm GW 17 (120.00) while, the lowest number of
pods were observed in PS 26 (6.40).
Based on the number of pods per plant, the genotypes were grouped into three
categories as less (< 35.00), medium (35.00-40.00) and more (> 40.00) pods per plant
(Table 4.8). The findings were as follows:

55
Table 4.8- Number of pods per plant
No. of pods per plant Germplasm lines Checks Total
Less 62 13 75
Medium 53 3 56
More 179 8 187
Total 294 24 318
Pod pubescence
The pod pubescence i.e, presence of hair has been observed on plot basis in
each treatment at advanced pod filling stage and grouped in two categories i.e, present
& absent (Table 4.9). The findings were as follows:

Table 4.9- Pod pubescence

Pod pubescence Germplasm lines Checks Total
Present 271 15 286
Absent 23 9 32
Total 294 24 318
Pod pubescence colour
The pod pubescence colour has been observed on plot basis in each treatment
at advanced pod filling stage and grouped in two categories i.e, grey & tawny (Table
4.10). The findings were as follows:

Table 4.10- Pod pubescence colour

Pod pubescence colour Germplasm lines Checks Total
Grey 38 4 42
Tawny 233 11 244
Total 271 15 286

4.2.4 Seed morphological characters

4.2.4.1 100 seed weight (g)

The hundred seed weight varied among different Soybean genotypes. The
mean hundred seed weight of the genotypes was 12.63 g (Table 4.13). The highest
hundred seed weight was observed in the KDS 753 (21.31 g), while the lowest was
observed in GW 361 (7.54 g).
Based on the hundred seed weight, the genotypes were grouped into three categories
as light weight with the hundred seed weight 10.00 g or less, medium weight with the
hundred seed weight 10.01-13.00 g and heavy weight with the hundred seed weight
more than 13.00 g (Table 4.11). The findings were as follows:

56
Table 4.11- 100 seed weight(g)
100 seed weight (g) Germplasm lines Checks Total
Light 22 0 22
Medium 152 14 166
Heavy 120 10 130
Total 294 24 318

4.2.4.2 Seed yield per row (g)

The seed yield (kg/ha) is different for different genotypes. The mean value for
seed yield per row was 241.56 g (Table 4.13). The highest seed yield per row was given
by GW 25 (409 g), while the lowest seed yield per row was observed in GW 189 (36
g).
4.2.4.3 Seed yield per plant (g)

The seed yield per plant varied among different Soybean genotypes. The mean
seed yield per plant was 8.05 g (Table 4.13). The highest seed yield per plant was
observed in GW19 (19.07 g), while the lowest seed yield per plant was observed in
GW10 (4.30 g)

4.2.5 Other characters

4.2.5.1 Days to maturity

The days to harvesting varied significantly among the genotypes. The average
days required by the genotypes for harvesting were 102.72 (Table 4.13). The days to
maturity ranged from 93.5 days in NRC 130 to 109.50 in JS 71-05.

Based on the days to harvesting, the genotypes were grouped into three
categories as early (100.00 days or less) and late (>100.00 days) (Table 4.12). The
findings were as follows:

Table 4.12- Days to maturity

Days to maturity Germplasm lines Checks Total
Early 87 17 104
Late 207 7 214
Total 294 24 318

57
Table 4.13 Mean performances of Soybean genotypes for morphological,
physiological, yield and yield contributing traits.
No of Seed
Name of Days Plant pod No of 100 Seed yield/
to 50% Days to height clusters/ pods/ seed yield/ plant(
SN entry flower maturity (cm) plant plant wt(g) row(g) g)
1 GW 3 44.5 103.0 61.6 17.6 50.2 14.7 264.5 13.9
2 GW 4 41.5 100.5 60.4 19.4 62.9 12.2 316.5 10.7
3 GW 6 33.5 93.5 32.2 9.4 23.3 14.5 170.0 6.2
4 GW 7 37.0 96.0 51.8 12.5 28.5 12.3 239.5 8.0
5 GW 8 40.5 99.5 66.8 12.3 52.4 13.0 237.5 7.9
6 GW 9 38.5 97.5 52.7 8.3 92.5 17.4 335.5 12.0
7 GW 10 33.5 92.0 21.8 6.8 29.2 11.0 106.5 4.3
8 GW 12 37.5 96.5 72.0 11.7 51.2 10.7 224.0 7.7
9 GW 13 33.5 94.5 70.6 15.2 50.0 10.5 252.0 8.4
10 GW 14 38.0 97.5 73.6 24.5 99.2 10.5 250.0 8.5
11 GW 15 42.5 101.5 49.7 14.4 86.9 13.6 149.0 11.9
12 GW 16 40.5 102.5 42.3 15.9 95.8 15.1 301.0 12.8
13 GW 17 39.0 98.5 66.7 21.5 120 13.2 87.0 11.7
14 GW 18 39.0 97.0 56.6 26.2 111 12.8 86.5 13.3
15 GW 19 45.5 103.5 55.6 15.7 57.2 17.0 248.0 19.1
16 GW 20 36.5 95.5 50.4 15.4 41.3 15.5 211.0 7.0
17 GW 22 39.5 98.5 62.7 25.2 64.4 11.7 287.5 9.6
18 GW 23 45.5 104.5 62.2 13.5 23.3 15.0 165.0 6.9
19 GW 24 36.5 95.0 45.2 8.8 25.2 11.5 158.5 5.3
20 GW 25 42.5 100.5 54.3 12.0 91.6 16.0 409.0 13.6
21 GW 26 42.0 101.5 51.6 16.8 51.6 14.0 149.0 9.6
22 GW 27 35.0 95.5 52.2 8.4 46.0 16.0 234.0 8.1
23 GW 28 44.5 103.5 54.0 22.7 67.6 15.2 202.0 11.3
24 GW 30 38.0 97.5 85.9 24.9 88.0 16.6 284.5 12.4
25 GW 31 39.5 98.5 64.4 13.3 24.2 12.8 212.0 7.2
26 GW 32 42.5 101.5 98.8 15.9 32.9 11.1 165.0 5.5
27 GW 34 42.0 101.0 87.2 17.1 31.1 13.3 273.0 9.1
28 GW 36 38.5 98.5 49.6 16.5 43.4 12.4 266.0 9.0
29 GW 37 39.0 98.5 42.4 16.3 51.1 13.3 121.5 6.5
30 GW 38 41.5 100.5 50.7 12.6 53.4 12.6 206.5 8.6
31 GW 39 40.5 99.5 48.2 12.3 32.4 14.2 209.0 7.2
32 GW 42 42.0 101.5 97.3 30.4 83.3 12.7 293.5 10.1
33 GW 43 38.5 97.5 40.6 12.2 41.3 12.0 166.0 6.4
34 GW 44 38.5 97.0 50.7 9.6 34.4 17.3 227.0 8.0
35 GW 45 41.0 100.5 44.8 17.3 41.7 12.8 265.5 8.9
36 GW 46 36.5 95.5 56.7 13.6 50.4 15.0 206.5 9.3
37 GW 47 41.0 100.5 53.3 13.7 40.6 12.4 267.0 10.2

58
Table 4.13 continued…
No of 100
Name Days Plant pod No of seed Seed Seed
to 50% Days to height clusters pods/ wt yield/ yield/
SN of entry flower maturiy (cm) /plant plant (g) row(g) plant(g)
38 GW 48 38.5 97.5 78.9 27.1 53.0 14.1 252.0 8.7
39 GW 49 43.0 102.5 67.2 16.3 55.9 11.2 251.0 8.4
40 GW 50 39.5 98.5 97.1 27.7 59.0 13.7 217.0 8.2
41 GW 51 36.5 94.5 40.8 8.3 48.2 11.0 100.0 4.7
42 GW 52 37.5 95.5 68.0 13.4 91.2 13.3 329.5 11.0
43 GW 53 46.5 105.5 72.8 18.9 47.7 14.6 241.5 8.1
44 GW 54 42.0 101.5 44.3 17.2 31.7 15.1 166.5 6.3
45 GW 55 42.5 101.0 94.9 15.3 60.9 9.9 248.5 8.3
46 GW 57 44.0 102.5 70.5 12.5 45.3 14.3 272.0 10.9
47 GW 58 42.5 101.5 66.7 13.9 51.9 11.5 195.0 6.5
48 GW 59 38.5 97.5 43.4 13.6 28.9 12.9 216.0 7.2
49 GW 60 40.0 99.5 57.8 15.5 84.5 12.2 300.5 10.0
50 GW 61 47.5 106.5 56.6 21.1 70.5 12.3 189.0 10.1
51 GW 62 37.0 96.5 32.0 8.5 42.8 16.7 163.5 8.4
52 GW 63 42.5 102.5 62.6 17.3 55.2 12.6 305.0 10.2
53 GW 64 43.0 102.5 55.4 15.2 73.6 12.1 255.0 10.4
54 GW 65 43.0 102.0 68.4 17.7 45.9 12.5 290.5 10.8
55 GW 66 38.0 96.5 42.9 8.4 41.9 15.8 251.5 8.5
56 GW 67 39.5 97.5 39.6 15.7 51.3 14.7 285.0 10.8
57 GW 68 40.5 99.5 57.4 12.2 46.9 12.4 307.5 10.4
58 GW 69 45.5 104.5 62.4 14.3 51.3 13.1 238.0 8.7
59 GW 70 37.0 96.5 42.7 15.7 36.0 11.1 123.0 5.7
60 GW 71 41.0 100.5 53.1 13.6 52.5 13.3 258.5 8.6
61 GW 73 44.0 103.5 55.9 13.3 42.7 13.9 258.5 8.6
62 GW 74 42.5 101.5 65.7 16.5 49.6 12.9 252.5 8.4
63 GW 75 40.0 99.5 62.4 13.6 36.1 12.2 221.0 7.7
64 GW 76 40.5 99.5 55.1 15.0 50.4 12.6 236.0 7.9
65 GW 77 41.0 100.5 62.1 15.4 57.6 13.1 270.5 10.2
66 GW 78 41.5 100.5 47.0 12.4 38.2 14.4 187.0 8.0
67 GW 79 43.5 103.5 60.5 15.4 60.2 12.0 255.5 8.5
68 GW 80 43.0 102.5 68.3 15.0 31.8 11.8 221.0 7.5
69 GW 81 45.5 104.5 57.9 9.7 41.6 14.2 242.0 8.1
70 GW 82 37.0 95.5 63.8 20.7 35.4 10.5 192.0 6.7
71 GW 83 43.0 102.5 47.6 14.3 33.4 11.6 96.0 6.5
72 GW 84 44.0 103.5 64.6 14.3 51.1 11.9 193.0 6.4
73 GW 85 55.5 113.5 64.1 27.2 49.6 11.5 191.0 6.4
74 GW 86 45.0 104.5 63.7 14.6 37.0 13.3 160.5 6.2
75 GW 87 44.5 103.5 59.8 19.5 39.2 14.0 150.0 6.5
76 GW 88 42.5 102.5 49.3 14.2 36.1 11.5 237.0 7.9
77 GW 89 42.5 101.5 69.1 11.6 34.1 7.8 85.0 5.4
78 GW 90 42.5 102.5 60.6 13.5 37.9 11.3 264.0 10.2
79 GW 94 42.0 101.5 46.7 14.5 30.5 13.6 197.0 10.1

59
Table 4.13 continued…
No of 100
Days Plant pod No of seed Seed Seed
Name of to 50% Days to heigh cluster pods/ wt(g yield/ yield/
SN entry flower maturiy t(cm) s/ plant plant ) row(g) plant(g)
80 GW 95 35.5 94.5 38.9 9.1 31.4 11.7 162.5 5.9
81 GW 96 41.5 101.5 61.5 14.3 42.2 12.6 243.5 8.1
82 GW 97 41.5 100.5 57.3 14.3 52.2 15.4 267.5 10.9
83 GW 99 40.0 99.5 57.4 17.2 50.3 12.2 236.0 8.5
84 GW 100 44.5 103.5 49.7 15.2 39.5 14.9 107.0 13.4
85 GW 101 40.5 99.5 50.1 6.9 51.5 13.6 231.5 8.6
86 GW 102 38.0 97.5 67.7 14.1 92.2 11.6 191.5 7.6
87 GW 103 37.5 96.5 50.1 9.6 30.9 14.5 202.5 6.8
88 GW 105 42.0 101.5 68.0 12.7 50.2 11.3 203.0 6.8
89 GW 106 37.5 97.5 48.7 11.4 48.8 11.8 190.0 6.6
90 GW 107 46.0 105.5 77.5 17.7 52.5 13.9 249.0 9.2
91 GW 108 43.0 102.5 63.1 18.1 68.4 10.4 250.0 8.3
92 GW 110 42.5 101.5 52.9 12.4 52.6 12.0 232.0 8.6
93 GW 111 44.5 103.5 60.1 23.2 57.3 12.4 80.0 11.4
94 GW 112 43.0 102.0 76.5 19.5 36.8 13.9 223.0 9.3
95 GW 113 43.5 102.5 81.4 18.1 36.2 13.4 205.0 7.6
96 GW 114 35.5 96.5 50.2 17.1 42.3 11.5 188.5 10.5
97 GW 115 39.0 98.5 48.6 13.5 21.8 13.7 274.0 9.1
98 GW 116 40.5 99.5 37.1 12.3 72.0 15.5 182.5 13.0
99 GW 117 37.5 96.5 33.7 12.9 57.7 14.2 216.0 10.3
100 GW 118 36.5 97.5 37.4 10.2 26.6 14.4 170.5 6.0
101 GW 119 48.0 107.5 89.4 30.5 60.2 10.9 302.5 10.3
102 GW 120 39.5 99.5 68.4 18.1 59.8 13.3 216.0 8.5
103 GW 123 39.5 98.5 52.7 13.6 46.0 12.6 207.5 7.6
104 GW 124 45.5 104.5 57.1 17.8 52.5 13.3 238.0 9.1
105 GW 125 43.5 103.0 57.0 13.7 59.2 12.4 180.5 7.2
106 GW 126 41.0 100.5 59.8 15.9 36.9 14.5 245.0 10.0
107 GW 128 43.5 102.5 102 29.2 80.3 15.5 345.5 12.1
108 GW 129 43.0 102.5 60.5 11.5 21.8 15.5 223.0 7.4
109 GW 130 43.5 103.0 58.2 13.6 56.0 12.8 273.0 9.1
110 GW 131 39.5 98.5 98.9 18.3 57.6 12.8 232.5 7.8
111 GW 132 38.5 97.5 57.9 17.0 28.7 15.2 141.5 8.9
112 GW 133 41.5 100.5 68.1 15.4 42.2 11.6 244.0 8.4
113 GW 134 41.0 100.5 94.3 22.0 44.7 14.3 239.0 8.2
114 GW 135 37.5 96.5 67.3 17.1 44.5 12.9 141.5 8.2
115 GW 138 42.5 101.5 61.7 12.4 23.7 11.5 246.0 8.2
116 GW 139 44.0 103.5 83.2 13.6 29.6 14.9 232.0 7.9
117 GW 140 43.5 102.5 63.8 15.0 42.4 15.4 327.0 12.1
118 GW 142 42.0 101.5 68.4 22.9 19.4 13.2 198.0 6.6
119 GW 143 43.0 102.5 77.3 25.4 45.8 12.4 230.0 7.7
120 GW 144 41.5 100.5 52.1 11.9 39.2 13.5 159.0 6.2
121 GW 145 42.5 101.5 59.0 15.4 45.5 10.6 186.0 6.4

60
Table 4.13 continued…
Days No of
Days to Plant pod No of 100 Seed Seed
Name of to 50% matu height clusters/ pods/ seed yield/ yield/
SN entry flower riy (cm) plant plant wt(g) row(g) plant(g)
122 GW 146 36.0 95.5 32.8 13.0 32.6 12.5 178.0 5.9
123 GW 147 45.5 104.5 78.6 21.1 81.0 11.5 309.0 10.3
124 GW 148 45.5 104.5 97.2 24.4 108.0 12.5 290.0 10.3
125 GW 149 41.0 100.5 40.6 8.2 20.3 14.5 150.0 6.4
126 GW 150 43.5 103.5 89.8 15.6 71.0 16.1 154.5 14.7
127 GW 151 43.5 102.5 67.7 15.2 41.5 13.2 310.0 10.3
128 GW 152 43.5 102.5 62.7 13.9 47.4 9.9 161.5 5.4
129 GW 153 43.5 102.5 59.5 15.9 47.3 16.4 322.0 10.7
130 GW 154 42.5 101.5 45.0 12.8 36.7 13.2 155.0 6.6
131 GW 155 41.0 100.5 82.1 23.1 41.0 12.5 222.5 7.5
132 GW 157 45.0 104.0 91.1 19.4 27.2 14.2 213.0 7.1
133 GW 158 43.0 102.5 82.7 14.1 36.7 12.0 194.0 6.5
134 GW 159 42.5 101.5 60.5 11.0 23.4 11.7 161.0 5.4
135 GW 160 43.5 103.5 87.1 14.1 56.8 11.4 193.0 6.4
136 GW 161 44.5 103.5 81.5 12.2 26.2 12.7 124.0 5.7
137 GW 162 42.5 101.5 60.7 16.7 57.9 13.3 238.0 8.4
138 GW 163 38.0 97.5 91.9 29.8 70.1 11.1 241.0 8.0
139 GW 164 45.5 105.0 80.0 19.4 78.6 11.0 192.0 10.0
140 GW 165 45.0 104.5 90.6 11.9 45.5 13.4 241.5 8.6
141 GW 166 45.5 104.5 68.5 10.2 51.2 15.6 140.5 10.5
142 GW 167 43.0 102.5 57.8 20.9 42.6 10.3 213.0 7.1
143 GW 168 43.5 102.0 71.1 25.3 39.2 12.6 208.0 6.9
144 GW 169 44.5 104.0 72.3 15.0 24.5 10.4 146.0 5.2
145 GW 170 44.0 102.5 61.0 16.8 37.6 15.9 243.0 8.1
146 GW 171 44.5 103.5 63.0 15.7 39.8 9.8 209.5 7.0
147 GW 172 38.5 97.0 60.5 11.0 37.1 12.3 237.0 7.9
148 GW 173 50.0 108.5 91.8 25.6 59.1 13.5 257.5 8.6
149 GW 174 41.0 100.5 50.1 14.3 48.6 10.9 198.5 6.6
150 GW 175 43.5 103.5 60.9 16.9 70.5 10.9 257.0 8.6
151 GW 176 47.0 106.5 79.7 13.9 48.6 10.3 187.0 6.2
152 GW 177 43.5 102.5 62.3 15.9 39.9 11.2 205.5 6.9
153 GW 179 54.5 112.0 72.0 16.0 39.1 11.7 178.0 5.9
154 GW 180 43.0 102.5 75.5 13.7 47.8 14.8 280.5 9.5
155 GW 181 46.5 105.5 54.3 15.7 30.0 10.2 138.0 4.8
156 GW 182 44.5 103.5 67.3 11.0 50.2 11.0 235.5 7.9
157 GW 183 43.5 102.5 45.9 11.0 29.1 11.8 208.0 7.7
158 GW 184 38.0 97.5 49.7 12.7 29.6 12.7 261.0 8.7
159 GW 185 38.5 98.5 49.7 15.6 48.8 12.5 244.5 8.2
160 GW 186 43.5 102.5 68.8 17.4 47.9 15.5 248.0 8.4
161 GW 187 44.0 103.0 57.1 13.2 51.3 14.5 226.0 7.8
162 GW 188 43.5 103.0 56.5 17.3 40.2 16.3 226.0 7.5
163 GW 189 47.5 107.0 45.3 18.2 36.5 11.6 36.0 5.6

61
Table 4.13 continued…
Days No of
Name of Days to Plant pod No of 100 Seed Seed
to 50% matu height clusters/ pods/ seed yield/ yield/
SN entry flower riy (cm) plant plant wt(g) row(g) plant(g)
164 GW 190 44.0 103.5 40.8 10.7 38.5 11.4 256.0 8.5
165 GW 191 44.5 103.5 70.8 9.4 39.4 9.8 207.0 6.9
166 GW 192 47.5 106.5 74.5 17.5 55.7 10.6 185.0 6.6
167 GW 193 43.5 102.0 60.4 13.7 34.5 17.0 302.5 10.1
168 GW 195 46.5 106.0 77.2 14.5 38.8 14.2 247.0 8.2
169 GW 196 43.5 103.0 58.3 6.9 40.0 11.5 177.5 5.9
170 GW 197 43.0 102.0 60.8 9.1 29.0 16.2 228.5 7.6
171 GW 198 42.5 101.5 52.7 13.6 26.6 13.3 229.0 7.6
172 GW 199 42.5 101.5 79.4 18.1 49.3 12.7 190.0 6.3
173 GW 200 43.0 102.5 59.9 9.2 30.4 12.7 187.5 6.3
174 GW 201 44.5 103.5 81.2 8.1 40.3 11.3 198.5 6.6
175 GW 202 44.0 103.5 71.0 16.5 40.5 12.7 196.0 6.5
176 GW 203 45.5 105.0 78.9 11.8 32.1 12.1 232.5 7.8
177 GW 204 43.5 103.5 81.1 11.6 39.8 12.0 210.0 7.0
178 GW 205 44.0 103.5 70.1 15.7 41.3 11.8 262.5 8.8
179 GW 206 45.5 104.5 80.3 17.3 29.5 10.4 157.5 5.3
180 GW 207 48.0 107.5 61.8 16.8 30.1 14.6 308.0 10.3
181 GW 208 44.5 103.5 68.0 11.5 35.5 11.4 203.0 6.8
182 GW 209 41.5 101.0 68.0 16.0 40.7 14.3 217.0 7.2
183 GW 210 45.5 105.5 98.5 23.9 66.5 15.5 291.0 9.7
184 GW 211 43.0 102.5 73.3 16.3 35.5 12.5 196.0 6.5
185 GW 212 41.0 100.5 61.9 17.1 67.5 10.4 194.0 6.5
186 GW 213 45.5 104.5 80.4 25.2 73.2 13.6 287.0 9.6
187 GW 214 44.5 103.5 70.9 16.3 49.3 16.0 218.5 7.3
188 GW 215 44.0 103.5 105.2 15.9 59.3 10.6 240.0 8.0
189 GW 216 44.0 104.0 77.5 10.2 43.8 13.6 205.0 6.8
190 GW 217 44.5 104.5 93.0 13.8 43.0 11.4 179.5 6.0
191 GW 218 45.0 104.5 87.2 13.2 57.3 11.1 211.5 7.1
192 GW 219 43.5 102.5 101.7 16.2 61.2 10.0 198.0 8.7
193 GW 220 45.0 104.5 87.2 8.4 41.7 14.8 223.0 7.4
194 GW 221 44.0 103.5 64.2 11.5 57.0 11.5 233.0 7.8
195 GW 222 44.5 103.5 66.4 14.3 46.0 13.2 253.0 9.1
196 GW 223 46.0 105.5 59.0 10.3 55.9 12.5 265.0 8.8
197 GW 224 49.0 109.0 94.7 25.0 61.6 10.7 186.0 6.2
198 GW 225 44.5 103.0 61.5 15.7 59.3 10.6 198.5 6.6
199 GW 226 47.0 106.5 60.7 19.2 60.7 11.0 188.5 6.3
200 GW 230 44.5 103.0 59.2 15.3 45.9 13.2 297.0 9.9
201 GW 231 47.5 107.5 99.2 18.6 79.8 10.6 249.0 8.3
202 GW 232 48.0 107.5 71.0 10.6 62.8 10.1 211.5 7.1
203 GW 233 43.0 102.5 62.3 8.6 50.6 14.7 244.0 8.1
204 GW 234 52.5 109.5 83.6 14.9 51.5 9.4 181.0 6.0
205 GW 235 44.5 104.0 79.3 18.2 42.0 11.3 190.0 6.3

62
Table 4.13 continued…
Days Days No of Seed
Name to to Plant pod No of 100 Seed yield/
50% matu heigh clusters/ pods/ seed yield/ plant(g
SN of entry flower riy t (cm) plant plant wt(g) row(g) )
206 GW 236 48.5 108.5 82.1 12.8 57.5 10.3 188.5 6.3
207 GW 237 52.0 111.0 99.6 13.3 48.5 9.5 219.5 7.3
208 GW 238 50.5 109.5 93.8 13.3 39.2 10.3 162.0 5.4
209 GW 239 46.5 105.5 83.0 19.7 46.2 11.3 186.0 6.8
210 GW 242 48.5 107.5 84.1 14.4 99.5 10.6 272.0 10.1
211 GW 244 43.5 102.5 95.2 12.2 31.9 11.6 170.5 5.7
212 GW 245 48.5 108.5 75.6 18.4 51.2 13.1 230.0 7.7
213 GW 247 46.5 105.5 71.6 18.0 35.8 13.2 178.5 6.0
214 GW 248 44.5 103.5 60.7 13.6 37.1 15.5 257.0 8.6
215 GW 249 51.5 111.0 73.2 17.3 60.9 9.9 170.0 5.7
216 GW 250 49.5 108.5 69.8 13.9 36.2 10.0 173.0 5.8
217 GW 251 39.0 98.5 54.4 16.1 57.5 13.0 252.5 8.4
218 GW 252 47.5 107.0 70.0 14.1 49.2 15.4 280.0 9.3
219 GW 253 53.0 113.0 71.9 8.7 62.1 10.5 183.0 7.3
220 GW 254 43.0 103.0 54.8 17.1 37.4 14.3 263.0 8.8
221 GW 255 44.0 104.0 61.7 14.8 31.8 15.3 332.0 11.1
222 GW 258 43.5 104.5 51.9 19.6 47.0 12.6 266.0 8.9
223 GW 259 49.5 108.5 60.5 28.1 66.7 8.7 167.5 5.6
224 GW 260 48.0 106.5 61.9 24.2 102.9 9.1 251.0 8.4
225 GW 263 44.0 103.5 54.7 10.9 29.4 13.0 163.0 5.4
226 GW 264 47.0 106.5 56.6 25.2 97.1 9.5 249.0 8.3
227 GW 265 42.5 101.0 50.9 18.8 29.8 10.0 222.0 7.4
228 GW 266 43.5 104.5 57.0 20.3 91.0 10.6 222.5 7.4
229 GW 267 52.5 111.5 58.9 26.4 86.5 11.4 250.5 8.4
230 GW 268 44.0 102.5 72.1 21.2 71.5 13.2 293.5 9.8
231 GW 270 51.5 111.0 65.9 26.7 63.1 10.8 198.5 6.6
232 GW 272 44.0 104.0 62.5 16.9 49.5 10.4 209.0 7.0
233 GW 273 52.5 113.0 62.3 18.8 41.2 15.0 216.5 8.4
234 GW 275 49.0 109.0 114.7 20.5 60.6 9.4 173.5 6.1
235 GW 276 53.0 112.5 57.7 26.3 79.6 15.0 259.0 9.6
236 GW 278 49.5 108.5 61.1 20.3 68.3 9.6 182.5 6.1
237 GW 279 43.0 102.5 69.1 18.0 36.5 12.0 186.0 6.9
238 GW 280 46.5 105.5 68.9 17.5 49.2 14.0 205.0 6.8
239 GW 281 41.5 100.5 49.6 10.6 41.0 11.0 195.0 6.7
240 GW 282 51.0 110.5 61.8 12.2 50.9 11.2 208.0 6.9
241 GW 283 56.0 114.0 80.6 11.3 56.3 10.6 200.0 6.7
242 GW 284 46.5 105.5 61.0 11.3 32.2 12.5 181.0 6.0
243 GW 285 49.5 108.5 74.0 15.1 47.2 8.3 182.0 6.1
244 GW 286 49.0 108.5 86.7 17.2 38.6 11.6 193.5 6.5
245 GW 287 49.5 107.5 82.5 15.5 42.9 9.2 173.5 5.8
246 GW 288 49.5 107.5 72.6 12.5 32.9 14.1 218.5 7.3
247 GW 289 47.5 107.0 71.7 12.6 50.6 10.5 188.5 6.3

63
Table 4.13 continued…
Days Days No of Seed
Name to to Plant pod No of 100 Seed yield/
50% matu heigh clusters/ pods/ seed yield/ plant(g
SN of entry flower riy t (cm) plant plant wt(g) row(g) )
248 GW 290 45.0 104.5 82.5 10.9 36.6 11.0 188.5 6.3
249 GW 291 49.5 108.5 62.0 8.9 42.9 11.0 187.5 6.6
250 GW 292 49.5 109.0 93.5 16.4 51.9 12.0 230.5 7.7
251 GW 293 50.5 108.5 115.6 19.6 50.6 10.4 174.0 6.5
252 GW 294 47.5 106.5 68.0 9.3 38.4 14.4 201.0 6.7
253 GW 295 49.5 108.5 83.7 10.1 52.0 12.3 233.0 7.8
254 GW 296 50.5 109.5 87.9 12.7 40.0 11.7 194.0 6.5
255 GW 297 44.5 103.5 66.7 10.5 49.1 14.5 234.0 7.8
256 GW 298 45.5 104.5 82.3 16.6 37.2 14.5 224.5 7.5
257 GW 299 47.5 108.5 91.3 14.6 38.2 10.7 183.5 6.3
258 GW 300 44.5 104.5 71.4 10.5 51.3 15.3 232.0 7.7
259 GW 301 44.0 103.5 76.4 11.5 49.1 10.5 198.5 6.6
260 GW 302 49.0 108.5 60.0 8.9 36.3 13.0 145.0 6.3
261 GW 303 48.5 107.5 70.1 9.7 30.1 13.0 186.5 6.2
262 GW 304 44.5 104.0 60.3 9.1 30.2 14.5 237.0 7.9
263 GW 305 56.0 117.0 86.7 15.2 71.0 9.5 207.0 6.9
264 GW 306 47.5 106.5 60.1 9.0 33.6 12.5 181.5 6.1
265 GW 308 53.5 112.5 82.8 10.2 25.7 11.7 150.0 5.0
266 GW 309 39.5 98.5 86.1 9.6 71.4 13.1 253.5 9.1
267 GW 310 51.0 109.5 78.8 12.5 23.1 10.6 144.0 4.8
268 GW 311 45.5 105.0 60.2 12.9 30.7 13.0 198.5 6.6
269 GW 312 41.0 100.5 62.1 9.7 30.8 14.1 201.5 6.7
270 GW 313 50.5 109.5 93.8 15.3 53.0 10.0 158.0 5.3
271 GW 314 43.5 103.5 90.3 9.0 27.6 9.8 158.0 5.3
272 GW 315 43.5 102.5 58.5 9.2 27.8 15.2 294.0 9.8
273 GW 316 44.5 103.5 80.7 9.6 36.1 13.2 294.0 9.8
274 GW 317 51.0 110.5 90.7 19.0 41.5 9.9 167.0 5.6
275 GW 318 38.0 97.5 82.2 13.8 66.7 10.7 238.5 8.0
276 GW 319 38.5 97.5 72.3 17.4 29.5 11.9 177.5 6.1
277 GW 358 43.0 102.5 79.2 12.1 51.0 11.8 209.5 7.0
278 GW 360 45.0 104.5 73.2 8.8 27.9 14.0 194.0 6.5
279 GW 361 39.5 97.5 60.3 16.1 33.6 7.5 198.0 6.6
280 GW 362 42.5 101.5 79.8 14.3 42.4 11.2 192.0 6.4
281 GW 365 33.5 93.0 30.8 6.4 32.4 14.8 246.0 8.2
282 GW 368 38.5 97.5 42.7 7.3 64.9 11.8 98.0 9.4
283 GW 369 42.5 103.5 50.1 8.6 62.0 16.0 197.0 11.5
284 GW 370 54.5 113.0 82.0 18.0 71.5 11.6 242.0 8.1
285 GW 372 52.5 110.5 80.9 14.1 28.7 9.9 211.5 7.1
286 GW 373 43.0 102.5 65.7 8.9 27.3 14.3 175.0 8.8
287 GW 374 37.5 96.5 52.4 8.9 41.3 14.2 240.0 8.1
288 GW 375 40.5 99.5 47.1 8.6 28.4 13.1 239.0 8.0

64
Table 4.13 continued…
Days
to Days Plant No of No of 100 Seed Seed
Name of 50% to heigh pod seed yield/ yield/
flowe maturi cluster pods/ wt row plant
SN entry r y t (cm) s/ plant plant (g) (g) (g)
289 GW 376 41.0 100.5 62.8 11.2 56.0 11.9 268.5 9.0
290 GW 377 32.5 91.5 26.5 6.4 19.3 13.9 190.0 7.1
291 GW 378 39.0 98.5 63.5 9.0 73.1 12.9 300.5 10.0
292 GW 379 37.5 96.5 52.6 8.8 27.3 12.5 232.0 9.6
293 GW 382 40.0 99.5 47.0 7.3 37.4 11.0 196.0 6.5
294 GW 383 38.0 97.5 77.5 9.9 67.5 14.6 317.0 11.1
Checks
295 JS 97-52 42.5 101.5 63.5 9.2 59.1 11.0 268.5 9.0
296 JS 20-69 37.5 96.5 53.1 8.7 29.4 11.3 74.0 6.5
297 PK 472 42.5 101.5 36.7 6.8 28.8 16.1 329.0 11.0
298 NRC 37 37.5 96.5 59.4 8.3 58.6 12.9 271.5 9.1
299 JS 71-05 50.5 109.5 117.3 16.8 37.3 12.0 203.0 7.1
300 NRC 128 42.5 101.5 42.7 7.2 28.2 13.7 205.0 7.2
301 JS 20-116 41.5 100.5 49.4 7.3 31.1 11.6 233.5 8.5
AMS 2014-
302 1 38.5 98.5 55.4 6.8 27.2 10.7 263.5 8.8
303 RSC 11-07 38.0 97.0 51.3 8.2 46.8 10.6 289.0 9.6
304 SL 1074 39.5 98.5 48.6 9.1 49.8 12.4 221.0 7.9
305 RKS 113 41.5 101.5 63.1 10.3 35.1 11.3 167.0 5.6
306 MACS 1407 38.0 97.5 53.2 9.9 46.9 12.1 231.0 7.7
307 VLS 59 37.5 96.5 34.2 6.5 31.4 12.9 168.0 7.9
308 VLS 89 40.5 99.5 47.0 7.7 56.2 14.3 296.0 10.4
309 VLS 63 36.5 95.5 51.9 7.1 57.4 13.9 257.0 9.2
310 RSC 10-52 42.5 101.0 62.8 11.8 36.1 14.2 299.0 10.0
311 PS 26 34.5 94.0 39.0 7.1 16.7 11.1 162.0 5.4
312 NRC 138 34.5 94.5 35.3 7.4 27.0 10.1 229.5 7.7
313 NRC 130 34.5 93.5 35.4 6.9 29.1 15.8 333.0 11.9
314 KDS 753 45.5 105.5 61.9 8.6 26.8 21.3 394.0 14.1
315 DSB 34 40.0 99.5 51.7 8.1 25.9 13.5 246.0 8.3
Palam Soya
316 1 37.5 96.5 47.8 7.1 48.4 12.6 257.5 8.9
317 SL 955 41.5 101.0 52.2 9.6 26.7 12.9 239.0 8.9
RVSM
318 2011-35 38.5 97.5 45.2 6.5 29.4 15.2 327.0 11.3

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4.3 Genetic Parameters- range, mean, GCV, PCV, Heritability and Genetic
advance

The characters in the investigation were studied for genotypic variance,

phenotypic variance, genotypic coefficient of variation (GCV), phenotypic coefficient
of variation (PCV), heritability (broad sense) and expected genetic advance as percent
of mean (EGA). The results are present in Table 4.14.

4.3.1 Days to 50% flowering

Days to 50 percent flowering in germplasm lines ranged from 33 days (GW

377) to 56 days (GW 2) with average being 43.16 days (Table 4.14). Genotypic
variance (18.90%) was lower than phenotypic variance (19.25%). The genotypic
coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) for days
to 50 % flowering were 10.07 % and 10.17 %, respectively. High heritability estimates
(98.2%), low genetic advance (8.87) and moderate genetic advance as percent of mean
(20.56) observed for their trait.

4.3.2 Days to maturity

The days to maturity ranged from 91.5 days in GW 377 to 117 in GW 305 with
an average of 102.41 days (Table 4.14). Genotypic variance (18.51) was lower than
the phenotypic variance (19.15). The genotypic coefficient of variation (GCV) was
4.20% and phenotypic coefficient of variation (PCV) was 4.27%. The high amount of
heritability (96.7 %) coupled with low genetic advance (8.71) and low genetic advance
as percent of mean (8.51) observed for days to maturity.

4.3.3 Plant height (cm)

The highest plant height was observed in JS 71-05 (117.30cm), while, the
lowest plant height was observed in GW 10 (21.80 cm) and average plant height
observed was 65.08 cm (Table 4.14). Genotypic coefficient of variation and
phenotypic coefficient of variation was 25.50% and 25.53% respectively, while lower
amount of genotypic variance (275.48) than phenotypic variance (275.9%observed for
plant height. High heritability (99.8%) along with high genetic advance (34.15) and
high genetic advance as percent of mean (52.48) was observed for the character plant
height.

66
4.3.4 Number of pod clusters per plant

The mean number of pod clusters per plant of the germplasm lines was 14.58
(Table 4.14). Highest number of pod clusters per plant was observed in GW 119
(31.00) while, the lowest number of pod clusters were observed in GW 377 (6.40).
Genotypic coefficient of variation and phenotypic coefficient of variance were 34.45%
and 34.69% respectively, while lower amount of genotypic variance (25.24) than
phenotypic variance (25.58) observed for number of pod clusters per plant. High
heritability (98.7%) along with low genetic advance (10.28) and high genetic advance
as percent of mean (70.50) were observed for this trait.

4.3.5 Number of pods per plant

The mean number of pods per plant of the genotypes was 47.45 (Table 4.14).
The highest number of pods was recorded in the germplasm GW 17 (120.00) while,
the lowest number of pods were observed in GW 365 (6.40) & GW 377 (6.40).
Genotypic variance (316.35) observed was lower than phenotypic variance (317.24)
for number of pods per plant. The GCV was 37.49 percent and PCV was 37.54 percent
with the high amount of heritability (99.7%, bs), high expected genetic advance (36.59)
and high genetic advance as percent of mean (77.11).
4.3.6 100 seed weight (g)

The mean hundred seed weight of the genotypes was 12.63 g (Table 4.14). The
highest hundred seed weight was observed in the KDS 753 (21.31 g), while the lowest
was observed in GW 361 (7.54 g). Genotypic variance (3.70) was lower than
phenotypic variance (3.80). The genotypic coefficient of variation (GCV) observed
was 15.22% while the phenotypic coefficient of variation was 15.42%. The high
heritability (97.5 %) coupled with low genetic advance (3.91) and high genetic advance
as percent of mean (30.96) was observed for 100 seed weight.

4.3.8 Seed yield per row (g)

The seed yield (kg/ha) is different for different genotypes (Table 4.14). The
mean value for seed yield per row was 241.56 g. The highest seed yield per row was
given by GW 25 (409 g), while the lowest seed yield per row was observed in GW 189

67
(36 g). Genotypic variance observed was (501.64) which is lower than the phenotypic
variance (521.84). The genotypic coefficient of variation and phenotypic coefficient of
variation were 24.27% and 24.47% respectively. High heritability (96.2%) with low
genetic advance (3.48) and high genetic advance as percent of mean (47.24) was also
observed for grain yield per row.
4.3.9 Seed yield per plant (g)

Mean seed yield per plant was 8.05 g (Table 4.14). The highest seed yield per
plant was observed in GW19 (19.07 g), while the lowest seed yield per plant was
observed in GW10 (4.30 g). Genotypic variance observed was 3.72, which is lower
than the phenotypic variance which was 3.89. The genotypic coefficient of variation
and phenotypic coefficient of variation was 23.94 percent and 24.49 percent,
respectively. High heritability (95.6%, bs) with low amount of genetic advance (3.88)
and high genetic advance as percent of mean (48.21) observed for grain yield per plant.

In the present investigation genotypic coefficient of variation was lower than

phenotypic coefficient of variation for all the traits but the differences between them
were of lower magnitude.

The narrow differences between PCV and GCV indicated that there is relative
resistance to environmental changes. This marked effect of environmental factors for
the phenotype expression of genotypes was poor than the greater chance of improving
these traits through selection depends on the outputs of phenotypes. The high values of
PCV and GCV indicates that there was a chance of improvement of these traits through
direct selection.

According to Agarwal et al. (2001) genotypic and phenotypic coefficient of

variance can be grouped as low (≤ 10%), moderate (10-20%) and high (≥ 20%).

In the present investigation high estimates of genotypic and phenotypic

coefficient of variation were observed for plant height, number of pod clusters per
plant, number of pods per plant, seed yield per row, seed yield per plant, similar result
were reported by Pushpa and Rao (2013) and Pagde et al. (2015).

68
 Moderate PCV and GCV values were recorded for days to 50% flowering, 100-
seed weight, similar result obtained by Badkul et al. (2014), Gohil et al. (2006), Jain
et al. (2015).

Lower values were observed for days to physiological maturity, similar result
obtained by Patil et al. (2011), Barb et al. (2014), Ghodrati (2013), Bangar et al. (2003)
Chettri et al. (2005), Athoni and Basavaraja (2012) and Baraskar et al. (2014).

The genotypic coefficient of variation alone does not show the proportion to
total heritable variation. High heritability shows that efficacy of selection based on
phenotypic performance but does not necessarily mean a high genetic gain for single
character. According to Singh (2001), heritability of a character can be categorized as
low (< 30%), moderate (30-60%) and high (> 60%) by Johnson et al. (1955).
According to this, for all traits under study high heritability was observed (Table 4.14).
High value of heritability indicates that there is a favourable effect of environment and
the traits were under genotypic control. For such traits selection could be easy and
improvement is possible by using selective breeding methods for these characters.

In the present study heritability ranged from 96.2 to 99.8%. High value of
heritability in broad sense were observed for plant height, number of pods per plant,
followed by number of pod clusters per plant, days to 50% flowering, 100 seed weight,
days to maturity, seed yield per row and lastly seed yield per plant.

Similar findings were observed for plant height by Bekele et al. (2012), and
Malek et al. (2015), and number of pods per plant by Barh et al. (2014), Malek et al.
(2015) and Padge et al. (2015), Jain et al. (2015), 100 seed weight by Osekita et al.
(2013), Jain et al. (2015) and Malek et al. (2015), days to 50% flowering by Osekita
et al. (2013) and Jain et al. (2015), days to maturity by Osekita et al. (2013) and
Baraskar et al. (2014), number of pod cluster per plant by Badkul et al. (2014) and
Baraskar et al. (2014).

High heritability coupled with high genetic advance as per cent mean was
observed for plant height, number of pod clusters per plant, number of pods per plant,
100 seed weight, seed yield per row and seed yield per plant suggesting that these traits
are under the control of additive gene action and can be improved through simple
selection procedure. Similar results were reported by Yadav (2007), Malek et al.(2015)

69
for plant height; Baraskar et al. (2014) Barh et al. (2014) and Malek et al. (2015) for
number of pod clusters per plant, number of pods per plant; Sahay et al. (2005) and
Baraskar et al. (2014) for seed yield per row and per plant, Abady et al.(2013) and
Badkul et al. (2014) for 100 seed weight. This indicates the lesser influence of
environments in expression of characters and prevalence of additive gene action in
their inheritance, since are amenable for simple selection.

High heritability with moderate genetic advance as percent of mean was

recorded for days to 50% flowering. Similar results were obtained by Bekele et al.
(2012), Badkul et al. (2014), Baraskar et al. (2014) and Mahbub et al. (2015) for days
to 50 per cent flowering. The results indicate that these characters were less influenced
by environment but governed by additive and non-additive gene action.

High heritability with low genetic advance as percent mean was recorded for
days to maturity. High heritability coupled with low genetic advance is indicative of
non-additive gene action. Improvement in such traits would be achieved through
heterosis breeding

From the above discussions, it can be concluded that high GCV and PCV
coupled with high heritability were observed for the traits plant height, number of pod
clusters per plant, number of pods per plant, seed yield per row and seed yield per
plant. This indicates that there is a lesser influence of environment in the expression of
character which is amenable for selection. The character viz, days to 50 % flowering,
days to maturity, 100 seed weight showed high heritability but low level of variability.
Hence, these characters are not amenable for selection in the present study.

70
Table 4.14- Genetic variability parameters for grain yield, it’s related traits in Soybean

Genetic parameters Days to Days to Plant No of pod No of 100 Seed Seed

50% maturity height clusters/plant pods/plant seed yield/row(g) yield/plant(g)
flowering (cm) wt(g)
General Mean 43.16 102.41 65.08 14.58 47.45 12.63 241.56 8.05
Range (maximum) 56.00 117.00 117.30 30.50 120.00 21.31 409.00 19.07
Range (minimum) 32.50 91.50 21.80 6.40 16.70 7.54 36.00 4.30
Genotypic variance 18.90 18.51 275.40 25.24 316.35 3.70 501.64 3.72
Phenotypic variance 19.25 19.15 275.98 25.58 317.24 3.80 521.84 3.89
GCV 10.07 4.20 25.50 34.45 37.49 15.22 24.27 23.94
PCV 10.17 4.27 25.53 34.69 37.54 15.42 24.47 24.49
h² (Broad Sense) in % 98.2% 96.7% 99.8% 98.7% 99.7% 97.5% 96.2% 95.6%
Genetic Advancement 5% 8.87 8.71 34.15 10.28 36.59 3.91 3.48 3.88
Gen.Adv as % of Mean 5% 20.56 8.51 52.48 70.50 77.11 30.96 47.24 48.21

71
4.4 Correlation studies

The correlation study was undertaken to find out the inter relationship of
different yield contributing characters with seed yield per plant at both genotypic (G)
and phenotypic (P) level which is presented in Table 4.15 & Table 4.16 respectively.

4.4.1 Association of seed yield per plant(g) with its attributing traits

The characters number of pod clusters/ plant (G=0.123, P=0.121), number of

pods/ plant (G=0.430, P=0.419), 100 seed weight (G=0.551, P=0.534), seed yield per
row (G=0.621, P=0.567) were significantly and positively correlated with seed yield
per plant at genotypic and phenotypic level. A significant and negative correlation was
observed between seed yield/ plant and days to 50% maturity (G= -0.191, P= -0.183),
days to maturity (G= -0.195, P= -0.180), plant height (G= -0.127, P= -0.124) at both
genotypic and phenotypic level (Table 4.15 & Table 4.16).

4.4.2 Association among other attributing traits

4.4.2.1 Days to 50% flowering

A highly significant and positive association of days to 50% flowering with

days to maturity (G=0.997, P=0.985), plant height (G=0.528, P=0.523), number of pod
clusters per plant (G=0.283, P=0.287), number of pods per plant (G=0.117, P=0.116)
at both genotypic and phenotypic level. 100 seed weight (G= -0.247, P= -0.241) and
seed yield per plant (G= -0.191, P= -0.183) showed significant and negative association
at genotypic and phenotypic level. While seed yield per row (G= -0.093, P= -0.082)
showed negative association at genotypic and phenotypic level (Table 4.15 & Table
4.16). Similar result obtained by Machikowa et al. (2011), Salimi and Abola (2013),
Salimi and Abola (2013), Okonkwo and Idahosa (2013) and Ghodrati et al. (2013)

4.4.2.2 Days to maturity

The character days to maturity reported a significant and positive association
with plant height (G=0.532, P=0.522), number of pod clusters per plant (G=0.283,
P=0.283), number of pods per plant (G=0.125, P=0.122) at both genotypic and
phenotypic level. 100 seed weight (G= -0.246, P= -0.239) and seed yield per plant (G=
-0.195, P= -0.180) showed significant and negative association at genotypic and
phenotypic level. While seed yield per row (G= -0.088, P= -0.077) showed negative

72
association at genotypic and phenotypic level (Table 4.15 & Table 4.16). Similar result
was obtained by Ganeshamurthy and Seshadri (2004), and Gohil et al. (2006).

4.4.2.3 Plant height(cm)

The character plant height revealed significant and positive association with
number of pod clusters per plant (G=0.408, P=0.404), number of pods per plant
(G=0.219, P=0.219) at both genotypic and phenotypic level. Plant height showed
positive association with seed yield per row (G=0.044, P=0.038) while showed
significant and negative association with 100 seed weight (G= -0.260, P= -0.257) and
seed yield per plant (G= -0.127, P= -0.124) at genotypic and phenotypic level (Table
4.15 & Table 4.16). Same result was obtained by Bhairav et al. (2006), Raut et al.
(2001), Rajanna et al. (2000)

4.4.2.4 Number of pods clusters per plant

The character number of pod clusters per plant showed a positive and
significant association with number of pods per plant (G=0.462, P=0.459) at both
genotypic and phenotypic level while showed positive association with seed yield per
row (G=0.063, P=0.055) and seed yield per plant (G= 0.123, P= 0.121) at both
genotypic and phenotypic level. The character number of pod clusters per plant showed
significant and negative association with 100 seed weight (G= -0.137, P= -0.136) at
genotypic and phenotypic level (Table 4.15 & Table 4.16). Similar result was obtained
by Rajanna et al. (2000).

4.4.2.5 Number of pods per plant

Number of pods per plant registered positive and significant association with
seed yield per row (G=0.245, P=0.213) and seed yield per plant (G= 0.430, P= 0.419)
at genotypic and phenotypic level. The character number of pods per plant showed
negative association with 100 seed weight (G= -0.095, P= -0.094) at genotypic and
phenotypic level (Table 4.15 & Table 4.16). Similar result was found with Faisal et al.
(2006), Athoni and Basavaraja (2012) and Barh et al. (2014)

73
4.4.2.6 100 seed weight(g)

The character 100 seed weight exhibited a positive and significant association
with seed yield/row (G=0.392, P=0.339) and seed yield/plant (G= 0.551, P= 0.534) at
both genotypic and phenotypic level, respectively (Table 4.15 & Table 4.16). Similar
result were obtained by Machikowa et.al. (2011) and Salimi and Abola (2013), Athoni
et.al. (2012), Bueno et.al. (2013) and Ghodrati et.al. (2013)

4.4.2.7 Seed yield /row(g)

The character seed yield/row revealed positive and significant association with
seed yield/plant (G= 0,621, P= 0.567) at both levels (Table 4.15 & Table 4.16).
Correlation coefficient is an important statistical constant which indicates the degree
of association among the various characters.

The genotypic correlations were generally higher than the phenotypic

correlations indicating the inherent association between various characters. Seed yield
is a complex character and is dependent on number of component trait. Therefore,
study of relationship of characters with each other and with seed yield become more
important in crop improvement programme. Therefore, it is essential to find out
relative contribution of each of the component character in yield for giving few
weightages during selection.

74
Table 4.15- Genotypic Correlation coefficient for yield and it’s attributing characters

Characters Days to Days to Plant No of pod No of 100 seed Seed Seed

50% flower maturity height (cm) clusters/plant pods/plant wt(g) yield/row(g) yield/plant(g)
Days to 50% flower 1 0.997 ** 0.528 ** 0.283 ** 0.117 ** -0.247 ** -0.093 * -0.191 **
Days to maturity 1 0.532 ** 0.283 ** 0.125 ** -0.246 ** -0.088 * -0.195 **
Plant height 1 0.408 ** 0.219 ** -0.260 ** 0.044 -0.127 **
No of pod clusters/plant 1 0.462 ** -0.137 ** 0.063 0.123 **
No of pods/plant 1 -0.095 * 0.245 ** 0.430 **
100 seed wt 1 0.392 ** 0.551 **
Seed yield/row 1 0.621 **
Seed yield/plant 1
*, ** denotes significance at 5% and 1% respectively.

75
Table 4.16- Phenotypic Correlation coefficient for yield and it’s attributing characters

Characters Days to Days to Plant No of pod No of 100 seed Seed Seed

50% flower maturity height(cm) clusters/plant pods/plant wt(g) yield/row(g) yield/plant(g)
Days to 50% flower 1 0.985 ** 0.523 ** 0.287 ** 0.116 ** -0.241 ** -0.082 * -0.183 **
Days to maturity 1 0.522 ** 0.283 ** 0.122 ** -0.239 ** -0.077 -0.180 **
Plant height 1 0.404 ** 0.219 ** -0.257 ** 0.038 -0.124 **
No of pod clusters/plant 1 0.459 ** -0.136 ** 0.055 0.121 **
No of pods/plant 1 -0.094 * 0.213 ** 0.419 **
100 seed wt 1 0.339 ** 0.534 **
Seed yield/row 1 0.567 **
Seed yield/plant 1
*, ** denotes significance at 5% and 1% respectively.

76
Fig 4.1- Genotypic Correlation coefficient for yield and it’s attributing
characters.

Fig 4.2- Phenotypic Correlation coefficient for yield and it’s attributing
characters

77
4.5 Path analysis
The path analysis studies were carried out to find out the direct and indirect
contribution of each character towards the seed yield per plant because the correlation
studies did not show the clear picture of direct and indirect effects of yield contributing
characters. Direct effect on seed yield per plant of any trait gives idea about reliability
of indirect selections to be done through that trait brings about increase in grain yield.
If direct effects as well as correlation are high and significant then correlation describes
its true relationship and selections would be effective for that character.

The residual effect describes how dependent factor variability (seed yield per
plant) is better compensated by the causative factors. If the residual factor values are
higher or moderate it shows that there is some other factors that contribute to yield
besides the character studied.

Direct and indirect effects of yield contributing traits and their genotypic and
phenotypic correlation with grain yield per plant were presented in Table 4.17 and
Table 4.18 respectively.

4.5.1 Days to 50 percent flowering

At genotypic level, positive direct effect (1.447) on seed yield per plant was
exerted by days to 50 percent flowering and it had maximum positive indirect effect
on seed yield per plant through number of pods/plant (0.048) followed by number of
pod clusters/plant (0.007). Whereas, maximum negative indirect effect on seed
yield/plant was recorded via days to maturity (-1.507) followed by 100 seed wt (-
0.101), plant height (-0.051), seed yield/row (-0.033) at genotypic level (Table 4.17).

At phenotypic level, days to 50 percent flowering had shown positive direct

effect (0.042) on seed yield per plant and it had maximum positive indirect effect on
seed yield per plant through number of pods/plant (0.0471), number of pod
clusters/plant (0.009). However, maximum negative indirect effect on seed yield/plant
was recorded via 100 seed wt (-0.102), days to maturity (-0.101), plant height (-0.05),
seed yield/row(-0.027) at genotypic level (Table 4.18).

78
4.5.2 Days to maturity
At genotypic level, negative direct effect (-1.511) on seed yield per plant was
exerted by days to maturity and it had maximum positive indirect effect on seed yield
per plant through days to 50% flowering (1.441) followed by number of pods/plant
(0.051), number of pod clusters/plant (0.007). Whereas, maximum negative indirect
effect on seed yield/plant was recorded via 100 seed wt (-0.101), plant height (-0.052),
seed yield/row (-0.032) at genotypic level (Table 4.17).

At phenotypic level, days to maturity had shown negative direct effect (-0.102)
on seed yield per plant and it had maximum positive indirect effect on seed yield per
plant through number of pods/plant (0.049), days to 50% flowering (0.041), number of
pod clusters/plant (0.009). However, maximum negative indirect effect on seed
yield/plant was recorded via 100 seed wt (-0.101), days to maturity (-0.101), plant
height (-0.05), seed yield/row (-0.025) at genotypic level (Table 4.18).

4.5.3 Plant height(cm)

At genotypic level, plant height recorded negative direct effect (-0.097) on seed
yield per plant. It had shown maximum positive indirect effect days to 50% flowering
(0.765) followed by number of pods/plant (0.089), number of pod clusters/plant
(0.01),seed yield per row (0.016). The negative indirect effect was through days to
maturity (-0.804), 100 seed wt (-0.107) (Table 4.17).

At phenotypic level, plant height had shown the negative direct effect (-0.097)
on seed yield per plant. It has shown the maximum positive indirect effect on seed yield
per plant through days to 50% flowering (0.021) followed by number of pods/plant
(0.088), number of pod clusters/plant (0.012), seed yield per row (0.012). The negative
indirect effect was through days to maturity (-0.053), 100 seed wt (-0.109). Similar
result was observed by Wankhede et al. (1985) (Table 4.18).

4.5.4 Number of pod clusters/plant

At genotypic level, number of pod clusters/plant recorded positive direct effect
(0.026) on seed yield/plant and had exerted maximum positive indirect effect through
days to 50% flowering (0.41) followed by number of pods/plant (0.189), seed yield per
row (0.023). Whereas, maximum negative indirect effect on seed yield/plant was
recorded via days to maturity (-0.428), 100 seed wt (-0.056), plant height (-0.04) (Table
4.17).

79
 At phenotypic level, number of pod clusters/plant recorded positive direct
effect (0.032) on seed yield/plant and had exerted maximum positive indirect effect
through number of pods/plant (0.185) followed by seed yield per row (0.018), days to
50% flowering (0.011). Whereas, maximum negative indirect effect on seed yield/plant
was recorded via 100 seed wt (-0.057), plant height (-0.039), days to maturity (-0.029)
(Table 4.18).

4.5.5 Number of pods/plant

At genotypic level, number of pods/plant recorded positive direct effect (0.410)

on seed yield/plant and had exerted maximum positive indirect effect through days to
50% flowering (0.17) followed by seed yield per row (0.089), number of pod
clusters/plant (0.011). Whereas, maximum negative indirect effect on seed yield/plant
was recorded via days to maturity (-0.19), 100 seed wt (-0.039), plant height (-0.021)
(Table 4.17).

At phenotypic level, number of pods/plant recorded positive direct effect

(0.404) on seed yield/plant and had exerted maximum positive indirect effect through
number of pod clusters/plant (0.014) followed by seed yield per row (0.07), days to
50% flowering (0.004). Whereas, maximum negative indirect effect on seed yield/plant
was recorded via 100 seed wt (-0.04), plant height (-0.021) (Table 4.18).

4.5.6 100 seed wt(g)

At genotypic level, 100 seed wt recorded positive direct effect (0.411) on seed
yield/plant and had exerted maximum positive indirect effect through days to maturity
(0.372) followed by seed yield per row (0.142), plant height (0.025). Whereas,
maximum negative indirect effect on seed yield/plant was recorded via days to 50%
flowering (-0.357), number of pods/plant (-0.039), number of pod clusters/plant (-
0.003) (Table 4.17).

At phenotypic level, 100 seed wt recorded positive direct effect (0.425) on seed
yield /plant and had exerted maximum positive indirect effect through seed yield per
row (0.111) followed by plant height (0.025), days to maturity (0.024). Whereas,
maximum negative indirect effect on seed yield/plant was recorded via number of
pods/plant (-0.038) followed by days to 50% flowering (-0.01), number of pod
clusters/plant (-0.004) (Table 4.18).

80
4.5.7 Seed yield per row(g)
At genotypic level, seed yield per row recorded positive direct effect (0.363) on
seed yield/plant and had exerted maximum positive indirect effect through 100 seed wt
(0.161) followed by days to maturity (0.133), number of pods/plant (0.1), number of
pod clusters/plant (0.001). Whereas, maximum negative indirect effect on seed
yield/plant was recorded via days to 50% flowering (-0.135), plant height (-0.004)
(Table 4.17).

At phenotypic level, seed yield per row recorded positive direct effect (0.329)
on seed yield/plant and had exerted maximum positive indirect effect through 100 seed
wt (0.144) followed by number of pods/plant (0.086), days to maturity (0.008), number
of pod clusters/plant (0.001). Whereas, maximum negative indirect effect on seed
yield/plant was recorded via days to plant height (-0.004), 50% flowering (-0.003)
(Table 4.18).

The approach to enhance Soybean yield should thus be comprehensive and take
into account the direct and indirect involvement of each individual character. But there
is clearly a limit to the number of characters that can be enhanced at the same time in
any breeding method. Even with the use of molecular markers to assist selection, not
many characters can be efficiently handled at one time because many of these
characters are quantitative and several genes or genomic regions may account for their
appearance. Thus, the breeder must make a pick between the many apparently related
characters and spotlight on those that are most essential.

Thus, it may be concluded from present investigation that the characteristics

like days to 50% flowering, number of pod clusters/plant, number of pods/plant, 100
seed wt, seed yield/row had greater importance for indirect selection for seed yield, so
these traits should be given due consideration while planning a breeding programme
for increase in seed yield in kharif Soybean. Similar results were observed by
G.Neelima (2017), Thi et al. (2019), Shrishti Mehra et al. (2020).

Residual effect
The genotypic residual effect was 0.581 whereas phenotypic residual effect was
0.625. High residual effect was observed in present study, it shows that there is some
other factors that contribute to yield besides the character studied.

81
Table 4.17 Direct and indirect effects (genotypic level) of yield components on seed yield in Soybean
Days to Days to Plant No of pod No of 100 seed Seed
Characters 50% flower maturity height clusters/plant pods/plant wt yield/row
Days to 50% flower 1.447 1.443 0.765 0.411 0.170 -0.358 -0.135
Days to maturity -1.507 -1.512 -0.804 -0.429 -0.190 0.373 0.134
Plant height -0.052 -0.052 -0.098 -0.040 -0.022 0.026 -0.004
No of pod clusters/plant 0.007 0.007 0.010 0.026 0.012 -0.004 0.002
No of pods/plant 0.048 0.052 0.090 0.190 0.410 -0.039 0.101
100 seed wt -0.102 -0.101 -0.107 -0.057 -0.039 0.411 0.161
Seed yield/row -0.034 -0.032 0.016 0.023 0.089 0.143 0.363
Seed yield/plant -0.192 -0.195 -0.128 0.124 0.430 0.551 0.621

Table 4.18 Direct and indirect effects (phenotypic level) of yield components on seed yield in Soybean
Days to Days to Plant No of pod No of 100 seed Seed
Characters 50% flower maturity height clusters/plant pods/plant wt yield/row
Days to 50% flower 0.042 0.041 0.022 0.012 0.005 -0.010 -0.003
Days to maturity -0.101 -0.102 -0.054 -0.029 -0.013 0.025 0.008
Plant height -0.051 -0.050 -0.097 -0.039 -0.021 0.025 -0.004
No of pod clusters/plant 0.009 0.009 0.013 0.032 0.015 -0.004 0.002
No of pods/plant 0.047 0.049 0.089 0.186 0.404 -0.038 0.086
100 seed wt -0.103 -0.101 -0.109 -0.058 -0.040 0.425 0.145
Seed yield/row -0.027 -0.025 0.013 0.018 0.070 0.112 0.329
Seed yield/plant -0.184 -0.180 -0.124 0.121 0.420 0.534 0.563
Bold values denote direct effects.
Residual effect- 0.581 (genotypic level), 0.625(phenotypic level).

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Fig 4.3- Genotypical Path Diagram for seed yield/ plant(g)

Fig 4.4- Phenotypical Path Diagram for seed yield/ plant(g)

83
4.6 Genetic Diversity

Genetic diversity in the crop species is of greatest importance and makes

appearance due to geographical separation or genetic hurdle to crossability. The seed
yield and yield related traits are standard tools to analyze the genetic diversity.

4.6.1 Mahalanobis’s generalized distance (D2)

Wilk’s criterion showed significant differences between the genotypes for the
pooled effect of the twenty characters studied. Hence, further analysis was done to
calculate D2 values for all the possible pairs of comparison among 318 genotypes.

Based upon the observations of eight characters, the Mahalanobis’s D2 statistics

was computed for all possible pairs of 318 genotypes of Soybean in order to assess the
genetic diversity present among the genotypes under study.

4.6.2 Clustering pattern of the genotypes

The clustering pattern obtained on the basis of magnitude of D2 values studied,

are presented in Table 4.19

These 318 genotypes were grouped into 20 clusters. The cluster 15 was with the
highest number of genotypes (24) followed by cluster 17 (23), cluster 2 (22), cluster 8
(20), cluster 12 (20), cluster 13 (20), cluster 9 (18), cluster 16 (18), cluster 18 (18),
cluster 14 (17),cluster 4 (16), cluster 6 (16), cluster 11 (16), cluster 1 (15), cluster 7
(15), cluster 3 (13), cluster 12 (10), cluster 19 (7), cluster 5 (5), cluster 20 (5).

4.6.3 Intra and inter cluster divergence

The average intra and inter cluster D2 values are presented in Table 4.20. The
intra cluster distance (D2) range from 0 to 1146.7, whereas inter cluster distance D2
ranges from 55 to 7442.2. Maximum inter cluster distance (D2 = 7442.2) was observed
between cluster 15 and cluster 18. The minimum inter cluster distance (D2 = 55) was
between clusters 5 and 6. Diagram showing Mahalanobis Euclidean distance clusters
by Tocher’s method is given in Fig-4.5
4.6.4 Cluster means for different characters
The cluster means for the eight characters are presented in Table 4.21. A
considerable inter-cluster variation was observed among the cluster means for the
characters studied viz. days to 50 per-cent flowering, days to maturity, plant height (cm),

84
number of pod clusters per plant, number of pods per plant, 100 seed weight (g), seed
yield per row & seed yield per plant.

The cluster mean for days to 50 per cent flowering varied from 33.50 (cluster
1) to 56.00 (cluster 14), the cluster mean for days to maturity ranged between 94.50
(cluster 10) to 117.00 (cluster 14), the cluster mean for plant height was from 38.83
(cluster 7) to 117.30 (cluster 15), the cluster mean for number of pod clusters per plant
ranged from 9.40 (cluster 6) to 30.00 (cluster 17), the cluster mean for number of pod
clusters per plant ranged from 7.10 (cluster 6) to 27.20 (cluster 17), the cluster mean for
100 seed weight (g) ranged from 19.40 (cluster 19) to 109.63 (cluster 20), the cluster
mean for seed yield per row ranged from 183.00 (cluster 13) to 370.39 (cluster 2), the
cluster mean for seed yield per plant ranged from 6.37 (cluster 17) to 12.84 (cluster 18).

The most important and difficult task in the initiation of hybridization

programme is by selecting genotypes with high per cent performance for yield and
yield contributing components with suitable genetic divergence among them. From the
genetic variability estimates, it would be possible to identify desirable genotypes but
unless we have sound knowledge about the divergence between them it's difficult to
expect any extraordinary results from their progeny.

D2 statistics, a concept developed by Mahalonobis (1936) is an important tool

for plant breeders. It is useful in quantifying the degree of divergence between the
biological population at genotypic level and to assess the relative contribution of
different components to the total divergence at both, intra- and inter cluster levels. Rao
(1952) suggested the application of this technique for the assessment of genetic
diversity in plant breeding.

The aim of cluster formation and measuring inter and intra cluster distance is
to provide the basis for a hybridization programme. The theoretical concept behind
such grouping is that the genotypes grouped into the same cluster presumably are less
diverse from each other than those belonging to the different clusters and will not give
the expected desired heterotic response and segregants in further generations.

85
 Consequently, the breeding programme should be designed in such a way that,
the parents are selected from different clusters with wider genetic diversity in the
genotypes.

The crosses involving the parents with extreme divergence have also been
reported to exhibit a decrease in heterosis. Therefore, while selecting the parents by
considering the genetic diversity, their performance and cluster mean for the characters
also needed due consideration in the crop improvement programme.

The intra cluster distance (D2) range from 0 to 1146.7, whereas inter cluster
distance D2 ranges from 55 to 7442.2 as given in Table 4.20. Maximum inter cluster
distance (D2 = 7442.2) was observed between cluster 15 and cluster 18. Indicating that
the genotypes falling in the clusters 15 and 18 were highly divergent from each other
implying a large amount of diversity within and between groups, which could be
exploited in breeding programme. The minimum inter cluster distance (D2 = 55) was
between clusters 5 and 6 indicating that this cluster is less divergent. The cluster means
for different characters showed considerable differences among the clusters for all the
characters.

A considerable inter-cluster variation was observed among the cluster means for
the characters studied viz., days to 50 per-cent flowering, days to maturity, plant height
(cm), number of pod clusters per plant, number of pods per plant, 100 seed weight (g),
seed yield per row & seed yield per plant.

86
Table 4.19- Composition of three hundred and eighteen genotypes of Soybean into different clusters by Tocher’s method.
Cluster No. of Genotypes included in the cluster
No. genotypes

1 15 6 10 11 12 13 14 20 42 49 86 224 226 228 229 235

2 22 22 31 33 34 41 44 48 55 59 66 71 76 79 120 130 163 164 227 239 287 303 316
3 13 3 7 51 80 100 122 281 290 297 307 311 312 313
4 16 68 77 118 144 165 176 181 184 213 216 246 252 261 273 276 278
5 5 29 56 98 99 282
6 16 2 17 23 50 53 91 93 102 150 185 199 223 230 231 236 291
7 15 24 32 40 101 107 123 124 126 138 139 148 183 186 201 210
8 20 26 27 116 132 136 174 179 190 193 208 211 244 248 254 257 265 267 271 274 285
9 18 133 140 151 153 168 177 189 204 205 209 241 243 245 253 256 259 277 280
10 20 18 25 108 115 134 155 169 170 173 225 242 260 262 264 268 269 272 286 305 314
11 16 38 90 119 131 135 166 172 191 206 212 215 263 266 275 284 294
12 10 45 110 113 188 192 197 207 234 270 299
13 20 61 63 69 74 78 81 106 111 121 145 147 152 162 167 180 200 203 249 279 310
14 17 8 9 39 43 47 54 62 73 143 154 160 187 202 218 219 238 247
15 24 5 46 70 72 75 88 112 114 117 127 128 141 146 156 175 178 182 195 232 233 237 240
255 258
16 18 4 19 87 97 125 157 158 171 288 292 293 296 300 301 302 315 317 318
17 23 1 15 52 58 64 65 67 82 83 104 105 109 129 137 142 161 194 196 198 217 289 295 298
18 18 36 37 57 60 84 85 89 92 96 103 149 159 222 283 304 306 308 309
19 7 94 95 214 220 221 250 251
20 5 16 21 28 30 35

87
Table 4.20- Average intra and inter cluster distance D2 values in Soybean.
Cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 396.2 972.7 1384.1 2333.8 593.2 583.5 957.5 906.5 1055.5 574.6 1458.1 2064.5 993 1832.1 3367.1 641.1 1085.6 2388.4 988.1 3832

2 387.1 1693.2 1315.5 1729.1 1531.8 2500.8 972.1 790.1 741.3 837.2 2991.1 835.2 864.9 1261.5 1084.3 1241.2 3731.6 1220.1 3609.6

3 579.8 1667.8 1309.3 1290.2 2370.3 1288.4 1346.8 928.4 1227.1 1246.2 911 1295 3906.6 1423.7 1015.8 1097.7 2273.7 1495.1

4 733.6 3301.8 3023.6 4431 1209.4 2107.7 1274.8 1254.3 3483.3 1327.4 1223.1 1513 1775.9 1509.1 3794.3 2065.2 2408.5

5 0 55 639.3 1893.9 1007 1002.4 1505 876.7 913.4 2227 5030.2 1414.2 1838.3 1248.7 2193.8 3443.3

6 0 757 1846.6 927.4 811 1172.6 850 676.4 2077.5 4594.3 1341.5 1970.9 1383.3 2166.2 3291.3

7 703 2109.6 2253.5 1552.2 2977.8 2363 2153.7 3751.1 6165.1 1453.7 2273.4 2507.8 1980.1 5348.7

8 0 1985.4 473.9 1880.4 3307.1 1466.3 1855.5 2442.6 181.2 325.5 3140.4 239.9 3428.2

9 0 1221 578.8 1637.9 616.2 422.8 2472 1989.1 1624.3 2432.6 2573.8 2864

10 0 878.8 2004.3 557.5 1447.2 2558.7 283.7 961.7 2226.3 832.1 2716.1

11 0 1426.5 119.6 580.4 1875.7 1970 2103.5 2412.8 2707.5 1973.9

12 0 988.8 2361.3 6185.4 3124.7 2978.6 379.1 4441.3 1814.3

13 0 841.6 2377.3 1459.8 1713.7 1757.4 2175.7 1924.4

14 0 1586.3 2289.7 1431.1 2960 2805.5 2103.2

15 0 3107.5 3029.5 7442.2 2772.6 5281.5

16 0 674.5 3012.7 241.7 3899.9

17 0 2553.1 821.8 2967.4

18 0 4407.6 1684.2

19 0 5224

20 1146.7

88
Table 4.21- Cluster means of different characters to genetic diversity in Soybean.
Characters Days to 50% Days to Plant height No of pod No of 100 seed Seed Seed
flower maturity (cm) clusters/plant pods/plant wt(g) yield/row yield/plant
Cluster 1 42.45 101.69 58.83 16.12 13.15 41.01 242.98 7.97
Cluster 2 46.57 105.77 83.28 16.97 14.12 42.50 237.18 6.73
Cluster 3 44.56 103.77 63.60 23.26 20.05 76.54 242.71 9.11
Cluster 4 43.73 103.10 95.29 27.29 23.46 62.63 230.00 8.89
Cluster 5 40.50 99.50 47.00 10.70 7.70 56.20 295.50 10.40
Cluster 6 36.50 95.50 51.90 9.40 7.10 57.40 220.00 9.19
Cluster 7 37.98 97.28 38.83 12.85 9.95 37.91 246.50 8.76
Cluster 8 43.50 102.00 71.10 28.70 25.30 39.20 246.00 6.93
Cluster 9 53.00 113.00 71.90 10.70 8.70 62.10 207.50 7.28
Cluster 10 33.50 94.50 70.60 20.30 15.20 50.00 261.25 8.40
Cluster 11 39.50 98.50 86.10 12.30 9.60 71.40 218.00 9.07
Cluster 12 38.50 97.50 52.70 11.60 8.30 92.50 370.39 12.00
Cluster 13 38.00 97.50 77.50 13.60 9.90 67.50 183.00 11.11
Cluster 14 56.00 117.00 86.70 18.30 15.20 71.00 193.50 6.90
Cluster 15 50.50 109.50 117.30 20.20 16.80 37.30 194.00 7.13
Cluster 16 37.00 95.50 63.80 24.70 20.70 35.40 264.35 6.70
Cluster 17 55.50 113.50 64.10 30.00 27.20 49.60 232.00 6.37
Cluster 18 40.50 102.50 42.30 19.70 15.90 95.80 366.43 12.84
Cluster 19 42.00 101.50 68.40 26.90 22.90 19.40 230.00 6.60
Cluster 20 43.00 101.88 76.14 25.24 21.60 109.63 237.63 11.36

89
Fig 4.5- Mahalonobis Eucledean distance using Trocher Method (not to scale)

90
 Chapter – V

SUMMARY AND CONCLUSIONS

Chapter V

SUMMARY AND CONCLUSION

The present investigation on “STUDY OF GENETIC DIVERGENCE IN

CORE GERMPLASM ACCESSIONS OF SOYBEAN” was undertaken during Kharif
2021-22. Field work at the experimental farm of “All India Coordinated Research
Project on Soybean” Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani. The
experimental material comprised of 294 germplasm lines and 24 checks with following
objectives:-

1. To access the variability in germplasm lines of soybean for qualitative and

quantitative traits.
2. To determine character association among yield and its contributing traits.
3. To access the diversity in yield and its contributing traits.

The observations were recorded for days to 50% flowering, days to

physiological maturity, plant height, number of pod clusters per plant, number of pods
per plant, 100-seed weight, seed yield per row, seed yield per plant, plant growth, leaf
colour, leaf shape, presence of pubescence, pubescence colour. The mean, variability,
heritability, genetic advance percent mean, correlation, the magnitude of direct and
indirect effect and genetic divergence were analyzed for these traits.

The soybean genotypes were planted in randomized block design with two
replications. The observations were recorded on five randomly selected plants for
qualitative and quantitative characters from each replication.

The findings of present investigation are summarised as under:-

1 Analysis of variance revealed highly significant differences among

treatments in respect of all the traits studied. It shows that all the accessions
under study were genetically diverse i.e, there was presence of genetic
variability.
2 On the basis of mean performance for grain yield and it’s attributing traits
genotype GW 19 recorded highest grain yield per plant which is higher

91
 than all the checks. GW 3, GW 9, GW 15, GW 16, GW 18, GW 25, GW
30, GW 100, GW 116, GW 128, GW 140 recorded grain yield higher than
high yielding checks viz, PK 482, NRC 130, RVSM 2011-35 except KDS
753 which was the highest yielding check.
3 PCV was higher in magnitude than the GCV in respect of all the characters.
In the present investigation high estimates of genotypic and phenotypic
coefficient of variation were observed for plant height, number of pod
clusters per plant, number of pods per plant, seed yield per row, seed yield
per plant along with high heritability and high genetic advance.
4 The genotypic correlations were generally higher than the phenotypic
correlations indicating the inherent association between various characters.
Number of pod clusters per plant, number of pods per plant, 100 seed
weight, seed yield per row showed positive and significant correlation with
seed yield per plant at both phenotypic and genotypic level.
5 Traits like number of pod clusters per plant, number of pods per plant, 100
seed weight, seed yield per row had positive direct effect and had positive
correlation with grain yield. The residual factor was high (0.581 and 0.625)
at both genotypic and phenotypic levels respectively.
6 On the basis of D2 analysis by Tocher’s method the results of multivariate
analysis gives an idea about the subsistence of sufficient genetic divergence
among 318 genotypes. For 8 morphological traits 318 genotypes were
grouped into 20 clusters. The cluster 15 was with the highest number of
genotypes (24) followed by cluster 17 (23), cluster 2 (22), cluster 8 (20),
cluster 12 (20), cluster 13 (20), cluster 9 (18), cluster 16 (18), cluster 18
(18), cluster 14 (17), cluster 6 (16), cluster 6 (16), cluster 11 (16), cluster 1
(15), cluster 7 (15), cluster 3 (13), cluster 12 (10), cluster 19 (7), cluster 5
(5), cluster 20 (5).
7 Based on genetic divergence through D2 analysis, maximum inter cluster
distance was observed between cluster 15 and cluster 18.

92
Conclusion:
Based on the results the following conclusion can be drawn-
The genetic variability studies shows that the genotypes used in the study
had considerable variability which provides sufficient basis for the
selection. The characters controlled by additive gene effect can be improved
by most appropriate method of breeding would be pedigree method of
selection. In contrast to it other characters were controlled by non-additive
gene effects in different crosses, hence those could be successfully
improved by heterosis breeding or hybridization. The significant high
estimates of genotypic and phenotypic coefficient of variation were
observed for plant height, number of pod clusters per plant, number of pods
per plant, seed yield per row, seed yield per plant along with high
heritability and high genetic advance. Selection based on these traits
exhibiting high heritability and high expected genetic advance under such
situation can be effective in getting promising genotypes. Heritability of
this trait was due to additive gene action in controlling the traits, hence
pedigree method of breeding will be a rewarding one to improve the trait
under investigation.
Number of pod clusters per plant, number of pods per plant, 100 seed
weight, seed yield per row showed positive and significant correlation with
seed yield per plant at both phenotypic and genotypic level which indicates
simple selection based on these traits would be rewarding.
Traits like number of pod clusters per plant, number of pods per plant, 100
seed weight, seed yield per row had positive direct effect and had positive
correlation with grain yield indicating simple selection based on these traits
would be rewarding. High residual factor (0.581 and 0.625) at both
genotypic and phenotypic levels respectively pointed out that there may be
environmental component and other traits which is contributing towards
grain yield.
Based on genetic divergence through D2 analysis, maximum inter cluster
distance was observed between cluster 15 and cluster 18 indicating that
there was wide diversity present among these clusters which can be
exploited to breed cultivars with desirable agronomic traits.

93
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APPENDIX
 APPENDIX-I

Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani-431 402

(Maharashtra)
Meteorological data for the year 2022-23
Met. Temperature (0C) Humidity (%)
Rainfall
Week Period
(mm) Max. Min. RH1 RH2
No.
1 01-07 Jan 0.0 29.4 13.0 89 39
2 08-14 Jan 0.0 26.5 15.9 87 55
3 15-21 Jan 0.0 27.2 11.8 92 44
4 22-28 Jan 0.0 26.1 9.9 78 34
5 29-04 Feb 0.0 30.2 8.0 83 19
6 05-11 Feb 0.0 30.8 11.8 75 28
7 12-18 Feb 0.0 31.6 14.4 73 25
8 19-25 Feb 0.0 34.2 14.6 74 20
9 26-04 Mar 0.0 30.6 14.4 55 15
10 05-11 Mar 0.0 34.3 17.5 62 21
11 12-18 Mar 0.0 38.1 16.3 56 10
12 19-25 Mar 0.0 39.1 20.6 43 13
13 26-01 Apr 0.0 40.7 18.6 43 8
14 02-08 Apr 0.0 41.3 21.3 48 10
15 09-15 Apr 0.0 40.1 24.2 48 16
16 16-22 Apr 0.0 41.3 22.3 38 12
17 23-29 Apr 1.5 41.5 24.0 46 12
18 30-06 May 0.0 42.4 24.7 42 13
19 07-13 May 0.0 41.9 26.8 39 17
20 14-20 May 0.0 40.9 28.1 48 23
21 21-27 May 24.7 38.8 25.4 60 30
22 28-03 June 10.7 40.2 26.0 52 21
23 04-10 June 11.8 38.3 26.3 52 26
24 11-17 June 17.1 36.2 24.4 90 45
25 18-24 June 56.0 34.2 24.0 80 47
26 25-01 July 64.8 33.6 23.0 85 55
27 02-08 July 65.4 30.0 23.0 91 71
28 09-15 July 255.2 25.9 22.0 94 87
29 16-22 July 34.1 30.2 22.7 87 70
30 23-29 July 91.3 28.9 22.2 93 76
31 30-05 Aug 12.3 31.7 23.0 87 62
32 06-12 Aug 48.8 28.9 22.5 88 73
33 13-19 Aug 6.0 30.2 21.8 87 65

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Met.
Rainfall Temperature (0C) Humidity (%)
Week Period
(mm) Max. Min. RH1 RH2
No.
34 20-26 Aug 6.6 31.6 22.3 85 58
35 27-02 Sept 3.6 32.6 22.5 75 46
36 03-09 Sept 120.4 30.7 22.3 92 68
37 10-16 Sept 35.8 30.0 21.8 94 74
38 17-23 Sept 9.3 30.7 21.4 103 81
39 24-30 Sept 47.4 32.0 21.2 92 58
40 01-07 Oct 11.0 31.8 21.4 88 56
41 08-14 Oct 57.1 31.0 22.1 91 64
42 15-21 Oct 87.8 30.3 21.6 91 62
43 22-28 Oct 0.0 30.7 14.2 86 26
44 29-04 Nov 0.0 30.7 12.9 86 28
45 05-11 Nov 0.0 31.5 12.6 83 25
46 12-18 Nov 0.0 30.4 12.2 86 28
47 19-25 Nov 0.0 29.3 11.4 80 26
48 26-02 Dec. 0.0 30.1 11.4 86 32
49 03-09 Dec. 0.0 29.7 13.8 87.7 34.7
50 10-16 Dec. 1.2 29.9 17.4 84.9 46.6
51 17-23 Dec. 0.0 30.6 12.1 88.7 26.7
52 24-31 Dec. 0.0 31.6 14.2 88.8 36.3
Jan- Dec 1079.9
Total
June-Dec 1053.7

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CURRICULUM VITAE
 CURRICULLUM VITAE

1) Name of Student : Mr. Sourav Banik

2) Date of Birth : 17/01/1998
3) Nationality : Indian
4) Department : Department of Botany (Genetics and Plant Breeding)
5) Residential Address : Santipara, Agartala, Tripura West, Pin code : 799001
6) Mobile No. : 8787791541
7) Email id : sourav4pogo@gmail.com
8) Title of the thesis : Study of genetic divergence in core germplasm accessions
of soybean (Glycine max (L.) Merrill)

Academic qualifications
Course/ Name of the University/ Year of Percentage(%) Class/
Degree School/ Board passing / CGPA Grade
College or
University
10th Sri Krishna Central Board 2015 10 Grade A1
Mission School of Secondary
Education
(CBSE)
12th Sri Krishna Central Board 2017 87.45 Grade-A2
Mission School of Secondary
Education
(CBSE)
B.Sc. College of 2021 8.46 First
Hons Agriculture, AAU, Assam division
(Agricul Jorhat, Assam with
ture) distinction

Place:
Date: / / 2023 Sourav Banik

107