This document summarizes the research conducted on genetic variability, diversity and yield contributing traits in soybean genotypes. 41 soybean genotypes along with 4 checks were evaluated. Significant genetic variability was observed for all traits. Pod bearing length showed the highest GCV (11.13%) and PCV (11.21%). High heritability and genetic advance was observed for most traits indicating additive gene action. Based on D2 analysis, genotypes were grouped into 8 clusters with maximum diversity observed between clusters I and IV. Pod bearing length (31.01%) and 100 seed weight (29.19%) contributed most to diversity. Correlation and path analysis identified pod bearing length and number of pods per plant as important yield contributing traits.
mechanisms creating heterosis in the genotypes at molecular level i.e., in the areas of transcriptomics, proteomics and metabolomics by DNA methylation, small RNAs, histone modifications and parent-of-origin effect
mechanisms creating heterosis in the genotypes at molecular level i.e., in the areas of transcriptomics, proteomics and metabolomics by DNA methylation, small RNAs, histone modifications and parent-of-origin effect
28. Breeding for resistance to abiotic stresses – drought resistance – mechanisms of drought resistance (drought escape, avoidance, tolerance, and resistance) – features associated with drought resistance – sources of drought resistance – breeding methods for drought resistance – limitations – achievements; breeding for resistance to water logging – effects of water logging mechanism of tolerance – ideotype for flooded areas – breeding methods.
A measure of group distance based on multiple charaters.
It introduce by P.C.Mahalanobis in 1928.
Rao 1952 use this technique for assessment of genetic diversity in plant breeding.The genotypes for study of genetic diversity includes germplasm lines, and varieties.
3.Grouping of genotypes into clusters
4.Average Intra and Inter-cluster Distance
5.Cluster Diagram
6.Contributation of individual characters towards total divergence
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
28. Breeding for resistance to abiotic stresses – drought resistance – mechanisms of drought resistance (drought escape, avoidance, tolerance, and resistance) – features associated with drought resistance – sources of drought resistance – breeding methods for drought resistance – limitations – achievements; breeding for resistance to water logging – effects of water logging mechanism of tolerance – ideotype for flooded areas – breeding methods.
A measure of group distance based on multiple charaters.
It introduce by P.C.Mahalanobis in 1928.
Rao 1952 use this technique for assessment of genetic diversity in plant breeding.The genotypes for study of genetic diversity includes germplasm lines, and varieties.
3.Grouping of genotypes into clusters
4.Average Intra and Inter-cluster Distance
5.Cluster Diagram
6.Contributation of individual characters towards total divergence
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
Presentation during the Bureau of Agricultural Research (BAR) 13th Agriculture and Fisheries Technology Forum and Product Exhibition Seminar Series on August 9, 2017 at BAR Grounds, cor. Visayas Ave., Elliptical Rd., Diliman, Quezon City
Integrated Nutrient Management refers to maintenance of soil fertility and the plant nutrient supply at an optimum level for sustaining the desired productivity through optimization of the benefits from all the possible sources of Organic, Inorganic & biological component in an integrated manner.
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Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's entangled aventures in wonderlandRichard Gill
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Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
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Nutraceutical market, scope and growth: Herbal drug technology
Genetic variability and diversity analysis for yield and its contributing characters in soybean [Glycine max (L.) Merrill]
1. COLLEGE OF AGRICULTURE, RAIPUR
INDIRA GANDHI KRISHI VISHWAVIDIYALA, RAIPUR, (C.G.).
GUIDED BY
Dr. S.K. Nag
SPEAKER
Ankit Tigga
M.Sc. (Ag) Final Year
Genetic Variability and Diversity Analysis for Yield and Its Contributing
Characters in Soybean [Glycine max (L.) Merrill]
DEPARTMENT OF GENETICS AND PLANT BREEDING
(Scientist /Assistant Professor) 21 SEPTEMBER 2021
2. INTRODUCTION
Crop
Soybean (Glycine max L. Merrill)
Chromosome no. – 2n-40
Family – Leguminaceae
Self-pollinated crop
Plant type – C3 and short day plant
INDIA
Area – 11.72 M ha
Production – 135.83 lakh
tonnes
Productivity – 1192 kg/ha
Source – Ministry of
Agriculture and Farmers
Welfare, Yearly report 2020-
21
CHHATTISGARH
Area – 0.776 M ha
Production – 8.84 lakh
tonnes
Productivity – 1145 kg/ha
Source – SOPA data bank
2019-20
The crop is considered as an important oilseed in the globe due to its high Oil content 16-21%
and protein content 36-42%.
Nutritional and health's benefits. Good source of protein, unsaturated fatty acids and minerals,
like calcium and phosphorus including vitamins A, B, C and D.
Brazil is the world largest producer of soybean in 2020, followed by the USA, Argentina,
China and India. Source -USDA (Untied state department of agriculture).
India is the world's 5th largest producer of soybean.
3. ADVISORY COMMITTEE
Dr. S.K. Nag
(Major Advisor and Chairman)
Dr. Rajeev Shrivastava
(Member of department)
Dr. V.B. Kuruwanshi
(Member of other department)
Dr. Ravi R Saxena
(Member from supporting department)
Dr. R.K. Yadav
(Additional member)
4. OBJECTIVES
1. To study the genetic variability, heritability and genetics advance
for yield and its contributing characters.
2. To study genetic divergence through D2 statistics technique for
grain yield and its components in soybean.
3. To determine the direct and indirect effects of various yield
attributing characters on yield through correlation and path
coefficient analysis.
5. TECHNICAL PROGRAM OF WORK
(Material, location and season for the work done)
Experimental material : 41 genotypes with 4
checks total 45 genotypes.
The experiment was carried out in
Randomized Block Design with three
replications under the All India Co-ordinated
Research Project on Soybean during Kharif
2020 at the Research Cum Instructional Farm
under Department of Genetics and Plant
Breeding, College of Agriculture, IGKV,
Raipur, C.G.
6. OBSERVATIONS RECORDED
S. No. Characters S. No. Characters
1 Days to 50% flowering 7 Number of pods per plant
2 Days to maturity 8 Number of seeds per plant
3 Plant height (cm) 9 100 seed weight (g)
4. Pod bearing length (cm) 10 Protein content (%)
5 Number of pod bearing nodes per plant 11 Oil content (%)
6. Number of primary branches per plant 12 Seed yield per plant (gm)
7. Source of
variation
DF DTF DTM PH PBL NOPBN NOPBPP NOPPP NOSPP SW PC OC SYPP
Replication 2 3.4700 0.496 0.5700 0.615 0.347 0.051 9.062 74.274 0.010 1.058 0.021 0.694
Treatment 44 48.49** 22.09** 50.65** 59.75** 1.91** 0.93** 177.62** 901.19** 5.98** 6.01** 2.51** 11.70**
Error 88 3.489 3.027 0.263 0.275 0.247 0.055 3.881 26.827 0.044 0.510 0.156 0.454
DF stands for degree of freedom
* = Significant at a 5% probability level ** = Significant at a 1% probability level
{ DTF = days to 50% flowering; DTM = Days to maturity; PH = Plant height (cm); PBL = Pod bearing length (cm); NOPBN = No.
of pod bearing nodes per plant; NOPBPP = No. of primary branches per plant; NOPPP = No. of pods per plant; NOSPP = No. of
seed per plant; SW = 100 seed weight (g); PC = Protein content (%); OC = Oil content (%), SYPP = Seed yield per plant (gm) }
ANALYSIS OF VARIANCE
Results of Objective 1
8. Character Mean Range GCV
(%)
PCV
(%)
h2 (bs) Genetic
Advance
GA as % of
mean
Min. Max.
Days to 50%
flowering
37.37 31.33 44.33 10.36 11.50 81.12 7.18 19.22
Days to maturity 96.51 91 102.33 2.61 3.17 67.74 4.27 4.42
Plant height (cm)
49.79 40.75 57.71 8.23 8.29 98.45 8.37 16.82
Pod bearing length
(cm)
39.99 30.27 49.45 11.13 11.21 98.63 9.11 22.77
No. of pod bearing
nodes per plant
10.26 8.97 11.76 7.25 8.72 69.15 1.27 12.43
No. of primary
branches per plant 3.11 2.14 4.43 17.37 18.94 84.06 1.02 32.81
No. of pods per
plant
37.74 12.33 44.66 20.15 20.82 93.71 15.17 40.20
No. of seeds per
plant
85.19 37.33 112.66 20.03 20.94 91.57 33.65 39.50
100-seed weight (g)
10.68 8.63 14.22 13.17 13.31 97.81 2.86 26.83
Protein content
(%)
38.28 34.76 40.25 3.53 3.99 78.25 2.46 6.44
Oil content (%) 19.57 18.53 22.48 4.52 4.95 83.44 1.66 8.51
Seed yield per
plant- (g)
9.32 3.47 12.82 20.77 22.00 89.20 3.76 40.42
GENETIC PARAMETERS OF VARIATION
High
Medium
Low
9. 0 5 10 15 20 25
Days to 50% flowering
Days to maturity
Plant height (cm)
Pod bearing length (cm)
No. of pod bearing nodes
No. of primary branches per plant
No. of pods per plant
No. of seeds per plant
100-seed weight (g)
Protein content (%)
Oil content (%)
Seed yield per plant (g)
10.36
2.61
8.23
11.13
7.25
17.37
20.15
20.03
13.17
3.53
4.52
20.77
11.5
3.17
8.29
11.21
8.72
18.94
20.82
20.941
13.31
3.99
4.95
21.99
PCV % GCV %
GRAPHICAL REPRESENTATION OF GCV AND PCV FOR VARIOUS ASSOCIATED TRAITS OF SEED
YIELD
10. GRAPHICAL REPRESENTATION OF HERITABILITY, GENETIC ADVANCE AND
GENETIC ADVANCE AS A % OF MEAN
0
5
10
15
20
25
30
35
40
45
Days to
50%
flowering
Days to
maturity
Plant height
(cm)
Pod bearing
length (cm)
No. of pod
bearing
nodes
No. of
primary
branches
per plant
No. of pods
per plant
No. of seeds
per plant
100-seed
weight (g)
Protein
content (%)
Oil content
(%)
Seed yield
per plant
(g)
0
20
40
60
80
100
120
h2 (bs) Genetic Advance GA as % of mean
11. Number
of cluster
The
genotype
number
included
Name of genotypes
I 21
DS 3105, CAUMS 2, RSC 11-39, AS-15, PS 1664, JS 22-14, DS 3144, DLSb-1, JS 20-
116, RVS 2012-10, RVS 2011-10, ASb 36, MACS 1701, KDS 1096, KDS 1144, BAUS
96-17, TS 20-5, SL 1212, SL 1250, RSC 11-17, RSC 11-36
II 8
HIMSO 1691, VLS 101, PS 1661, Himso- 1692, ASb 9, AUKS 207, MACS 1460, RSC
11-15
III 6 DSb-38, DLSb-2, RSC 10-46, NRC 109, MACS 1691, RSC 11-22
IV 3 JS 22-11, PS 1670, MAUS 806
V 2 MAUS 768, BAUS 31-17
VI 3 RVSM 2012-11, NRC 128, RSC 11-35
VII 1 DS 1312
VIII 1 AUKS 206
SOYBEAN GENOTYPES IN VARIOUS CLUSTERS
ANALYSIS OF GENETIC DIVERGENCE
Results of Objective 2
12. S.No. Characters
No. of times appearing
first in ranking
Contribution towards divergence (%)
1 Days to 50% flowering 7 0.71
2 Days to maturity 4 0.40
3 Plant height (cm) 223 22.53
4 Pod bearing length (cm) 307 31.01
5 Number of pod bearing nodes per plant 1 0.10
6 Number of primary branches per plant- 16 1.62
7 Number of pods per plant 63 6.36
8 Number of seeds per plant 46 4.65
9 100-seed weight (g) 289 29.19
10 Protein content (%) 4 0.40
11 Oil content (%) 18 1.82
12 Seed yield per plant (g) 12 1.21
Total 990 100
CONTRIBUTION OF VARIOUS TRAITS TO DIVERGENCE AMONG 45 SOYBEAN GENOTYPES
13. Days to 50% flowering
0.70%
Days to maturity
0.40%
Plant height (cm)
22.53%
Pod bearing length (cm)
31%
Number of pod bearing nodes per
plant
0.10%
Number of primary branches per
plant-
1.62%
Number of pods per plant
6.36%
Number of seeds per plant
4.65%
100-seed weight (g)
29.19%
Protein content (%)
0.40%
Oil content (%)
1.82%
Seed yield per
plant (g)
1.21%
Figure - Relative contribution of different characters towards genetic divergence in percentage
14. Cluster I II III IV V VI VII VIII
I
199.66
(14.13)
610.53
(24.71)
413.96
(20.35)
1060.47
(32.56)
444.49
(21.08)
859.09
(29.31)
602.69
(24.55)
517.61
(22.75)
II
202.97
(14.25)
425.69
(20.63)
433.54
(20.82)
1262.73
(35.53)
602.59
(24.55)
379.03
(19.47)
1294.73
(35.98)
III
193.59
(13.91)
412.62
(20.31)
1143.80
(33.82)
544.46
(23.33)
832.67
(28.86)
914.78
(30.25)
IV
161.65
(12.71)
2240.25
(47.33)
699.46
(26.45)
1135.07
(33.69)
1935.68
(44.00)
V
263.60
(16.24)
1524.69
(39.05)
803.41
(28.34)
450.03
(21.21)
VI
299.88
(17.32)
1020.59
(31.95)
920.43
(30.34)
VII
0.00
(0.00)
1245.83
(35.30)
VIII
0.00
(0.00)
SOYBEAN GENOTYPES INTER AND INTRA-CLUSTER DISTANCE
INTRA CLUSTER
DISTANCE
INTER CLUSTER
DISTANCE
* Figure given in diagonals bold is intra-cluster D2 values and figure in parenthesis is 𝐷2 values
MAXIMUM
MINIMUM
15. DIFFERENT INTER AND INTRA CLUSTER DISTANCES ARE REPRESENTED
DIAGRAMMATICALLY
16. Characters
Cluster
Entries
DTF DTM PH PBL
NOPBNP
P
NOPBPP NOPPP NOSPP SW PC OC SYPP
I 21 37.17 96.84 51.94 42.60 10.24 3.09 40.57 90.90 10.07 38.21 19.49 9.87
II
8 38.33 94.58 47.48 37.56 10.36 3.03 38.73 87.79 12.70 38.65 19.61 10.29
III 6 36.89 98.33 47.08 36.43 10.65 3.37 38.41 88.89 9.68 37.59 19.72 8.94
IV 3 37.11 95.44 42.33 31.80 9.91 3.27 41.32 95.22 10.96 39.43 19.98 10.15
V 2 39.00 96.67 57.57 48.30 10.47 3.27 38.50 77.50 10.81 36.83 19.97 9.14
VI 3 37.11 95.11 45.75 35.93 10.11 2.82 17.00 39.67 10.55 39.32 19.35 3.81
VII 1 31.67 99.00 52.25 42.99 9.55 2.55 40.78 81.00 14.20 36.46 19.27 10.34
VIII 1 40.67 99.00 56.20 43.18 9.26 3.29 13.67 48.33 9.13 39.35 19.11 5.69
CLUSTER MEAN OF SOYBEAN GENOTYPES
MIN
MAX
DTF = Days to 50% flowering NOPPP = Number of pods per plant
DTM = Days to maturity NOSPP = Number of seeds per plant
PH = Plant height (cm) SW = 100 seed weight (g)
PBL = Pod bearing length (cm) PC = Protein content (%)
NOPBNPP = Number of pod bearing nodes per plant OC = Oil content (%)
NOPBPP = Number of primary branches per plant SYPP = Seed yield per plant (g)
17. S.No. Characters
Genotypes
I II
1 Days to 50% flowering DS 1312 NRC 109
2 Days to maturity HIMSO 1691 VLS 101
3 Plant height (cm) JS 22-11 PS 1670
4 Pod bearing length (cm) PS 1670 BAUS 31-17
5 No. of pod bearing nodes per plant DSb-38 NRC 109
6 No. of primary branches per plant- DLSb-2 RSC 10-46
7 No. of pods per plant JS 22-11 PS 1670
8 No. of seeds per plant MAUS 806 JS 22-11
9 100-seed weight (g) DS 1312 PS 1661
10 Protein content (%) JS 22-11 PS 1670
11 Oil content (%) MAUS 806 JS 22-11
12 Seed yield per plant (g) DS 1312 PS 1661
DESIRABLE GENOTYPES FOR DIFFERENT TRAITS IN SOYBEAN
18. Characters DTF DTM PH PBL NOPBN NOPBPP NOPPP NOSPP SW PC OC SYPP
DTF
G
P
1.000
1.000
DTM
G
P
0.257**
0.283**
1.000
1.000
PH
G
P
0.057
0.052
0.213*
0.172*
1.000
1.000
PBL
G
P
0.121
0.105
0.277**
0.230**
0.862**
0.850**
1.000
1.000
NOPBN
G
P
0.086
0.061
0.040
0.025
-0.045
-0.044
-0.120
-0.104
1.000
1.000
NOPBPP
G
P
0.139
0.102
0.227**
0.153
-0.070
-0.065
0.005
0.004
-0.010
-0.017
1.000
1.000
NOPPP
G
P
0.052
0.067
0.158
0.119
0.138
0.133
0.145
0.140
0.231**
0.177*
0.151
0.156
1.000
1.000
NOSPP
G
P
0.075
0.046
0.285**
0.225**
0.113
0.098
0.095
0.099
0.135
0.121
0.252**
0.204*
0.807**
0.743**
1.000
1.000
SW
G
P
-0.049
-0.033
-0.242**
-0.189*
-0.179*
-0.170*
-0.128
-0.126
-0.115
-0.098
-0.179*
-0.160
0.081
0.82
-0.039
-0.042
1.000
1.000
PC
G
P
0.138
0.128
-0.241**
-0.141
-0.264**
-0.232**
-0.249**
-0.217*
-0.104
-0.095
0.132
0.114
-0.157
-0.123
-0.106
-0.101
0.130
0.120
1.000
1.000
OC
G
P
0.150
0.158
-0.017
0.011
-0.043
-0.039
-0.008
-0.007
0.020
-0.024
0.087
0.066
0.119
0.130
0.214*
0.186*
0.032
0.039
-0.316**
-0.243**
1.000
1.000
SYPP
G
P
0.103
0.082
0.282**
0.191*
0.199*
0.183*
0.180*
0.180*
0.126
0.081
0.178*
0.152
0.882**
0.806**
0.883**
0.829**
0.140
0.129
-0.100
-106
0.144
0.143
1.000
1.000
** Significant at 1% probability level *Significant at 5% probability level
{DTF = days to 50% flowering; DTM = Days to maturity; PH = Plant height (cm); PBL = Pod bearing length (cm); NOPBN = No. of pod bearing nodes/ plant; NOPBPP = No. of primary branches
per plant; NOPPP = No. of pods per plant; NOSPP = No. of seed per plant; SW = 100 seed weight(g); PC = Protein content (%); OC = Oil content (%), SYPP = Seed yield per plant (g)}
ANALYSIS OF CORRELATION COEFFICIENT
Genotypic and phenotypic correlation coefficient for seed yield and its contributing traits in Soybean
Results of Objective 3
19. Character Days to
50%
flowering
Days to
maturity
Plant
height
(cm)
Pod bearing
length (cm)
No. of pod
bearing
nodes/plant
No. of primary
branches per
plant-
No. of pods
per plant
No. of seeds
per plant
100-seed
weight (g)
Protein
content
(%)
Oil
content
(%)
Seed yield
per plant
(g)
Days to 50% flowering 0.020 0.027 0.009 -0.009 -0.003 -0.001 0.025 0.035 -0.007 0.007 0.001 0.103
Days to maturity 0.005 0.106 0.034 -0.021 -0.001 -0.002 0.075 0.135 -0.037 -0.012 -0.000 0.282**
Plant height (cm) 0.001 0.023 0.159 -0.065 0.002 0.001 0.066 0.054 -0.027 -0.013 -0.000 0.199*
Pod bearing length (cm) 0.002 0.029 0.137 -0.075 0.004 -0.000 0.069 0.045 -0.020 -0.012 -0.000 0.180*
No. of pod bearing nodes/ plant 0.002 0.004 -0.007 0.009 -0.033 0.000 0.110 0.064 -0.018 -0.005 0.000 0.126
No. of primary branches per
plant-
0.003 0.024 -0.011 -0.000 0.000 -0.008 0.072 0.119 -0.027 0.006 0.000 0.178*
No. of pods
per plant
0.001 0.017 0.022 -0.011 -0.008 -0.001 0.475 0.382 0.012 -0.008 0.000 0.882**
No. of seeds per plant 0.002 0.030 0.018 -0.007 -0.005 -0.002 0.383 0.474 -0.006 -0.005 0.001 0.883**
100-seed weight (g) -0.001 -0.026 -0.028 0.010 0.004 0.001 0.039 -0.018 0.153 0.006 0.000 0.14
Protein content (%) 0.003 -0.025 -0.042 0.019 0.004 -0.001 -0.075 -0.050 0.020 0.049 -0.001 -0.1
Oil content (%) 0.003 -0.002 -0.007 0.001 -0.001 -0.001 0.057 0.101 0.005 -0.016 0.003 0.144
ANALYSIS OF PATH COEFFICIENT
The genotypic path coefficient (direct and indirect effects) of different traits influencing seed yield per plant
Residual effect -
0.10086
Positive direct
effect with
Significant
correlation
on Seed yield
Positive
indirect effect
with significant
correlation on
seed yield
20. Conclusion
• The present genetic variability gives an opportunity to select higher-level genotypes that can be
obtained by evaluating them.
• The high percentage of GCV and PCV was recorded for seed yield per plant, number of pods per plant
and number of seeds per plant. The existence of a large genotypic coefficient of variation suggesting
that the population has a lot of variability and provides opportunities for genetic enhancement through
trait selection.
• The number of pods per plant, number of seeds per plant, number of primary branches per plant and
100-seed weight were observed high heritability with high genetic advance as percent of mean.
Suggesting that these traits are governed by additive gene effects, which is fixable, In such situation
direct selection for seed yield may be effective.
• High heritability coupled with low genetic advance was found for protein content and oil content. It
suggests that non-additive gene action. The high heritability shown by the positive environmental
influence rather than the genotype and selection of such traits is not effective for yield.
• Two components, that is, days to maturity and number of pod bearing nodes per plant, show medium
heritability, it reveals that environmental effects are heavily influenced on these two traits and genetic
improvement through selection is challenging because of its high environmental effects.
Cont.
21. • The traits like, days to maturity, plant height, pod-bearing length, number of pods per plant, number of
seed per plant, and number of primary branches per plant all had positive and significant correlations
with seed yield per plant. selection for improvement of such character may be rewarding.
• Protein content was revealed to have a negative relationship with oil content.
• The number of pods per plant and number of seed per plant had the greatest positive direct effect and
correlation on seed yield per plant. it reveals true relationship between them and direct selection for
these traits will be rewarding for yield improvement.
• Path coefficient analysis results revealed that number of pod bearing nodes and number of primary
branches per plant had negatively direct effects on yield, the selection based on these features might
lead to the loss of soybean yield. The characters number of pods per plant and number of seeds per
plant exhibited positive direct effects and significant correlation with seed yield, so these characters
concluded that the main yield contributing components in soybean.
• 45 genotypes were grouped into eight cluster. Cluster-I had the greatest intra-cluster distance,
followed by other clusters. It should be given emphasis, while selection of parents for hybridization
programme since most of the elite breeding cultivars were included this cluster.
• The genotypes with high number of pods per plant, number of seeds per plant and pod bearing length
may be utilized to improve the seed yield of soybean genotype through hybridization and selection.