J. Agric. Environ. Sci. Vol. 7 No. 1 (2022) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 63
Genetic Variability of Yield and Yield Related Traits in Bread Wheat (Triticum aestivum
L.) Genotypes under Irrigation Condition in South Omo, Southern Ethiopia
Yimegnushal Bekele Tessema
1*
, Bulti Tesso Obsa
2
, Agdew Bekele W/Sillase
3
1
Jinka Agricultural Research Center, Jinka, Ethiopia
2
Department of Plant Science, Haramaya University, Dire Dawa, Ethiopia
3
Southern Agricultural Research Institute, Hawassa Ethiopia
*Corresponding author: yimegnu48@gmail.com
Received: March 9, 2022 Accepted: June 27, 2022
Abstract: Bread wheat (Triticum aestivum L), is a self-pollinating annual plant in the true grass family Gramineae
(Poaceae), and is the largest cereal crop extensively grown as staple food source in the world. The objective of this
study was to assess the genetic variability and genetic diversity among genotypes using a triple lattice design in
Bena-Tsemay district in 2020 under irrigation conditions. The analysis of variance revealed highly significant
variation (P≤0.01) among the genotypes for yield and yield components. Wide ranges of the mean values were
observed for most of the traits like grain yield, plant height, days to maturity, and grain filling period, indicating the
existence of variations among the tested genotypes. Moderate Phenotypic coefficient of variability and genotypic
coefficient of variability was recorded for days to maturity, grain yield, and harvest index; while high heritability
values were observed for plant height and days to heading. Among the studied characters grain yield showed high
genetic advance. The D
2
analysis grouped the 36 genotypes into six clusters. The maximum inter-cluster distance
was observed between clusters V and VI (D
2
=777.99), followed by that between clusters III and V (D
2
=525.49) and
I and III (D
2
=310.81), which showed that the genotypes included in these clusters are genetically more divergent
from each other than those in any other clusters. Principal components (PC1 to PC6) having Eigen value greater
than one, accounted for 75.6% of the total variation. The first three principal components, i.e., PC1, PC2, and PC3,
with values of 22.0, 35.7, and 47.9, respectively, contributed more to the total variation. Generally, the results of
this study showed the presence of variations among the studied genotypes for agro-morphology traits that could
allow selection and/or hybridization of genotypes.
Keywords: Genetic advance, GCV, Heritability, PCV
This work is licensed under a Creative Commons Attribution 4.0 International License
1. Introduction
Bread wheat (Triticum aestivum L), a self-pollinating
annual plant in the true grass family Gramineae
(Poaceae), is the largest cereal crop extensively
grown as staple food source in the world
(Mollasadeghi and Shahryari, 2011). It is one of the
most important exports and strategic cereal crops in
the world and in Ethiopia in terms of production and
utilization. Ethiopia is the first largest wheat producer
in Sub-Saharan Africa followed by South Africa and
fourth in Africa with the harvested area of 1.8 million
hectares with a production of 5.3 million tons and an
average yield of 2.97 t ha-1 (CSA, 2021). The narrow
genetic background has rendered improved varieties
less tolerant to biotic and abiotic stresses (Maqbool et
al., 2010).
Reduction in genetic variability makes the crops
increasingly vulnerable to diseases and adverse
climatic changes (Aremu, 2012). Therefore, precise
information on the nature and degree of genetic
variability and divergence present in wheat would
help to select parents for evolving superior varieties.
For a successful breeding program, the presence of
genetic variability plays a vital role. It is true that the
more diverse plants, the greater chance of exploiting
to generate productive recombinants and broad
variability in segregating generations during genetic
improvement (Mohammadi and Prasanna, 2003).
From 1974 to 2011, the country's research efforts
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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 64
resulted in the development of more than 87 bread
wheat varieties: thirty varieties from 1974 to 1997
(Degewione and Alamerew, 2013), and fifty-seven
varieties from 1998 to 2011, with some of them in
production in various agro-ecological zones.
Significant genetic variability was reported in bread
wheat (Tarekegne et al., 1994; Degewione et al.,
2013). Previous research has shown that in the study
area, little information is generated about the genetic
variability of yield and yield component attributes of
bread wheat genotypes under irrigation. Thus, the
present study was conducted to assess the extent of
genetic variability of yield and yield-related traits of
bread wheat genotypes under irrigation conditions.
2. Materials and Methods
2.1. Description of the study area
The genotypes were tested at Bena-Tsemay Weyito
Nasa Agricultural-Farm in 2019/2020 E.C. The
experimental site has an altitude of 550 m a.s.l. with
an annual rainfall of 750 mm with an average
minimum and maximum temperature of 22 °C and
32°C, respectively. The soil type of the site is
classified as vertisol and the textural class of the
experimental area is sandy loam soil with a pH of
7.9-8.1 (Haileslassie et al., 2015).
2.2. Experimental materials
The experimental materials consisted of 36 bread
wheat (Triticum aestivum) genotypes including nine
standard checks (Fentale, ADEL-2, Fentale-2, Atila-
7, GAMBO, Amibara, Amibara-2, LUCY, and
Kakaba). The genotypes were obtained from the
National Wheat Research Program, specifically from
Werer (WARC) Agricultural Research Center. The
genotypes were selected based on adaptation to heat
stress and classified under lowland type.
2.3. Experimental design and trial management
The experiment was carried out in a 6 x 6 triple
Lattice design comprising six incomplete blocks
where each block contains 6 test entries and 4 checks
(randomly allocated) with a total of 36 genotypes in
each block. The genotypes were grown under
irrigation conditions. Each genotype was sown in six
rows of 1.8 m long and 30 cm apart, with a seed rate
of 7.5g, 120 kg/h. Weeds were controlled manually
by hand weeding. Planting was done by hand drilling
on July 05, 2011, EC. Recommended fertilizer rate of
100kg/ha NPSB in (19% N, 38%P: 7% S, and 2.5%
B) at the rate of 50 kg ha
-1
in the shallow furrow
depths and mixed with soil at the same time during
sowing.
2.4. Statistical analysis
2.4.1. Analysis of variance
The analysis of variance (ANOVA) was carried out
to dissect total variability of the entries into sources
attributable to genotype and error using the SAS
software version (9.2) (SAS, 2008). The statistical
model for the augmented design was the same as that
of the randomized complete block design (Federer,
1956) as indicated below [1].
          [1]
Where
yij = observation of treatment i in jth block
µ = general mean,
g = effect of test treatment,
cj = effect of control treatments in a jth
block
βj = block effects
ε = error
2.4.2. Estimation of variance components
The phenotypic, genotypic, and environmental
variances were calculated as indicated below.
    [2]
Where
σ2 p = phenotype variance
σ2g = genotypic variance
σ2 e = environmental variance


[3]
Where
σ2g = genotypic variance
MTS = MST mean square treatment
σ2e = environmental variance
Msg = mean square of genotype,
Mse = mean square of error,
r = number of replications


   [4]


   [5]
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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 65
Where
= Grand mean of the character studied
PCV = phenotypic coefficient of variability
GCV = genotypic coefficient of variability
PCV and GCV values were categorized as low (0-
10), moderate (10-20), and high (>20) as indicated by
Burton and de vane (1953).
2.5. Broad sense heritability
Broad sense heritability (H
2
B) for all characters was
estimated as the ratio of genotypic variance to the
phenotypic variance and expressed in percentage [6]
according to the methods suggested by Falconer et al.
(1996).



   [6]
Broad sense heritability values were categorized as
High (>60%), Moderate (30-60%), and Low (0-30%
as described by Johnson et al. (1955).
2.6. Genetic advance under selection
The expected genetic advance expressed under
selection (GA) in a broad sense, assuming selection
intensity of 5% of the superior progeny was
estimated in accordance with the methodology
methods illustrated by Johnson et al. (1955).
     [7]
Where
GA = Genetic advance
SDp = Phenotypic standard deviation on
mean basis
H2 = Heritability in the broad sense.
K = the standardized selection differential at
5% selection intensity (K = 2.063).
2.7. Genetic advance as percent of mean
Genetic advance as percent of mean (GAM) was
estimated following the formula described by
(reference), which is indicated below [8].


   [8]
Where
GAM = Genetic advance as percent of
mean,
GA = Genetic advance
Genetic advance as percent of mean was categorized
as low (0 - 10%), moderate (10 – 20%) and high
(>20%) as suggested by Johnson et al. (1955).
3. Results and Discussion
3.1. Analysis of variance
Mean squares of the 13 yield and yield-related traits
from the analysis of variance (ANOVA) showed
highly significant differences among genotypes
(P≤0.01) for days to heading, days to maturity, grain
filling period, 1000 kernel weight, kernel number,
plant height, spike length, number of spikelet’s per
spike and grains yield (Table1). Significant
differences were observed in the number of
productive tillers per plant, biomass yield, and seeds
per spike. In line with the present results, many
scholars also reported highly significant differences
among all the wheat genotypes for all the traits
(Mohammed et al., 2011; Dergicho et al., 2015;
Gezahegn et al., 2015). Significant differences
among genotypes for all traits except for plant height
and number of spike lets per plant were reported by
Adhiena et.al. 2016. Similarly, moderate values for
the phenotypic and genotypic coefficients of
variation in wheat were reported by Kolakar et al.
(2012), Mohammed et al. (2011), and Berhanu et al.
(2017) for grain yield, biomass yield, plant height,
spike length, number of productive tillers per plant,
number of spikelets per spike, number of grains per
spike and 1000-grain weight.
J. Agric. Environ. Sci. Vol. 7 No. 1 (2022) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 66
Table 1: Mean squares of thirty six bread wheat genotypes evaluated at Bena-Tsemay weyito during the 2020 growing season
Traits
Replication
Block within
rep.
(Adj.)(d.f=15)
Treatment
Intra block
error (DF =
55)
RCBD
error
CV (%)
(df=35)
unadj.)
(Adj.)
Days to heading
13.12*
0.78**
140.76
109.11***
0.82
0.8
2.272
Grain filling period
0.25ns
2.751*
297.14
248.67***
1.42
1.70
1.727
Days to maturity
2.56ns
1.08ns
1469.03
1098.59***
0.97
0.99
1.720
Plant height (Ph)
4.014ns
5.21ns
45.69
131.08***
7.11
6.7060
4.721
No. of fertile tillers/plant
3.45**
0.59ns
1.064
0.96*
0.58
0.5880
23.658
Spike length (cm)
0.031ns
2.36ns
4.40
4.32 **
2.21
2.2484
14.676
No. of spikelet/spike
0.703ns
0.55ns
3.02
2.49***
0.38
0.4180
18.712
No. of kernels/ spike
90.19**
16.34ns
35.85
31.41**
11.66
12.6690
19.888
Seeds/spike
0.70ns
0.55ns
3.02
60.51*
0.38
0.41
6.01
1000-kernel weight (g)
46.001*
13.61ns
25.71
20.91**
8.39
9.5129
14.521
Grain yield (kg/ha)
971899
1202694
32942
1421576***
1165619
1173564
6.086
Biomass yield (t/ha)
3.91**
0.42ns
1.4995
1.23*
0.49
0.4800
29.881
Note: ** and * indicates highly significant at (1%) and significant at (5%) probability levels, respectively, DF = degree freedom, RE = relative efficiency, RCBD
= completely randomized block design, CV= coefficient of variations, adj. = adjusted treatment, unadj. = unadjusted treatment
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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 67
3.2. Phenotypic and genotypic coefficient of
variability
The PCV of traits ranged from 15.87% for grain
filling period to 92.85% for Plant height whereas
GCV ranged from 8.25% for the spike length to
85.76% for plant height (Table 4). In the present
study, high PCV coupled with high GCV of traits
were observed for days to maturity, grain yield, and
biomass yield and harvest index. Considering the
GCV estimates, number of fertile tillers per plant,
thousand-kernel weight, and kernel number and seeds
per spike exhibited moderate values. Moderate GCV
with moderate PCV was observed for a number of
grain filling periods, thousand-kernel weight, and
spike length. Accordingly, the genotypes coefficient
of variation (GCV) ranged from 5.67% for the plant
height to 14.74% for a number of fertile tillers plant-
1, whereas the phenotypic variation (PCV) ranged
from 7.06% for days to maturity to 19.08% for a
number of fertile tillers plant-1 (Table 4).
Moderate GCV coupled with moderate PCV was
observed for the grain filling period and thousand
seed weight. Considering the GCV estimates, days to
heading, fertile tiller per plant, and seeds per spike
exhibited moderate values. The studied characters
that had high GCV coupled with high PCV values
were days to maturity, plant height, spikelet’s number
per spike, grain yield, biomass yield, and harvest
index.
The high PCV and GCV indicate that selection may
be effective based on these traits. In support of such a
study, several workers reported high PCV and GCV
for grain yield, biomass, harvest index, 1000 grain
weight, and plant height in wheat. Medium PCV and
GCV values were recorded for the rest of the
characters. The high and medium PCV and GCV
indicate that selection may be effective based on
these traits. In support of this study, Tarekegne et al.
(1994) reported high PCV and GCV for grain yield,
biomass, harvest index, 1000 grain weight, and plant
height in wheat. In addition, the findings of Ali and
Shakor (2012) reported medium PCV and GCV for
grain yield per plot in 20 bread wheat genotypes.
Degewione et al. (2013) reported medium PCV and
GCV for 1000-grain weight, plant height, and days to
heading in twenty-six bread wheat genotypes. Similar
to the current finding, Berhanu et al. (2004) reported
that higher GCV and PCV values were observed for
grain yield, thousand-kernel weight, harvest index,
tillers per plant, spikes per plant, spike length, kernels
per spike, and grain protein yield while lowest GCV
and PCV values (<5%) were observed for days to
maturity in bread wheat. Similar results of PCV to
GCV estimates for most characters were also
reported by Dawit et al. (2012) and Adhiena et al.
(2016).
Similar observations showed moderate values for the
PCV and GCV in wheat were reported by Kolakar et
al. (2012), Mohammmed et al. (2011), and Berhanu
et al. (2017) for grain yield, biomass yield, plant
height, number of productive tillers per plant, spike
length, number of spikelets per spike, number of
grains per spike and 1000-grain weight. Similar to
the current finding, Berhanu et al. (2004) reported
that higher GCV and PCV values for grain yield,
thousand kernel weight, harvest index, tillers per
plant, spikes per plant, spike length, kernels per
spike, and grain protein yield while contrary lowest
GCV and PCV values (< 5 %) were observed for
spike length in bread wheat.
Adhiena (2015) reported high heritability for days to
heading which supports this finding. Similarly,
Kyosev and Desheva (2015) and Desheva and
Cholakov (2014) reported a high heritability value for
spike length. Kyosev and Desheva (2015) also
reported high estimates of heritability for spike length
with awns (74.93%), spike length without awns
(80.48%), spikelets per spike (63.96%), grain weight
per spike (67.47)% and thousand-grain weight
(73.51%) in their study on variability, heritability,
genetic advance.
3.3. Estimates of heritability In Broad Sense
In the present study, the heritability in broad sense
(H
2
B) estimates ranged from 19.56 % for days to
heading to 100.2% for grain yield (Table 2). High
heritability was noticed for days to maturity (99.87%)
followed by grain filling period (98.30%), plant
height (85.31), grain yield (97.67%), and the number
of spikelet’s per spike (64.91). Moderate heritability
values were also recorded for No. of kernels/ spike,
1000-kernel weight, and biomass yield. The
remaining traits like fertile tiller per plant, spike
length, and seeds per spike had low heritability. High
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heritability values for these traits indicated that the
variation observed was mainly under genetic control
and was less influenced by the environment and the
possibility of progress from the selection. This may
be attributed due to uniform environmental
conditions during the conduct of the experiment. The
results of the present study were in agreement with
the results of El-Mohsen et al. (2012) who noticed
higher heritability values for plant height, days to 50
percent flowering, number of productive tillers per
plant, grain yield per plot, and number of grains per
spike. Further, Salem et al (2008), and Ali et al.
(2008) recorded high heritability estimates for grain
yield, the number of kernels per the main spike, plant
height, thousand kernel weights, and the number of
tillers per plant.
The results obtained in the present study are similar
to that of results reported by El-Mohsen et al., (2012)
and Farshadfar and Mohammed (2012). Rahim et al.
(2010) noticed higher heritability value for plant
height, days to 50% flowering, the number of
productive tillers per meter length, grain yield per
plot, and the number of grains per spike. Awale et al.
(2013) also reported high heritability values for plant
height, tillers per meter, and spike length. In contrary
with the current result, Berhanu et al. (2017) reported
moderate heritability for grain filling period, kernels
per spike, plant height, biomass, thousand-kernel
weight in bread wheat genotypes. The present results
were also in line with the results of Dergicho et al.
(2015) who reported high heritability was observed
for, days to heading, thousand-grain weight, grain
filling period, days to maturity, spike length, and the
number of spikelets per spike in 68 bread wheat
genotypes. Jericho et al. (2015) reported similar
findings for high heritability associated with high
genetic advance for grain yield per plot and harvest
index which supports the present findings.
Mohammed et al. (2011) and Berhanu et al. (2017)
also reported similar results, showing relatively high
estimates of genetic advance (as a percentage of
mean) for grain yield and yield-related traits like the
number of fertile tiller per m2, plant height,
thousand-kernel weight, kernel number per spike and
harvest index.
The contrasting results as compared to the present
investigation, Berhanu et al. (2017) reported as high
heritability is coupled with moderate genetic advance
as percent of the mean for days to heading and days
to maturity in bread wheat genotypes. This finding is
in part similar to those reported by Gezahegn et al.
(2015). Rehman et al. (2015) report explained that
high heritability is coupled with high genetic
advance. Contrasting results as compared to the
present investigation, high heritability associated
with high genetic advance noticed for days to
heading, grain filling period, fertile productive tillers,
spikelet per spike, spike length, kernel per spike,
thousand-grain weight, and biomass yield per plot
respectively by Dergicho et al. (2015); moderate
heritability coupled with high genetic advance
observed for grain yield (41.71%, 63.05%) whereas
high heritability coupled with moderate genetic
advance as percent of mean was observed for 1000
kernel weight (74.28%, 20.13%), and plant height
(69.43%, 10.27%), respectively (Gezahegn et al.,
2015).
3.4. Estimates of expected genetic advance
Genetic advance (GAM %) as a percentage of the
mean was high for grain yield (92.08%) followed by
the number of days to maturity (62.50%), the number
of spikelets per spike (42.35%), biomass yield
(33.92), and grain filling period (32.20%) as
indicated in Table 2. plant height, spike length, days
to heading, thousand-kernel weight, number of
kernels per spike, seeds per spike, and harvest index
showed moderate GAM. It was also moderate for
seeds per spike (18.79%) and the number of kernels
per spike (18.31%), harvest index (14.50%), and
seeds per spike (10.12).
Accordingly, high heritability with high genetic
advance as a percent of mean shows for grain yield
(97.67%, 92.08%), spikelet’s number per spike
(64.91%, 42.35%), grain filling periods (98.30 %,
32.20%) and days to maturity (99.87%, 62.50%).
High heritability coupled with moderate genetic
advance as percent of mean were noticed for grain
filling periods, and spike lets per spike. Moderate
heritability associated with high genetic advance was
observed for the number of kernels per spike,
biomass yield, and harvest index, whereas moderate
heritability coupled with moderate genetic advance as
percent of mean was observed for seeds per spike,
and harvest index.
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The estimates of genetic advances help in
understanding the type of gene action involved in the
expression of various polygenic characters. High
values of genetic advance are indicative of additive
gene action whereas low values are indicative of non-
additive gene action (Singh 2009). Accordingly,
Heritability and genetic advance are important
selection parameters. The estimate of genetic advance
is more useful as a selection tool when considered
jointly with heritability estimates (Johnson et al.,
1955). high heritability associated with high genetic
advance was observed for days to heading, grain
filling period, fertile productive tillers, spikelet per
spike, spike length, kernel per spike, thousand-grain
weight, grain yield per plot, biomass yield per plot,
and harvest index respectively. These are simply
inherited traits indicates that most likely the
heritability is due to additive gene effects and
selection may be effective in early generations for
these traits. Kalimullah et al. (2012) reported similar
findings for plant height, biomass yield per plot, and
1000 grain weight, which supports the present
studies.
Table 2: Estimates of Ranges, Mean, Phenotypic (PV) and Genotypic (GV) Coefficient of Variation, Broad Sense
Heritability and Genetic Advance as Percent of Mean of traits
Traits
Range
Mean+ SE
PCV (%)
GCV (%)
H
2
B (%)
GA
GAM (%)
Days to heading (days)
17-65
38.79 ± 1.16
82.89
16.21
19.56
3.67
9.46
Grain filling
period(days)
42-83
57.66 ± 1.02
15.87
15.74
98.30
18.56
32.20
Days to maturity (days)
23-107
63.22 ± 2.17
30.29
30.25
99.87
39.51
62.50
Plant height (cm)
41.6-74
56.17± 0.66
92.85
85.76
85.31
1.63
2.90
fertile tillers/plant (no)
1.2-5.8
2.31 ± 0.08
30.77
12.57
17.65
0.11
4.84
spikelets/spike (no.)
2-6
3.29 ±0.16
31.62
25.60
64.91
1.39
42.35
Spike length(cm)
0.2-12.8
10.16 ±0.10
16.81
8.25
24.11
0.85
8.36
kernels/ spike (no)
10.2-41.4
17.35±0.50
24.61
14.78
46.07
3.75
18.31
Seeds per spike(no)
12-46.2
24.90±0.66
26.71
11.46
18.36
2.52
10.12
1000-kernel weight (g)
11.8-32.5
19.95 ±0.66
19.77
13.41
46.07
3.75
18.79
Grain yield (kg/ha)
183.3-1333.3
691.38±0.08
45.44
45.04
97.67
636.62
92.08
Biomass yield (kg/plot)
0.4-4.8
1.75 ±0.37
49.21
28.44
33.41
0.59
33.92
Note: PCV = Phenotypic coefficient of variation; GCV = Genotypic coefficient of variation; H2b = Broad sense
heritability; GA = Genetic advance and GAM = Genetic advance as percent of mean
3.5. Clustering of genotypes
The D2 values based on the pooled mean of
genotypes resulted in classifying the 36 bread wheat
genotypes into six clusters (eight groups and one
solitary). It was indicated that the tested bread wheat
genotypes were moderately divergent. The genotypes
were clustered in such a way that 40% genotypes in
cluster I (38.88%), 10% in cluster II (27%), 6%
genotypes in III (16.67%), 3% genotypes in cluster
IV (8.33%) whereas another 2% genotypes in cluster
V (5.56%) and 1% genotypes in cluster VI (2.78),
respectively (Table 3). This indicates that the
crossing between superior genotypes of the above
diverse cluster pairs might provide desirable
recombinants for developing high-yielding bread
wheat varieties.
3.6. Average intra and inter-cluster distance (D2)
The average inter-cluster distance (D2) values are
presented in (Table no.10). Maximum inter-cluster
distance was observed between clusters V and VI
(D2 = 777.98770), followed by that between clusters
III and IV (D2 = 525.49337). The lowest inter-cluster
distance D2 was recorded in clusters III and VI
(D2=62.04524) (Table 3). According to Rahim et al.
(2010) Hybrid of genotypes with maximum distance
resulted in high yield; the crosses between those
genotypes can be used in a breeding program to
achieve maximum heterosis. Therefore, more
emphasis should be on clusters V and VI for selecting
genotypes as parent for crossing with the genotype of
the cluster, which may produce new recombinants
with desired traits. This indicates that the crossing
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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 70
between superior germ plasm of above diverse cluster
pair’s might provide desirable recombinants for
developing high-yielding bread wheat varieties.
Similarly, Degewione and Alamerew (2013) grouped
26 bread wheat genotypes into six clusters;
Shashikala (2006) grouped 169 wheat genotypes into
11 clusters.
Table 3: Average intra (bold) and inter-cluster (off-diagonal) distance values (D2) among six clusters in 36 bread wheat
genotypes
I
II
III
IV
V
VI
I
31.14
109.67856**
310.81179**
19.80514ns
36.66184*
506.65355**
II
22.00
70.91886**
63.07811**
251.71101**
205.14023**
III
7.41
243.29602**
525.49337 **
62.04524*
IV
24.04
78.46869**
436.79455**
V
0.01
777.98770**
VI
13.23
x2 = 82.529 at 5% probability level and x2 =92.010 at 1% probability level, *= Significant at 0.05 probability level
,**= Highly significant at 0.01 probability level., where = X
2
is Chi-square.
3.7. Genetic divergence
Genetic divergence analysis quantifies the genetic
distance among the selected genotypes and reflects
the relative contribution of specific traits towards the
total divergence. Divergence analysis is a technique
used to categorize genotypes that are as similar as
possible into one group and others into a different. D-
square statistics (D2) developed by Mahalanobis
(1936). It has been used to classify the divergent
genotypes into different groups. The extent of
diversity present between genotypes determines the
extent of improvement gained through selection and
hybridization.
The lowest intra cluster distance D2 was recorded in
cluster IV (19.80514), which shows the presence of
less genetic variability or diversity within this cluster.
The diversity among clusters or inter cluster distance
D2 ranged from 85.15 to 174.32. Cluster V and VI
showed maximum inter cluster distance of (D2 =
777.98770), followed by that between clusters III and
IV (D2 = 525.49337) and I and VI (D2 = 506.65355).
The lowest inter cluster distance was noticed between
clusters I and IV (19.80514), followed by that
between clusters I and V (36.66184). Evaluation of
genetic diversity can be useful for the selection of the
most efficient genotypes. The results of this study
showed the presence of a high genetic divergence
among wheat genotypes, which is similar to the
findings of Ali et al. (2008) who reported that cluster
analysis can be useful for finding high yielding wheat
genotypes. According to Rahim et al. (2010) hybrids
genotypes with maximum distance resulted in high
yield. Thus the cross between these genotypes can be
used in breeding programs to achieve maximum
heterosis. Therefore, more emphasis should be given
on cluster V and VI for selecting genotypes as
parents for crossing with the genotypes of cluster,
which may produce new recombinants with desired
traits.
The chi-square test for the clusters indicated that
there was a statistically significant difference in all
characters (Table 7). The χ2- test for the six clusters
indicated that there was a statistically significant
difference in all characters.
J. Agric. Environ. Sci. Vol. 7 No. 1 (2022) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 71
Table 4: Bread wheat genotypes in six clusters tested based on D2 analysis
Clusters
Number and (% ) of Genotypes
Genotypes (G*)
I
14 (38.88%)
G13,G16,G22,G14,G15,G10,G11,G25,G7,G8,G26,G28,D5,G33
II
10 (27%)
G30, G32, G3 ,G19, G34, G36, G17, G18, G2, G9
III
6 (16.67%)
G27, G31, G21, G29, G1,G24
IV
3(8.33%)
G12 , G23, G4 (Atila-7)
V
2 (5.56%)
G6 (ETBW5957), G35(kakaba)
VI
1 (2.38%)
G20 (ANGI-2/HUBARA-3)
G*=genotype number
3.8. Principal component (PC) analysis
The eigenvalues are often used to determine how
many factors to retain. The first four components
together accounted for about 75.6% of the total
variation among the genotypes with respect to all the
13 traits evaluated and showed the presence of
considerable genetic diversity among the genotypes
for most of the traits under consideration.
Individually, PC1, PC2, PC3, PC4, PC5, and PC6 in
that order accounted for about 22%, 13%, 12%, 11%,
9% and 7% of the gross variation among the 36 bread
wheat genotypes evaluated for 13 traits. The traits,
which contributed more to PC1, were days to
heading, plant height, grain filling period, and Spike
length. Whereas for second PC grain yield, days to
maturity, fertile tiller/plant, 1000-kernel weight, and
harvest index. For the third PC, biomass yield, No. of
kernels/ spike, and harvest index while for the fourth
PC, Fertile tiller/plant and The first two principal
components PC1 and PC2 with values of 22% and
13% respectively, contributed more than half to the
total variation. Therefore, the present study
confirmed that the bread wheat genotypes showed
significant variations for the characters studied and it
suggested the many opportunities for genetic
improvement through selection. Similar results were
reported by Sajjad et al (2011), El-Mohsen et al.
(2012) and Degewione and Alamerew (2013).
Table 5: Eigenvalues and Eigenvectors of the first six principal components (PCs) for 13 traits of 36 bread wheat
genotypes tested at Benatsemay weyito kebele during the 2020 growing season
Characters
PC1
PC2
PC3
PC4
PC6
Days to heading(days)
0.744
0.189
-0.135
-0.185
0.120
Grain filling period (days)
0.594
0.163
0.177
-0.020
-0.408
Days to maturity (days)
0.028
0.498
0.056
0.766
0.025
Plant height(cm)
0.783
0.171
0.237
0.246
0.160
Fertile tiller (no)
0.198
0.509
-0.149
-0.247
0.606
No. of spikelet/spike (no.)
0.088
0.259
-0.631
0.204
0.297
Spike length (cm)
0.594
0.204
-0.268
-0.009
-0.465
No. of kernels/ spike (no.)
-0.320
0.753
0.559
0.452
0.208
Thousand-kernel weight (g)
-0.320
0.753
0.187
-0.129
-0.160
Grain yield (t/ha)
-0.135
0.536
-0.131
-0.208
-0.208
Biomass yield (t/ha)
0.077
-0.111
0.569
-0.435
0.168
Eigen value
3.083
1.926
1.698
1.548
1.028
Variance explained (%)
22
13.7
12.1
11
7.3
Cumulative variance explained (%)
22
35.7
47.9
58.9
75.6
Difference
1.156
0.227
0.150
0.237
0.112
J. Agric. Environ. Sci. Vol. 4 No. 1 (2019) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Journal of the College of Agriculture & Environmental Sciences, Bahir Dar University 72
4. Conclusion
The present study comprises thirty-six bread wheat
germplasm that were evaluated at weyito Nasa
agricultural farm with the objective of assessing the
genetic variability of yield and yield-related traits.
Analysis of variance revealed that highly
significant differences were obtained among the
treatments for all thirteen traits. Selected
quantitative characters indicated adequate
variability among the germplasm considered in this
study. The estimates of ranges of mean values
revealed that bread wheat germplasm possesses a
good amount of genetic variability. Productive
tillers per plant, spike length, kernel per spike,
thousand-grain weights, biomass yield per plot, and
grain yield per plot showed a high phenotypic
coefficient of variation (PCV) and genotypic
coefficient of variation (GCV) values. Heading
date, maturity date, grain filling period, and plant
height showed a medium phenotypic coefficient of
variation (PCV) and genotypic coefficient of
variation (GCV). The high to a medium phenotypic
coefficient of variation (PCV) and genotypic
coefficient of variation (GCV) values of characters
suggest the possibility of improving the desired
traits through selection. The values of heritability
for all the quantitative characters were high. The
expected genetic advance as a percentage of the
mean ranged from 0.11% No of fertile tiller per
plant to 636.62% 5% for grain yield(GY)
Characters with high genetic advance as a percent
of mean allow the improvement of the characters
through selection. The cluster analysis based on D2
analysis on the pooled mean of genotypes classified
the thirty-six genotypes into six clusters, which
makes them moderately divergent. There was a
statistically approved difference between all the
clusters. It was obvious from the analysis that three
PCs out of thirteen were selected having >1
Eigenvalues and contributed 75.6 % variation
among thirty-six bread wheat genotypes for all
parameters. It was noted that the principal
component first contributed 22%, the principal
component second 13%, and the principal
component third 12%, of the total genetic
variability for all the genotypes Productive tillers
per plant, spikelet per spike, spike length, kernel
per spike thousand-grain weight and harvest index
showed high heritability with the high genetic
advance of percent mean, these traits may be
included as components of indirect selection.
Acknowledgements
The authors would like to thank the South
Agricultural Research Institute (SARI) in South
Nations and Nationalities People State for its
financial support. And also, our special thanks go
to Werer Agricultural Research center for
providing planting materials for the research
purpose.
Conflict of interest
The authors declare no conflict of interest
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