J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 40
Heritability, Genetic Advance and Gene Action Determination for Seed Yield and Yield
Components Using Generations of Finger Millet [Eleusine coracana (L.) Gaertn]
Wossen Tarekegne
1*
, Firew Mekbib
2
, and Yigzaw Dessalegn
3
1
Bahir Dar University, Department of Plant Sciences, Bahir Dar, Ethiopia,
2
Haramaya University, School of plant Sciences, Dire Dawa, Ethiopia
3
ILRI, LIVES Project, Bahir Dar, Ethiopia
*Corresponding author: wossentarekegne1@gmail.com
Received: December 13, 2020 Accepted: May 5, 2021
Abstract: Finger millet (Eleusine coracana (L.)Gaertn.) is a small seed crop grown in low rainfall areas and
its diverse cultural conditions make it an important food security crop; however, its productivity is low in
Ethiopia. This research was done to estimate heritability and gene numbers for yield and yield components in
parental, filial and backcross generations derived from a cross of two-finger millet cultivars at Koga and Adet
Agricultural Research Centers, Northwestern Ethiopia in 2014/15. The experiment comprised six basic
generations and four reciprocals of finger millet evaluated in randomized complete block design with two
replications. Data on yield and yield component traits were recorded. The result showed the number of genes
estimated in both locations ranged from -0.23 to 88.78, indicating that the presence of many genes with small
cumulative effect and epistasis gene effect will bias an estimate of the number of genes. Medium to high narrow-
sense heritability value coupled with high genetic advance showed the influence of additive variance and ease of
improvement for biomass yield and number of ears in this population. While low, medium and high narrow-
sense heritability observed together with the low genetic gain in most traits; which showed the presence of small
additive variance in most traits with the influence of epistasis; hence intensive selection is required to exploit
the characters. In most traits, the number of genes estimated to be negative and/or very small indicates that
epistasis was significant and the existence of environmental effect in both locations. The results indicate the
presence of genetic variability for developing improved varieties through crossing and selection.
Keywords: Additive variance, Epistasis, Genetic variability, Polygene
This work is licensed under a Creative Commons Attribution 4.0 International License
1. Introduction
Finger millet [Eleusine coracana (L.) Gaertn] is an
important staple crop grown under rain-fed
conditions in Northwestern Ethiopia which has
96.9% area coverage from the Amhara region
(CSA, 2020). The total area coverage of finger
millet in Ethiopia is 455,580.47 ha with a total
production of 112595.79 tons whereas in Amhara
Region it covers 236,124.66 ha of land with the
production of 59140.23 tons, which has a
proportion of 51.83% and 52.5% to the national
area coverage and production, respectively (CSA,
2020). However, it is the most neglected cereal
crop grown on marginal lands under poor
management condition and resulted in very low
yield (Salasya et al., 2009). Degu et al. (2009) also
reported that lack of improved varieties is one of
the major constraints in finger millet production.
This low productivity of the crop emanates due to
lack of genetic improvement that hinders overall
progress of the crop in developing countries; even
though environmental factors also contribute to
large losses in yield (Zerihun, 2009).
The knowledge of genetic system present in a
given crop species of the character under
improvement is of paramount importance for the
success of any plant-breeding program (Azizi et al.,
2006). Hence, estimation of genetic parameters
helps researchers understanding genetic variances,
heritability, and the number of genes and to
facilitate the selection of a desirable breeding
method.
The basic and key to bringing about genetic
improvement in any crop is the availability of
genetic variability. Variability is the occurrence of
differences among individuals due to differences in
their genetic composition and/or the environment
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 41
in which they are raised (Allard, 1960; Falconer et
al., 1996).
Heritability of crops provides information used for
breeders in designing appropriate breeding
strategies. The magnitude of such estimates also
suggests the extent to which improvement is
possible through selection. However, Johnson et al.
(1955) stated that heritability estimates together
with genetic advance are more important than
heritability alone to predict the resulting effect of
selecting the best individuals. Likewise, Bisne et
al. (2009) also reported that heritability estimates
along with genetic advance are normally more
helpful in predicting the gain under selection than
heritability estimates alone. Genetic advance is also
of considerable importance because it indicates the
magnitude of the expected genetic gain from one
cycle of selection (Hamdi et al., 2003). Genetic
advance as percent of the mean (GAM) is a more
reliable index for understanding the effectiveness
of selection in improving the traits because the
estimates are derived by the involvement of
heritability, phenotypic standard deviation and
intensity of selection. Thus, genetic advance (GA)
along with heritability provides a clear picture
regarding the effectiveness of selection for
improving the plant characters.
In order to develop a high yielding variety, it is
very important to know about the genetic structure
of each trait including, variability, gene mode of
action, heritability and number of controller genes.
This information enables breeders to develop
improved varieties. Hence, the present
investigation is carried out to gather information on
heritability and the number of genes governing the
expression of yield and yield component traits of
finger millet to design appropriate breeding
strategies
2. Materials and Methods
2.1. Description of the study area
The study was undertaken in the Northwestern
Ethiopia at the research field of Mecha and Adet
Agricultural Research Center.
Table 1: Geographical description of the experimental sites
Location
Elevation
(masl)
Latitude
Longitude
Temperature (
°
C)
Annual
rainfall (mm)
Maximum
Minimum
Mecha
1960
11
o
25’20” N
37
o
10’20” E
27.9
9.4
1557.9
Adet
2240
11
o
16’19’’N
37
o
28’38’’E
26.4
10.9
1215.2
Source: WAMSC, 2014
Table 2: Soil physical and chemical properties of the experimental area
Location
Soil pH
Textural
class
Soil type
O.M (%)
Total N (%)
Available P (ppm)
Mecha*
5.09-5.3
Clay
Nitosol
2.34-4.44
0.18-0.24
3.54-8.7
Adet**
5.38-5.48
Clay
Luvisol
2.67-2.86
0.17-0.47
2.64-2.76
Source: Berhanu et al., 2014*; NSRC, 2006**; O.M = Organic matter; ppm = parts per million
2.2. Experimental materials
The experimental materials produced using
generation mean analysis, with model parameters
of (m), (a) and (d) that consisted of basic
generations (P1, P2, F1, F2, BC1, and BC2) and
their reciprocals (RF1, RF2, RBC1 and RBC2)
derived from a cross of improved variety Necho
(P1) and local Tikur dagusa/Abate tikur (P2). The
parent varieties were chosen primarily based on
their difference in seed yield, yield components and
other traits.
2.3. Experimental design
The six basic generations and their four reciprocals
were evaluated in a Randomized Complete Block
Design with 2 replications at the research field of
Mecha and Adet Agricultural Research Center.
Each plot for various generations was sown in one,
two, and three rows with five-meter lengths for
parental, F1 and RF1 generations; for backcross
and reciprocal backcross and for F2 and RF2
generations, in the same order (Akhtar and
Chowdhary, 2006; Yadav and Singh, 2011). Each
generation was planted in a plot of 5 m length with
row to row spacing of 40 cm and a within row
spacing of 15 cm.
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 42
2.4. Management of experimental plants
The seed rate of 15 kg/ha and fertilizer rate of
100/50 kg/ha for DAP and UREA were applied in
rows, respectively (Molla, 2012). The total required
amount of phosphorous applied at basal but from
the total nitrogen applied half was used at planting
and the remaining was top-dressed at tillering
stage. Hand hoeing and weeding were made one
and two times, respectively over the growing
season to put the experimental plots free of weeds.
Other agronomic management practices were done
as required.
2.5. Data collection
The number of plants sampled for traits in each
experimental unit (plot) varied among generations
depending on the expected level of heterogeneity in
the generation. Accordingly, sampled numbers
were 10 plants for non-segregating generations
such as P1, P2, F1 and RF1 due to its homogeneity;
correspondingly for segregating generations, 20
from each backcross and its reciprocals and 30
plants from each F2 and RF2 generations due to its
heterogeneity (Akhtar and Chowdhary, 2006;
Yadav and Singh, 2011).
The measured traits on a plant basis included plant
height, number of effective tillers, number of ears,
number of fingers/ear and finger length data were
recorded. Other parameters such as days to
flowering, days to maturity, grain yield, biomass
yield, harvest index and thousand seed weight were
recorded on a plot basis. The measurement was
done according to the International Board for Plant
Genetic Resources (IBPGR, 1985) descriptor.
2.6. Data analysis
Analysis of variance and mean comparison using
Duncan’s Multiple Range Test at 5% probability
level was done with SAS statistical software model
with computer application (SAS, 2002).
2.6.1. Generation variance component analysis
Variance components under generation mean
analysis (additive, dominance and environment)
were estimated as per Kearsey and Pooni (1996)
and Mather and Jinks (1971) using the following
equations.
[1]
Where:
V(E) = Environment variance; VP1 = Variance
parent one; VP2 = Variance parent two and VF1 =
Variance first filial generation
[2]
Where:
V(A) = Additive variance; VF2 = variance second
filial generation; VBC1 = Variance backcross one
and VBC2 = Variance backcross two
[3]
Where:
V(D) = Dominance variance; VF2 = Variance
second filial generation; V[d] = Variance
dominance and V[E] = Environmental variance
[4]
Where
= Average degree of dominance variance;
V[D] = Dominance variance; V[A] = Additive
variance
[5]
Where
F = Association between D and A in all loci;
VBC1= variance backcross one; VBC2 = variance
backcross two
2.6.2. Heritability analysis
Narrow sense heritability (h
2
n) was estimated
following the methods described by Warner (1952).
[6]
Where:
h
2
n = narrow sense heritability; VBC1 variance
backcross one; variance backcross two; variance
second filial generation
According to Robinson et al. (1949) heritability
(H
2
) with the values of H
2
<0.2 is classified as low
while, values between 0.2 and 0.4 and greater than
0.4 are considered as moderate and high,
respectively.
2.6.3. Genetic advance analysis
Genetic advance i.e. the expected genetic gains
from selection were calculated using the formula
described by Johnson et al. (1955) indicated under
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 43
formula 7 while the predicted genetic advance
where the expected genetic gain upon selection was
expressed as a percentage of F2 mean using the
formula under 8.
[7]
Where:
ΔG = Genetic advance; h
2
n = narrow sense
heritability; σF2 = standard deviation second filial
generation
[8]
Where:
ΔG (%) = Genetic advance as percentage of second
filial generation; F2 = second filial generation
2.6.4. Minimum number of gene analysis
In order to evaluate the effect of those genes which
are involved in yield and yield component traits
minimum number of gene was computed using the
formula described by Lande (1981).
)]2
2
1
2
(2
2
2[8
2
)21(
BCBCF
PP
MNG
[9]
Where:
MNG = Minimum number of gene; P1 =parent one
cultivar; P2 = Parent two cultivar; σ
2
= variance; F2
= second filial generation; BC1 = Backcross one;
BC2 = Backcross two
3. Results and Discussion
3.1. Analysis of variance
Analysis of variance indicated the presence of
significant differences (P<0.01) among generation
for all traits at Adet (Table 3) and for all traits
except plant height (P<0.05) at Mecha (Table 4). A
significant difference between treatments indicated
the existence of genetic variability in genetic
materials for the traits studied. These results were
in agreement with the findings of Foroozanfar and
Zeynali (2013) in bread wheat. The foregoing
statement ensures the presence of high genetic
potential among these generations so that these
results are similar as generation effects found
significantly different as suggested by Dvojković et
al. (2010).
Table 3: Analysis of variance of yield and yield component traits of all generations in finger millet cross at Adet
Source of
variation
DF
PH
(cm)
FL
(cm)
NT
NF
NE
DTF
DTM
SY (kg)
BMY (kg)
HI
TSW (g)
Replication
1
0.55
0.41
0.98
0.2
138.28
31.25
2.45
185978.75
2830528.8
36.96
0.07
Generation
9
24.24
**
7.09
**
3.32*
*
4.71
**
13.99*
*
7.25*
*
56.72
**
584122.50
**
241071.13
**
112.80
**
0.07**
Error
9
2.74
0.14
0.32
0.18
0.44
0.58
0.45
2373.57
13455.13
4.2
0.01
CV (%)
2.3
3.62
5.39
4.68
4.89
0.88
0.49
2.14
2.48
4.24
2.36
*, ** = 0.05 and 0.01, respectively; DF = Degree of Freedom, PH = Plant Height, FL = Finger Length, NT = Number of
Tiller, NF = Number of Finger, NE = Number of Ears, DTF = Days to Flowering, DTM = Days to Maturity, SY = Seed
Yield, BMY = Bio Mass Yield, HI = Harvest Index, TSW = Thousand Seed Weight
Table 4: Analysis of variance of yield and yield component traits of all generations in finger millet cross at Mecha
Source of
variation
DF
PH
(cm)
FL
cm)
NT
NF
NE
DTF
DTM
SY (kg)
BMY (kg)
HI
TSW
(g)
Replication
1
25.88
0.5
0.17
0.06
1.38
5
0.8
75651.15
581746.05
6.48
0.01
Generation
9
20.48
*
4.96
**
5.75*
*
5.46*
*
4.79*
*
8.98*
*
38.31
**
254498.40
**
383926.90
**
27.34
**
0.08**
Error
9
4.62
0.07
0.19
0.21
0.27
0.44
0.69
5451.06
23189.83
2.11
0.01
CV%
3.32
2.59
6.68
5.77
5.11
0.72
0.63
3.69
3.8
2.92
3.31
*, ** = 0.05 and 0.01, respectively; DF = Degree of Freedom, PH = Plant Height, FL = Finger Length, NT = Number of
Tiller, NF = Number of Finger, NE = Number of Ears, DTF = Days to Flowering, DTM = Days to Maturity, SY = Seed
Yield, BMY = Bio Mass Yield, HI = Harvest Index, TSW = Thousand Seed Weight
3.2. Mean performance of the generations
The mean performance of the generations for yield
and its components are presented in Table 5and
Table 6. The results revealed the presence of
genetic variability for these characters in the
studied materials. The F1’s mean value for all traits
except plant height, days to flowering, days to
maturity and thousand seed weight were greater
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 44
than the mid parental value of finger length,
number of tillers, number of fingers, number of
ears, seed yield, biomass yield and harvest index.
The F2’s mean value was significantly below that
of the F1’s, except for traits days to flowering, days
to maturity and thousand seed weight; whereas, its
mean value was better than the mid-value of
parental lines for the traits finger length, the
number of fingers, days to maturity, seed yield,
harvest index and thousand seed weight. The
backcross to P1 was significantly different from
backcross to P2 excluding thousand seed weight
character at Adet (Table 5). Similarly, at Mecha the
F1’s mean value was greater than to the mid parent
and mean of F2’s value of all traits except to days
to flowering, days to maturity and thousand seed
weight. Backcross to P1 was significantly different
from backcross to P2 except for seed yield, harvest
index and thousand seed weight (Table 6).
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 45
Table 5.The mean, standard error and DMRT of main and reciprocal effect generations of finger millet at Adet
Generation
PH
FL
NT
NF
NE
DTF
DTM
SY
BMY
HI
TSW
P1
67.30+0.70
e
11.45+0.35
b
9.40+0.30
c
8.90+0.40
b
14.70+3.60
b
83.00+1.0
d
130.50+0.5
e
2387.35+57.10
b
4966.00+484.0
a
48.43+3.58
b
3.05+0.05
b
P2
77.75+1.05
a
8.70+0.40
d
10.98+0.03
b
6.45+0.55
d
10.20+2.20
d
90.50+0.5
a
149.00+1.0
a
1550.68+76.68
e
4305.00+395.0
c
38.29+0.60
d
3.25+0.05
a
F1
72.10+0.60
bcd
13.75+0.25
a
12.50+0.10
a
11.50+0.30
a
18.00+2.10
a
86.50+1.5
bc
135.00+0.0
c
3203.85+109.55
a
5188.50+418.5
a
61.75+3.12
a
3.05+0.05
b
F2
70.20+1.00
cde
10.25+0.22
c
9.33+0.13
c
9.05+0.05
b
12.02+2.89
c
86.50+1.5
bc
140.50+0.5
b
2139.30+57.00
c
4592.50+307.5
b
46.70+1.90
bc
3.40+0.10
a
BC1
68.61+1.39
de
9.40+0.15
cd
9.50+0.30
c
9.00+0.20
b
14.00+2.60
b
86.00+2.0
c
133.00+0.0
d
2233.83+50.63
c
4622.50+519.5
b
46.97+2.02
bc
2.90+0.10
b
BC2
75.15+0.85
ab
8.73+0.48
d
11.25+0.75
ab
7.84+0.16
c
11.25+2.25
cd
88.00+1.0
b
139.50+0.5
b
1844.35+131.75
d
4301.50+299.5
c
42.87+0.08
cd
3.00+0.00
b
RF1
72.75+0.75
bc
13.40+0.10
a
12.40+0.30
a
11.4+0.40
a
17.65+2.65
a
86.50+0.5
bc
135.50+0.5
c
3178.30+124.30
a
5220.00+294.0
a
60.95+1.05
a
3.05+0.15
b
RF2
70.18+1.28
cde
10.25+0.12
c
9.30+0.34
c
9.00+0.00
b
12.00+2.50
c
86.50+1.5
bc
140.00+1.0
b
2127.40+107.10
c
4632.50+354.5
b
46.02+1.22
bc
3.40+0.00
a
RBC1
68.60+1.80
de
9.43+0.38
cd
9.50+0.80
c
8.90+0.30
b
13.85+3.15
b
86.00+2.0
c
133.00+0.0
d
2210.25+101.25
c
4631.50+401.5
b
47.88+1.98
bc
2.90+0.10
b
RBC2
75.75+1.25
ab
8.70+0.20
d
11.23+0.58
ab
7.85+0.15
c
11.69+2.36
cd
88.00+1.0
b
139.50+0.5
b
1849.45+148.95
d
4288.00+288.0
c
43.09+0.59
cd
3.00+0.00
b
PH- Plant Height, FL- Finger Length, NT- Number of Tiller, NF- Number of Finger, NE- Number of Ears, DTF- Days To Flowering, DTM- Days To Maturity, SY- Seed Yield, BMY- Bio
Mass Yield, HI- Harvest Index, TSW- Thousand Seed Weight, P1-Parent one, P2-Parent two, F1-First filial, F2- Second filial, BC1- Backcross one, BC2- Backcross two, RF1- Reciprocal First
filial, RF2-Reciprocal Second filial, RBC1- Reciprocal Backcross one, RBC2- Reciprocal Back cross two
Table 6.The Mean, Standard error and DMRT of main and reciprocal effect generations of finger millet at Mecha
Generation
PH
FL
NT
NF
NE
DTF
DTM
SY
BMY
HI
TSW
P1
59.45+1.15
d
10.75+0.45
b
6.70+0.40
c
8.80+0.80
b
10.70+0.10
b
89.50+0.5
d
121.50+0.5
d
2169.60+29.40
b
4296.00+104.00
ab
50.52+0.54
b
3.20+0.20
bc
P2
69.55+3.45
a
8.35+0.15
e
4.70+0.10
d
5.50+0.20
e
7.35+0.35
d
97.00+0.0
a
137.50+0.5
a
1506.15+17.85
d
3194.50+78.50
d
47.16+0.60
bc
3.50+0.10
a
F1
67.50+3.50
ab
13.10+0.10
a
9.00+0.20
a
10.40+0.20
a
12.30+1.00
a
92.00+0.0
c
129.00+1.0
c
2603.25+32.00
a
4634.00+246.00
a
56.30+2.30
a
2.93+0.03
d
F2
63.83+0.93
bcd
10.15+0.15
bc
5.30+0.30
d
8.30+0.10
bc
9.30+0.10
c
93.00+1.0
c
134.00+1.0
b
1881.50+11.50
c
4028.00+12.00b
c
46.68+0.12
c
3.38+0.03
ab
BC1
61.18+0.48
cd
9.9+0.30
c
7.65+0.35
bc
7.25+0.15
cd
10.48+0.18
bc
91.50+0.5
c
129.50+0.5
c
1921.63+130.63
c
3975.50+247.50
bc
48.32+0.28
bc
3.10+0.00
cd
BC2
66.10+0.90
abc
9.05+0.25
d
4.90+0.10
d
6.30+0.30
de
9.25+0.25
c
95.00+0.0
b
133.00+0.0
b
1751.00+84.30
c
3662.00+229.00
c
47.84+0.67
bc
3.08+0.03
cd
RF1
67.53+2.23
ab
12.95+0.35
a
8.90+0.40
a
10.25+0.25
a
12.50+0.50
a
93.00+0.0
c
129.50+0.5
c
2590.00+40.00
a
4605.00+261.0
a
56.38+2.33
a
2.90+0.00
d
RF2
63.50+0.50
bcd
10.20+0.10
bc
5.40+0.20
d
8.40+0.10
b
9.25+0.25
c
93.00+1.0
c
134.50+0.5
b
1891.65+13.35
c
4037.50+6.50
bc
46.85+0.25
c
3.40+0.00
ab
RBC1
62.10+0.90
cd
9.85+0.15
c
7.75+0.55
b
7.30+0.20
cd
10.50+0.30
bc
92.00+1.0
c
129.50+0.5
c
1926.50+153.50
c
3962.50+262.50
bc
48.57+0.67
bc
3.10+0.00
cd
RBC2
66.10+0.60
abc
9.08+0.08
d
4.93+0.08
d
6.33+0.08
de
9.30+0.50
c
95.00+1.0
b
133.00+0.0
b
1752.50+102.50
c
3675.50+258.50
c
47.73+0.58
bc
3.05+0.05
cd
PH- Plant Height, FL- Finger Length, NT- Number of Tiller, NF- Number of Finger, NE- Number of Ears, DTF- Days To Flowering, DTM- Days To Maturity, SY- Seed Yield, BMY- Bio
Mass Yield, HI- Harvest Index, TSW- Thousand Seed Weight, P1-Parent one, P2-Parent two, F1-First filial, F2- Second filial, BC1- Backcross one, BC2- Backcross two, RF1- Reciprocal First
filial, RF2-Reciprocal Second filial, RBC1- Reciprocal Backcross one, RBC2- Reciprocal Back cross two
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 46
3.3. Component of genetic variation
Mather (1943, 1973) stated that genetic variability
obtained because of crossing, segregation and
recombination of parental lines redistributed among
the various states, in which it can exist. The
existence of genetic variation in the cross shows
how much of the variation is heritable and what
types of gene effects are involved. Estimates of
additive, dominance and environmental variances,
degree of dominance, the direction of dominance,
heritability values, genetic advance and number of
genes are presented in Table 7 and Table 8.
Table 7: Variance components estimates of generations for various characters of finger millet ‘Necho x Tikurdagusa’
cross at Adet
Traits
A
D
E
(H/D)
1/2
F
MNG
Plant height (cm)
-0.000045
0.000355
0.000031
-2.81
0.000157
-10.63
Finger length (cm)
-0.001142
0.00166
0.000243
-1.21
-0.000884
-1.29
Number of tiller
-0.001749
0.003864
0.000121
-1.49
-0.000093
-0.27
Number of finger
-0.000249
-0.00262
0.000789
3.24
0.000035
-7.76
Number of ear
0.007348
0.002888
0.012312
0.63
0.003896
2.44
Days to flowering
-0.000013
0.000332
0.000041
-5.05
0.000153
-6.9
Days to maturity
0.000033
-0.000022
0.000008
-0.82
-0.000005
12.37
Seed yield
-0.001411
0.003016
0.000593
-1.46
-0.001531
-3.12
Biomass yield
-0.002061
0.001018
0.002512
-0.7
0.001847
-0.23
Harvest index
-0.000503
-0.000624
0.000654
1.11
0.000977
-2.42
1000 seed weight
-0.000151
-0.000068
0.000142
0.67
0.000249
-0.36
A = Additive variance, D = Dominance variance, E = Environmental variance (E), (D/A)
1/2
= Degree of dominance, the F =
Direction of dominance, MNG = The minimum number of gene
Table 8: Variance components estimates of generations for various characters of finger millet ‘Necho x Tikurdagusa’
cross at Mecha
Traits
A
D
E
(H/D)
1/2
F
MNG
Plant height (cm)
0.000093
-0.002254
0.000587
-4.92
-0.000033
5.96
Finger length (cm)
-0.000112
-0.000164
0.000165
1.21
0.000004
-10.94
Number of tiller
0.000393
0.000564
0.000516
1.20
0.001207
5.38
Number of finger
-0.000484
-0.002399
0.000853
2.23
-0.00006
-8.1
Number of ear
-0.000306
-0.001312
0.000624
2.07
-0.00029
-8.82
Days to flowering
0.00003
0.000072
0.000009
1.55
0.000034
4.99
Days to maturity
0.000004
-0.000008
0.000005
-1.41
0.000006
88.78
Seed yield
-0.003037
0.005776
0.000081
-1.38
0.000933
-1.03
Biomass yield
-0.003221
0.00346
0.000746
-1.04
0.000051
-0.64
Harvest index
-0.000064
-0.001232
0.000342
4.39
-0.00003
-1.68
1000 seed weight
0.000007
-0.00107
0.000272
-12.36
-0.000009
16.45
A = Additive variance, D = Dominance variance, E = Environmental variance (E), (D/A)
1/2
= Degree of dominance, the F =
Direction of dominance, MNG = The minimum number of gene
3.3.1. Additive variance
The predominance of additive gene action depicted that it
is fixable in nature and selection will be very effective,
but the existence of low and negative additive variance in
most traits in this cross-required intensive selection to
exploit the traits due to the presence of epistasis gene
effect. The negative value of dominance and additive
variances for the characters indicates that the negative
sign may arise due to genotype by environment
interaction (Robinson et al., 1955; Haque et al., 2013).
The environmental variance was higher than the additive
variance for the number of fingers, number of ears, and
harvest index at both locations and days to flowering and
biomass yield at Adet. Whereas, plant height, finger
length, number of tillers, days to maturity and thousand
seed weight at Mecha, which indicated that, this
character lacks value for selection in this cross. At the
same time, estimates of low narrow-sense heritability for
these traits were also other indicators for a low value of
additive variances. This suggests that the environment
and non-additive gene effect had influenced the
expression of the yield and yield component traits.
Therefore, breeding methodologies that can reduce these
variations may help improve the rate of gain from the
traits; and suggesting that additive variance was playing
a major role in the improvement of these traits.
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 47
3.3.2. Average degree of dominance
The average degree of dominance revealed high
variation in both sites. It ranged from 0.63 (number
of ears) to -5.05 (days to flowering) at Adet and -
1.04 (biomass yield) to -12.36 (thousand seed
weight) at Mecha site (Table 7and Table 8).
According to Kearsey and Pooni (1996) and
Farshadfar (1998) average degree of dominance is
used to determine the importance of dominance
effects in relation to the additive deviations of
genes and is estimated as partial when the value
was less than unity while dominance was greater
than unity for traits influenced by over-dominance
effects. Hence, in this study partial dominance,
dominance and over dominance gene effects
present in the inheritance of traits with the range of
0.63 to -12.36.
The results indicated that except the number of
ears/plant, days to maturity, biomass yield and
thousand seed weight at Adet and biomass yield at
Mecha; the other traits in both locations determined
by over dominance gene effects. Kutlu and Olgun
(2015) reported similar findings where over
dominance gene effect was observed for harvest
index and grain yield per plant in the mean of
average degree of dominance value. This foregoing
statement showed a low, narrow-sense heritability
because of a strong environmental effect on the
expression of this trait.
3.3.3. Direction of dominance
The direction of dominance (F) estimated for the
studied traits (Table 7and Table 8)showed positive
value for most of the traits except for finger length,
number of tillers/plant, days to maturity and seed
yield at Adet, and plant height, number of fingers,
number of ears, harvest index and thousand seed
weight at Mecha. These results indicated that the
traits controlled by dominant gene action so that
dominant alleles were found more than recessive
alleles in the parents. Likewise, Shahrokhi et al.
(2013) observed the importance of dominant gene
action in the inheritance of the above traits. The
negative values of F mean, the additive genetic
variation controlled the inheritance of the traits.
Selection methods are effective to improve these
traits in this cross.
3.3.4. Minimum number of genes
Determination of the number and effects of this
polygene desired for obtaining optimal genotypes
in breeding practice. Hence, estimates of the
minimum number of genes controlling yield and
yield-related traits are shown in Table 7and Table
8. The estimates of both locations ranged from -
0.23 to 88.78 number of genes.
According to individual location estimated number
of genes ranged from -0.23 (biomass yield) to
12.37 (days to maturity) at Adet whereas from -
0.64 (biomass yield) to 88.78 (days to maturity) at
Mecha were controlled by many genes and this
happened because of divergence of the two parents,
so these cultivars can be used for future breeding
programs as genetic materials. The negative sign
and small values of the number of genes may
indicate the presence of epistasis and
environmental effect (Coates and White, 1998).
Similarly, Yield and its component traits controlled
by polygene, whose expression greatly affected by
environments (Ahmed and Khaliq, 2007).
Therefore, the estimates of the minimum number of
genes of the cross are likely to be inaccurate with
the effect of environment and epistasis. Despite a
situation, most of the estimates indicate that the
yield and its components are quantitatively
inherited traits that are amenable to selection.
3.4. Heritability
Heritability estimates for studied characters
between Adet and Mecha varied considerably and
presented in Table 9, respectively. Narrow-sense
heritability estimated in the range of 3.4 (days to
flowering) to52.4 (days to maturity) at Adet and
0.52 (thousand seed weight) to 43.37 (biomass
yield) at Mecha, respectively. The high heritability
of days to maturity and thousand seed weight at
Adet and biomass yield at Mecha estimate indicates
the selection procedures are simpler and lead to fast
genetic improvement of the traits (Khan et al.,
2008) since these traits are highly heritable from
parents to progenies. In addition to that, for traits
that expressed high to medium heritability, simple
selection would be an effective method (Feyissa
and Zinaw, 2014). While, low heritability values
were indicating selections might be difficult or
virtually impractical and revealed only slow
progress for the characters due to some variances
constituting the environment variance or the
masking effect of environment on genotypic effects
(Eid, 2009). The estimated values of narrow-sense
heritability (h
2
n) were higher in some of the traits
in both studied areas due to additive variance being
higher. These indicated that additive gene action
engaged in the expression of these traits and then
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 48
selection becomes effective from segregate
generations to obtain high performing cultivar
(Kutlu and Olgun, 2015).In contrary to the above
finding, the additive variance was lower than the
environmental variance for traits found in Adet
(days to flowering and biomass yield), Mecha
(plant height, finger length, number of tillers, days
to maturity and thousand seed weight ) and in both
locations (number of fingers/plant, number of
ears/plant and harvest index). This may be
suggesting the influence of environmental factors
in the inheritance of these characters.
The traits such as plant height, number of finger
and days to flowering at Adet and plant height,
number of fingers, number of ears and harvest
index at Mecha detected low narrow-sense
heritability; this condition may happen when
dominance and epistasis gene effects are increased
(Warner, 1952). This is because of narrow-sense
heritability depending on additive variance only.
Therefore, traits with low to high narrow-sense
heritability indicated the occurrence of complex
inheritance for the traits studied. Hence, the
recurrent selection method required for the
improvement of traits since it allows recombination
and breaking up of undesirable linkage (Ganesh
and Sakila, 1999). This cyclic method should
continue until a high level of gene fixation attained
with early and intensifies selection of later
generations (Arora et al., 2010).
3.5. Genetic advance
The estimated values of genetic advance and
genetic advance as percent of F2 mean for different
characters are presented in Table 9. Selection
efficiency depends on both heritability and genetic
advance as indicated by Johnson et al. (1955) and
Ubi et al. (2001) because the genetic advance is a
useful indicator when selection applies to the
relevant population to predict the progress that can
be expected. In the present study, high heritability
coupled with high genetic advance noticed for
biomass yield at Mecha while medium heritability
along with high genetic advance was recorded for
the number of ears and biomass yield at Adet. This
indicated the additive nature of genetic variation
transmitted from the parents to the progeny. In
addition, this trait can easily fix in the genotypes by
selection in early generations. These results were in
harmony with the finding of the previous
researcher (Yadav et al., 2011) for biological yield.
The information on heritability and genetic
advance ascribed the additive gene effects are may
be more essential for the above traits than non-
additive effects and can be improved through
simple or progeny selection methods (Johnson et
al., 1955; Panse, 1957).
Medium to high heritability accompanied by low
genetic advance for finger length, number of tillers,
seed yield, harvest index, days to maturity and
thousand seed weight at Adet; similarly, finger
length, number of tillers, days to flowering, and
days to maturity, seed yield and thousand seed
weight at Mecha. The result showed that the traits
could be improved by inter-mating superior
genotypes of segregating population developed
from a combination of genotypes with recurrent
selection method since non-additive gene actions'
was predominance than other gene action. In
agreement with this study, consistent estimates
reported in previous studies of Yadav et al. (2011).
Low heritability with low genetic advance values
found for plant height and number of finger and
days to flowering at Adet while plant height,
number of finger, number of ear and harvest index
at Mecha, indicating slow progress through
selection for these traits. The reason for the low
heritability for these characters was a result of
some variances constituting the environment
variance. These results find support from the earlier
study reported (Eid, 2009) for plant height and
number of grain.
J. Agric. Environ. Sci. Vol. 6 No. 1 (2021) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 49
Table 9: Estimates of heritability, genetic advance, and genetic advance at a percentage of mean at Adet and Mecha
Traits
Adet
Mecha
h
2
n
G
G
h
2
n
G
G
Plant height
10.4
0.0021
0.29
3.17
0.0005
0.07
Finger length
37.5
0.0067
0.69
25.4
0.003558
0.37
Number of tiller
30.5
0.01332
1.34
26.7
0.046917
4.19
Number of finger
6.8
0.00013
0.01
13
0.000295
0.03
Number of ear
32.6
1.12204
117.95
13.6
0.004006
0.4
Days to flowering
3.4
0.0008
0.11
27.03
0.002339
0.33
Days to maturity
52.4
0.0021
0.31
23.53
0.000242
0.04
Seed yield
28.1
0.037
6.75
34.15
0.000422
0.08
Biomass yield
36.9
0.132
25.26
43.37
89.34
16963.31
Harvest index
28.2
0.0143
1.85
3.91
0.000016
0.002
1000 seed weight
41.8
0.00423
0.34
0.52
0.000009
0.0007
4. Conclusion and Recommendations
According to generation variance analysis additive,
genetic variance and dominance genetic variance
influenced the expression of finger millet traits.
This indicated that both additive and non-additive
gene action involved in the control of traits. The
average degree of dominance values indicated that
number of ears, biomass yield and thousand seed
weight at Adet showed partial dominance, while
the other traits implied over dominance gene
actions. Medium to high narrow-sense heritability
value coupled with high genetic advance showed
the influence of additive variance and ease of
improvement of these important traits in this
population. While low narrow-sense heritability
along with low genetic advance indicated the
occurrence of complex inheritance for the traits
studied. Hence, the recurrent selection method
required for the improvement of traits since it
allows recombination and breaking up of
undesirable linkage. The number of genes
governing the inheritance of the characters in both
locations ranged low to high indicating the
inheritance of the traits depends on polygenic
action. In connection to this, the result showed the
presence of dominance and epistasis, which bias an
estimate of a minimum number of genes. Besides,
the small and negative value of the number of
genes on the study traits indicated the probable
presence of epistasis and environmental effects.
The results of this study concluded the existence of
sufficient genetic variability as well as additive and
non-additive type of gene effects in the inheritance
of the traits. Therefore, the possibility of
developing lines and hybrids were showed clearly
in this study; so that, improvement of high
heritability coupled with high genetic advance
noticed for biomass yield at Mecha while medium
heritability along with high genetic advance was
recorded for the number of ear and biomass yield at
Adet. Medium to high heritability accompanied by
low genetic advance for finger length, number of
tillers, seed yield, harvest index, days to maturity
and thousand seed weight at Adet; similarly, finger
length, number of tillers, days to flowering, and
days to maturity, seed yield and thousand seed
weight at Mecha. These indicated the presence of
additive, dominance and epistasis gene action and
its improvement could be achieved through
recurrent selection at early and later generations.
Acknowledgement
The authors thank the Ministry of Science and
Higher Education and Debre Markos University for
granting fund for the completion of this research
work. We would also like to thank the Amhara
Region Agricultural Research Institute for
providing office and internet access, Adet
Agricultural Research Center and finger millet
breeding team for their kind cooperation on
research facilities and in-field works and also to
Amhara Regional State Metrology Agency for
providing weather data of the study areas.
References
Ahmed, N.C.M. and Khaliq, I.M.M. (2007). The
inheritance of yield and yield components of
five wheat hybrid populations under drought
conditions. Indonesian Journal of Agricultural
Science. 8(2): 53-59.
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 50
Akhtar, N. and Chowdhry, M. N. (2006). Genetic
analysis of yield and some other quantitative
traits in bread wheat. International Journal of
Agricultural Biology. 8(4):523–527.
Allard, R.W. (1960). Principles of Plant Breeding.
pp. 254. John Wiley and Sons, Inc., New York.
Arora, D. S., Jindal, K. and Ghai, T. R. (2010).
Quantitative inheritance for fruit traits in inter
varietal crosses of okra (Abelmoschus
esculentus L. Moench). Electronic Journal of
Plant Breeding. 1(6): 1434-1442.
Azizi, F., Rezai, A. M. and Saeidi, G. (2006).
Generation mean analysis to estimate genetic
parameters for different traits in two crosses of
corn inbred lines at three planting densities.
Journal of Agricultural Science. 8: 153-169.
Berhanu, A., Anteneh, A., and Dereje, A. (2014).
Response of irrigated onion (Allium cepa L.) to
nitrogen and phosphorus fertilizers at Ribb and
Koga irrigation schemes in Amhara Region,
North Western Ethiopia. International
Research Journal of Agricultural Science and
Soil Science.4: 95-100.
Bisne, R., Sarawgi, A. K. and Verulkar, S. B.
(2009). Study of heritability and genetic
advance and variability for yield contributing
characters in rice. Bangladesh Journal of
Agricultural Research. 34(2): 175-179.
Coates, S.T. and White, D. G. (1998). Inheritance
of Resistance to Gray Leaf spot in Crosses
Involving Selected Resistant Inbred Lines of
Corn. Phytopathology. 88: 972-982.
CSA (Central Statistical Agency). (2020).
Agricultural sample survey report on area and
production for major crops (Private peasant
holdings Meher season). Central Statistical
Agency, Statistical Bulletin. 1(587). Addis
Ababa, Ethiopia.
Degu, E., Asfaw, A., Taye, T., and Tesfaye, T.
(2009). Genetic resources, breeding and
production of millets in Ethiopia. In: New
approaches to plant breeding of orphan crops
in Africa. Proceedings of an International
Conference, 19-21 September 2007. Bern,
Switzerland.
Dvojković, K., Drezner, G., Novoselović, D., Lalić,
A., Kovaćević, J., Babić. D. and Barić, M.
(2010). Estimation of some genetic parameters
through generation means analysis in two
winter wheat crosses. Periodicum Biology.
112: 247-251.
Eid M. H. (2009). Estimation of heritability and
genetic advance of yield traits in wheat
(Triticum aestivum L.) under drought
condition. International Journal of Genetics
and Molecular Biology. 1(7): 115-120.
Falconer, D. S., Mackay, T. F. and Franchum, R.
(1996). Introduction to Quantitative Genetics,
4
th
Edition. Trends in genetics. 12(7): 280.
Farshadfar, E. (1998). Application of biometrical
genetics in plant breeding.Razi Uni. Press.
Kermanshah, Iran.
Feyissa, T., and Zinaw, D. (2014). Genetic
Variability, Heritability and Character
Association of Twelve Sugar Cane Varieties in
Finchaa Sugar Estate West Wolega Zone
Oromia Region of Ethiopia. International
Journal of Advanced Research in Biological
Sciences. 1(7): 1-7.
Foroozanfar, M. and Zeynali, H. (2013).
Inheritance of some correlated traits in bread
wheat using generation mean analysis.
Advanced Crop Science. 3(6): 436–443
Ganesh, S. K., and Sakila, M. (1999). Generation
mean analysis in sesame (Sesamum indium L.)
crosses. Sesame and Safflower Newsletter. 14:
8 – 14.
Hamdi, A., El-Ghareib, A. A., Shafey, S. A. and
Ibrahim, M. A. M. (2003). Genetic variability,
heritability and expected genetic advance for
earliness and seed yield from selection in
lentil. Egypt Journal Agricultural Research.
8(1): 125–137.
Haque, A. F. M. M., Samad, M. A., Sarker, N.,
Sarker, J. K., Azad, A. K. and Deb, A. C.
(2013). Gene effects of some agronomic traits
through single cross analysis in blackgram
(Vigna mungo L. Hepper). International
Journal of Biosciences. 3(6): 220-225.
IBPGR (International Board for Plant Genetic
Resources). (1985). pp. 1-20. Descriptors for
Finger millet. IBPGR Secretariat. FAO, Rome,
Italy.
Johnson, H. W., Robinson, H. F. and Comstock, R.
E. (1955). Estimation of genetic and
environmental variability in soybeans.
Agronomy Journal. 47: 314–318.
Khan, H., Rahman, H., Ahmed, H. and Ali, H.
(2008). Magnitude of heterosis and heritability
in sunflower over environments. Pakistan
Journal of Botany. 1: 301-330.
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 51
Kearsey, M. J. and Pooni, H. S. (1996). The
genetically analysis of quantitative traits, 1
st
Edition. Chapman and Hall, London.
Kutlu, I. and Olgun, M. (2015). Determination of
genetic parameters for yield components in
bread wheat. International Journal of
Biosciences. 6(12):61-70.
Lande, R. (1981). The minimum number of genes
contributing to quantitative variation between
and within populations. Genetics. 99: 541-553.
Mather, K. (1943). Polygenic inheritance and
natural selection. Biological Reviews.18(1):
32-64.
Mather, K, and Jinks, J.L. (1971). Biometrical
genetics, 2
nd
edition Chapman and Hall,
London.
Molla, F. (2012). Participatory evaluation and
selection of improved finger millet varieties in
North Western Ethiopia. International
Research Journal of Plant Science, 3: 141-146.
NSRC (National Soil Research Center). (2006).
Soils of Adet Agricultural Research Center
Testing Sites. Addis Ababa, Ethiopia.
Robinson, H.F., Comstock, R.E. and Harvey, P.H.
(1949). Estimates of heritability and the degree
of the dominance in corn. Agronomy Journal.
41: 353-359.
Robinson, H. F., Comstock, R. E. and Harvey, P.
H. (1955). Genetic variance in open pollinated
varieties of corn. Genetica. 40: 45–60.
Salasya, B., Oduori, C., Ambitsi, N., Onyango, E.,
Oucho P. and Lumuli, J. (2009). The status of
finger millet production in western Kenya.
African Crop Science Soon area and
production for major crop society. 9: 719–723.
SAS (Statically Analysis of Software). (2002).
Guide for personal computers, 6
th
Edition.
S.A.S. Institute Inc., Cary, NC, USA.
Shahrokhi, M.,Khorasani, S. K. and Asa, E. (2013).
Study of genetic components in various maize
(Zea mays L.) traits, using generation mean
analysis method. International Journal of
Agronomy and Plant Production. 4(3): 405-
412.
Ubi, EB.,Mignouna, H. and Obigbesan,G. (2001).
Segregation for seed weight, pod length and
days to flowering following cowpea cross.
Africa Crop Science Journal. 9(3): 463- 470.
WAMSC (Western Amhara Metrological Services
Center). (2014). Seasonal agro metrological
data. Bahir Dar, Ethiopia.
Warner, J. N. 1952. A method for estimating
heritability. Agronomy Journal.44: 427-430.
Yadav, H. K. and Singh, S. P. (2011). Inheritance
of quantitative traits in opium poppy (Papaver
somniferum L). Genetika. 43(1):113 -128.
Yadav, S. K., Pandey, P., Kumar, B. and Suresh, B.
G. (2011). Genetic Architecture, Inter-
relationship and Selection Criteria for Yield
Improvement in Rice (Oryza sativa L.).
Pakistan Journal of Biological Sciences. 14(9):
540-545.
Zerihun, T. (2009). Role of orphan crops in
enhancing and diversifying food production in
Africa. African Technology Development
Forum Journal. 6(3/4): 9-15
