The experiment was conducted to assess genetic variability, heritability and genetic advance for yield and yield related traits in some upland rice genotypes. A total of 23 rice genotypes were evaluated in a randomized complete block design with three replications in 2017 at Pawe and Assosa. Analysis of variance revealed significant difference among the genotypes for most of the traits at individual and across locations, and error variances of the two locations were homogenous for most of the traits including grain yield. Moreover, the genotypes showed wider variability for grain yield in the range between 3707-6241kg/ha, 4853-7282kg/ha and 4280-6761kg/ha at Pawe, Assosa and over locations, respectively. A relatively high (>20%) phenotypic and genotypic coefficient of variations were estimated merely for number of unfilled grains per panicle. High heritability estimates (> 60%) were obtained for all of the traits, except plant height and Protein content. A relatively high genetic advance was obtained for traits like unfilled grains per panicle and fertile tiller per plant. Thus, this study revealed that there was higher genetic variability among the tested genotypes, which could be potentially exploited in future breeding programs.

1. Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice (Oryza sativa L.) Genotypes for Yield and Yield Related Traits in
BenishangulGumuz, Ethiopia
IJPBCS
Genetic Variability, Heritability and Genetic Advance Analysis
in Upland Rice (Oryza sativa L.) Genotypes for Yield and Yield
Related Traits in Benishangul Gumuz, Ethiopia
1Atsedemariyam Tewachew*, 2Wassu Mohammed, 3Alemayehu Assefa
1,2,3Pawe Agricultural Research Centre, Haramaya University and Ethiopian Institute of Agricultural Research, Ethiopia
The experiment was conducted to assess genetic variability, heritability and genetic advance for
yield and yield related traits in some upland rice genotypes. A total of 23 rice genotypes were
evaluated in a randomized complete block design with three replications in 2017 at Pawe and
Assosa. Analysis of variance revealed significant difference among the genotypes for most of the
traits at individual and across locations, and error variances of the two locations were
homogenous for most of the traits including grain yield. Moreover, the genotypes showed wider
variability for grain yield in the range between 3707-6241kg/ha, 4853-7282kg/ha and 4280-
6761kg/ha at Pawe, Assosa and over locations, respectively. A relatively high (>20%) phenotypic
and genotypic coefficient of variations were estimated merely for number of unfilled grains per
panicle. High heritability estimates (> 60%) were obtained for all of the traits, except plant height
and Protein content. A relatively high genetic advance was obtained for traits like unfilled grains
per panicle and fertile tiller per plant. Thus, this study revealed that there was higher genetic
variability among the tested genotypes, which could be potentially exploited in future breeding
programs.
Key words: Genetic advance, Genetic variability, Heritability, Upland Rice and Yield related traits.
INTRODUCTION
Rice (Oryza sativa L.) is one of the most important food
crops in the world. It is a staple food crop for more than
half of the world’s human population. Rice contains more
than 20 species. However, only two species are economic
importance of which Oryza sativa L. cultivated in
Southeast Asian countries and Japan, and Oryza
glaberrima Steud cultivated in West Africa. Rice grain
contains 75 to 80% starch, 12% water and 7% protein
(Okoet al., 2012). China, India, Indonesia, Bangladesh and
Viet Nam are the major producing countries in the world
(FAOSTAT, 2014). Rice was introduced in Ethiopia in the
1970s (MoARD, 2010). However, it has been cultivated in
small pocket areas though the country has suitable
ecologies for rice productions but unsuitable for production
of other food crops. In the country, four rice ecosystems
are identified and these are: upland rice, hydrophilic (rain
fed lowland) rice, irrigated lowland ecosystem, paddy rice
(with or without irrigation) (MoARD, 2010). The national
average yield of rice in Ethiopia is 2.81 ton/ha (CSA, 2017),
which is much lower than the world’s average rice yield of
4.6 ton/ha (FAOSTAT, 2017). In Ethiopia, rice variety
development is mainly through introduction and then
selection out of breeding materials. The development of
new varieties requires knowledge about the genetic
variability in the germplasm being handled by breeder. The
knowledge about genetic variability can help to know if
these variations are heritable or non-heritable. The
magnitude of variation due to heritable component is very
important because, it would be a guide for selection of
parents for crop improvement (Dutta et al., 2013).
Considering the importance of generating information on
genetic variability, heritability and genetic advance that
could be help development of high yielding rice genotypes
as pre-requisite for breeding program.
*Corresponding Author: Atsedemariyam Tewachew,
Pawe Agricultural Research Centre, Haramaya University
and Ethiopian Institute of Agricultural Research, Ethiopia.
Email: atsdemary21@gmail.com
International Journal of Plant Breeding and Crop Science
Vol. 5(3), pp. 437-443, December, 2018. © www.premierpublishers.org.ISSN: 2167-0449
Research Article

2. Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice (Oryza sativa L.) Genotypes for Yield and Yield Related Traits in
BenishangulGumuz, Ethiopia
Tewachew et al. 438
Further, information on the association of agro-
morphological and grain quality traits in rice breeding
programs will help to select the most desirable genotype(s)
leading to evolution of new rice varieties. Therefore,
present research was conducted to assess genetic
variability, heritability and genetic advance of upland rice
genotypes to understand the mechanism of genetic
variation and further selection of genotypes on strong
genetic basis.
MATERIALS AND METHODS
The experiment was conducted in Benishangul Gumuze
region at two locations, namely Assosa and Pawe
Agricultural Research Center (PARC) during the main
cropping season of 2017.PARC is located 575Kmfrom
Addis Ababa. Its geographical location is between
11015’and 11023” North latitude and 36030’ East longitudes
at an altitude of 1120 meters above sea level and the soil
type of vertisol with clay loam texture. A total of 23 upland
rice genotypes wereobtained from Fogera National Rice
Research and Training Center (FNRRTC). The genotypes
were introduced from Africa Rice Center and EMBRAPA
(Brazilian Agricultural Research Enterprise). The
experiment was conducted in Randomized Complete
Block Design (RCBD) with three replications. A plot
consisting six rows of 5 m long by 1.2m width (6 m2) with
spacing of 0.2 m between rows, 0.3 m between plots and
1.5 m between blocks was used. The seed rate of 60 kg/h
was used and a seed was drilled in a row. The
recommended fertilizer urea and DAP (diammonium
phosphate) was applied at a rate of 64 N and 46 P2O5 kg
ha-1. Data on grain yield and other important agronomic
traits and quality traits were collected on plot and individual
plant basis at each location. Crop phenology parameters
were registered by visual observation of plants grown in a
net plot, growth characters were measured from pre-
tagged 5 randomly taken plants from four central rows
while yield and yield components were measured plants
from 4m2 net plots. The grain quality parameter was
measured from randomly taken grain samples from each
plot.
Data Analysis
Analysis of variance (ANOVA) for two locations was
computed for all data recorded using the statistical
analysis software computer program (SAS Ins, 2014).
Genotypic means were compared following Fisher’s Least
Significant Difference (LSD) test at 5% levels of probability.
The following linear additive model was used:
Yijk= μ + Gi+ Lj+ (G x L)ij+ Rj(k)+ Eijk
Where;
Yijk is the observation on the ith genotype in the jth location
in the kth replication,
μ is the general mean,
Gi is the fixed effect of the jth genotype,
Lj is the effect of the jth location, (G x L)
ij is the interaction of the jth genotype with jth location,
Rk (j)is the effect of kth randomized block within thejth
location and Eijk is the experimental error associated within
the ijkth observation.
Table 1: A two-factor (combined) analysis of variance over
locations in randomized complete block design.
Source of variation DF MS EMS
Genotype (G) G-1 MSG σ2E+ rσ2GL+ rlσ2G
Replication
(Location)
(r-1) L MSR(L)
Location (L) L-1 MSL σ2E+ rσ2GL+ rgσ2L
Genotype x
Location (G x L)
(G-1)
(L-1)
MSGE σ2E+ rσ2GL
Residual/ Pooled
error
(r-1)
(G-1) L
MSE σ2E
Total GLr-1
FAO, Model
4.http://www.fao.org/docrep/005/y4391e/y4391e07.htm.T
he F-test for genotype, location and genotype x location
mean squares is against pooled error.
Where, r = number of replications; G = number of
genotypes; L = number of locations, MSR(L) = mean
square due to replications over location; MSG = mean
square due to genotypes; MSL= mean square due to
locations, MSGE= mean square of genotype x location
interaction, MSE = mean square of error; σ2E+ rσ2GL+
rlσ2G, σ2E+ rσ2GL+ rgσ2L and σ2E+ rσ2GL are variances
or expected mean squares due to genotype, location and
genotype x location interaction, respectively, whileσ2E is
expected mean squares due to error.
Phenotypic and Genotypic Variability Analysis
The phenotypic and genotypic coefficient of variation was
computed for each location using the formula suggested
by Burton and de Vane (1953) as follows.
Genotypic variance (σ2g) =
𝑀𝑔−𝑀𝑒
𝑟
Where, σ2g = genotypic variance, Mg= mean square of
genotype, Me = mean square of error, r = number of
replications
Phenotypic Variance (σ2p) = σ2g + σ2e
Where, σ2g = Genotypic variance, σ2e = Environmental
variance, σ2p = phenotypic variance
Where: PCV= Phenotypic coefficient of variation,
GCV= Genotypic coefficient of variation, 𝐱 = population
mean of the character being evaluated.
Sivasubramaniah and Menon (1973), phenotypic
coefficients of variation and genotypic coefficients of
variation were categorized as low (0-10%), moderate
(10-20%) and high (>20%).
100
x
_
2
p
PCV 











 100
x
_
2
g
GCV 













3. Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice (Oryza sativa L.) Genotypes for Yield and Yield Related Traits in
BenishangulGumuz, Ethiopia
Int. J. Plant Breed. Crop Sci. 439
Heritability and Genetic Advance
Broad Sense (b.s) heritability values were estimated for
each location using the formula adopted by Falconer et
al (1996) as follows
𝐇𝟐 =
𝛔𝟐𝐠
𝛔𝟐𝐩
𝑿𝟏𝟎𝟎Where, H2 = heritability in broad sense,
σ2p = phenotypic variance, σ2g = Genotypic variance
Genetic advance in absolute unit (GA) and percent of
the mean (GAM), assuming selection of superior 5% of
the genotypes were estimated for each location in
accordance with the methods illustrated by Johnson et
al. (1955) as:
GA = K * SDp * H2
Where, GA = Genetic advance, SDp = Phenotypic
standard deviation on mean basis of the character.
H2 = Heritability in the broad sense, k = the
standardized selection differential at 5% selection
intensity (K = 2.063).
The heritability values as percentage could be
categorized as low (<30%), medium (30-60%) and high
(>60%) (Robinson et al., 1949).
Genetic advance as percent of mean was estimated as
follows:
GAM =
GA
X
X100
Where, GAM = Genetic advance as percent of mean,
GA = Genetic advance. X= Population mean of the
character being evaluate.
Johson et al. (1955) genetic advance as percent of mean
is classified as low (<10%), moderate (10-20%) and high
(>20%).
RESULTS AND DISCUSSION
Analysis of Variance
The analysis of variance revealed that the error mean
squares (EMS) ratio was <3 for all traits except days to
heading and flowering. Moreover, the calculated F-values
were non-significant for half of the traits including grain
yield. Therefore, combined analysis of variance over
locations was performed (Table 2).The performance of
genotypes was evaluated based on the pooled mean
values for the traits that the homogenous error variances
and non-significant mean squares for genotype x location
were evident. The results of combined ANOVA over
locations revealed that the mean squares for genotype and
location were significant for all traits except for kernel
length and kernel thickens in which mean squares for
location were non-significant. The significant mean square
due to genotypes indicated the existence of variability
among the genotypes, which could be an opportunity to
apply selection breeding to improve the respective traits
that genotypes exhibited significant differences. Similar
findings were also presented by Ogunbayo et al., 2014;
Seyou et al., 2015; Ekka et al., 2015; Munganyinka et al.,
2015 and Lingaiah et al., 2014. The mean squares for
genotype x location interaction (GLI) were significant for
days to maturity, unfilled grains per panicle, kernel length,
kernel thickens, length width ratio and protein content
whereas mean squares for GLI were non-significant for the
rest of the traits. Moreover, the contribution of genotype x
location to the total sum square was much lower than the
contribution of genotype and location. This implies that the
variation observed among genotypes could be explained
by the inherent characteristics of the genotypes and
location effects for most of the traits.
Table 2: Mean square values of characters from combine
analysis over two locations (Pawe and Assosa) during
2017.
Trait Rep (2)
Genotypes
(22)
Location (1)G x L (22)
Error
(90)
CV
(%)
DE 0.86 0.21NS 10.46** 0.40 0.25 5.7
DH 16.75 55.39** 1921.92** 7.33 4.07 2.7
DF 45.66 38.72** 3490.12** 5.27 5.2 3.1
DM 4.48 46.1** 2968.12** 9.68 3.37 1.8
PH 300.65 75.52* 1072.98** 35.72 29.39 6.7
PL 0.53 7.94** 118.35** 1.4 1.72 6.2
FTPP 0.65 9.88** 137.47** 0.41 0.78 12.1
FGPP154.89 484.020NS 697.3NS 262.27 340.8514.6
UGPP1.02 81.39** 44.19** 13.27 2.15 13.5
NSPP8.47 2.720NS 244.27** 1.33 2.37 11.49
BY 70460485
38819777N
S
4927582541*
*
35583488
26334
538
24.69
HI 0.001 0.002NS 2.40** 0.005 0.003 18.5
GP 0.89 1.430NS 74.99** 1.41 1.14 1.08
MC 1.04 0.94NS 14.03** 1.16 1.03 7.8
TGW 6.12 6.04** 22.56** 1.12 0.76 3.6
KL 0.03 0.77** 0.00005NS 0.035 0.02 2
KW 0.01 0.034** 0.11** 0.0022 0.002 1.9
KT 0.01 0.012** 0.00012NS 0.0025 0.001 2
LWR 0.01 0.31** 0.19** 0.008 0.005 2.4
PC 0.05 1.57** 0.41* 1.09 0.09 4.3
GY 90820.00
36575286.7
1**
52960472.68
**
457357.5
8
50698
6.1
13.7
*and **= significant and highly significant at P<0.05 and
P<0.01, respectively. CV (%) = coefficient of variation in
percent.DE= days to emergence, DH= days to heading,
DF= days to flowering, DM= days to maturity, PH (cm)=
plant height, PL(cm)= panicle length, FTPP=fertile tiller
per plan , FGPP = number of filled grains per panicle,
UGPP = unfilled grains per panicle, NSPP = number of
spikelets per panicle, BY(kg/h)= biomass yield, HI =
harvest index, GP = grain purity, MC (%)= moisture content,
TGW (g)=thousand grain weight, KL(mm)=kernel length,
LW(mm)= kernel width, KT(mm)= kernel thickens,
LWR(mm)= length width ratio, PC(%)= protein content and
GY(kg/h)= grain yield.

4. Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice (Oryza sativa L.) Genotypes for Yield and Yield Related Traits in
BenishangulGumuz, Ethiopia
Tewachew et al. 440
Table 3: Estimates of variance components, broad sense heritability and genetic advance evaluated over two locations
(Pawe and Assosa) in 23 rice genotypes during 2017.
Traits Mean Min Max σ2g σ2p GCV(%) PCV(%) H2(%age) GA (%) GAM(%)
DH 81.14 76.67 87.17 8.01 9.23 3.49 3.74 86.77 5.44 6.7
DF 86.1 82.17 91.33 5.58 6.45 2.74 2.95 86.39 4.53 5.26
DM 115.68 112.2 122 6.07 7.68 2.13 2.4 79 4.52 3.91
PH(cm) 95.39 88.7 103.73 6.63 12.59 2.7 3.72 52.7 3.86 4.04
PL(cm) 21.26 19.1 23.9 1.09 1.32 4.91 5.41 82.37 1.95 9.19
FTPP 10.78 4.83 10.27 1.58 1.65 11.65 11.9 95.85 2.54 23.54
UGPP 7.24 6.23 20.36 11.35 13.57 46.54 50.87 83.7 6.36 87.84
TGW(g) 27.16 25.47 29.23 0.82 1.01 3.33 3.69 81.46 1.69 6.21
KL(mm) 6.88 6.46 7.67 0.12 0.13 5.08 5.2 95.45 0.71 10.25
KW(mm) 2.39 2.23 2.5 0.005 0.006 3.05 3.15 93.53 0.15 6.08
KT(mm) 1.86 1.76 1.96 0.002 0.002 2.14 2.41 79.17 0.07 3.93
LWR(mm) 2.89 2.63 3.42 0.05 0.052 7.76 7.87 97.42 0.46 15.81
PC (%) 6.73 5.8 7.57 0.08 0.26 4.2 7.6 30.57 0.32 4.79
GY(kg/ha) 5234 4280 6771 200859.5 265930.6 8.56 9.85 75.53 803.54 15.35
σ2g= Genotypic variance, σ2p = Phenotypic variance, GCV = genotypic coefficient of variation PCV = phenotypic coefficient
of variation, H2 (%age) = heritability in broad sense GA= Genetic Advance, GAM (%) = genetic advance as percent of
mean at 5% selection intensity DH= days to heading, DF= days to flowering, DM= days to maturity, PH= plant height, PL=
panicle length, FTPP=fertile tiller per plant, UGPP = unfilled grains per panicle, TGW=thousand grain weight, KL=kernel
length, KW= kernel width, KT= kernel thickens, LWR= length width ratio, PC= Protein content and GY= grain yield.
Mean Performance of Genotypes
The genotypes had 115.7 days of grand mean of maturit.
Eight genotype (ART15-13-2-2-2-1-1-B-1-2, NM1-29-4-B-
P-80-8, ART16-9-14-16-2-2-1-B-1-2, ART16-21-4-7-2-2-
B-2-2, ART15-16-31-2-1-1-1-B-1-1, ART16 5-10-22-4-B-
1-B-B-1, ART16-9-9-25-2-1-1-B-2-1, ART16-9-19-11-2-2-
2-B-1-2) were identified with the earliest maturity varying
from 112 to 114 days of mean maturity (Table 4). Osman
et al.(2012) and Bitewet al.(2016)evaluated 13 and 22 rice
genotypes respectively and found that genotypes had
variation in days to maturity from 88 to 110and 104 to115
days respectively. The genotypes showed a wide range of
variation for plant height, panicle length and fertile tiller per
plant which ranged from 88.7 to 103.73 cm, 19.1 to 23.9
cm and 4.87 to 10.27 per plant respectively. Akinwale et
al. (2011) also reported similar findings.
A wide range of variability was observed in panicle length
ranged from 19.1to 23.9cm for ART16-9-9-25-2-1-1-B-2-1
and ART16-5-10-2-3-B-1-B—1-1 with a mean value of
21.27. The genotypes also showed a wide range of
variation for number of fertile tiller per plant and number of
unfilled grains per panicle. The mean values of number of
fertile tiller per plant and unfilled grains per panicle were
ranged from 4.83 to 10.27 and 6.23 to 20.36 with mean
value of 7.25 and 10.78 respectively. The three genotypes
(ART16-5-9-22-2-1-1-B-1-2, ART16-9-5-28-3-13-1-B-2-1
and ART16-9-33-2-1-1-1-B-1-2) showed highest mean
values of grain yield significantly different from other
genotypes that had about 5801 to 6761kg grain yield per
hectare. Abebe et al. (2017) evaluated 34rice genotypes
and reported grain yield in the range between 2886 and
6759 kg/ha which showedwide range of variation among
the varieties.
The genotypes showed variation for kernel length, kernel
width and kernel thickness ranged from 6.46 to 7.67mm,
2.23 to 2.5mm and 1.76 to 1.96mm respectively, with the
mean value of 6.88mm, 2.39mm and 1.86mm respectively.
The three genotypes (PARC.DAT.V-1.2013,
PARC.DAT.V-2.2013 and PARC.DAT.V-3.2013) had
significantly longest kernel length. Girma et al. (2016) also
reported there are significant difference among 15 rice
genotypes for kernel length, breadth, thickness and length-
to-breadth ratio. The mean value of length width ratioand
protein content ranged from 2.63 to 3.42mm and 5.8 to
7.57%, with mean value of 2.89mm and 6.73%
respectively. Similar findings were reported by Diako et al.
(2011) protein content ranged from 5.10 to 5.9% and
Girma et al. (2016) found protein content of 5.3 to 10.55%
from 15 rice genotypes.
Estimates of Variance Components
The estimates of phenotypic coefficient of variation ranged
from 2.4 to 50.87% and values for genotypic coefficient of
variation were in the range between 2.13 and 46.54% in
which the lowest and highest values were estimated for
days to maturity and number of unfilled grains per panicle,
respectively (Table 3). According to Sivasubramaniah and
Menon (1973), phenotypic coefficients of variation and
genotypic coefficients of variation were categorized as low
(0-10%), moderate (10-20%) and high (>20%). The values
of PCV were slightly higher than the corresponding GCV
values for all traits and the magnitude of differences
between the two values were low for most of the traits. This
indicated that the traits were less influenced by the
environment. The environmental influence on any
character is indicated by the magnitudes between the
genotypic and phenotypic coefficients of variation. Babu et
al. (2012), Konate et al. (2015) and Srivastava et al. (2017)

5. Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice (Oryza sativa L.) Genotypes for Yield and Yield Related Traits in BenishangulGumuz, Ethiopia
Int. J. Plant Breed. Crop Sci. 441
Table 4: Mean performance of 23 rice genotypes for yield, yield related and quality traits evaluated across two locations (Pawe and Assosa) in 2017.
Genotypes DH DF DM PH PL FTPP UFGPP TGW GY KL KW KT LWR PC
NM1-29-4-B-P-80-8 79.33e-h 84f-j 114g-k 91.07def 20.33g-j 6.18hij 12.09d 26.27ghi 4901d-h 6.91cd 2.32hi 1.83e-h 2.98bcd 6.78d-h
ART16-9-29-12-1-1-2-B-1-1 77.83hi 82.83ij 114.3g-k 98.33a-d 20.93d-i 7.35d-g 20.36a 26.47f-i 4509gh 6.46j 2.42c-f 1.87cde 2.67jki 6.54hi
ART16-9-14-16-2-2-1-B-1-2 76.67i 82.17j 112.8ijk 92.77b-f 22.27bcd 7.73def 11.98d 25.47i 5472b-e 6.88cd 2.33gh 1.81h 2.95bcd 7.02c-f
ART16-9-33-2-1-1-1-B-1-2 85.5ab 88.83abc 118.7bc 99.8ab 20.4f-j 6.68ghi 14.6c 27.03d-g 5801bc 6.97cd 2.32ghi 1.82fgh 3.00bc 7.1cd
ART16-9-122-33-2-1-1-B-1-181.5c-f 86.83c-f 116.3c-g 96.77a-e 21.9b-f 6.89f-i 9.9ef 27.63b-e 5264b-g 6.99bc 2.41def 1.87b-e 2.91de 6.77d-h
ART15-19-5-4-1-1-1-B-1-1 81.5c-f 86.17c-g 115e-i 92.73b-f 21.03c-i 6.97f-i 8.4f-i 29.23a 4792e-h 7.15b 2.45a-d 1.89bcd 2.92cde 7.27abc
ART16-5-9-22-2-1-1-B-1-2 83.17bcd 87.67bcd 118cd 99abc 20.17hij 7.41d-g 12.31d 27.7b-e 6761a 7.14b 2.39ef 1.84e-h 2.99bc 7.28abc
ART16-21-4-7-2-2-B-2-2 79.5e-h 85.17d-j 113.5h-k 96.1b-e 21.67b-h 7.03f-i 8.73fgh 27.03d-g 5100b-h 6.61hij 2.43b-e 1.87cde 2.72hij 7.15bc
ART16-9-16-21-1-2-1-B-1-1 83.5bc 87.17cde 117c-f 95.77b-f 22.53abc 8.99bc 6.73ij 26.45f-i 5315b-g 6.65gh 2.37fg 1.83fgh 2.81fg 6.45hij
ART15-13-2-2-2-1-1-B-1-2 78.17ghi 84.33e-j 112.2k 95.9b-f 21.53b-h 9.83ab 6.23j 28.17abc 4280h 6.85c-f 2.44b-e 1.88bcd 2.81fg 5.8m
ART15-16-45-1-B-1-1-B-1-2 81.17c-f 86c-h 114.7f-j 95.37b-f 20.5e-j 6.73f-i 14.27c 26.37f-i 4552fgh 6.49ij 2.41def 1.89bcd 2.69i-l 6.99c-g
ART16-5-10-2-3-B-1-B—1-1 80.67d-g 86.17c-g 115.7d-h 103.73a 23.9a 6.7ghi 8.53fgh 27.73bcd 5694bcd 6.84c-f 2.47ab 1.86c-g 2.76ghi 6.76e-h
ART16-4-1-21-2-B-2-B-1-2 81.67c-f 86.83c-f 117.2cde 99.07abc 22.63ab 7.34d-g 9.34efg 26.67d-h 5579b-e 6.56hij 2.5a 1.86c-f 2.63l 7.57a
PARC.DAT.V-1.2013 86.67a 91.33a 122a 93.3b-f 22b-e 8.13cde 14.32c 27.3c-g 5061b-h 7.65a 2.27ijk 1.79hi 3.36a 6.23ijk
PARC.DAT.V-2.2013 86.5a 90.83a 120.5ab 98.8abc 22.73ab 10.27a 18.18b 27.43c-f 5187b-g 7.62a 2.23k 1.76i 3.42a 6.73fgh
PARC.DAT.V-3.2013 87.17a 90.33ab 121.2a 93.37b-f 21.8b-g 6.20hij 12.4d 28.33abc 5560b-e 7.67a 2.3hij 1.82gh 3.35a 7.46ab
ART15-16-31-2-1-1-1-B-1-1 79.67e-h 85.67d-i 113.8h-k 95.77b-f 21.8b-g 7.14e-h 8.7fgh 26.3ghi 5229b-g 6.69fgh 2.4def 1.87cde 2.78fgh 7.09cde
ART16 5-10-22-4-B-1-B-B-1 79.17f-i 83.67g-j 113.5h-k 93.83b-f 19.6ij 8.06cde 6.52j 28.88a 5262b-g 6.71e-h 2.47abc 1.92b 2.72h-k 5.91klm
ART16-4-13-1-2-1-1-B-1-1 79.83e-h 86c-h 114.2g-k 92.6b-f 20ij 6.09ij 10.8de 28.63ab 4773e-h 6.86cde 2.41def 1.89bcd 2.85ef 6.66gh
ART16-9-5-28-3-13-1-B-2-1 78hi 83hij 114.7f-j 92.07c-f 20.27hij 8.19cd 9.2efg 25.67hi 5851b 6.81d-g 2.25jk 1.79hi 3.02b 6.18jkl
ART16-9-9-25-2-1-1-B-2-1 77.83hi 82.83ij 112.3jk 88.7f 19.1j 5.27jk 7.1hij 26.83d-g 5097b-h 6.62hi 2.46abc 1.96a 2.69i-l 6.16jkl
ART16-9-19-11-2-2-2-B-1-2 81.83cde 87c-f 114g-k 98.43abc 20.97d-i 6.67ghi 9.53ef 26.6e-h 4994c-h 6.47ij 2.45a-d 1.85d-g 2.64kl 5.89lm
NERICA-4(Check) 79.33e-h 85.5d-i 115.2e-i 90.73ef 21.03c-i 4.83k 7.68g-j 26.5f-i 5343b-f 6.71e-h 2.41def 1.89bc 2.79fgh 7.06c-f
Mean 81.14 86.1 115.68 95.39 21.27 7.25 10.78 27.16 5233.7 6.88 2.39 1.86 2.89 6.73
LSD (5%) 2.7 3.1 3.34 10.29 2.14 1.42 2.36 1.6 1162 0.22 0.07 0.06 0.11 0.47
indicated that the estimates of PCV were slightly higher than the corresponding
GCV estimates for all the traits studied in rice and indicated the characters were
less influenced by the environment.
High value of PCV and GCV were obtained for only unfilled grains per panicle.
The results were also in agreement with the earlier reports of (Ekka et al., 2015
and Fathelrahman et al., 2015). High PCV and GCV values of these traits
suggested that the possibility of improving the traits through selection. Kernel
thickness, Kernel width and length-to-width ratio showed relatively similar PCV
and GCV values. This indicates that there is low environmental influence for
the phenotypic expression of these traits and the phenotypic variation of these
traits is more of due to their genotypic variations.
Heritability and Expected Genetic Advance
The estimated heritability in broad sense ranged from 30.57 % for protein
content to 97.42% for length width ratio. The heritability values as percentage
could be categorized as low (<30%), medium (30-60%) and high (>60%)
(Robinson et al., 1949). Heritability estimates were high for all traits except
Plant height and Protein content. Therefore, the traits with high heritability
estimates could be amenable for selection breeding since the traits are less
influenced by environmental factors and easily expressed in the selected
genotypes. Khare et al. (2015) also reported that estimate of heritability was
high for days to 50% flowering, days to maturity and grain yield in rice
genotypes. Mulugeta et al. (2012) for days to flowering, days to maturity, panicle

6. Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice (Oryza sativa L.) Genotypes for Yield and Yield Related Traits in
BenishangulGumuz, Ethiopia
Tewachew et al. 442
length, thousand grain weight; Babu et al. (2012) for days to
flowering and Rashid et al. (2017) for days to flowering, days
to maturity, panicle length, unfilled grains per panicle and
thousand grain weight reported high estimates of heritability.
Unfilled grains per panicle showed high heritability coupled
with high genotypic coefficient of variation. High heritability
coupled with high genotypic coefficient of variation of the
traits indicated that this trait may respond effectively to
phenotypic selection.
The estimated value of expected genetic advance
expressed as percentage of mean ranged from 3.91% for
days to maturity to 87.84% for number of unfilled grains per
panicle. With this range, a relatively high expected genetic
advance was obtained from traits with high and moderate
heritability but with better genotypic coefficient of variation.
According to Johson et al. (1955) genetic advance as
percent of mean is classified as low (<10%), moderate (10-
20%) and high (>20%). Based on this argument, in the
present study, traits such as unfilled grains per panicle
(87.87%) and fertile tiller per plant (23.54%) gave high
genetic advance as percent of mean, while moderate
genetic advance as percent of mean was computed for
kernel length (10.25%), length width ratio (15.81%) and
grain yield (15.35%). The present finding is corresponding
to the work of Abebe et al. (2017) for number of unfilled
grains per panicle. Bitew (2016) also reported moderate
genetic advance for grain yield. Generally, from this study,
traits such as unfilled grains per panicle and fertile tiller per
plant have the potential to respond positively to selection
because of their better broad sense heritability coupled with
relatively high genetic advance.
CONCLUSION
This study identified the existence of adequate genetic
variability among 23 tested genotypes for grain yield
andrelated traits. The genotypes ART16-5-9-22-1-1-B-1-,
ART16-9-5-28-3-13-1-B-2-1 and ART16-9-33-2-1-1-1-B-1-2
had shown highest grain yield significantly superiors from
rest of the genotypes. Thetraitfertile tillers per plant, unfilled
grains per panicle and grain yield have great potential which
may respond positively to selection owing to their better
broad sense heritability coupled with high genetic advance.
Hence, the information generated from this present study
can be helpful for, rice breeder to exploit genetic parameters
for future rice breeding program. Although this study has
been carried out for one season and at two locations, further
evaluation of these breeding materials at more locations and
year, different environments and agro-ecologies is advisable
to confirm the promising results observed in the present
study.
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Accepted 24 November 2018
Citation: Tewachew A, Mohammed W, Assefa A (2018).
Genetic Variability, Heritability and Genetic Advance
Analysis in Upland Rice (Oryza sativa L.) Genotypes for
Yield and Yield Related Traits in Benishangul Gumuz,
Ethiopia. International Journal of Plant Breeding and Crop
Science 5(3): 437-443.
Copyright: © 2018 Tewachew et al. This is an open-
access article distributed under the terms of the Creative
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use, distribution, and reproduction in any medium,
provided the original author and source are cited.