Genetic variability and correlation analysis of rice (Oryza sativa L.) inbred lines based on agro-morphological traits

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Vol. 11(35), pp. 3340-3346, 1 September, 2016
DOI: 10.5897/AJAR2016.11415
Article Number: 6A4542660278
ISSN 1991-637X
Copyright ©2016
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Full Length Research Paper
Genetic variability and correlation analysis of rice
(Oryza sativa L.) inbred lines based on agro-
morphological traits
Abdourasmane K. Konate1*, Adama Zongo2, Honore Kam1, Ambaliou Sanni3 and Alain
Audebert4
1Institut de l’Environnement et de Recherches Agricoles (INERA) BP 910 Bobo-Dioulasso, Burkina Faso.
2Université Ouaga 1 Pr Joseph Ki-Zerbo BP 7021 Ouagadougou, Burkina Faso.
3Université d’Abomey-Calavi, BP 526 Cotonou, Bénin.
4Centre de Coopération International en Recherche Agronomique pour le Développement (CIRAD), CA UPR, Riziculture
Montpellier, France.
Received 10 July, 2016; Accepted 12 August, 2016
In order to evaluate genetic variability of agro-morphological traits and also determine the correlation
between grain yield with its components in rice lines, 17 recombinants inbred lines, their parents and a
check variety were grown in research station of Africa rice center in Benin republic during two
consecutive years 2013 and 2014. The experiments were laid out in a randomized complete block
design with four replications. Phenotypic coefficients of variance were higher than genotypic
coefficients of variance in all the characters across the two years. High heritability in broad sense (H2)
estimates were obtained for biomass (68.77%), date of 50% flowering (98.11%), plant height (81.94%),
leaf area (82.90%), number of panicles (64.40%), leaf dry weight (72.91%), root weight (67.43%) and
yield/plant (62.23%) suggesting that the traits were primarily under genetic control. A joint
consideration of broad sense heritability (H2) and genetic advance as per cent mean expected (GAM)
revealed that leaves dries weight and roots weight combined high heritability and high GAM.
Furthermore, high (H2) and high GAM recorded in these characters could be explained by additive gene
action. However, high estimates (H2) combined with moderate GAM recorded for biomass, day to 50%
flowering, leaf area, number of panicle and yield/plant could be due to non-additive gene effect. Grain
yield/plant recorded positive and significant correlation with stem weight (r=0.5262) and biomass
(r=0.9291). This result indicates that selection based on these two characters will be highly effective for
yield improvement in rice.
Key words: Agro-morphological traits, correlation, genetic variability, heritability, rice.
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 grain contains
75 to 80% starch, 12% water and 7% protein (Oko et al.,
2012; Hossain et al., 2015). Minerals like calcium,
magnesium and phosphorus are present along with some
traces of iron, copper, zinc and manganese. In addition,
rice is a good source of niacin, thiamine and riboflavin
(Yousaf, 1992; Oko et al., 2012).
Rice is grown in 117 countries across all habitable

continents covering a total area of about 163 mha with a
global production of about 740 mt and an average yield of
about 4,539 kg / ha (FAOSTAT, 2014). The Asian
continent ranks first with over 90.1% of world production,
followed by the American continent (5.1%), African
continent (4.2%), Europe (0.5%) and Oceania (0.1%).
The major producing countries are China (206.5 million
tons), India (157.2 million tons), Indonesia (70.8 million
tons), Bangladesh (52.2 million tons) and Viet Nam with
44.9 million tons (FAOSTAT, 2014). In Africa, rice is
grown and consumed in more than 40 countries. Its
production has increased significantly from year 2000 to
2013. More than 20 million farmers in Africa are engaged
on rice production in these countries and about 100
million people are dependent on it directly for their
livelihood (Nwanze et al., 2006). For example in Burkina
Faso, rice production has grown from 103,087 tons on
year 2000 to 305,382 tons on 2013. It is no longer a
luxury food but has become the cereal that constitutes a
major source of calories for almost urban and rural
people of Africa (Seck et al., 2013).
In a report published by United Nations the world
population is going to cross the 8 billion mark by 2030
and 9.6 billion by the year 2050 and rice production must
be increased by 50% in order to meet the growing
demand. This demand in Sub-Saharan Africa is expected
to grown substantially as the population is currently
growing at the rate of 3 to 4% per annum (Ogunbayo et
al., 2014). To meet the future demand resulting from
population growth, development of news high yielding
rice genotypes is therefore a necessity. Thus, to meet
this demand and attend rice self-sufficiency, plant
breeders have to develop high yielding cultivars with
desirable agronomic traits for diverse ecosystems.
The development of new genotypes requires some
knowledge about the genetic variability presents in the
germplasm of the crop to build efficient breeding
programme. 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). Therefore, selection for high yield requires
knowledge about genetic variability and good
understanding of correlation between yield and yield
components regarding to the genetic material that is on
hand. Genetic variability for agronomic traits is the key
component of breeding programme for broadening the
gene pool of rice (Dutta et al., 2013).
Heritability estimates provide authentic information
about a particular genetic attribute which will be
transmitted to the successive generations and constitute
an efficient guide for breeders in the choice of parents for
Konate et al. 3341
crop improvement programmes (Rafi and Nath, 2004).
However, heritability in broad sense alone may not be
helpful for selection based on phenotype, because it’s
influenced by environment. Thus, estimate heritability
along with genetic advance conjointly are reliable helpful
in predicting the gain under selection than heritability
alone (Ogunbayo et al., 2014). Moosavi et al. (2015)
reported that grain yield is a complex trait, quantitative in
nature and a combined function of a number of
constituent traits. Consequently, selection for yield may
not be satisfying without taking into consideration yield
component traits. Thus, positives correlated between
yield and yield components are requires for effective yield
component breeding increasing grain yield in rice
(Ogunbayo et al., 2014). So, it is important for plant
breeders to understand the degree of correlation between
yield and its components.
Therefore, the objective of the present study was to
assess and evaluate genetic variability of rice
recombinant inbred lines based on agro-morphological
traits and analyse the relationships between these traits.
MATERIALS AND METHODS
The experiment was conducted in the greenhouse of Africa rice
center research station in Cotonou (Benin republic) from March to
July 2013 and from February to June 2014. The site is located
between 6°25.415N and 2°19.684E at an altitude of 21 m above
sea level. Experiments were conducted in greenhouse using a
randomized complete block design with four replications. Individual
plant of each genotype was grown in 2 L pots containing natural
field soil. Management practices such as irrigation and fertilization
were performed by following the standard procedures (IRRI, 2002).
A total of 20 genotypes consisting of 17 F5 inbred lines from the
indica’s cross included their two parents and the indica variety APO
were used in this study. The inbred lines were obtained from the
indica’s cross IR64 X B6144F-MR-6-0-0.The parent, B6144F-MR-6-
0-0, a drought resistant landrace was crossed with the variety IR64,
a variety which possesses many agronomical superior traits. The
variety APO, a popular variety known for its long term adaptation in
drought prone ecosystem was used as a control.
Observations were recorded on one plant of each pot. Thus,
morphological and agronomical data were collected for 11
quantitative characters at appropriate growth stage of rice plant
following the standard evaluation system indicated by IRRI (IRRI,
2002). The characters that were evaluated included days to 50%
flowering (50%DF, day), plant height (PH, cm), leaf area (LA, cm2),
number of tillers (NT), number of panicles (NP), number of fertile
spikelets (NFS), leaf dry weight (LDW, g), roots weight (RW, g),
panicles weight (PW, g), stem weight (SW, g), 1000 grains weight
(1000GW, g), biomass (biom, g) and grain yield per plant
(yield/plant, g/plant).
The data recorded on 12 morphological and agronomical traits
from the genotypes used, were subjected to statistical analysis.
Analysis of variance (ANOVA) was carried out to access the
genotype effect and their interaction using R program package. The
correlation analysis was performed using the same software to
*Corresponding author. Email: kadougoudiou@gmail.com.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License

3342 Afr. J. Agric. Res.
Table 1. Mean square of combined analysis of variance for all the characters studies of 20 rice genotypes.
Source
df
Biom
D50F
PH
LA
NT
NP
NFS
LDW
RW
PW
SW
1000GW
Yield/plant
Rep
3
31.6
4.126
49.06
95.51
1.22
48.35
75.88
8.32
15.47
3.26
20.73
9.68
24.95
Geno
19
89.1**
337.24**
66.37**
2992.44**
6.20**
11.66**
234.93**
4.72**
31.20**
1.03**
8.89**
11.35**
80.94**
Year
1
278.44**
120.16**
1863.36**
14.5ns
46.71**
30.11**
1956.69**
27.23**
38.43**
3.48**
1.52ns
47.91**
222.34**
G x Y
19
6ns
2.23ns
4.80ns
333.7**
0.22ns
0.20ns
53.25ns
0.49ns
2.59ns
0.21ns
2.40ns
6.21**
14.44ns
Error
117
10.58
1.4
2.923
71.56
1.26
2.04
65.6
0.36
3.74
0.33
2.38
2.57
8.64
1000 GW: 1000 grains weight; D50F: day to 50% flowering; LA: leaf area; LDW: leaf dry weight; NP: number of panicle; NFS: number of fertile spikelets; NT: number of tiller; PH: plant height;
PW: panicle weight; RW: root weight; SW: stem weight; Yield/p: yield per plant; biom: biomass; ns: no significant; * : significant at 5% level probability; **: significant at 1% level probability.
determine the degree of correlation between yield and its
components. In order to assess and quantify the genetic
variability among the genotypes for the characters under
study the variance components and values of heritability
and genetic advance were estimated following the formula
given by Burton and De Vane (1953); and Johnson et al.
(1955) and applied by Tuhina-Khatun et al. (2015).
Phenotypic and genotypic variances were estimated
using the following formula:
Vg = (MSg – MSgxy) / ry
Vgxy = (MSgxy- MSe) / r
Ve = MSe
Vp = Vg + Vgxy / y + Ve / ry
Where, Vg = genotypic variance; Vgxy = genotype x year
variance; Ve = environment variance; Vp= phenotypic
variance; MSg = mean square of genotypes, MSgxy= mean
square of genotype x year; MSe= mean square of error, r =
number of replications and y = number of year.
Both genotypic and phenotypic coefficients of variability
were estimated using the formula below:
  

  

Where GCV = genotypic coefficient of variability; PCV =
Phenotypic coefficient of variability;  = genotypic
standard deviation;  = phenotypic standard deviation
and X= general mean of the character.
Heritability in broad sense (H2) was computed as the ratio
of genetic (Vg) variance to the total phenotypic variance
(Vp).

 
The genetic advance (GA) and genetic advance as per
cent of mean (GAM) were estimate using the formula given
below:
GA = H² k
  

Where H2= heritability in broad sense; k = Selection
differential which is equal to 2.06 at 5% intensity of
selection; X= general mean of the character.
RESULTS
Analysis of variance
The results of combined analysis of variance for
the all characters are shown in Table 1. Significant
effects of genotype were observed for all the
characters under study. High significant effect of
year for all the characters except the leaf area
(LA) and stem weight (SW) were shown from this
analysis. The genotype x year was highly
significant only for the characters leaf area (LA)
and 1000 grain weight (1000 GW). Environmental
conditions were not similar in two years, meaning
that climate changes were observed during the
study suggesting that the differences among
genotypes were not stable across years.
Estimate of genetic parameters
Estimates of genotypic (Vg) and phenotypic
variances (Vp), genotypic coefficient of variation
(GCV) and phenotypic coefficient of variation
(PCV), broad sense heritability (H2), genetic
advance (GA) and genetic advance as percentage
of the mean (GAM) are shown in Table 2.
High genotypic and phenotypic variances were
recorded with leaf area, 332.34 and 400.89,
respectively. High genotypic and phenotypic
variances were equally observed for 50%
flowering day (41.88 and 42.68) and the number
of fertile spikelets 22.71 and 53.97, respectively.
The low values of genotypic and phenotypic
variances were observed with the characters
panicle weight (0.10 and 0.26), stem weight (0.81
and 2.01) leaf dries weight (0.53 and 0.73), number

Konate et al. 3343
Table 2. Genotypic (Vp) and phenotypic variance (Vp), genotypic coefficient (GCV) and phenotypic coefficient of variance (PCV), broad
sense heritability (H2) , genetic advance (GA) and genetic advance as per cent of mean (GAM) for all the traits.
Characters
Min
Means
Max
Vg
Vp
GCV
PCV
H2 (%)
GA
GAM
Biomass (g)
39.79
50.34
62.85
10.39
15.11
6.40
7.72
68.77
5.51
10.94
Day to 50% flowering
81.60
92.67
118.30
41.88
42.68
6.98
7.05
98.11
13.20
14.25
Plant height (cm)
79.00
87.24
100.30
7.70
9.39
3.18
3.51
81.94
5.17
5.93
Leaf area (cm2)
175.20
213.60
264.70
332.34
400.89
8.53
9.37
82.90
34.19
16.01
Number of tillers
10.00
13.43
16.89
0.75
1.25
6.44
8.33
59.79
1.38
10.26
Number of panicles
6.50
10.29
15.07
1.43
2.23
11.63
14.50
64.40
1.98
19.23
Number of fertile spikelets
8.81
86.39
101.30
22.71
53.97
5.52
8.50
42.08
6.37
7.37
Leaf dry weight (g)
3.02
5.09
9.35
0.53
0.73
14.31
16.75
72.91
1.28
25.16
Roots weight (g)
3.05
9.44
16.86
3.58
5.30
20.03
24.39
67.43
3.20
33.87
Panicle weight (g)
1.17
2.17
8.73
0.10
0.26
14.78
23.30
40.25
0.42
19.32
Stem weight (g)
3.48
8.00
13.78
0.81
2.01
11.27
17.71
40.49
1.18
14.77
1000 grains weight (g)
12.85
23.49
29.28
0.64
2.39
3.41
6.58
26.95
0.86
3.65
Yield/plant (g/plant)
22.79
36.04
45.79
8.31
13.36
8.00
10.14
62.23
4.69
13.00
number of tiller (0.75 and 1.25) and 1000 grains weight
(0.64 and 2.39), respectively. In general, the phenotypic
variances were higher than genotypic variances for all the
characters.
Genotypic coefficients of variance (GCV) were ranged
from 3.18% for plant height to 20.03% for root weight,
whereas phenotypic coefficients of variance (PCV) were
ranged from 3.51% for plant height to 24.39% for root
weight. According to Sivasubramanian and Menon (1973)
PCV and GCV values more than 20% are regarded as
high, whereas values less than 10% are considered to be
low and values between 10 and 20% to be moderate.
Based on this delineation, GCV and PCV values were
low for biomass, day to 50% flowering, plant height and
1000 grains weight; medium for number of panicle, leaf
dry weight and stem weight; high for root weight. The
panicle weight and plant height were recorded with low
GCV and moderate PCV. In addition, PCV values were
higher than theirs corresponding GCV values for all the
characters considered. However, this difference was low
for all the characters except the panicle weight and stem
weight.
Heritability analyses estimate the relative contributions
of differences in genetic and non-genetic factors to the
total phenotypic variance in a population. It is an
important concept in quantitative genetics, particularly in
selective breeding. The heritability in broad sense (H2)
estimate varied from 26.95% to 98.11%, respectively for
1000 grains weight and day to 50% flowering. All the
characters studies had high heritability (>60%) except the
numbers of fertile spikelets, panicle weight, stem weight
and 1000 grains weight. This result indicates that these
characters could be easily improved by selection.
Genetic advance (GA) under selection refers to the
improvement of characters in genotypic value for the new
population compared with the base population under one
cycle of selection at a given selection intensity (Wolie et
al., 2013). The high value of GA was recorded with
leaves area (34.19) and the low (0.42) with panicle
weight. Estimates of GA for yield/plant was 4.69 g/plant
indicating that whenever we select the best, 5% high
yielding genotypes as parents, average grain yield/plant
of progenies could be improved by 4.69 g/plant.
Genetic advance as per cent mean expected (GAM)
had a general range between 3.65% for 1000 grains
weight and 33.87% for roots weight. Among the
characters high values of GAM (>20%) were recorded
only for roots weight and leaf dry weight (25.16%). It was
moderate (10 to 20%) for biomass, day to 50% flowering,
leaf area, number of tillers, number of panicle, panicle
weight, stem weight and yield/plant; low (<10%) for plant
height, number of fertile spikelets and 1000 grains
weight. Leaf dry weight and roots weight had high
heritability and high GAM, whereas biomass, day to 50%
flowering, leaf area, number of panicle and yield/plant
had high heritability but moderate GAM. Panicle weight
and stem weight had both moderates heritability and
GAM.
Correlation
The degree of correlation between the traits is important
in plant breeding. It can be used as tool for indirect
selection. Correlation studies help the plant breeder
during selection and provide the understanding of yield
components. The results of correlation analysis showed
in Table 3 reveals that there was positive and highly
significant correlation between day to 50% flowering
(50%DF) with leaf dry weight (r=0.6223), number of
panicle (r=0.7091) and stem weight (r=0.5566). The
characters which had positive relationship with grain
yield/ plant were day to 50% flowering (r=0.3997), leaf
area (r = 0.0382), leaf dry weight (r=0.101), number of

3344 Afr. J. Agric. Res.
Table 3. Correlation coefficients among agronomical and morphological characters in twenty recombinants inbreed rice lines.
Correlation
100GW
D50F
LA
LDW
NP
NFS
NT
PH
PW
RW
SW
Yield/p
100GW
-
D50F
-0.0842ns
-
LA
0.119ns
-0.3095ns
-
LDW
0.2171ns
0.6223**
-0.08ns
-
NP
-0.2698ns
0.7091**
-0.5265*
0.4235ns
-
NFS
0.3861ns
-0.2657ns
-0.1247ns
0.1743ns
-0.1863ns
-
NT
-0.0061ns
0.4241ns
-0.0837ns
0.1964ns
0.5345*
-0.3501ns
-
PH
0.0526ns
0.4329ns
-0.1598ns
0.0653ns
0.2572ns
-0.4627*
0.1598ns
-
PW
-0.207ns
0.3297ns
-0.4161ns
0.039ns
0.5134*
-0.3552ns
0.1554ns
0.2856ns
-
RW
0.1602ns
-0.0833ns
0.0456ns
0.1133ns
0.0952ns
-0.0023ns
0.0906ns
-0.064ns
0.2736ns
-
SW
0.037ns
0.5566*
0.0116ns
0.4164ns
0.2989ns
-0.3386ns
0.1536ns
0.255ns
0.1883ns
0.1161ns
-
Yield/p
-0.0241ns
0.3997ns
0.0382ns
0.101ns
0.1286ns
-0.3007ns
-0.1425ns
0.2794ns
0.112ns
-0.1489ns
0.5262*
-
Biom
-0.0366ns
0.3658ns
0.1106ns
0.0596ns
0.1783ns
-0.3813ns
-0.1343ns
0.2902ns
0.1895ns
-0.0332ns
0.5556*
0.9291**
1000GW: 1000 grains weight; D50F: day to 50% flowering; LA: leaf area; LDW: leaf dry weight; NP: number of panicle; NFS: number of fertile spikelets; NT: number of tiller; PH: plant height; PW:
panicle weight; RW: root weight; SW: stem weight; Yield/p: yield per plant; biom: biomass; ns: no significant; * : significant at 5% level probability; **: significant at 1% level probability.
panicle (0.1286), plant height (r=0.2794), panicle
weight (r=0.112), stem weight (r=0.5262) and
biomass (r=0.9291). Among these characters only
stem weight and biomass showed significant
positive correlation with grain yield per plant. On
the contrary 1000 grains weight (r=-0.024), number
of fertile spikelets (r=-0.3007), number of tiller (r=-
0.1425) and root weight (r=-0.1489) were
inversely but no significant correlated with grain
yield per plant. Plant height had positive
correlation with all the characters except leaves
area, number of fertile spikelets and root weight.
In the same time, number of fertile spikelets had
negative correlation with almost the characters
except 1000 grains weight and leaf dry weight.
Positive and significant correlation were shown for
biomass with root weigh (r=0.5556) and for
number of panicle with number of tillers and
panicle weight (r=0.5345 and r=0.5134,
respectively). However, plant height and number
of panicle showed significant negative correlation
with number of fertile spikelets (r=-0.4627) and
leaf (r=0.5265), respectively.
DISCUSSION
Genetic diversity in breeding is very important. It
is the key of crop improvement. More variability is
observed in the basic population more is the
chance of improvement. In the present study,
results from ANOVA showed highly significant
difference among the genotypes for all the
characters, indicating huge genetic variability
existing among the genotypes. So, the parents
used for crossing were genetically different.
Significant year effects were observed for all the
characters except leaf area and stem weight,
meaning that climate change were observed
during the study. Environmental conditions were
not similar during the two years. The results are in
accordance to those found by Ogunbayo et al.,
(2014). All the characters, except leaf area and
1000 grains weight, exhibited stability across the
seasons since the significance of genotype × year
interaction was not detected and the differences
among genotypes were clear. This appears to
show that further improvement through selection
for these characters may be effective. On the
other hand, the significant effect of genotype x
year interaction for leaf area and 1000 grains
weight, indicating that genotypic difference in
these characters was greatly influenced by the
environment.
The current study suggests that phenotypic
variance (Vp) and phenotypic coefficient variance
(PCV) were higher than theirs corresponding
genotypic variance (Vg) and genotypic coefficient
of variance (GCV) respectively for all the charac-
ters studies, indicating that the expression of these

characters was influenced by environment. Similar results
were reported by Dutta et al., (2013); Singh et al., (2014)
and Tuhina-Khatun et al., (2015) in rice. It is interesting to
note that this difference was low for all the characters
except panicle weight and stem weight, indicating that
these characters were less influenced by environment. It
also suggests that selection based on these characters
would be effective for future crossing. Similar result was
also found by Prajapati et al., (2011) and Singh et al.,
(2014) for these traits. However the other traits like
panicle weight and stem weight which showed a higher
difference between PCV and GCV indicated that
environmental effect on the expression of those traits is
higher.
The most important function of the heritability in the
genetic study of quantitative characters is its predictive
role to indicate the reliability of the phenotypic value as a
guide to breeding value (Falconer and Mackay, 1996; Al-
Tabbal et al., 2012). High heritability estimates for
yield/plant, plant height, day to 50% flowering, biomass
and number of tillers, indicated a high response to
selection in these traits. Similar results were also
reported by Al-Tabbal et al., 2012; Dutta et al., 2013;
Rafii et al., 2014, which support the present findings.
Heritability in broad sense and the genetic advance are
also important selection parameters. It is more useful as
a selection tool when considered jointly with heritability.
The estimates of genetic advance can help to understand
the type of gene action of various polygenic characters.
Johnson et al. (1955) suggested that high heritability
estimates along with the high genetic advance is more
helpful in predicting gain under selection than heritability
estimates alone. Thus, the heritability estimates will be
reliable if accompanied by high genetic advance. The
present study revealed high heritability accompanied with
high genetic advance as percent of the mean for leaf dry
weight and root weight; high heritability and moderate
genetic advance as percent of the mean for biomass, day
to 50% flowering, leaf area, number of panicle and
yield/plant. These results could be explained by additive
gene action and their selection may be done in early
generations. Similar findings have been reported by
Wolie et al. (2013), Ogunbayo et al. (2014) on rice and
Reza et al. (2015) on wheat. However, panicle weight
and stem weight had moderate heritability coupled with
low to moderate genetic advance as percent of the mean
indicates non-additive gene effects; suggesting that these
characters could be improved by developing varieties
through recurrent selection method (Ogunbayo et al.,
2014).
Selection of traits contributing simultaneously to a
character will improve it in subsequent segregation
population (Nor et al., 2013). Hence, the correlation
analysis is therefore necessary to determine the direction
of selection and the numbers of characteristics need to
be considered in improving any character such as grain
yield. The present study showed that there was a highly
Konate et al. 3345
significant correlation between grain yield per plant with
biomass at the 1% level and stem weight at the 5% level
indicating that simultaneous selection for these
characters would result in improvement of yield. Similar
findings were earlier reported by Gulzar and Subhasl
(2012), Azarpour (2013) and Moosavi et al. (2015). Also,
day to 50% flowering exhibits a significant positive
correlation with leaf area, number of panicle and stem
weight. The observed positive correlation of date to 50%
flowering was supported by earlier researchers such as
Zhou et al. (2010) and Khan et al. (2014) for number of
panicle. Plant height has not significant correlated with
yield per plant. This result is in accordance with those of
Golam et al. (2011) and Nor et al. (2013). However, this
is in contrast with the previous study of Khan et al. (2014)
and Moosavi et al. (2015) that presented the negative
correlation between plant height and yield per plant.
Positive and significant correlation was shown between
plant height and number of fertile spikelets. Similar
results were early reported by Aris et al. (2010) and
Kohnaki et al. (2013). Azarpour (2013) also reported that
plant height in rice had significant and positive correlated
with spikelet fertility per plant.
Conclusion
The present study highlighted the existence of diversity
among the 17 rice recombinant inbred lines, their parents
and the check variety. High heritability in broad sense
recorded for biomass, day to 50% flowering, plant height,
leaf area, number of panicles, leaf dry weight, roots
weight and grain yield per plant demonstrates that these
characters could be successfully transferred to offsprings
if their selection is performed in hybridization programme.
The correlation analysis revealed that 8 agronomical
traits such as day to 50% flowering, leaf area, leaf dry
weight, number of panicle, plant height, panicle weight,
stem weight and biomass have the positive contribution
to grain yield. Among these characters, biomass and
stem weight showed significant correlation with grain
yield. So, these two traits may be considered as the
selection criteria for the improvement of grain yield in
rice.
Conflict of Interests
The authors have not declared any conflict of interest.
ACKNOWLEDGMENTS
The authors are thankful to all the team of INERA in
Burkina Faso, Africa Rice center in Benin, and CIRAD
Montpellier in France. Financial support from General
Challenge Program (GCP) is dully acknowledged.

3346 Afr. J. Agric. Res.
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