INTRODUCTION
Rice is one of the most important cereal
crops and provides the staple food for about half of the worlds population (Moosavi et al., 2015). Genetic enhancement is one of the
important tools to improve the productivity. (Babu et al., 2012). Knowledge on the nature and
magnitude of the genetic variation governing the inheritance of quantitative
character like yield and its components is essential for effecting genetic
improvement. (Rakesh et al., 2015).
A critical analysis of the genetic variability parameters, namely, genotypic
coefficient of variability (GCV), phenotypic coefficient of variability (PCV),
heritability and genetic advance for different traits of economic importance is
a major pre-requisite for any plant breeder to work with crop improvement programmes. (Kishore et al., 2015).
Genetic variability for agronomic traits is the key component of breeding
programs for broadening the gene pool of rice and other crops. The genetic
coefficient of variation together with heritability estimate would give the
best picture of the amount of advance to be expected from selection. The amount
of genetic advance under selection depends mainly on the amount of genetic
variability. (Immanuel et al., 2011).
Development of high
yielding varieties requires the knowledge of existing genetic variability.
Hence, rice breeders are interested in developing varieties with improved yield
and other desirable agronomic characters. (Idris and Mohamed, 2013). Most agronomically
significant characters are inherited quantitatively and are known to be
affected by environmental factors. Selection based on the phenotype would be
difficult for such difficult traits. In breeding programs, it is often
difficult to manipulate such traits, since several inter-componential characters indirectly control them. (Immanuel
et al., 2011).
Genetic variability among traits is important
for breeding and in selecting desirable types. The low heritability of grain
characters made selection for high yielding varieties possible usually using
various components traits associated with yield. (Mulugeta et al., 2012). High magnitude of
variability in a population provides the opportunity for selection to evolve a
variety having desirable characters. (Bornare
et al., 2014). Therefore, the objectives of this study is to determine
the genetic variation and diversity analysis of two hundred and twenty nine
rice genotypes and an improved Nigerian variety (FARO44) were evaluated based
on agronomic traits.
MATERIALS
AND METHODS
The experiment was conducted at experimental
field of African Rice Centre at International Institute of Tropical Agriculture
(IITA), Old Oyo road, Ibadan, Oyo state, Nigeria. During 2020
cropping season. IITA is located at longitude 7Ί308N, latitude
3Ί5437E and at elevation 243m above sea level (Ariyo et al., 2018). The experimental materials of
this study consisted of two
hundred and thirty-nine (239) accessions of rice anther culture derived from
South Korea, of O. sativa L. and one improved and adapted Nigeria rice
variety (FARO 44) collected from African Rice Center, IITA used as check. The
experiment was established in alpha lattice design (rows Χ column) with four
columns and planted sixty entries in each column (4Χ60) with two replications.
The seedlings were
raised in the wet prepared nursery bed and then transplanted to the field after
25 days under irrigated system. A single row with size of 0.2Χ3 m was used as
plot. A single seedling was transplanted per hill at the spacing of 20Χ20 cm
between rows and between hills. The chemical fertilizer NPK (15:15:15) was
applied as a basal application of 200 kg/ ha (N2, P2O5 and K2O). Urea was applied at the rate of 65 kg/ha at tillering stage and the second rate of 35 kg/ha was applied
at the beginning of panicle initiative (booting) stage. The weeds were controlled twice by using selective herbicide Vespanil Plus (250 ml/ 20 liters of water) at early stage
of crop development and before flowering.
Data was collected at
appropriate stage of the crop development. The agronomic characters were
measured by randomly selected plants from each experimental unit (row). The
Standard Evaluation System (SES) by International Rice Research Institute
(IRRI) for Rice reference manual IRRI, 2002 was used for all trait measurements
except where stated otherwise. The data collected were plant height (cm),
number of tillers per plant, days to 50% flowering, flag leaf length (cm),
panicle fertility (%), productive tillers per plant, panicle length (cm), panicle
weight (g), biomass (g), number of grains per panicle, 1000 grain weight (g),
grain yield per plant (g) and grain yield per plot (kg/ha). Leaf area (
was measured manually following the formula:
LA=WΧLΧK
Where:
LA≡ Leaf Area, W ≡ Leaf Width, L ≡ Leaf length and K ≡
Constant = 0.75
Leaf area index (
) was
calculated as described (Efisue and
Dike,
2020) as follow:
LAI = LA / area covered by the plant
Where: LAI ≡ Leaf Area Index, LA ≡
Leaf Area
Genetic parameters were estimated to identify
genetic variation among accessions and to determine genetic and environmental effects
on various characters. These genetic parameters were calculated as described
(Burton and Devane, 1953 and Johnson et al., 1955).
Genotypic variance: 
= MSG-MSE/r
Where MSG is the mean square of genotypes,
MSE is mean square of error, and r is number of replications
Phenotypic variance:
= Χ 
Where 
is
the genotypic variance and 
is the mean squares of error.
Genotypic coefficient of variance (GCV) and
phenotypic coefficient of variance (PCV):
GCV (%) = √
/
Χ 100
PCV (%) = √
/ Χ 100
Where 
= Genotypic variance, 
= Phenotypic variance, = grand mean of the trait.
GCV and PCV values were categorized as low
(0-10%), moderate (10-20%) and high (20% and above).
Heritability (Broad sense) was computed as
described (Amegan et al.,
2020).
= 
Χ 100
Where is the genotypic variance and is the phenotypic variance
The Heritability was categorized as low
(0-30%), moderate (30-60%) and high (60% and above) as described by Robinson et
al, (1949).
Expected
genetic advance (GA):
Expected genetic advance (GA) was computed
according to (Allard, 1960) cited by (Saeed et
al., 2018) as:
GA = 
Χ 
Χ k
Where, GA = Expected genetic advance,
= Phenotypic standard deviation, = Heritability in broad sense
and k =
the standardize selection differential at 5% selection intensity (K = 2.063).
Principal component analysis was computed by
using the principal component procedure of multivariate technique in
Statistical Analysis System SAS, version 9.4 (2018) for analyzing data for all
the characters. Cluster analysis was carried out to clustering the genotypes
into different groups using R software version 3.9.6.
RESULTS AND DISCUSSION
Genetic Variability parameters
High
variability in the initial breeding material ensures better chances of
producing desired crop plant. Genotypic coefficient of variation (GCV) measures
the variability of any trait. The extent of the environmental influence on any
trait is indicated by the magnitude of the differences between the genotypic
and phenotypic coefficients of variation. Large differences reflect high
environmental influence as reveal by high phenotypic influence, while small
differences reveal high genetic influence (Idris
and Mohamed, 2013). In this study phenotypic
coefficients of variance were higher than the corresponding genotypic
coefficients of variance for all the studied traits (Table 1). This indicates
the presence of environmental influence to some degree in the phenotypic
expression of the characters (Iftekharuddeula et
al., 2001 and Idris et al., 2012) observed
similar results. Likewise (Singh et al., 2019, Sameera et al., 2016, Rakesh et al., 2015 and Mulugeta et al., 2012).
The differences were observed between genotypic and phenotypic
coefficients of variance for days to 50% flowering, plant height, panicle
length, 1000 grain weight and fertility (Table 1). This indicates that
genotypic influence had played an important role rather than phenotypic
influence indicating less influence of environment on these traits. Therefore,
selection on the basis of phenotype alone can be effective for the improvement
of these traits (Idris and Mohamed, 2013). High
differences between genotypic and phenotypic coefficients of variance were
observed for biomass, grain yield, grain yield per
plant, number of grains per panicle, panicle weight, and effective tillers.
Therefore, indicating that phenotypic influence assert more on the existence of
environment influence on these traits.
The
characters studied in the present investigation exhibited high, moderate to low
PCV and GCV values. The high PCV (>20%) and GCV values were recorded for
grain yield, grain yield per plant, biomass, number of tillers, panicle weight,
effective tillers, leaf area, leaf area index and number of grains per panicle
(Table 1). The result in conformity with the reports of (Kishore et al., 2015). for
grain yield; (Prasad et al., 2013) for number of tillers per plant; (Sameera et al., 2016) for
number of effective tillers and number of grains per panicle; (Hasib et al., 2004) for grain yield per plant; (Bekele et al., 2013) for effective tillers per
plant; (Bornare et al., 2014) for effective tillers and grain yield per
plant; (Singh et al., 2019)
also recorded similar observation for grain yield per plant and number of
tillers. Hence, selection on the basis of these phenotypic characters in these
genotypes can also be effective for improvement of grain yield. However,
moderate (10-20%) genotypic and phenotypic coefficients of variance were
recorded in the present study for plant height (PH), days to 50% flowering
(D50%F), panicle length (PL) and 1000 grain weight (TGW) (Table 1). These
results corroborate the earlier reports (Singh et al., 2019) for days to 50% flowering, panicle weight
and 1000 grain weight and for days to 50% flowering (Bornane
et al., 2014 and Kishore et al., 2015). In contrast, low (<10%) estimates of
genotypic and phenotypic coefficients of variation were observed in fertility
(FER).
Heritability
The heritability estimates act as predictive instrument in expressing
the reliability of phenotypic value. Therefore, good knowledge of transmission
of a particular trait assists the plant breeders in predicting the behavior of
succeeding generations because high heritability of a trait simplifies the
selection procedure (Khaliq et al., 2009). In this
study, heritability in broad sense was calculated for all characters under
study and is presented in (table 1). Heritability was classified as low
(<30%), medium (30-60%) and high (>60%). High heritability was recorded
for days to 50% flowering (80.07), plant height (79.65), 1000 grain weight
(75.44), panicle length (74.93) and number of tillers (62.38). These results
are in conformity with the reports of (Idris et al.,
2013) for 1000 grain weight and plant height; (Bisne
et al., 2009) for number of tillers; (Dhanwani et
al., 2013) for days to 50% flowering. Similar results were also quoted by (Rakesh et al., 2015), (Subbaiah et al.,
2011) and Ananadarao et al., 2011). Low
heritability value was recorded for fertility (20.24). High heritability values
indicate that the characters under study are less influenced by environment in
their expression, therefore, the selection may base on the phenotypic
expression of these characters in the individual plant by adopting simple
selection methods. High heritability indicates the scope of genetic improvement
of these characters through selection. The low heritability recorded for
fertility indicate that direct selection for this
trait will be ineffective.
Genetic
advance
The
genetic advance is a useful indicator of the progress that can be expected as
result of exercising selection on the pertinent population. Genetic advance I (Table 1) was highest for grain yield . This result is in conformity with the findings of (Kishore et al., 2015, Dhanwani et al., 2013 and Bornare et al., 2014). This suggest that this
character is predominantly controlled by additive gene action. Hence genetic
improvement through selection for this trait may be effective
(Singh et al., 2019). The
lowest genetic advance was noticed for leaf area index (3.188).
The
information on genetic variation, heritability and genetic advance helps to
predict the genetic gain that could be obtained in later generations, if
selection is made for improving the particular trait under study. Selection for the traits having high heritability
coupled with high genetic advance is likely to accumulate more additive genes
leading to further improvement of their performance (Panse
and Suhatme, 1957) cited by (Rakesh et al., 2015).
High heritability along with high genetic advance was noticed for traits days
to 50% flowering and plant height (Table 1) this was also reported by (Seyoum et al., 2012 and Rakesh et al., 2015). Characters which showed high heritability coupled with
moderate or low genetic advance such as 1000 grain weight, panicle length and
number of tillers can be improved by intermating
superior genotypes of segregating population developed from combination
breeding (Samadhia, 2005), (Babu et al., 2012).
Cluster
Analysis of Rice Genotypes Evaluated
Cluster
analysis of some agronomic traits and yield was conducted and dendrogram was constructed using rice genotypes values
presented in the (Figure 1). Four major groups (A, B, C, and D) were observed
among 240 rice genotypes including an improved variety (FARO44). Cluster analysis provide a useful means for estimating morphological
diversity within and between genotypes evaluated. Its useful
tool to detect the potential of breeding value. Cluster A consisted of
73 genotypes, Cluster B had least genotypes (20) of the group with two sub
groups; B1 contain three genotypes (UPN_643, UPN_611 and UPN_639) and B2
comprise 17 genotypes. Cluster C consisted 35 genotypes.
Cluster D recorded 112 genotypes which is the biggest and the check (FARO44)
was clustered in this group (Figure 1) and FARO 44 contains fortified iron (Ikuli et al., 2017) that showed more genetic relatedness
among the genotypes as compared to any other cluster. Cluster
analysis showed the genetic
variation among 240 genotypes
indicating wide diversity of genetic material. Hence, crossing between two
groups could give
good result and cluster also assist breeders in making good choice in selection
of parents in crop improvement. However, breeders need to evaluate genotypes
accurately in each grouping before use in rice breeding programme
(Maji et al., 2012). For the
selection of parents, genetic diversity is one of the important decisive
factors that will enhance crop improvement (Tuhina-khatun et al., 2015).
Principal
Components Analysis
The result
of principal component analysis explained the genetic diversity of the rice
accessions for all traits under study. The PCA with eigenvalues >1 and which
explained 20% of the variation were considered. Out of fifteen only three
principal components exhibited more than one eigenvalue, and showed 64.78% of
total cumulative variability among the traits studied (Table 2). It also revealed
that the first principal component accounted for 37.17% of total variance and
traits such as plant height (0.32), panicle weight (0.32), panicle length
(0.31), biomass (0.31) and grain yield per plant (0.30) are the most positively
contributed. The second
component accounted for 20.39% of the total variance with most positively
contribution variables such as effective tillers (0.37), number of tillers per
plant (0.31) and grain yield (0.30). The third principal component accounted
for 7.22% and the variables contributing most positively were number of tillers
(0.55), biomass (0.39) and effective tillers (0.32). These results conformed
presence of strong differences among genotypes. Through the PCA, the number of
characters, which are responsible for the observed variation within a group could be identified. The principal component with
higher eigenvalues and variables which had high loading were considered as best
representative of system attributes (Pachauri et
al., 2017), which
may be considered in the utilization of these traits for crop improvement.
These result in agreement with the findings of (Ayenew et al., 2020)
and (Worede et al., 2014) explained 73.5% of the
total variability and (Singh et al., 2013)
explained 62.72% of total variation in rice using the first three principal
components.
Agronomic
parameters projection in the PCA plots showed the phenotypic variation among
the populations this indicated how dispersed along each principal component
(Figure 2). The genotypes UPN540 and UPN632 on the top right part of the plot
showed the highest performance in either axis of principal component 1 and 2. While the genotypes UPN643 is the lowest
performance in either axis of the principal component 1 (Figure 2). The first
three principal components accounted for 64% of the total variation, which
indicated a very strong correlation among the characters being studied.
Accordingly, in the first PCA, the plant height, panicle length, biomass,
panicle weight, and grain yield per plant were important in separating the
genotypes due to their high loadings. Similarly, (Tuhina-khatun et al., 2015 and Worede et al.,
2014) explained 61.2% of the total variability using the first and second
principal components.
CONCLUSION
Results of
variability indicated that there is adequate genetic variability among the genotypes
studied. Characters such as grain yield per plant, biomass and number of
tillers showed high genotypic and phenotypic coefficient variance whereas days
to 50% flowering, plant height, panicle length and 1000 grain weight showed
high heritability likewise number of grains per panicle, biomass, leaf area,
days to 50% flowering and plant height showed high genetic advance suggesting a
scope for improvement of grain yield through selection. Dendrogram
cluster analysis based on quantitative traits classified 240 rice genotypes
including an improved variety (FARO44) into four groups (A, B, C and D) which
detected the divergences among genotypes. The principal component analysis
revealed that the first three components (PC1, PC2 and PC3) accounted for 64.78%
of the total cumulative variability among the traits studied confirmed the
presence of ample genetic diversity for use in improvement.
Acknowledgement
Authors wish to express their gratitude to KAFAC of RDA Korea for
providing the genetic materials (anther culture
derived) used for this study under the project KAR20190112. Gratitude
to Pan African University, Life and Earth Science Institute (PAULESI) for
granting Salim Hassan Kafi scholarship
for this study.
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