Genetic Diversity and Selection in Blast Resistant Rice

Genetic Diversity and Selection Criteria in Blast Resistance Rice (Oryza sativa) Genotypes/Lines under Tropical Environments
Genetic Diversity and Selection Criteria in Blast Resistance
Rice (Oryza sativa) Genotypes/Lines under Tropical
Environments
*Hamisu Almu1,6, Zulkefly Sulaiman2,3, Mohd R. Yusop1,3, Mohd R. Ismail1,3, Abdul R. Harun4,
Asfaliza Ramli5 and Uzaifa A. Karfi6
1Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
2Laboratory of Plantation and Technology. Institute of Plantation Studies, Universiti Putra Malaysia, Serdang, Selangor,
Malaysia
3Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
4Agrotechnology and Bioscience Division, Malaysian Nuclear Agency, Kajang, Selangor, Malaysia
5Paddy and Rice Research Centre, Malaysian Agricultural Research and Development Institute, Serdang, Selangor,
Malaysia
6Department of Soil Science, Faculty of Agriculture and Agricultural Technology, Kano State University of Science and
Technology, Wudil, Kano State, Nigeria
Genetic diversity has been a critical step in knowing the different growth traits for selection and
varietal improvement of rice. The present study aimed to estimate the phenotypic and genotypic
correlation coefficients among the growth traits and to work out how to select between traits.
Three field experiments were carried out in Malaysia during the cropping season of 2016/2018.
Sixteen advanced blast resistant rice lines were studied in order to find out the genetic diversity
in some quantitative characters and to find out the relationship between yield and yield related
components by using the multivariate analysis. The field trials were conducted in a split-plot
design replicated three times in a plot of 35 × 28.5 m2
. The planting distant was 25 × 25 cm spacing
and the plot size was 2 × 1.5 m2
unit for genotype in each replication. There was a significant
difference among the characters, most of the genotypes responded significantly. The high
phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), heritability,
relative distance and genetic advance indicated that different traits especially tonnes per hectare
(Tha), grain weight per plot (GWTPP) and kilogram per plot (kgplot) significantly influenced the
yield trait. In addition, the genotypes were grouped into 9 major clusters based on the assessed
characters by using the UPGMA dendrogram. Group 1 with Group VII could be hybridized in order
to attain higher heterosis or the best between the genotypes, which becomes helpful in
developing a good selection in rice.
Keywords: genetic diversity, multivariate analysis, phenotypic variance, genotypic variance, cluster analysis, rice (Oryza
sativa)
INTRODUCTION
Rice (Oryza sativa L) happens to be the most important
food and staple grain for more than three and a half billion
people living in the rural and urban areas of the world (Wu
et al., 2013). Rice is cultivated on more than 163.24 million
hectares, and annual world production is around 499.31
*Corresponding Author: Hamisu Almu; Institute of
Tropical Agriculture and Food Security, Universiti Putra
Malaysia, Serdang, Selangor, Malaysia.
Email: hamisualmu@gmail.com
International Journal of Plant Breeding and Crop Science
Vol. 7(1), pp. 689-699, June, 2020. © www.premierpublishers.org, ISSN: 2167-0449
Research Article

Almu et al. 690
million metric tonnes (USDA, 2019). In order to increases
grain yield, yield component traits would have to be
heritable in their genetic variability so that they would be
heritable and positively correlated with grain yield (Hasan
et al., 2013). In the breeding program, it is difficult to
achieve genetic gain in yield in rice potential crops, hence
breeders in their studies, applied indirect selection for yield
based on plant traits (Yaun et al., 2011). Many factors
controlled the existence of grain yield such as polygene,
environment and genetic variability (Singh et al., 2000).
The complexity of the grain yield and its relationship with
other yield components, selection to increase the amount
yield in grain other yield component traits should be
considered not only in the yield. A combination of several
estimates like genotypic and phenotypic coefficients of
variation and genetic advances would bring a sound mean
value between genotypes (Wani and Khan, 2006).
The growth performance of plants, in other words the
phenotypic properties, come up as a result of the
interaction of genetic structure and environmental
conditions (Hrivnák et al., 2017; Turkyilmaz et al.,
2018a,b,c), and it is known that each genetic structure may
react differently to the same environmental conditions
(Yucedag et al., 2019; Cetin et al., 2018a,b)
Rice varieties MR219, MR263 and MR269 were
developed by the Malaysian Agricultural Research and
Development Institute (MARDI) and officially released for
commercial production in Malaysia. Characteristics of the
formation of the panicle were the emphasis of their
choices. For example, variety MR219, are found to be
mainly of the grain size and number of grains per panicle.
A single grain of MR219 can weigh as much as 29-30 mg,
and the number of grains can be as high as 200-210.
These are found to be higher than the local varieties. There
are various characteristic value of these varieties than the
local varieties which include short life cycle (105-111
days), long fairly with strong culms, and its resistance to
blast diseases and bacterial blight. These allow it to be
marketed as a long grain variety extensively. Also, a
cooked variety of MR219 has a soft texture (amylose
content of about 21.4%). Therefore, a preference to most
local consumers. The rice variety becomes the most
cultivated in Malaysia covering about 90% the of cultivated
area (Bashar et al., 2014).
Conservation of genetic diversity in species requires the
proper performance of conservation superiorities and
sustainable handling plans that should be based on
universal information on population structures, including
genetic diversity resources among and between breeds
(Ruzina et al., 2010; Shamsalddini et al., 2016). Genetic
diversity is an essential element for genetic improvement,
preserving populations, evolution and adapting to variable
environmental situations (Mousavizadeh et al., 2009;
Vajed Ebrahimi et al., 2017; Mostafavi et al., 2020).
A large amount of well-conserved genetically diverse
material was recognised in rice along with genetic
polymorphism. The breeders are interested in evaluating
genetic diversity based on morphological traits. These
traits can be inherited without either specific biochemical
or molecular techniques. Nowadays, classification for plant
in science was based on morphological traits (Kumar,
1999, Din et al., 2010). The rice plant (Oryza sativa) shows
great morphological variation, especially in growth
characteristic, yield and yield components. Recognition of
the relationship between grain yield and yield components
are essential in order to derive an efficient selection for the
improvement in characters that are good for economic
development (Kumar et al., 2014).
Appearance (phenotypic) traits can be more effective in
crop growth and genotypic variations, pointing out the
existence of significant genetic variances for different traits
and showing no influence to the environment (Dhanwani
et al., 2013). There is a yield of constituent trait which does
not occur freely, they become correlated with each other
in order to bring the yield. A plant breeder with knowledge
of heritability assist him to make appropriate selection and
a magnitude of improvement in genetics allows for proper
selection. The aim of the study is to select a potentials
genotypes and to identify breeding programmes by
exploiting the genetics advancement in different traits and
other related attributes of sixteen rice genotypes.
MATERIALS AND METHODS
Husbandry of the plant: The plant materials used for this
experiment were 13 advanced blast rice resistant lines
which were obtained from the crossing between MR219 ×
Pongsu Seribu1.Three check varieties were used MR219,
MR263 and MR269 (Table 1). These three varieties
served as reference lines for validating the existence of a
significant varieties of rice known for high yield, fine and
long grain quality. The field trials were done in three
location where the cropping seasons are two times (six
environment, combination, season and location) pertaining
to Malaysia environment viz: Tanjung Karang, Malaysian
Agriculture Research and Development Institute (MARDI),
Penang, Seberang Perai, (MARDI) and Kedah (Kota
Sarang Semut) Malaysia Agricultural Development
Authority (MADA). This was shown as an example in
Appendix 1. The three locations were described and
characterized into two seasons of the research areas as
shown in Table 2. The locations are the best places where
rice is grown in Malaysia. Split-plot design was used
having three replications in each environment. The plot
size was 35 by 24 m2, having each environment with each
replication a subplot of the size of 2 by 1.5 m2 unit. The
establishment of rice plant was done at the optimum date
for each location in accordance with the farmer’s schedule.

Int. J. Plant Breed. Crop Sci. 691
An irrigation was carried out throughout the experiments
with average of 10 cm above sea level. There was a
regular hand weeding in order to free the plants from
interspecific competition. The 16 rice genotypes were
grown on five (5) different nitrogen fertilizer rates at N @
60kg ha-1; 80kg ha-1; 100kg ha-1; 120kg ha-1 (Control) and
140 kg ha-1, and were applied by following the
recommendation by MARDI in form of Urea and
Compound fertilizer. There were applied in triplicate
starting on day 15, 55, 75 after transplanting. Phosphorous
(triple superphosphate) was applied on the 15 days at the
rate of 57kg ha-1, Potassium (Muriate of potash) at 42kg
per hectare. Melathion and Hopper Gun insecticides were
used at the recommended rate to take care of the insect
pests.
Table 1: Rice genotypes used
Code number of
the genotype
Name of the accession
G1 P 411
G2 P 416
G3 P 4150
G4 P 4742
G5 MR 269 (Checked varieties)
G6 P 4129
G7 P 4619
G8 P 4114
G9 P 4137
G10 P 4131
G11 P 4716
G12 P 4712
G13 P 4159
G16 P 4170
Table 2: Three locations in two seasons (descriptive and characterized environments)
Code Location Season Location Altitude Average Temperature Average
humidity
Rainfall
(mean)Min-Max
TK1 Tanjung Karang Main 30 25’0N 101010’E 3m 230c-310C 83% 782.4(195.6)
TK2 Tanjung Karang Off 30 25’0N 101010’E 3m 250C-370C 65% 482.7(120.7)
MADA1 Kedah Main 5059’N1000 24’E 18m 220C-330C 91% 550.6(137.7)
MADA2 Kedah Off 5059’N1000 24’E 18m 250C-380C 63% 486.9(121.7)
PP1 Penang Main 05025’N 100015’E 3m 220C-300C 88% 934.7(233.7)
PP2 Penang Off 05025’N 100015’E 3m 240C-370C 71% 766.6(191.7)
Data collection: Five rice stands/plot/genotypes were
sampled and recorded per each replicate for all the
characters measured. These observations include plant
height, tillers per hill, panicles per hill, panicle length, filled
grains per panicle, unfilled grains per panicle, length of
grain, width of grain, 1000 weight of grains, kilogram per
plot, and grain weight per hill. The amount of yield was
estimated from the threshed weight of grains in 2×1.5 m2
tonnes per hectare (ton/ha) without the border rows.
(Table 3).
Variance components: Estimation of the difference
variance were determined among the genotypes to assess
the genetic and environmental consequence on different
traits. Estimation of variance component was performed as
follows:
Genotypic Variance: σ2
g =
MSG− MSGE
𝑟
(1)
Where MSG = mean square of genotype, MSGE = mean
square of genotype by environment, r = number of
replications and σ2g = genotypic variance.
Genotypic by environment variance (G×E):
σ2
ge =
MSG − MSE
𝑟
(2)
r
Where mean square (MSGE) = G×E, mean square of error
(MSE), r = number of replications.
Error Variance: σ2
e = MSE
Where MSE = mean square of error.
Phenotypic Variance: σ2
p = σ2
g + σ2
ge + σ2
e (3)
σ2
p = genotypic variance,
σ2
g = genotypic variance,
σ2
ge = G×E variance and
σ2
e = mean squares due to the experimental error
Phenotypic coefficient of variation (PCV):
PCV (%) =
√σ2
p
𝑥̅
𝑥 100 (4)
Genotypic coefficient of variation (GCV)
GCV% =
√σ2
g
𝑥̅
𝑥 100 (5)
The estimate of phenotypic coefficient of variation and
genotypic coefficients of variation were acquired through
the explanation by Singh and Chaudhary (1985) as
aforementioned.
σ2
p = phenotypic variance,
σ2
g = genotypic variance and
x = mean of the trait.
PCV and GCV values were said to be low (0-10), moderate
(10-20) and high (20% and above) values. This was shown
through the work of Sivasubramanian and Madhava
(1973).

Almu et al. 692
Estimation of heritability
Referred to as broad sense heritability (h2
B) = (σ2
g)/(σ2
p)
which was presented by (Falconer, 1989).
H2
B (%) =
σ2
g
σ2p
𝑥 100 (6)
σ2
g = genotypic variance,
σ2
p = phenotypic variance and the heritability percentage
is agreed to be low when the value is (0-30%), moderate
(30-60%) and high when the value is (>60%) as
categorised by (Johnson et al., 1951).
Genotypic advance
This was calculated as a percentage of the mean as
described by Assefa et al., 1999, methodology (GA) where
a selection of 5% was the intensity (K). Where, GA was
described as low when the value is (0-10%), moderate (10-
20%) and high (>20 %) as explained by (Johnson et al.,
1955).
GA% = K
√σ2
p
𝑥̅
𝑥 𝐻2
𝐵 𝑥 100 (7)
K = constant (stands for selection intensity).
K = 20%, the value is 1.40, √σ2p
𝑋̅ = the phenotypic standard deviation,
H2
B = heritability and
𝑋̅ = traits mean
Cluster analysis:
Data were subjected to analysis base on Jaccard’s
similarity coefficient using the NTSYS-pc software (version
2.1). Clustering were used for UPGMA algorithm and
SAHN systems. These were used for generating relative
genetic advances amongst the traits. Sixteen varieties of
advance blast resistant genotypes of rice were subjected
to PCA analysis using EIGEN and PROJ modules of
NTSYS-pc.
Table 3: List of quantitative traits of rice recorded during the period of the experiment
Growth trait Abbreviation Method of evaluation
Plant height PHT(cm) The average height of from the base to the tip of the last leaf
Tillers per hill NTH (no) By counting the number of tillers per hill
Panicle per hill NPH (no) By counting the number of panicle per hill
Filled grain per panicle FGPC (no) By counting the number of spikelets per panicle
Unfilled grain per panicle UFGPC (no) By counting the number of unfilled spikelets per panicle
Percentage filled grain per panicle PFGPC (%) By calculating the percentage filled grain
Panicle length PL (cm) By measuring up to the first superior spikelet node length of
the panicle below the lowest branch.
Grain length GLTH (mm) By measuring the length of the grain
Grain width GW (mm) By measuring the width of the grain
One thousand grain weight OneTGWT(g) By weighing 1000 filled grains
Grain weight per plot GWTPP (g) By weighing total number of grains per hill
Kilogram per plot Kgplot(kg/pt) By weighing one kilogram per plot
Yield per tonnes Tha (t/ha) By weighing tonnes per plot that is threshed in 2 × 1.5 m2).
Data analysis
The compilation of the observations for each environment
was done by taking and averaging the mean data from all
replications were rice plant randomly selected. (Table 4).
The data were subjected to analyses of variance (ANOVA)
procedure in the statistical software package (SAS version
9.4). The treatment means were compared using the
Tukey’s range test (p ≤ 0.05). To measure the observed
property five plants were selected at random from each
plot at harvest. The determination of the relationship
among traits were done using correlation analysis. The
data generated were separated in to genetics variance
which were analyse based on Euclidian distance method.
Relationship between rice genotypes were determined by
using UPGMA algorithm and SAHN methods. These were
achieved with NTSYS-pc 2.1 software.
RESULTS AND DISCUSSION
Genetic variation among the 13 growth traits in 16
advanced blast resistant rice lines
In the traits assessed were 16 advanced blast resistant
rice lines which showed significant differences (p ≤ 0.05).
Looking at the pool analysis of the three sites, there were
significant differences among the growth traits (Table 4).
There was a highly significant difference (p ≤ 0.01) in most
of the traits in their variation. About nine traits including
plant height (PHT), number of tillers per hill (NTH), number
of panicle per hill (NPH), filled grain per panicle (FGPC),
percentage filled grain per panicle (PFGPC), grain weight
per plot (GWTPP), one thousand grain weight per plot (one
TGWT), grain mass per plot (kgplot) and tonnes per
hectare (Tha) were highly significant (p ≤ 0.01) in
environment, nitrogen, genotype and genotype by
environment variation and the rest of the variation showed
significant difference in their interaction (p ≤ 0.05). There
was no significant difference in three traits which were

panicle length (PCL), grain length (GLTH) and grain width
(GWD) in environment, nitrogen, genotype and genotype
by environment interactions
Genetic diversity is the richness of the hereditary
information in the gene pole of one species. High level of
inter-species genetic diversity is an assurance for
adaptation to changing environmental conditions, an
indication for adaptation potential of the species and an
important part of the ecosystem stability. Also, genetic
diversity is a raw material for tree improvement studies. As
such, most of the researches about the genetic diversity
are in high priority in plants improvement programs (Sevik
et al., 2012, 2016). Genetic diversity can be determined by
morphological and physiological characters or molecular
markers. Morphological and physiologic which is
especially enough to get information after obtaining to
reach the details of information of izoenzim and DNA
studies (Yigit et al., 2016; Sevik and Topacoglu, 2015). For
this, there are very many studies about genetic diversity
on the different plant species.
Table 4: Mean square of analysis of Variance of 13 growth traits among 16 advanced blast resistant rice lines
Source DF PHT NTH NPH FGPC UFGFC PFGPC PCL
Rep(ENV) (R)121 12 1.7** 331.9** 206.8** 6033.1** 388.3** 369.8** 41.9ns
Env(E) 5 12474.5** 18434.6** 10972.7** 420689.6** 10483.2** 5358.4** 122.6ns
Nitro 4 250.6** 519.7** 113.4** 2715.6* 317.3* 222.9* 95ns
E×N 20 174.3** 79.1** 75.9** 1980.6** 308.2** 131** 97.1*
E×N×R 48 74.4** 24ns 36.8* 940.3ns 115.2ns 57.1* 79.8ns
Genotypes(G) 15 113.7** 72.5** 69.9** 5770.4** 161.6* 115.3** 41.8ns
G×N 60 33* 20.6ns 14.8ns 1105.8ns 123.8* 44.2ns 50.8ns
G×E 75 47.9** 81.8** 73.7** 1471.7** 140.3** 75.3** 47.8ns
N×E 300 23.9ns 23.8ns 19.5ns 1026.3* 107.2* 42.3* 50.8ns
Error 870 20.8** 21.2** 19.1** 777.9** 75.7** 33.8** 49.5ns
Table 4: continued
Source DF GLTH GWD GWTPP oneTGWT kgplot Tha
Rep (Env) (R) 12 331.5* 0.04ns 839.2** 1.2* 1.9** 21.7**
Env (E) 5 337ns 1.4** 5950.1** 172.1** 15** 167.8**
Nitro (N) 4 390.9ns 0.3* 2128.4** 31.9** 4.7** 51.5**
E × N 20 310* 0.1* 804.4** 11.4** 1.9** 21.3**
E×N × R 48 328.7** 0.04ns 178.6* 1** 0.4* 4.9*
Genotype (G) 15 146.7ns 0.06ns 600.1** 57** 1.4** 16.7**
G × N 60 148.6ns 0.05ns 188.7* 1.7** 0.4* 4.7*
G × E 75 154.1ns 0.07* 325.6** 1.9** 0.8** 8.6**
G × N × E 300 154.3ns 0.05ns 143.4* 1.2** 0.3* 3.7ns
Error 900 153.4ns 0.05** 111.7** 0.5** 0.3** 2.9**
** : significant at p ≤ 0.01, * : significant at p ≤ 0.05, ns :not significant. ENV.; Environment, Nitro; Nitrogen, Source of
variation, DF: Degree of freedom, NTH : Number of tillers per hill, NPH : Number of panicle per hill, FGPC : Filled grain
per panicle UFGPC : Unfilled grain per panicle, PFGPC : Percentage filled grain per panicle, PLC : Panicle length, GLTH
: Grain length GWD : Grain width, GWTPP : Grain weight per plot, kgplot : Kilogram per plot, Tha : Tonnes per hectare
Variance component analysis
The results of the phenotypic and genotypic coefficient of
variance for the character was shown in Table 5.
Phenotypic coefficient of variation (PCV) ranges from
0.13% for grain length to 39.18 % for unfilled grain per
panicle while the genotypic coefficient of variation (GCV)
ranges from 0% for number of tillers per hill, number of
panicles per hill, panicle length and grain width to 4.55%
for tonnes per hectare. Slight difference between the
phenotypic coefficient of variation and genotypic
coefficient variation indicated that the traits are less
accounted for being constrain by environmental factor or
maybe a small influence to the factors of environments on
the traits. The Percentage of GCV and PCV as suggested
by Sivasubramanian et al., (1973) are said to be low, if the
value range between 0-10%, moderate if the values are
between 10-20% and high if the values are above 20%.
The measuring traits unit would limit the magnitude of
variation. The coefficient of variation act freely to measure
its unit therefore make it more reliable to compare the
population. In these studies, the amount of PCV and GCV
in most of the traits were low and low values is a
manifestation of the need of variability either through
hybridization or mutation which later yield to selection.
However, the magnitude of PCV in this study was found to
be more than the GCV for most of the studied character,
these happens because of the influence of environment on
the development of a character. These was in agreement
with findings of Idris and Muhamed, 2012. There was a
small difference between PCV and GCV, in accordance to
the above research and therefore performance of these
character would be efficient and competent to make
changes that would lead to genetics advancement.

Almu et al. 694
Heritability
Genetic variability is an important aspect of genetic
variability and hence affected by environmental factors.
Genetic variability affected by heritability which changes
from generation to the other generation, hence the
removing of heritable character from non-heritable
character implies the continuation of selection for plant
breeder. The breeders have to get a new method of
selection so as to obtain a heritable character from non-
heritable character for a judicious breeding program. The
phenotypic and genotypic variation being measured by
heritability is an attribute to genetic variability. The
heritability is considered a very good aspect of genetic
variability and it is viewed as low value if it is ranges from
0-30%, moderate if it ranges from 30-60% and if it ranges
above 60% is considered as high (Falconer, 1989). It
happens that most of the characters in this study showed
no heritability, low and moderate heritability (it ranges from
0-44.20%). One thousand grain weight (oneTGWT)
recorded (44.20%) moderate sense of heritability. The
characters with the lowest sense of heritability recorded
were filled grain per panicle (FGPC) (4.98), kilogram per
plot and tonnes per hectare (Tha) which were at 4.2% and
4.2% sense heritability respectively. There were followed
by grain weight per plot (GWTPP) (3.96) and percentage
filled grain per panicle (PFGPC) (1.09) and grain length
(GLTH) (0.39). However, no sense of heritability was
recorded in number of tillers per hill (NTH), number of
panicles per hill (NPH), Panicle length (PCL) and Grain
width (Table 5). These findings were supported by earlier
researchers who reported almost the same findings
Oladosu et al., (2014), Abebe et al., (2017). As a predictive
measure in expressing heritability one needs to know at
what measure heritability help in efficient selection for
specific traits. Low heritability suggests that such trais
have a greater control over environments and as such
breeders can go and select base on phenotypic
appearances. The low values recorded could mean that all
the traits would go on for hybridization or mutation which
later may be good for improvement.
Table 5: Mean, phenotypic variation, genotypic variation, heritability, phenotypic coefficient of variation, genotypic
coefficient of variation, Relative distance and Genetic advance for different characters in rice genotype
Character Mean Phenotypic Genotypic Environmental Heritability Ph. Coef. Ge. Coef. RD GA
% variation variation variation % % % %
PHT 103.72 24.62 0.63 51.04 2.56 4.78 0.76 84.17 0.3
NTH 28.39 25.67 0 75.13 0 17.85 0 100 36.76
NPH 25.41 22.6 0 44.52 0 18.7 0 100 38.52
FGPC 151.94 941.84 46.93 1722.1 4.98 20.19 4.51 19.55 2.05
UFGPC 24.13 89.4 0.06 41.18 0.07 39.18 1.02 97.44 0.06
PFGPC 85.74 39.38 0.43 20.34 1.09 7.32 0.76 89.04 0.17
PCL 26.9 49.65 0 0.11 0 0.26 0 100 53.56
GLTH 10.39 1.82 0.007 0.03 0.39 0.13 0.81 93.83 0.1
GWD 2.25 0.05 0 0.005 0 9.93 0 100 20.46
GWTPP 53.89 141.84 5.62 17.51 3.96 22.1 4.39 80 1.99
OneTGWT 30.56 1.38 0.61 0.66 44.2 3.84 2.56 31.58 0.2
Kgplot 2.58 0.33 0.01 0.05 4.2 22.17 4.51 79.3 1.9
Tha 8.51 3.68 0.15 0.05 4.2 22.33 4.55 79.1 1.9
PHT: Plant height NTH: Number of tillers per hill; NPH: Number of panicle per hill; FGPC: Filled grain per panicle; UFGPC:
Unfilled grain per panicle; PFGPC; Percentage filled grain per panicle; PCL: Panicle length; GLTH: Grain length; GWD:
Grain width; GWTPP: Grain weight per plot; oneTGWT: One thousand grain weight; kgplot: Kilogram per plot; Tha: Tonnes
per hectare; Ph. Coef. % variation: Percentage phenotypic coefficient of variation; Ge. Coef. % variation, Percentage
genotypic coefficient variation; RD: Relative distance; GA: Genetic advance
Genetic advance
For genetic improvements, heritability alone cannot
provide a sufficient selection for an individual genotype,
therefore, there is a need to have a knowledge of the
genetic advancements. Therefore, characters where
simply inherited in nature and controlled by few genes or
possessed additive gene effects to become effective for
improving such characters. A more reliable selection is
based on the selection in the well adapted population to
see that heritable character did not insist a high genetics
gain, but should be an assistant to high genetic advance.
It has been proposed by Johnston et al., (1955) that it is
considered low when the value ranges from (0-10%),
moderate when it ranges from (10-20) and considered high
when the value is (> 20%). According to these findings
(Table 5), the characters with high genetic advance were
the panicle length (PCL), number panicle per hill (NPH),
number of tillers per hill (NTH) and grain width (GWD)
which have of 53.56, 38.52, 36.76 and 20.46 respectively
(Table 5). However, the genetic advance recorded for yield
was found to be low, this characterised the fact that
environment had an influence on this character. Their
expression is governed by additive gene effects, therefore
simple selection of this character is not good source for s
for improving their character though, its low in its
heritability but it produces an up to date in a subsequent
generation might which become a good result for

improvement. The non-additive (epistatic mode) gene
action and the effect of environmental causes to take part
in the vivid representation of genotypic characters.
Phenotypic relations
When two traits combine to form an association especially
grain yield traits, these two traits in combination with other
traits which ultimately results in the formation of yield. It
results in a relation which may be positive or negative
relationship. Pearson’s correlation coefficient assists in
finding the relationship in two different characters, though
it does give the starting point of the relationship between
two characters but it initiates the magnitude of association.
Quantitative traits were analysed among the rice
genotypes by using SAS software (version 9.4). These
was shown in Table 6. For general interpretation of
correlation coefficient, it was reported by Ratner (2009)
that the r-value for pearson’s correlation starts from 0, +1
and -1 which shows no linear relation, a perfect Positive
linear relation and a negative relation respectively. Values
ranging from 0 to -0.3, 0.3 to -0,7 and -0.7 to -1 showed a
low, moderate and strong negative linear relation
respectively. A positive linear correlation was found
between most traits, yield per hectare showed a positive
correlation with other traits. Yield showed a strong positive
correlation with GWTPP and kgplot. A strong positive
correlation was observed between NTH and NPH. There
was a strong negative correlation between FGPC and
UFGPC. The more the filled grain per panicle the less will
be the unfilled grain per panicle. The coefficient of
correlation between kgplot and GWTPP showed a strong
positive relation, for these traits to be very significant for
rice production. There was a strong moderate coefficient
of correlation between yield and NPH which becomes very
important for the production of rice (Table 6).
Nevertheless, the phenotypic correlation coefficient of all
the characters ranges from 0.0005 to 0.99, this shows the
magnitude is high for phenotypic relation in the processes.
Therefore, the amount of produce in a given hectare
showed a highly significant and strongly positive
correlation with grain weight per plot and kilogram per plot,
selection should hence be applied on these two
characters since there are effective and contribute towards
grain yield improvement.
Table 6: Pearson coefficient of phenotypic relationship among the investigated traits of sixteen rice genotypes
PHT NTH NPH FGPC UFGPC PFGPC PCL GLTH GWD GWTPP one
TGWT
kgplot Tha
PHT
NTH -0.19**
NPH -0.3** 0.71**
FGPC -0.04ns -0.05ns 0.09ns
UFGPC 0.19** 0.08* -0.04ns 0.06n
PFGPC -0.19** -0.18** 0.04n 0.52** -0.73**
PCL 0.05ns 0.02ns 0.006ns 0.03ns 0.05ns 0.02ns
GLTH 0.01ns -0.08* 0.005ns -0.002n -0.005ns 0.01ns 0.0005ns
GWD -0.04n -0.19** 0.12** 0.11** -0.02ns 0.12** 0.001ns 0.02ns
GWTPP -0.08* 0.43** 0.43** 0.18** -0.13** 0.19** 0.02ns -0.03ns -0.09ns
oneTGW
T
0.13** 0.07ns 0.11** 0.42** 0.12** 0.12** 0.02ns 0.01ns 0.04ns 0.19**
kgplot -0.09* 0.44** 0.44** 0.18** -0.13** 0.18** 0.01ns -0.03ns -0.09* 0.99** 0.18**
Tha -0.09* 0.44** 0.44** 0.18** -0.13** 0.18** 0.02ns -0.03ns -0.09* 0.99** 0.18** 0.99**
** : Highly significant; * : Significant; ns : Not significant; PHT : Plant height; NTH : Number of tillers per hill; NPH : Number
of panicle per hill; FGPC : Filled grain per panicle; UFGPC : Unfilled grain per panicle; PFGPC : Percentage filled grain
per panicle; PCL : Panicle length; GLTH : Grain length; GWD : Grain width; GWTPP : Grain weight per plot; oneTGWT :
One thousand grain weight; kgplot :Kilogram per plot; Tha : Tonnes per hectare
Cluster analysis
Sixteen genotypes of advanced blast resistant rice were
subjected to cluster analysis. Standardised morphological
data was used in a UPGMA dendrogram structure. There
were clustered in to 9 groups by 12 quantitative traits as
was shown in fig 1 and Table 7. Cluster 1 was the largest
amongst the clusters (6 varieties), cluster VIII and IX have
2 varieties respectively, while clusters II, III, IV, V, VI, VII
were the smallest amongst the clusters. Cluster IX has the
highest in comparison with the other clusters in terms of
yield per hectare (9.37t/ha), one thousand grain weight
(32.22mm), grain weight per plot (58.7g/plot), filled grain
per panicle (165.29) and number of tillers per hill (29.43).
Cluster VIII also recorded a high grain yield (9.2t/ha), one
thousand grain weight (31.53mm), grain weight per plot
(58.7), filled grain per panicle (163.23) and number of
tillers per hill (27.59).
In using UPGMA dendrogram systems which clustered the
rice genotypes to 9 major groups at 3.38 dissimilarity
coefficient (Table 7 and figure 1). There is a high level of
interaction between the morphological property of the rice
genotypes. The quantitative or morphological traits were
effective in grouping the rice genotypes. The genetic
divergence analysis has been a very important

Almu et al. 696
phenomena in trying to classify and differentiate
genotypes of rice in different population (Franco et al.,
2001). It has been generally agreed upon that genetic
divergence analysis act as a prime factor in selection of
diverse genotypes for improvement of rice through
breeding (Shahidullahi et al., 2010). Quantitative traits
were used to classify 16 genotypes of rice into 9 major
clustering group for better handling and transport of
diverse genotypes. These classes were used for the gain
in vigour and energetic offspring when crossed. Another
clustering of 58 genotypes of rice into four groups were
carried out by Ahmadikhah et al., 2008) into 18
morphological traits and another clustering was done by
Mazid et al., 2013) who classified 41 rice genotypes in to
6 classes on the basis of 13 morphological traits. On this
basis a succeeding genotypes breeding can be said to
occur on the outcome of their researches, group 1 could
hybridized with group VII to produce group X. Therefore,
there is a notable genetic diversity of the genotypes of
advanced blast resistant rice in this study.
Table 7: Distribution of 16 blast resistance rice varieties
in to nine cluster
Cluster
(in group)
Number of
genotypes
Genotypes
I 6 GI, G3, G4, G8, G12, G10
II 1 G6
III 1 G11
IV 1 G13
V 1 G7
VI 1 G2
VII 1 G9
VIII 2 G5, G14
IX 2 G16, G15
Note: G1 : P 411; G2 : P 416; G3 : P 4150; G4 : 4742; G5
MR 269; G6 : P 4129; G7 : P 4619; G8 : P 4114; G9 : P
4137; G10 : P 4131; G11 : P 4716; G12 : P 4712; G13 : P
4159; G14 : MR 219; G15 : 263; G16 : P 41
CONCLUSION
The most important aspect is that the findings lead to the
development of rice genotypes across the genetic and
phenotypic concepts of the quantitative traits. Genetic
variations provide a good atmosphere for recombinants
which are necessary for the raising up of a new genotype.
Sixteen advanced blast resistant rice genotypes were
used for genetic variability in trying to develop their genetic
aspects. There was significant difference in genotypes and
their traits which results in variation in growth and yield
components. Henceforth, all the traits were positively
related with the final yield. An analysis was conducted in
such a way that the evaluated genotypes were run into
groups of nine different clusters with the assistant of
UPGMA dendrogram. These allows the hybridization of
group 1 having genotypes G1, G3, G4, G8, G10 and G12
with group VII to produced group X. These allows to bring
energetic, high heterosis and vigorous rice species. To
developed reliable selection in the rice species, these
evaluations becomes significant in the breeding program
as it gives way to improvement in rice production.
Molecular and morphological approaches to explore the
agronomic potentials in rice, hence it is recommended that
future research should be focus on molecular strategies as
a means of developing genotype and confirm the outcome
of the research to establish the relationship between the
two methods.

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Appendix 1: Examples of Field layout in Tanjung Karang
Main plot
A (N100) B (N60) C(N120) D(N80) E(N140)
G1 G10 G5 G3 G16 G10 G2 G6 G14 G16 G G7 G3 G7 G11 G5 G2 G4 G4 G14
REP
1 G6 G16 G11 G13 G13 G7 G3 G11 G16 G13 G2
G12
G9 G12 G6 G16 G9 G6 G16 G15
G8 G G4 G9 G4 G12 G5 G15 G8 G4 G6 G8 G15 G1 G10 G2 G15 G14 G3 G9
G12 G2 G7 G15 G1 G14 G9 G8 G3 G9 G11 G1 G8 G14 G13 G4 G3 G16 G6 G2
REP
2 G1 G3 G2 G11 G8 G16 G9 G4 G5 G12 G2
G16
G5 G7 G16 G13 G14 G9 G6 G13
REP
3 G16 G10 G15 G12 G4 G1 G16 G14 G14 G4 G13
G11
G11 G6 G9 G4 G16 G1 G4 G15
G2 G4 G13 G11 G7 G1O G8 G9 G3 G7 G1 G9 G12 G14 G5 G13 G7 G13 G2 G5

Accepted 16 May 2020
Citation: Almu H, Sulaiman Z, Yusop MR, Ismail MR, Harun AR, Ramli A, Karfi UA (2020). Genetic Diversity and Selection
Criteria in Blast Resistance Rice (Oryza sativa) Genotypes/Lines under Tropical Environments. International Journal of
Plant Breeding and Crop Science, 7(1): 689-699.
Copyright: © 2020: Almu et al. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
author and source are cited.

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