There is a relation between yield and yield-related traits in the evaluation of rice plant, the direct and indirect traits have a significant effect and influence on rice production and the pattern of grain yield. The direct and indirect effects of various traits determine the selection criterion for high grain yield. An evaluation of 16 rice genotypes was done under a tropical condition at three environments during two planting seasons. The experiment was split-plot design replicated three times across the environment. Data were collected on vegetative, yield and yield-related components. The pooled data base on the analysis of variance revealed that there were highly significant different (p ≤ 0.01) among the 16 genotypes in all the characters studied except panicle length and grain width which show no significant difference. There was highly significant and highly positive correlation at a phenotypic level at the number of tillers per hill (0.46), number of panicles per hill (0.41), grain weight per plot (0.99) and yield per plot (kg) (0.99) with the yield per hectare. Also, a significant and positive correlation was observed by filled grain per panicle (0.19). However, in contrast, the number of empty grain per panicle (-0.02) which recorded negative significant correlation with the yield . It could be concluded that number of tillers per hill, number of panicles per hill, grain weight per plot and yield per plot could be used as selection criteria to improve grain yield of rice.

1. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Genetic Variability of Rice (Oryza sativa L.) Genotypes
under Different Level of Nitrogen Fertilizer in Malaysia
Hamisu Almu1,7*, M.Y. Rafii1,3, Zulkefly Sulaiman2,3, Mohd Razi Ismail1,3, Abdul Rahim Harun4,
Asfaliza Ramli5, Mohd Yusoff A.S.6 and Jamilu Halidu8
1Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
2 Laboratory of Plantation and Technology. Institute of Plantation Studies, Universiti Putra Malaysia, Serdang, Selangor,
Malaysia
3,8Department 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 Land management, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
7Department of Soil Science, Faculty of Agriculture and Agricultural Technology, Kano State University of Science and
Technology, Wudil, Kano State, Nigeria
There is a relation between yield and yield-related traits in the evaluation of rice plant, the direct
and indirect traits have a significant effect and influence on rice production and the pattern of
grain yield. The direct and indirect effects of various traits determine the selection criterion for
high grain yield. An evaluation of 16 rice genotypes was done under a tropical condition at three
environments during two planting seasons. The experiment was split-plot design replicated three
times across the environment. Data were collected on vegetative, yield and yield-related
components. The pooled data base on the analysis of variance revealed that there were highly
significant different (p ≤ 0.01) among the 16 genotypes in all the characters studied except panicle
length and grain width which show no significant difference. There was highly significant and
highly positive correlation at a phenotypic level at the number of tillers per hill (0.46), number of
panicles per hill (0.41), grain weight per plot (0.99) and yield per plot (kg) (0.99) with the yield per
hectare. Also, a significant and positive correlation was observed by filled grain per panicle (0.19).
However, in contrast, the number of empty grain per panicle (-0.02) which recorded negative
significant correlation with the yield . It could be concluded that number of tillers per hill, number
of panicles per hill, grain weight per plot and yield per plot could be used as selection criteria to
improve grain yield of rice.
Keywords: Rice genotypes, Path coefficient analysis, phenotypic coefficient correlation analysis, yield traits.
INTRODUCTION
Rice (Oryza sativa L.) is an important cereal crop,
accepted globally and constitute a staple food grain for
more than three and a half billion people living in the rural
and urban areas of the World (FAO, 2016).
The relationship between yield and yield component traits
isan important constituent before making any successful
breeding programme. Grain yield is a complex trait which
is controlled by many factors, such as genetic variability
and environment (Singh et al., 2000). These factors may
be the fertility of the soil which could be the ability of the
rice plant to increase in yield in response to applied
nitrogen. There are a lot of nitrogen fertilizer losses which
might be from volatilization, denitrification and leaching
which reduces the yield of the rice plant (Wissum et al.,
2009). The ability of the rice plant to use nitrogen is what
is called agronomic nitrogen use efficiency. Therefore, for
yield increase, one has to achieve a greater uptake
*Corresponding Author: Hamisu Almu, Institute of
Tropical Agriculture and Food Security, Universiti Putra
Malaysia, Serdang, Selangor, Malaysia.
E-mail: hamisualmu@gmail.com, mrafii@upm.edu.my,
zulkefly@upm.edu.my
Analysis
Vol. 6(1), pp. 487-497, March, 2019. © www.premierpublishers.org, ISSN: 2167-0449
International Journal of Plant Breeding and Crop Science

2. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Almu et al. 488
efficiency of the nitrogen being applied to the soil, Nitrogen
and rice management must to be set in the cropping
season so that nitrogen could be applied at proper time in
order to maximise yield. This reduces the amount of
nitrogen lost from the soil (Barraclough et al., 2014). There
was a significant increase in the yield of rice due to
nitrogen fertilizer levels (Bereket et al., 2011). In order to
increase grain yield, yield component traits would have to
be heritable (directly or indirectly) in their genetic variability
so that they would be heritable and positively correlated
with grain yield (Hasan et al., 2013). In breeding program,
it is difficult to achieve genetic gain in yield in rice potential
crops, breeders in their studies applied indirect selection
for yield based on plant traits (Yaun et al., 2011). For
partitioning correlation analysis, path coefficient analysis is
a reliable statistical method whereby direct or indirect
effects are observed. Contribution of each character to
yield could be estimated in this technique, it is direct or
indirect influence on the grain yield. As a selection guide
for plant breeding it is therefore, a very significant
technique. Yield-related components such as plant height,
tillers number per plot, panicle number per plot, weight of
grain per plot etc tend to bring about the ultimate yield
(Usman et al., 2017). The yield components trait does not
occur independently, they become interrelated with each
other in order to bring the yield. Path coefficient analysis
being a fundamental technique in finding the relationship
between some characters, first order and second order
components were analysed in order to analyse their
effects over a dependent variable such as yield (Usman et
al., 2017). This study was conducted in order to determine
the nature of relationship between grain yield and their
component. The yield components that would be
considered are plant height, tillers per hill, panicle per hill,
panicle length, numbers of filled and empty grains, 1000
grain weight, total grain weight per panicle etc. Data on the
above mentioned characters can be applied for the
calculation of the impact of path analysis on the yield and
yield components (Ahmed et al., 2003). Path coefficient
analysis had been used in the production of rice by
Oladosu et al., 2018, in chilli (Usman et al., 2017), in barley
(Shahinnia et al., 2005), in Wheat (Naserian et al., 2007).
They all applied path analysis to express clearly the
relationship between yield parameters.
For organising and presentation of the cause and
relationship between the predictor character and the
response of character based on the results of the
experiments provide a reliable technique in path analysis
procedure. Relationship between traits and yield
components to quantify the path analysis into direct and
indirect influences on the yield and yield components
which allow the contribution of each to be estimated (Ali et
al., 2009). The most important components in path
analysis is the direct effect of a predictor character to its
response character. Another second important component
is the indirect effect of a predictor character. It is through
these two relationships that response of characters results
in another character(s). It is through these processes of
path analysis that led to the identification of characters in
selection for the purposes of increases yield in agriculture
(Bagheri et al., 2011). The least second-order component
variables constitute the path analysis of the treated
previous research amongst traits which resulted in the
presence of multicollinearity in path coefficients with
values greater than one. The grain yield and yield
component traits of the path analysis indicated that at least
second-order component varies in their path diagram in
rice. The yield related traits is the result of the expression
of characters which becomes associated with yield
(Usman et al., 2017). The yield –related components
include plant height, tillers per hill, panicle per hill, panicle
length, number of filled grain and empty grain per panicle,
grain length, yield per plot are so significant and therefore
a good strategy in making a fundamental impact on
agronomic efficiency. In calculating the quantitative impact
on yield, path analysis can be used for direct or indirect
effects resulted from one or the other traits component
(Anwar et al., 2011).
Improvement and a good selection efficiency present the
whole systems in the procedure asa variable in form of
diagram referred to as path diagram (Usman et al., 2017).
The relationship between the two characters being
presented by correlation coefficient measures, this study
tries to reveal the relative performance of each trait and
the aim is to determine the nature of the relationship of
grain yield and yield components.
MATERIALS AND METHODS
Plant Husbandry: The number of genotypes that were
used in this experiment were 16 genotypes of which 13 are
advanced blast rice resistant lines which were obtained
from crossing between MR219 × Pongsu Seribu1 and
three commercial varieties were used MR219, MR263 and
MR269 which were developed by the Malaysian
Agricultural Research and Development Institute (MARDI)
and released in 2001. These three varieties served as
reference lines for validating the performance of the rice
varieties. The reference lines have high yield, fine and long
grain quality. They possess the characteristics of short life
cycle (105-112 days), strong to fairly long culms and also
being tolerant to blast and bacterial leaf blight. The field
trials were conducted in three locations in two different
cropping seasons (six environment combinations) in
peninsular Malaysia viz: Tanjung Karang, Malaysian
Agriculture Research and Development Institute (MARDI),
Penang, Seberang Perai, (MARDI) and Kedah (Kota
Sarang Semut) Malaysia Agricultural Development
Authority (MADA). The description and characterization of
the three locations in two seasons of the research areas
were given in Table 1. These locations were chosen to
represent major rice growing areas in Malaysia. The
periods of the cultivation were shown in Table 1. At each

3. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Int. J. Plant Breed. Crop Sci. 489
environment, the experiment was laid out in a Split plot
design with three replications. Plot size was 35 by 24 m2,
with subplot size of 2 by 1.5 m2 unit for each genotype in
each replication. Transplanting was done at the optimum
date (appropriate time) for each location in accordance
with the farmer’s schedule. 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. 60; 80; 100; 120 (Control) and 140 kg/ha
were applied following the recommendation by MARDI in
the form of urea and compound fertilizer. There were
applied in triplicate starting on day 15, 55 and 75 after
transplanting. Phosphorous (as triple superphosphate)
was applied at 15 days at the rate of 57 kg/ha, Potassium
(Muriate of potash) at 42 kg per hectare. Melathion and
Hopper Gun insecticides were used at recommended rate
to take care of the insect pests.
Soil Characteristics
Soil sample were taken before and after the studies (in
each location) in order to provide the physico-chemical
analysis of the soil sample used. Collection of soil sample
was done from five points in the field which were later
bulked to produce a composite sample. The composite
sample were air dried and crushed so that a 2 mm mesh
was used for proper analysis. The results of the physico-
chemical characteristics of the soil sample used was
shown in Table 2.
Data Collection
Five rice stands/plot/genotypes were sampled and
recorded per each replicate for all the characters
measured. These observations included plant height,
tillers per hill, panicles per hill, panicle length, filled grains
per panicle, empty grains per panicle, length of grain, width
of grain, 1000 weight of grains, grain weight, kilogram per
plot, and grain weight per plot. Yield in ton per hectare
(ton/ha) was estimated from the weight of threshed grains
from the all panicles in 2 × 1.5 m2 excluding the border
rows (Table 3 is the list of the quantitative traits collected).
Path Coefficient analysis
Correlation coefficient analysis were carried out on
genotypic and phenotypic variation using SAS version 9.3
(SAS Institute Inc., Cary, NC, USA). Genotypic and
phenotypic analysis were done in order to carry out the
various association of the various characters with yield per
hectare as expressed by Kashiani and Saleh, 2010. The
components of direct and indirect effects were partitioned
in to phenotypic correlation according to Wright (1921).
Usman et al., 2017 calculated the path coefficient analysis,
where a set of simultaneous equations were arranged in
matrix revealing the relationship between the correlation
analyses. where: r, represents the phenotypic correlation
values between variables, p values of coefficient value
(direct effects) of one variable upon another and rijPij
indirect effects. The definition in their serial number in
accordance to the observation.
1 = Plant height
2 = Tillers number per hill
3 = Panicles number per hill
4 = Filled grains per panicle
5 = Empty grains per panicle
6 = Percentage filled grains per panicle
7 = Panicle length
8 = Grain length
9 = Width of grain
10 = Grain weight per plot
11 = 1000 grain weight per hill
12 = Yield per hectare
Effects of yield component variables on yield per
hectare
r13 = P13 + r12 × P12 + r13 × P13 + r14 × P14 + r15 × P15 + r16
× P16 + r17 × P17 + r18 ×P18 + r19 ×P19 + r110 × P110 +
r111 × P111 + r112 × P112
r213 = P213 + r121 x P121 + r124 x P124 + r125 x P125 + r126 x P126
+ r127 x P127 + r128 x P128 + r129 x P129 + r210 x P210 +
r211 x P211 + r212 x P212
r313 = P313 + r313 x P313 + r332 x P332 + r333 x P333 +r334 x P334
+ r335 x P335 + r336 x P336 + r337 x P337 +r338 x P338 + r339
x P339 + r310 x P310 +r311x P311 + r312 + p312
r413 = P413 + r413 x P413 + r423 x P423 + r433 x P433 + r443 x P443
+ r453 x P453 +r463 x P463 + r473 x P473 + r483 x P483 +
r493 x P493 + r410 x P410 + r411 x P411 + r412 x P412
r513 = P513 + r513 x P513 + r523 x P523 + r533 x P533 + r543 x P543
+ r553 x P553 + r563 x P563 + r573 x P573 + r583 x P583 +
r593 x P593 + r510 x P510 + r511 x P511 + r512 x P512
r613 = P613 + r613 x P613 +r623 xP623 +r633 x P633 + r643 x P643 +
r653 x P653 + r663 x P663 + r673 x P673 + r683 x P683 + r693
x P693 + r610 x P610 + r611 x P611 + r612 x P612
r713 = P713 + r713 x P713 + r723 x P723 + r733 x P733 + r743 x P743
+ r753 x P753 + r763 x P763 + r773 x P773 + r783 x P783 +
r793 x P793 + r710 x P710 + r711 x P711 + r712 x P712
r813 = P813 + r813 x P813 +r823 x P823 + r833 x P833 + r843 x P843
+ r853 x P853 + r863 x P863 + r873 x P873 + r883 x P883 +
r893 x P893 + r810 x P810 + r811 x P811 + r812 x P812
r913 = P913 + r913 x P913 +r923 x P923 + r933 x P933 + r943 x P943
+ r953 x P953 + r963 x P963 + r973 x P973 + r983 x P983 +
r993 x P993 + r910 x P910 + r911 x P911 + r912 x P912
r1013 = P1013+ r1013 x P1013 + r1023 x P1023 + r1033 x P1033 + r1043
x p1043 + r1053 x P1053 + r1063 x P1063 + r1073 x P1073 + r1083
x P1083 + r1093 x P1093+ r1010 x PI010 + r1011 x P1011 + r1012
x P1012
r1113 = P1113+ r1113 x P1113 + r1123 x P1123 + r1133 x P1133 + r1143
x P1143 + r1153 x P1153 +r1163 xP1163 + r1173 x P1173 + r1183
x P1183 + r1193 x P1193 + r1110 x P1110 + r1111 x P1111 +
r1112 x P1112
r1213 = P1213 + r1213 x P1213 + r1223 x P1223 + r1233 x P1233 + r1243
x P1243 + r1253x P1253 + r1263x P1253 + r1273 x P1273 + r1283
x P1283 + r1293 x P1293 + r1210 x P1210 + r1211 x P1211 +
r1212 x P1212

4. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Almu et al. 490
The traits were then divided into two different stages of
relationship, first order components and second order
components. The first order components include plant
height, number of tillers per hill, number of panicles per hill
and empty grain per panicle. The second order
components also include filled grain per panicle, panicle
length, grain length, grain width, grain weight per plot and
one thousand grain weight. The principle aspect was that
the cause and effective relations between the two
components were subsequently subjected to simultaneous
equations which were arranged in matrix notation as
shown below.
Effects of first-order components on filled grain per
panicle, panicle length, grain length, grain width, grain
weight per plot and 1000 grain weight.
Filled grain per panicle
r15 = P15 + P25 × r12 + P35 × r13 + P45 × r14
r25 = P25 + P15 × r21 + P35 × r23 + P45 × r24
r35 = P35 + P15 × r31 + P25 × r32 + P45 × r34
r45 = P45 + P15 × r41 + P25 × r42 + P45 × r43
Panicle length
r16 = P16 + P27 × r12 + P37 × r13 + P47× r14
r26 = P25 + P17 × r21 + P37 × r23 + P47 × r24
r36 = P35 + P17 × r32 + P37 × r32 + P47 × r34
r46 = P45 + P15 × r47 + P257× r42 + P47 × r43
Grain length
r17 = P17+ P27 × r12 + P37 × r13 + P45 × r14
r37 = P27 + P17 × r21 + P37 × r23 + P47 × r24
r47 = P35 + P15 × r31 + P25 × r32 + P45 × r34
r57 = P45 + P15 × r41 + P25 × r42 + P45 × r43
Grain weight
r18 = P15 + P28 × r12 + P38 × r13 + P48 × r14
r38 = P38 + P15 × r21 + P38 × r23 + P48 × r24
r38 = P35 + P15 × r38 + P38× r32 + P48 × r34
r48 = P48 + P15 × r41 + P25 × r42 + P48 × r43
Grain weight per plot
r19 = P15 + P29 × r12 + P39 × r13 + P49 × r14
r39 = P39 + P15 × r21 + P39 × r23 + P49 × r24
P49 = P49 + P15 × r39 + P39 × r32 + P49 × r34
P510 = P510+ P15 × r41 + P25 × r42 + P510 × r43
One thousand grain weight
r21= P15 + P21 × r12 + P22 × r13 + P23 × r14
r510 = P510 + P15 × r21 + P35 × r23 + P45 × r24
r511= P35 + P15 × r31 + P25 × r32 + P45 × r34
r512 = P45 + P15 × r41 + P25 × r42 + P45 × r43
Effects of second order components on yield per
hectare
r511 = P511 + P611 × r56 + P711 × r57 + p811 × r58 +
P911 × r59 + P1011 × r510
r611 = P611 + P511 × r65 + P711 × r67 + P811 × r68 +
P911 × r69 +P1010 × r610
r711 = P711 + P711 × r75 + P711 × r77 + P811 × r78 +
P911 × r79 + P1010 × r710
r811 = P811 + P811 × r85 + P811 × r87 + P811 × r88 +
P911 × r89 + P1010 × r810
r911 = P911 + P911 × r95 + P911 × r97 + P811 × r98 +
P911 × r99 + P1010 × r910
r1111 = P1111 + P1111 × r105 + P11101× r107 + P811 ×
r108 + P911 × r109 + P1010 × r1110
Table 1: Description and characterisation of the three location during two planting seasons
Code Location Season Location Altitude Average
temperature
Min-Max
Average
humidity
Rainfall (mean) Year of planting
TK1
Tanjung
Karang Main 3025' ON 1010E 3m 230C-310C 83% 782.4 (195.6) Sept. 2015 to Dec. 2015
TK2
Tanjung
Karang Off 3025' ON 1010E 3m 250C-370C 65% 482.7 (120.7) Mar. 2016 to June 2016
MADA1 Kedah Main 5059'N100 24'E 18m 220C-330C 91% 550.6 (137.7) Nov. 2016 to Feb. 2017
MADA2 kedah Off 5059'N100 24'E 18m 250C-380C 63% 486.9 (121.7) Mar. 2015 to June 2015
PP1 Penang Main 05025'N 100015'E 3m 220C-300C 88% 934.7 (233.7) Oct. 2015 to Jan. 2016
PP2 Penang Off 05025'N 100015'E 3m 240C-370C 71% 766.6 (191.7) Feb. 2015 to May 2015

5. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Int. J. Plant Breed. Crop Sci. 491
Table 2: Physico-chemical characteristics of the soil sample in the six locations
Parameters Units TK1 TK2 MADA1 MADA2 PP1 PP2
pH 0C 4.9 4.89 5.03 5.01 4.92 4.98
EC ds m-1 0.09 0.11 0.11 0.11 0.11 0.1
Avai. P mg/kg 9.93 10.75 11.68 11.92 10.89 11.17
CEC Cmolc kg-1 17.68 18.12 18.51 19.23 20.28 19.17
Ca " 4.27 4.56 3.88 3.84 4.29 3.74
Mg " 1.9 2.12 2.48 2.57 2.52 2.31
K " 0.31 0.34 0.34 0.38 0.35 0.31
Na " 0.46 0.46 0.42 0.42 0.5 0.43
Al " 3.38 3.1 2.06 2.17 3.09 2.73
Clay % 44.81 44.61 49.04 48.89 49.7 49.07
Silt % 35.89 37.07 35.05 36.51 32.27 34.75
Sand % 38.61 36.64 31.86 29.22 26.05 32.38
Table 3: List of quantitative traits collected
Growth trait Abbreviation Method of evaluation
Plant height PHT (cm) The average height from the base to the top 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
Empty grain per panicle EGPC (no) By counting the number of empty spikelet per panicle
Percentage filled grain per panicle PFGPC (%) By calculating the percentage filled grain grain
Panicle length PLC (cm) By measuring the length from the node below the lowest
branch on the panicle to the top of first superior spikelet
Grain length GLTH (mm) By measuring the length of the grain
One thousand grain length One TGWT (g) By weighing 1000 filled grain
Grain weight per plot GWTPP (g) By weighing total number of grains per hill
Kilogram per plot kgplot By weighing one kilogram per plot
Yield per tonnes Tha (t/ha)
By weighing tonnes per plot that is thrashed in 2×1.5 m2
excluding the border rows
Table 4: Mean square of vegetative traits, yield and yield component of rice genotype
Traits Rep in Env Genotypes Environments Nitro G×E Error
(df = 12) (G) (df = 15) (E) (DF = 5) (df = 4) (df = 75) (df = 900)
PHT 121.7** 113.7** 12474.5** 250.6** 47.9** 20.8**
NTH 339.9** 72.5** 18434.6** 519.7** 81.8** 21.2**
NPH 206.8** 69.9** 10972.7** 113.4** 73.7** 19.1**
FGPC 6033.1** 5770.4** 420689.6** 2751.6* 1471.7** 779.9**
EGPC 388.3** 161.6* 10483.2** 317.3* 140.3** 75.7**
PFGPC 369.8** 115.3** 5358.4** 222.9* 75.3** 33.8**
PCL 41.9ns 41.8ns 122.6ns 95.0ns 47.8ns 49.5ns
GLTH 331.5* 146.7ns 337.0ns 390.9ns 154.1ns 153.4ns
GWD 0.04ns 0.06ns 1.4** 0.3* 0.07* 0.05**
GWTPP 839.2** 600.1** 5950.1** 2128.4** 325.6** 117.7**
One TGWT 1.2** 57.0** 172.1** 31.9** 1.9** 0.5**
YLD 21.7** 16.7** 167.8** 51.5** 8.6** 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, PHT= Plant height, NTH = Number of tillers per hill, FGPC = Number of filled grain per
panicle, EGPC = empty 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
RESULTS AND DISCUSSION
Variance analysis
From the presented data in Table 4, there are significant
differences in the pooled analysis of variance for the
vegetative, yield and yield components for all the
genotypes across the environments. There was a
significant difference among the genotypes, nitrogen,
genotype by nitrogen and genotype by environment as
shown in Table 4. These differences might be because of
variation in their genetic variability and geographical
backgrounds. It could be seen that there was a highly
significant difference in nitrogen in plant height, number of
tillers per hill, number of panicle per hill, grain weight per
plot, one thousand grain weight and yield per hectare

6. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Almu et al. 492
(Table 4). There was a significant difference in nitrogen
interaction in empty grain per panicle, percentage filled
grain per panicle and grain width. The ability of rice plant
to use nitrogen become a very important constituent to
greater yield. The amount of nitrogen applied lead to the
increase in yield of rice. Taalab et al., 2015 reported that
the more nitrogen uptake is applied the greater the yield of
the rice plant, however, nitrogen application is dependent
on genetic potential of the varieties. The agronomic
nitrogen uses efficiency of nitrogen decreased because of
the increased rates of nitrogen application, this showed
that a greater efficiency of N when nitrogen was applied at
lower rate. This might be because the increases in yield
per kilogram N become low with increasing nitrogen rates
as reported by Woldeyesus et al. (2004). It has been
reported that high agronomic efficiency of N is obtainable
in increase yield per unit of applied N, the applied N being
high for reduction in amount of N losses, Volatilization,
denitrification and leaching (Abebe, 2012). The chances
of getting brighter desirable trait is enhanced which could
be used for heterosis (Oladoso et al., 2015). The
differences were possible because of the genetic
background and their origin. A lot of scientists propose
differences in genotype with regard to their phenotypic and
genotypic variations. A highly significant difference were
reported among 40 rice accession in using 12 qualitative
traits (Pandey et al., 2009). Also, Rao, (1991) reported
about 95% of differences in 20 quantitative traits among
rice varieties.
Direct and Indirect Effects of Growth Traits on Yield
per Plant
There was a significant effect of phenotypic direct and
indirect yield-related traits per rice plant as shown in fig.1
and Table 5. The number of tillers per hill, number of
panicles per hill, filled grain per panicle, grain weight per
plot and kilogram per plot indicated positive direct effects
on yield per plant. In these, number of tillers per hill,
number of panicles per hill, grain weight per plot and
kilogram per plot indicated positive and highly significant
direct effects on yield of rice plant. The number of filled
grains per panicle showed positive and significant to the
rice plant yield. Base on the knowledge of selective
breeding the association of between yield and yield-related
traits becomes significant in asserting the most important
traits towards the general yield. Path coefficient analysis is
one of the most important techniques used for
improvement of crop in breeding program. It is therefore,
an analysis that is used in investigating direct effects and
indirect effects among the yield related traits by partitioning
of correlations coefficient (Chaudhary and Joshi, 2005)..
On the other hand, most traits as found by this study did
not have a significant direct effect on rice plant yield. The
indirect effects found were kilogram per plot and grain
weight per plot. These two traits have shown positive
indirect effects on the yield of rice plant. It is therefore a
significant effect on the yield of rice which have direct and
indirect influence on the development of rice. These
provide a successful breeding plan base on the correlation
of direct and indirect effects of yield (Chaudhry et al.,
1986).Selection base on these traits that have direct
influence on yield and yield components will be very
effective since these traits have the maximum positive
directs effects on grain yield of rice plant. Gangashetty et
al., (2013) reported high direct of number of tillers, grain
yield and number of panicle per hill to influence desired
improvement in selection of rice genotypes.
Indirect effects were also reported by Oladosu et al., 2014,
Usman et al., 2016. There was an indirect effects of
kilogram per plot via grain weight per plot, panicle length
and percentage filled grain per panicle. These indirect
effects gave a way to high positive significant correlation
of each traits with the rice grain yield per plant.
To boost the yield potential of rice, it is necessary to
improve the direct and indirect effects of traits so that
selection should be appropriate for yield and yield factors.
Path coefficient analysis is the most commonly used
techniques in finding the relationship between traits. Grain
yield does not exist in isolation but rather it is the result of
an association with other traits that form a complex
relationship that ultimately affect the yield. It was
suggested by Wright, 1921 that allows the separation of
direct and indirect effects through other traits by assigning
the correlation for interpretation of cause and effect
relationship.
Two-Stage Relations
There are two-stage of the growth and production factor of
rice phenotypically: First-order component which
comprises of plant height, number of tillers per hill, number
of panicles per hill and empty grain per panicle. The other
one which is regarded as the yield component traits or
called principal yielding factors for the production of rice
comprises filled grain per panicle, panicle length, grain
length, grain width, grain weight per plot and one thousand
grain weight of rice. These two components were
presented in Table 6 and 7 below.
First-Order Component Relation on Second Order
Component
The relationship between first-order and second order
component shown on Table 6 and 7 revealed that there
was a significant interaction of various trait components. .
The path analysis with first-order component with filled
grain per panicle revealed that number panicle per hill had
the highest value of direct correlation. It can be seen that
in the first-order component of variation that the number of
tillers per hill exhibited a direct positive variation with filled
grain per panicle (Table 6). The path analysis of first-order
component with panicle length showed a positive direct
correlation with number of tillers per hill, followed by the
empty grain per panicle and plant height. The other

7. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Int. J. Plant Breed. Crop Sci. 493
Table 5: Phenotypic path analysis of the direct (BOLD) and indirect effects of 12 characters on yield per hectare
in rice genotypes
Direct effect,pPLH NTH NPH FGPC EGPC PFGPC PLC GLTH GWD GWTPP oneTGW kplot
PLH -0.005 -0.0048 0.0006 0.00039 0.00006 0.00029 -0.00024 -0.00014 -0.00024 -0.00042 0.00037 -0.00037 0.0004
NTH -0.004 0.0005 -0.0041 -0.00358 -0.00039 0.0003.0 -0.00041 -0.00031 0.00037 0.00031 -0.00186 0.00014 -0.00191
NPH 160 -0.0001 0.0014 0.0016 0.00006 -8.60E-05 0.00008 8.45E-05 -0.00013 -0.00011 0.00065 -8.60E-05 0.00066
FGPC -0.007 0.0001 -0.0007 -0.00028 -0.00737 -0.00041 -0.00237 -0.00109 0.00119 0.00027 -0.00137 -0.00108 -0.00147
EGPC 0.009 -0.0006 -0.0008 -0.00049 0.00051 0.00912 -0.00831 0.00034 -0.00011 0.00116 -0.00011 -9.80E-05 -0.00015
PFGPC 0.012 0.0006 0.0012 0.00065 0.00395 -0.01118 0.01227 0.00024 -0.00055 -0.00163 0.00100 0.0005 0.00109
PLC 0.003 0.0001 0.0002 0.00017 0.00046 0.00012 0.00006 0.00315 -0.00025 0.00002 0.00022 0.00042 0.00023
GLTH 0.001 0.00004 -0.0001 -0.00006 -0.00012 -9.00E-06 -0.00003 -5.70E-05 0.00072 -0.00001 -8.60E-05 -6.30E-05 -0.00008
GWD -9.00E-05 -0.00001 0.00001 0.00001 0.000003 -1.10E-05 0.00001 -4.20E-07 0.000001 -0.00009 0.000008 0.000002 0.00001
GWTPP 0.001 -0.0001 0.00061 0.00055 0.00025 -0.00002 0.00011 9.22E-05 -0.00016 -0.00019 0.00135 0.00025 0.00134
oneTGW -0.003 -0.0003 0.00011 0.00018 -0.00048 0.00004 -0.00013 -0.00043 0.00029 0.00007 -0.00063 -0.00329 -0.00061
gplot 0.999 -0.0839 0.46402 0.4114 0.19871 -0.01608 0.08927 0.07349 -0.10984 -0.08907 0.98935 0.18522 0.99854
Corr. With yld -0.09NS 0.46** 0.41** 0.19* -0.02NS 0.09NS 0.08NS -0.12NS -0.09NS 0.99** 0.18NS 0.99**
* ≤ 0.05, ** ≤ 0.01, PHT: plant height, NTH: number of tillers hill, NPH: number of panicle per hill, FGPC: filled grain per
panicle, EGPC: Empty grain per panicle, PFGPC: percentage filled grain per panicle, PCL: panicle length, GLTH: grain
length, GWD: grain weight, GWTPP: grain weight per plot, one TGWT: one thousand grain weight, kgplot: kilogram,
Tha: yield in tonnes per hectare
Figure1: Path diagram and coefficients of factors on the influence of order components (first order and second
order components and the latter on yield per hectare of rice. Pij values are the direct effects, rij values are the correlation
coefficients)
parameter (number of panicles per hill) exhibited a
negative correlation with the panicle length. The path
analysis relationship with first-order component with grain
length revealed that only plant height had a positive direct
effect while the rest of the parameters show a negative
direct influence (Table 6). There was a significant
correlation of path analysis of first-order component with
grain width which showed a positive direct correlation
(0.1295), followed by plant height (0.0891) while the other
parameters showed a negative direct effect. The path
analysis with the first-order component on grain weight per
plot indicated that number of panicle per hill, number of
tillers per hill and showed a positive direct correlation while
plant height showed a negative direct effect (Table 6).
Moreover, plant height (0.0779) and number of tillers per
hill (0.0709) showed a positive direct effect on 1000 grain
weight while the number of panicles per hill and empty
grain per panicle showed a negative direct correlation.
The positive direct correlation effect on panicle length on
number of tillers as shown above, it showed a direct
influence in the path analysis these agreed with findings of
Moldenhauer and Gibboons, (2003) who stated that the
main important aspect in plant breeding is tillering because
it affect the number of panicles that are produced. There
had been a lot of studies in order to identify the genes
responsible for rice tiller development (Miyamoto et al.,
2004 and Zou et al., 2005). Environmental factors alter
tillering, however some important aspect should be
considered like light intensity and temperature as
suggested by Yoshida (1973). Tillers rate will continue to
expand with increased in nitrogen concentration in rice
development (Zhong et al., 2003). Panicle length has been

8. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Almu et al. 494
a very important aspect in determining the sink capacity of
rice plant. These was described by many plant breeders
(Mei et al., 2006, Xing et al., 2008). A large variation of sink
capacity had become a major objective of plant breeder. A
very important agronomic trait in plant breeding is tillering
which assist in grain production (Ling, 2000).
Where tillers are in abundance it results in a dense canopy
which provide a good situation where diseases and pests
prevailed. If the tillers are few it results in insufficient
number of panicles which will result in low amount of grains
that are produced (Cu et al., 1996). In this study there had
been an adequate amount of nitrogen fertilizer in most of
the treatments which was applied to the rice plants which
therefore, enhances the number of tillers produced. It
should be noted that nitrogen fertilizer plays a very
significant effect in the production of tillers (Sakakibara et
al., 2006) which contributed in production of rice yield. This
agreed with the findings of Wang et al (2016) where rice
grain yield was increased.
It had been stated by Oladosu et al., (2014) that fewer
number of tillers give rise to fewer panicles while too much
of tillers give higher tiller abortion, fewer panicles and the
yield would be reduced. It can also be seen that in one
thousand grain weight, the empty grain per panicle and
number of panicles per hill had a negative value of direct
correlation effect while plant height and number of panicles
per hill had a positive direct correlation. It should therefore
be taken into consideration that yield is a complex trait, it
becomes necessary to bring all the several
interdependable quantitative traits in order to make it
function in the right direction. Other yield-related
components or traits have to be considered which directly
or indirectly participated in the programme. Improvement
in selection is therefore a good exercise so that a trait that
is highly yield is selected, these affect a number of other
correlated traits and hence having to know the different
association of traits with yield in order to furnish a path to
the plant breeder for improvement in plant selection as it
go a long way in establishing the genetic and non-genetic
factors in plant breeding (Rahman et al., 2012).
Second-Order Component on Yield
There was a significant interaction between second-order
components on yield as presented in Table 7. It was found
out that grain weight per plot with a value of (0.989) had a
maximum positive direct influence on yield. This was
followed by filled grain per panicle with a value of (0.013),
with direct positive influence on yield. This agreed with the
work of Datta et al., (2017) who suggested that genotypes
with high grains per panicle showed higher grains yield of
rice. This was most suitable in selection criteria in
providing the rice yield. The dependence for yield and yield
components on grain weight per plot for direct positive
correlation. Panicle length and grain length also have a
direct positive correlation influence on yield. Grain length
is a very significant aspect of yield and yield components,
it therefore affects yield (Zuo and Li, 2015). The panicle
length had the direct positive correlation while one
thousand grain had the negative correlation. Grain weight
had been a significant effect on the yield of rice, there was
a strong direct positive correlation with yield. These agreed
with the findings of Seyoum et al., (2012) and Hairmansis
et al., (2013). These should be considered during selection
of rice improvement program so that a better and high
yielding rice varieties were achieved.
Table 6: Relationship between first-order and second-
order
Variable PHT NTH NPH EGPC
Filledgrain/
panicle
PHT 0.0111 -0.0014 -0.0009 -0.0007
NTH -0.0343 0.2687 0.2340 -0.0196
NPH 0.0160 -0.1670 -0.1920 0.0100
EGPC -0.0040 -0.0050 -0.0037 0.0660
FGPC -0.0116ns 0.0950ns 0.0380** 0.0560ns
Panicle
length
PHT 0.0450 -0.0060 -0.0040 -0.0030
NTH -0.0180 0.1410 0.1220 -0.0100
NPH 0.0050 -0.0550 -0.0640 0.0034
EGPC -0.0029 -0.0030 -0.0030 0.0470
PLC 0.0290ns 0.0750ns0.0530** 0.0370ns
Grainlength
PHT 0.0392 -0.0050 -0.0032 -0.0024
NTH 0.0086 -0.0673 -0.0587 0.0049
NPH 0.0018 -0.0195 -0.0224 0.0012
EGPC 0.0009 0.0012 0.0008 -0.0158
GLTH 0.0506ns -0.0907ns -0.0835* -0.0121
Grainwidth
PHT 0.0891 -0.0114 -0.0073 -0.0055
NTH 0.0039 -0.0312 0.0023 0.0023
NPH 0.0021 -0.0226 -0.0259 0.0014
EGPC -0.0080 -0.0095 -0.0069 0.1295
GWD 0.0872ns -0.0746ns -0.0673ns 0.1277*
Grain
weight/plot
PHT -0.0184 0.0024 -0.0820 0.0011
NTH -0.0541 0.4233 0.8720 -0.0309
NPH -0.0028 0.0299 1.0000 -0.0019
EGPC -0.0012 -0.0015 -0.0015 0.0199
GWTPP -0.0766ns 0.4541* 1.7363ns -0.0119ns
oneTGWT
PHT 0.0779 -0.0099 -0.0064 -0.0048
NTH -0.0091 0.0709 0.0618 -0.0052
NPH 0.0089 -0.0956 -0.1097 0.0059
EGPC 0.0005 0.0005 0.0004 -0.0066
One
TGWT
0.0782ns -0.0342ns -0.0539** -0.0107**
PHT: Plant height, NTH: Number of tillers per hill, NPH:
Number of panicles per hill, EGPC:Empty grain per
panicle, oneTGWT: One thousand grain weight

9. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Int. J. Plant Breed. Crop Sci. 495
Table 7: Second order component on yield per plant
Variable FGPC PCL GLTH GWD GWTPP oneTGWT
FGPC 0.0135 0.1474 -0.162 -0.0372 0.1864 0.1471
PCL 0.1474 0.0078 -0.079 0.0047 0.0683 0.1317
GLTH -0.162 -0.079 0.01135 -0.0157 -0.1194 -0.0878
GWD -0.0372 0.0047 -0.0157 -0.0023 -0.0875 -0.0205
GWTPP 0.1864 0.0683 -0.1194 -0.0875 0.98874 0.1916
oneTGWT 0.1471 0.1317 -0.0878 -0.0205 0.1916 -0.009
Tha 0.196* 0.075NS -0.109NS -0.089NS 0.989** 0.183*
FGPC: Filled grain per panicle, PCL: Panicle length, GLTH: Grain length, GWD: Grain width, GWTPP: Grain weight per
plot, oneTGWT: one thousand grain weight, Tha: Tonnes per hectare
CONCLUSION
There was a need for the development of rice genotypes
for the attainment of food security and exploring its effect
in quantitative traits. Evaluation of 16 rice genotypes was
done in order to understand their effects. Three
environments were involved in two planting seasons. The
experiment was split-plot design replicated three times
across the environment. Data were collected on
vegetative, yield and yield-related traits. There was a
significant relation (p ≤ 0.01) in the pooled analysis of
variance among the 16 genotypes in all the characters
except panicle length and grain length. There was a highly
significant difference (p ≤ 0.01) in nitrogen rates in plant
height, number of tillers per hill. Number of panicles per
hill, grain weight per plot, one thousand grain weight and
grain yield. There was a highly significant increases in
yield. However, nitrogen fertilizer rates being a very
important for increased yield, management becomes
critical, so that increase yield and nitrogen use efficiency
would be maintained. Furthermore, the direct and indirect
yield related traits in the path coefficient analysis were
discussed. The direct effects were critically analysed to
reveal that coefficient interaction exist between characters
such as number of tillers, number of panicles per hill, filled
grain per panicle and grain weight per plot indicated
positive direct effects on the yield of rice plant. These
permits the selection base on these traits to be very
effective since the traits showed a maximum positive direct
effect on grain yield. It showed that since there is a direct
positive correlation in tillers per hill and grain weight per
plot, this allow a good association leading to high grain
yield per hectare, there were also indirect effects of grain
weight per plot, panicle length and percentage filled grain
per panicle. A high positive significant correlation with rice
yield allows the potential to boost rice production in both
the direct and indirect effects. Path coefficient were also
used to separate the different association between traits.
Yield showed a strong positive correlation with GWTPP
and kgplot.
There was a strong positive correlation between NTH and
NPH. There was a strong negative correlation between
FGPC and EGPC. These showed that the more the filled
grain per panicle the less will be the empty grain per
panicle. The strong positive relation for these traits showed
a significant interaction for the production of rice.
Therefore, these two effects possess positive direct effect
with the yield per hectare. It should be noted that selection
can therefore be made efficiently.
REFERENCES
Abebe, G., (2012). Soil characterisation and evaluation of
slow release urea fertilizer rates on yield components
and grain yields of wheat and teff on Vertisols of Jamma
district of south Wollo Zone,Ambra Region . MSc
Thesis, Haramaya University, Haramaya, Ethiopia
Ali, Y.Z A. and Hussain, F., (1992). Genotype and
environment interaction effect on yield of cotton under
naturally salt stress condition, Genotype and
environment interaction effect on yield of cotton under
naturally salt stress. International Journal of
Environmental Science and Technology Vol. 2, pp 169-
173
Barraclough, P. B., Howarth, J.R., Jones, J., Lopez,-B. and
Parmar, S., (2014). Nitrogen efficiency of wheat:
genotypic and environmental variation and prospects
for improvement. European Journal of Agronomy, 33:
1-114(5):363-368
Bereket, H., Tjeerd, J.S. and Ellis, H., (2011).
Tef(Eragrastic tef) production constraints on Vertsol
Ethopia: farmers perceptions and evaluation of low soil
zinc as yield limiting factor. Journal of soil science and
plant nutrition, 57: 4, 587-596.
Chaudhry, A.R, Shah, A.H, Ali, L and Bashir, M., (1986).
Path coefficient analysis of yield and yield components
in wheath. Pakistan Journal of Agricultural Research,
7: 71-75
Cu, R.M., Mew, T.W., Cassman, K.G. andTeng, P.S.,
(1996). Effect of sheath blight on yield in tropical,
intensive rice production system. Plant
Disease 80:1103–1108.
Datta, A.L and Ferdous Z., (2017). Water Management in
Rice B.S. Chauhan, K. Jabran, G. Mahajan (Eds.), Rice
Production Worldwide., Springer, Dordrecht, the
Netherlands (2017), pp. 255-277
Hairmansis A, Kustianto, B. and Suwarno, S., (2013).
Correlation analysis of agronomic characters and grai
yield of rice for tidal swamp areas. Indonesian Journal
of Agricultural Science, 11:11-15

10. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Almu et al. 496
Hasan, M.J.M., Kulsum, U., Akter, A., Masuduzzaman,
A.S.M., Ramesha, M.S., (2013). Genetic Variability
and Character Association for Agronomic Traits in
Hybrid Rice (Oryza Sativa L.). Bangladesh Journal of
Plant Breeding and Genetics > Vol 24, No 1
Kashiani, P and Ghizan Sale. (2010). Estimation of
Genetic Correlations on Sweet Corn Inbred Lines Using
SAS Mixed Model. American Journal of Agricultural and
Biological Sciences. 5: 309-314
Food And Agriculture Organization Of United Nations
(FAO). 2016. Statistical database. [online] Rome: Food
and Agriculture Organization of the United Nations.
Available at: http://faostat3.fao. org/home/E. [Accessed
2016, August 24].
Gangashetty P.I., Salimath, P.M. and Hanamaratti. N.G.,
(2013). Genetic variability studies in genetically diverse
non-basmati local aromatic genotypes of rice (Oryza
sativa L.). Rice Genomics and Genetics. 4 (2): 4-8.
Ling, Q.H. (2000). Crop population quality. Shanghai
Scientific & Technical Publishers: Shanghai, China, pp
32–36.
Mei, H.W., Xu. J.L., Li, Z.K., Yu, X.Q., Guo, LB., Wang,
Y.P., Ying, C.S. and Luo, L.J., (2006). QTLs infl uencing
panicle size detected in two reciprocal introgressive line
(IL) populations in rice (Oryza sativa L.). Theoretical
Applied Genetics 112: 648-656.
Miyamoto, N., Goto, Y., Matsui, M., Ukai, Y., Morita, M.
and Nemoto, K., (2004). Quantitative trait loci for
phyllochron and tillering in rice. Theorotical Applied
Genetics 109: 700-706.
Moldenhauer, K.A.K. and Gibbons, J.H., (2003). Rice
Morphology and Development. In C.W. Smith and R.H.
Dilday eds., Rice: Origin, History, Technology, and
Production. John Wiley & Sons, Inc., New Jersey. 103-
128.
Naserian, B., Asadi, A.A., Rahimi, M. and Ardakani, M.R.,
(2007). Evaluation of wheat cultivars and mutants for
morphological and yield traits and comparing of yield
components under irrigated and rain fed conditions.
Asianournal of Plant science, 6(2), 214-224.
Pandey, P., John A. P., Tiwari, D. K., Yadav, S.K. and
Kumar, B., (2009). Genetic variability, diversity and
association of quantitative traits with grain yield in rice.
(Oryza sativa L.) Journal of Bio-Science.17(1):77–82,
Rahman, M.M., Syed, M.A., Adil, M. and Rshid, M.M.,
(2012). Genetic Variability, Correlation and Path
Coefficient Analysis of some Physiological Traits of
Transplanted Aman Rice (Oryza sativa.L). Middle-East
Journal of Science Research, 11(5): 563-566.
Rao, S.A., Khan, M.A., Neilly, T.M. and Khan, A.A.,
(1997). Cause and effect relations of yield and yield
components in rice (Oryza sativa L.). Journal of
Genetics and Breeding, 51: 1-5.
Oladosu, Y., M. Y. Rafii., Magaji, Abdullahi, N., Miah, G.,
Chukwu, S.C., Hussin, G., Ramli, A., and Kareem, I.,
( 2018). Genotypic and Phenotypic Relationship among
Yield Components in Rice under Tropical Conditions,
BioMed Research International Volume 2018, Article ID
8936767, 10 pages https://doi.org/10.1155/2018/
8936767
Oladosu, Y., M Y. Rafii, Abdullah,N., Malek, M.A.,
Rahim, H.A., Hussin, G., Latif, M.A. and Kareem, I.,
(2014). Genetic variability and selection criteria in rice
mutant lines as revealed by quantitative traits
Oladosu, Y., M Y. Rafii., Abdullah, N., Magaji,U.,
Miah, G.,Hussin, G. and Ramli, A., (2015). Genotype ×
Environment interaction and stability analyses of yield
and yield components of established and mutant rice
genotypes tested in multiple locations in Malaysia. Acta
Agriculturae Scandinavica, Section B — Soil & Plant
Science 67:7, pages 590-606.
Sakakibara, H., Takei, K. and Hirose, N., (2006).
Interactions between nitrogen and cytokinin in the
regulation of metabolism and development. Trends in
Plant Science 11: 440–448.
Seyoum, M., Alamerew, S., and Bantte, K (2012). Genetic
variability, heritability correlation coefficient and path
analysis for yield and yield related traits in upland rice
(Oryza sativa L.). Journal of Plant Sciences, 7(1):13-22
Shahinnia, F., Rezai, A.M. and Tabatabaei, B.E.S.,
(2005). Variation and path coeffiecient analysis of
important agronomic traits in two- and six-rowed
recombinant inbred lines of barley (Hordeum vulgare
L.). Czech Journal of Genetics and Plant Breeding 41:
246-250.
Taalab, S., Mahmoud, A. and Hanan, S., (2015).
Implication of rate and time of nitrogen application on
yield and nitrogen use efficiency of barley in sandy soil,
International Journal of Crop Research 8(6): 412-422
Usman, M.., Fagam, G.A.S., Rilwanu, Dayi, U. and Isah.
Z., (2016). Phenotypic Response of Two Garlic
Varieties to Different Nitrogen Fertilization Grown under
Irrigation in Sudan Savannah Ecological Zone of
Nigeria. International Journal of Agronomy
Volume 2016, Article ID 2495828, 9 pages
Wang, Y., Ren, T., Lu, J.W., Ming, R., Li, P.F., Saddam,
H., Cong R.H. and Li, X.K., (2016). Heterogeneity in
rice tillers yield associated with tillers formation and
nitrogen fertilizer. Agronomy Journal 108:1717–1725.
Usman,,M.G., M Y.Rafii., Martini, M.Y., Oladosu A.Y.,
Ismail, M.R. and Miah, G., (2017). Molecular analysis
of Hsp70 mechanisms in plants and their function in
response to stress, Biotechnology and Genetic
Engineering Reviews,
Wissum, M, Mazzola, M. and Picard, C., (2009). Novel
approaches in plant breeding for Rhizosphere related
traits. Journal of plant and animal science. 321, 409-
430
Woldeyesus, S, Gretzmarher, R. and Edelbaue, A.,
(2004). Genotypic Variation for nitrogen use efficiency
in Ethopia barley. Field Crops Research, 85: 43-60
Wright S. (1921). Correlation and causation. Journal of
Agricultural Research, 20: 557-585
Yoshida S. (1973). Effects of temperature on growth of the
rice plant (Oryza sativa L.) in a controlled environment.
Soil Science and Plant Nutrition 19: 299-310.
Yuan, W., Peng, S., Cao, C., Yirk, P., Xing, D., Zhang, Y.,
Yisperas, R.M. and Laza, R.C., (2011). Agronomic

11. Genetic Variability of Rice (Oryza sativa L.) Genotypes under Different Level of Nitrogen Fertilizer in Malaysia
Int. J. Plant Breed. Crop Sci. 497
Performance of Rice Breeding Lines Selected Based
on Plant Traits or Grain Yield. Field crops research,
121: 168-174
Xing, Y.Z., Tang, W.J., Xue, W.Y., Xu,C.G. and Zhang, Q.,
(2008). Fine Mapping of a major quantitative trait loci,
qssp7, controlling the number pf spikelets per panicle
as a single mendelian factor in rice. Theor. Appl. Genet.
116: 789-796
Zou, Q et al. (2015). Effect of nitrogen fertilizer and
planting density on canopy structure and population
characteristic of rapeseed with direct seeding
treatment. Acta Agronomica Sinica, 41: 758-765
Zhong, X.H., Peng, S.B., Sanico, A.L. and Liu, H.X.,
(2003). Quantifying the Interactive Effect of Leaf
Nitrogen and Leaf Area on Tillering of Rice. Journal of
Plant Nutrition, 26:1203–1222.
Zou, J., Chen, Z., Zhang, S., Zhang, W., Jiang, G., Zhao,
X., Zhai, W., Pan, X. and Zhu, L., (2005).
Characterizations and fine mapping of a mutant gene
for high tillering and dwarf in rice (Oryza sativa L.).
Planta, 222, 604–612.
Accepted 25 February 2019
Citation: Almu H, Rafii MY, Sulaiman Z, Ismail R, Harun
AR, Ramli A, Yusoff A.S., Halidu J (2019). Genetic
Variability of Rice (Oryza sativa L.) Genotypes under
Different Level of Nitrogen Fertilizer in Malaysia.
International Journal of Plant Breeding and Crop Science,
6(1): 474-484.
Copyright: © 2019: 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.