CONTACT : MOHD RAFII YUSOP mrafii@upm.edu.my
1
Abstract
Rice (Oryza sativa L.) is a cereal and staple food crop of over half of the world’s
population. Blast, bacteria leaf blight and drought stresses affect yield of rice
drastically ranging from 1-100% loss depending on the severity of disease and
water deficit condition. Resistance and tolerance high yielding varieties of blast
(Putra1) and drought (MR219 IR99784-156-137-1-3) respectively and also
IRBB60 (bacteria leaf blight) were used. The research considered the genetic
inheritance of the new improved lines and their interactions. Pedigree breeding
method was used to develop two single, double and three-way (and reciprocal)
crosses through marker-assisted selection. Southern blot analysis was used to
determine success of introgression of resistance/tolerance genes/QTLs and
selection, also validated by phenotyped results. Agro-morphological and yield
parameters of the various populations were analysed. The results indicated
levels of significant differences amongst and between treatments for non-
drought stress (NS) and reproductive drought stress (RS) and their interactions.
There were significant variation among parents and improved lines on some
traits in NS treatment, but RS significantly affected parameters of DF, FFG, YM
and most especially the susceptible parent, while the improved lines were
tolerant. Significant interactions was recorded (P≤0.05) between treatment and
variety (Trt*Var.) on PL, T, FFG and GLW. Cluster analysis and PCA of relationship
among the 9 traits in the two treatments revealed that each of single, double
and three-way (and reciprocal) crosses had good lines either under NS and RS.
ISSN : 2580-2410
eISSN : 2580-2119
Genetic inheritance of multiple traits of blast, bacteria leaf blight
resistant and drought tolerant rice lines
Ibrahim Silas Akos1,2, Mohd Rafii Yusop*1,3, Mohd Razi Ismail1,3, Shairul Izan Ramlee3,
Noraziyah Abd Aziz Shamsudin4, Asfaliza Binti Ramli5, Jalloh Momodu1, Jamilu Halidu1, &
Senesie Swaray1
1Laboratory of Climate-Smart Food Crop Production, Institute of Tropical Agriculture and
Food Security, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
2Department of Crop Science, Faculty of Agriculture, Kaduna State University, Kafanchan
Campus, Nigeria.
3Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia, 43400 UPM
Serdang, Selangor, Malaysia.
4School of Environmental and Natural Resource Sciences, Faculty of Science and Technology,
Universiti Kebangsaan Malaysia.
5Malaysian Agricultural Research and Development Institute, Rice Research Centre,
Persiaran Mardi-UPM, 43400, Serdang, Selangor, Malaysia.
OPEN ACCESS International Journal of Applied Biology
Keyword
Rice,
Genetic,
Inheritance,
Resistant,
Tolerance.
Article History
Received 25 September 2019
Accepted 29 December 2019
International Journal of Applied Biology is licensed under a
Creative Commons Attribution 4.0 International License,
which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work is properly cited.
International Journal of Applied Biology, 3(2), 2019
2
Introduction
Rice, a multi-environmental (rain-fed lowland, upland and deep water) (Ou, 1985, Latif
et al., 2011) cereal and staple food crop of the world, it is one of the most cultivated crops
along with wheat and corn. It provides nutrient and meets the economic need of the farming
populace. It is consumed by over 50% of the world’s population as their major source of
calorie (Luo et al., 1998). Blast and bacteria leaf blight are two major important diseases of
rice that causes significant yield loss to farmers (Asghar et al., 2007, Jia et al., 2000, Zhang et
al., 2015).
The management practice for the disease can be fungicide, biotechnological methods,
agronomic practices and cultivation of resistant cultivars (Ribot et al., 2008). The use of the
resistant variety is the most environmental and economically friendly approach to blast threat
(Castano et al., 1990, Saifullah et al., 1995, Khan et al., 2001, Haq et al., 2002). The challenge
is often that due to the changeful nature of the virulent races, the resistance traits may be
lost in the cause of time. The most effective strategy to minimize yield losses due to blast,
bacteria leaf blight and drought stress is through the development of durable, broad-
spectrum resistant varieties (Jena and Mackill, 2008; Kumar et al., 2014; Sundaram et al.,
2014). Sundaram et al. (2014) reported that atleast 40 genes that confer bacteria blight
resistance have been identified, while many have been fine-mapped and cloned (Natrajkumar
et al., 2012). 101 blast resistant (R) genes (Rajashekara et al., 2014) and 350 QTLs have been
identified (Sharma et al., 2012). Closely linked markers available for many blast resistance and
bacteria blight genes have been identified (Sundaram et al., 2014). Drought stress is a
condition of water availability deficit, it could be water deficit condition at any of the stages
of rice development which has the potential of affecting yield. Drought stress is increasingly
becoming a challenge to farming communities today with a global scale, it affects over
23million hectares of farming areas in Asia (Bray et al., 2000; Kumbhar et al., 2015). The
increasing human population with approximated 10billion by the year 2050 also requires 50%
increase to current global rice production to meet the food projection demand (Maclean et
al., 2002, Bourman et al., 2007). This places a demand for strategy to increase yield
production in the phase of climate change to develop rice adapted to drought stress (Pandey
and Sukla, 2015). Understanding plant diversity in relation to behaviour and adaptation of
drought-prone environment is important (Alonso-Blanco et al., 2009), the ability to design an
effective strategy of phenotyping requires good understanding of plant survival mechanism
under drought stress.
Understanding of plant diversity is relevant to assessment of genotypic variability
under different water deficit condition as an important pre-condition for a successful drought
tolerance breeding programme (Sarkar et al., 2013, Abenavoli et al., 2016, Anower et al.,
2017). Blast, bacteria leaf blight (BLB) and drought stress could affect rice drastically, and
result showed that high incidences caused yield losses of 100% (Zhai and Zhu, 1999), 1-50%
(Scardaci et al., 2003) and 100% depending on the stage of rice development and duration
respectively. 50% yield loss was recorded for abiotic stress (Bray et al., 2000, Iqbal et al., 2013,
Li et al., 2014).
The parameters of panicle length, effective tillers, grain length and width ratio, 100
grain weight, fully grain weight were amongst those considered important agronomic trait
which has correlation with increased yield potential in rice, considered as quantitative traits
which could be affected by the influence of environment (Han et al., 2004, Guo et al., 2003,
Han et al., 2006, IRRI-SES, 2013, Taglea et al., 2016, Chang et al., 2016).New improved lines
has been developed with three resistance/tolerant traits of blast (Magnaporthe grisea)
International Journal of Applied Biology, 3(2), 2019
3
bacteria leaf blight (Xanthomonas oryzae) and drought tolerance (MR219) through
pyramiding of marker-assisted selection. This research considered the genetic inheritance of
the target genes and their interactions.
Materials and Methods
Plant material and breeding design
Plant materials used were new developed lines from three parents with resistances to
blast, M. grisea anomorph P. oryzae, a popular Malaysia high yielding rice variety known as
Putra1. Bacteria leaf blight resistance variety known as IRBB60, and a drought tolerant
Malaysia variety referred to as MR219 (IR99784-156-137-1-3 Drought tolerance). these
varieties were developed as single crosses, double cross and three-way cross with reciprocal
cross as well. These new lines were developed in a glass house at Rice Research Centre and
Laboratory of Climate and Smart-Food Crop Production, Universiti Putra Malaysia.
DNA extraction and nanodrop spectrophotometry
Three-four weeks old leaves samples were collected and ground using mortar and
pestle in liquid nitrogen. The procedure was according to Doyle and Doyle (1987)
cetyltrimethylammonium bromide CTAB method with modification following the protocol of
McCouch et al (1988).
The DNA pellet was diluted in 50µl TE buffer, 2µl was pipetted on nanodrop
spectrophotometry machine to measure the quantity and purity of the DNA samples. This
formed the basis for the constitution of the DNA working solution for polymerized chain
reaction (PCR) amplification.
Molecular markers
Simple sequence repeat (SSR) markers polymorphic and linked to the genes/QTLs for
the three parental varieties were purchased. Putra1, a blast resistance variety had two
polymorphic markers that were also linked to the genes of resistance (R), MR219 (IR 99784-
156-137-1-3) drought tolerance variety used had three qDTY and IRBB60 had four R genes
(Pinta et al. 2013, Miah et al. 2016, Kumar et al. 2014, Khan et al. 2015, Pradhan et al. 2015,
Shamsudin et al. 2016, www.gramene.org).
Polymerized chain reaction and electrophoresis
The pairs of primers for the various genes/QTLs were optimized to amplify simple
sequence repeat loci for polymerase chain reaction (PCR). Survey of the three parental
varieties was carried out to identify SSR markers polymorphic to each of them. Total PCR
reaction of 15μL which contained 70ng DNA template had 7.5μL master mix (Thermo
Scientific, Waltham, MA, USA), 4.5μL nucleaus free water and 1.0 μmol L-1 concentration of
each primer (Forward and Reverse). The PCR amplification was conducted in a thermocycler
(T100TM, Bio-Rad, Hercules, CA, USA) following the touchdown protocol with the lid
temperature of 1050C. The initial denaturation, annealing and extension temperatures were
thus; 940C for 3mins followed by 940C for 30sec, a temperature of 620C for 1min., +10C per
cycle followed by 720C for 30sec., then a returned to step 2, 9× followed by 940C, 30sec., 520C,
1min, 720C, 2mins., another return to step 6, 29×., and finally 720C, 10mins. Followed by rapid
cooling to 40Cꝏ prior to analysis.
http://www.gramene.org/
International Journal of Applied Biology, 3(2), 2019
4
Southern blot analysis using gel electrophoresis was carried out. 2.0% MetaphorTM
agarose (Lonza) gel containing 5μL Midori green in 1× TBE buffer (0.05 mol L−1 Tris, 0.05 mol
L−1 boric acid, 1 mmolL−1 EDTA, pH 8.0) was run with 5μL of PCR product mixed with loading
dye. The gel run was at a constant voltage of 80V for 45-60 minutes. Molecular imager system
(GelDocTM XR, BioRad) was used to analyzed band pattern under UV light for amplified
products.
Identification of polymorphic markers and progeny selection
The basis for a successful marker-assisted selection was the identification of the
polymorphic markers for each of the parent trait. Blast resistance had RM6836 and RM8225,
IRBB60 had Xa13 prom, RM122, RG136, pTA248 and RM224 while MR219 IR99784-156-137-
1-3 drought tolerance had RM511, RM1261 and RM520 polymorphic to the drought tolerance
QTLs.
Two F1s with double bands corresponding with the parent plants were selected, that
was in a cross between a common recipient parent Putra 1 and donor IRBB60, Putra1 and
MR219. The selected progenies formed the bases for a double cross and three-way and
reciprocal crosses.
The selection of the subsequent generations of F1(2) and F1 three-way crosses were
based on southern blot analysis of the bands which coincide with the first recipient and the
introgressed variety. Those were selected and advanced until a non-segregating F3(2) double,
F4 single, and F3 three-way crosses pure-line generation was reached.
Fungal, bacteria culture and inoculation
Most virulent strains of fungus Magnaporthe grisea pathotype P7.2 and bacterium
Xanthomonas oryzae pv oryzae MX01552 were obtained from Malaysia Agricultural Research
and Development Institute (MARDI). They were sub-cultured in potato dextrose agar (PDA)
for 14 days in 250C and nutrient agar (NA) for two days in 300C respectively (Suresh et al.
2013, Mahdieh et al. 2013).
The M. grisea mycelia culture was prepared in sterile distilled water suspension at
concentration of 1.9×106 conidia mL-1. The Xoo culture was also prepared in a suspension
concentration of 109.
The fungus suspension was sprayed on young leaves of 2-3 weeks old at relative
humidity >90% for 48hours for disease infection and after 7 days disease scoring was carried
out according to the IRRI-SES, (2013) glass house scoring, thus; from 0-2, resistant (R), 3
considered as moderately resistant (MR) and score 4-6 as MS, while 7-9 as S. The Xoo
pathogen was clip inoculated on 3-4 weeks old leaves at 1-2cm from the tip of the leaves. The
scoring was by measuring the length of infected area with meter rule in centimeter according
to IRRI-SES, 2013. Glass house scale (Banito et al. 2012) and modification according to
Amante-Bordeos et al. (1992), thus; 0-5 considered as R, >5-10 MR, >10-15 MS, while >15
were considered as S.
Drought stress imposition
Reproductive drought stress was imposed on the rice lines carrying drought tolerance
QTLs. According to IRRI-SES (2013) one week of water deficit stress for glass house experiment
is enough to affect yield. The reproductive stage was between 70-90 days after sowing. Stress
was imposed for >2weeks with leaves turned from U-shaped to 0-shaped as criterion for
International Journal of Applied Biology, 3(2), 2019
5
scoring drought (IRRI-SES, 2013, Kadioglu and terzi, 2007), dried soil and soil moisture meter
with >15cm depth measured dried.
Experimental layout and cultivation
The experiment was set out in a glass house at Rice Research Centre, Universiti Putra
Malaysia with 3 plants each per bucket and label accordingly. The leaves of the parents to
their progenies were collected and genotyped using southern blot analysis to determined the
ideal plants for selection until line stability of the progenies as non segregating generation
reached. The non-segregating (pure-lines) and parent plants were laid in rows according to
their genotypes.
Data collection
Nine quantitative traits were measured and data collected from the 3 parents and
their progenies (improved lines) in single trial under non drought stress (NS) and reproductive
drought stress (RS) treatment in five (5) replications in around 120 days after seeds sowing.
These quantitative traits included; days to 50% flowering (DF), height of plant (HP), panicle
length (PL), effective tillers (ET), tillers produced (T), fully filled grain (FFG), 100-grain weight
(100-GW), grain length and width ratio (GLW) and yield maturity(YM) as indicated in Table 4.
for NS and Table 7. for RS treatments.
Statistical Analysis
All evaluated data were subjected to analysis of variance (ANOVA) using Statistical
Analysis Software (SAS) version 9.4 in Complete Randomized Design (CRD). The expressed
results were in mean, mean square, correlation coefficient (CV) and standard deviation to set
the relationship among the parameters (traits). To determine the genetic variability among
the 9 quantitative traits, cluster analysis was employed. The genetic relationship among the
parent varieties and improved rice lines was determined in conformity with unweighted pair
group method using arithmetic average (UPGMA) algorithm and sequential, agglomerative,
hierarchic and non-overlapping (SAHN) method using Numerical Taxonomy Multivariate
Analysis System, Exeter Software, Setauket, NY, USA software (NTSYS v2.1).
Table 1. Description of major symbols
Symbol Description
PB
PD
PBD
PDB
DPB
DF
HP
PL
ET
T
FFG
GLW
100GW
Cross between Putra1 and IRBB60
Cross between Putra1 and MR219
Three-way cross between putar1 and IRBB60(F1) and
MR219 Drought tolerant
Double cross(from two F1s; P×D and P×B
Three-way reciprocal cross (between MR219 drought
tolerant and F1 Putra1×IRBB60)
Days to 50% flowering
Height of plant measured from the base to the tip of
panicle
Panicle length measured in centimetre
Effective tiller refers to those that had gains
Total number of tillers produced
Fully filled grain obtained by counting the number of
grains produced per panicle
Grain length and width ratio of a grain
100grain weight is a measure of 100 grains in grams
International Journal of Applied Biology, 3(2), 2019
6
Results and discussion
Genotypic and phenotypic selection of improved lines
Electrophoresis analysis (gel scoring) results with bands showing alignment to the
recipient parents were selected as pure and non-segregating lines in the F4 single crosses for
PD and PB, F3 three-way and reciprocal crosses (PBD, DPB) and F3(2) (double) cross (PDB) as
shown in the Figures 1, 2, 3, 4 and 5 below.
Challenging the plants with the disease pathogens and subjection to water deficit
stress for the improved lines and susceptible varieties was undertaken. The results showed
that the improved lines were resistant and moderately resistant to blast and bacterial leaf
blight and as well tolerant to drought stress according to acceptable yield percentage for
tolerant rice (Miah et al. 2016, Ashkani et al. 2011, Yambao and Ingram, 1988). The
susceptible varieties in each case were truly susceptible to the disease pathogens and drought
stress.
Figure 1. F3(2) cross for improved lines(PDB)
Figure 2. F3 three-way reciprocal cross of improved lines(DPB)
Figure 3. F3 three-way cross of improved lines PBD
YM
DS
NS
Yield maturity is the number of days from sowing to
harvest.
Reproductive drought stress
Non-drought stress
International Journal of Applied Biology, 3(2), 2019
7
Figure 4. F4 single cross improved lines(PD)
Figure 5. F4 single cross for PB improved lines
Agronomic trait assessment of improved lines
These are also considered as agronomic traits; days to 50% flowering, height of plant,
panicle length, number of tillers, effective tillers, fully filled grain, 100-grain weight, grain
length and width ratio, yield maturity. The effect of blast, bacteria leaf blight and drought on
any of these parameters could result in poor yield and quality. Improved lines were evaluated
under two treatments condition, namely; non-drought stress (NS) and reproductive drought
stress (RS) condition. Under NS the data of both parents and improved lines were analysed,
whereas in RS treatment, a susceptible and improved lines were analysed. They were to
further validate the results of the southern blot analysis (genotyping) and the phenotyping
results that showed resistances and tolerance to blast, bacteria leaf blight (BLB) and drought.
8
Table 2. Polymorphic, linked/quantitative trait loci and flanking simple sequence repeat (SSR) markers for genes/QTLs used.
Variety SSR markers Genes/QTLs Chromosom
e position
Expected
base pair size
Description
Putra1
Blast resistance
RM6836
Piz, Pi2, Pi9
6
240
Polymorphic/linked
RM8225 Piz 6 221 Polymorphic/linked
IRBB60
Bacteria leaf blight resistance
RM224
Xa4
11
157
Polymorphic/linked
RM122
RM153
RM13
xa5
xa5
xa5
5
5
5
227
201
141
Polymorphic/linked
Linked
Polymorphic/linked
RG136
Xa13Prom
xa13
xa13
8
8
246
Polymorphic/linked
Polymorphic/linked
RM21
pTA248
Xa-21
Xa-21
11
11
157 Polymorphic/linked
Polymorphic/linked
MR219 IR99784-156-137-1-3
Drought tolerance
RM511
qDTY12.1
12
130
Polymorphic/linked
RM1261 qDTY12.1 12 167 Polymorphic/flanking marker
RM28099 qDTY12.1 12 120 Flanking marker
RM28076 qDTY12.1 12 287 Flanking marker
RM520 qDTY3.2 3 247 Polymorphic/linked
RM236 qDTY2.2 2 174 Linked
RM276 qDTY2.2,3.1 6 149 Flanking marker
Askani et al., 2011, Miah et al., 2016, Shamsudin et al., 2016, www.gramene.org, He et al, 2006, Khan et al., 2015, Pradhan et al.,
2015
http://www.gramene.org/
International Journal of Applied Biology, 3(2), 2019
9
Table 3. ANOVA for the parameters showing level of significance for non-drought stress (NS)
SOURCE DF DF(no) HP(cm) PL(cm) ET(no) T(no) FFG(no) 100GW(no) GLW(cm) YM(days)
Var 13 6.15* 73.9** 33.39* 13.73ns 16.90** 336.66* 0.02** 0.61** 3.48*
Rep. 4 3.31ns 50.4ns 0.28ns 2.13ns 5.32ns 239.19ns 0.02ns 0.07ns 2.19ns
Error 52 3.13ns 24.6ns 1.04ns 5.31ns 5.33ns 117.92ns 0.009ns 0.1ns 1.52ns
C. TotaL 69
∗Significant at P≤0.05, ∗∗highly significant at P≤0.01, ns: non-significant P> 0.05, DF. (degree of freedom), Var. (Variety), REP. (Replications),
C. Total (Corrected total), DF (Days to 50% flowering), HP (cm) (Height of plant measured in centimetres), PL (Panicle length in centimetres),
ET (Effective tillers), Tillers (total number of tillers), FFG (Fully filled grains), 100-GW (100 grain weight in grams)
International Journal of Applied Biology, 3(2), 2019
10
Figure 6. Comparison of agro-morphogical and yield parameters of parent plants and their progenies (improved lines) in non-
drought stress condition
0
20
40
60
80
100
120
140
160
180
200
M
e
a
n
Parent plant and progenies
Mean comparison of agro-morphogical and yield parametersbetween parents and progenies in non-
drought stress condition
DF
HP
PL
ET
T
FFG
100GW
GLW
YM
International Journal of Applied Biology, 3(2), 2019
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Table 4. Mean squares, least significant differences, coefficient of variations, standard deviation and broad sense heritability of
vegetative traits and yield components of different rice varieties (genotypes) for reproductive drought stress and non drought stress
treatments.
VARIETIES
DF(no)
RS NS
HP(cm)
RS NS
PL(cm)
NS RS
ET(no)
NS RS
T(no)
NS RS
FFG
NS RS
PUTRA1 89a - 103.19c -
25.03a
b - 10.2bcd - 10.6bcde - 172.8ab -
DROUGHT 88ab - 100.3c - 25.9ab - 13.6a - 13.8a - 168.4abc -
IRBB60 87.8abc 96.6a 103.61c 97.52a 25.06b 20.68c 7.8de 9ab 8.8de 9.6ab 172.4ab 26.4c
PB12 87.6abc - 104.34c - 24.9b - 9.4bcde - 9.6cde - 177.6a -
PB15 86.8abcd - 102.64c - 25.9ab - 9cde - 9.2de - 166abcd -
PD14 85.8bcd 91.8d 102.56c 96.6a 25.28b 23.7ab 10.6bcd 11.4a 11abcd 11.4ab 178.4a 52.4ab
PD15 87.4abcd 92.8cd 104.54c 97.32a 24.66b 22.6ab 9.6bcde 8b 11abcd 8.4b 157.2cd 49.8ab
PDB3 87.4abcd 92.8 104.06c 100.6a 24.88b 24.1a 11.2abc 10ab 13ab 10ab 153.4d 48.2ab
PBD1 87.6abc 94bc 104.72c 100.5a 24.9b 22.1bc 11abc 11.6a 12.4abc 11.6a 170.6abc 45.4b
PBD3 88ab 94bc 99.9c 96.22a 22.86c 23.3ab 12ab 11.6ab 13.2ab 10.6ab 168.4abc 59.4a
DPB7 85.2d 91.8d 113.44a 102.1a 25.04b 24a 9.4bcde 11.8a 9.8cde 11.8a 161.4bcd 53.4ab
DPB12 86bcd 92.8cd 106.1bc 99.68a 25.04b 23.4ab 7.2e 9.2ab 7.8e 9.4ab 152.4d 52.2ab
DPB13 86.2bcd 93cd 103.6c 98.44a 25.18b 23.4ab 9.4bcde 10.2ab 9.6cde 11.4ab 161bcd 43.76b
DPB20 85.6cd 95b 112.3ab 96.3a 26.58a 23.7ab 9cde 10.2ab 9.2de 10.4ab 165.8abd 46.2b
LSD 2.25 1.56 6.29 11.82 1.3 1.74 2.92 3.33 2.93 3.07 13.78 11.83
CV 2.03 1.3 4.74 9.35 4.07 5.87 23.13 25.43 21.69 22.86 6.54 19.34
Mean 87.03 93.46 104.67 98.54 25.09 23.09 9.96 10.2 10.64 10.46 166.13 47.72
Std Dev.
h2 B (%)
1.93
16.09
1.79
56.18
5.95
28.63
8.51
-17.54
1.20
31.13
1.55
28.40
2.59
24.03
2.65
2.18
2.74 2.43
30.24 -0.93
12.89
27.06
11.85
41.43
Heritability percentage categorised as low (0-30%), moderate (30-60%), and high (≥60%)
12
Table 5. Continued
VARIETIES
100GW(g)
NS RS
GLW(cm)
NS RS
YM(days)
NS RS
PUTRA1 2.41de - 4.97bc - 117.4ab -
DROUGHT 2.47cde - 4.98b - 117.4ab -
IRBB60 2.35e 2.43a 5.61a 4.42a 116.8b 133.2a
PB12 2.55abc - 4.96bc - 118.2a -
PB15 2.49cde - 4.81bcd - 117.6ab -
PD14 2.55abc 2.34a 4.86bcd 4.49a 117.4ab 128.4b
PD15 2.52abcd 2.4a 4.86bcd 4.49a 117.6ab 127.6b
PDB3 2.47cde 2.38a 4.53d 4.5a 117ab 127.8b
PBD1 2.63a 2.42a 4.59bcd 4.51a 117.6ab 127.8b
PBD3 2.52abcd 2.38a 4.63bcd 4.47a 117.6ab 129b
DPB7 2.52abcd 2.32a 4.51cd 4.57ab 117ab 127.4b
DPB12 2.52abcd 2.35a 4.57cd 4.75a 117.6ab 128.2b
DPB13 2.6ab 2.39a 4.51d 4.57ab 117ab 128.2b
DPB20 2.56abc 2.43a 4.08e 4.53 117.8ab 127.6b
LSD 0.12 0.11 0.4 0.2 1.31 1.8
CV 3.83 3.75 6.63 3.5 0.88 1.09
Mean 2.51 2.39 4.75 4.53 117.43 128.52
Std Dev.
h2 B (%)
0.11
16.67
0.09
0.5
0.44
50.50
0.17
6.25
1.02
-7.27
2.05
56.22
Var. (variety), IRBB60 (B) (Bacteria leaf blight resistance), Drought (D) (MR219 Drought tolerance),
reciprocal cross with MR219 Drought, Putra 1 and IRBB60 (DPB), Putra1, IRBB60 and MR219 Drought
tolerance (PBD), Putra1, MR219 Drought tolerance and IRBB60 (PDB), Putra1 and MR219 drought
tolerance (PD), Putra1 and IRBB60 (PB), LSD(Least significant difference), CV(Coefficient of variation),
Mean(average), DF(Days to 50% flowering), HP(cm)(Height of plant measured in centimeters),
PL(Panicle length in centimeters)ET(Effective tillers), Tillers (Total number of tillers), FFG(Fully filled
grains), 100-GWT(100 grain weight in grams), YM (yield maturity counted on number of days from
sowing to physiological maturity).
Agro-morphological and yield traits data for single cross (F4), double cross (F3(2)), and three-
way cross (F3) for non-drought stress.
Some parameters of pure-line agro-morphological and yield traits were observed and
measured to ascertain the differences between the various rice lines and the parent plants.
Table 3. shows an analysis of variance (ANOVA) of 9 traits, the varieties were all significant
with the exception of effective tillers (ET). Days to 50% flowering (DF), panicle length (PL) and
yield maturity (YM) were significant at 5% level of probability (P≤0.05), while height of plant
(HP), tillers (T), fully filled grain (FFG), 100GW, grain length and width ratio (GLW) were
significant at 1% level of probability (P≤0.01). No significant differences among the
replications were recorded in all the morphological and yield traits under non drought stress.
Comparison of parameters of non-drought stress (NS) and reproductive drought stress (RS).
Table 4. and Table 5. compares nine (9) mean data of the two treatments of non-
drought stress (NS) and reproductive drought stress (RS). The comparison was between
improved lines from the three crossed methods. Even though there were 14 lines in all on NS
and 10 on RS because, non drought stress had the three (3) parent plants and two (2) single
cross lines PB12, PB15 which did not have drought tolerance QTLs to be subjected in to
International Journal of Applied Biology, 3(2), 2019
13
reproductive drought stress treatment. Now, the parameters of days to 50% flowering (DF),
height of plants (HP), fully filled grain (FFG), yield maturity (YM) showed that the means of
reproductive drought stress (RS) was higher on all the improved lines and each compared to
their non-drought stress lines. While days to 50 % flowering on NS has the highest and lowest
number of days as 89 and 86 days respectively. RS had 91.8 and 96 days for lowest and highest
respectively. Height of plants (HP) for NS measured 102.3-112.3 cm and RS 96.3-102.1 cm for
lowest and highest respectively. The length of panicle for NS was 22.86 cm-26.5 cm and RS
was 22.6 cm-24.1 cm for lowest and highest respectively. There was an exceptional difference
between the line PBD3, which showed that the RS measured 23.2 cm in length whereas the
NS was 22.86 cm in length. The mean number of fully filled grain on NS lines had lowest mean
as 152.2 and highest 178, and RS was 43.7 and 59.4 for lowest and highest count respectively.
The parameter of 100-grains weigh measurement taken indicated that the lowest weight for
NS was 2.52g and the highest weight was 2.63g, for the RS it was 2.32-2.43g, lowest and
highest respectively. There were slight variations between the two treatments among some
lines on the parameters of grain length and weight ratio. Even though, the NS ratio was higher
than RS on five (5) improved lines, four (4) of the RS and reciprocal lines of DPB7, DPB12,
DPB13, DPB20 were higher than their counterpart lines. Effective tillers (ET) and number of
tiller (T) didn’t show any clear difference between the two treatments. It was reported that
water deficit stress affect these quantitative traits which was also confirmed in this study
(Latiffe et al., 2004; Atlin et al., 2006; Barnabas et al., 2008; Garrity and O’Toole, 1994). There
were alternating variation among them. This was because the treatment for drought was at
the reproductive stage when the tillers were matured and could not be affected by just about
two weeks of water deficit stress. Record has it that drought affect tillers at vegetative stage
(Cruz et al., 1986).
In consideration of non drought stress (NS) in the Table 4. and Table 5. with 14 lines
(parental and improved lines), it indicated the mean days to 50% flowering of Putra1 was 89
days. This was also corroborated with the findings of Miah et al. (2015) on the same trait of
the same variety (Putra 1) and significantly different from other traits, but all other traits were
not significantly different from each other. DPB7 was significantly different on height of plant,
while the other 8 traits were not different from each other. They tally with their parent plants,
which indicated their was no variation. DPB20 and PBD3 were significantly different from each
other and the other genotypes on parameter of panicle length. The other genotypes including
the parent plants were not different from each other. All other parameters did not show
difference between parents and progenies except the parameters of effective tillers, tillers
and grain length and width ratio showing significant difference between IRBB60 and others,
although with some similarities with other genotypes.
Considering the means of reproductive drought stress (RS), it has indicated that the
improved lines were different from the susceptible (control) variety because, the improved
lines were introgressed with drought tolerance QTLs (qDTY) which conferred on it ability to
adjust to some level of water deficit stress as against the control check that had no drought
tolerance qDTY. Field evaluation of MR219 carrying these three qDTY2.2, qDTY3.2, qDTY12.1,
confirmed tolerance to reproductive drought stress with grain yield of 756-2521 kg ha-1 for
2013 and 903-2523 kg ha-1 in the year 2014, while the susceptible variety had grain yield of
13 kg ha-1 in 2013 and didn’t flower in 2014 (Shamsuddin et al., 2016). Venuprasad et al.
(2009) and Swamy et al. (2013) reported tolerance ability of qDTY3.1 and qDTY2.2 respectively.
Mishra et al. (2013) reported that twenty one (21) experiments conducted in eastern India
International Journal of Applied Biology, 3(2), 2019
14
and IRRI confirmed that qDTY12.1 has an effect that increases with increasing severity of water
deficit stress. These confirms the reason the improved lines performed better compared with
its susceptible check.
Heritability estimate on non drought stress (NS) and reproductive drought stress (RS)
treatments.
Broad sense heritability is the total ratio of genetic variance to phenotypic variance.
In order words, the proportion of the parental gene inherited in the progenies that are
influenced by the environment and expressed in the phenotypic traits. The percentage ratio
is expressed as low, medium and high represented as 0-30%, 30-60% and ≥60% respectively.
The broad sense heritability of characters studied for non drought stress (NS) and
reproductive drought stress (RS) treatments (Table 4 and Table 5) were all within the range
of 0 to 56.22, which was within low to moderate heritability. These indicated the magnitude
of heritability that was influenced by environmental factors. Low percentage is higher
environmentally influenced and moderate heritability is lesser. Climatic factors which are
environmental in nature presents a great challenge to rice plant, there are various optimum
temperature requirements for various growth phases and stages, outside the range it affects
that phase of development (Tashiro and Wardlaw, 1989, Baker and Allen, 1993, Singh et al.,
1996, http:ricepedia.org). Control experiment and field experiment often presents a variation
in response to environmental factors. For instance, IRRI-SES (2013) categorised scoring and
protocols for diseases and tolerance evaluation for green or glass house experiment
differently from field. The low and medium heritability may not present a true genotypic
content of the improved lines in respect of the treatment since heritability estimate in this
case is not restrictive and does not consider experimental environment. The varieties used as
recipient parents were all high yielding, resistant and tolerant. And confirmed to be so
resistant and tolerant by the genotyped and phenotyped results.
The magnitude of heritability among the parameters of non drought stress (NS)
showed that all characters were within the lowest percentage of heritability which suggests
environmental influence, except days to 50% flowering (DF), fully filled grains (FFG) and yield
maturity (YM) which were medium ranged as; 56.18%, 41.43% and 56.22% respectively.
Reproductive drought stage (RS) treatment showed characters that were also low ranged with
exception of traits of panicle length 31.13%, tillers (T), 30.24% and grain length and width
ratio (GLW), 50.50% as medium heritability, indicated as having lesser environmental
influence compared to low heritability. All ranges of heritability percentage estimates were
also reported by Oladosu et al. (2014), Meena et al. (2016), Ridzuan et al. (2018). The low
heritability may not present the true nature of the inherited genes, because controlled
environment of research may present a variation different than when in a field environment.
International Journal of Applied Biology, 3(2), 2019
15
Table 6. Estimates of correlation coefficients on the phenotype among 9 traits in rice lines for non drought stress
∗Significant at P≤ 0.05, ∗∗highly significant at P≤ 0.01, ns: non-significant P> 0.0
DF(no) HP(cm) PL(cm) ET(no) T(no) FFG(no) 100GW(g) GLW(cm) YM(days)
DF(N0) 1
HP(cm) -0.26246* 1
PL(cm) -0.2019 0.30623** 1
ET(no) 0.00606 -0.07271 -0.11752 1
T(no) 0.00196 -0.12024 -0.24529* 0.92492** 1
FFG(no) 0.00394 -0.13601 0.06923 0.08221 -0.00771 1
100GW(g) -0.2765* 0.07116 0.06867 0.01494 -0.03721 0.13656 1
GLW(cm) 0.34151** -0.26206* -0.11788 0.01078 0.00824 0.09208 -0.29898 1
YM(days) -0.01376 0.1382 0.05447 0.11179 0.04018 0.23267 0.17624 -0.12587 1
16
Correlation coefficient relationship for non-drought stress
The r-values and test of significance provided by proc corr in SAS program (Table 6)
showed days to 50% flowering (DF) and height of plant (HP), 100-grain weight were
significantly different (P≤0.05), while with grain length and width ratio showed high significant
difference (P≤0.01). All the traits were low and negatively correlated. Height of plant (HP)
with panicle length (PL) and grain length and width ratio (GLW) were significant at (P≤0.05)
and (P≤0.01) respectively. Both were similarly low and negatively correlated. Panicle length
(PL) and effective tillers (ET) were jointly significant to tillers (T) at (P≤0.05) and (P≤0.01). while
the former is low and negatively correlated, the later is high and positively correlated. Notable
high correlated relationship was observed between height of plant (HP), effective tillers and
100GW. The relationship with ET though both were high but it was strongly negative, while
with 100GW was strong positive linear relationship. Ridzuan et al. (2019) and Oladosu et al.
(2018) also used phenotypic traits to determine relationships.
Karl Pearson’s correlation coefficient r-value helps to identify association that exists
between two unique traits, even though it is not able to measure the magnitude (extent) of
association but gives a clue as to the relationship. The interpretation of correlation coefficient
was given, but Ratner (2009) provides an accepted standard guideline.
The r-value could indicate no linear relationship, positive linear relationship and
negatively linear relationship represented by 0, +1, and -1 respectively. Low, moderate and
strong positive linear relationships are represented with values ranging from 0-0.3, 0.3-0.7
and 0.7-1, respectively while 0 to -0.3, -0.3 to -0.7, and -0.7 to -1 would indicate low, moderate
and strong negative linear relationship respectively.
International Journal of Applied Biology, 3(2), 2019
17
Table 7. ANOVA for the parameters showing level of significance for reproductive stage drought stress (RS)
∗Significant at p ≤ 0.05, ∗∗highly significant at p ≤ 0.01, ns: non-significant p > 0.05, DF. (degree of freedom), REP.(Replications),
Cor. Total (Corrected total), DF(Days to 50% flowering), HP(cm)(Height of plant measured in centimetres), PL(Panicle length in centimetres),
ET(Effective tillers),Tillers (total number of tillers), FFG(Fully filled grains), 100-GW(100 grain weight in grams)
Source DF DF(day) HP(cm) PL(cm) ET(no) T(no) FFG(no) 100GW(g) GLW(cm) YM(days)
Var 9 11.02** 21.53ns 5.47** 7.47ns 6.22ns 384.90** 0.01ns 0.04ns 14.63**
Rep. 4 0.93ns 74.74ns 0.62ns 8.65ns 7.13ns 86.59ns 0.01ns 0.02ns 0.97ns
Error 36 1.49ns 84.94ns 1.84ns 6.73ns 5.72ns 84.83ns 0.01ns 0.03ns 1.97ns
Cor. Total 49
18
Table 8. Estimates of correlation coefficients on the phenotype among 9 traits in rice varieties for reproductive drought stress
DF(days) HP(cm) PL(cm) ET(no) T(no) FFG(no) 100GW(g) GLW(cm) YM(days)
DF(days) 1
HP(cm) -0.11295 1
PL(cm) -0.34917* 0.04025 1
ET(no) -0.1276 0.02553 0.06464 1
T(no) -0.11063 0.02957 0.05282 0.98204** 1
FFG(no) -0.35762* -0.06439 0.24756 0.1528 0.1108 1
100GW(g) 0.26915 -0.0041 -0.18723 -0.13435 -0.13343 -0.11789 1
GLW(cm) -0.21732 0.08631 0.17222 0.07339 0.0711 0.2032 -0.00037 1
YM(days)
0.59004** -0.11094 -0.53245** -0.0833 -0.06109 -0.4332* 0.13789 -0.22637 1
19
ANOVA for Agro-morphological and yield traits data for single cross (F4) double cross F3(2),
and three-way cross and reciprocal (F3) under reproductive drought stress (RS).
Phenotyping for reproductive drought tolerance was carried out to determine the
level of improved lines tolerance to water deficit condition. This is very importance due to
challenges of climate change and irregular rainfall for rainfed rice and also for irregular
irrigation water supply. The ANOVA Table 7. showed highly significance difference (P≤0.01)
among varieties (improved lines) on the parameters for DF, PL, FFG, YM, while there was non
among the replicates. This clearly indicated the effect of drought on these very important
parameters (quantitative traits) whose effect affect yield drastically (Taglea et al., 2016,
Chang et al., 2016).
Correlation coefficient relationship for non-drought stress
In Table 8 which estimated the relationship among phenotypes of the reproductive
drought stress traits indicated that days to 50% flowering (DF) had a significant relationship
with panicle length (PL) and fully filled grain (FFG) at (P≤ 0.05) although negative and moderate
but yield maturity (YM) at (P≤0.01), it was positive and moderate correlated relationship.
Panicle length (PL) and yield maturity (YM), effective tillers (ET) and fully filled grain (FFG)
were both significant at P≤0.01. while the former is negative and moderately correlated the
later was low but positively correlated.
The strongest positively and high correlated relationship but not significant was
between effective tillers (ET) and total number of tillers per plant (T). It is because most of
the tillers were effective despite the drought stress. Stress was introduced at the
reproductive stage when tillers were already matured and it could not affect it.
International Journal of Applied Biology, 3(2), 2019
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Table 9. ANOVA for the parameters showing Interaction levels of significance
∗Significant at p ≤ 0.05, ∗∗highly significant at p ≤ 0.01, ns: non-significant p > 0.05, DF. (degree of freedom), REP.(Replications), Cor.. Total (Corrected total),
DF(Days to 50% flowering), HP(cm)(Height of plant measured in centimetres), PL(Panicle length in centimetres), ET (Effective tillers),Tillers (total number of
tillers), FFG(Fully filled grains), 100-GW(100 grain weight in grams), YM (yield maturity counted on number of days from sowing to physiological maturity).
SOV DF DF(days) HP(cm) PL(cm) ET(no) T(no) FFG(no) 100GW(g) GLW(cm) YM(days)
Treatment 1 1020.17*
*
1091.15*
*
69.17** 7.04ns 0.02ns 325366.67*
*
0.60** 0.18ns 2733.4**
Varieties 10 6.22* 67.75ns 3.70** 10.30* 11.87*
*
307.53** 0.01ns 0.19** 1.09ns
Replications 4 0.89ns 89.62ns 1.89ns 10.26ns 8.51ns 165.90ns 0.01ns 0.08ns 1.92ns
Trt*Var 8 3.6ns 42.50ns 2.59* 5.43 10.66* 211.25* 0.01ns 0.17* 0.75ns
Error
Total
76
99
2.52 53.68 1.13 4.33 4.40 90.70 0.01 0.06 1.25
International Journal of Applied Biology, 3(2), 2019
21
Table 10. Correlation coefficient of interaction of non drought stress (NS) and reproductive drought stress (RS) treatments.
DF(days) HP(cm) PL(cm) ET(no) T(no) FFG(no) 100GW(g) GLW(cm) YM(days)
DF(days) 1
HP(cm) -0.43695** 1
PL(cm) -0.5883** 0.39373** 1
ET(no) 0.12171ns -0.00503ns -0.22585* 1
T(no) 0.02853ns -0.00643ns -0.23369* 0.92342** 1
FFG(no) -0.87446** 0.37901** 0.55552** -0.09132ns 0.0005ns 1
100GW(g) -0.60745** 0.30235** 0.35449** -0.1395ns -0.0953ns 0.68357** 1
GLW(cm) -0.00568ns -0.05578ns -0.02718ns 0.10715ns 0.11803ns 0.15551ns 0.11917ns 1
YM(days) 0.87083** -0.41243** -0.56762** 0.13997ns 0.01887ns -0.95227** -0.64997** -0.14577ns 1
∗Significant at p ≤ 0.05, ∗∗highly significant at p ≤ 0.01, ns: non-significant p ≤ 0.05, DF. (degree of freedom), REP.(Replications), Cor..Total (Corrected total),
DF(Days to 50% flowering), HP(cm)(Height of plant measured in centimeters), PL(Panicle length in centimeters), ET(Effective tillers),Tillers (total number of
tillers), FFG(Fully filled grains), 100-GW(100 grain weight in grams), YM (yield maturity counted on number of days from sowing to physiological maturity).
International Journal of Applied Biology, 3(2), 2019
22
Interactions of reproductive drought stress (RS) and non drought stress (NS).
There were interactions between treatment and varieties (TRT*VAR.) in Table
9. Panicle length (PL), Tillers (T), Fully filled grain (FFG) and Grain length and width
ratio (GLW) interacted at P≤0.05. while the other parameters were not significant. The
significant interactions was because of the variation in treatments (reproductive
drought stress (RS) and non drought stress (NS)) while the varieties comprised of data
from the improved lines (progenies) subjected to stress and non stress conditions.
Shortage of water was responsible because of its dire need for living cells functioning
and turgidity (Sukhla, 2012). The parameters affected are yield borne especially fully
filled grains (FFG). Juraimi et al. (2009), Sikuku et al. (2009), Masitah, (2018) reported
that days to flowering, yield maturity, fully filled grain, panicle length are affected by
drought stress. Variation of significance on major yield component was also attributed
to biochemical, physiological, morphological and anatomical effect of water deficit
condition (Serraj et al., 2009).
Reproductive stage of rice is a very sensitive stage and responded to drought
stress in affecting flowering and heading as also confirmed by Davatgar et al. (2009).
drought stress often results in low tissue water potential for rice (Sikuku et al., 2010).
Generally, low yield underscore the importance of water (Juraimi et al., 2009).
Correlation coefficient of interaction
The interaction correlation coefficient Table 10. showed highly significant
interaction between days to 50% flowering (DF) with height of plant (HP), panicle
length (PL), fully filled grain (FFG), 100 grain weight (100GW) and yield maturity (YM).
All correlation coefficient relationships were either negatively medium or high, with
the exception of yield maturity which was both strong and positively correlated
relationship. This clearly indicated the influence of days to 50% flowering to yield
maturity due basically to water stress treatment which delayed maturity because of
distortion of the normal physiological and biochemical processes of the rice plant
(Serraj, 2009; Juraimi, et al., 2009). For height of plant (HP), there were highly
significant (P≤0.01), negatively medium correlation relationship with panicle length
(PL), effective tillers (ET), 100grain weight (100GW) and yield maturity (YM). the
relationship between effective tillers (ET) and tillers (T) was significant P≤0.01 but with
strong negative correlation. Fully filled grains (FFG) was also highly significant (P≤0.01)
with 100 grain weight (100GW) and yield maturity (YM) at medium and strongly
negative correlated relationship respectively. The parameter of 100 grain weight
(100GW) was highly significant (P≤0.01), medium and negatively correlated.
Clustering and principal component analysis
The analysis of genetic variability is one important criterion for parental
selection by the estimate of the extend of variation that existed among the genotypes.
The specific information on the nature and degree of genetic variability is critical for
selection of ideal parent so as to minimize the number of crosses that would have
been required. (Guerra et al., 1999, Yatung et al., 2014).
International Journal of Applied Biology, 3(2), 2019
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Non drought stress
The study considered 14 genotypes which comprised of the 3 parents and their
progenies (improved lines). They were clustered into four main groups based on their
quantitative characteristics at genetic similarity of 0.122 in order of distance to the
various genotypes in the population. Clustering of 39 genotypes according to
morphological characteristics majorly on the size of fruit was reported by Geleta et al.,
2005. The genetic distance dendrogram was constructed in accordance to the UPGMA
method using NTSYS-pc version 2.1. The Figure 7. showed the clustered genotypes
into four groups.
Groups III and IV comprised of two genotypes each namely DPB7, DPB20 and
PB15, PBD3 respectively. Group I and II comprised of 6 and 4 genotypes respectively.
While Putra 1, IRBB60, PBD1, Drought (MR219 drought tolerance), PB12,PD14 were
clustered in group I, PD15, PBD3, DPB13 and DPB12 were clustered in group II. There
parental plants and were clustered along with the first two crossed generations that
produced the three-way and double crosses.
The PCA results are indicated in Figure 8. the farthest genotypes from the
centroid were PBD3. PB15, PD14, PB12, DPB7, DPB12, while the closest to the
centroid were DPB13, PD15, PDB3. three genotypes were intermediary between the
farthest and closest, which included; PUTRA1, IRBB60 and PBD1.
International Journal of Applied Biology, 3(2), 2019
24
Figure 7. Clustering pattern of the agro-morphological and yield based on 14 trait at dissimilarity coefficient of 0.122
Coefficient
0.050.230.410.590.77
DROUGHT
PUTRA1
IRBB60
PBD1
DROUGHT
PB12
PD14
PD15
PDB3
DPB13
DPB12
DPB7
DPB20
PB15
PBD3
Group I
Group IV
Group III
Group
II
0.122
25
Figure 8. Principal component analysis (PCA) of relationship among fourteen (14) rice lines in a 2-dimentional graph
PC1
PC2
26
Reproductive drought stress
In this study, 10 genotypes (lines) were clustered together into six groups based on
nine quantitative traits at the reproductive stage drought stress (RS) to separate different
genotypes in the population. UPGMA method using NTSYS-pv v2.1 was similarly used to
construct the genetic distance dendrogram. The genotypes here were clustered into 6-group
at 0.094 genetic similarity as shown in Figure 9.
Group I, III, IV and VI comprised of only one line (genotype) each such as CONTROL,
DPB7, PDB3 and PBD3 respectively. While group II and V each comprised of three genotypes;
PD14, PD15, DPB12 and PBD1, DPB13, DPB20 genotypes respectively.
Figure 10. showed the result of PCA. The genotypes distanced away from the centroid
in the order of distance comprised of CONTROL, PBD3, DPB7, and three genotypes were
intermediary such as PBD1, DPB20, PD14, while the others were closest to the centroid, and
they included PD15, DPB13, DPB12 and PDB3. Group I stood out as the lowest with yield since
it was the control and was susceptible to RS.
Table 11. Grouping of 14 and 10 improved and selected lines for non drought stress (NS)
and reproductive drought stress (RS) respectively for cluster analysis
Treatment Cluster
number
Number of
genotypes
Genotypes
NS I 6 PUTRA1, IRBB60, PBD1, DROUGHT, PB12, PD14
II 4 PD15, PDB3, DPB13, DPB12
III 2 DPB7, DPB20
IV 2 PB15, PBD3
RS I 1 CONTROL
II 3 PD14, PD15, DPB12
III 1 DPB7
IV 1 PDB3
V 3 PBD1, DPB13, DPB20
VI 1 PBD3
International Journal of Applied Biology, 3(2), 2019
27
Figure 9. Relationship among the 10 developed and selected lines (genotypes) based on 9 characteristics (traits) using
SAHN clustering of UPGMA method
Coefficient
0.050.160.260.360.47
CONTROL
CONTROL
PD14
PD15
DPB12
DPB7
PDB3
PBD1
DPB13
DPB20
PBD3
0.094
Group I
Group IV
Group V
Group VI
Group II
Group III
International Journal of Applied Biology, 3(2), 2019
28
Figure 10. Principal component analysis of relationship amongst 10 improved lines and selected lines in two dimensional graph
PC1
PC2
29
Conclusion
The two recipient parents (Putra1 and MR219 drought tolerant) used in the different
crossing methods were high yielding, resistant and tolerant to blast (Magnaporthe grisea)
and drought respectively. And a donor IRBB60 resistant to bacteria leaf blight (Xanthomonas
oryzae). The yield parameters showed that there was no yield loss on non-drought stress
treatment on improved lines except on reproductive drought stress treatment. The
interactions showed that reproductive drought stress affected days to flowering, fully filled
grain and yield maturity significantly. The profitability of these parameters are often
dependent on the reproductive stage, but shortage of water tempered with their due
performances. Even though there was reduction in yield, but it was still within average yield
performance of drought tolerant variety due to the presence of introgressed genes/QTLs
compared with the susceptible variety in reproductive drought stress (RS) treatment.
Prior knowledge of the rice genotype is important in making informed decision on
selection of the best cultivars for variety improvement. Genetic variation is an indication of
the possibility of diverse source of origin and the result gave a clue as to the genetic/QTL
variance of the rice populations.
In consideration of the variation pattern and other agro-morphological and yield
performance, the improved and selected lines PB12, PBD1, and PD14 in group I, PDB3 in group
II, DPB20 in group III and PB15, PBD3 in group IV could be considered as better lines on good
yield under non drought stress (NS) treatment. In reproductive drought stress (RS) treatment,
PD14, DPB12 in group II, DPB7 in group III and PDB3in group IV. While in groups V and VI were
PBD1, DPB20 and PBD3 respectively. Generally, the yield performances of the different lines
for both NS and RS were all within considerable high yielding range, with high potentials for
increased yield with different environmental trials since the QTLs could be influenced by
different environments, even though the recommended ones were better.
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