ORIGINAL RESEARCH
published: 08 July 2022
doi: 10.3389/fpls.2022.866742
The Combining Ability for Grain Yield
and Some Related Characteristics in
Rice (Oryza sativa L.) Under Normal
and Water Stress Conditions
Mohamed S. Abd El-Aty 1 , Youssef S. Katta 1 , Abd El Moaty B. El-Abd 2 ,
Samiha M. Mahmoud 2 , Omar M. Ibrahim 3 , Mohamed A. Eweda 3 ,
Mohamed T. El-Saadony 4 , Synan F. AbuQamar 5* , Khaled A. El-Tarabily 5,6,7* and
Amira M. El-Tahan 3
1
Edited by:
Cengiz Toker,
Akdeniz University, Turkey
Reviewed by:
Pasala Ratnakumar,
Indian Institute of Oilseeds Research
(ICAR), India
Haiyan Wei,
Yangzhou University, China
*Correspondence:
Synan F. AbuQamar
sabuqamar@uaeu.ac.ae
Khaled A. El-Tarabily
ktarabily@uaeu.ac.ae
Specialty section:
This article was submitted to
Plant Breeding,
a section of the journal
Frontiers in Plant Science
Received: 31 January 2022
Accepted: 13 April 2022
Published: 08 July 2022
Citation:
Abd El-Aty MS, Katta YS,
El-Abd AEMB, Mahmoud SM,
Ibrahim OM, Eweda MA,
El-Saadony MT, AbuQamar SF,
El-Tarabily KA and El-Tahan AM
(2022) The Combining Ability for Grain
Yield and Some Related
Characteristics in Rice (Oryza sativa
L.) Under Normal and Water Stress
Conditions.
Front. Plant Sci. 13:866742.
doi: 10.3389/fpls.2022.866742
Department of Agronomy, Faculty of Agriculture, Kafr El Sheikh University, Kafr El-Sheikh, Egypt, 2 Rice Research
and Training Center, Agricultural Research Center, Field Crops Research Institute, Kafr El-Sheikh, Egypt, 3 Department
of Plant Production, Arid Lands Cultivation Research Institute, The City of Scientific Research and Technological
Applications, SRTA-City, Alexandria, Egypt, 4 Department of Agricultural Microbiology, Faculty of Agriculture, Zagazig
University, Zagazig, Egypt, 5 Department of Biology, College of Science, United Arab Emirates University, Al-Ain, United Arab
Emirates, 6 Khalifa Center for Genetic Engineering and Biotechnology, United Arab Emirates University, Al-Ain, United Arab
Emirates, 7 Harry Butler Institute, Murdoch University, Murdoch, WA, Australia
Drought is considered a major threat to rice production. This study aimed to determine
the effects of drought stress on the estimates of heterosis and the combining ability
of rice genotypes for the number of days to 50% heading, plant height, number of
panicles per plant, panicle length, number of filled grains per panicle, and grain yield
per plant. Field experiments were conducted at the Rice Research and Training Center,
Kafr El Sheikh, Egypt, during the rice-growing season in 2018 and 2019. Eight rice
genotypes (Giza178, Giza179, Sakha106, Sakha107, Sakha108, WAB1573, NERICA4,
and IET1444) were crossed in a half-diallel cross in the rice-growing season in 2018,
which yielded a wide range of variability in numerous agronomic traits and drought
tolerance measurements. In 2019, these parents and their 28 F1 crosses were produced
by employing a three-replication randomized complete block design under normal
and water stress conditions. The results showed remarkable differences across the
studied genotypes under normal and water stress conditions. Under both conditions,
Sakha107 was the best general combiner for earliness and short stature. Giza179
and Sakha108 were the best general combiners for grain yield per plant and one or
more of its characteristics. Furthermore, in both normal and water stress conditions,
Giza179 exhibited the highest general combining ability effects for all attributes that
were evaluated. Under normal and water stress conditions, the Giza179 × Sakha107
cross demonstrated substantial and desirable specific combining ability effects on all
the examined traits, which suggested that it could be considered for use in rice hybrid
breeding programs. Therefore, we recommend that these vital indirect selection criteria
to be considered for improving rice grain yield under drought conditions.
Keywords: combining ability, earliness, heterosis, rice genotypes, water stress conditions
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1
July 2022 | Volume 13 | Article 866742
Abd El-Aty et al.
Rice Cultivars Under Water Stress
INTRODUCTION
of interest, including additive, dominant, and non-allelic
interaction effects (Ullah Zaid et al., 2018; Suvi et al.,
2021).
At present, utilizing heterosis in self-pollinated crops,
particularly in rice, is impossible. Furthermore, heterosis
management requires the ability of genotypes in hybrids to mix
in general and specialized ways. Plant breeders can use the diallel
analysis to determine a breeding system that can be adopted and
breeding materials that possess the best likelihood of achieving
success in selection. This strategy, which was initially adopted
by Griffing (1956) can be used for several crop to carry out
self-pollination.
The present study aimed to identify optimum cultivars and
cross combinations by assessing five native and three “alien” rice
genotypes and their F1 diallel crosses. Thus, the potential for
heterosis expression was determined for a set of agronomic and
grain yield-related variables to evaluate the combining ability
effects and gene action modes in the inheritance of grain yieldrelated agronomic traits.
Rice (Oryza sativa L.) is a well-known crop that is consumed
by most of the world’s population. It has a low fat content
and high carbohydrate, protein, vitamin, and mineral content.
Rice is widely grown in many parts of the world, including
Egypt (Ullah Zaid et al., 2018; El-Mowafi et al., 2021; Abd
El-Aty et al., 2022a). The global population is estimated
to increase from 7.7 billion today to 9 billion in 2035.
Therefore, there would be an increase in global rice demand
from 763 million tons to 850 million tons. However, in the
past decade, only a 1% annual increase in rice yield was
reported, and this average was the highest among rice-producing
countries. Rice consumption continues to increase with the
increase in global population (Kumar, 2018; Gaballah et al.,
2022).
In contrast, scarcity of irrigation water is a major stumbling
block to improve rice production worldwide (El-Mowafi et al.,
2021; Abd El-Aty et al., 2022b). Water stress is a critical
limiting factor during the early stages of rice development and
establishment, which affects both stem elongation and leaf area
expansion during growth. In the past three decades, rice sector
in Egypt has outperformed that in the rest of the world in
terms of rice production and yield (Sari et al., 2020; Gaballah
et al., 2022). Rice yield per hectare increased from 5.7 t ha−1
in the 1980s to 9.52 t ha−1 in the 2000s as a result of the
widespread adoption of semi-dwarf and early maturing Egyptian
cultivars. However, the rice production could not keep up
with the growing population and diminishing water resources
(Dianga et al., 2020).
Using the hybrid rice technology that employs heterosis is
an effective approach for enhancing the rice yield. This method
utilizes heterosis, which refers to the superiority of an F1
hybrid over its parents. Compared with the traditional highyielding inbred varieties, the F1 hybrid rice has a 15%–20%
yield advantage (Sari et al., 2020). Identifying good parental lines
to develop hybrid combinations is crucial so that hybrid rice
technology is effective. As a result, rice breeders continuously
select suitable parental lines (Suvi et al., 2021). The combining
ability has been employed to understand the potential of a given
parental line to pass on its genetic information to its descendants
to overcome this challenge. The general combining ability (GCA)
indicates an additive gene action and measures the average
performance of parental lines. The specific combining ability
(SCA) represents a non-additive gene action associated with
dominance, overdominance, and epistatic effects and quantifies
the performance of hybrid combinations (Dianga et al., 2020).
In hybrid combinations, genetic variations in parental
lines have a remarkable impact. Many morphological and
molecular techniques have been used to assess the genetic
variability of parental lines utilized in hybrid rice breeding.
Although the biological basis for drought resistance is
unknown, several plant breeding initiatives have focused
on selecting genotypes with better output in drought-prone
areas. Any selection or hybridization breeding programs
for developing drought-tolerant varieties require precise
estimates of genetic variance components for the traits
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MATERIALS AND METHODS
This study was conducted at an experimental farm of the Rice
Research and Training Center, Sakha, Kafr Elsheikh, Egypt,
during the rice-growing seasons in 2018 and 2019. Eight rice
cultivars were chosen to reflect various agronomic traits and
drought resistance measurements (five local varieties: Giza178,
Giza179, Sakha106, Sakha107, and Sakha108 and three exotic
varieties: WAB1573, NERICA4, and IET1444). Rice cultivar
grains at 140 kg ha−1 were prepared for sowing in the nursery.
Sowing was performed in late April 2018 and 2019. Table 1
lists the names, origins, and other agronomic characteristics
of these parents.
A permanent field was prepared, as recommended by the
Ministry of Agriculture, Egypt. Calcium superphosphate (15.5%
P2 O5 ) was applied at a rate of 238 kg ha−1 during soil tillage.
Then, 25-day-old seedlings were transplanted in a 20 × 20 cm2
field, with 4–5 seedlings hill−1 . Potassium sulfate (48% K2 O) was
added at a concentration of 58 kg K2 O ha−1 and divided into
two equal doses at 15 and 35 days after transplantation (DAT).
Nitrogen fertilizers in the form of urea (46% N) at a concentration
of 165 kg ha−1 were also added and divided into three equal doses
at 15, 35, and 55 DAT.
During the 2018 rice-growing season, the parents were crossed
in 8 × 8 diallel crosses, eliminating reciprocals, yielding 28
crosses. In the 2019 season, the parents and their F1 hybrid
seeds were sown in a dry seedbed, and 30-day-old seedlings
were transplanted individually into field plots in two separate
irrigating experiments. A well-watered condition was maintained
using continuous flooding every 4 days, with an adequate
submersion depth to ensure that all surface areas were covered by
water in each irrigation incident. A water-deficit treatment was
maintained by the application of irrigation water every 10 days
without standing water. A stress condition was applied 15 days
after the transplantation date and until maturity. A flow meter
was used to measure the applied irrigation quantities for each
2
July 2022 | Volume 13 | Article 866742
Abd El-Aty et al.
Rice Cultivars Under Water Stress
TABLE 1 | Origin and main characteristics of the eight rice genotypes used as parents in the half-diallel cross.
No
Genotype
Parentage
Origin
Grain shape
Variety group
Drought tolerance
Moderate
1
Giza178
Giza175/Milyang49
Egypt
Medium
Indica/Japonica
2
Giza179
GZ6296-12/GZ1368-5-S-4
Egypt
Short
Indica/Japonica
Tolerant
3
Sakha106
Giza177/Hexi30
Egypt
Short
Japonica
Sensitive
4
Sakha107
Giza177/BL1
Egypt
Short
Japonica
Sensitive
5
Sakha108
Sakha101/HR5824/Sakha101
Egypt
Short
Japonica
Sensitive
6
IET
TN1 × CO29
India
Short
Indica/Japonica
Tolerant
7
WAB1573
Introduced
Côte d’Ivoire
Long
Indica
Tolerant
8
NERICA4
CG14/WAB56-104
Africa rice center
Long
Indica
Tolerant
treatment, which were 13,090 and 8330 m3 ha−1 under wellwatered and water-deficit conditions, respectively.
Water stress treatment was performed at 10 DAT. Two trials
were set up in a three-replication randomized complete block
design. Each replicate had five rows of parents and three rows of
F1 hybrids. The row was 5-m long, with a spacing of 20 × 20 cm2
between rows of seedlings.
The number of days to 50% heading was recorded at the
heading stage. In contrast, the plant height, number of panicles
per plant, panicle length (cm), number of filled grains per panicle,
and grain yield per plant (g) at maturity of 25 randomly chosen
single plants from each entry were recorded.
For each attribute, heterosis was calculated based on parents
vs. crosses sum of squares by partitioning the sum of squares of
the genotype to its components. All characters were subjected
to analysis of variance, as described by Steel et al. (1997), using
IRRISTAT and R software version 4.1.0 2021. The obtained mean
values were compared using the least significant difference. GCA
and SCA were analyzed using method 11, model 1 according to
the techniques of Griffing (1956).
Supplementary Figure 1 shows the weather data (rain in mm,
average temperature in ◦ C, relative humidity in %, and radiation
in MJ m2 ) recorded in 2018 and 2019, which were obtained from
https://power.larc.nasa.gov.
Chemical and mechanical analyses of soil and organic matter
were performed according to Page et al. (1982) at the Agricultural
Research Center, Ministry of Agricultural, Egypt. Some chemical
and physical properties of the soil present in the experimental site
at a depth of 0–30 cm are presented in Table 2.
Twenty-two stress tolerance indices were calculated for all
the genotypes based on grain yield under non-stress and stress
conditions. The names, equations, and references of the stress
tolerance indices are listed in Supplementary Table 1.
large extent among all the genotypes used in this study. These
results indicated that the rice genotypes responded differently to
stress and non-stress conditions.
For all the measured traits, both GCA and SCA mean squares
were highly significant. This finding highlights the significance
of additive and non-additive genetic factors in influencing these
traits and consequently the performance of the rice genotypes.
Mean Performance
The mean performances of the parents and F1 hybrids for the
traits studied under drought and regular irrigation conditions are
shown in Table 4.
The number of days to 50% heading of the parental varieties
Sakha107, Giza179, and IET1444 were 98.67, 98.67, and 99.00
days under regular irrigation and 92.23, 91.33, and 93.33 days
under stress conditions, respectively. Thus, these were the
earliest varieties under regular irrigation. Meanwhile, Sakha107,
Giza179, and IET1444 were the earliest varieties under water
TABLE 2 | Some physical and chemical properties of the experimental soil before
sowing at the two locations.
Soil properties
Mechanical:
Clay (%)
56.00
Silt (%)
32.00
Sand (%)
12.00
Texture
Clayey
Chemical:
RESULTS
Organic matter (%)
1.50
pH (1:2.5 soil suspension)
8.44
Ec (ds m−1 )
3.34
Total N (ppm)
430.50
Available P (ppm)
12.00
Available K (ppm)
432
Soluble anions (meq L−1 ):
Analysis of Variance
Table 3 shows the analysis of variance for yield and its
components under drought and regular watering conditions.
For all the traits studied under drought and regular irrigation
conditions, the mean squares of the genotypes and their
partitions, parents, crosses, and parents vs. crosses, were
significantly different, indicating the presence of diversity to a
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Value
HCO− 3
6.20
Cl−
9.10
Soluble cations (meq L−1 ):
Ca++
3
10.70
Mg++
5.00
Na++
2.00
K+
15.60
Total carbonate (%)
14.00
July 2022 | Volume 13 | Article 866742
Abd El-Aty et al.
Rice Cultivars Under Water Stress
TABLE 3 | Mean square estimates of ordinary analysis and combining ability analysis for yield and related traits under normal and water stress conditions.
SOV
df
Number of days to 50% heading (day)
N
Plant height (cm)
D
N
D
Number of panicle per plants
N
D
Replications
2
2.06
0.01
6.04
0.23
0.42
0.65
Genotypes
35
148.38**
171.24**
359.20**
333.85**
261.15**
68.52**
Parents
7
161.31**
172.61**
114.54**
159.00**
200.74**
33.69**
Crosses
27
150.46**
177.23**
367.94**
383.18**
285.75**
79.91**
Parents vs. Crosses
1
1.65
0.00
1835.84**
226.06**
19.86**
4.91**
Error
70
1.46
0.93
3.57
1.58
5.69
1.31
GCA
7
212.82**
227.83**
165.73**
330.21**
323.38**
85.74**
SCA
28
8.62**
14.39**
108.23**
56.55**
27.97**
7.11**
Error term
70
0.49
0.31
1.19
0.53
1.90
0.44
2.611
1.616
0.154
0.588
1.233
1.277
GCA/SCA
SOV
df
Panicle length (cm)
N
Number of filled grains per panicle
D
N
D
Grain yield per plant (g)
N
D
Replications
2
13.85
0.05
102.72
0.15
0.66
4.73
Genotypes
35
173.16**
74.47**
2609.70**
2083.67**
113.29**
41.28**
Parents
7
23.37**
20.50**
3918.01**
2055.40**
66.08**
16.36**
Crosses
27
209.31**
76.75**
2194.94**
1862.59**
120.87**
46.47**
Parents vs. Crosses
1
245.93**
390.59**
4650.19**
8250.68**
238.97**
75.56**
Error
70
7.35
1.13
92.56
5.85
0.21
0.94
GCA
7
117.86**
64.36**
3370.35**
2466.83**
135.34**
40.67**
SCA
28
42.69**
14.94**
244.79**
251.49**
13.37**
7.03**
Error term
70
2.45
0.38
30.85
1.95
0.07
0.31
0.287
0.439
1.561
0.988
1.017
0.601
GCA/SCA
** indicates significance at 0.01 probability level. GCA, general combining ability; SCA, specific combining ability; SOV, sources of variation; df, degrees of freedom; N,
normal condition; D, water stress condition.
Giza178 × IET1444, Sakha106 × IET1444, Giza178 × Giza179,
and Sakha106 × Sakha107) were earlier than their parental
lines. Regarding plant height, the mean performances of the
crosses varied significantly. Three crosses (Giza178 × Giza179,
Sakha107 × Sakha108, and Sakha107 × NERICA4) out of
28 were shorter than their parents under normal and stress
conditions. These crosses produced high grain yield and were
resistant to lodging. The cross that had the highest mean
number of panicles per plant (39 and 35.33 panicles per
plant under normal and stress conditions, respectively) was
Sakha106 × IET1444.
Sakha108 × WAB1573 had the highest panicle length (29.33
and 27.00 cm under normal and stress conditions, respectively).
Furthermore, WAB1573 × NEICA4 and IET1444 × NERICA4
had a higher number of filled grains per panicle under
normal conditions, whereas IET1444 × NERICA4 and
Sakha107 × NERICA4 had a higher number of filled grains
per panicle under stress conditions.
stress conditions, with 91.33, 92.33, and 93.33 days to 50%
heading, respectively.
For the number of panicles per plant, the parental
varieties Giza179, Giza178, and IET1444 exhibited the
highest mean values of 46, 42.87, and 40.33 panicles per
plant under regular irrigation and 27.67, 25.83, and 32.33
panicles per plant under stress conditions, respectively. In
contrast, NERICA4 and Sakha106 had the lowest values
of 24.47 panicles per plant under normal conditions
and 22.00 panicles per plant under stress conditions,
respectively.
The parental variety of IET1444 had the tallest panicle length
(28.83 and 26.33 cm) compared with the other parents under
normal and stress conditions, respectively. For the number of
filled grains per panicle, the parental varieties Giza179 and
WAB1573 exhibited the highest mean values of 177.17 and 180
under normal and stress conditions, respectively. The parental
varieties Sakha107 and Sakha108 had the highest grain yield per
plant of 47.30 and 52.03 g per plant under normal and stress
conditions, respectively.
Seven crosses (Giza178 × Sakha107, Giza178 × Giza179,
Giza178 × IET1444, Giza179 × Sakha107, Sakha106 × IET1444,
Sakha106 × Sakha107, and Sakha106 × Sakha108) were earlier
combinations than their parental mean values under normal
and stress conditions, while five crosses (Giza178 × Sakha107,
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Combining Ability
The mean square values of the GCA and SCA effects are provided
in Table 3. Under both conditions, the data revealed extremely
significant GCA and SCA estimates for all the traits studied.
Both types of combining abilities appeared to contribute to the
inheritance of these characteristics. Under both conditions, the
4
July 2022 | Volume 13 | Article 866742
Abd El-Aty et al.
Rice Cultivars Under Water Stress
TABLE 4 | Mean performance of parents and their F1 generation in a half-diallel cross for yield and some related traits under normal and water stress conditions.
No.
Genotypes
Number of days to 50% heading (day)
Plant height (cm)
Number of panicles per plant
N
D
N
D
N
D
25.83
1
Giza178
105.00
99.00
102.28
86.67
42.87
2
Giza179
98.67
92.33
127.67
88.80
46.00
27.67
3
Sakha106
104.00
109.00
113.28
94.00
32.00
22.00
4
Sakha107
98.67
91.33
107.53
90.67
35.73
29.33
5
Sakha108
112.00
108.67
106.00
84.00
30.00
23.67
32.33
6
IET1444
99.00
93.33
112.33
85.33
40.33
7
WAB1573
108.33
105.67
109.50
93.00
32.00
30.07
8
NERICA 4
108.00
105.02
134.87
123.84
24.47
23.67
9
Giza178 × Giza179
92.67
88.67
97.60
82.33
34.87
26.80
10
Giza178 × Sakha106
107.00
109.00
100.00
80.00
36.00
25.33
11
Giza178 × Sakha107
89.00
86.67
104.40
82.67
34.00
29.00
12
Giza178 × Sakha108
102.00
103.33
110.60
80.33
27.07
22.33
13
Giza178 × IET1444
93.00
88.33
108.40
86.00
25.00
23.65
14
Giza178 × WAB1573
108.33
104.67
103.87
96.20
26.10
24.00
15
Giza178 × NERICA4
101.67
98.67
124.40
93.67
22.33
22.00
16
Giza179 × Sakha106
106.00
102.00
108.80
90.87
25.53
18.67
17
Giza179 × Sakha107
95.67
96.67
121.67
91.67
25.00
22.00
18
Giza179 × Sakha108
114.33
111.00
112.73
87.00
23.00
19.00
19
Giza179 × IET1444
101.00
94.33
136.00
98.67
32.00
27.00
20
Giza179 × WAB1573
116.00
108.00
125.33
101.00
20.00
18.33
21
Giza179 × NERICA4
107.00
105.33
122.78
98.33
18.73
17.00
22
Sakha106 × Sakha107
95.00
91.00
114.47
103.53
38.07
25.67
24.00
23
Sakha106 × Sakha108
98.00
93.33
106.33
87.00
30.00
24
Sakha106 × IET1444
94.00
88.67
118.33
92.00
39.00
35.33
25
Sakha106 × WAB1573
103.00
96.67
139.33
116.67
26.33
24.00
18.67
26
Sakha106 × NERICA4
101.00
91.33
116.67
110.33
20.00
27
Sakha107 × Sakha108
112.00
106.00
98.33
86.33
27.00
22.33
28
Saakha107 × IET1444
109.00
98.67
113.67
88.67
35.00
30.00
29
Sakha107 × WAB1573
118.00
108.67
109.67
101.00
20.00
18.00
30
Sakha107 × NERICA4
111.00
106.67
99.33
87.33
19.00
17.00
31
Sakha108 × IET1444
100.33
96.00
106.73
88.00
37.13
28.00
32
Sakha108 × WAB1573
104.00
95.00
114.67
103.67
31.33
27.67
19.00
33
Sakha108 × NERICA4
102.00
93.33
116.33
105.67
19.00
34
IET1444 × WAB1573
113.00
111.00
111.73
99.00
23.37
23.00
35
IET1444 × NERICA4
109.67
108.33
134.57
115.67
22.00
19.00
36
WAB1573 × NEICA4
106.00
104.00
109.37
96.33
20.00
19.90
L.S.D 0.05
1.98
1.58
3.08
2.05
3.89
1.88
L.S.D 0.01
2.63
2.10
4.10
2.73
5.17
2.50
No.
Genotypes
Panicle length (cm)
Number of filled grains per panicle
N
D
N
D
Grain yield per plant
N
D
1
Giza178
26.40
24.10
103.00
98.00
44.53
34.67
2
Giza179
27.43
25.68
177.17
161.67
46.83
37.67
3
Sakha106
22.30
20.00
112.67
104.67
41.83
32.33
4
Sakha107
27.47
26.40
118.33
101.00
47.30
38.33
5
Sakha108
27.10
23.67
146.00
129.00
52.03
40.00
6
IET1444
28.83
26.33
163.67
152.67
46.80
36.67
7
WAB1573
22.30
20.27
180.00
162.00
36.93
30.00
8
NERICA 4
26.53
23.67
151.13
147.33
40.30
35.67
9
Giza178 × Giza179
25.90
22.61
112.67
101.00
45.90
36.00
10
Giza178 × Sakha106
24.67
23.48
98.33
94.33
50.73
38.33
(Continued)
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TABLE 4 | (Continued)
No.
Genotypes
Panicle length (cm)
N
Number of filled grains per panicle
D
N
D
Grain yield per plant
N
D
11
Giza178 × Sakha107
25.13
22.33
115.33
105.00
53.20
40.33
12
Giza178 × Sakha108
25.18
22.00
142.67
132.00
54.70
41.33
13
Giza178 × IET1444
25.33
23.00
163.22
155.00
45.10
38.00
14
Giza178 × WAB1573
25.30
24.00
143.33
141.67
42.97
30.00
15
Giza178 × NERICA4
25.30
22.00
155.33
147.00
44.87
33.00
16
Giza179 × Sakha106
20.23
16.67
86.33
81.00
40.90
31.00
17
Giza179 × Sakha107
25.00
22.00
90.00
87.00
44.77
32.00
18
Giza179 × Sakha108
23.00
19.00
115.67
112.33
46.77
35.00
19
Giza179 × IET1444
25.07
23.00
109.00
98.00
43.30
33.67
20
Giza179 × WAB1573
20.00
18.67
132.67
131.00
33.87
28.33
30.33
21
Giza179 × NERICA4
18.73
17.00
142.33
138.67
36.33
22
Sakha106 × Sakha107
20.13
17.33
94.00
87.00
42.33
33.00
23
Sakha106 × Sakha108
27.67
24.33
122.00
110.33
49.37
37.67
24
Sakha106 × IET1444
28.00
26.00
134.33
122.00
50.83
37.67
25
Sakha106 × WAB1573
26.00
24.67
152.00
138.00
37.87
32.33
30.67
26
Sakha106 × NERICA4
20.00
18.67
164.67
153.33
35.83
27
Sakha107 × Sakha108
23.33
18.33
132.40
118.67
44.07
30.00
28
Saakha107 × IET1444
25.00
23.00
168.33
154.67
55.97
38.33
29
Sakha107 × WAB1573
20.00
18.00
175.33
161.00
44.57
35.33
30
Sakha107 × NERICA4
19.00
17.67
178.67
165.00
42.03
31.67
31
Sakha108 × IET1444
24.69
21.00
153.60
134.67
38.80
34.00
32
Sakha108 × WAB1573
29.33
27.00
155.33
143.00
40.90
32.33
33
sakha108 × NERICA4
21.00
19.00
162.00
153.33
35.83
28.33
34
IET1444 × WAB1573
19.67
18.33
171.87
142.00
32.70
29.67
35
IET1444 × NERICA4
24.00
23.67
180.67
175.33
33.63
28.67
36
WAB1573 × NEICA4
20.80
19.33
180.97
150.33
35.13
31.00
L.S.D 0.05
1.53
146
15.66
3.93
0.77
1.56
L.S.D 0.01
2.03
1.94
20.83
5.22
0.99
2.07
N, normal condition; D, water stress condition.
In addition, the genotypes Sakha108, IET1444, WAB1573, and
NERICA4 showed highly significant positive GCA effects under
both conditions. All genotypes showed highly significant positive
GCA effects under both conditions for grain yield per plant,
with the exception of Sakha106, WAB1573, and NERICA4. This
finding could be useful for rice breeding programs that intend to
generate cultivars having a high yield.
GCA/SCA ratios exceeded unity for the number of days to 50%
heading (day), panicles per plant, number of filled grains per
panicle, and grain yield per plant (g), indicating that additive
gene action is more important than non-additive gene action in
controlling these features.
General Combining Ability
Table 5 shows the impacts of GCAs on the number of days
to 50% heading. The parental varieties Giza179, Sakha107, and
IET1444 exhibited desirable significant adverse GCA effects for
the number of days to 50% heading under both conditions.
These adverse GCA effects suggested that these parents were
the strongest general combiners for earliness. In contrast, the
parents Giza178, Giza179, Sakha108, and IET1444 demonstrated
highly significant negative GCA effects under stress conditions,
whereas Sakha106 and IET1444 were deemed as the best
donors for earliness.
The parents Giza178, Giza179, Sakha107, and IET1444
exhibited highly significant positive GCA effects for the
number of panicles per plant under normal conditions. Under
both conditions, the parents Giza178, Giza179, Sakha107, and
IET1444 exhibited beneficial significant positive GCA effects.
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Specific Combining Ability
SCA effects for the crosses can estimate the non-additive impact.
Estimates of SCAs of F1 hybrids are presented in Supplementary
Table 2. The SCA for the number of days to 50% heading (day)
was negative and highly significant in eight crosses under normal
and stress conditions, indicating that one or more combinations
could assist in the selection of early maturing parental lines of
rice. Six crosses showed adverse and highly significant desirable
SCA effects for plant height under both conditions. Only two
crosses exhibited positive and highly significant desirable SCA
effects for the number of panicles per plant under both conditions
(Giza178 × Giza179 and Sakha108 × IET1444). For panicle
length, 12 crosses showed desirable SCA effects under both
conditions. Nine out of the 28 cross combinations showed
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TABLE 5 | Estimates of general combining ability effects for yield and some related traits for parental genotypes under normal and water stress conditions.
No.
Genotypes
Number of days to 50% heading (day)
N
Plant height (cm)
D
N
D
Number of panicles per plant
N
D
1
Giza178
0.28
0.59**
−0.92**
−1.61**
6.62**
2.51
2
Giza179
−5.08**
−3.71**
−5.04**
−7.67**
2.62**
1.23
−2.73
3
Sakha106
2.12**
3.99**
2.45*
−1.67**
−2.23**
4
Sakha107
−6.65**
−7.08**
1.83**
2.90**
2.61**
1.84
5
Sakha108
5.25**
4.49**
−6.99**
−6.11**
−2.20**
−1.66
6
IET1444
−3.32**
−5.38**
0.83*
−1.31**
3.59**
3.57
7
WAB1573
5.75**
5.16**
3.55**
7.61**
−3.56**
−0.82
8
NERICA 4
1.65**
1.93**
4.28**
7.84**
−7.45**
−3.92
S.E (gi )
0.21
0.16
0.32
0.21
0.41
0.20
S.E (gl -gj )
0.31
0.25
0.49
0.32
0.61
0.30
L.S.D 0.05
0.41
0.33
0.65
0.43
0.81
0.39
L.S.D 0.01
0.55
0.44
0.86
0.57
1.08
No.
Genotypes
Panicle length
N
D
Number of filled grains per panicle
0.52
Grain yield per plant (g)
N
D
N
D
1.42**
1
Giza178
1.83**
1.87**
−1.63
−1.79**
1.11**
2
Giza179
1.37**
1.22**
−5.05**
−3.36**
4.02**
2.48**
3
Sakha106
−1.72**
−1.91**
−29.79**
−24.43**
−1.06**
−1.38**
4
Sakha107
0.31*
0.35*
−18.66
−18.16**
1.39**
0.92**
5
Sakha108
−0.29*
−1.12
4.22*
2.94**
4.36**
1.35**
6
IET1444
1.55**
1.38
9.18**
7.58**
0.64**
0.72**
7
WAB1573
−1.02**
−0.26
19.18**
16.38**
−5.38**
−2.95**
8
NERICA4
−2.03**
−1.52**
22.55**
20.84**
−5.09**
−2.55**
S.E (gi )
0.16
0.15
1.64
0.41
0.08
0.17
S.E (gl -gj )
0.24
0.23
2.48
0.62
0.12
0.25
L.S.D 0.05
0.32
0.31
3.28
0.8
0.16
0.33
L.S.D 0.01
0.43
0.41
4.38
1.09
0.21
0.44
* and ** indicate significance at 0.05 and 0.01 probability levels, respectively. N, normal condition; D, water stress condition.
Supplementary Table 3 presents 22 stress indices and yield
values under stress and non-stress conditions. The first 14 indices
are maximum value indices, where the maximum values indicates
tolerance. Simultaneously, the last eight indices are minimum
value indices, where the minimum value indicates tolerance.
Supplementary Table 4 presents the yield ranks under stress
and non-stress conditions, ranks of the 22 stress indices, and the
average of these ranks.
The results in Table 6 and Supplementary Table 3 revealed
that G13 (Giza178 × IET1444) was the most tolerant genotype,
with an average rank (AR) of nine (Figure 1); however, G14
(Giza178 × WAB1573) and G27 (Sakha107 × Sakha108) were
the least tolerant genotypes (AR, 30). Both G8 (NERICA4)
and G4 (Sakha107) exhibited a similar intolerance, with an
AR of 10, and were the second and third tolerant genotypes.
G2 (Giza179) was the fourth tolerant genotype (AR, 11). As
the average rank values increased, the tolerance of genotypes
decreased, as shown in Supplementary Table 3. Taking the
average ranks of the different indices was helpful because of the
other results of the indices. This is obvious for the rank of G34
(IET1444 × WAB1573), with an AR of 17, as 11 indices ranked
this genotype first as the most tolerant genotype.
positive and highly significant desirable SCA effects for the
number of filled grains per panicle under both normal and stress
conditions. Furthermore, the estimates of SCA effects for grain
yield were positive and highly significant in eight crosses under
both normal and stress conditions.
Drought Stress Tolerance
Stress indices can be used to quantify the stress response
based on the crop yield. These are more readily usable
owing to their more straightforward interpretation compared
with raw yield data. Many drought tolerance indices have
been proposed (Supplementary Table 1) to assess stresstolerant genotypes using mathematical equations, describing
the relationship between yields under stress and non-stress
conditions. These indices can be classified into two categories: the
first includes indices with maximum values that indicate highstress tolerance, while the other category includes indices with
minimum values that indicate high-stress tolerance. The use of
these indices will assist in the identification of stable, high-yield,
and drought-tolerant genotypes (Sofi et al., 2018). Owing to the
long labels of crosses, an abbreviation was used to present the
results and figures (Table 6).
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TABLE 6 | Abbreviations of genotypes.
G1
Giza178
G19
Giza179 × IET1444
G2
Giza179
G20
Giza179 × WAB1573
G3
Sakha106
G21
Giza179 × NERICA4
G4
Sakha107
G22
Sakha106 × Sakha107
Sakha106 × Sakha108
G5
Sakha108
G23
G6
IET1444
G24
Sakha106 × IET1444
G7
WAB1573
G25
Sakha106 × WAB1573
G8
NERICA4
G26
Sakha106 × NERICA4
G9
Giza178 × Giza179
G27
Sakha107 × Sakha108
G10
Giza178 × Sakha106
G28
Saakha107 × IET1444
G11
Giza178 × Sakha107
G29
Sakha107 × WAB1573
G12
Giza178 × Sakha108
G30
Sakha107 × NERICA4
G13
Giza178 × IET1444
G31
Sakha108 × IET1444
G14
Giza178 × WAB1573
G32
Sakha108 × WAB1573
G15
Giza178 × NERICA4
G33
Sakha108 × NERICA4
G16
Giza179 × Sakha106
G34
IET1444 × WAB1573
G17
Giza179 × Sakha107
G35
IET1444 × NERICA4
G18
Giza179 × Sakha108
G36
WAB1573 × NEICA4
G34 significantly lower or as the lowest tolerant genotype because
most of them considered the yield of G34 relative to that of the
other genotypes under stress and non-stress conditions.
G28 (Saakha107 × IET1444) ranked first based on yield under
non-stress conditions; however, its AR was 19 and occupied the
21st position. Moreover, G12 (Giza178 × Sakha108) was ranked
first based on the yield under stress conditions; however, its
average rank was 14 and occupied the eighth position. These
results ensure that the ranking of the genotypes depends on the
amount of reduction in yield or the difference between the yields
under stress and non-stress conditions, in this order.
Correlation Analysis
The upper triangle in Figure 2 shows the Spearman coefficient
correlation matrix between each pair of the studied traits under
normal and water stress conditions. Both plant height and the
number of days to 50% heading (HD) showed a negative and
significant correlation with grain yield per plant under normal
and water stress conditions. Meanwhile, the number of filled
grains per panicle showed a negative and insignificant correlation
with grain yield per plant. Both panicle number per plant and
panicle length showed a positive and significant correlation with
grain yield per plant under normal and water stress conditions.
Subjecting rice to water stress did not change the relationship
between grain yield per plant and the other traits. In contrast,
water stress weakened the relationship between panicle number
per plant and filled grains per panicle, wherein it was significant
under normal conditions and not significant under water stress
conditions. This change was caused by the decrease in panicle
number per plant under water stress conditions, as illustrated
in the density plots in the diagonal of Figure 2, where the
peak of panicle number per plant under water stress conditions
corresponded to the lower value on the x-axis.
These indices included yield stability index, relative stress
index, golden mean, tolerance index, stress susceptibility index,
stress susceptibility percentage index, yield reduction, abiotic
stress tolerance index, mean productivity index, Schnieders
stress susceptibility index, and sensitivity drought index. These
indices were primarily driven by the difference in yield between
stress and non-stress conditions; however, it did not consider
the yield of the G34 genotype relative to that of the other
genotypes under stress and non-stress conditions. In contrast,
the indices like mean productivity, geometric mean productivity,
harmonic mean, stress tolerance index, yield index, modified
stress tolerance index-I, modified stress tolerance index-II,
relative efficiency index, and mean relative performance ranked
FIGURE 1 | Tolerance of genotypes according to the average rank of 22 stress indices (small number of average ranks signifies tolerance). All Gs correspond to the
genotype identified in Table 6.
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FIGURE 2 | Spearman correlation matrix for the traits studied under normal and water stress conditions. HD, number of days to 50% heading; PH, plant height;
PNPP, panicle number per plant; PL, panicle length; FGNPP, number of filled grains per panicle; GYPP, grain yield per plant. *, ** and *** indicate significance at 0.05,
0.01 and 0.001 probability levels, respectively.
the average of the traits studied is summarized in Table 7.
The structure of the clusters remained the same when the
genotypes were subjected to water stress conditions, with the
exception of the Sakha108 × WAB1573, Sakha108 × IET1444,
Sakha106 × WAB1573, Giza179 × Sakha108, Sakha106,
Giza179 × IET1444, Giza178 × WAB1573, Giza179 × Sakha107,
Giza178 × NERICA4, and Sakha107 × Sakha108 genotypes,
which moved from cluster 1 under normal conditions to
cluster 2 under water stress conditions because they were less
stable than the other members of their cluster. Overall, a
heatmap was generated to determine the relationship between
genotypes and the studied traits under normal and water stress
conditions (Figure 4).
The diagonal in Figure 2, illustrates the density plots of the
traits studied using the smoothed function of the values. The
highest density of the values was indicated by the area under the
curve and the peak of the density plot. From the density plots, it
was observed that all the traits studied had different peaks under
normal and water stress conditions. Thus, the values of these
traits under normal conditions are concentrated at higher values
compared to those under water stress conditions. However, a
remarkable overlap was observed in panicle length and HD
values, i.e., the water stress effects were not stable on these traits.
The density plots thus showed that the effects of water stress were
mainly on both the magnitude of the traits and their density.
Cluster Analysis
To cluster the genotypes under normal and water stress
conditions, cluster analysis was performed using Euclidean
as a distance measure of dissimilarity and Ward’s algorithm
on R software version 4.1.0 2021. Grain yield per plant and
panicle length values were used to construct a distance matrix
and generate a tangle-gram to show the similarities among
all the genotypes under normal and water stress conditions
(Figure 3). The data were standardized owing to their different
scales. Because the results of Fuzzy C-Means showed low
overlap between clusters, hard clustering methods were used
to construct the tangle-gram (Figure 3). Six methods were
compared using agglomerative coefficients to choose the most
efficient method for clustering the data. These methods were
average, generalized average, single, weighted, complete, and
Ward. The agglomerative coefficients were 0.81, 0.88, 0.71,
0.84, 0.89, and 0.94, respectively, under normal conditions and
0.86, 0.90, 0.64, 0.86, 0.91, and 0.95, respectively, under water
stress conditions.
Based on Figure 3, all the genotypes were grouped into
two clusters under normal and water stress conditions, and
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DISCUSSION
Choosing proper parents and crosses is a challenge for breeders
of high-yielding rice varieties with improved grain qualities.
The hybrid combining abilities of parental lines of rice can be
an efficient tool to improve their rice production. Combining
ability analysis is one of the most practical approaches to
estimate its effects for the selection of desired parents and
crosses (El-Mowafi et al., 2015; Akanksha and Jaiswal, 2019).
Moreover, the combining ability was investigated to identify
the best genetic potential for developing cross combinations
with desirable characteristics and to observe the genetic impact
involved in trait expression (Sprague and Tatum, 1942).
For all the traits tested, the results of combining ability showed
that both GCA and SCA mean squares were extremely significant.
This finding emphasizes the relevance of both additive and nonadditive genetic factors in determining how these traits perform
under normal and stress conditions. Under normal and stress
conditions, the GCA/SCA ratios were more significant than
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FIGURE 3 | A tangle-gram showing results of cluster analysis based on Euclidean coefficient and Ward method under normal and water stress conditions.
TABLE 7 | Average of the traits studied for the two clusters under normal and water stress conditions.
Condition
Cluster
HD
PH
PNPP
PL
FGNPP
Normal
Cluster1
100.59
111.44
33.76
25.39
130.76
46.33
Cluster2
108.73
117.29
22.09
22.18
155.94
39.12
Cluster1
95.25
88.02
26.94
23.31
119.02
36.92
Cluster2
103.70
99.99
21.23
20.31
140.30
31.35
Water stress
GYPP
HD, number of days to 50% heading; PH, plant height; PNPP, panicle number per plant; PL, panicle length; FGNPP, number of filled grains per panicle; GYPP, grain yield
per plant.
FIGURE 4 | A heatmap of the relationship between genotypes and the traits studied under normal (.N) and water stress (.S) conditions. GYPP, grain yield per plant;
PL, panicle length; PNPP, panicle number per plant; HD, number of days to 50% heading; FGNPP, number of filled grains per panicle; PH, plant height.
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optimum number of clusters in the data, internal validation was
performed, where voting among 30 indices was used to determine
the relevant number of clusters in the data (Ward, 1963; Charrad
et al., 2014).
unity for the number of days to 50% heading (day), number of
panicles per plant, number of filled grains per panicle, and grain
yield per plant (g) under regular irrigation conditions. The SCA
estimation of the hybrid combinations demonstrated that all the
hybrids showed significantly positive SCA effects for at least one
yield characteristic.
The analysis of variance of several genotypes under different
conditions (drought and non-drought) at both sites revealed
significant differences for all the characteristics assessed, implying
that the germplasm utilized in the study possessed substantial
genetic diversity. Thus, the studied genotypes can improve grain
yield and other agronomic traits in drought-prone crops.
Similar results were found in other studies (Ganapati et al.,
2020; Abd El-Aty et al., 2022a). However, under deficit irrigation,
non-additive gene effects, appear to be highly important for
plant height, panicle length, number of filled grains per panicle,
and grain yield per plant, as shown by Akanksha and Jaiswal
(2019). Thus, additive gene effects significantly contribute to
the inheritance of these qualities, and a pedigree technique of
selection can be used to improve them. Such results demonstrated
the role of the cumulative effects of additive × additive
interactions of positive alleles. Some previous studies reported
the effects of additive gene action on yield quality and quantity
traits (El-Hity et al., 2015; El-Mowafi et al., 2018; Herwibawa
et al., 2019). Simultaneously, other studies revealed the effects of
additive and non-additive genes and their benefits on developing
hybrid rice varieties (Sreeramachandra et al., 2000; Huang et al.,
2015).
For traits regulated by non-additive gene activities,
hybridization followed by selection in subsequent generations
may be performed. The earliest parents were the Sakha107,
Giza179, and IET1444 varieties with 98.66, 98.67, and 99.00 days
under regular irrigation and 92.23, 91.33, and 93.33 days under
stress conditions, respectively. Under both conditions, the
parental varieties Giza179 and WAB1573 showed the most
significant mean number of filled grains per panicle. Under
normal and stressful conditions, the parental varieties Sakha107
and Sakha108 exhibited the highest grain yield per plant. Vanaja
et al. (2006) used a 6 × 6 half-diallel cross to evaluate the
combining ability of rice yield and yield components. They
proposed that additive and non-additive gene effects are essential
in determining the yield and the components showing the most
yield (Mandal and Roy, 2001; Kusaka et al., 2005; Moradi et al.,
2012; El-Malky and Al-Daej, 2018; Manjunath et al., 2020).
Because the Giza179 × Sakha107 cross showed highly
significant desirable SCA effects for all the studied traits under
normal and water stress conditions, they could be recommended
for use in rice hybrid breeding programs. Earlier findings have
also confirmed these results (El-Mowafi et al., 2015; Manjunath
et al., 2020).
The cubic clustering criterion (Milligan and Cooper, 1985)
was used to identify whether there were clusters in the data.
Fuzzy C-Means is a soft clustering algorithm (Bezdek, 1973, 1981)
and was used to determine if overlapping existed between the
clusters. The Ward method had the highest coefficient compared
with the other five methods under normal and water stress
conditions; thus, it was chosen for cluster analysis. To validate the
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CONCLUSION
Under normal and water stress conditions, Sakha107 was
the best general combiner for earliness and short stature.
Giza179 and Sakha108 were the best general combiners for
grain yield per plant and one or more of its characteristics.
Furthermore, in both normal and water stress conditions,
Giza179 displayed the highest GCA impacts for all attributes
evaluated. In addition, under normal and water stress conditions,
the Giza179 × Sakha107 cross demonstrated highly substantial
and desirable SCA effects on all the examined traits.
DATA AVAILABILITY STATEMENT
The original contributions presented in this study are included
in the article/Supplementary Material, further inquiries can be
directed to the corresponding author/s.
AUTHOR CONTRIBUTIONS
MA, YK, SA, and KE-T conceived and designed the research.
MA, SA, KE-T, and AE-T supervised the study and wrote the
manuscript. MA, YK, AE-A, SM, OI, and AE-T performed field
experiments. MA, YK, AE-A, SM, OI, and ME developed the
biochemical and physiological analyses. MA, ME-S, SA, KE-T,
and AE-T analyzed the data. YK, AE-A, SM, OI, ME, and ME-S
assisted with experiments and/or data evaluation. All authors
critically revised the manuscript and approved the final version.
FUNDING
This project was funded by the Khalifa Center for Biotechnology
and Genetic Engineering-UAEU (grant no. 31R286) to SA
and Abu Dhabi Research Award (AARE2019) for Research
Excellence-Department of Education and Knowledge (ADEK;
Grant #: 21S105) to KE-T.
ACKNOWLEDGMENTS
KE-T would like to thank the library at the Murdoch
University, Australia for the valuable online resources and
comprehensive databases.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.
866742/full#supplementary-material
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