Content uploaded by Ahmed El Hosary
Author content
All content in this area was uploaded by Ahmed El Hosary on Jul 09, 2022
Content may be subject to copyright.
Egypt. J. Plant Breed. 19(6):1765 – 1781 (2015)
GENETIC ANALYSIS OF WATER STRESS
TOLERANCE ATTRIBUTES IN F1 MAIZE
DIALLEL CROSSES
A.A.A. EL-Hosary
Department of Agronomy, Faculty of Agriculture, Benha University, Egypt.
ABSTRACT
This investigation aimed to estimate the type and relative amount of genetic
variance components and their interactions with irrigation treatments for earliness, some
physiological traits, yield and its components. A half diallel set of crosses involving
seven parental inbred lines were evaluated in two separate experiments (normal
irrigation and water deficit conditions). All genotypes were grown in a randomized
complete block design with four replications in each of the two experiments at
Agricultural Research and Experimental Station of the Faculty of Agriculture,
Moshtohor, Benha University. Data were genetically analyzed according to Griffing
method 2 model 1 and gene action Wr via Vr regression analysis was determined as
described by Jinks methods. Significant differences between the two studied
environments were detected for all studied traits. Mean squares for genotypes including
parents, crosses, and parents vs crosses and their interactions with irrigation treatment
reached the significance level of probability for most of the traits studied. The crosses
P1xP2, P1xP3, P2xP3, P3xP4 and P4xP6 were significantly superior over SC 168 for grain
yield plant-1 by 13.00, 7.14, 12.37, 5.73 and 6.94%, respectively. Mean squares due to
general and specific combining abilities were significant for all traits. Also, GCA/SCA
ratios equal unity were obtained for relative water content, grain yield and most of its
components, indicating that additive and non-additive types of gene action have the same
importance in the performance of these traits. The parental inbred line P1 seemed to be
the best general combiner for earliness and grain yield plant-1. The results indicated that
significant desirable
ij
S
^
effects were represented by cross P4xP5 for days to 50% silking
and P1xP2, P2xP3, P3xP4 and P4xP6 for grain yield plant-1. The genetic analysis by means
of diallel cross graphs showed that significant and negative intercept was obtained,
suggesting over dominance for most studied traits. The parental inbred line P7 in both
irrigation treatments seemed to carry the most dominant genes responsible of the
expression for proline content, grain yield plant-1 and its components. However, the
inbred line P2 for proline content, P4 for RWC, No of rows ear-1, No. of kernels row-1 and
grain yield plant-1 in both irrigation treatments, seemed to carry the most recessive genes
responsible the expression of respective traits.
Key words: Maize, Drought stress, Relative water content, Chlorophyll content,
Combining ability, Graphical analysis.
INTRODUCTION
Maize (Zea mays L.) is considered the most widely cultivated and
dominat food crops in the world. It is grown throughout various
environments in the world. This crop can be used for human consumption or
livestock feed and as a raw material for industrial products. It is grown on
more than 160 million hectares in the world, with annual production of
more than 1017 million megagrams in 2103 (FAO 2015). Increasing maize
production to narrow the gap between production and consumption is vital
in Egypt.
1766
Considerable variations in maize productivity in different parts of
Egypt should be reduced to attain a projected high productivity. This could
be achieved through diversification of maize breeding programs and
developing new set of maize varieties.
Water stress affects 45% of the world’s crops and arable lands. It is
a major constraint in maize production and the most negative factor causing
yield reduction in semiarid regions (El-Hosary 2013 and Umar et al 2014).
Maize growth and yield affected by soil moisture regime caused yield
deterioration, especially if water deficit occurs during the flowering or
reproductive phase (Bassetti and Westgate 1993)
Drought avoidance includes mechanisms which reduce water loss
from plant or keep water absorption. Drought tolerance is the ability of plant
to resist low water supply by enhancing tissue water potentials. Therefore, a
main objective in maize plant breeding programs is improving drought
resistant genotypes. The ideal maize genotype should be high yielding under
all environmental conditions. However as genetic effects are not
independent of environmental effects, most genotypes may differ by
environmental changes (Banziger et al 2000 and El-Hosary et al 2011).
Considerable importance is now being given to interaction of
genotypes with a wide range of soil water stress in maize breeding
programs. Therefore, great attention should be paid to select drought
tolerant maize genotypes combined with high yielding properties under
different drought environments, which could be achieved by testing maize
genotypes continuously across different drought environments.
Combining ability is a concept newly developed to help the breeder
in selection of parental stocks appropriate to the designed breeding
procedure (Basbag et al 2007). Parents of the best potentiality to transmit
high yielding ability or improved earliness and drought tolerance traits to
their progeny of new combinations, are those exhibiting the highest values
for specific combining ability (SCA) effects. Whereas, combinations of the
highest values for SCA demonstrate exploition of heterosis concept. The
utility of diallel cross design in investigating the genotypic by
environmental interactions has been considered by Allard (1956) and Allard
and Bradshaw (1964).
The aim of this work was to identify of gene action controlling the
inheritance of studied traits, to estimate the magnitude of combining ability
(GCA and SCA) in the first generation and to enhance yield potentiality and
resistance to drought stress of maize genotypes.
MATERIALS AND METHODS
The endeavor of understanding the importance of combining ability
in the first hybrid generation in maize, a field experiment procedure was
executed at the Agricultural Research and Experimental Station of the
1767
Faculty of Agriculture, Moshtohor, Benha University in successful growing
season of 2014 and 2015.
In the first season, 21 F1 crosses were made using half diallel mating
between seven yellow maize inbred lines, i.e. M601 (P1), M602 (P2), M603
(P3), M604 (P4), M605 (P5), M606 (P6) and M607 (P7), which differed
considerably in their characters. To achieve the previous crosses the seven
parental inbred lines were grown at two planting dates (15th and 25th May)
in order to overcome the differences of parental inbred lines in flowering
time and to secure enough hybrid seeds.
The seven parents and their resulting 21 F1 hybrids along with the
commercial check hybrid single cross 168 (SC 168) were evaluated in the
second season under two independent experiments (normal irrigation), by
adding irrigation every 13 days and water-stress (drought environment)
irrigation every 21 days) using a randomized complete block design
(RCBD) with four replications. The planting date was 31th May 2015. Plots
were two ridges, 6 m long and the spacing between ridges and hills were 70
and 25 cm, respectively. To adjust the plant stand, three kernels were
planted hill-1 on one side of the ridge then seedlings were later thinned to
one plant hill-1. The other cultural practices were followed as usual for
ordinary maize field in the area. Mean data were calculated after recorded
measurements on 15 plants chosen at random from each plot for parents, F1
crosses and SC 168, except days to 50% silking where the mean of plot was
used. The following traits were measured at flowering stage; leaf proline
content (mg g-1) determined according to protocol of Bates et al (1973) as a
physiological indicator of plant status under the implemented water stress treatments.,
relative water content (RWC%) estimated according to the protocol of Barrs
and Weatherley (1962), then compensation account in the following
formula:
Where, FW is sample fresh weight, DW is sample dry weight and TW =
sample full turgor weight and chlorophyll content (mg m-2); determined as
SPAD units which using the apparatus devised by Soil and Plant Analysis Dept.
Minolta Co SPAD units which were transformed to mg m-2 as described by Monje
and Bugbee (1992). Then after harvest the following traits were determined; number
of rows ear-1, number of kernels row-1, 100-kernel weight and grain yield
plant-1 adjusted to a uniform moisture basis of 15.5%. Drought
susceptibility index (DSI) was calculated for grain yield plant-1 using the
following formula: DSI = YS/YP, where, Ys= yield of genotype in stress
condition and Yp= yield of genotype in non-stress condition.
The data obtained for each trait were statistically analyzed using
computer statistical program MSTAT-C. Superiority of grain yield was
1768
calculated for individual crosses as the percentage deviation of F1 mean
performance from check variety SC 168 average value. The analysis of
variance for combining ability and the estimation of various effects were
done following the procedure of Griffing (1956) for method 2 model 1.
Whenever, homogeneity of error variance was found, the combined analysis
across experiments was carried out according to Gomez and Gomez (1984).
The data were also subjected to Wr-Vr regression analysis to determine
gene action as described by Jinks (1954).
RESULTS AND DISCUSSION
Table 1 presents the analysis of variance for earliness, physiological
studied traits, grain yield and its components at each and across
environments. Significant differences between the two irrigation
environments were detected for all studied traits, indicating overall
differences between the stress and normal irrigation treatment with high
yield mean performance of normal compared to those in drought one (Table
2). The increase in normal irrigation may be due to ability of plants to do
metabolic process in perfect way leading to better vegetative growth and
therefore reflected to high grain yield. Therefore, normal irrigation seemed
to be non-stress environment. On the other side, proline accumulation
increased continuously during the stress period. Genotypes which
accumulate larger concentrations of proline are able to survive more under
extreme stress conditions than those with lower proline levels.
Leaf proline content is an indicator of drought stress tolerance.
Selection for stress tolerance should give a positive yield response under
stress. On the contrary, the results indicated that selection under the
irrigated environment would be less effective for improving grain yield
under drought stress than direct selection in the stress conditions. Such
results are in good agreement with those reported by Souza et al (2009), and
Shiri et al (2010) and El-Hosary et al (2012).
Genotypes and their fragmentations (parents, crosses and parents vs
crosses) mean squares were significant for all studied traits in both and
across irrigation treatments and accounted for a major portion of phenotypic
variation. This reveals over all differences between genotypes in the present
study. Significant mean squares for genotypes, parents, crosses and parents
vs crosses x environment interactions were obtained for all studied traits,
except days to 50% silking (Tables 1), revealing that the performance of
genotypes (parents and crosses) and heterosis differed in repeatability from
normal to drought environment.
The mean performances of the tested parental inbred lines, their 21
F1 hybrids and SC 168 at the combined analysis for all traits, susceptibility
index for grain yield plant-1 as well as grain yield plant-1 and relative
superiority over SC 168 at both and across irrigation treatments were shown
1769
Table 1. Mean squares from ordinary and combining ability analysis
for all studied traits at normal irrigation, drought stress
environment and across them.
SOV
df
Mean squares
Days
to 50%
silking
Chlorophyll
content
Relative
Water
content
Prolien
content
No. of
rows ear-1
No.
of kernels
row-1
100-
kernel
weight
Grain
yield
plant -1
Normal irrigation treatment
Replication
3
29.25**
207.69
4.67
12.85**
0.23
0.60
1.17
180.79*
Genotype (G)
27
14.78**
3503.31**
215.98**
23.98**
9.28**
193.07**
85.59**
13934.93**
Parent (P)
6
12.92*
1268.57**
213.91**
32.84**
2.40**
25.00**
15.18**
911.62**
Crosses (F1)
20
11.85*
2850.69**
190.85**
20.07**
1.70**
87.74**
19.35**
6911.44**
P vs. F1
1
84.48**
29964.07**
731.13**
49.02**
202.16**
3308.18**
1832.84**
232544.62**
Error
81
0.30
112.40
2.95
0.46
0.19
1.37
1.14
42.85
GCA
6
5.60**
307.31**
57.37**
8.21**
0.49**
47.13**
13.53**
3221.33**
SCA
21
3.15**
1038.26**
53.03**
5.36**
2.84**
48.59**
23.65**
3558.71**
Error
81
0.75
28.10
0.74
0.11
0.05
0.34
0.29
10.71
GCA/SCA
1.78
0.30
1.08
1.53
0.17
0.97
0.57
0.91
Drought stress environment
Replication
3
93.11**
119.84
5.58
26.24**
0.40
5.63**
4.78
74.89
Genotype (G)
27
23.19**
11350.73**
115.76**
248.55**
10.52**
166.68**
54.71**
10669.07**
Parent (P)
6
11.62*
10310.20**
105.49**
403.27**
4.90**
8.74**
16.66**
862.25**
Crosses (F1)
20
26.67**
9095.98**
110.96**
193.35**
3.65**
97.52**
36.99**
6732.87**
P vs. F1
1
23.05**
62689.02**
273.49**
424.22**
181.50**
2497.46**
637.31**
148233.76**
Error
81
2.68
71.19
4.02
0.75
0.16
1.06
1.26
29.77
GCA
6
10.81**
2933.10**
23.49**
154.74**
1.94**
40.76**
20.16**
3034.07**
SCA
21
4.37**
2810.42**
30.50**
35.68**
2.83**
41.93**
11.82**
2562.46**
Error
81
0.67
17.80
1.00
0.19
0.05
0.26
0.31
7.44
GCA/SCA
2.48
1.04
0.77
4.34
0.68
0.97
1.71
1.18
Combined across irrigation treatments (I)
Environments
(I)
1
910.14
**
293661.65
**
5206.31
16733.07
**
119.30
**
1556.92
**
1400.00
**
81066.48
**
Rep/I
4
61.18**
163.76
5.12
19.55**
0.31
3.11
2.98
127.84
Genotype (G)
35
29.20**
11560.07**
257.24**
190.11**
18.85**
350.97**
128.25**
24174.63**
Parent (P)
7
18.24*
7718.49**
243.46**
312.62**
6.24**
26.65**
27.30**
1474.78**
Crosses (F1)
27
29.06**
8807.19**
226.76**
143.82**
4.41**
176.96**
49.15**
13390.67**
P vs. F1
1
97.89**
89667.27**
949.48**
380.82**
383.38**
5777.20**
2315.86**
376052.76**
G x I
35
8.76
3293.97**
74.51**
82.41**
0.94*
8.78**
12.05**
429.37**
P x I
7
6.30
3860.28**
75.93**
123.48**
1.06*
7.09*
4.54*
299.09**
F1 x I
27
9.45
3139.48**
75.05**
69.59**
0.94*
8.30**
7.19*
253.64**
P vs. F1 x I
1
9.64
2985.82**
55.14**
92.42**
0.28
28.44**
154.29**
4725.61**
Error
140
2.85
91.80
3.48
0.60
0.18
1.21
1.20
36.31
GCA
6
14.76**
1987.22**
65.61**
110.08**
1.98**
83.16**
32.24**
6139.26**
SCA
21
5.17*
3147.96**
63.94**
29.65**
5.49**
89.05**
32.01**
6016.34**
GCA x I
27
8.76**
3293.97**
74.51**
82.41**
0.94**
8.78**
12.05**
429.37**
SCA x I
6
1.65
1253.19
15.25
52.87**
0.45**
4.73**
1.45
116.14
Error
21
2.35
700.72
19.59
11.38
0.17
1.47
3.46
104.83
GCA/SCA
2.86
0.63
1.03
3.71
0.36
0.93
1.01
1.02
GCA x I/GCA
0.59
1.66
1.14
0.75
0.48
0.11
0.37
0.07
SCA x I/SCA
0.32
0.40
0.24
1.78
0.08
0.05
0.05
0.02
* p 0.05; ** p 0.01
1770
Table 2. Mean performance of all genotypes at the combined analysis
across the two irrigations treatments for physiological and
yield component traits.
Genotypes
Days
to 50%
silking
Chlorophyll
content
Relative
water
content
Proline
content
No
of rows
ear-1
No
of kernel
row-1
100-kernel
weight
(g)
P1
72.09 ej
502. hi
74.65 eg
27.35 c
11.36 m
29.01 n
32.78 g
P2
74.28 bd
412.9 q
70.89 jk
11.34 o
10.59 n
24.06 p
29.66 i
P3
75.09 bc
449.2 np
72.39 hj
23.88 f
10.52 n
26.85 o
32.94 g
P4
75.22 b
458.8 ln
60.42 m
23.87 f
9.010 p
23.58 p
29.81 hi
P5
73.94 be
416.8 q
68.97 k
14.84 m
9.980 o
26.31 o
28.22 j
P6
71.22 gk
439.4 p
75.73 ef
12.61 n
10.77 n
26.71 o
30.84 h
P7
73.03 dg
469.7 k
76.46 de
19.64 i
11.68 m
25.94 o
28.81 ij
P1xP2
69.91 k
516.2 eg
83.37 a
30.34 a
14.10 ce
43.24 ab
39.81 ac
P1xP3
70.84 ik
466.3 kl
84.00 a
28.91 b
14.72 a
43.69 a
39.25 bc
P1xP4
70.44 jk
451.6 mo
79.59 bc
24.92 e
14.42 ac
40.71 df
39.84 ac
P1xP5
70.00 k
503.7 hi
76.63 de
23.34 fg
14.21 bc
39.41 gh
39.34 bc
P1xP6
70.63ik
542.4 b
73.41 gi
24.87 e
14.39 ac
40.14 eg
39.47 bc
P1xP7
73.41 bf
465.6 kl
74.15 fh
16.03 l
13.07 ik
30.63 m
34.16 f
P2xP3
72.06 ej
522.4 ce
83.18 a
23.28 fg
14.66 ab
40.48 dg
39.44 bc
P2xP4
72.91 dh
553.0 a
78.96 bc
27.70 c
14.39 ac
41.53 cd
39.91 ac
P2xP5
71.34 gk
511.1 fh
76.14 df
18.68 j
13.58 fh
37.48 ij
36.13 e
P2xP6
70.81 ik
530.1 c
75.73 ef
23.35 fg
13.71 dg
40.89 de
38.81 cd
P2xP7
75.00 bc
470.4 k
70.75 jk
18.15 jk
12.66 kl
31.90 kl
36.53 e
P3xP4
73.19 cg
526.4 ce
83.76 a
25.84 d
14.12 cd
42.21 bc
40.06 ac
P3xP5
70.41 jk
491.7 j
79.02 bc
22.76 g
13.09 ik
38.56 hi
38.06 d
P3xP6
70.28 jk
518.3 df
72.55 hj
20.22 i
13.53 gi
41.35 ce
40.78 a
P3xP7
74.06 bd
494.1 ij
65.94 l
19.94 i
13.17 hj
32.78 k
35.53 e
P4xP5
70.28 jk
506.9 gh
71.85 ij
24.02 f
13.32 gi
39.60 fh
39.63 ac
P4xP6
71.38 gk
527.1 cd
77.80 cd
21.24 h
13.65 eg
41.04 ce
40.31 ab
P4xP7
77.00 a
449.9 mp
68.94 k
16.01 l
12.67 kl
31.83 kl
35.66 e
P5xP6
71.78 fk
468.6 kl
80.98 b
17.67 k
12.78 jl
36.87 j
36.09 e
P5xP7
72.59 di
441.9 op
69.07 k
15.41 lm
12.41 l
28.19 n
32.13 g
P6xP7
74.25 bd
460.1 km
72.57 hj
21.06 h
12.52 l
31.16 lm
34.19 f
SC 168
71.00 hk
447.0 op
80.00 b
21.53 h
14.00 cf
41.43 cd
39.00 cd
1771
Table 2. Cont.
Genotype
Grain yield plant-1
(g)
Grain yield
susceptibilit
y index
(DSI)
Relative
superiority over SC 168
N
D
C
P1
122.6 k
90.10 h
106.3 j
0.74
P2
94.41 mn
60.68 j
77.54 n
0.64
P3
99.48 lm
85.69 h
92.58 l
0.86
P4
75.41 o
73.73 i
74.57 no
0.98
P5
86.41 n
51.95 k
69.18 o
0.6
P6
108.1 l
89.61 h
98.86 k
0.83
P7
95.66 mn
75.32 i
85.49 m
0.79
N
D
C
P1xP2
259.5 a
214.0 a
236.7 a
0.82
13.53**
12.37**
13.00**
P1xP3
255.1 a
193.8 c
224.4 b
0.76
11.58**
1.8
7.14**
P1xP4
237.3 b
189.0 c
213.1 c
0.8
3.79
-0.74
1.74
P1xP5
222.5 cd
172.3 d
197.4 d
0.77
-2.65
-9.51**
-5.77**
P1xP6
220.5 ce
171.9 d
196.2 d
0.78
-3.55
-9.72**
-6.35**
P1xP7
158.1 i
114.9 g
136.5 g
0.73
-30.84**
-39.66**
-34.85**
P2xP3
254.8 a
215.6 a
235.2 a
0.85
11.45**
13.25**
12.27**
P2xP4
223.6 cd
175.7 d
199.6 d
0.79
-2.18
-7.72**
-4.70**
P2xP5
209.6 f
155.0 e
182.3 e
0.74
-8.33**
-18.61**
-13.00**
P2xP6
216.1 df
173.6 d
194.9 d
0.8
-5.47**
-8.80**
-6.99**
P2xP7
147.7 j
116.7 g
132.2 gh
0.79
-35.38**
-38.69**
-36.89**
P3xP4
237.2 b
205.4 b
221.3 b
0.87
3.75
7.89**
5.63**
P3xP5
197.9 g
169.5 d
183.7 e
0.86
-13.43**
-10.99**
-12.32**
P3xP6
187.1 h
156.5 e
171.8 f
0.84
-18.16**
-17.80**
-18.00**
P3xP7
145.4 j
111.0 g
128.2 h
0.76
-36.39**
-41.71**
-38.81**
P4xP5
217.5 df
171.1 d
194.3 d
0.79
-4.84*
-10.16**
-7.26**
P4xP6
235.8 b
212.3 ab
224.0 b
0.9
3.15
11.50**
6.94**
P4xP7
144.9 j
88.41 h
116.6 i
0.61
-36.64**
-53.57**
-44.33**
P5xP6
211.9 ef
145.1 f
178.5 e
0.68
-7.31**
-23.81**
-14.81**
P5xP7
129.2 k
84.79 h
107.0 j
0.66
-43.47**
-55.47**
-48.92**
P6xP7
144.3 j
109.1 g
126.7 h
0.76
-36.88**
-42.73**
-39.54**
SC 168
228.6 bc
190.4 c
209.5 c
0.83
Means followed by the same letter for each tested parameter are not significantly
different by Duncan’s test (P < 0.05). * p 0.05; ** p 0.01
1772
in Table 2. The mean values for days to 50% silking ranged from 69.91
days for cross P1 xP2 to 77.00 for P4xP7. Insignificant differences between
SC 168 and the cross P1xP2 was detected for earliness.
The cross P2xP4 gave significantly the highest value for chlorophyll
content followed by cross P1xP6. However, the P5 had the lowest one. For
relative water content, the three crosses P1xP3, P2xP3 and P3xP4 gave
significantly higher mean values, while the parental inbred line P4 had the
lowest one.
The proline content ranged from 11.34 for P2 to 30.34 for cross
P1xP2. Eleven crosses expressed positive significant differences from the
check hybrid (SC 168). The two crosses P1xP3 and P2xP3 for No. of rows
ear-1; P1xP2 and P1xP3 for No. of kernels row-1 and P3xP6 and P4xP6 for 100-
kernel weight had significantly higher mean values compared to SC 168
(check hybrid) for these traits. Also, the crosses P1xP2, P1xP3 and P2xP3 at
normal irrigation treatment, P1xP2, P2xP3, P3xP4 and P4xP6 at stress
conditions and P1xP2, P1xP3, P2xP3, P3xP4 and P4xP6 at the combined
analysis seemed to be the best crosses for grain yield plant-1 and had
significantly out-yielded SC 168 (Table 2). Also, the cross P1xP4 in both
and across irrigation treatments insignificantly out-yielded the check hybrid
SC 168. The useful superiority in yield over SC 168 ranged from 6.84 to
13.00% in the combined data. Also, these crosses gave higher susceptibility
index (Table 2). Hence it could be concluded that these crosses offer
possibility for improving grain yield of maize.
The mean squares associated with both types of combining ability
were significant for all traits under study at normal and stress environments
and across them (Table 1), revealing that, both additive and non-additive
types of gene action were involved in determining the performance of
single-cross progeny. To determine the genetic effects of greater
importance, GCA/SCA ratio was computed. For days to 50% silking and
proline content at both and across the two environments as well as 100-
kernel weight at drought environment, high ratio, which largely exceeded
the unity was obtained, indicating that large part of the total genetic
variability associated with these traits was of additive and additive by
additive gene action. No of rows ear-1 at both and across environments,
chlorophyll content and 100 kernel weight at normal environment and
relative water content at drought environment showed GCA/SCA ratio less
than unity. Therefore, it could be concluded that the large portion of the
total genetic variability associated with these traits is due to non-additive
gene action as it reflected on the largest superiority magnitude expressed by
the previous traits as the deviation of particular F1 mean performance from
check (SC 168). The remaining cases had GCA/SCA ratio equal unity,
indicating that additive and non-additive types of gene action have the same
importance in the performance of these cases. Several investigators
1773
reported similar results (El-Badawy 2013 and El-Hosary 2014 ). On the
other hand, Akbar et al (2008), Hefny (2010) and Aminu and Izge 2013 and
Aminu et al 2014 reported that both additive and non-additive were
important in genetic expression of the yield and its components in maize.
The interactions between irrigation treatments and general
combining ability were significant for all traits. Such results refer to
magnitude of additive and additive x additive gene action appeared to be
more affected by environments. Meanwhile, insignificant interaction mean
squares between environments and specific combining ability were detected
for all traits except SCA x E for proline content, No. of rows ear-1 and No.
of kernels row-1, revealing that, the non-additive type of gene action was not
influenced by environmental changes. It is fairly evident that the ratio for
GCA x D/ GCA was higher than ratio of SCA x D/ SCA for proline content.
This result indicated that additive effects were more influenced by the
environmental conditions than non-additive. Vice versa, for No. of rows ear-
1 and No. of kernels row-1, the non- additive effects were more influenced
by change in irrigation treatments. For remaining traits, the insignificant
SCAxE were detected along with significant GCAxE. This result indicated
that additive effects were more influenced by the environmental conditions
than non- additive effects. This conclusion is in well agreement with those
reported by Gilbert (1958).
Estimation of general combining ability effects (
i
g
ˆ
) for individual
inbred lines at combined analysis were presented in Table 3.
Table 3. General combining ability effects (
i
g
ˆ
) of the studied parental
inbred lines for all studied traits across the two irrigation
treatments.
Parents
Days to
50%
silking
Chlorophyll
content
Relative
Water
content
Proline
content
No. of
rows ear-1
No. of
kernels
row-1
100-
kernel
weight
Grain
yield
plant -1
P1
-1.10**
8.23**
2.34*
3.61**
0.56**
1.88**
1.04**
17.60**
P2
0.15
5.85**
1.17*
-0.72**
0.19**
0.53**
0.21**
8.60**
P3
0.20
4.60**
1.54**
2.01**
0.19**
1.54**
1.22**
10.13**
P4
0.71**
6.25**
-1.96**
1.87**
-0.22**
0.58**
0.78**
6.62**
P5
-0.55**
-13.19**
-0.86**
-2.12**
-0.36**
-0.68**
-1.14**
-8.57**
P6
-0.86**
5.47**
0.57**
-1.89**
-0.05
0.66**
0.37**
3.46**
P7
1.46**
-17.21**
-2.79**
-2.75**
-0.30**
-4.50**
-2.47**
-37.83**
L.S.D(0.05) gi
0.22
1.25
0.24
0.10
0.05
0.14
0.14
0.79
L.S.D(0.01) gi
0.30
1.70
0.33
0.14
0.07
0.20
0.19
1.07
L.S.D(0.05) gi-gj
0.39
2.21
0.43
0.18
0.10
0.25
0.25
1.39
L.S.D(0.01) gi-gj
0.53
3.00
0.59
0.24
0.13
0.35
0.34
1.89
* p 0.05; ** p 0.01
1774
High positive values would be of interest for all studied traits in
question, except days to 50% silking where high negative would be useful
from the breeder point of view. The parental inbred line P1 showed significant
desirable (
i
g
ˆ
) effects for all studied traits. The parental lines P2 and P3
expressed significant positive (
i
g
ˆ
) effects for chlorphyll content, RWC,
proline, grain yield plot-1 and its components. However, it gave significant
undesirable or insignificant (
i
g
ˆ
) effects for days to 50% silking. The parental
inbred line P4 gave significant positive (
i
g
ˆ
) effects for chlorophyll content,
proline content, No. of kernls row-1, 100-kernel weight and grain yield plant-1.
The parental lines P5 and P6 showed undesirable (
i
g
ˆ
) effects for all traits,
except No. of days to 50% silking. The parental inbred line P7 gave undesirable
(
i
g
ˆ
) effects for all traits under study.
It is worth noting that the inbred line which possessed high (
i
g
ˆ
)
effects for grain yield plant-1 showed the same effect for one or more of the
traits contributing to grain yield. From the previous result, it could be
concluded that the parental inbred line P1 seemed to be the best general
combiner for earliness and grain yield plant-1.
Specific combining ability effects for all possible combinations with
respect to traits studied are presented in Table (4). One, thirteen, nine, eight,
thirteen, fifteen, thirteen and fourteen crosses expressed significant
desirable
ij
S
^
effects for days 50% silking, cholorphyll content, relative
water content, proline content, No. of rows ear-1, No. of kernels row-1, 100-
kernel weight and grain yield plant-1, respectively . Also, results indicated
that significant negative
ij
S
^
effects was represented by cross P4xP5 for days
to 50% silking. Also, The high and positive inter- and intera – allelic
interactions were exhibited by crosses P1xP6 and P2xP4 for cholorphyll
content, P3xP4 and P5xP6 for relative water content, P1xP2 and P2xP4 for
porline content, P1xP3, P1xP4, P1xP5, P1xP6, P2xP3, P2xP4 and P3xP4 for No.
of rows ear-1, P1xP2, P1xP3, P2xP4, P3xP4, P4xP5 and P4xP6 for No of kernels
row-1, P1xP5, P2xP4, P2xP7, P3xP6, P4xP5 and P4xP6 for 100-kernel weight
and P1xP2, P2xP3, P3xP4 and P4xP6 for grain yield plant-1. In these crosses
showing perfect
ij
S
^
effects containing only one good combiner, such
combinations would show desirable transgressive segregates, providing that
the additive genetic system present in the good combiner as well as the
complementary and epistatic effects present in the cross, act in the same
direction to reduce undesirable plant characteristics and maximize the
character in view. Therefore, the previous crosses might be of prime
importance in breeding programs for traditional breeding procedures. In
most traits, the values of SCA effects were mostly different from irrigation
treatment to another.
1775
Table 4. Specific combining ability effects (
ij
S
^
) of the studied diallel
crosses for all studied traits across the two irrigation
treatments.
Crosses
Days
to 50%
silking
Chlorophyll
content
Relative
water
content
Prolien
content
No.
of rows
ear-1
No. of
kernels
row-1
100-
kernel
weight
Grain
yield
plant -1
P1xP2
-1.55
17.54**
4.94**
6.12**
0.53*
5.97**
2.55**
53.21**
P1xP3
-0.67
-31.02**
5.20**
1.97**
1.15**
5.41**
0.98
39.39**
P1xP4
-1.58
-47.46**
4.29**
-1.88**
1.26**
3.39**
2.02**
31.59**
P1xP5
-0.76
24.12**
0.23
0.52
1.19**
3.35**
3.43**
31.05**
P1xP6
0.17
44.20**
-4.42**
1.83**
1.06**
2.73**
2.05**
17.80**
P1xP7
0.64
-9.96
-0.33
-6.15**
-0.01
-1.61*
-0.42
-0.61
P2xP3
-0.69
27.43**
5.55**
0.66
1.46**
3.54**
2.01**
59.14**
P2xP4
-0.36
56.36**
4.83**
5.22**
1.60**
5.57**
2.91**
27.11**
P2xP5
-0.66
33.90**
0.91
0.19
0.93**
2.77**
1.05
24.90**
P2xP6
-0.89
34.27**
-0.93
4.63**
0.75**
4.84**
2.23**
25.48**
P2xP7
0.99
-2.77
-2.56*
0.29
-0.05
1.02
2.79**
4.12
P3xP4
-0.12
31.02**
9.26**
0.64
1.32**
5.23**
2.06**
47.22**
P3xP5
-1.64
15.72*
3.42**
1.54**
0.44
2.84**
1.98**
24.81**
P3xP6
-1.47
23.71**
-4.48**
-1.22*
0.56*
4.28**
3.19**
0.89
P3xP7
0.00
22.21**
-7.74**
-0.65
0.45
0.88
0.78
-1.44
P4xP5
-2.28*
29.31**
-0.25
2.94**
1.08**
4.85**
3.98**
38.92**
P4xP6
-0.88
30.84**
4.27**
-0.07
1.10**
4.94**
3.16**
56.64**
P4xP7
2.43*
-23.67**
-1.24
-4.44**
0.37
0.89
1.35
-9.49*
P5xP6
0.78
-8.22
6.35**
0.35
0.37
2.03**
0.86
26.26**
P5xP7
-0.72
-12.22*
-2.21
-1.05*
0.25
-1.48*
-0.27
-3.92
P6xP7
1.24
-12.73*
-0.13
4.37**
0.05
0.14
0.29
3.71
LSD Sij 5%
2.10
11.92
2.32
0.97
0.52
1.37
1.36
7.50
LSD Sij 1%
2.85
16.17
3.15
1.31
0.71
1.86
1.85
10.17
LSD sij-sik 5%
1.56
8.85
1.72
0.72
0.39
1.02
1.01
5.57
LSD sij-sik 1%
2.12
12.01
2.34
0.97
0.53
1.38
1.37
7.56
LSD sij-skl 5%
0.55
3.13
0.61
0.25
0.14
0.36
0.36
1.97
LSD sij-skl 1%
0.75
4.25
0.83
0.34
0.19
0.49
0.49
2.67
* p 0.05; ** p 0.01
1776
This finding coincided with that reached above, where significant
SCA by environment mean squares were detected. These findings are
similar to those of Aminu and Izge (2013) and Umar et al (2014). Some of
the recent crosses having one or two parents with high combiner, revealing
that parents would have chance of having excellent complimentarity with
other parents
The data obtained herein were further subjected to genetic analysis by
means of diallel cross graphs as constructed by Jinks (1954). The regression
of parent offspring covariance (Wr) on parental array variances (Vr) and
their limiting parabola of the seven parental diallel crosses for all traits
studied are illustrated in Fig. 1 through 8.
Fig 1. Wr/Vr graph for days to 50% silking at normal (N) and drought stress (D)
treatments.
r= correlation coefficient values between parental inbred lines (Yr) and (Wr+Vr)
Fig 2. Wr/Vr graph for cholorophyll content at normal (N) and drought stress (D)
treatments.
r= correlation coefficient values between parental inbred lines (Yr) and (Wr+Vr)
1777
Fig 3. Wr/Vr graph for relative water content at normal (N) and drought
stress (D) treatments.
r= correlation coefficient values between parental inbred lines (Yr) and
(Wr+Vr)
Fig 4. Wr/Vr graph for prolein content at normal (N) and drought stress (D)
treatments.
r= correlation coefficient values between parental inbred lines (Yr) and
(Wr+Vr)
Fig 5. Wr/Vr graph for No of rows ear-1 content at normal (N) and drought
stress (D) treatments.
r= correlation coefficient values between parental inbred lines (Yr) and
(Wr+Vr)
1778
Fig 6. Wr/Vr graph for No of kernels row-1 content at normal (N) and drought
stress(D) treatments.
r= correlation coefficient values between parental inbred lines (Yr) and
(Wr+Vr)
Fig 7. Wr/Vr graph for 100-kernel weight at normal (N) and drought stress(D)
treatments.
r= correlation coefficient values between parental inbred lines (Yr) and
(Wr+Vr)
Fig 8. Wr/Vr graph for grain yield plant-1 at normal (N) and drought stress(D)
treatments.
r= correlation coefficient values between parental inbred lines (Yr) and
(Wr+Vr)
1779
With the exception of silking date and relative water content at
normal irrigation and proline content and chlorophyll content at stress
conditions, the regression coefficient "b" of (Wr,Vr) is different from unity,
indicating that a complementary tupe of epistasis was involved. For the
exceptional cases, regression coefficients "Wr" on "Vr" is not different from
unity, suggesting that the gentic system can be deduced be additive without
complication of non-allelic interaction. With the exception of relative water
content at stress irrigation treatment, significant negative intercept was
obtained, suggesting over dominance. For the exceptional case, the presence
of partial dominance was concluded.
The array points were scattered along the regression line for most
traits, indicating that genetic diversity among the parents.
Parental inbred lines P6 and P1; P 7 and P2 for silking date, P7 and P5;
P7 and P3 for chlorphyll content seemed to carry the most dominance genes
at normal and stress irrigation treatments, respectively. However, P4 for
both traits (silking and chlorophyll content) in both irrigation treatments,
seemed to carry the most recessive genes.
The parental inbred line P7 in both irrigation treatments seemed to
carry the most dominat genes responsible of the expression for proline
content, grain yield plant-1 and its components. However, the inbred line P2
for proline content, P4 for RWC, No of rows ear-1, No. of kernels row-1 and
grain yield plant-1 in both irrigation treatments, P5 in normal irrigation and
P6 in stress irrigation treatment for 100-kernel weight seemed to carry the
most recessive genes responsible for the expression of respective traits.
The correlation coefficient values between parental inbred lines (Yr)
and (Wr+Vr) for each array were significantly positive for silking date in
both irrigation treatments, indicating that decreaser genes were dominat
over increasers and the earliness behaved as dominat trait. On the contrary,
significant negative correlation values between (Yr) and (Wr+Vr) obtained
for other traits indicated that increaser genes were dominat over decreasers.
REFFERENCES
Akbar, M., M Saleem, F. Muhammad, M.K. Ashraf and R.A. Ahmed (2008).
Combining ability analysis in maize under normal and high temperature conditions.
J. Agric. Res. 64: 27-38.
Allard, R.W. (1956). The analysis of genetic-environmental interaction by means of diallel
crosses. Genetics 41: 305-318.
Allard, R.W. and A.D. Bradshaw (1964). Implications of genotype-environmental
interactions in applied plant breeding. Crop Sci. 4: 503–508.
Aminu, D. and A. U. Izge (2013). Gene action and heterosis for yield and yield traits in
maize (Zea mays L.), under drought conditions in Northern guinea and Sudan
savannas of Borno State, Nigeria. Peak Journal of Agricultural Sciences 1 (1):17-
23.
Aminu, D., S.G. Muhammed and B.G. Kabir (2014). Estimates of combining ability and
heterosis for yield and yield traits in maize population (Zea mays L.) under drought
conditions in the Northern Guinea and Sudan savanna zones of Borno State,
1780
Nigeria. International Journal of Agriculture, Innovations and Research 2(5): 824-
830.
Banziger, M., G.O. Edmeades , D. Beck and M. Bellon (2000). Breeding for Water
Stress and N Stress Tolerance in Maize: From Theory to Practice. CIMMYT,
Mexico, D.F., Mexico.
Barrs, H.D. and P.E. Weatherley (1962). A re-examination of the relative turgidity
technique for estimating water deficits in leaves. Australian Journal of Biological
Sciences 15:413-428.
Basbag, S., R. Ekinci and O. Gencer (2007). Combining ability and heterosis for
earliness characters in line x tester population of Gossypium hirsutum L. Hereditas
144: 185-190.
Bassetti, P., and M.E. Westgate (1993). Senescence and receptivity of maize silks. Crop
Sci. 33: 275-278.
Bates, L. S., R. P. Waldren and I. D. Teare (1973). Rapid determination of free proline
for water-stress studies. Plant Soil 39: 205-207.
El-Badawy, M.E.M. (2013). Heterosis and combining ability in maize using diallel
crosses among seven new inbred lines. Asian J. Crop Sci. 5: 1-13.
El-Hosary A.A., M.E.El-Badawy, T.A.E. Abdallah, A.A.A. El-Hosary and I.A. Abou
Hussen (2013). Evaluation of diallel maize crosses for physiological and chemical
traits under drought stress. The 8th Plant Breeding International Conf. 14-15 May.
Special Issue. Egypt. J. Plant Breed. 17:(2) 357-374.
El-Hosary, A.A.A. (2014). Comparison between some methods of diallel cross analysis
in maize. Egypt. J. Plant Breed. 18 (4):715 –736.
El-Hosary, A.A.A., A.S. Sedhom and M.EL.M. EL-Badawy (2011). Genetic and
Biotechnological Studies for Important Traits in Maize. LAP Lambert Academic
Publishing, ISBN 978-3-8454-4108-5, paperback, 280 Pages
http://www.bod.com/index.php?id=3435&objk_id=570116
FAO (2015). FAO Statistics Devision. Food and Agriculture Organization (FAO) of the
United Nations, Roma, Italy
Gilbert, N.E.G. (1958). Diallel cross in plant breeding. Heredity 12: 477-492.
Gomez, K.N. and A.A. Gomez (1984). Statistical Procedures for Agricultural Research.
John. Wiley and Sons. Inc., New York, 2nd ed.
Griffing, B. (1956). Concept of general and specific combining ability in relation to diallel
crossing systems. Austr. J. of Biol. Sci. 9: 463-493.
Hefny, M., (2010). Genetic control of flowering traits, yield and its components in maize
(Zea mays L.) at different sowing dates. Asian J. Crop Sci. 2: 236-249.
Jinks, J.L. (1954). The analysis of continuous varation in a diallel cross of Nicotiana
reustica varieties. Genetics 39: 767-788.
Monje, O. A. and B. Bugbee (1992). Inherent limitations of nondestructive chlorophyll
meters. A comparison of two types of meters. Hort. Sci. 27: 69-71.
Shiri, M., R. T. Aliyev and R. Choukan (2010). Water stress effects on combining ability
and gene action of yield and genetic properties of drought tolerance indices in
maize. Res. J. Environment. Sci. 4(1): 75-84.
Souza, L. V. de., G. V. Miranda, J. C. C. Galvão, L. J. M. Guimarães and I. C. dos.
Santos (2009). Combining ability of maize grain yield under different levels of
environmental stress. Pesq. Agropec. Brasilia., 43: 1517-1523. [C.F. Computer
Search, Science Direct].
Umar U.U., S.G. Ado, D.A. Aba and S.M. Bugaje (2014). Estimates of combining ability
and gene action in maize (Zea mays L.) under water stress and non-stress
conditions. Journal of Biology, Agriculture and Healthcare 4 (25): 247-253.
1781
Griffing 1965
Jinks
P1xP2 P1xP3P2xP3P3xP4P4xP6
P1
P4xP5P1xP2P2xP3
P3xP4P4xP6
