Heterosis and Combining Ability of Maize (Zea mays L.) Grain Protein, Oil and Starch Content and Yield as Affected by Water Stress

Abstract

Information on heterosis and combining ability of available germplasm would help maize breeder in
identifying proper genotypes and breeding procedures for improving tolerant varieties to water
stress. The objective of this investigation was to assess the performance, heterobeltiosis, general
combining ability (GCA) and specific combining ability (SCA) for grain quality and yield traits
among inbred lines of maize under water stress (WS) and well watering (WW) conditions. Six
inbred lines of maize differing in drought tolerance and their diallel F
1
crosses were evaluated in
2013 and 2014 seasons, in two separate experiments; one under WW and one under WS. In most
cases, heterobeltiosis under WS was higher than WW. The GCA (additive) variance was higher
than SCA (non-additive) variance for grain protein content (GPC) and/or grain oil content (GOC)
and grain starch content (GSC) under WS, but the opposite was true for the rest of traits. Under
WS, there were significant correlations between inbred mean and GCA effects for GPC, grain
yield/plant (GYPP), grain yield/ha (GYPH), protein yield/ha (PYPH) and starch yield/ha (SYPH),between hybrid mean and SCA effects for GYPP, GYPH, PYPH and SYPH, between hybrid mean
and heterobeltiosis for GPC, GOC and GSC and between SCA effects and heterobeltiosis for GOC
only. The breeding method of choice is selection for improving GPC, GOC and GSC, and heterosis
for GYPP, GYPH, PYPH, OYPH and SYPH. Mean performance for yield traits of a given inbred
and hybrid could be considered as an indication of its GCA and SCA effects, respectively.

Full text

_____________________________________________________________________________________________________
*Corresponding author: Email: ahmedmedhatalnaggar@gmail.com;
Archives of Current Research International
4(4): 1-15, 2016, Article no.ACRI.27508
ISSN: 2454-7077
SCIENCEDOMAIN international
www.sciencedomain.org
Heterosis and Combining Ability of Maize
(Zea mays L.) Grain Protein, Oil and Starch
Content and Yield as Affected by Water Stress
A. M. M. Al-Naggar
1*
, M. M. M. Atta
1
, M. A. Ahmed
2
and A. S. M. Younis
2
1
Department of Agronomy, Faculty of Agriculture, Cairo University, Giza, Egypt.
2
Field Crops Research Department, National Research Centre (NRC), Dokki, Giza, Egypt.
Authors’ contributions
This work was carried out in collaboration between all authors. Author AMMAN designed the study,
wrote the protocol and wrote the first draft of the manuscript. Authors MMMA and MAA managed the
literature searches. Author ASMY managed the experimental process and performed data analyses.
All authors read and approved the final manuscript.
Article Information
DOI: 10.9734/ACRI/2016/27508
Editor(s):
(1) Arturo Pérez Vázquez, Commercial Horticulture, Universidad Veracruzana, Mexico.
Reviewers:
(1) Chunqing Zhang, Shandong Agriculture University, Taian, Shandong, China.
(2)
Hidayat Ullah, The University of Swabi, Pakistan.
(3)
Juan Ma, Cereal Crops Research Institute, Henan Academy of Agricultural Sciences, China.
Complete Peer review History:
http://www.sciencedomain.org/review-history/15488
Received 5
th
June 2016
Accepted 16
th
July 2016
Published 24
th
July 2016
ABSTRACT
Information on heterosis and combining ability of available germplasm would help maize breeder in
identifying proper genotypes and breeding procedures for improving tolerant varieties to water
stress. The objective of this investigation was to assess the performance, heterobeltiosis, general
combining ability (GCA) and specific combining ability (SCA) for grain quality and yield traits
among inbred lines of maize under water stress (WS) and well watering (WW) conditions. Six
inbred lines of maize differing in drought tolerance and their diallel F
1
crosses were evaluated in
2013 and 2014 seasons, in two separate experiments; one under WW and one under WS. In most
cases, heterobeltiosis under WS was higher than W W. The GCA (additive) variance was higher
than SCA (non-additive) variance for grain protein content (GPC) and/or grain oil content (GOC)
and grain starch content (GSC) under WS, but the opposite was true for the rest of traits. Under
WS, there were significant correlations between inbred mean and GCA effects for GPC, grain
yield/plant (GYPP), grain yield/ha (GYPH), protein yield/ha (PYPH) and starch yield/ha (SYPH),
Original Research Article

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
2
between hybrid mean and SCA effects for GYPP, GYPH, PYPH and SYPH, between hybrid mean
and heterobeltiosis for GPC, GOC and GSC and between SCA effects and heterobeltiosis for GOC
only. The breeding method of choice is selection for improving GPC, GOC and GSC, and heterosis
for GYPP, GYPH, PYPH, OYPH and SYPH. Mean performance for yield traits of a given inbred
and hybrid could be considered as an indication of its GCA and SCA effects, respectively.
Keywords: Heterobeltiosis; grain chemical composition; gene action; water stress at flowering.
1. INTRODUCTION
The development of maize (Zea mays L.)
cultivars with high and stable yields under
drought is an important priority as access to
drought-adapted cultivars may be the only
affordable alternative to many small-scale
farmers [1]. Maize is considered more
susceptible than most other cereals to water
stresses at flowering, when yield losses can be
severe through barrenness or reductions in
kernels per ear [2,3]. Egypt produces about 5.8
million tons of maize grain per year cultivated in
approximately 0.75 million hectares [4]. Maize is
used primarily for animal feed, especially for
poultry in Egypt and ranks second to wheat in
land under cereal cultivation. Maize is a summer
season crop in Egypt and depends on flood
irrigation from River Nile and its branches.
However, the amount of water available for
irrigation is reducing, especially at the ends of
canals, due to expanding maize cultivation into
the deserts and competition with other crops;
especially rice. In order to stabilize maize
production in Egypt, there is need to develop
maize hybrids with drought tolerance.
Heterosis is the genetic expression of the
superiority of a hybrid in relation to its parents [5].
The term heterobeltiosis has been suggested to
describe the increased performance of the hybrid
over the better parent [6]. Since inbreds are more
sensitive to environmental differences, some
traits have been found to be more variable among
inbreds than among hybrids [7]. Similarly, Betran
et al. [8] reported extremely high expression of
heterosis in maize under stress, especially under
severe water stress because of the poor
performance of inbred lines under these
conditions.
Combining ability has been defined as the
performance of a line in hybrid combinations [9].
Since the final evaluation of inbred lines can be
best determined by hybrid performance, it plays
an important role in selecting superior parents for
hybrid combinations and in studying the nature of
genetic variation [10-12]. In general, diallel
analysis has been used primarily to estimate
general combining ability (GCA) effects and
specific combining ability (SCA) effects from
crosses of fixed lines [10,13].
Grain quality is an important objective in maize
(Zea mays L.) breeding [14-18]. In maize grain, a
typical hybrid cultivar contains approximately 4%
oil, 9% protein, 73% starch, and 14% other
constituents; mostly fiber [16]. Some of the most
important traits of interest in the maize market
are those related to the nutritional quality of the
grain, especially protein and oil content [19]. The
protein content in maize is a quantitative trait
[20]. Additive and non-additive effects are
important and dominance occurs essentially for
the reduction of this trait [21]. Significant
environment and genotype × environment
interaction effects are in general detected for
protein content [16,21]. Among the environment
factors that influence protein content, availability
of water is the most important [22]. The oil
content in maize grains was reported as a
quantitative trait [23]. The additive genetic
variance seems to be the main component in the
control of this trait [23]. However, non-additive
gene effects including dominance and epistasis
had the predominant role in the inheritance of
grain oil content in maize [24-26]. Knowledge
about the heterosis and combining ability
of maize kernel composition in diverse
environments is essential for plant breeding
programs. The objectives of the present study
were to: (i) assess performance, heterosis and
combining ability among maize inbreeds under
optimum and drought conditions for grain protein,
oil and starch content and yield traits, (ii) identify
suitable parents and hybrids for further breeding
studies on improving maize drought tolerance
and (iii) analyze interrelationships among inbred
and hybrid per se performance, general and
specific combining ability and heterosis for grain
quality traits.
2. MATERIALS AND METHODS
This study was carried out at the Agricultural
Experiment and Research Station of the Faculty
of Agriculture, Cairo University, Giza, Egypt

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
3
(30° 02' N latitude and 31° 13' E longitude with
an altitude of 22.50 meters above sea level), in
2012, 2013 and 2014 seasons.
2.1 Plant Material
Based on the results of previous experiments
[27], six maize (Zea mays L.) inbred lines in the
8
th
selfed generation (S
8
), showing clear
differences in performance and general
combining ability for grain yield under water
stress, were chosen in this study to be used as
parents of diallel crosses (Table 1).
2.2 Making F
1
Diallel Crosses
In 2012 season, all possible diallel crosses
(except reciprocals) were made among the six
parents, so seeds of 15 direct F
1
crosses were
obtained. Seeds of the 6 parents were also
increased by selfing in the same season (2012)
to obtain enough seeds of the inbreds in the 9
th
selfed generation (S
9
).
2.3 Evaluation of Parents and F
1
's
Two field experiments were carried out in each
season of 2013 and 2014 at the Agricultural
Experiment and Research Station of the Faculty
of Agriculture, Cairo University, and Giza. Each
experiment included 21 genotypes (15 F
1
crosses and their 6 parents). The first experiment
was done under well irrigation by giving all
required irrigations, but the second experiment
was done under water stress at flowering stage
by skipping the fourth and fifth irrigations. A
randomized complete blocks design with three
replications was used in each experiment. Each
experimental plot consisted of one ridge of 4 m
long and 0.7 m width, i.e. the experimental plot
area was 2.8 m
2
. Seeds were sown in hills at 20
cm apart, thereafter (before the 1
st
irrigation)
were thinned to one plant/hill to achieve a plant
density of 76,400 plants/ha, respectively. Sowing
date of the two experiments was on May 5 and
May 8 in 2013 and 2014 seasons, respectively.
The soil of the experimental site was clayey
loam. All other agricultural practices were
followed according to the recommendations of
ARC, Egypt. The analysis of the experimental
soil, as an average of the two growing seasons
2013 and 2014, indicated that the soil is clay
loam (4.00% coarse sand, 30.90% fine sand,
31.20% silt, and 33.90% clay), the pH (paste
extract) is 7.73, the EC is 1.91 dSm-1, soil bulk
density is 1.2 g cm-3, calcium carbonate is
3.47%, organic matter is 2.09%, the available
nutrient in mg kg-1are Nitrogen (34.20),
Phosphorous (8.86), Potassium (242), hot water
extractable B (0.49), DTPA - extractable Zn
(0.52), DTPA - extractable Mn (0.75) and DTPA
- extractable Fe (3.17). Meteorological variables
in the 2013 and 2014 growing seasons of maize
were obtained from Agro-meteorological Station
at Giza, Egypt. For May, June, July and August,
mean temperature was 27.87, 29.49, 28.47 and
30.33°C, maximum temperature was 35.7, 35.97,
34.93 and 37.07°C and relative humidity was
47.0, 53.0, 60.33 and 60.67% respectively, in
2013 season. In 2014 season, mean temperature
was 26.1, 28.5, 29.1 and 29.9°C, maximum
temperature was 38.8, 35.2, 35.6 and 36.4°C
and relative humidity was 32.8, 35.2, 35.6 and
36.4%, respectively. Precipitation was nil in all
months of maize growing season for both
seasons. Sibbing was carried out in each entry
for the purpose of determining the grain contents
of protein, oil and starch.
2.4 Data Recorded
Grain protein content (GPC) (%), grain oil
content (GOC) (%) and grain starch content
(GSC) (%) were determined using the non-
destructive grain analyzer, Model Infratec TM
1241 Grain Analyzer, ISW 5.00 valid from S/N
12414500, 1002 5017/Rev.1, manufactured by
Foss Analytical AB, Hoganas, Sweden. Grain
yield per plant (GYPP) (g) estimated by dividing
the grain yield per plot (adjusted at 15.5% grain
moisture) on number of plants/plot at harvest.
Grain yield per hectare (GYPH) in ton, by
adjusting grain yield/plot to grain yield per
hectare. Protein yield per hectare (PYPH), by
multiplying grain protein content by grain yield/ha
in kg. Oil yield per hectare (OYPH), by
multiplying grain oil content by grain yield/ha in
kg. Starch yield per hectare (SYPH), by
multiplying grain starch content by grain yield/ha
in kg.
2.5 Biometrical and Genetic Analyses
Analysis of variance of the RCBD was performed
on the basis of individual plot observation using
GENSTAT 10
th
addition windows software.
Combined analysis of variance across the two
seasons was also performed if the homogeneity
test was non-significant. Least significant
differences (LSD) values were calculated to test
the significance of differences between means
according to Steel et al. [28]. Diallel crosses were
analyzed to obtain general (GCA) and specific

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
4
Table 1. Designation, origin and most important traits of 6 inbreds lines used for making diallel
crosses of this study
Inbred line Origin Institution-
country
Prolificacy Productivity under
water stress
Leaf
Angle
L20-Y SC 30N11 Pion. Int. Co. Prolific High Erect
L53-W SC 30K8 Pion. Int. Co. Prolific High Erect
Sk 5-W Teplacinco - 5 ARC-Egypt Prolific High Erect
L18-Y SC 30N11 Pion. Int. Co. Prolific Low Wide
L28-Y Pop 59 ARC-Thailand Non-Prolific Low Wide
Sd7-W A.E.D. ARC-Egypt Non-Prolific Low Erect
ARC = Agricultural Research Center, Pion. Int. Co. = Pioneer International Company in Egypt, SC = Single cross, A.E.D. =
American Early Dent, an open pollinated variety, W = White grains and Y = Yellow grains
(SCA) combining ability variances and effects for
studied traits according to Griffing [29] Model I
(fixed effect) Method 2. The significance of the
various statistics was tested by ‛‛t” test, where ‛‛t”
is a parameter value divided by its standard
error. However, for making comparisons between
different effects, the critical difference (CD) was
calculated using the corresponding comparison
as follows: CD = SE × t (tabulated).
Heterobeltiosis was calculated as a percentage
of F
1
relative to the better-parent (BP) values as
follows: Heterobeltiosis (%) = 100 [(F

1
-BP




) /BP




]
Where: F

1
= mean of an F
1
cross and BP




= mean
of the better parent of this cross. The significance
of heterobeltiosis was determined as the least
significant differences (L.S.D) at 0.05 and 0.01
levels of probability according to Steel et al. [28]
using the following formula: LSD
0.05
= t
0.05
(edf) x
SE, LSD
0.01
= t
0.01
(edf) x SE, Where: edf = the
error degrees of freedom, SE= the standard
error, SE for heterobeltiosis =(2MS
e
/r)
1/2
Where:
t
0.05
and t
0.01
are the tabulated values of 't' for the
error degrees of freedom at 0.05 and 0.01 levels
of probability, respectively. MS
e
: The mean
squares of the experimental error from the
analysis of variance table. r: Number of
replications.
Rank correlation coefficients were calculated
between per se performance of inbred lines and
their GCA effects; between per se performance
of F
1
crosses and their SCA effects and between
SCA effects and heterobeltiosis of F
1
crosses for
studied traits under WW and WS conditions by
using SPSS 17 computer software and the
significance of the rank correlation coefficient
was tested according to Steel et al. [28]. The
correlation coefficient (r
s
) was estimated for each
pair of any two parameters as follows: r
s
=1- (6
∑d
i
2
)/(n
3
-n), Where, d
i
is the difference between
the ranks of the i
th
genotype for any two
parameters, n is the number of pairs of data. The
hypothesis Ho: r
s
= 0 was tested by the r-test with
(n-2) degrees of freedom.
3. RESULTS AND DISCUSSION
3.1 Analysis of Variance
Combined analysis of variance of a randomized
complete blocks design for 8 traits of 21 maize
genotypes (6 inbreds + 15 F
1
's) under two
environments (WW and WS); across two
seasons is presented in Table 2. Mean squares
due to parents and crosses under both
environments were very significant for all studied
traits, indicating the significance of differences
among studied parents and among F
1
diallel
crosses in all cases.
Mean squares due to parents vs. F
1
crosses
were very significant for all studied traits under
both environments, except for GSC under WS,
suggesting the presence of significant average
heterosis for most studied cases. Mean squares
due to the interactions parents × years (P × Y)
and crosses × years (F
1
× Y) were significant for
all studied traits under both environments, except
GYPH under WW for P × Y and F
1
× Y, GSC
under WW for P × Y, PYPH under WW for P × Y
and WW for F
1
× Y, OYPH under WW for P × Y
and SYPH under WW for P × Y and F
1
× Y.
Mean squares due to parents vs. crosses ×
years were significant in 8 out of 16 cases,
indicating that heterosis differ from season to
season in these cases (Table 4). It is observed
from Table 2 that the largest contributor to total
variance was parents vs. F
1
's (average heterosis)
variance for 12 cases, followed by F
1
crosses
(4 cases).
3.2 Mean Performance
Means of each inbred and cross for studied grain
quality and yield traits under contrasting irrigation
regimes, i.e. well watering and water stress at
flowering across two years are presented in
Table 3. The highest mean grain yield per plant
and per hectare, protein yield, oil yield and starch

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
5
yield per hectare was recorded for the inbred line
L53 followed by L20 and Sk5 under both
irrigation regimes, while the lowest ones were
exhibited by Sd7, L28 and L18. The first three
inbreds are high yielding under both water stress
and non-stress conditions. The second three
inbreds are low-yielders under both irrigation
regimes. The present results assure the diversity
of the parental inbreds in tolerance to drought at
silking stage and therefore are valid for diallel
analysis. It is observed that the inbred L18
showed the highest grain protein content under
both water stress and non-stress conditions.
Moreover, the highest grain oil content and
starch content were shown by the parental
inbreds L28 and L20, respectively under water
stress conditions.
Results in Table 3 indicated the existence of
cross × irrigation regime interaction in most
studied F
1
crosses for all studied traits. This
conclusion is in agreement with that reported by
Al-Naggar et al. [30]. The rank of crosses for
studied traits under well watering was changed
from that under water stress conditions. The
highest mean grain yield per hectare under water
stress was shown by the F
1
cross L20 × L53
(11.23 ton/ha) followed by L20 × L28 (7.79
ton/ha) and L53 × Sd7 (8.96 ton/ha).
Most of highest yielding crosses showed low
percentages of grain protein and/or oil contents.
However, it was observed that the cross L53 ×
Sd7 showed the highest grain oil content, under
water stress as well as well watering and was
one of the three highest yielding crosses. Several
investigators [20,30,31] reported a negative
correlation between grain yield and either grain
protein content or grain oil content, but our
results and Al-Naggar et al. [16-18,30] indicated
that it is possible to break such linkage between
high yield and low grain protein or oil content
genes of maize and obtain genotypes of high
grain yield and high oil or protein content
simultaneously. On the contrary, the lowest grain
yield/ha under WS was exhibited by the cross
L18 × L28 (5.57 ton/ha) followed by Sk5 × Sd7
(6.86 ton/ha), but these two crosses showed the
highest GPC (12.32%) and GOC (4.75%) under
WS, respectively.
3.3 Heterobeltiosis
Estimates of better parent heterosis
(heterobeltiosis) across all F
1
crosses, maximum
values and number of crosses showing
significant favorable heterobeltiosis for all studied
traits under the two environments (WW and WS)
across 2011 and 2012 years are presented in
Table 4. Favorable heterobeltiosis in the studied
crosses was considered positive for all studied
traits under both irrigation regimes. It is observed
that the heterobeltiosis for all studied grain
quality and yield traits was more pronounced
under water stress than under well watering
conditions. Similarly, Betran et al. [8] reported
extremely high expression of heterosis in maize
under stress, especially under severe water
stress because of the poor performance of inbred
lines under these conditions. This was also
observed under high density stress in maize [32]
and under low-N stress in wheat [33-36]. In
general, the highest average significant and
positive (favorable) heterobeltiosis was shown by
oil yield per feddan (186.25 and 302.71%) under
WW and WS, respectively followed by GYPP,
SYPH, GYPH and SYPH traits. On the contrary,
the lowest average significant heterobeltiosis
was shown by grain starch content (-0.09 and -
0.48%) under WW and WS, respectively. The
traits GPC, GSC under both environments,
showed on average unfavorable heterobeltiosis.
The traits GYPP, GYPH, PYPH, OYPH and
SYPH, showed the highest maximum
heterobeltiosis (736.00, 813.39, 710.95, 876.66
and 816.74%, respectively) under WS
environment. The reason for getting the highest
average heterobeltiosis estimates for such traits
under WS environment could be attributed to the
large reduction in grain yield of the parental
inbreds compared to that of F
1
crosses due to
negative effects of water stress at flowering
stage in this environment. In general, maize
hybrids typically yield two to three times as much
as their parental inbred lines. However, since a
cross of two extremely low yielding lines can give
a hybrid with high heterosis, a superior hybrid is
not necessarily associated with high heterosis
[11]. This author suggested that a cross of two
high yielding inbreds might exhibit less heterosis
but nevertheless produce a high yielding hybrid.
Besides, a hybrid is superior not only due to
heterosis but also due to other heritable factors
that are not influenced by heterosis. On the
contrary, the WW environment (non-stressed)
showed the lowest average favorable
heterobeltiosis for all yield traits, viz. GYPP
(49.55%), GYPH (46.71%), OYPH (52.24%),
PYPH (29.38%) and SYPH (47.32%) (Table 4).

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
6
Table 2. Combined analysis of variance of RCBD across two years for studied traits of 6
parents (P) and 15 crosses (F
1
) and their interactions with years (Y) under water stress (WS)
and well watering (WW) conditions
SOV df %Sum of squares
WW WS WW WS WW WS WW WS
GPC GOC GSC GYPP
P 5 14.22** 12.88** 18.01** 8.11** 7.93** 10.32** 5.50** 3.71**
F
1
14 10.27** 16.08** 19.84** 30.07** 36.19** 48.73** 9.66** 17.83**
P vs F
1
1 42.39** 28.04** 7.37** 13.16** 1.54* 0.00 75.18** 70.56**
P × Y 5 2.05* 3.30** 3.33** 3.81** 2.67 8.91** 0.37** 0.18*
F
1
× Y 14 2.31* 6.12** 17.06** 5.70* 15.69** 9.88** 1.91** 1.95**
P vs F
1
× Y 1 9.14** 6.04** 6.62** 0.43 0.26 0.72** 0.01 0.17**
GYPH PYPH OYPH SYPH
P 5 4.98** 4.39** 5.60** 4.81** 3.86** 3.20** 5.01** 4.43**
F
1
14
13.70**
23.44**
16.94**
25.66**
17.45**
26.45**
12.72**
23.15**
P vs F
1
1 75.76** 67.23** 71.64** 63.13** 73.06** 64.78** 76.43** 67.28**
P × Y 5 0.12 1.11** 0.25 1.34** 0.10 0.78** 0.11 1.15**
F
1
× Y 14 0.42 1.43** 0.38 1.12** 1.83** 1.39** 0.41 1.50**
P vs F
1
× Y 1 0.01 0.06* 0.38** 0.02 0.05 0.09 0.01 0.06*
WW= Well watering, WS= Water stress, GPC= Grain protein content, GOC= Grain oil content, GSC= Grain starch content,
GYPP= Grain yield/plant, GYPH= Grain yield/ha, PYPH= Protein yield/ha, OYPH= Oil yield/ha, SYPH= Starch yield/ha, * and **
indicate significance at 0.05 and 0.01 probability levels, respectively
Table 3. Means of each inbred parent (P) and cross (F
1
) for studied grain quality and yield traits
under well watering (WW) and water stress (WS) across two years
Genotypes WW WS WW WS WW WS WW WS
GPC% GOC% GSC% GYPP (g)
Inbreds
L20 10.97 11.88 4.23 3.67 71.0 72.1 106.6 57.7
L53 11.82 11.18 4.15 4.15 70.5 71.0 132.1 85.5
Sk5 12.80 13.08 3.48 3.57 71.3 70.6 77.6 46.9
L18 13.52 13.12 4.03 3.88 70.4 71.1 46.7 34.8
L28 12.88 12.63 4.55 4.15 69.9 70.5 44.4 21.2
Sd7 12.57 12.38 4.40 4.03 70.8 71.2 55.1 13.2
Aver. (P) 12.43 12.38 4.14 3.91 70.6 71.1 77.1 43.2
F
1
crosses
L20 × L53 9.73 10.37 4.38 4.07 71.7 71.6 277.4 242.7
L20 ×SK5 10.55 10.67 4.80 4.25 70.1 71.5 221.7 166.8
L20 × L18 10.95 10.82 4.05 3.72 71.6 73.0 219.2 182.1
L20 × L28 10.63 11.07 4.38 4.53 71.2 70.7 232.8 171.7
L20 × Sd7 10.33 11.00 4.50 4.12 71.0 70.8 226.7 179.9
L 53 × Sk5 10.58 11.05 4.12 4.42 70.8 70.5 245.5 203.0
L53 × L18 10.57 11.60 4.27 4.40 70.8 70.7 197.5 138.9
L53 × L28 10.63 11.45 4.53 4.32 70.8 70.9 237.5 171.6
L53 × Sd7 10.50 11.32 4.57 4.47 70.9 70.9 241.0 197.3
Sk5 × L18 11.35 11.58 4.10 3.85 71.1 72.0 234.8 183.7
Sk5 × L28 11.42 11.23 4.40 4.17 70.4 71.2 223.2 177.2
Sk5 × Sd7 10.83 11.03 4.68 4.75 70.0 69.8 207.2 147.7
L18 × L28 11.57 12.32 4.45 4.17 70.7 70.7 171.1 124.0
L18 × Sd7 10.85 11.53 4.42 4.25 71.1 70.7 213.3 154.2
L28 × Sd7 10.67 10.85 4.32 4.28 70.8 71.3 227.6 177.2
Aver. (F
1
) 10.74 11.19 4.40 4.25 70.9 71.1 225.1 174.5
LSD05 0.32 0.33 0.15 0.12 0.3 0.4 13.5 10.8
GYPH (kg) PYPH (kg) OYPH (kg) SYPH (kg)
Inbreds
L20 4.95 2.39 542 285 210 88 3513 1728

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
7
Genotypes WW WS WW WS WW WS WW WS
L53 6.13 3.52 735 391 252 146 4319 2501
Sk5 3.60 2.17 462 283 126 77 2566 1534
L18 2.16 1.49 295 195 87 58 1523 1057
L28 2.06 0.87 265 108 93 36 1440 618
Sd7 2.01 0.63 257 78 87 26 1423 452
Aver. (P) 3.49 1.85 426 223 143 72 2464 1315
F
1
crosses
L20 × L53 12.88 11.23 1254 1166 564 456 9230 8043
L20 ×SK5 10.22 7.75 1082 832 492 333 7149 5533
L20 × L18 10.15 8.33 1111 902 412 310 7273 6076
L20 × L28 10.81 7.97 1149 882 474 362 7689 5633
L20 × Sd7 10.53 8.31 1088 913 473 342 7470 5882
L 53 × Sk5 11.40 9.31 1206 1029 469 411 8072 6561
L53 × L18 8.99 6.45 950 749 384 284 6363 4559
L53 × L28 11.03 7.95 1173 911 500 343 7804 5635
L53 × Sd7 11.19 8.96 1175 1013 511 401 7928 6351
Sk5 × L18 10.90 8.43 1237 977 447 324 7755 6068
Sk5 × L28 10.34 8.17 1180 919 455 341 7281 5815
Sk5 × Sd7 9.58 6.86 1038 758 448 325 6705 4787
L18 × L28 7.91 5.76 915 709 352 240 5592 4068
L18 × Sd7 9.88 7.16 1072 827 436 304 7022 5059
L28 × Sd7 10.49 7.97 1116 874 463 348 7405 5667
Aver. (F
1
) 10.42 8.04 1116 897 459 342 7383 5716
LSD05 0.47 0.43 48 45 19 12 258 207
WW= Well watering, WS= Water stress, GPC= Grain protein content, GOC= Grain oil content, GSC= Grain starch content,
GYPP= Grain yield/plant, GYPH= Grain yield/ha, PYPH= Protein yield/ha, OYPH= Oil yield/ha, SYPH= Starch yield/ha
Table 4. Estimates of average (Aver) and maximum (Max) heterobeltiosis and number (No.) of
crosses showing significant favorable heterobeltiosis for quality traits under water stress (WS)
and well watering (WW) conditions across two seasons
Parameter WW WS WW WS WW WS WW WS
GPC GOC GSC GYPP
Aver -17.11 -12.75 0.97 4.75 -0.09 -0.48 151.79 236.58
Max -11.38 -6.1 13.39 17.77 0.94 1.29 313.14 736.00
Min -21.82 -18.47 -5.13 -4.29 -1.75 -2.04 49.55 62.37
No. 0 0 1 2 0 3 15 15
GYPH PYPH OYPH SYPH
Aver 162.31 264.08 129.7 234.38 186.25 302.71 162.95 263.41
Max 409.27 813.39 321 710.95 402.92 876.66 414.13 816.74
Min 46.71 82.98 29.38 91.32 52.24 94.28 47.32 82.31
No. 15 15 15 15 15 15 15 15
WW= Well watering, WS= Water stress, GPC= Grain protein content, GOC= Grain oil content, GSC= Grain starch content,
GYPP= Grain yield/plant, GYPH= Grain yield/ha, PYPH= Protein yield/ha, OYPH= Oil yield/ha, SYPH= Starch yield/ha
The largest significant favorable heterobeltiosis
for GYPP in this study (736.00%) was shown by
the cross (L28 × Sd7) under WS environment
(Table 5). This cross showed also the highest
significant and favorable heterobeltiosis under
WS for GYPH (813.39%), PYPH (710.95%),
OYPH (876.66%) and SYPH (816.74%). Under
the environments WW and WS, the highest
estimates of GYPP heterobeltiosis were
generally obtained by the cross (L28 × Sd7)
(313.14, and 736.00 %), respectively, followed by
the crosses L18 × Sd7 and L18 × L28 in the
same environments.
The highest heterobeltiosis for PYPH, OYPH and
SYPH, GYPH and GYPP under WS as well as
WW environments was shown by L28 × Sd7
followed by L18 × Sd7, L18 × L28, Sk5 × L18
and Sk5 × L28. The two crosses L20 × Sk5 and
Sk5 × Sd7 showed significant heterobeltiosis for
grain oil content under water stress conditions
(15.91 and 17.77%, respectively). These crosses

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
8
could therefore be recommended for plant
breeding programs aiming at improving such
traits under water stress conditions.
3.4 Combining Ability Variances
Estimates of variances due to general (GCA) and
specific (SCA) combining ability of the diallel
crosses of maize for combined data across two
seasons under the two environments (WW and
WS) are presented in Table 6. Mean squares
due to GCA and SCA were significant (P≤ 0.01
or 0.05) for GYPP, GYPH, PYPH, OYPH and
SYPH under both environments, GPC under WW
and GOC under W S, suggesting that both
additive and non-additive gene effects play
important roles in controlling the inheritance of
such traits under respective environments.
Moreover, SCA variance (non-additive variance)
was significant for GPC and GSC under WS
conditions. A similar conclusion was reported by
Al-Naggar et al. [16-18,36].
In the present study, the magnitude of GCA
mean squares was higher than SCA mean
squares (the ratio of GCA/SCA mean squares
was higher than unity) for two traits (GPC and
GOC) under both environments and GSC under
WS, suggesting the existence of a greater
portion of additive and additive x additive than
non-additive variance in controlling the
inheritance of these traits under respective
environments. These results are in agreement
with those reported by Al-Naggar et al. [16-18].
On the contrary, the magnitude of SCA mean
squares was higher than GCA mean squares
(the GCA/SCA ratio was less than unity) for the
rest of cases, i.e. the five traits GYPP,
GYPH, PYPH, OYPH and SYPH under both
environments (WW and WS). A similar
conclusion was reported by several investigators
[16-18,37-38].
Table 5. Estimates of heterobeltiosis (%) for selected quality and yield traits of diallel F
1
crosses under WW and WS conditions during 2013 and 2014 seasons
Cross WW WS WW WS WW WS
GOC GYPP GYPH
L20 × L53 3.54 -2.01 110.04** 183.73** 110.04** 218.57**
L20 ×SK5 13.39** 15.91** 107.99** 188.90** 106.46** 223.44**
L20 × L18 -4.33 -4.29 105.63** 215.33** 105.16** 247.67**
L20 × L28 -3.66 9.24* 118.39** 197.36** 118.39** 232.91**
L20 × Sd7 2.27 2.07 112.69** 211.62** 112.69** 246.99**
L 53 × Sk5 -0.8 6.43 85.93** 137.29** 85.93** 164.14**
L53 × L18 2.81 6.02 49.55** 62.37** 46.71** 82.98**
L53 × L28 -0.37 4.02 79.87** 100.64** 79.87** 125.69**
L53 × Sd7 3.79 7.63 82.47** 130.68** 82.47** 154.21**
Sk5 × L18 1.65 -0.86 202.76** 291.88** 202.76** 289.17**
Sk5 × L28 -3.3 0.4 187.76** 278.14** 187.19** 277.19**
Sk5 × Sd7 6.44 17.77** 167.16** 215.14** 165.98** 216.59**
L18 × L28
-
2.2
0.4
266.42**
256.34**
265.32**
286.98**
L18 × Sd7
0.38
5.37
287.11**
343.24**
356.40**
381.35**
L28 × Sd7 -5.13 3.21 313.14** 736.00** 409.27** 813.39**
PYPH OYPH SYPH
L20 × L53 70.64** 197.93** 123.35** 211.94** 113.71** 221.62**
L20 ×SK5 99.66** 191.54** 134.74** 279.46** 103.51** 220.19**
L20 × L18 105.10** 216.06** 96.42** 252.42** 107.04** 251.61**
L20 × L28 112.08** 209.22** 126.22** 311.43** 118.91** 226.00**
L20 × Sd7 100.78** 220.11** 126.00** 289.27** 112.67** 240.37**
L 53 × Sk5 64.21** 162.92** 86.02** 181.28** 86.88** 162.37**
L53 × L18 29.38** 91.32** 52.24** 94.28** 47.32** 82.31**
L53 × L28 59.63** 132.80** 98.24** 134.56** 80.68** 125.32**
L53 × Sd7 59.91** 158.95** 102.44** 174.00** 83.56** 153.96**
Sk5 × L18 167.99** 244.70** 255.40** 323.69** 202.27** 295.72**
Sk5 × L28 155.57** 224.26** 261.65** 344.88** 183.79** 279.19**
Sk5 × Sd7 124.72** 167.66** 256.38** 324.60** 161.35** 212.17**
L18 × L28 210.84** 263.54** 276.44** 315.34** 267.23** 284.90**
L18 × Sd7 263.99** 323.74** 402.92** 426.85** 361.15** 378.66**
L28 × Sd7 321.00** 710.95** 395.26** 876.66** 414.13** 816.74**
WW= Well watering, WS= Water stress, GOC= Grain oil content, GYPP= Grain yield/plant, GYPH= Grain yield/ha, PYPH=
Protein yield/ha, OYPH= Oil yield/ha, SYPH= Starch yield/ha, * and ** indicate significance at 0.05 and 0.01 probability levels,
respectively

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
9
Table 6. Mean squares due to general (GCA) and specific (SCA) combining ability and their
interactions with years (Y) for studied characters under water stress (WS) and well watering
(WW) during 2013 and 2014 seasons
Parameter WW WS WW WS WW WS WW WS
GPC GOC GSC GYPP
GCA 7.48* 4.56 0.64 0.64* 1.24 4.71 12189** 9558**
SCA 5.14** 3.26** 0.42 0.49* 1.33 2.30* 39215** 32244**
GCA/SCA 1.45 1.40 1.52 1.30 0.93 2.05 0.30 0.30
GCA×Y 0.94 1.28 0.28 0.09 1.68* 1.59* 1067** 632**
SCA×Y 1.23* 1.05 0.38 0.20 1.33* 0.98 797.8** 1206**
GCA×Y/SCA×
Y
0.76 1.22 0.74 0.43 1.26 1.62 1.30 0.52
GYPH PYPH OYPH SYPH
GCA 247.12** 167.1* 37811.00 29653* 9470* 6167* 2476627** 1683194*
SCA 777.60** 639.4** 153673** 146260** 31507** 23372** 7696247** 6364526**
GCA/SCA 0.32 0.26 0.25 0.20 0.30 0.26 0.30 0.26
GCA×Y 21.91** 16.3** 8262** 5519** 1428** 917** 185787** 158568**
SCA×Y 16.85** 21.5** 5116** 6738** 1757** 1342** 138944** 203145**
GCA×Y/SCA×
Y
1.30 0.76 1.62 0.82 0.80 0.68 1.30 0.78
WW= Well watering, WS= Water stress, GPC= Grain protein content, GOC= Grain oil content, GSC= Grain starch content,
GYPP= Grain yield/plant, GYPH= Grain yield/ha, PYPH= Protein yield/ha, OYPH= Oil yield/ha, SYPH= Starch yield/ha, * and **
indicate significance at 0.05 and 0.01 probability levels, respectively
Results in Table 6 indicate that mean squares
due to the SCA × year and GCA x year
interactions were significant for the six traits
GSC, GYPP, GYPH, PYPH, OYPH and SYPH
under both environments, except SCA × year for
GSC under WS, indicating that additive and non-
additive variances for these traits under the
respective environments were affected by years.
This was not true for GPC and GOC traits under
both environments, except SCA × year for GPC
under WW, suggesting that additive and non-
additive variances for these cases were not
affected by years.
The mean squares due to SCA × year was
higher than GCA × year for OYPH and GOC
under both environments, GYPP, GYPH, PYPH
and SYPH in WS environment, and GPC in WW
environment, suggesting that SCA (non-additive
variance) is more affected by years than GCA for
these cases. On the contrary, mean squares due
to GCA × year was higher than those due to SCA
× year in both environments for GSC, in WS for
WS and in WW for GYPP, GYPH, PYPH and
SYPH (Table 6), indicating that GCA (additive)
variance is more affected by years than SCA
(non-additive) variance for these traits under the
respective environments.
3.5 GCA Effects of Parental Inbreds
Estimates of general combining ability (GCA)
effects of parental inbreds for studied traits under
the two environments (WW and WS) across two
seasons are presented in Table 7. The best
parental inbreds were those showing positive
and significant GCA effects for all studied traits.
For GYPP and GYPH traits, the best inbred in
GCA effects was L53 in both environments (WW
and WS) followed by L20 and Sk5. These best
general combiners for grain yield (L53, L120 and
Sk5) were also the best ones in per se
performance for the same traits under the
respective environments (Table 3).
On the contrary, the inbred lines L18, L28 and
Sd7 were the worst in GCA effects for GYPP and
GYPH in this study (Table 7) and the worst in per
se performance for the same traits under the
same environments (Table 3). Superiority of the
inbreds L53, L20 and Sk5 in GCA effects for
GYPH and GYPP was associated with their
superiority in GCA effects for all yield-related
traits, i.e. PYPH, OYPH and SYPH.
For high PYPH, the inbred L53 under both
environments, inbred L20 under WW were the
best general combiners. The inbreds L53 and
L20 were the best general combiners for high
OYPH and high SYPH under both environments.
Inbred Sk5 was also the best combiner for SYPH
under WW and WS environments. For the grain
quality traits, i.e. GPC, GOC and GSC, the
magnitude of GCA effects was small and not
significant. However, the largest values of GCA
effects were exhibited by L18 under WW and WS
for GPC, Sd7 under WW and L18 under WS for
GOC and L20 under WW, L53 under WS for

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
10
GSC trait. In previous studies [39], the inbred
lines L53, L20 and Sd5 were also the best
general combiners for GYPP and GYPH under
high plant density stress.
3.6 SCA Effects of Diallel Crosses
Estimates of specific combining ability effects
(SCA) of F
1
diallel crosses for studied traits
under the two environments are presented in
Table 8. The best crosses in SCA effects were
considered those exhibiting significant positive
SCA effects for the all studied traits. For GYPP,
GYPH and SYPH, the largest positive (favorable)
and significant SCA effects were recorded by the
cross Sk5 × L18 followed by L20 × L53 and L28
× Sd7 under the water stressed and non-
stressed environments. For OYPH, the highest
(favorable) positive and significant SCA effects
were exhibited by the cross Sk5 × L18 and L20 ×
L53 under both environments and L20 × L18
under WS environment. For PYPH, the highest
positive and significant SCA effects were shown
by the cross Sk5 × L18 under both environments
followed by L20 × L18, L53 × Sd7, L20 × L53
and L28 × Sd7 under WS environment. The
above-mentioned crosses may be recommended
for maize breeding programs for the
improvement of respective traits under water
stress conditions [40-44].
Table 7. Estimates of general combining ability (GCA) effects of parents for studied characters
under water stress (WS) and non-stress (WW) across 2013 and 2014 seasons
Parent WW WS WW WS WW WS WW WS
GPC GOC GSC GYPP
L20 -0.38 -0.15 0.03 -0.04 0.32 0.16 13.05** 13.85**
L53 -0.43 -0.32 -0.03 -0.12 0.15 0.39 18.35** 18.16**
Sk5 0.25 -0.17 0.03 0.03 -0.45 -0.07 1.74 3.54
L18 0.39 0.59 -0.18 -0.16 0.26 -0.04 -22.40** -21.66**
L28 0.3 -0.14 0.02 0.15 -0.12 -0.13 -8.31** -9.93**
Sd7 -0.14 0.19 0.12 0.13 -0.15 -0.32 -2.42 -3.96
SE g
i
-g
j
0.56 0.55 0.52 0.52 0.6 0.55 3.08 3.61
GYPH PYPH OYPH SYPH
L20 1.86** 3.07** 10.62 41.26** 12.68** 16.44** 199.3** 311.8**
L53 2.54** 4.04** 18.47* 49.17** 14.15** 17.01** 260.8** 423.7**
Sk5
0.26
0.63*
16.91
2.89
1.94
5.55
5.2**
57.3**
L18 -3.19** -4.78** -31.07** -52.17** -27.57** -35.69** -305.4** -476.3**
L28 -1.14** -2.11** -5.1 -38.46** -5.23 -4.78 -119.9** -218.0**
Sd7 -0.33 -0.85** -9.84 -2.69 4.03 1.48 -40.1** -98.4**
SE g
i
-g
j
0.42 0.47 13.23 11.2 4.97 5.1 0.71 0.71
WW= Well watering, WS= Water stress, GPC= Grain protein content, GOC= Grain oil content, GSC= Grain starch content,
GYPP= Grain yield/plant, GYPH= Grain yield/ha, PYPH= Protein yield/ha, OYPH= Oil yield/ha, SYPH= Starch yield/ha, * and **
indicate significance at 0.05 and 0.01 probability levels, respectively
Table 8. Estimates of specific combining ability (SCA) effects for studied characters under
water stress (WS) and non-stress (WW) across 2013 and 2014 seasons
Cross WW WS WW WS WW WS WW WS
GPC GOC GSC GYPP
L20 × L53 -0.2 -0.22 -0.02 -0.17 0.35 0.38 20.88** 16.72**
L20 ×SK5 -0.07 0.49 0.34 0.26 -0.6 -0.83 -18.21** -19.40**
L20 × L18 0.2 0.17 -0.2 0.06 0.21 -0.23 3.43 13.87**
L20 × L28 -0.03 -0.13 -0.07 -0.06 0.1 0.27 2.93 2.44
L20 × Sd7 0.1 -0.31 -0.05 -0.09 -0.05 0.42 -9.03* -13.63**
L 53 × Sk5 0.01 -0.29 -0.28 -0.18 0.26 0.09 0.34 2.68
L53 × L18 -0.14 -0.27 0.08 0.13 -0.51 -0.26 -23.56** -26.55**
L53 × L28 0.02 0.27 0.14 0.18 -0.12 -0.5 2.4 -0.04
L53 × Sd7 0.32 0.51 0.08 0.04 0.02 0.3 -0.06 7.18
Sk5 × L18 -0.04 -0.22 -0.15 -0.3 0.48 1.06 30.40** 26.39**
Sk5 × L28 0.12 0.25 -0.05 0.02 0.12 -0.14 4.67 10.05*
Sk5 × Sd7 -0.03 -0.23 0.14 0.2 -0.25 -0.17 -17.21** -19.72**
L18 × L28 0.13 -0.05 0.21 0.06 -0.28 0.18 -23.29** -26.17**

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
11
Cross WW WS WW WS WW WS WW WS
L18 × Sd7 -0.15 0.36 0.07 0.05 0.1 -0.75 13.02** 12.46*
L28 × Sd7 -0.24 -0.34 -0.23 -0.2 0.18 0.21 13.28** 13.72**
SE Sij – Sik 0.97 0.95 0.89 0.9 1.05 0.95 5.34 6.24
SE Sij – Skl 0.79 0.77 0.73 0.73 0.85 0.77 4.36 5.1
GYPH PYPH OYPH SYPH
L20 × L53 2.97** 4.42** 28.52 57.37** 17.23* 14.46* 315.91** 466.46**
L20 ×SK5 -2.73**
-4.22** -42.12* -42.94** -0.73 -7.18 -302.79** -469.04**
L20 × L18 0.53 2.79** 18.25 55.71** -4.94 18.99** 59.90** 267.41**
L20 × L28 0.44 0.18 8.15 -3.21 -1.06 -2.82 49.39** 33.19**
L20 × Sd7 -1.22* -3.18** -12.81 -66.94** -10.5 -23.45** -122.41** -298.02**
L 53 × Sk5 0.14 0.64 2.41 -5.44 -11.60* -4.72 23.36** 68.99
L53 × L18 -3.62**
-5.75** -57.09** -100.25** -17.88** -24.99** -383.72** -590.08**
L53 × L28 0.43 -0.42 10.29 7.03 8.52 5.97 35.93** -66.28**
L53 × Sd7 0.09 1.10* 15.87 41.29** 3.73 9.28 8.52** 120.90**
Sk5 × L18 4.39** 5.67** 64.97** 86.21** 20.81** 19.28** 456.77** 614.31**
Sk5 × L28 0.65 2.08** 14.91 40.24** 1.78 12.09* 72.15** 204.34**
Sk5 × Sd7 -2.45**
-4.18** -40.17* -78.08** -10.26 -19.47** -249.48** -418.60**
L18 × L28 -3.20**
-5.41** -48.29** -94.74** -12.14* -31.08** -326.89** -529.31**
L18 × Sd7 1.90** 2.69** 22.17 53.06** 14.14* 17.80* 193.95** 237.66**
L28 × Sd7 1.68** 3.57** 14.94 50.67** 2.9 15.84* 169.42** 358.06**
SE Sij – Sik 0.72 0.82 22.91 19.4 8.61 8.83 1.22 1.22
SE S
ij
– S
kl
0.59 0.67 18.71 15.84 7.03 7.21 1 1
WW= Well watering, WS= Water stress, GPC= Grain protein content, GOC= Grain oil content, GSC= Grain starch content,
GYPP= Grain yield/plant, GYPH= Grain yield/ha, PYPH= Protein yield/ha, OYPH= Oil yield/ha, SYPH= Starch yield/ha, * and **
indicate significance at 0.05 and 0.01 probability levels, respectively
For grain quality traits (GPC, GOC and GSC),
the values of SCA effects were mostly non-
significant and small in magnitude. However, the
highest positive SCA effects were shown by L53
× Sd7 under WW and L20 × SK5 under WS for
GPC, L20 × Sk5 under both environments, Sk5 ×
Sd7 under WS, L18 × L28 under WW for GOC
and Sk5 × L18 under both environments, for
GSC trait. In this study, it could be concluded
that the F
1
cross Sk5 x L18 is superior to other
crosses in SCA effects for grain yield/plant,
GYPH, PYPH, OYPH, SYPH under water
stressed and non-stressed environments, The
crosses L20 × L53, L18 × Sd7 and L28 × Sd7
follow the cross Sk5 x L18 in superiority for such
traits. These crosses could be offered to plant
breeding programs for improving tolerance to
drought tolerance at flowering stage. It is worthy
to note that for the studied traits, most of the best
crosses in SCA effects for a given trait included
at least one of the best parental inbred lines in
GCA effects for the same trait. This conclusion
was also reported by other investigators [16-18,
34,36].
3.7 Correlations between Performance,
GCA, SCA and Heterosis
Rank correlation coefficients calculated between
mean performance of inbred parents (
p
) and
their GCA effects, between mean performance
of F
1
's (

c
) and their SCA effects and
heterobeltiosis and between SCA effects and
heterobeltiosis, for studied characters are
presented in Table 9. Out of 8 studied traits,
significant (P≤ 0.05 or 0.01) correlations between


p
and GCA effects existed for 6 traits, namely
GPC, GYPP, GYPH, PYPH, OYPH (except WW)
and SYPH. Such significant correlations between
(
p
) and their GCA effects in this investigation
representing 68.75% of all studied cases (11 out
of 16 cases) suggest the validity of this concept
in the majority of studied traits, especially yield
traits under both environments. These results
indicate that the highest performing inbred lines
are also the highest general combiners and vice
versa for the previously mentioned traits and
therefore, the mean performance of a given
parent for these traits under the both
environments is an indication of its general
combining ability. This conclusion was previously
reported by several investigators [33,34,36,45,
46] in wheat.
All correlations between 

p
and GCA effects in
the present study were positive for all traits. The
traits which did not show any correlation between


p
and GCA effects under both environments
were GOC and GSC. In general, the non-
stressed environment showed higher correlation
x
x

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
12
coefficient between
p
and GCA effects for all
studied traits. The strongest correlation (highest
in magnitude) between
p
and GCA effects was
shown by GYPP, GPC and SYPH traits under
WW (0.91, 0.89 and 0.89, respectively) (Table 9).
For F
1
crosses, rank correlation coefficients
calculated between mean performance of F
1
crosses (

c
) and their SCA effects (Table 6)
showed that out of 8 studied traits, significant
(P≤ 0.05 or 0.01) correlations existed for 4 traits
under both environments, namely GYPP, GYPH,
PYPH and SYPH and 3 traits under WW, namely
GOC, GSC and OYPH. Such significant
correlations between (

c
) and SCA effects in this
investigation representing 68.75% of all studied
cases (11 out of 16 cases) suggest the validity of
this concept in the majority of studied traits and
environments. All correlations between (

c
) and
SCA effects in the present study were positive for
all traits. These results indicate that the highest
performing crosses are also the highest specific
combiners and vice versa for the previously
mentioned traits and therefore, the mean
performance of a given cross for these traits
under the respective environments is an
indication of its specific combining ability. This
conclusion was previously reported by Srdic et
al. [47] and Al-Naggar et al. [33,34,36]. In
general, the non-stressed environment showed
significant correlations between (

c
) and SCA
effects for all studied traits. This conclusion was
also reported by Le Gouis et al. [45] and Yildirim
et al. [46] under stress conditions. The strongest
correlation (highest in magnitude) between 

c
and SCA effects was shown by GOC and GYPH
traits under WW (0.82 and 0.83, respectively)
(Table 8).
Significant correlations between mean
performance of crosses (
c
) and heterobeltiosis
(Table 8) were exhibited only in the three quality
traits GPC, GOC and GSC under both
environments. For these traits, the mean
performance of a cross could be used as an
indicator of its useful heterosis under WW and
WS environments. The traits GYPP, GYPH,
PYPH, OYPH and SYPH did not exhibit any
correlation between
c
and heterobeltiosis
under both environments and therefore,
heterobeltiosis of crosses could not be expected
from their per se performance in such cases.
Only one significant correlation was observed
between SCA effects and heterobeltiosis was
exhibited in one trait, namely GOC under WW
and WS environments (Table 8). For this trait,
the useful heterosis of a cross could be used as
an indicator of its SCA effects under both
environments. The rest of studied traits did not
exhibit any correlation between SCA effects and
heterobeltiosis under both environments and
therefore, SCA effects of crosses could not be
expected from their heterobeltiosis values in
such cases.
Summarizing the above mentioned results, it
cloud be concluded from this investigation that
under water stressed environment, the mean
performance of a given parent could be
considered an indication of its general combining
ability for six traits (GPC, GYPP, GYPH, PYPH,
OYPH and SYPH) and the mean performance of
Table 9. Rank correlation coefficients among mean performance of inbreds (

p
) and their GCA
effects and between mean performance of F
1
’s (

c
) and their SCA effects and between
heterosis (H) and each of 

c
and SCA effects under water stress (WS) and non-stress (WW)
across 2013 and 2014 seasons
Correlation WW WS WW WS WW WS WW WS
GPC GOC GSC GYPP

̅
p
vs. GCA 0.89* 0.59* 0.23 0.17 -0.27 0.25 0.91* 0.76*

̅
c
vs. SCA 0.33 0.07 0.82** 0.36 0.65** 0.13 0.67** 0.66**

̅
c
vs. H. 0.52* 0.72* 0.65** 0.80** 0.85** 0.73** -0.36 -0.04
SCA vs .H 0.37 -0.01 0.66** 0.54* 0.33 0.13 0.27 0.36
GYPH PYPH OYPH SYPH

̅
p
vs. GCA 0.88* 0.76* 0.77* 0.71* 0.72* 0.51 0.89** 0.78*

̅
c
vs. SCA 0.68** 0.65** 0.83** 0.66** 0.53* 0.41 0.69** 0.68**

̅
c
vs. H. -0.28 -0.07 -0.18 -0.08 -0.25 -0.12 -0.27 -0.05
SCA vs. H 0.30 0.39 0.21 0.34 0.33 0.29 0.30 0.40
WW= Well watering, WS= Water stress, GPC= Grain protein content, GOC= Grain oil content, GSC= Grain starch content,
GYPP= Grain yield/plant, GYPH= Grain yield/ha, PYPH= Protein yield/ha, OYPH= Oil yield/ha, SYPH= Starch yield/ha, * and **
indicate significance at 0.05 and 0.01 probability levels, respectively
x
x
x
x

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
13
a given cross could be considered an indication
of its specific combining ability for four traits
(GYPP, GYPH, PYPH and SYPH). But the mean
performance of a given cross could be
considered an indication of its heterobeltiosis for
only three traits (GPC, GOC and GSC), and the
heterobeltiosis of a given
cross could be used as
indication of its SCA effects for only one trait
(GOC).
4. CONCLUSIONS
The highest mean grain yield, protein yield, oil
yield and starch yield was recorded by inbred line
L53 followed by L20 and Sk5 and crosses L20 ×
L53, L20 × L28 and L53 × Sd7 under WS
conditions. The inbred L18 showed the highest
GPC, inbreds L28 and L20 showed the highest
GOC and GSC under WS conditions. It is
observed that the heterobeltiosis for all studied
grain quality and yield traits was more
pronounced under water stress than under well
watering conditions. Crosses L28 × Sd7, L18 ×
Sd7, L18 × L28, Sk5 × L18 and Sk5 × L28
showed significant heterobeltiosis for grain
quality and yield traits. The results indicated the
existence of a greater portion of additive and
additive × additive variance than non-additive
variance in controlling the inheritance of GPC,
GOC and GSC and therefore selection methods
are the best choice for improving such traits
under WS. On the contrary, results indicated
predominance of non-additive variance for
GYPP, GYPH, PYPH, OYPH and SYPH, and
therefore heterosis breeding is the best choice
for such traits. The best inbreds in cross
combinations for grain yield were L53, L120 and
Sk5, for high PYPH, SYPH and OYPH were L53,
for high GPC was L18, for high GOC were Sd7
and L18 and for high GSC were L20 and L53
under WS. These inbreds and their hybrids could
be offered to maize breeding programs for
improving grain quality and yield traits under WS
conditions. Results also concluded that under
WS conditions, the mean performance of a given
inbred could be considered an indication of its
general combining ability for 6 out of 8 traits
(GPC, GYPP, GYPH, PYPH, OYPH and SYPH)
and the mean performance of a given cross
could be considered an indication of its specific
combining ability for 4 traits (GYPP, GYPH,
PYPH and SYPH), but the mean performance of
a given cross could be considered an indication
of its heterobeltiosis for only three traits (GPC,
GOC and GSC), and the heterobeltiosis of a
given
cross could be used as indication of its
SCA effects for only one trait (GOC).
COMPETING INTERESTS
Authors have declared that no competing
interests exist.
REFERENCES
1. Monneveux P, Sanchez C, Beck D,
Edmeades GO. Drought tolerance
improvement in tropical maize source
populations: Evidence of progress. Crop
Sci. 2006;46(1):180-191.
2. Bolanos J, Edmeades GO. The
importance of the anthesis-silking interval
in breeding for drought tolerance in
tropical maize. Field Crops Res. 1996;48:
65–80.
3. El-Ganayni AA, Al-Naggar AMM, El-
Sherbeiny HY, El-Sayed MY. Genotypic
differences among 18 maize populations
in drought tolerance at different growth
stages. J. Agric. Sci. Mansoura Univ.
2000;25(2):713–727.
4. FAOSTAT.
Available:http://faostat.fao.org/; 2014
(Access date: 20.05.2014.)
5. Miranda FJB. Inbreeding depression. In
Coors, J.G. & Pandey, S. (Eds.), The
genetics and exploitation of heterosis in
crops. ASA, CSS, and SSSA. Madison,
Wisconsin, USA. 1999;69-80.
6. Fonseca S, Patterson Fl. Hybrid vigor in a
seven parent diallel cross in common
wheat (Triticum aestivum L.). Crop Sci.
1968;8:85-88.
7. Falconer AR. Introduction to quantitative
genetics. Third Edition. Longman, New
York; 1989.
8. Betran JF, Ribaut JM, Beck DL, Gonzalez
De Leon D. Genetic diversity, specific
combining ability, and heterosis in
tropical maize under stress and non stress
environments. Crop Sci. 2003;43:797-806.
9. Kambal AE, Webster OJ. Estimation of
general and specific combining ability in
grain sorghum (Sorghum vulgare Pers.)
Crop Sci. 1965;5:521-523.
10. Hallauer AR, Miranda JB. Quantitative
genetics in maize breeding. 2
nd
ed. Iowa
State University Press. Ames, USA; 1988.
11. Duvick DN. Commercial strategies for
exploitation of heterosis. In Coors J.G. &
Pandey, S. (Eds.), The genetics and
exploitation of heterosis in crops. ASA,
CSS, and SSSA. Madison, W isconsin,
USA. 1999;19-29.

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
14
12. Koutsika-Sotiriou M. Hybrid seed
production in maize. In Basra, A. S. (Ed.),
Heterosis and Hybrid Seed Production in
Agronomic Crops. Food Products Press,
New York. 1999;25-64.
13. Sughroue JR, Hallauer AR. Analysis of the
diallel mating design for maize inbred
lines. Crop Sci. 1997;37:400-405.
14. Mazur B, Krebbers E, Tingey S. Gene
discovery and product development for
grain quality traits. Science. 1999;
285:372-375.
15. Wang XL, Larkins BA. Genetic analysis of
amino acid accumulation in opaque-2
maize endosperm. Plant Physiol. 2001;
125:1766-1777.
16. Al-Naggar AMM, El-Lakany MA, El-
Sherbieny HY, El-Sayed WM. Combining
abilities of newly-developed quality protein
and high-oil maize inbreds and their
testcrosses. Egypt. J. Plant Breed. 2010;
14(2):1-15.
17. Al-Naggar AMM, El-Lakany MA, El-
Sherbieny HY, El-Sayed WM. Diallel
analysis of maize inbred lines with
contrasting protein contents. Egypt. J.
Plant Breed. 2010;14(2):125-147.
18. Al-Naggar AMM, El-Lakany MA, El-
Sherbieny HY, El-Sayed WM. Inheritance
of grain oil content and yield
characteristics in maize. Egypt. J. Plant
Breed. 2010;14(2):239-264.
19. Mittelmann A, Miranda JB, Lima GJM,
Haraklein C, Tanaka RT. Potential of the
ESA23B maize population for protein and
oil content improvement. Sci. Agric. 2003;
60(2):319-327.
20. Dudley JW, Lambert RJ. Ninety
generations of selection for oil and protein
in maize. Maydica 1992;37:81–87.
21. Berke TG, Rocheford TR. Quantitative
trait loci for flowering, plant and ear height,
and kernel traits in maize. Crop Sci. 1995;
35:1542-1549.
22. Oikeh SO, Kling JG, Okoruwa AE.
Nitrogen fertilizer management effects on
maize grain quality in the West African
moist savanna. Crop Sci. 1998;(38):
1056-1061.
23. Dudley JW. Seventy-six generations of
selection for oil and protein percentage in
maize. p. 459-473. In E. Pollak et al. (ed.)
Int. Conf. on Quant. Genet. Proc. Iowa
State Univ. Press, Ames, IA. 1977;
459-473.
24. Amit D, Joshi VN. Heterosis and
combining ability for quality and yield in
early maturing single cross hybrids of
maize (Zea mays L.). Indian J. Agric. Res.
2007;41(3):210-214.
25. Dudley JW. Epistatic interactions in
crosses of Illinois high oil × Illinois low oil
and of Illinois high protein × Illinois low
protein corn strains. Crop Sci. 2008;48:
59-68.
26. Dudley JW, Johnson GR. Epistatic models
improve prediction of performance in corn.
Crop Sci. 2009;49:763-770.
27. Al-Naggar AMM, Shabana R, Rabie AM.
Per se performance and combining ability
of 55 newly – developed maize inbred
lines for tolerance to high plant density.
Egypt. J. Plant Breed. 2011;15(5):59-82.
28. Steel RGD, Torrie JH, Dickey DA.
Principles and procedures of statistical
analysis. Biometerical Approach. 3
rd
Edition. McGraw-Hill Book Company, New
York; 1997.
29. Griffing B. Concept of general and specific
combining ability in relation to diallel
crossing systems. Aust. J. Biol. Sci. 1956;
9;463-493.
30. Al-Naggar AMM, Atta MMM, Ahmed MA,
Younis ASM. Grain protein, oil and starch
contents and yields of maize (Zea mays
L.) as affected by water stress, genotype
and their interaction. International Journal
of Plant and Soil Science. 2016;10(1):
1- 21.
31. Misevic D, Alexander DE. Twenty-four
cycles of phenotypic recurrent selection
for percent oil in maize. I. per se and
testcross performance. Crop Sci. 1989;
29:320-324.
32. Al-Naggar AMM, Shabana R, Atta MMM,
Al-Khalil TH. Heterosis and type of gene
action for some adaptive traits to high
plant density in maize. Egypt. J. Plant
Breed. 2014;18(2):189-209.
33. Al-Naggar AMM, Shabana R, Abd El-
Aleem MM, El-Rashidy Zainab A.
Response of performance and combining
ability of wheat parents and their F2
progenies for N efficiency traits to
reducing N-fertilizer level. American
Research Journal of Agriculture. 2015;
1(3):49-59.
34. Al-Naggar AMM, Shabana R, Abd El-
Aleem MM, El-Rashidy Zainab A. Diallel
analysis of wheat grain protein content
and yield in F
1
and F
2
generations under
contrasting nitrogen conditions. American
Research Journal of Agriculture. 2015;
1(4):12-29.

Al-Naggar et al.; ACRI, 4(4): 1-15, 2016; Article no.
ACRI.27508
15
35. Al-Naggar AMM, Shabana R, Abd El-
Aleem MM, El-Rashidy Zainab A.
Heterobeltiosis in Wheat (Triticum
aestivum L.) F
1
diallel Crosses under
Contrasting Soil-N Conditions British
Biotechnology Journal. 2015;10(4):1-12.
36. Al-Naggar AMM, Shabana R, Abd El-
Aleem MM, El-Rashidy Zainab A.
Performance and combining ability for
grain yield and quality traits of wheat
(Triticum aestivum L.) F
1
diallel crosses
under low-N and high-N environments.
Scientia Agriculturae. 2015;12(1):13-22.
37. Mostafa MAN, Abd-Elaziz AA, Mahgoub
GHA, El-Sherbiney HYS. Diallel analysis
of grain yield and natural resistance to late
wilt disease in newly developed inbred
lines of maize. Bull. Fac. Agric., Cairo
Univ. 1996;47:393-404.
38. Ahsan M, Hussnain H, Saleem M, Malik
TA, Aslam M. Gene action and progeny
performance for various traits in maize.
Pak. J. Agric. Sci. 2007;44(4):608-613.
39. Al-Naggar AMM, Shabana R, Al-Khalil TH.
Alternative screening criteria for selecting
nitrogen-use efficient genotypes of maize.
Egypt. J. Plant Breed. 2011;15(1):27-40.
40. Buren LL, Mock JJ, Anedrson IC.
Morphological and physiological traits in
maize associated with tolerance to high
plant density. Crop Sci. 1974;14:426-429.
41. Beck DL, Betran J, Bnaziger M, Willcox M,
Edmeades GO. From landrace to hybrid:
Strategies for the use of source
populations and lines in the development
of drought tolerant cultivars. Proceedings
of a Symposium, March 25-29, 1996,
CIMMYT, El Batan, Mexico; 1997.
42. Vasal SK, Cordova H, Beck DL,
Edmeades GO. Choices among breeding
procedures and strategies for developing
stress tolerant maize germplasm.
Proceedings of a Symposium, March 25-
29, CIMMYT, El Batan, Mexico. 1997; pp.
336-347.
43. Al-Naggar AMM, Atta MMM, Ahmed MA,
Younis ASM. Grain protein, oil and starch
contents and yields of maize (Zea mays
L.) as affected by water stress, genotype
and their interaction. International Journal
of Plant and Soil Science. 2016;10(1):
1- 21.
44. Meseka SK, Menkir A, Ibrahim AES, Ajala
SO. Genetic analysis of maize inbred lines
for tolerance to drought and low nitrogen.
Jonares. 2013;1:29-36.
45. Le Gouis J, Beghin D, Heumez E,
Pluchard P. Genetic differences for
nitrogen uptake and nitrogen utilization
efficiencies in winter wheat. Eur. J. Agron.
2000;12:163–173.
46. Yildirim M, Bahar B, Genc I, Korkmaz K,
Karnez E. Diallel analysis of wheat
parents and their F2 progenies under
medium and low level of available N in
soil. J. Plant Nutri. 2007;30:937–945.
47. Srdic J, Nikolic A, Pajic Z, Drinic SM,
Filipovic M. Genetic similarity of sweet
corn inbred lines in correlation with
heterosis. Maydica. 2011;56:251-256.
© 2016 Al-Naggar et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution
License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
Peer-review history:
The peer review history for this paper can be accessed here:
http://sciencedomain.org/review-history/15488