Acta Scientific AGRICULTURE (ISSN: 2581-365X)
Volume 8 Issue 7 July 2024
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield,
Nutritional Traits and Milling Quality in South African Maize Hybrids
Khajoane TJ
1,2
, MT Labuschagne
1
and NW Mbuma
1,2
*
1
Department of Plant Sciences, University of the Free State, South Africa
2
Department of Plant Breeding, Agricultural Research Council-Vegetables, Industrial
and Medicinal Plants Institute, South Africa
*Corresponding Author: Mbuma N, Researcher, Department of Plant Breeding, Agricultural
Research Council-Vegetables, Industrial and Medicinal Plants Institute, South Africa.
Research Article
Received: April 16, 2024
Published: June 15, 2024
© All rights are reserved by
NW Mbuma., et al.
Abstract
Maize is a staple food crop that can provide multiple dietary components, and has the potential to improve food security and
address malnutrition. The objectives of this study were to determine the phenotypic correlation variation among maize hybrids for
grain yield, nutritional quality traits and milling quality, to identify superior maize hybrids, and to determine the interrelationship
between measured traits. Eighteen maize hybrids (nine commercial and nine experimental) were planted in a randomised complete
block design with six replicates in seven different environments in South Africa. Genotype, environment and their interaction effects
differences of these traits were mostly attributed by environmental factors. Grain yield, protein, moisture and fat had broad sense
Milling quality was positively correlated with almost all traits measured, indicating the possibility of multiple trait selection. Starch
was negatively associated with protein and grain yield. The clustered heat map distinguished three distinct clusters of maize hybrids
quality and fat. High-yielding maize hybrids with good nutritional quality can be tested for adaptability and stability and recom-
mended for commercial release.
Abbreviations
G: Genotype; GE Interaction: Genotype by Environment Interac-
E:
G
GE
:
2
G
: Genotypic
2
GXE
: Genotype by Environment Interaction Variance;
2
P
: Phenotypic Variance; H
2
: Broad Sense Heritability; SD: Pheno-
-
-
fective Grain; PC: Principal Component; PCA: Principal Component
Analysis; C: Commercial Hybrids; Ex: Experimental Genotypes;
MC: Moisture Content
Introduction
Maize (Zea mays L.) is a primary food source for millions of
people in sub-Saharan Africa, particularly in Southern Africa where
[1].
-
[2].
which is responsible for water retention in humans during food di-
gestion [3,4]. -
tophan, it contains considerable amounts of methionine and cyste-
ine. Due to its nutritional composition, the crop is mainly produced
for human consumption as food and as an animal feed.
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
35
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
Maize is grown in diverse environments with varying tempera-
tures, soil types and rainfall, making it a suitable crop for climate
smart agriculture [5]. Researchers have been developing climate
smart maize hybrids with high grain yield, improved nutritional
quality and other desired traits [6,7]. Previous studies have indi-
cated that grain yield and most of the nutritional quality traits are
polygenic with small additive genetic effects [8]. Polygenic traits
broad sense heritability estimates, which ultimately reduces selec-
[9]. Thus, comprehensive and ongoing research that
-
mental conditions on the phenotypic expression and heritability of
grain yield, nutritional quality traits and milling quality is required
[10].
Genotypic and phenotypic variance as well as heritability are
the most important parameters that determine the selection ef-
[11]. Genotypic variance
-
types in diverse environments [10]. Whereas phenotypic variance
explains the total variation among genotypes in different environ-
ments. Both genotypic and phenotypic variance are used to deter-
mine the broad-sense heritability. Broad-sense heritability is the
amount of phenotypic variance that is attributable to the overall
-
[12].
Previous studies conducted in Ethiopia [9,13], Nigeria [14,15],
[16], Kenya [17], Ghana [18], Uganda [19], India [20]
and South Africa [21,22], focused on the assessment of phenotypic
diversity of maize hybrids in various environments for grain yield
and yield components and to some extent on nutritional composi-
-
tions.
Multiple trait selection is required for developing maize hybrids
with the desired characteristics and to understand how improving
[1]. Phenotypic correlation is
one of the methods used to study relationships between traits in a
population [23]. In a maize breeding programme, phenotypic cor-
relations between pairs of variables give opportunities for simul-
taneous selection [24,25]. Other methods such as principal com-
ponent analysis and clustered maps have been used to assess the
interrelationships among maize phenotypic and nutritional char-
acteristics and their association with genotypes [26-28].
Therefore, the evaluation of the phenotypic diversity of maize
hybrids for grain yield, nutritional quality characteristics and mill-
ing index is important in order to determine the variation present
in the available material, which will ultimately enable the identi-
understanding the interrelationship between the traits associated
with superior hybrid genotypes is expected to guide and improve
determine the phenotypic variation among maize hybrids for grain
yield, nutritional quality traits and milling quality, to identify supe-
rior maize hybrids, and to determine the interrelationship between
measured traits.
Materials and Methods
Study material and experimental environments
Eighteen maize genotypes (nine commercial hybrids: G1-C, G2-
C, G3-C, G5-C, G6-C, G7-C, G8-C, G9-C and G10-C and nine experi-
mental hybrids: G4-Ex, G11-Ex, G12-Ex, G13-Ex, G14-Ex, G15-Ex,
G16-Ex, G17-Ex and G18-Ex) were obtained from a private seed
company in South Africa. All maize hybrids were obtained from the
-
cial release). Maize hybrids were planted in seven different envi-
ronments during the 2020/2021 cropping season. The experimen-
tal sites represented different environmental conditions and were
located in the eastern part of the maize production areas of South
Africa (Table 1).
Environment
Environment
(E) code
Latitude
(S)
Longi-
tude (E)
Altitude
ASL (m)
Average seasonal max
temperature (
O
C)
Average seasonal min
temperature (
O
C)
Average seasonal
rainfall (mm)
Bethal E1 26°46’ 29°47’ 1661 31 2 478
Leandra E2 26°37’ 28°92’ 1687 32 1 616
Middleburg E3 25°46’ 29°27’ 1479 27 5 958
Wonderfontein E4 25°80’ 28°88’ 1459 33 -3 421
Petit E5 26°09’ 28°39’ 1649 32 -1 649
Kriel E6 26°27’ 29°23’ 1552 33 2 616
Amersfoort E7 26°89’ 29°85’ 1652 31 0 705
Table 1: Description of the experimental sites.
ASL = Above sea level.
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
36
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
Experimental design, trial establishment and management
design with six replications. The experimental plots consisted of
four rows of 6 to 12 m long with a spacing 0.75 m between rows
and 0.25 m between plants. All trials were planted using private
seed company plot planters and managed by commercial farmers.
-
ommendations for each environment.
Data collection
-
ing the combine harvester from the two inner rows in the four-row
plot and converted to ton per hectare. The following formula was
used to calculate grain yield:
MC) (plot area)])
MC = moisture content
Nutritional quality traits
Two sub-samples for each sample were used to determine the
milling quality of maize kernels and nutritional quality traits such
-
Analyzer, Model DA 7250, Perten, Instruments AB, Sweden) with a
wavelength ranging from 900 to 1700 nm.
Defective grain (DEFG)
After thorough mixing, a random 100 g sample of maize grain
per hybrid was weighed and sieved manually using a 6.35 mm
round-hole sieve. Maize grains that remained above the sieve were
insect or rodent damage, water damage and pinking. Kernels that
passed through the sieve were weighed separately as ‘under sieve’.
-
tal 100 g sample per hybrid.
Statistical analysis
Combined analysis of variance (ANOVA) were done for grain
yield, nutritional quality traits and milling quality using GenStat
version 2021 [29] and SAS software [30]
because they represented all advanced maize hybrids before com-
mercial release. Environments represented a random sample of all
possible environments that represented maize growing environ-
ments in the eastern parts of South Africa. Genotypic and pheno-
typic variances were calculated from the mean squares generated
from ANOVA [31].
Genotype, genotype by environment (GE) interaction and phe-
notypic variances for combined analysis were calculated using the
following formulae:
Where MS
G
= mean squares of genotypes, MS
GE
= mean squares
of genotype by environment interaction, MS
e
= mean squares of
error, e = environments, and r = replications.
Broad sense heritability (H
2
) was calculated using the following
formulae:
Predicted selection gains (Gs) was calculated using the follow-
ing formulae:
-
σ
P
= phenotypic standard deviation, and H
2
= broad
sense heritability.
Phenotypic correlations for grain yield, nutritional quality traits
and milling quality were estimated using SAS software [30]. The
principal component analysis (PCA) was done to visualize the per-
-
STAT 2022 [32]. A clustered heat map was generated to visualize
the hierarchy of clusters among maize genotypes for grain yield,
nutritional quality traits and milling quality using NCSS [33].
Results
Combined ANOVA
quality (Table 2). Phenotypic variances were higher than genotypic
variances for all traits. H2 estimates were high for milling quality
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
37
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
Source of
Variation
DF
Grain yield
(ton/ha)
Protein
(%)
Starch (%) Fibre (%) Fat (%)
Moisture
(%)
Milling quality
(%)
DEFG (%)
E 5 1021.27*** 50.52*** 566.86*** 0.37*** 5.41*** 20.26*** 1821.66*** 272.94***
Block (E) 30 3.9 0.44 0.93 0.01 0.4 0.15 15.13 31.46
G 17 3.29* 1.43*** 3.57*** 0.06*** 1.04*** 0.29* 1282.73*** 152.39***
GE interaction 85 2.71*** 0.76*** 1.41*** 0.02*** 0.64*** 0.20* 224.99*** 63.40***
Error 510 1.62 0.32 0.83 0.01 0.31 0.14 15.07 21.94
Total 647 6332.08 499.56 841.4 8.23 254.79 203.6 64687.1 21658.5
E
260.71*** 123.67*** 54.41*** 57.19*** 15.17*** 147.57*** 130.12*** 8.56***
G
2.04* 4.42*** 2.78*** 8.10*** 3.35*** 1.97* 85.67*** 7.33***
GE
1.67*** 2.24*** 1.72*** 2.95*** 2.06*** 1.38* 14.97** 2.9***
σ
2
G
0.0101 0.027 0.0659 0.0012 0.028 0.005 29.465 6.7606
σ
2
GE
0.1884 0.0673 0.0881 0.0025 0.06 0.01 34.9867 8.4545
σ
2
P
0.0865 0.0471 0.1036 0.0019 0.0466 0.0106 35.7147 8.7791
H
2
17.63 46.85 60.61 78.57 38.19 30.86 82.5 58.25
Gr. mean ± SD 6.77 ± 1.27 6.84 ± 0.57 66.89 ± 0.91 2.48 ± 0.08 4.30 ± 0.56 11.02 ± 0.37 69.79 ± 3.88 6.06 ± 4.68
Gs 0.46 0.55 1.14 0.13 0.44 0.24 6.6 5.62
6.79 8.04 1.7 5.24 10.23 2.18 9.46 92.8
Table 2: Combined ANOVA for maize grain yield, nutritional quality traits and milling quality.
*PPP
E
G
GE
2
G
=
Genotypic variance, σ
2
GXE
2
P
= Phenotypic variance, H
2
= Broad sense heritability, SD =
s
Defective grain.
Combined analysis mean values
The genotypes showed large variation across the environments
for all traits, ranging from 6.47 to 7.25 ton/ha for grain yield, 6.20
-
(Table 3). On average, hybrid G15-Ex had the highest grain yield
Phenotypic correlations
-
-0.334), fat with protein (r = -0.308).
Principal component analysis (PCA) for grain yield, nutrition-
al quality traits and milling quality
The eight measured traits were reduced to three principal com-
-
served in the dataset when a minimum threshold eigenvalue of one
was used (Table 5). Only PC1 and PC2 were interpreted because
they accounted for the majority of the variation in the data set. Mill-
ing quality, fat, and moisture contributed the most to PC1 (posi-
The PCA was used to visualise genotype and trait associations
-
brid genotypes according to their associated traits. Maize hybrid
genotypes G2-C, G4-Ex, G10-C, G11-Ex, G12-Ex, G15-Ex and G18-Ex
were on the positive side of PC1 suggesting that these genotypes
had high values for milling quality, fat and moisture and low values
Ex were displayed on the positive side of PC2 indicating that these
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
38
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
hybrids had high values for grain yield and protein and low values
for starch content. The PCA further distinguished four different
groups of hybrid genotypes according to their associated traits,
namely, I) G11-Ex and G12-Ex were associated with high starch
content, II) G2-Ex, G4-Ex, G10-C, G15-Ex and G18-Ex were associ-
G3-C, G5-C, G6-C, G8-C, G16-Ex and G17-Ex were associated with
Genotypes
Grain Yield
(ton/ha)
Protein (%) Starch (%) Fibre (%) Fat (%) Moisture (%)
Milling quality
(%)
DEFG (%)
G1-C 7.15 ± 3.49 7.09 ± 0.99 66.50 ± 1.14 2.50 ± 0.12 4.02 ± 0.57 10.93 ± 0.52 68.74 ± 6.27 5.50 ± 4.46
G2-C 6.74 ± 3.15 6.70 ± 0.87 67.10 ± 1.08 2.41 ± 0.09 4.43 ± 0.80 11.12 ±0.57 80.67 ± 8.61 3.34 ± 2.58
G3-C 6.75 ± 3.09 7.30 ± 1.01 67.02 ± 1.42 2.43 ± 0.09 4.12 ± 0.63 11.03 ± 0.58 76.75 ± 4.06 4.45 ± 3.60
G4-Ex 6.91 ± 3.22 6.68 ± 0.91 66.55 ± 1.31 2.44 ± 0.07 4.49 ± 0.61 11.23 ± 0.56 65.23 ± 3.52 3.34 ± 2.21
G5-C 7.09 ± 3.35 6.80 ± 1.01 66.50 ± 1.28 2.49 ± 0.13 4.44 ± 0.54 11.10 ± 0.66 69.13 ± 11.57 5.15 ± 5.84
G6-C 7.14 ± 3.19 6.66 ± 0.90 66.86 ± 1.12 2.48 ± 0.11 4.45 ± 0.58 11.03 ± 0.64 67.14 ± 6.03 3.69 ± 2.52
G7-C 6.94 ± 3.03 6.85 ± 0.81 67.24 ± 1.13 2.47 ± 0.11 4.05 ± 0.66 10.92 ± 0.70 75.49 ± 4.55 8.03 ± 7.43
G8-C 7.11 ± 3.19 6.66 ± 0.98 66.82 ± 1.04 2.47 ± 0.11 4.41 ± 0.63 10.96 ± 0.63 62.99 ± 4.37 5.36 ± 7.54
G9-C 6.79 ± 3.18 7.01 ± 0.87 67.07 ± 1.32 2.49 ± 0.11 4.05 ± 0.42 10.96 ± 0.51 68.95 ± 7.13 8.45 ± 6.51
G10-C 6.56 ± 3.13 6.68 ± 0.79 67.04 ± 1.10 2.44 ± 0.10 4.44 ± 0.75 10.95 ± 0.48 76.60 ± 7.78 9.08 ± 9.08
G11-Ex 6.80 ± 3.15 6.52 ± 1.09 67.77 ± 1.25 2.48 ± 0.11 4.12 ± 0.54 11.05 ± 0.60 66.09 ± 8.48 4.05 ± 2.98
G12-Ex 5.53 ± 3.62 6.20 ± 0.57 67.70 ± 0.89 2.45 ± 0.14 4.66 ± 0.70 11.07 ± 0.73 80.70 ± 7.56 7.50 ± 7.45
G13-Ex 6.59 ± 3.03 6.94 ± 0.81 66.85 ± 1.00 2.51 ± 0.11 4.47 ± 0.84 11.15 ± 0.54 63.52 ± 4.10 3.77 ± 4.01
G14-Ex 6.56 ± 3.07 6.75 ± 0.91 66.84 ± 0.97 2.60 ± 0.16 4.11 ± 0.52 10.87 ± 0.56 54.98 ± 10.45 4.06 ± 4.22
G15-Ex 7.25 ± 2.75 6.76 ± 092 66.66 ± 1.21 2.44 ± 0.11 4.90 ± 0.91 11.25 ± 0.67 83.47 ± 7.59 16.80 ± 9.76
G16-Ex 6.64 ± 3.30 7.36 ± 1.20 66.69 ± 1.42 2.51 ± 0.16 4.03 ± 0.58 10.78 ± 0.50 66.51 ± 11.06 7.69 ± 4.48
G17-Ex 6.47 ± 3.40 7.01 ± 0.67 66.32 ± 1.32 2.51 ± 0.09 4.16 ± 0.64 10.95 ± 0.61 63.70 ± 10.15 4.54 ± 4.39
G18-Ex 6.47 ± 3.29 6.96 ± 1.03 66.57 ± 1.21 2.41 ± 0.09 4.25 ± 0.48 11.22 ± 064 71.62 ± 12.57 4.45 ± 3.17
Minimum 6.47 6.2 66.32 2.41 4.02 10.78 54.98 3.34
Maximum 7.25 7.36 67.77 2.6 4.9 11.25 83.47 16.8
LSD 0.56 0.21 0.3 0.02 0.20 0.12 1.21 0.12
Table 3: Combined mean values ± standard deviation of 18 maize hybrids for grain yield (ton/ha), nutritional quality traits and milling
quality.
high protein and grain yield, and IV) G7-C, G9-C, G13-Ex and G14-
-
positively correlated with grain yield and these traits were nega-
tively correlated with starch.
Traits Grain yield Protein Starch Fibre Fat Moisture Milling quality
Protein 0.173**
Starch -0.134** -0.636***
0.191 0.085* -0.123**
-0.071 -0.308*** -0.174*** -0.117**
Moisture -0.163** 0.040 -0.341*** -0.334*** -0.020
Milling quality 0.047 0.002 0.044 -0.411*** 0.101* 0.044
0.034 -0.112** 0.149*** -0.100* 0.129** -0.053 0.217***
Table 4: Phenotypic correlation of maize grain yield, nutritional quality traits and milling quality.
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
39
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
Traits PC1 PC2 PC3
Grain yield -0.10 0.57 -0.09
Protein -0.33 0.42 0.36
Starch 0.20 -0.59 0.26
-0.43 -0.16 -0.11
0.46 0.11 -0.31
Moisture 0.41 0.21 -0.46
Milling quality 0.46 0.08 0.46
0.25 0.25 0.52
Eigenvalue 3.10 1.71 1.20
38.81 21.37 14.94
38.81 60.19 75.13
Table 5: Principal component (PC) analysis for grain yield,
nutritional quality traits and milling quality.
Clustered heat map for grain yield, nutritional quality traits
and milling quality
-
tive clusters for traits were observed, namely, 1) grain yield, fat
into three distinctive clusters namely, 1) G1-C, G7-C, G9-C, G13-Ex,
G14-Ex, G16-Ex and G17-Ex were positively associated with high
2) G4-Ex, G5-C, G6-C, G8-C, G11-Ex were positively associated with
G12-Ex, G15-Ex and G18-Ex were positively associated with milling
and grain yield.
Figure 1:
defective grain. Genotypes with Ex were experimental hybrids while genotypes with C were commercial hybrids.
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
40
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
Figure 2: Clustered heat map illustrating associations between maize hybrids for grain yield, nutritional quality traits and milling
Discussion
suggested the presence of genetic variation among maize hybrids.
This suggests that high performing hybrids can be selected. Sig-
yield and its components in Western Ethiopia [9].
interaction effects for grain yield, nutritional quality traits and
variability among hybrids. This indicates the importance of testing
maize hybrids in diverse environments for grain yield, nutritional
for grain yield of six open pollinated maize genotypes evaluated in
three different locations over two seasons was reported by [13].
The phenotypic variance was higher than the genotypic variance
for all traits, which suggested that the phenotypic expression of
[20] -
cant GE interaction effect for grain yield in maize hybrids across
moisture regimes. [15]
for grain yield of pro-vitamin A maize hybrids in Nigeria over two
cropping seasons under drought and low nitrogen environments.
High H
2
suggesting that the phenotypic differences were due to genotypic
effects. This could further indicate that these traits could be select-
ed with high precision based on their genotypic differences [34].
Low H
2
was seen for grain yield, protein, moisture and fat content,
which indicated that the phenotypic differences of these traits were
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
41
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
mostly affected by the environment [35]. In contrast with the cur-
rent study, high H
2
for grain yield in maize populations were found
in Nigeria [36]. This could imply that the broad sense heritability
values for grain yield in maize depends on genetic material and
their background. Generally, most of the quantitative traits are
polygenic with small additive effects, resulting in large GE inter-
action which results in low heritability [37]. It is challenging to
improve traits with low H
2
and low percentage selection gains
through selection because of high environment variance and low
genetic variance.
On average grain yield ranged from 6.56 to 7.15 ton/ha, protein
-
-
-
variability. The results also indicated high values for grain yield,
nutritional quality traits and milling quality. Bojtor., et al. [38] eval-
uated 10 maize hybrids in Hungary and reported lower values for
-
in this study. Two experimental hybrids (G17-Ex and G11-Ex) had
the highest values for each trait in all test environments, which in-
dicated that these hybrids may be recommended for commercial
release and production. However, they should be tested for pest
and disease resistance before commercial release. These results
could also imply that there is a need to exploit recurrent selection
in maize breeding programmes, this will ensure that the released
commercial varieties are further exploited as potential parents,
which is expected to increase the number of superior experimental
hybrids. Moreover, these hybrids can further be evaluated for trait
between environments, which indicated that hybrids performed
differently across environments. These results also showed that a
hybrid that performs well in one environment will not necessar-
open pollinated varieties of maize in three different locations were
found in Nepal [10]. Salinger., et al. [39]
in maize grain yield in Veneto under the winter North Atlantic
Oscillation, summer North Atlantic Oscillation, West African Mon-
Superior experimental hybrids G15-Ex (grain yield, fat con-
tent, and milling quality), G16-Ex (protein content and low mois-
-
ronments, which suggested that they may be considered for com-
mercial production after further evaluation for stability and adapt-
ability in the main maize growing regions in South Africa. [40] also
reported that the variation in multiple environmental trials for
maize was largely attributed by the effect of the environment.
-
cated that when one of the traits are selected, an improvement may
grain yield with protein suggested possible simultaneous improve-
ment of these traits. Previous studies also reported positive cor-
relation for grain yield with protein content in QPM and non-QPM
hybrids [41]. Contrasting results have been reported previously, for
example, strong negative correlations have been observed between
grain yield and protein content [42]. The strong negative correla-
tion of starch with grain yield and protein indicates the complex-
ity of simultaneously improving these traits, as the improvement
protein, therefore if protein is increased starch will decrease and
vice versa. This might be because starch synthase enzyme activity
and grain size [43]. Another cause might be that the protein struc-
ture which is a ball-like structure, rather than a matrix linked by
molecular acid bonding as in rye, barley, and wheat, which sticks to
the starch granules, negatively altering the rheological characteris-
tics of starch [44]. This could mean that protein and starch content
should be monitored on a regular basis to identify cultivars that
combine grain yield with desirable nutritional quality traits [45].
-
will have a negative effect on most of the maize nutritional quality
traits whereas grain yield and protein content can be simultaneous
improved in maize hybrids.
Improving maize nutritional traits such as protein content and
milling quality should be considered when selecting for high yield-
Positive correlations were observed for milling quality with almost
all traits, indicating that an increase in grain yield and nutritional
quality traits would results in a positive effect on the milling qual-
ity. However, milling quality was strongly and negative correlated
-
-
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
42
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
reduces the milling ability and the quality of end products such as
rice with different degrees of milling showed that each milling step
led to increased total starch content and amylose while decreas-
[46]. Quinoa, wheat and barley
were also reported to have similar physiochemical properties as
Simiao rice [47].
with milling quality, both traits are important contributors to the
cholesterol and reduce other health risk factors such as heart dis-
ease and stomach cancer [48]
-
bre and their correlation with milling quality and other nutritional
quality traits to successful breed for balance of traits in maize crop.
and G17-Ex were associated with high protein and grain yield,
indicating the possibility of simultaneous improvement of maize
grain yield and protein content for these hybrids. These hybrids
can further be used as potential varieties for commercial release
and production. It is important to note that the majority of African
nations and other regions of the world suffer from malnutrition
and undernourishment. This might be attributable to the fact that
most of these nations’ daily meals are staple foods (maize, wheat,
and rice). When compared to legume crops, most staple meals give
a large amount of carbohydrates as a source of energy and a low
amount of protein, essential minerals and vitamins. Although car-
bohydrates provide energy, a healthy diet should include protein,
vitamins, and other minerals. As a result, effectively breeding im-
proved maize varieties with high protein content along with nutri-
two experimental hybrids (G16-Ex and G17-Ex) were associated
with high grain yield and protein, further indicating a need to in-
corporate the commercial inbred varieties as potential parents in a
breeding programme to introduce these desirable traits.
The PCA showed that hybrids G2-C, G4-Ex, G10-C, G15-Ex and
G18-Ex were associated with good milling quality, fat and mois-
ture. These hybrids are most desirable in the maize industry due
to the high milling quality. However, milling quality, fat and mois-
Ex, G10-C, G15-Ex and G18-Ex indicating that an improvement of
these traits will be associated with high levels of defective grain.
A selection index may be used to ensure the balance of grain yield
with quality traits. Milling quality or milling index is a measure of
the milling ability and quality of maize kernels, with better milling
quality implying more extractable and high-grade of lucrative prod-
ucts such as samp, maize rice, and maize grits (degermed goods)
made from the corneous part of the endosperm [49]. Most of the
population in SSA consumes maize in the form of samp and pap/
porridge and the quality of these products depends on maize mill-
ing ability. Breeding for superior maize hybrids with high starch,
and as well as high milling quality is required to reduce food inse-
curity and hunger in SSA and other regions of the world.
The clustered heat map did not group the hybrids based on
whether they were commercial or experimental hybrids. The one
cluster of hybrids (G4-Ex, G5-C, G6-C, G8-C, and G11-Ex) was as-
sociated with high grain yield but also associated with lower values
for important traits such as protein and milling quality. Interest-
ing was that the cluster of hybrids (G3-C, G10-C, G12-Ex, G15-Ex,
and G18-Ex) that was associated with high milling quality, was also
associated with low values for protein and grain yield, indicating
that the improvement of these two important maize traits will have
could indicate that there is a need for the development of a selec-
tion index in a maize breeding programme to ensure multiple trait
selection and improvement of traits without a penalty to other
traits.
Conclusions
The study revealed the presence of variability among maize hy-
brids and across environments which can be exploited for future
crop improvement. Broad-sense heritability and predicted selec-
tion gain for grain yield were low, highlighting the complexity in the
genetic improvement of grain yield. Broad-sense heritability was
high for almost all the nutritional quality traits. Superior experi-
mental hybrids G15-Ex (grain yield, fat content, and milling qual-
ity), G16-Ex (protein and low moisture content), G11-Ex (starch
-
indicated the possibility of simultaneous trait selection and im-
provement in maize breeding programmes. Grain yield and protein
were positively correlated and both traits were negatively corre-
considered for multiple traits improvement. Only two experimental
hybrids (G16-Ex and G17-Ex) were associated with both high grain
yield and protein content. Hybrids G2-C, G4-Ex, G10-C, G15-Ex and
Citation: NW Mbuma., et al. “Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling
Quality in South African Maize Hybrids". Acta Scientific Agriculture 8.7 (2024): 34-45.
43
Phenotypic Variation, Broad-Sense Heritability and Interrelationship between Grain Yield, Nutritional Traits and Milling Quality in South
African Maize Hybrids
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G18-Ex were associated with good milling quality, fat, low moisture
C, G10-C, G12-Ex, G15-Ex and G18-Ex were associated with high
milling quality. Maize breeding programmes should consider the
development of a selection index to ensure multiple trait selection
and for improving grain yield and nutritional quality traits, which
will ultimately ensure the balance in trait combinations within su-
perior cultivars. However, these results were only based on single
year multi-environment trial in the eastern region of South Africa,
thus these hybrids need to be evaluated in the western region and
over seasons to derive a more robust conclusion on their perfor-
mance and interrelationships among traits.
Acknowledgements
We thank the Private seed company in South Africa, for pro-
viding the study material, trial management, and data collection.
(UID 84647) in South Africa, for funding the project.
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