Testing of genotypes in multi-environments is an important to estimate genotype x environment interaction (GEI) and identify stable genotypes with superior performance. The study was to evaluate different maize hybrids at multi-environments as well as to identify high yielding and stable maize hybrids. Twenty maize hybrids were tested across eight environments in a randomized complete block design in the 2015 cropping season. Combined analysis of variance and AMMI analysis showed that genotype, environment and GEI effect were highly significant (p < 0. 01) for grain yield. Genotype, environment and GEI explained 6.62, 84.87 and 4.50% of the total experimental variations, indicating the importance of environment for variations in grain yield. Mean grain yield of tested hybrids ranged from 4.98 t ha-1 in G2 to 7.51 t ha-1 in G16. As evident from significant GEI, performances of the hybrids were inconsistent across environments indicated that suitable to specific environment. Based on AMMI stability value and mean ranking of GGE biplot indicated that G18 (BH 546) had high grain yield (7.16 t ha-1) and more stable across tested environments. This study identified maize hybrids with high grain yield and stable across environments that need to be further validated for possible new maize variety release and or the newly released hybrid is used for possible commercial production.

1. Genotype x Environment Interaction and Grain Yield Stability of Maize (Zea mays L.) Hybrids Tested in Multi-Environment Trials
Genotype x Environment Interaction and Grain Yield
Stability of Maize (Zea mays L.) Hybrids Tested in Multi-
Environment Trials
Belay Garoma1*, Sintayehu Alamirew2, Bitew Tilahun3
1,3Bako National Maize Research center; Department-Cereal, Ethiopian Institute of Agricultural Research (EIAR), P.O.
Box 03, Bako, Ethiopia
2Jimma university, Faculty of Agriculture and Veterinary medicine, P.O. Box 307, Jimma, Ethiopia
Testing of genotypes in multi-environments is an important to estimate genotype x environment
interaction (GEI) and identify stable genotypes with superior performance. The study was to
evaluate different maize hybrids at multi-environments as well as to identify high yielding and
stable maize hybrids. Twenty maize hybrids were tested across eight environments in a
randomized complete block design in the 2015 cropping season. Combined analysis of variance
and AMMI analysis showed that genotype, environment and GEI effect were highly significant (p
< 0. 01) for grain yield. Genotype, environment and GEI explained 6.62, 84.87 and 4.50% of the
total experimental variations, indicating the importance of environment for variations in grain
yield. Mean grain yield of tested hybrids ranged from 4.98 t ha-1
in G2 to 7.51 t ha-1
in G16. As
evident from significant GEI, performances of the hybrids were inconsistent across environments
indicated that suitable to specific environment. Based on AMMI stability value and mean ranking
of GGE biplot indicated that G18 (BH 546) had high grain yield (7.16 t ha-1
) and more stable across
tested environments. This study identified maize hybrids with high grain yield and stable across
environments that need to be further validated for possible new maize variety release and or the
newly released hybrid is used for possible commercial production.
Keywords: Genotype x environment Interaction, stability, grain yield, GGE biplot, hybrid, maize
INTRODUCTION
The concept of genotype by environment interaction might
be mimicked when plants adapting to the new or
fluctuating of environment. The other idea of genotype x
environment interaction (GEI) could be considered as the
pleiotropic effect of particular variants across
environments (Malosetti et al., 2013; Juenger, 2013). The
term genotype refers to the genetic makeup of an
organism while environment refers to biophysical factors
that have an effect on the growth and development of crop.
Genotype x environment interaction refers to two or more
genotype performs differently in different environments
(Kim et al., 2014).
Maize growers require varieties that are reliable and stable
across environments as well as have high yield potential
under favorable conditions. However, the response of
different genotypes under different environments can be
varied. This might be due to fluctuation of rainfall during
cropping season, emerging and remerging disease as well
insects) and variation in soil status, drought and others
stresses (Farshadfar et al., 2012; Ngaboyisonga et al.,
2016). Consequently, a variety which performs well in one
environment during one season may not perform in
different testing sites. This showed that Genotype ×
environment interaction impede on superior genotypes
across environment (Higginson.and Reader 2009). In
such situation, it is difficult for breeder to select high yield
stable genotype across tested environments (Dai et al.,
2012). Thus, plant breeders are stimulated to test multi-
environment trials (METs) and select superior as well as
stable maize hybrids that show high grain yield
performance across environments.
*Corresponding Author: Belay Garoma, Bako National
Maize Research center; Department-Cereal, Ethiopian
Institute of Agricultural Research (EIAR), P.O. Box 03,
Bako, Ethiopia. Email: belaygaroma@gmail.com
International Journal of Plant Breeding and Crop Science
Vol. 7(2), pp. 763-770, August, 2020. © www.premierpublishers.org, ISSN: 2167-0449
Research Article

2. Genotype x Environment Interaction and Grain Yield Stability of Maize (Zea mays L.) Hybrids Tested in Multi-Environment Trials
Garoma et al. 764
Several biometrical methods reported to analysis
Genotype by environmental interaction (GEI) and stability
in multi- environment trials, METs. Among these
biometrical, Additive Main Effects and Multiplicative
Interaction (AMMI) and Genotype and Genotype by
Environment Interaction (GGE) Biplot are the most
common statistical tools used to analysis METs data to
reveal pattern of Genotype × environment interaction
(Gauch and Zobel, 1997; Yan et al., 2000). To compute
METs, AMMI model analysis the variance (ANOVA) for
genotype, environment and their interaction as well as
decompose GEI into principal components. It also used to
determine stability of genotype across locations using
principal component axis. AMMI also an effective tool to
detect the GEI patterns graphically. However,
interpretation of output from principal components (PCA)
is likely difficult for genotype targeting environment. Thus,
GGE biplot is suggested and superior to the AMMI to
visualize GEI graphically at mega-environments (Yan et
al., 2007). Furthermore, GGE biplot analysis is efficient to
identify: the best performing genotype in the given tested
environment, the discriminating power of environment and
it rank the cultivars based on mean yield and stability of
cultivars (Yan and Tinker, 2006). Moreover, it helps to
assess the relationship between environments and re-
planning the targeted environments to test cultivars in
plant breeding program (Fan et al., 2007; Dehghani et al.,
2009). Therefore, the objective of this study was to
evaluate different maize genotypes at multi-environments,
to identify high yielding as well as stable maize hybrids and
discriminating environments using GGE-biplot analysis.
MATERIALS AND METHODS
The experiments were conducted at eight environments
which represent a mid-altitude sub-humid agro-ecology of
Ethiopia. Geographical location of altitude ranges from
1000 to 1800 m a.s.l. and receive about 1200-1500 mm
rainfall annually. The testing locations were namely; Bako,
Hawasa, Jimma, Asosa, Pawe, Arsi-negale, Fenote-
Selam and Tepi in 2015/16 main cropping season of
Ethiopia. A total of twenty maize hybrids were used in the
study and the experiments arranged in randomized
complete block design with three replications and each
genotype was planted on a 5.1 m long consisted two rows
with spacing of 75 cm and 30 cm between rows and plant,
respectively. Agronomic practices including fertilizer
application (DAP and UREA), weed management as well
herbicide application and others were kept constant or
applied as per research recommendation.
Data Collection
All important agronomic traits’ data were collected but ears
weight (unshelled grain weights) were recorded on plot
basis to estimate grain yield adjusted to 80 % shelling
percentage and 12.5 % grain moisture content and later
on converted in t ha-1 for this study.
Statistical analysis
Combined analysis of variance was performed using
Genstat software (version 13) to test the significant of
Genotype, Environment and Genotype by Environment
interaction prior to subsequent analysis.
The combined ANOVA model:
Yijk = µ + Gi + Ej + Bk + GEij + Ɛijk
Where, Yijk is the observed mean of the ith genotype (Gi)
in the environment (Ej), in the kth block (Bk); µ is the overall
mean, Gi is the effect of ith genotype, Ej is the effect of jth
environment; Bk is block effect of the ith genotype in the jth
environment; GEij is the interaction effect of the ith
genotype as well jth environment and Ɛijk is the error term.
If GIE significant from the combined ANOVA, it portioned
into principal components. i.e the genotype and
environment effects were portioned into the additive main
effect for genotype and environments, and non-additive
effect due to GIE (Multiplicative interaction). This means
that, AMMI model estimates the additive effects of
genotypes and environments whereas PCA estimates the
GEI.
According to (Gauch, 2006) AMMI model:
Where: (i = 1, 2……….22: j = 1…….8); Yij = The
performance of the ith genotype in the jth environment; =
The grand mean; Gi = Additive effect of the ith genotype
(genotype mean minus the grand mean); Kn = Eigen
value of the PCA axis n,; Ej = Additive effect of the jth
environment (environment mean deviation); Uni&Snj =
Scorer of genotype i and environment j for the PCA axis
n; Qij = Residual for the first n multiplicative components,
and; eij = error.
In this study, GGE biplot method was used to investigate
Genotype and Genotype x Environment interaction
analysis was conducted using GGE biplot software to
evaluate grain yield stability, identify superior genotypes
and to visualize pattern of environments graphically. Thus,
GGE biplot analyzed according to (Yan et al., 2000).
To see the yield stability analysis, the formula suggested
by (Purchase et al., 2000) was used:
Where, ASV = AMMI stability value, IPCA1 = interaction
principal component analysis 1, IPCA2 = interaction
principal component analysis 2, SSIPCA1 = sum of
squares of the interaction principal component one,
SSIPCA2 = sum of squares of the interaction principal
component two
 
22
2
1
2)1( sccoreIPCAscoreIPCA
SS
SS
ASV
IPCA
IPCA

ijij
n
njninjiij eQSUKEGy  1
)(

3. Genotype x Environment Interaction and Grain Yield Stability of Maize (Zea mays L.) Hybrids Tested in Multi-Environment Trials
Int. J. Plant Breed Crop Sci. 765
RESULTS AND DISCUSSION
Combined analysis of variance showed that mean squares
due to genotype, environment and Genotype x
environment interaction (GEI) revealed highly significant
(p<0.01) for grain yield (Table 1), indicated that variations
observed among the tested genotypes at individual as well
as across environments and possibility of selecting
favored hybrids. In addition, significant difference
observed (P < 0.05) for all IPCA axes, but IPCA1 and
IPCA2 taken according to significant values. Based on
IPCA1 scores, some maize genotypes had relatively high
to lower positive interaction and others negative interaction
with the environments, indicating that the presence of GEI.
Genotype, environment and Genotype x environment
interaction explained 6.62%, 84.84% and 4.50% the total
variation. This showed that environments were divers and
affects the grain yield potential of hybrids. This might be
due to fluctuation of rainfall pattern during cropping
season, different soil statues and other biotic stress
(Farshadfar et al., 2012). This finding also agreed with
(Fan et al., 2007; Mitrovic et al., 2012) reported in METs of
maize for which environment contributed about 83.4 % of
total variation in grain yield whereas Genotype and
Genotype x environment interaction contributed only 1.5%
and 11%, respectively. Further explanation, the biggest
total variation was attributed by environment for the
maximum variation in grain yield performance in different
environments while relatively smaller variation contributed
by genotype and Genotype x environment interaction. The
present study showed that the magnitude of GEI sum of
square was double than genotype mean, indicating the
difference responses of genotype across environments.
Mean grain yield of 20 maize genotypes for eight
environments presented in Table 2, The highest grain yield
in t ha-1 was recorded for the genotype G16 (7.51) whereas
the genotype G2 showed the lowest yield (4.98).However,
the performances of hybrids were inconsistent across
environments, indicating that their unstable performance
of genotypes or crossing over interaction, this phenomena
showed that the presence of Genotype x environment
interaction (Table 2). In addition to this, the responses of
genotypes were fluctuated in their yield potential with
changing of environments, suggesting that some genotype
suitable to specific environment. Similar finding reported
that the ranking maize hybrids across environments
(Mebratu et al., 2019). This could be impede on selecting
stable superior performance maize hybrids across
environments
Table 1: AMMI analysis of variance for grain yield (t /ha)
of 20 maize hybrids tested at eight environments.
Source df SS MS F SS%
Explained
Treatment 159 2196.2 13.81 9.44 **
Genotypes 19 279.3 14.70 10.05
**
6.62
Environments 7 1319.0 188.43 7.41 ** 84.84
Genotype x
Env.
133 597.8 4.50 3.07 ** 4.50
IPCA1 25 219.2 8.77 5.99 **
IPCA2 23 137.0 5.96 4.07 **
Residual 85 241.7 2.84 1.94 **
Error 304 444.8 1.46 -
Write the means of appreciations.
Table 2: Mean of grain yield (t ha-1) of maize hybrids tested at eight environments.
Genotype Bk Hw Jm As Pw AN Fs Tp Mean ASV
G1 10.06 5.13 4.63 3.86 8.10 5.20 3.49 7.55 6.00 0.17
G2 4.61 4.92 4.19 3.95 5.28 5.15 3.83 7.88 4.98 0.26
G3 8.91 8.36 4.47 4.39 6.45 6.48 5.29 8.10 6.56 0.23
G4 7.94 5.70 4.45 4.48 6.54 5.67 2.73 8.34 5.73 0.21
G5 7.55 8.17 3.97 4.66 6.32 5.57 6.75 8.48 6.43 0.07
G6 8.09 6.69 3.58 4.71 6.76 5.49 4.86 9.48 6.21 0.52
G7 9.84 6.44 4.26 3.96 7.48 5.91 2.20 6.29 5.80 0.11
G8 8.74 4.31 4.58 4.08 7.34 4.97 3.19 8.74 5.74 0.98
G9 8.91 7.24 4.50 4.78 5.06 5.53 3.13 6.84 5.75 0.22
G10 10.18 6.62 4.77 3.82 6.62 5.60 6.01 8.20 6.47 0.66
G11 6.99 5.51 3.80 4.08 7.57 3.60 2.52 8.42 5.31 0.27
G12 7.35 7.27 4.69 4.56 5.92 6.04 4.41 6.39 5.83 0.14
G13 10.00 8.10 4.58 4.56 7.49 6.00 6.42 8.77 6.99 0.12
G14 5.86 11.75 4.84 4.66 7.02 6.28 5.18 9.65 6.90 0.06
G15 10.36 7.64 5.61 5.09 7.38 6.25 7.27 8.77 7.30 0.21
G16 11.50 7.98 4.35 5.40 8.25 5.78 5.24 11.56 7.51 0.77
G17 6.69 3.86 4.00 3.99 7.71 4.65 1.92 8.16 5.12 0.08
G18 11.20 8.96 5.12 5.06 5.90 5.47 5.43 10.10 7.16 0.03
G19 10.39 6.52 3.81 4.70 8.26 4.14 5.38 8.91 6.51 0.26
G20 10.15 6.09 4.95 4.89 7.57 3.75 5.20 7.79 6.30 0.02
Environments: Bk = Bako, Hw = Hawasa, Jm = Jima, As = Asosa, Pw = Pawe, AN = Arsi-Neggale, Fs = Fenote-Selam,
Tp = Tepi
Genotypes: G1 up to G17 were promising three way and single crosses/hybrids, G18 and G19 were recently released
maize hybrids (BH 546 and BH 547) and G20= BHQPY 545 as 3rd check

4. Genotype x Environment Interaction and Grain Yield Stability of Maize (Zea mays L.) Hybrids Tested in Multi-Environment Trials
Garoma et al. 766
ASV analysis for stability and AMMI biplot
AMMI stability value (ASV) is aid to select relatively stable
and high yielding genotypes. Genotypes would have high
mean grain yield than average performance and with lower
value of ASV is relatively stable (Purchase et al. 2000).
Accordingly, Genotype 18 (BH 546) had higher mean yield
over than average performance (grand mean) as well as
relatively small ASV value (0.03) and showed the best
stable genotype (table 2). On other hand, G16 was the
among the hybrids with the highest grain yield but not
stable due to high ASV.
AMMI biplot is to visualize the stability and adaptability of
genotypes across tested environments (Gauch and Zobel,
1997; Gauch, 2006). Moreover, the model also useful on
grouping similar performance genotypes and / or
environments and also provide some information about
GEI in order to identify the genotypes adapted to specific
environment. Accordingly, hybrids, (G16), (G18), (G14),
(G15), (G13) and (G19) were generally exhibited high yield
with high main effects showing positive IPCA1 score.
These genotypes adapted to E1, E2, E5 and E8. These,
environments considered as favorable and good selection
to identify the best performing genotypes. Particularly, the
environment, E1 has large positive IPCA1 score as well
discriminating power of environment to select the
response of genotypes. Similarly, E5 showed small
positive IPCA1 score with high mean value and had small
interaction effects indicating that most of genotypes
performed well in this location. (Ebdon and Gauch, 2002)
reported that the genotypes with IPCA1 scores close to
zero likely adapted to wide environment or less influenced
by the environments. On other hand, Genotypes had
negative IPCA scores showed below average yield.
Accordingly, G2 and G7 would have low performance and
adapted to poor environments; E3, E4, E6 and E7. In
contrast, G11 showed poor performance during cropping
season across tested environments (Fig. 1). Over all,
genotypes on the right side (I and IV quadrants) of the
midpoint of x- axis had higher yields than these of left hand
side whereas, genotypes lies on left sides showed low
yield performances.
Figure 1. AMMI biplot showing mean grain yield performance and adaptability of 20 maize genotypes across eight tested
environments., Bk(E1) = Bako, Hw(E2) = Hawasa, Jm = Jima(E3), As = Asosa(E4), Pw = Pawe(E5), AN = Arsi-Neggale
(E6), Fs = Fenote-Selam (E7), Tp = Tepi(E8)
Polygon View of the GGE- biplot
The GGE biplot graphically shows GEI of METs and visual
genotype to which environment and mega-environments
identification (Yan et al., 2000). The polygon view of the
GGE biplot (Fig. 2), displaying which genotype performed
well at where environments. According to Yan et al. (2007),
when different test environments occurred in different
sectors, it showed that they have different yielding ability
of genotypes for those sectors and thus, an indicating the
existence of Genotype x environment interaction. In these
vertexes, the genotype showed the one that give the
highest grain yield for each environment in which
genotypes lie. Accordingly, G16 was the best at Bako,
G13 was good at Tepi as well Fenote-sealm and G14 was
the best at Hawasa (Fig. 2), suggesting that these
genotype won to which environment lie. Genotypes
positioned on the polygon vertex might be have the longest

5. Genotype x Environment Interaction and Grain Yield Stability of Maize (Zea mays L.) Hybrids Tested in Multi-Environment Trials
Int. J. Plant Breed Crop Sci. 767
distance from biplot origin, supposed to the most
responsive. However, responsive hybrids either have the
best or poorest yield (Yan and Rajcan, 2002). In this study,
hybrids G16, G18, G15 and G14 had the highest yield
whereas G2, G11 and G17 had lowest grain yield because
no suiting environments in their vertex. In addition, no
location fell into sector with G2 and G11 vertex, implies
that it was the poorest genotypes in some or all of the
locations. Accordingly (Fig. 2), the eight locations fell into
three sectors. However, this should be re-proved using
environmental factors and repeated data across locations
and overs years.
.
Figure 2. Polygon View of the GGE- biplot based on genotype by environment grain yield data of 20 maize genotypes in
eight locations,: Bk = Bako, Hw = Hawasa, Jm = Jima, As = Asosa, Pw = Pawe, AN = Arsi-Neggale, Fs = Fenote-Selam,
Tp = Tepi
Identification of stable hybrids based on mean ranking
and their relationship with the environments
The stability of hybrids and their performance can be
evaluated by average environment coordination (AEC)
method (Yan, 2001). The vertical line separates the
genotypes with below mean yield from those with above
mean yield (Fig. 3). Based on the rank of genotypes; maize
hybrids with above mean yield of genotypes were lie from
G3 to G18 (right side) whereas genotypes with below-
average means indicated from G12 to G2 (Fig. 3). The
stability of the hybrids is determined by their projection
onto the middle horizontal line or stability of the genotypes
depends on their distance from the Average environment
coordinator (AEC) x-axis. Genotypes which are positioned
near to abscissa with short arrow are stable in the part of
GGE-biplot (Yan et al., 2007). Accordingly, G18 and G15
are stable, of all G18 (BH 546) had a good yield as well as
more stable across environments and this results agrees
with those obtained by Mitrovic et al. (2012). In addition, it
also showed using AMMI stability value (Table 2).
Whereas, G2 had the lowest mean grain yield, poor
performance during cropping season and unstable across
tested environments (Fig. 3). On other hand, G14 showed
the highest yield at Hw (Hawasa) location and adapted to
specific environment.

6. Genotype x Environment Interaction and Grain Yield Stability of Maize (Zea mays L.) Hybrids Tested in Multi-Environment Trials
Garoma et al. 768
Figure 3. Mean vs. stability” view of the GGE biplot of grain yield for 20 hybrids evaluated across the eight environments
Discriminating and relationship among test
environments
GGE biplot also view the discriminating and
representativeness ability of environments to identify an
environment that efficiently discernment the superior
genotype in the tested environments. A long
environmental vector from origin showed a high capacity
to discriminate genotype (Yan and Tinker, 2006). Among
tested environments, Bako and Hawasa were the most
discriminating environments that provided adequate
information on the performance of the hybrids. Further
explanation, Bako and Hawasa environments were
powerful for genotype evaluation and interesting sites to
identify superior genotypes. On other hand, Asosa
environment had short vector and falls close to the bi-plot
origin and the least discriminating environment (Fig. 4) and
it provided little information about the performance
difference of genotypes. Thus, difference of genotypes
may be not reliable and likely reflect noise effect. This may
be due to rainfall situation and others stresses during
cropping season at Asosa site. The relationship among
testing environments can be obtained by the environment-
vector view of GGE biplot. the angle between the vectors
of two environments estimates the association coefficient
between them (Yan 2002).i.e if the angles between
environments were less than 90, showed that high
associations amongst environments...In this study, Arsi-
negale showed strong positive correlation with Hawasa
site, Jima also showed positively associated to Bako
because they had the smallest angles (acute angle)
between the vectors of environments. According to
(Abakemal et al., 2016) reported on Kulumsa and Ambo
sites had small angles revealing strong positive correlation
among them over two years. This showed that similar
information about response of genotype can be obtained
in these environments (Yan and Tinker, 2006). In such
case, indirect selection of genotype for grain yield can be
applied. However, a very close association between
locations should be again validated using over years data
analysis later on re-planning to minimize the cost of maize
breeding program. The other tested environments vectors
formed a wide obtuse angle and right angle indicates poor
correlation between environments showed relatively large
Genotype x environment interaction.
-6 -4 -2 0 2 4
-4-2024 Mean vs. Stability
AXIS1 43.07 %
AXIS224.14%
G1
G10G11
G12
G13
G14
G15
G16G17
G18
G19
G2
G20
G3
G4
G5
G6
G7G8
G9
AN
As
Bk
FS
Hw
Jmpw
TP

7. Genotype x Environment Interaction and Grain Yield Stability of Maize (Zea mays L.) Hybrids Tested in Multi-Environment Trials
Int. J. Plant Breed Crop Sci. 769
Figure 4. Environment-vector view of the GGE-biplot showing the relationship among eight environments;
CONCLUSION
Multi-environment trials study showed that genotypes,
environments and genotype by environment interaction
were significant for grain yield. The genotypes therefore
response varied with respect to grain yield under test
different environments, indicated that the presence of
Genotype x environment interaction. Hybrid of G16 had
the highest mean grain yield (7.51 t ha-1) to be considered
the most performed hybrid. However, it was not stable over
locations. G18 had high yield than average yield and stable
across tested environments. G18 (BH 546) is a recently
released three way maize hybrid that suitable for mid-
altitude sub-humid agro-ecology of Ethiopia. Thus, farmers
should be used for further commercial production.
Out of eight locations, Bako and Hawasa sites were the
most suitable environments in discriminating maize
genotypes, selection of superior maize hybrids and being
a representative test environment during cropping
seasons. Some sites showed close association between
them and thus, should be again validated using over years
data analysis later on re-planning to minimize the cost of
maize breeding program
REFERENCES
AbakemalD, Shimelis H and Derere J (2016). Genotype-
by-environment interaction and yield stability of quality
protein maize hybrids developed from tropical-highland
adapted inbred lines. Eup, 209:757-769.
Dehghani H, Sabaghnia N, Magadha M (2009).
Interpretation of genotype by environment interaction
for late maize hybrids grain yield using a biplot method.
Turkish Journal of Agriculture and Forestry 33: 1-10.
Dia M, Wehner T, Hassell R, Price D, Boyhaa G, Olson S,
King S, Davis A, Tolla E, Bernier J and Juarez B,
(2012). Mega-environment identification for
watermelon yield testing in the US. In Cucurbitaceae.
Proceedings of the Xth EUCARPIA, (pp. 385-390).
University of Cukurova, Ziraat Fakultesi
Ebdon JS and Gauch HG (2002). Additive main effect and
multiplicative interaction analysis of national turfgrass
performance trials: Interpretation of genotype x
environment interaction. Crop Sci. 42: 489-496.
Fan XM, Kang MS, Chen H, Zhang Y, Tan J, XU C (2007).
Yield stability of maize hybrids evaluated in multi-
environment trials in Yunnan, China. Agronomy Journal
99: 220-228.

8. Genotype x Environment Interaction and Grain Yield Stability of Maize (Zea mays L.) Hybrids Tested in Multi-Environment Trials
Garoma et al. 770
Farshadfar ER, Mohammadi M, Aghaee, Vaisi Z (2012).
GGE biplot analysis of genotype x environment
interaction in wheat barley disomic addition lines. Aust.
J. Crop Sci. 6: 1074-1079.
Gauch HG and Zobel RW (1997). Identifying mega-
environment and targeting genotypes. Crop Sci.
37:311-326.
Gauch HG (2006). Statistical analysis of yield trials by
AMMI and GGE. Crop Sci. 46: 1488-1500.
Higginson AD.and Reader T (2009). Environmental
heterogeneity, genotype-by-environment interactions
and the reliability of sexual traits as indicators of mate
quality. Proc. R. Soc. B Biol. Sci. 276, 1153–1159
Juenger TE (2013). Natural variation and genetic
constraints on drought tolerance. Curr. Opin. Plant Biol.
16: 274–281.
Kim J, Taeheon L, Hyun-Jeong L, Heebal K (2014).
Genotype-environment interactions for quantitative
traits in Korea Associated Resource (KARE) cohorts.
Genetics 15: 18.
Malosetti M, Ribaul JM, van Eeuwijk FA (2013). The
statistical analysis of multi-environment data: modeling
genotype-by-environment interaction and its genetic
basis. Frontiers in Physiology, 4: 44.
Mebratu A., Wegary D, MohammedW, Teklewold A and
Tarekegne A, 2019. Genotype× Environment
Interaction of Quality Protein Maize Hybrids under
Contrasting Management Conditions in Eastern and
Southern Africa. Crop Science, 59(4), pp.1576-1589.
Mitrovićc B, Dušan S, Sanja T, Milisav S, Mile I, Goran B,
Miloš R (2012). Evaluation of experimental maize
hybrids tested in multi-location trials using AMMI and
GGE biplot analyses. Turkish Journal of Field Crops 17:
35-40.
Ngaboyisonga C, Nyombayire M, Gafishi ,F Nizeyimana A.
Uwera A and T. Ndayishimiye. (2016). Adaptability and
genotype by environment interaction of maize
commercial hybrid varieties from east african seed
companies in rwandan environments. GJAR 4, 32-40.
Purchase JL, Hatting H, Van Deventer CS (2000).
Genotype x environment interaction of winter wheat (T.
aestivum) in South Africa: Stability analysis of yield
performance. S Afr J Plant Soil 17: 101-107.
Yan WL, Hunt WA, Sheng Q, Szlavnics Z (2000). Cultivar
evaluation and mega-environment investigation based
on the GGE biplot. Crop Sci. 40: 597-605.
Yan W (2001). GGE biplot Windows application for
graphical analysis of multi environment trial data and
other types of two-way data. Agronomy Journal 93:
1111-1118.
Yan W and Rajcan J (2002). Biplot analysis of test sites
and trait relations of soybean in Ontario. Crop Sc 42:
11-20.
Yan W (2002). Singular-value partitioning in biplot analysis
of multi-environmental trial data. Agron J 94:990–996
Yan W and Tinker NA (2006). Biplot analysis of multi-
environment trial data: Principles and participatory plant
breeding: an example of demand applications.
Canadian Journal of Plant Science 86: 623-645.
Yan W, Kang MS, Woods MB (2007). GGE biplot vs. AMMI
analysis of genotype-by-environment data. Crop Sci.
47: 643-653.
Accepted 23 July 2020
Citation: Garoma B, Alamirew S, Tilahun B (2020).
Genotype x Environment Interaction and Grain Yield
Stability of Maize (Zea mays L.) Hybrids Tested in Multi-
Environment Trials. International Journal of Plant Breeding
and Crop Science, 7(2): 763-770.
Copyright: © 2020: Garoma et al. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.