International Research Journal of Plant Science (ISSN: 2141-5447) Vol. 2(10) pp. 317-322, October, 2011
Available online http://www.interesjournals.org/IRJPS
Copyright © 2011 International Research Journals
Full length Research Paper
Genotypes X environment interaction in bread wheat
(Triticum aestivum L.) cultivar development in Ethiopia
1*
Kemelew Muhe and 2Alemayehu Assefa
1*
Ghent University, Faculty of Science, Kwintensberg 54, Ghent 9000, Belgium, and College of Agriculture and
Veterinary Medicine, Wollo University, P.O Box 1145, Dessie, Ethiopia.
2
Ethiopian Institute of Agricultural Research, Holetta Agricultural Research Center, P.O.Box 2003, Addis Ababa,
Ethiopia.
Accepted 27 September, 2011
Multi-environment bread wheat yield trial comprised of 18 cultivars along with the standard check
HAR1899 was conducted at three locations, Inewary, Molale and Mehalmeda during main seasons, July
to December in 2001-2004. The objective of the experiment was to identify stable and high yielding
bread wheat cultivar suitable for the rainfall wheat production system in Ethiopia. Analysis of variance
using grain yield data from twelve environments made of three locations and four years revealed that
both the main and interaction components were significant at (P < 0. 01%),suggesting that no matter
how productive cultivars may be, selection of cultivars based on grain yield is not reliable if the cultivar
x environment interaction is statistically significant. Additive main effect and multiplicative interaction
analysis of grain yield combined over ten environments showed that some of the tested bread wheat
cultivars were most stable and high yielding, some were less stable and high yielding and one cultivars
was stable and low yielding cultivars. The result demonstrated that application of Additive main effect
and multiplicative interaction analysis is important in handling the cultivar x environment interaction
component and developing specific and widely adapted wheat varieties.
Keywords: Genotypes X, bread wheat, Ethiopia.
INTRODUCTION
Plant breeders have long been aware of the various
implications of cultivar x environment interaction/ GEI/ in
varietal development endeavors. Given the diversity of
wheat agro-ecologies in Ethiopia and elsewhere in the
world, differential responses of genotypes to the growing
environments is inevitable. Stemming from the differential
responses of genotypes to the growing environments,
GEI is ubiquitous and complicating wheat varietal
development, seed multiplications and production. As the
magnitude of a significant GEI interaction increases, the
usefulness and reliability of the main effects
correspondingly decrease. GEI reduces the correlation
between phenotypic and genotypic values, increasing the
difficulty in identifying truly superior genotypes across
environments, especially in the presence of crossover
GEI. Knowledge on the patterns and nature of GEI is
*Corresponding E-mail: kemelewmuhe2001@gmail.com
essential to design strategy leading for progressive wheat
breeding program. If the goal of breeding programs is the
development of cultivars suitable for possible
recommendation domains, information on GEI should be
handled properly (Bridges, 1989; Shafii and Price, 1998).
In a barley trial, estimates of environmental contribution
to yield were 10 to 30% and those of genetic contribution
were 30 to 60%; GEI accounted for the remaining 25 to
45% of yield gain (Simmonds, 1981). Thus, detection and
quantification of GEI is important to develop specifically
or widely adapted cultivars. Stable performance in yield
and quality traits across a wide ranges of growing
conditions is desirable for management, marketing and
profit (Gutierrez et al., 1994). Several methods have been
developed to analyze GEI (Kang and Gauch, 1996;
Piepho, 1998). The regression approach (Finlay and
Wilkinson, 1963; Eberhart and Russell, 1966) are among
others. The additive main effects and multiplicative
interaction (AMMI) model (Gauch and Zobel, 1996) has
received attention in dealing with GEI since recently.
318 Int. Res. J. Plant Sci.
Ethiopian wheat producing areas have diverse agroecologies, that inevitably bringing about differential
performance of commercial wheat cultivars and
landraces. Seasonal and spatial wheat yield fluctuation
due to climate and soil variability is a common
phenomenon in Ethiopia. Thus, GEI complicates wheat
breeding and entails uncertainties in wheat production,
thereby jeopardize the food security endeavors of millions
of Ethiopian wheat farmers. This would be more
pronounced in the foreseen climate changes. In this
paper, effort was made to apply the additive main effects
and multiplicative interaction (AMMI) in analyzing multienvironment wheat variety yield trial to facilitate the
identification of high yielding and stable wheat cultivars in
Ethiopia.
Statistical data analyses
Analyses
of
variance
combined
over
twelve
environments, made up of four years x three locations
was done using general mixed linear model assuming
that year and replications had random effects, whereas
the tested wheat cultivars and location were assumed to
have fixed effects. Statistical software SAS V8.12 was
used for the analysis of variance. Additive main effect
and multiplicative interaction/MMI/ model was used
partition the cultivars x environment interaction sum
square and stability of the tested wheat cultivars analyses
was done using AMMI model in AGROBASE99. Stratified
ranking of bread wheat cultivars was done using AMM
adjusted and normal ANOVA mean yield recorded in
each locations and yeas using the procedure (Fox et al.,
1990). The AMMI model had the following equation:
MATERIALS AND METHODS
Study areas
Inewary is located at an altitude of 2600 m.a.sl with the
slope about 2% and the soil is pellic vertisol with total
clay content of 75-78%, which classifies the texture to be
heavy clay. The soil at Inewary is characterized by
slightly acid (pH = 6.41), low organic matter (1.18%),
Phosphorus (7.25 ppm), nitrogen (0.17%) and low C: N
(6.4) ratio. The plateau Molale testing site is located at an
altitude of 3000 m.a.s.l. and slops of 1.5%. The soil at the
site is pellic vertisol with a clay content of 65-73%. The
pH is slightly acid (6.11), and with very low organic matter
(1.73%) and low phosphorous 8.93 PPM. The total
nitrogen status of the soil is low (0.16%) with a narrow C:
N ratio (6.41). Mehalmeda site is moderately plateau,
with an elevation of 3075 m.a.s.l. The soils are cambisols
and the clay content is 57-65%. The pH is slightly acid
(6.28) with relatively low organic matter (2.3%) and better
phosphorous supply (49.81 PPM). The total nitrogen
status of the soil is low (0.17%) with narrow C:N ratio
(8.08).
Experimental designs
In this study,18 superior bread wheat cultivars were
evaluated in bread wheat yield trial at three locations,
Inewary, Molale and Mehalmeda in four years (20012004). The tested bread wheat cultivars and the then
recent standard commercial cultivar, HAR1899 were
arranged in randomized complete block design with three
replications. Each plot contained six rows, each being 2.5
m long and an interrow spacing of 20 cm, made the plot
size of 3 m2. Fertilizer was applied uniformly to all
experimental plot at the rate of 60/60 kg ha-1 N/P2O5.
The seed rate was 150kg ha-1. Data on days to heading
and maturity, plant height and grain yield were recorded
on plot basis.
is the yield of cultivars ( ) in environment ( ) ; is the
grand mean;
is the cultivar mean deviation (the
cultivar mean minus the grand mean);
is the
environment mean deviation (the environment mean
th
minus the grand mean);
is the eigenvalue of n
principal components analysis (IPCA) axis n;
is the
cultivar eigenvector value for IPCA axis n;
is the
environment eigenvector value for IPCA axis n ;
is
the residual and
is the error.
RESULTS
Analysis of variance
Analysis of variance combined over three locations and
four years showed that the main effects of cultivar (G),
year (Y), locations (L) and all possible interaction effects
of these factors were significant (P<0. 01%) for grain
yield (Table 1). The result disclosed statistically
significant (P<0.0001) cultivar x environment interaction
components, suggesting selection based on their
respective main effects would not be valid and reliable.
Thus, partitioning the relative contribution of each wheat
cultivars to the total cultivar x environment interaction
effect was important.
AMMI analysis
Additive main effects and multiplicative interaction
analysis based on grain yield data showed statistically
significant difference (P < 0.0001) for cultivar and
environments main effects, and cultivar x environment
interaction. The postdictive assessment of AMMI model
resulted in four significant (p<0.02%) interaction principal
Muhe and Assefa 319
Table 1. Analysis of variance combined over 12 environments, four years (2001-2004) and three locations (Inewary, Molale and Mehalmeda), Ethiopia
Sources of variation
Block
Cultivar(G)
Location (L)
Year(Y)
L* G
Y*G
L*Y
L*Y*G
Error
Total
2
R
Degree of freedom
2
18
2
3
36
54
6
108
454
683
Sum square
1253211.4
62963064.4
38832824.8
180262861.2
29632821.6
21525058.1
129763101.9
39785953.1
106730142.1
610749038.6
Mean square
626605.700
3497948.00
19416412.4
60087620.4
823133.900
398612.200
21627183.60
368388.5000
235088.4000
0.83
Coefficient of
variation%
16.59
F-value
2.67
14.88
82.59
255.60
3.50
1.70
92.00
1.57
Pr > F
0.0707
<.0001
<.0001
<.0001
<.0001
<.0023
<.0001
<.0009
-1
Table 2. Additive Main effects and Multiplicative Interaction (AMMI) for grain yield kg ha combined over four years (2001-2004) and three
locations (Inewary, Molale and Mehalmeda), Ethiopia
Source
Total
Environments(Env.)
Reps within Env.
Cultivars
Cultivars X Env.
IPCA 1
IPCA 2
IPCA 3
IPCA 4
Residual
Grand mean = 3066.2
Degree of freedom
Sum square
569
429345538.3
9
265567233.5
20
57312.30
18
63168269.7
162
59752927.4
26
30147402.6
24
11417246.3
22
6213130.6
20
3927181.5
408
85214703.200
C.V. = 10.98%;
Mean square
24426771.1
739833.4
5927871.5
476969.4
1159515.5
475718.6
282415.0
196359.1
208859.567
R2 = 0.91
F-value
Pr> F
33.02
0.0000
12.43
2.28
10.23
4.20
2.49
1.73
0.0000
0.0000
0.0000
0.0000
0.0003
0.0268
Genetic variance for entries = 104683.436, with a std. error of 37016.688 and genetic variance for entries x env. = 85170.822, with a std.
error of 13864.705
component axis explained about 86.53% of the
interaction sum square in the data set, and relegating the
remaining sum squares to residual component. The first
interaction principal component axis account for 50.45%,
and the next three IPAC axis explained 19.11, 10.40 and
6.57%, respectively. The model was adequate enough to
explain the total cultivar x interaction component.
Biploting was done using each environment and cultivar
-1
main effects (mean grain yield tha ) on an x-axis and first
Interaction principal component axes (IPCA1) scores as
y-axis. In the biplot, the broken vertical line passing
through the center of the biplot was the grand mean
(3066 2kg ha-1) of the experiment, and the solid
horizontal line passed through at the IPCA1 axis score =
0 (Figure 1). In the biplot, nine bread wheat cultivars (c,
e, f, g, j , n, o, q and r) and five environments (A, B, D, J
and G) located at the right side of the grand mean were
considered as high yielding cultivars and environments
while their corresponding low yielding counterparts were
located at the left side of the grand mean (Figure 1). The
relative contribution of wheat cultivars to cultivars x
environment interaction sum square were represented by
the magnitude of the respective IPCA score (Table 3),
which in turn determined their position in the biplot. Bread
wheat cultivars (b, f, m, n, q and r) located far from the
IPCA axis contributed more to the *cultivar environment
interaction sum square than cultivars (a, c, e, and g) that
were located either on or closer to IPCA1 axis = 0 (Figure
1). Bread wheat cultivars (a, c, e and g) had IPCA score
value closer to zero, and were classified as highly stable
whereas the IPCA scores of cultivars (f, j, k, o, p and r)
were moderately large, and these group of bread wheat
cultivars could be classified as less stable. On the other
hand, bread wheat cultivars (b, c, d, h, m, n, and q) had
large IPCA score and unstable. Stability analysis of bread
wheat cultivars produced three categories of responses:
(1) most stable and high yielding cultivars, HAR2975,
HAR2941 and HAR3076, (2) less stable and high
320 Int. Res. J. Plant Sci.
Figure 1. Biplot with abscissa (X-axis) plotting means from 2186.06 to 4270.97 and with
ordinate (Y-axis) plotting IPCA1 from -36.841 to 32.476. Wheat Cultivars plotted as a, b ,
c, ... and environments as A, B, C, as cross-referenced in the IPCA1 axis scores shown.
Note lower cases represents wheat cultivars coded in Table 2 and environments
designated by capital letter ‘A’, ‘B’, ‘C’ and ’D’ were data from same location, Inewary in
2001,2002,2003 and 2004; ‘E’, ‘F’,’G’ were data from Molale in 2001,2002, and 2004
whereas ‘H’ ‘I’,’J’, were data from Mehalmeda in 2001,2002, and 2004 cropping season
Table 3. Mean grain yield (kg/ha) and other agronomic traits of the 18 bread wheat Cultivars tested at three locations (Inewary,
Molale and Mehalmeda) for four years (2001-2004), Ethiopia
Cultivars
names
HAR2448
HAR3009
HAR2975
HAR2939
HAR3076
HAR2481
HAR2941
HAR2938
HAR3004
HAR3008
HAR2932
HAR1407
HAR3016
HAR2923
HAR3030
HAR3010
HAR3080
HAR1899
Mean
Standard
error
LSD 0.05
Cultivar
code
a
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
Cultivar IPCA 1
score
2.9382
-33.3423
-0.3012
14.9911
0.5719
12.3335
0.7898
-16.6205
10.0244
11.3338
3.4943
-10.6109
-18.6018
15.2985
-5.4785
-5.2459
14.9461
11.0879
Grain yield
kg/ha
2883.47
2525.25
3373.84
2887.74
3239.81
3312.71
3221.20
2940.44
2925.88
3607.14
2890.42
2949.05
2295.35
3357.09
3155.30
2641.90
3430.43
3463.35
2976.4
108.02
Days to
heading
84
87
80
79
76
75
85
79
87
83
77
88
84
85
86
86
87
80
79.45
0.3
Days to
maturity
158
160
149
150
144
145
151
152
158
152
149
157
150
152
150
157
156
149
145.34
1.4
Plant height
'cm'
77.60
76.60
76.40
79.00
77.10
77.30
74.50
79.00
82.30
70.80
77.50
74.70
70.50
78.10
68.30
68.10
83.70
74.50
72.85
0.90
211.720
0.588
2.744
1.764
Muhe and Assefa 321
Figure 2. Comparison of analysis of variance and AMMI on the ranking of two superior bread
-1
wheat cultivars in the first top three ranks based on grain yield kg ha recorded over ten
environments
yielding cultivars, HAR3008, HAR3080, HAR3030,
HAR2923, HAR1899 and HAR2481, and (3) most stable
and low yielding cultivar, HAR2481 ( Figure 1). Stratified
ranking of wheat cultivars based on AMMI adjusted and
unadjusted mean grain yield data from ten environments
showed that the use of AMMI brought considerable effect
on cultivars ranking. Bread Wheat cultivar, HAR3008
appeared in the top three ranks in three environments
based on mean grain yield estimated in analysis of
variance while it appeared in the top three ranks in nine
environments when AMMI was used for grain yield
estimation (Figure 2). The commercial standard check
cultivar, HAR1899 took the first three top ranks in six and
eight environments when grain yield data were analyzed
using analysis of variance and AMMI, respectively
(Figure 2). The remaining tested cultivars didn’t appear in
the top three ranks in any of the ten environments. As
the magnitude of a significant interaction between two
factors increases, the usefulness and reliability of the
main effects correspondingly decrease. GEI reduces the
correlation between phenotypic and genotypic values,
increasing the difficulty in identifying truly superior
genotypes across environments, especially in the
presence of crossover GEI.
DISCUSSION
Genotype x environment interactions (GEI) is one of the
major factors complicating plant breeding endeavors,
development and decelerating the breeding progress. In
this study, combined analysis of variance over three
locations and four years disclosed statistically significant
main effects of cultivar, year and locations and cultivar x
environment interactions components, suggesting that
the data deviate from the additivity assumption of
analysis of variances. Thus, no matter how productive
cultivars may be, selection based on mean grain yield
doesn’t necessarily lead for progress while the interaction
components are statistically significant. Genotype x
environment interactions are problematic for both the
agronomist and breeder because cultivar means
(averaged over environments) are unreliable for
predicting performance of a cultivar (Ebdon Gauch,
2002). Several methods have been developed to analyze
GEI (Kang and Gauch, 1996; Piepho, 1998). In the past
decade, the additive main effects and multiplicative
interaction (AMMI) model (Gauch and Zobel, 1996) has
received attention in dealing with Genotype x
environment interactions. Quantifying the contribution of
each bread wheat cultivars to cultivar x environment
interaction sum squares explained is important to know
the stability of cultivars and to determine which cultivar do
well in which environments. In the biplot, nine high
yielding bread wheat cultivars ( labeled as lower cases)
have been located at the right side of the grand mean
while low yielding counterparts are positioned at the left
side of the grand mean (Figure 1). Regardless of the sign
of IPCA score, bread wheat cultivars having high IPCA
score would be located far from the IPCA axis = 0, and
contribute more to the cultivar x environment interaction
sum square than cultivars with zero or very low IPCA
score. The latter group of cultivars contribute none or less
to cultivar x environment interaction sum square, and
positioned either on or very closer to IPCA axis = 0
(Figure 1). These group of bread wheat cultivars are
classified as most stable whereas cultivars which have
high IPCA score are classified unstable. Stability analysis
identified three most stable and high yielding cultivars,
HAR2975, HAR2941 and HAR3076, six less stable and
high yielding cultivars, HAR3008, HAR3080, HAR3030,
HAR2923, HAR1899 and HAR2481, and one most stable
and low yielding cultivar, HAR2481 ( Figure 1).
In the biplot, cultivars and environment (labeled as
322 Int. Res. J. Plant Sci.
upper cases) combinations with IPCA score of same sign
produced positive and desirable interaction effects,
whereas combinations of opposite sign had undesirable
or negative specific interaction. For instance, genotypes
HAR3009 and HAR3016 with IPCA score < 0 have a
positive interaction with the environments (A, B, C and
D) which have negative IPCA score while their
counterparts, such as HAR3008, HAR2923 and
HAR1899 produced a negative interactions with
environments (A, B, C and D) , but interacted positively
with environments (E, F, H,I ,J and G) (Figure 1). In other
words, cultivars and environments positioned below or
above IPCA axis = 0 interact positively and the
otherwise interact negatively. This finding has been
supported by (Thillainathan and Fernandez, 2001), who
reported that integrating biplot display enables cultivars to
be grouped based on similarity of performance across
diverse environments. Application of AMMI analysis in
multi-environment cultivar yield trial has been reported
(Ebdon and Gauch, 2002; Gauch and Zobel, 1989).
Stratified ranking of cultivars using AMMI and analysis of
variance ranked the same cultivar differently, indicating
how genotype x environment interaction affects the
performance of a given cultivar across environments
(Figure 2). Earlier studies indicate that cultivar ranks on
individual environment based on observed data can be
quite different from expected according to AMMI model
(Crossa et al, 1990; Gauch and Zoble , 1997; Ebdon and
Gauch ,2002).
CONCLUSION
Knowledge on the nature, pattern and causes of cultivar x
environment interaction is vital in plant breeding, including
varietal development, parent selection. establish breeding
objectives, identify ideal test sites and formulate
recommendations domains that can optimize wheat
adaptation. The analysis of variance doesn’t give
adequate and reliable information to make decisions in
varietal selection program if the cultivar x environment
interaction is statistically significant. Thus, handling of
cultivar x environment interaction using AMMI deemed
necessary not only to account all confounding variances
and determine the specific and wide adaptation potential
of wheat cultivars but to streamline seed multiplication
and production of the released cultivars. The superiority
of AMMI over joint regression for modelling adaptive
responses has already been reported for cereal and
forage crops as well as for several grain crops worldwide.
However, the application of AMMI would be more
powerful if soil and climatic variables should be included
to know which abiotic factors contributed more to the
genotype x environment interaction.
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