
Research Article


GenotypeEnvironment Interaction and Stability Analysis for Grain Yield of Maize (Zea mays L.) in Ethiopia


Solomon Admassu,
Mandefro Nigussie
and
Habtamu Zelleke


ABSTRACT

Fifteen maize genotypes were tested at nine different locations in 2005 under rainfed condition to determine stable maize genotypes for grain yield and determine genotypes with high yield and form homogenous grouping of environments and genotypes. The experiment was conducted using Randomized Complete Block Design with three replications. There was considerable variation among genotypes and environments for grain yield. Stability was estimated using the Additive Main Effects and Multiplicative Interactions (AMMI). Based on the stability analysis, genotypes 30H83, BH540, Ambo Synth1, AMH800 and BHQP543 were found to be stable for grain yield. The first two Interaction Principal Component axis (IPCA1 and IPCA2) were significant (p<0.01) and cumulatively contributed 70.27% of the total genotype by environment interaction. The coefficient of determination (R^{2}) for genotypes 30H83 was as high as 0.92, confirming its high predictability to stability. Among the genotypes, the highest grain yield was obtained from genotype 30H83 and BH541 (8.98 and 8.05 t ha^{1}) across environments. Clustering of AMMIestimate values grouped genotypes in to four clusters and the environment in to three clusters. Environment Goffa was unique as it is grouped differently from all other environments. 




INTRODUCTION
Information about phenotypic stability is useful for the selection of crop varieties and breeding programs. Plant breeders invariably encounter GenotypexEnvironment Interactions (GEI) when testing varieties across a number of environments. Depending up on the magnitude of the interactions or the differential genotypic responses to environments, the varietal ranking can differ greatly across environments (Kaya et al., 2002). The phenotypic performance of a genotype is not necessarily the same under diverse agroecological conditions. The concept of stability has been defined in several ways and several biometrical methods including univariate and multivariate ones have been developed to assess stability (Crossa, 1990; Ngeve and Bouwkamp, 1993; Sneller et al., 1997; Scapim et al., 2000). A combined analysis of variance can quantify the interactions and describe the main effects. However, analysis of variance is uninformative for explaining GEI. Other statistical models for describing GEI such as the Additive Main Effects and Multiplicative Interaction (AMMI) model are useful for understanding GEI.
The AMMI model is a hybrid that involves both additive and multiplicative components of the twoway data structure. AMMI biplot analysis is considered to be an effective tool to diagnose GEI patterns graphically. In AMMI, the additive portion is separated from interaction by Analysis of Variance (ANOVA). Then the Principal Component Analysis (PCA), which provides a multiplicative model, is applied to analyze the interaction effect from the additive ANOVA model. The biplot display of PCA scores plotted against each other provides visual inspection and interpretation of the GEI components. Integrating biplot display and genotypic stability statistics enables genotypes to be grouped based on similarity of performance across diverse environments (Thillainathan and Fernandez, 2001).
The AMMI model combines the analysis of variance for the genotype and environment main effects with principal components analysis of the genotype environment interaction (Kaya et al., 2002). The results can be graphed in a useful biplot that shows both main and interaction effects for both genotypes and environments (Annicchiarico, 2002). AMMI combines Analysis of Variance (ANOVA) into a single model with additive and multiplicative parameters.
AMMI is ordinarily the model of first choice when main effects and interaction effects are both important, which is a case in most yield trials (Mandel, 1971). If, for example, only main effects (additive structure) are present in the data, then the AMMI can be reduced to an ANOVA model. Whereas, if nonadditive structure is only present then the PCA model is reflected. AMMI results can be readily used to diagnose these and other sub cases (Gabriel, 1978). The pattern portion of GEI sum of squares captured by the regression approach (heterogeneity among regressions) can at best capture only the amount of GEI sum of squares modeled by the simplest AMMI model. Therefore, AMMI analysis can potentially glean more patterns from the GEI than the regression approach (Sneller et al., 1997). In addition, the AMMI analysis can be applied to data sets where regression analysis may be inappropriate.
The combination of analysis of variance and principal components analysis in the AMMI model, along with prediction assessment, is a valuable approach for understanding GEI and obtaining better yield estimates. Therefore, the objectives of this study were to estimate GenotypeEnvironment (GE) interactions, to determine stable maize genotypes for grain yield and to determine genotypes with high yields, depending on the differential genotypic responses to environments and to form homogenous grouping of environments and genotypes.
MATERIALS AND METHODS
Fifteen maize genotypes were evaluated at nine locations in 2005 crop under
rainfed condition. Randomized Complete Block Design (RCBD) with three replications
was used. Each plot had four rows of 5.1 m length with spacing of 75 cm between
rows and 30 cm between plants. Two seeds were planted per hill and then thinned
to one plant per hill to have a final plant density of about 44,444 plants ha^{1}.
To reduce border effects, data were recorded from the two central rows of each
plot. Other management practices were done as recommended for each location.
Fifteen maize genotypes of diverse origin were included in the study. The genotypes
include top crosses, single crosses, threeway crosses and synthetics (Table
1).
The locations where the experiment was conducted were different in soil type,
altitude and mean annual rainfall and considered as individual environment (Table
2). Several traits were assessed but only data for grain yield (t ha^{1},
at 12.5% grain moisture, estimated on the basis of two row plot) is reported
here.
Analysis of variance for each environment was done for grain yield and other
traits, using the SAS computer program (SAS, 2001). Bartlett`s test was used
to assess homogeneity of error variances prior to combine analysis over environments.
Genotypexenvironment interaction was quantified using the most common procedure;
i.e., pooled analysis of variance, which partitions the total variance into
its components (genotype, environment, genotypexenvironment interaction and
pooled error). Environments were considered as random factors while the effect
of genotypes was regarded as fixed. AMMI was used to test the stability of genotypes.
Table 1: 
Description of maize genotypes used for the study 

SF: Semi Flint; D: Dent; SD: SemiDent; F: Flint; TWC: ThreeWay
Cross; SC: Single Cross hybrid; TC: Top Cross; Syn: Synthetic; ESE: Ethiopian
Seed Enterprise; QPM: Quality Protein Maize; BNMR: Bako National Maize Research
and AMR: Ambo Maize Research 
Table 2: 
Description of the test locations 

*: Mean of 10 years 
RESULTS AND DISCUSSION
The AMMI analysis of variance for grain yield of 15 maize genotypes evaluated
at nine locations showed that the total SS 31.3% was attributed to environmental
effects for genotypic effects 24.2 and 22.8% was due to genotypexsignificant
MS of environment indicated that the environments were diverse, with large differences
among environmental means causing most of the variation in grain yield, which
is in harmony with the findings by Taye et al. (2000), Kaya et al.
(2002) and Alberts (2004). This indicated that the overwhelming influence that
environments have on the performance of maize genotypes. Sneller et al.
(1997), Tiruneh (1999), Taye et al. (2000) , Abush (2001) Kaya et
al. (2002) and Alberts (2004) also reported similar results whose all the
genotypes, environmental and genotypexenvironment effects were declared significant
in the ANOVA of AMMI. Mean grain yield of the maize genotypes varied among environments
ranging from 5.99 t ha^{1} for environment Jinka to 8.98 t ha^{1}
for environment Bako. The mean grain yield of the 15 genotypes ranged from 5.63
to 8.98 t ha^{1} and the highest grain yield was obtained from genotype
30H83 and BH541 (Table 4).
Table 3: 
Additive main effects and multiplicative interaction analysis
of variance for grain yield of genotypes across environments 

NS: NonSignificant, **: Significant at p<=0.01 level, respectively,
Grand mean = 7.25 t ha^{1}, R^{2} = 0.8279, CV = 13.5% 
Table 4: 
IPCA1, IPCA2, R^{2} and grain yield t ha^{1}
for 15 genotypes 

Results from AMMI analysis (Table 3) also showed that the
first Interaction Principal Component Axis (IPCA1) of the interaction captured
46.6% of the interaction sum of squares. Similarly, the second Interaction Principal
Component Axis (IPCA2) explained a further 23.67% of the GEI sum of squares.
The mean square for IPCA1 and IPCA2 were significant at p<=0.01 and cumulatively
contributed to 70.27% of the total GEI. The Ftest at p<=0.01 suggested that
the two principal component axes of the interaction were significant for the
model with 40°C of freedom. Hence, the AMMI with only two interaction principal
component axes was the best predictive model, which is in agreement with Zobel
et al. (1988) and Annicchiarico (2002). Further interaction principal
component axes captured mostly noise and therefore did not help to predict validation
of observations. Thus, the interaction of the 15 genotypes with nine environments
was best predicted by the first two principal components of genotypes and environments.
Most accurate model for AMMI can be predicted by using the first two PCAs (Gauch and Zobel, 1996; Yan et al., 2000; Annicchiarico, 2002). Conversely, Sivapalan et al. (2000) recommended a predictive AMMI model with the first four PCAs. These results indicate that the number of the terms to be included in an AMMI model cannot be specified a priori without first trying AMMI predictive assessment.
By plotting both the genotypes and the environments on the same graph, the
associations between the genotypes and the environments can be seen clearly
(Fig. 1). The IPCA scores of genotypes in the AMMI analysis
are an indication of the stability or adaptation over environments (Gauch and
Zobel, 1996; Purchase, 1997; Alberts, 2004). The greater the IPCA scores, the
more specific adapted is a genotype to certain environments. The more the IPCA
scores approximate to zero, the more stable or adapted the genotype is over
all the environments sampled.
A biplot is generated using genotypic and environmental scores of the first two AMMI components (Vergas et al., 1999). A biplot has four sections, depending upon signs of the genotypic and environmental scores. In Fig. 2, the sites fell into four sections: the best genotype with respect to site Alemaya, Awada and ArsiNegelle were genotypes BH544 and FH625259 xF7215x1447b: Genotypes BH660, BH670 and AMH800 were best for sites Bako and Areka on the other hand the best genotypes for Hirna, Awassa and Jinka were BH541, BHQP542 and SC715, while Genotype PHB3253 and ESE203 were best for site Goffa.
Estimation of environmental indices (I_{j}) were used to classify environments
into three classes viz., positive significant as good (favorable environments),
positive or negative nonsignificant as average environments and negatively
significant as poor (unfavorable) environments (Table 6).
Based on the results of the analysis Bako, Awassa and Hirna were favorable environments
with environmental index of positive and significant. Awada, Jinka and Goffa
were poor (unfavorable) environments with negative and significant environmental
index, while Alemaya and ArsiNegelle were average environments.

Fig. 1: 
AMMI1 biplot of main effects and interactions. Where, 1 =
BH541; 2 = BH660; 3 = BH670; 4 = BHQP542; 5 = BHQP543; 6 = FH625259xF7215
x1447b; 7 = BH544; 8 = BH540; 9 = Ambo Synth1; 10 = Ambo Synth5; 11
= AMH800; 12 = SC715; 13 = PHB 3253; 14 = 30H83, 15 = ESE203; AW = Awassa;
AR = Areka; GO = Goffa; AN = ArsiNegelle; JI = Jinka; BK = Bako; AD = Awada;
HI = Hirna and AL = Alemaya 
Table 4 and 5 shows the AMMI analysis with
the IPCA1 and IPCA2 scores for the genotypes and environments, respectively.
When looking at the environments it was clear that there is a good variation
in the different environments sampled, ranging from the lower yielding environments
in quadrants 3 and 4 and the high yielding environments in quadrants 1 and 2.
With respect to the test sites, Goffa, was most discriminating as indicated
by the longest distance between its marker and the origin (Fig.
2). However, due to its large PCA2 score, genotypic differences observed
at Goffa may not exactly reflect the genotypes in average yield overall sites.
Site Awassa was not the most discriminating, but genotypic differences at Awassa
should be highly consistent with those averaged over sites, because it had near
zero PCA2 scores compared to the other.
Awassa and Bako were the most favorable environments for all genotypes with
nearly similar yield response but slight difference in interaction (Fig.
1). Jinka and Awada were the least favorable environments for all genotypes,
with different interaction and different yield response.
The genotypes had considerably less variation than the environments around
the mean yield of 7.25 t ha^{1}. The genotypes 30H83, BH541 and FH625259x
F7215x 1447b are mainly adapted to higher yielding environments. Considering
only the IPCA1 scores, genotypes BH660, BH541 and BH670 was unstable genotypes
and also adapted to the higher yielding or more favorable environments. Genotypes
adapted to lower yielding environments were BH540, AMH800 and Ambo Synth1.
The most stable genotypes based on IPCA1 scores, were BH540, 30H83, AMH800,
Ambo Synth1 and BHQP543.

Fig 2: 
Interaction biplot for the AMMI2 model. Where, 1 = BH541;
2 = BH660; 3 = BH670; 4 = BHQP542; 5 = BHQP543; 6 = FH625259xF7215x1447b;
7 = BH544; 8 = BH540; 9 = Ambo Synth1; 10 = Ambo Synth5; 11 = AMH800;
12 = SC715; 13 = PHB 3253; 14 = 30H83, 15 = ESE203; AW = Awassa; AR =
Areka; GO = Goffa; AN = ArsiNegelle; JI = Jinka; BK = Bako; AD = Awada;
HI = Hirna and AL = Alemaya 
Since IPCA2 scores also play a significant role (23.67%) in explaining the
GEI, the IPCA1 scores were plotted against IPCA2 scores to further explore adaptation
(Fig. 2). SC715 was the most unstable in addition, PHB3253,
BH541, BH660, BH544 and BHQP542 were unstable to a lesser extent. BH540,
BHQP543, 30H83, AMH800 and Ambo Synth1 were stable, when plotted on the IPCA1
and IPCA2 scores.
Adaptation of the genotypes based on the AMMI 2 model: The AMMI model shown patterns and relationships of genotypes and environments successfully. The hybrid that best adapted to most environments was 30H83 inclining average to favorable environments and it was also stable to all environments. BH541 was better performing in the lower to high yielding environments (Table 6). FH625259xF7215x1447b showed adaptation to specific environments. It is clear that the AMMI model can be used to analyze the GxE and identify the superior genotypes. It can also be used in the selection of the best environments for genotype evaluation.
Cluster analysis: Genotypes were clustered using the AMMI2 estimated
values of nine environments used as attributes and, conversely, the environments
were grouped using estimated value of AMMI2 for the 15 genotypes. Dendrogram
for clustering of cultivars and environments are shown in Fig.
3. At the two group level of genotype clustering genotypes, BH660, BH670,
FH625259xF7215x1447b, BH544 and BH541 were discriminated from the remaining.
These genotypes are characterized by high yield (above the grand mean) with
high positive interaction. In contrast, the second genotypes, BHQP542, BHQP543,
Ambo Synth1, Ambo Synth5, AMH800 and SC715 are low yielders (below the grand
mean) with a IPCA score ranging from 3 to 3.
Table 5: 
IPCA1, IPCA2 scores and environmental index for nine locations 

NS, *, **: Non Significant and significant at p<=0.05 and
0.01 level, respectively, EN mean: Environmental mean and EN index: Environmental
index 
Centroid clustering methods strongly recommended a class of four for this data
set. Therefore, splitting the down the main first branch of the dendrogram resulted
in two subclusters, while splitting down the second main branch resulted also
in two subclusters (Fig. 3a). The first subcluster of the
first group comprised genotypes BHQP543, Ambo Synth1, Ambo Synth5 and AMH800,
low yielders and having low interaction. Except genotype BHQP543 (quality protein
maize) all of these genotypes are highland materials developed for highland
altitude. The second subcluster (BHQP542, BH540, SC715, PHB3253 and ESE203)
is characterized by genotypes with low yield and negative interactions (Fig.
3a).
The splitting down of the second main branch at two cluster levels simply isolated
genotype No.1 and 14 from the rest. As it was observed in the biplot, these
genotypes were highest yielders and most adapted to many environments and could
probably be the reason for the AMMI2 estimate clustering to group them differently
from the other high yielding genotypes. The second subcluster (BH660, BH670,
FH625259xF7215x1447b and BH544) was characterized by genotypes with high
yield and high interaction. Three way cross hybrids, BH660 and BH670 have
the same single cross female parent and they only differ in their male parents.

Fig. 3a: 
Genotype dendrogram representing four genotype cluster. Where,
FH1447b = FH625259xF7215x1447b 

Fig. 3b: 
Environmental dendrogram representing three clusters of test
locations 
Table 6: 
Best five high yielding genotypes based on AMMI model selections 

FH1447b = FH625259xF7215x1447b; Gen1 = 1st yielder at
a location; Gen2 = 2nd yielder; Gen3 = 3rd yielder; Gen4 = 4th yielder;
Gen5 = 5th yielder at a location 
Environments were clustered using the nearest neighbor. The three clusters
sufficiently described this data set (Fig. 3b). The first
group comprised of only environment Goffa and the second group comprised of
all environments except Goffa. Goffa is unique environment, where there was
high and erratic rainfall and situated at low altitude as compared to others.
The splitting down of the second main branch at two cluster levels simply isolated
environment Awassa and Bako together which are high yielding (above grand mean)
environments. This could also be attributed to similarities between the two
locations in altitude, humidity and length of growing seasons.

REFERENCES 
Abush, T., 2001. Genotype environment interaction in landrace derived advanced lines of Sorghum (Sorghum bicolar L. Moench). M.Sc. Thesis, Alemaya University of Agriculture, School of Graduate Studies, Alemaya, Ethiopia.
Alberts, M.J.A., 2004. Comparison of statistical methods to describe genotype environment interaction and yield stability in multilocation maize trials. M.Sc. Thesis, University of the Free State.
Annicchiarico, P., 2002. Genotype environment interactionschallenges and opportunities for plant breeding and cultivar recommendation. Food and Agriculture Organization (FAO), Rome.
Crossa, J., 1990. Statistical analyses of multilocation trials. Adv. Agron., 44: 5585. CrossRef 
Gabriel, K.R., 1978. The biplotgraphical display of matrices with application to principal component analysis. Biometrika, 58: 453467.
Gauch, H.G. and R.W. Zobel, 1996. AMMI Analysis of Yield Trials. In: GenotypebyEnvironment Interaction, Kang, M.S. and H.G. Gauch Jr. (Eds.). Taylor and Francis, Boca Raton, FL., ISBN: 9780849340031, pp: 85122.
Kaya, Y., C. Palta and S. Taner, 2002. Additive main effects and multiplicative interactions analysis of yield performance in bread wheat genotypes a cross environments. Turk. J. Agric., 26: 275279. Direct Link 
Mandel, J., 1971. A new analysis of variance model for nonadditive data. Technometrics, 13: 118.
Ngeve, J.M. and J.C. Bouwkamp, 1993. Comparison of statistical methods to assess yield stability in sweetpotato. J. Am. Soc. Hortic. Sci., 118: 304310.
Purchase, J.L., 1997. Parametric analysis to describe genotype × environment interaction and yield stability in winter wheat. Ph.D. Thesis, University of the Orange Free State, Bloemfontein, South Africa.
SAS, 2001. SAS/STAT User's Guide. 8.02 Edn., SAS Institute Inc., Cary, NC.
Scapim, C.A., V.R. Oliveira, A.L. Braceini, C.D. Cruz, C.A. Andrade and M.C.G. Vidial, 2000. Yield stability in Maize (Zea mays L.) and correlation among the parameters of the Eberhart and Russell, Lin and Binns and Huehn models. Genet. Mol. Biol., 23: 387393. CrossRef 
Sivapalan, S., L.O. Brien, G.O. Ferrara, G.L. Hollamby, I. Barclay and P.J. Martin, 2000. An adaptation analysis of Australian and CIMMYT/ICARDA wheat germplasm in Australian production environments. Aust. J. Agric. Res., 51: 903915. Direct Link 
Sneller, C.H., L. KilgoreNorquest and D. Dombek, 1997. Repeatability of yield stability statistics in soybean. Crop Sci., 37: 383390. CrossRef  Direct Link 
Taye, G., T. Getachew and G. Bejiga, 2000. AMMI adjustment for yield estimate and classification of genotypes and environments in field pea (Pisum sativum L.). J. Genet. Breed., 54: 183191.
Thillainathan, M. and G.C.J. Fernandez, 2001. SAS applications for Tai's stability analysis and AMMI model in GenotypeHEnvironmental Interaction (GEI) effects. J. Hered., 92: 367371. Direct Link 
Tiruneh, K., 1999. . Assessment of genotype environment interaction for grain yield and yield related traits in Tef (Eragrostis tef) Zucc. Trotter. M.Sc. Thesis, Alemaya University of Agriculture, School of Graduate Studies, Alemaya, Ethiopia.
Vargas, M., J. Crossa, F.A. van Eeuwijk, M.E. Ramirez and K. Sayre, 1999. Using partial least squares regression, factorial regression and AMMI models for interpreting genotype x environment interaction. Crop Sci., 39: 955967. CrossRef  Direct Link 
Yan, W., L.A. Hunt, Q. Sheng and Z. Szlavnics, 2000. Cultivar evaluation and megaenvironment investigation based on the GGE biplot. Crop Sci., 40: 597605. CrossRef  Direct Link 
Zobel, R.W., M.J. Wright and H.G. Gauch, 1988. Statistical analysis of yield trial. Agron. J., 80: 388393.



