
Research Article


Additive Main Effects and Multiplicative Interactions Analysis of Grain Yield Performances in Rice Genotypes Across Environments


Kayode A. Sanni,
O.J. Ariyo,
D.K. Ojo,
G. Gregorio,
E.A. Somado,
I. Sanchez,
M. Sie,
K. Futakuchi,
S.A. Ogunbayo,
R.G. Guei
and
M.C.S. Wopereis


ABSTRACT

Genotype by Environment Interaction (GEI) is a major complications in plant breeding. We used Additive Main Effects and Multiplicative Interaction (AMMI) to evaluate the effects of GEI in NERICA rice genotype and their adaptation in two years at three locations; Ibadan7°30’ N, 3°58’ E, 210 m.a.s.l. (Nigeria), Cotonou6°24’ N, 2°19’ E, 15.5 m.a.s.l. and Deve6°48’ N, 1°47’ E, 72 m.a.s.l. (Benin Republic). Twenty two rice genotypes were grown in 2005 and 2006 under upland condition, using randomized complete block design with three replications. Main effects due to environments (E), genotypes (G) and GxE interaction (GEI) were significant (p<0.01), with the highest variation of 43.1% accounted for by environmental effects. The first four Interaction Principal Component Axes (IPCA1, 2, 3 and 4) were significant (p<0.01) and cumulatively contributed 98.5% of the total GEI. AMMI biplot accounted for 91.4% of the total sum of squares. The stability study indicated that NERICAs 3, 10, 11 and 18 could be considered stable in any of the environments, due to their low interactions. However, NERICA 11 was the most promising of the genotypes, with high yield (5.15 t ha^{1}) and a broad environmental adaptation.




How
to cite this article:
Kayode A. Sanni, O.J. Ariyo, D.K. Ojo, G. Gregorio, E.A. Somado, I. Sanchez, M. Sie, K. Futakuchi, S.A. Ogunbayo, R.G. Guei and M.C.S. Wopereis, 2009. Additive Main Effects and Multiplicative Interactions Analysis of Grain Yield Performances in Rice Genotypes Across Environments. Asian Journal of Plant Sciences, 8: 4853. DOI: 10.3923/ajps.2009.48.53 URL: https://scialert.net/abstract/?doi=ajps.2009.48.53



INTRODUCTION
Rice is grown and consumed in about 39 countries of Africa. Fuelled by changing
lifestyle, population growth and consumer preference, the demand for rice in
West and Central Africa (WCA), is growing at the rate of 6% per annum, which
is fasterthan anywhere else in the world, while production increase at the rate
of 4% per annum (WARDA, 2004). Africa consumes a total
of 11.6 million tonnes of milled rice per year (FAO, 1996),
of which 3.3 million tonnes (33.6%) is imported. Among the 39 African countries
that produce and consume rice, only ten have attained any appreciable levels
of rice selfsufficiency (7599.9%), while the remaining 29 are heavy importers
with selfsufficiency levels ranging between 0 and 62.8% (Oteng
and Santâ€™Anna, 1999). There is no doubt that rice production in Africa
must increase if the high demand in Africa will be met.
One of the main objectives of rice improvement in SubSaharan Africa (SSA)
is to improve yield and increase local production. However, its production in
SubSaharan Africa (SSA) has been limited by the typical African stresses (biotic
and abiotic). Oryza consists of two cultivated species Oryza sativa
which originates from Asia and O. glaberrima which is indigenous to
Africa. O. glaberrima which has been discovered and cultivated in Africa
over 3500 years ago (Chang, 1976) is well adapted to
the various adverse conditions of Africa, such as the blast, rice yellow mottle
virus, rice gall midge, insects, nematodes, acidity, iron toxicity, salinity
and drought, but with poor yield. Although, the Asian rice (O. sativa)
which was introduced to Africa about 2000 years ago has a better yield but it
is not sufficiently adapted to the typical African stresses.
Research work at the Africa Rice Centre (WARDA) has led to a major success
with the development of improved upland rice varieties from interspecific cross
between O. sativa and O. glaberrima to produce what is now referred
to as the New Rice for Africa (NERICA). NERICA has great potential to benefit
African farmers because of its highyielding ability under typical upland conditions
in this region (Ikeda et al., 2007). Eighteen
of these NERICA varieties have been named by WARDA and are being widely disseminated
across the region by WARDA and its partners.
The adoption of these named NERICA varieties is increasingly spreading in the upland ricebased systems, which covers the most extensive rice ecosystem in SSA. As a result of this, it is therefore important to assess the adaptation and yield stability of these promising upland genotypes of rice across environments.
Several statistical methods have been developed for the analysis of Genotype
by Environment Interactions (GEI) and phenotypic stability (Hill,
1975; Lin et al., 1986; Wescott,
1986; Crossa, 1990; Flores et
al., 1998). Regression technique has been widely used (Eberhart
and Russell, 1966; Perkins and Jinks, 1968) due
to its simplicity and the fact that its information on adaptive response is
easily applicable to locations (Annicchiarico, 1997).
The Principal Component Analysis (PCA) method that shows the mean squares of
the principal components axes (Gauch and Furnas, 1991)
has also been used. Zobel et al. (1988) compared
the traditional statistical analysis such as Analysis of Variance (ANOVA), Principal
Component Analysis (PCA) and Linear Regression with AMMI analyses, and showed
that the traditional analyses were not always effective in analyzing the multienvironment
trial data structure. The ANOVA is an additive model that describes main effects
effectively and determines if GE interaction is a significant source of variation,
but it does not provide insight into the patterns of genotypes or environments
that give rise to the interaction. The PCA is a multiplicative model that contains
no sources of variation for additive G or E main effects and does not analyze
the interactions effectively. The linear regression method uses environmental
means, which are frequently a poor estimate of environments, such that the fitted
lines in most cases account for a small fraction of the total GE and could be
misleading (Byth et al., 1976; Zobel
et al., 1988; Ariyo, 1999).
Additive main effects and multiplicative interaction (AMMI) has been proved
to be a suitable method for depicting adaptive responses (Gauch,
1993; Annicchiarico, 1997; Gauch
and Zobel, 1989; Ariyo, 1999). AMMI analysis has
been reported to have significantly improved the probability of successful selection
(Gauch and Zobel, 1989) and has been used to analyse
GxE interaction with greater precision in many crops (Bradu,
1984; Gauch 1990; Crossa et
al., 1991; Ariyo, 1999). The model combines the
conventional analysis of variance for genotype and environment main effects
with principal components analysis to decompose the GEI into several Interaction
Principal Component Axes (IPCA). With the biplot facility from AMMI analysis,
both genotypes and environments are plotted together on the same scatter plot
and inferences about their interaction can be made.
This study reports the use of AMMI model to analyse yield data of twenty two genotypes of rice from two years evaluation in three locations. The objective was to quantify genotypexenvironment interaction for grain yield and the stability of NERICA rice genotypes in different environments.
MATERIALS AND METHODS
Plant materials: Twenty two rice genotypes comprising of the 18 named interspecific NERICA rice varieties (Oryza sativaxO. glaberrima) and their parents O. glaberrima steud (CG 14) and O. sativa L. (WAB56104, WAB5650 and WAB18118) were used in this study (Table 1). Apart from CG 14 which is a landrace from Senegal, all the genotypes originated from WARDA, BouakÃ©, CÃ´te dâ€™Ivoire. The seeds were obtained from the rice germplasm collection of the Genetic Resources Unit at the Africa Rice Center (WARDA).
Field trials: The experiment was carried out during the rainy seasons (June to October) of 2005 and 2006 under upland condition at three locations in two West African countriesNigeria (Ibadan) and Benin (Cotonou and Deve). The geographical coordinates and agroclimatic characteristics of the locations are shown in Table 2. At each location, the genotypes were grown through direct seeding, by dibbling in randomized complete block design with three replications. The size of each plot was 1x5 m made up of 6 rows, with a spacing of 20 cm apart. The interplot spacing was 50 cm. Fertilizer application at each site consisted of NPK (101818) used as basal application at the rate of 100 kg ha^{1} during land preparation. Urea (N: 46%) was applied at the rate of 65 kg ha^{1} as topdressing, first at tillering and second at panicle development (booting) stage. The plots were handweeded regularly to minimize weed infestation. Bird damage was controlled using bird scares.
Statistical analysis: Data on grain yield for the 22 genotypes of rice
in 6 yearlocation environments were subjected to AMMI analysis. To implement
the AMMI, the data was input into the microcomputer software program MATMODEL
Version 2.2. The AMMI model is:
Y_{ger} = Î¼+Î±_{g}+Î²_{e}+Î£Î»_{n}Î³_{gn}Î·_{en}+Ï?_{ge}+Îμ_{ger} 
Table 1: 
List of the studied genotypes 

IBP: Interbreeding population, HB: High input
homogeneous bulk, P: Panicle selection, BL: Blast, DV: Drought at vegetative
phase 
Table 2: 
Description of the three experimental locations 

where, Y_{ger} is the yield of genotype^{ }g in environment
e for replicate r, Î¼ is the grand mean, Î±_{g} is genotype mean
deviation (mean minus the grand mean),^{ }Î²_{e} is the
environment mean deviations, N is the number^{ }of PCA axes retained
in the model,^{ }Î»_{n} is the singular value of PCA axis
n, Î³_{gn} is the genotype^{ }eigenvector values for PCA
axis n, Î·_{en} is the environment^{ }eigenvector values
for PCA axis n, I?_{ge} is the AMMI residuals and Îµ_{ger}
is the residual error.
AMMI uses ordinary ANOVA to analyse the main effects (additive part) and Principal
Component Analysis (PCA) to analyse the non additive residual left over by the
ANOVA (Gauch, 1993). In the analysis, each combination
of a single location and year was considered as an environment.
The interaction is the genotype PCA score multiplied by that of the environment.
When a cultivar and an environment have the same sign on their respective first
PCA axis, their interaction is positive; if different, their interaction is
negative. An AMMI plot is a graph where aspects of both genotypes and environments
are plotted on the same axis so that interrelationship can be visualised. It
provides a pictorial view of the transformed GxE interaction (Kempton,
1984) for easy interpretation. In a biplot where, PCA 1 score is on the
vertical axis and the mean yield on the horizontal, genotypes that appear almost
on a perpendicular line had similar means and those that fall almost on a horizontal
line had similar interaction patterns. Similarly, environment that occurred
almost on a perpendicular line had similar means and those on horizontal lines
had similar interaction patterns. Genotypes or environments with large PCA 1
scores, either positive or negative had large interactions, whereas genotypes
with PCA 1 score of zero or nearly zero had smaller interaction (Crossa
et al., 1990). The biplot of the first two IPCA axes demonstrates
the relative magnitude of the GEI for specific genotypes and environments. The
further away from the axes center a genotype or environment is, the larger the
GEI.
RESULTS AND DISCUSSION
Genotype (G) which accounted for 41.6% of the total Sum of Square (SS), environmental
effects (E) which explained 43.1% and the GxE Interaction (GEI) effects which
captured 15.3%, were all significant (p = 0.01), indicating that all sources
are important in the analysis (Table 3). The result showed
that the environment main effect (E) was the most important source of variation,
due to its large contribution to the total sum of squares for yield (Kaya
et al., 2002). Variation due to G was larger than that due to GEI,
but GEI was significant p = 0.01, meaning that differences among genotypes vary
across environments (Admassu et al., 2008).
The presence of GEI was clearly demonstrated by the AMMI model, when the interaction
was partitioned among the first four Interaction Principal Component Axis (IPCA)
as they were significant p = 0.01 in a postdictive assessment.
Table 3: 
AMMI analysis of variance for rice grain yield 

**Significant at 1% probability level, ns:
Not significant 
The IPCA1 explained 45.51% of the interaction sum of square in 25% of the interaction
degree of freedom (df). Similarly, the second, third and fourth principal component
axis (PCA 24) explained a further 33.58, 13.2 and 6.19% of the GEI sum of square,
respectively. They cumulatively captured 98.50% of the total GEI SS, using 88
df. This implied that the interaction of the 22 genotypes of rice with six environments
was predicted by the first four principal components of genotypes and environments,
which is in agreement with the recommendation of Sivapalan
et al. (2000). This is also in concordance with the results of Van
Oosterom et al. (1993), where as much as the first five IPCAs were
significant. However, this contradicted the findings of Gauch
and Zobel (1996), which recommended that the most accurate model for AMMI,
can be predicted using the first two IPCAs. These results indicate that the
number of the terms to be included in an AMMI model cannot be specified a prior
without first trying AMMI predictive assessment (Kaya et
al., 2002).
The AMMI analysis provided a biplot (Fig. 1) of main effects
and the first principal component scores of interactions (IPCA1) of both genotypes
and environments. The differences among genotypes in terms of direction and
magnitude along the Xaxis (yield) and Y axis (IPCA 1 scores) are important.
In the biplot display, genotypes or environments that appear almost on a perpendicular
line of the graph had similar mean yields and those that fall almost on a horizontal
line had similar interaction (Crossa et al., 1990).
Thus the relative variability due to environments was greater than that due
to genotype differences. Genotypes or environments on the right side of the
midpoint of the perpendicular line have higher yields than those on the left
side. Consequently, NERICA11, 10, 17, 16 and 9 were generally high yielding
(5.15, 4.71, 4.55, 4.45 and 4.36 t ha^{1}, respectively) with NERICA11
being the overall best with yield of 5.15 t ha^{1} (Fig.
1). In contrast, CG 14, WAB5650 and WAB56104 were generally low yielding
genotypes.

Fig. 1: 
Biplot of grain yield of 22 rice genotypes and environments AMMI plot
for rice grain yield trials with 22 genotypes grown in six environments.
IPC1: Firstinteraction principal component, C: Cotonou, D: Deve; IB: Ibadan,
N: NERICA, W18: WAB18118; W50: WAB5650, W104: WAB56104 
The Deve and Ibadan locations which were always on the right hand side of the
midpoint of the main effect axis, seemed to be favourable environments, with
Deve as the most favourable, while Cotonou is the less favourable environment.
Genotypes or environments with large negative or positive IPCA1 scores have
high interactions, while those with IPCA1 scores near zero (close to the horizontal
line) have little interaction across environments and vice versa for environments
(Crossa et al., 1991; Egesi
and Asiedu, 2002) and are considered more stable than those further away
from the line. In the biplot, NERICA11, 10 and 18 fell almost on a horizontal
line near the zero point on IPCA1, but were vertically distant apart. This implies
that NERICA 11 and 10 showed high and stable yield; yield of NARICA 18 is also
stable against environmental changes but its potential is lower than NERICA
11 and 10.
Since, the IPCA 2 scores was also important (33.58% of GxE SS) in explaining
GEI, the biplot of the first two IPCAs was also used to demonstrate the relative
magnitude of the GEI for specific genotypes and environments (Fig.
2). The IPCA scores of genotypes in the AMMI analysis is an indication of
stability or adaptation over environments (Gauch and Zobel,
1996). The greater the IPCA scores, the more specifically adapted is a genotype
to certain environments (Admassu et al., 2008).
The more the IPCA scores approximate to zero, the more stable or adapted the
genotype is over all the environments sampled. A biplot of the first two IPCA
show that the best adapted genotype to most environments was NERICA11 with the
IPCA scores close to zero, indicating its stability over the entire environments.
WAB56104 and WAB18118 were well adapted to the high yielding environments
of Deve 2005 and 2006, while CG 14 was well adapted in the low yielding environments
of Cotonou 2005 and 2006. NERICA15 and 14 performed best at Ibadan in 2006 while
the best performing variety at Ibadan in 2005 was NERICA6.

Fig. 2: 
Biplot of the second interaction principal component axis (IPCA 2) against
the first interaction principal component axis (IPCA 1) scores for grain
yield of 22 rice genotypes in six environments. C = Cotonou, D = Deve, IB
= Ibadan, N = NERICA, W18 = WAB18118, W50 = WAB5650, W104 = WAB56104 
Considering the environments tested in this study, no single location had IPCA
1 scores close to zero line. Also the IPCA 2 scores for environments were far
from zero. This indicates that all the environments had potential for large
GEI.
CONCLUSION
The AMMI statistical model has shown that the largest proportion of the total variation in grain yield was attributed to environments in this study. NERICA 11 and 10 had the highest yield and were hardly affected by the GEI effects as a result of which they will perform well across a wide range of environments. The yield of NARICA 18 was also stable against environmental changes but its potential was lower than NERICA 11 and 10.

REFERENCES 
Admassu, S., M. Nigussie and H. Zelleke, 2008. Genotypeenvironment interaction and stability analysis for grain yield of maize (Zea mays L.) in Ethiopia. Asian J. Plant Sci., 7: 163169. CrossRef  Direct Link 
Annicchiarico, P., 1997. Joint regression vs. AMMI analysis of genotypeenvironment interactions for cereals in Italy. Euphytica, 94: 5362. CrossRef 
Ariyo, O.J., 1999. Use of additive main effects and multiplicative interaction model to analyse multilocation soybean varietal trials. J. Genet. Breed., 53: 129134.
Bradu, D., 1984. Response surface model diagnosis in twoway tables. Commun. Statist. Theor. Meth., 13: 30593106.
Byth, D.E., R.L. Eisemann and I.H. de Lacy, 1976. Twoway pattern analysis of a large data set to evaluate genotypic adaptation. Heredity, 37: 215230. CrossRef  Direct Link 
Chang, T.T., 1976. The origin, evolution, cultivation, dissemination and diversification of Asian and African rices. Euphytica, 25: 425441. CrossRef 
Crossa, J., 1990. Statistical analyses of multilocation trials. Adv. Agron., 44: 5585. CrossRef 
Crossa, J., H.G. Gauch and R.W. Zobel, 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci., 30: 493500. CrossRef  Direct Link 
Crossa, J., P.N. Fox, W.H. Pfeiffer, S. Rajaram and H.G. Gauch Jr., 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theoret. Applied Genet., 81: 2737. CrossRef  Direct Link 
Eberhart, S.A. and W.A. Russell, 1966. Stability parameters for comparing varieties. Crop Sci., 6: 3640. CrossRef  Direct Link 
Egesi, C.N. and R. Asiedu, 2002. Analysis of yam yields using the additive main effects and multiplicative interaction (AMMI) model. Afr. Crop Sci. J., 10: 195201. Direct Link 
FAO, 1996. Production year book. Vol. 50. Rome.
Flores, F., M.T. Moreno and J.I. Cubero, 1998. A comparison of univariate and multivariate methods to analyze GxE interaction. Field Crops Res., 56: 271286. CrossRef  Direct Link 
Gauch Jr., H.G. and R.W. Zobel, 1989. Accuracy and selection success in yield trials analysis. Theor. Applied Genet., 77: 443481. CrossRef  Direct Link 
Gauch, H.G. Jr, 1993. MATMODEL version 2.0. AMMI and related analyses for twoway data Matrices. Soil. Crop Atmosp. Sci.
Gauch, H.G. Jr., 1990. Full and reduced models for yield trials. Theor. Applied Genet., 80: 153160. CrossRef 
Gauch, H.G. and R.E. Furnas, 1991. Statistical analysis of yield trials with MATMODEL. Agron. J., 83: 916920. Direct Link 
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.
Hill, J., 1975. Genotypeenvironment interactionsa challenge for plant breeding. J. Agric. Sci. Camb., 85: 477493. CrossRef 
Ikeda, R., Y. Sokei and I. Akintayo, 2007. Reliable multiplication of seed for NERICA varieties of rice, Oryza sativa L. Genet. Resour. Crop Evol., 54: 16371644. CrossRef  Direct Link 
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 
Kempton, R.A., 1984. The use of biplots in interpreting variety by environment interactions. J. Agric. Sci., 103: 123135. CrossRef  Direct Link 
Lin, C.S., M.R. Binns and L.P. Lefkovitch, 1986. Stability analysis: Where do we stand? Crop Sci., 26: 894900. CrossRef  Direct Link 
Oteng, J.W. and R. Sant'Anna, 1999. Rice production in Africa: Current situation and issues. Int. Rice Com. Newsl., 48: 4151. Direct Link 
Perkins, J.M. and J.L. Jinks, 1968. Environmental and genotypeenvironmental components of variability III. Multiple lines and crosses. Heredity, 23: 339356. CrossRef 
Sivapalan, S., L.O. Brien, G. OrtizFerrara, 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. CrossRef  Direct Link 
Van Oosterom, E.J., D. Kleijn, S. Ceccarelli and M.M. Nachit, 1993. Genotypebyenvironment interactions of barley in the mediterranean region. Crop Sci., 33: 669674. CrossRef  Direct Link 
WARDA, 2004. Strategic plan 20032012. Bouake, Cote dâ€™Ivoire, pp: 64. http://www.warda.org/publications/Strategic%20Plan/warda_strategic_plan_2003.htm.
Wescott, B., 1986. Some methods of analyzing genotypeenvironment interaction. Heredity, 56: 243253. CrossRef  Direct Link 
Zobel, R.W., M.J. Wright and H.G. Gauch, 1988. Statistical analysis of yield data. Agron. J., 80: 388393.



