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its prevailing environmental conditions (exploitation of specific adaptation) or of
genotypes with low frequency of poor yield or crop failure (exploitation of yield
stability) (Ceccarelli 1996 ; Annicchiarico et al. 2005 ; Mohammadi et al. 2010 ).
Yan and Kang ( 2003 ) described the different types of GE interactions and high-
lighted the implications of these in plant breeding and crop production. Crossover
GE interactions (change in rankings of genotypes across environments) are of great-
est interest to breeders as these directly affect genotype selection in specific envi-
ronments. Consequently, promising selections in one environment may perform
poorly in another. Ignoring significant GE interactions in favor of resource savings
can have detrimental effects. With regards to genetic gains from selection, large GE
interactions, as components of total phenotypic variance, affect heritability (propor-
tion of total phenotypic variance that is due to genetic variance) negatively. The
larger the GE interaction, the smaller the heritability estimate; thus, progress from
selection would be reduced as well (Yan and Kang 2003 ). GE interaction has been
a focus of plant breeders as early as the 1950s, and there is a wide range of literature
outlining examples and methods of dealing with this phenomenon. The primary
objective of multi-environment trials (METs) is to identify superior genotypes for a
target region, and to determine if the target region can be subdivided into different
mega-environments (Yan et al. 2000 ). A mega-environment may be defined as a
portion of a crop species’ growing region with a homogeneous environment that
causes some genotypes to perform similarly (Gauch and Zobel 1997 ), and is nor-
mally identified through analysis of MET data. Currently, there is a wide range of
statistical techniques used for the analysis of yield trials data collected from METs.
These models can be linear formulations such as joint-regression (Yates and Cochran
1938 ; Finlay and Wilkinson 1963 ; Eberhart and Russell 1966 ; Tai 1971 ), multivari-
ate clustering techniques (Lin and Butler 1990 ), and multiplication approaches such
as additive mean effects and multiplicative interaction (AMMI; Zobel et al. 1988 ;
Gauch 1992 ) and genotype plus GE interaction (GGE; Yan et al. 2000 ) biplot analy-
ses. Modeling GE interaction in METs helps to determine phenotypic stability of
genotypes, but this concept has been defined in different ways and increasing num-
bers of stability parameters have been developed (Gauch and Zobel 1997 ).
The effectiveness of genotype evaluation as part of breeding is influenced by (i)
understanding of GE interaction and (ii) the degree to which the environments sam-
pled in the MET represent the target population of environments (TPE) (Podlich
and Cooper 1998 ). While statistical approaches to estimate GE interaction have
developed over more than 50 years with specific environment variables being
included in mixed models (van Eeuwijk et al. 2005 ) and with environment classifi-
cations being identified in principal component analysis (Yan et al. 2000 ). In paral-
lel with these methodological improvements to analyze GE interactions within a
MET, characterization of the complete TPE has been conducted based on (i) climate
and soil data (Pollak and Corbett 1993 ; Hodson and White 2007 ), (ii) physiological
traits that integrate various stresses that plants experience, for example, flowering
Q. Sohail et al.