Abstract
The combined ANOVA analysis for grain yield of ten wheat genotypes at 12 environments showed that bread wheat grain yield was significantly affected by environment, which explained 75.01% of the total treatment (genotype + environment + genotype by environment interactions) variation, whereas the G and GEI were significant and accounted for 9.48 and 15.5%, respectively. The additive main effects and multiplicative interactions (AMMI) analysis indicated that three principal component analysis (PCA) were significant (P < 0.01). PCA 1, 2 and 3 accounted for 65.49, 17.10 and 10.11% of the genotype + environment (GE) interaction, respectively. Graphical display of genotype by environment interaction (GGE-biplot) based on genotype-focused scaling was depicted in order to detect the locations of genotypes, whereas the wheat genotypes were divided into four groups based on their scores of PCA 1 and 2: Three stable and high yielding genotypes (G2, 10 and 6), two unstable high yielding genotypes (G4 and 1), three stable low yielding genotypes (G5, 7 and 8) and two low yielding unstable genotypes (G9 and 3). The correlation coefficients among the twelve test environments and the vector view of the GGE-biplot provided a succinct summary of the interrelationship between the environments whereby only 38 of the 67 correlation coefficients were significant. All environments were positively correlated except the environment E5 which was negatively correlated with E9, 12 and 10. The G10 (Giza 168) is adaptable for a wide range of environment conditions. Key words: Additive main effects and multiplicative interactions (AMMI), additive main effects and multiplicative interaction, bread wheat, genotype by environment interaction (GEI), genotype x environment interaction, principal component analysis (PCA).
Highlights
Wheat is an important cereal crop which is receives the most attention of specialists in plant breeding and production
The combine ANOVA and AMMI analysis for grain yield at 12 environments showed that bread wheat grain yield was significantly affected by E, which explained 75.01% of the total treatment (G+E+genotype by environment interaction (GEI)) variation, whereas the G and GEI were significant and accounted for 9.48 and
The portioning of GEss data matrix by using AMMI analysis indicated that three principal component analysis (PCA) were significant (P < 0.01)
Summary
Wheat is an important cereal crop which is receives the most attention of specialists in plant breeding and production. Genotype by environment interaction (GEI) refers to the differential responses of different genotypes across a range of environments (Kang, 2004). This is a universal universal issue relating to all living organisms, from bacteria to plants to humans (Kang, 1998), and it is important in agricultural, genetic, evolutionary, and statistical research. Genotype x environment interaction (G x E) interactions cause many difficulties, whereas the environmental factors such as temperature and drought stress affect the performance of genotypes. Genotype + environment (GE) interaction reduces the genetic progress in plant breeding programs through minimizing the association between phenotypic and genotypic values (Comstock and Moll, 1963). Multienvironment yield trails are essential in estimation of genotypebyenvironmentinteraction(GEI)and identification of superior genotypes in the final selection cycles (Kaya et al, 2006 and Mitrovic et al, 2012)
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