Moreover, the GGE biplot provides greater insight, as it illustrates the relationship between the genotype and its GE interaction [8]. However, the GGE biplot results need to be validated with the original data. According to the original data, genotypes G4 and G6 had respectively the highest and lowest mean yield performances across environments, an inference supported graphically by fitting the GGE model to the original data (Fig. 4 and Table 1), suggesting that the GGE biplot results are in agreement with the original yield data. These results are in accord Trichostatin A in vivo with those of other studies [16] and [17] that found agreement between GGE biplot results and the

original yield data. Phenotypic yield stability is a trait of special interest for plant breeders and farmers. This trait can be quantified if genotypes are evaluated in different environments [30]. No correlation was found between yield ranks and stability ranks that were based on measuring GE interaction, including AMMI distance in the AMMI model; stability index in the GGE biplot; S2di Staurosporine in the JRA; and σ2 in the YSi statistic, indicating that these stability indices describe static stability and accordingly could be used if selection is to be based primarily on stability. This conclusion is in agreement with other reports on cereal crops for which stability indices based on measuring GE effects are not correlated with mean yield in bread

wheat, durum wheat and barley [31]. It is also supported by other reports

[32], [33], [34], [35] and [36]. Helms [32] found that the correlations of oat yield with σ2 and S2di were poor. Jalaluddin and Harrison [33] reported no correlation of wheat grain yield with σ2 or S2di. Sneller et al. [35] also found no relationship of soybean yield with the statistics AMMI, σ2, and S2di. Many statistical methods have been developed to analyze data from MET to gain a better understanding and interpretation of observed GE interaction patterns, with the aim of identifying outstanding new cultivars with high stability in crop breeding programs. A worthwhile discussion of many of these methods and their efficiency in identifying superior click here genotypes in MET data can be found in reviews [10], [11], [12], [13], [16], [17] and [18]. Fan et al. [14] and Mohammadi et al. [15] reported high rank correlations between GGE and YSi and concluded that YSi should be useful in selecting superior genotypes in the absence of GGE biplot software. Baxevanos et al. [37] also reported a high correlation between YSi and GGE distance. Goyal et al. [17] reported some agreement between JRA and GGE biplot methods in identifying stable genotypes with high yield performance. According to Goyal et al. [17], S2di and GGE biplot models were not in general agreement in identifying high-yielding and stable genotypes, a conclusion differing from that of Alwala et al. [16].