The additive main effect and multiplicative interaction (AMMI) has been widely used for studying and interpreting genotype × environment interaction (GEI) in agricultural experiments using multi-environment trials (METs). When METs are performed across several years the interaction is referred to as a 3-mode (3-way) data array, in which the modes are genotypes, environments, and years. The 3-way array can be applied to other conditions or factors artificially created by the researcher, such as different sowing dates or plant densities, etc. Three-way interaction data can be studied using the AMMI analysis. The objective of this study is to apply the 3-mode AMMI to 2 datasets. Dataset 1 comprises genotype (25) × location (4) × sowing time (4) interaction; 8 traits were measured. The structure of dataset 2 is genotype (20) × irrigation regimes (4) × year (3) on grain yield. Results of the 3-way AMMI on dataset 1 show that several important 3-way interactions were not detected when condensing location (4) × sowing time (4) into environments (16). An alternative 3-way array, genotype × attribute × locations for the early sowing date in Year 1, is considered. Results of the 3-way AMMI on dataset 2 show that different patterns of response of genotypes can be found at different irrigation methods and years.