Sensitivity of Genotype by Environment Interaction Models to Outlying Observations

Plant breeding program depends on its ability to provide farmers with genotypes with guaranteed superior performance (phenotype) in terms of yield and/or quality across a range of environmental conditions. To achieve this aim, it is necessary to have an understanding of the model suitable for or leading to a good phenotype. In this study, two cases of scenarios were considered to have a clearer view of the performance of Genotype by Environment Interaction on the following four models; Additive Main Effect and Multiplicative Interaction (AMMI), Finlay Wilkinson (FW), Genotype and Genotype by Environment Interaction (GGE) and Mixed Model. We experiment the inference behind the violation of the assumption of normal distribution by observing the data contamination of two case scenarios (Lowest and Highest outlying observations). It was observed on the two data Types of Balance and Unbalance designs with different Levels of generations. We achieved that by comparative performance of the data contamination techniques under the two case scenarios; Case I scenario was done for Lowest Outlying Observations where 50%, 100% and 500% data contamination on the First Quarter (P1), Mid quarter (P2) and Last Quarter (P3). We then deduced from the result of the model evaluation that, at each levels of data contamination for Balance and Unbalance design, Mixed model was the ideal model for interaction. Case II scenario was done for Highest Outlying Observations where 50%, 100% and 500% data contamination on the First Quarter (P1), Mid quarter (P2) and Last Quarter (P3) were examined on each levels of generations. We then observed from the result of the model evaluation that, at each levels of data contamination for Balance and Unbalance design, Mixed model also outperformed the other three models.