In plant breeding, Multi-Environment Field Trials (MET) are essential for evaluating genotypes across multiple traits and estimating their genetic breeding value through Genomic Prediction (GP). The presence of outliers in MET data adversely affects the accuracy of GP, necessitating robust Outlier Detection (OD) mechanisms. Despite this, OD in MET is frequently neglected. MET data are prone to heteroscedasticity, leading to swamping and masking effects where actual data points are misclassified as outliers. Thus, a robust OD algorithm is critical for enhancing GP accuracy, particularly with limited sample sizes. Our previous study identified the Subspace Outlier Detection Method as the most effective technique among various OD methods, including Mahalanobis Distance, Principal Component Analysis (PCA), and Local Outlier Factor (LOF), based on its performance across eleven real-world MET datasets. In this extended study, we apply the Subspace Outlier Detection Method to a different publicly available wheat dataset from the Triticeae Toolbox (T3/Wheat) to validate its robustness and generalizability. We conducted a comprehensive evaluation using precision, recall, and F1-score metrics, injecting artificial outliers to simulate real-world scenarios. Our findings reaffirm the method’s superior sensitivity and robustness in detecting true outliers, thereby significantly improving the accuracy of subsequent GP analyses. This extended analysis demonstrates the method’s applicability across diverse datasets, offering valuable insights for researchers in selecting appropriate outlier detection techniques for MET data analysis.