Abstract: Outlier detection is an important preliminary step in the data analysis often conducted through a form of residual analysis. A complex data, such as those that are analysed by linear mixed models, gives rise to distinct levels of residuals and thus offers additional challenges for the development of an outlier detection method. Plant breeding trials are routinely conducted over years and multiple locations with the aim to select the best genotype as parents or commercial release. These so-called multi-environmental trials (MET) is commonly analysed using linear mixed models which may include cubic splines and autoregressive process to account for spatial trends. We consider some statistics derived from mean and variance shift outlier model (MSOM/VSOM) and the generalised Cook’s distance (GCD) for outlier detection. We present a simulation study based on a set of real wheat yield trials.