In western Canada and in many agricultural areas around the world, new crop genotypes are evaluated over a number of locations and years in multi-environment trials (MET) to investigate yield, yield stability, agronomic, and quality characteristics, with the ultimate goal to predict future genotype performance in commercial fields. This evaluation informs decisions about the commercial value of new crop genotypes, with a primary user of this information being farmers. Currently in many regions of Canada as the first step in analysis of this MET data, values usually are expressed as a percentage of a designated check genotype value at each site-year (trial), usually followed by a relatively simplistic statistical analysis of this percentage data. There are a number of problems with this traditional approach including selection of an appropriate check genotype or genotypes, and the necessary consistent performance of the check genotype over a number of locations and years. Following the recent approach of other countries and jurisdictions, MET spring wheat genotype yield data (kg ha^-1) that had been collected from 2000 to 2009 from various locations in Manitoba, Canada were subjected to mixed model statistical analysis. The results of the mixed model analysis compared very favourably to the historical traditional approach, and proved to be superior in situations such as a specific year in the dataset (2007) when the designated check genotype performed anomalously poorly. These results indicated that as little as five trial sites in a single year provided sufficient data for reliable prediction of a new genotype's yield performance, given a background dataset comprised of approximately 45 spring wheat genotypes tested over eight years. The wheat genotype yield data also was subjected to estimation of several different stability measures to investigate differences in yield stability between genotypes in the dataset. Results indicated relatively stable yield performance for most genotypes over a range of site-years (environments).