Breeding oilseed rape for oil and protein contents may have led to differences in seedling emergence in genotypes. New opportunities for deep automated phenotyping of germination and seedling growth are being developed on phenotyping platforms. Our aim was to demonstrate that using these data to parameterize a crop emergence model complements field experiments for the evaluation of differences among genotypes. Five genotypes, chosen in a diverse set of winter oilseed rape for their different germination speeds, were phenotyped for germination at different temperatures and water potentials as well as for radicle and hypocotyl growth. These data were used as parameters to run the SIMPLE crop emergence model over a period of 27 years (1985–2012), at two locations, one in France and one in Germany, and at four sowing dates. Field experiments were performed in 2012, 2013 and 2014, and the emergence of the five genotypes was measured at early and late sowing dates. First, model predictions were compared with observed field emergence in the French sowing trials in 2014. The model proved to be rather good at predicting the emergence of the genotypes. Then, for the simulation study, the model extended the observed differences between locations and sowing dates over a greater number of years. The model also identified the main reasons for non-emerging seedlings and their frequencies in the simulated sowings. Differences between the five genotypes were on average very small, but complex interactions appeared that led to bigger differences under certain sowing conditions. This study demonstrates that combining deep phenotyping with crop models in simulation studies paves the way for more precise and detailed evaluation of genotypes