Seven different model approaches to calculate the onset of sour cherry blossom for the main growing regions in Rhineland-Palatinate (Germany) were compared. Three of the approaches were pure forcing models (M1, M2, M2DL) and the remaining four models were combined sequential chilling-forcing (CF) models. Model M1 was the commonly used growing degree day (GDD) model in which the starting date of temperature accumulation (t1), the base temperature (TBF) and the forcing requirement F* were optimized on the basis of observed data. Because of a relatively late optimal starting date (t1=1 March), the model can be applied only to calculate the onset of cherry blossom for present climate conditions. In order to develop forcing models that could possibly be used to estimate possible shifts in the timing of cherry blossom due to climate change, the starting date t 1 of the models was intentionally set to 1 January (M2, M2DL). Unfortunately, model M2 failed in both the optimization and validation period. The introduction of a daylength term (DL) in model M2DL improved model performance. In order to project possible shifts in the timing of plant phenological events, combined CF-models are preferred over pure GDD-models. For this reason four CF-models were developed with (M3DL, M4DL) and without (M3, M4) consideration of daylength in the GDD-approach. The chilling requirement was calculated using chilling hours (M3, M3DL) and chill portions (M4, M4DL). Both models without daylength estimated implausible model parameters and failed model validation. However, models M3DL and M4DL showed meaningful model parameter estimations and the error between modelled and observed data was markedly reduced. Moreover, the models optimized and validated (internal validation) for one sour cherry growing region in Germany, were applied successfully to calculate the beginning of the blossom period in other regions in Europe and even at one station in North America (external validation).