Many fruit and nut tree species depend on low temperatures during the cold season to overcome dormancy. Lacking chill may result in diminished bud burst rates and uncoordinated blooming, both of which can cause severe yield losses. With global warming leading to rising winter temperatures in many growing regions, assessments of temperature requirements during dormancy and projections of tree phenology have gained renewed attention. A number of studies in recent decades have attempted to improve the predictive performance of phenology models and to integrate ecophysiological as well as genetic processes. Nevertheless, the development of modeling frameworks that are readily applicable to data are still lacking. As a result, many practitioners must use outdated models, which have not been parameterized for specific species and/or varieties, and have also not been validated for the climate of interest. To help close the gap between scientific developments and the need for applied analyses, we recently re-implemented the Dynamic Model of chill accumulation in the widely distributed programming environment R. We linked this sub-model with a heat model to project bloom dates and developed a framework for automated model fitting. Here, we present the application of the framework to long-term data sets of bloom dates of sweet cherry (Prunus avium) to demonstrate the use of the model and its predictive performance.