Climate change is threatening food security. To ensure food security, we do not only have to safeguard agricultural production but also optimally distribute the yields between regions. For that, decision-makers need reliable crop yield forecasts so that they can plan which regions are likely to experience crop yield losses and which regions will produce a surplus. Earth observation and machine learning are key tools to calculate such forecasts. Especially Sentinel-1 and 2 data has been used a lot as it provides regular high-resolution information about the state of crops and soil moisture. However, crop yield forecasts based on machine learning are strongly limited by the availability of field-level crop yield data, which farmers often do not like to share publicly. In this study, we evaluated if a model trained with data from a certain region can be applied elsewhere, to use training data more efficiently. Our field-level crop yield forecasts were trained using crop yield data from a farm (846 fields) in the Czech Republic for winter wheat. It was based on Sentinel-1 and 2 data and the machine learning model Extreme Gradient Boosting. The model was then tested for various farms with increasing geographical distance. The baseline was a forecast for fields of the same farm, that were not used for training. Next, the model was applied to another farm in Czechia, one in Ukraine and one in the Netherlands. The model transferability worked well for the other farm in Czechia (R²=0.64 between the forecast 1 month before harvest and observed yield). However, the model performed poorly further away than that (R²<0.13). This was related to very different weather conditions. Adding meteorological predictors or applying the model to more similar areas may help in the future to improve the transferability of the forecasts.