  Predictor inputs and label data for crop yield forecasting are not always
available at the same spatial resolution. We propose a deep learning framework
that uses high resolution inputs and low resolution labels to produce crop
yield forecasts for both spatial levels. The forecasting model is calibrated by
weak supervision from low resolution crop area and yield statistics. We
evaluated the framework by disaggregating regional yields in Europe from parent
statistical regions to sub-regions for five countries (Germany, Spain, France,
Hungary, Italy) and two crops (soft wheat and potatoes). Performance of weakly
supervised models was compared with linear trend models and Gradient-Boosted
Decision Trees (GBDT). Higher resolution crop yield forecasts are useful to
policymakers and other stakeholders. Weakly supervised deep learning methods
provide a way to produce such forecasts even in the absence of high resolution
yield data.
