  Climate change is posing new challenges to crop-related concerns including
food insecurity, supply stability and economic planning. As one of the central
challenges, crop yield prediction has become a pressing task in the machine
learning field. Despite its importance, the prediction task is exceptionally
complicated since crop yields depend on various factors such as weather, land
surface, soil quality as well as their interactions. In recent years, machine
learning models have been successfully applied in this domain. However, these
models either restrict their tasks to a relatively small region, or only study
over a single or few years, which makes them hard to generalize spatially and
temporally. In this paper, we introduce a novel graph-based recurrent neural
network for crop yield prediction, to incorporate both geographical and
temporal knowledge in the model, and further boost predictive power. Our method
is trained, validated, and tested on over 2000 counties from 41 states in the
US mainland, covering years from 1981 to 2019. As far as we know, this is the
first machine learning method that embeds geographical knowledge in crop yield
prediction and predicts the crop yields at county level nationwide. We also
laid a solid foundation for the comparison with other machine learning
baselines by applying well-known linear models, tree-based models, deep
learning methods and comparing their performance. Experiments show that our
proposed method consistently outperforms the existing state-of-the-art methods
on various metrics, validating the effectiveness of geospatial and temporal
information.
