Data Sheet 1_A deep learning approach to county-level soybean yield forecasting using large-scale environmental data.docx

However, current yield forecasting approaches often lack in scale and are hard to interpret agronomically within real world scenarios. To fill the gap, this study developed an interpretable soybean yield forecasting framework using quantifiable county-level climate and soil attributes at scale for early, accurate yield predictions months ahead of harvest.</p>Methods<p>Here, 388 climate, soil, and elevation attributes were collected from a ~100-year time span over the continental US.

After preprocessing, Shapley Additive Explanation (SHAP) values were computed for each factor to aid in the interpretability of forecasts within the framework. Using the combined climate factors, a deep learning neural network was tuned and constructed to produce yield forecasts in May, June, July, and August.</p>Results<p>On a hold-out test set, these forecasts produced robust predictions, indicating the framework can reliably predict yields up to five months ahead of harvest.