A Large Yield Model for Crop Production and Design in Western Canada

Abstract

With a changing climate, disease pressure, and other production threats, it is critical to ensure that crop producers are well-positioned to protect and optimize yields. In this work we present LYM-1, the first large-scale, multi-crop model for the prediction of yield performance in the Canadian prairies. This is enabled by a large dataset containing over 4.7 million yield observations across 10 different crop types, distributed over 23 growing years. Leveraging additional data sources for weather and soil properties allows the model to reason about the complex interactions between genetics, environment, and management which underlie yield. The trained model is not only effective at predicting the yield for held-out data, but also reveals scientifically and agronomically relevant effects such as the interaction between solar radiation and nitrogen uptake. We show that the architecture and training approach used in LYM-1 allows it to perform inference given incomplete queries where some model inputs are unspecified, while also establishing an advantage over popular linear and machine learning models in predictive performance.