Ubbens, J.; Loliencar, P.; Kagale, S.

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 anticipate that large yield models can be used for both the optimization of crop production by producers, as well as by plant breeders and industry for crop design.