Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model

Abstract

A major challenge for food security worldwide is the large inter-annual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed Long Short-Term Memory model with attention (LSTMatt) outperforms other ML models (including other variations of LSTM developed in this study), and explains 73% of the spatiotemporal variance of the observed maize yield, in contrast to 16% explained by the regionally calibrated PBM; the magnitude of yield prediction errors in LSTMatt is about one-third of that in the PBM. When applied to the extreme drought year 2012 that has no counterpart in the training data, the LSTMatt performance drops but still shows advantage over the PBM. Findings from this study suggest a great potential for out-of-sample application of the 𝐿𝑆𝑇𝑀𝑎𝑡𝑡 model to predict crop yieldunder a changing climate.