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This study investigates whether coupling crop modeling and machine learning(ML) improves corn yield predictions in the US Corn Belt. The main objectivesare to explore whether a hybrid approach (crop modeling + ML) would result inbetter predictions, investigate which combinations of hybrid models provide themost accurate predictions, and determine the features from the crop modelingthat are most effective to be integrated with ML for corn yield prediction.Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost)and six ensemble models have been designed to address the research question.The results suggest that adding simulation crop model variables (APSIM) asinput features to ML models can decrease yield prediction root mean squarederror (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion ofAPSIM features in the ML prediction models and we found soil moisture relatedAPSIM variables are most influential on the ML predictions followed bycrop-related and phenology-related variables. Finally, based on featureimportance measure, it has been observed that simulated APSIM average droughtstress and average water table depth during the growing season are the mostimportant APSIM inputs to ML. This result indicates that weather informationalone is not sufficient and ML models need more hydrological inputs to makeimproved yield predictions.

Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt