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

TL;DR

This paper demonstrates that integrating crop simulation variables with machine learning models significantly enhances corn yield prediction accuracy in the US Corn Belt, highlighting the importance of hydrological features.

Contribution

It introduces a hybrid modeling approach combining crop simulation outputs with ML, identifying key features that improve yield prediction accuracy.

Findings

Adding APSIM variables reduces RMSE by up to 20%.

Soil moisture and water table depth are the most influential features.

Hydrological inputs are essential for accurate yield predictions.

Abstract

This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that 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) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.