Modeling Nitrate Leaching from Danish Agricultural Fields Using a Machine Learning Approach

The environmental and economic impacts of nitrate leaching from agricultural soils highlight the need for accurate prediction to support effective nitrogen management. This study evaluated six machine learning (ML) models—Multiple Linear Regression, Elastic Net, K-Nearest Neighbors, Decision Trees, Extra Trees, and Multi-Layer Perceptron—using 2993 field observations to predict nitrate leaching. The models incorporated crop sequence, manure and fertilizer inputs, soil, and percolation and are benchmarked against the empirical NLES5 model used in Danish nitrogen regulation. Four ML models achieved higher predictive accuracy than NLES5 when evaluated on independent test data, with the Extra Trees model achieving the best performance (R2 = 0.63, RMSE = 23.3 kg N ha−1), exceeding NLES5 (R2 = 0.39, RMSE = 29.8 kg N ha−1). Model interpretability analyses identified winter percolation, winter vegetation cover, and soil type as key drivers of nitrate leaching. The Extra Trees model was further evaluated using scenario analyses of long-term leaching trends, marginal responses to spring-applied mineral nitrogen, and spatial patterns within the Bolbro Bæk catchment. Findings highlight the potential of ML models to improve nitrate leaching predictions in ungauged areas. Future research could incorporate additional variables, including crop yield and tillage practices, to enhance model accuracy and support sustainable agricultural practices that maintain productivity while reducing nitrate leaching.