Machine learning-based prediction of nitrous oxide emissions from arable farming: Exploring management practices as predictor variables

• Machine learning models for N 2 O emission prediction in arable farming were compared. • Random forest model exceled in weekly, Feedforward neural network in annual fluxes. • Management practice “days after hoeing” was most relevant predictor variable. • Further training is needed for large-scale application. Nitrous oxide emissions from agricultural activities significantly contribute to the global greenhouse gas balance, with approximately 60 % originating from agricultural soils, primarily due to nitrogen fertilizer application. Estimating these emissions from croplands for national reporting and mitigation strategies presents a complex challenge, considering the intricate interplay of meteorological factors, soil conditions, and management practices governing microbial processes such as nitrification and denitrification. Current estimation methods, including the 1 % IPCC approach and process-based models, face limitations due to incomplete process representation, parameter uncertainties, and complex initialization procedures. This study explores the potential of machine learning to improve the prediction of nitrous oxide emissions. We evaluated three machine learning algorithms (Random forest (RF), Extreme gradient boosting (XGBoost), and Feedforward neural network (FNN)) for their ability to predict weekly fluxes, peak flux, and annual emissions using data from a field study with seven different management treatments. A comprehensive set of predictor variables, including meteorological, soil, and management factors, was utilized. Cross-validation results demonstrate the superior performance of the RF model, achieving a root mean squared error of 8.51, surpassing the XGBoost model (9.28) and FNN model (9.08). Remarkably, analysis of cumulative emissions reveals that the FNN model, in particular, exhibits better predictive capability for annual trends compared to other models, with 72.5 % of predictions falling within the standard error range. The inclusion of agricultural management variables such as “Days after Hoeing” emerged as the dominant predictor, contributing to 40 % (RF)/55 % (XGBoost) of the prediction accuracy. These results demonstrate the potential of machine learning to become a robust, and time-efficient method for predicting N 2 O fluxes at different scales. Due to its potential generalizability, the large-scale application, e.g. for national greenhouse gas reporting, is envisioned. This requires further training with data from multiple locations with different site factors and land uses.

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