Predicting nitrous oxide emissions from soil planted to sugarcane under various irrigation regimes using machine learning models

A scalable, data-driven machine learning model for N2O emissions was refined with data from sugarcane crops. A simulation model for forecasting nitrous oxide emissions under various environmental conditions was developed. The efficacy of the random forest model for predicting N2O emissions from irrigated sugarcane fields was determined. The relative impacts of watering regimes, soil types and environmental factors on N2O emissions from sugarcane crops are presented.