Agriculture accounts for a large percentage of nitrous oxide ($N_{2}O$) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and $N_{2}O$ volatilization processes. $N_{2}O$ emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and $N_{2}O$ emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect $N_{2}O$ emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict $N_{2}O$ emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect $N_{2}O$ emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches.