Fine-scale agricultural fertilizer management promotes global NH3 emission reduction

Mitigating NH₃ emission from cropland soil is crucial for further improving air quality. Despite crop-specific emission factors (EFs) being provided by several studies, the lack of high-resolution EFs under different human management practices limits the further exploration of global cropland NH₃ mitigation potential. Agricultural NH₃ mitigation potential varies widely depending on where emissions are released. Thus, comprehensively and systematically assessing impacts on a fine scale is useful when developing strategies to efficiently mitigate the effects of NH₃ emission. In this study, we performed several machine learning models on a global NH₃ emission factors response dataset to find the relationship between EFs and climate conditions, soil properties, and human management. The random forest (RF) model with an R² of 0.78 and an RMSE of 0.88 showed the best estimation ability. Our data-driven approach indicated that the EFs were mainly affected by temperature, water input, N placement, crop type, and fertilizer type. The combine-effect of N application rate and temperature need to be further studied since these two variables interacted most in our RF model. Using the RF model, we provided five-arcminute high-resolution NH₃ emission factor maps under different management practices. The results showed that under proper management, the global NH₃ emission of rice, wheat, and maize production has a reduction potential of 44%, 34%, and 37%, respectively. The effects of different human management practices vary everywhere due to the interaction of environmental conditions and management. Our management-specific EFs can provide insights for fine-scale NH₃ emission control.