Fertilizer management for global ammonia emission reduction
Crop production is a large source of atmospheric ammonia (NH3), which poses risks to air quality, human health and ecosystems1–5. However, estimating global NH3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy4,5. Here we develop a machine learning model for generating crop-specific and spatially explicit NH3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH3 emissions from rice, wheat and maize fields in ...