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 ecosystems^1–5. However, estimating global NH₃ emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy^4,5. 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 2018 were 4.3 ± 1.0 Tg N yr⁻¹, lower than previous estimates that did not fully consider fertilizer management practices^6–9. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH₃ emissions by about 38% (1.6 ± 0.4 Tg N yr⁻¹) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH₃ emissions reductions of 47% (44–56%) for rice, 27% (24–28%) for maize and 26% (20–28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH₃ emissions could increase by 4.0 ± 2.7% under SSP1–2.6 and 5.5 ± 5.7% under SSP5–8.5 by 2030–2060. However, targeted fertilizer management has the potential to mitigate these increases.