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 2018 were 4.3 ± 1.0 Tg N yr⁻¹, lower than previous estimates that did not fully consider fertilizer management practices6–9. 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.A machine learning model for generating crop-specific and spatially explicit NH₃ emission factors globally shows that global NH₃ emissions in 2018 were lower than previous estimates that did not fully consider fertilizer management practices. [ABSTRACT FROM AUTHOR]