Fertilizer management for global ammonia emission reduction.

Abstract

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.