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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

United statesUnited kingdomVan dammeCommunity emissions data systemResearch centreEuropean commissionAgriculture organization of the united nationsChange biolAir pollutantJoint research centreModel devAgriculture organizationUnited nations