Spatially Optimized Nutrient Management as a Climate-Resilient Strategy to Reduce Nitrogen Runoff from Global Croplands

Nitrogen fertilizer, as an indispensable input in crop
production,
has played a crucial role in enhancing crop yields. However, the associated
cropland nitrogen runoff has significantly intensified surface water
eutrophication, posing serious threats to aquatic ecosystems globally.
Accurate estimation of cropland nitrogen runoff is essential for designing
effective mitigation strategies, yet research in this area remains
constrained by limited data availability. To address this gap, we
developed a machine learning model based on a globally compiled data
set of field observations. Using a global spatial interpolation approach
driven by the similarity of environmental and management variables,
we generated crop-specific, high-resolution global maps of nitrogen
runoff emission factors. Our results showed that synthetic nitrogen
fertilizer application in rice, wheat, and maize fields generated
approximately 2.33 Tg N yr–1 (95% CI: 1.93–2.70
Tg N yr–1) of nitrogen runoff in 2020. Under future
climate change scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5), global
nitrogen runoff was projected to increase by −0.2–1.4%
by 2060 and 0.8–3.2% by 2100. We further evaluated 96 crop-nutrient
management scenarios and identified region-specific, rather than universal,
nitrogen runoff mitigation pathways. Spatially optimized nutrient
management could reduce cropland nitrogen runoff by 22.2% (0.52 Tg
N yr–1) without changing total nitrogen fertilizer
input and showed sustained mitigation effects across future climate
change scenarios. This study provides a climate-resilient and scalable
spatial optimization strategy for mitigating cropland nitrogen runoff
pollution and offers actionable guidance for sustainable cropland
nutrient management.