Spatially Optimized Nutrient Management as a Climate-Resilient Strategy to Reduce Nitrogen Runoff from Global Croplands.
Chen B, Tang J, Nan L, Li Y, Liu Z
Summary
PubMedExcessive nitrogen fertilizer runoff from global farmland pollutes waterways and harms aquatic life. Researchers used machine learning and global field data to create high-resolution maps showing how much nitrogen different crops lose, enabling farmers and policymakers to optimize fertilizer application to reduce pollution while maintaining yields.
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Key Findings
Nitrogen runoff from cropland fertilizers is a major driver of freshwater eutrophication with serious threats to aquatic ecosystems worldwide
Machine learning model trained on global field observations generated crop-specific, high-resolution nitrogen runoff emission factor maps for spatial optimization
Rice, wheat, and maize fields with synthetic nitrogen fertilizer produce approximately 2.33 Tg N per year in runoff emissions
Original Abstract
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
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