Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management

• Published in: Global change biology 2016-10, Vol.22 (10), p.3383-3394
• Main Authors: Gerber, James S., Carlson, Kimberly M., Makowski, David, Mueller, Nathaniel D., Garcia de Cortazar-Atauri, Iñaki, Havlík, Petr, Herrero, Mario, Launay, Marie, O'Connell, Christine S., Smith, Pete, West, Paul C.
• Format: Article
• Language: English
• Subjects: Climate change; Emissions; Estimates; flooded rice; greenhouse gas; Greenhouse gases; Life Sciences; manure; meta-analysis; N2O; Nitrogen; nitrous oxide; Pastures; sustainable agriculture
• ISSN: 1354-1013; 1365-2486
• DOI: 10.1111/gcb.13341

With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N2O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N2O emissions at the country scale by aggregating all crops, under the assumption that N2O emissions are linearly related to N application. However, field studies and meta‐analyses indicate a nonlinear relationship, in which N2O emissions are relatively greater at higher N application rates. Here, we apply a super‐linear emissions response model to crop‐specific, spatially explicit synthetic N fertilizer and manure N inputs to provide subnational accounting of global N2O emissions from croplands. We estimate 0.66 Tg of N2O‐N direct global emissions circa 2000, with 50% of emissions concentrated in 13% of harvested area. Compared to estimates from the IPCC Tier 1 linear model, our updated N2O emissions range from 20% to 40% lower throughout sub‐Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak nonlinear response of N2O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N2O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high‐resolution N application data are critical to support accurate N2O emissions estimates.