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

Gerber, J.S., Carlson, K.M., Makowski, D., Mueller, N.D., Garcia de Cortazar-Atauri, I., Havlik, P. ORCID: https://orcid.org/0000-0001-5551-5085, Herrero, M., Launay, M., O'Connell, C.S., Smith, P., & West, P.C. (2016). Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management. Global Change Biology 22 (10) 3383-3394. 10.1111/gcb.13341.

Preview

Text Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (688kB) | Preview

Abstract

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 20-40% lower throughout Sub-Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak non-linear response of N2O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N2O emissions. Since aggregated fertilizer data generate underestimation bias in nonlinear models, high-resolution N application data are critical to support accurate N2O emissions estimates.

Actions (login required)

View Item