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Spatially explicit estimates of N 2 O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management
Author(s) -
Gerber James S.,
Carlson Kimberly M.,
Makowski David,
Mueller Nathaniel D.,
Garcia de CortazarAtauri Iñaki,
Havlík Petr,
Herrero Mario,
Launay Marie,
O'Connell Christine S.,
Smith Pete,
West Paul C.
Publication year - 2016
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.13341
Subject(s) - greenhouse gas , environmental science , fertilizer , agriculture , manure , climate change , soil water , manure management , atmospheric sciences , agronomy , soil science , geography , ecology , archaeology , geology , biology
Abstract With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N 2 O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N 2 O emissions at the country scale by aggregating all crops, under the assumption that N 2 O emissions are linearly related to N application. However, field studies and meta‐analyses indicate a nonlinear relationship, in which N 2 O 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 N 2 O emissions from croplands. We estimate 0.66 Tg of N 2 O‐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 N 2 O 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 N 2 O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N 2 O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high‐resolution N application data are critical to support accurate N 2 O emissions estimates.