Abstract
Mitigating soil nitrous oxide (N2O) emissions is essential for staying below a 2 °C warming threshold. However, accurate assessments of mitigation potential are limited by uncertainty and variability in direct emission factors (EFs). To assess where and why EFs differ, we created high-resolution maps of crop-specific EFs based on 1,507 georeferenced field observations. Here, using a data-driven approach, we show that EFs vary by two orders of magnitude over space. At global and regional scales, such variation is primarily driven by climatic and edaphic factors rather than the well-recognized management practices. Combining spatially explicit EFs with N surplus information, we conclude that global mitigation potential without compromising crop production is 30% (95% confidence interval, 17–53%) of direct soil emissions of N2O, equivalent to the entire direct soil emissions of China and the United States combined. Two-thirds (65%) of the mitigation potential could be achieved on one-fifth of the global harvested area, mainly located in humid subtropical climates and across gleysols and acrisols. These findings highlight the value of a targeted policy approach on global hotspots that could deliver large N2O mitigation as well as environmental and food co-benefits.
| Original language | English |
|---|---|
| Pages (from-to) | 886-893 |
| Number of pages | 8 |
| Journal | Nature Food |
| Volume | 2 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - Nov 2021 |
Bibliographical note
Funding Information:This study was supported by the National Natural Science Foundation of China (grant nos 41671464 to F.Z. and 41830751 to X.J.), the China Postdoctoral Science Foundation (grant no. 2019M660301 to X.C.), the US National Science Foundation (grant no. 1903722 to H.T.) and the French ANR under the CLAND ‘Investissements d'avenir’ programme (grant no. ANR-16-CONV-0003 to P.C.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank Z. Shang from the University of Aberdeen for sharing the dataset that includes growing-season and whole-year EFs and the fallow EF models. We acknowledge field experimentalists for publishing or sharing chamber-based observations of N2O flux. We acknowledge FAO and IFA for national statistics on synthetic fertilizers, and we thank 38 agencies for subnational statistics on synthetic fertilizers. The views expressed in this publication are those of the authors and do not necessarily reflect the views or policies of FAO. We also acknowledge IIASA, CGIAR, the University of East Anglia, the University of Minnesota and V. Porwollik from the Potsdam Institute for Climate Impact Research for sharing other model input data.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.
Funding
This study was supported by the National Natural Science Foundation of China (grant nos 41671464 to F.Z. and 41830751 to X.J.), the China Postdoctoral Science Foundation (grant no. 2019M660301 to X.C.), the US National Science Foundation (grant no. 1903722 to H.T.) and the French ANR under the CLAND ‘Investissements d'avenir’ programme (grant no. ANR-16-CONV-0003 to P.C.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank Z. Shang from the University of Aberdeen for sharing the dataset that includes growing-season and whole-year EFs and the fallow EF models. We acknowledge field experimentalists for publishing or sharing chamber-based observations of N2O flux. We acknowledge FAO and IFA for national statistics on synthetic fertilizers, and we thank 38 agencies for subnational statistics on synthetic fertilizers. The views expressed in this publication are those of the authors and do not necessarily reflect the views or policies of FAO. We also acknowledge IIASA, CGIAR, the University of East Anglia, the University of Minnesota and V. Porwollik from the Potsdam Institute for Climate Impact Research for sharing other model input data.