Data-driven estimates of global nitrous oxide emissions from croplands
Abstract Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961–2014, usi...
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| Published in | National Science Review Vol. 7; no. 2; pp. 441 - 452 |
|---|---|
| Main Authors | , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
China
Oxford University Press
01.02.2020
Oxford Academic |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Abstract
Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961–2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990–2014, but give a ∼15% lower EF in 1961–1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr–1) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways. |
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| AbstractList | Croplands are the single largest anthropogenic source of nitrous oxide (N$_2$O) globally, yet their estimatesremain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on ClimateChange (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961–2014, usingN-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributedsites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrativeunits. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990–2014, but give a ∼15% lowerEF in 1961–1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ±0.34 Tg N yr$^{–1}$) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, therest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2Oemissions from site-level observations to global croplands provides a new benchmark for constrainingIPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to informadvancement towards more realistic and effective mitigation pathways. Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961–2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990–2014, but give a ∼15% lower EF in 1961–1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr–1) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways. Croplands are the single largest anthropogenic source of nitrous oxide (N 2 O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N 2 O emissions in 1961–2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990–2014, but give a ∼15% lower EF in 1961–1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr –1 ) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N 2 O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways. Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961-2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990-2014, but give a ∼15% lower EF in 1961-1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr-1) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways.Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961-2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990-2014, but give a ∼15% lower EF in 1961-1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr-1) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways. Croplands are the single largest anthropogenic source of nitrous oxide (N O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N O emissions in 1961-2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990-2014, but give a ∼15% lower EF in 1961-1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr ) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways. Abstract Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961–2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990–2014, but give a ∼15% lower EF in 1961–1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr–1) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways. |
| Author | Tubiello, Francesco N Tian, Hanqin Winiwarter, Wilfried Wang, Qihui Tao, Shu Canadell, Josep G Zhou, Feng Ciais, Philippe Cui, Xiaoqing Janssens-Maenhout, Greet Piao, Shilong Shang, Ziyin Jackson, Robert B |
| AuthorAffiliation | 4 The Institute of Environmental Engineering , University of Zielona Góra, Zielona Góra 65-417, Poland 3 International Institute for Applied Systems Analysis (IIASA), Laxenburg A-2361, Austria 8 International Center for Climate and Global Change Research , School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama 36849, USA 7 European Commission , Joint Research Centre, Ispra 21027, Italy 6 Statistics Division, Food and Agricultural Organization of the United Nations, Via Terme di Caracalla , Rome 00153, Italy 9 Global Carbon Project , CSIRO Oceans and Atmosphere, Canberra ACT 2601, Australia 1 Sino-France Institute of Earth Systems Science , Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China 5 Department of Earth System Science , Stanford University, Stanford 94305, USA 2 Laboratoire des Sciences du Climat et de l'Environnement , LSCE, CEA CNRS UVSQ, Gif sur Yvette 91191, France |
| AuthorAffiliation_xml | – name: 6 Statistics Division, Food and Agricultural Organization of the United Nations, Via Terme di Caracalla , Rome 00153, Italy – name: 9 Global Carbon Project , CSIRO Oceans and Atmosphere, Canberra ACT 2601, Australia – name: 4 The Institute of Environmental Engineering , University of Zielona Góra, Zielona Góra 65-417, Poland – name: 3 International Institute for Applied Systems Analysis (IIASA), Laxenburg A-2361, Austria – name: 2 Laboratoire des Sciences du Climat et de l'Environnement , LSCE, CEA CNRS UVSQ, Gif sur Yvette 91191, France – name: 7 European Commission , Joint Research Centre, Ispra 21027, Italy – name: 1 Sino-France Institute of Earth Systems Science , Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China – name: 5 Department of Earth System Science , Stanford University, Stanford 94305, USA – name: 8 International Center for Climate and Global Change Research , School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama 36849, USA |
| Author_xml | – sequence: 1 givenname: Qihui surname: Wang fullname: Wang, Qihui organization: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China – sequence: 2 givenname: Feng orcidid: 0000-0001-6122-0611 surname: Zhou fullname: Zhou, Feng email: zhouf@pku.edu.cn organization: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China – sequence: 3 givenname: Ziyin surname: Shang fullname: Shang, Ziyin organization: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China – sequence: 4 givenname: Philippe surname: Ciais fullname: Ciais, Philippe organization: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China – sequence: 5 givenname: Wilfried surname: Winiwarter fullname: Winiwarter, Wilfried organization: International Institute for Applied Systems Analysis (IIASA), Laxenburg A-2361, Austria – sequence: 6 givenname: Robert B surname: Jackson fullname: Jackson, Robert B organization: Department of Earth System Science, Stanford University, Stanford 94305, USA – sequence: 7 givenname: Francesco N surname: Tubiello fullname: Tubiello, Francesco N organization: Statistics Division, Food and Agricultural Organization of the United Nations, Via Terme di Caracalla, Rome 00153, Italy – sequence: 8 givenname: Greet surname: Janssens-Maenhout fullname: Janssens-Maenhout, Greet organization: European Commission, Joint Research Centre, Ispra 21027, Italy – sequence: 9 givenname: Hanqin surname: Tian fullname: Tian, Hanqin organization: International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama 36849, USA – sequence: 10 givenname: Xiaoqing surname: Cui fullname: Cui, Xiaoqing organization: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China – sequence: 11 givenname: Josep G surname: Canadell fullname: Canadell, Josep G organization: Global Carbon Project, CSIRO Oceans and Atmosphere, Canberra ACT 2601, Australia – sequence: 12 givenname: Shilong surname: Piao fullname: Piao, Shilong organization: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China – sequence: 13 givenname: Shu surname: Tao fullname: Tao, Shu organization: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34692059$$D View this record in MEDLINE/PubMed https://hal.science/hal-02971355$$DView record in HAL |
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| Keywords | emission inventories flux upscaling nitrous oxide temporal trend emission factor agricultural soils |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0 The Author(s) 2019. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. Attribution: http://creativecommons.org/licenses/by |
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Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier... Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3... Croplands are the single largest anthropogenic source of nitrous oxide (N O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3... Croplands are the single largest anthropogenic source of nitrous oxide (N$_2$O) globally, yet their estimatesremain difficult to verify when using Tier 1 and 3... Croplands are the single largest anthropogenic source of nitrous oxide (N 2 O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3... |
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| SubjectTerms | Continental interfaces, environment Ocean, Atmosphere Sciences of the Universe |
| Title | Data-driven estimates of global nitrous oxide emissions from croplands |
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