Activity data on crop management define uncertainty of CH4 and N2O emission estimates from rice: A case study of Vietnam
Background Globally, rice systems are a major source of atmospheric CH4 and for major rice‐producing countries, such as Vietnam, CH4 as well as N2O emissions from agricultural land used for rice production may represent about one‐fourth of total national anthropogenic greenhouse gas (GHG) emissions....
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Veröffentlicht in: | Journal of plant nutrition and soil science 2022-12, Vol.185 (6), p.793-806 |
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creator | Butterbach‐Bahl, Klaus Kraus, David Kiese, Ralf Mai, Van Trinh Nguyen, Tanh Sander, Björn Ole Wassmann, Reiner Werner, Christian |
description | Background
Globally, rice systems are a major source of atmospheric CH4 and for major rice‐producing countries, such as Vietnam, CH4 as well as N2O emissions from agricultural land used for rice production may represent about one‐fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national‐scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality.
Aims
Here, we used the biogeochemical model LandscapeDNDC to calculate emissions of CH4 and N2O from rice systems in Vietnam (Tier 3 IPCC approach). Our objectives were to identify hotspot regions of emissions and to assess the contribution of N2O to the total non‐CO2 (CH4+N2O) GHG balance of rice systems as well as the seasonal and interannual variability of fluxes in dependence of uncertain input data on field management .
Methods
The biogeochemical model LandscapeDNDC model was linked to publicly available information on climate, soils, and land management (fertilization, irrigation, crop rotation) for calculating a national inventory in daily time steps of CH4 and N2O emissions from rice systems at a spatial resolution of 0.083° × 0.083°. Uncertainty in management practices related to fertilization, use of harvest residues or irrigation water, and its effects on simulated CH4 and N2O fluxes was accounted for by Latin Hypercube Sampling of probability distribution functions.
Results
Our study shows that CH4 and N2O fluxes from rice systems in Vietnam are highly seasonal, with national CH4 and N2O emissions totaling to about 2600 Gg CH4 year–1 and 42 Gg N2O year–1, respectively. Highest emissions were simulated for double and triple rice cropping systems in the Mekong Delta region. Yield‐scaled emissions varied largely in a range of 300–3000 kg CO2‐eq Mg–1 year–1, with CH4 emissions during the rice season(s) dominating (>82%) the total annual non‐CO2 GHG balance of rice systems. In our study, uncertainty in field management information (nitrogen fertilization, ratio synthetic to organic fertilization, residue management, availability of irrigation water) were major drivers of uncertainty of the national CH4 and N2O emission inventory.
Conclusions
Our study shows that Tier 3 approaches, that is, process‐oriented model approaches combined with GIS databases, for estimating national‐scale GHG emissions from rice systems are ready to be applied at national scale. Generally, this approach is powerful as it allows t |
doi_str_mv | 10.1002/jpln.202200382 |
format | Article |
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Globally, rice systems are a major source of atmospheric CH4 and for major rice‐producing countries, such as Vietnam, CH4 as well as N2O emissions from agricultural land used for rice production may represent about one‐fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national‐scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality.
Aims
Here, we used the biogeochemical model LandscapeDNDC to calculate emissions of CH4 and N2O from rice systems in Vietnam (Tier 3 IPCC approach). Our objectives were to identify hotspot regions of emissions and to assess the contribution of N2O to the total non‐CO2 (CH4+N2O) GHG balance of rice systems as well as the seasonal and interannual variability of fluxes in dependence of uncertain input data on field management .
Methods
The biogeochemical model LandscapeDNDC model was linked to publicly available information on climate, soils, and land management (fertilization, irrigation, crop rotation) for calculating a national inventory in daily time steps of CH4 and N2O emissions from rice systems at a spatial resolution of 0.083° × 0.083°. Uncertainty in management practices related to fertilization, use of harvest residues or irrigation water, and its effects on simulated CH4 and N2O fluxes was accounted for by Latin Hypercube Sampling of probability distribution functions.
Results
Our study shows that CH4 and N2O fluxes from rice systems in Vietnam are highly seasonal, with national CH4 and N2O emissions totaling to about 2600 Gg CH4 year–1 and 42 Gg N2O year–1, respectively. Highest emissions were simulated for double and triple rice cropping systems in the Mekong Delta region. Yield‐scaled emissions varied largely in a range of 300–3000 kg CO2‐eq Mg–1 year–1, with CH4 emissions during the rice season(s) dominating (>82%) the total annual non‐CO2 GHG balance of rice systems. In our study, uncertainty in field management information (nitrogen fertilization, ratio synthetic to organic fertilization, residue management, availability of irrigation water) were major drivers of uncertainty of the national CH4 and N2O emission inventory.
Conclusions
Our study shows that Tier 3 approaches, that is, process‐oriented model approaches combined with GIS databases, for estimating national‐scale GHG emissions from rice systems are ready to be applied at national scale. Generally, this approach is powerful as it allows to identify regions with elevated emissions, thereby accounting not only for CH4, but as well for N2O emissions. However, our study also shows that specifically better information on land management is required to narrowing uncertainties.</description><identifier>ISSN: 1436-8730</identifier><identifier>EISSN: 1522-2624</identifier><identifier>DOI: 10.1002/jpln.202200382</identifier><language>eng</language><publisher>Weinheim: Wiley Subscription Services, Inc</publisher><subject>Agricultural land ; Agricultural practices ; Anthropogenic factors ; Availability ; biogeochemical modeling ; Biogeochemistry ; Carbon dioxide ; Cereal crops ; Crop management ; Crop production ; Crop rotation ; Cropping systems ; Distribution functions ; Emission analysis ; Emission inventories ; Emissions ; Estimates ; Farm buildings ; Fertilization ; field management ; Fluxes ; Greenhouse gases ; Hypercubes ; Information management ; Intergovernmental Panel on Climate Change ; Irrigation ; Irrigation water ; Land management ; Land use planning ; Latin hypercube sampling ; Methane ; Nitrous oxide ; Organic fertilizers ; Probability distribution ; Probability distribution functions ; Residues ; Rice ; Rice fields ; rice systems ; Seasonal distribution ; Seasonal variations ; soil CH4 and N2O emissions ; Spatial discrimination ; Spatial distribution ; Spatial resolution ; Uncertainty ; uncertainty assessment</subject><ispartof>Journal of plant nutrition and soil science, 2022-12, Vol.185 (6), p.793-806</ispartof><rights>2022 The Authors. Journal of Plant Nutrition and Soil Science published by Wiley‐VCH GmbH</rights><rights>2022. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-0847-4185 ; 0000-0001-7032-8683 ; 0000-0003-2485-8389 ; 0000-0002-7967-6147 ; 0000-0002-2814-4888 ; 0000-0001-9499-6598</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjpln.202200382$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjpln.202200382$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Butterbach‐Bahl, Klaus</creatorcontrib><creatorcontrib>Kraus, David</creatorcontrib><creatorcontrib>Kiese, Ralf</creatorcontrib><creatorcontrib>Mai, Van Trinh</creatorcontrib><creatorcontrib>Nguyen, Tanh</creatorcontrib><creatorcontrib>Sander, Björn Ole</creatorcontrib><creatorcontrib>Wassmann, Reiner</creatorcontrib><creatorcontrib>Werner, Christian</creatorcontrib><title>Activity data on crop management define uncertainty of CH4 and N2O emission estimates from rice: A case study of Vietnam</title><title>Journal of plant nutrition and soil science</title><description>Background
Globally, rice systems are a major source of atmospheric CH4 and for major rice‐producing countries, such as Vietnam, CH4 as well as N2O emissions from agricultural land used for rice production may represent about one‐fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national‐scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality.
Aims
Here, we used the biogeochemical model LandscapeDNDC to calculate emissions of CH4 and N2O from rice systems in Vietnam (Tier 3 IPCC approach). Our objectives were to identify hotspot regions of emissions and to assess the contribution of N2O to the total non‐CO2 (CH4+N2O) GHG balance of rice systems as well as the seasonal and interannual variability of fluxes in dependence of uncertain input data on field management .
Methods
The biogeochemical model LandscapeDNDC model was linked to publicly available information on climate, soils, and land management (fertilization, irrigation, crop rotation) for calculating a national inventory in daily time steps of CH4 and N2O emissions from rice systems at a spatial resolution of 0.083° × 0.083°. Uncertainty in management practices related to fertilization, use of harvest residues or irrigation water, and its effects on simulated CH4 and N2O fluxes was accounted for by Latin Hypercube Sampling of probability distribution functions.
Results
Our study shows that CH4 and N2O fluxes from rice systems in Vietnam are highly seasonal, with national CH4 and N2O emissions totaling to about 2600 Gg CH4 year–1 and 42 Gg N2O year–1, respectively. Highest emissions were simulated for double and triple rice cropping systems in the Mekong Delta region. Yield‐scaled emissions varied largely in a range of 300–3000 kg CO2‐eq Mg–1 year–1, with CH4 emissions during the rice season(s) dominating (>82%) the total annual non‐CO2 GHG balance of rice systems. In our study, uncertainty in field management information (nitrogen fertilization, ratio synthetic to organic fertilization, residue management, availability of irrigation water) were major drivers of uncertainty of the national CH4 and N2O emission inventory.
Conclusions
Our study shows that Tier 3 approaches, that is, process‐oriented model approaches combined with GIS databases, for estimating national‐scale GHG emissions from rice systems are ready to be applied at national scale. Generally, this approach is powerful as it allows to identify regions with elevated emissions, thereby accounting not only for CH4, but as well for N2O emissions. However, our study also shows that specifically better information on land management is required to narrowing uncertainties.</description><subject>Agricultural land</subject><subject>Agricultural practices</subject><subject>Anthropogenic factors</subject><subject>Availability</subject><subject>biogeochemical modeling</subject><subject>Biogeochemistry</subject><subject>Carbon dioxide</subject><subject>Cereal crops</subject><subject>Crop management</subject><subject>Crop production</subject><subject>Crop rotation</subject><subject>Cropping systems</subject><subject>Distribution functions</subject><subject>Emission analysis</subject><subject>Emission inventories</subject><subject>Emissions</subject><subject>Estimates</subject><subject>Farm buildings</subject><subject>Fertilization</subject><subject>field management</subject><subject>Fluxes</subject><subject>Greenhouse gases</subject><subject>Hypercubes</subject><subject>Information management</subject><subject>Intergovernmental Panel on Climate Change</subject><subject>Irrigation</subject><subject>Irrigation water</subject><subject>Land management</subject><subject>Land use planning</subject><subject>Latin hypercube sampling</subject><subject>Methane</subject><subject>Nitrous oxide</subject><subject>Organic fertilizers</subject><subject>Probability distribution</subject><subject>Probability distribution functions</subject><subject>Residues</subject><subject>Rice</subject><subject>Rice fields</subject><subject>rice systems</subject><subject>Seasonal distribution</subject><subject>Seasonal variations</subject><subject>soil CH4 and N2O emissions</subject><subject>Spatial discrimination</subject><subject>Spatial distribution</subject><subject>Spatial resolution</subject><subject>Uncertainty</subject><subject>uncertainty assessment</subject><issn>1436-8730</issn><issn>1522-2624</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNo9kM1PwzAMxSMEEmNw5RyJc0fqNG3CbZqAgaaNA3CNsnygTGtamhTYf0_H0E62pffs5x9C1zmZ5ITA7abdhgkQAEIohxM0yhlABiUUp0Nf0DLjFSXn6CLGDSGkyAWM0M9UJ__l0w4blRRuAtZd0-JaBfVhaxsSNtb5YHEftO2S8mGQNg7P5gVWweAlrLCtfYx-sNqYfK2Sjdh1TY07r-0dnmKtosUx9ebP-e5tCqq-RGdObaO9-q9j9PZw_zqbZ4vV49NsushaqChka-aoAcZB5MaWfF0JVoqqYEI7XhhFha6ocbwUlnHDSl0Wlld56YRzToNZ0zG6Oextu-azHxLKTdN3YTgpoSp4zgQZsIyROKi-_dbuZNsNf3Q7mRO5Ryv3aOURrXx-WSyPE_0FKzxv4g</recordid><startdate>202212</startdate><enddate>202212</enddate><creator>Butterbach‐Bahl, Klaus</creator><creator>Kraus, David</creator><creator>Kiese, Ralf</creator><creator>Mai, Van Trinh</creator><creator>Nguyen, Tanh</creator><creator>Sander, Björn Ole</creator><creator>Wassmann, Reiner</creator><creator>Werner, Christian</creator><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>7ST</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-0847-4185</orcidid><orcidid>https://orcid.org/0000-0001-7032-8683</orcidid><orcidid>https://orcid.org/0000-0003-2485-8389</orcidid><orcidid>https://orcid.org/0000-0002-7967-6147</orcidid><orcidid>https://orcid.org/0000-0002-2814-4888</orcidid><orcidid>https://orcid.org/0000-0001-9499-6598</orcidid></search><sort><creationdate>202212</creationdate><title>Activity data on crop management define uncertainty of CH4 and N2O emission estimates from rice: A case study of Vietnam</title><author>Butterbach‐Bahl, Klaus ; Kraus, David ; Kiese, Ralf ; Mai, Van Trinh ; Nguyen, Tanh ; Sander, Björn Ole ; Wassmann, Reiner ; Werner, Christian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p2732-b5f3d258291de68b795697459cf84da39c73df869e58d56c64e8716f9fffc2db3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Agricultural land</topic><topic>Agricultural practices</topic><topic>Anthropogenic factors</topic><topic>Availability</topic><topic>biogeochemical modeling</topic><topic>Biogeochemistry</topic><topic>Carbon dioxide</topic><topic>Cereal crops</topic><topic>Crop management</topic><topic>Crop production</topic><topic>Crop rotation</topic><topic>Cropping systems</topic><topic>Distribution functions</topic><topic>Emission analysis</topic><topic>Emission inventories</topic><topic>Emissions</topic><topic>Estimates</topic><topic>Farm buildings</topic><topic>Fertilization</topic><topic>field management</topic><topic>Fluxes</topic><topic>Greenhouse gases</topic><topic>Hypercubes</topic><topic>Information management</topic><topic>Intergovernmental Panel on Climate Change</topic><topic>Irrigation</topic><topic>Irrigation water</topic><topic>Land management</topic><topic>Land use planning</topic><topic>Latin hypercube sampling</topic><topic>Methane</topic><topic>Nitrous oxide</topic><topic>Organic fertilizers</topic><topic>Probability distribution</topic><topic>Probability distribution functions</topic><topic>Residues</topic><topic>Rice</topic><topic>Rice fields</topic><topic>rice systems</topic><topic>Seasonal distribution</topic><topic>Seasonal variations</topic><topic>soil CH4 and N2O emissions</topic><topic>Spatial discrimination</topic><topic>Spatial distribution</topic><topic>Spatial resolution</topic><topic>Uncertainty</topic><topic>uncertainty assessment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Butterbach‐Bahl, Klaus</creatorcontrib><creatorcontrib>Kraus, David</creatorcontrib><creatorcontrib>Kiese, Ralf</creatorcontrib><creatorcontrib>Mai, Van Trinh</creatorcontrib><creatorcontrib>Nguyen, Tanh</creatorcontrib><creatorcontrib>Sander, Björn Ole</creatorcontrib><creatorcontrib>Wassmann, Reiner</creatorcontrib><creatorcontrib>Werner, Christian</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Journal of plant nutrition and soil science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Butterbach‐Bahl, Klaus</au><au>Kraus, David</au><au>Kiese, Ralf</au><au>Mai, Van Trinh</au><au>Nguyen, Tanh</au><au>Sander, Björn Ole</au><au>Wassmann, Reiner</au><au>Werner, Christian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Activity data on crop management define uncertainty of CH4 and N2O emission estimates from rice: A case study of Vietnam</atitle><jtitle>Journal of plant nutrition and soil science</jtitle><date>2022-12</date><risdate>2022</risdate><volume>185</volume><issue>6</issue><spage>793</spage><epage>806</epage><pages>793-806</pages><issn>1436-8730</issn><eissn>1522-2624</eissn><abstract>Background
Globally, rice systems are a major source of atmospheric CH4 and for major rice‐producing countries, such as Vietnam, CH4 as well as N2O emissions from agricultural land used for rice production may represent about one‐fourth of total national anthropogenic greenhouse gas (GHG) emissions. However, national‐scale estimates of GHG emissions from rice systems are uncertain with regard to its magnitude, spatial distribution, and seasonality.
Aims
Here, we used the biogeochemical model LandscapeDNDC to calculate emissions of CH4 and N2O from rice systems in Vietnam (Tier 3 IPCC approach). Our objectives were to identify hotspot regions of emissions and to assess the contribution of N2O to the total non‐CO2 (CH4+N2O) GHG balance of rice systems as well as the seasonal and interannual variability of fluxes in dependence of uncertain input data on field management .
Methods
The biogeochemical model LandscapeDNDC model was linked to publicly available information on climate, soils, and land management (fertilization, irrigation, crop rotation) for calculating a national inventory in daily time steps of CH4 and N2O emissions from rice systems at a spatial resolution of 0.083° × 0.083°. Uncertainty in management practices related to fertilization, use of harvest residues or irrigation water, and its effects on simulated CH4 and N2O fluxes was accounted for by Latin Hypercube Sampling of probability distribution functions.
Results
Our study shows that CH4 and N2O fluxes from rice systems in Vietnam are highly seasonal, with national CH4 and N2O emissions totaling to about 2600 Gg CH4 year–1 and 42 Gg N2O year–1, respectively. Highest emissions were simulated for double and triple rice cropping systems in the Mekong Delta region. Yield‐scaled emissions varied largely in a range of 300–3000 kg CO2‐eq Mg–1 year–1, with CH4 emissions during the rice season(s) dominating (>82%) the total annual non‐CO2 GHG balance of rice systems. In our study, uncertainty in field management information (nitrogen fertilization, ratio synthetic to organic fertilization, residue management, availability of irrigation water) were major drivers of uncertainty of the national CH4 and N2O emission inventory.
Conclusions
Our study shows that Tier 3 approaches, that is, process‐oriented model approaches combined with GIS databases, for estimating national‐scale GHG emissions from rice systems are ready to be applied at national scale. Generally, this approach is powerful as it allows to identify regions with elevated emissions, thereby accounting not only for CH4, but as well for N2O emissions. However, our study also shows that specifically better information on land management is required to narrowing uncertainties.</abstract><cop>Weinheim</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/jpln.202200382</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-0847-4185</orcidid><orcidid>https://orcid.org/0000-0001-7032-8683</orcidid><orcidid>https://orcid.org/0000-0003-2485-8389</orcidid><orcidid>https://orcid.org/0000-0002-7967-6147</orcidid><orcidid>https://orcid.org/0000-0002-2814-4888</orcidid><orcidid>https://orcid.org/0000-0001-9499-6598</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Agricultural land Agricultural practices Anthropogenic factors Availability biogeochemical modeling Biogeochemistry Carbon dioxide Cereal crops Crop management Crop production Crop rotation Cropping systems Distribution functions Emission analysis Emission inventories Emissions Estimates Farm buildings Fertilization field management Fluxes Greenhouse gases Hypercubes Information management Intergovernmental Panel on Climate Change Irrigation Irrigation water Land management Land use planning Latin hypercube sampling Methane Nitrous oxide Organic fertilizers Probability distribution Probability distribution functions Residues Rice Rice fields rice systems Seasonal distribution Seasonal variations soil CH4 and N2O emissions Spatial discrimination Spatial distribution Spatial resolution Uncertainty uncertainty assessment |
title | Activity data on crop management define uncertainty of CH4 and N2O emission estimates from rice: A case study of Vietnam |
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