Attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of Argentina
The increasing proportion of agricultural lands worldwide makes it necessary to intensify the research concerning the carbon exchange at agricultural sites. In order to determine the Net Ecosystem Exchange (NEE) in an agricultural landscape in the province of Buenos Aires, Argentina, we carried out...
| Autores principales: | , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2018
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/3595 https://rd.springer.com/article/10.1007%2Fs10666-013-9395-x https://doi.org/10.1007/s10666-013-9395-x |
| _version_ | 1855483267696820224 |
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| author | Posse Beaulieu, Gabriela Richter, Klaus Lewczuk, Nuria Andrea Cristiano, Piedad Maria Gattinoni, Natalia Noemí Rebella, Cesar Manuel Achkar, Antonio |
| author_browse | Achkar, Antonio Cristiano, Piedad Maria Gattinoni, Natalia Noemí Lewczuk, Nuria Andrea Posse Beaulieu, Gabriela Rebella, Cesar Manuel Richter, Klaus |
| author_facet | Posse Beaulieu, Gabriela Richter, Klaus Lewczuk, Nuria Andrea Cristiano, Piedad Maria Gattinoni, Natalia Noemí Rebella, Cesar Manuel Achkar, Antonio |
| author_sort | Posse Beaulieu, Gabriela |
| collection | INTA Digital |
| description | The increasing proportion of agricultural lands worldwide makes it necessary to intensify the research concerning the carbon exchange at agricultural sites. In order to determine the Net Ecosystem Exchange (NEE) in an agricultural landscape in the province of Buenos Aires, Argentina, we carried out eddy covariance measurements with a flux tower, which was placed between two agricultural fields. Therefore, the measured CO2 flux represents the accumulated flux from both areas, i.e., from different crop types. We here present an analysis method which attributes the flux to the two crop types. For this analysis, we applied the Hsieh footprint model to identify the contributing source area to the flux measurement. We then applied a multiple regression analysis to calculate the NEE in the growing season 2011/2012 for each field separately. The pronounced differences in the time courses of the CO2 fluxes in the two fields can be explained by the different sowing times and different growth stages of both cultivations. The time courses furthermore show that the CO2 uptake of the plants was strongly affected by the drought which lasted from December 2011 to January 2012. For the growth cycle of maize (216 days), the NEE was −240 g C m−2 and for the growth cycle of soybean (154 days) −231 g C m−2. In order to obtain the NEE of a complete agricultural cycle (from harvest to harvest), we also considered the NEE of autumn and winter 2011. Uncertainties of the spatially partitioned NEE are quantified and discussed. |
| format | Artículo |
| id | INTA3595 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INTA35952022-09-12T16:16:09Z Attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of Argentina Posse Beaulieu, Gabriela Richter, Klaus Lewczuk, Nuria Andrea Cristiano, Piedad Maria Gattinoni, Natalia Noemí Rebella, Cesar Manuel Achkar, Antonio Dióxido de Carbono Sequía Maíz Zea Mays Soja Glycine Max Cobertura de Suelos Carbon Dioxide Drought Maize Soybeans Land Cover Argentina Eddy Covariance Footprint Model Heterogeneous Land Cover The increasing proportion of agricultural lands worldwide makes it necessary to intensify the research concerning the carbon exchange at agricultural sites. In order to determine the Net Ecosystem Exchange (NEE) in an agricultural landscape in the province of Buenos Aires, Argentina, we carried out eddy covariance measurements with a flux tower, which was placed between two agricultural fields. Therefore, the measured CO2 flux represents the accumulated flux from both areas, i.e., from different crop types. We here present an analysis method which attributes the flux to the two crop types. For this analysis, we applied the Hsieh footprint model to identify the contributing source area to the flux measurement. We then applied a multiple regression analysis to calculate the NEE in the growing season 2011/2012 for each field separately. The pronounced differences in the time courses of the CO2 fluxes in the two fields can be explained by the different sowing times and different growth stages of both cultivations. The time courses furthermore show that the CO2 uptake of the plants was strongly affected by the drought which lasted from December 2011 to January 2012. For the growth cycle of maize (216 days), the NEE was −240 g C m−2 and for the growth cycle of soybean (154 days) −231 g C m−2. In order to obtain the NEE of a complete agricultural cycle (from harvest to harvest), we also considered the NEE of autumn and winter 2011. Uncertainties of the spatially partitioned NEE are quantified and discussed. Instituto de Clima y Agua Fil: Posse Beaulieu, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Richter, Klaus. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Lewczuk, Nuria Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Cristiano, Maria Piedad. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Gattinoni, Natalia Noemí. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Rebella, Cesar Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Achkar, Antonio. Universidad Católica de Santa Fe. Área Informática; Argentina 2018-10-16T12:09:22Z 2018-10-16T12:09:22Z 2014-10 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/3595 https://rd.springer.com/article/10.1007%2Fs10666-013-9395-x 1420-2026 1573-2967 (Online) https://doi.org/10.1007/s10666-013-9395-x eng info:eu-repo/semantics/restrictedAccess application/pdf Argentina (nation) Springer Environmental modeling & assessment 19 (5) : 361–372. (October 2014) |
| spellingShingle | Dióxido de Carbono Sequía Maíz Zea Mays Soja Glycine Max Cobertura de Suelos Carbon Dioxide Drought Maize Soybeans Land Cover Argentina Eddy Covariance Footprint Model Heterogeneous Land Cover Posse Beaulieu, Gabriela Richter, Klaus Lewczuk, Nuria Andrea Cristiano, Piedad Maria Gattinoni, Natalia Noemí Rebella, Cesar Manuel Achkar, Antonio Attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of Argentina |
| title | Attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of Argentina |
| title_full | Attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of Argentina |
| title_fullStr | Attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of Argentina |
| title_full_unstemmed | Attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of Argentina |
| title_short | Attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of Argentina |
| title_sort | attribution of carbon dioxide fluxes to crop types in a heterogeneous agricultural landscape of argentina |
| topic | Dióxido de Carbono Sequía Maíz Zea Mays Soja Glycine Max Cobertura de Suelos Carbon Dioxide Drought Maize Soybeans Land Cover Argentina Eddy Covariance Footprint Model Heterogeneous Land Cover |
| url | http://hdl.handle.net/20.500.12123/3595 https://rd.springer.com/article/10.1007%2Fs10666-013-9395-x https://doi.org/10.1007/s10666-013-9395-x |
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