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Abstract
Estimations of Net Ecosystem Exchange (NEE) are crucial to assess the carbon sequestration/carbon source capacity of agricultural systems. Although several global models have been built to describe carbon flux patterns based on flux tower data, South American ecosystems (and croplands in particular) are underrepresented in the databases used to calibrate these models, leading to large uncertainties in regional and global NEE estimation. Despite the fact [ver mas...]
dc.contributor.authorMarconato, Ulises Mariano
dc.contributor.authorFernández, Roberto J.
dc.contributor.authorPosse Beaulieu, Gabriela
dc.date.accessioned2022-09-23T10:23:42Z
dc.date.available2022-09-23T10:23:42Z
dc.date.issued2022-06-23
dc.identifier.issn2673-8619
dc.identifier.otherhttps://doi.org/10.3389/fsoil.2022.903544
dc.identifier.urihttp://hdl.handle.net/20.500.12123/12946
dc.identifier.urihttps://www.frontiersin.org/articles/10.3389/fsoil.2022.903544/full
dc.description.abstractEstimations of Net Ecosystem Exchange (NEE) are crucial to assess the carbon sequestration/carbon source capacity of agricultural systems. Although several global models have been built to describe carbon flux patterns based on flux tower data, South American ecosystems (and croplands in particular) are underrepresented in the databases used to calibrate these models, leading to large uncertainties in regional and global NEE estimation. Despite the fact that almost half of the land surface is used worldwide for agricultural activities, these models still do not include variables related to cropland management. Using enhanced vegetation index (EVI) derived from MODIS imagery (250m) and monthly CO2 exchange from a 9-year record of an eddy covariance (EC) flux tower in a crop field in the Inland Pampas region, we developed regression models to predict monthly NEE. We tested whether including a term for crop identity/land cover as a categorical variable (maize, soybean, wheat, and fallow) could improve model capability in capturing monthly NEE dynamics. NEE measured at the flux tower site was scaled to croplands across the Inland Pampa using crop-type maps, from which annual NEE maps were generated for the 2018–2019, 2019–2020, and 2020–2021 agricultural campaigns. The model based solely on EVI showed to be a good predictor of monthly NEE for the study region (r2 = 0.78), but model adjustment was improved by including a term for crop identity (r2 = 0.83). A second set of maps was generated taking into account carbon exports during harvest to estimate Net Biome Productivity (NBP) at the county level. Crops across the region as a whole acted as a carbon sink during the three studied campaigns, although with highly heterogeneous spatial and temporal patterns. Between 60% and 80% of the carbon sequestered was exported during harvest, a large decrease from the carbon sequestration capacity estimated using just NEE, which further decreased if fossil carbon emissions from agricultural supplies are taken into account. Estimates presented in this study are a first step towards upscaling carbon fluxes at the regional scale in a South American cropland area, and could help to improve regional to global estimations of carbon fluxes and refine national greenhouse gas (GHG) inventories.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherFrontiers Mediaes_AR
dc.relationinfo:eu-repograntAgreement/INTA/PNNAT-1128023/AR./Emisiones de gases con efecto invernadero.
dc.relationinfo:eu-repograntAgreement/INTA/2019-PD-E3-I058-001/2019-PD-E3-I058-001/AR./EMISIONES (GEI) EN LOS SISTEMAS AGROPECUARIOS y FORESTALES. MEDIDAS DE MITIGACIÓN
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceFrontiers Soil Science 2 : 903544 (June 2022)es_AR
dc.subjectAgricultureeng
dc.subjectAgriculturaes_AR
dc.subjectModerate Resolution Imaging Spectroradiometereng
dc.subjectEspectrorradiómetro de Imágenes de Resolución Moderadaes_AR
dc.subjectCropseng
dc.subjectCultivos
dc.subjectTierras Agrícolas
dc.subjectFarmlandeng
dc.subject.otherNet Biome Productivityeng
dc.subject.otherProductividad Neta del Biomaes_AR
dc.subject.otherUpscalineeng
dc.subject.otherCarbon Fluxeseng
dc.subject.otherFlujos de Carbonoes_AR
dc.subject.otherCrop Type Mappingeng
dc.subject.otherMapeo de Tipos de Cultivoses_AR
dc.titleCropland Net Ecosystem Exchange Estimation for the Inland Pampas (Argentina) Using EVI, Land Cover Maps, and Eddy Covariance Fluxeses_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVesiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.description.filFil: Marconato, Ulises. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentinaes_AR
dc.description.filFil: Fernandez, Roberto J. Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA),; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Ecología; Argentinaes_AR
dc.description.filFil: Posse Beaulieu, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentinaes_AR
dc.subtypecientifico


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