Modelling pasture management practices for soil organic carbon gain in livestock systems

Autores
Schimpf, Karen Gisele; Errecart, Pedro Manuel; Canziani, Graciela Ana
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Our ability to develop strategies to mitigate climate change includes an understanding of, and our capacity to predict soil organic carbon (SOC) dynamics in livestock systems. Here we assess the capability of the Sustainable Grazing System (SGS) Pasture Model for predicting pasture growth (elongated wheatgrass, Thinopyrum ponticum) and SOC accumulation in different environments and under a range of pasture management practices in hydrohalomorphic soils located in South-eastern Buenos Aires Province, Argentina. After Model calibration, aerial net primary productivity (ANPP) and TSOC content under two grazing intensities (7.5 and 11 cm post-grazing target heights) and two N fertilization levels (0 and 100 kg N ha−1 yr−1) were simulated over a 10 year-period. The SGS Pasture Model predicted 87% of the observed ANPP, with observed and predicted ANPPs averaging 1.46 and 1.42 Mg ha−1 yr−1, respectively. There were differences in simulated ANPP between fertilized and unfertilized treatments both at high and low grazing intensities for the last year of the period. Total SOC contents from the modelling showed differences between high (83.7 to 84.2 Mg ha−1) and low (86.8 to 87.5 Mg ha−1) grazing intensities, with treatments receiving N also showing higher carbon stocks. The positive effect of reduced grazing intensity on soil carbon was explained by an increased input of aerial and subterranean dry matter into the soil. Sensitivity analysis showed that SGS is a robust model, capable of performing effectively under a variety of conditions. Hence, it can be used for exploring management practices to mitigate the impact of livestock systems on emissions and SOC stocks.
EEA Balcarce
Fil: Schimpf, Karen Gisele. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; Argentina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires; Argentina. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.
Fil: Schimpf, Karen Gisele. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Errecart, Pedro Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.
Fil: Canziani, Graciela Ana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; Argentina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires; Argentina. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.
Fuente
Grass and Forage Science : 1-13 (First published: 13 September 2022)
Materia
Modelización
Carbono Orgánico del Suelo
Manejo de Praderas
Modelling
Soil Organic Carbon
Grassland Management
Livestock Systems
Sistemas Pecuarios
Pasto
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/13972
Acceder
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spelling Modelling pasture management practices for soil organic carbon gain in livestock systemsSchimpf, Karen GiseleErrecart, Pedro ManuelCanziani, Graciela AnaModelizaciónCarbono Orgánico del SueloManejo de PraderasModellingSoil Organic CarbonGrassland ManagementLivestock SystemsSistemas PecuariosPastoOur ability to develop strategies to mitigate climate change includes an understanding of, and our capacity to predict soil organic carbon (SOC) dynamics in livestock systems. Here we assess the capability of the Sustainable Grazing System (SGS) Pasture Model for predicting pasture growth (elongated wheatgrass, Thinopyrum ponticum) and SOC accumulation in different environments and under a range of pasture management practices in hydrohalomorphic soils located in South-eastern Buenos Aires Province, Argentina. After Model calibration, aerial net primary productivity (ANPP) and TSOC content under two grazing intensities (7.5 and 11 cm post-grazing target heights) and two N fertilization levels (0 and 100 kg N ha−1 yr−1) were simulated over a 10 year-period. The SGS Pasture Model predicted 87% of the observed ANPP, with observed and predicted ANPPs averaging 1.46 and 1.42 Mg ha−1 yr−1, respectively. There were differences in simulated ANPP between fertilized and unfertilized treatments both at high and low grazing intensities for the last year of the period. Total SOC contents from the modelling showed differences between high (83.7 to 84.2 Mg ha−1) and low (86.8 to 87.5 Mg ha−1) grazing intensities, with treatments receiving N also showing higher carbon stocks. The positive effect of reduced grazing intensity on soil carbon was explained by an increased input of aerial and subterranean dry matter into the soil. Sensitivity analysis showed that SGS is a robust model, capable of performing effectively under a variety of conditions. Hence, it can be used for exploring management practices to mitigate the impact of livestock systems on emissions and SOC stocks.EEA BalcarceFil: Schimpf, Karen Gisele. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; Argentina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires; Argentina. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.Fil: Schimpf, Karen Gisele. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Errecart, Pedro Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Canziani, Graciela Ana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; Argentina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires; Argentina. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.Wiley2023-02-14T14:10:46Z2023-02-14T14:10:46Z2022-09-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/13972https://onlinelibrary.wiley.com/doi/10.1111/gfs.125800142-5242 (print)1365-2494 (online)https://doi.org/10.1111/gfs.12580Grass and Forage Science : 1-13 (First published: 13 September 2022)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2026-05-07T11:49:16Zoai:localhost:20.500.12123/13972instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-05-07 11:49:18.0INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Modelling pasture management practices for soil organic carbon gain in livestock systems
title Modelling pasture management practices for soil organic carbon gain in livestock systems
spellingShingle Modelling pasture management practices for soil organic carbon gain in livestock systems
Schimpf, Karen Gisele
Modelización
Carbono Orgánico del Suelo
Manejo de Praderas
Modelling
Soil Organic Carbon
Grassland Management
Livestock Systems
Sistemas Pecuarios
Pasto
title_short Modelling pasture management practices for soil organic carbon gain in livestock systems
title_full Modelling pasture management practices for soil organic carbon gain in livestock systems
title_fullStr Modelling pasture management practices for soil organic carbon gain in livestock systems
title_full_unstemmed Modelling pasture management practices for soil organic carbon gain in livestock systems
title_sort Modelling pasture management practices for soil organic carbon gain in livestock systems
dc.creator.none.fl_str_mv Schimpf, Karen Gisele
Errecart, Pedro Manuel
Canziani, Graciela Ana
author Schimpf, Karen Gisele
author_facet Schimpf, Karen Gisele
Errecart, Pedro Manuel
Canziani, Graciela Ana
author_role author
author2 Errecart, Pedro Manuel
Canziani, Graciela Ana
author2_role author
author
dc.subject.none.fl_str_mv Modelización
Carbono Orgánico del Suelo
Manejo de Praderas
Modelling
Soil Organic Carbon
Grassland Management
Livestock Systems
Sistemas Pecuarios
Pasto
topic Modelización
Carbono Orgánico del Suelo
Manejo de Praderas
Modelling
Soil Organic Carbon
Grassland Management
Livestock Systems
Sistemas Pecuarios
Pasto
dc.description.none.fl_txt_mv Our ability to develop strategies to mitigate climate change includes an understanding of, and our capacity to predict soil organic carbon (SOC) dynamics in livestock systems. Here we assess the capability of the Sustainable Grazing System (SGS) Pasture Model for predicting pasture growth (elongated wheatgrass, Thinopyrum ponticum) and SOC accumulation in different environments and under a range of pasture management practices in hydrohalomorphic soils located in South-eastern Buenos Aires Province, Argentina. After Model calibration, aerial net primary productivity (ANPP) and TSOC content under two grazing intensities (7.5 and 11 cm post-grazing target heights) and two N fertilization levels (0 and 100 kg N ha−1 yr−1) were simulated over a 10 year-period. The SGS Pasture Model predicted 87% of the observed ANPP, with observed and predicted ANPPs averaging 1.46 and 1.42 Mg ha−1 yr−1, respectively. There were differences in simulated ANPP between fertilized and unfertilized treatments both at high and low grazing intensities for the last year of the period. Total SOC contents from the modelling showed differences between high (83.7 to 84.2 Mg ha−1) and low (86.8 to 87.5 Mg ha−1) grazing intensities, with treatments receiving N also showing higher carbon stocks. The positive effect of reduced grazing intensity on soil carbon was explained by an increased input of aerial and subterranean dry matter into the soil. Sensitivity analysis showed that SGS is a robust model, capable of performing effectively under a variety of conditions. Hence, it can be used for exploring management practices to mitigate the impact of livestock systems on emissions and SOC stocks.
EEA Balcarce
Fil: Schimpf, Karen Gisele. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; Argentina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires; Argentina. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.
Fil: Schimpf, Karen Gisele. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Errecart, Pedro Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.
Fil: Canziani, Graciela Ana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas; Argentina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires; Argentina. Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable; Argentina.
description Our ability to develop strategies to mitigate climate change includes an understanding of, and our capacity to predict soil organic carbon (SOC) dynamics in livestock systems. Here we assess the capability of the Sustainable Grazing System (SGS) Pasture Model for predicting pasture growth (elongated wheatgrass, Thinopyrum ponticum) and SOC accumulation in different environments and under a range of pasture management practices in hydrohalomorphic soils located in South-eastern Buenos Aires Province, Argentina. After Model calibration, aerial net primary productivity (ANPP) and TSOC content under two grazing intensities (7.5 and 11 cm post-grazing target heights) and two N fertilization levels (0 and 100 kg N ha−1 yr−1) were simulated over a 10 year-period. The SGS Pasture Model predicted 87% of the observed ANPP, with observed and predicted ANPPs averaging 1.46 and 1.42 Mg ha−1 yr−1, respectively. There were differences in simulated ANPP between fertilized and unfertilized treatments both at high and low grazing intensities for the last year of the period. Total SOC contents from the modelling showed differences between high (83.7 to 84.2 Mg ha−1) and low (86.8 to 87.5 Mg ha−1) grazing intensities, with treatments receiving N also showing higher carbon stocks. The positive effect of reduced grazing intensity on soil carbon was explained by an increased input of aerial and subterranean dry matter into the soil. Sensitivity analysis showed that SGS is a robust model, capable of performing effectively under a variety of conditions. Hence, it can be used for exploring management practices to mitigate the impact of livestock systems on emissions and SOC stocks.
publishDate 2022
dc.date.none.fl_str_mv 2022-09-13
2023-02-14T14:10:46Z
2023-02-14T14:10:46Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/13972
https://onlinelibrary.wiley.com/doi/10.1111/gfs.12580
0142-5242 (print)
1365-2494 (online)
https://doi.org/10.1111/gfs.12580
url http://hdl.handle.net/20.500.12123/13972
https://onlinelibrary.wiley.com/doi/10.1111/gfs.12580
https://doi.org/10.1111/gfs.12580
identifier_str_mv 0142-5242 (print)
1365-2494 (online)
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv Grass and Forage Science : 1-13 (First published: 13 September 2022)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 13.1485815

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