An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems

Autores: Vazquez Amabile, Gabriel; Studdert, Guillermo; Ogle, Stephen M.; Beltran, Marcelo Javier; Said, Andrés Demián; Galbusera, Sebastián; Montiel, Fátima Soledad; Moreno, Rocio; Ricard, María Florencia
Año de publicación: 2025
Idioma: inglés
Tipo de recurso: artículo
Estado: versión publicada
Descripción: The estimation of changes in soil organic carbon (SOC) is a key issue for national green-house gasses (GHG) inventories, climate change mitigation programs and the estimation of carbon footprint of farm products in life cycle assessments. Any strategy related to net-zero carbon in agricultural systems needs to quantify the SOC balance. In this way, SOC models help decision makers involved in agriculture to understand the dynamics of the SOC and the interaction between all variables related to soil, climate, land use, and management, to design the best solution to reduce emissions or enable carbon sequestration. Likewise, it is important to identify suitable models for the region. This study aims to address three main subjects: a) a discussion on the importance of SOC estimation for GHG inventories and the carbon footprint of crops, using the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method and AMG model; b) an evaluation and brief description of the IPCC “Steady State Method” (SSM), using experimental data from two sites in Argentina, comparing these results to AMG and RothC models (both previously validated at those sites); and c) a brief discussion about the potential use of SOC models for what-if management scenarios, their real limitations and future research needs. The three models were consistent in predicting the impact of tillage and the long-term trends in changes in SOC stocks under different management practices. The SSM model was evaluated for the first time in Argentina and performed even better than the other two models. It was consistent with the observed values, when predicting the effect of tillage system under different crop rotations, including pasture systems. Regarding efficiencies of the models, they showed acceptable Nash-Sutcliffe Efficiency (NSE) values, and the root mean square error (RMSE) was also acceptable between 3 % and 7 %, within a range of 4–5 Mg C.ha−1. Therefore, the SSM model proved to be a valuable tool to estimate SOC trends for crop and pasture rotations under different management scenarios (i.e., tillage systems and fertilization), to identify best practices that allow for a zero or positive SOC balance, in two different soil and climate conditions of the Pampean Region of Argentina. In our study, the SSM did have a better fit to the data and, furthermore, this Tier 2 method is simpler than the Tier 3 models, and, therefore, is advantageous for conducting regional assessments and GHG inventories.
Instituto de Suelos
Fil: Vazquez Amabile, Gabriel. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina.
Fil: Vazquez Amabile, Gabriel. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina.
Fil: Studdert, Guillermo Alberto. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; Argentina
Fil: Studdert, Guillermo Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; Argentina
Fil: Ogle, Stephen M. Colorado State University. Department of Ecosystem Science and Sustainability; Estados Unidos
Fil: Ogle, Stephen M. Colorado State University. Natural Resource Ecology Laboratory; Estados Unidos
Fil: Beltran, Marcelo Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina
Fil: Beltran, Marcelo Javier. Universidad Nacional de San Antonio de Areco; Argentina
Fil: Said, Andrés Demián. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Said, Andrés Demián. Secretaría de Agricultura, Ganadería y Pesca de la Nación; Argentina
Fil: Galbusera, Sebastián. Secretaría de Turismo, Ambiente y Deportes de la Nación, Dirección Nacional de Cambio Climático; Argentina
Fil: Montiel, Fátima Soledad. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; Argentina
Fil: Montiel, Fátima Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; Argentina
Fil: Moreno, Rocío. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; Argentina
Fil: Moreno, Rocío. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; Argentina
Fil: Ricard, María Florencia. Provincia de La Pampa. Secretaría de Ambiente y Cambio Climático; Argentina
Fil: Ricard, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: Ricard, Maria Florencia. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: Ricard, Maria Florencia. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales; Argentina
Fuente: Soil and Tillage Research 246 : 106342. (February 2025)
Materia: Carbono Orgánico del Suelo
Carbono
Sistemas de Explotación
Huella de Carbono
Gases de Efecto Invernadero
Soil Organic Carbon
Carbon
Farming Systems
Carbon Footprint
Greenhouse Gases
Agricultural Systems
Nivel de accesibilidad: acceso restringido
Condiciones de uso: http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
Institución: Instituto Nacional de Tecnología Agropecuaria
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spelling	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systemsVazquez Amabile, GabrielStuddert, GuillermoOgle, Stephen M.Beltran, Marcelo JavierSaid, Andrés DemiánGalbusera, SebastiánMontiel, Fátima SoledadMoreno, RocioRicard, María FlorenciaCarbono Orgánico del SueloCarbonoSistemas de ExplotaciónHuella de CarbonoGases de Efecto InvernaderoSoil Organic CarbonCarbonFarming SystemsCarbon FootprintGreenhouse GasesAgricultural SystemsThe estimation of changes in soil organic carbon (SOC) is a key issue for national green-house gasses (GHG) inventories, climate change mitigation programs and the estimation of carbon footprint of farm products in life cycle assessments. Any strategy related to net-zero carbon in agricultural systems needs to quantify the SOC balance. In this way, SOC models help decision makers involved in agriculture to understand the dynamics of the SOC and the interaction between all variables related to soil, climate, land use, and management, to design the best solution to reduce emissions or enable carbon sequestration. Likewise, it is important to identify suitable models for the region. This study aims to address three main subjects: a) a discussion on the importance of SOC estimation for GHG inventories and the carbon footprint of crops, using the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method and AMG model; b) an evaluation and brief description of the IPCC “Steady State Method” (SSM), using experimental data from two sites in Argentina, comparing these results to AMG and RothC models (both previously validated at those sites); and c) a brief discussion about the potential use of SOC models for what-if management scenarios, their real limitations and future research needs. The three models were consistent in predicting the impact of tillage and the long-term trends in changes in SOC stocks under different management practices. The SSM model was evaluated for the first time in Argentina and performed even better than the other two models. It was consistent with the observed values, when predicting the effect of tillage system under different crop rotations, including pasture systems. Regarding efficiencies of the models, they showed acceptable Nash-Sutcliffe Efficiency (NSE) values, and the root mean square error (RMSE) was also acceptable between 3 % and 7 %, within a range of 4–5 Mg C.ha−1. Therefore, the SSM model proved to be a valuable tool to estimate SOC trends for crop and pasture rotations under different management scenarios (i.e., tillage systems and fertilization), to identify best practices that allow for a zero or positive SOC balance, in two different soil and climate conditions of the Pampean Region of Argentina. In our study, the SSM did have a better fit to the data and, furthermore, this Tier 2 method is simpler than the Tier 3 models, and, therefore, is advantageous for conducting regional assessments and GHG inventories.Instituto de SuelosFil: Vazquez Amabile, Gabriel. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina.Fil: Vazquez Amabile, Gabriel. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina.Fil: Studdert, Guillermo Alberto. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; ArgentinaFil: Studdert, Guillermo Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; ArgentinaFil: Ogle, Stephen M. Colorado State University. Department of Ecosystem Science and Sustainability; Estados UnidosFil: Ogle, Stephen M. Colorado State University. Natural Resource Ecology Laboratory; Estados UnidosFil: Beltran, Marcelo Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Beltran, Marcelo Javier. Universidad Nacional de San Antonio de Areco; ArgentinaFil: Said, Andrés Demián. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Said, Andrés Demián. Secretaría de Agricultura, Ganadería y Pesca de la Nación; ArgentinaFil: Galbusera, Sebastián. Secretaría de Turismo, Ambiente y Deportes de la Nación, Dirección Nacional de Cambio Climático; ArgentinaFil: Montiel, Fátima Soledad. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; ArgentinaFil: Montiel, Fátima Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; ArgentinaFil: Moreno, Rocío. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; ArgentinaFil: Moreno, Rocío. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; ArgentinaFil: Ricard, María Florencia. Provincia de La Pampa. Secretaría de Ambiente y Cambio Climático; ArgentinaFil: Ricard, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; ArgentinaFil: Ricard, Maria Florencia. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; ArgentinaFil: Ricard, Maria Florencia. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales; ArgentinaElsevier2025-03-20T13:13:58Z2025-03-20T13:13:58Z2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/21751https://www.sciencedirect.com/science/article/abs/pii/S016719872400343X0167-19871879-3444https://doi.org/10.1016/j.still.2024.106342Soil and Tillage Research 246 : 106342. (February 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-16T09:32:13Zoai:localhost:20.500.12123/21751instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-16 09:32:13.75INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems
title	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems
spellingShingle	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems Vazquez Amabile, Gabriel Carbono Orgánico del Suelo Carbono Sistemas de Explotación Huella de Carbono Gases de Efecto Invernadero Soil Organic Carbon Carbon Farming Systems Carbon Footprint Greenhouse Gases Agricultural Systems
title_short	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems
title_full	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems
title_fullStr	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems
title_full_unstemmed	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems
title_sort	An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems
dc.creator.none.fl_str_mv	Vazquez Amabile, Gabriel Studdert, Guillermo Ogle, Stephen M. Beltran, Marcelo Javier Said, Andrés Demián Galbusera, Sebastián Montiel, Fátima Soledad Moreno, Rocio Ricard, María Florencia
author	Vazquez Amabile, Gabriel
author_facet	Vazquez Amabile, Gabriel Studdert, Guillermo Ogle, Stephen M. Beltran, Marcelo Javier Said, Andrés Demián Galbusera, Sebastián Montiel, Fátima Soledad Moreno, Rocio Ricard, María Florencia
author_role	author
author2	Studdert, Guillermo Ogle, Stephen M. Beltran, Marcelo Javier Said, Andrés Demián Galbusera, Sebastián Montiel, Fátima Soledad Moreno, Rocio Ricard, María Florencia
author2_role	author author author author author author author author
dc.subject.none.fl_str_mv	Carbono Orgánico del Suelo Carbono Sistemas de Explotación Huella de Carbono Gases de Efecto Invernadero Soil Organic Carbon Carbon Farming Systems Carbon Footprint Greenhouse Gases Agricultural Systems
topic	Carbono Orgánico del Suelo Carbono Sistemas de Explotación Huella de Carbono Gases de Efecto Invernadero Soil Organic Carbon Carbon Farming Systems Carbon Footprint Greenhouse Gases Agricultural Systems
dc.description.none.fl_txt_mv	The estimation of changes in soil organic carbon (SOC) is a key issue for national green-house gasses (GHG) inventories, climate change mitigation programs and the estimation of carbon footprint of farm products in life cycle assessments. Any strategy related to net-zero carbon in agricultural systems needs to quantify the SOC balance. In this way, SOC models help decision makers involved in agriculture to understand the dynamics of the SOC and the interaction between all variables related to soil, climate, land use, and management, to design the best solution to reduce emissions or enable carbon sequestration. Likewise, it is important to identify suitable models for the region. This study aims to address three main subjects: a) a discussion on the importance of SOC estimation for GHG inventories and the carbon footprint of crops, using the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method and AMG model; b) an evaluation and brief description of the IPCC “Steady State Method” (SSM), using experimental data from two sites in Argentina, comparing these results to AMG and RothC models (both previously validated at those sites); and c) a brief discussion about the potential use of SOC models for what-if management scenarios, their real limitations and future research needs. The three models were consistent in predicting the impact of tillage and the long-term trends in changes in SOC stocks under different management practices. The SSM model was evaluated for the first time in Argentina and performed even better than the other two models. It was consistent with the observed values, when predicting the effect of tillage system under different crop rotations, including pasture systems. Regarding efficiencies of the models, they showed acceptable Nash-Sutcliffe Efficiency (NSE) values, and the root mean square error (RMSE) was also acceptable between 3 % and 7 %, within a range of 4–5 Mg C.ha−1. Therefore, the SSM model proved to be a valuable tool to estimate SOC trends for crop and pasture rotations under different management scenarios (i.e., tillage systems and fertilization), to identify best practices that allow for a zero or positive SOC balance, in two different soil and climate conditions of the Pampean Region of Argentina. In our study, the SSM did have a better fit to the data and, furthermore, this Tier 2 method is simpler than the Tier 3 models, and, therefore, is advantageous for conducting regional assessments and GHG inventories. Instituto de Suelos Fil: Vazquez Amabile, Gabriel. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola; Argentina. Fil: Vazquez Amabile, Gabriel. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentina. Fil: Studdert, Guillermo Alberto. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; Argentina Fil: Studdert, Guillermo Alberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; Argentina Fil: Ogle, Stephen M. Colorado State University. Department of Ecosystem Science and Sustainability; Estados Unidos Fil: Ogle, Stephen M. Colorado State University. Natural Resource Ecology Laboratory; Estados Unidos Fil: Beltran, Marcelo Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; Argentina Fil: Beltran, Marcelo Javier. Universidad Nacional de San Antonio de Areco; Argentina Fil: Said, Andrés Demián. Universidad de Buenos Aires. Facultad de Agronomía; Argentina Fil: Said, Andrés Demián. Secretaría de Agricultura, Ganadería y Pesca de la Nación; Argentina Fil: Galbusera, Sebastián. Secretaría de Turismo, Ambiente y Deportes de la Nación, Dirección Nacional de Cambio Climático; Argentina Fil: Montiel, Fátima Soledad. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; Argentina Fil: Montiel, Fátima Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; Argentina Fil: Moreno, Rocío. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Unidad Integrada; Argentina Fil: Moreno, Rocío. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Unidad Integrada; Argentina Fil: Ricard, María Florencia. Provincia de La Pampa. Secretaría de Ambiente y Cambio Climático; Argentina Fil: Ricard, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina Fil: Ricard, Maria Florencia. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina Fil: Ricard, Maria Florencia. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales; Argentina
description	The estimation of changes in soil organic carbon (SOC) is a key issue for national green-house gasses (GHG) inventories, climate change mitigation programs and the estimation of carbon footprint of farm products in life cycle assessments. Any strategy related to net-zero carbon in agricultural systems needs to quantify the SOC balance. In this way, SOC models help decision makers involved in agriculture to understand the dynamics of the SOC and the interaction between all variables related to soil, climate, land use, and management, to design the best solution to reduce emissions or enable carbon sequestration. Likewise, it is important to identify suitable models for the region. This study aims to address three main subjects: a) a discussion on the importance of SOC estimation for GHG inventories and the carbon footprint of crops, using the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method and AMG model; b) an evaluation and brief description of the IPCC “Steady State Method” (SSM), using experimental data from two sites in Argentina, comparing these results to AMG and RothC models (both previously validated at those sites); and c) a brief discussion about the potential use of SOC models for what-if management scenarios, their real limitations and future research needs. The three models were consistent in predicting the impact of tillage and the long-term trends in changes in SOC stocks under different management practices. The SSM model was evaluated for the first time in Argentina and performed even better than the other two models. It was consistent with the observed values, when predicting the effect of tillage system under different crop rotations, including pasture systems. Regarding efficiencies of the models, they showed acceptable Nash-Sutcliffe Efficiency (NSE) values, and the root mean square error (RMSE) was also acceptable between 3 % and 7 %, within a range of 4–5 Mg C.ha−1. Therefore, the SSM model proved to be a valuable tool to estimate SOC trends for crop and pasture rotations under different management scenarios (i.e., tillage systems and fertilization), to identify best practices that allow for a zero or positive SOC balance, in two different soil and climate conditions of the Pampean Region of Argentina. In our study, the SSM did have a better fit to the data and, furthermore, this Tier 2 method is simpler than the Tier 3 models, and, therefore, is advantageous for conducting regional assessments and GHG inventories.
publishDate	2025
dc.date.none.fl_str_mv	2025-03-20T13:13:58Z 2025-03-20T13:13:58Z 2025-02
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/20.500.12123/21751 https://www.sciencedirect.com/science/article/abs/pii/S016719872400343X 0167-1987 1879-3444 https://doi.org/10.1016/j.still.2024.106342
url	http://hdl.handle.net/20.500.12123/21751 https://www.sciencedirect.com/science/article/abs/pii/S016719872400343X https://doi.org/10.1016/j.still.2024.106342
identifier_str_mv	0167-1987 1879-3444
dc.language.none.fl_str_mv	eng
language	eng
dc.rights.none.fl_str_mv	info:eu-repo/semantics/restrictedAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv	restrictedAccess
rights_invalid_str_mv	http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	Elsevier
publisher.none.fl_str_mv	Elsevier
dc.source.none.fl_str_mv	Soil and Tillage Research 246 : 106342. (February 2025) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str	INTA Digital (INTA)
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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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An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems

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