An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems
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 syste...
| Main Authors: | , , , , , , , , |
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
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Elsevier
2025
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| Subjects: | |
| Online Access: | 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 |
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| author | 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_browse | Beltran, Marcelo Javier Galbusera, Sebastián Montiel, Fátima Soledad Moreno, Rocio Ogle, Stephen M. Ricard, María Florencia Said, Andrés Demián Studdert, Guillermo 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_sort | Vazquez Amabile, Gabriel |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA21751 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA217512025-03-20T13:22:13Z An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems Vazquez Amabile, Gabriel Studdert, Guillermo Ogle, Stephen M. Beltran, Marcelo Javier Said, Andrés Demián Galbusera, Sebastián Montiel, Fátima Soledad Moreno, Rocio Ricard, María Florencia 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 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 2025-03-20T13:13:58Z 2025-03-20T13:13:58Z 2025-02 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf Elsevier Soil and Tillage Research 246 : 106342. (February 2025) |
| spellingShingle | 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 Vazquez Amabile, Gabriel Studdert, Guillermo Ogle, Stephen M. Beltran, Marcelo Javier Said, Andrés Demián Galbusera, Sebastián Montiel, Fátima Soledad Moreno, Rocio Ricard, María Florencia 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 |
| 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_short | An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems |
| title_sort | evaluation of soil carbon models and their role on finding ways to net zero carbon in 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 |
| 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 |
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