Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina
Soil organic carbon (SOC) stocks play an important role in ecosystem functioning and climate regulation. These stocks are declining in many tropical dry forests due to land-use change and degradation. Data on topsoil (0–300 mm) organic C stocks from six experiments conducted in the Dry Chaco region,...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Artículo |
| Language: | Inglés |
| Published: |
MDPI
2025
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/22478 https://www.mdpi.com/2071-1050/17/11/5012 https://doi.org/10.3390/su17115012 |
| _version_ | 1855486942663147520 |
|---|---|
| author | Filip, Iván Daniel Peri, Pablo Luis Banegas, Natalia Romina Nasca, Jose Andres Sacido, Mónica Faverin, Claudia Vibart, Ronaldo |
| author_browse | Banegas, Natalia Romina Faverin, Claudia Filip, Iván Daniel Nasca, Jose Andres Peri, Pablo Luis Sacido, Mónica Vibart, Ronaldo |
| author_facet | Filip, Iván Daniel Peri, Pablo Luis Banegas, Natalia Romina Nasca, Jose Andres Sacido, Mónica Faverin, Claudia Vibart, Ronaldo |
| author_sort | Filip, Iván Daniel |
| collection | INTA Digital |
| description | Soil organic carbon (SOC) stocks play an important role in ecosystem functioning and climate regulation. These stocks are declining in many tropical dry forests due to land-use change and degradation. Data on topsoil (0–300 mm) organic C stocks from six experiments conducted in the Dry Chaco region, the world’s largest dry tropical forest, were used to test the predictive performance of the Rothamsted Carbon Model (RothC) after its implementation in an object-oriented graphical programming language. RothC provided promising predictions (i.e., precise and accurate) of the SOC stocks under two representative land covers in the region, native forest and Rhodes grass [relative prediction error (RPE) < 10%, concordance correlation coefficient (CCC) > 0.9, modelling efficiency (MEF) > 0.7]. Comparatively, model predictions of the SOC stocks under degraded Rhodes grass swards were suboptimal. The predictions were sensitive to C inputs; under native forests and Rhodes grass, a high C input improved the predictive performance of the model by reducing the mean bias and increasing the MEF values, compared with mean and low C inputs. Larger datasets and revisiting some of the underlying assumptions in the SOC modelling will be required to improve the model’s performance, particularly under the degraded Rhodes grass land cover. |
| format | Artículo |
| id | INTA22478 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA224782025-06-04T10:43:32Z Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina Filip, Iván Daniel Peri, Pablo Luis Banegas, Natalia Romina Nasca, Jose Andres Sacido, Mónica Faverin, Claudia Vibart, Ronaldo Carbono Orgánico del Suelo Bosques Bosque Primario Praderas Estimación de las Existencias de Carbono Soil Organic Carbon Forests Primary Forests Grasslands Carbon Stock Assessments Bosque Nativo Región Chaqueña, Argentina Chaco Seco Soil organic carbon (SOC) stocks play an important role in ecosystem functioning and climate regulation. These stocks are declining in many tropical dry forests due to land-use change and degradation. Data on topsoil (0–300 mm) organic C stocks from six experiments conducted in the Dry Chaco region, the world’s largest dry tropical forest, were used to test the predictive performance of the Rothamsted Carbon Model (RothC) after its implementation in an object-oriented graphical programming language. RothC provided promising predictions (i.e., precise and accurate) of the SOC stocks under two representative land covers in the region, native forest and Rhodes grass [relative prediction error (RPE) < 10%, concordance correlation coefficient (CCC) > 0.9, modelling efficiency (MEF) > 0.7]. Comparatively, model predictions of the SOC stocks under degraded Rhodes grass swards were suboptimal. The predictions were sensitive to C inputs; under native forests and Rhodes grass, a high C input improved the predictive performance of the model by reducing the mean bias and increasing the MEF values, compared with mean and low C inputs. Larger datasets and revisiting some of the underlying assumptions in the SOC modelling will be required to improve the model’s performance, particularly under the degraded Rhodes grass land cover. EEA Las Breñas Fil: Filip, Iván Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigación y Transferencia Formosa; Argentina Fil: Filip, Iván Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Las Breñas; Argentina Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina. Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina. Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Banegas, Natalia Romina. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Investigación Animal del Chaco Semiárido; Argentina Fil: Banegas, Natalia Romina. Universidad Nacional de Tucumán. Facultad de Agronomía, Zootecnia y Veterinaria; Argentina Fil: Nasca, Jose Andres. Terratio; Argentina Fil: Sacido, Mónica. Investigadora independientes; Argentina Fil: Faverin, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina Fil: Faverin, Claudia. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales; Argentina Fil: Vibart, Ronaldo. AgResearch. Grasslands Research Centre; Nueva Zelanda 2025-06-04T10:39:33Z 2025-06-04T10:39:33Z 2025-06 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/22478 https://www.mdpi.com/2071-1050/17/11/5012 2071-1050 https://doi.org/10.3390/su17115012 eng info:eu-repograntAgreement/INTA/2019-PD-E3-I062-001, Estrategias de producción que incrementen el secuestro de C en suelo para la mitigación del Cambio Climático info:eu-repograntAgreement/INTA/2019-PE-E1-I006-001, Respuestas tecnológicas para el manejo sustentable y eficiente de pasturas megatérmicas en sistemas ganaderos del norte y centro de Argentina info:eu-repograntAgreement/INTA/2023-PD-L02-I097, Emisiones de gases de efecto invernadero y captura de carbono en sistemas agropecuarios y forestales info:eu-repo/semantics/openAccess 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 MDPI Sustainability 17 (11) : 5012 (June 2025) |
| spellingShingle | Carbono Orgánico del Suelo Bosques Bosque Primario Praderas Estimación de las Existencias de Carbono Soil Organic Carbon Forests Primary Forests Grasslands Carbon Stock Assessments Bosque Nativo Región Chaqueña, Argentina Chaco Seco Filip, Iván Daniel Peri, Pablo Luis Banegas, Natalia Romina Nasca, Jose Andres Sacido, Mónica Faverin, Claudia Vibart, Ronaldo Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina |
| title | Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina |
| title_full | Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina |
| title_fullStr | Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina |
| title_full_unstemmed | Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina |
| title_short | Predicting Soil Organic Carbon Stocks Under Native Forests and Grasslands in the Dry Chaco Region of Argentina |
| title_sort | predicting soil organic carbon stocks under native forests and grasslands in the dry chaco region of argentina |
| topic | Carbono Orgánico del Suelo Bosques Bosque Primario Praderas Estimación de las Existencias de Carbono Soil Organic Carbon Forests Primary Forests Grasslands Carbon Stock Assessments Bosque Nativo Región Chaqueña, Argentina Chaco Seco |
| url | http://hdl.handle.net/20.500.12123/22478 https://www.mdpi.com/2071-1050/17/11/5012 https://doi.org/10.3390/su17115012 |
| work_keys_str_mv | AT filipivandaniel predictingsoilorganiccarbonstocksundernativeforestsandgrasslandsinthedrychacoregionofargentina AT peripabloluis predictingsoilorganiccarbonstocksundernativeforestsandgrasslandsinthedrychacoregionofargentina AT banegasnataliaromina predictingsoilorganiccarbonstocksundernativeforestsandgrasslandsinthedrychacoregionofargentina AT nascajoseandres predictingsoilorganiccarbonstocksundernativeforestsandgrasslandsinthedrychacoregionofargentina AT sacidomonica predictingsoilorganiccarbonstocksundernativeforestsandgrasslandsinthedrychacoregionofargentina AT faverinclaudia predictingsoilorganiccarbonstocksundernativeforestsandgrasslandsinthedrychacoregionofargentina AT vibartronaldo predictingsoilorganiccarbonstocksundernativeforestsandgrasslandsinthedrychacoregionofargentina |