High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia
Soil organic carbon (SOC) is critical for sustaining agricultural productivity, enhancing resilience to climate change, and supporting ecosystem functions, particularly in fragile regions facing increasing aridity like Patagonia. Knowledge of SOC is often represented by decades old, coarse-scale map...
| 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/20.500.12123/23466 https://www.sciencedirect.com/science/article/pii/S0341816225006551 https://doi.org/10.1016/j.catena.2025.109353 |
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| author | Trinco, Fabio Daniel Zeraatpisheh, Mojtaba Turner, Hannah C. El Mujtar, Veronica Andrea Tittonell, Pablo Adrian Galford, Gillian L. |
| author_browse | El Mujtar, Veronica Andrea Galford, Gillian L. Tittonell, Pablo Adrian Trinco, Fabio Daniel Turner, Hannah C. Zeraatpisheh, Mojtaba |
| author_facet | Trinco, Fabio Daniel Zeraatpisheh, Mojtaba Turner, Hannah C. El Mujtar, Veronica Andrea Tittonell, Pablo Adrian Galford, Gillian L. |
| author_sort | Trinco, Fabio Daniel |
| collection | INTA Digital |
| description | Soil organic carbon (SOC) is critical for sustaining agricultural productivity, enhancing resilience to climate change, and supporting ecosystem functions, particularly in fragile regions facing increasing aridity like Patagonia. Knowledge of SOC is often represented by decades old, coarse-scale maps or sparse data, limiting its utility for land managers and policymakers. This study leverages a novel SOC database (1,724 samples) integrated with remote sensing and spatial variables in a machine learning model to produce high-resolution (30 m) SOC data that captures decision-relevant scales of variability across diverse land covers and uses. Results revealed that Random Forest modelling performed best in the NW Patagonian mountainous region. Feature selection procedures identified soil depth, spectral indices, and climatic factors such as evapotranspiration and aridity as important co-variates. We found significant heterogeneity in SOC distribution, ranging from the greatest SOC concentration in Nothofagus pumilio forests (132.4 ± 19.2 t ha−1 at 0–30 cm depth), to the lowest in the grasslands of the Monte ecoregion (27.6 ± 8.0 t ha−1). Due to landmass size, the grasslands of the Steppe ecoregion have the most carbon (276.5 million tons), followed by Nothofagus pumilio forests (103.7 million tons). These SOC (t ha−1) estimates agree with other studies, showing little difference for forests (10 %) and grasslands (14 %). The resulting maps of this study provide a critical baseline for evaluating SOC distribution, informing land management strategies, and guiding future climate resilience efforts in Patagonia and other similarly vulnerable regions across the globe. |
| format | info:ar-repo/semantics/artículo |
| id | INTA23466 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA234662025-08-19T11:31:20Z High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia Trinco, Fabio Daniel Zeraatpisheh, Mojtaba Turner, Hannah C. El Mujtar, Veronica Andrea Tittonell, Pablo Adrian Galford, Gillian L. Carbono Orgánico del Suelo Medio Ambiente Cobertura de Suelos Paisaje Sistemas de Información Geográfica Soil Organic Carbon Environment Land Cover Landscape Geographical Information Systems SIG Región Patagónica GIS Soil organic carbon (SOC) is critical for sustaining agricultural productivity, enhancing resilience to climate change, and supporting ecosystem functions, particularly in fragile regions facing increasing aridity like Patagonia. Knowledge of SOC is often represented by decades old, coarse-scale maps or sparse data, limiting its utility for land managers and policymakers. This study leverages a novel SOC database (1,724 samples) integrated with remote sensing and spatial variables in a machine learning model to produce high-resolution (30 m) SOC data that captures decision-relevant scales of variability across diverse land covers and uses. Results revealed that Random Forest modelling performed best in the NW Patagonian mountainous region. Feature selection procedures identified soil depth, spectral indices, and climatic factors such as evapotranspiration and aridity as important co-variates. We found significant heterogeneity in SOC distribution, ranging from the greatest SOC concentration in Nothofagus pumilio forests (132.4 ± 19.2 t ha−1 at 0–30 cm depth), to the lowest in the grasslands of the Monte ecoregion (27.6 ± 8.0 t ha−1). Due to landmass size, the grasslands of the Steppe ecoregion have the most carbon (276.5 million tons), followed by Nothofagus pumilio forests (103.7 million tons). These SOC (t ha−1) estimates agree with other studies, showing little difference for forests (10 %) and grasslands (14 %). The resulting maps of this study provide a critical baseline for evaluating SOC distribution, informing land management strategies, and guiding future climate resilience efforts in Patagonia and other similarly vulnerable regions across the globe. EEA Bariloche Fil: Trinco, Fabio Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche; Argentina Fil: Trinco, Fabio Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Zeraatpisheh, Mojtaba. University of Vermont. Gund Institute for Environment and Rubenstein School of Environment and Natural Resources; Estados Unidos Fil: Turner, Hannah C. University of Vermont. Gund Institute for Environment and Rubenstein School of Environment and Natural Resources; Estados Unidos Fil: El Mujtar, Veronica Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche; Argentina Fil: El Mujtar, Veronica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Tittonell, Pablo Adrian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche; Argentina Fil: Tittonell, Pablo Adrian. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Tittonell, Pablo Adrian. Groningen University. Groningen Institute of Evolutionary Life Sciences; Países Bajos Fil: Tittonell, Pablo Adrian. Universite de Montpellier. Centre de cooperation Internationale en Recherche Agronomique pour le Developpement. Agroecologie et Intensification Durable; Francia. Fil: Galford, Gillian L. University of Vermont. Gund Institute for Environment and Rubenstein School of Environment and Natural Resources; Estados Unidos 2025-08-19T11:27:44Z 2025-08-19T11:27:44Z 2025-11 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/23466 https://www.sciencedirect.com/science/article/pii/S0341816225006551 0341-8162 1872-6887 https://doi.org/10.1016/j.catena.2025.109353 eng 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 Elsevier CATENA 259 : 109353. (November 2025) |
| spellingShingle | Carbono Orgánico del Suelo Medio Ambiente Cobertura de Suelos Paisaje Sistemas de Información Geográfica Soil Organic Carbon Environment Land Cover Landscape Geographical Information Systems SIG Región Patagónica GIS Trinco, Fabio Daniel Zeraatpisheh, Mojtaba Turner, Hannah C. El Mujtar, Veronica Andrea Tittonell, Pablo Adrian Galford, Gillian L. High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia |
| title | High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia |
| title_full | High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia |
| title_fullStr | High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia |
| title_full_unstemmed | High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia |
| title_short | High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia |
| title_sort | high resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in patagonia |
| topic | Carbono Orgánico del Suelo Medio Ambiente Cobertura de Suelos Paisaje Sistemas de Información Geográfica Soil Organic Carbon Environment Land Cover Landscape Geographical Information Systems SIG Región Patagónica GIS |
| url | http://hdl.handle.net/20.500.12123/23466 https://www.sciencedirect.com/science/article/pii/S0341816225006551 https://doi.org/10.1016/j.catena.2025.109353 |
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