High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia

Autores
Trinco, Fabio Daniel; Zeraatpisheh, Mojtaba; Turner, Hannah C.; El Mujtar, Veronica Andrea; Tittonell, Pablo Adrian; Galford, Gillian L.
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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
Fuente
CATENA 259 : 109353. (November 2025)
Materia
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
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/23466
Acceder
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oai_identifier_str oai:localhost:20.500.12123/23466
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in PatagoniaTrinco, Fabio DanielZeraatpisheh, MojtabaTurner, Hannah C.El Mujtar, Veronica AndreaTittonell, Pablo AdrianGalford, Gillian L.Carbono Orgánico del SueloMedio AmbienteCobertura de SuelosPaisajeSistemas de Información GeográficaSoil Organic CarbonEnvironmentLand CoverLandscapeGeographical Information SystemsSIGRegión PatagónicaGISSoil 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 BarilocheFil: Trinco, Fabio Daniel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche; ArgentinaFil: Trinco, Fabio Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Zeraatpisheh, Mojtaba. University of Vermont. Gund Institute for Environment and Rubenstein School of Environment and Natural Resources; Estados UnidosFil: Turner, Hannah C. University of Vermont. Gund Institute for Environment and Rubenstein School of Environment and Natural Resources; Estados UnidosFil: El Mujtar, Veronica Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche; ArgentinaFil: El Mujtar, Veronica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Tittonell, Pablo Adrian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias de Bariloche; ArgentinaFil: Tittonell, Pablo Adrian. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Tittonell, Pablo Adrian. Groningen University. Groningen Institute of Evolutionary Life Sciences; Países BajosFil: 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 UnidosElsevier2025-08-19T11:27:44Z2025-08-19T11:27:44Z2025-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/23466https://www.sciencedirect.com/science/article/pii/S03418162250065510341-81621872-6887https://doi.org/10.1016/j.catena.2025.109353CATENA 259 : 109353. (November 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-04-23T10:40:03Zoai:localhost:20.500.12123/23466instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-04-23 10:40:03.713INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv 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
spellingShingle High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia
Trinco, Fabio Daniel
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
title_short 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_sort High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia
dc.creator.none.fl_str_mv Trinco, Fabio Daniel
Zeraatpisheh, Mojtaba
Turner, Hannah C.
El Mujtar, Veronica Andrea
Tittonell, Pablo Adrian
Galford, Gillian L.
author Trinco, Fabio Daniel
author_facet Trinco, Fabio Daniel
Zeraatpisheh, Mojtaba
Turner, Hannah C.
El Mujtar, Veronica Andrea
Tittonell, Pablo Adrian
Galford, Gillian L.
author_role author
author2 Zeraatpisheh, Mojtaba
Turner, Hannah C.
El Mujtar, Veronica Andrea
Tittonell, Pablo Adrian
Galford, Gillian L.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv 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
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
dc.description.none.fl_txt_mv 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
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.
publishDate 2025
dc.date.none.fl_str_mv 2025-08-19T11:27:44Z
2025-08-19T11:27:44Z
2025-11
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/20.500.12123/23466
https://www.sciencedirect.com/science/article/pii/S0341816225006551
0341-8162
1872-6887
https://doi.org/10.1016/j.catena.2025.109353
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
identifier_str_mv 0341-8162
1872-6887
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv 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)
eu_rights_str_mv openAccess
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 CATENA 259 : 109353. (November 2025)
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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