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, Verónica Andrea; Tittonell, Pablo; 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.
Fil: Trinco, Fabio Daniel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Zeraatpisheh, Mojtaba. University Of Vermont.; Estados Unidos
Fil: Turner, Hannah C.. University Of Vermont.; Estados Unidos
Fil: El Mujtar, Verónica Andrea. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Tittonell, Pablo. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; Francia. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Galford, Gillian L.. University Of Vermont.; Estados Unidos
Materia
LAND COVER
RANDOM FOREST
MACHINE LEARNING
GIS
SPATIAL MODELLING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/281837
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
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, Verónica AndreaTittonell, PabloGalford, Gillian L.LAND COVERRANDOM FORESTMACHINE LEARNINGGISSPATIAL MODELLINGhttps://purl.org/becyt/ford/4.1https://purl.org/becyt/ford/4Soil 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.Fil: Trinco, Fabio Daniel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Zeraatpisheh, Mojtaba. University Of Vermont.; Estados UnidosFil: Turner, Hannah C.. University Of Vermont.; Estados UnidosFil: El Mujtar, Verónica Andrea. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Tittonell, Pablo. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; Francia. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Galford, Gillian L.. University Of Vermont.; Estados UnidosElsevier Science2025-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/281837Trinco, Fabio Daniel; Zeraatpisheh, Mojtaba; Turner, Hannah C.; El Mujtar, Verónica Andrea; Tittonell, Pablo; et al.; High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia; Elsevier Science; Catena; 259; 11-2025; 1-140341-8162CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0341816225006551info:eu-repo/semantics/altIdentifier/doi/10.1016/j.catena.2025.109353info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-03-11T13:17:27Zoai:ri.conicet.gov.ar:11336/281837instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-03-11 13:17:28.207CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
LAND COVER
RANDOM FOREST
MACHINE LEARNING
GIS
SPATIAL MODELLING
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, Verónica Andrea
Tittonell, Pablo
Galford, Gillian L.
author Trinco, Fabio Daniel
author_facet Trinco, Fabio Daniel
Zeraatpisheh, Mojtaba
Turner, Hannah C.
El Mujtar, Verónica Andrea
Tittonell, Pablo
Galford, Gillian L.
author_role author
author2 Zeraatpisheh, Mojtaba
Turner, Hannah C.
El Mujtar, Verónica Andrea
Tittonell, Pablo
Galford, Gillian L.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv LAND COVER
RANDOM FOREST
MACHINE LEARNING
GIS
SPATIAL MODELLING
topic LAND COVER
RANDOM FOREST
MACHINE LEARNING
GIS
SPATIAL MODELLING
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.1
https://purl.org/becyt/ford/4
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.
Fil: Trinco, Fabio Daniel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Zeraatpisheh, Mojtaba. University Of Vermont.; Estados Unidos
Fil: Turner, Hannah C.. University Of Vermont.; Estados Unidos
Fil: El Mujtar, Verónica Andrea. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Tittonell, Pablo. Centre de Coopération Internationale en Recherche Agronomique pour le Développerment; Francia. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina
Fil: Galford, Gillian L.. University Of Vermont.; 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 2025-11
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/11336/281837
Trinco, Fabio Daniel; Zeraatpisheh, Mojtaba; Turner, Hannah C.; El Mujtar, Verónica Andrea; Tittonell, Pablo; et al.; High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia; Elsevier Science; Catena; 259; 11-2025; 1-14
0341-8162
CONICET Digital
CONICET
url http://hdl.handle.net/11336/281837
identifier_str_mv Trinco, Fabio Daniel; Zeraatpisheh, Mojtaba; Turner, Hannah C.; El Mujtar, Verónica Andrea; Tittonell, Pablo; et al.; High-resolution soil organic carbon mapping for enhancing predictive accuracy of environmental drivers in heterogeneous and mountainous landscapes in Patagonia; Elsevier Science; Catena; 259; 11-2025; 1-14
0341-8162
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0341816225006551
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.catena.2025.109353
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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