Better estimates of soil carbon from geographical data: a revised global approach

Autores
Duarte Guardia, Sandra; Peri, Pablo Luis; Amelung, Wulf; Sheil, Douglas; Laffan, Shawn W.; Borchard, Nils; Bird, Michael I.; Dieleman, Wouter; Pepper, David A.; Zutta, Brian; Jobbagy Gampel, Esteban Gabriel; Silva, Lucas C. R.; Bonser, Stephen P.; Berhongaray, Gonzalo; Piñeiro, Gervasio; Martinez, Maria Jose; Cowie, Annette L.; Ladd, Brenton
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Soils hold the largest pool of organic carbon (C) on Earth; yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a global baseline of SOC stocks. We used partial least square (PLS) regression and available geographic datasets that describe SOC, climate, organisms, relief, parent material and time. The accuracy of the model was determined by the root mean square deviation (RMSD) of predicted SOC against 100 independent measurements. The best predictors were related to primary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 m were found in boreal forests (254 ± 14.3 t ha−1) and tundra (310 ± 15.3 t ha−1). Deserts had the lowest C stocks (53.2 ± 6.3 t ha−1) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha−1), tropical and subtropical forests (94 - 143 t ha−1) and grasslands (99-104 t ha−1). Solar radiation, evapotranspiration, and annual mean temperature were negatively correlated with SOC, whereas soil water content was positively correlated with SOC. Our model explained 49% of SOC variability, with RMSD (0.68) representing approximately 14% of observed C stock variance, overestimating extremely low and underestimating extremely high stocks, respectively. Our baseline PLS predictions of SOC stocks can be used for estimating the maximum amount of C that may be sequestered in soils across biomes.
EEA Santa Cruz
Fil: Duarte Guardia, Sandra. Universidad Nacional de la Patagonia Austral; Argentina
Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Amelung, Wulf. University of Bonn. Soil Science and Soil Ecology. Institute of Crop Science and Resource Conservation (INRES); Alemania
Fil: Sheil, Douglas. Norwegian University of Life Sciences. Faculty of Environmental Sciences and Natural Resource Management; Noruega. Jalan Cifor Rawajaha. Center for International Forestry Research (CIFOR); Indonesia
Fil: Borchard, Nils. Forschungszentrum Jülich GmbH. Agrosphere Institute (IBG-3); Alemania. Jalan Cifor Rawajaha. Center for International Forestry Research (CIFOR); Indonesia. Ruhr-University Bochum, Institute of Geography, Soil Science/Soil Ecology; Alemania. Plant Production Natural Resources Institute Finland (Luke); Finlandia
Fil: Laffan, Shawn W. University of New South Wales. School of Biological, Earth and Environmental Sciences; Australia
Fil: Bird, Michael I. James Cook University. College of Science, Technology and Engineering and Centre for Tropical Environmental and Sustainability Science; Australia
Fil: Dieleman, Wouter. James Cook University. College of Science, Technology and Engineering and Centre for Tropical Environmental and Sustainability Science; Australia
Fil: Pepper, David A. University of New South Wales. School of Biological, Earth and Environmental Sciences; Australia. University of Canberra. Institute for Applied Ecology; Australia
Fil: Zutta, Brian. Perú. Ministerio del Ambiente. Programa Nacional de Conservación de Bosques; Perú
Fil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis; Argentina
Fil: Silva, Lucas C. R. University of Oregon. Institute of Ecology & Evolution. Department of Geography. Environmental Studies Program; Estados Unidos
Fil: Bonser, Stephen P. University of New South Wales. School of Biological, Earth and Environmental Sciences. Evolution and Ecology Research Centre; Australia
Fil: Berhongaray, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral.Facultad de Ciencias Agrarias; Argentina
Fil: Piñeiro, Gervasio. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Ecología. Laboratorio de Análisis Regional y Teledetección; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de la República. Facultad de Agronomia; Uruguay
Fil: Martinez, Maria Jose. Universidad Científica del Sur. Escuela de Agroforestería; Perú
Fil: Cowie, Annette L. NSW Department of Primary Industries; Australia. University of New England. School of Environmental and Rural Science; Australia
Fil: Ladd, Brenton. Universidad Científica del Sur. Escuela de Agroforestería; Peru. UNSW Australia. School of Biological. Earth and Environmental Sciences, Evolution and Ecology Research Centre; Australia
Fuente
Mitigation and Adaptation Strategies for Global Change : 1–18 (May 2018)
Materia
Clima
Cambio Climático
Suelo
Carbono
Sistemas de Información Geográfica
Climate
Climate Change
Soil
Carbon
Geographical Information Systems
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/2925

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oai_identifier_str oai:localhost:20.500.12123/2925
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network_name_str INTA Digital (INTA)
spelling Better estimates of soil carbon from geographical data: a revised global approachDuarte Guardia, SandraPeri, Pablo LuisAmelung, WulfSheil, DouglasLaffan, Shawn W.Borchard, NilsBird, Michael I.Dieleman, WouterPepper, David A.Zutta, BrianJobbagy Gampel, Esteban GabrielSilva, Lucas C. R.Bonser, Stephen P.Berhongaray, GonzaloPiñeiro, GervasioMartinez, Maria JoseCowie, Annette L.Ladd, BrentonClimaCambio ClimáticoSueloCarbonoSistemas de Información GeográficaClimateClimate ChangeSoilCarbonGeographical Information SystemsSoils hold the largest pool of organic carbon (C) on Earth; yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a global baseline of SOC stocks. We used partial least square (PLS) regression and available geographic datasets that describe SOC, climate, organisms, relief, parent material and time. The accuracy of the model was determined by the root mean square deviation (RMSD) of predicted SOC against 100 independent measurements. The best predictors were related to primary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 m were found in boreal forests (254 ± 14.3 t ha−1) and tundra (310 ± 15.3 t ha−1). Deserts had the lowest C stocks (53.2 ± 6.3 t ha−1) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha−1), tropical and subtropical forests (94 - 143 t ha−1) and grasslands (99-104 t ha−1). Solar radiation, evapotranspiration, and annual mean temperature were negatively correlated with SOC, whereas soil water content was positively correlated with SOC. Our model explained 49% of SOC variability, with RMSD (0.68) representing approximately 14% of observed C stock variance, overestimating extremely low and underestimating extremely high stocks, respectively. Our baseline PLS predictions of SOC stocks can be used for estimating the maximum amount of C that may be sequestered in soils across biomes.EEA Santa CruzFil: Duarte Guardia, Sandra. Universidad Nacional de la Patagonia Austral; ArgentinaFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Amelung, Wulf. University of Bonn. Soil Science and Soil Ecology. Institute of Crop Science and Resource Conservation (INRES); AlemaniaFil: Sheil, Douglas. Norwegian University of Life Sciences. Faculty of Environmental Sciences and Natural Resource Management; Noruega. Jalan Cifor Rawajaha. Center for International Forestry Research (CIFOR); IndonesiaFil: Borchard, Nils. Forschungszentrum Jülich GmbH. Agrosphere Institute (IBG-3); Alemania. Jalan Cifor Rawajaha. Center for International Forestry Research (CIFOR); Indonesia. Ruhr-University Bochum, Institute of Geography, Soil Science/Soil Ecology; Alemania. Plant Production Natural Resources Institute Finland (Luke); FinlandiaFil: Laffan, Shawn W. University of New South Wales. School of Biological, Earth and Environmental Sciences; AustraliaFil: Bird, Michael I. James Cook University. College of Science, Technology and Engineering and Centre for Tropical Environmental and Sustainability Science; AustraliaFil: Dieleman, Wouter. James Cook University. College of Science, Technology and Engineering and Centre for Tropical Environmental and Sustainability Science; AustraliaFil: Pepper, David A. University of New South Wales. School of Biological, Earth and Environmental Sciences; Australia. University of Canberra. Institute for Applied Ecology; AustraliaFil: Zutta, Brian. Perú. Ministerio del Ambiente. Programa Nacional de Conservación de Bosques; PerúFil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis; ArgentinaFil: Silva, Lucas C. R. University of Oregon. Institute of Ecology & Evolution. Department of Geography. Environmental Studies Program; Estados UnidosFil: Bonser, Stephen P. University of New South Wales. School of Biological, Earth and Environmental Sciences. Evolution and Ecology Research Centre; AustraliaFil: Berhongaray, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral.Facultad de Ciencias Agrarias; ArgentinaFil: Piñeiro, Gervasio. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Ecología. Laboratorio de Análisis Regional y Teledetección; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de la República. Facultad de Agronomia; UruguayFil: Martinez, Maria Jose. Universidad Científica del Sur. Escuela de Agroforestería; PerúFil: Cowie, Annette L. NSW Department of Primary Industries; Australia. University of New England. School of Environmental and Rural Science; AustraliaFil: Ladd, Brenton. Universidad Científica del Sur. Escuela de Agroforestería; Peru. UNSW Australia. School of Biological. Earth and Environmental Sciences, Evolution and Ecology Research Centre; Australia2018-07-31T12:18:13Z2018-07-31T12:18:13Z2018-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://link.springer.com/article/10.1007/s11027-018-9815-yhttp://hdl.handle.net/20.500.12123/29251381-23861573-1596https://doi.org/10.1007/s11027-018-9815-yMitigation and Adaptation Strategies for Global Change : 1–18 (May 2018)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-29T13:44:22Zoai:localhost:20.500.12123/2925instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:23.099INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Better estimates of soil carbon from geographical data: a revised global approach
title Better estimates of soil carbon from geographical data: a revised global approach
spellingShingle Better estimates of soil carbon from geographical data: a revised global approach
Duarte Guardia, Sandra
Clima
Cambio Climático
Suelo
Carbono
Sistemas de Información Geográfica
Climate
Climate Change
Soil
Carbon
Geographical Information Systems
title_short Better estimates of soil carbon from geographical data: a revised global approach
title_full Better estimates of soil carbon from geographical data: a revised global approach
title_fullStr Better estimates of soil carbon from geographical data: a revised global approach
title_full_unstemmed Better estimates of soil carbon from geographical data: a revised global approach
title_sort Better estimates of soil carbon from geographical data: a revised global approach
dc.creator.none.fl_str_mv Duarte Guardia, Sandra
Peri, Pablo Luis
Amelung, Wulf
Sheil, Douglas
Laffan, Shawn W.
Borchard, Nils
Bird, Michael I.
Dieleman, Wouter
Pepper, David A.
Zutta, Brian
Jobbagy Gampel, Esteban Gabriel
Silva, Lucas C. R.
Bonser, Stephen P.
Berhongaray, Gonzalo
Piñeiro, Gervasio
Martinez, Maria Jose
Cowie, Annette L.
Ladd, Brenton
author Duarte Guardia, Sandra
author_facet Duarte Guardia, Sandra
Peri, Pablo Luis
Amelung, Wulf
Sheil, Douglas
Laffan, Shawn W.
Borchard, Nils
Bird, Michael I.
Dieleman, Wouter
Pepper, David A.
Zutta, Brian
Jobbagy Gampel, Esteban Gabriel
Silva, Lucas C. R.
Bonser, Stephen P.
Berhongaray, Gonzalo
Piñeiro, Gervasio
Martinez, Maria Jose
Cowie, Annette L.
Ladd, Brenton
author_role author
author2 Peri, Pablo Luis
Amelung, Wulf
Sheil, Douglas
Laffan, Shawn W.
Borchard, Nils
Bird, Michael I.
Dieleman, Wouter
Pepper, David A.
Zutta, Brian
Jobbagy Gampel, Esteban Gabriel
Silva, Lucas C. R.
Bonser, Stephen P.
Berhongaray, Gonzalo
Piñeiro, Gervasio
Martinez, Maria Jose
Cowie, Annette L.
Ladd, Brenton
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Clima
Cambio Climático
Suelo
Carbono
Sistemas de Información Geográfica
Climate
Climate Change
Soil
Carbon
Geographical Information Systems
topic Clima
Cambio Climático
Suelo
Carbono
Sistemas de Información Geográfica
Climate
Climate Change
Soil
Carbon
Geographical Information Systems
dc.description.none.fl_txt_mv Soils hold the largest pool of organic carbon (C) on Earth; yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a global baseline of SOC stocks. We used partial least square (PLS) regression and available geographic datasets that describe SOC, climate, organisms, relief, parent material and time. The accuracy of the model was determined by the root mean square deviation (RMSD) of predicted SOC against 100 independent measurements. The best predictors were related to primary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 m were found in boreal forests (254 ± 14.3 t ha−1) and tundra (310 ± 15.3 t ha−1). Deserts had the lowest C stocks (53.2 ± 6.3 t ha−1) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha−1), tropical and subtropical forests (94 - 143 t ha−1) and grasslands (99-104 t ha−1). Solar radiation, evapotranspiration, and annual mean temperature were negatively correlated with SOC, whereas soil water content was positively correlated with SOC. Our model explained 49% of SOC variability, with RMSD (0.68) representing approximately 14% of observed C stock variance, overestimating extremely low and underestimating extremely high stocks, respectively. Our baseline PLS predictions of SOC stocks can be used for estimating the maximum amount of C that may be sequestered in soils across biomes.
EEA Santa Cruz
Fil: Duarte Guardia, Sandra. Universidad Nacional de la Patagonia Austral; Argentina
Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Amelung, Wulf. University of Bonn. Soil Science and Soil Ecology. Institute of Crop Science and Resource Conservation (INRES); Alemania
Fil: Sheil, Douglas. Norwegian University of Life Sciences. Faculty of Environmental Sciences and Natural Resource Management; Noruega. Jalan Cifor Rawajaha. Center for International Forestry Research (CIFOR); Indonesia
Fil: Borchard, Nils. Forschungszentrum Jülich GmbH. Agrosphere Institute (IBG-3); Alemania. Jalan Cifor Rawajaha. Center for International Forestry Research (CIFOR); Indonesia. Ruhr-University Bochum, Institute of Geography, Soil Science/Soil Ecology; Alemania. Plant Production Natural Resources Institute Finland (Luke); Finlandia
Fil: Laffan, Shawn W. University of New South Wales. School of Biological, Earth and Environmental Sciences; Australia
Fil: Bird, Michael I. James Cook University. College of Science, Technology and Engineering and Centre for Tropical Environmental and Sustainability Science; Australia
Fil: Dieleman, Wouter. James Cook University. College of Science, Technology and Engineering and Centre for Tropical Environmental and Sustainability Science; Australia
Fil: Pepper, David A. University of New South Wales. School of Biological, Earth and Environmental Sciences; Australia. University of Canberra. Institute for Applied Ecology; Australia
Fil: Zutta, Brian. Perú. Ministerio del Ambiente. Programa Nacional de Conservación de Bosques; Perú
Fil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis; Argentina
Fil: Silva, Lucas C. R. University of Oregon. Institute of Ecology & Evolution. Department of Geography. Environmental Studies Program; Estados Unidos
Fil: Bonser, Stephen P. University of New South Wales. School of Biological, Earth and Environmental Sciences. Evolution and Ecology Research Centre; Australia
Fil: Berhongaray, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral.Facultad de Ciencias Agrarias; Argentina
Fil: Piñeiro, Gervasio. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Ecología. Laboratorio de Análisis Regional y Teledetección; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de la República. Facultad de Agronomia; Uruguay
Fil: Martinez, Maria Jose. Universidad Científica del Sur. Escuela de Agroforestería; Perú
Fil: Cowie, Annette L. NSW Department of Primary Industries; Australia. University of New England. School of Environmental and Rural Science; Australia
Fil: Ladd, Brenton. Universidad Científica del Sur. Escuela de Agroforestería; Peru. UNSW Australia. School of Biological. Earth and Environmental Sciences, Evolution and Ecology Research Centre; Australia
description Soils hold the largest pool of organic carbon (C) on Earth; yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a global baseline of SOC stocks. We used partial least square (PLS) regression and available geographic datasets that describe SOC, climate, organisms, relief, parent material and time. The accuracy of the model was determined by the root mean square deviation (RMSD) of predicted SOC against 100 independent measurements. The best predictors were related to primary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 m were found in boreal forests (254 ± 14.3 t ha−1) and tundra (310 ± 15.3 t ha−1). Deserts had the lowest C stocks (53.2 ± 6.3 t ha−1) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha−1), tropical and subtropical forests (94 - 143 t ha−1) and grasslands (99-104 t ha−1). Solar radiation, evapotranspiration, and annual mean temperature were negatively correlated with SOC, whereas soil water content was positively correlated with SOC. Our model explained 49% of SOC variability, with RMSD (0.68) representing approximately 14% of observed C stock variance, overestimating extremely low and underestimating extremely high stocks, respectively. Our baseline PLS predictions of SOC stocks can be used for estimating the maximum amount of C that may be sequestered in soils across biomes.
publishDate 2018
dc.date.none.fl_str_mv 2018-07-31T12:18:13Z
2018-07-31T12:18:13Z
2018-05
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 https://link.springer.com/article/10.1007/s11027-018-9815-y
http://hdl.handle.net/20.500.12123/2925
1381-2386
1573-1596
https://doi.org/10.1007/s11027-018-9815-y
url https://link.springer.com/article/10.1007/s11027-018-9815-y
http://hdl.handle.net/20.500.12123/2925
https://doi.org/10.1007/s11027-018-9815-y
identifier_str_mv 1381-2386
1573-1596
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Mitigation and Adaptation Strategies for Global Change : 1–18 (May 2018)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
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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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