Better estimates of soil carbon from geographical data: a revised global approach
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...
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ftunivnewengland:oai:rune.une.edu.au:1959.11/58391 2024-05-12T08:12:09+00:00 Better estimates of soil carbon from geographical data: a revised global approach Duarte-Guardia, Sandra Peri, Pablo L Amelung, Wulf Sheil, Douglas Laffan, Shawn W Borchard, Nils Bird, Michael I Dieleman, Wouter Pepper, David A Zutta, Brian Jobbagy, Esteban Silva, Lucas C R Bonser, Stephen P Berhongaray, Gonzalo Piñeiro, Gervasio Martinez, Maria-Jose Cowie, Annette L School of Environmental and Rural Science Ladd, Brenton 2019-03 https://hdl.handle.net/1959.11/58391 en eng Springer Dordrecht 10.1007/s11027-018-9815-y https://hdl.handle.net/1959.11/58391 une:1959.11/58391 Climate change impacts and adaptation Journal Article 2019 ftunivnewengland 2024-04-17T23:39:19Z 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. Article in Journal/Newspaper Tundra Research UNE - University of New England at Armidale, NSW Australia |
institution |
Open Polar |
collection |
Research UNE - University of New England at Armidale, NSW Australia |
op_collection_id |
ftunivnewengland |
language |
English |
topic |
Climate change impacts and adaptation |
spellingShingle |
Climate change impacts and adaptation Duarte-Guardia, Sandra Peri, Pablo L Amelung, Wulf Sheil, Douglas Laffan, Shawn W Borchard, Nils Bird, Michael I Dieleman, Wouter Pepper, David A Zutta, Brian Jobbagy, Esteban Silva, Lucas C R Bonser, Stephen P Berhongaray, Gonzalo Piñeiro, Gervasio Martinez, Maria-Jose Cowie, Annette L School of Environmental and Rural Science Ladd, Brenton Better estimates of soil carbon from geographical data: a revised global approach |
topic_facet |
Climate change impacts and adaptation |
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. |
format |
Article in Journal/Newspaper |
author |
Duarte-Guardia, Sandra Peri, Pablo L Amelung, Wulf Sheil, Douglas Laffan, Shawn W Borchard, Nils Bird, Michael I Dieleman, Wouter Pepper, David A Zutta, Brian Jobbagy, Esteban Silva, Lucas C R Bonser, Stephen P Berhongaray, Gonzalo Piñeiro, Gervasio Martinez, Maria-Jose Cowie, Annette L School of Environmental and Rural Science Ladd, Brenton |
author_facet |
Duarte-Guardia, Sandra Peri, Pablo L Amelung, Wulf Sheil, Douglas Laffan, Shawn W Borchard, Nils Bird, Michael I Dieleman, Wouter Pepper, David A Zutta, Brian Jobbagy, Esteban Silva, Lucas C R Bonser, Stephen P Berhongaray, Gonzalo Piñeiro, Gervasio Martinez, Maria-Jose Cowie, Annette L School of Environmental and Rural Science Ladd, Brenton |
author_sort |
Duarte-Guardia, Sandra |
title |
Better estimates of soil carbon from geographical data: a revised global approach |
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 |
publisher |
Springer Dordrecht |
publishDate |
2019 |
url |
https://hdl.handle.net/1959.11/58391 |
genre |
Tundra |
genre_facet |
Tundra |
op_relation |
10.1007/s11027-018-9815-y https://hdl.handle.net/1959.11/58391 une:1959.11/58391 |
_version_ |
1798834432716570624 |