TY - JOUR
T1 - Better estimates of soil carbon from geographical data
T2 - a revised global approach
AU - Duarte-Guardia, Sandra
AU - Peri, Pablo L.
AU - Amelung, Wulf
AU - Sheil, Douglas
AU - Laffan, Shawn W.
AU - Borchard, Nils
AU - Bird, Michael I.
AU - Dieleman, Wouter
AU - Pepper, David A.
AU - Zutta, Brian
AU - Jobbagy, Esteban
AU - Silva, Lucas C.R.
AU - Bonser, Stephen P.
AU - Berhongaray, Gonzalo
AU - Piñeiro, Gervasio
AU - Martinez, Maria Jose
AU - Cowie, Annette L.
AU - Ladd, Brenton
N1 - Publisher Copyright:
© 2018, Springer Science+Business Media B.V., part of Springer Nature.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
KW - Baseline
KW - Climate
KW - Geographic information systems
KW - Global
KW - Pristine ecosystems
KW - Soil organic carbon
UR - http://www.scopus.com/inward/record.url?scp=85047264134&partnerID=8YFLogxK
U2 - 10.1007/s11027-018-9815-y
DO - 10.1007/s11027-018-9815-y
M3 - Artículo
AN - SCOPUS:85047264134
SN - 1381-2386
VL - 24
SP - 355
EP - 372
JO - Mitigation and Adaptation Strategies for Global Change
JF - Mitigation and Adaptation Strategies for Global Change
IS - 3
ER -