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

This title appears in the Scientific Report : 2019

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 a gl...

Personal Name(s):	Duarte-Guardia, Sandra (Corresponding author)
	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. / Ladd, Brenton
Contributing Institute:	Agrosphäre; IBG-3
Published in:	Mitigation and adaptation strategies for global change, 24 (2019) 3, S. 355 - 372
Imprint:	Dordrecht [u.a.] Springer Science + Business Media B.V 2019
DOI:	10.1007/s11027-018-9815-y
Document Type:	Journal Article
Research Program:	Terrestrial Systems: From Observation to Prediction
Link:	Restricted Restricted OpenAccess OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.1007/s11027-018-9815-y in citations.
Please use the identifier: http://hdl.handle.net/2128/23127 in citations.

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