Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/58391
Title: Better estimates of soil carbon from geographical data: a revised global approach
Contributor(s): Duarte-Guardia, Sandra (author); Peri, Pablo L (author); Amelung, Wulf (author); Sheil, Douglas (author); Laffan, Shawn W (author); Borchard, Nils (author); Bird, Michael I (author); Dieleman, Wouter (author); Pepper, David A (author); Zutta, Brian (author); Jobbagy, Esteban (author); Silva, Lucas C R (author); Bonser, Stephen P (author); Berhongaray, Gonzalo (author); Piñeiro, Gervasio (author); Martinez, Maria-Jose (author); Cowie, Annette L  (author); Ladd, Brenton (author)
Publication Date: 2019-03
Early Online Version: 2018-05-23
DOI: 10.1007/s11027-018-9815-y
Handle Link: https://hdl.handle.net/1959.11/58391
Abstract: 

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.

Publication Type: Journal Article
Source of Publication: Mitigation and Adaptation Strategies for Global Change, v.24, p. 355-372
Publisher: Springer Dordrecht
Place of Publication: The Netherlands
ISSN: 1573-1596
1381-2386
Fields of Research (FoR) 2020: 4101 Climate change impacts and adaptation
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science

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