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

Duarte-Guardia, Sandra, Peri, Pablo, 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, Pineiro, Gervasio, Martinez, Maria-Jose, Cowie, Annette L., and Ladd, Brenton (2019) Better estimates of soil carbon from geographical data: a revised global approach. Mitigation and Adaptation Strategies for Global Change, 24 (3). pp. 355-372.

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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 toprimary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 mwere 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 +/- 6.3tha(-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, withRMSD (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 soilsacross biomes.

Item ID: 57093
Item Type: Article (Research - C1)
ISSN: 1573-1596
Keywords: Soil organic carbon, Geographic information systems, Climate, Global, Pristine ecosystems, Baseline
Copyright Information: (C) Springer Science+Business Media B.V., part of Springer Nature 2018
Funders: INTA Argentinia
Date Deposited: 13 Feb 2019 07:39
FoR Codes: 41 ENVIRONMENTAL SCIENCES > 4101 Climate change impacts and adaptation > 410102 Ecological impacts of climate change and ecological adaptation @ 50%
41 ENVIRONMENTAL SCIENCES > 4106 Soil sciences > 410604 Soil chemistry and soil carbon sequestration (excl. carbon sequestration science) @ 50%
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