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

Sandra Duarte-Guardia (), Pablo L. Peri (), Wulf Amelung (), Douglas Sheil (), Shawn W. Laffan (), Nils Borchard (), Michael I. Bird (), Wouter Dieleman (), David A. Pepper (), Brian Zutta (), Esteban Jobbagy (), Lucas C. R. Silva (), Stephen P. Bonser (), Gonzalo Berhongaray (), Gervasio Piñeiro (), Maria-Jose Martinez (), Annette L. Cowie () and Brenton Ladd ()
Additional contact information
Sandra Duarte-Guardia: Universidad Nacional de la Patagonia Austral (UNPA)
Pablo L. Peri: INTA EEA Santa Cruz, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)-cc332
Wulf Amelung: University of Bonn
Douglas Sheil: Norwegian University of Life Sciences
Shawn W. Laffan: University of New South Wales
Nils Borchard: Forschungszentrum Jülich GmbH
Michael I. Bird: James Cook University
Wouter Dieleman: James Cook University
David A. Pepper: University of New South Wales
Brian Zutta: Programa Nacional de Conservación de Bosques, Ministerio del Ambiente (National Forest Conservation Program, Ministry of the Environment)
Esteban Jobbagy: Universidad Nacional de San Luis y CONICET
Lucas C. R. Silva: University of Oregon
Stephen P. Bonser: University of New South Wales
Gonzalo Berhongaray: Universidad Nacional del Litoral
Gervasio Piñeiro: Universidad de Buenos Aires
Maria-Jose Martinez: Universidad Científica del Sur
Annette L. Cowie: NSW Department of Primary Industries
Brenton Ladd: University of New South Wales

Mitigation and Adaptation Strategies for Global Change, 2019, vol. 24, issue 3, No 2, 355-372

Abstract: 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.

Keywords: Soil organic carbon; Geographic information systems; Climate; Global; Pristine ecosystems; Baseline (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11027-018-9815-y

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