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

Duarte-Guardia, Sandra; 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

doi:10.1007/s11027-018-9815-y

Author(s)

Duarte-Guardia, Sandra

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

Publication Date

2019-03

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.

Citation

Mitigation and Adaptation Strategies for Global Change, v.24, p. 355-372

ISSN

1573-1596

1381-2386

Link

https://hdl.handle.net/1959.11/58391

Language

en

Publisher

Springer Dordrecht

Title

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

Type of document

Journal Article

Entity Type

Publication

Author(s)	Duarte-Guardia, Sandra 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
Publication Date	2019-03
Abstract	<p>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<sup>−1</sup>) and tundra (310 ± 15.3 t ha<sup>−1</sup>). Deserts had the lowest C stocks (53.2 ± 6.3 t ha<sup>−1</sup>) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha<sup>−1</sup>), tropical and subtropical forests (94 - 143 t ha<sup>−1</sup>) and grasslands (99-104 t ha<sup>−1</sup>). 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.</p>
Citation	Mitigation and Adaptation Strategies for Global Change, v.24, p. 355-372
ISSN	1573-1596 1381-2386
Link	https://hdl.handle.net/1959.11/58391
Language	en
Publisher	Springer Dordrecht
Title	Better estimates of soil carbon from geographical data: a revised global approach
Type of document	Journal Article
Entity Type	Publication

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

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