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, Brenton Ladd

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)355-372
Number of pages18
JournalMitigation and Adaptation Strategies for Global Change
Volume24
Issue number3
DOIs
Publication statusPublished - Mar 2019

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soil carbon
organic carbon
soil
biome
climate
parent material
temperate forest
tundra
anthropogenic effect
boreal forest
evapotranspiration
soil type
solar radiation
relief
desert
grassland
soil water
water content
topography
productivity

Cite this

Duarte-Guardia, S., Peri, P. L., Amelung, W., Sheil, D., Laffan, S. W., Borchard, N., ... Ladd, B. (2019). Better estimates of soil carbon from geographical data: A revised global approach. Mitigation and Adaptation Strategies for Global Change, 24(3), 355-372. https://doi.org/10.1007/s11027-018-9815-y
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. / Better estimates of soil carbon from geographical data : A revised global approach. In: Mitigation and Adaptation Strategies for Global Change. 2019 ; Vol. 24, No. 3. pp. 355-372.
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Duarte-Guardia, S, Peri, PL, Amelung, W, Sheil, D, Laffan, SW, Borchard, N, Bird, MI, Dieleman, W, Pepper, DA, Zutta, B, Jobbagy, E, Silva, LCR, Bonser, SP, Berhongaray, G, Piñeiro, G, Martinez, MJ, Cowie, AL & Ladd, B 2019, 'Better estimates of soil carbon from geographical data: A revised global approach', Mitigation and Adaptation Strategies for Global Change, vol. 24, no. 3, pp. 355-372. https://doi.org/10.1007/s11027-018-9815-y

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.

In: Mitigation and Adaptation Strategies for Global Change, Vol. 24, No. 3, 03.2019, p. 355-372.

Research output: Contribution to journalArticle

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T1 - Better estimates of soil carbon from geographical data

T2 - A revised global approach

AU - Duarte-Guardia, Sandra

AU - Peri, Pablo L.

AU - Amelung, Wulf

AU - Sheil, Douglas

AU - Laffan, Shawn W.

AU - Borchard, Nils

AU - Bird, Michael I.

AU - Dieleman, Wouter

AU - Pepper, David A.

AU - Zutta, Brian

AU - Jobbagy, Esteban

AU - Silva, Lucas C.R.

AU - Bonser, Stephen P.

AU - Berhongaray, Gonzalo

AU - Piñeiro, Gervasio

AU - Martinez, Maria Jose

AU - Cowie, Annette L.

AU - Ladd, Brenton

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N2 - 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.

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KW - Baseline

KW - Climate

KW - Geographic information systems

KW - Global

KW - Pristine ecosystems

KW - Soil organic carbon

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