% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{DuarteGuardia:864364,
      author       = {Duarte-Guardia, Sandra and Peri, Pablo L. and Amelung, Wulf
                      and Sheil, Douglas and Laffan, Shawn W. and Borchard, Nils
                      and Bird, Michael I. and Dieleman, Wouter and Pepper, David
                      A. and Zutta, Brian and Jobbagy, Esteban and Silva, Lucas C.
                      R. and Bonser, Stephen P. and Berhongaray, Gonzalo and
                      Piñeiro, Gervasio and Martinez, Maria-Jose and Cowie,
                      Annette L. and Ladd, Brenton},
      title        = {{B}etter estimates of soil carbon from geographical data: a
                      revised global approach},
      journal      = {Mitigation and adaptation strategies for global change},
      volume       = {24},
      number       = {3},
      issn         = {1573-1596},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2019-04159},
      pages        = {355 - 372},
      year         = {2019},
      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},
      cin          = {IBG-3},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000456264900002},
      doi          = {10.1007/s11027-018-9815-y},
      url          = {https://juser.fz-juelich.de/record/864364},
}