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000864364 1001_ $$0P:(DE-HGF)0$$aDuarte-Guardia, Sandra$$b0$$eCorresponding author
000864364 245__ $$aBetter estimates of soil carbon from geographical data: a revised global approach
000864364 260__ $$aDordrecht [u.a.]$$bSpringer Science + Business Media B.V$$c2019
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000864364 520__ $$aSoils hold the largest pool of organic carbon (C) on Earth; yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a global baseline of SOC stocks. We used partial least square (PLS) regression and available geographic datasets that describe SOC, climate, organisms, relief, parent material and time. The accuracy of the model was determined by the root mean square deviation (RMSD) of predicted SOC against 100 independent measurements. The best predictors were related to primary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 m were found in boreal forests (254 ± 14.3 t ha−1) and tundra (310 ± 15.3 t ha−1). Deserts had the lowest C stocks (53.2 ± 6.3 t ha−1) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha−1), tropical and subtropical forests (94 - 143 t ha−1) and grasslands (99-104 t ha−1). Solar radiation, evapotranspiration, and annual mean temperature were negatively correlated with SOC, whereas soil water content was positively correlated with SOC. Our model explained 49% of SOC variability, with RMSD (0.68) representing approximately 14% of observed C stock variance, overestimating extremely low and underestimating extremely high stocks, respectively. Our baseline PLS predictions of SOC stocks can be used for estimating the maximum amount of C that may be sequestered in soils across biomes
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000864364 7001_ $$0P:(DE-HGF)0$$aPeri, Pablo L.$$b1
000864364 7001_ $$0P:(DE-Juel1)129427$$aAmelung, Wulf$$b2$$ufzj
000864364 7001_ $$00000-0002-1166-6591$$aSheil, Douglas$$b3
000864364 7001_ $$0P:(DE-HGF)0$$aLaffan, Shawn W.$$b4
000864364 7001_ $$0P:(DE-Juel1)145704$$aBorchard, Nils$$b5
000864364 7001_ $$0P:(DE-HGF)0$$aBird, Michael I.$$b6
000864364 7001_ $$0P:(DE-HGF)0$$aDieleman, Wouter$$b7
000864364 7001_ $$0P:(DE-HGF)0$$aPepper, David A.$$b8
000864364 7001_ $$0P:(DE-HGF)0$$aZutta, Brian$$b9
000864364 7001_ $$0P:(DE-HGF)0$$aJobbagy, Esteban$$b10
000864364 7001_ $$0P:(DE-HGF)0$$aSilva, Lucas C. R.$$b11
000864364 7001_ $$0P:(DE-HGF)0$$aBonser, Stephen P.$$b12
000864364 7001_ $$0P:(DE-HGF)0$$aBerhongaray, Gonzalo$$b13
000864364 7001_ $$0P:(DE-HGF)0$$aPiñeiro, Gervasio$$b14
000864364 7001_ $$0P:(DE-HGF)0$$aMartinez, Maria-Jose$$b15
000864364 7001_ $$0P:(DE-HGF)0$$aCowie, Annette L.$$b16
000864364 7001_ $$0P:(DE-HGF)0$$aLadd, Brenton$$b17
000864364 773__ $$0PERI:(DE-600)2004169-X$$a10.1007/s11027-018-9815-y$$gVol. 24, no. 3, p. 355 - 372$$n3$$p355 - 372$$tMitigation and adaptation strategies for global change$$v24$$x1573-1596$$y2019
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