Partitioning Soil Organic Carbon Into Its Centennially Stable and Active Fractions with Machine-Learning Models Based on Rock-Eval®
Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval® thermal analysis (PARTYSOCv2.0 and PARTYSOCv2.0EU) 5 Lauric Cécillon1,2, François Baudin3, Claire Chenu4, Bent T. Christensen5, Uwe Franko6, Sabine Houot4, Eva Kanari2,3, Thomas Kätterer7, Ines Merbach8, Folkert van Oort4, Christopher Poeplau9, Juan Carlos Quezada10,11,12, Florence Savignac3, Laure N. Soucémarianadin13, Pierre Barré2 1Normandie Univ., UNIROUEN, INRAE, ECODIV, Rouen, France 10 2Laboratoire de Géologie, École normale supérieure, CNRS, PSL Univ., IPSL, Paris, France 3Institut des Sciences de la Terre de Paris, Sorbonne Université, CNRS, Paris, 75005, France 4UMR 1402 ECOSYS, INRAE, AgroParisTech, Univ. Paris Saclay, Thiverval-Grignon, 78850, France 5Department of Agroecology, Aarhus University, AUFoulum, 8830 Tjele, Denmark 6Department of soil system science, Helmholtz Centre for Environmental Research, UFZ, 06120 Halle Germany 15 7Department of Ecology, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden 8Department Community Ecology, Helmholtz Centre for Environmental Research, UFZ, 06246 Bad Lauchstädt, Germany 9Thünen Institute of Climate-Smart Agriculture, 38116 Braunschweig, Germany 10Laboratory of Ecological Systems ECOS and Laboratory of Plant Ecology Research PERL, School of Architecture, Civil and Environmental Engineering ENAC, École Polytechnique Fédérale de Lausanne EPFL, 1015 Lausanne, Switzerland 20 11Swiss Federal Institute for Forest, Snow and Landscape Research
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