Jeroen Meersmans;Manuel Martín;Eva Lacarce;Thomas G. Orton;Sarah De Baets;M. Gourrat;Nicolas P.A. Saby;Johanna Wetterlind;Antonio Bispo;Timothy A. Quine;Dominique Arrouay

French National Institute for Agriculture Research (INRA), InfoSol Unit, Orléans 45075 (France; French National Institute for Agriculture Research (INRA), InfoSol Unit, Orléans 45075 (France);French National Institute for Agriculture Research (INRA), InfoSol Unit, Orléans 45075 (France);Earth and Life Institute (ELI), Université Catholique de Louvain, Louvain-la-Neuve 1348 (Belgium);College of Life and Environmental Sciences (CLES), Department of Geography, University of Exeter, Rennes Drive, EX4 4RJ Exeter (UK);Agriculture and Forestry Department, French Environment and Energy Management Agency (ADEME), Angers 49004 (France); Earth and Life Institute (ELI), Université Catholique de Louvain, Louvain-la-Neuve 1348 (Belgium)

c input;values;specific;soc stock;crop;modelling approac

Development of a quantitative understanding of soil organic carbon (SOC) dynamics is vital for management of soil to sequester carbon (C) and maintain fertility, thereby contributing to food security and climate change mitigation. There are well-established process-based models that can be used to simulate SOC stock evolution; however, there are few plant residue C input values and those that exist represent a limited range of environments. This limitation in a fundamental model component (i.e., C input) constrains the reliability of current SOC stock simulations. This study aimed to estimate crop-specific and environment-specific plant-derived soil C input values for agricultural sites in France based on data from 700 sites selected from a recently established French soil monitoring network (the RMQS database). Measured SOC stock values from this large scale soil database were used to constrain an inverse RothC modelling approach to derive estimated C input values consistent with the stocks. This approach allowed us to estimate significant crop-specific C input values (P < 0.05) for 14 out of 17 crop types in the range from 1.84 +/- 0.69 t C ha(-1) year(-1) (silage corn) to 5.15 +/- 0.12 t C ha(-1) year(-1) (grassland/pasture). Furthermore, the incorporation of climate variables improved the predictions. C input of 4 crop types could be predicted as a function of temperature and 8 as a function of precipitation. This study offered an approach to meet the urgent need for crop-specific and environment-specific C input values in order to improve the reliability of SOC stock prediction.