Lefèvre, Romain2; Barré, Pierre3; Moyano, Fernando E.2; Christensen, Bent Tolstrup8; Bardoux, Gérard2; Eglin, Thomas4; Giradin, Cyril2; Houot, Sabine5; Kätterer, Thomas6; van Oort, Folkert7; Chenu, Claire2
1 Department of Agroecology - Soil Fertility, Department of Agroecology, Science and Technology, Aarhus University2 Bioemco Laboratory, UPMC, CNRS, INRA, AgroParisTech3 Laboratoire de Géologie, Paris4 Direction Productions et Energies Durable, Angers5 EGC laboratory, UMR INRA-AgroParisTech6 Department of Ecology, Swedish University of Agricultural Sciences, Uppsala7 Pessac Laboratory, INRA, Versailles8 Department of Agroecology - Soil Fertility, Department of Agroecology, Science and Technology, Aarhus University
The impact of climate change on the stability of soil organic carbon (SOC)remains a major source of uncertainty in predicting future changes in atmospheric CO2 levels. One unsettled issue is whether the mineralization response to temperature depends on SOC mineralization rate. Long-term (>25 years) bare fallow experiments (LTBF) in which the soil is kept free of any vegetation and organic inputs, and their associated archives of soil samples represent a unique research platform to examine this issue as with increasing duration of fallow, the lability of remaining total SOC decreases. We retrieved soils from LTBF experiments situated at Askov (Denmark), Grignon (France), Ultuna (Sweden), and Versailles (France) and sampled at the start of the experiments and after 25, 50, 52, and 79 years of bare fallow, respectively. Soils were incubated at 4, 12, 20, and 35 °C and the evolved CO2 monitored. The apparent activation energy (Ea) of SOC was then calculated for similar loss of CO2 at the different temperatures. The Ea was always higher for samples taken at the end of the bare-fallow period, implying a higher temperature sensitivity of stable C than of labile C. Our results provide strong evidence for a general relationship between temperature sensitivity and SOC stability upon which significant improvements in predictive models could be based.
Global Change Biology, 2014, Vol 20, Issue 2, p. 633-640