Soil sampling and preparation for monitoring soil carbon
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INRA, InfoSol Unit, 45075 Orléans, France
Max-Planck-Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745 Jena, Germany
Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
Mazingira Centre, International Livestock Research Institute (ILRI), P.O. Box 30709, 00100, Nairobi, Kenya
Antwerpen University, D.C.219, 2610 Wilrijk, Belgium
Univ Orleans, CNRS, ISTO, UMR 7327, F-45071 Orleans, France
UMR 8079 CNRS / Université Paris Sud, Laboratoire Ecologie, Systématique et Evolution. Avenue du Doyen André Guinier, 91405 Orsay, France
INRA, UMR IUREP, 63100 Clermont Ferrand, France
INRA, UMR 1391 ISPA, 33140 Villenave d’Ornon, France
Publish date: 2018-11-23
Int. Agrophys. 2018, 32(4): 633–643
There is an urgent need for standardized monitoring of existing soil organic carbon stocks in order to accurately quantify potential negative or positive feedbacks with climate change on carbon fluxes. Given the uncertainty of flux measurements at the ecosystem scale, obtaining precise estimates of changes in soil organic carbon stocks is essential to provide an independent assessment of long-term net ecosystem carbon exchange. Here we describe the standard procedure to monitor the soil organic carbon stocks within the footprint of an eddy covariance flux tower, as applied at ecosystem stations of the Integrated Carbon Observation System. The objectives are i) to ensure comparability between sites and to be able to draw general conclusions from the results obtained across many ecosystems and ii) to optimize the sampling design in order to be able to prove changes in time using a reduced number of samples. When sampling a given site at two periods, the objective is generally to assess if changes occurred in time. The changes that can be detected (i.e., demonstrated as statistically significant) depend on several parameters such as the number of samples, the spatial sampling design, and the inherent within-site soil variability. Depending on these parameters, one can define the ‘minimum detectable change’ which is the minimum value of changed that can be statistically proved. Using simulation studies, we address the trade-off between increasing the number of samples and getting lower minimum detectable changes of soil organic carbon stocks.