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dc.contributor.authorRODEGHIERO Mircoen_GB
dc.contributor.authorCESCATTI ALESSANDROen_GB
dc.date.accessioned2010-02-25T16:14:58Z-
dc.date.available2009-01-30en_GB
dc.date.available2010-02-25T16:14:58Z-
dc.date.created2009-01-30en_GB
dc.date.issued2008en_GB
dc.date.submitted2007-06-25en_GB
dc.identifier.citationFOREST ECOLOGY AND MANAGEMENT vol. 255 no. 1 p. 106-112en_GB
dc.identifier.issn0378-1127en_GB
dc.identifier.urihttp://dx.doi.org/10.1016/j.foreco.2007.08.025en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC37726-
dc.description.abstractSoil respiration is the second largest flux of carbon between terrestrial ecosystems and the atmosphere and is affecting climate sensitivity and vulnerability of the terrestrial carbon stock. Monitoring soil carbon dioxide efflux is a complex task, due to the high spatial and temporal variability of the fluxes. For this reason, more than 30 sampling points are required to attain reliable estimates of ecosystem soil respiration. However, the number of sampled points is often limited by labour, time and budget constraints. Stratified sampling is an alternative to random sampling as a method to reduce the number of sampling points when an effective proxy variable is available for the definition of the strata. In order to evaluate different sampling strategies we tested, with a Monte Carlo simulation, the effectiveness of random and stratified samplings, using experimental data collected in three alpine ecosystems (two forests and one grassland). We evaluated an innovative method for defining the strata to be sampled. The method is based on an initial sampling of soil respiration from a large number of candidate points in order to account for the spatial variability. The minimum number of sampling points required to adequately represent soil respiration for the entire area were then selected using stratified statistical sampling.We show that this method is unbiased and that it reduces considerably the uncertainty in the sampling process compared to random sampling. The method was highly effective in the two forest ecosystems, characterized by a high spatial variability in soil respiration and by a high temporal correlation of the fluxes. On the contrary the method was not so effective in the grassland site, where fluxes have lower spatial variability and temporal correlation. However, the stratified sampling offered a consistent reduction of the error (%) of the estimated annual soil CO2 efflux in all the ecosystems. At the grassland ecosystem the average reduction of the error (%) of the annual CO2 efflux was about 12%, while at the forest ecosystems the average reductions were 55% and 57%, respectively. # 2007 Elsevier B.V. All rights reserved.en_GB
dc.description.sponsorshipJRC.H.2-Climate changeen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherELSEVIER SCIENCE BVen_GB
dc.relation.ispartofseriesJRC37726en_GB
dc.titleSpatial Variability and Optimal Sampling Strategy of Soil Respirationen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1016/j.foreco.2007.08.025en_GB
JRC Directorate:Sustainable Resources

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