Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 509161
Title Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales—review and recommendations
Author(s) Vanguelova, E.I.; Bonifacio, E.; Vos, B. De; Hoosbeek, M.R.; Berger, T.W.; Vesterdal, L.; Armolaitis, K.; Celi, L.; Dinca, L.; Kjønaas, O.J.; Pavlenda, P.; Pumpanen, J.; Püttsepp, ; Reidy, B.; Simončič, P.; Tobin, B.; Zhiyanski, M.
Source Environmental Monitoring and Assessment 188 (2016)11. - ISSN 0167-6369 - 24 p.
DOI https://doi.org/10.1007/s10661-016-5608-5
Department(s) Chair Soil Chemistry and Chemical Soil Quality
WIMEK
Publication type Refereed Article in a scientific journal
Publication year 2016
Keyword(s) Carbon stocks - European - Forest soils - Landscape - National - Plot - Sampling - Soil profile
Abstract

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales—sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

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