Abstract

Climate-smart land management offers an opportunity to reconcile biodiversity goals with climate change mitigation. An important first step towards this, is an accurate estimate of the soil organic carbon (SOC) stock and the identification of locations with a large share in storage. General-purpose, regional assessments are not always useful to identify such hotspots, since they tend to even out extremes of SOC stocks in the landscape. The objectives of this study were to (i) develop and validate a digital soil mapping procedure to assess the SOC stock of nature conservation areas in Flanders, N. Belgium, and (ii) identify soil carbon hotspots in these areas. To this end, different soil inventories, encompassing 864 soil profiles, were combined in a digital soil mapping approach.The resulting boosted regression trees model was able to explain 75% of the variability in the training dataset (R2 = 0.75, RMSE = 9.83 kg m−2) and 59% after 10-fold cross-validation (R2 = 0.59, RMSE = 12.38 kg m−2). Reference Soil Group according to World Reference Base (WRB), vegetation type, highest groundwater level and clay fraction were identified as key predictors of the SOC stock in the upper 100 cm. The total SOC stock stored under 86,828 ha of nature conservation area was estimated to be 21,565 kton OC, or 24.8 kg m−2 on average. According to the presented hotspot definition, taking the SOC stock of the surrounding landscape into account, 10.5–11.3% of the nature conservation areas was classified as a hotspot. However, validation of the results in five study sites showed the local variability in predictive performance, with relatively accurate predictions in the two data-rich sites in the eastern part of the territory and poor predictions in the three remaining sites. Local, site-specific variations, together with uncertainties in the input data and unmapped elements can hamper the accurate estimation of SOC stocks and hotspots.

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