Abstract
Accuracy in assessing the distribution of soil organic carbon (SOC) is an important issue because of playing key roles in the functions of both natural ecosystems and agricultural systems. There are several studies in the literature with the aim of finding the best method to assess and map the distribution of SOC content for Europe. Therefore this study aims searching for another aspect of this issue by looking to the performances of using aggregated soil samples coming from different studies and land-uses. The total number of the soil samples in this study was 23,835 and they’re collected from the “Land Use/Cover Area frame Statistical Survey” (LUCAS) Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and “Soil Transformations in European Catchments” (SoilTrEC) Project (samples from local soil data coming from six different critical zone observatories (CZOs) in Europe). Moreover, 15 spatial indicators (slope, aspect, elevation, compound topographic index (CTI), CORINE land-cover classification, parent material, texture, world reference base (WRB) soil classification, geological formations, annual average temperature, min-max temperature, total precipitation and average precipitation (for years 1960–1990 and 2000–2010)) were used as auxiliary variables in this prediction. One of the most popular geostatistical techniques, Regression-Kriging (RK), was applied to build the model and assess the distribution of SOC. This study showed that, even though RK method was appropriate for successful SOC mapping, using combined databases was not helpful to increase the statistical significance of the method results for assessing the SOC distribution. According to our results; SOC variation was mainly affected by elevation, slope, CTI, average temperature, average and total precipitation, texture, WRB and CORINE variables for Europe scale in our model. Moreover, the highest average SOC contents were found in the wetland areas; agricultural areas have much lower soil organic carbon content than forest and semi natural areas; Ireland, Sweden and Finland has the highest SOC, on the contrary, Portugal, Poland, Hungary, Spain, Italy have the lowest values with the average 3%.
Highlights
Numerous environmental and socio-economic models require soil parameters as inputs to estimate and forecast changes in our future life conditions
For the combination 2; elevation, slope, CTI, Average temperature, average precipitation, total precipitation, Texture class 6 (Peat soils), WRB classes 21,10,6 (Vertisol, Gypsisol, Calcisol), CORINE Classes 1,2,4,5,6 predictors were found as statistically significant (p < 0.01) and 41% of the soil organic carbon (SOC) distribution was best explained by these covariates
This study showed that the SOC distribution of Europe was successfully mapped using Regression-Kriging method with good accuracy (R2 = 0.4, 0.41 and 0.33 (Table 1))
Summary
Numerous environmental and socio-economic models require soil parameters as inputs to estimate and forecast changes in our future life conditions. The availability of soil data is limited on both national and European scales. There are several reasons for working on assessing the distribution of this important chemical parameter, such as; SOC is a quantifiable indicator which is of high importance for evaluating the state of soils in Europe; SOC is of high interest for environmental policy making in Europe; existence of comparable modelling datasets exist at local/national and European level; and availability of auxiliary datasets (environmental covariates) for the best application of a modelling platform. Studies use various approaches to predict soil properties or classes including univariate and multi-variate statistical, geostatistical and hybrid methods, and process-based models that relate soils to environmental covariates considering spatial and temporal dimensions [2]
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