Predicted distribution of SOC content in Europe (based on LUCAS, BioSoil and CZO) in the context of the EU-funded SoilTrEC project.

These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the 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 five different critical zone observatories (CZOs) in Europe).
Author - Contributors: 
Ece Aksoy , Yusuf Yigini , Luca Montanarella
Publisher: 
PLoS ONE 11(3): e0152098. doi:10.1371/journal.pone.0152098
Year: 
2016

The maps of predicted distribution of SOC content in Europe are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), the BioSoil Project (samples from European forest soil), and the “Soil Transformations in European Catchments” (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe). The Predicted SOC content was the lowest in permanent crops and arable lands; highest values are found in wetlands and grasslands. Moreover, Hungary and Portugal show the lowest SOC content with the averages 2.21% and 2.68%, whereas Ireland (13.29%) and Sweden (11.15%) hshow ighest SOC contents.  

Spatial coverage: 25 European Union Member States (excluded Romania, Bulgaria, Croatia), and Switzerland
Pixel size: 1Km
Projection: ETRS89-LAEA-10-52
Temporal coverage: 2014
Input data source: LUCAS, BioSoil and CZOs point data

Via this page you can register for downloading the output and input data that are mentioned in the paper

The data are described in:

Combining soil databases for topsoil organic carbon mapping in Europe” (E. Aksoy, Y.Yigini and L. Montanarella), published in PLOS ONE. doi: 10.1371/journal.pone.0152098, that is summarized as follows: 

Accuracy in assessing the distribution of soil organic carbon (SOC) is important because it plays a key role 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. This study aims to search for the effects and 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 were collected from the “Land Use/Cover Area frame Statistical Survey” (LUCAS) Project (samples from agricultural soil), the BioSoil Project (samples from forest soil), and the “Soil Transformations in European Catchments” (SoilTrEC) Project (samples from local soil data coming from five 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 the 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 the RK method was appropriate for successful SOC mapping, using combined databases did not increase the statistical significance of the method results for assessing the SOC distribution as much as expected. Combining local data coming from CZOs with LUCAS samples was found as more significant than combining the two big datasets of the LUCAS and Biosoil Projects. Moreover, the effect of the chosen auxiliary variables on SOC prediction seems more important than increasing the number of the soil samples. According to the results: SOC variation was mainly affected by elevation, slope, CTI, average temperature, average and total precipitation, texture, WRB and CORINE variables at the European scale in the model. Moreover, the highest average SOC contents were found in the wetland areas; agricultural areas have much lower SOC content than forest and semi natural areas; Ireland, Sweden and Finland show the highest SOC values; Portugal, Poland, Hungary, Spain and Italy show the lowest values with an average 3%.

 

Data (available: 3 output datasets and 1 input dataset):

0.     Input: soil sample point data; attribute: SOC for each point.

1.     Predicted distribution of SOC content by using 1 dataset (LUCAS) (Figure 3 in the article)

2.     Predicted distribution of SOC content by using 2 datasets (LUCAS-CZOs) (Figure 4 in the article)

3.     Predicted distribution of SOC content by using 3 datasets (LUCAS-CZOs-BIOSOIL) (Figure 5 in the article)

 

Acknowledgments

Funding support is acknowledged from the European Commission FP 7 Collaborative Project “Soil Transformations in European Catchments” (SoilTrEC) (Grant Agreement no. 244118).

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