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. 2016 Mar 24;11(3):e0152098.
doi: 10.1371/journal.pone.0152098. eCollection 2016.

Combining Soil Databases for Topsoil Organic Carbon Mapping in Europe

Ece Aksoy  1 , Yusuf Yigini  2 , Luca Montanarella  2
Affiliations

Combining Soil Databases for Topsoil Organic Carbon Mapping in Europe

Ece Aksoy et al. PLoS One. .

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%.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Distribution of SOC samples.
Very low (<1%) Low (1–2%) Medium (2–6%) High (> 6%).
Fig 2
Fig 2. Locations of CZOs in SoilTrEC Project CZOs.
Fig 3
Fig 3. Predicted distribution of SOC content by using combination 1 dataset.
Fig 4
Fig 4. Predicted distribution of SOC content by using combination 2 dataset.
Fig 5
Fig 5. Predicted distribution of SOC content by using combination 3 dataset.
See this image and copyright information in PMC

References

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