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dc.contributor.authorFERNANDEZ UGALDE OIHANEen_GB
dc.contributor.authorTÓTH GERGELYen_GB
dc.date.accessioned2017-10-22T00:22:33Z-
dc.date.available2017-10-20en_GB
dc.date.available2017-10-22T00:22:33Z-
dc.date.created2017-10-18en_GB
dc.date.issued2017en_GB
dc.date.submitted2017-07-21en_GB
dc.identifier.citationEUROPEAN JOURNAL OF SOIL SCIENCE vol. 68 no. 5 p. 716-725en_GB
dc.identifier.issn1351-0754en_GB
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1111/ejss.12464/fullen_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC107431-
dc.description.abstractThere is an increasing demand for information on organic carbon (OC) in subsurface horizons, because subsurface horizons down to the bedrock can contribute to more than half of soil carbon stocks. In this study, we developed pedotransfer functions (PTFs) for predicting OC content in subsurface horizons of European soils. We used a dataset with a wide geographical coverage in Europe. The dataset was stratified sequentially into land cover and soil categories. For each category, PTFs were developed by multiple linear regression with the main soil and climatic factors of soil OC storage as predictive variables: OC in topsoil (0–20 cm), depth of subsurface horizons, texture and bulk density (BD) in subsurface horizons, and mean annual temperature and precipitation. Three land-cover categories were separated: woodland, a combined category of grassland and non-permanent arable land, and permanent arable land. For the combined land-cover category, two soil categories were identified: (i) soils with clay-rich subsoil and soils with little horizon development, and (ii) organic-rich soils and soils rich in Fe and Al compounds. The adjusted R2 of all PTFs was above 0.62. When PTFs were applied to independent data, the adjusted R2 was above 0.51 for all of them. The PTFs showed good prediction ability, with root mean square error (RMSE) values between 2.43 and 13.82 g C kg-1 soil. The adjusted R2 and RMSE of PTFs were better when BD was used as a predictive variable. The PTFs could be implemented easily for applications at the continental scale in Europe.en_GB
dc.description.sponsorshipJRC.D.3-Land Resourcesen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherWILEY-BLACKWELLen_GB
dc.relation.ispartofseriesJRC107431en_GB
dc.titlePedotransfer functions for predicting organic carbon in subsurface horizons of European soilsen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1111/ejss.12464en_GB
JRC Directorate:Sustainable Resources

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