Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil data
Soil Electrical conductivity (EC) is a measure of the ability of soil to conduct an electric current, which is primarily influenced by the concentration of soluble salts in the soil solution that takes place principally through water-filled pores. Ions (Ca2+, Mg 2+, K +, Na +, and NH 4+, SO42-, Cl-,...
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Elsevier
2025-02-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0016706125000370 |
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| author | Calogero Schillaci Simone Scarpa Felipe Yunta Aldo Lipani Fernando Visconti Gábor Szatmári Kitti Balog Triven Koganti Mogens Greve Giulia Bondi Georgios Kargas Paraskevi Londra Fuat Kaya Giuseppe Lo Papa Panos Panagos Luca Montanarella Arwyn Jones |
| author_facet | Calogero Schillaci Simone Scarpa Felipe Yunta Aldo Lipani Fernando Visconti Gábor Szatmári Kitti Balog Triven Koganti Mogens Greve Giulia Bondi Georgios Kargas Paraskevi Londra Fuat Kaya Giuseppe Lo Papa Panos Panagos Luca Montanarella Arwyn Jones |
| author_sort | Calogero Schillaci |
| collection | DOAJ |
| description | Soil Electrical conductivity (EC) is a measure of the ability of soil to conduct an electric current, which is primarily influenced by the concentration of soluble salts in the soil solution that takes place principally through water-filled pores. Ions (Ca2+, Mg 2+, K +, Na +, and NH 4+, SO42-, Cl-, NO3–, and HCO3–) from soluble salts dissolved in soil water carry electrical charges and conduct the electrical current. EC is considered a proxy of soil salinity and other soil characteristics, whose monitoring is much needed in the context of climate change, increasing irrigation in agricultural areas and sea level rise. The pan-European LUCAS soil monitoring scheme, established in 2009, provided EC1:5 in the topsoil (0–20 cm) in the surveys of the years 2015 and 2018 for almost 20,000 samples. In this work, using the LUCAS 2018 dataset, we provide an empirically-derivedpedotransfer function to convert diluted EC1:5 to saturated ECe using the LUCAS soil texture and soil organic carbon, and a framework for ECe mapping with a machine-learning algorithm named Quantile Regression Forest. The final model resulted in an R2 of 0.302 with an RMSE of 0.265 dS m−1 for soil samples not used for model calibration. The results are presented as predicted ECe in the topsoil, and they reveal that in Atlantic and Northern Europe, salts may accumulate in soils through several natural processes, i.e., primary salinization, but in Mediterranean and Southern Europe, they accumulate because of human interventions on the soil water and solute regimes. Among these interventions, seawater intrusion into coastal aquifers, irrigation with waters containing soluble salts, poor drainage of irrigated lands and of naturally saline soils, stand out. Hotspot analysis per country or Nomenclature of Territorial Units for Statistics (NUTS 0) revealed high topsoil ECe levels occurred in Spain 0.11 %. Increasing ECe can led to constrained crop productivity in irrigated farming. With this assessment, we try to determine the hotspots for future monitoring and understanding the main drivers for sustainable soil management. Future challenges for ECe mapping that need to be address are sample numerosity and depth and availability of a consistent set of ECe data measured to provide a regression based PTF for the use of diluted EC1:5. |
| format | Article |
| id | doaj-art-ade59e4110de4564b09e3ff4b4a8d849 |
| institution | Kabale University |
| issn | 1872-6259 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
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| series | Geoderma |
| spelling | doaj-art-ade59e4110de4564b09e3ff4b4a8d8492025-02-10T04:33:27ZengElsevierGeoderma1872-62592025-02-01454117199Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil dataCalogero Schillaci0Simone Scarpa1Felipe Yunta2Aldo Lipani3Fernando Visconti4Gábor Szatmári5Kitti Balog6Triven Koganti7Mogens Greve8Giulia Bondi9Georgios Kargas10Paraskevi Londra11Fuat Kaya12Giuseppe Lo Papa13Panos Panagos14Luca Montanarella15Arwyn Jones16JRC European Commission, Ispra, Italy; Corresponding author.JRC European Commission, Ispra, ItalyJRC European Commission, Ispra, ItalyUniversity College London, United KingdomCSIC Centro de Investigaciones sobre Desertificación-CIDE Valencia, SpainInstitute for Soil Sciences, Centre for Agricultural Research, HUN-REN, Budpest, HungaryInstitute for Soil Sciences, Centre for Agricultural Research, HUN-REN, Budpest, HungaryAgroecology Dept. Aarhus University, DenmarkAgroecology Dept. Aarhus University, DenmarkTEAGASC, Dublin, IrelandDepartment of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, GreeceDepartment of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, GreeceDepartment of Soil Science and Plant Nutrition, Faculty of Agriculture, Isparta University of Applied Sciences, Isparta, 32260, TürkiyeUniversity of Palermo, ItalyJRC European Commission, Ispra, ItalyJRC European Commission, Ispra, ItalyJRC European Commission, Ispra, ItalySoil Electrical conductivity (EC) is a measure of the ability of soil to conduct an electric current, which is primarily influenced by the concentration of soluble salts in the soil solution that takes place principally through water-filled pores. Ions (Ca2+, Mg 2+, K +, Na +, and NH 4+, SO42-, Cl-, NO3–, and HCO3–) from soluble salts dissolved in soil water carry electrical charges and conduct the electrical current. EC is considered a proxy of soil salinity and other soil characteristics, whose monitoring is much needed in the context of climate change, increasing irrigation in agricultural areas and sea level rise. The pan-European LUCAS soil monitoring scheme, established in 2009, provided EC1:5 in the topsoil (0–20 cm) in the surveys of the years 2015 and 2018 for almost 20,000 samples. In this work, using the LUCAS 2018 dataset, we provide an empirically-derivedpedotransfer function to convert diluted EC1:5 to saturated ECe using the LUCAS soil texture and soil organic carbon, and a framework for ECe mapping with a machine-learning algorithm named Quantile Regression Forest. The final model resulted in an R2 of 0.302 with an RMSE of 0.265 dS m−1 for soil samples not used for model calibration. The results are presented as predicted ECe in the topsoil, and they reveal that in Atlantic and Northern Europe, salts may accumulate in soils through several natural processes, i.e., primary salinization, but in Mediterranean and Southern Europe, they accumulate because of human interventions on the soil water and solute regimes. Among these interventions, seawater intrusion into coastal aquifers, irrigation with waters containing soluble salts, poor drainage of irrigated lands and of naturally saline soils, stand out. Hotspot analysis per country or Nomenclature of Territorial Units for Statistics (NUTS 0) revealed high topsoil ECe levels occurred in Spain 0.11 %. Increasing ECe can led to constrained crop productivity in irrigated farming. With this assessment, we try to determine the hotspots for future monitoring and understanding the main drivers for sustainable soil management. Future challenges for ECe mapping that need to be address are sample numerosity and depth and availability of a consistent set of ECe data measured to provide a regression based PTF for the use of diluted EC1:5.http://www.sciencedirect.com/science/article/pii/S0016706125000370Electrical conductivityEC1:5ECePedotransfer functionLUCAS soil surveyPredictive modelling |
| spellingShingle | Calogero Schillaci Simone Scarpa Felipe Yunta Aldo Lipani Fernando Visconti Gábor Szatmári Kitti Balog Triven Koganti Mogens Greve Giulia Bondi Georgios Kargas Paraskevi Londra Fuat Kaya Giuseppe Lo Papa Panos Panagos Luca Montanarella Arwyn Jones Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil data Geoderma Electrical conductivity EC1:5 ECe Pedotransfer function LUCAS soil survey Predictive modelling |
| title | Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil data |
| title_full | Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil data |
| title_fullStr | Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil data |
| title_full_unstemmed | Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil data |
| title_short | Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topsoil data |
| title_sort | empirical estimation of saturated soil paste electrical conductivity in the eu using pedotransfer functions and quantile regression forests a mapping approach based on lucas topsoil data |
| topic | Electrical conductivity EC1:5 ECe Pedotransfer function LUCAS soil survey Predictive modelling |
| url | http://www.sciencedirect.com/science/article/pii/S0016706125000370 |
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