Browsing by Author "Reimann, Clemens"
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- GEMAS: Boron as a geochemical proxy for weathering of European agricultural soilPublication . Négrel, Philippe; Ladenberger, Anna; Demetriades, Alecos; Reimann, Clemens; Birke, Manfred; Sadeghi, Martiya; The EuroGeoSurveys GEMAS Project TeamABSTRACT: bout a century ago, B was recognised as an essential element for the normal growth of plants and terrestrial organisms. Limitations for plant development have been recognised in agricultural systems, particularly in highly weathered soil. Boron is rarely analysed in whole rock or soil analysis, as it requires specific analytical techniques. It is often determined, after partial extraction (aqua regia or Ca-Cl), usually on a limited number of samples. Many more questions than answers exist about the environmental behaviour of B. We present B contents in agricultural soil samples (0-10 cm) collected in 33 European countries (5.6 million km2) during the GEMAS (GEochemical Mapping of Agricultural and grazing land Soil) continental-scale project. The B content, determined by ICP-MS following hot aqua regia extraction, varies in European agricultural soil from 0.5 to 49 mg/kg (median 2.42 mg/kg, n = 2108), which is somewhat similar to total B estimates for the Upper Continental Crust (17-47 mg/kg). Its spatial distribution in agricultural soil shows a patchy pattern with low values in regions with granitic bedrock and high contents in soil formed over limestone and in volcanic areas. Boron geochemical behaviour in soil is strongly dependent on other factors such as pH, CEC, presence of organic matter, clay and secondary oxides and hydroxides. Boron geochemical mapping at the continental scale in arable soil allows investigations of plant health, i.e., the beneficial and adverse effects due to the nutritional status of boron.
- Prediction of the concentration of chemical elements extracted by aqua regia in agricultural and grazing European soils using diffuse reflectance mid-infrared spectroscopyPublication . Soriano-Disla, J. M.; Janik, L.; McLaughlin, M. J.; Forrester, S.; Kirby, J. K.; Reimann, Clemens; The EuroGeoSurveys GEMAS Project TeamPrediction of the concentration of chemical elements extracted by aqua regia in agricultural and grazing European soils using diffuse reflectance mid-infrared spectroscopy / J. M. Soriano-Disla... [et al.]. - Amsterdam : Elsevier, 2013. - il., 2 figuras e 3 tabelas ; 30 cm The aim of this study was to develop partial least squares (PLS) models to predict the concentrations of 45 elements in soils extracted by the aqua regia (AR) method using diffuse reflectance Fourier Transform mid-infrared (MIR; 4000–500 cm-1) spectroscopy. A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were selected. From the full soil set, 1000 samples were randomly selected to develop PLS models. Cross-validation was used for model training and the remaining 3130 samples used for model testing. According to the ratio of standard deviation to root mean square error (RPD) of the predictions, the elements were allocated into two main groups; Group 1 (successful calibrations, 30 elements), including those elements with RPD ? 1.5 (the coefficient of determination, R2, also provided): Ca (3.3, 0.91), Mg (2.5, 0.84), Al (2.4, 0.83), Fe (2.2, 0.79), Ga (2.1, 0.78), Co (2.1, 0.77), Ni (2.0, 0.77), Sc (2.1, 0.76), Ti (2.0, 0.75), Li (1.9, 0.73), Sr (1.9, 0.72), K (1.8, 0.70), Cr (1.8, 0.70), Th (1.8, 0.69), Be (1.7, 0.66), S (1.7, 0.66), B (1.6, 0.63), Rb (1.6, 0.62), V (1.6, 0.62), Y (1.6, 0.61), Zn (1.6, 0.60), Zr (1.6, 0.59), Nb (1.5, 0.58), Ce (1.5, 0.58), Cs (1.5, 0.58), Na (1.5, 0.57), In (1.5, 0.57), Bi (1.5, 0.56), Cu (1.5, 0.55), and Mn (1.5, 0.54); and Group 2 for 15 elements with RPD values lower than 1.5: As (1.4, 0.52), Ba (1.4, 0.52), La (1.4, 0.52), Tl (1.4, 0.51), P (1.4, 0.46), U (1.4, 0.45), Sb (1.3, 0.46), Mo (1.3, 0.43), Pb (1.3, 0.42), Se (1.3, 0.40), Cd (1.3, 0.40), Sn (1.3, 0.38), Hg (1.2, 0.33), Ag (1.2, 0.32) and W (1.1, 0.19). The success of the PLS models was found to be dependent on their relationships (directly or indirectly) with MIR-active soil components.
- The use of diffuse reflectance mid-infrared spectroscopy for the prediction of the concentration of chemical elements estimated by X-ray fluorescence in agricultural and grazing European soilsPublication . Soriano-Disla, J. M.; Janik, L.; McLaughlin, M. J.; Forrester, S.; Kirby, J.; Reimann, Clemens; The EuroGeoSurveys GEMAS Project TeamThe aim of this study was to develop partial least-squares (PLS) regression models using diffuse reflectance Fourier transform mid-infrared (MIR) spectroscopy for the prediction of the concentration of elements in soil determined by X-ray fluorescence (XRF). A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were used for the development of models to predict concentrations of Al, As, Ba, Ca, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hf, K, La, Mg, Mn, Na, Nb, Ni, P, Pb, Rb, Sc, Si, Sr, Th, Ti, V, Y, Zn and Zr in soil using MIR spectroscopy. The results were compared with those obtained where MIR models were developed with the same soils but using the concentration of elements extracted with aqua regia (AR). The PLS models were cross-validated against the experimental log-transformed XRF values of all the elements. The calibration models were derived from a set of 1000 randomly selected calibration samples. The rest of the samples (3130) were used as an independent validation set. According to the residual predictive deviation (RPD), predictions were classified as follows: “Good quality”, Ca (2.9), Mg (2.5), Al (2.3), Fe (2.2), Ga (2.2), Si (2.1), Na (2.0); “Indicator quality”, V (1.9), Ni (1.9), Sc (1.9), K (1.8), Ti (1.8), Rb (1.8), Zn (1.7), Co (1.7), Zr (1.6), Cr (1.6), Sr (1.6), Y (1.6), Nb (1.6), Ba (1.5), Mn (1.5), As (1.5), Ce (1.5); “Poor quality”, Cs (1.4), Th (1.4), P (1.4), Cu (1.4), Pb (1.3), La (1.2), Hf (1.1). Good agreement was observed between the RPD values obtained for the elements analysed in this study and those from the AR study. Despite the different elemental concentrations determined by the XRF method compared to the AR method, MIR spectroscopy was still capable of predicting elemental concentrations.