A high resolution map of soil types and physical properties for Cyprus : a digital soil mapping optimization

Fine-resolution soil maps constitute important data for many different environmental studies. Digital soil mapping techniques represent a cost-effective method to obtain detailed information about soil types and soil properties over large areas. The main objective of the study was to extend predictions from 1:25,000 legacy soil surveys (including WRB soil groups, soil depth and soil texture classes) to the larger area of Cyprus. A multiple-trees classification technique, namely Random Forest (RF), was applied. Specific objectives were: (i) to analyze the role and importance of a large data set of environmental predictors, (ii) to investigate the effect of the number of training points, forest size (ntree), the numbers of predictors sampled per node (mtry) and tree size (nodesize) in RF; (iii) to compare RF-derived maps with maps derived with a multinomial logistic regression model, in terms of validation error (test set and independent profiles) and map uncertainty, using the confusion index and a newly developed reliability index. The optimized RF model was run using half of the input points available (over a million) and with ntree equal to 350. The mtry parameter was set to 5 (close to half the number of the environmental variables used) for both soil series and soil properties. The nodesize calibration showed no relevant performance increase and was kept at its default value (1). In terms of environmental variables, the model used 10 predictors, covering all the soil formation factors considered in the scorpan formula, to derive the three maps. Soil properties, derived from geochemistry data, showed a high importance in deriving soil groups, depths and texture. Random Forest constructed a better predictive model than multinomial logistic regression, showing comparable predictive uncertainty but much lower validation error. The RF-derived maps show very low out of bag (OOB) errors (around 10% for both soil groups and soil properties) but relatively high validation error from independent profiles (45% for soil depth, 51% for soil texture). The resulting reliability index was low in the main mountainous area of Cyprus, where predictions were extrapolations as indicated by the multivariate environmental similarity surface, but medium to high in the main agricultural areas of the country

Hexavalent chromium leads to differential hormetic or damaging effects in alfalfa (Medicago sativa L.) plants in a concentration-dependent manner by regulating nitro-oxidative and proline metabolism

Chromium has been proven to be extremely phytotoxic. This study explored the impacts of increasing Cr(VI) exposure (up to 10 mg L−1 K2Cr2O7) on the growth and development of alfalfa plants and adaptation responses employed, in an environmentally relevant context. The threshold concentration of K2Cr2O7 in irrigation water beyond which stress responses are initiated is 1 mg L−1. Lower Cr(VI) exposure (0.5 mg L−1 K2Cr2O7) induced hormesis, evident through increased biomass and larger leaves, likely mediated by increased NO content (supported by elevated NR enzymatic activity and overexpression of NR and ndh genes). Elevated Cr(VI) exposure (5 and 10 mg L−1 K2Cr2O7) resulted in reduced biomass and smaller leaves, and lower levels of photosynthetic pigment (10 mg L−1 K2Cr2O7). Higher levels of lipid peroxidation, H2O2 and NO contents in these plants suggested nitro-oxidative stress. Stress responses included increased SOD and CAT enzymatic activities, further supported to some extent by MnSOD, FeSOD, Cu/ZnSOD and CAT transcripts levels. GST7 and GST17 gene expression patterns, as well as proline content, P5CS enzymatic activity and corresponding P5CS and P5CR gene expression levels emphasized the role of proline and GSTs in the adaptation responses. Results highlight the importance of managing Cr(VI) levels in irrigation water. © 2020 Elsevier Lt

Arsenic in agricultural and grazing land soils of Europe

Arsenic concentrations are reported for the <2 mm fraction of ca. 2200 soil samples each from agricultural (Ap horizon, 0\u201320 cm) and grazing land (Gr, 0\u201310 cm), covering western Europe at a sample density of 1 site/2500 km2. Median As concentrations in an aqua regia extraction determined by inductively coupled plasma emission mass spectrometer (ICP-MS) were 5.7 mg/kg for the Ap samples and 5.8 mg/kg for the Gr samples. The median for the total As concentration as determined by X-ray fluorescence spectrometry (XRF) was 7 mg/kg in both soil materials. Maps of the As distribution for both land-use types (Ap and Gr) show a very similar geographical distribution. The dominant feature in both maps is the southern margin of the former glacial cover seen in the form of a sharp boundary between northern and southern European As concentrations. In fact, the median As concentration in the agricultural soils of southern Europe was found to be more than 3-fold higher than in those of northern Europe (Ap: aqua regia: 2.5 vs. 8.0 mg/kg; total: 3 vs. 10 mg/kg). Most of the As anomalies on the maps can be directly linked to geology (ore occurrences, As-rich rock types). However, some features have an anthropogenic origin. The new data define the geochemical background of As in agricultural soils at the European scale

Geogenic and agricultural controls on the geochemical composition of European agricultural soils

Concern about the environmental impact of agriculture caused by intensification is growing as large amounts of nutrients and contaminants are introduced into the environment. The aim of this paper is to identify the geogenic and agricultural controls on the elemental composition of European, grazing an nd agricultural soils

Prediction of the concentration of chemical elements extracted by aqua regia in agricultural and grazing European soils using diffuse reflectance mid-infrared spectroscopy

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 cm1) 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 RPDP1.5 (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