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Many rural villages in Eastern Europe are severely impacted by aeolian smelter dust/deposits. Commonly, elemental studies in Romania have suffered from limited sampling numbers (n = 5–10) and/or incomplete digestion offering only semi-total quantification of elements using traditional, laboratory-based techniques. These approaches are simply inadequate for evaluating potentially hazardous soils and their spatial extent, particularly at urban/rural interfaces of variable land use. Portable x-ray fluorescence (PXRF) spectroscopy can accurately quantify contamination rapidly, in-situ with a wide dynamic range and little to no sample preparation for analysis of regulated elements (e.g., As, Cd, Cu, Mn, Pb, V, Zn) and other common soil elements such as Ca, Fe, K, Rb, Sr, Ti, and Zr. A contemporary PXRF spectrometer was used to scan 61 soil samples across multiple land use types in urban/rural interfaces on-site. Each site was georeferenced with elemental data inputted into a geographic information system for high resolution kriging interpolation. These models were superimposed over modern aerial imagery to evaluate the extent of pollution for each government-regulated element with simultaneous consideration and quantification of spatial variability in naturally occurring soil elements. Pb exceeded governmental action limits across 100% of the area, while V, Mn, Cu, and Zn were exceeded in 2.2, 2.1, 39.6, and 9.8% of the area. Furthermore, many regulated elements were closely correlated to natural soil elements. In short, georeferenced PXRF data proved a powerful new tool for on-site assessment of contaminated soils; one which has rarely been utilized in Eastern Europe to date. [ABSTRACT FROM AUTHOR]

The paper presents a concise overview of available statistics for rabbit livestock production and compares It with other meat production (chicken, pig, cattle). Data reflects quantities for production, trade, at European and world level and for Romania. [ABSTRACT FROM AUTHOR]

• Proximal sensors were used to determine calcic horizons in-situ. • PXRF in isolation was adept at differentiating chernozems and phaeozems. • Combined VisNIR-PXRF models modestly increased predictive model stability. The Transylvanian Plain (TP), Romania is widely used for agronomic production. The Chernisol soil class covers a vast majority of the TP as defined by the Sistemul Roman De Taxonomie A Solurilor (SRTS). Chernisols are fertile, dark soils similar to Mollisols in the United States. Chernisols have four key soil types, two of them occur in the TP: chernozems and phaeozems. While these two classifications appear similar to those found in the World Reference Base (WRB) for Soil Resources , only the Romanian system (SRTS) is applied in this paper. Chernozems require a chroma of ≤2 in the Am horizon when moist and a calcic horizon or secondary CaCO 3 within 125 cm. Phaeozems require chroma of ≤3.5 when wet and a calcic horizon or secondary CaCO 3 deeper than 125 cm. Traditionally, morphological assessment in combination with laboratory data has been used to assess the depth of secondary CaCO 3 , thus establishing the taxonomic classification. Herein, the efficacy of portable X-ray fluorescence (PXRF) and visible near infrared spectroscopy (VisNIR) were evaluated to make such determinations in lieu of laboratory data on 25 soil cores collected across five toposequences. Cores were scanned on-site with both sensors at 10 cm increments to determine depth to CaCO 3 accumulation. Comparing Ca percentages from only PXRF with traditional laboratory pressure calcimetry via simple linear regression (SLR), the following model validation data (based on whole core splitting, Core val) were obtained: R2 = 0.92; root mean squared error (RMSE) = 0.81%; residual prediction deviation (RPD) = 3.27: ratio of performance to interquartile range (RPIQ): 5.34. Thus, most of the Ca in soils of the TP is associated with secondary CaCO 3. Using the three most prominent latent variables, VisNIR spectra (smoothed to 10 nm bands) were combined with PXRF data via partial least squares regression (PLSR) to determine if any improvements could be achieved by the combined approach. Combined Core val models produced the following: R2 = 0.89; RMSE = 0.98%; RPD = 2.73; RPIQ = 4.46. Boosted regression tree Core val modeling produced similar results (R2 = 0.90; RMSE = 0.89%; RPD = 2.99; RPIQ = 4.87). With deference to the law of parsimony, use of PXRF data without VisNIR for calcic horizon identification and quantification in conjunction with morphological assessment and interpretation appears preferable for most Pedological applications given its robust, strong performance. Minimal differences were observed using two different sample splitting schemes (whole core vs. full sample set) relative to PXRF data predictive models for CaCO 3 prediction, especially for PXRF with no VisNIR contribution. Of the sites investigated, PXRF identified six phaeozems (P) and nineteen chernozems (C). Specifically, the following were identified on different slope profiles: summits (1P/4C), shoulders (1P/2C), backslopes (3P/7C), footslopes (0P/4C), and toeslopes (1P/2C). Localized landslides and erosion precluded the identification of a common landscape model differentiating P/C on landscapes. Nonetheless, proximal sensors were adept at informing soil properties needed for taxonomic classification on-site, with minimal need for traditional pressure calcimetry. [ABSTRACT FROM AUTHOR]

As countries of Eastern Europe look to advance their agricultural markets through large scale agronomic production, high resolution mapping of soil resources will be essential. Portable X-ray fluorescence (PXRF) spectrometry and diffuse reflectance spectroscopy (DRS) are non-invasive, proximal sensing techniques which provide quantitative data germane to physicochemical soil properties in seconds. While these techniques have been widely used to characterize individual soil samples, sample sets, or variability across individual fields, less work has been done at the catena scale (even less so in Eastern Europe), where variability due to topographic differences substantively affects a wide number of soil properties. The present study was conducted on three catenas of the Transylvanian Plain, Romania, each with 100 sampling points randomly established in ArcGIS. Laboratory analysis (particle size analysis, total carbon, total nitrogen, soil organic matter) was conducted at Texas Tech University, USA. Following Savitzky–Golay first derivative transformation, DRS spectra were used to predict soil physicochemical parameters of interest via support vector regression. The whole dataset was randomly divided into a 70% training (n = 210) and 30% test set (n = 90). Across all catenas, a combined PXRF + DRS approach showed better parameter prediction relative to either sensor independently as evidenced by higher R 2 , lower RMSE, higher RPD, and higher RPIQ values. For each parameter, the 100 points per catena were used as input data to develop a PXRF + DRS predictive model, and the output data from each model was kriged using ArcGIS 10.3.1. Spatial analysis strongly reflected management and landscape dynamics across the catenas. Combined proximal sensor approaches show considerable advantages over traditional laboratory approaches, allowing for high sample throughput, greater analytical density, and less expensive data, with minimal fall off in data quality. The combined PXRF + DRS approach showed excellent potential for providing the data needed to support optimized soil resource mapping and land management decisions in Eastern Europe or worldwide. [ABSTRACT FROM AUTHOR]