According to the Scopus database, over the last five years, 91 scientific papers with the keyword “pXRF” (portable X-ray fluorescence) were published in indexed journals in the domain of environmental science and agricultural science, which indicates more frequent applications of this technique in scientific research. The pXRF method is characterized by speed, precision, accuracy, and the possibility of a simultaneous analysis of a large number of elements, albeit with higher limits of detection (LODs) as a major disadvantage. The presence of metals in certain phosphate fertilizers is well established, though not to the same extent as in mineral nitrogen fertilizers. The aim of this research was to determine the metal content (As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Si, Sr, Th, U, Zn, Zr, and Y) in thirteen commercial mineral nitrogen fertilizers via the pXRF method. Six straight fertilizers (ammonium nitrate, ammonium sulphate nitrate, limestone ammonium, nitrate, and urea) and seven complex fertilizers (various NPK formulations), which are different even according to their production technology, produced in Croatia were analyzed using the handheld Vanta C (Olympus) XRF analyzer according to the loose powder method and “point and shoot” technique. Data quality control was performed by analyzing the reference fertilizer samples and certified and reference soil samples. The results revealed that the determined contents of Cd, Mn, and Th were relatively higher in the single-component fertilizers, while the contents of As, Cr, Fe, Ni, Si, Sr, Zn, Zr, Y, and U were relatively higher in the complex fertilizers. Due to the higher LODs of Co and Pb (3 mg/kg) and Mo (2 mg/kg), the pXRF method was not appropriate for the determination of these metals in the analyzed fertilizers. The quantified metal content in the analyzed fertilizers varied as follows: 2.0–8.0 mg As/kg; 11.5–31.3 mg Cd/kg; 29.8–118.5 mg Cr/kg; 7.8–26.3 mg Cu/kg; 16.5–2209 mg Fe/kg; 20.3–5290 mg Mn/kg; 6.2–27.8 mg Ni/kg; 1156–4581 mg Si/kg; 2.0–469.8 mg Sr/kg; 3.0–35.3 mg Th/kg; 2.0–82.8 mg U/kg; 1.4–166 mg Zn/kg; 9.7–15.3 mg Zr/kg; and 16.5–128.0 mg Y/kg. The results indicated that the pXRF method is particularly suitable for measurement and metal detection in complex nitrogen mineral fertilizers with higher amounts of metals, but it is not suitable for the detection and quantification of the lower amounts of As, Zr, Y, Cu, Ni, and Cr in single-component nitrogen fertilizers. Compared to all of the investigated fertilizers, the highest amounts of As, Cr, Cu, Fe, Ni, U, Zn, and Zr were quantified in the NPK 7-20-30 formulation.

Changes in soil chemistry after invasion by bracken (Pteridium aquilinum) have been studied in heathlands, but comparable studies in meadows are lacking. We investigated if bracken invasion into P-deficient meadows alters the soil nutrient-resource pool, as well as the mechanisms behind it linked to soil processes and bracken nutrition. Furthermore, we investigated how community composition responds to differences in soil chemistry before and after the invasion. Soil and plant material sampling, along with vegetation survey, were performed during bracken peak biomass. Data analyses included analysis of variance and canonical correspondence analysis (CCA). Bracken invasion increased soil P availability, soil organic C concentration, as well as C:N, C:S and N:S ratios, while decreasing Fe and Co concentrations. Bracken pinnae were rich in P, and its rhizomes were rich in K, whereas N:P of pinnae and rhizomes was low. CCA showed contrasting abundance patterns of frequent meadow species related to P and K availability. Holcus lanatus exhibited competitive advantage under extremely low P availability. Increase in P availability under bracken may have occurred through promoting the leaching of Fe and Al. By increasing P availability for its growth and increasing N limitation for other species, bracken can gain a competitive advantage from the soil resource-niche perspective. Its ability to increase soil P availability, along with the physiological mechanisms behind its high P acquisition efficiency, seem to differentiate bracken from other species of competitive ecological strategy, which are mainly confined to nutrient-rich environments. This enabled bracken to invade P-deficient meadows.

The research objective was to use proximal spectroscopy in visible and near infrared (VNIR) spectra to determine the total leaf nitrogen (TN) content and the above-ground biomass of Miscanthus × giganteus (MxG) grown in the open-roof greenhouse experiment on soil contaminated with cadmium and mercury (100 mg Cd/kg soil; 20 mg Hg/kg soil), in dependence of different soil amendments in four treatments (I-soil without amendment; II-sludge; III-mycorrhizae; IV-MxG ash). Leaf reflectance was acquired using a field spectroradiometer (350–1050 nm) at the end of the vegetation period between 2018 and 2019 (n = 24). TN content was determined using the dry combustion method, while biomass was weighted immediately after the harvest. In terms of the treatment effect, sludge showed the greatest contribution in TN content. Regarding the biomass quantity, MxG ash revealed the best results as soil amendment. Applying the partial least squares regression, complete correlation and low root mean squared error (RMSE) were obtained between predicted and measured values for the validation dataset of TN content (R² = 0.87, RMSE = 0.139%), while a strong correlation was calculated for biomass (R² = 0.53, RMSE = 0.833 t/ha). As an additional tool with analytical methods, proximal spectroscopy is suitable to integrate the optical and physiological properties of MxG, and to assess nutrient stress in crop grown on contaminated soils.

This paper aims to evaluate the ability of VNIR proximal soil spectroscopy to determine post-fire soil chemical properties and discriminate fire severity based on soil spectra. A total of 120 topsoil samples (0–3 cm) were taken from 6 ha of unburned (control (CON)) and burned areas (moderate fire severity (MS) and high fire severity (HS)) in Mediterranean Croatia within one year after the wildfire. Partial least squares regression (PLSR) and an artificial neural network (ANN) were used to build calibration models of soil pH, electrical conductivity (EC), CaCO₃, plant-available phosphorus (P₂O₅) and potassium (K₂O), soil organic carbon (SOC), exchangeable calcium (^exCa), magnesium (^exMg), potassium (^exK), sodium (^exNa), and cation exchange capacity (CEC), based on soil reflectance data. In terms of fire severity, CON samples exhibited higher average reflectance than MS and HS samples due to their lower SOC content. The PCA results pointed to the significance of the NIR part of the spectrum for extracting the variance in reflectance data and differentiation between the CON and burned area (MS and HS). DA generated 74.2% correctly classified soil spectral samples according to the fire severity. Both PLSR and ANN calibration techniques showed sensitivity to extract information from soil features based on hyperspectral reflectance, most successfully for the prediction of SOC, P₂O₅, ^exCa, ^exK, and CEC. This study confirms the usefulness of soil spectroscopy for fast screening and a better understanding of soil chemical properties in post-fire periods.