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European soils are under increasing pressure, making it difficult to maintain the provision of soil ecosystem services (SESs). A better understanding of soil processes is needed to counteract soil threats (STs) and to promote sustainable soil management. The EJP SOIL programme of the EU provides a framework for the necessary research. However, different definitions of soil‐related terms potentially lead to varied understandings of concepts. Furthermore, there are numerous indicators available to quantify STs or SESs. As unclear communication is a key barrier that hinders the implementation of research results into practice, this study aimed to answer the question about whether the terminology of large‐scale initiatives is adequately understood within the soil‐science community and non‐research stakeholders. An online questionnaire was used to provide definitions for 33 soil‐related terms in both scientific and plain language, as well as indicators for seven SESs and 11 STs. Participants were asked to rate their agreement with the definitions and indicators on a seven‐grade Likert scale. The level of agreement was calculated as the percentage of ratings above 4, the neutral position. The survey was available from June to September 2023 and was distributed by a snowball approach. More than 260 stakeholders assessed the survey; 70% of respondents were researchers, and 15% were practitioners. Mean agreement levels for the definitions and indicators were generally high, at 85% and 78% respectively. However, it was apparent that the lowest agreement was found for terms that are relatively new, such as Ecosystem Services and Bundle, or unfamiliar for certain subgroups, such as ecological terms for stakeholders working at the farm scale. Due to their distinct majority, the results of this study primarily reflect the opinions of scientists. Thus, broad conclusions can only be drawn by comparing scientists with non‐scientists. In this regard, the agreement was surprisingly high across all types of questions. The combined outcomes indicate that there is still a need to facilitate communication between stakeholders and to improve knowledge distribution strategies. Nevertheless, this study can support and be used by future projects and programmes, especially regarding the harmonization of terminology and methods. [ABSTRACT FROM AUTHOR]

Soil sand particles play a crucial role in soil erosion because they are more susceptible to being detached and transported by erosive forces than silt and clay particles. Therefore, in soil erosion assessment and mitigation, it is crucial to model and predict soil sand particles at unsampled locations using appropriate methods. The study was aimed to evaluate the ability of a multivariate approach based on non-stationary geostatistics to merge LiDAR and visible-near infrared (Vis-NIR) diffuse reflectance data with laboratory analyses to produce high-resolution maps of soil sand content. Remotely sensed, high-resolution LiDAR-derived topographic attributes can be used as auxiliary variables to estimate soil textural particle-size fractions. The proposed approach was compared with the commonly used univariate approach of ordinary kriging to evaluate the contribution of auxiliary variables. Soil samples (0–0.20 m depth) were collected at 135 locations within a 139 ha forest catchment with granitic parent material and subordinately alluvial deposits, where soils classified as Typic Xerumbrepts and Ultic Haploxeralf crop out. A number of linear trend models coupled with different auxiliary variables were compared. The best model for predicting sand content was the one with elevation derived from LIDAR data as the only auxiliary variable. Although the improvement in estimation over the univariate model was rather marginal, the proposed approach proved very flexible and scalable to include any type of auxiliary variable. The application of LiDAR data is expected to expand as it allows the high-resolution prediction of soil properties, generally insufficiently sampled, at different spatial scales. [ABSTRACT FROM AUTHOR]

Radon gas from the ground is the main source of indoor concentrations in buildings, regardless of construction characteristics. According to Council Directive 2013/59/EURATOM, EU Member States must establish national reference levels for indoor radon concentrations in workplaces and draw up a National Radon Plan. In Calabria (Southern Italy), maps of indoor measurements at regional scale are not available. A set of 1434 average annual measurements, taken between 2010 and 2021, has been analysed. For a limited sector, a geostatistical approach allowed to map the expected concentrations at ground floor, the spatial uncertainty of estimates, and the probability of exceedance of the 300 Bq m−3 Italian threshold for workplaces. Highest values characterize the eastern border of the Sila massif. Obtained maps might be used to optimize locations of additional dosimeters, based on geological constraints. Such studies may support urban planning policies and provide recommendations on building techniques. [ABSTRACT FROM AUTHOR]

Sustainable forest land management requires measuring and monitoring soil organic carbon. Visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR, 350–2500 nm), although it has become an important method for predicting soil organic carbon (SOC), requires further studies and methods of analysis to realize its full potential. This study aimed to determine if the size of the study area and soil sampling density may affect the performance of Vis-NIR diffuse reflectance spectroscopy in the prediction of soil organic carbon. Two forest sites in the Calabria region (southern Italy), which differ in terms of area and soil sampling density, were used. The first one was Bonis catchment area (139 ha) with a cover consisting mainly of Calabrian pine, while the second was Mongiana forest area (33.2 ha) within the "Marchesale" Biogenetic Nature Reserve, which is covered by beech. The two study areas are relatively homogeneous regarding parent material and soil type, while they have very different soil sampling density. In particular, Bonis catchment has a lower sampling density (135 samples out of 139 ha) than Mongiana area (231 samples out of 33.2 ha). Three multivariate calibration methods (principal component regression (PCR), partial least square regression (PLSR), and support vector machine regression (SVMR)) were combined with different pretreatment techniques of diffuse reflectance spectra (absorbance, ABS, standard normal variate, SNV, and Savitzky–Golay filtering with first derivative (SG 1st D). All soil samples (0–20 cm) were analyzed in the laboratory for SOC concentration and for measurements of diffuse reflectance spectra in the Vis-NIR region. The set of samples from each study area was randomly divided into a calibration set (70%) and a validation set (30%). The assessment of the goodness for the different calibration models and the following SOC predictions using the validation sets was based on three parameters: the coefficient of determination (R2), the root mean square error (RMSE), and the interquartile range (RPIQ). The results showed that for the two study areas, different levels of goodness of the prediction models depended both on the type of pretreatment and the multivariate method used. Overall, the prediction models obtained with PLSR and SVMR performed better than those of PCR. The best performance was obtained with the SVMR method combined with ABS + SNV + SG 1st D pretreatment (R2 ≥ 0.77 and RPIQ > 2.30). However, there is no result that can absolutely provide definitive indications of either the effects of the study area size and soil sampling density in the prediction of SOC by vis-NIR spectroscopy, but this study fostered the need for future investigations in areas and datasets of different sizes from those in this study and including also different soil landscapes. [ABSTRACT FROM AUTHOR]

In some regions of Italy, low-intensity farming systems, together with variable climate conditions, have lowered soil organic carbon (SOC) content and soil quality attributes. This work aims to investigate on some aspects of (1) total organic carbon (TOC) prediction using Vis-NIR reflectance spectroscopy in combination with partial least squares regression (PLSR); (2) the most appropriate pre-processing techniques of Vis-NIR absorbance spectra; (3) the composition of organic carbon using variable importance of prediction (VIP). The study area was an olive grove, located at Montecorvino Rovella (Salerno, southwestern Italy), characterized by a calcaric soil (Leptic Calcisols) and (Luvic Phaeozem), with a low content of TOC (mean 2.03 g kg−1), caused by a low-intensity farming. Results of univariate PLSR analyses showed a good agreement between measured and predicted values both for TOC (R2: 0.66) and total carbonate content (R2: 0.93), when pH, electrical conductivity (EC) and absorbance spectra were used as predictors. The best results were obtained using as pre-treatments of the spectral data: 1) standard normal variate (SNV); 2) Savitzky-Golay algorithm; 3) first derivative. Variable Importance for Prediction (VIP) statistics showed to be a good tool to gain insights in TOC composition also when its content is low and influenced by carbonate. [ABSTRACT FROM AUTHOR]