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We evaluated the performance of a new, simple test to evaluate soil structural stability. The QuantiSlakeTest (QST) consists in a quantitative approach of the slake test, a dynamic weighing of a dried structured soil sample once immersed in water. The objective of this work was threefold: we aimed to (i) derive indicators from QST curves to evaluate soil structural stability, (ii) establish the relationship between soil properties and QST indicators, and (iii) assess how QST indicators respond to contrasting soil management practices. To reach these goals, we sampled the soil of 35 plots from three long-term field trials in the silt loam region of Belgium dealing respectively with contrasting organic matter inputs, tillage and P–K fertilisation. For each plot, indicators calculated from QST curves (e.g. total relative mass loss, disaggregation speed and time to meet a threshold values of mass loss) were compared to the results of the three tests of , used as a reference method for the measurement of soil aggregate stability. Shortly after immersion in water, soil mass increases due to the rapid replacement of air by water in soil porosity. Then soil mass reaches a maximum before decreasing, once mass loss by disaggregation exceeds mass gain by air loss. Our results confirmed that the early mass loss under water is mainly related to slaking, whereas after a longer time period, clay dispersion and differential swelling become the dominant processes of soil disaggregation. The overall soil structural stability was positively correlated to the soil organic carbon (SOC) content and negatively correlated to the clay content of soil. Consequently, the SOC : clay ratio was closely related to QST indicators. Nevertheless, for a similar mean annual carbon (C) input, green manure and crop residues were more efficient in decreasing clay dispersivity and differential swelling, whereas farmyard manure promoted SOC storage and was more efficient against slaking. QST curves had a strong discriminating power between reduced tillage and ploughing regardless of the indicator, as reduced tillage increases both total SOC content and root biomass in the topsoil. The QST has several advantages. It (i) is rapid to run, (ii) does not require expensive equipment or consumables, and (iii) provides a high density of information on both specific mechanisms of soil disaggregation and the overall soil structural stability. As an open-access programme for QST data management is currently under development, the test has a strong potential for adoption by a widespread community of end users. [ABSTRACT FROM AUTHOR]

Micro-near-infrared (Micro-NIR) spectroscopy has emerged as a promising technique for accurate and cost-effective estimation of soil attributes compared to traditional wet chemistry methods and conventional NIR spectroscopy. Despite its potential, the full extent of its capabilities and applications under different soil conditions remained unexplored. This study has evaluated the potential of a low-cost micro-NIR sensor for predicting soil organic carbon (SOC) and clay contents in agricultural soils under dry and fresh conditions. Two sets of soil samples i.e., A (92 samples, Netherlands) and B (92 samples, Belgium, France, and Germany) were collected and analysed for SOC and clay contents using standard laboratory methods. A micro-NIR of 2000 ∼ 2450 nm spectral range with 18–28 nm resolution (NIRONE D2.5, Spectral Engine, Germany) was used to scan both fresh and air-dried soil samples. Partial least squares regression (PLSR) models were developed for each dataset separately with and without feature selection using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and slime mould algorithm (SMA). Micro-NIR sensor performed differently for the two datasets, predicting SOC and clay contents accurately for the dataset A while failing for the dataset B. The best accuracy achieved for SOC in the dataset A-fresh (coefficient of determination in prediction (R P 2) = 0.76 and root mean square errors in prediction (RMSE P) = 0.27 %) was improved with the dry samples (R P 2 = 0.81 and RMSE P = 0.27 %). The prediction of clay content was rather poor (R P 2 = 0.48 and RMSE P = 5 %). Feature selection by SMA and CARS methods improved SOC prediction for the dataset A of fresh (R p 2 = 0.79 and RMSE P = 0.25 %) and dry soils (R p 2 = 0.84 and RMSE P = 0.25 %), respectively. Using CARS improved the results of clay prediction for the dataset A of dry soil (R p 2 = 0.56; RMSE P = 4.62 %). Poor performance for the dataset B could be attributed to the non-normal data distribution (SOC skewness = 4.30 and clay skewness = 3.05) and the larger soil variability encountered including soil types. Therefore, this study indicates that a micro-NIR sensor is a potential innovation and can predict SOC accurately and clay with rather moderate accuracy for a normally distributed dataset. • Micro-NIR sensor can accurately predict soil organic carbon. • Micro-NIR sensor can predict soil clay content to a certain degree of accuracy. • Feature analysis can enhance the accuracy of micro-NIR for SOC and clay content predictions. • Sample's attributes distribution has influence on the prediction using micro-NIR. [ABSTRACT FROM AUTHOR]

[Display omitted] • - A methdological alternative to handle highly censored As data is developed. • - Higher risk associated with warmer, higher areas with clayey and phosphorus-rich soils. • - Higher As concentrations in Portugal, Spain, Austria, France and Belgium. • - Countries present a non-negligible risk of exceeding the 45 mg/kg limit of action. • - As concentrations exceed background values, but the threshold adopted is uncertain. Arsenic (As) is a versatile heavy metalloid trace element extensively used in industrial applications. As is carcinogen, poses health risks through both inhalation and ingestion, and is associated with an increased risk of liver, kidney, lung, and bladder tumors. In the agricultural context, the repeated application of arsenical products leads to elevated soil concentrations, which are also affected by environmental and management variables. Since exposure to As poses risks, effective assessment tools to support environmental and health policies are needed. However, the most comprehensive soil As data available, the Land Use/Cover Area frame statistical Survey (LUCAS) database, contains severe limitations due to high detection limits. Although within International Organization for Standardization standards, the detection limits preclude the adoption of standard methodologies for data analysis. The present work focused on developing a new method to model As contamination in European soils using LUCAS soil samples. We introduce the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, we produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). We found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection. [ABSTRACT FROM AUTHOR]