This study focused on one of the few but critical sample preparations required in soil spectroscopy (i.e., grinding), as well as the effect of soil particle size on the FTIR spectral database and the partial least squares regression models for the prediction of eight soil properties (viz., TC, TN, OC, sand, silt, clay, Olsen P, and CEC). Fifty soil samples from three Moroccan region were used. The soil samples underwent three preparations (drying, grinding, sieving) to obtain, at the end of the sample preparation step, three ranges of particle size, samples with sizes < 500 µm, samples with sizes < 250 µm, and a third range with particles < 125 µm. The multivariate models (PLSR) were set up based on the FTIR spectra recorded on the different obtained samples. The correlation coefficient (R²) and the root mean squared error of cross validation (RMSECV) were chosen as figures of merit to assess the quality of the prediction models. The results showed a general trend in improving the R2 as the finer particles were used (from <500 µm to 125 µm), which was clearly observed for TC, TN, P₂O₅, and CEC, whereas the cross-validation errors (RMSECV) showed an opposite trend. This confirmed that fine soil grinding improved the accuracy of predictive models for soil properties diagnosis in soil spectroscopy.

Groundwater has been the main source of irrigation in Morocco for many decades. However, irrigated agriculture is now constrained by groundwater depletion, severe soil salinity and alkalinity, among other factors. Irrigation practices with poor quality groundwater can limit crop choices, reduce agricultural yields and lead to long-term deterioration of soil quality and fertility. This study aims to identify the hydrogeochemical processes controlling groundwater composition in the Triffa plain study area and evaluates the effects of depth (shallow vs. deep) on its suitability for irrigation, in relation to dominant soil types, by adopting a comprehensive approach, combining the Irrigation Water Quality Index (IWQI), multiple traditional indices (e.g., SAR, SARadj, %Na, PS, PI, KR, SSP, MAR, RSBC, TH), USSL/Wilcox/Doneen diagrams, statistical analyses and GIS technique. The hydrogeochemical analysis reveals that groundwater in the Triffa plain is dominated by Cl⁻ and Na⁺ ions, with three main hydrochemical facies identified: Na⁺–Cl⁻, Ca²⁺–Mg²⁺–Cl⁻–SO₄²⁻, and Ca²⁺–Mg²⁺–Cl⁻ mixed type. The salinity and ionic composition of groundwater in the Triffa plain are primarily influenced by evaporite dissolution minerals, carbonate weathering and ion exchange processes, with no evidence of seawater intrusion, as demonstrated by the Cl⁻/Br⁻ ratio. Most groundwater samples were found very hard (TH > 300 mg/L) and display high ECw and TDS values, indicating poor suitability for irrigation, except dam water. IWQI results show that 88.88% of shallow and deep borehole groundwater falls within high to severe restriction use categories, which is confirmed by traditional indices and diagrams (USSL and Wilcox). Only 11.12% of samples were classified in the low to moderate restriction categories, especially those from dam water. Spatial analysis reveals that water quality deteriorates from the southern/southeastern zones to the central and northern regions of the plain, with the highest restrictions found in the northern regions, which present elevated values across different indices (e.g., SAR, %Na, KR), ECw, TDS, Na⁺, and Mg²⁺. To address salinity and sodicity risks, especially in Mollisols and Ultisols, this study advises soil-specific management, especially under high to severe restriction zones, with application of periodic leaching, very frequent irrigation, gypsum amendments, low-volume irrigation, and the use of polyacrylamides (PAMs) to mitigate salinity effects and preserve soil structure. Also, this study offers a promising and complete approach for assessing water irrigation quality in semi-arid areas for water management, soil quality, and sustainable agriculture.

Soil salinization is a major form of land degradation that diminishes soil fertility and health, thereby severely impacting the economic development and livelihood improvement of agrarian countries worldwide. Salt-affected soils generally include both saline soils, characterized by excess soluble salts that hinder plant growth, and sodic soils, where high exchangeable sodium levels disrupt soil structure and permeability. Frequent and accurate monitoring of farmland salinity is a vital action for the timely management and control of salinization. Thus, this study was carried out in irrigated fields of the Tassaout region of Morocco, where irrigation has been practiced for centuries, with the objective of evaluating the effectiveness of using portable X-ray fluorescence (pXRF) for direct soil salinity measurement by comparing the results with EC measured values in soil-to-water (S:W) extracts at various ratios, namely 1:1, 1:2.5, and 1:5. In addition, pertinent soil physico-chemical properties, including pH and organic matter content, were determined for correlation analysis. The study proved that pXRF can be a reliable, cost-effective, and quick option for the electrical conductivity of saturated extract (EC_e) measurement both in the laboratory and in situ. Furthermore, the study developed predictive models for soil EC_e estimation by complementing the pXRF technique, EC meter, and machine learning algorithms. The models were trained on 75% of the dataset using k-fold cross-validation and the remaining 25% for validation. The models performances were significantly better for EC_1:1 (R² = 0.94), EC_1:2.5 (R² = 0.93), and EC_1:5 (R² = 0.97). In conclusion, pXRF can be a reliable, cost-effective, and quick option for direct EC measurement both in the laboratory and in situ. In addition, the predictive models developed are promising tools for accurately inferring EC soil-to-water extract (EC_S:W) values either and EC_e from pXRF or EC-meter readings. The study recommends using the pXRF technique and the predictive models developed for large-scale salinity monitoring.

Soil salinity monitoring requires accurate measurement of saturated paste extract electrical conductivity (ECe) which is considered the most trustworthy measure of salinity hazard in many laboratories globally. It’s a time-consuming and technically demanding process. In contrast, the measurement of EC values from the 1:1, 1:2.5, and 1:5 soil-to-water ratios is simple, rapid and cheap. This study aims to develop a pedotransfer functions to estimate ECe from diluted soil-to-water extracts (EC1:1, EC1:2.5, EC1:5) and presents an innovative FTIR spectroscopy approach coupled with machine learning for rapid EC prediction. A total of 59 soil samples were collected from 22 profiles across three depths (0–20, 20-50 and 50-100 cm) in Morocco and analyzed for EC at different soil-to-water extracts and scanned using a Bruker-Tensor-II-HTS-XT spectrometer. Random Forest (RF) and Partial Least Squares Regression (PLSR) models were employed to predict ECe and diluted EC values. Results demonstrated a significant linear correlation between the ECe and diluted extract EC values, with an R ² >0.89 across all extracts. In addition, the conversion factors (CF) (ECe = CF × EC _{soil-to-water ratios} ) varied significantly among soil types, indicating the critical role of soil-type parameters for accurate ECe estimation. FTIR developed models demonstrated high predictive accuracy across all soil-to-water extracts (R ² = 0.86-0.91, RMSE = 0.41-3.69 dS/m), with distinct spectral features at 1970-2550 cm⁻ ¹ and 2867-3086 cm⁻ ¹ identified as the most sensitive regions for EC prediction. Random Forest (RF) models accurately predicted ECₑ from EC1:5 (R ² = 0.92, RMSE = 2.05 dS/m), with enhanced performance when including CEC and CaCO₃ content (R ² =0.95, RMSE=1.51 dS/m). In conclusion, FTIR spectroscopy combined with machine learning offers an accurate, rapid and minimal sample preparation method to make it particularly valuable for large-scale precision agriculture. Our findings demonstrate that mid-infrared spectroscopy enabling a rapid ECₑ estimation without saturated paste analysis a significant advancement for salinity hazard monitoring. Graphical Abstract This study aims to develop pedotransfer functions to convert soil electrical conductivity of the saturated paste extract (ECe), a key indicator of soil salinity based on easily measured soil-to-water extract ratios (1:1, 1:2.5, and 1:5) in the irrigated perimeter of the Triffa plain, northeastern of Morocco. In addition, it presents an innovative approach for rapid estimation of the ECe using mid-infrared spectroscopy combined with machine learning techniques. Soil sampling was conducted across three depth intervals and different soil types including Mollisols, Ultisols, Histosols, Entisols and Aridisols. Laboratory analyses included EC measurements from both saturated paste extracts and diluted soil-to-water extracts was conducted. Simultaneously, mid-infrared spectral data were scanned using Fourier-transform infrared (FTIR) spectroscopy. Machine learning algorithms, specifically Random Forest (RF) and Partial Least Squares Regression (PLSR) were used to establish relationships between spectral data and EC values. The study also evaluates the influence of integrating two key soil properties, calcium carbonate (CaCO₃) content and cation exchange capacity (CEC), on model performance and prediction accuracy. The results reveal a strong correlation between the ECe and soil-to-water ratio of 1:1, 1:2.5 and 1:5 with an R ² >0,89 across all ratios. In addition, the conversion factors (CF) used to transform soil-to-water ratios into ECe (ECe = CF × EC _{soil-to-water} ) varied significantly by soil type for 1:1, 1:2.5 and 1: respectively: Mollisols (1.87, 5.30, 9.52), Ultisols (1.77, 6.03, 8.66), Histosols (2.32, 6.73, 8.74), Aridisols (1.82, 3.76, 7.57), and combined soils (2.09, 5.51, 8.60). Furthermore, the models developed from Mid-infrared spectral data were validated and were recorded as high accuracy for EC1:1 (R ² =0.86, RMSE=1.65 dS/m), EC1:2.5 (R ² =0.91, RMSE=0.41 dS/m), EC1:5 (R ² =0.86, RMSE=0.42 dS/m), and ECe (R ² =0.87, RMSE=3.69 dS/m). Moreover, the study reveals that spectral ranges 1970–2550 cm⁻ ¹ and 2867–3086 cm⁻ ¹ were identified as the most sensitive for EC prediction. On the other hand, RF models also demonstrated the strong model performance for ECe prediction from EC1:5 ratio (R ² =0.92 and RMSE=2.05 dS/m). Worth noting that integration of soil properties such as CEC and CaCO ₃ , has enhanced the model prediction performances (i.e. R ² =0.95 and RMSE=1.51 dS/m). In concluding, FTIR spectroscopy enables accurate prediction of ECe using the (EC) of diluted soil-to-water extracts of 1:1, 1:2.5 and 1:5. Additionally, the RF machine learning algorithms, demonstrated strong potential in estimating ECe from EC1:5, with CEC and CaCO₃ serving a key role in enhancing model performance. Finally, the study recommends that mid FTIR spectroscopy coupled with chemometrics method is a robust, quick and cost-effective method for ECe measurement for soil salinity monitoring.

Purpose Microplastics (MPs) are an emerging soil contaminant, whereas potentially toxic elements (PTEs) are long-recognized persistent pollutants; their co-occurrence and potential interactions in terrestrial ecosystems are of growing concern. In semi-arid regions such as Marrakech (Morocco), where wastewater reuse and plastic-intensive agriculture are common, their accumulation in soils may pose long-term ecological risks. This study aimed to assess the occurrence, characteristics, and ecological implications of MPs and PTEs in agricultural and peri-urban soils of Tamansourt, on the outskirts of Marrakech, based on 30 soil samples collected from three sites across two depths. Methods Soil samples representing different land uses and irrigation practices were analyzed for MPs using density separation, stereomicroscopy, and Fourier Transform Infrared Spectroscopy (FTIR). Potentially toxic elements (Pb, Cd, Ni, Cu, Zn, and Co) were determined by acid digestion followed by Atomic Absorption Spectrometry (AAS). Results Microplastics were detected in all samples, with abundances ranging from 0 to 220 items kg⁻¹ dry weight, and significantly higher concentrations in surface soils (0–10 cm; mean = 64 items.kg⁻¹) than in subsurface layers (mean = 21.3 items.kg⁻¹; p < 0.05). Fragments dominated, with polyethylene and polypropylene as the main polymers. Potentially toxic elements showed moderate enrichment, especially Zn and Cu in peri-urban sites. Weak to moderate correlations were observed between MPs and Zn (r ≈ 0.38) and Cu (r ≈ 0.24). Pollution indices indicated unpolluted to moderately polluted conditions, while Cd contributed most to ecological risk. Conclusion The co-occurrence of MPs and PTEs, concentrated in surface horizons of a rapidly expanding peri-urban agricultural zone, identifies Tamansourt as an early-stage but intensifying contamination hotspot. These baseline data establish a reference point for North African drylands and make a concrete case for embedding microplastics, alongside potentially toxic elements, into national soil-monitoring and wastewater-reuse standards before contamination becomes entrenched.