This study aims to identify the influence of soil organic matter (OM) content and calcium carbonates (CaCO₃) on soil reflectance and select the optimum spectral bands for discriminating between topsoils of different soil types situated in the irrigated perimeter of the Triffa plain (Morocco) using VIS-NIR reflectance spectroscopy. Soil samples were collected from the plow layer in 26 sampling sites. The spectral measurements were conducted in the field using an ASD Fieldspec portable spectroradiometer (350–2500 nm), while the soil samples were analyzed in the laboratory. The spectral data were pre-processed to remove the noise effects and then analyzed with the CovSel (selected covariance) method, validated by linear discriminant analysis in order to select the most optimal spectral variables to discriminate between topsoils of different soil types in the plain. The results of the soils reflectance curves showed that low reflectance intensity marked the soils with high OM contents throughout the VIS spectrum. The influence of the soil OM content was very apparent in the VIS range (between 580–750 nm). Regarding the CaCO₃ content, it was noted that the soil samples with a high percentage of CaCO₃ increase the reflectance in all spectral domains situated between 350 and 2500 nm. The spectral bands of 1999, 686, 1280, 2340 and 1951 nm were the most optimal for the soil discrimination in the Triffa plain. This study concluded that the VIS-NIR spectroscopy demonstrates an excellent ability to characterize and discriminate between topsoils in the Triffa plain.

The use of standard laboratory methods to estimate the soil texture is complicated, expensive, and time-consuming and needs considerable effort. The reflectance spectroscopy represents an alternative method for predicting a large range of soil physical properties and provides an inexpensive, rapid, and reproducible analytical method. This study aimed to assess the feasibility of Visible (VIS: 350-700 nm) and Near-Infrared and Short-Wave-Infrared (NIRS: 701-2500 nm) spectroscopy for predicting and mapping the clay, silt, and sand fractions of the soils of Triffa plain (north-east of Morocco). A total of 100 soil samples were collected from the non-root zone of soil (0-20 cm) and then analyzed for texture using the VIS-NIRS spectroscopy and the traditional laboratory method. The partial least squares regression (PLSR) technique was used to assess the ability of spectral data to predict soil texture. The results of prediction models showed excellent performance for the VIS-NIRS spectroscopy to predict the sand fraction with a coefficient of determination R2 = 0.93 and Root Mean Squares Error (RMSE) =3.72, good prediction for the silt fraction (R2=0.87; RMSE = 4.55), and acceptable prediction for the clay fraction (R2 = 0.53; RMSE = 3.72). Moreover, the range situated between 2150 and 2450 nm is the most significant for predicting the sand and silt fractions, while the spectral range between 2200 and 2440 nm is the optimal to predict the clay fraction. However, the maps of predicted and measured soil texture showed an excellent spatial similarity for the sand fraction, a certain difference in the variability of clay fraction, while the maps of silt fraction show a lower difference.

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 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.

Exploration of mineral resources had been part of the major means to promote economic development in Morocco, but the devastating effect of mining on the environment is inevitable. Based on the factors analysis of the environmental disasters caused by the mining activities, the Jbel Tirremi mine,...