Modeling of land use and land cover (LULC) is a very important tool, particularly in the agricultural field: it allows us to know the potential changes in land area in the future and to consider developments in order to prevent probable risks. The idea is to give a representation of probable future situations based on certain assumptions. The objective of this study is to make future predictions in land use and land cover in the watershed “9 April 1947”, and in the years 2028, 2038 and 2050. Then, the maps obtained with the climate predictions will be integrated into an agro-hydrological model to know the water yield, the sediment yield and the water balance of the studied area by 2050.The future land use and land cover (LULC) scenarios were created using a CA-Markov forecasting model. The results of the simulation of the LULC changes were considered satisfactory, as shown by the values obtained from the kappa indices for agreement (κstandard) = 0.73, kappa for lack of information (κno) = 0.76, and kappa for location at grid cell level (κlocation) = 0.80. Future scenarios modeled in LULC indicate a decrease in agricultural areas and wetlands, both of which can be seen as a warning of crop loss. There is, on the other hand, an increase in forest areas that could be an advantage for the biodiversity of the fauna and flora in the “9 April 1947” watershed.

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.

Several studies have pointed out the promising use of nutritional diagnosis methods for the determination of optimum nutrient contents in plant tissues. The present investigation was carried out in different oases in Southern Tunisia to determine reference values for the interpretation of leaf analyses of date palm (Phoenix dactylifera) Deglet Nour cultivar with the Critical Value Approach (CVA) and the Compositional Nutrient Diagnosis (CND). A database (n = 100) of yield and mineral concentrations taken from date palm leaflets in October, at the maturity stage of dates, was used. The yield cut-off between low-yield and high-yield subpopulations, selected from cumulative variance ratio functions across survey data, was 76 kg palm⁻¹ and the global nutrient imbalance index (CNDr²) was 10.06. Critical CND nutrient indices were found to be symmetrical around zero as follows: (1.59; +1.59) for I_N, (−0.44, +0.44) for I_P, (−0.63, +0.63) for I_K, (−0.94, +0.94) for I_Ca, (−1.05, +1.05) for I_Mg, (−0.80, +0.80) for I_Fe, (−0.74, +0.74) for I_Cu, (−0.80, +0.80) for I_B, (−0.93, +0.93) for I_Zn, (−1.04, +1.04) for I_Mn, and (−1.03, +1.03) for the residual value. Compared to CND, the CVA approach shows weak detection of the nutrients that cause nutritional imbalance. CND indices revealed, except for N, the presence of nutrient imbalances and the necessity to correct the mineral nutrition of date palm in the Kebeli oases.

Understanding the interplay between climate change and land use/land cover (LULC) dynamics is critical for sustainable resource management, particularly in vulnerable Mediterranean ecosystems. This study introduces a novel framework integrating the Soil and Water Assessment Tool (SWAT) with future LULC and climate scenarios to predict hydrology and sediment dynamics in the 9th April watershed, a data-scarce river basin catchment in northern Morocco. By exploring four temporal scenarios up to 2050, the study comprehensively analyses erosion and water yield dynamics under changing environmental conditions. The framework employs robust calibration and validation using five statistical metrics, including Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE), with NSE as the objective function. Sensitivity analysis identifies critical parameters such as bulk density (SOL_BD), curve number (CN2), and soil evaporation compensation factor (ESCO), which govern water retention and runoff. For sediment transport, parameters like soil erodibility (USLE_K) and supporting practice factor (USLE_P) significantly influence deposition rates, projected to decrease from 49.6 t/ha/year to 16 t/ha/year under Scenario 4. This research demonstrates the novelty of integrating climate and LULC projections in a single modeling framework for Mediterranean drainage basins, offering insights into adaptive water management and erosion control strategies. The results highlight key vulnerabilities and adaptive opportunities, emphasizing the global applicability of this approach for data-scarce regions experiencing similar challenges. This study provides a predictive tool to address gaps in understanding hydrological and sediment dynamics under future climate and land-use change, providing practical insights for policymakers and resource managers. By identifying the key drivers of erosion and water yield, the results will support targeted interventions to enhance climate resilience and the sustainable management of land and water resources. The adaptability of the framework highlights its potential as a benchmark for addressing environmental challenges in climate-sensitive ecosystems worldwide. Future research should refine these models by incorporating socio-economic factors and stakeholder engagement to ensure practical application in decision-making.

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.