Soil organic carbon (SOC) is an essential component, which soil quality depends on. Thus, understanding the spatial distribution and controlling factors of SOC is paramount to achieving sustainable soil management. In this study, SOC prediction for the Ourika watershed in Morocco was done using four machine learning (ML) algorithms: Cubist, random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM). A total of 420 soil samples were collected at three different depths (0–10 cm, 10–20 cm, and 20–30 cm) from which SOC concentration and bulk density (BD) were measured, and consequently SOC stock (SOCS) was determined. Modeling data included 88 variables incorporating environmental covariates, including soil properties, climate, topography, and remote sensing variables used as predictors. The results showed that RF (R² = 0.79, RMSE = 1.2%) and Cubist (R² = 0.77, RMSE = 1.2%) were the most accurate models for predicting SOC, while none of the models were satisfactory in predicting BD across the watershed. As with SOC, Cubist (R² = 0.86, RMSE = 11.62 t/ha) and RF (R² = 0.79, RMSE = 13.26 t/ha) exhibited the highest predictive power for SOCS. Land use/land cover (LU/LC) was the most critical factor in predicting SOC and SOCS, followed by soil properties and bioclimatic variables. Both combinations of bioclimatic–topographic variables and soil properties–remote sensing variables were shown to improve prediction performance. Our findings show that ML algorithms can be a viable tool for spatial modeling of SOC in mountainous Mediterranean regions, such as the study area.

Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of this study is to predict the spatial distribution of gully erosion at the scale of this municipality and to evaluate the predictive capacity of three prediction methods (frequency ratio (FR), logistic regression (LR), and random forest (RF)) for the characterization of gullying vulnerability. Twelve predisposing factors underlying gully formation were considered and mapped (elevation, slope, aspect, plane curvature, slope length (SL), stream power index (SPI), composite topographic index (CTI), land use, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, and vegetation cover (C factor). Furthermore, 894 gullies were digitized using high-resolution imagery. Seventy-five percent of the gullies were randomly selected and used as a training dataset, whereas the remaining 25% were used for validation purposes. The prediction accuracy was evaluated using area under the curve (AUC). Results showed that the factor that most contributed to the prevalence of gullying was topographic (slope, CTI, LS). Furthermore, the fitted models revealed that the RF model had a better prediction quality, with the best AUC (91.49%). The produced maps represent a valuable tool for sustainable management, land conservation, and protecting human lives against natural hazards (floods).

Terraced farming play several roles, from improving ecosystem services to enhancing associated population livelihoods. In this study, we were interested in evaluating the roles of mountain terraces in controlling floods and erosion risks, in particular in the Ourika watershed, located in the High Atlas mountains of Morocco. Rainfall simulation tests were conducted to measure infiltration, runoff and initial abstraction, while the Cesium-137 isotope technique was used to quantify soil loss. The results highlighted high infiltration for dense forests (78.00 ± 2.65 mm/h) and low for rangelands (27.12 ± 2.82 mm/h). For terraces, infiltration was found to be about 70.36 ± 0.56 mm/h, confirming the role of terraces in promoting infiltration. The runoff coefficient obtained was lowest for dense forests, followed by cultivated terraces, and highest for rangelands (62.71 ± 3.51). Thus, outside dense forests, infiltration and runoff were significantly very high and low, respectively, for agricultural terraces compared to other land use. The assessment of soil erosion rates showed a significant soil loss for rangelands compared to the agricultural terraces, further underlining the role of terraces in soil conservation. Terraces in the Ourika watershed, by increasing water infiltration, reduce the rate of surface runoff, and consequently, flood risks and soil degradation.

Desertification affects over 90% of Moroccan territory, leading to soil degradation that reduces agricultural productivity, diminishes biodiversity, and alters environmental functions. This study estimates the total economic cost of desertification in Morocco using a zonal approach based on regional sensitivity. The methodology includes two stages: quantifying productivity losses from water and wind erosion, salinization, overgrazing, silting of dams, carbon storage loss, and land-use changes; and monetizing impacts using methods such as productivity change, replacement cost, and the social cost of carbon. The total cost is estimated at USD 2.1 billion per year, with 78.02% from agricultural and grazing land productivity losses, 2.95% from dam silting, 18.47% from carbon storage loss, and 0.56% from land-use changes. These findings underscore the urgency of public policies, including land use planning, sustainable agriculture, irrigation modernization, and community engagement. Drawing on successful initiatives in the MENA region and globally, Morocco can mitigate desertification’s impacts and foster sustainable development.

This study delves into the complex interplay between land use dynamics, hydrological services, and intangible benefits within the context of Ifrane National Park (INP) in Morocco. Due to its extensive mountain forests and numerous wetlands, INP is a crucial contributor to the nation’s water supply and a vital source of hydrological ecosystem services (HES). However, climate change and evolving land use patterns have led to diminishing water resources and the desiccation of certain wetlands. This research used the Integrated Valuation of Ecosystem Services and Tradeoffs software (InVEST 3.10.2) for HES quantification and environmental economics approach for monetization to comprehend how HES values respond to challenges posed by urbanization, intensive agriculture, and other land use alterations. This work underscores INP’s role as a significant “water tower”, emphasizing the evolution of its services amidst challenges. Our findings reveal an annual decrease in HES economic value by USD 4000. This economic assessment serves as a compelling tool to enlighten decision-makers and park users about the imperative need to preserve natural ecosystems and use water resources judiciously. It advocates for investments in conservation and restoration within protected areas to sustain these vital services.