Increasing agricultural production is a major concern that aims to increase income, reduce hunger, and improve other measures of well-being. Recently, the prediction of soil-suitability has become a primary topic of rising concern among academics, policymakers, and socio-economic analysts to assess dynamics of the agricultural production. This work aims to use physico-chemical and remotely sensed phenological parameters to produce soil-suitability maps (SSM) based on Machine Learning (ML) Algorithms in a semi-arid and arid region. Towards this goal an inventory of 238 suitability points has been carried out in addition to14 physico-chemical and 4 phenological parameters that have been used as inputs of machine-learning approaches which are five MLA prediction, namely RF, XgbTree, ANN, KNN and SVM. The results showed that phenological parameters were found to be the most influential in soil-suitability prediction. The validation of the Receiver Operating Characteristics (ROC) curve approach indicates an area under the curve and an AUC of more than 0.82 for all models. The best results were obtained using the XgbTree with an AUC = 0.97 in comparison to other MLA. Our findings demonstrate an excellent ability for ML models to predict the soil-suitability using physico-chemical and phenological parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment.

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Recent climate change effects worsened water scarcity in Morocco and forced the country to seek alternative water resources such as domestic and industrial wastewater. In this context, we assessed the treatment efficiency of wastewater treatment plants (WWTP) of the BeniMellal-Khenifra region based on physicochemical and biological parameters. Vegetation cover evolution near WWTPs was also analysed using satellite images (Landsat TM and OLI). Six WWTP were evaluated based on treated water quality and a survey of nearby farmers and residents. Results showed treated wastewater is in line with Moroccan standards, and can be reused for irrigation and aquaculture without harmful effects. The survey pointed to the Boujaad WWTP as a model in the region. The vegetation cover evolution before and after WWTP existence showed an important improvement of cultivated lands. As a conclusion, wastewater reuse will allow the BeniMellal-Khenifra region to secure agricultural irrigation to safeguard freshwater quantities and quality despite climate change.

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The Mediterranean Region is presumed to be one of the locations where climate change will have the most effect. This impacts natural resources and increases the extent and severity of natural disasters, in general, and soil water erosion in particular. The focus of this research was to assess how climate change might affect the rate of soil erosion in a watershed in the High Atlas of Morocco. For this purpose, high-resolution precipitation and temperature data (12.5 × 12.5 km) were collected from EURO-CORDEX regional climate model (RCM) simulations for the baseline period, 1976–2005, and future periods, 2030–2060 and 2061–2090. In addition, three maps were created for slopes, land cover, and geology, while the observed erosion process in the catchment was determined following field observations. The erosion potential model (EPM) was then used to assess the impacts of precipitation and temperature variations on the soil erosion rate. Until the end of the 21st century, the results showed a decrease in annual precipitation of −32% and −46% under RCP 4.5 for the periods 2030–2060 and 2061–2090, respectively, −28% and −56% under RCP 8.5 for the same periods, respectively, and a large increase in temperature of +2.8 °C and +4.1 °C for the RCP 4.5 scenario, and +3.1 °C and +5.2 °C for the RCP 8.5 scenario for the periods 2030–2060 and 2061–2090, respectively. The aforementioned changes are anticipated to significantly increase the soil erosion potential rate, by +97.11 m3/km2/year by 2060, and +76.06 m3/km2/year by 2090, under the RCP 4.5 scenario. The RCP 8.5 predicts a rise of +124.64 m3/km2/year for the period 2030–2060, but a drop of −123.82 m3/km2/year for the period 2060–2090.