The availability of water for agricultural production is under threat from climate change and rising demands from various sectors. In this paper, a simulation-optimization model for optimizing the irrigation schedule in the Bilate watershed was developed, to save irrigation water and maximize the yield of deficit irrigation. The model integrated the Soil and Water Assessment Tool (SWAT) and an irrigation-scheduling optimization model. The SWAT model was used to simulate crop yield and evapotranspiration. The Jensen crop-water-production function was applied to solve potato and wheat irrigation-scheduling-optimization problems. Results showed that the model can be applied to manage the complicated simulation-optimization irrigation-scheduling problems for potato and wheat. The optimization result indicated that optimizing irrigation-scheduling based on moisture-stress-sensitivity levels can save up to 25.6% of irrigation water in the study area, with insignificant yield-reduction. Furthermore, optimizing deficit-irrigation-scheduling based on moisture-stress-sensitivity levels can maximize the yield of potato and wheat by up to 25% and 34%, respectively. The model developed in this study can provide technical support for effective irrigation-scheduling to save irrigation water and maximize yield production.

Surface-based global positioning system (GPS) measurements of zenith tropospheric delay (ZTD) can be used for deducing vertically integrated water vapor (IWV) of the atmosphere. In the present study ZTD data from the Moroccan ground-based GPS meteorology stations are converted into IWV, which is validated using equivalent values from radiosonde during one half-year 2016 (15 May-31 December). In particular, the GPS analysis is accomplished in near-real time to furnish estimates of the IWV for evaluating the potential of GPS observations for operational meteorological purposes. The results show that the IWV values derived from GPS observations had an agreement with the radiosonde (RS) observations with a correlation of 0.98 associated with a standard deviation of 2.51 mm and a bias of 1.41 mm.

The information on land is a basic resource at the economic, social, and industrial levels. Many approaches have been made to enhance integrating the geospatial technology, enable managing the geospatial features, and provide decision makers with capabilities for best land governance. The goal behind this chapter is to develop an approach dealing with a necessary paradigm of improving land information management. Such approach is made up of a series of the fundamental requirements and principles. The requirements encompass the fundamental geodetic network supporting spatial infrastructure, a series of large-scale maps, an integrated land administration system, and a basic conceptual core model for the land information system. The principles of the new paradigm will be discussed. To achieve such objective, the object oriented approach as an emerging methodology was followed to improve land administration and management.

Climate change is having a major impact on the world's forests, compromising their health and resilience. Agroforestry, which consists of integrating trees into agricultural systems, appears to be a solution for strengthening this resilience. This study assesses the suitability of agroforestry in Tazekka National Park, Morocco, using its ecological features to explore the contribution of agroforestry to reducing the effects of climate change. Using Google Earth Engine (GEE), the Land Use and Land Cover (LULC) classification was generated by comparing two major machine learning algorithms: Support Vector Machine (SVM) and Random Forest (RF). The most accurate LULC classification, as determined by the algorithms, was integrated with various environmental variables such as rainfall, temperature, soil pH, soil texture, slope, vegetation indices (NDVI, NDWI), population density, erosion risk, and tree cover. These factors were incorporated into the analysis using Multi-Criteria Analysis (MCA) to calculate and generate a suitability index for agroforestry. The Analytical Hierarchy Process (AHP) was employed to assign weights to the variables based on their relative importance. The results of the LULC classification revealed that SVM outperformed RF, achieving an accuracy of 95.05% compared to 85.15% for RF. The final results produced a more accurate suitability map for agroforestry, effectively identifying areas with low, medium, and high suitability for agroforestry interventions. This research highlights the potential of agroforestry at the local level and proposes a strategic framework for sustainable land management in the face of climate change at regional and global levels.