Agroforestry systems are critical for building resilience to socio-environmental shocks, especially in drought-prone arid and semi-arid areas. This study the ecological impact of drought on the Mediterranean Globally Important Agricultural Heritage Systems (GIAHS) site of Ait Saoub-Ait Mansour in Morocco. Machine learning-based random forest (RF) classification and change detection were used to explore the change in land use classes such as built-up areas, agricultural areas, woodland, and bare land. Spatiotemporal analysis of temperature, precipitation, and Precipitation-Evapotranspiration Index (SPEI) is performed using Mann-Kendall and Sen’s slope test for the period 1983–2020. Additionally, the Normalised Difference Vegetation Index (NDVI) is analyzed from 2000 to 2020 for some specified crop species such as Argan tree (Argania spinosa (L.) Skeels), cereal, palm tree, and cereal mixed. The results show a decrease in the area of agricultural and woodland and a consistent increase in drought (decrease in SPEI-6 and SPEI-12) over time. Annual and seasonal trends in NDVI for Argan tree, cereal, palm tree, and mixed cereal are negative. These findings provide consistent evidence of the ongoing degradation of this ecosystem and the changing climate. The resilience implications are discussed to inform socio-economic actors and stakeholders.

Land use and land cover (LULC) change is an important factor when solving environmental issues such as water resources, agricultural productivity, and soil preservation. This study looks at the spatial and temporal trends of LULC over a 33-year period, in an area of the Sebou basin in Morocco,...

The Eddy Covariance station provides observations of agrometeorological variables and surface energy fluxes, collected from 2019 to 2022, in a citrus orchard located in the Souss-Massa plain, Morocco. The present dataset comprises measurements recorded via a set of aboveground and subsurface sensors. The aboveground setup consistently measures air temperature, relative humidity, wind speed, net radiation, and precipitation. Additionally, the subsurface setup continuously tracks soil temperature, moisture, and electrical conductivity at depths from 5 to 80 cm, along with soil heat flux. Moreover, these setups enable the measurement of turbulent fluxes (sensible and latent heat). Given the limited availability of long-term agrometeorological data in semi-arid regions of the Mediterranean, this paper addresses a critical data gap by providing a reliable agrometeorological dataset. The latter consists of two types of data: 30 min interval files and high-frequency files (20 Hz, i.e., one measurement every 50 ms). The processing of this data involved Card Convert, MATLAB EC-Pack, and Excel, with data quality control performed by removing outliers and excluding nighttime fluxes. The dataset is organized in a table and provided in a .csv format with standard metadata. It is designed for a wide range of applications, including evapotranspiration modeling, satellite product validation, agroclimatic monitoring, determining crop irrigation requirements, precision irrigation planning, and water management. Additionally, the dataset can be reused for crop and hydrological model calibration, as well as soil moisture and crop stress prediction using machine learning algorithms.