Decarbonization strategies aim at increasing Renewable Energy Source (RES) capacity, including new photovoltaic (PV) systems. Utility-scale PV installations are often placed in agricultural areas, resulting in a reduction of agricultural land, and affecting the environment. To balance agricultural and energy policies, PV development should not limit agricultural purposes, allowing for sustainable exploitation under specific technological and environmental conditions, and particularly in areas of actual or potential abandonment.

Studying agricultural abandonment is a complex due to its multifaceted nature, lack of a clear definition, and challenges in acquiring cartographic data. This study introduces and compares two methodologies to identify abandoned agricultural areas, aiming to delineate macro-areas of potential abandonment and examine patterns in conversion to energy use, with a focus on Toscana, a region (NUTS 2) in central Italy which has experienced cropland reduction unrelated to urbanization.

The first simplified approach analyzes land cover changes from 2000 to 2018, while the second method provides a more detailed abandonment detection by means of medium spatial resolution satellite imagery from the Harmonized Landsat and Sentinel-2 dataset. A Random Forest classifier combined with Object-Based Image Analysis (OBIA) is applied to satellite data to map annual active/non-active croplands. Annual maps are then validated with a trajectory-based approach to detect agricultural land abandonment. This second methodology can help in providing spatially and timely estimates of abandoned agricultural areas to be recovered for energy purposes, and promote the sustainable growth of PV systems.