Climate change threatens agriculture, altering growing seasons and challenging viticulture in traditionally suitable regions. Grape quality, essential for good wine, depends on proper management and suitable territories. In Northern Italy's Oltrepò Pavese region, enhancing wine production and economic stability for farmers is crucial. Moreover, detection of problematic vineyards is also a key point to enhance wine production. To address them, a PhD project is co-funded by Confagricoltura Pavia, Sezione Vino, and PNRR’s NODES Spoke 6 VINO.Abandoned vineyards harbour diseases like flavescence dorée: an incurable phytoplasma disease, spread by the vector Scaphoideus titanus that infects nearby fields annually. Control relies on insecticides and uprooting of infected vines, making abandoned vineyards persistent reservoirs of infection. Identifying such vineyards is challenging due to legal complications for owners.This study employs the YOLOv8 deep learning model to detect active and abandoned vineyards using high-resolution satellite imagery. A custom dataset was created from Google Earth Pro imagery of the Lombardy region in Italy, comprising 188 images of abandoned vineyards and 178 images of active vineyards. Pre-processing techniques, including auto-orientation, static cropping with 25–75% horizontal and vertical regions, resizing to 640×640 pixels, and adaptive equalization for contrast adjustment, were applied to enhance image quality. Augmentation technique was also applied on the dataset to increase the overall dataset size.Preliminary results show the YOLOv8 model detects accurately (F1 score = 58%) active and abandoned vineyards, providing a reliable, systematic tool for vineyard management. This approach addresses a critical gap in regional viticulture, aiding in the mitigation of disease spread and supporting sustainable agricultural practices. The research results proved that the proposed method has strong generalization and good detection performance for identifying vineyard abandonment using satellite images and machine learning. This work can contribute as part of the economic stability and sustainability of wine grape production in Oltrepò Pavese. This work was funded by the European Union - NextGenerationEU, Mission 4 Component 1.5 - ECS00000036 - CUP F17G22000190007.

Climate change is increasingly challenging global food production systems, highlighting the need for robust and climate-responsive land suitability assessments. This review examines the most widely used methods and indices for vineyard land suitability analysis, with particular attention to their application under changing climatic conditions. A systematic search was conducted using the Scopus and Web of Science databases, covering the period from 2000 to September 2025, resulting in the selection of 39 peer-reviewed studies that met inclusion criteria. Results indicate a growing reliance on spatial technologies, including Geographic Information Systems (GIS), unmanned aerial vehicles (UAVs), and satellite data, which has driven the widespread adoption of Multi-Criteria Analysis (MCA). However, only a limited proportion of studies explicitly incorporate future climate scenarios, with most relying on historical climate data. Additionally, the selection and weighting of suitability criteria vary substantially across studies, reducing methodological consistency and limiting the comparability of results. These findings highlight critical gaps in current approaches, particularly the lack of standardized frameworks and the insufficient integration of climate projections. Addressing these limitations is essential for developing more robust and transferable suitability models capable of supporting vineyard adaptation and long-term resilience under climate change.

In Mediterranean semi-arid regions, rainfed olive groves are increasingly being abandoned due to drought, low profitability, and rural depopulation. The long-term impact of abandonment on soil conditions is debated, as it may promote vegetation recovery or lead to degradation. In contrast, some farmers are adopting low-disturbance management practices that allow spontaneous vegetation to establish. These contrasting scenarios offer valuable opportunities for comparison. This study aims to develop a framework to assess the impact of different management regimes on soil health and to investigate (1) the impact of spontaneous vegetation cover (SVC) and tillage regimes on soil organic carbon (SOC), and (2) the long-term ecological dynamics of abandoned groves, through a combination of field surveys, remote sensing, and object detection. SOC was assessed using both ground-based and remote sensing-derived indicators. Vegetation cover was quantified via a grid point intercept method. Field data were integrated with a land-use monitoring framework that includes abandonment assessment through historical orthophotos and a deep learning model (YOLOv12) to detect active and abandoned olive groves. Results show that abandoned zones are richer in SOC than active ones. In particular, the active groves with SVC exhibit a mean SOC of 1%, which is higher than that of tilled groves, where SOC is 0.45%, with no apparent moisture loss. Abandoned groves can be reliably identified from aerial imagery, achieving a recall of 0.833 for abandoned patches. Our results demonstrate the potential of YOLOv12 as an innovative and accessible tool for detecting zones undergoing ecological regeneration or degradation. The study underscores the ecological and agronomic potential of spontaneous vegetation in olive agroecosystems.