Fig (Ficus carica L.) is an ancient Mediterranean fruit species characterized by extensive clonal propagation, high phenotypic variability, and a complex domestication history. However, the relationships between genetic structure, phenotypic diversity, and agronomically relevant traits remain insufficiently understood, limiting the efficient use of germplasm resources in breeding programs. To address this gap, we integrated molecular, phenotypic, and association analyses to characterize 235 fig genotypes conserved in the “Giardini di Pomona” repository. Genetic diversity was assessed using 66 polymorphic SSR alleles, while a representative subset of 83 genotypes was evaluated for 27 morphological, fruit, tree, and phenological traits. Population analyses revealed a structured but highly admixed genetic organization, with two major genetic groups identified by Bayesian clustering. Principal Coordinate Analysis explained 44.4% of the total molecular variation, and STRUCTURE analysis assigned 105 and 130 genotypes to the two groups, respectively. Despite this genetic structure, Shannon diversity and AMOVA indicated that most diversity was maintained within geographic groups (93.2% and 61% of total variation, respectively), highlighting the importance of historical germplasm exchange and vegetative propagation in shaping Mediterranean fig diversity. Phenotypic analyses revealed broad but continuous variation across all trait categories, with several cultivars displaying distinctive combinations of tree, fruit, and phenological characteristics. Association mapping identified significant SSR–trait relationships involving leaf morphology, tree growth habit and vigor, fruit weight and pigmentation, cropping type, and ripening time, identifying candidate markers with potential utility for early selection. By combining population genetics, phenotypic characterization, and marker–trait associations within a single germplasm collection, this study provides a comprehensive framework for cultivar identification, conservation prioritization, and marker-assisted breeding in fig.

Agrivoltaic (AV) systems are emerging as a promising strategy to reconcile agricultural management and productivity with renewable energy generation, particularly in climate-vulnerable Mediterranean regions. This study evaluated the effects of static photovoltaic shading on the vineyard microenvironment, as well as the vegetative, productive, and microbiological responses of Vitis vinifera cv. ‘Falanghina’ grown under semi-arid conditions in Southern Italy, comparing high and low shading configurations with full sun (FS) exposure. Although the study was conducted during a single growing season, the vines in the AV system had been growing and adapting to their respective shading regimes for approximately eight years. The AV system modified the vineyard microclimate by reducing solar radiation, air temperature, and wind speed, thereby lowering atmospheric and soil water demand. These morphological adjustments were associated with improved photochemical performance and enhanced vine water status, as well as more effective gas exchange. The physiological and structural responses translated into variations in grapevine productivity: moderate shading (AVL) significantly increased yield per vine by 97.7% compared to FS conditions and by 54.6% compared to high shading (AVS). This increased productivity was achieved while maintaining berry quality comparable to the control, with only an 18% to 15% increase in titratable acidity with respect to FS vines. Beyond vine responses, under the assessed conditions, AV environment also influenced the endophytic culturable microbial community of phyllosphere, inducing early shifts in the relative abundance of dominant morphotypes without detectable changes in richness or Shannon diversity at either stage, although replication was limited. Under the conditions evaluated, AVL showed a possible favorable combination of physiological and productive responses. These findings support the potential of established agrivoltaic systems to mitigate environmental stress and maintain grapevine performance under Mediterranean conditions subjected to drought and high temperatures, while further multi-year studies are needed to assess the consistency of these responses across different growing seasons.

Societal Impact Statement: Agriculture faces increasing pressure to produce high‐quality food while reducing water use under intensifying climate change and water scarcity. This study compared sensor‐based precision irrigation, drone monitoring, and near‐infrared spectroscopy with conventional farmer management in table grape and processing tomato production systems. Precision agriculture improved crop water status, photosynthetic performance, yield, and marketable quality, while reducing irrigation by 8%–15% in table grape and approximately 15% in processing tomato. These findings support wider adoption of integrated digital tools through targeted incentives and farmer training, offering a scalable strategy to strengthen long‐term water security, farm resilience, and sustainable food production globally. Summary: The study aimed to evaluate the effectiveness of precision agriculture (PA) technologies, specifically sensor‐based irrigation and drones, on table grape (cv. Allison ) and processing tomato (cv. Taylor ) production compared to traditional farming methods (control). The research involved field experiments in two locations in the Puglia region, southeastern Italy, in 2023 and 2024. For table grape and processing tomato, two different irrigation managements (PA vs. Control/Farmer) were compared, monitoring physiological, morphological, yield, and quality parameters. For processing tomato, drone imagery, and ground measurements were also conducted. Predictive models for fruit ripeness and quality traits of both species were also developed using near‐infrared (NIR) spectroscopy data, preprocessing techniques, and PLS regression. For table grape, the PA vines showed greater water potential stability, more uniform stomatal conductance, and higher chlorophyll content, resulting in higher and more consistent production with 8%–15% water savings. For processing tomato, PA management improved plant vegetative indicators, total and marketable yields, and reduced water consumption by approximately 15%. Three out of four calibrated models using NIR showed predictive performance suitable for future practical applications. The findings highlight the potential of PA to improve water resource utilization, crop development, yield, and fruit quality, contributing to more sustainable agricultural systems in regions facing water scarcity and climate change. Moreover, this work demonstrates how sensor‐driven irrigation of the two crops (with more equilibrated plants) directly influenced high‐accuracy predictive modelling. The integration of environmental sensors (for irrigation) and optical sensors (for quality) will represent the core of modern smart farming.