Climate change significantly affects viticulture, with noticeable impacts on yield and quality. The increase in average temperatures, often coupled with decreased precipitation, accelerates the phenological development of grapevines, leading to rapid sugar accumulation and concentration and decreased acidity. This study aimed to evaluate the impact of black shading nets with two levels (26% and 40%) on vine phenology, vegetative growth, yield, and grape ripening, as a potential strategy to mitigate the adverse effects of rising temperatures and reduced precipitation. The research was conducted in southwestern Sicily, in the Menfi (AG) area, using Grillo and Syrah grapevines. Black shading nets were applied during the pea-sized berry stage (BBCH 75). The results demonstrated that shading effectively delayed vine phenology and altered grape ripening, with significant reductions in sugar content (up to 10%) and increases in total acidity (up to 10%) at harvest compared to non-shaded vines. However, shading also reduced berry size, resulting in lower cluster weight and yield per plant (up to 15%). These findings highlight the potential of shading nets as a tool for adapting viticulture to climate change, while emphasizing the need to carefully assess their large-scale applicability, considering economic and operational factors.

Introduction: HyTan, a new chestnut tannin extracted from chestnut wood via hydrodynamic cavitation at low temperature in water only, is an effective biostimulant for the nursery production of high-quality grapevine planting material.Methods: Experiments were carried out in Sicily with HyTan aqueous extracts obtained at different temperatures (45, 60 and 75°C) and dosage (pure extract or diluted with water) on the above-ground and below-ground characteristics of cv. Zibibbo cuttings grafted on rootstock 1103 Paulsen.Results and discussion: HyTan tannin extracted at low temperature (45°C) favored the growth of both thinner root fraction and total above-ground plant as well as the leaf chlorophyll content. These findings open the route to the widespread use in agriculture of this newly extracted chestnut tannin rich in ellagic acid, nonhydrolyzed ellagitannins (castalagin and vescalagin) and gallic acid.

Accurate yield estimation is crucial for optimising vineyard management and logistical organisation. Traditional methods relying on manual and destructive flower or berry counts are labour-intensive and unsuitable for large-scale applications. To address these limitations, this study evaluated the potential of RGB image analysis for non-destructive estimation of flower number in grapevine inflorescences and its relationship with yield components. The objectives were: (i) to correlate the number of actual flowers with the number of pixels in inflorescence images, considering the projected area and not visible flower count; (ii) to assess the model’s ability for predicting yield components (number of berries and bunch weight) on the plant scale. The study was conducted during the 2023 vintage in a cv. Catarratto/1103P vineyard located in Sicily, on 36 vines. The vines were trained on a vertical shoot positioning trellis system with a bilateral spur pruned cordon. All the inflorescence images were acquired using a smartphone and white cardboard under field conditions. Images were analysed using FIJI/ImageJ© software, inflorescences were segmented via Otsu’s method, and pixel counts were used to estimate flower number through linear regression. Flower counts were validated through manual counts of detached calyptra. Vines were harvested to measure bunch weight, and the number of berries was counted to calculate the fruit set rate. External model validation was performed on datasets from Catarratto, Chardonnay, and Vermentino cvs. Results showed a strong correlation between inflorescence pixel count and flower number, both on single inflorescences (R2 = 0.78; MAPE 29 %; n = 300) and in terms of total flowers per vine (R2 = 0.95; MAPE 12 %), with estimation accuracy slightly varying according to inflorescence length. External validation yielded an MAPE of 20 % in Catarratto, 40 % in Chardonnay, and 34 % in Vermentino. The model also reliably predicted the number of berries (R2 = 0.94; MAPE 13 %) and bunch weight (R2 = 0.79; MAPE 20 %) per vine. While environmental factors such as fruit set can affect yield, this study highlights the potential for early yield prediction with image analysis. The methodology holds promise for scalable applications and integration into vineyard management technologies in wine and table grapes.

Pruning wood mass is crucial for grapevine management, as it reflects the vine’s vigor and balance. However, traditional manual measurement methods are time-consuming and labor-intensive. Recent advances in digital imaging offer non-invasive techniques, but limited research has explored pruning wood weight estimation, especially regarding the use of artificial backgrounds and lighting. This study assesses the use of image analysis for estimating wood weight, focusing on image acquisition conditions. This research aimed to (i) evaluate the necessity of artificial backgrounds and (ii) identify optimal daylight conditions for accurate image capture. Results demonstrated that estimation accuracy strongly depends on the sun’s position relative to the camera. The highest accuracy was achieved when the camera faced direct sunlight (morning on the northwest canopy side and afternoon on the southeast side), with R² values reaching 0.90 and 0.93, and RMSE as low as 44.24 g. Artificial backgrounds did not significantly enhance performance, suggesting that the method is applicable under field conditions. Leave-One-Group-Out Cross-Validation (LOGOCV) confirmed the model’s robustness when applied to Catarratto cv. (LOGOCV R² = 0.86 in NB and 0.84 in WB), though performance varied across other cultivars. These findings highlight the potential of automated image-based assessment for efficient vineyard management, using minimal effort adjustments to image collection that can be incorporated into low-cost setups for pruning wood weight estimation.