Search Results (91)

Agricultural intensification has led to landscape homogenization and the widespread loss of semi-natural habitats, contributing to biodiversity decline in agroecosystems. Semi-natural areas embedded within croplands may mitigate these effects by acting as reservoirs and steppingstones for species providing ecosystem services such as pollination. We assessed the role of remnants of semi-natural habitats in sustaining the diversity and abundance of nocturnal Lepidoptera within a Mediterranean vineyard landscape (southern Italy) using monthly light trap sampling over almost one year. Assemblages were compared between vineyards and adjacent semi-natural patches. Multivariate analyses revealed marked differences in community composition between semi-natural habitat types. Species richness was consistently higher in semi-natural habitats, even when represented by small residual patches. Vineyard assemblages were characterized by reduced richness and a predominance of generalist species with high dispersal ability, indicating a simplified community structure. Nevertheless, some taxa of biogeographical interest were recorded. These findings demonstrate the importance of semi-natural habitats in maintaining nocturnal Lepidoptera diversity in vineyard-dominated landscapes and support their integration into sustainable agricultural management to enhance biodiversity conservation and the provision of ecosystem services. Full article

Criolla grape varieties are native South American cultivars that represent an important reservoir of genetic and microbiological diversity. This study aimed to investigate the oenological potential of Saccharomyces and non-Saccharomyces yeasts isolated from three criolla grape varieties in order to support the future design of wine starters. Yeasts were isolated at different fermentation stages from four vineyards in Mendoza, Argentina. A total of 485 isolates were recovered and molecularly identified, revealing 12 species belonging to eight genera. Saccharomyces cerevisiae, Hanseniaspora guilliermondii and Hanseniaspora uvarum were the dominant species. Isolates were screened for H₂S and acetic acid production, followed by physiological and enzymatic characterisation. Selected strains were further evaluated in small-scale fermentations to assess fermentative kinetics and metabolic performance. Significant variability was observed, particularly among non-Saccharomyces isolates, which generally exhibited lower ethanol yields and acetic acid production compared to S. cerevisiae. Several isolates of H. guilliermondii showed balanced fermentative behaviour and favourable metabolic and enzymatic profiles. Through the applied selection strategy, twelve strains emerged as promising wine starter candidates. These isolates combined a low production of undesirable metabolites, relevant enzymatic activities, and favourable fermentative performance. Overall, the results highlight the oenological potential of autochthonous yeasts as a resource for innovative winemaking strategies. Full article

Mealybugs (Hemiptera: Pseudococcidae) are one of the prevalent pests infesting wine grapes in the eastern United States. Their close association with ants (Hymenoptera: Formicidae) provides them with protection against natural enemies. Although sugar-based dispensers have been proposed as a strategy to disrupt this trophobiotic interaction, their field performance and indirect effects on mealybug infestation remain poorly understood. This study addresses this gap by identifying mealybug species present in Virginia vineyards, characterizing dominant ant genera associated with mealybugs, and evaluating the impact of sugar dispensers (with and without insecticide) on ant activity, mealybug density, and fruit cluster infestation. Field trials were conducted in two commercial vineyards in Virginia, USA, both with a history of mealybug infestations. Sampling plots with or without sugar dispensers were compared to assess differences in mealybug and ant population densities and fruit cluster infestation levels. Two mealybug species, Pseudococcus maritimus (Ehrhorn) and Ferrisia gilli Gullan, were detected at both sites. Some dominant ant genera, including Tetramorium Mayr, Lasius Fabricius, Solenopsis molesta (Say), Crematogaster Lund, and Pheidole Westwood, were found in close association with mealybugs. Ant activity remained low in untreated plots, whereas insecticide-treated dispensers initially attracted high ant numbers, which declined over time. Fruit cluster infestation was highest in plots lacking dispensers, indicating that dispenser deployment reduced mealybug impact. These findings demonstrate that sugar dispensers, particularly those containing insecticide, can suppress ant activity and reduce mealybug-related fruit damage, offering a practical non-disruptive tool for integrated pest management in small- and medium-sized vineyards. Full article

Grapevine trunk diseases, particularly those caused by Neofusicoccum parvum, represent a major threat to vineyard productivity and are increasingly difficult to control with conventional fungicides. Green synthesis of silver nanoparticles (AgNPs) using biocontrol fungi offers a promising alternative, but the factors governing the efficiency and bioactivity of biogenic nanoparticles remain poorly understood. Here, three Trichoderma species (T. harzianum, T. asperellum and T. virens) were evaluated as biological nanofactories for AgNP production. Cell-free fungal filtrates were used to synthesize AgNPs, which were characterized by UV–visible spectrophotometry, Dynamic Light Scattering (DLS) and transmission electron microscopy, while fungal redox metabolism was assessed using DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging assays and HPLC profiling of extracellular metabolites. AgNPs were tested against two isolates of N. parvum in vitro. The Trichoderma strains differed markedly in nanoparticle yield, size and antifungal activity, with T. harzianum T0 producing the highest amounts of small, well-dispersed AgNPs that exerted a strong fungistatic effect on N. parvum. Nanoparticle production correlated with antioxidant capacity and the abundance of redox-active metabolites. Integration of these parameters into a Fungal Nanofactory Efficiency Index (FNEI) revealed that nanoparticle bioactivity depends on both dose and biological origin. These results demonstrate that fungal metabolism is a key determinant of biogenic nanoparticle performance and identify Trichoderma as a platform for sustainable nanotechnology-based control of grapevine trunk pathogens. Full article

Bioclimatic monitoring at vineyard scale is essential for irrigation management and disease-risk assessment, yet many systems rely on expensive commercial stations or generic IoT nodes with limited validation and little focus on small and medium-sized winegrowers. This application-driven engineering work investigates whether decision-support-grade bioclimatic data for precision viticulture can be obtained from a low-cost station, by proposing a solar-powered proximal node that integrates soil, plant, and atmospheric sensors on a dedicated PCB that communicates via LoRaWAN. The node operates in a 15-min cycle, with sensing parameters selected to provide the minimum information required for key Precision Viticulture applications. It was deployed in a commercial vineyard side by side with a commercial station, quantifying sensor agreement, communication reliability, and energy consumption. The results show low error rates and consistent agronomic interpretation of environmental conditions, disease risk, precipitation events, and soil and water dynamics. The LoRaWAN link reached a 97% packet-delivery ratio with an average consumption of about 2.5 Wh per day. Material cost is approximately 260 €, one order of magnitude lower than a comparable station. These results indicate that, under real vineyard conditions and compared with a commercial reference, the proposed low-cost system provides agronomically useful, reliable bioclimatic monitoring. Full article

Monitoring groundcover diversity in vineyards is a complex task, often limited by the time and expertise required for accurate botanical identification. Remote sensing technologies and AI-based tools are still underutilized in this context, particularly for classifying herbaceous vegetation in inter-row areas. In this study, we introduce a novel approach to classify the groundcover into one of nine categories, in order to simplify this task. Using UAV images to train a convolutional neural network through a deep learning methodology, this study evaluates the effectiveness of different backbone structures applied to a UNet network for the classification of pixels into nine classes of groundcover: vine canopy, bare soil, and seven distinct cover crop community types. Our results demonstrate that the UNet model, especially when using an EfficientNetB0 backbone, significantly improves classification performance, achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%. Although this study demonstrates the potential of integrating remote sensing and deep learning for vineyard biodiversity monitoring, its applicability is limited by the small image coverage, as data were collected from a single vineyard and only one drone flight. Future work will focus on expanding the model’s applicability to a broader range of vineyard systems, soil types, and geographic regions, as well as testing its performance on lower-resolution multispectral imagery to reduce data acquisition costs and time, enabling large-scale and cost-effective monitoring. Full article

Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM, a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board, developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars (A27, noble, and Floriana) with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management. Full article

Spatial variability in vine vigour and yield components is a major challenge for vineyard management and consistent grape quality, particularly in hilly landscapes. This study evaluates the impact of on-the-go variable-rate fertilisation (VRA) in reducing within-vineyard variability in an 8.5 hectares commercial vineyard in the Conegliano Valdobbiadene Prosecco DOCG region (Italy). Over two growing seasons, a proximal NDVI sensor (GreenSeeker) guided real-time fertiliser applications without prescription maps. Vine vigour, yield components, and grape quality were evaluated using geostatistical analysis and coefficient of variation (CV) metrics. VRA reduced total spatial variability (sill) by 55% and erratic variance (nugget effect) by 39% for NDVI measurements. Variability in yield components also decrease (−21.1% for cluster number, −6.25% for cluster weight), while grape composition parameters (total soluble solids, titratable acidity, and pH) was not significantly altered despite a slightly higher variability (in titratable acidity and pH), indicating that fertiliser modulation did not compromise grape quality. Nitrogen input was reduced by 50%, highlighting economic and environmental benefits (−302 kg CO₂). These results show that simplified, sensor-based, on-the-go VRA is a practical and sustainable precision viticulture tool, even in small and heterogeneous vineyards typical of the Conegliano Valdobbiadene Prosecco DOCG area. Full article

Italy is a global top wine producer, with emphasis on high-quality wines. This study investigates the Carbon Footprint (CF), Water Footprint (WF), and Ecological Footprint (EF) of twelve red wine producers in Piedmont, Northern Italy. The analysis was based on a 0.75 L wine bottle as functional unit (FU). Twelve producers were interviewed and given questionnaires, which made it possible to gather primary data for the environmental evaluation that described vineyard and agricultural operations and wine production. The average CF was 0.88 ± 0.3 kg ${CO}_{2eq},$ with 44% of CF associated with the glass bottle, 20% to the diesel fuel fed to the agricultural machines, 32% to electricity consumption, and 4% to other contributions. The average WF was 881 ± 252.4 L, with 98% Green WF due to evapotranspiration, and 2% Blue and Grey WF. The average EF was 81.3 ± 57.2 global ha, 73% ascribed to the vineyard area and 27% to CO₂ assimilation. The obtained CF and WF values align with existing literature, while no comparison is possible for the EF data, which are previously unknown. To reduce the environmental impacts of wine production, actions like using recycled glass bottles, electric agricultural machines and renewable energy can help. However, high-quality wine production in Piedmont is deeply rooted in tradition and mostly managed by small producers. Further research should investigate the social acceptance of such actions, and policies supporting economic incentives could be key enablers. Full article

Background/Objective: Ganoderma spp. have long been studied for their bioactive pharmacological properties, and their biomass and extracts have been obtained from various sources. This study adopts a novel approach: enriching a liquid culture of Ganoderma mexicanum with a vineyard pruning waste extract to identify bioactive compounds with antiproliferative activity through enriched chromatographic fractions. Methods: The ethanolic extract from a mycelial culture was separated following a partitioning process, and the hexane fraction was subsequently separated in a chromatographic column. The fractions were evaluated for their antiproliferative properties against cancer cell lines. The interactions of the molecules identified with peroxisome proliferator-activated receptor gamma (PPAR-γ) were analyzed via molecular docking. Results: Three chromatographic fractions (FH11–FH13) exhibited antiproliferative activity which was significantly more effective against non-small lung cancer cells (A549). The cells treated with the crude extract and fractions presented a balloon-like morphology. A chemical analysis of the active fractions allowed us to identify four compounds: one fatty acid (9-Hydroxy-10E,12Z-octadecadienoic acid) and three triterpenes (ganoderic acids DM, TQ, and X). These compounds showed interactions with the PPAR-γ receptor through molecular docking. Conclusions: Ganoderma mexicanum is a promising source of compounds with antiproliferative activity that could serve as natural ligands for PPAR-γ and has possible applications in lung cancer therapy. Full article

Accurate irrigation volume prediction is crucial for sustainable agriculture. This study enhances precision irrigation by integrating diverse datasets, including historical irrigation records, soil moisture, and climatic factors, collected from a small-scale commercial estate vineyard in southwestern Idaho, the United States of America (USA), over a period of three years (2017–2019). Focusing on long-term irrigation forecasting, addressing a critical gap in sustainable water management, we use machine learning (ML) methods to predict future irrigation needs, with improved accuracy. We designed, developed, and tested a Long Short-Term Memory (LSTM) model, which achieved a Mean Squared Error (MSE) of 0.37, and evaluated its performance against a simpler baseline linear regression (LinReg) model, which yielded a higher MSE of 1.29. We validate the results of the LSTM model using a cross-validation technique, wherein a mean MSE of 0.18 was achieved. The low value of the statistical analysis (p-value = 0.0009) of a paired t-test confirmed that the improvement is significant. This research shows the potential of Artificial Intelligence (AI) to optimize irrigation planning and advance sustainable precision agriculture (PA), by providing a practical tool for long-term forecasting and that supports data-driven decisions. Full article

Soil management in vineyards is a crucial component of sustainable viticulture. Weed control under the row has traditionally been addressed using mechanical, physical, and chemical techniques, but herbicides pose environmental and health risks. The circular economy offers an alternative approach by converting organic waste into a resource, such as compost. This study explores the effectiveness of compost derived from the organic fraction of municipal solid waste (MSW) not only as a mulching technique but also as a potential biological agent for weed control through allelopathic mechanisms in vineyards. Experiments were conducted both in the field and under controlled conditions. In the field, compost was applied under the vine row as mulch and incorporated into the soil. Under controlled conditions, germination tests were performed to assess weed inhibition at different compost concentrations. Field results demonstrated that compost applications, both as mulch and incorporated into the soil, significantly inhibited weed growth during the first period after application compared to the tilled control without compost. Thus, this inhibition is not limited to physical mulching; it also applies to the release of allelopathic compounds from compost. Controlled condition experiments showed strong inhibition of germination in Cichorium intybus and Foeniculum vulgare seeds, confirming the anti-germinative effects of compost, particularly on small-seeded weed species. Compost is a promising tool for sustainable vineyard management, offering fertilization and weed-suppression benefits while reducing herbicide use. Full article

In this study, we developed, validated and applied an NGS-based SNP genotyping protocol for the molecular identification of Vitis vinifera varieties, demonstrating a reliable and efficient approach for distinguishing grapevine cultivars. By utilizing a small but highly informative set of SNP loci, this method provides effective molecular genotyping while capturing the genetic diversity needed for accurate identification. This straightforward and accessible approach allows for the rapid generation of genetic profiles, which can be compared with the profiles in existing databases to precisely identify grapevine varieties, even in cases where traditional morphological methods fall short due to environmental variability or developmental differences. The process is designed to be both time-efficient and cost-effective, making it a practical tool for routine use in vineyard management, breeding programs, and conservation efforts. Furthermore, the workflow minimizes the need for whole-genome sequencing or other resource-intensive techniques, making molecular profiling accessible to a wider range of researchers, growers, and industry professionals. Analysis of the molecular profiles of known varieties validated the accuracy of the protocol. Moreover, 14 autochthonous Greek grapevine varieties that have not been previously identified were also genotyped and the data were compared with those of all Greek varieties in the Vitis International Variety Catalogue, revealing no matching multilocus genotypes across Greece. Full article

Grape leafhoppers (Erythroneura spp.) are major pests in grape cultivation, leading to significant economic losses. Accurate and efficient damage level assessment is crucial for effective pest management and reducing financial impact. In this study, we categorized damage into uninfested leaves and five damage levels (I–V) and constructed a grape leafhopper damage dataset. Based on this dataset, we developed a lightweight detection model for grape leafhopper damage levels, incorporating improvements to the YOLOv8n architecture. The model employs FasterNet as the backbone network to enhance computational efficiency and reduce model complexity. It substitutes for the nearest-neighbor upsampling with CARAFE to improve small target detection capabilities. Additionally, the SE attention mechanism is integrated to optimize leaf feature extraction, thereby enhancing recognition accuracy in complex vineyard environments. The experimental results demonstrate that CSF-YOLO achieves a mAP of 90.15%, which is 1.82% higher than the baseline model, YOLOv8n. Additionally, the model’s inference results can be accessed via mobile devices, demonstrating the feasibility of real-time vineyard pest monitoring. This study provides a solid technical foundation for advancing intelligent pest monitoring systems in vineyards and the development of smart agriculture. Full article

Albania, situated in southeastern Europe, enjoys a Mediterranean climate that is well-suited for grape cultivation. The vineyard area totals 7.202 million hectares. Since the 1990s, the country has experienced a resurgence in its rich winemaking traditions, which have gained considerable attention over the last decade. Alongside its significant wine production, large amounts of vine pruning waste and fermentation by-products are generated, estimated at over 50,000 t of prunings and 35,900 t of grape pomace annually. This waste is often burned, and the ash is used as fertilizer, releasing considerable CO₂ emissions, and contributing to greenhouse gas levels. However, recycling these prunings into fertilizer by chipping and grinding them in the vineyard presents a sustainable choice, providing key minerals, nitrogen, phosphorus, and various micronutrients, thereby reducing the dependence on synthetic fertilizers in farming. Characterized by small plantations, the vine pruning issue needs site-specific, economically feasible solutions for the farmer. Additionally, there is unexplored potential for applying wine fermentation pressing residues as fertilizer for agricultural land or vineyards. The Albanian wine sector has significant untapped opportunities, such as employing vine pruning ash as a mineral fertilizer to help achieve sustainability goals. Full article