1. At the global scale, vineyards are usually managed intensively to optimize wine production without considering possible negative impacts on biodiversity and ecosystem services (ES) such as high ...soil erosion rates, degradation of soil fertility or contamination of groundwater. Winegrowers regulate competition for water and nutrients between the vines and inter-row vegetation by tilling, mulching and/or herbicide application. Strategies for more sustainable viticulture recommend maintaining vegetation cover in inter-rows, however, there is a lack of knowledge as to what extent this less intensive inter-row management affects biodiversity and associated ES. 2. We performed a hierarchical meta-analysis to quantify the effects of extensive vineyard inter-row vegetation management in comparison to more intensive management (like soil tillage or herbicide use) on biodiversity and ES from 74 studies covering four continents and 13 wine-producing countries. 3. Overall, extensive vegetation management increased above- and below-ground biodiversity and ecosystem service provision by 20% in comparison to intensive management. Organic management together with management without herbicides showed a stronger positive effect on ES and biodiversity provision than inter-row soil tillage. 4. Soil loss parameters showed the largest positive response to inter-row vegetation cover. The second highest positive response was observed for biodiversity variables, followed by carbon sequestration, pest control and soil fertility. We found no trade-off between grape yield and quality vs. biodiversity or other ES. 5. Synthesis and applications. Our meta-analysis concludes that vegetation cover in inter-rows contributes to biodiversity conservation and provides multiple ecosystem services. However, in drier climates grape yield might decrease without irrigation and careful vegetation management. Agri-environmental policies should therefore focus on granting subsidies for the establishment of locally adapted diverse vegetation cover in vineyard inter-rows. Future studies should focus on analysing the combined effects of local vineyard management and landscape composition and advance research in wine-growing regions in Asia and in the southern hemisphere.
Introduction
Young people represent a vulnerable population, with 75% of mental disorders first emerging before 25 years of age. This pilot stems from the acknowledged need to design and test ...non-stigmatizing programs that are appealing to young people and suited for the protean mental health problems that they experience.
Objectives
The study involves a group of youths (aged 16-25) with different forms of mental ill-health in a locally and culturally meaningful activity, namely hand-harvesting grape in the renowned area of Langhe (Italy). The aim is to investigate viticultural practices as possibly effective in supporting recovery by promoting social interaction and fostering a sense of belonging in the broader process of winemaking.
Methods
The project is multidisciplinary in its design and implementation, involving psychiatrists, psychologists, rehabilitation specialists and sociologists. Research methods include clinical assessment, participant observation, and semi-structured interviews with the participants.
Results
During the harvest season, a stable group of participants has been involved in a one-to-one relationship with professional vine growers. This relational geometry was built around the performance of a practical task: that of filling in a box with manually harvested grape and moving it along the rows of vines. Within each dyad, which represents the most fragile and intimate of all social forms, practical knowledge has been conveyed from the experienced worker to the youth. Most importantly, the repeated encounters provided an opportunity for human interaction and exchange that went beyond the activity being performed, involving the gradual disclosure of self, the ability to listen, connect and empathize with personal stories from diverse backgrounds. Participants’ narratives collected during and after the pilot describe the vineyard as a psychic more than a physical place – a landscape of the mind, structured around the emotional and sensorial contents of the experience. The study’s core finding emerging from fieldwork and youths’ accounts is the beneficial effects of the intervention on transdiagnostic factors such as social anxiety symptoms, low self-efficacy and poor social skills.
Conclusions
The pilot provides suggestions to orient meaningful and non-stigmatising programs for vulnerable young people, hosted in landscapes that can become therapeutic not by virtue of their aesthetic features, but because of the access they provide to social (i.e. opportunities for new relationships), material (occasions to create and share something tangible) and affective (promotion of positive emotions, containment of loneliness and feelings of inadequacy) resources.
Disclosure of Interest
None Declared
Wine Aroma Compounds in Grapes: A Critical Review González-Barreiro, Carmen; Rial-Otero, Raquel; Cancho-Grande, Beatriz ...
Critical reviews in food science and nutrition,
01/2015, Letnik:
55, Številka:
2
Journal Article
Recenzirano
Volatile organic compounds are vital to wine quality, determining their aroma and varietal characteristics. Which are present, and in what quantity, depends on the cultivar, the situation and soil of ...the vineyard, weather, cultivation methods, and wine-making practices. Here, we review the literature on the development of wine aroma compounds in grapes, and how it is affected by the above-named factors. Increasing understanding of these processes at the molecular level will aid vine growers in the optimal selection of harvest dates and other decisions favoring the consistent production of balanced, flavorful berries.
Automated Visual Yield Estimation in Vineyards Nuske, Stephen; Wilshusen, Kyle; Achar, Supreeth ...
Journal of field robotics,
September/October 2014, Letnik:
31, Številka:
5
Journal Article
Recenzirano
Odprti dostop
We present a vision system that automatically predicts yield in vineyards accurately and with high resolution. Yield estimation traditionally requires tedious hand measurement, which is destructive, ...sparse in sampling, and inaccurate. Our method is efficient, high‐resolution, and it is the first such system evaluated in realistic experimentation over several years and hundreds of vines spread over several acres of different vineyards. Other existing research is limited to small test sets of 10 vines or less, or just isolated grape clusters, with tightly controlled image acquisition and with artificially induced yield distributions. The system incorporates cameras and illumination mounted on a vehicle driving through the vineyard. We process images by exploiting the three prominent visual cues of texture, color, and shape into a strong classifier that detects berries even when they are of similar color to the vine leaves. We introduce methods to maximize the spatial and the overall accuracy of the yield estimates by optimizing the relationship between image measurements and yield. Our experimentation is conducted over four growing seasons in several wine and table‐grape vineyards. These are the first such results from experimentation that is sufficiently sized for fair evaluation against true yield variation and real‐world imaging conditions from a moving vehicle. Analysis of the results demonstrates yield estimates that capture up to 75% of spatial yield variance and with an average error between 3% and 11% of total yield.
The study of the soil properties and geochemistry of vineyard soils of the Méntrida Protected Designation of Origin (PDO) (central Spain) was performed to better understand the role of soil as a ...terroir component and to contribute to sustainable vineyard management. Soil physico‐chemical characteristics were determined, along with the content of trace elements (by placing emphasis on rare earths) in topsoils and subsoils. The dominant soil types were Inceptisols, Alfisols and Entisols (USDA soil classificacion), i.e., Cambisols, Luvisols and Regosols in the World Reference Base for Soil Resources (WRB) system. They were all deep and well‐structured. Our findings reveal that soil‐available nitrogen (N) ranged from 0.03 to 0.21 (%), with soil‐available phosphorus (P) ranging from 0.20 to 40.50 (mg·kg−1). pH had a mean of 6.33 (range: 4.4–8.7) in the ten observations, and a degree of mesotrophic saturation in bases (V%), except in one that had a 100% value (soils where carbonates were absent, save exceptional cases). A medium to high cation exchange capacity (CEC) was found (range: 9.8–38.3 cmol·kg−1). The mean values of elements' contents were (mg·kg−1): Sc 14.8, V 32.13, Cr 23.09, As 4.01, Ni 8.57, Cu 6.43, Zn 30.13, Rb 143.49, Sr 63.03, Y 15.39, Ba 374,44, La 25.87, Ce 43.77, Pb 22.25, Nd 21.28. They decreased in this order: Ba > Rb > Sr > Ce > V > Zn > La > Cr > Pb > Nd > Y > Sc > Cu > As. The results confirm the importance of soils for the PDO, with an optimum condition to produce grapevine quality laying the foundations to obtain a good wine. Our findings provide insights into the importance of pedological properties for regulating soil nutrient availability across soil types, and support soil resource utilization management in regional viticulture.
•An optimized method for multimodal image registration.•A new semi-automatic method for labelling images at pixel-wise.•Data augmentation based on ground truth to enrich databases.•SegNet models for ...vine disease detection in UAV visible and infrared images.•Fusion of visible and infrared segmented images to obtain a robust map of disease.
One of the major goals of tomorrow’s agriculture is to increase agricultural productivity but above all the quality of production while significantly reducing the use of inputs. Meeting this goal is a real scientific and technological challenge. Smart farming is among the promising approaches that can lead to interesting solutions for vineyard management and reduce the environmental impact. Automatic vine disease detection can increase efficiency and flexibility in managing vineyard crops, while reducing the chemical inputs. This is needed today more than ever, as the use of pesticides is coming under increasing scrutiny and control. The goal is to map diseased areas in the vineyard for fast and precise treatment, thus guaranteeing the maintenance of a healthy state of the vine which is very important for yield management. To tackle this problem, a method is proposed here for Mildew disease detection in vine field using a deep learning segmentation approach on Unmanned Aerial Vehicle (UAV) images. The method is based on the combination of the visible and infrared images obtained from two different sensors. A new image registration method was developed to align visible and infrared images, enabling fusion of the information from the two sensors. A fully convolutional neural network approach uses this information to classify each pixel according to different instances, namely, shadow, ground, healthy and symptom. The proposed method achieved more than 92% of detection at grapevine-level and 87%at leaf level, showing promising perspectives for computer aided disease detection in vineyards.
In this century, one of the main objectives of agriculture is sustainability addressed to achieve food security, based on the improvement of use efficiency of farm resources, the increasing of crop ...yield and quality, under climate change conditions. The optimization of farm resources, as well as the control of soil degradation processes (e.g., soil erosion), can be realized through crop monitoring in the field, aiming to manage the local spatial variability (time and space) with a high resolution. In the case of high profitability crops, as the case of vineyards for high-quality wines, the capability to manage and follow spatial behavior of plants during the season represents an opportunity to improve farmer incomes and preserve the environmental health. However, any field monitoring represents an additional cost for the farmer, which slows down the objective of a diffuse sustainable agriculture.
Satellite multispectral images have been widely used for production management in large areas. However, their observation is limited by the pre-defined and fixed scale with relatively coarse spatial resolution, resulting in limitations in their application.
In this paper, encouraged by recent achievements in convolutional neural network (CNN), a multiscale full-connected CNN is constructed for the pan-sharpening of Sentinel-2A images by UAV images. The reconstructed data are validated by independent multispectral UAV images and in-situ spectral measurements. The reconstructed Sentinel-2A images provide a temporal evaluation of plant responses using selected vegetation indices. The proposed methodology has been tested on plant measurements taken either in-vivo and through the retrospective reconstruction of the eco-physiological vine behavior, by the evaluation of water conductivity and water use efficiency indexes from anatomical and isotopic traits recorded in vine trunk wood.
In this study, the use of such a methodology able to combine the pro and cons of space-borne and UAVs data to evaluate plant responses, with high spatial and temporal resolution, has been applied in a vineyard of southern Italy by analyzing the period from 2015 to 2018. The obtained results have shown a good correspondence between the vegetation indexes obtained from reconstructed Sentinel-2A data and plant hydraulic traits obtained from tree-ring based retrospective reconstruction of vine eco-physiological behavior.
•CNN is constructed for pan-sharpening of Sentinel-2A images by UAV images.•Reconstructed data are validated on MS UAV images and in-situ spectral measures.•Reconstructed VIs from Sentinel-2A data agreed with dendroecological plant traits.
Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management ...and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used-the sensors, the UAV, and the flight operations-the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analising and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications.
1. Mechanisms responsible for the success or failure of agricultural diversification are often unknown. Most studies of arthropod pest management focus on enhancing natural enemy effectiveness. ...However, non-crop plants can also change crop host quality by reducing or adding soil nutrients or water, and therefore improve or hamper pest suppression. Native perennial ground covers may provide food or habitat to natural enemies and, in terms of competition for soil nutrients or water, be more compatible with crop management than exotic annuals. 2. We conducted a 3-year vineyard study to examine the impacts of native perennial grasses on pests, natural enemies, crop plant condition and soil properties. We included three ground cover treatments: bare soil with a grower standard drip irrigation; native grasses with drip irrigation; and native grasses with drip irrigation as well as an additional flood irrigation to keep the grasses green and growing during the season. 3. Numbers of leafhopper pests Erythroneura spp. decreased in both native grass treatments, where parasitism rates were higher. Vine petiole nitrate levels were lower in grass treatments, indicating competition for soil nitrogen, which is most often considered to be detrimental. Berry weight was higher in the irrigated treatment but did not differ between the bare soil and non-irrigated grass treatment. Grape °Brix was similar in the bare soil and native grass treatments, suggesting native grasses did not compromise grape quality. In fact, leaf water stress was lower and soil moisture higher not only in the irrigated grass treatment but, at times, in the non-irrigated grass treatment, compared with the bare soil treatment. 4. synthesis and applications. Our work shows that native grasses contribute to a reduction in vineyard leafhopper pests by reducing host quality through competition for soil nitrogen and providing food resources and/or habitat for natural enemies. Native grasses also improve soil water content and may be part of a water conservation program for perennial crops in dry climate regions.
This article presents VineSens, a hardware and software platform for supporting the decision-making of the vine grower. VineSens is based on a wireless sensor network system composed by autonomous ...and self-powered nodes that are deployed throughout a vineyard. Such nodes include sensors that allow us to obtain detailed knowledge on different viticulture processes. Thanks to the use of epidemiological models, VineSens is able to propose a custom control plan to prevent diseases like one of the most feared by vine growers: downy mildew. VineSens generates alerts that warn farmers about the measures that have to be taken and stores the historical weather data collected from different spots of the vineyard. Such data can then be accessed through a user-friendly web-based interface that can be accessed through the Internet by using desktop or mobile devices. VineSens was deployed at the beginning in 2016 in a vineyard in the Ribeira Sacra area (Galicia, Spain) and, since then, its hardware and software have been tested to prevent the development of downy mildew, showing during its first season that the system can led to substantial savings, to decrease the amount of phytosanitary products applied, and, as a consequence, to obtain a more ecologically sustainable and healthy wine.
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