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189 results for “Vineyards”
Augmentative release of two <em>Drosophila</em> parasitoids did not suppress competitor populations in a vineyard
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Data from: Weed management modifies functional properties of both weeds and microbial nitrogen-cycling communities in Mediterranean vineyards
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Data from: Invasive Drosophila suzukii facilitates Drosophila melanogaster infestation and sour rot outbreaks in the vineyards
How do invasive pests affect interactions between members of pre-existing agrosystems? The invasive pest Drosophila suzukii is suspected to be involved in the aetiology of sour rot, a grapevine disease that otherwise develops following Drosophila melanogaster infestation of wounded berries. We combined field observations with laboratory assays to disentangle the relative roles of both Drosophila in disease development. We observed the emergence of numerous D. suzukii, but no D. melanogaster flies, from bunches that started showing mild sour rot symptoms days after field collection. However, bunches that already showed severe rot symptoms in the field mostly contained D. melanogaster. In the laboratory, oviposition by D. suzukii triggered sour rot development. An independent assay showed the disease increased grape attractiveness to ovipositing D. melanogaster females. Our results suggest that in invaded vineyards, D. suzukii facilitates D. melanogaster infestation and, consequently, favours sour rot outbreaks. Rather than competing with close species, the invader subsequently permits their reproduction in otherwise non-accessible resources and may cause more frequent, or more extensive, disease outbreaks.
Data from: Landscape diversity and crop vigor influence biological control of the western grape leafhopper (E. elegantula Osborn) in vineyards
This study evaluated how the proportional area of natural habitat surrounding a vineyard (i.e. landscape diversity) worked in conjunction with crop vigor, cultivar and rootstock selection to influence biological control of the western grape leafhopper (Erythroneura elegantula Osborn). The key natural enemies of E. elegantula are Anagrus erythroneurae S. Trjapitzin & Chiappini and A. daanei Triapitsyn, both of which are likely impacted by changes in landscape diversity due to their reliance on non-crop habitat to successfully overwinter. Additionally, E. elegantula is sensitive to changes in host plant quality which may influence densities on specific cultivars, rootstocks and/or vines with increased vigor. From 2010–2013, data were collected on natural enemy and leafhopper densities, pest parasitism rates and vine vigor from multiple vineyards that represented a continuum of landscape diversity. Early in the season, vineyards in more diverse landscapes had higher Anagrus spp. densities and lower E. elegantula densities, which led to increased parasitism of E. elegantula. Although late season densities of E. elegantula tended to be lower in vineyards with higher early season parasitism rates and lower total petiole nitrogen content, they were also affected by rootstock and cultivar. While diverse landscapes can support higher natural enemy populations, which can lead to increased biological control, leafhopper densities also appear to be mediated by cultivar, rootstock and vine vigor.
Data from: Native grass ground covers provide multiple ecosystem services in Californian vineyards
1. The mechanisms responsible for the success or failure of agricultural diversification are often unknown. Most studies of arthropod pest management focus on enhancing the effectiveness of natural enemies, but non-crop plants can also improve or hamper pest suppression by changing the host quality of crop plants by reducing or adding available soil nutrients or water. Native perennial ground covers may provide resources and long-term habitat to resident natural enemies and be more compatible than exotic annuals for crop management in terms of competition for soil nutrients or water. 2. A three-year study was conducted in a California vineyard to examine the impacts of native perennial grasses on pests, natural enemies, crop plant condition and soil properties. Three ground cover treatments were included: bare soil with a grower standard drip irrigation, native grasses with the drip irrigation, or native grasses with the drip irrigation and 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 and spider densities were higher. 4. Nitrate levels in vine leaf petioles were lower in grass treatments, indicating competition with vines 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 native grass treatments. Grape quality (brix) was similar in the bare soil and native grass treatments, suggesting that increased soil moisture in the presence of native grasses did not compromise grape quality. In fact, leaf water stress was lower and available soil moisture higher not only in the irrigated native grass treatment but, at times, in the non-irrigated native grass treatment, in comparison to the no ground cover treatment. 5. We conclude that native grasses contributed to a reduction in leafhopper density by reducing host quality through competition with vines for soil nitrogen and providing food resources and/or habitat for natural enemies. Native grasses also improved soil water content and may be part of a water conservation program for perennial crops in dry climate regions.
Data from: Genetic diversity and structure of Lolium perenne ssp. multiflorum in California vineyards and orchards indicates potential for spread of herbicide resistance via gene flow
Management of agroecosystems with herbicides imposes strong selection pressures on weedy plants leading to the evolution of resistance against those herbicides. Resistance to glyphosate in populations of Lolium perenne L. ssp. multiflorum is increasingly common in California, USA, causing economic losses and the loss of effective management tools. To gain insights into the recent evolution of glyphosate resistance in L. perenne in perennial cropping systems of northwest California and to inform management, we investigated the frequency of glyphosate resistance and the genetic diversity and structure of 14 populations. The sampled populations contained frequencies of resistant plants ranging from 10% to 89%. Analyses of neutral genetic variation using microsatellite markers indicated very high genetic diversity within all populations regardless of resistance frequency. Genetic variation was distributed predominantly among individuals within populations rather than among populations or sampled counties, as would be expected for a wide-ranging outcrossing weed species. Bayesian clustering analysis provided evidence of population structuring with extensive admixture between two genetic clusters or gene pools. High genetic diversity and admixture, and low differentiation between populations, strongly suggests the potential for spread of resistance through gene flow and the need for management that limits seed and pollen dispersal in L. perenne.
Data from: Deployment of organic farming at a landscape scale maintains low pest infestation and high crop productivity in vineyards
Organic farming is a promising way to reduce pesticide use but increasing the area under organic farming at the landscape scale could increase pest infestations and reduce crop productivity. Examining the effects of organic farming at multiple spatial scales and in different landscape contexts on pest communities and crop productivity is a major step in the ecological intensification of agricultural systems. We quantified the infestation levels of two pathogens and five arthropod pests, the intensity of pesticide use and crop productivity in 42 vineyards. Using a multi-scale hierarchical design, we unravelled the relative effects of organic farming at both field and landscape scales from the effects of semi-natural habitats in the landscape. At the field scale, pest communities did not differ between organic and conventional farming systems. At the landscape scale, increasing the area under organic farming did not increase pest infestation levels. Three out of seven pest taxa were affected both by local farming systems and the proportion of semi-natural habitats in the landscape. Our findings revealed that the proportion of semi-natural habitats reduced pest infestation for two out of seven pest taxa. Organic vineyards had much lower treatment intensities, very similar levels of pest control and equal crop productivity levels. Synthesis and Applications. Our results clearly indicate that policies promoting the development of organic farming in conventional vineyard landscapes will not lead to greater pest and disease infestations but will reduce the pesticide treatment intensity and maintain crop productivity. Moreover, the interactions between semi-natural habitats in landscape and local farming practices suggest that the deployment of organic farming should be adapted to landscape contexts.
Data from: Pest consumption in a vineyard system by the lesser horseshoe bat (Rhinolophus hipposideros)
Herbivorous arthropods cause immense damage in crop production annually. Consumption of these pests by insectivorous animals is claimed to be crucial to counteract their adverse effects. Bats are considered amongst the most voracious predators of arthropods, of which some species are known to consume crop pests. In vineyard dominated Mediterranean agroecosystems, several crops are damaged by the attack of insect pests. In this study we therefore aimed 1) to explore the diet and pest consumption of the lesser horseshoe bat Rhinolophus hipposideros and 2) analyse whether the composition of pest species in its diet changes throughout the season. For that, we employed a dual-primer DNA metabarcoding analysis of DNA extracted from faeces collected in three bat colonies of a wine region in Southwestern Europe during the whole active period of most pest species. Overall, 395 arthropod prey species belonging to 11 orders were detected; lepidopterans and dipterans were the most diverse orders in terms of species. Altogether, 55 pest species were identified, among them 25 are major pests and 8 are regarded as pests affecting grapevines. The composition of pest species in faeces changed significantly with season. As a whole, the results suggest that R. hipposideros acts as a suppressor of a wide array of agricultural pests in Mediterranean agroecosystems. Therefore, appropriate management measures to favour the growth of this bat populations should be considered.
FIGURE 1 in Phytoseiid mites (Acari: Phytoseiidae) from vineyards in Rio Grande do Sul State, Brazil
FIGURE 1. Vineyards selected for the study of the diversity of Phytoseiidae mites in the State of Rio Grande do Sul: Bento Gonçalves (Cabernet Sauvignon and Pinot Noir), Candiota (Cabernet Sauvignon, Pinot Noir and Alfrocheiro) and Encruzilhada do Sul (Pinot Noir), Boqueirão do Leão (Bordeaux and Cabernet Sauvignon) and Dois Lajeados (Bordeaux and Cabernet Sauvignon).
FIGURE 8 in Stigmaeid mites (Acari: Stigmaeidae) from vineyards in the state of Rio Grande do Sul, Brazil
FIGURE 8. Zetzellia ampelae Johann and Ferla sp. nov. (female). A: dorsum; B: palp; C: anogenital region ventrally; D: legs I and II in dorsal view.
FIGURE 4. Agistemus mendozensis Simons, 1967 in Stigmaeid mites (Acari: Stigmaeidae) from vineyards in the state of Rio Grande do Sul, Brazil
FIGURE 4. Agistemus mendozensis Simons, 1967 (female). A: dorsum; B: palp; C: anogenital region ventrally; D: legs I and II in dorsal view.
FIGURE 1 in Stigmaeid mites (Acari: Stigmaeidae) from vineyards in the state of Rio Grande do Sul, Brazil
FIGURE 1. Geographical location of the municipalities sampled for the study of the diversity of Stigmaeidae mites in the State of Rio Grande do Sul: Bento Gonçalves, Boqueirão do Leão, Candiota, Dois Lajeados and Encruzilhada do Sul.
FIGURE 5 in Stigmaeid mites (Acari: Stigmaeidae) from vineyards in the state of Rio Grande do Sul, Brazil
FIGURE 5. Agistemus riograndensis Johann and Ferla sp. nov. (female). A: dorsum; B: palp; C: anogenital region ventrally; D: legs I and II in dorsal view.
FIGURE 3. Agistemus floridanus Gonzales, 1965 in Stigmaeid mites (Acari: Stigmaeidae) from vineyards in the state of Rio Grande do Sul, Brazil
FIGURE 3. Agistemus floridanus Gonzales, 1965 (female). A: dorsum; B: palp; C: anogenital region ventrally; D: legs I and II in dorsal view.
FIGURE 2 in Stigmaeid mites (Acari: Stigmaeidae) from vineyards in the state of Rio Grande do Sul, Brazil
FIGURE 2. Agistemus brasiliensis Matioli, Ueckermann and Oliveira, 2002 (female). A, dorsum; B, palp; C, anogenital region ventrally; D, legs I and II in dorsal view.
FIGURE 6 in Stigmaeid mites (Acari: Stigmaeidae) from vineyards in the state of Rio Grande do Sul, Brazil
FIGURE 6. Zetzellia agistzellia Hernandes and Feres, 2005 (female). A: dorsum; B: palp; C: anogenital region ventrally; D: legs I and II in dorsal view.
FIGURE 7 in Stigmaeid mites (Acari: Stigmaeidae) from vineyards in the state of Rio Grande do Sul, Brazil
FIGURE 7. Zetzellia malvinae Matioli, Ueckermann and Oliveira 2002 (female). A: dorsum; B: palp; C: anogenital region ventrally; D: legs I and II in dorsal view.
Identifying candidate host plants for trap cropping against Drosophila suzukii in vineyards
<p>Raw data for the identification of candidate trap crop species against <i>Drosophila suzukii </i>in vineyards. Overall, oviposition attractiveness and suitability for larval development was assessed for the fruits of 60 different plant species in no-choice and multiple-choice preference test conducted in 2018 in relation to fruit traits such as skin hardness, skin elasticity, acidity and sugar content. </p>
MOTS-annotated UAV Vineyard Dataset captured using Multiple Perspectives to avoid Leaf Occlusion for Object Detection and Tracking
<p>This dataset contains UAV RGB videos (MP4) recorded with a Phantom4 RTK in a vineyard during the harvesting campaign of 2023. It also includes frames and annotations (PNG) to boost Object Detection and Tracking of grape bunches. There are two types of videos: (1) videos capturing the side of the canopy from a frontal point of view only, and (2) videos that collect the data from multiple perspectives to avoid leaf occlusion, common in commercial vineyards. All flights were executed 3 meters above ground level, with a clear sky and wind speed below 0.5 m/s. </p>
BSC Post-processed Seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the seasonal climate prediction system SEAS5 (ECMWF).</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term. In contrast, a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: European Center for Medium-Range Weather Forecasts (ECMWF) SEAS5 and post-processed by BSC.</p> </li> <li> <p>Issue frequency: Monthly (~15th of each month)</p> </li> <li> <p>Lead times: months 1 to 3 (e.g. For a forecast initialised in June, forecasts will be monthly averages for July, August and September). The initialization date is indicated in the name of each file (e.g. 20210701). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, incoming solar radiation</p> </li> <li> <p>Ensemble size: 51 members</p> </li> <li> <p>Postprocessing: Downscaling from original (1°x 1°) resolution to 0.1°x 0.1° for three domains and monthly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ’douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p> </p> <p>For each prediction, there are several files containing the seasonal variables values, probabilities, definition of the categories and skill scores.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20210701.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (1st of the month).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20210701.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (1st of the month).</p> <p>It contains the 51 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania-percentiles_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33th percentile is the boundary between below normal and normal, and the 66th percentile is the boundary between the normal and above normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <p><strong> </strong></p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania-skill_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the month for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p>
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OpenNeuro
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