Disease Of Grapevine Research Articles

Transcriptome analysis reveals mechanisms involved in the enhanced antagonistic efficacy of Sporidiobolus pararoseus Y16 treated by γ-aminobutyric acid

Our previous study showed that γ-aminobutyric acid (GABA) can significantly enhance the biological control efficacy of Sporidiobolus pararoseus Y16 on postharvest diseases of grapes. The possible physiological biocontrol mechanisms of the yeast have been reported previously, however, the associated molecular mechanisms were unclear. This study aimed to investigate the possible molecular mechanisms behind the enhanced biological control efficacy of S. pararoseus Y16 cultured with GABA based on transcriptome analysis. In addition, the activities of catalase (CAT) and chitinase (CHI) and the content of malondialdehyde (MDA) in S. pararoseus Y16 were investigated. The results showed that after the addition of 4 mg/mL GABA, the activities of CAT and CHI in S. pararoseus Y16 were significantly increased, and the content of MDA accumulation was decreased. Transcriptome analysis found that GABA-treated S. pararoseus Y16 can improve the ability of yeast ion transport, cell wall remodeling, antioxidants, growth and reproduction, resistance to stress, amino acid synthesis, etc., resulting in increased antagonistic efficacy of S. pararoseus Y16. These results provide a reference for future research on the use of antagonistic yeasts to prevent and control postharvest diseases of fruits and vegetables, and provide a theoretical basis for the practical application of antagonistic yeasts.

Fungal species associated with grapevine trunk diseases in Washington wine grapes and California table grapes, with novelties in the genera Cadophora, Cytospora, and Sporocadus.

Grapevine trunk diseases cause serious economic losses to grape growers worldwide. The identification of the causal fungi is critical to implementing appropriate management strategies. Through a culture-based approach, we identified the fungal species composition associated with symptomatic grapevines from wine grapes in southeastern Washington and table grapes in the southern San Joaquin Valley of California, two regions with contrasting winter climates. Species were confirmed through molecular identification, sequencing two to six gene regions per isolate. Multilocus phylogenetic analyses were used to identify novel species. We identified 36 species from 112 isolates, with a combination of species that are new to science, are known causal fungi of grapevine trunk diseases, or are known causal fungi of diseases of other woody plants. The novel species Cadophora columbiana, Cytospora macropycnidia, Cytospora yakimana, and Sporocadus incarnatus are formally described and introduced, six species are newly reported from North America, and grape is reported as a new host for three species. Six species were shared between the two regions: Cytospora viticola, Diatrype stigma, Diplodia seriata, Kalmusia variispora, Phaeoacremonium minimum, and Phaeomoniella chlamydospora. Dominating the fungal community in Washington wine grape vineyards were species in the fungal families Diatrypaceae, Cytosporaceae and Sporocadaceae, whereas in California table grape vineyards, the dominant species were in the families Diatrypaceae, Togniniaceae, Phaeomoniellaceae and Hymenochaetaceae. Pathogenicity tests demonstrated that 10 isolates caused wood discoloration similar to symptomatic wood from which they were originally isolated. Growth rates at temperatures from 5 to 35°C of 10 isolates per region, suggest that adaptation to local climate might explain their distribution.

Multiclass Classification of Grape Diseases Using Deep Artificial Intelligence

Protecting agricultural crops is essential for preserving food sources. The health of plants plays a major role in impacting the yield of agricultural output, and their bad health could result in significant economic loss.This is especially important in small-scale and hobby-farming products such as fruits. Grapes are an important and widely cultivated plant, especially in the Mediterranean region, with an over USD 189 billion global market value. They are consumed as fruits and in other manufactured forms (e.g., drinks and sweet food products). However, much like other plants, grapes are prone to a wide range of diseases that require the application of immediate remedies. Misidentifying these diseases can result in poor disease control and great losses (i.e., 5–80% crop loss). Existing computer-based solutions may suffer from low accuracy, may require high overhead, and be poorly deployable and prone to changes in image quality. The work in this paper aims at utilizing a ubiquitous technology to help farmers in combatting plant diseases. Particularly, deep-learning artificial-intelligence image-based applications were used to classify three common grape diseases: black measles, black rot, and isariopsis leaf spot. In addition, a fourth healthy class was included. A dataset of 3639 grape leaf images (1383 black measles, 1180 black rot, 1076 isariopsis leaf spot, and 423 healthy) was used. These images were used to customize and retrain 11 convolutional network models to classify the four classes. Thorough performance evaluation revealed that it is possible to design pilot and commercial applications with accuracy that satisfies field requirements. The models achieved consistently high performance values (>99.1%).

Two-stage automatic diagnosis of Flavescence Dorée based on proximal imaging and artificial intelligence: a multi-year and multi-variety experimental study

“Flavescence dorée” (FD) is a grape vine disease caused by the bacterial agent “Candidatus Phytoplasma vitis” and spread by the leafhopper Scaphoideus titanus Ball (Hemiptera: Cicadellidae). The disease is very closely monitored in Europe, as it reduces vine productivity and causes vine death and is also highly transmissible. Currently, the control method used against this disease is a two-pronged approach: i) the spraying of insecticide on a regular basis to kill the vector, and ii) a survey of each row in a vineyard by experts in this disease. Unfortunately, these experts are not able to carry out such a task every year on every vineyard and need an aid for planning their survey.In this study, we propose and evaluate an original automatic method for the detection of FD based on computer vision and artificial intelligence algorithms applied to images acquired by proximal sensing. A two-step approach was used, mimicking an expert’s scouting in the vine rows: (i) the three known isolated symptoms (red or yellow leaves depending on variety, together with a lack of shoot lignification and the presence of desiccated bunches) were detected, (ii) isolated detections were combined to make a diagnosis at image scale; i.e., vine scale. A detection network was used to detect and classify non-healthy leaves into three classes: ‘FD symptomatic leaf', 'Esca leaf' and 'Confounding leaf'; while a segmentation network was used for the retrieval of FD symptomatic shoots and bunches. Finally, the association of detected symptoms was performed by a RandomForest classifier for diagnosis at the image scale. The experimental evaluation was conducted on more than 1000 images collected from 14 blocks planted with five different grape varieties. The detection of the isolated symptoms achieved a precision of between 0.67 and 0.82 and a recall of between 0.39 and 0.59. The classification at the image scale obtained very good results when applied to images acquired under the same conditions, with the same grape varieties as the training images (precision and recall of more than 0.89). The results of the tests on the other grape varieties show the importance of having some of them in the training base in these AI-based approaches.

Summary of the worldwide available crop disease risk simulation studies that were driven by climate change scenarios and published during the past 20 years

AbstractHere, published crop disease risk simulations are summarized, which were driven by climate change scenarios on a timescale terminating between 2001 and 2100. Thirty different agricultural and horticultural crops were specifically considered. Wheat diseases (mainly leaf rust) were most often simulated, followed by rice diseases (mainly leaf blast), grapevine diseases (mainly downy mildew) and potato diseases (mainly late blight). Most simulations suggest that within the projection period simulated, crop disease risk will more often increase (86 simulations) than decrease (45 simulations) or remain similar (12 simulations). The majority of crop disease risk simulations focus on Europe and Brazil. For example, there is agreement across crop disease risk simulations that in Europe the risk of leaf rust of wheat will increase. In other cases, there is disagreement across crop disease risk simulations, for example, due to contemporary climatic differences of locations. Therefore, it is risky to extrapolate results across locations. Mitigation and adaptation methods should be more often integrated in the crop disease risk simulations in order to inform about potential methods to reduce disease risk in future.

Comparison of Deep Neural Networks in Detecting Field Grapevine Diseases Using Transfer Learning

Plants diseases constitute a substantial threat for farmers given the high economic and environmental impact of their treatment. Detecting possible pathogen threats in plants based on non-destructive remote sensing and computer vision methods offers an alternative to existing laboratory methods and leads to improved crop management. Vine is an important crop that is mainly affected by fungal diseases. In this study, photos from healthy leaves and leaves infected by a fungal disease of vine are used to create disease identification classifiers. The transfer learning technique was employed in this study and was used to train three different deep convolutional neural network (DCNN) approaches that were compared according to their classification accuracy, namely AlexNet, VGG-19, and Inception v3. The above-mentioned models were trained on the open-source PlantVillage dataset using two training approaches: feature extraction, where the weights of the base deep neural network model were frozen and only the ones on the newly added layers were updated, and fine tuning, where the weights of the base model were also updated during training. Then, the created models were validated on the PlantVillage dataset and retrained using a custom field-grown vine photo dataset. The results showed that the fine-tuning approach showed better validation and testing accuracy, for all DCNNs, compared to the feature extraction approach. As far as the results of DCNNs are concerned, the Inception v3 algorithm outperformed VGG-19 and AlexNet in almost all the cases, demonstrating a validation performance of 100% for the fine-tuned strategy on the PlantVillage dataset and an accuracy of 83.3% for the respective strategy on a custom vine disease use case dataset, while AlexNet achieved 87.5% validation and 66.7% accuracy for the respective scenarios. Regarding VGG-19, the validation performance reached 100%, with an accuracy of 76.7%.

Leaf Removal at Véraison and Foliar K+ Application to Beibinghong Vines Improved Berry Quality under Cold-Climate Conditions.

(1) Background: Beibinghong is a grapevine variety that is well distributed in Northeastern China due to its adaptation to extreme cold conditions and vine diseases. Nonetheless, Beibinghong wines are extremely acidic and rich in phenolic compounds. The aim of this research was to study the effects of leaf removal at véraison and foliar K+ applications on Beibinghong vines to reduce the acidity and increase their polyphenol content. (2) Methods: Beibinghong berries were harvested when they reached close to 20 °Brix, and the physicochemical parameters were determined. (3) Results: Leaf removal at véraison plus K+ foliar applications to Beibinghong vines decreased the titratable acidity and increased the total phenolic and phenolic acid contents compared with the control. Moreover, the titratable acidity in the Beibinghong berries was negatively related to their total contents of phenols, proanthocyanidins, and anthocyanins. (4) Conclusions: Leaf removal at véraison performed with foliar K+ applications to vines could be an interesting alternative for Beibinghong production under cold-climate viticulture because it allows for a decrease in the acidity and an increase in the phenolic content of the berries, without incurring the risk of sunburn.

Comparative genomic and functional analyses of Paenibacillus peoriae ZBSF16 with biocontrol potential against grapevine diseases, provide insights into its genes related to plant growth-promoting and biocontrol mechanisms

Paenibacillus peoriae is a plant growth-promoting rhizobacteria (PGPR) widely distributed in various environments. P. peoriae ZBFS16 was isolated from the wheat rhizosphere and significantly suppressed grape white rot disease caused by Coniella vitis. Here, we present the complete genome sequence of P. peoriae ZBFS16, which consists of a 5.83 Mb circular chromosome with an average G + C content of 45.62%. Phylogenetic analyses showed that ZBFS16 belongs to the genus P. peoriae and was similar to P. peoriae ZF390, P. peoriae HS311 and P. peoriae HJ-2. Comparative analysis with three closely related sequenced strains of P. peoriae identified the conservation of genes involved in indole-3-acetic acid production, phosphate solubilization, nitrogen fixation, biofilm formation, flagella and chemotaxis, quorum-sensing systems, two-component systems, antimicrobial substances and resistance inducers. Meanwhile, in vitro experiments were also performed to confirm these functions. In addition, the strong colonization ability of P. peoriae ZBFS16 was observed in soil, which provides it with great potential for use in agriculture as a PGPR. This study will be helpful for further studies of P. peoriae on the mechanisms of plant growth promotion and biocontrol.

Viewpoint of Chitosan Application in Grapevine for Abiotic Stress/Disease Management towards More Resilient Viticulture Practices

Chitosan is a biopolymer with various favorable properties (biotic/abiotic stress mitigation, qualitative improvement, bio-fertilizer, bio-stimulant and postharvest management) to meet multiple agricultural objectives. Grapevine is an important crop and has an enormous impact on the world’s economy due to its derived products, notably the different wine styles. In viticulture, chitosan application made significant developments towards higher contents of beneficial metabolites in grape berries as well as stress and postharvest management during recent decades, although the reports are limited. Recent investigations by our group demonstrated chitosan as a potential elicitor molecule at a molecular level and opened the possibility to use chitosan for trunk disease management; moreover, there are not yet any methods to combat trunk diseases in grapevine. The present viewpoint aimed to summarize the different aspects of chitosan application in grapevine in facilitating the development of inclusive and more integrated sanitary viticulture practices in a sustainable manner.

Spatial-Spectral Analysis of Hyperspectral Images Reveals Early Detection of Downy Mildew on Grapevine Leaves.

Downy mildew is a highly destructive disease of grapevine. Currently, monitoring for its symptoms is time-consuming and requires specialist staff. Therefore, an automated non-destructive method to detect the pathogen before the visible symptoms appear would be beneficial for early targeted treatments. The aim of this study was to detect the disease early in a controlled environment, and to monitor the disease severity evolution in time and space. We used a hyperspectral image database following the development from 0 to 9 days post inoculation (dpi) of three strains of Plasmopara viticola inoculated on grapevine leaves and developed an automatic detection tool based on a Support Vector Machine (SVM) classifier. The SVM obtained promising validation average accuracy scores of 0.96, a test accuracy score of 0.99, and it did not output false positives on the control leaves and detected downy mildew at 2 dpi, 2 days before the clear onset of visual symptoms at 4 dpi. Moreover, the disease area detected over time was higher than that when visually assessed, providing a better evaluation of disease severity. To our knowledge, this is the first study using hyperspectral imaging to automatically detect and show the spatial distribution of downy mildew on grapevine leaves early over time.

Adoption of Best Management Practices for Grapevine Leafroll and Red Blotch Diseases: A Survey of West Coast Growers

Grapevine leafroll disease (GLD) and red blotch disease (RBD) threaten the sustainability of the U.S. wine grape industry. To understand the factors influencing the adoption of disease management practices, we surveyed wine grape industry professionals in California, Oregon, and Washington ( n = 154). Economic factors were the fundamental cost of implementing management practices and the ability to sell products from diseased vines (salability). Respondents who encountered reduced salability were more likely to adopt virus testing, replace infected vines, and view these practices as economically favorable. Salability was a strong driver for adoption among Californian respondents but less so in Washington, where wineries appeared more willing to accept infected products. Respondents who had acquired technical knowledge of disease ecology were more likely to adopt management practices and to perceive them as economical. Conversely, when there was a lack of knowledge of GLD ecology, notably that mealybugs transmit the pathogen, adoption was reduced, and practices were considered less economical. Factors affecting adoption were broadly generalizable across diseases. However, knowledge of GLD ecology was more strongly associated with adoption, likely reflecting the remaining knowledge gaps in RBD related to vector ecology and field spread. An emphasis on grower knowledge acquisition and the development of economical disease management practices can improve adoption of best management practices for viral diseases of grapevine. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

A public decision support system for the assessment of plant disease infection risk shared by Italian regions.

Integrated pest management (IPM) practices proved to be efficient in reducing pesticide use and ensuring economic farming sustainability. Digital decision support systems (DSS) to support the adoption of IPM practices from plant protection services are required by European legislation. Available DSSs used by Italian plant protection services are heterogeneous with regards to disease forecasting models, datasets for their calibration, and level of integration in operational decision-making. This study presents the MISFITS-DSS, which has been jointly developed by a public research institution and nine regional plant protection services with the objective of harmonizing data collection and decision support for Italian farmers. Participatory approach allowed designing a predictive workflow relying on specific domain expertise, in order to explicitly match actual user needs. The DSS calibration entailed the risk of grapevine downy mildew infection (5-point scale from very low to very high), and phenological observations in 2012-2017 as reference data. Process-based models of primary and secondary infections have been implemented and tested via sensitivity analysis (Morris method) under contrasting weather conditions. Hindcast simulations of grapevine phenology, host susceptibility and disease pressure were post-processed by machine-learning classifiers to predict the reference infection risk. Results indicate that IPM principles are implemented by plant protection services since years. The accurate reproduction of grapevine phenology (RMSE=4-14 days), which drove the dynamic of host susceptibility, and the use of weather forecasts as model inputs contributed to reliably predict the reference infection risk (88% balanced accuracy). We did a pioneering effort to homogenize the methodology to deliver decision support to Italian farmers, by involving plant protection services in the DSS definition, to foster a further adoption of IPM practices.

Transcriptomic and methylation analysis of susceptible and tolerant grapevine genotypes following Plasmopara viticola infection.

Downy mildew, caused by the biotrophic oomycete Plasmopara viticola, is one of the most economically significant grapevine diseases worldwide. Current strategies to cope with this threat rely on the massive use of chemical compounds during each cultivation season. The economic costs and negative environmental impact associated with these applications increased the urge to search for sustainable strategies of disease control. Improved knowledge of plant mechanisms to counteract pathogen infection may allow the development of alternative strategies for plant protection. Epigenetic regulation, in particular DNA methylation, is emerging as a key factor in the context of plant-pathogen interactions associated with the expression modulation of defence genes. To improve our understanding of the genetic and epigenetic mechanisms underpinning grapevine response to P. viticola, we studied the modulation of both 5-mC methylation and gene expression at 6 and 24 hpost-infection (hpi). Leaves of two table grape genotypes (Vitis vinifera), selected by breeding activities for their contrasting level of susceptibility to the pathogen, were analysed. Following pathogen infection, we found variations in the 5-mC methylation level and the gene expression profile. The results indicate a genotype-specific response to pathogen infection. The tolerant genotype (N23/018) at 6 hpi exhibits a lower methylation level compared to the susceptible one (N20/020), and it shows an early modulation (at 6 hpi) of defence and epigenetic-related genes during P. viticola infection. These data suggest that the timing of response is an important mechanism to efficiently counteract the pathogen attack.

Morphological Characterization and Biological Management of Gloeosporium ampelophagum (Pass.) Sacc Causing Anthracnose of Grapes in India

Grape an important fruit crop, has been found to face a serious threat due to anthracnose disease. The disease on leaves appeared as small irregular dark brown lesions, which later developed greyish centre with dark brown margins that eventually dried and dropped resulting in peculiar shot hole appearance. On twigs (vines) the disease initially appeared as light brown circular lesion which on elongation became elliptical and developed sunken ashy grey centre. Coalescing of numerous such lesions culminated in canker formation. Berry symptoms were initiated as circular reddish brown lesions, which later coalesced, resulting in shrivelling and mummification of berries. The pathogen was isolated on potato dextrose agar medium and its pathogenicity was proved. The fungus produced circular, cottony colony with light green centre and creamish margins which later turned olive green with radial furrows. Acervuli and conidia were formed 10 days after incubation at 25±2 oC. Conidia (6.21 × 3.87μm) were oblong and hyaline to brownish in colour. Based on its colony characters, morphological characters and by comparing with authentic descriptions as well as through pathogenicity the pathogen causing anthracnose disease of grapes was identified as Gloeosporium ampelophagum (Pass.)Sacc. The fungus showed highest mycelial sporulation of 0.81×106/ml on oat meal agar with a pH 6.5 at 25-30 oC. Among the bio-agents screened, Trichoderma harzianum exhibited maximum inhibition in mycelial growth of 62.53 per cent that can be effectively used for the management of the disease.

Molecular and functional characterization of two RGA type genes in the PdR1b locus for Pierce’s disease resistance in Vitis arizonica/candicans

Pierce’s disease is a deadly disease of grapevines caused by the bacterial pathogen Xylella fastidiosa (Xf). A Pierce’s disease resistance locus from Vitis arizonica/candicans b43-17 segregated as a single dominant gene and mapped as PdR1a and PdR1b in two F1 sibling selections. The physical mapping of the PdR1b allele allowed the identification of five ORFs of the Leucine-Rich Repeat Receptor Kinase gene family. Two ORFs: V.ari-RGA14 and V.ari-RGA18 were used to transform embryogenic callus of V. vinifera Chardonnay (CH) and Thompson Seedless (TS) and V. rupestris St George (SG) via Agrobacterium tumefaciens. Regenerated plants were inoculated with Xf under greenhouse conditions. Genetic transformation with RGA14 and 18 did not generate resistance in CH and TS, although some lines of CH showed significantly lower stem bacterial concentration and/or exhibited reduced symptoms. In transgenic SG14, improved regrowth was accompanied with lower bacterial titers and decreased pectin lyase and ß-1,3-glucanase 3 gene expression. The limited effects of the transgenes on PD resistance could be explained by the lack of suitable partners or the presence of susceptibility factors that could not be overcome under these experimental conditions. The involvement of RGA17 in b43-17 resistance to Xf should not be discarded.

Partial double-pruning after bloom delays bunch rot epidemics in <i>Vitis vinifera</i> L. cvs. Riesling and Pinot gris

Bunch rot caused by Botrytis cinerea is a major fungal disease in grapevines. Under humid climatic conditions, bunch rot development on grapes cannot be completely suppressed and bunch rot control strategies mainly aim to delay the epidemic. In the present study, we investigated the potential of the innovative cultural practice “partial double-pruning after bloom (PDP)” to delay the bunch rot epidemic on Pinot gris and Riesling cultivars over five consecutive seasons (2016-2020) in Remich/Luxembourg. Control vines were pruned at winter to one 10-node fruiting cane per vine, while in PDP, two 10-node fruiting canes per vine were kept; one of the two canes was removed at BBCH 73 (2-3 weeks after bloom).In all the 10 cultivar*year combinations, the bunch rot disease severity at the final assessment date (shortly before harvest) was lower in PDP than in the control. This reduction was significant (P £ 0.05) in 7 of the 10 cultivar*year combinations. PDP significantly delayed the date when 5 % disease severity was reached; in data pooled over the five years this delay ranged between 10.3 (Pinot gris) and 8.3 days (Riesling). The proportion of non-marketable fruit was significantly reduced by 41 % (Pinot gris) and 53 % (Riesling). Total yield per plant was reduced by 10 % (Pinot gris) and 19 % (Riesling), with a significant increase in total soluble solids at harvest in the case of Riesling. An additional evaluation in the year 2020 revealed reduced cluster compactness in PDP for both cultivars.PDP turned out to be an innovative, efficient, reliable and relatively cost-efficient cultural practice to delay the bunch rot epidemic in grapes. It can be integrated as one module into the best practice strategy to control bunch rot and contributes to pesticide reduction in viticulture.

Image-based disease classification in grape leaves using convolutional capsule network

Crop protection is the prime hindrance to food security. Plant diseases destroy the overall quality and quantity of agricultural products. Grape is an important fruit and a major source of vitamin C nutrients. The automatic decision-making system plays a paramount role in agricultural informatics. This paper aims to detect the diseases in grape leaves using convolutional capsule networks. The capsule network is a promising neural network in deep learning. This network uses a group of neurons as capsules and effectively represents spatial information of features. The novelty of the proposed work relies on the addition of convolutional layers before the primary caps layer, which indirectly decreases the number of capsules and speeds up the dynamic routing process. The proposed method has experimented with augmented and non-augmented datasets. It effectively detects the diseases of grape leaves with an accuracy of 99.12%. The method's performance is compared with state-of-the-art deep learning methods and produces reliable results.

A Comparison of Three Types of "Vineyard Management" and Their Effects on the Structure of Plasmopara viticola Populations and Epidemic Dynamics of Grape Downy Mildew.

Grape downy mildew (GDM) is a destructive grapevine disease caused by Plasmopara viticola that occurs worldwide. In this study, we determined the characteristics of GDM epidemics and the grapevine canopy micro-climate in open-field, fungicide-spray, and rain-shelter plots during two constitutive years (2016 and 2017). It was found that rain shelter can significantly delay the disease occurrence by 28 and 21 days, reduce the epidemic phase by 28 and 21 days, and decrease the final disease index by 82% and 83%. Furthermore, it can block precipitation, reduce the relative humidity by 11% and 8%, and reduce the leaf wetness duration by 85% and 76% compared with open-field cultivation. A total of 3861, 783, and 1145 lesions were collected from the open-field, fungicide-managed, and rain-shelter plots, respectively, for analyses of the genetic diversity, population differentiation, and epidemic mode with seven microsatellite markers. In terms of genetic diversity, the Nei’s diversity index ranged from 0.569 to 0.680 and Shannon’s information index ranged from 0.958 to 1.226, showing high levels of diversity across populations. Similar to fungicide management, a rain shelter can significantly reduce the population’s genetic diversity. Low pairwise FST values (0.003–0.047) and high gene flow (Nm = 1.548–20.699) were observed among the three populations each year. In addition, most of the genetic variation occurred within populations. The epidemic mode of GDM in the open-field, fungicide-managed, and rain-shelter cultivation showed moderate, low, and high levels of clonality, respectively, in the case study.

Using X-ray Micro-Computed Tomography to Three-Dimensionally Visualize the Foregut of the Glassy-Winged Sharpshooter (Homalodisca vitripennis).

Simple SummaryThe foregut of the glassy-winged sharpshooter is difficult to dissect and observe in three dimensions with SEM or TEM. We describe an X-ray micro-computed tomography (microCT)-based method for studying the GWSS foregut including the precibarium and the cibarium, with high resolution. MicroCT imaging showed great promise for the study of the tissues and structures of small insects.Dissecting the heads of small insects belonging to the order Hemiptera for detailed anatomical investigation with light or scanning electron microscopy is difficult, time-consuming, and destructive, often resulting in sample preparation artifacts. Nevertheless, the structural details of these insects often hold critical information regarding their interactions with bacterial pathogens. For example, the glassy-winged sharpshooter (GWSS) is an efficient vector of the bacterium Xylella fastidiosa, the causal agent of Pierce’s disease in grape vines, but the foregut of this species is exceptionally difficult to dissect. Here, we describe a simple, non-destructive method to investigate the structure of the anterior gut of GWSS using high-resolution micro-computed X-ray tomography (microCT). The use of microCT eliminates the need for destructive dissection and reveals the morphology of small insects in three dimensions, allowing the user to virtually dissect the sample. The use of microCT imaging is a promising and powerful tool in the entomological sciences for studying the structures of vector insects, especially for difficult-to-dissect regions such as the foregut.

Grapevine roditis leaf Discoloration-associated virus: express pest risk analysis for Ukraine

Aim. To conduct an express pest risk analysis of Grapevine Roditis leaf discoloration-associated virus (GRLDaV) for Ukraine, a virus that has been related to a grapevine disease and was included in the EPPO Alert List in 2018. Methods. The phytosanitary risk analysis was carried out on the basis of an analytical review of expert literature and in accordance with the EPPO Decision-support scheme for an Express Pest Risk Analysis (EPPO, 2012) and with methodological recommendations for Ukraine (Pylypenko et al, 2012). The possibility of further spread and the potential range of the virus were determined using modern software packages AgroAtlas (Afonin and Li, 2011; Shumilin and Li, 2009), MapInfo Pro15.0 (ESTIMap®) and IDRISI SELVA (Clarklabs®). Results. An express pest risk analysis of GRLDaV for Ukraine was carried out for the first time. Outbreaks of the virus were first detected in the 1980ies in Greece (Rumbos, Avgelis, 1989) and from 2014–2018 in: Italy (Chiumenti et al, 2015, 2016; Maliogka et al, 2015), Turkey (Adan, 2016; Serçe et al, 2018) and Croatia (Vončina et al, 2018). Climatic predictors were analyzed in the outbreaks of the countries where the causal agent of the disease was identified. The potential range of GRLDaV in Ukraine has been established in case of importing infected planting material with further spreading of the virus. Risk management measures are proposed, which envisage including GRLDaV in the list of the Regulated Non-Quarantine Harmful Organisms of Ukraine. Conclusions. There is a possibility of introduction, further spread, and harmfulness of Grapevine Roditis leaf discoloration-associated virus in Ukraine, which is due to the presence of the host plant (grapevine, Vitis vinifera ssp. vinifera L.) and the corresponding climatic conditions of the southern and (part of) western Ukraine, where grapevine is cultivated on an industrial scale. The introduction of GRLDaV into Ukraine is possible as a result of the import of GRLDaV-infected grapevine planting material from the countries where the virus has been reported and presumably is still present. The current Ukrainian phytosanitary measures cannot reliably prevent the risk of intoduction of GRLDaV into Ukraine. The inclusion of GRLDaV in the List of the Regulated Non-Quarantine Harmful Organisms of Ukraine with the recommendation of permission to import grapevine planting material certified for the absence of GRLDaV (from the countries where the virus is reported) or mandatory testing of imported grapevine planting material for the presence of GRLDaV (from areas where such certification is absent), can be an effective risk management measure. It requires the producers and importers of grapevine planting material to include GRLDaV virus in their certification schemes to prevent eventual further spreading of the virus. It is recommended to do a nation-wide survey to determine the likelihood of the presence of GRLDaV in the region. Further research to identify possible natural virus vectors and to develop methods of (latent) GRLDaV diagnostics are needed. Finally additional studies on prevention and control of the virus (including breeding for resistance) are also required.