Source: WASHINGTON STATE UNIVERSITY submitted to NRP
PATHOGEN MONITORING AND DISEASE MANAGEMENT WITHIN A VINEYARD FRAMEWORK
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
Reporting Frequency
Annual
Accession No.
1032933
Grant No.
2024-51181-43184
Cumulative Award Amt.
$4,125,422.00
Proposal No.
2024-05462
Multistate No.
(N/A)
Project Start Date
Sep 1, 2024
Project End Date
Aug 31, 2028
Grant Year
2024
Program Code
[SCRI]- Specialty Crop Research Initiative
Recipient Organization
WASHINGTON STATE UNIVERSITY
240 FRENCH ADMINISTRATION BLDG
PULLMAN,WA 99164-0001
Performing Department
(N/A)
Non Technical Summary
In the U.S. grape industries, quality and economically-viable yields are the drivers of regionally-relevant and nationally-successful farming enterprises. A key factor to this quality and quantity is successful disease management. Many factors influence this success, but nothing can upset the system like fungicide resistance. This upset has been growing in the US grape industry, as the nationally-relevant diseases of powdery mildew, downy mildew, and Botrytis bunch rot have seen field-level control failures due to pathogen resistance to several key fungicide groups. To prevent crop loss, grape growers not only need tools for understanding and forecasting disease pressure in their vineyards, but also tools that can help them identify potential fungicide resistance challenges on a timescale that allows for actionable changes. But access to data is not the same as data usability; concerted educational efforts targeting all sectors of the industry - from vineyard laborers to product manufacturers - are needed so that everyone understand how data can be used, and how to translate that information into a plan that improves disease management.Through the research and extension efforts in this SCRI-SREP project, we will: DEVELOP better pathogen sampling approaches and rapid early detection tools; ADAPT models from the vineyard to satellite-scale to improve predictions of disease risk and optimize sampling and scouting practices; and EMPOWER stakeholders along the production continuum - from field scouts, producers and managers, to consultants and Extension professionals - with access to durable educational programming to build their knowledge in disease management and fungicide resistance mitigation.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
1321139117020%
2121139104020%
2121139116020%
9031139303020%
4021139208020%
Goals / Objectives
Through this SREP, we will: Develop better pathogen sampling approaches and rapid early detection tools (Objective 1); Adapt models from the vineyard to satellite-scale to improve predictions of disease risk and optimize sampling and scouting practices (Objective 2); and Empower stakeholders along the production continuum - from field scouts, producers and managers, to consultants and Extension professionals - with technical knowledge and emerging tools for spore detection and risk assessment to improve fungicide stewardship for multiple pathogens across the local, regional, and national scale (Objective 3).A list of those objective themes and their supporting activities are below:Objective 1: Monitoring - Molecular and Field-Ready Tools for Pathogen Monitoring. Develop sampling guidelines for air samplers and active sample collection devices.Examine scouting effectiveness.Use simulation tools to create non-random scouting strategiesTest 3D printed high volume cyclonic spore samplers.Develop molecular diagnostic tools to facilitate testing for resistance in multiple grape pathogens.Develop advanced diagnostic tools, including rapid CRISPR-based isothermal tools, for in-field detection of fungicide resistance in P. viticola and E. necator.Objective 2: Mapping - Local to Regional Pathogen Prediction and Disease Risk Assessment.Develop data-model fusion to build regional and vineyard scale disease risk maps for improved vineyard management Data layer assembly.Ground truth and field data collection.Data modeling and Fusion data layer generation. Objective 3: Educating - Building and Delivering Training Materials on a National Scale.Empower new and existing networks of Extension professionals with the skills to disseminate knowledge of pathogen detection and fungicide resistance through a train-the-trainer program.Use collaborative tools and platforms to share actionable information in real time on pathogen detection, fungicide resistance risk, and the incidence and distribution of fungicide resistant pathogen isolates.In-season updates of the Fungicide Resistance Dashboard, with additional FRAC groups and grape pathogens.Develop a universal "Spray Program Evaluation Checklist".Build hands-on Extension workshops to engage growers and other stakeholders in understanding how diseases spread in vineyards.Support existing grower networks and influential individuals through targeted dissemination of information at workshops and training involving hands-on activities, group work, and skills-building.Develop and deliver national curriculum (series of training modules) on fungicide stewardship targeted to crop consultants and commercial grape growers.Engage individuals in peer-learning networks to disseminate emerging information and support adoption of decision-support tools.
Project Methods
Objective 1 - MonitoringGeneralized Methods: We will use models developed in our previous work to create sampling and scouting guidelines for disease identification based on local knowledge of past infections, plant physiology, microclimate, topography, and surrounding land use. We will do this through a series of experiments, which include developing sampling guidelines for air samplers and active collection devices, buidling / 3-D pringing high volume cyclonic spore samplers, developing meolcular diagnostic tools to faciliated testing for resistance in multiple grape pathogens, and developing advanced diagnostic tools (including CRISPR-based isothermal tools), for in-field detection of fungicide resistance.Generalized Results Analyses: Sampler designs will be compared to impaction samplers and glove sampling using latent class analyses (Lowder et al. 2023) in Phase 1 & 2 and only those samplers performing statistically (p>0.05) better than impaction samplers will be advanced to the next phase. An advisory panel of growers and consultants will be used to test usability of designs and refinements before designs move to Phase 3. Only those devices that detect pathogens sooner and at lower levels than impaction sampling, visual scouting, and glove sampling will be made publicly available. Sensitivity and specificity of the assays will be assessed using known sensitive and resistant isolates from different geographic regions. Resulting molecular detection and phenotyping will be posted to the resistance dashboard and used to build a grower decision-support "tree" for multiple pathogens and resistance profiles. The multiplex qPCR results compared to individual assessments will demonstrate suitability of each approach for monitoring multiple pathogens and fungicide resistance. For in-field based resistance detection tools, G143A assays will be compared using latent class analysis and assays for other markers will be assessed by alternative means.Objective 2 - MappingGeneralized Methods: We will try data-model fusion approaches to build regional and vineyard-scale disease risk maps for improved vineyard management. This will include the combination of on-site and large-scale data sets. It will be done through data layer assembly of multiple crop, pathogen and environment data sets, which will the subsequently be evaluated with ground-truthing at specific vineyard sites, including the development of better crop-phenology models that are the primary driver of disease risk and timing of intervention. These will then be fused using a machine-learning approach to improve model development with real-time grower-inputed and satelite-generated data.Generalized Results Analyses: The dataset and derived products described above will be partitioned randomly at a ratio of 4:1 into training sets and a test set to create host growth and growth stage models. To aid the process of model selection and fitting, we will include model comparisons by using a cross-validation framework. Models will be evaluated using K-fold cross-validation with 10 folds and 10 replications for each algorithm; for each replicate the training data will be divided randomly into 10 folds, one of which will be used to evaluate the model calibrated using the other 9 folds, to give more precise projections. Model performance will be evaluated using two methods, a threshold-independent statistic (the area under the curve), and several threshold-dependent statistics (overall accuracy, Kappa, True Skill statistic). The models will be ranked based on all these performance criteria and the highest-ranking model in the validation method will be selected. The new phenology model described above will be combined with microclimate, weather data, and grower activities (e.g., spray program, training) to drive short term pathogen forecast models (Fig. 10). For pathogen spread, we will use the models created by the research team during FRAME (see Obj. 1 and past activities). The ultimate output will be risk maps for potential future spread that identify regions at risk for future outbreaks.Objective 3 - EducatingGeneralized Methods: We will empower new and existing networks of Extension professionals with the skills to disseminate knowledge of pathogen detection and fungicide resistance through a train-the-trainer program. We will use collaborative tools and platforms to share actionable information in real time on pathogen detection, fungicide resistance risk, and the incidence and distribution of fungicide resistant pathogen isolates. This includes the development of a GIS dashboard to report and display incidences of fungicide resistance, development of a useable "spray program checklist" to assist decision makers in evaluating their management decisions for optimal fungicide stewardship, and finally, developing hands-on Extension workshops to engage growers and other stakeholders in understanding how disease spreads within a management unit and between sites. Finally, we will work on supporting existing grower networks and influential individuals through targeted dissemination of information at workshops and training involving hands-on activities, group work, and skills-building. This includes the development of a national curriculum on fungicide stewardship, and developing peer-learning groups in key regions across the US.Generalized Results Analyses: To evaluate our train the trainer program, we will collect feedback on both content style and delivery. We will also be following-up with participants (1 year after workshop attendance) to determine if they have independently delivered the workshop to their target clientele. We will be monitoring the dashboard website traffic to determine where our viewers are coming from, and when they are most likely to engage with the presented data. For regions that also have their own grower-focused digital support tools we will explore linking or integrating the Dashboard to those platforms to extend reach and promote use. The effectiveness of the workshops in enhancing grower knowledge of aspects of airborne disease management will be evaluated using quiz-based learning modules measuring pre- and post-workshop understanding of key concepts. With IRB approval, we can also examine changes in pathogen spread from first simulation run to final simulation run for each user during the workshop and assess changes in timing and types of control measures employed. Finally, for evaluating our support of existing peer-network groups, we will evaluate grower responses to different activities (i.e., post-workshop surveys on change in knowledge or anticipated behavioral adjustments). These results will be used to progressive adjust and amend approaches to better suit the needs of these peer-learning networks, which will likely differ by composition and region.

Progress 09/01/24 to 08/31/25

Outputs
Target Audience:The primary audiences for this project are U.S. grape growers (juice, wine, and table), vineyard managers, Extension professionals, and consultants. We also target fellow scientists, and our work on low-cost sensor systems appeals to general environmental scientists. During the first year, we reached these groups through national meetings, regulatory briefings, academic seminars, and grower-focused programs, with details described below. Grape growers, viticulturists, and crop consultants are targeted to conduct on-farm research that yields real-world conditions to test the robustness of approaches and ease in on-the-ground implementation. Implementation is increased by on-farm research and ongoing sharing of data collected for their vineyards. This allows for real-time adjustments to manage variance in farming practices based on region or market demands. We engaged with these individuals at annual grower meetings, University-led field days, and through traditional Extension communication such as emails, phone calls, and email blasts. We developed a curriculum, which includes factsheets, to deliver a workshop series to improve technical knowledge, communication and collaboration for fungicide resistance detection, mitigation and management. We chose this target audience to tap into agricultural networks to enhance the uptake of recommended practices for fungicide resistance detection and management. Researchers, industry scientists, and diagnosticians are targeted for adopting our diagnostic assays and sampling equipment since they are the most likely to provide them as services to growers or farming companies. Their inclusion early will build understanding and comfort with the technology, as well as aid in getting feedback that will improve the technology. This will increase the probability of continued service availability and implementation after the project ends. Changes/Problems:Objective 1 - Monitoring The hand-held cyclone samplers were not as effective as the glove swab in detecting E. necator using the same DNA extraction protocol. We will examine some inexpensive PCR cleanups to see if we improve detection without decreasing the return on investment due to increased costs.Disruptions in federal funding delayed the 3D printing of some vacuum samplers at USDA-ARS. Objective 2 - Mapping Due to unforeseen administrative and logistical delays in the postdoctoral hiring process at the host institution, progress on Objective 2 was initially slower than anticipated. However, core activities remained largely on track, as interim support and continuity in the work were maintained during the transition period. Objective 3 - Educating No major delays or challenges in Project Year 1. While the grape industry, particularly the wine grape industry, has had a difficult economic year, industry support and general project participation has been steady. What opportunities for training and professional development has the project provided?In summer 2025, the P.I. Gold hosted Cadet Grover from the United States Air Force Academy for a 10wk internship. Cadet Grover's internship was funded by a prestigious STAMP act scholarship to enable her to conduct this on-site research experience. She actively participated in on-farm discussions covering viticulture practices, phenology, and the use of technology in viticulture. She received training through visits to various research and commercial vineyard sites across the Erie and Finger Lakes regions, engaging in detailed observations and discussions throughout the season. P.I. Mahaffee worked with undergraduate students, by providing data and advice which allowed student to build a simulation environment that can test the effect of fungicide applications on disease development, under different scenarios where the targeted pathogen has varying concentrations of resistant individuals in its population. This training allowed students to do a mock evaluation of how fungicide rotation and other practices to mitigate fungicide resistance. P.I. Miles worked with several undergraduate and graduate students, providing opportunities to learn about data collection, vineyard field scouting, and scientific writing (publication of peer-reviewed manuscripts). How have the results been disseminated to communities of interest?The FRAME team uses a variety of approaches and platforms to share project outcomes to our target audiences. Two more static approaches include the use of the FRAME website (framenetworks.wsu.edu), where we share publications, resources, and newsclips. FRAME also maintains a LinkedIn profile that is used to help push information and findings to peers and stakeholders. For year 1, the more dynamic approaches we used (and their approximate attendance) are described below: Grower, viticulturist and crop consultant audience: 1) Presentations at the Great Lakes Fruit and Vegetable Expo (75), Northwest Orchard and Vineyard Show (150), Southwest Michigan Horticultural Days (100), Pennsylvania Wine Grape Growers Meeting (100), Michigan Vineyard meetings (75-100 each), B.E.V New York (150), New York vineyard meetings (16), GALLO Wine Company Spring IPM Meeting (291), Washington State vineyard meetings (111), Oregon State vineyard meetings (300); 2) Trade Articles and Newsletters (7 - Wine Business Monthly, Good Fruit Grower, Finger Lakes Grape Program, Oregon State University Vine to Wine); and 3) Podcasts (2) including Vineyard Underground and Pacific Northwest Ag Network. Scientist and diagnostician audiences: 1) Presenting at the APS Plant Health 2024 Fungicide Workshop (Memphis, TN; 150 attendees), AMS Denver Summit (304); 2) Engaging with EPA staff (200 attendees) on fungicide selection and resistance management; and 3) Publication in peer-reviewed journals. What do you plan to do during the next reporting period to accomplish the goals?Objective 1 - Monitoring We will further improve upon our pathogen detection approaches by developing and testing a tractor-mounted cyclone sampler. This "mechanized" sampler will be compared to traditional stratified sampling (the grower standard). We will also continue the development of rapid screening assays using fungicide resistance markers, and screen new fungal isolates for these markers. This marker development is not trivial work, as each new marker needs to be validated using traditional fungicide sensitivity assays. In addition, we plan to expand resistance monitoring by incorporating a microbial ecology approach to detect multiple forms of fungicide resistance in Erysiphe necator, Botrytis cinerea, and Plasmopara viticola. Building on progress in air sampling and diagnostics, we will refine assays, begin sequencing, and generate preliminary datasets linking resistance markers to pathogen populations. Objective 2 - Mapping We will continue to deploy LEMS in vineyards across the US to continue our nationwide data set on vineyard development, microclimate, and winds that can be integrated into our modeling described below. We will also complete and evaluate our 2D disease epidemic model, so that it can be used in Objective 1's goals of improved monitoring, and Objective 3's goals of providing new educational tools for teaching about disease development. Part of this effort will be to characterize existing historical disease distribution and spray data collected by the FRAME team. This characterization will be the basis of our 2D disease spread model evaluation. Also in year 2, we will integrate our remote-sensed and grower collected data across three layers: 1) Weather network (air temperature and humidity); 2) Land surface temperature (as a proxy for canopy microclimate), and 3) Multispectral HLS imagery (for canopy progression). This integrated dataset will support more accurate regional-scale disease risk modeling and phenology tracking. Objective 3 - Educating In the next reporting period, we plan to have all of our FRAME educational factsheets published, to be used within the 4 training modules the team is currently building. We will also be delivering 2 of those 4 modules in the next reporting cycle, and beta testing a third module. We've been invited to participate in peer training activities using two national platforms - the American Society for Enology and Viticulture annual conference, and the National Viticulture and Enology Extension Leadership Conference. The train-the-trainer framework will be strengthened by working with Extension educators and consultants to broaden dissemination During this time, we also plan to complete our "Universal Checklist" for evaluating spray programs, and plan to disseminate that through our website, as well as draw attention to its existence through informational plugs in trade magazines.

Impacts
What was accomplished under these goals? Objective 1 - Monitoring - Molecular and Field-Reay Tools for Pathogen Monitoring In Year 1, we advanced low-cost approaches for vineyard pathogen monitoring. A handheld vacuum cleaner was optimized for air sampling, and weather stations were deployed across major grape regions to track conditions linked to disease outbreaks. This sampler was deployed across the country for general evaluation. In addition, it was specifically compared to the gold-standard "glove swab" method for pathogen detection in 16 vineyards in Oregon. We produced alignments of fungicide resistance markers, improving the foundation for molecular diagnostics. We began testing new multiplex detection assay for Bortytis, E. necator, and P. viticola and continued maintaining a collection of 53 E. necator isolates and isolate testing for fungicide resistance. We also collected 15 new E. necator isolates and tested ~ 50 Botrytis sp. isolates for tolerance to 9 fungicides. Objective 1 Impact- Our work is delivering affordable monitoring tools, validated diagnostics, and practical training that growers can apply now. These accomplishments strengthen grape disease management and support the long-term sustainability and profitability of U.S. vineyards. Objective 2 - Mapping - Local to Regional Pathogen Prediction and Disease Risk Assessment. We deployed multiple Low-cost Environmental Monitoring Systems (LEMS) at Chardonnay wine grape sites across the US, including Oregon, Washington, California, Michigan and New York. These off-the-shelf sensors allow for the monitoring of vineyard microclimate, winds, and plant growth. This effort directly addressed Obj. 2's tasks to collect ground truth and field data to use in data modeling and will provide future data for system evaluation. To develop better grapevine disease risk prediction, we began efforts to track local grapevine phenology (development stages) and canopy growth. This included the use of game camera tracking at sites where LEMS were deployed, as well as an addition 25 sites in Upstate New York, which included direct measurements by trained scientists, general monitoring and scouting by grape growers, and game cameras. One component to this monitoring was the improvement of an App that allows users (growers) to take pictures and identify key vine growth stages, which can then be geo-located and time stamped for further disease risk modelling use. Key features of this app include offline data entry and block-level profiling. This app is being beta tested under the "myCollector" module of the myEV app. MyEV is funded by USDA NIFA AFRI Small Farms as well as NASA Acres Domestic Agriculture Research Consortium. One of our main goals is to provide regional disease risk predictions, that growers can use to get a better sense of "how the season is progressing." To do this, satellite imagery has been passively collected all season. As part of the NASA AVIRIS4Acres-NY airborne science campaign, high resolution (0.5-1.25m) hyperspectral imagery was captured over 30 vineyards in upstate New York between July 28 - August 9. NASA Acres additionally tasked high-resolution commercial satellite imagery from Wyvern LLC. Additional imagery was collected at a research vineyard that included harmonized multispectral-thermal UAV imagery, ground side canopy RGB imagery (PhytoPatholoBot), and handheld spectroscopy reflectance. Combined, the local LEMS data and vine development data are being integrated with satellite datasets such as Harmonized Landsat-Sentinel and Landsat Land Surface Temperature based products to further compare the field scale canopy density variation and phenology modelling which will ultimately be used to predict risk for grapevine diseases such as downy mildew and powdery mildew. Finally, we continued to develop our 2D fungal pathogen disease spread models and have included components such as spray intervention, to determine how the timing of sprays can influence overall disease development. Objective 2 Impacts: Upon success, these approaches of using low-cost, low-maintenance ground cameras to calibrate open-source satellite imagery, as well as models that help describe how different management strategies can influence plant disease outcomes, has the potential to significantly enhance large-scale growers', extension agents', and educators' capabilities in disease forecasting and vineyard management. Objective 3: Educating - Building and Delivering Training Materials on a National Scale. The primary goal of this objective is to ensure that everyone involved in grape disease management - from growers to Extension specialists, from scientists to consultants - all have the same baseline awareness and understanding of what it takes to manage diseases, and how fragile our reliance on fungicides can be. We are building this baseline using both traditional Extension methods, and novel curriculum-based approaches. In this first year, we are focused on building the curriculum for the extension workshops to be held in the 2nd project year. This consists principally of developing content for factsheets on topics such as extension delivery, network-based extension, resistance sampling, and understanding the concepts of fungicide resistance. We will be developing the workshop schedule and identifying content for each session in the workshop series. The workshops will empower extension professionals and consultants todisseminate knowledge on pathogen detection and fungicide resistance, to improve uptake and reduce economic consequences of fungicide resistance. For growers and crop consultants, we also continued to update our "FRAC 11 fungicide resistance decision tree" which provides specific guidelines of how grape growers can adjust their spray programs if they have detected FRAC 11 fungicide resistance in their vineyards. This has been made available on our website, and has also been widely publicized in trade magazines, as well as made available to diagnostic labs offering the FRAC 11 fungicide resistance testing service (to include with their test results). We built and handed out over 1,100 spray coverage assessment kits to grape growers across the west and east coast; these kits assist growers in identifying whether or not their fungicide applications are reaching the grapevine. We have also continued to develop a pipeline of trade magazine articles highlighting grape disease management principles - this puts information into the venues growers, consultants, and farm owners use. Objective 3 Impacts: The national curriculum we are developing will ensure crop consultants and extension professionals have a baseline knowledge of fungicide stewardship and grapevine disease management as they move through their careers. This should reduce erroneous crop management recommendations and subsequent actions that can lead to the selection of fungicide resistance.

Publications

  • Type: Peer Reviewed Journal Articles Status: Awaiting Publication Year Published: 2025 Citation: Heger, L., Sharma, N., McCoy, A., Martin F.N, Miles L.A., Chilvers, M.I., Naegele, R.P., Miles, T.D..2025.Multiplexed real-time and digital PCR tools to differentiate clades of Plasmopara viticola causing downy mildew in grapes.Plant Disease.First Look. DOI:10.1094/PDIS-01-25-0173-SR