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)
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.