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United States Department of Agriculture

Agricultural Research Service

Research Project: Automated Mesoscale Pest Risk Forecast Maps for Agricultural Production and Potential Plant Biosecurity threats

Location: Horticultural Crops Research

2012 Annual Report


1a.Objectives (from AD-416):
Incorporate real-time weather data into disease forecasting models for diseases of grapevines, forage grass, and hops.


1b.Approach (from AD-416):
In conjunction with cooperation growers, virtual weather data and weather maps received will be evaluated for running disease forecasting models in the Willamette Valley. Actual weather data will be collected from automated weather stations installed at three field sites for each crop. Weather data to be used include temperature, daily rainfall, and leaf wetness. Virtual and actual weather data will be collected and compared.


3.Progress Report:

The purpose of this project is to develop, implement and support new methods of modeling and visualizing plant pest and pathogen threats. Technologies being developed include new terrain-sensitive ways to estimate point-based and gridded hourly weather data needed as inputs for disease risk models, improved weather forecasts that provide greater skill in disease prediction, and new ways to visualize disease risk using real-time risk maps and animations. A stepwise, end-user-feedback driven, systems approach to developing these tools is being used to quickly learn from any missteps and allow integration of multiple sources of technology. To date, information technologies are progressing for the several types of weather data streaming into the spatial modeling system: real time/near real-time (PRISM Group and IPPC), and forecast (Fox Weather, LLC, and National Weather Service NDFD). Integration of model inputs, models, and production of model outputs (virtual weather stations, virtual weather-based QA/QC, risk alert maps, and high-resolution disease risk forecast maps) are being evaluated by ARS in Grape, Grass seed and Hop production systems. The focus during 2010-2012 has been on validation studies of virtual weather estimates relevant to several plant disease models, both within and adjacent to crop canopies. Disease levels were also assessed regularly in plots established at the validation locations.

In western Oregon, daily mean temperature measurements in hop yards, grass fields, and vineyards were little affected by time resolution (15 or 60 min collection intervals). However, sensor placement at standard height compared to in-canopy had larger impacts on temperature estimates in grass seed canopies than in hop yards and vineyards. While virtual station estimates of standard height daily mean temperatures recorded hourly were well correlated with observed values, virtual station estimates of rain and relative humidity had weaker correlations with respective observed values. These errors caused differences in the disease risk indexes calculated from observed and virtual weather data, especially if the index rules involved rain. However, overall management recommendations were nearly identical for powdery mildew indexes for hop or grape when calculated with observed verses virtual weather data. As a result, overestimation of rain led to increased predicted risk levels for hop downy mildew and consequently a tendency for increased recommended treatments. For grass seed, estimation errors for leaf wetness reduced infection risk calculations in a stem rust simulation model, leading to an underestimation of stem rust hazard. Field tests were conducted during 2 years that happened to have relatively unfavorable weather for stem rust development; in these cases there was little effect on management decisions or disease outcomes of the error from the virtual weather inputs to the epidemic model. Because the rust decision aid is based on a simulation model, it is additionally possible to estimate the effects of input errors by simulation. A beta group of producers and/or their crop advisers was assembled to trial a web interface. Individualized training and focus groups were conducted during beta-testing in 2011 and early 2012. For hops, beta group users also made presentations at field events to other growers and crop consultants to aid in dissemination and uptake of the technology. Individual training sessions were conducted with beta users at least three times during the season to assist the users in interpreting disease model outputs and to obtain feedback on system improvements. Model outputs were disseminated weekly via social media to producers, crop advisers, and other stakeholders. Testing materials and scenario analyses were developed and given to participants over time to document changes in awareness of factors favoring downy mildew and powdery mildew diseases, disease management decision making, and grower confidence resulting from these educational activities. Five commercial hop yard and vineyard experimental plots were monitored in 2010 and 2011 for disease development along with collection of site specific weather data. Additional field experiments to validate powdery mildew control efficacy with on-site vs. virtual weather data inputs to the hop and grape powdery mildew risk indexes and stem rust simulation model are underway in 2012. Grass seed crop consultants have provided feedback on use of the stem rust model website operated with actual and virtual weather data. This research was conducted in support of objective 2C of the parent project.


Last Modified: 4/23/2014
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