About WSU-Potato Decision Aid System

A collaborative project between Washington State University, the University of Idaho, Oregon State University, USDA, and potato industry stakeholders.

The PNW Potato DAS is a web platform designed to transfer time-sensitive information to decision makers in the potato industry. This platform features spatial models of insect pests, allowing for better anticipation of highly mobile pest populations. It also runs models for some pests to estimate pest development and provide links to management and pesticide considerations.

The DAS is a comprehensive system that incorporates weather data from WSU-AgWeatherNet (AWN), virtual weather stations and forecasts powered by Weather Source, historic weather data from Daymet, and other information sources such as the Northwest Potato Research Consortium. DAS attempts to support reasonably common smart phones and tablets (iOS and Android) as well as desktop computers in all functions. As designed, DAS also has a data/account management subsystem that simplifies use for decision makers and allows them to tailor the information they receive.

Mission Statement

The PNW Potato Decision Aid System (DAS) was launched in 2021 to provide time-sensitive information for management in Washington, Idaho, and Oregon State potato crops. We are open and eager to collaborate with scientists to integrate tools, management information, and models into DAS that will assist Pacific Northwest potato growers make the best management decisions and remain competitive in the world economy.

Strategic Vision

The development of DAS is guided by four goals and principles:

  1. Implement science-based tools that help with management of time-sensitive problems in entomology and other allied sciences.
  2. Use technology to simplify and expand our ability to provide information in a broad range of areas to decision-makers in the potato industry.
  3. Work with scientists to implement web and smartphone-based solutions to key technical problems faced by the potato industry.
  4. Collaborate with other scientists in the region to implement science-based tools for the transfer of time-sensitive information to decision-makers. This includes sharing tools already developed by DAS or implementing tools developed by other groups.

How It Works

Who is it for?
  • for pest management decision makers in the potato industry
What does it provide?
  • time-sensitive information on pest development, spatial tools, management options, and pesticide considerations
  • accessibility on common smartphones and tablets (iOS and Android)
How does it work?
  1. sign up for an account (currently free with ability to subscribe to 3 stations)
  2. choose weather stations near your farm
  3. view model output
    • current and forecasted status of insect pests
    • management recommendations depending on the pest status
  4. access management information from the PNW Potato IPM Guidelines

Development of the Potato Pest Mapping System

Potato growers must manage a suite of pests, including insects and diseases. Sampling networks, where pests are monitored regularly across a broad region, allow growers to visualize “hot spots” of high pest activity and anticipate mobile pest populations. Our team currently conducts weekly monitoring of about 50 potato fields throughout the Columbia Basin in Washington, and similar numbers of sites in Idaho, for pests including aphids, beet leafhoppers, potato tuberworms, and potato psyllids (Fig. 1).

A map of pest monitoring points in potatoe fields.
Figure 1
A map of insect densities interpolated from a survey of insect trap counts.
Figure 2

Using geographical information systems (GIS) technology and these monitoring data, we generate predictions of pest densities across broad regions using a technique called “interpolation” (Fig. 2). This produces a continuous prediction of insect densities throughout the region, where areas of similar pest density are grouped together and shown on maps using the same color coding (Fig. 2).

Our predictive models are based on over 8 years of data, and are based on extensive validation. This was done by comparing predicted pest densities from the interpolations with actual pest densities observed through sampling. In the validation stage, we used a portion of the sampling data to develop the interpolation models, and the rest to validate the models. We have demonstrated that our simple GIS approach produces 60-70% congruence between predictions and observed pest densities. This is a high degree of precision, considering that many factors (such as insecticide use) can influence pest densities. However, it should be noted that the insect maps should be used as a guideline for what to expect, and not a definitive count of the number of insects in any given field at any given point in time. Variability in many factors, including grower management strategies, could cause predictions to vary from observed counts.

Contact us if you have any questions about the pest mapping project.

Useful Links

Staff

David Crowder
Interim Director of DAS
Associate Professor of Entomology

Washington State University
Vince Jones
Professor of Entomology
WSU Tree Fruit Research & Extension Center
Stefano Borghi
Information Systems Manager
WSU Tree Fruit Research & Extension Center
Javier Gutierrez Illan
Ecological Model Developer
Research Assistant Professor

Washington State University
Ethan Federman
Web Developer
WSU Tree Fruit Research & Extension Center
Carrie Wohleb
Professor of Extension
Washington State University
Robert Clark
Ecological Model Developer
Research Assistant Professor

Washington State University
Gengping Zhu
Ecological Model Developer
Postdoctoral Associate

Washington State University
Vera Pfeiffer
Ecological Model Developer
Postdoctoral Associate

Washington State University
Abigail Cohen
Ecological Model Developer
PhD Candidate

Washington State University
Jillian Foutz
Insect Detection
MS Candidate

Washington State University
Camille Wagstaff
Pathogen Detection
PhD Candidate

Washington State University

Credits

Vince Jones
Overall concept, insect models, insect management recommendations, model interface
WSU-Tree Fruit Research & Extension Center, Wenatchee

Stefano Borghi
DAS Information Systems Manager
WSU-Tree Fruit Research & Extension Center, Wenatchee

Ethan Federman
Software Developer
Spokane, WA

Jeremy Phillips
Web Developer
Pacific, WA

Near-real time data:
Forecasting powered by:
Weather Source Logo
Historic data:
Daymet Logo