Introduction

This risk index uses weather data to estimate fire blight infection risk for each day. Categories of low, moderate, and high risk are assigned to index values. These categories can be used to adjust application intervals of fungicides or other management actions such as fungicide selection and intensity of scouting.

This app is a newer, streamlined interface for the existing model, which is also available through MyPest Page.

Using this App

Select the "Inputs" tab, search for weather stations near you, and choose one either from a list or a map. Be aware that some weather stations have poor quality or missing data. Our software estimates the quality of each station, but you may wish compare results for several nearby stations.

Optionally, you may choose a start date and a span (number) of days. If you leave the start date blank, the end of the output will be five days in the future, using forecast weather data, and the app will count back to a start date in the recent past.

You should also choose a local risk level, which describes the prevalence of fireblight in your vicinity.

Then, select "Graph" or "Table" to run the model. Your output will appear after 10-20 seconds depending on server load.

Interpreting the Results

Fire blight infection can be predicted by measuring temperature following the occurrence of a wetting event, such as rain or dew, while the tree is in blossom and inoculative Erwinia amylovora bacteria are present. These bacterial colonies grow in warm temperatures (10-30°C / 50-86°F), increasing the risk of infections. This model (The Cougar Blight 2010 model by WSU) predicts the risk of infection based on two main factors:

1. Historical presence of fire blight in the location:

   • No fire blight present during the prior year
   • Fire blight present during the prior year (Normal in areas with known fire blight problems)
   • Fire blight present and active in the current year

2. Hourly temperature data (which assumes nighttime dew is sufficient, as is generally the case in springtime) calculates a risk index value and a risk category using a lookup table (Smith, WSU, see link above):

   Low

   Temperature conditions are not historically conducive to infection.

   Caution

   Temperature conditions are not likely to lead to infections unless blossoming trees are near an infected tree. Consider preventative management.

   High

   Temperature conditions are historically conducive to blight infection. Risk is increased at later stages of primary bloom and petal fall. Secondary blossoms are at risk of infection.

   Extreme

   Conditions are historically conducive to severe outbreaks due to the abundance of blossoms present and highly favorable temperature.

   Exceptional

   Similar to Extreme risk, but conditions are even more historically conducive to outbreaks.

Note that this model is only useful when blossoms are open. It is not intended for use, and has no predictive value, during high summer heat.

For a detailed description of the model, see the Cougar Blight 2010 page at WSU Extension. For general information about fire blight, see the fire blight page from the Pacific Northwest Disease Management Handbook.

Disclaimer: The index is intended to inform your decisions about management actions, such as choice and timing of control measures and intensity of scouting. It should supplement, not replace, the other factors you consider in making these decisions. Use at your own risk.

Email Subscription

Automated email delivery of the disease risk index outputs displayed in this app is available at no cost. An email subscription offers model results for this and several other plant disease models, for up to three weather stations, on a schedule that you select. To subscribe, you will need a uspest.org account.

Acknowledgements

• This app was funded in part by the USDA National Institute of Food and Agriculture, Crop Protection, and Pest Management, Extension Implementation Program (USDA-NIFA-CPPM-EIP).
• Dan Upper provided programming for the project.
• Location search uses data © OpenStreetMap contributors and data from MaxMind.