Brown rot, caused by several fungi in the genus Monilinia, is a major disease of stone and pome fruits—including peach, plum, nectarine, apricot, cherry, apple, pear, and quince—worldwide. This tool estimates the risk of brown rot infection from observed and forecast weather data.
In North America, brown rot is primarily caused by Monilinia fructicola, with M. laxa also present in some regions. The disease affects a wide range of Prunus species—such as peach, cherry, plum, apricot, and nectarine—and is considered one of the most destructive diseases of these crops. Some Monilinia species affect pome fruits, including apple, pear, and quince. This forecast applies primarily to M. fructicola and to M. fructigena, which is the species present in Europe that attacks pome fruits and may at some point become invasive to the U.S.
Brown rot infections need favorable temperatures during periods of sufficient moisture in the form of rainfall, dew, and high humidity during bloom, fruit development, and near harvest. Symptoms often appear rapidly. Infection can occur during periods of sufficient moisture over a wide temperature range, from 33 to 93F, with an optimum of 77F. Left unmanaged, the disease can spread quickly between fruits, reducing both yield and fruit quality.
This tool forecasts cumulative daily risk of Monilinia infection using a similar approach to our boxwood blight infection risk model. Both are based on hourly temperatures during periods of leaf wetness. We estimate leaf wetness using our implementation of a leaf wetness estimation algorithm.
This model has not been published. We do have a spreadsheet documenting the development and implementation of this model. A white paper is under development.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. The weather forecast data is from the National Weather Service's "NDFD" forecasts.
Then, select "Graph" or "Table" to run the model. Your output will appear after 10-20 seconds depending on server load.
To interpret our Monilinia infection risk forecasts, one should consider both the relative susceptibility of the current stage and cultivar and whether disease inoculum may be present. For example, the bloom period in an orchard that has had disease in recent months or years can be a very susceptible time for new infections to occur if the risk model indicates that the environmental conditions are favorable.
Understanding when brown rot infections are most likely to occur can help growers, land managers, and home fruit producers make more informed decisions about when to take management actions such as applying chemical treatment, pruning trees, or removing infected fruit. For specific information on preferred treatment options in your region, we recommend contacting your local extension agent. For more information on treatment guidance, visit University of Florida IFAS Extension’s Peach Brown Rot publication.
We also have spatial risk maps of Monilinia brown rot infection risk. And the USA-National Phenology Network has Phenocast maps for brown rot, which are based on our DDRP risk maps. Generally, the site based and spatial risk maps are based on the same model, but are different implementations. The site-based model uses weather station data at an hourly interval, while the spatial risk maps are based on daily PRISM and National Weather Service gridded spatial data at 4km spatial resolution, and use 2 to 4 day time windows. You may want to compare these model implementations and decide which are best for your needs.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.
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.
Next, use one of these buttons to run the model and see the output in the form of your choice.
To get this information by email, log in to or sign up for USPEST.org email notifications. To see the model output together with relevant weather inputs, go to MyPest Page.
To get this information by email, log in to or sign up for USPEST.org email notifications. To see the model output together with relevant weather inputs, go to MyPest Page.