Research and IPM

California PestCast: Disease Model Database

This database is a clearinghouse of information about models developed for economically important crop and turf diseases in California. A model is included in the database if it uses weather, host,å and/or pathogen data to predict risk of disease outbreak. This database is a part of a project called "PestCast," a regional weather network to support the development, validation, and implementation of crop disease models.

An initial survey of University of California researchers identified crop disease forecasting models in various stages of development in California. Communication with the model developers, validators, and implementers, as well as software and weather station manufacturers, identified other models that are relevant to California growers. A literature review uncovered other models in use around the world. Finally, some models were identified through use of an electronic form available on the World Wide Web.

Dr. Janet C. Broome, plant pathologist with the UC Sustainable Agriculture Research and Education Program, is leading the database development, with assistance from Joyce Fox Strand, agricultural meteorologist with the UC Statewide IPM Program. Research assistance was provided by Nena Bloom, then a graduate student in the Plant Protection and Pest Management program at UC Davis, and by Dr. Remigio Guzman, then a a graduate student in the Department of Plant Pathology at UC Davis.

A plant disease model is a mathematical description of the interaction between environmental, host, and pathogen variables that can result in disease. A model can be presented as a simple rule, an equation, a graph, or a table. The output of a model can be a numerical index of disease risk, predicted disease incidence or severity, and/or predicted inoculum development.

Note: Plant disease models typically are developed in specific climates and regions around the world. Before using a model not field tested or validated for a specific location, test the model for one or more seasons under local conditions to verify that it will work in this location. Models may contain assumptions about site specific conditions that might not apply for all areas. Input variables and/or other parameters, such as timing of model initiation, may need adjustment due to pathogen biology, host phenology, and variety in a specific area.

Model development

Plant disease models typically are developed from laboratory and/or field studies by researchers cooperating with extension personnel. The goal is to predict the risk of disease and/or development of inoculum, based on monitoring key environmental, host, and pathogen variables. Based on management options and goals, action thresholds can be incorporated into the model to provide advice on fungicide treatments. Models should be evaluated in the field, and actual disease compared to predicted disease.

Model validation

To ensure widespread applicability, these descriptive models must be validated across a variety of microclimates over a number of years by researchers and extension personnel cooperating with pest control advisors and growers. Models are tested to see how well they predict disease incidence, and then are revised and refined based on these findings. Frequently, disease incidence of plants treated according to the model is compared to disease managed by traditional spray schedules, as well as unsprayed plots. Models developed in one area are frequently validated by researchers in other areas. Under such circumstances, the models may need region-specific modifications.

Model implementation

After being found to predict disease or inoculum levels adequately, models can be used with microscale weather data obtained through the use of on-site, user-friendly electronic weather stations that monitor microclimate variables such as air temperature, relative humidity, hours of free moisture, and precipitation. These new technologies can be transferred through public and private means. Growers and pest control advisors can use disease risk indices for on-farm disease management. Frequently, local implementation efforts are supported by extension personnel and researchers through demonstrations and other outreach activities.

Disease model information was first assembled from published literature, as well as written documents supplied by the researchers, and then presented in a standard format. When several models are available for a disease, they are listed in reverse chronological order, by date of publication. When information is incomplete, the field is left blank, or termed "in progress" or "unspecified."

The fields of the database include:

Crop

A crop of economic importance to California agriculture.

Disease

A disease of economic importance in California.

Pathogen

The scientific name of the pathogen that causes the disease.

Model developer and citation

The citation(s) of the published model. When models have been modified by either the original researcher or another researcher, the most recent model is listed first. The original model also is listed separately from the modified model.

Weather station and sensor location

The location of weather monitoring equipment relative to the crop canopy. The sensors that monitor the environmental variables may be located within a crop canopy or may be part of a standardized reporting station at the edge of the field or other nearby location. To reproduce model results, it is important to place the sensors in the same location as they were in the research and validation work.

Input variables

The data used to run a disease model. Measured environmental variables are recorded by automated weather stations or other types of monitoring equipment. Variables typically monitored include temperature, precipitation, relative humidity, and leaf wetness. Leaf wetness is a very site-specific variable; therefore, the sensor must be placed in the appropriate place relative to the crop canopy. Forecast environmental variables are developed by weather services. Calculated environmental variables are measured variables that are transformed by mathematical calculations, such as degree-hours or dew points. Host variables include crop growth stage, cultivar, and other host factors. Pathogen variables include inoculum potential, maturity of spores, and other pathogen factors.

Model description

The mathematical relationship that describes the interaction between the environment, host, and pathogen variables, and disease. The model can be presented as an equation, a graph, a table, or a simple rule. The output of a model can be a numerical index of disease risk. For further information on the model, see the original model reference, listed under Model Developer and Citation.

Action threshold

Describes the level of disease above which significant economic loss is predicted. A treatment advisory is issued when this threshold is exceeded. Some models present the user with several levels of disease risk, to allow the user to decide the amount of risk that is tolerable.

Model validation and evaluation

"Validation" means testing of the model in the field over several cropping seasons and/or locations to evaluate the ability of a model to assess or predict disease. Typically a researcher will fine-tune and re-test a model several times. "Evaluation" means testing the model under local conditions, generally by someone other than the original researcher. The citation of the published work or work under way is listed.

Model implementation

The use of the model to guide the timing of fungicide applications. The citation of the published implementation work or the group doing the work is listed.

Current limitations of model

Describes drawbacks to parts of the model based on the researcher's observations, or from published literature about the model. When information about the model is unspecified or absent, this may be listed as a limitation if it is considered critical to using the model.

Future directions of model

Describes potential future directions of work on a model by a researcher.

Related work

Lists additional research relevant to the use of the model, by original model developers or others working with the model.

This database is a collection of information developed by many researchers all over the world. The information should not be interpreted as pesticide recommendations by the University of California, California EPA, or the Department of Pesticide Regulation. For the latest UC pest management guidelines relating to disease control, see the UC Pest Management Guidelines database. For pesticides registered for particular crop uses, see the DPR Pesticide Registration database.

These plant disease models can be used to predict the timing of fungicide applications. However, exercise caution when using these models because disease control in the field depends on many additional variables, some of which may not be included in any one model. Important variables include a fungicide's activity, such as whether a material is protective, eradicative, or curative, as well as fungicide coverage and the time intervals between applications. Other variables that might affect disease control include additional environmental variables that might not be included in the model, host phenology or growth stage, and pathogen virulence.

Disease model database

This database is a clearinghouse of information about models developed for economically important crop and turf diseases in California. A model is included in the database if it uses weather, host, and/or pathogen data to predict risk of disease outbreak. This database is a part of a project called "PestCast," a regional weather network to support the development, validation and implementation of crop disease models.

A plant disease model is a mathematical description of the interaction between environmental, host and pathogen variables that can result in disease. A model can be presented as a simple rule, an equation, a graph or a table. The output of a model can be a numerical index of disease risk, predicted disease incidence or severity, and/or predicted inoculum development.

• Description of the Database Contents
• What Are Plant Disease Model Development, Validation, and Implementation?
• Fungicides and Disease Forecasting

This symbol    indicates work conducted in California.

Almond	Lettuce	Strawberry
Shot hole Scab	Downy mildew	Botrytis Powdery mildew
	Sclerotinia drop
Apple	Pear	Tomato
Fire blight Scab	Fire blight	Powdery mildew
	Scab
Carrot	Pistachio	Tomato, processing
Alternaria leaf blight	Alternaria late blight	Blackmold
		Late blight
Celery	Potato	Turf
Septoria late blight	Late blight	Rhizoctonia  blight (brown patch)
Grape	Stone Fruit	Walnut
Botrytis bunch rot	Brown rot	Walnut blight
Powdery mildew
Downy mildew

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