Technical Documentation and Links for Plant Disease Risk and Other Hourly Weather Driven Models
Len Coop
Assoc. Dir. Decision Support Systems
Oregon IPM Center
Oregon State University
Our disease model system accesses hourly and sub-hourly data from numerous
real-time and archival weather stations (>33,000 stations, 2021) to compute
a variety of plant disease risk and horticultural models. Currently the
following models are available:
- Apple Scab infection risk
- Pear Scab infection risk
- Gubler Thomas Powdery Mildew
- Hop Powdery Mildew
- Cherry Powdery Mildew
- Pearson-Gadoury Powdery Mildew
- Cleistothecial Powdery Mildew
- Strawberry Powdery Mildew
- Anjou Pear Scald
- Fire Blight (Cougar Blight)
- Fox-Broome Botrytis infection risk
- Blueberry Mummy Berry
- Boxwood Blight infection risk
- Dollar Spot (turfgrass)
- Potato / Tomato Late Blight
- TomCast Disease Severity Values (DSV)
- MelCast Environmental Favorability Index (EFI)
- Vapor Drift Risk Estimation Model
- Custom Degree-Hour Accumulation Model
- Chilling Hours models
Use these models with caution. Please see our Disclaimer.
-
Apple Scab Infection Risk
-
Pear Scab Infection Risk
-
Gubler Thomas powdery mildew risk index
-
Hop Powdery Mildew
- This disease, caused by the fungus Podosphaera macularis,
is documented in
the PNW
Plant Disease Management Handbook.
- This is actually two models, an older model for highly
susceptible varieties, and a newer model for "Cascade" and similar
varieties. Both are models of risk for the conidial stage only.
Requires hourly weather data. Developed for use in the Pacific
Northwest.
- The older model was developed by Mahaffee and Gent, modified from
the Gubler-Thomas powdery mildew model. An early version of the Hop
PM model is documented
at Mahaffee,
W. F., Thomas, C. S., Turechek, W. W., Ocamb, C. M., Nelson, M. E.,
Fox, A. Gubler, W. D. 2003. Responding to an introduced
pathogen: Podosphaera macularis (hop powdery mildew) in the Pacific
Northwest. Online. Plant Health Progress
doi:10.1094/PHP-2003-1113-07-RV.
- The Cascade model, developed by Dave Gent, has not been published
as of Feb 2020. It is similar to the older model, but with these
rules:
- If there were ≥6 continuous hours at ≥28°C, then subtract 20
points, else;
- If there were ≥2 continuous hours at ≤4°C, then subtract 10
points; else
- If there were ≥2.5 mm rain, then subtract 10 points,
else;
- If there were ≥6 continuous hours at ≥28°C on the previous
day, then there is no change in the index, else;
- If there were ≥6 continuous hours of temperatures from 16 to
27°C, then add 20 points, else;
- If none of the above rules apply, then subtract 10
points.
Index values still accumulated over time, with minimum and maximum
values of 0 and 100, respectively. Values of 0 to 30, 40 to 60, and
70 to 100 indicate conditions of low, moderate, or high infection
risks, respectively.
-
These models are available via
our web app and
our email subscription
service.
-
Cherry Powdery Mildew
-
Pearson-Gadoury (1987) Powdery Mildew Model
-
Cleistothecial Powdery Mildew
- Grape powdery mildew, caused by Erysiphe necator (Also
called Uncinula necator), is documented in
the PNW
Plant Disease Management Handbook.
- This model is for the primary inoculation of grape by powdery
mildew ascospores released from cleistothecia, and is often
used in conjunction with the Gubler-Thomas Powdery Mildew model,
expecially in California.
-
Risk index model for initial infection in grape by ascospores,
also known as cleistothcia. Requires hourly weather data.
- Model source: Thomas, C. S., Gubler, W. D., and Leavitt,
G. 1994. Field testing of a powdery mildew disease forecast model
on grapes in California. Phytopathology 84:1070 (abstr.). This
model is a version of the model also known as the "2/3 Mills table"
model.
- Also documented by
the UC
Statewide IPM Program.
-
- Strawberry Powdery Mildew Index Model
-
Pear Scald - Anjou Pear Model
-
Fire Blight (Cougar blight)
-
Fox-Broome Botrytis infection risk
-
Blueberry Mummy Berry
-
Boxwood Blight Infection Risk
-
Dollar Spot (turfgrass)
- Dollar Spot Disease, caused by fungal pathogen Sclerotinia
homoeocarpa, is described
at UC
Davis Dollar
Spot, Purdue
University Dollar Spot, and
APS
Dollar Spot of turfgrass.
- The dollar spot model predicts risk of dollar spot epidemics in
turfgrass. Thresholds for treatment are likely to need local
calibration. Currently the authors are recommending a threshold of
20% probability for an epidemic. The model includes a parameter for
fungicide treatment within the last 15 days, which will lower the
probability of an epidemic.
- The geographic range of the model may be limited to those areas
thus far tested (Wisconsin, Oklahoma, Pennsylvania, Tennessee, and
Mississippi).
- Like most weather driven risk models, this one assumes that
sufficient inoculum is present for causing infections. It uses
5-day average temperature and relative humidity to predict the
relative probability of an epidemic, and need for management using
fungicides, so it requires hourly weather data.
- This model is from work by Smith, Kerns, et al, published
as Smith
DL, Kerns JP, Walker NR, Payne AF, Horvath B, Inguagiato JC, et
al. (2018) Development and validation of a weather-based
warning system to advise fungicide applications to control dollar
spot on turfgrass. PLoS ONE 13(3): e0194216.
https://doi.org/10.1371/journal.pone.0194216. See also
this model
description and validation summary (pdf).
- See also U. Wisc.
Dollar Spot Model, which describes the model and its use.
-
This model is available via
our web app
and is available through
our email subscription
service.
-
Potato / Tomato Late Blight
- Late blight is caused by the fungus Phytophthora infestans
in potato and tomato. The disease is documented for
tomato
and
for potato
in PNW Plant Disease Management Handbook.
- Requires hourly weather data. Model may underestimate disease
when irrigation increases leaf wetness or humidity.
- This model based on the Smith and the Winstel versions, and is
calculated at 6 am each day from prior 24 hr data. It uses these rules:
- If 10 consecutive hours of (leafwetness > 3 OR relative
humidity > 90 % ) AND temperature > 10°C, then there is an
"infectious spore production" event.
-
If "infectious spore production" event is at least 24 hours old
and no more than ten days old, and during that time there are
two consecutive days with a maximum temperature between 23 and
30°C, there is an "infection possible" event is.
- The "infections possible" is active for three days. Lesions
are likely to be visible after that.
-
Otherwise the late bight risk is "low".
- See http://www.ipm.ucdavis.edu/DISEASE/DATABASE/potatolateblight.html.
See Model 4 for Winstel version: Winstel, K. 1993. Krautund knollenfaule
der kartoffel eine eeue prognosemoglichkeit-sowie
bekampfungsstrategien. Med. Fac. Landbouww. Univ. Gent, 58/3b, and See
Model 14 for Smith version: Smith, L. P. 1956. Potato blight forecasting
by 90% humidity criteria. Plant Pathology 5:83-87.
-
This model is available via
our web app and
our email subscription
service.
-
TomCast Disease Severity Values (DSV)
-
MelCast Environmental Favorability Index (EFI)
-
Delta-T Vapor/Thermal Drift Risk
-
Custom Degree-Hour Accumulation Model
- A tool for plant disease epidemiologists to diagnose new and
invasive plant diseases, and to develop new models
-
This tool can be configured to accumulate degree-hours above a
lower threshold and below an upper threshold, as specified by the user.
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The option of whether these accumulations depend upon leaf wetness
is also available. With leaf wetness required, the model can
emulate other models such as apple scab, pear scab, cleistothecial
powdery mildew, and TomCast DSV.
-
Without the leaf wetness requirement, this tool can be used for
such purposes as chilling requirements and general degree-hour
accumulations.
-
Use trial and error to configure new model parameters for diseases
for which you have field data but no published model. This allows
this website to be used for invasive species when models have been
hypothesized but not yet researched or validated.
-
Chilling Hours models used in Horticulture to predict completion
of dormancy
Last updated 7/7/2023
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