Predictive Models for Turf Grass Diseases
Paul Vincelli, Extension Plant Pathologist(email@example.com)
These models attempt to provide weather-based forecasts of
several important turfgrass diseases in
1. Valid models that are effective on some sites may not perform well in others.
2. All of these models are weather-based; none can account for how one=s management practices influence disease, which can have a profound impact on disease development.
3. None of these models has yet
received adequate validation under
Users of these models are invited to provide comments on predictive successes or failures of these models to forecast disease activity to
Brown Patch Model #1
Forecasts brown patch on perennial
ryegrass. Limited evaluations
suggest that this model (as modified slightly from the original publication) is
the accurate for predicting brown patch activity on perennial ryegrass for
E = -21.5 + 0.15 RH + 1.4T - 0.033T2, in which RH is the mean relative humidity and T is the minimum daily air temperature. As originally published, a threshold value of E > 6 constituted a brown patch warning. My evaluations in 1999 suggest that we will have greater forecasting accuracy with a threshold value of 5.
Reference: Fidanza, M.. A., Dernoeden, P. H., and Grybauskas, A. P. 1996. Development and field validation of a brown patch warning model for perennial ryegrass turf. Phytopathology 86:385-390.
Brown patch Model #2
Forecasts brown patch disease on
cool-season grasses. Limited
Relative Humidity: >95% (=leaf wetness) for 10 hr or more
Soil Temperatures: mean >70o F (21o C); minimum > 64o F (18o C)
Air Temperatures: mean > 68o F (20o C); minimum > 59o F (15o C)
Rainfall/Irrigation: 0.1 inches (3 mm) received in the 36 hr preceding the tenth
hour of high relative humidity.
Temperatures apply to the 24 hr preceding the 10th hour of high relative humidity. Disease can also occur at somewhat lower air temperatures following heavy rainfall and extended hours of leaf wetness. If temperature falls below 59o F (15o C) in the 48 hr following the environmental conditions necessary for disease development, no disease will occur.
Reference: Schumann, G. L., Vittum,
P. J., Elliott, M. L., and Cobb, P. P. 1997. IPM Handbook for
Golf Courses. Ann
Arbor Press, Inc,
Pythium Model #1
Forecasts Pythium cottony blight on
cool-season grasses. In
Algorithm: Predicts cottony blight activity on a day ( of the previous day to of the current day) when the maximum air temperature > 82o F, the minimum air temperature > 68o F, and hours of relative humidity > 90% or hours leaves wet > 9.
Shane, W. W. 1994. Use of disease models
for turfgrass management decisions. Pages 397-404 in Handbook of Integrated
Pythium Model #2
Forecasts Pythium cottony blight on cool-season grasses. In
The risk is high when a maximum temperature > 86 F is followed by at least 14 h of relative humidity exceeding 90% with a minimum temperature > 68 F. Days run from noon to noon.
Reference: Nutter, F. W., Cole, H., Jr., and Schein, R. D. 1983. Disease forecasting system for warm weather Pythium blight of turfgrass. Plant Disease 67:1126-1138.
foliar anthracnose on Poa annua; it has not been evaluated for
forecasting accuracy on creeping bentgrass, the other host commonly affected in
Prediction factors are based on hours of leaf wetness (LW) and average daily temperature (T).
$ Hours of leaf wetness (LW)
$ Average daily temperature (T)
Points are accumulated daily using this equation:
ASI = 4.0233 - 0.2283LW - 0.5308T - 0.0013LW2 + 0.0197T2 + 0.0155LWXT
In sites with a history of the disease, infection may be possible whenever ASI >2.
Reference: Danneberger, T.K., Vargas, J. M., and Jones, A. L. 1984. A model for weather-based forecasting anthracnose on annual bluegrass. Phytopathology 74:448-451