Weather-based Disease Risk Assessment in Turf R. Latin
Weather-based Disease Risk Assessment in Turf R. Latin Purdue University Review unique aspects of turf environment Discuss attempts to develop WB systems for turf Describe system used in Indiana / Midwest Interesting aspects of the turf environment... 1. Agronomics... Genetics Cultural options No measureable yield
Interesting aspects of the turf environment... 2. Economics... Fungicides are expensive Supers spend other peoples money Turf grows back! Connecting the dots between environmental variables and turf disease...
Connecting the dots between environmental variables and turf disease... Brown Brown patch patch Gray Gray leaf leaf spot
spot Dollar Dollar spot spot Pythium Pythium blight blight Summer Summer patch patch
Pink Pink snow snow mold mold Anthracnose Anthracnose Published works describing the use of temperature and moisture variables to predict disease outbreaks* Dollar spot Hall, 1984 (Temp / RH) Smith, 2009 (Temp / moisture)
Environmental Data Data Processor Temp Hours wetness Precip. RH Y = a + b1T +b2W
Daily Disease Risk Examples of on-site data acquisition instruments Environmental data resources Air Temp Hours Dew RH Precip
Components of Disease Forecasters Data Processor Environmental Data Temp Hours wetness Precip. RH Y = a + b1T +b2W
Daily Disease Risk Weather-based models may be rushed to publication without adequate testing! Brown patch decision rule: Environmental risk is based on daily relative humidity and minimum temperatures. To calculate the E-value for a 24 hour period, substitute values for average relative humidity (RH) and daily minimum temperature in degrees Celsius (T) in the equation below: E = -21.5 + 0.15(RH) + 1.4(T) - 0.033(T2)
Risk is high if E 6.0 Disadvatage: Underestimates risk E-value Advantages: Convenience 3-5 day forecast Data source: SkyBit Algorithms: brown patch / Fidanza (T/RH)
Gross (T/LW) Pythium blight / Nutter (T/LW) dollar spot / Purdue (T/LW) Delivery: application server / website
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