1. MICHELLE M. MOYER CORNELL UNIVERSITY NYSAES-GENEVA Use of pan evaporation and temperature data in Powdery Mildew forecasting

2. Principles of disease forecasting Diseases Amenable to Meteopathological Prediction (Bourke 1970)  Causes economically significant damage  Variable seasonal impact (directly/indirectly weather related)  Control measures are available To adjust spray times To reduce total number of sprays To optimize timing of sprays  Protection, eradication  Maximization of chemistries; resistance management

3. Forecasting the Powdery Mildews Generally a focus on infection periods and general favorability for severity development Temperature (Grape, Apple, Beet, Wheat) Rainfall (Grape, Apple, Beet, Wheat) Humidity (Grape, Apple, Wheat) Vapor Pressure Deficit (Apple) Leaf Wetness (Grape) Wind (Wheat)

4. Forecasting Grape Powdery Mildew Development of a risk assessment model for grapevine powdery mildew in the NE. Ran existing models for the region Development of a new model based on a variety of weather inputs.  In NY, powdery mildew severity on clusters is related to temperature the previous fall, and  In-season pan evaporation levels

5. In Season Weather: How “wet” or “dry” has the weather been (based on pan evaporation). What is the upcoming forecast? In-season weather is conducive. What was the inoculum potential? In-season weather not conducive. 100% Chance of “Mild Year” High inoculum potential with average to highly conducive weather. Potential realized. 100% Chance of “Severe Year” Wet to Average (<6.07 mm)Dry (>=6.07 mm) Moderate to low inoculum load. How conducive is the weather to maximize the impact this load? Warm previous fall (>=450 DD) Cool previous fall (<450 DD) Conducive (<5.45 mm) Average (>=5.45 mm) Low inoculum load, but weather is extremely favorable to maximize potential. 60% Chance of “Severe Year” Low inoculum load with just average weather conditions means a poor start for PM. 80% Chance of “Mild Year” Estimating Powdery Mildew Risk

6. Advantages of using E pan Integration of multiple weather parameters Single measurement Global pan evaporation networks available Conceptually easy to explain to growers  “Water Stress”  “Clothesline” Example

7. E pan E to Evaporation from an open surface Measured parameter Evaporation from open and vegetative surfaces Calculated parameter E pan vs. E to

8. Requirements in using E to /E pan E pan maintenance crucial for accurate readings Need daily to weekly E pan averages Calculation of E to is only as good as the input values While E pan and E to are challenges to forecast, use of historical averages can help

9. YearR2R2 Slope (p-value) x Intercept (p-value) y 19850.861.00 (1.00)-0.31 (0.09) 19860.810.95 (0.19)-0.04 (0.86) 19870.780.97 (0.55)-0.02 (0.93) 19880.761.06 (0.25)-0.19 (0.51) 19890.790.9 (0.01)-0.29 (0.17) 19900.780.93 (0.07)-0.27 (0.22) 19910.570.86 (0.02) 0.46 (0.23) 19920.610.84 (0.01)-0.003 (0.99) 19930.750.97 (0.48)-0.24 (0.37) 19940.560.76 (<0.01) 0.60 (0.05) 19950.590.93 (0.30) 0.29 (0.44) 19960.570.81 (<0.01) 0.56 (0.07) 19970.450.77 (<0.01) 1.08 (0.01) 19980.490.77 (<0.01) 0.92 (0.02) 19990.580.95 (0.48) 0.24 (0.55) 20000.440.75 (<0.01) 0.99 (0.02) 20010.590.94 (0.35) 0.47 (0.20) 20020.380.50 (<0.01) 2.34 (<0.01) 20030.300.49 (<0.01) 2.18 (<0.01) 20040.040.17 (<0.01) 3.37 (<0.01) 20050.130.45 (<0.01) 2.66 (<0.01) 20060.560.86 (0.04) 0.41 (0.28) 20070.470.80 (0.01) 0.11 (0.02) x Testing that slope is different than 1 (2-tailed). y Testing that the intercept is different than 0 (2-tailed). Resulting regression parameters from comparing calculated E to to actual E pan

10. Annual E pan values decreasing Average Daily E pan Historically (May-Oct,170)= 5.97mm Average Daily E pan 2000-2007 (May-Oct,170)= 4.63mm

11. Trends vs. Events E pan as a general favorability indicator Temperature most common and easily accessible weather input How to temperature trends vs. specific events influence PM development?

12. What we know: What we don’t: Suboptimal temperature effects on existing colonies Suboptimal effects on epidemic development What do cold temperatures do? Temperature and powdery mildew

13. Cold-induced resistance * Percent in Class (%) Appressorium Branched hyphae

14. Cold kills (or at least hurts a little…) Four-day-old colony grown at 25°C:: A)Line Sketch of colony footprint, and B) Same colony visualized with a vital stain Four-day-old colony exposed to 2°C for 8h at 3dpi:: C)Line Sketch of colony footprint, and D) Same colony visualized with a vital stain

15. It is true… New York is cold

16. Similar cold events occur globally

17. Considerations E pan, in theory, is a useful environmental parameter to use for disease forecasting  Incorporates multiple weather parameters and their interaction on water stress and availability to the pathogen.  May also help understand plant stress- useful in obligate biotroph systems. Temperature in PM forecasting may best be used as an indicator of acute unfavorable events for disease or pathogen development

18. Questions?

19. Air temperature is deceiving