1. Weather-based prediction of diseases of horticulture commodities in Oklahoma
Damon L. Smith and Andrea F. Payne
Department of Entomology and Plant Pathology,
Oklahoma State University
Stillwater, OK
James P. Kerns
Department of Plant Pathology
University of Wisconsin
Madison, WI

2. Dollar spot prediction on creeping bentgrass
Pecan scab prediction
Grape black rot prediction

3. Location: OSU Turfgrass Research Center, OK (Fall, Spring 2008; Spring 2009); OJ Noer Turfgrass Center, WI (3 locations – 2009 growing season)
Host: Creeping bentgrass
Treatments: Non-treated, Preventative, Curative (vinclozolin 14-day interval)
New dollar spot foci counted daily
Hourly weather data recorded

4. Many zeros in the dataset = Foci counts transformed to binomial output (Foci = 1; No Foci = 0) and averaged across replicates for each treatment
Weather data were transformed to 5-day moving averages
Used Kendall’s Correlation Procedure
(PROC CORR; Kendall)
-Identified correlated independent variables
-Identified independent variables correlated with foci events
Binomial disease data and weather variables used in logistic regression analysis to predict the probability of dollar spot symptom development
(PROC LOGISTIC; STEPWISE option)

5. Moving Average Best Model Max-Rescaled R2 Concordant (%)
Area Under ROC
5-day
(MEANRH) – 2.33 (SEASON) – 3.30 (FUNG)
0.50 92
0.92

6. Fall, No Fungicide
Fall, Fungicide
Spring, No Fungicide
Spring, Fungicide

7. * = Probability of dollar spot development ≥ 10%
= Fungicide protection
intervals *

8. = Probability of dollar spot
development ≥ 10%
= Fungicide protection
intervals

9. n=423 (2,538 obs averaged across rep)
Moving Average
Best Model
Max-Rescaled R2
Concordant (%)
Area Under ROC
5-day
(FUNG) – 0.12 (MINAT) (MEANRH)
0.46 88
0.88
n=423 (2,538 obs averaged across rep)
Oklahoma and Wisconsin data

10. = Probability of dollar spot
development ≥ 50%
development ≥ 30% *

11. Internet Accessed, site-specific, weather-based disease advisory
Uses the Oklahoma Mesonet = 115 fully outfitted weather stations
Calculates the number of scab hours (hours during which T ≥ 21 C and RH ≥ 90%) accumulated over the last 14 non-fungicide protected days (Research by Gottwald et al. implemented by Driever and Vonbroemson)
Sprays advised based on scab hour thresholds for specific cultivars

12. Highly susceptible cultivars = 10 scab hours
Moderately susceptible cultivars = 20 scab hours
Resistant cultivars = 30 scab hours

13. OSU Pecan Scab Advisory

15. OSU Pecan Scab Advisory

16. Several reports of Pecan Scab in orchards with no urgent scab advisory
Advisories work well for some folks, and not so well for others
Complaints about Mesonet weather station placement

17. Weather station placement might be an issue, but can be corrected with some calibration?
T and RH thresholds set incorrectly?
Need a weighted T or RH factor, instead of “hard” cutoff ?
Could be different fungal ecotypes?

18. According to Sentelhas et al., 2008, Agri. & Forest Meteor.

19. *Will use the no spray check in new regression analyses
Combinations of relative humidity and temperature thresholds as treatments
90 %/ 70 ̊F
85 %/ 60 ̊F
80 %/ 60 ̊F
80 %/ 65 ̊F
No Spray*
*Will use the no spray check in new regression analyses

20. *No significant Difference between treatments
* P < .10
*No significant Difference between treatments
70F / 90%
Control
60F / 85%
60F / 80%
65F / 80%
5/19/09 -
6/29/09
6/12/09
6/8/09
6/10/09
7/31/09
7/1/09
6/26/09
7/23/09
8/25/09
8/14/09

21. What to do about leaf wetness and its measurement or interpolation?
Refining scales of weather data measurement to drive site-specific models – Improving site-specific weather measurement
Improving weather forecasts

22. Oklahoma n m b R2 ME
Temp. Min.
101
0.94
1.17
0.74
0.44
RH Avg.
0.87
8.54
0.46
0.17

23. Wisconsin n m b R2 ME
Temp. Min. 93
1.11
-2.64
0.86
-1.1
RH Avg.
0.96
8.30
0.83
5.6