1. Strawberry Disease Monitoring and Forecasting System Clyde Fraisse Willigthon Pavan Natália Peres University of Florida Climate Prediction Applications Workshop Norman, Oklahoma March 24-27, 2009

2. FL Strawberry Industry Overview  FL ~ 8,000 ac 15% total strawberry production in the U.S. 16 million flats per year $200 million industry  Plant City – “Winter strawberry capital of the world” 25 7500 220 Clyde Fraisse – University of Florida IFAS

3. Strawberry Production Cycle in West Central Florida Peak bloom periods Land prep / planting Peak harvest periods Cropping season is affected by El Niño - Southern Oscillation (ENSO) cycles

4. Major fruit rot diseases Botrytis fruit rot or Gray Mold caused by the fungus Botrytis cinerea Anthracnose fruit rot caused by the fungus Colletotrichum acutatum Clyde Fraisse – University of Florida IFAS

5. Spray program for control of BFR and AFR in FL Planting 1 st Bloom 1 st Harvest 2 nd Bloom 2 nd Harvest Botrytis Protective sprays Bloom sprays Legard, D.E., MacKenzie, S.J. Mertely, J.C., Chandler, C.K., Peres, N.A. 2005. Development of a reduced use fungicide program for control of Botrytis fruit rot on annual winter strawberry. Plant Dis. 89:1353-1358 Anthracnose sprays

6. Calendar vs Predictive System  Disease management currently relies on calendar-based protective applications of fungicides  Disease management with predictive system, application of fungicides are made only when necessary (requires a good understanding of the conditions suitable for disease development, i.e., host, pathogen, environment) Clyde Fraisse – University of Florida IFAS

7. Objectives  Develop/adapt disease models by correlating weather data and disease incidence from past seasons or based on laboratory studies (growth chambers) Models require leaf wetness duration and temperature  Develop a decision support system to help producers decide when to apply fungicides Weather monitoring combined with short-term forecast  Develop a system to predict seasonal disease pressure based on ENSO forecast Clyde Fraisse – University of Florida IFAS

8. Objectives  Develop/adapt disease models by correlating weather data and disease incidence from past seasons or based on laboratory studies (growth chambers) Models require leaf wetness duration and temperature  Develop a decision support system to help producers decide when to apply fungicides Weather monitoring combined with short-term forecast  Develop a system to predict seasonal disease pressure based on ENSO forecast Clyde Fraisse – University of Florida IFAS

9. Objectives  Develop/adapt disease models by correlating weather data and disease incidence from past seasons or based on laboratory studies (growth chambers) Models require leaf wetness duration and temperature  Develop a decision support system to help producers decide when to apply fungicides Weather monitoring combined with short-term forecast  Develop a system to predict seasonal disease pressure based on ENSO forecast Clyde Fraisse – University of Florida IFAS

10. Strawberry Project Forecast Value Weather short-term SeasonalDecadal Multi- decadal Time Scale Farmers Grain Trading Companies USDA, Government Agencies Perceived Value of Forecasts Short- term SeasonalDecadal Multi- decadal Decisions PlantingVariety selection Define Ag. policies FertilizingCrop allocation Research priorities SprayingPlanting date Infrastructure investments HarvestingInsurance TradingImporting - Exporting Trading Clyde Fraisse – University of Florida IFAS

11. Status of the project National Digital Forecast Database Clyde Fraisse – University of Florida IFAS

12. Seasonal Forecasting

13. Disease Models - Inputs  Leaf wetness Sensors Physical models Empirical models  Temperature  High temporal resolution (15 minutes) Clyde Fraisse – University of Florida IFAS

14. Seasonal forecasting approach  Modeling leaf wetness using physical and empirical methods Penman-Monteith RH threshold Penman-Monteith approach is showing promising results, we may completely replace the use of sensors by modeling

15. Seasonal Forecasting Approach Daily Tmin Tmax Precip. Cooperative observer network (NCDC TD 3200) Hourly Temp. Daily max. and min. temp. and daylength generate hourly temperature data (Parton and Logan, 1981) Hourly RH Tdew = Tmin Disease Models Historical number of moderate and high risk events Clyde Fraisse – University of Florida IFAS RH threshold

16. Seasonal forecasting approach  Hourly estimates of temperature and relative humidity will be used to generate seasonal numbers of moderate and high risk events for different ENSO phases Number of Applications Clyde Fraisse – University of Florida IFAS