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“A disease forecast system for timing fungicide applications to control strawberry fruit rots”

“A disease forecast system for timing fungicide applications to control strawberry fruit rots” Natalia Peres and Steve Mackenzie, UF – Gulf Coast REC Clyde Fraisse and Willingthon Pavan UF – Agriculture & Eng. Dept. FL strawberry industry overview. FL ~ 8,500 ac

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“A disease forecast system for timing fungicide applications to control strawberry fruit rots”

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  1. “A disease forecast system for timing fungicide applications to control strawberry fruit rots” Natalia Peres and Steve Mackenzie, UF – Gulf Coast REC Clyde Fraisse and Willingthon Pavan UF – Agriculture & Eng. Dept.

  2. FL strawberry industry overview • FL ~8,500 ac • 2ndbiggest producer in U.S. • 15% total strawberry production • $300 million industry • Plant City – “Winter strawberry capital of the world” 25 8000 220

  3. Strawberry Production Cyclein West Central Florida Peak harvest periods Peak bloom periods Land prep / planting

  4. Major Strawberry Fruit Rot Diseases in Florida Botrytis fruit rot or Gray Mold (caused by Botrytis cinerea) Anthracnosefruit rot (caused by Colletotrichumacutatum)

  5. Spray program for control of BFR and AFR in FL Botrytis Anthracnose Bloom sprays Late season sprays X X X Protective sprays (captan) Planting 1st Bloom 1st Harvest 2nd Bloom 2nd Harvest 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

  6. Calendar system vs. Forecast system • Disease management currently relies on calendar-based protective applications of fungicides • Disease management with a forecast 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)

  7. Development of a forecast system • Disease models published by others to predict the incidence of Botrytis and anthracnose fruit rots were evaluated for their effectiveness to time fungicide applications in replicated field trials during the 3 consecutive strawberry seasons • Fungicides applied at variable intervals according to models and compared to a standard calendar program and an untreated control

  8. BotrytisBulger - Madden model and Broome model • Length of most recent wetness period • Average temperature during wetness event Bulger, M. A., Ellis, M. A. and L. V. Madden. Influence of Temperature and Wetness Duration on Infection of Strawberry Flowers by Botrytis cinerea and Disease Incidence of Fruit Originating from Infected Flowers. Phytopathology 77: 1225-1230, 1987. Broome, J. C., English, J. T., Marois, J. J., Latorre, B. A. and Aviles, J. C. Development of an Infection Model for Botrytis Bunch Rot of Grapes Based on Wetness Duration and Temperature. Phytopathology 85: 97-102, 1995.

  9. BotrytisXumodel • Average day time relative humidity (%) (8:00 am to 7:45 pm) • Average day time temperature (8:00 am to 7:45 pm) • Average night time temperature (8:00 pm to 7:45 am) • Duration of leaf wetness (hr) previous night X. Xu, D.C. Harris, A.M. Berrie. Modeling infection of strawberry flowers by Botrytis cinerea using field data. Phytopathology, 90:13671373, 2000.

  10. Botrytis - Treatments evaluated

  11. 2006-07 season

  12. 2007-08 season

  13. 2008-09 season

  14. AnthracnoseWilson-Madden infection curves Infection curve for mature berries (cv. Midway) 42F 50F 59F 68F 77F 86F Wilson, L. L., Madden, L. V., and Ellis, M. A. 1990. Influence of temperature and wetness duration on infection of immature and mature strawberry fruit by Colletotrichum acutatum. Phytopathology 80:111-116.

  15. Anthracnose - Treatments evaluated

  16. 2006-07 season

  17. 2007-08 season

  18. 2008-09 season

  19. Treatments selected to develop the disease forecast system • Botrytis: Bulger-Madden %INF>0.5 • Anthracnose: Wilson-Madden INF>0.15; INF>0.5 (pre-symptom) • Length of most recent wetness period • Average temperature during wetness event

  20. Development of the disease forecasting tool in AgroClimate http://agroclimate.org/tools/strawberry/

  21. AgroClimate.org Peres, N.A., and Fraisse, C.W. Development of a disease forecasting system for strawberries as a tool on AgClimate. (USDA/RMA)

  22. Strawberry Disease System

  23. Current risk level

  24. Disease simulation

  25. Weather data

  26. Spray recommendation

  27. Spray recommendation

  28. Email and SMS alerts

  29. 2009-10 Grower trials • 2 treatments: Grower standard and model-timed applications • 3 farms – 5 to 13 acres • Disease incidence – 60 plants per treatment • ~20 growers signed up to receive disease risk alerts

  30. 2009-10 Grower trials

  31. Future plans • USDA-NIFA-SCRI project funded to: • Validate and expand the forecast system to North Carolina, South Carolina, Ohio and Iowa • Evaluate the use of models to estimate leaf wetness duration • Determine baseline sensitivities of B. cinereaand C. acutatumand develop a resistance monitoring system

  32. Questions??

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