Strawberry Disease Monitoring and Forecasting
System Clyde Fraisse
Willigthon PavanNatália Peres
University of Florida
Climate Prediction Applications WorkshopNorman, OklahomaMarch 24-27, 2009
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
7500220
Clyde Fraisse – University of Florida IFAS
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
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
Spray program for control of BFR and AFR in FL
Planting 1st Bloom 1st Harvest 2nd Bloom 2nd Harvest
BotrytisBotrytis
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
Calendar vs Predictive SystemDisease 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
ObjectivesDevelop/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
ObjectivesDevelop/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
forecastDevelop a system to predict seasonal disease
pressure based on ENSO forecastClyde Fraisse – University of Florida IFAS
ObjectivesDevelop/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
Strawberry ProjectStrawberry Project
Fo
reca
st V
alu
e
Weather short-term
Seasonal Decadal Multi-decadalTime Scale
Farmers
Grain Trading Companies
USDA,
Gover
nmen
t
Agenc
ies
Perceived Value of ForecastsShort-term
Seasonal DecadalMulti-decadal
Decisions
Planting Variety selection
Define Ag. policies
Fertilizing Crop allocation
Research priorities
Spraying Planting date
Infrastructure investments
Harvesting Insurance
Trading Importing - Exporting
Trading
Clyde Fraisse – University of Florida IFAS
Status of the project
National Digital Forecast Database
Clyde Fraisse – University of Florida IFAS
Seasonal Forecasting
Disease Models - InputsLeaf wetness
Sensors Physical models Empirical models
Temperature
High temporal resolution (15 minutes)
Clyde Fraisse – University of Florida IFAS
Seasonal forecasting approachModeling 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
Seasonal Forecasting Approach
DailyTmin Tmax
Precip.
Cooperative observer network(NCDC TD 3200)
HourlyTemp.
Daily max. and min. temp. and daylength generate hourly temperature data (Parton and Logan, 1981)
HourlyRH
Tdew = Tmin
Disease ModelsHistorical number
of moderate and high risk events
Clyde Fraisse – University of Florida IFAS
RH threshold
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