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