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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. FL Strawberry Industry Overview. FL ~ 8,000 ac

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Presentation Transcript
slide1

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

slide2

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

slide3

Strawberry Production Cycle in West Central Florida

Peak harvest periods

Peak bloom periods

Land prep / planting

Cropping season is affected by El Niño - Southern Oscillation (ENSO) cycles

slide4

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

slide5

Spray program for control of BFR and AFR in FL

Bloom

sprays

Anthracnose

sprays

Protective sprays

Botrytis

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

slide6

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

slide7

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

slide8

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

slide9

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

slide10

Perceived Value of Forecasts

Strawberry Project

Farmers

Forecast Value

Grain Trading Companies

USDA, Government Agencies

Multi-decadal

Weather short-term

Seasonal

Decadal

Time Scale

Clyde Fraisse – University of Florida IFAS

slide11

Status of the project

National Digital Forecast Database

Clyde Fraisse – University of Florida IFAS

slide13

Disease Models - Inputs

  • Leaf wetness
    • Sensors
    • Physical models
    • Empirical models
  • Temperature
  • High temporal resolution (15 minutes)

Clyde Fraisse – University of Florida IFAS

slide14

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

slide15

Seasonal Forecasting Approach

Daily max. and min. temp. and

daylength generate hourly temperature data (Parton and Logan, 1981)

Cooperative observer network

(NCDC TD 3200)

Daily

Tmin Tmax

Precip.

Hourly

Temp.

Hourly

RH

Tdew = Tmin

RH threshold

Disease

Models

Historical number of moderate and high risk events

Clyde Fraisse – University of Florida IFAS

slide16

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