environmental and geographical variables associated with
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Environmental and Geographical Variables Associated with TYLCV Epidemics in Southwest Florida
Bill Turechek, USDA-ARS, Fort Pierce, FL
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Project began in summer of 2007 ◦ After a meeting w/ grower groups, industry reps, and
University personnel in the wake of the 2006 epidemic
We suggested conducting a regional survey of growers fields to gather info. on TYLCV/WF ◦ Goal is to improve management of TYLCV and whiteflies
Greatly facilitated when “we” received funding for 3 years through NIFA’s Specialty Crops Research Initiative (SCRI) program o The SCRI project focuses on managing whitefly-transmitted
viruses of vegetables
o Project team: Scott Adkins, Shaker Kousik, Susan Webb, Pam Roberts, Phil Stansly, Charlie Mellinger
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An understanding of which factors contribute to “local” outbreaks of whiteflies & the viruses they transmit ◦ Identify climatic variables and common features
associated with problem locations (e.g., influence of location, production practices)
◦ Develop a strategy to reduce epidemics
Decision Support System ◦ GPS/GIS, internet, and smart phone-based
technology ◦ Develop a information collection, processing and
delivery system ◦ In cooperation with ZedX Inc.
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1
2
Future Expansion
Current Locations
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Collier
Hendry Lee
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2006-2007
Dates
0 25 50 75 100 125 150 175 200 225 250 275
Wh
ite
fly
& V
iru
s
1.0
0.5
0.0
0.5
1.0Mean Virus
Mean Whitefly
Start End Season
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2007-2008
Dates
0 25 50 75 100 125 150 175 200 225 250 275
Wh
ite
fly &
Viru
s
1.0
0.5
0.0
0.5
1.0Mean Virus
Mean Whitefly
Start End Season
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2008-2009
Dates
0 25 50 75 100 125 150 175 200 225 250 275
Wh
ite
fly &
Viru
s
1.0
0.5
0.0
0.5
1.0Mean Virus
Mean Whitefly
Start End Season
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2009-2010
Dates
0 25 50 75 100 125 150 175 200 225 250 275
Wh
ite
fly &
Viru
s
1.0
0.5
0.0
0.5
1.0Mean Virus
Mean Whitefly
Start End Season
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Can we rely on weather conditions to predict whitefly and/or virus outbreaks? ◦ …Yes and No
Can we rely on geographical attributes or simply location to predict whitefly and/or TYCLV outbreaks?
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Can we rely on weather conditions to predict whitefly and/or virus outbreaks? ◦ …Yes and No
Can we rely on geographical attributes or simply location to predict whitefly and/or TYCLV outbreaks?
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Windowpane or Moving-Window Analysis ◦ Variables
Temperature (max, min, avg.)
Wind speed, gusts, direction (max, min, avg.)
Dewpoint & Relative humidity (max, min, avg.)
Precipitation (max, min, avg.)
Visibility
◦ Window sizes 5, 10, 15, 30, 60, 90 and 120 days
Determine the correlation between TYLCV severity or whitefly density and associated weather variables
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Variable Whitefly TYLCV
Wind Speed + +
Wind Direction ± (30 day) ± (30 day)
Precipitation − −
Minimum Temperature ± −
Average Temperature ± −
Maximum Temperature ± −
Pest management practices considerably alter counts Averaging over the region helps to mediate this effect
Varietal differences alter epidemic development
Data is still sparse to fully overcome limitations
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Minimum temperature and total daily rainfall recorded in Immokalee
2003 2004 2005 2006 2007 2008 2009 2010
Min
imum
tem
pe
ratu
re
0
20
40
60
80
Rain
fall
(in.)
0
2
4
6
8
10
2h2h
0.5h 0.5h 1h 5h 0.25h 6.5h7h
4h 1.5h 5.25h9.25h5.45h
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Can we rely on weather conditions to predict whitefly and/or virus outbreaks? ◦ …Yes and No
Can we rely on geographical attributes or simply location to predict whitefly and/or TYCLV outbreaks?
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Variable Mean Whitefly Maximum Virus
06/07 07/08 08/09 06/07 07/08 08/09
East-West Direction 0.35 0.31 0.39 0.39 0.21 0.21
North-South Direction 0.02 0.49 0.40 0.16 0.29 0.35
Perimeter -0.28 -0.05 -0.05 -0.18 -0.19 -0.07
Area -0.32 -0.06 -0.06 -0.23 -0.23 -0.09
Date Planted 0.74 0.23 0.21 0.74 0.36 -0.12
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High : 1.99723
Value
Low : 0
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High : 1.99723
Value
Low : 0
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High : 1.99723
Value
Low : 0
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Mean Whitefly Density
0.0001 0.001 0.01 0.1 1 10
Mean W
hitefly D
ensity
in N
eig
hboring F
ield
0.0001
0.001
0.01
0.1
1
10
2006-2007
2007-2008
2008-2009
log(y) = -0.1 + 0.94 log(x)
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07/08 Virus
Distance (~miles)
0 1 2 3 4 5 6 7 8 9 10
Corr
ela
tion
-0.50
-0.25
0.00
0.25
0.50
07/08 Whitefly
-0.50
-0.25
0.00
0.25
0.50
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An understanding of which factors contribute to “local” outbreaks of whiteflies & the viruses they transmit ◦ Identify common features associated with problem
locations (e.g., land usage, border fields, production practices)
◦ Develop a strategy to reduce epidemics
Decision Support System ◦ GPS/GIS, internet, and smart phone-based
technology ◦ Develop a information collection, processing and
delivery system ◦ In cooperation with ZedX Inc.
![Page 24: Environmental and Geographical Variables Associated with](https://reader033.vdocuments.site/reader033/viewer/2022051204/62781252acdaf01751321304/html5/thumbnails/24.jpg)
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It is possible to predict the severity of TYLCV & whitefly density with select weather variables ◦ Cold events
Geographical features are perhaps the best predictor of these pests ◦ Natural scale of these pests is regional
Developing or coordinating an area-wide pest management protocol is perhaps the best bet for maximizing control ◦ The Decision Support Tool could facilitate such an
effort.
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Relative of Tomato spotted wilt virus (TSWV) ◦ Thrips vectored
Found in south Florida tomatoes in fall 2009 and spring 2010 ◦ Glades Crop Care
◦ Dr. Scott Adkins
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Agmart Gargiulo Glades Crop Care Immokalee Tomato
Growers Pacific Tomato Red Gator Consulting Six L’s West Coast Tomato Wolf Island
Lisa Rouse