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“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 PavanUF – Agriculture & Eng. Dept.
FL strawberry industry overview
FL ~ 8,500 ac 2nd biggest producer in U.S. 15% total strawberry production $300 million industry
Plant City – “Winter strawberry
capital of the world”
25
8000220
Strawberry Production Cyclein West Central Florida
Sept Oct Nov Dec Jan Feb Mar Apr
Peak bloom periods
Land prep / planting
Peak harvest periods
Major Strawberry Fruit Rot Diseases in Florida
Botrytis fruit rot or Gray Mold (caused by Botrytis cinerea)
Anthracnose fruit rot (caused by Colletotrichum acutatum)
October November December January February March
Planting 1st Bloom 1st Harvest 2nd Bloom 2nd Harvest
Spray program for control of BFR and AFR in FL
Botrytis
Protective sprays (captan)
Bloom sprays
X X X
Late season sprays
Anthracnose
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
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)
Calendar system vs. 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
Development of a forecast system
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.
BotrytisXu model
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.
Botrytis - Treatments evaluated
Treatment Fungicide spray
1. Xu model DI=0.5
2. Xu model DI=0.8
3. Broome DI=0.5
4. Broome DI=1.4
5. Bulger-Madden DI=0.5
6. Bulger-Madden DI=0.7
7. Bulger-M Captan DI=0.5;
Captevate DI=0.7
8. Calendar standard
9. Untreated control
If %INF>0.5 captan early, Captevate at bloom
If %INF>0.8 captan early, Captevate at bloom
If DI>0.5 captan early, Captevate at bloom
If DI>1.4 captan early, Captevate at bloom
If %INF>0.5 captan early, Captevate at bloom
If % INF>0.5 captan early, Captevate at
bloom
If %INF>0.5 captan,
if %INF>0.7 Captevate
captan early, Captevate at bloom
N/A
2006-07 seasonDisease incidence (%)
Treatment # SpraysSweet Charlie Festival
Calendar standard 20 3.9 d 0.4
Xu model DI=0.5 15 4.1 d 1.0
Bulger-M Captan DI=0.5; Captevate DI=0.7 12 4.1 d 1.1
Bulger-Madden DI=0.5 9 4.6 cd 1.2
Broome DI=0.5 11 5.1 cd 1.5
Xu model DI=0.8 12 6.2 bcd 1.1
Bulger-Madden DI=0.7 8 7.7 bc 1.6
Broome DI=1.4 5 9.1 ab 3.5
Untreated control 0 12.7 a 3.3
2007-08 seasonDisease incidence (%)
Treatment # SpraysSweet Charlie Festival
Xu model DI=0.5 14 0.9 a 0.4
Bulger-Madden DI=0.5 8 1.7 ab 0.4
Calendar standard 16 1.8 ab 0.3
Broome DI=0.5 11 1.9 ab 0.4
Bulger-Madden DI=0.7 4 2.4 bc 0.3
Xu model DI=0.8 12 2.6 bc 0.6
Bulger-M Captan DI=0.5;
Captevate DI=0.710 3.2 bc 0.2
Broome DI=1.4 4 3.8 c 0.6
Untreated control 0 3.9 c 0.8
Disease incidence (%)
Treatment # SpraysSweet Charlie Festival
Bulger-Madden DI=0.5 3 0.6 0.3
Calendar standard 17 0.6 0.2
Combined anthracnose and Botrytis fruit rot INF
6 0.8 0.4
Forecasted 3 0.8 0.3
Bulger-M Captan DI=0.5;
Captevate DI=0.74 0.9 0.4
Untreated control 0 1.3 0.5
2008-09 season
Anthracnose Wilson-Madden infection curves
00.10.20.30.40.50.60.70.80.9
1
0 6 12 18 24 30 36 42 48
Leaf Wetness (hr)
Dis
ease
In
cid
ence 6 C
10 C
15 C
20 C
25 C
30 C
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.
Infection curve for mature berries (cv. Midway)
42F
50F
59F
68F
77F
86F
Anthracnose - Treatments evaluated
Treatment Symptoms required Fungicide spray
1. Calendar captan only
No captan weekly
2. Calendar captan or pyraclostrobin
No captan weekly early season, pyraclostrobin late season
3. Pre-symptom W-M captan only
No If INF > 0.15 - captan
4. Pre-symptom W-M captan or pyraclostrobin
No If INF > 0.15 - captan, If INF > 0.5 - pyraclostrobin
5. Post-symptom W-M captan only
Yes If INF > 0.15 - captan
6. Post-symptom W-M captan or pyraclostrobin
YesIf INF > 0.15 - captan,
If INF > 0.5 or 1st INF >0.15 -pyraclostrobin
7. Untreated control N/A N/A
2006-07 seasonDisease incidence (%)
Treatment# Sprays
(captan; Cabrio) Camarosa Festival
Pre-symptom W-M captan or pyraclostrobin
10(6;4) 2.1 a 0.4 ab
Calendar captan or pyraclostrobin
16(12;4) 3.1 ab 0.4 ab
Calendar captan only
16(16;0) 4.0 ab 0.1 a
Post-symptom W-M captan or pyraclostrobin
6(4;2) 4.8 ab 1.2 bc
Pre-symptom W-M captan only
9(9;0) 5.5 bc 0.5 ab
Post-symptom W-M captan only
5(5;0) 9.4 cd 0.9 bc
Untreated control 0 14.3 d 2.0 c
2007-08 season# Sprays Disease incidence (%)
Treatment(captan; Cabrio)
Camarosa Festival
Calendar captan or pyraclostrobin
16(12;4)
15.5 a 5.2 a
Pre-symptom W-M captan or pyraclostrobin
11(9;2)
17.6 ab 6.3 ab
Calendar captan only
16(16;0)
20.2 abc 5.9 a
Post-symptom W-M captan or pyraclostrobin
8(6;2)
24.7 bc 7.9 ab
Pre-symptom W-M captan only
11(11;0)
27.6 c 9.8 bc
Post-symptom W-M captan only
9(9;0)
38.4 d 13.4 c
High threshold post-symptoms captan or pyraclostrobin
1 (0;1)
51.9 e 28.4 d
Untreated control 0 58.5 e 35 e
2008-09 seasonDisease incidence (%)
Treatment # Sprays Camarosa Festival
Calendar captan or pyraclostrobin 17 0.8 a 0.2
Calendar captan only 17 1.1 a 0.2
Pre-symptom W-M captan or pyraclostrobin 5 1.5 a 0.3
Combined anthracnose and Botrytis fruit rot INF 5 1.8 a 0.8
Post-symptom W-M captan or pyraclostrobin 5 2.0 a 0.4
Untreated control 0 8.3 b 0.4
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
Development of the disease forecasting tool in AgroClimate
http://agroclimate.org/tools/strawberry/
AgroClimate.org
Peres, N.A., and Fraisse, C.W. Development of a disease forecasting system for strawberries as a tool on AgClimate. (USDA/RMA)
Strawberry Disease System
Current risk level
Disease simulation
Weather data
Spray recommendation
Spray recommendation
Email and SMS alerts
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
Treatment Cultivar # Sprays BFR (%) AFR (%)
Farm 1
Model Festival 4 21.3 2.8
Grower Festival 14 30.4 0.7
Farm 2
Model Alafia 6 10.2 1.0
Grower Alafia 13 9.9 2.4
Model Sanibel 6 12.4 0.0
Grower Sanibel 13 13.8 6.0
Farm 3
Model Treasure (1) 5 42.1 5.7
Grower Treasure (1) 14 41.6 0.5
Model Treasure (2) 5 37.4 14.3
Grower Treasure (2) 14 32.0 0.9
Model Treasure (3) 5 19.2 60.4
Grower Treasure (3) 14 23.2 23.8
2009-10 Grower trials
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. cinerea and C. acutatum and develop a resistance monitoring system