Disease Warning Systems for Apples

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Weather Stations and
Disease Warning Systems
for Apple and Grape
What are they and what can they do for you?
Patty McManus and Steve Jordan
What are we talking about?
•
Disease warning system
•
Disease risk advisory system
•
Disease forecasting system
•
Disease prediction system
What are we talking about?
Disease model
– Mathematical formula that you plug
environmental data into
– Can be very simple (e.g., degree days) or
more complex requiring a computer
Models use quantitative data (e.g., amount of rain,
temperature, % RH, number of hours of leaf
wetness)
Disease warning systems usually include
qualitative information (e.g., cultivar, growth
stage, disease history)
Disease Warning Systems
Based on sound science, validated in real
orchards, but not foolproof
Environmental conditions vary across an orchard
or vineyard and even within a canopy
Individuals in a fungal population vary in their
optimal temperature/wetness requirements for
germination and growth
Different cultivars differ in susceptibility
IPM tool to be used with other IPM tools
Main Objective
Improve your understanding of environmental
monitoring equipment and disease warning
systems so that you can use the technology to
your best advantage.
Collecting Environmental Data
•
•
On-site instruments (e.g., Spectrum, Hobo,
Metos, Campbell); most require computer
software to run models.
Remotely sensed data (e.g., Skybit),
advisory e-mailed or faxed daily.
Purchasing a Weather Station
• Number of makes and
models available
• Spectrum, Hobo, Metos,
Campbell
• What suits your needs and
budget (and disease models)
Necessary Components
• temperature sensor
• Relative humidity
sensor
• Typically housed
within a radiation
shield
Necessary Components
• Leaf Wetness Sensor
– Measures the duration of
water on the surface of
“leaves”
– Northern exposure is
optimum (last to dry out)
– Necessary for most
models
Necessary Components
• Rain Gauge
– Number of different
types
– Tipping bucket
most common
Other Useful Components
• Anemometer
– used for measuring
wind speed
– Avoid spray drift
• Weather vane
– Wind direction
Powering your Weather Station
• Number of options depending on your weather station
– Small batteries (AA, 9 Volt, etc..)
• Cheap, reliable?
– Marine battery
• Relatively affordable, but must charge or change
out batteries periodically
– Solar panel
• Self-sustaining, can be unreliable depending on
weather and energy needs of the weather station
– Direct line from a power grid
• Ideal, but requires close proximity to a power
source and wiring compatiblity to the weather
station
Communicating with your
Weather Station
• “Read-out” display on station
– Simplest way
– Can limit the amount of information available
– Requires interacting directly with the station
• Data shuttle or laptop computer
– Download data to a small data shuttle or laptop
– Requires interacting directly with the station
• Permanent cable link to a computer
– Buried computer cable from you desktop to the weather station
– Great if vineyard is next to your house
Communicating with your Weather
Station
• line-modem link
– Weather station is linked to a phone line
– Uses a modem to download and send data to your computer
– mains power supply and telephone point necessary
• Cellular modem link
– Uses a cellular modem (think cell phone) to send data to your
computer
– Does not require a main power supply or telephone point, so
good for a remote station
– Requires good cellular reception
– Can be expensive
Apple scab
•
•
Fire blight
Sooty blotch-flyspeck
Major diseases, especially eastern North
America
Warning systems commercially available
For scab and fire blight:
•
Biology as it relates to warning systems
•
Origin and development of the systems
•
How to make them work for you
•
Take-home message
Secondary
infections
Scab Biology
Fall
LWD and temp to
identify infection
periods
Degree-day model
estimates % of
ascospores mature
Primary
infections
Spring
Ascospores
Origin and Development of
Scab Warning Systems
•
•
Degree-day model for estimating percentage of
ascospores mature
Leaf wetness- and temperature- based model
for identifying infection periods (Mills)
Scab Infection Period Models
•
•
•
Mills as modified by Jones
– Temperature and leaf wetness duration
(LWD) hours
– Established “Light,” “Medium,” and “Heavy”
infection periods
Washington State model requires more LWD
hours at temps below 47 oF
Cornell model requires the fewest LWD hours
at all temperatures and does away with L,M,
and H infection periods
Often overlooked points:
• LWD hours required for infection are
approximate, not absolute
• Scab fungus exists as a population of individuals
with a range of germination/infection
requirements
• Models often developed and validated under
“worst-case” scenario:
Highly susceptible cultivar
“Abundant” inoculum
Making the scab infection
model work for you
•
•
•
What it will do:
– Identify when conditions have been favorable for
infection (assumes presence of spores and
susceptible cultivar)
What it won’t do:
– Predict the amount of scab that develops
– Predict when scab will appear
Amount of scab that develops depends on amount
of inoculum, cultivar susceptibility, and tissue age
Making the scab infection
model work for you
•
•
Rely primarily on preventing infection with
protectant fungicides
When spraying post-infection, use appropriate
fungicides
– Don’t count on 4 days “kick-back” from sterol
inhibitor or strobilurin fungicides
Making the scab infection
models work for you
Scab warning systems are IPM tools that work
best if integrated with inoculum reduction and
host resistance
Keeping records of weather and infection periods
helps you sort out what went wrong when, so
that you can do better in the future
Take-home Message
•
Scab warning systems are IPM tools that work
best if integrated with inoculum reduction, host
resistance, and a preventative spray schedule.
Canker blight
Blossom blight
Erwinia amylovora:
doubles every 30
minutes on stigma
Internal movement
of bacteria...
…causes shoot infections
Bacteria spread by rain,
wind, possibly insects
to new shoots…
…shoot blight
Rootstock Blight
Most deadly form of fire blight
Not accounted for by fire blight warning systems
Special challenges with
fire blight
•
•
Disease development is explosive:
– Pathogen grows exponentially
– Can infect with just minutes of wetness
Pathogen can persist asymptomatically, or in
hard-to-find cankers, from year to year in apple
trees
Special challenges with
fire blight
•
•
•
Internal and systemic nature of the pathogen
limits the effectiveness of chemicals and
pruning
Streptomycin is the only consistently effective
bactericide
Sporadic disease, so we tend to forget stuff and
repeat mistakes!
Origin and Development of
Fire Blight Warning Systems
• Two most used systems in North America:
–
Maryblyt: P. Steiner, Univ. Maryland
–
Cougarblight: T. Smith, Wash. State Univ.
Maryblyt and Cougarblight
• Both require:
•
Open blossoms
•
Accumulation of degree
hours (DH) sufficient for E.
amylovora to multiply on
the stigma
•
Rain or dew to wash the
pathogen into nectarthodes
Maryblyt and Cougarblight
• Key differences:
•
Maryblyt assumes presence
of pathogen; Cougarblight
incorporates fire blight history
into the risk assessment
•
Maryblyt predicts the onset of
symptoms; Cougarblight
provides low, marginal, high,
or extreme infection risk
warning
Which should you use?
•
•
E. amylovora is VERY sensitive to
environment
Disease warning systems tend to perform
best in the region(s) in which they were
developed and validated
Maryblyt
• Predicts infection and onset of symptoms:
– Canker blight
– Blossom blight
– Shoot blight
– Trauma blight
Canker Blight
Starting at green
tip,196 DD (base 55F)
Blossom Blight
INFECTION
1. 198 DH (base 65F)
within the last 80 DD
(base 40F)
2. Heavy dew or 0.01 inch
rain during current day
or 0.1 inch rain previous
day
3. Current daily average
temp 60F or more
103 DD (base 55F)
Epiphytic Inoculum Potential (EIP)
•
•
•
E. amylovora
population is building
up on stigma, and EIP
approaches 100
EIP = 100 is the
infection threshold
EIP = 100 reached
after 198 DH (base
65F) within the last 80
DD (base 40F)
EIP is dynamic
Maryblyt assumes cold weather or
streptomycin will reduce E. amylovora to
marginal levels
A 3-day cool period reduces EIP to 0, unless
EIP had previously exceeded 200
After streptomycin application, EIP is reset to
zero
Shoot Blight
Trauma Blight
Wind, rainsplash, insects (?)
103 DD (base 55F) and
daily average temp 60F
or more
Do insects spread E.
amylovora and/or
facilitate infection?
White apple leafhopper—NO
Green apple aphid—NO
Potato leafhopper—MAYBE
Making fire blight warning
systems work for you
•
•
What they will do:
– Indicate when blossom infection is likely
– Guide timing of streptomycin application during
bloom (best if applied before infection)
– Predict the onset of symptoms to guide scouting
early removal of diseased tissues
What they won’t do:
– Predict rootstock blight
– Save a lot of sprays in any one year
– Substitute for common sense
Take-home Messages
•
•
Sporadic, explosive, systemic nature of fire
blight makes it impossible to control in some
years despite timely sprays and removal of
diseased tissues
Cost savings of using warning systems will be
realized over several years
Grape Disease Models
• 5 Models currently available
– Powdery mildew
– Phomopsis cane and leaf spot
– Botrytis
– Downy mildew
– Black rot
Powdery Mildew
• Requires air temperature and leaf wetness
• Predicts two infectious stages, an ascospore stage and a
conidial stage
– Ascospores are released in the spring from
overwintering structures (primary infections)
– Ascospore infection period described as “Heavy”
– Condia are released from lesions through the growing
season (secondary infections)
– Conidial Index:
0 - 30 = Light infection risk
40 - 50 = Medium infection risk
60 - 100 = Heavy infection risk
Powdery Mildew
Powdery Mildew
Phomopsis Cane and Leaf Spot
• Requires air temperature,leaf wetness, and rain data
• Developed using two different varieties, Catawba and
Seyval
– Risk calculated as expected number of lesions per
leaf
– 1 – 30 = light infection risk
31 – 90 = medium infection risk
90 + = heavy infection risk
Phomopsis Cane and Leaf Spot
Phomopsis Cane and Leaf Spot
Botrytis
• Model requires air temperature and leaf wetness data
• Three levels of infection risk; light, moderate and high
• U.C. Davis recommends spraying when risk is above 0.5
Botrytis
Botrytis
Downy Mildew
• Model requires leaf wetness, relative humidity, and
temperature data
• The model estimates three levels of infection likelihood
• Level 1, or “Possible Infection”, infection can occur but
conditions are not optimal
• Level 2, or “Medium Risk of Infection”, possible light infection,
low risk
• Level 3, or “High Risk of Infection”, conditions are optimal for
infection
Downy Mildew
Downy Mildew
Black Rot
• model uses temperature and leaf wetness period to
estimate the onset of an infection period
• The model uses a risk rating system to determine the
likelihood of infection
• Value of 1 = shortest possible period for successful infection
• As values go up, the “window” for infection increases
Black Rot
Black Rot
Black Rot
Black Rot Scenario
• You have had black rot troubles in previous years, but
are “on top of it” this year
• Last application of fungicide (Mancozeb and Rally) on
June 1st
• Its now the 10th, and its rained several times in the
last week with lots of dew and relatively warm
temperatures at night. No rain is forecasted until the
15th
• Do you spray now, wait until your next “scheduled”
spray date (the 14th) and what product(s) do you
apply?
90.0
80
70
60
• Last application of fungicide (mancozeb
and rally) on June 1st
80.0
100
90
80
• Its now the
and its rained several
times in the last week with lots of dew and
relatively warm temperatures at night.
Rain is forecasted for the 15th with warm
days and nights expected
70
10th,
70.0
60.0
50.0
40.0
Jun 01
60
50
40
30
20
Jun 03
Jun 05
Jun 07
Jun 09
Jun 11
• Do you spray now, wait until your next
“scheduled” spray date (the 14th) and what
product(s) do you apply?
Jun 13
Jun 15
TMP
Risk
Jun 17
RNF
Jun 19
WET
Jun 21
Jun 23
Jun 25
Jun 27
Jun 29
10
0
90.0
100
90
80
80.0
80
70
70
70.0
60
50
60
60.0
40
30
50.0
20
10
40.0
Jun 01
0
Jun 03
Jun 05
Jun 07
Jun 09
Jun 11
Jun 13
Jun 15
TMP
Jun 17
RNF
Jun 19
WET
Jun 21
Jun 23
Jun 25
Jun 27
Jun 29
Questions?
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