1.5 prediction of disease outbreaks introduction principles of disease forecasting forecasting the...
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1.5 Prediction of disease outbreaks
•Introduction
•Principles of disease forecasting
•Forecasting the amount of initial inoculum
•Forecasting the rate of pathogen proliferation
•Forecasting host response
•Concluding remarks
Why do we need to predict disease outbreaks?or
What are the uses of disease forecasts ?
For making strategic decisions
•Prediction of the risks involved in planting a certain crop.
•Deciding about the need to apply strategic control measures (soil treatment, planting a resistant cultivar, etc.)
Time
Dis
ease
inte
nsi
ty
For making tactical decisions
Deciding about the need to implement disease management measure
?
The principles of disease forecasting are based on:
The nature of the pathogen
Effects of the environment
The response of the host to infection
Activities of the growers
The disease pyramidgrower
pathogen
environment
host
disease
Time
Dis
ease
sev
erit
y (%
)Monocyclic pathogens
Time
Dis
ease
sev
erit
y (l
ogit
)Time
Dis
ease
sev
erit
y (%
)
Polycyclic pathogens
Initial disease
rate
Monocyclic pathogens
Complete only one disease cycle in a growing season
(100 - y)QRdy
dt
Q = amount of initial inoculum
R = infection efficacy of the inoculum
y = disease intensity
Prediction of a monocyclic pathogen that complete only one disease cycle in a growing season - indirect prediction
Severe infections occur after moderate winters.
Mild infections occur after cold winters.
Average Temp. in December, January
and February0.7oC
High probability for severe epidemic
-1.1oC
Low probability for severe epidemic
Wilt disease in maize induced by Erwinia stewartii
Consequences from predicting the severity of Erwinia stewartii in maize on grower’s action
High probability for severe epidemic
Do not sow maizeat all
Sow only resistant cultivars
Low probability for severe epidemic
Sow maize as planned
Prediction of a monocyclic pathogen that complete only one disease cycle in a growing season - direct prediction
No. of sclerotia in soil sample
Dis
ease
sev
erit
y
Soil sample
Sclerotia
Soil
Wilt disease in sugar beat induced by Sclerotium rolfsii
Consequences from predicting the severity of S. rolfsii in sugar beat on grower’s actions
Many sclerotia in the soil sample
Do not sow sugar beat at all
Sow only resistant cultivars
Apply soil treatment
Few sclerotia in the soil sample
Sow sugar beat as planned
Monocyclic pathogens
Complete only one disease cycle in a growing season
(100 - y)QRdy
dt
Q = amount of initial inoculum
R = infection efficacy of the inoculum
y = disease intensity
The disease pyramidgrower
pathogen
environment
host
disease
Temperature (oC)
Du
rati
on o
f R
H>
90%
(h
rs)
No disease
Mod. disease
Severe disease
mild disease
Apple scab induced by Venturia inaequalis
1. Amount of initial inoculum is high (ascospores)
2. Only young leaves are susceptible
3. Film of water on the leaves and proper temperatures are needed for infection
Prediction of a polycyclic pathogen that complete very few disease cycles in a growing season
Consequences from predicting the occurrence of infections of apples by V. inaequalis on grower’s actions
Temperature (oC)
Du
rati
on o
f R
H>
90%
(h
rs)
No disease
Mod. disease
Severe disease
mild diseaseNo control
Protectant fungicide
Systemic fungicide
High dose of systemic fungicide
Decision concerning the need for fungicide spraying is made daily during the beginning of the season
Polycyclic pathogens
r ydy
dt(100 - y)
r = apparent infection rate
y = disease intensity
Complete several disease cycles in a growing season
Prediction of a polycyclic pathogen - the time of disease onset
1. The rate of disease progress (apparent infection rate) is not affected by the environment
2. Epidemics in different fields vary only in the time of disease onset
Time
Dis
ease
sev
erit
y (%
)
Sunflower rust induced by Puccinia helianthi
Time
Dis
ease
sev
erit
y (%
)
Prediction of a polycyclic - the time of disease onset
3. One assessment of the disease, at any time, may be used for future disease prediction Critical
severity
Sunflower rust induced by Puccinia helianthi
Time
Dis
ease
sev
erit
y (%
)
Critical severity
Time for critical severity (days)
Yie
ld lo
ss (
%)
The critical time model
Consequences from predicting the time for critical severity on rust management in sunflower
spore germination
establishment
lesion formation
reproductive growth
spore formation
spore dissemination
Why the environment did not affect P. helianthi?
Time
Dis
ease
sev
erit
y (%
)
Effects of the environment on P. helianthi life cycle
spore germination
lesion formation
reproductive growth
spore disseminationestablishment
germ
inat
ion
(%
)
Temperature (oC)
10 25
Duration of wetness (hours)
germ
inat
ion
(%
)
2 6
spore germination
establishment
lesion formation
reproductive growth
spore formation
spore dissemination
Late
nt p
erio
d
Lat
ent
per
iod
(d
ays)
Temperature (oC)
10 35
Effects of the environment on P. helianthi life cycle
spore germination
establishment
lesion formation
reproductive growth
spore dissemination
spore formation
No.
of
spor
es
Temperature (oC)
5 38
Effects of the environment on P. helianthi life cycle
Induction of light
Wetness duration (hrs)
No.
of
spor
es
Relative humidity (%)
No.
of
spor
es
70 95
spore germination
establishment
lesion formation
reproductive growth
spore formation
spore dissemination
Time
Dis
ease
sev
erit
y (%
)
Effects of the environment on pathogens
En
viro
nm
enta
l fa
ctor
Time
Dis
ease
sev
erit
y (%
)
Rain
Periods of high relative humidity
High or low temperatures
Hail
Sand storms
Environmental factors
Effects of the environment on pathogens
Measurement of weather parameters
ParameterVariability over
distancesPrecision of
measurement
Temperature
Wind
Rain
Relative humidity
Leaf wetness
Radiation intensity
Cloudiness
Low precisionHigh precision
Low variabilityHigh variability
Where to put the weather sensors?
Weather station
Precision of predictionParameter
Variability over distances
Prediction of weather parameters
Low precisionHigh precision
Low variabilityHigh variability
Temperature
Wind
Relative humidity
Leaf wetness
Radiation intensity
Cloudiness
Rain
Time
Dis
ease
sev
erit
y (%
)
Prediction of a polycyclic pathogen - the time of disease onset
1. Amount of initial inoculum is very low (infected tubers).
Potato late blight induced by Phytophthora infestans
5. The time of disease onset is governed by the environment.
2. Disease progress rate may be very high.3. Potential loss - high.4. Preventive sprays are highly effective.
Prediction of the time of late blight onset
Hyre’s system
Late blight appears 7-14 days after accumulation of 10 “rain favorable-days” since emergence.
Average Temp. in the last five days 7.2oC25.5oC
“A rain-favorable day”
Rain quantity in the last five days 30 mm
and
Prediction of the time of late blight onset
Wallin’s system
Late blight appears 7-14 days after accumulation of 18-20 “severity values” since emergence.
Temperature Hours with RH>90%
7.2 - 11.6
11.7 - 15.0
15.1 - 26.6
15
12
9
16-18
13-15
10-12
19-21
16-18
13-15
22-24
19-21
16-18
25+
22+
19+
Severity values 0 1 2 3 4
Prediction of the subsequent development of late blight and determining the need for spraying
NW7d5d
<3 3 4 5 6 >6
<4
>4
Severity values during the last 7 days
N N W 7d 7d 5d
N W 7d 5d 5d 5d
No. rain-favorable days during the last 7 days
No spraylate blight warning7-day spraying schedule5-day spraying schedule
Recommendation for action
The disease pyramidgrower
pathogen
environment
host
disease
Time
Hos
t re
sist
ance
1. Amount of initial inoculum is very high (infected plant debris)
2. The pathogen develops at a wide range of conditions
3. Potential loss - low
4. Disease progress is governed by the response of the host
Prediction of disease development in relation to host response to the pathogen
Potato early blight induced by Alternaria solani
Age related resistance
Time
Hos
t re
sist
ance Res.
Suc.
emergence
The source-sink relationships of the plant determines its response to the pathogen
Vegetative phase
tuber initiation
Reproductive phase
harvest
Time
Hos
t re
sist
ance Res.
Suc.
emergence tuber initiation
harvest
Consequences from predicting the age related resistance of potatoes on management of early blight
No need to control
Supplement control measures
The disease pyramidfarmer
pathogen
environment
host
disease
Time
Dis
ease
sev
erit
y (%
)
Irrigation
Fertilization
Heating
Ventilating
Spraying
Harvesting
Grower’s actions
Effects of grower’s actions on the epidemic
Grower’s actions
Botrytis rot in basil induced by Botrytis cinerea
Time
Dis
ease
sev
erit
y (%
)
Prediction of disease outbreaks based on the environment and grower actions
Botrytis rot in basil induced by Botrytis cinerea
2. The wounds are healed within 24 hours and are not further susceptible for infection.
3. A drop of water is formed (due to root pressure) on the cut of the stem.
4. If humidity is high, the drop remains for several hours.
1. The pathogen invades the plants through wounds that are created during harvest.
Time
Dis
ease
sev
erit
y (%
)ra
in
Harvests
Botrytis rot in basil induced by Botrytis cinerea
5. During rain, growers do not open the side opening of the greenhouses.
6. Disease outbreaks occur when harvest is done during a rainy day.
Consequences from predicting grey mold outbreaks in basil on disease management
Time
Dis
ease
sev
erit
y (%
)ra
in
Harvests
If harvesting is done during rainy days, apply a fungicide spray once, soon after harvest
To minimize the occurrence of infection, harvesting should be avoided during rainy days.
Concluding remarks
The principles of disease forecasting should be based on:
•The nature of the pathogen (monocyclic or polycyclic)
•Effects of the environment on stages of pathogen development
•The response of the host to infection (age-related resistance)
•Activities of the growers that affect the pathogen or the host