1. 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

2. Why do we need to predict disease outbreaks
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
Disease intensity
For making tactical decisions
Deciding about the need to implement disease management measure ?

3. The disease pyramid
grower
host
environment
pathogen

4. 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

5. The disease pyramid
grower
host
environment
disease
pathogen

6. Disease severity (logit)
Time
Disease severity (%)
Monocyclic pathogens
Time
Disease severity (%)
Polycyclic pathogens
Time
Disease severity (logit)
Initial disease
rate

7. Complete only one disease cycle in a growing season
Monocyclic pathogens
Complete only one disease cycle in a growing season
(100 - y) QR dy dt
Q = amount of initial inoculum
R = infection efficacy of the inoculum
y = disease intensity

8. 0.7oC -1.1oC Wilt disease in maize induced by Erwinia stewartii
Prediction of a monocyclic pathogen that complete only one disease cycle in a growing season - indirect prediction
Wilt disease in maize induced by Erwinia stewartii
Severe infections occur after moderate winters.
Mild infections occur after cold winters.
Average Temp. in December, January and February
0.7oC
High probability for severe epidemic
-1.1oC
Low probability for severe epidemic

9. Low probability for severe epidemic
Consequences from predicting the severity of Erwinia stewartii in maize on grower’s action
Low probability for severe epidemic
Sow maize as planned
High probability for severe epidemic
Do not sow maize at all
Sow only resistant cultivars

10. Wilt disease in sugar beat induced by Sclerotium rolfsii
Prediction of a monocyclic pathogen that complete only one disease cycle in a growing season - direct prediction
Wilt disease in sugar beat induced by Sclerotium rolfsii
No. of sclerotia in soil sample
Disease severity
Sclerotia
Soil
Soil sample

11. Few sclerotia in the soil sample
Consequences from predicting the severity of S. rolfsii in sugar beat on grower’s actions
Few sclerotia in the soil sample
Sow sugar beat as planned
Many sclerotia in the soil sample
Do not sow sugar beat at all
Sow only resistant cultivars
Apply soil treatment

12. Complete only one disease cycle in a growing season
Monocyclic pathogens
Complete only one disease cycle in a growing season
Q = amount of initial inoculum
R = infection efficacy of the inoculum
y = disease intensity
(100 - y) QR dy dt

13. The disease pyramid
grower
host
environment
disease
pathogen

14. Apple scab induced by Venturia inaequalis
Prediction of a polycyclic pathogen that complete very few disease cycles in a growing season
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
Temperature (oC)
Duration of RH>90% (hrs)
No disease
Mod. disease
Severe disease
mild disease

15. Duration of RH>90% (hrs)
Consequences from predicting the occurrence of infections of apples by V. inaequalis on grower’s actions
Decision concerning the need for fungicide spraying is made daily during the beginning of the season
Temperature (oC)
Duration of RH>90% (hrs)
No disease
Mod. disease
Severe disease
mild disease
High dose of systemic fungicide
Systemic fungicide
Protectant fungicide
No control

16. Complete several disease cycles in a growing season
Polycyclic pathogens
Complete several disease cycles in a growing season
r y dy dt
(100 - y)
r = apparent infection rate
y = disease intensity

17. Prediction of a polycyclic pathogen - the time of disease onset
Sunflower rust induced by Puccinia helianthi
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
Disease severity (%)

18. Prediction of a polycyclic - the time of disease onset
Sunflower rust induced by Puccinia helianthi
3. One assessment of the disease, at any time, may be used for future disease prediction
Time
Disease severity (%)
Critical severity

19. The critical time model
Consequences from predicting the time for critical severity on rust management in sunflower
Time
Disease severity (%)
Critical severity
The critical time model
Time for critical severity (days)
Yield loss (%)

20. Why the environment did not affect P. helianthi?
spore germination
establishment
lesion formation
reproductive growth
spore formation
spore dissemination
Time
Disease severity (%)

21. Effects of the environment on P. helianthi life cycle
germination (%)
Temperature (oC) 10 25
spore dissemination
establishment
spore germination
Duration of wetness (hours)
germination (%) 2 6
reproductive growth
lesion formation

22. Effects of the environment on P. helianthi life cycle
spore dissemination
spore formation
spore germination
Latent period (days)
Temperature (oC) 10 35
Latent period
reproductive growth
establishment
lesion formation

23. Effects of the environment on P. helianthi life cycle
No. of spores
Temperature (oC) 5 38
spore dissemination
Induction of light
spore formation
spore germination
Relative humidity (%)
No. of spores 70 95
Wetness duration (hrs)
No. of spores
reproductive growth
establishment
lesion formation

24. Effects of the environment on pathogens
spore dissemination
spore formation
spore germination
Time
Disease severity (%)
reproductive growth
establishment
lesion formation

25. Effects of the environment on pathogens
Rain
Periods of high relative humidity
High or low temperatures
Hail
Sand storms
Environmental factors
Environmental factor
Time
Disease severity (%)

26. Measurement of weather parameters
Variability over distances
Precision of measurement
Temperature
Rain
Relative humidity
Leaf wetness
Radiation intensity
Cloudiness
Wind
Low precision High precision
Low variability High variability

27. Where to put the weather sensors?
Weather station

28. Prediction of weather parameters
Precision of prediction
Parameter
Variability over distances
Temperature
Rain
Relative humidity
Leaf wetness
Radiation intensity
Cloudiness
Wind
Low precision High precision
Low variability High variability

29. Prediction of a polycyclic pathogen - the time of disease onset
Potato late blight induced by Phytophthora infestans
1. Amount of initial inoculum is very low (infected tubers).
2. Disease progress rate may be very high.
Time
Disease severity (%)
3. Potential loss - high.
4. Preventive sprays are highly effective.
5. The time of disease onset is governed by the environment.

30. 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
7.2oC
25.5oC
“A rain-favorable day”
Rain quantity in the last five days
30 mm
and

31. Prediction of the time of late blight onset
Wallin’s system
Late blight appears 7-14 days after accumulation of “severity values” since emergence.
Temperature Hours with RH>90% 15 12 9
16-18
13-15
10-12
19-21
22-24
25+
22+
19+
Severity values

32. Severity values during the last 7 days Recommendation for action
Prediction of the subsequent development of late blight and determining the need for spraying
N W 7d 5d
< >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 spray late blight warning 7-day spraying schedule 5-day spraying schedule
Recommendation for action

33. The disease pyramid
grower
host
environment
disease
pathogen

34. Potato early blight induced by Alternaria solani
Prediction of disease development in relation to host response to the pathogen
Potato early blight induced by Alternaria solani
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
Time
Host resistance

35. Age related resistance
Time
Host resistance
Res.
Suc.
emergence
Vegetative phase
tuber initiation
Reproductive phase
harvest
The source-sink relationships of the plant determines its response to the pathogen

36. Supplement control measures
Consequences from predicting the age related resistance of potatoes on management of early blight
Time
Host resistance
Res.
Suc.
emergence
tuber initiation
harvest
No need to control
Supplement control measures

37. The disease pyramid
farmer
host
environment
disease
pathogen

38. Effects of grower’s actions on the epidemic
Irrigation
Fertilization
Heating
Ventilating
Spraying
Harvesting
Grower’s actions
Time
Disease severity (%)

39. Prediction of disease outbreaks based on the environment and grower actions
Botrytis rot in basil induced by Botrytis cinerea
Time
Disease severity (%)

40. Botrytis rot in basil induced by Botrytis cinerea
1. The pathogen invades the plants through wounds that are created during harvest.
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.

41. Botrytis rot in basil induced by Botrytis cinerea
Time
Disease severity (%)
rain
Harvests
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.

42. Consequences from predicting grey mold outbreaks in basil on disease management
Time
Disease severity (%)
rain
Harvests
To minimize the occurrence of infection, harvesting should be avoided during rainy days.
If harvesting is done during rainy days, apply a fungicide spray once, soon after harvest

43. 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