1.5 Prediction of disease outbreaks

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?orWhat are the uses of disease forecasts ? Disease intensity Time • 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.) For making tactical decisions Deciding about the need to implement disease management measure ?

3. 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

4. The disease pyramid grower host environment disease pathogen

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

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

7. Wilt disease in maize induced by Erwinia stewartii Average Temp. in December, January and February 0.7oC -1.1oC High probability for severe epidemic Low probability for severe epidemic 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.

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

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

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

11. dy (100 - y) QR dt 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

12. The disease pyramid grower host environment disease pathogen

13. Severe disease Duration of RH>90% (hrs) Mod. disease mild disease No disease Temperature (oC) 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

14. High dose of systemic fungicide Severe disease Systemic fungicide Duration of RH>90% (hrs) Mod. disease Protectant fungicide mild disease No control No disease Temperature (oC) 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

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

16. Sunflower rust induced by Puccinia helianthi Disease severity (%) Time 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

17. Sunflower rust induced by Puccinia helianthi Critical severity Disease severity (%) Time 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

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

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

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

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

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

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

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

25. Measurement of weather parameters Precision of measurement Variability over distances Parameter Temperature Rain Relative humidity Leaf wetness Radiation intensity Cloudiness Wind Low precisionHigh precision Low variabilityHigh variability

26. Where to put the weather sensors? Weather station

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

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

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

30. 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 time of late blight onset Wallin’s system Late blight appears 7-14 days after accumulation of 18-20 “severity values” since emergence.

31. No. rain-favorable days during the last 7 days Severity values during the last 7 days <3 3 4 5 6 >6 N N W 7d 7d 5d <4 >4 N W 7d 5d 5d 5d NW7d5d No spraylate blight warning7-day spraying schedule5-day spraying schedule Recommendation for action Prediction of the subsequent development of late blight and determining the need for spraying

32. The disease pyramid grower host environment disease pathogen

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

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

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

36. The disease pyramid farmer host environment disease pathogen

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

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

39. 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.

40. rain Harvests Disease severity (%) Time 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.

41. rain Harvests Disease severity (%) Time Consequences from predicting grey mold outbreaks in basil on disease management 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

42. Concluding remarks The principles of disease forecasting should be based on: • The nature of thepathogen (monocyclic or polycyclic) • Effects of theenvironment on stages of pathogen development • The response of thehosttoinfection (age-related resistance) • Activities of thegrowers that affect the pathogen or the host