This document discusses disease forecasting models that use weather data to predict outbreaks. It provides examples of models for rice blast, potato late blight, wheat yellow rust, and more. The potato late blight model for India, JHULSACAST, is specifically discussed. Disease forecasting is useful for giving advance warning to apply protective chemicals before infection starts and help control economically important crop diseases. Both empirical and fundamental forecasting systems are covered, along with their components and requirements for developing useful forecasting.

1. DISEASE FORECASTING

2. Forecasting
It is an advance warning/ forewarning to
prevent the outbreak of disease and to cover the
crop with a protective chemical before infection
starts.

3. Forecasting models
• Blast L (Japan – Haslumoto et al. 1984)
• Pyricularia (Japan – 1986)
• LEAF BLAST (Choi, Korea – 1987)
• EPIBLAST (Choi, Korea – 1987)
• EPIMAY (Southern corn leaf blight)
• EPIPRE (Westend, wheat yellow rust, 1978)
• BLITECAST- Potato late blight
• SIMCAST (effect of climate, fungicide and host resistance)
• PROGEB, PHYTOPRE, NEGFRY, PROPHY AND
SIMPHYT (pathogen lifecycle, weather conditions, fungicides and
host resistance)

5. POTATO LATE BLIGHT FORECASTING
MODEL IN INDIA
• JHULSACAST: Singh et al, (2000) developed JHULSACAST, a
computerized forecast of potato late blight in Western Uttar
Pradesh for rainy and non rainy year.
• Weather data included temperature, relative humidity and rainfall on
hourly basis. The current version of JHULSACAST was developed
in Fox Pro under DOS environment and can be run on Pentium
computer MS DOS/Window 9x operating system.
• The main menu of the programme is late blight forecasting
model(s), data entry, modification and model execution.

6. GEOGRAPHIC INFORMATION SYSTEM
(GIS)
• GIS – computer based system. Capture, store,
manipulate, analyze, manage and present all
geographical data.
(Used for predicting, monitoring and controling
the diseases)

7. Forecasting period
(based on critical period concept)
• An identifiable life span
(weather is conducive for sporulation,
dispersal and infection)
1. Empirical forecasting system
2. Fundamental forecasting system
inductive/ logical/ derived

8. System
• Each ends with a model
1. Biological model
2. Climatological model
3. Synoptic model

9. Biological model
• Leads to a formalized statement of
environmental condition.
• Needed for induction of sporulation, dispersal
and penetration.

10. Climatological model
• The translation of biological methods into real
meteorological situations.

12. Synoptic model
• Comparison between Climatological model
and real weather situation using synoptic
weather maps.

13. Empirical forecasting system
• Developed by studying and comparing
historical records of disease occurrence and
concurrent weather conditions in a same
locality
• Formulation of rules
- Forecasting based on synoptic charts
developed

14. Fundamental forecasting system
• Based on data obtained from lab/ field.
• Regarding relationship of biological and
environmental conditions.

15. After developing empirical forcasting……
- The system is tested and modified to fit
various geographical and environmental
conditions.
- Some questions arise during the conduct of
experiments in controlled condition.
- The relationships are defined mathematically.
Ex. Potato late blight.
(based on stepwise multiple regression using
five meteorological parameters)

16. Ex.
• Meteorological influence on sporangium production
• Meteorological influence on sporangium germination
• Meteorological influence on colonization
• Magnitude and duration of high humidity periods
• Effect of dry period on disease development

17. • 1 to 150 ratings- no forecasting alert
• After 150- an alert is given that the infection will
be occurring within 15- 45 days
• After 270- an alert is given that the infection will
be occurring within 15 days
• Control measures to be taken

18. Requirements for developing useful and
successful forecasting systems
1. The disease must cause economically
significant damage in terms of quantity and
quality.
2. The onset, speed of spread and
destructiveness of disease are variable,
mostly due to dependence which is variable.
3. Control measures must be available at an
economically acceptable cost.

19. Usefulness of forecasting
• By predicting dreadful diseases in advance, the
costly plant protections measures could be
saved.
• Forecasting of disease in advance is useful for
farmers to have enough time to go for disease
resistant crop in the place of susceptible one.
• Forecasting a disease is useful to estimate the
loss of crop in different locations to be expected
and useful to estimate the yield.

20. • The information on weather and disease
relations should be known fully for a particular
disease.
• Forecasting should be developed only for the
diseases which are not controlled by genetic
resistance and which can completely destroy a
major crop.