In future, disease forecasting systems may become more useful as computing power increases and the amount of data that is available to plant pathologists to construct models. Good forecasting systems also may become increasingly important with climate change.

1. Course seminar
Khushboo Dubey
Id no.- A-9015/15
Ph.D Plant Pathology

2.  Introduction
 Signification
 Modules
 Mechanism with example
 Conclusion

3.  Forecasting of plant diseases means
predicting for the occurrence of plant disease
in a specified area ahead of time, so that
suitable control measures can be undertaken
in advance to avoid losses. Disease forecasts
are predictions of probable outbreaks or
increase in intensity of disease. It involves
well organized team work and expenditure of
time, energy and money. It is used as an aid
to the timely application of chemicals.

14.  Fore casts based on amount of initial inoculum
 Heald (1921) Number of spores on infected
grains of wheat
 Wilhelm (1950) spores of V. alboatrum in soil
 No. of sclerotia an nematodes cysts in soil
 Fore casts based on weather conditions between
the cropping seasons
 Stewart wilt of corn (E. stewartii) survive in flea
beetle vector, so conditions during Dec to Feb
(temp less than -1oC) affect survival
 Downy mildew of tobacco

16.  Models typically are developed in specific
climates for specific diseases
 Early blight model for Midwest tomatoes
 Models may contain assumptions about site
specific conditions that might not apply for all
areas
 Early blight in Midwest, but what about the west
coast, the desert?
 Variables such as timing of model initiation, host
phenology, and host range may effect predictions
Host plant resistance, variety & fungicide
selection

18. Name of the programme Disease Pathogen
EPIDEM Early blight of potato Alternaria solani
EPIMAY Southern leaf blight of maize Helminthosporium maydis
EPICORN Southern corn blight Helminthosporium maydis
CERCOS Blight of cerely Cercospora spp.
MYCOS Blight of chrysanthemum Mycosphaerella spp.
TOM-CAST(Pitbaldo,1992) Early blight of tomato Alternaria solani
PLASMO(Rosa et al.,.,1993) Downy mildew of grapes Plasmopara viticola
EPIVET Viral disease of potato Contact with aphid transmitted viruses
EPIPIRE Cereal rusts and aphids Puccinia graminis tritici

19. BLIGHT CAST(Karuse et al.1975) Late blight of potato Phytophthora infestans
SIMCAST(Grunwald,2002) Late blight of potato Phytophthora infestans
NEGFRY(Runno and Kopple,2002) Late blight of potato Phytophthora infestans
EPIVEN Apple scab Venturia inequalis
EPIDEMIC Strip rust Puccinia striiformis
BLIGHT CAST(Karuse et al.1975) Late blight of potato Phytophthora infestans

20.  Purpose of Disease Model
 Specific disease, crop, climate, & region
 Model Development
 Assumptions & monitoring variables
 Model Validation
 Testing the assumptions
 Model Implementation
 Going public or private

24.  In these diseases, the rate of increase is mathematically analogous to the simple interest
in money.
 There is only one generation of the pathogen in the life of the infected crop.
 The primary inoculum is seed or soil borne and secondary infection is rare.
 Formula
 Y= EK

27.  In these diseases, the rate of increase is mathematically analogous to compound interest
in money. The pathogen produces spores at a very rapid rate which are disseminated by
external agencies like air, there by infecting other plants
 R=2-3/t2-t1(log10 x2/1-x2-log x1/1-x1)
 Where,x1=initial inoculum at time T1
 X2=inocume present at time T2
 R=infection rate

39.  In future, disease forecasting systems may
become more useful as computing power
increases and the amount of data that is
available to plant pathologists to construct
models. Good forecasting systems also may
become increasingly important with climate
change. Over all, disease forecasting modules
facilitated the development of strategic to
cam bat at the appropriate stage time,
amount at economic cost.