Role of Forecasting system in plant disease management and new technology in plant disease management

2. Presented by-
DINESH CHAND
(Reg. No. 22DPPC0101)
Ph.D. Scholar Pl. Patho.
Department of Plant Pathology
(COLLEGE OF AGRICULTURE, JODHPUR)
Chairperson
Dr. R. K. SHARMA
Associate Professor
Dept. of Plant Pathology
Doctoral Seminar
on
ROLE OF FORECASTING SYSTEM IN PLANT DISEASE MANAGEMENT
2

3. CONTENTS
Introduction
History
Components of plant disease.
Pre-requesting for disease forecasting system.
Method of disease forecasting system.
Examples of Disease Forecasting system.
GIS (Geographic Information system).
Successful plant disease forecasting system.
Uses of forecasting system.
Conclusion
References

5. DISEASE FORECASTING
 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.
 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
before infection.
 Among the first spray warning services to be established for
growers, were the grapevine downy mildew forecasting

6. HISTORY
 In1911- One of the first attempts at predicting forecasting
was made by 'Lutman' who concluded that epidemics
were favoured in wet and cold conditions.
 In1926- 'Van Everdingen' in Holland proposed the first
model based on four climatic conditions necessary for
disease development .
 In 1933- In England, Beaumont and Stanilund emphasized
the importance of late blight occurrence.
 In 1956- Burke described the "Irish Rule " that describes
the conditions favourable for disease forecasting.

7.  In1959- Simcast is derived from a simulation model
describing the effects of climate, fungicide and host
resistance on Phytophthora infestans.
 In1978- a computerized forecasting system called FAST
was developed for Alternaria solani, a fungus that infects
tomato, to identify periods when environmental conditions
are favorable for early blight development (Madden, et al,
1978).
 In1985- a modified FAST program called Tom- Cast was
developed to aid in the management of anthracnose,

8. Components for successful Disease
Development
The most three important components of plant Disease:-
 Susceptible Host
 Virulent pathogen
 Favorable environment
For disease to occur all three of these must be present.
No Disease Severe Disease

9. Host Factors
a. Prevalence of susceptible varieties in the given
locality.
b. Response of host at different stages of the growth to
the activity of host.
c. Density and distribution of the host in a given locality.

10. Pathogen factors
a. Amount of primary initial, inoculum in the air, soil
or planting material.
b. Dispersal of inoculum.
c. Spore germination.
d. Infection.
e. Incubation period.
f. Sporulation on the infected host.
g. Re-dispersal / Dissemination of spores.
h. Inoculum potential and density in the seed, soil
and air.

11. Environmental factors
a. Humidity
b. Temperature
c. Wind Velocity
d. Light Intensity
e. Soil pH
f. Toxic chemical
g. Nutrient deficiency or excess
h. Other non living agents.

12. Pre-requisites for disease forecasting system
or
Developing a forecasting system
These factors are most necessary:
 The crop must be a cash crop or economically valuable.
 The disease must have potential to cause losses (yield losses)
which is economically significant.
 Control measures should be available at a economically
acceptable cost(reasonable cost).
 The disease must vary each season in the timing of the first
infections and its subsequent rate of progress i.e. the disease
should not be regular.

13.  The model used in making a prediction must be based on
sound investigational work carried out in the laboratory
and in the field and tested over a number of years to
establish its accuracy and applicability in all the locations
where its use is envisaged
 Growers must have sufficient man power and equipments
to follow the management measures when disease
warning is given.
 Long-term warnings or predictions are more useful than
short-term warning or predictions.

14. Methods of disease forecasting
system
1. Forecasting based on primary inoculums:
2. Forecasting based on weather conditions:
3. Forecasting based on correlative information:
4. Use of computer for disease forecasting:
EPIDEM
BLITECAST
FAST

15. 1. Forecasting based on primary inoculums
 Presence of primary inoculum, its density and
viability are determined in the air, soil or
planting material.
 Occurrence of viable spores or propagules in
the air can be assessed by using different air
trapping devices.
 In the case of soil-borne diseases the primary
inoculum in the soil can be determined by
monoculture method.
 E.g. Loose smut of wheat, ergot of pearl millet
and viral diseases of potato.

16. 2. Forecasting based on weather
conditions
 Weather parameters like temperature, relative
humidity, rainfall, light, wind velocity etc., during
the crop season and during the inter crop
season are measured.
 Weather conditions above the crop and at the
soil surface are also recorded.
 Forecasting based on weather conditions given
by late blight of potato(Dutch rules).

17. Dutch rules (In Holland)
Van Everdingen (1926): After evaluating the
weather parameters that are associated with the
development and spred of late blight of potato
caused by phytophthora infestans.
He had proposed a set of essential conditions called
‘Four Dutch Rules’ which combined the following for
temperature, leaf wetness, cloudiness and rainfall.
1. Night temperature below dew point at least for 4
hours.
2. A minimum temp. of 10°C or below.
3. A mean cloudiness on the next day of 80% of the
sky covered by cloud.
4. During periods at least 0.1mm rainfall during the
next 24 hours.

18. 3. Forecasting based on correlative
information
 Weather data of several years are collected
and correlated with the intensity of the
diseases.
 The data are compared and then the
forecasting of the disease is done.
 Forecasting criteria developed from
comparisons of disease observation with
meteorological data have been provided for
diseases like leaf blotch of wheat, fire blight of
apple and barley powdery mildew.

19. 4. Use of computer for disease
forecasting
 In some advanced countries forecasting of disease is
made by the use of computers which gives the quick
result.
 One such computer-based programs in the USA is
known as ‘Blitecast‘ for potato late blight.
 Many computers simulations models have been
developed to predict plant disaeses.
 Waggoner and Horsfall (1969) Developed the first
computer simulation model (EPIDEM) against
Alternaria solani causing early blight disease of
tomato and potato.

20. Some Computer Disease Forecasting
Models
 SIMCAST- It is derived from a simulation model
describing the effect of climate, fungicide and host
resistance on Phytophthora infestans development
 PLASMO- Downy mildew of grapevine caused by
Plasmopara viticola.
 FAST/TOMCAST-Early blight of potato and tomato
caused by Alternaria solani.
 WISDOM(BLITECAST)-Late blight on tomatoes &
potatoes caused by Phytophthora infestans.
 EPICORN-Southern corn leaf blight Helminthosporium
maydis.

21.  EPIPRE- Diseases of winter wheat especially
yellow rust caused by Puccinia striformis.
 CERCOS- Cercospora blight of celery.
 Mary blight- Fire blight of apples caused by
Erwinia amylovora
 MYCOS- Mycosphaerella blight of
chrysanthemums.
 EPIVEN- Apple scab caused by Venturia
inaequalis.
 EPIGRAM-Barley powdery mildew caused by
Blumeria graminis
 BARSIM-I- Barley leaf rust caused by Puccinia

22. Examples of Disease Forecasting
models
Sclerotinia Stem Rot forecasting
Rice blast forecasting
Early and late leaf spots of groundnut
Potato late blight forecasting model in
India
TOMCAST(Tomato disease

23. Sclerotinia Stem Rot forecasting
 Sclerotinia stem rot (Sclerotinia
sclerotiorum) is one of the most
important diseases on spring
sown oilseed rape.
 forecasting method of
Sclerotinia stem rot has been
developed in Sweden in the
year 1998.
 The method is mainly based
upon a number of risk factors,
such as crop density, crop
rotation.
 Formation of sclerotia rainfall

24. Rice blast forecasting
In India, forecasting rice blast
(Pyricularia oryzae) is done by
correlative information method.
• The disease is predicted on the
basis of minimum night
temperature 20-26°C in
association with high
relative humidity of 90% or
above.
• Computer based forecasting
system has also been developed
for rice blast in India.

25. Early and late leaf spots of groundnut
 Early and late leaf spots of
groundnut caused
respectively
by Cercospora arachidicola
and C. personata.
 A technique has been
developed for forecasting
early and late leaf spots of
groundnut in the U.S.A.
 When the groundnut foliage
remains wet for a period
greater than or equal to 10 h
and the minimum temperature
is 21°C or higher for two

26. Potato late blight forecasting model in India
 Development of late blight mainly depends on
moisture, temperature and cloudiness. In India, the
rains are heavy and the weather is cool and cloudy
during summer in hills but in the plains the weather is
generally clear with scanty rains.
 There are several models for late blight forecasting
developed in India by various Scientists but the most
successful models are: JHULSACAST.
 Singh et al. (2000) developed JHULSACAST, a
computerized forecast of potato late blight in Western
Uttar Pradesh for rainy and non rainy year.
Cont….

27.  The weather parameters used in this programme are
daily rainfall in millimeter, hourly temperature and
relative humidity.
 Arora et al.,(2012) modified JHULSACAST and
developed a modified model for late blight forecasting
at Punjab.
 The model specifies that 7 day moving sum of RH
less than 85% for at least 90 hr coupled with a 7 day
moving sum of temperature between 7.2 to 26.6°C for
at least 115 hrs. would predict appearance of late
blight within 10 days of satisfying the conditions.
 One such computer based programmes in the USA is
known as “Blistcast” for potato late blight.
(Singh et al., Arora et al.,
HARDWICK, N. Disease Forecasting . In The

28. BLITECAST Disease Spectrum
Late blight of Potato and Tomato

29. TOMCAST
TOMato disease
foreCASTing
 Purpose: Assist processing tomato growers
with fungicide application timing based on early
blight development, using a “protectant”
fungicide program.
 Use local weather to guide fungicide schedule.
 Alternative to 7-14 day calendar spray
programs.
 Only a PART of the Disease management
component of an overall IPM Program for
tomatoes.

30. PREDICTED
Early Blight Anthracnose
NOT PREDICTED
Bacterial Canker Tomato spotted Late Blight Septoria leaf
TOMCAST Disease Spectrum

31. Geographic information system
 Geographic information systems (GIS) and related
technologies like remote sensing are increasingly used to
analyze the geography of disease, specifically the relationships
between pathological factors (causative agents, vectors and
hosts, people) and their geographical environments.
 A GIS is a computer system designed to capture, store,
manipulate, analyze, manage and present all types of spatial or
geographical data.
 GIS provide important tools that can be applied in predicting,
monitoring and controlling diseases.
 Use of GIS tools on data collected to identify critical
intervention areas to combat the spread of Banana
Xanthomonas wilt (BXW).

33. Successful plant disease forecasting
system
 Reliability - use of sound biological and environmental
data.
 Simplicity - The simpler system and more likely it will be
applied and used by producers.
 Importance -The disease is of economic importance to the
crop, but sporadic enough that the need for treatment is
not a given.
 Usefulness -The forecasting model should be applied
when the disease or pathogen can be detected reliably.

34. ❖ Availability - necessary information about the
components of the disease triangle should be available
❖ Multipurpose applicability- monitoring and decision-
making tools for several diseases and pests should be
available.
❖ Cost effectiveness - forecasting system should be
cost affordable relative to available disease
management tactics.

35. USES OF FORECASTING SYSTEM
 Forecasting and assessment of disease is
important for crops production management
 For timely plant protection measures :
Information whether the disease status is
expected to be below or above the threshold
level.
 For loss assessment : Forecasting actual
intensity of loss and yield reduction can be
predicted.
 Plant pathologists and meteorologists have often
collaborated to develop disease forecasting or
warning systems that attempt to help growers
make economic decisions for managing

36.  These types of warning systems may consist of
supporting a producer’s decision-making
process for determining cost and benefits for
applying pesticides, selecting seed or
propagation materials, or whether to plant a
crop in a particular area.
 This system is boon to growers as it
encourages use of pesticide economically and
as required.
 This not only saves the money and energy of
the farmers without risking the crop health, but
also avoids the environmental pollution.

37. CONCLUSION
 A successful plant disease forecasting system is
attributed to its reliability, simplicity, economic
importance of crop, usefulness, availability of
necessary information about the components of the
disease triangle, multipurpose applicability and cost
effectiveness.
 Plant disease forecasting systems have been
developed to help growers to make important
economic decisions about disease management.
 Although there are various problems in successful
application of forecasting system but accuracy in
prediction system with proper time management, it
could create a revaluation in future farming system.

38. REFERENCE
 Agrios, G.N. Introduction to plant pathology. In Plant
Pathology, 3rd ed.; Academic Press: San Diego, CA, USA,
1988; pp. 3–40.
 Arora, R.K., Ahmad, I., & Singh, BP. (2012). Forecasting
late blight of potato in Punjab using Jhulacast model.
Potato J . 39(2), 173-176.
 David H Gent, W. F. (2013). The use and role of predictive
systems in disease management. Annual Review of
Phytopathology , 51, 267-289.
 Francl, L. The Disease Triangle: A plant pathological paradigm
revisited. Plant Health Instr. 2001.
 HARDWICK, N. (2006). Disease Forecasting . In The
epidemiology of plant Diseases. Springer , Dordrecht ,
239-267.

39.  JHULSACAST: A computerized forecast of potato late
blight in Western Uttar Pradesh. J. Indian Potato
Assoc., 27, 25-34.
 Lu, B.; Dao, P.D.; Liu, J.; He, Y.; Shang, J. Recent
advances of hyperspectral imaging technology and
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 Remote Sens. 2020, 12, 2659. [CrossRef] 31.
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 Sunita Mahapatra, P. S. (2018). Plant disease
forecasting in the era of climate change : Trands and
applications. Recent approaches for management of
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 Weiss, M.; Jacob, F.; Duveiller, G. Remote sensing for