forecasting is the first step for IPM. forecasting reduce the protection cost.various models and software are now known to present days ,Which are useful in control the pest.

1. Forecasting Models for major
Insect pests
1

2. 2
Forecasting involves all the activities in ascertaining
and notifying the growers of community that
conditions are sufficiently favourable for certain
insect pest, that application of control measures
will result in economic gain or on the other hand
and just as important that the amount expected is
unlikely to be enough to justify the expenditure of
time, energy and money for control.
Miller and O’Brien (1952)

3. Crop – Pests - Weather
Relationship
Weather
Crop
Pests

4. Pre-requisites for developing a Forecast System
• The crop must be a cash crop(economic
yield)
• The insect must have potential to cause
damage(yield losses)
• The Insect pest should not be regular
(uncertainty)
• Effective and economic control known
(options to growers)
• Reliable means of communication with
farmers
• Farmer should be adaptive and have
purchase power

5. 5
National Consultation on a Framework for Climate Services in Belize

6. Criteria for successful Insect pest
forecasting system
 Reliability -use of sound biological and environmental data
 Simplicity - The simpler the system, the more likely it will be
applied and used by producers
 Importance -The insect pest is of economic importance to the crop,
 Usefulness -The forecasting model should be applied when the
insect can be detected reliably
• 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 insect pest management tactics.

7. 7
Observed weather
Weather generator
Re-sampling approach
Climate change
scenarios
Future hourly
weather data
Pest and
disease
models
Future pest/disease
scenarios
Experimental pest and
disease data
Sensitivity
studies
Schematic overview of the Forecast
model for Insect and disease
calibration
calibration

8. 8

9. Forecast Model- Types
 Between year models
◦ These models are developed using previous years’ data.
◦ The forecast for pests and diseases can be obtained by
substituting the current year data into a model developed upon
the previous years.
 Within year models
◦ Sometimes, past data are not available but the pests status at
different points of time during the current crop season are
available.
◦ In such situations, within years growth model can be used,
provided there are 10-12 data points between time of first
appearance of pests and maximum or most damaging stage.
(Amrender,2006)

10. Forecast Models Developed in the Past
for insect pests
 Techniques used were essentially Statistical
(Correlation and Regression Analysis)
◦ T.P. Trivedi had proposed a regression model to predict the
pest attack.
 Model seems to work only for some years (1992-1994)
◦ Correlation analysis was used by C.P. Srivastava to explore
the relationship between the rainfall and pest abundance in
different years.
 The technique is not effective as the attributes don’t follow normal
distribution

11. 11
Model based on Regression method

12. Weather Related Forecasting
Model
Observations-
Crop data: Phenological development, Growth, Leaf area
and Variety
Insect pest : Pest population
Weather Data Required (hourly for ten or more
years)
 Precipitation
 Temperature
 Sunshine/cloudiness
 Relative humidity
 Leaf wetness
 Wind direction and speed
12
National Consultation on a Framework for Climate Services in Belize

13. Degree-Day Models
 Degree-days (DD) are used in models because they allow a
simple way of predicting development of cold-blooded
organisms (insects, mites, bacteria, fungi, plants).
 Degree-day models have long been used as part of decision
support systems to help growers predict spray timing or when
to begin pest scouting.
 1 degree-day (DD)-DD is way of measuring of Insect growth
and development in response to daily temperature
http://www.ipm.ucdavis.edu/MODELS
13

14. Calculating degree-days
• Degree days = (Maximum temperature + minimum
temperature)/2 - Base Temperature
• Accumulated growing degree days was derived by
using the formula
𝐴𝐺𝐷𝐷 =
Where,
 Tmax = maximum temperature (°C)
 Tmin = minimum temperature (°C)
 Tb = base temperature (°C) (Iwata,1984)
14


n
i
bT
TT
0
minmax
)
2
(

15. 15
Phenology model
Deviation Model
Degree-Days Models types
Simulation model

16. Phenology Models
• Phenology models are driven by several weather
parameters on hourly basis.
• Relationships between temperature and stage specific
development rates of the insect life cycles are
established in through laboratory experiments under
controlled conditions.
• For validation, implemented model predictions are
compared with independent field observations from
several years.
16
(Stinner et al., 1974)

17. Two Methods to Manage
Codling moth Larvae
Calendar Approach
 Treat 3 weeks after full
bloom
Degree Day Model
 Monitor adult flight with
pheromone traps
 Biofix = 1st consistent
catch of moths in traps
 Treat at 250 DD after
Biofix
17

18. 18

19. 19

20. Degree-day maps
Degree-day
calculator and
models
IPPC weather data homepage (http://pnwpest.org/wea)
Select location,
forecast location
historical average
location

21. Example on-line DD models:
Fruit and Nut Crops:
a) codling moth
b) western cherry Fruit Fly
c) oblique-banded leafroller
d) filbertworm
e) orange tortrix
and 6 others
Vegetable Crops:
a) bertha armyworm
b) black cutworm
c) cabbage looper
d) corn earworm
e) sugarbeet root maggot
Peppermint:
5 species
Other crops:
4 species
IPPC weather data homepage (http://pnwpest.org/wea)

22. GDD approach
This method is based on the assumption that the pest becomes
inactive below a certain temperature known as base
temperature
GDD =  (mean temperature – base temperature)
• Not much work on base temperature for various diseases and
insect pest. Normally base temperature is taken as 50 C
• Under Indian conditions, mean temperature is seldom below 50 C
• Need for work on base temperature and initial time of calculation

23. 23

24. Limitations of Degree-Day Models
 Insect response to temperature is not linear
 Lower Thresholds Temperature known for very few
species.
 Measured temperatures not the same as those
experienced by the pest.
24

25. Models developed at IASRI
• Mustard
• Aphid
• Cotton
• American boll worm
• Pink boll worm
• Spotted boll worm
• Whitefly
• Groundnut
• Spodoptera litura
• Onion
• Thrips
• Sugarcane
• Pyrilla
• Early shoot borer &
• Top borer
• Pigeon pea
• Pod fly
• Pod borer
• Rice
• BPH
• Gall midge
• Mango
• hoppers
• fruit-fly

26. S.
No.
Parameters
Pest infestation in
10-14 standard
week
High Medium Low
1 Sudden rise in the minimum
temperature by >50C around
7-8 standard week
+ + + - + - -
2 Rainfall during 1-9 standard
weeks
+ + - + - + -
3 Base adult moth population
>15 per week during 5-7
standard week
+ - + + - - +
26
Forecasting model for H. armigera
(Indian Institute of Pulses Research)

27. 27
Forewarning model given by National research Center
for Rapeseed Mustard

28. 28

29. Potato aphid
 Potato aphid (Myzus persicae) is an abundant potato pest and
vector of potato leaf-roll virus, potato virus Y , etc.
 Potato aphid population – Pantnagar (weekly models)
 Data used: 1974-96 on MAXT, MINT and RH
– [X1 to X3) lagged by 2 weeks
 Model for December 3rd week
Y = 80.25 + 40.25 cos (2.70 X12 - 14.82)
+ 35.78 cos (6.81 X22 + 8.03)
Trivedi et al 1999

30. Potato Aphid population in 3rd week of December
at Pantnagar
Trivedi et al 1999

31. 31
(Yadav and singh,2015)

32.  Useful when only 5-6 year data available for different
periods
 Week-wise data not adequate for modeling
 Combined model considering complete data.
 Not used for disease forewarning but in pest
forewarning
 Assumption : pest population in particular year at a
given point of time composed of two components.
◦ Natural cycle of pest
◦ Weather fluctuations
Deviation method
(Mehta et al,2001)

33. Mango
 Mango fruit fly – Lucknow (weekly models)
 Data used: 1993-94 to 1998-99 on MAXT, MINT
and RH – [X1 to X3]
 Model for natural pattern
2t
t0067.0t16.01
t79.164.33
Y



t = Week no.
Yt = Fruit fly population count at week t
Mehta et al.(2001)

34. 0
50
100
150
200
250
300
April
I
II
III
IV
M
ay
I
II
III
IVJune
I
II
III
IVJuly
I
II
III
IVAugust
I
II
III
IV
Week
Population
Observed Expected
Mango fruit fly population prediction model based on
deviation method in Lucknow
Mehta et al.(2001)

35. Pest simulation models
Pre –requisites for Simulation models –
• Mathematical descriptions of biological data.
• Computer programs or software to run these models.
• Application of these models in understanding
population dynamics and dissemination of pest
forecasts for timely pest management decisions.
(Coulson and Saunders, 1987 )
13

36. EPIPRE
EPIPRE (EPidemics PREdiction and PREvention) is a system of
supervised control of diseases and pests in wheat.
The participating farmers do their own pest monitoring, simple
and reliable observation and sampling techniques.
Farmers send their field observations to the central team, which
enters them in the data bank. Field data are updated daily by
means of simplified simulation models. Expected damage and
loss are calculated and used in a decision system, that leads to
one of three major decisions :
• treat
• don't treat
• make another field observation
Ex-Rhopalosiphum padi

37. 37
Organization scheme for EPIPRE in relation to farmers and
research organizations

38. Generic Pest Forecast System (GPFS)
◦ Combination of multiple weather variables and
biological processes into a single predictive
model Temperature, relative humidity, leaf
wetness (hourly)
◦ Growth, mortality, infection
 Predict timing and abundance of specific life stages
 Estimate damage to specific host plants based on pest
and host phenology
38
Hong et al,2013

39. GPFS Model Modules
• Development Response to temperature
• Simple linear model (Tmin, Topt1, Topt2, Tmax)
• Mortality factors: Heat, cold, aging, soil moisture,
food availability
39
Case Studies
Insects:
• Bactrocera dorsalis
(oriental fruit fly)
• Epiphyas postvittana
(light brown apple moth)
• Helicoverpa armigera
(boll worm)
Hong et al,2013

40. Oriental Fruit Fly
 Highly polyphagous and extremely invasive
 Development is temperature driven
 Available data sets of distribution records and
seasonal observations available for validation
 Model validation in three locations: India, USA (HI),
China
 Comparisons made with CLIMEX-compare locations
40

41. Case Study I: Bangalore, India
41(Jayanthi and Verghese, 2011)
Offruit
fly

42. Ordinal logistic model – model for qualitative data
 pest / disease outbreak can be taken even if the information on
the extent of severity is not available but merely the epidemic
status is accessible
 models have added advantage that these could be obtained
even if the detailed and exact information on pest count /
disease severity is not available but only the qualitative status.
where z is a function of weather variables.
 Forecast / Prediction rule:
 If P ≥.5 more chance of occurrence of epidemic
 If P < .5 probability of occurrence of epidemic is minimum
42
(Mehta et al. 2001; Mishra et al. 2004; Johnson et al. 1996; Agrawal, et al. 2004)
e
z
EP 


)exp(1
1
)1(

43. 43
Crop
(Location)
Pest Important
variables
Time of Workers
Damage Forecast
Cotton
(south India)
Whitefly MAXT,MINT
,RH I & RH
II
Mid Dec. Mid Nov. Agrawal et al.
(2004)
Sugarcane
(Muzaffarnagar)
Pyrilla MAXT &
RHM
Oct.-
Nov.
May Mehta et al.
(2001)
Mango
(Lucknow)
Fruit fly MAXT,MINT
& RH I
May-
June
2nd week
of March
Misra et al.
(2004)
Forecasting outbreak of pest using Ordinal Logistic
model
MAXT = maximum temperature, MINT =minimum temperature, RH 1= relative humidity
(morning),RHII=relative humidity (evening) and RHM=mean relative
humidity

44.  ANN provides an efficient alternative tool for forecasting.
 Neural Networks don’t make any distributional
assumption about the data.
 It learns the patterns in the data, while statistical
techniques try to do model fitting.
ANNs can often correctly infer the unseen part of a
population even if data contains noisy information.
This makes neural network modeling a powerful tool for exploring
complex, nonlinear biological problems like pest incidence.
Uses of ANN(artificial neural network
technique)
(Agrawal et al. 2004; Dewolf et al. 1997, 2000; Kumar, et al. 2010)

45. 45(Wu et al., 2008)
Remote
Sensing
• Remote sensing technologies are used to gather information about the
surface of the earth from a distant platform, usually a satellite or
airborne sensor
• The integration of remote sensing, GPS and GIS are valuable tools that
can enable resource managers to develop maps showing the distribution
of insect infestations over large areas
• Site-specific data, such as type of insect, level of damage, stage of attack,
yield loss are collected from different sources, stored and managed in
spatial database , either contained within the GIS or connected to the
GIS from an external source.

46. Predicting the potential geographical
distribution of sugarcane wooly aphid
 Prediction of potential
geographical distribution of
sugarcane wooly aphid using
its current distribution and
data on range of
environmental parameters.
 Two approaches for the
purpose:
 GPS (Global Positioning
System)
 GIS (Geographic
Information System)
46
(Prabhakar et al., 2012)

47. Infrastructure to calculate risk maps
met.
data
Geo.
data
combine with
GIS
Interpolation
Calculation of forecasting models with
interpolated input parameters
Presentation of results
step1
step 2
step 3
step 4

48. The forecasting
models database
 The CIPRA (Computer Centre for Agricultural Pest
Forecasting) software allows the user to visualise forecasts of
insect development .
 Within CIPRA, there is forecasting models for a total of 35
pests (25 insects and 10 diseases). In vegetables ( cabbage,
carrot, onion, potato, tomato), fruits(apple, grape, strawberry),
and cereals (wheat, barley, corn).
 Each crop has its own independent computer programs file,
known as DLL (Dynamic Link Library), which makes possible
their integration in other specialized software.
(Bourgeois et al., 2008)
48

49. DEGREE-DAYS MODELS AVAILABLE IN
CIPRA
Crop
• Apple
• Crucifers
• Potato
• Tomato
• Cotton
Insect
 Codling moth
 Diamond back moth
 Colorado potato beetle
 Tomato fruit borer
 Spotted boll worm
• Mathematical models vary from simple degree-days approach
based on air temperature to more detailed epidemiological
system based on air temperature, relative humidity and
duration of leaf wetness.

50. 50

51. 51

52. Computer Software Model for Prediction for
Insect pest
• PEST-MAN is a computerized forecasting tool for apple and
pear pests-Canada
• MORPH is predictive computer model for horticultural pest-
UK
• SOPRA is applied as a decision support system for eight
major insect pests of fruit orchards -Switzerland and southern
Germany
• The SIMLEP decision support system for Colorado potato
beetle (Leptinotarsa decemlineata)-Germany and Austria
52

53. 53
mainstream technology of computer software for
forecasting

54. 54

55. 55
Insect pest forecasting
concept

56. 56

57. 57

58. 58

59. success of a forecasting
The success of a forecasting system depends, among
other things, on
• The commonness of epidemics (or need to intervene)
• The accuracy of predictions of epidemic risk (based on
weather, for example)
• The ability to deliver predictions in a timely fashion
• The ability to implement a control tactic (Insecticide
application, for example)
• The economic impact of using a predictive system

60. Uses of insect pest forecasts
Forewarning or assessment of insect pest important for crop
production management
 for timely plant protection measures
information whether the insect pest status is expected to be
below or above the threshold level is enough, models based on
qualitative data can be used – qualitative models
 loss assessment
forewarning actual intensity is required - quantitative model
 For making strategic decision-
• 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)

61.  For making tactical decision-
Deciding about the need to implement insect pest management measure
 Entomologist and meteorologists have often collaborated to develop
insect pest forecasting or warning systems that attempt to help
growers make economic decisions for managing insect.
 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.
forecasting models in order to optimize timing of
monitoring, management and control measures of insect
pests

62. Limitations of forecasting Models-
 Statistical models are simple in their usage and less parameter-
intensive, but they are limited in the information they can provide
outside the range of values for which the model is parameterized.
 Crop simulation models are rather hard to use and parameterize. The
need for calibration can be quite data extensive and not applicable to
some developing countries.
 The major limitation in use of satellite-borne(remote Sensing) data in
pest forewarning is the timely availability of cloud-free data with the
desired spatial and spectral resolution
62

63. Thank You