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Weather based Forewarning of
Pests/Diseases -Efforts in India
YS Ramakrishna
Director
Central Research InstituteforDrylandAgriculture
Hyderabad
Forewarning
System Pest
Weather
Crop
Pest Associated Losses
�Crop losses curren
tly estim
ated
at 14% of the total ag
ricu
lturalproduction
�Additional costs in the form
of pesticides applied
for pest control estimated
at $1 billion every year
�30% of the total cost of cu
ltivation is spen
t towards pesticides
in cotton
�Maxim
um share of pesticide consumption (70%) in India is on cotton, rice &
veg
etab
les
Weather based pest / disease forewarning systems for need-based
crop protection towards successful implementation of IPM
A typical
Pest Management System
Recommendation
algorithm
Agroecosystem:
•Crops
•Pests
•Natural enemies
Sampling
Action
Farmer /
Specialist
Tactics:
•Improved Variety
•Planting dates
•Bio-control
•Pesticides
Yield
Data
Recommendation
Weather
Weather data & Forecasts
Trend in pest forecasting research
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Forecast models
Action / economic thresholds
Population dynamics
Pest weather relationships
Monitoring / seasonal occurrence
% Publications
Cotton in India
Cotton abroad%
Rice in India %
Rice abroad %
Pest Forewarning: a few isolated attempts
Weather Based Advisory Scheme (WBAS) for
Groundnut Leaf Spot in AP (Leaf wetness index)
ICRISAT / ICAR /
SAU
Thumb rule for prediction of mustard aphid in
North India (temperature & cloudiness)
CRIDA / AICRP
on Agromet
Thumb rule -prediction of pod borer on
pulses in AP and Karnataka (rainfall)
NCIPM and
ICRISAT
Empirical model for potato aphid(temp)
NCIPM & SAU
Simulation model for rice blast in AP (temp &
RH)
ANGRAU and
DRR
Weather Based Advisory Scheme (WBAS) for Groundnut Leaf Spot
3.15
WI Total
0.50
10
7
0.20
46
0.65
22
5
0.80
16
4
0.60
12
3
0.00
02
0.40
WI
8WH (H)
1Day
Two criteria to decide on making a fungicide application
1. Disease threshold
2. 7-days wetness index total
Procedure for calculating wetness index:
If W
etness Hours(W
H) is 20 h or less, then W
etness Index(W
I)= W
H/20
If W
His greater than 20 h, then W
I= 4.5-0.175 W
H.
Advisory Fungicide should only be applied if 7-
days WItotal is ≥2.3 and disease incidence
exceeds the 10% threshold.
ICRISAT
Leaf spot
420 –525
6>70
<13.0
17 –60
363 –70
13.0 –15.0
1-16
2<63
>15.0
Aphid
Population on
30 plants
Cloud
Amount
(Octas)
Relative
Humidity
(%)
Mean Daily
Tem
perature
(Deg
C)
Combination of low temperature, high humidity and cloudiness
is conducive for rapid aphid m
ultiplication on Mustard
Aphid
AICRPAM
Thumb Rule
A thumb rule to predict Level of attack by Helicoverpa in
pulses
A+B-
A+B+
A-B-
A-B+
LOW
MODERATE
MODERATE
SEVERE
ARainfall during the m
onths of June-October
BRainfall during the m
onth of November
+/-
Above/ Belownorm
al rainfall
Helicoverpa
NCIPM
Blast
EPIBLA –A simulation model for rice blast
Observed and simulated number of
P.oryza spores
0
10
20
30
40
50
60
715
31-
Dec
15
31-
Jan
15
21-
Feb
No. of spores / m3 of air
Observed
Simulated
Estimated number of spores
= 123.068 –
3.0X1 + 0.321X2–0.37X3
(R2=0.65) Where X1= Daily m
ax temperature X2= Daily m
ax RH X3= Daily m
in RH
Est. disease incidence
= 1.0428 –
0.00007z 1
+ 0.0102 z
2–0.0659z 3
(R2=0.97)where z
1= Total No. of spores for 7-d period z 2= Average
maximum RH z 3= Total dew fall for 7-d preceding disease onset
ANGRAU & DRR
Constraints
�Limited number of validated models for
few pests and crops
�Scattered data sources
�Information, qualitative
Problems in Historical data
�missing data
�limited seasonal data (time series data)
�lack of sowing dates
�inconsistency in sampling
�inconsistency in data form
ats
�lack of absolute population counts
�lack of data on crop damage
Development of Weather-Based
Forewarning
Systems for Crop Pests and Diseases
Mission Mode Sub-Project (MM-III-17)
Concerted research efforts initiated
�Main Lead Centre, AICRPAM-CRIDA
Cotton:
Rice:
Groundnut:
Sugarcane:
Pigeonpea:
Mustard:
Groundnut satellite stations
•Anantapur
•Vriddhachalam
Mission Mode Project
6 Leadand
14 CooperatingCentres
Budget: Rs.45 million
Consortium
AICRPAM
CRIDA
CICR
Cotton
DRR
Rice
NRCG
Groundnut
IISR
Sugarcane
IIPR
Pigeonpea
IASRI
Statistics
IIPR
Mustard
NCIPM
IPM
+ IISc, Bangalore and NCMRWF, New Delhi
Database Architecture
Weather-Crop-Pest-Disease
RDBMS
•Application programs for data input & Retrieval
•Procedures for data conversion viz.,
daily, weekly and m
onthly
Data File
Data Dictionary
Main Lead Centre
Cooperating Institutes
Lead & Coop. Centres
Others
Database Users
Quality Check
&Compilation
Data Form
atting
Compiled data
Form
atted data
Historical Data
•Weather
•Pest
•Disease
•Crop
Data From
•Crop Lead Centres
•Cooperating Centres
•Other Sources
Data from Centres
Raw data
RDBMS: Crop-Pest-Disease-Weather
Data collected for 75 locations across India
Major components of model development
Collection and compilation of
historical databases on clim
ate,
crop, pests and diseases
Conduct of field experiments
with special emphasis on
pest-disease data collection
Development of statistical regression m
odels
to predict pest-disease incidence from weather data
Validation of models in farm
ers’fields
Integration of seasonal and m
edium range weather forecasts
with m
odels for real-time forewarning
MMP-17
Developed & Validated models
•Simple prediction/thumb rules
•Established crop-pest/disease weather
relationships
•Models based on weather indices
•Logistic models for qualitative data
•Day degree models
•Decision support tools
•Neural network models
•Remote sensing techniques
Cumulative GDD During 1
stto 25thJanuary in Delhi
0
50
100
150
200
250
13
57
911
13
15
17
19
21
23
25
Date (January)
Degree days
1979
1980
1994
1995
1998
1999
2000
2001
2002
Low incidence
1979
High incidence
2001
aa
Aphid prediction using Growing Degree Days (GDD)
Mustard
IARI
Forewarning of aphid population at IARI, New Delhi
Date
Cumulative Degree-days from 1st January
Optimum for
Observed in
High
Infestation
Low
Infestation
2001
2002
15thJan
90
140
85
117
20thJan
115
190
111
161
25thJan
150
245
157
198
Peak Aphid population
2546
1790
Mustard
Multi Layer Neural Networks
Inputs
Outputs
Hidden Layer
Data Mining: A Knowledge Discovery in
Database (KDD) Process
Kharif
0
200
400
600
800
1000
1200
1400
1600
1800
323640
4448
313539
4348
343842
33
374145
Standard week
YSB population
Observed
Predicted
1997
1998
2000
1999
Validation of Neural Network m
odel on rice YSB
Neural Network
Validation
0
500
1000
1500
2000
2500
3000
323844
6121824303642
Standard week
YSB Population
Predicted 1997
Observed
Predicted 1998
Observed
Post-monsoon
Dissemination of forewarning
information to benefit
farming community
Market
Inputs
services
New
Enterprise
Disease
Weather
Insurance
Govt.
Scemes
Credit
Farmers are not satisfied with only technical information.
Farmers are not satisfied with only technical information.
Farmers are not satisfied with only technical information.
Farmers are not satisfied with only technical information.
He is in need of all information related to his farm business
He is in need of all information related to his farm business
He is in need of all information related to his farm business
He is in need of all information related to his farm business
•Despite technological developments for the last few decades,
Indian agriculture is still rain dependent
•Increased activity of the extreme weather events such as
-Drought 2002
-Cold wave 2002-03
-Heat wave 2003
-Increased temperatures during rabi2003-04 and 2004-05
-Deficit rainfall in many parts of the country during 2004
-Excess rains in 2005 (100cm in Mumbai, floods in MP,
Gujarat, Orissa, AP, Tamilnaduand Karnataka)
Affected the food grain production
Importance of Agro advisory services
Thus timely and accurate weather based agro
advisories is the need of the hour for sustainable
agricultural production
Video
Conferencing
�Inform
ation support in advance to
avoid the crop losses
�Online solutions to their problems
through emails, video conferencing
�Regular updating the inform
ation
�Advisory for crises management
�Consultancies for project
preparation , credit linkage, insurance
Mission 2007
Village Knowledge Centres
RajivInternet Village, e-chouppaletc.
Use of Information technology
in Agroadvisoryservices
Proposed National Agro-
Advisory Network
KVK
National Level
National Level
State Level
District Level
ELURU
CRIDA
Rainfall Departures
Soil Moisture Status
ICRISAT
Satellite Cloud Maps
JNTU
Inform
ation on Cloud,
Surface-& Ground-W
ater
Agro-advisories
using information from
Research stations, inputs from
CRIDA, NCMRWF
JNTU & ICRISAT
NCMWRF
Weather Forecast
From Research Stations
Weather
Crop Condition
Diseases & Pests
District Level -KVK
West GodavariDistrict has
1.
Rice Research Station, Maruteru& Pulla
2.
Banana Research Station, Kovvur
3.
Buffaloes Research Station, Venkataramannagudem
4.
Oilpalm, Horticultural crops, Vijayarai
5.
Fresh Water Prawns & Fish, Undi
6.
National Research Centre for Oilpalm, Pedavegi
Mandal/ Block Level
Weather based Agro-advisories
In Regional & EnglishLanguages
Mission 2007
Village Knowledge Centres
RajivInternet Village, e-chouppaletc.
Maruteru
Kovvuru
Vijayral
Eluru
Malkapuram
Chodimella
Gudivakalanka
Village Rural hubs
Individual Farmer queries
Agroclimatic Zone
District
Crop weather outlook website
AMFU (NCMRWF)
Click here
Andhra Pradesh
Zone-wise agroclimatic information for all
127 Agroclimatic zones by CRIDA
&NCMRWF
National Crop-
weather Watch Group
(ICAR, IMD, NCMRWF, Ministry
of Agriculture, Ministry of I&T)
Crop weather outlook website
Hence
there
is
a need t
o
Hence
there
is
a need t
o
initia
te p
rogra
mm
es
at e
ach
initia
te p
rogra
mm
es
at e
ach
Sta
te level fo
r st
rength
enin
g
Sta
te level fo
r st
rength
enin
g
the A
gro
the A
gro
-- advisory
netw
ork
at
advisory
netw
ork
at
the N
atio
nal
level
the N
atio
nal
level
New NAIP Project Proposal
NEW ACTIVITY
Development of decision support
systems for crop pest / disease
risk management
Objectives
1.Generate cropping system based
information for population biology of insect
pests and diseases for robust model
development
2.Evaluate role of indigenous technical
knowledge (ITKs) in pest forewarning
3.Develop pest forewarning models and
decision support systems in rice and cotton
Experiments planned to generate data on
•Development rates & time affected by
environmental and host variables
–Pest / disease life stages
–Thresholds
•Field incidence vis-à-vis crop
environment, macro and micro weather
–Field incidence & life tables (mortality rates)
–Apparent rates of infection / infestation
–Experiments at multi-stations with multiple
sowings for data maximization
What Data and What models
•Historical data
•Model parameters from
lab experiments
•Current field
experimental data, pest /
disease distribution data;
crop data
•Qualitative data /
knowledge / assumptions
•Differential equations
•Multivariate analysis
•Population dynamics
modeling
–Quantitative / size
•Simulation modeling
–Timing of attack
–Stochastic
•Simple probabilistic
•Complex
–Monte carlo
What the project aims at
What the project aims at
•Primary
–DSS driven by pest and disease models
for risk management in rice and cotton
•Back-up
–Knowledge & data driven DSS
–Web based DSS
Future Thrusts
�Systematized data collection on crop –
pest –
disease -
weather and development of a centralized RDBMS facility in
the country with access to all researchers
�Strengthening multi-disciplinary network programmes
formonitoring pest/disease dynamics
�Development
of disease forewarning models based on
biological & physical processes.
Contd…
�Development
of
decision support systems
through
integration of crop models and disease forewarning
subroutines
to
generate
inform
ation
on
possible
scenarios and m
anagement options.
�Use of Remote Sensing and GIS tools for identification of
hot spots andpest m
igration
�Linking pest forewarning m
odels w
ith w
eather forecasts
for improvingagro-advisories towards need-based pest
management
All these efforts can lead to:
•better pest / disease management
•reduced cost of cultivation through rational pesticide
use
•minimizing damage and crop losses
and contribute towards improved food security
