2 YS Ramakrishna - Welcome to CRIDA 4/S-IX/YS Ramakrishna.pdf · 2008-03-02 · Weather Based...

transcript

Weather based Forewarning of

Pests/Diseases -Efforts in India

YS Ramakrishna

Director

Central Research InstituteforDrylandAgriculture

Hyderabad

Forewarning

System Pest

Weather

Pest Associated Losses

�Crop losses curren

tly estim

at 14% of the total ag

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 &

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

Recommendation

Weather

Weather data & Forecasts

Trend in pest forecasting research

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 /

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 &

ANGRAU and

Weather Based Advisory Scheme (WBAS) for Groundnut Leaf Spot

WI Total

8WH (H)

Two criteria to decide on making a fungicide application

1. Disease threshold

2. 7-days wetness index total

Procedure for calculating wetness index:

etness Hours(W

H) is 20 h or less, then W

etness Index(W

His greater than 20 h, then W

I= 4.5-0.175 W

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

17 –60

363 –70

13.0 –15.0

Population on

30 plants

Amount

(Octas)

Relative

Humidity

Mean Daily

perature

Combination of low temperature, high humidity and cloudiness

is conducive for rapid aphid m

ultiplication on Mustard

AICRPAM

Thumb Rule

A thumb rule to predict Level of attack by Helicoverpa in

pulses

MODERATE

SEVERE

ARainfall during the m

onths of June-October

BRainfall during the m

onth of November

Above/ Belownorm

al rainfall

Helicoverpa

EPIBLA –A simulation model for rice blast

Observed and simulated number of

P.oryza spores

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

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

�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:

Groundnut:

Sugarcane:

Pigeonpea:

Mustard:

Groundnut satellite stations

•Anantapur

•Vriddhachalam

Mission Mode Project

6 Leadand

14 CooperatingCentres

Budget: Rs.45 million

Consortium

AICRPAM

Cotton

Groundnut

Sugarcane

Pigeonpea

Statistics

Mustard

+ IISc, Bangalore and NCMRWF, New Delhi

Database Architecture

Weather-Crop-Pest-Disease

•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

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

crop, pests and diseases

Conduct of field experiments

with special emphasis on

pest-disease data collection

Development of statistical regression m

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

Date (January)

Degree days

Low incidence

High incidence

Aphid prediction using Growing Degree Days (GDD)

Mustard

Forewarning of aphid population at IARI, New Delhi

Cumulative Degree-days from 1st January

Optimum for

Observed in

Infestation

15thJan

20thJan

25thJan

Peak Aphid population

Mustard

Multi Layer Neural Networks

Inputs

Outputs

Hidden Layer

Data Mining: A Knowledge Discovery in

Database (KDD) Process

Kharif

323640

313539

343842

374145

Standard week

YSB population

Observed

Predicted

Validation of Neural Network m

odel on rice YSB

Neural Network

Validation

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

Enterprise

Disease

Weather

Insurance

Scemes

Credit

Farmers are not satisfied with only technical information.

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

Conferencing

�Inform

ation support in advance to

avoid the crop losses

�Online solutions to their problems

through emails, video conferencing

�Regular updating the inform

�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

National Level

State Level

District Level

Rainfall Departures

Soil Moisture Status

ICRISAT

Satellite Cloud Maps

Inform

ation on Cloud,

Surface-& Ground-W

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

Rice Research Station, Maruteru& Pulla

Banana Research Station, Kovvur

Buffaloes Research Station, Venkataramannagudem

Oilpalm, Horticultural crops, Vijayarai

Fresh Water Prawns & Fish, Undi

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

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

a need t

initia

te level fo

rength

te level fo

rength

-- advisory

advisory

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

•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

decision support systems

through

integration of crop models and disease forewarning

subroutines

generate

inform

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

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

•minimizing damage and crop losses

and contribute towards improved food security

2 YS Ramakrishna - Welcome to CRIDA 4/S-IX/YS Ramakrishna.pdf · 2008-03-02 · Weather Based...

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