forecasting model for insect pests - PowerPoint PPT Presentation

View by Category
About This Presentation
Title:

forecasting model for insect pests

Description:

various forecasting model and advance software are enlisted....with their examples – PowerPoint PPT presentation

Number of Views:1994
Slides: 64
Provided by: shweta patel
Category: Pets & Animals

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: forecasting model for insect pests


1
Forecasting Models for major Insect pests
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
OBrien (1952)
3
Crop Pests - Weather Relationship
Crop
Weather
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
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
Experimental pest and disease data
Sensitivity studies
Observed weather
calibration
calibration
Pest and disease models
Weather generator Re-sampling approach
Future hourly weather data
Climate change scenarios
Future pest/disease scenarios
Schematic overview of the Forecast model for
Insect and disease
8
(No Transcript)
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
    dont follow normal distribution

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

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

14
Calculating degree-days
  • Degree days (Maximum temperature minimum
    temperature)/2 - Base Temperature
  • Accumulated growing degree days was derived by
    using the formula
  • ????????
  • Where,
  • Tmax maximum temperature (C)
  • Tmin minimum temperature (C)
  • Tb base temperature (C)
    (Iwata,1984)

15
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.

(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

18
(No Transcript)
19
(No Transcript)
20
IPPC weather data homepage (http//pnwpest.org/wea
)
21
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
(No Transcript)
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.

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
Forecasting model for H. armigera
S. No. Parameters Pest infestation in 10-14 standard week Pest infestation in 10-14 standard week Pest infestation in 10-14 standard week
S. No. Parameters High Medium Low
1 Sudden rise in the minimum temperature by gt50C around 7-8 standard week - - -
2 Rainfall during 1-9 standard weeks - - -
3 Base adult moth population gt15 per week during 5-7 standard week - - -
(Indian Institute of Pulses Research)
27
Forewarning model given by National research
Center for Rapeseed Mustard
28
(No Transcript)
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
(Yadav and singh,2015)
32
Deviation method
  • 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

(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

t Week no. Yt Fruit fly population count
at week t
Mehta et al.(2001)
34
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 )

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
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

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
  • 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

41
Case Study I Bangalore, India
Of fruit fly
(Jayanthi and Verghese, 2011)
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 lt .5 probability of occurrence of epidemic
    is minimum

(Mehta et al. 2001 Mishra et al. 2004 Johnson
et al. 1996 Agrawal, et al. 2004)
43
Forecasting outbreak of pest using Ordinal
Logistic model
Crop (Location) Pest Important variables Time of 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)
MAXT maximum temperature, MINT minimum
temperature, RH 1 relative humidity
(morning),RHIIrelative humidity (evening) and
RHMmean relative humidity
44
Uses of ANN(artificial neural network technique)
  • ANN provides an efficient alternative tool for
    forecasting.
  • Neural Networks dont 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.

(Agrawal et al. 2004 Dewolf et al. 1997, 2000
Kumar, et al. 2010)
45
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.

(Wu et al., 2008)
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)

(Prabhakar et al., 2012)
47
Infrastructure to calculate risk maps
step1
met. data
Geo.data
step 2
combine with GIS
Interpolation
step 3
Calculation of forecasting models with
interpolated input parameters
step 4
Presentation of results
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)

49
DEGREE-DAYS MODELS AVAILABLE IN CIPRA
  • 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.
  • Crop
  • Apple
  • Crucifers
  • Potato
  • Tomato
  • Cotton
  • Insect
  • Codling moth
  • Diamond back moth
  • Colorado potato beetle
  • Tomato fruit borer
  • Spotted boll worm

50
(No Transcript)
51
(No Transcript)
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

53
mainstream technology of computer software for
forecasting
54
(No Transcript)
55
Insect pest forecasting concept
56
(No Transcript)
57
(No Transcript)
58
(No Transcript)
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 producers 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(rem
    ote Sensing) data in pest forewarning is the
    timely availability of cloud-free data with the
    desired spatial and spectral resolution

63
Thank You
About PowerShow.com