Use of GIS and Weather data for Use of GIS and Weather data for Online Crop and Pest Online Crop and Pest Management Models Management Models

Len CoopLen Coop

Integrated Plant Protection CenterIntegrated Plant Protection Center

Oregon State UniversityOregon State University

Project Support Provided by:

USDA National Plant Diagnostic Network (2005-2007) USDA NRI Plant Biosecurity (2006-2009), CAR Program (2005-

2008) USDA Western Region IPM Grants Program (1996-98, 1999-2002,

2003-2005) USDA Pest Management Centers - W. Region (2001-2003) IPPC (OSU Integrated Plant Protection Center) - state level IPM Commodity grants (Oregon Vegetable Commission, Oregon Essential

Oil Growers League, Oregon Cherry Commission)

Major topicsMajor topics

• Brief intro. to 1 other current project• History and status of IPPC weather-driven

modeling website• Development of W. Region Weather

Workgroup• Site-specific Models: Degree-days• Plans for site-specific disease models and

forecasts

Leafroller parasite lifecycle studies: a) Caneberry field, b) Orange tortrix adult and eggs, c) Phytodietus parasitoid, d) Oncophanes larval parasitoid, e) Apanteles cocoon (a)

(d)(c)

(b)

(e)

● Leafrollers are key pests of processed caneberries● Broad spectrum pesticides are a short term fix but a long term cause of orange tortrix outbreaks● Pesticides harm the key natural enemies (mainly, parasitoid wasps) that normally keep leafroller levels below significant contamination levels● Earlier research found that unsprayed fields have, on average, one-third the population densities and three times the parasitism rates of leafrollers found in sprayed caneberries

Caneberry project rationale:

● Caneberry PMSP (Pest Management Strategic Plan)

● PMSP's: A major initiative by USDA to systematically organize IPM priorities by region and commodity

● The current caneberry CAR grant proposal addressed 23 pest management research and Extension needs/priorities cited in the caneberry PMSP

Caneberries – a case study in phenological research

Raspberry

Marionberry

Evergreen blackberry

Other blackberry

Boysenberry

Caneberry IPM field studies – first year sample sites

Leafroller parasite lifecycle studies (previous results) - Developmental (degree-day) model of Apanteles, a parasite of the orange tortrix leafroller

Using Degree-Day and Disease Models in IPM

The Western IPM Center Weather The Western IPM Center Weather WorkgroupWorkgroup

United United SStates Orographically Effective Terraintates Orographically Effective Terrain

DataCollection

Forecasting SpatialInterpolation

Model InputEstimation

DiseaseModeling

Figure 1. Hypothesized uncertainty profiles for a given set of conditions in the western and eastern US.

Rel

ativ

e U

nce

rtai

nty

Co

ntr

ibu

tio

n

Western USEastern US

Theoretical uncertainty profiles for a given set of conditions

Weather workgroup goal: to expand access to, and use of, Weather workgroup goal: to expand access to, and use of, effective models and forecasts that enhance the precision of effective models and forecasts that enhance the precision of IPM decisions and reduce reliance on insurance pesticide IPM decisions and reduce reliance on insurance pesticide

treatments, treatments,

i. e. support site-specific pest management. i. e. support site-specific pest management.

Typical IPM questions and Typical IPM questions and representative decision representative decision

tools:tools: "Who?" and "What?"

Identification keys, diagnostic guides, management guides

"When?" Phenology models (crops, insects, weeds),

Risk models (plant diseases) "If?"

Economic thresholds, crop loss models, sequential and binomial sampling plans

"Where?" GPS, GIS, precision agriculture

Degree-day calculationsSimplest: (daily max + min)/2 -TL

Example: single triangle case with Tmax > T

U, Tmin < T

L

Single triangle compared with typical daily fluctuation

Weather and Degree-day Concepts1)Degree-day models: accumulate a daily "heat unit

index" (DD total) until some event is expected (e. g. egg hatch)

Eggs hatch: 152 cumulative DDs

Eggs start developing: 0 DDs

70o(avg)-50o(threshold) = 20DD

1)Day DD DDcum.

2)1. 20 203)2. 18 384)3. 32 705)4. 14 846)5. 22 1067)6. 20 1268)7. 26 152

Weather and Degree-day Concepts • We assume that development rate is linearly related

to temperature above a low threshold temperature

30 40 50 60 70 80 90 1000

0.01

0.02

0.03

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0.06

Temperature versus development

Development time (days)Rate (1/days)

Temperature (F)

Ra

te (

1/d

ays

)

Low temperature threshold = 32o F

Graph of typical insect development rate

Rate of development is linear over most temperatures

Thinking in degree-days: Predator mites example - very little activity Oct-Mar; so no spider mite control expected if you release predators during these months

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

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Predator mite avg DDs/month - W. OR

Deg

ree-d

ays/

mon

th

http://pnwpest.org/cgi-bin/ddmodel.pl?spp=nfa

Active Period

Weather and Degree-day Concepts● Some DD models sometimes require a local

"biofix", which is the date of a biological monitoring event used to initialize the model:● Local field sampling is required, such as: sweep net data, pheromone trap catch, etc.

IPPC weather data homepage IPPC weather data homepage (http://pnwpest.org/wea)(http://pnwpest.org/wea)

Degree-day maps

Degree-day

calculator and

models

IPPC weather data homepage IPPC weather data homepage (http://pnwpest.org/wea)(http://pnwpest.org/wea)

Insect Models with potentialsignificance in field crops:-black cutworm-bertha armyworm-variegated cutworm-corn earworm-Lygus bug-mint flea beetle, mint root borer-strawberry root weevil, Crop Models:-barley, chick pea, canola, flax-lentil, mustard, oats, pea-safflower, sunflower-wheat, winter wheatWeed Models:-downy brome (cheatgrass)-Orobanche (small broomrape)

Degree-day models: UC Davis Database of Degree-day models: UC Davis Database of Degree-day models – 97 pest insects, 11 Degree-day models – 97 pest insects, 11 beneficial insects, 2 nematodes, 6 weeds, 9 beneficial insects, 2 nematodes, 6 weeds, 9 crop plants – beware that many are not crop plants – beware that many are not relevant to Pacific NW (or California)relevant to Pacific NW (or California)

Model Runs0

2000

4000

6000

8000

10000

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16000

Calculator/model usage per year

1999

2000

2002

2003

2004

2005 (est)

•Degree-day/Phenology Calc./Model Usage – PNWPEST.ORG •Example 1999 2000 2002 2003 2004 2005-Oct24•================================================================================•Degree-Day Calculator generic 454 3219 6048 5162 7761 7599•codling moth [apple & pear] 83 1123 2019 2053 2428 1827•fire blight [apple & pear] 17 300 699 1115 778 560•

Degree-day models: standardized user Degree-day models: standardized user interfaceinterface

Select species

Select location,forecast locationhistorical average location Click to run model

Model inputs:-links to documentation-model description-validation status

Key events table:-cumulative DDs-name of event

Degree-day models: Codling moth Degree-day models: Codling moth example example

Key events table:-cumulative DDs-name of event

Model outputs:-month, day, max, min-precipitation-daily and cumulative Dds-events

Degree-day models: Codling moth example Degree-day models: Codling moth example (cont.)(cont.)

Model outputs:-month, day, max, min-precipitation-daily and cumulative Dds-events

Degree-day models: Codling moth example Degree-day models: Codling moth example (cont.)(cont.)

Forecasted weather link into the system: 1) weather.com 45 sites (10-day) 2) NWS zone forecasts entire US (7-day)

Degree-day models: Codling moth example Degree-day models: Codling moth example (cont.)(cont.)

Date (x-axis)-dates of key events

Cumulative DDs (y-axis)

Current Dds (with forecasted afterwards)

Historical DDs

Model Summary Graph

Key events

Control of cheatgrass (downy brome) in fallow wheat fields - model under development by Dan Ball, OSU Pendleton, and cooperators throughout the Western U.S.

Downy brome: treat before May 16 2006 (Hermiston)

DD Models map – select weather station from DD Models map – select weather station from mapmap

(example for 10 Stations in Hood River Network – codling moth model)

Weather station selected

IPPC – Weather networks expansion 2003-IPPC – Weather networks expansion 2003-20062006

DD calculator map – select weather station DD calculator map – select weather station from mapfrom map

(example with 6300 stations in US/SW (example with 6300 stations in US/SW Canada)Canada)

Weather station selected

Calculator

Online Models - IPPC- Wide range of weather and climate data - driven pest modeling decision support products

Daily and custom degree-day maps available for coterminous USA by state and region

Online Models - IPPC- Daily updated map: 32, 41, 50 degrees thresholds, Jan 1 - yesterday

Today's base 32 map: 5401 out of 6300+ stations passed quality tests to be included in todays national map

PRISM Knowledge PRISM Knowledge BaseBase

• Elevation influence on climate

• Terrain-induced climate transitions (topographic facets, moisture index)

• Coastal effects

• Two-layer atmosphere and topographic index

• Orographic effectiveness of terrain

• Persistence of climatic patterns (climatically-aided interpolation)

Oregon Annual Precipitation

Mean Annual Precipitation, 1961-90

Full PRISM ModelMax ~ 3300 mm

Simple distance interpolation

Max ~ 7900 mm

GRASS -free & open source for over 25 years: the "Linux" of GIS Simple scripting w/GRASS, e. g.:#!/bin/shd.rast NW_41usd.sites stations_06 color=red type=box size=2d.sites stations_03 color=green type=box size=2d.vect statelines color=blackNew GUI (graphical user interface) Several Web user

interfaces: GRASSLinks, Mapserver

Client side programsOS: Linux/BSD Windows XPWeb/Email: Firefox MS OutlookOffice Suite: OpenOffice MS 95/97/2000/XPPhoto: GIMP PhotoshopStats: R S+GIS: GRASS ESRI Arc*

Open vs non-open source options

• Uses CAI (PRISM temperature climatologies)

• Interpolates current anomalies from mean climatology

PRISM climate

Today’s Anomalies

Today’s Map

Near Real-Time Temperature and Degree-Day Calculation

Initial PRISM-derived DD map, 4 km , Initial PRISM-derived DD map, 4 km , corrected using near-real time site datacorrected using near-real time site data

IPPC, weather-degree days decision support tools: basic map IPPC, weather-degree days decision support tools: basic map generation example, Hood River tree fruit, Oregongeneration example, Hood River tree fruit, Oregon

Hood River, OR – tree fruitHood River, OR – tree fruit1. 2 km resolution2. GWR downscaled to 100 m3. GWR downscaled to 30 m

Online Models - IPPCCustom online degree-day maps available for coterminous USA by state and region

GIS interface: zoom, pan, query, modeling forms

User selected modeling and mapping options

DD mapping of downy brome model -Hermiston region

Downy brome: green "too late" moss "treat now"

Online Models - IPPCNew - date of event phenology maps – we will test if “date” prediction maps are easier to use than “degree-day” prediction maps

Support for hand-held devices, e. g. 320 x 240Online Models - IPPC

Regional/national systemRegional/national system• Develop an insect, weed and plant disease

phenology and risk modeling server for W. USA to be on line (1st services) in 2005

• Services to all regions will evolve, supplemented by Weather Workgroup partnership (e. g. plant disease models, site-specific forecasts)

• Some biosecurity-focused analyses already taking place

• Integrate with East/Midwest workgroups to build a national system “PIPE”

• Weather Workgroup/NRI Biosecurity proposal to:

• conduct uncertainty analyses for model inputs.

• as well as sensitivity analyses of the models with respect to those inputs.

• make a coordinated effort for model implementation, support, and validation from experts across a range of pest, pathogen and crop systems working with physical scientists and technology transfer teams.

Len CoopLen Coop - IPPC, Oregon State University - IPPC, Oregon State University Christopher DalyChristopher Daly, Director, Spatial Climate Analysis Service, Oregon State , Director, Spatial Climate Analysis Service, Oregon State UniversityUniversityAlan FoxAlan Fox – Foxweather, LCC – Foxweather, LCC Gary Grove - Gary Grove - Washington State University Washington State University Doug GublerDoug Gubler – University California – University California Paul Jepson – Paul Jepson – Director, IPPC, Oregon State University Director, IPPC, Oregon State University Ken JohnsonKen Johnson – Botany and Plant Pathology, Oregon State University – Botany and Plant Pathology, Oregon State University Walter MahaffeeWalter Mahaffee – USDA-ARS – USDA-ARS William PfenderWilliam Pfender – USDA-ARS – USDA-ARS Fran PierceFran Pierce - Director, Center for Precision Agricultural Systems, Washington - Director, Center for Precision Agricultural Systems, Washington State UniversityState UniversityJoyce StrandJoyce Strand - University of California - Information Systems Manager and - University of California - Information Systems Manager and MeteorologistMeteorologistCarla S. Thomas -Carla S. Thomas -National Plant Diagnostic Network, University California National Plant Diagnostic Network, University California

W IPMC Weather WorkgroupW IPMC Weather Workgroup

Disease risk models: Pear scab (Venturia pirina)

Spotts et al. Pear Scab infection risk model:Spotts et al. Pear Scab infection risk model:

pear scab degree-hours = avg hourly temp – 32notes:-lower threshold = 32 F-upper threshold = 66 F (so substitute 66 if avg temp > 66)-accumulate degree-hours during times of leaf wetness (using leaf wetness sensors or estimated leaf wetness)

Risk Index Table (pear scab):

Cumulative degree-hours Risk Level

< 250 No scab risk > 250 Scab sycle nearing > 320 Scab cycle started

Online Models - IPPCPlant disease models

online – Crop disease

models w/specific

grower networks, e. g.

Hood River pear scab &

GT powdery mildew

Model outputs shown w/input weather data for veracity

Generic disease models applicable to a variety of diseases and crops:

Model Disease Crops==============================================================Gubler-Thomas Powdery Mildew grape, tomato, lettuce,

cherry, hops

Broome et al. Botrytis cinerea grape, strawberry, tomato,

flowers

Mills tables scab, powdery apple/pear, grapemildew

TomCast DSV Septoria, celery, potato, tomato, Alternaria

almond

Bailey Model Sclerotinia, peanut/bean, rice, melon rice blast,

downy mildew

Xanthocast Xanthomonas walnut--------------------------------------------------------------

Gubler/Thomas Model for Grape Powdery Mildew

• A simple hourly temperature, rule based model • Developed 1990-1995

– Funded by the Ag-chemical Industry• Pilot Implementation and Public Release 1995

– A partnership funded by UC state-wide IPM, Adcon Telemetry, growers

• Full Implementation 1997– Privatization

• Terra Spase• Western Farm Service• Ag Unlimited• FieldWise• Metos

– Ongoing university networks • Pest Cast

Why was a model developed?

• Numerous control failures

• Disease development is explosive

• Rapid development of fungicide resistance

• Only available control options are protectant fungicides

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Epidemics are Explosive

3.53x105spores/cm2

30-40 generations per season

Gubler/Thomas Model• Adapted or modified for other powdery

mildews– Cherry (Grove et al, 2000)

– Hops (Mahaffee et al, 2003)

– Nectarine (Grove)

– Apple (Grove)

– Peach (Grove, Adaskaveg)

– Strawberry (Gubler)

– Melon (Gubler)

Plant disease models online – National Plant Disease Risk System (in development w/USDA)

Model outputs shown w/input weather data for veracity

GIS user interface

Advances in Interpolation are Still Needed for Sparse Weather Networks

Napa Valley, CA (45 x 10 miles)200 weather stations – 2 mile grid

Color differences reflect topography

Yakima Valley, WA (60 x 30 miles)20 weather stations – no grid

Color differences do not always reflect topography

Hop Powdery Mildew Hop Powdery Mildew Infection Risk ForecastInfection Risk Forecast

54%58%61%68%75%

Day 5Day 4Day 3Day 2Day 1

Forecast Accuracy

WA

OR

WA

OR

Region

815662003

91712.414

5

10

Number

of Fields

Potential Number of Fungicide Applications*

9177.6

815102002

14 Day Calendar Program

7 Day Calendar Program

Number of Fungicide

ApplicationsYear

Number of fungicide applications made by Growers Utilizing HPM Risk Model

* Assumes first potential application on May 1 and every 7 or 14 days

until Aug 10 for Oregon and August 20 for Washington.

Practical disease forecasts

====================================================================FIVE DAY DISEASE WEATHER FORECAST1537 PDT WED, OCTOBER 01, 2003 THU FRI SAT SUN MONDATE 10/02 10/03 10/04 10/05 10/06...SALINAS PINE...TEMP: 74/49 76/47 72/50 72/49 76/49RH %: 66/99 54/96 68/99 68/96 58/96WIND SPEED MAX/MIN (KT) 10/0 10/0 10/0 10/0 10/0BOTRYTIS INDEX: 0.12 0.03 0.09 0.48 0.50BOTRYTIS RISK: MEDIUM LOW LOW MEDIUM MEDIUMPWDRY MILDEW HOURS: 2.0 5.0 6.5 4.0 4.0TOMATO LATE BLIGHT: READY SPRAY READY READY SPRAYXANTHOCAST: 1 1 1 1 1WEATHER DRZL PTCLDY DRZL DRZL DRZL-------------------------------------------------------------------TODAY'S OBSERVED BI (NOON-NOON): -1.11; MAX/MIN SINCE MIDNIGHT: 70/50;-------------------------------------------------------------------...ALANFOX...FOX WEATHER...

Full simulation model online example Grass Seed Stem Rust Simulator (w/Bill Pfender, USDA)

Fungicideefficacy submodel

Automatic help window

Graphs of disease and crop development

Single screen user interface

User-input inoculum levels

Using MtnRain™ and MtnRTemps as forecast and simulation tools

Fox Weather, LLC

Northern California Office

662 Main Street, Fortuna, CA 95540

707 725-8013

805 469-1368

Fax 707 725-9380

DISEASE AND PEST MODEL INPUTS

• Leaf wetness can be estimated using custom sensors and from first principles (physics).

• A semi-quantitative approach, using fuzzy logic, is proposed by Kim & Gleason (Iowa).

• Fox Weather, with IPPC, is improving on this approach by incorporating orographic effects, and is developing algorithms for forecasting leaf wetness.

Feb 21, 2005 Storm MtnRain 60hr Forecast

OBS/FCST RAIN 22/20th-01/21st 1hrMx 3hrTotalEl Rio .32/0.3 .72/0.7 La Conchita .36/.35 .80/1.1Moorpark .36/.25 .84/.75OldManMtn 1.02/.64 2.44/2.0Opids Camp .78/0.6 1.79/1.8

80-hour MtnRain Forecast, 6-Hour Rainfall for Nov 6, 2005Northern San Francisco Bay Area, California

GFS Grid Cell

GFS Forecast: 1 grid cell = 1 value for the entire region

GFS Grid Cell

MtnRTemps+PRISM+CALMET

+

+ =>End Products:Gridded output (map layer) out 5

days of:1. Leaf Wetness (LW)2. Tmean during LW period

To be used for:Maps and web GIS of spatializedDisease and insect risk forecasts

PRISMMean DewPtTemperatureAug 2000

MtnRTemps

CALMET

ConclusionsConclusions

• IPM decision making resides with the grower: decision aids need to be resolved to the field/farm scale

• Advanced climate analysis is an effective starting point for development of tools and services

• Development model in OR, PNW, West, has recruited large numbers of growers, and is evolving

• Plant disease models, supported by improved forecasting, are in development; some released

• W IPM C Weather Workgroup is focusing on standards, quality control, and delivery of comprehensive regional and national services

• GIS-based tools offer scope for integration of other IPM decision tools relating to diagnostics, IPM options, and spatially resolved risk and risk mitigation factors