EpidemiologyRegional to Local Focus

Paul Jepson

• The concept in my mind is as follows:> >> > 1) explain what we are doing for NPDN in an IPM context (1-2 slides)> > 2) explain how IPM needs and uses have driven development of our system> > (1-2> > slides)> > 3) explain how the system is used in a fully integrated pest management> > setting> > (monitoring, diagnosis, decision support, outeach summaries)> > 4) Outline other collaborators and the composition of the WWG> > 5) outline PRISM, downscaling, quality control and weather data> > validation,> > model adoption and validation, use statistics in a well worked example (a> > well> > chosen example showing the screens accessed by users and the process they> > employ, as briefly as possible): including a slide showing the application> > in> > two parts of the country, one PNW, one relevant to SBR, ideally Florida)> > 6) summarize current activities and goals> > 7) capture the essence of what we offer, the inclusiveness of our approach> > and> > the need to incorporate IPM thinking

IPM and Weather Data

Long history of successful IPM programs which base IPM decisions on weather data, models and field observations

– Insect phenology and movement– Disease development and spread– Weed phenology– Cultural practices

• Planting• harvesting • Irrigation• others

IPM is Local

• IPM practitioners need a high level of precision at the local level

• Economy of scale leads management of infrastructure towards state or regional level

• Pests and Pathogens know no borders

IPPC’s Web-based System

• Multi-state, Multi-scale

• Serve local, state and national needs for IPM and biosecurity for weather-based risk

• Open and Collaborative Approach

• The system is a fully integrated pest management setting for monitoring, diagnosis, decision support, outreach

Online IPM weather, Degree-Day and Disease Models from

OSU/IPPCLen Coop, Assistant Professor (Senior Research)

Integrated Plant Protection Center, Botany & Plant Pathology Dept.Oregon State University

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)

38

20

18

32

14

22

20

26

daily:

cumulative: 20

70

84

106

126

152

Eggs hatch: 152 cumulative DDs

Eggs start developing: 0 DDs

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

Disease risk models: Pear scab (Venturia pirina)

NPDN / IPM Partnershipto meet overlapping needs

• IPM needs decision support system for daily decisions on pest management at the local scale.

• NPDN needs to know what is normal, what is not, at a national down to a local scale.

• NPDN has highly sensitive and confidential data.

• Industry has proprietary information.

• All groups need information on what to expect during an epidemic.

NPDN / IPM Partnershipto meet overlapping needs

IPPC / NPDN• Web based delivery tools integrate open information (distributed by

IPPC) with secured information (housed at CERIS)

• Maps and reports display pest/ disease incidence and estimate or predict crop risk.

• Economy of Scale for Infrastructure optomized NPDN and IPM resources.

• These decision support tools can be interfaced with other systems to create a distributed set of databases for decision support

• Novel technologies for example PRISM interpolation.

Western IPM Weather WorkgroupWestern IPM Center has funded a workgroup to tackle these

challenges in the western US

• Huge diversity in crops– Many disciplines represented– Large dependence on proximity to the Pacific Ocean and

elevation– Large number of operational IPM weather networks with long

history of success stories

Workgroup Membership• Joyce Strand, UC State-wide IPM• Doug Gubler, Small Fruit Pathology Extension Specialist• Dennis Johnson, WSU Potato Pathologist• Fran Pierce, WSU Center for Precision Agricultural Systems &• Gary Grove, WSU Fruit Pathologist, PAWS Weather Network • Walt Mahaffee, OSU and USDA-ARS, Fruit Pathologist, PNW• David Brent, OSU and USDA-ARS Hops Pathologist• Bill Phender, OSU and USDA-ARS, Grass Seed Pathologist• Paul Jepson, OSU IPPC, Entomologist• Len Coop, OSU IPPC, Entomologist• Chris Daly, OSU Spatial Climate Analysis Services• George Taylor, Oregon State Climatologist• Alan Fox, Fox Weather, LLC, Private Weather Forecaster• Carla Thomas, National Plant Diagnostic Network, Western

Region

IPPC GIS Visualization

Detecting geographic, climatological, temporal, distribution and association anomalies – high resolution weather and pest/ disease risk maps– daily crop and pest phenology maps– a degree-day mapping calculator for insect pests– a generic disease modeling system – Leverages funding

• WIPM Center • NRI Biosecurity Grant• OSU Integrated Plant Protection Center• OSU Spatial Climate Analysis Center

– IPM products in public domain

Oregon Annual Precipitation

Demonstration of Climatological Fingerprint

Climatology used in place of DEM as PRISM predictor grid

Evolution of Map Interpolation for Weather Related Information

Weather data and maps index page (http://pnwpest.org/US)

Thumbnail hyperlinks to daily maps

DD 32 for 2007

DD 32 for 30 year average

Difference showing the Eastern US is muchWarmer this year.

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)

38

20

18

32

1422

20

26

daily:

cumulative: 20

70

84

106

126152

Eggs hatch: 152 cumulative DDs

Eggs start developing: 0 DDs

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

Weather Data Infrastructure

• Labor and Equipment intensive

• Software/ Hardware Compatibility

• Quality Assurance / Control

• Interpolation Strategy

• Model Output and Interpretation

• Delivery platform

PRISMParameter-elevation Regressions on

Independent Slopes Model

Developed by Christopher Daly, Ph.D., Director, Spatial Climate Christopher Daly, Ph.D., Director, Spatial Climate Analysis Service, Oregon State UniversityAnalysis Service, Oregon State University

• a local, a local, moving-window, regression functionmoving-window, regression function between a between a climate variable and elevation that interacts with an climate variable and elevation that interacts with an encoded knowledge baseencoded knowledge base and and inference engineinference engine

• engine is a series of rules, decisions and calculations that set weights for the station data points entering the regression function.

• Rules account for the Rules account for the elevational influenceelevational influence on climate, on climate, terrain-induced climate transitionsterrain-induced climate transitions (e.g., rain shadows), (e.g., rain shadows), coastal effects, atmospheric layers, inversions, and the coastal effects, atmospheric layers, inversions, and the orographic effectiveness of terrainorographic effectiveness of terrain

• http://www.ocs.orst.edu/prism/http://www.ocs.orst.edu/prism/