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Use of GIS and Weather data for Online Crop and Pest Management Models Len Coop Integrated Plant Protection Center Oregon State University.

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Presentation on theme: "Use of GIS and Weather data for Online Crop and Pest Management Models Len Coop Integrated Plant Protection Center Oregon State University."— Presentation transcript:

1 Use of GIS and Weather data for Online Crop and Pest Management Models Len Coop Integrated Plant Protection Center Oregon State University

2 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)

3 Major 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

4 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)

5 ● 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:

6 ● 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

7 Raspberry Marionberry Evergreen blackberry Other blackberry Boysenberry Caneberry IPM field studies – first year sample sites

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

9 Using Degree-Day and Disease Models in IPM

10 The Western IPM Center Weather Workgroup

11 United S tates Orographically Effective Terrain

12 Theoretical uncertainty profiles for a given set of conditions Weather workgroup goal: to expand access to, and use of, effective models and forecasts that enhance the precision of IPM decisions and reduce reliance on insurance pesticide treatments, i. e. support site-specific pest management.

13 Typical IPM questions and representative decision 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

14 Degree-day calculations Simplest: (daily max + min)/2 -T L Example: single triangle case with Tmax > T U, Tmin < T L Single triangle compared with typical daily fluctuation

15 Weather and Degree-day Concepts 1) 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 70 o(avg) -50 o(threshold) = 20 DD 1) DayDDDD cum. 2) 1.2020 3) 2.1838 4) 3. 3270 5) 4.1484 6) 5.22106 7) 6.20126 8) 7. 26152

16 Weather and Degree-day Concepts We assume that development rate is linearly related to temperature above a low threshold temperature Low temperature threshold = 32 o F Graph of typical insect development rate Rate of development is linear over most temperatures

17 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 bin/ Active Period

18 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.

19 IPPC weather data homepage (

20 Insect Models with potential significance 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 wheat Weed Models: -downy brome (cheatgrass) -Orobanche (small broomrape)

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

22 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

23 Degree-day models: standardized user interface

24 Degree-day models: Codling moth example

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


27 Forecasted weather link into the system: 1) 45 sites (10-day) 2) NWS zone forecasts entire US (7-day) Degree-day models: Codling moth example (cont.)

28 Model Summary Graph

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

30 Downy brome: treat before May 16 2006 (Hermiston)

31 DD Models map – select weather station from map (example for 10 Stations in Hood River Network – codling moth model) Weather station selected

32 IPPC – Weather networks expansion 2003-2006

33 DD calculator map – select weather station from map (example with 6300 stations in US/SW Canada) (example with 6300 stations in US/SW Canada) Weather station selected Calculator

34 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

35 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


37 PRISM Knowledge Base 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)

38 Oregon Annual Precipitation Mean Annual Precipitation, 1961-90 Full PRISM Model Max ~ 3300 mm Simple distance interpolation Max ~ 7900 mm

39 GRASS -free & open source for over 25 years: the "Linux" of GIS Simple scripting w/GRASS, e. g.: #!/bin/sh d.rast NW_41us d.sites stations_06 color=red type=box size=2 d.sites stations_03 color=green type=box size=2 d.vect statelines color=black New GUI (graphical user interface) Several Web user interfaces: GRASSLinks, Mapserver

40 Client side programs OS:Linux/BSDWindows XP Web/Email:FirefoxMS Outlook Office Suite:OpenOfficeMS 95/97/2000/XP Photo:GIMP Photoshop Stats: R S+ GIS:GRASSESRI Arc* Open vs non-open source options

41 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

42 Initial PRISM-derived DD map, 4 km, corrected using near-real time site data IPPC, weather-degree days decision support tools: basic map generation example, Hood River tree fruit, Oregon

43 Hood River, OR – tree fruit 1. 2 km resolution 2. GWR downscaled to 100 m 3. GWR downscaled to 30 m

44 Online Models - IPPC Custom 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

45 DD mapping of downy brome model -Hermiston region

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

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

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

49 Regional/national system Develop an insect, weed and plant disease phenology and risk modeling server for W. USA to be on line (1 st 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”

50 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.

51 Len Coop - IPPC, Oregon State University Christopher Daly, Director, Spatial Climate Analysis Service, Oregon State University Alan Fox – Foxweather, LCC Gary Grove - Washington State University Doug Gubler – University California Paul Jepson – Director, IPPC, Oregon State University Ken Johnson – Botany and Plant Pathology, Oregon State University Walter Mahaffee – USDA-ARS William Pfender – USDA-ARS Fran Pierce - Director, Center for Precision Agricultural Systems, Washington State University Joyce Strand - University of California - Information Systems Manager and Meteorologist Carla S. Thomas -National Plant Diagnostic Network, University California W IPMC Weather Workgroup

52 Disease risk models: Pear scab (Venturia pirina)

53 Spotts et al. Pear Scab infection risk model: pear scab degree-hours = avg hourly temp – 32 notes: -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-hoursRisk Level < 250No scab risk > 250Scab sycle nearing > 320Scab cycle started

54 Online Models - IPPC Plant 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

55 Generic disease models applicable to a variety of diseases and crops: ModelDisease Crops ============================================================== Gubler-ThomasPowdery Mildewgrape, tomato, lettuce, cherry, hops Broome et al.Botrytis cinereagrape, strawberry, tomato, flowers Mills tables scab, powdery apple/pear, grape mildew TomCast DSVSeptoria, celery, potato, tomato, Alternaria almond Bailey ModelSclerotinia,peanut/bean, rice, melon rice blast, downy mildew XanthocastXanthomonaswalnut --------------------------------------------------------------

56 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

57 Why was a model developed? Numerous control failures Disease development is explosive Rapid development of fungicide resistance Only available control options are protectant fungicides 0 20 40 60 80 100 3/22/053/29/05 4/5/05 4/12/05 4/19/054/26/05 5/3/05 5/10/05 Percent Incidence Epidemics are Explosive 3.53x10 5 spores/cm 2 30-40 generations per season

58 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)

59 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

60 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

61 Hop Powdery Mildew Infection 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.

62 Practical disease forecasts ==================================================================== FIVE DAY DISEASE WEATHER FORECAST 1537 PDT WED, OCTOBER 01, 2003 THU FRI SAT SUN MON DATE 10/02 10/03 10/04 10/05 10/06...SALINAS PINE... TEMP: 74/49 76/47 72/50 72/49 76/49 RH %: 66/99 54/96 68/99 68/96 58/96 WIND SPEED MAX/MIN (KT) 10/0 10/0 10/0 10/0 10/0 BOTRYTIS INDEX: 0.12 0.03 0.09 0.48 0.50 BOTRYTIS RISK: MEDIUM LOW LOW MEDIUM MEDIUM PWDRY MILDEW HOURS: 2.0 5.0 6.5 4.0 4.0 TOMATO LATE BLIGHT: READY SPRAY READY READY SPRAY XANTHOCAST: 1 1 1 1 1 WEATHER DRZL PTCLDY DRZL DRZL DRZL ------------------------------------------------------------------- TODAY'S OBSERVED BI (NOON-NOON): -1.11; MAX/MIN SINCE MIDNIGHT: 70/50; -------------------------------------------------------------------...ALANFOX...FOX WEATHER...

63 Full simulation model online example Grass Seed Stem Rust Simulator (w/Bill Pfender, USDA) Fungicide efficacy submodel Automatic help window Graphs of disease and crop development Single screen user interface User-input inoculum levels

64 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

65 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.

66 Feb 21, 2005 Storm MtnRain 60hr Forecast OBS/FCST RAIN 22/20th-01/21st 1hrMx 3hrTotal El Rio.32/0.3.72/0.7 La Conchita.36/.35.80/1.1 Moorpark.36/.25.84/.75 OldManMtn 1.02/.64 2.44/2.0 Opids Camp.78/0.6 1.79/1.8

67 80-hour MtnRain Forecast, 6-Hour Rainfall for Nov 6, 2005 Northern San Francisco Bay Area, California GFS Grid Cell

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

69 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 spatialized Disease and insect risk forecasts PRISM Mean DewPt Temperatur e Aug 2000 MtnRTemps CALMET

70 Conclusions 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

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