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Weather Models and Pest Management Decision Timing Len Coop, Assistant Professor (Senior Research) Integrated Plant Protection Center, Botany & Plant Pathology.

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Presentation on theme: "Weather Models and Pest Management Decision Timing Len Coop, Assistant Professor (Senior Research) Integrated Plant Protection Center, Botany & Plant Pathology."— Presentation transcript:

1 Weather Models and Pest Management Decision Timing Len Coop, Assistant Professor (Senior Research) Integrated Plant Protection Center, Botany & Plant Pathology Dept. Oregon State University

2 Topics for today's talk: ● Weather data -driven models: degree-day and disease risk models - concepts and examples ● Some uses and features of the IPPC "Online weather data and degree-days" website, ● Focus on caneberries and phenology models ● Reasons for modeling

3 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

4 Weather and Degree-day Concepts in IPM Degree-days: a unit of accumulated heat, used to estimate development of insects, fungi, plants, and other organisms which depend on temperature for growth. Calculation of degree-days: (one of several methods) DDs = avg. temperature - threshold. So, if the daily max and min are 80 and 60, and the threshold is 50, then we accumulate » (80+60)/ = 20 DDs for the day

5 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) daily: cumulative: Eggs hatch: 152 cumulative DDs Eggs start developing: 0 DDs 70 o(avg) - 50 o(threshold) =20 DD

6 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

7 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.

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

9

10 Degree-day models: Examples in pest management ●Nursery crops - Eur. Pine Shoot Moth: Begin sprays at 10 percent flight activity, predicted by 1,712 degree-days above 28 F after Jan. 1 st. ●Tree Fruits - Codling moth: 1 st treatment 250 DD days after first consistent flight in traps (BIOFIX). ● Vegetables - Sugarbeet root maggot: if flies are collected in traps by 360 DD from March 1 then treat.

11 Degree-day models: standardized user interface

12 Model Summary Graph

13 Degree-day models: Orange tortrix example

14 Degree-day models: Orange tortrix example (cont.)

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

16 Thinking in degree-days: Predator mites example - very little activity Oct-Mar (Oct-Apr in C. OR) Active Period

17 New version of US Degree-day mapping calculator 1. Specify all regions and each state in 48- state US 2. Uses all US weather stations (current year) 3. Makes maps for current year, last year, diffs from last year, hist. Avg, diffs from hist. Avg maps

18 New version of US Degree-day mapping calculator 4. Animated show of steps used to create degree-day maps

19 New version of US Degree - day mapping calculator 5. Revised GRASSLinks interface 6. Improved map legends

20 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

21 Disease risk models: Like insects, plant pathogens respond to temperature in a more-or less linear fashion. Unlike insects, we measure development in degree-hours rather than degree-days. In addition, many plant pathogens also require moisture at least to begin an infection cycle.

22 Spotts et al. Pear Scab model (example “generic” degree- hour infection risk model): 1. Degree-hours = hourly temperature ( o F) – 32 (during times of leaf wetness) 2. Substitute 66 if hourly temp >66) 3. If cumul. degree-hours >320 then scab cycle started

23 Some 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, Alternariaalmond Bailey ModelSclerotinia,peanut/bean, rice, melon rice blast, downy mildew XanthocastXanthomonaswalnut

24 Online Models - IPPC 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

25 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: BOTRYTIS RISK: MEDIUM LOW LOW MEDIUM MEDIUM PWDRY MILDEW HOURS: TOMATO LATE BLIGHT: READY SPRAY READY READY SPRAY XANTHOCAST: WEATHER DRZL PTCLDY DRZL DRZL DRZL TODAY'S OBSERVED BI (NOON-NOON): -1.11; MAX/MIN SINCE MIDNIGHT: 70/50; ALANFOX...FOX WEATHER...

26 ● Pest models provide quantitative estimates of pest activity and behavior (often hard to detect): they can take much of the guess work out of timing control measures ● Pest models are expected to become NRCS cost share approved practices for certain crops and pests, proper spray timing is a recognized pesticide risk mitigation practice ● Models can be tied to local biological and weather inputs for custom predictions, and account for local population variations and terrain differences ● Models can be tied to forecasted weather to predict future events Why weather-driven models for IPM?


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