1. Using Degree-Day Tools To Improve Pest Management: Dont get caught off-guard ! Len Coop, IPPC, OSU Corvallis Jan 25, 2012

2. Phenology and degree-day concepts Some features of the IPPC "Online weather data and degree-days" website at uspest.org/wea Development and use of specific models – example applications Topics for today's session:

3. "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, sampling calculators, other decision tools "Where?" GIS, precision agriculture Typical IPM questions/decision tools:

4. "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, sampling calculators, other decision tools "Where?" GIS, precision agriculture Typical IPM questions/decision tools:

5. "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, sampling calculators, other decision tools "Where?" GIS, precision agriculture Typical IPM questions/decision tools:

6. "If?" Economic thresholds, crop loss models, sampling calculators, other decision tools "Where?" GIS, precision agriculture Typical IPM questions/decision tools:

7. What to know about degree-days: Insects have complex life cycles Eggs Larvae Pupae (most) Adults -Timing of all stages *is often* predictable using degree-days, which are a two dimensional “heat Unit“ of development for cold blooded organisms

8. Degree-day calculations – method varies: Simplest: (daily max + min)/2 – low threshold Single triangle compared with typical daily fluctuation How fast are they going?

9.  Simple average: daily max + min/2 - lower threshold (ignores any upper threshold)  Growing degree-days for corn: use simple avg but subst lower threshold in place of min (if min is lower), subst upper threshold in place of max (if max is higher)  Growing degree-days for cereals: same as simple average  Single and double triangle: simple geometric formulae (the latter uses tonights min for second half of today)  Single and double sine curve: more complex trig. formulae  Actual degree-days: computed in real time ex. once/minute  Degree-hours: require hourly data  Fireblight degree-days: 4-day running degree-day total  Heating and Cooling degree-days: used by the power industry Types of degree-days Used for insects, plants, plant diseases, other uses

11. Degree-day Models: A bit more detail

12. Developing degree-day models from lab studies

13.  Degree-day models: from lab studies

14.  Degree-day models: x-intercept method (Arnold 1959) x-intercept ~ Tlow = 37 F 1/slope = 1/0.0011 = 920 ~ DD requirement

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

16. Degree-days: a tool for decision support, not another tool we can store in the tool shed

17. Degree-days: help us time controls before The pest gets “out of hand”

18. Pest Management: ID your target first!

19. Phenology: the study of periodic phenomena in their relations to each other, as climatic and weather changes to plant life (from Torre-Bueno 1 st ed.) Heat unit concepts and examples first published by Reaumur (1736), modern refs. in Arnold (1959, 1960) Degree-days: A popular method of recording physiological time to represent development of many plants and animals that do not self regulate temperature Degree-day models in common use for timing of sampling and management events in agriculture; a cornerstone of IPM Phenology and degree-day concepts

20. Assume that developmental rate is linearly related to temperature above a lower threshold (Tlow) Work best in temperate regions for populations that have relatively few, non-overlapping generations Degree-day models: accumulate a daily "index of development" (DD total) until some event is expected (e. g. egg hatch) DD models often require a "biofix" - biological monitoring event used to initialize the model Degree-day Concepts

21. http.uspest.org/wea

22. DD model example - corn earworm, Helicoverpa zea

23. Corn earworm model output - summary, daily report

24. Corn earworm -degree-days and event labels

25. Missing data algorithm – simple: nearest date averaging

26. Corn earworm - graph of date vs. degree-days

27. Weather and Pest/Disease Models

28. Why is Climate Important? Climate provides a long-term context for weather events Weather is a variation on typical climate conditions The spatial patterns of long-term climate inform the spatial patterns of weather (“Climate Fingerprint”) 1971-2000 July Tmax 2003 July Tmax (One of Hottest on Record) Different values, but same spatial pattern

29. Why is Climate Important? Climate provides a long-term context for weather events Weather is a variation on typical climate conditions The spatial patterns of long-term climate inform the spatial patterns of weather (“Climate Fingerprint”) 1971-2000 July Tmax 2003 July Tmax (One of Hottest on Record) Different values, but same spatial pattern

30. What is Climate Mapping? The process of interpolating climate statistics at irregularly- spaced station locations to a regular grid “Geospatial Climatology” The study of the spatial patterns of climate on the earth’s surface and their causes

31. New USDA Plant Hardiness Zone Map Products Guided by PRISM Climatologies New USDA Plant Hardiness Zone Map

33. Prism Interpolated Degree-Days New for 2011 - Google Maps interface

34. NPDN Epidemiology uses for IPPC degree-day maps Tracking soybean rust – can show up at > 2500 DDs July 24, 2009 Oct. 1, 2009

35. Weather and Pest/Disease Models Over 69 DD/phenology models available Over 18 Disease risk/hourly driven models

36. Example multi-regional Disease Alert Map integrating real-time observed and forecast weather data into one disease index: Forecast fire blight risk model available at the IPPC website http://uspest.org/wea Other Disease Alert Maps include: Tomato-potato late blight, Tomcast DSV, Soybean rust, also animated movies are available for each of these. Click on pin to run full model

37. Summary Points: WSU DAS is perhaps most advance IPM online DSS supporting crops at the state level, provides a great model for other similar programs IPPC uspest.org/wea has evolved as a hybrid for support of State, Regional, and National needs Virtual weather still in development but appears to be useful now depending on needs “MyPest Home Page” now integrates numerous models and weather in one place The website is available for strategic alliances of DSS services with other IPM Regional Centers and state- level PIPES (working now with Montana State and Iowa PIPE projects)‏