Weather Models and Pest Management Decision Timing for Grass Seed and Vegetables Leonard Coop, Assistant Professor (Senior Research) Integrated Plant Protection Center, Botany & Plant Pathology Dept. Oregon State University

Topics for today's talk: Weather data -driven models: degree- day and disease risk models – some concepts and examples Some uses and features of the IPPC "Online weather data and degree-days" website, Focus on vegetable and grass seed models Reasons for modeling

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

When are they expected to get here? Sampling and control Sampling and control Pest Alerts Models

(+) Using the crop stage to integrate heat units and predict pest activity is perhaps the oldest and most used “predictive model” for timing pest management practices. (+) This makes sense for when pest is highly dependent on the plant (-) This may not be reliable if the pest and plant have very different lower temperature thresholds/responses to moisture, etc. Using Crop Stages as “heat unit indicators”

Issues and constraints to using degree- day/weather driven pest management models: Unfamiliarity with concepts, tools: in tree fruits the codling moth model has led the way for 25+ years Not all pests are “modeling-friendly” Availability of tools and weather data: must be easy to use and representative of local area. See for free online tools/data Lack of confidence in existing models: local research / Extension / data gathering / calibration / validation Lack of researchers for developing new models

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

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

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

Weather Station Network Checklist – What to look for: Weather station type – stand alone, networked, public Weather station hardware – reliability, maintenance, uptime, product lifetime, etc. - $$ Type of data transfer and database storage: Are missing data auto- resynchronized from sensor to user: station gateway server client partially or completely? Does the network involve standardized internet data collection and delivery or “Public Aggregation” (e. g. Agrimet, Utah NWS Mesowest) Is there “added value” regarding: model development and delivery, missing data estimation, intelligent spatial interpolation, integrated forecasts, and Extension service/other expert advising including model validations?

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 (

Example on-line DD models: Vegetable Crops: a) bertha armyworm b) black cutworm c) cabbage looper d) corn earworm e) sugarbeet root maggot f) cabbage maggot (new) g) onion maggot ?? Grass Seed: a) billbug (in devel.) b) crane fly (in devel.) c) cereal leaf beetle (in devel.) d) sod webworm (in devel.) e) slugs ??

Degree-day (DD) models: Examples in pest management Grass seed – Cereal leaf beetle: Expect first adults 176 DD and eggs 253 DD above 44.6 after Jan. 1 st (Montana). Vegetables - Sugarbeet root maggot: if flies are collected in traps by 360 DD then treat (above 47.5 after Mar. 1 st ).

Generalized flight pattern for cabbage maggot, Delia radicum in the Willamette Valley. MONTH and DEGREE DAYS above 40 F FLY CATCH Spring Plantings <1520 DD Fall Plantings >2700 DD Summer Plantings > DD - FLY CATCH Spring PlantingsFall Plantings Summer Plantings - Mar Apr May Jun Jul Aug Sept Oct Nov Cabbage/broccoli – Cabbage maggot: Flight activity/egg laying highest from DD and again in the fall after 3850 DD (above 40 o F, after Jan 1 st ) (Amy Dreves 2005)

Degree-day models: standardized user interface

Model Summary Graph

Degree-day models: Orange tortrix example

Degree-day models: Orange tortrix example (cont.) Model Outputs: -month, day, max, min -precipitation -daily and cumulative Dds -events

Degree-day models: forecasted weather Forecasted weather 1) weather.com (10-day) 2) NWS zone (7-day)

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

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

48-state US Degree-day mapping calculator 4. Animated show of steps used to create degree-day maps

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

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

Plant 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.  Disease risk models are not epidemiological – unless they include inoculum levels, population increase, etc.

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 infection cycle has started

Some generic disease models applicable to a variety of diseases and crops: ModelDiseaseCrops ======================================================================== 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

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

Grass seed stem rust model: 2 sprays (a)

Grass seed stem rust model: 2 sprays (b)

Grass seed stem rust model: 1 spray

Grass seed stem rust model: 3 sprays

Grass seed stem rust model: first spray better control with Strobilurin vs DMI

Practical disease forecasts Fox Weather/IPPC ==================================================================== 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...

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 of sampling and control measures Using weather data for pest models are expected to become NRCS cost share approved practices for certain regions, 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?