Weather Models and Pest Management Decision Timing

Len 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 - concepts and examples● Some uses and features of the IPPC "Online weather data and degree-days" website, http://pnwpest.org/wea ● Focus on caneberries and phenology 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

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)/2 - 50 = 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)

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

Weather and Degree-day Concepts • We assume that development rate is linearly related

to temperature above a low threshold temperature

30 40 50 60 70 80 901000

0.01

0.02

0.03

0.04

0.05

0.06Temperature versus development

Development time (days)Rate (1/days)

Temperature (F)

Rat

e (1

/day

s)

Low temperature threshold = 32o F

Graph of typical insect development rate

Rate of development is linear over most temperatures

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.

First consistent trap

catch required to

biofix (begin) codling

moth model

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

Degree-day maps

Degree-day

calculator and

models

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

Example on-line DD models:Fruit and Nut Crops:a) codling mothb) western cherry Fruit Flyc) oblique-banded leafrollerd) filbertworme) orange tortrixand 6 othersVegetable Crops:a) bertha armywormb) black cutwormc) cabbage looperd) corn earworme) sugarbeet root maggotPeppermint:5 speciesOther crops:4 species

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. 1st.●Tree Fruits - Codling moth: 1st treatment 250 DD days after first consistent flight in traps (BIOFIX).● Vegetables - Sugarbeet root maggot: if 40-50 flies are collected in traps by 360 DD from March 1 then treat.

Degree-day models: standardized user interface

Select species

Select location,forecast locationhistorical average location Click to run model

Model Summary Graph

Cumulative DDs (y-axis)

Current Dds (with forecasted afterwards)

Historical DDs

Date (x-axis)-dates of key events

Key events

Key events table:-cumulative DDs-name of event

Degree-day models: Orange tortrix example

Model outputs:-month, day, max, min-precipitation-daily and cumulative Dds-events

Model inputs:-links to documentation-model description-validation status

Model outputs:-month, day, max, min-precipitation-daily and cumulative Dds-events

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

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

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

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50

100

150

200

250

300

350

Predator mite avg DDs/month - W. OR

De

gre

e-d

ays/

mo

nth

http://pnwpest.org/cgi-bin/ddmodel.pl?spp=nfa

Active Period

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50

100

150

200

250

300

350

400

Predator mite avg DDs/month - Bend OR

Active Period

New version of US Degree-day mapping calculator

1. Specify all regions and each state in 48-state US2. Uses all 3200+ US weather stations (current year)3. Makes maps for current year, last year, diffs from last year, hist. Avg, diffs from hist. Avg maps

New version of 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 interface6. Improved map legends

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

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.

Spotts et al. Pear Scab model (example “generic” degree-hour infection risk model):

1. Degree-hours = hourly temperature (oF) – 32(during times of leaf wetness)

2. Substitute 66 if hourly temp >66)

3. If cumul. degree-hours >320 then scab cycle started

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

Model Disease Crops===================================================================Gubler-Thomas Powdery Mildew grape, tomato, lettuce,

cherry, hops

Broome et al. Botrytis cinerea grape, strawberry, tomato,flowers

Mills tables scab, powdery apple/pear, grapemildew

TomCast DSV Septoria, celery, potato, tomato, Alternaria almond

Bailey Model Sclerotinia, peanut/bean, rice, melon rice blast,

downy mildew

Xanthocast Xanthomonas walnut-------------------------------------------------------------------

Online Models - IPPCPlant disease models online – National Plant Disease Risk System (in development w/USDA)

Model outputs shown w/input weather data for veracity

GIS user interface

Practical disease forecasts

====================================================================FIVE DAY DISEASE WEATHER FORECAST1537 PDT WED, OCTOBER 01, 2003 THU FRI SAT SUN MONDATE 10/02 10/03 10/04 10/05 10/06...SALINAS PINE...TEMP: 74/49 76/47 72/50 72/49 76/49RH %: 66/99 54/96 68/99 68/96 58/96WIND SPEED MAX/MIN (KT) 10/0 10/0 10/0 10/0 10/0BOTRYTIS INDEX: 0.12 0.03 0.09 0.48 0.50BOTRYTIS RISK: MEDIUM LOW LOW MEDIUM MEDIUMPWDRY MILDEW HOURS: 2.0 5.0 6.5 4.0 4.0TOMATO LATE BLIGHT: READY SPRAY READY READY SPRAYXANTHOCAST: 1 1 1 1 1WEATHER 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 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?