weather models and pest management decision timing len coop, assistant professor (senior research)...
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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?