Weather Models and Pest Management Decision Timing for Grass Seed and Vegetables

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Weather Models and Pest Management Decision
Timingfor Grass Seed and Vegetables
Leonard Coop, Assistant Professor (Senior
Research) Integrated Plant Protection Center,
Botany Plant Pathology Dept. Oregon State
University
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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,
  http//pnwpest.org/wea
• Focus on vegetable and grass seed models
• Reasons for modeling

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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

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When are they expected to get here?
Sampling and control
Pest Alerts
Models
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Using Crop Stages as heat unit indicators

• () 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.

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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
  http//pnwpest.org/wea 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

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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
• (8060)/2 - 50 20 DDs for the day

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Weather and Degree-day Concepts

• Degree-day models accumulate a daily "heat unit
  index" (DD total) until some event is expected
  (e. g. egg hatch)

Eggs start developing 0 DDs
152
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126
20
106
22
84
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Eggs hatch 152 cumulative DDs
70
32
38
cumulative
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20
20
daily
70o(avg)-50o(threshold)20DD
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Weather and Degree-day Concepts

• We assume that development rate is linearly
  related to temperature above a low threshold
  temperature

Low temperature threshold 32o F
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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 stationgatewayserverclient
  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?

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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.

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IPPC weather data homepage (http//pnwpest.org/wea
)
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IPPC weather data homepage (http//pnwpest.org/wea
)
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 ??
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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. 1st (Montana).
• Vegetables - Sugarbeet root maggot if 40-50
  flies are collected in traps by 360 DD then treat
  (above 47.5 after Mar. 1st).

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• Cabbage/broccoli Cabbage maggot Flight
  activity/egg laying highest from 360-1520 DD and
  again in the fall after 3850 DD (above 40o F,
  after Jan 1st) (Amy Dreves 2005)

Generalized flight pattern for cabbage maggot,
Delia radicum in the Willamette Valley.
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Degree-day models standardized user interface
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(No Transcript)
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Degree-day models Orange tortrix example
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Degree-day models Orange tortrix example (cont.)
Model Outputs -month, day, max,
min -precipitation -daily and cumulative
Dds -events
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Degree-day models forecasted weather
Forecasted weather 1) weather.com (10-day) 2) NWS
zone (7-day)
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Thinking in degree-days Predator mites example -
very little activity Oct-Mar (Oct-Apr in C. OR)
Active Period
Active Period
http//pnwpest.org/cgi-bin/ddmodel.pl?sppnfa
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New version of US Degree-day mapping calculator
1. Specify all regions and each state in 48-state
US 2. Uses 5000 US weather stations 3. Makes
maps for current year, last year, diffs from last
year, normals, diffs from normals maps
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48-state US Degree-day mapping calculator
4. Animated show of steps used to create
degree-day maps
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5. Revised GRASSLinks interface 6. Improved map
legends
New version of US degree - day mapping calculator
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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
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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.

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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 infection cycle has started
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Some generic disease models applicable to a
variety of diseases and crops Model Disease
Crops
Gubler-Thomas Powder
y Mildew grape, tomato, lettuce, cherry,
hops Broome et al. Botrytis cinerea grape,
strawberry, tomato, flowers Mills tables
scab, powdery apple/pear, grape mildew To
mCast DSV Septoria, celery, potato, tomato,
Alternaria almond Bailey Model Sclerotinia,
peanut/bean, rice, melon rice blast, downy
mildew Xanthocast Xanthomonas walnut ----------
--------------------------------------------------
---
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Online Models - IPPC
Plant disease models online National Plant
Disease Risk System (in development w/USDA)
GIS user interface
Model outputs shown w/input weather data for
veracity
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Grass seed stem rust model 2 sprays (a)
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Grass seed stem rust model 2 sprays (b)
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Grass seed stem rust model 1 spray
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Grass seed stem rust model 3 sprays
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Grass seed stem rust model first spray better
control with Strobilurin vs DMI
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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
0.12 0.03 0.09 0.48
0.50 BOTRYTIS RISK MEDIUM LOW
LOW MEDIUM MEDIUM PWDRY MILDEW HOURS
2.0 5.0 6.5 4.0 4.0 TOMATO LATE
BLIGHT READY SPRAY READY READY
SPRAY XANTHOCAST 1 1 1
1 1 WEATHER DRZL
PTCLDY DRZL DRZL DRZL ------------------------
------------------------------------------- TODAY'
S OBSERVED BI (NOON-NOON) -1.11 MAX/MIN SINCE
MIDNIGHT 70/50 ---------------------------------
---------------------------------- ...ALANFOX...FO
X WEATHER...
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Why weather-driven models for IPM?

• 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