USPEST.ORG / Oregon IPM Center Hourly Weather and Plant Disease Risk Models Home

Technical documentation and links for plant disease risk and other hourly weather driven models

Len Coop
Assoc. Dir. Decision Support Systems
Oregon IPM Center
Oregon State University

This system accesses hourly and sub-hourly data from numerous real-time and archival weather stations (>32,000 stations, 2020) to compute a variety of plant disease risk and horticultural models. Currently the following models are available:

1. Apple Scab infection risk - developed as a degree-hour version of the Stensvand modification of the standard Mills table for apple scab infection risk, caused by the fungus Venturia inaequalis.
2. Pear Scab infection risk - developed by Spotts and Cervantes (1991), converted for degree-hour calculations by Coop and Spotts (2002). This is a fungal disease caused by Venturia pirina.
3. Gubler Thomas Powdery Mildew - well known index model for powdery mildew of many crops including grapes (caused by the fungus Erysiphe necator).
4. Hop Powdery Mildew - developed by Mahaffee and Gent for Pacific Northwest Hop fields, modified from G-T powdery mildew model. Caused by the fungus Podosphaera macularis.
5. Cherry Powdery Mildew - developed by G. Grove for Washington Sweet Cherries. Caused by the fungus Podosphaera clandestina.
6. Pearson-Gadoury Powdery Mildew - Grape PM primary infection alternative to Cleisto. PM model
7. Cleistothecial Powdery Mildew - similar to apple scab, used for initial infection in grape by ascospores, also known as cleistothcia.
8. Strawberry Powdery Mildew - similar to GT PM with thresholds determined by Miller et al. 2003. Caused by Sphaerotheca macularis f. sp..
9. Anjou Pear Scald - developed to estimate safe storage time based on temperatures prior to harvest.
10. Fire Blight 2000 - Cougarblight model for disease of pome fruits caused by Erwinia amylovora.
11. Fire Blight 2010 - Cougarblight model revised 2010 by Tim Smith, WSU.
12. Fox-Broome Botrytis infection risk - Used regularly for grape bunch mold and for Botrytis cinerea in other fruit crops.
13. Blueberry Mummy Berry - New/preliminary degree-hour/leaf wetness model to predict ascospore infection risk in blueberry.
14. Boxwood Blight infection risk - Model to estimate first infections and degree of infection risk
15. Dollar Spot Disease of turfgrass - Model of Smith and Kerns (Univ. Wisc.) to predict risk of dollar spot epidemics in turfgrass caused by the fungal pathogen Sclerotinia homeocarpa.
16. Potato Tomato Late Blight - Adapted from Smith version. A fungus caused by Phytophthora infestans in potato and tomato.
17. TomCast Disease Severity Values (DSV) - Used to estimate units of disease development for black mold caused by Alternaria solani on tomato, and by other pathogens in carrot, celery, and tomato.
18. Vapor Drift Risk Estimation Model - A tool to help with spray drift avoidance/mitigation
19. Custom Degree-Hour Accumulation Model - A tool for plant disease epidemiologists to diagnose new and invasive plant diseases, and to develop new models.
20. Chilling Hours models - Chilling requirements (hours or units) are typically computed to estimate when fruit trees complete dormancy.

Use these models with caution. Please see our Disclaimer.

  • Apple Scab model- inverted Mills Table: degree-hours - infection risk only
  • - hourly version: expecting good hourly data

    - developed as a degree-hour version of the Stensvand modification of the standard Mills table for apple scab infection risk, caused by the fungus Venturia inaequalis

    - This disease is documented in the PNW Plant Disease Management Handbook.
    - This version by Len Coop, documented at OSU IPPC.
    - See also http://www.ipm.ucdavis.edu/DISEASE/DATABASE/pearscab.html for more model documentation.

    Here is the algorithm used:

       ########
       if ( $lfwetness < 1 ) {
                    $Anomoistcount++;
                    $ADH = 0;
                    if ( $Anomoistcount > 8.8 ) { $AcumDH = 0 }
       }
       else {
          $Anomoistcount = 0;
          if ( $temp < 32 ) { $ADH = 0 }
          else {
            if ( $temp > 66 ) { $ADH = 66 - 30 }
            else { $ADH = $temp - 30 }
            $AcumDH += $ADH;
          }
       }
       if ( $AcumDH < 175 )           { $Alabel = $gre . "no app_scab" . $stp }
       if ( $AcumDH >= 175 )          { $Alabel = $yel . "scab near  " . $stp }
       if ( $AcumDH > 204 )           { $Alabel = $red . "APPLE SCAB!" . $stp }
       if ( $AcumDH > 275 )           { $Alabel = $red . "SCAB cycle!" . $stp }
       ########
    

  • Pear Scab model- Bob Spotts Pear Scab degree-hours - infection risk only
  • - hourly version: expecting good hourly data

    - developed by Spotts and Cervantes (1991), converted for degree-hour calculations by Coop and Spotts (2002). This is a fungal disease caused by Venturia pirina

    - This disease is documented in the
    PNW Plant Disease Management Handbook.
    - This model is documented at OSU IPPC.
    - See also http://www.ipm.ucdavis.edu/DISEASE/DATABASE/pearscab.html for more model documentation.

    Here is the algorithm used:

       ########
       if ( $lfwetness < 1 ) {
                    $Pnomoistcount++;
                    $PDH = 0;
                    if ( $Pnomoistcount > 11.8 ) { $PcumDH = 0 }
       }
       else {
          $Pnomoistcount = 0;
          if ( $temp < 32 ) { $PDH = 0 }
          else {
            if ( $temp > 66 ) { $PDH = 66 - 32 }
            else { $PDH = $temp - 32 }
            $PcumDH += $PDH;
          }
       }
       if ( $PcumDH < 250 )           { $Plabel = $gre . "no pearscab" . $stp }
       if ( $PcumDH >= 250 )          { $Plabel = $yel . "scab near  " . $stp }
       if ( $PcumDH > 320 )           { $Plabel = $red . "PEAR SCAB! " . $stp }
       if ( $PcumDH > 350 )           { $Plabel = $red . "SCAB cycle!" . $stp }
       ########
    

  • GTPM model-Gubler Thomas Powdery Mildew Index Model- conidial stage only
  • - hourly version: expecting good hourly data

    - well known index model for powdery mildew of many crops including grapes (caused by the fungus Erysiphe necator)

    - Powdery mildew in grape is documented at various websites, including the
    PNW Plant Management Handbook.
    - See https://www.ipm.ucdavis.edu/DISEASE/DATABASE/grapepowderymildew.html For more details.
    - This model is published at Gubler et al. 1999 - APSnet Online.

  • HOP PM model- Mahaffee Thomas Powdery Mildew Model- conidial stage only
  • - hourly version: expecting good hourly data

    - developed by Mahaffee and Gent for Pacific Northwest Hop fields, modified from G-T powdery mildew model.
    - Caused by the fungus Podosphaera macularis

    - This disease is documented in the
    PNW Plant Disease Management Handbook.
    - An early version of the Hop PM model is documented at Mahaffee, W. F., Thomas, C. S., Turechek, W. W., Ocamb, C. M., Nelson, M. E., Fox, A. Gubler, W. D. 2003. Responding to an introduced pathogen: Podosphaera macularis (hop powdery mildew) in the Pacific Northwest. Online. Plant Health Progress doi:10.1094/PHP-2003-1113-07-RV.

  • HOP Cascade PM model- Powdery Mildew Model for "Cascade" and similar cultivars- conidial stage only
  • - hourly version: expecting good hourly data

    - Personal communication from Dave Gent -- Not published as of Feb 2020.
    - Model is similar to Mahaffee Thomas Powdery Mildew Model, but with these rules:

    (i) If there were ≥6 continuous hours at ≥28°C, then subtract 20 points, else;
    (ii)If there were ≥2 continuous hours at ≤4°C, then subtract 10 points; else
    (iii) If there were ≥2.5 mm rain, then subtract 10 points, else;
    (iv) If there were ≥6 continuous hours at ≥28°C on the previous day, then there is no change in the index, else;
    (v) If there were ≥6 continuous hours of temperatures from 16 to 27°C, then add 20 points, else;
    (vi) If none of the above rules apply, then subtract 10 points.

    Index values still accumulated over time, with minimum and maximum values of 0 and 100, respectively. Values of 0 to 30, 40 to 60, and 70 to 100 indicate conditions of low, moderate, or high infection risks, respectively.

  • Cherry PM model- WSU/G. Grove Powdery Mildew Model- conidial stage only
  • - hourly version: expecting good hourly data

    - developed by G. Grove for Washington Sweet Cherries
    - caused by the fungus Podosphaera clandestina

    - This disease is documented in the
    PNW Plant Disease Management Handbook and WSU.
    - The basic Cherry PM model has not been fully documented and at this time is considered to be experimental. Use at your own risk.

  • Pearson-Gadoury (1987) Powdery Mildew Model
  • - depends upon good hourly data

    - Grape PM primary infection alternative to Cleistothecial PM model

    - This model is very simple: ascospores are released from overwintered cleistothecia (or chasmothecia) on days when the average daily temperature exceeds 10 degrees C (50 degrees F.) AND daily rainfall (or other form of precipitation) exceeds 0.1 inches (2.5 mm). The model is used during bud break and early shoot growth. The model was developed in New York State and has been adopted for use in Western Oregon.
    - References include
    Gadoury et al. (2012) and Cornell Extension Paper by Gadoury et al. (2006).

  • Cleistothecial Powdery Mildew Index Model
  • - hourly version: expecting good hourly data

    - similar to apple scab, used for initial infection in grape by ascospores, also known as cleistothecia

    - This model is for the primary inoculation of grape by powdery mildew ascospores released from cleistothecia and is a version of the model also known as the "2/3 Mills table" model, and is often used in conjunction with the GT PM model, expecially in California.
    - This disease is documented for grape in the
    PNW Plant Disease Control Guide.
    - UC Davis documentation includes this page.

  • Strawberry Powdery Mildew Index Model
  • - hourly version: expecting good hourly data

    - similar to GT PM with thresholds determined by Miller et al. 2003. Caused by Sphaerotheca macularis f. sp..

    - This disease is documented in the
    PNW Plant Disease Control Guide.
    - his model is documented by:
    Miller, T. C., Gubler, W. D., Geng, S., and Rizzo, D. M. 2003. Effects of temperature and water vapor pressure on conidial germination and lesion expansion of Sphaerotheca macularis f. sp. fragariae. Plant Dis. 87:484-492.
    and available at APSnet Online.

  • Pear Scald - Anjou Pear Model
  • - hourly version: expecting good hourly data

    - developed to estimate safe storage time based on temperatures prior to harvest

    - This disease is documented in the
    PNW Plant Disease Control Guide.
    - This model was developed by Jinhe Bai at OSU, and is documented at OSU IPPC.

  • Fire Blight - Cougar blight 2000 Model
  • - uses daily max and min from hourly temperatures (therefore expecting good hourly data)

    - Disease of pome fruits caused by Erwinia amylovora.

    - This disease is documented in the
    PNW Plant Disease Control Guide.
    - This model was developed by Tim Smith at WSU, and is documented at WSU Extension.
    - Note that this version reads hourly-data files and extracts the daily max and min values, and so may differ in output somewhat from the version in the degree-day models database. This version may be sensitive to bad or missing hourly temperature data.

  • Fire Blight - Cougar blight Model (version 2010 EZ)
  • Fire Blight - Cougar blight Model (version 2010 hourly)
  • - Hourly version: uses hourly data and a lookup table developed by Tim Smith (therefore expecting good hourly data)

    - This disease is documented in the
    PNW Plant Disease Control Guide.
    - See the WSU website for documentation: WSU Extension and Fire Blight Control in Organic Apples and Pears.

  • Broome Botrytis Model
  • - hourly version: expecting good hourly data

    - Used regularly for grape bunch mold and for Botrytis cinerea in other fruit crops

    - This disease is documented for grapes in the
    PNW Plant Disease Control Guide.
    - See http://www.ipm.ucdavis.edu/DISEASE/DATABASE/grapebotrytis.html for model documentation (see model 1 of 2).

  • Blueberry Mummy Berry Infection Risk Model
  • - hourly version: expecting good hourly data

    - New/preliminary degree-hour/leaf wetness model to predict ascospore infection risk in blueberry

    - This disease is documented at the online
    PNW Plant Disease Management Handbook.
    - The model is based on work by Hildebrand and Braun 1991 (Can. J. Plant Path. 13:232-240, see Fig. 5) and is documented in this spreadsheet.
    - There is a related degree-day "end of primary ascospore season" model also at uspest.org, also documented in the spreadsheet linked above.

  • Boxwood Blight Infection Risk Model
  • - hourly version 2.1: expecting good hourly data

    - A disease caused by Calonectria pseudonaviculata

    - Model to estimate first infections and degree of infection risk

    - See also mobile-friendly version of this model at:
    https://uspest.org/risk/boxwood_app.
    - See updated Extension Info. at this website: Virginia Cooperative Extension - Boxwood Blight Task Force.
    - See model version 2.1 summary document (pdf)
    - Model Assumptions: Currently (Summer 2020) this model is thought to fairly reflect infection potential for the two susceptible varieties it was based upon. Like all infection risk models, we assume that infectious inoculum is present. The model currently assumes that infection occurs during periods of leaf wetness caused by dew or rain. In some regions such as the southeastern US, high humidity may contribute to leaf wetness not currently accounted for in this model. The model may therefore sometimes underpredict disease under these conditions (high RH).

  • Dollar Spot (turfgrass) Risk Model of Smith and Kerns
  • - hourly version: expecting good hourly data

    - Model to predict risk of dollar spot epidemics in turfgrass caused by the fungal pathogen Sclerotinia homeocarpa.

    - Dollar Spot Disease is described at these websites:
    UC Davis - Dollar Spot, Purdue University - Dollar Spot, and APS - Dollar Spot of turfgrash.
    - The model algorithm has not been published, but see model description and validation summary (pdf) and for more info on the model and its intended use.
    - Model Assumptions: Like most weather driven risk models, this one assumes that sufficient inoculum is present for causing infections.
    - This model uses 5-day average temperature and relative humidity to predict the relative probability of an epidemic, and need for management using fungicides. The geographic range of the model may be limited to those areas thus far tested (Wisconsin, Oklahoma, Pennsylvania, Tennessee, and Mississippi).
    - The model is empirical although it is known that warmer temperatures and higher humidity result in higher dollar spot incidence, as this model will predict.
    - It is likely that thresholds for treatment will need local calibration. Currently the authors are recommending a threshold of 20% probability for an epidemic. The model includes a parameter for fungicide treatment within the last 15 days, which will lower the probability of an epidemic.

  • Potato Tomato Late Blight Model
  • - hourly version: expecting good hourly data

    - Late blight is caused by the fungus Phytophthora infestans in potato and tomato.

    - This model based on the Smith and the Winstel versions
    - Calculated at 6 am each day from prior 24 hr data.

    Model details:
    If 10 consecutive hours of (leafwetness > 3 OR relative humidity > 90 % ) AND temperature > 10 C, then print "infectious spore production"

    - If "infectious spore production" event is at least 24 hours old and no more than ten days old and during that time a "Infection possible" event is activated if there are two consecutive days with a maximum temperature between 23 and 30 C.
    - The "Infections possible" is active for three days. Lesions are likely to be visible after that.

    Otherwise "low" is the late bight risk.

    - This disease is documented for tomato in the PNW Plant Disease Control Guide.
    - This disease is documented for potato in the PNW Plant Disease Control Guide.
    - See http://www.ipm.ucdavis.edu/DISEASE/DATABASE/potatolateblight.html
    - See Model 4 for Winstel version
    - See Model 14 for Smith version

  • TomCast Disease Severity Values (DSV)
  • - hourly version: expecting good hourly data

    - Used to estimate units of disease development for black mold caused by Alternaria solani on tomato, and by other pathogens in carrot, celery, and tomato.

    - This is a widely-used model for diseases including Alternaria solani (black mold) on tomato,
    http://www.ipm.ucdavis.edu/DISEASE/DATABASE/tomatoblackmold.html (Calif.),
    Alternaria leaf blight on carrot, http://www.ipm.ucdavis.edu/DISEASE/DATABASE/carrotblight.html (Calif.), and Septoria apiicola on celery, http://www.ipm.ucdavis.edu/DISEASE/DATABASE/celeryblight.html (Calif.).
    Disease Severity Values available at: http://www.ipm.ucdavis.edu/DISEASE/DATABASE/dsvtable.html

  • Delta-T Vapor/Thermal Drift Risk
  • - hourly version: expecting good hourly data

    - A tool to help with spray drift avoidance/mitigation

    - This is a model adapted from a tool used in Australia to reduce/avoid vapor or thermal drift by monitoring Delta-T (the difference between wet and dry bulb Temperatures; the greater the difference, the more likely small spray droplets will volatilize (vaporize) into the atmosphere and thus become an invisible form of spray drift.
    - This model can be especially useful in arid climates such as the Western US during summer months. It may also be useful to maximize irrigation efficiency (avoid irrigating during periods of high Delta-T). Reference at:
    http://www.cottonmap.com.au/Content/documents/Weather%20for%20Pesticide%20Spraying.pdf (Australia Cotton)

  • Custom Degree-Hour Accumulation Model
  • - hourly version: expecting good hourly data

    - A tool for plant disease epidemiologists to diagnose new and invasive plant diseases, and to develop new models

    - This tool can be configured to accumulate degree-hours above a lower threshold and below an upper threshold, as specified by the user.
    - The option of whether these accumulations depend upon leaf wetness is also available. With leaf wetness required, the model can emulate other models such as apple scab, pear scab, cleistothecial powdery mildew, and TomCast DSV.
    - Without the leaf wetness requirement, this tool can be used for such purposes as chilling requirements and general degree-hour accumulations.
    - Use trial and error to configure new model parameters for diseases for which you have field data but no published model. This allows this website to be used for invasive species when models have been hypothesized but not yet researched or validated.

  • Chilling Hours models used in Horticulture to predict completion of dormancy
  • - hourly version: expecting good hourly data

    - Chilling requirements (hours or units) are typically computed to estimate when fruit trees complete dormancy

    - This calculator allows both the use of a simple count of hours between two temperature thresholds, such as above 32F and below 45F, and the more complex Utah model (Richardson, Seeley, Walker 1977).
    - Insufficient chill hours can result in delayed foliation, and reduced fruit set and quality.
    - See
    Victoria BC or UC Extension for more complete introductions to chill units and formulas for simple and Utah types of chill units.

    Last updated 06/04/2020

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