Cougar Blight Model Overview

Program Contact: Tianna DuPont, Regional Specialist, Tree Fruit
(509) 663-8181 • tianna.dupont@wsu.edu

THE DEVELOPMENT AND USE OF COUGARBLIGHT 1990 – 2010
A SITUATION-SPECIFIC FIRE BLIGHT RISK ASSESSMENT MODEL FOR APPLE AND PEAR.

Timothy J. Smith
Washington State University
400 Washington Street, Wenatchee, Washington 98801
United States of America

During the 1940’s through the 1980’s, fire blight caused sporadic, scattered, light to serious damage to pear orchards in the Pacific Northwest United States. Most orchardists did not see fire blight most years. Fire blight damage seemed to the average grower to have no relationship to weather and spray timing, as prophylactic control sprays applied during primary
bloom seemed to make little difference to the number of blight strikes.

Since the 1990’s, apple cultivars and rootstocks in this region have been converted to those that are much more susceptible, and fire blight, while outbreaks remain sporadic, has become a much more frequent and serious problem. However, most orchards do not have fire blight, even during what we consider a serious fire blight season.

In the late 1980’s, in an effort to find the most accurate method to predict fire blight infection, a number of fire blight models (Covey 1975, Mills 1955, Steiner 1990, Thompson et al. 1975) were tested, using weather data from the affected regions. These models were mainly built around the concept that blossom blight infection would occur on a day that had a 15.5ºC (60ºF)or higher mean temperature when flowers were wetted. (Mean temp is High + Low divided by 2.)

The models were difficult to adapt to the pear and apple orchards of the region, as infectionrarely occurred during the primary bloom period, as was assumed in the regions where these models had been developed. Also, since blight was uncommon, the contamination of blossoms by Erwinia amylovora was also not common, so blight rarely followed what we would expect to be an infection event. While primary bloom infection is possible on abnormal seasons, (somewhat less abnormal during recent years), in the Pacific Northwest USA, infection usually occurs through the secondary blossoms that occur on pears or apples during the seven to fourteen days after petal fall, or on the shoot tip (tertiary) blossoms that form on certain pear cultivars throughout the growing season.

All models tested tended to be highly over-predictive, often indicating that numerous infectionevents had occurred, when no fire blight resulted in unsprayed orchards (false positives). The models generally also predicted infection in instances that actual fire blight occurred. More importantly, at times, the models did not predict infection on actual infection events (false
negatives). When actual fire blight infection conditions occurred over a large region, it was noted that, usually, a relatively small percentage of orchards in that region were affected, and that disease severity was quite variable. It was determined that no fire blight model present at that time would adequately separate actual fire blight infection events from the more common “near infections.”

Temperatures and E. amylovora stigma colony development.

Contamination of flowers by E. amylovora does not necessarily lead to infection. After infesting the flower, populations of the pathogen have only a few days to grow to a colony size of at least 100,000 or 1 million live bacteria (CFU) prior to the potential infection event. The colony population increase is greatly influenced by daily temperatures.

The author theorized that a more effective model would need to more accurately evaluate and quantify the temperature conditions leading to the development of a potentially dangerous Erwinia amylovora (Burrill) Winslow et al. colony on the stigma surfaces of pear and apple flowers. It was noted that days with similar mean temperatures often had very
dissimilar hourly temperatures, and it was assumed that this variation could lead to inaccuracies in any model that used mean temperatures as a temperature assessment method. As many accurate entomological models use degree days or degree hours derived from sine wave measurements, it was theorized that measuring the number of degree hours that accumulated each hour of the days during the potential E.a. colony growth period would lead to a more accurate assessment of the potential colony growth rate and size. The degree hour values used were based on an asymmetric curve adapted from Schoutens’ revision of Billings’ Potential Doublings table (Schouten 1987), arbitrarily adjusted to a base temperature of 15.5ºC, with an optimum peak of 31ºC, with degree hour values declining to a maximum of 40.5ºC, above which bacterial
numbers were assumed to decline slightly. The temperature measurements used in the CougarBlight model were previously described as degree days above 60ºF. This has never been an accurate description, particularly when describing the 2010 version. The relationship between a temperature degree and its’ relative impact on CougarBlight temperature risk values varies greatly, dependent on its’ effect on the growth rate of E.a. For instance, increasing the temperature one degree from 78ºF to 79ºF would add one degree day, but this is worth 2.8 risk value units on the new hourly CougarBlight scale.

The 2010 version of the model altered this basis of temperature/risk evaluation:

This pathogen multiplies on the flower stigmas, slowly at temperatures below 70ºF, moderately at temperatures between 70º and 75ºF, and rapidly at temperatures between 75º and 93ºF. Optimum population size growth rate occurs between 82º and 90ºF. At temperatures over 95ºF, growth rapidly decreases to zero and populations decline in size at any temperature over about 99ºF.

Methods: The new “temperature risk value” units were developed from unpublished data for population growth of E. amylovora on stigmas.

Crab apple flowers were inoculated with E. a. using a suspension of 107 CFU/ml, (10,000,000 live bacteria per ml, or 50 million per teaspoon), resulting in a starting population of about 300 live bacteria per flower.  The flowers were held at 15 different temperatures between 39 and 102ºF for 24 hours.  The resulting population size was divided by 24 to estimate the increase in population per hour. That number was then divided by 1,000 to make the temperature value numbers smaller and more practical to manage.

Table 1.           Population size of Erwinia amylovora on stigmas of detached crab apple flowers
held at various temperatures for 24 hours, and the corresponding hourly average.

Temperatures ºC 3.48 7.89 12.22 15.27 18.21
24 hour CFU 502 397 435 10775 30637
1 hour average 20.9 16.5 18.1 449 1277
Temperatures ºC 20.16 22.5 24.27 26.06 27.17
24 hour CFU 65680 285230 357733 555333 907333
1 hour average 2737 11885 14906 23139 37806
Temperatures ºC 29.87 32.17 34.01 35.87 39.05
24 hour CFU 1153000 1259133 853667 210 0
1 hour average 48042 52464 35569 8.75 0

These numbers were used to develop a population increase curve fill in missing values for each half degree of temperature Celsius between 4ºC and 35ºC and the equivalent range in Fahrenheit (39 and 95F). (Note: models in both F and C versions now use the same values and temperature risk threshold numbers.)  These temperature values are used to compile heat unit values per hour relative to the potential population size of blight bacteria colonies.

Table 2.  Temperature risk values per hour during a specific day relative to its average temperature.  Values were derived from one hour averages in table 1, adjusted to fit a smoothed curve, and divided by 1000.

Temperatures ≤10 10.5 11 11.5 12 12.5 13 13.5 14
Hour risk values 0 0.05 0.10 0.15 0.20 0.22 0.25 0.3 0.35
Temperatures 14.5 15 15.5 16 16.5 17 17.5 18 18.5
Hour risk values 0.4 0.45 0.5 0.6 0.7 0.9 1.0 1.12 1.25
Temperatures 19 19.5 20 20.5 21 21.5 22 22.5 23
Hour risk values 1.6 2.1 2.7 3.2 3.9 4.75 5.6 7 8.9
Temperatures 23.5 24 24.5 25 25.5 26 26.5 27 27.5
Hour risk values 11.5 14.7 17.1 20.3 23 26 29 32 34.5
Temperatures 28 28.5 29 29.5 30 30.5 31 31.5 32
Hour risk values 37.5 40.5 44 46.5 48.2 50 51 52 52
Temperatures 32.5 33 33.5 34 34.5 35 35.5 36 36.5+
Hour risk values 51 50 45 35 20 10 0 0 0

 

Then, for forecasting blight risk, a table of average daily temperature risk values related to the daily high temperature was developed.  More than 2500 days in April, May and June at numerous sites and several years in central Washington State, USA, were assigned a value for every actual hourly temperature. These values were summed for every 24-hour period,
and sorted into groups relating to daily high temperature.

Table 3.           Average sum of all hourly risk values relative to daily high temperature.

Temperatures 10 10.5 11 11.5 12 12.5 13 13.5 14
Daily risk value 0.1 0.15 0.2 0.3 0.5 0.7 1.1 1.4 1.7
Temperatures 14.5 15 15.5 16 16.5 17 17.5 18 18.5
Daily risk value 2.0 2.3 2.8 3.4 4.0 4.7 5.7 6.8 8
Temperatures 19 19.5 20 20.5 21 21.5 22 22.5 23
Daily risk value 10 11.1 12.4 15.6 19.7 24.2 28.5 36.5 42
Temperatures 23.5 24 24.5 25 25.5 26 26.5 27 27.5
Daily risk value 50 61 78 95 114 133 155 186 212
Temperatures 28 28.5 29 29.5 30 30.5 31 31.5 32
Daily risk value 240 270 295 325 350 380 412 440 467
Temperatures 32.5 33 33.5 34 34.5 35 35.5 36 36.5
Daily risk value 490 508 525 535 540 535 450 310 120
Temperatures 37 37.5 38 38.5 39+
Daily risk value 60 30 15 5 0

While the temperature risk values usually tend to fall very close to the average, there can be significant variation away from this average for any specific actual day. Due to this inevitable variation, average risk values taken from the table and thresholds are considered as estimates and guidelines.  These average daily risk values may be used to run the simple form of the model, and will be used in the forecasting mode of any automated CougarBlight model system.  To accurately determine the actual daily temperature risk values, hourly temperatures must be monitored and assigned an individual corresponding risk value, which is summed with others for the day. Computer automation is almost required for this task.

Temperatures over time:
Since the stigma tips are open to contamination as soon as the flower opens, and remain in condition to support colony growth only for a low number of days until the flower degrades, it was theorized that temperatures must be evaluated as a total over a few days, rather than a single day as most models (Covey 1975, Steiner 1990) assumed at that time, or continuously throughout the entire blossom period, as others (Thompson et al. 1975, Zoller et al. 1976) did. As reported elsewhere (Smith 1996), a study of numerous actual fire blight infection events and “near infections” showed a more consistent relationship between temperatures accumulated over the four days prior to blossom wetting and actual blossom infection. Since this assumption was made, and became integral to the Cougarblight model, studies (Gouk 1998, Pusey 2006, 2007) have supported this four day temperature summation assumptionby showing that apple stigma tips support the growth of E. amylovora for the first four of the approximate six days that flower remains open and viable. Pusey found that flowers remain susceptible for longer periods, but only under cooler conditions.

For details about actual fire blight degree hour values, please contact me. I will be pleased to share them. Contact me.

Adjusting to the Local Orchard Situation:

The potential hazard fire blight presents to any specific orchard varies by variety, rootstock, vigor and age of the tree, and, especially, the recent history of fire blight in the neighborhood. Very often, when blight infection conditions are optimum, most unsprayed orchards escape infection because they are not blooming at that time, or there is very low presence of blight bacteria in the region.

As fire blight infection most often occurs during the four to six weeks following bloom, and the number of secondary bloom varies by year, variety and rootstock, the growers are advised to closely observe their orchards for blossom flushes, and to start running the model whenever open blossoms are present.

Since there is no available rapid test for growers to detect the presence and number of E. amylovora in their apple or pear flowers, growers are advised to use the recent history of fireblight in the region around their orchard as a guide to estimate potential pathogen pressure. The most important aspect of this bacterial presence guideline is whether or not fire blight was present in their neighborhood the previous year, or has been present during the current season. Degree hour thresholds are lowered in the model under these potentially high pressure situations, but it is assumed that E. amylovora is present, even though no fire blight has been seen in the region for the previous season. While outbreaks of fire blight are more scattered when infection conditions are experienced in “blight free” areas, this disease generally occurs in a region whenever susceptible hosts are subjected to high or extreme risk infection weather. Growers are advised that infection risk exists everywhere in the USA, it is just more severe some seasons in specific orchards. Blight has shown an ability to remain a potential problem in regions that apparently have had no infections for 20 years or more, flaring in seasons with abnormally conducive weather during blossom periods.

After the study of numerous infection periods, the minimum four-day Cougarblight degree hour accumulation that appear to be necessary for most infections to occur during a blossom wetting was set as follows:

For scenario 1, risk categories and temperature risk value ranges are: Low 0 – 150, Caution 150 – 500, High 500 – 800, Extreme 800-1000, and Exceptional 1000+.

For scenario 2, risk categories and temperature risk value ranges are: Low 0 – 100, Caution 100 – 200, High 200 – 350, Extreme 350 – 500, and Exceptional 501+.

For scenario 3, risk categories and temperature risk value ranges are:  Low 0 (there is no low risk in an infected orchard), Caution 0 – 100, High 100 – 200, Extreme 200 – 300, and Exceptional 301+.

The meaning of risk category terminology

Low: Wetting of flowers during these temperature conditions has not resulted in new flower blight infections in past years.  The flowers within a few meters of an active canker may be an exception.

Caution: Wetting of flowers under these temperature conditions is not likely to lead to infection, but the possibility increases as values approach the upper range.  Weather forecastsand risk values should be carefully monitored. If antibiotic materials are not being used, blossom protection with other materials should be initiated three or four days prior to entering a high infection risk period.  Continue appropriate protective sprays until the infection risk drops below the “high” threshold.

High: Under these temperature conditions, serious outbreaks of fire blight have occurred. Orchards that recently had blight are especially vulnerable.  The risk of severe damage from infection increases during the later days of the primary bloom period, and during petal fall, while blossoms are plentiful.  Infection is common, but more scattered when late blossoms
are wetted during high risk periods.  The potential severity of infection increases if a series of high risk days occur.

Extreme or Exceptional:  Some of the most damaging fire blight epidemics have occurred under these optimum temperature conditions, followed by blossom wetting.  These infections often lead to severe orchard damage, especially during primary bloom or when numerous secondary blossoms are present.  As the season progresses, secondary blossoms tend to form
less frequently, and hot summer temperatures of 35ºC and above greatly reduce the frequency of new blossom infections.

Further experience has shown that this temperature risk value number should be considered a usually trustworthy guideline only, and the degree of infection risk as determined by degree hour accumulations should be considered as an ever-increasing curve upwards, with “no risk” at the base, rising through “infection possible, but unlikely,” to “probable infection,” “high risk of infection” and “extreme risk of infection. “Stair-step” thresholds with abrupt changes in risk levels were found to be an improbable model assumption for two reasons:

  1. The ultimate size of the colony after a specific amount of time and heat may be greatly influenced by its’ CFU numbers when it was first placed upon the stigma surface, and:
  2. It is not likely that a single degree hour unit would be sufficient to actually move risk from “moderate” 198 four-day risk value total to a “high” 200 four-day temperature risk value total.

Due to the “fuzzy” nature of risk thresholds, one should be careful in interpretation of automated model systems.

The model has long since been automated. Most recently in the (Washington State University Decision Aid System”) Most on-line versions compile the hourly degree hour values over a 4 day period, and assign that total to each day, as of 08:00 (8 am). This gives a historical number for each day. (the degree hour total from midnight to 8 am is assigned to the prior day, so the “day” runs from 8 am to 8 am. So, if dew formed from 02:00 to 06:00 in the early morning, the total degree hours for the four days (96 hours) leading up to that wetting event are used in the risk assessment. If the wetting occurs later in the day, the past three days plus the current day are used to determine degree hour totals. These assessments may help the manager decide if an infection event occurred, so they might react accordingly.

However, the forecasted degree hours and risk are of greater practical value to the orchard  manager. The present day is given a forecasted degree hour value based on actual hourly temperatures for the past three days, plus an estimated value for “today,” based on the forecasted daily high temperature. The degree hour forecast for “tomorrow” is based on the actual
degree hours for the past two days, plus the estimates for “today and tomorrow.” ect. Using forecasted temperatures and estimated degree hour totals, the manager can see infection risk trends in the near future, and can adjust control programs accordingly.

Treating during the days leading up to high infection risk periods is especially important when non-antibiotic control products are used.  By spraying “biologicals” on the flowers that open during the tree or four days leading up to an infection event, the manager may be able to suppress the development of E.a. bacterial colonies, which may be much more effective than trying to control infections during or after an infection event.

Blossom Wetting:

Blossom wetting is often difficult to assess. Moisture measuring and monitoring devices are relatively untrustworthy over time, and, even if they were perfect, it is not possible to place them in every microclimatic condition in an orchard. It is possible to determine that wettingoccurred at the monitoring station, but not possible to determine that it did NOT occur in all areas of an orchard region being represented by that station.

Rain is the most common form of wetting. Observation of infections occurring in the absence of rain, and a high level of infection occurring only in low areas of orchards with poor air drainageindicates that dew alone will provide the wetness necessary to transport the E. amylovora stigma colony into the flower nectary. It is possible that duration of wetting makes a great difference to a successful infection. Fire blight outbreaks have occurred in some localities without measurable rain occurring during the 10 days to two weeks preceding symptom expression. Data recorded on remotely monitored leaf wetness sensors scattered throughout the Washington State fruitproduction area indicated that likely infection events coincided with leaf wetness duration readings of two or more hours. So, whenever possible, growers are advised to monitor leaf wetness, and consider leaf wetness readings of two or more hours as qualifying as sufficient blossom wetting in a potential infection event.

Apple and pear flowers are very often wetted by high volume sprayers during pest control, blossom thinning, or plant growth regulator applications, without any documented instance of resultant fire blight infection in the lower humidity of the western USA. It appears that this short-term wetting does not trigger fire blight infection in Western USA orchards, even when temperatures have been conducive to the development of large E. amylovora stigma colonies.

References

Covey R .P., 1975. Fire blight control strategies- a look into the future. Proc. 71st An.Mtg. Washington State Hort. Assoc., 1975: 64-65.

Gouk S.C., 1998. Influence of age of apple flowers on growth of Erwinia amylovora.Acta Hort., Proc. 8th Int. Workshop on Fire Blight , 489:525-528.

Johnson K. B., and Stockwell V. O., 1998. Secondary spread by antagonistic bacteriaamong pear and apple blossoms. Acta Hort., Proc. 8th Int. Workshop on Fire Blight,489:529.

Ley T. W., 1984. Washington State University Public Agriculture Weather System. Internet Site: http://frost.prosser.wsu.edu/

Mills W.D., 1955. Fire blight development on apple in Western New York. Pant Dis. Rptr. 39: 206-207.

Schouten H. J., 1987. A revision of Billing’s potential doublings table for fire blightprediction. Neth. J. Pl.Path., 93:55-60.

Smith T. J., 1996. A risk assessment model for fire blight of apple and pear. Acta Hort. 411:97-100.

Steiner P. W., 1990. Predicting apple blossom infections by Erwinia amylovora using the MARYBLYT model. Acta Hort. 273:149-158.

Thompson S.V., and Schroth M. N., 1975. Occurrence of fire blight of pears in relation to weather and epiphytic populations of Erwinia amylovora. Phytopathology 65:353-358.

Witney G.W., and Smith T.J., 1997. Washington State University Chelan/Douglas Extension. Internet site http://www.ncw.wsu.edu/treefrt.htm

Zoller B. G., and Sisevich J., 1976. Effect of temperature on blossoms and populations of Erwinia amylovora in Bartlett pear orchards in California during 1972-1976. (Abstract) Amer. Phytopath. Soc. Proc. 3:322.

Washington State University