Research and IPM
Crop: Pear and Apple
Disease: Fire Blight
Model 1 of 10
Steiner, P. W. and G. W. Lightner. 1996. MaryblytTM 4.3. A Predictive Program for Forecasting Fire Blight Disease in Apples and Pears. University of Maryland, College Park.
Steiner, P.W. 1990. Predicting Apple Blossom Infections by Erwinia amylovora Using the Maryblyt Model. Acta Horticulturae 273:139-148.
Steiner, P.W. 1990. Predicting Canker, Shoot and Trauma Blight Phases of Apple Fire Blight Epidemics Using the Maryblyt Model. Acta Horticulturae 273:149-158.
On-site, within canopy. Data from weather stations can also be used for general forecasts.
Environmental: Daily minimum and maximum temperature, rainfall, and leaf wetness.
Calculated: Degree-hours and degree-days.
Host: Bud phenology.
Maryblyt is a comprehensive computer program for predicting specific infection events and symptom development for most phases of fire blight epidemics in apples and pears. The program predicts specific infection events and the appearance of four distinct types of fire blight symptoms: blossom, canker, shoot and trauma blight. It does not predict rootstock blight. The program can be operated in real time to assess the current risks or progress of an epidemic, or in a simulation mode for predicting future events using forecasted weather data. Information generated in both modes of the program provides a basis for making decisions concerning when to make specific control treatments and when it is reasonably "safe" to delay them.
Maryblyt uses three cumulative heat unit measures to indirectly monitor the development of the host, pathogen populations, insect vector availability, and symptom development.
DD and DH are calculated by the program using a sine wave function with a 90F (32C) maximum and various minimum temperature thresholds.
Blossom Blight: The Maryblyt model assumes an abundance of inoculum. For a blossom infection to occur, four conditions must be met in sequence:
When all of these minimum conditions are met in sequence, infection occurs and first blossom blight symptoms are expected after an accumulation of 103 additional DD > 55F (57 DD > 12.7 C). DH are reduced by one-third, one-half or reset to zero if the temperature does not surpass a threshold of 64 F (17.8) during one, two or three days, respectively. However, once DH exceeds 400 (EIP = 200), no negative adjustments are made.
Canker Blight: Canker blight is predicted with the accumulation of at least 196 DD > 55F (109 DD > 12.7 C) after green tip.
Shoot Blight: Maryblyt forecasts only the first early shoot blight symptoms. Forecasts are based on the assumption that insect vectors are present. These early symptoms usually develop with the accumulation of 103 DD > 55F (57 DD > 12.7 C) after the first appearance of either blossoms or canker blight symptoms in the immediate area when average daily temperature is 60F (15.6 C) or above. Maryblyt can also be used to identify different insects contributing to shoot blight by subtracting 103 DD from the total of DD > 55 F shown by the program on the day of first shoot blight symptoms.
Trauma Blight: Trauma blight symptoms are based on infections associated with late frosts, hail or high winds. Symptoms can be expected when the EIP reaches 100, but are generally more severe when the EIP exceeds 200-250.
According to the model, risk is high if three of the four conditions for blossom infection are met. One antibiotic spray is recommended at bloom stage if the risk is high and an infection event is predicted for the next day.
Insecticide sprays to control insect vectors associated with fire blight (leafhoppers, plants bugs, psylla) should be maintained during the rapid vegetative growth phase to reduce the incidence of shoot blight. Treatments should begin when blossom or canker blight symptoms appear and, preferably, as the suspect insect species that vector the pathogen begin to appear.
Maryblyt can help signal when specific types of early symptoms begin. Removal of infected tissue may be effective if fire blight incidence is low or small outbreaks are localized in the orchard.
In addition to weather and crop development based timing of bactericide applications, a copper spray at bud break to reduce the efficiency of over-wintering inoculum plus a second spray not later than green tip stage may provide protection against fire blight.
Integrated fire blight management involves several other actions in addition to copper and antibiotic sprays. Dormant pruning of blighted limbs, shoots and cankers, fertility management to control the amount and duration of succulent tissues, stress avoidance, insecticide sprays and removal of infected shoots and limbs should also be considered.
By Brent Holtz, UCCE Madera County and Beth Teviotdale, Plant Pathology, UC Davis (at Kearney Agricultural Center), for apples as part of the Pestcast Project during the years 1997-1999.
van der Zwet, T., A. R. Biggs, R. Heflebower, and G. W. Lightner. 1994. Evaluation of the MARYBLYT Computer Model for Predicting Blossom Blight on Apple in West Virginia and Maryland. Plant Disease 78:225-230.
Bonn, W. G., and T. Leuty. 1993. An Assessment of the Maryblyt Computer Program for the Prediction of Fire Blight in Ontario, Canada. Acta Horticulturae 338:145-152.
Jones, A . L. 1992. Evaluation of the Computer Model MARYBLYT for Predicting Fire Blight Blossom Infection on Apple in Michigan. Plant Disease 76:344-347.
MARYBLYT is used by growers and in research, extension and teaching programs in 32 US States and in at least 36 countries.
The model does not always explain why fire blight does or does not develop in some locations.
The model stops the blossom blight prediction phase as soon as petal fall stage is entered. Therefore this stage should not be entered until the very last blossom has dropped its petals.
Certain wetness situations and frost damages have to be judged subjectively, which may influence model output.
Gemplers, Inc.Top of page
Model 2 of 10
Smith, T. J. 1993. A Predictive Model for Forecasting Fire Blight of Pear and Apple in Washington State. Acta Horticulturae 338:153-157.
Smith, T . J. A Risk Assessment Model for Fire Blight of Apple and Pear (Fahrenheit version). Washington State University Cooperative Extension document.
See the author's Web site for more information.
Environmental: Rainfall, leaf wetness and temperature.
Calculated: Degree-hours (number of hours above a 60 F temperature threshold).
Host: Variety, age, vigor, presence of blossoms.
Pathogen: Potential for pathogen presence due to proximity of fire blight cankers.
The "Cougarblight" model was developed for fire blight of pear and apple in Washington state. It uses temperature data to estimate the growth rate of fire blight bacteria (Erwinia amylovora) over the past three days plus the present day, if wetting occurs in the afternoon of evening, or the previous four days if wetting occurs in the morning. Each day blossoms are open, the degree-hours for the noted days are added to obtain a four day degree-hour total. The goal is to determine what sort of growing conditions the bacteria have had while on the stigma during the approximately 96 hours prior to a 3+ hour blossom wetting period. Degree-hours are calculated from average hourly temperatures, or from daily minimum/maximum temperatures based on an estimated degree-hour look up chart (see Table 1). Calculation of the "four-day degree-hour total" must be done for each day. If blossoms are wetted by rain, four or more hours of dew, or any significant wetting, refer to the degree-hour total and Table 2 below to evaluate the potential risk of infection.
|Daytime High||Nighttime Low 49.9 F or Less||Nighttime Low 50 F or More|
|Table from: Smith, T. J. Fire Blight Daily Risk Estimation Model Version 98F|
|Potential for Pathogen Presence||Low||Moderate||High||Extreme|
|No fire blight in area past two seasons||0-350||350-500||500-800||800+|
|Fire blight in local area past two seasons||0-300||300-500||500-750||750+|
|Fire blight in local area last year||0-250||250-450||450-700||700+|
|Fire blight in your orchard or your neighbor's orchard last year||0-200||200-350||350-500||500+|
|Active cankers present nearby||0-100||100-200||200-350||350+|
|Table from: Smith, T.J. Fire Blight Daily Risk Estimation Model Version 98F|
Control actions are advised if a high or extreme risk infection period is detected. Factors determining potential for fire blight damage to the trees (flower numbers, tree age, vigor and variety) should also be considered.
This model was validated in Washington state by T.J. Smith throughout the 1980s.
This model is being validated by Brent Holtz, UCCE Madera County and Beth Teviotdale, Plant Pathology, UC Davis (at Kearney Agricultural Center), for apples as part of the Pestcast Project during the years 1997-1999.
The Cougarblight model has been implemented by growers and advisors in Washington state since 1991. Growers and advisors who used the model in 1993 and 1994 sprayed far less frequently and with better control than those who followed more traditional control methods.
Degree-hour totals relative to daily high and low temperatures vary from region to region, therefore degree-hour accumulation tables should be developed for each location of intended use based on regional weather station data. As an alternative, individual growers can use their own on-site weather stations to calculate degree-hours, and then refer to Table 2 to assess the risk level.Top of page
Model 3 of 10
Gubler, W.D, Lindow, S., Zoller, B., and Duncan, R. 1999. Pear Diseases in Production and Handling of California Pears. University of California, Division of Agriculture and Natural Resources Publication.
Zoller, B.G. 1990. Controlling Fire Blight of Pear Using Heat Summation to Predict Blossom Blight. Proceedings for Pear Short Course. University of California Cooperative Extension, Konocti Harbor Resort, November 27-29.
van der Zwet, T., Zoller, B. G., and Thomson, V.S. 1988. Controlling Fire Blight of Pear and Apple by Accurate Prediction of the Blossom Blight Phase. Plant Disease 72: 464-472.
Standard weather shelter adjacent to the orchard.
Environmental: Temperature, relative humidity, precipitation.
Calculated: Degree-hours. 1 F-degree-hour = 1 F-DH = 1 degree above 65 F for 1 hour.
Host: European and Asian pear.
This model was developed from five years of field observations on the development of Erwinia amylovorain Bartlett pear blossoms. It is based on the correlation between the number of accumulated F-degree-hours and the incidence of random new blight infections per holdover infection during an 11 year period. Degree-hours accumulate each hour of the day until three consecutive days occur where the temperature is below 66F. In this case the accumulation of degree-hours is reset to zero, unless the accumulated total exceeds 400 F hours and either, it has rained or warm, humid conditions have occurred with the temperature at least 57F and the humidity at least 90%. If the orchard is being irrigated, the humidity threshold is reduced to 80% instead of 90%. Irrigation of the orchard should be timed so that it does not occur during the bloom period.
Use Table 1 to determine the appropriate action.
This model was developed using E. amylovorapopulation from 1972-76. The model was validated in California using data of disease observations from 1976-1986.
This model has been used in commercial orchards of California for more than 23 years.
Even when improved disease control had been achieved with the use of this model, the incidence of fire blight was 8.7 times greater in years when accumulated degree-hours during infection periods approached 1000 F-DH than when only 100 F-DH had accumulated during the 1976-86 period. Subsequent experience has shown that increasing treatment frequency during critical situations in high degree-hour seasons will further improve control, as noted in Table 1.
Different locations in California may experience differences in fire blight disease dynamics due to interactions of factors other than temperature.
|Accumulated F Degree-Hours||Description of Weather||Recommended Action|
|1-150 (Sacramento Valley)||Rain predicted within 24 hr.||Spray in the 24 hr. period prior to rain.|
|0-100 (Lake County)||Not relevant||None|
|100-250 (Lake County)||Rain predicted within 24 hr.||Spray in the 24 hr. period prior to rain.|
|150-500 (Sacramento Valley)||Predicted rain or warm, humid weather where the temperature is at least 57F and humidity is at least 90%.||Repeat treatment every 3-4 days with treatment in the 24 hours prior to predicted conducive weather.|
|250-600 (Lake County)||Predicted rain or warm, humid weather where the temperature is at least 57F and humidity is at least 90%.||Repeat treatment every 3-4 days with treatment in the 24 hours prior to predicted conducive weather.|
|Over 500 (Sacramento Valley)||Predicted rain or warm weather where the temperature is at least 57F and humidity is at least 90%.||Treat every other day during major bloom.|
|Over 600 (Lake County)||Possibly treat every other day during major bloom.|
Model 4 of 10
Thomson, S.V., Schroth, M.N., Moller, W.J., and Reil, W.O. 1982. A Forecasting Model for Fire Blight of Pear. Plant Disease 66:576-579.
Temperature probe placed in the warmest part of the orchard.
Environmental: Minimum and maximum temperature.
Calculated: Mean temperature (average of maximum and minimum temperature from midnight to midnight).
Prediction of flower colonization by bacteria (Erwinia amylovora) is based on the daily mean temperature rising above a line drawn from 16.7 C on March 1 to 14.4 C on May 1. Subsequent applications should be made every five days until the end of bloom.
Fig. 1. Populations of Erwinia amylovora during bloom are usually detected in flower samples taken shortly after the mean temperature exceeds the prediction line.
Initiate applications when the mean daily temperature rises above the action threshold, represented by a line drawn from 16.7 C. on March 1 to 14.4 C on May 1, as shown in Figure 1.
This model was validated in Lake, Mendocino, Solano, Santa Clara, Contra Costa, El Dorado, Glenn, Napa, Sacramento, Yolo and Yuba counties in a total of 132 orchards, using data from the 1970s.
In California, implemented on 16,200 ha in 1978, with an estimated savings of $1,200,000 per year due to a reduced number of bactericides.
The mean temperature line frequently is exceeded before bacterial detection in the North Coast Range area due to lower nighttime temperatures than in the Sacramento area.
The use of daily mean temperature to predict the need for bactericide applications is a conservative approach as it does not take into account the presence of the pathogen, indicating that bactericides may be applied when there is no pathogen present and hence no risk of disease.
Model 5 of 10
Billing, E. 1996. BIS95, an Improved Approach to Fire Blight Risk Assessment. Acta Horticulturae 411:121-126.
Official regionally based weather stations, field located data loggers, or minimum and maximum thermometers plus a rain gauge.
Environmental: Daily minimum, maximum and mean temperature (C) , rain (mm), mist and heavy dew, storms with strong winds (30 kt, 15 m/sec or more), hail and/or heavy rain, thunderstorms in the area and frosts (-2 C or lower). Recording time should be 7-9 a.m.
Calculated: Degree-days above two different temperature thresholds: 18 C and 13 C.
Host: Bud burst (green tip), blossom periods (first open flowers, full bloom, 80-90% petal fall), late flower production, secondary blossom production, shoot growth periods, tissue damage due to frost or wind.
Pathogen: New disease (pre-bloom, blossom, shoot and fruit blight) and signs of stem invasion.
Vector: Numbers and activity of bees or other insects that can spread the bacteria during bloomtime.
Billings integrated system (BIS) was designed as a substitute for Billings revised system (BRS) to assess the risk of fire blight in apples, pears and related hosts. It is flexible enough to be applied to all hosts in all climatic areas. The model uses two types of degree-day calculations, which are counted and summed on a daily basis, to help assess the risk of fire blight.
The model also includes a calculation of relative risk of direct infection, of which there are two kinds, blossom infection risk (BIR) and infection risk (IR). In general, infection risk depends on inoculum potential (IP), host/target susceptibility (Su) which is increased by tissue damage (TD), and warmth and wetness (WW) at the time of infection, such that
IR = IP+Su(TD)+WW.
Blossom infection risk (BIR) occurs in situations where IP levels depend on flower colonization by the pathogen and spread by insects. A day is classified as a BIR day if :
Infection risk (IR) events occur in situations where IP levels at the targets depend on the spread of ooze by rain. A day is classified as IR if:
In addition to degree-day and infection risk calculations based on weather data, the model also involves intensive orchard scouting and systematic recording of host growth stages, insect activity, and disease incidence to determine risk of new disease.
|Mean Temp (C) on Wet Day >=||WW Score When Rain (mm) is >=|
According to the model, blossom blight risk is moderate to high if the DD18 sum is 17 or more (47 for apples), while the risk is high if the DD18 sum is 34 or more. Blossom blight has not been observed with high WW scores alone. Shoot blight infection is common and the disease incidence highest when there is tissue damage by strong winds and/or hail during storms with wind and driven rain.
The DD13 sum is used to time orchard scouting for signs of new disease. From several years of observation the BIS95 model predicts that new disease will be seen in pears within 0 to 3 days of the DD13 sum reaching 17 (47 for apples).
Table 2 shows the values of DD18, maximum temperature, mean temperature, and rainfall which tend to correspond to each risk level.
This model has been tested in England.
No information available on this new version.
Usefulness of the model depends on the quantity and quality of the data input and the knowledge and experience of the user.
|Risk Symbol||DD18 sum >=||Max temp C >=||Mean temp C >=||Rainfall mm >=|
Model 6 of 10
Billing, E. 1992. Billing's Revised System (BRS) for Fire Blight Risk Assessment. Bulletin OEPP/EPPO 22: 1-102.
Billing, E. 1990. Fire Blight Concepts and a Revised Approach to Risk Assessment. Acta Horticulturae 273: 163-170.
Official regionally based weather stations.
Environmental: Daily maximum and minimum temperature, rainfall and storm activity recorded at 9 a.m. and credited to the previous day (thrown back).
Host: Bud phenology, in particular blossom periods (first open flowers, full bloom, 80-90% petal fall), late flower production, secondary blossom production, shoot growth periods, tissue damage due to frost or wind.
Pathogen: Potential daily doubling of bacteria (based on temperature), observed disease incidence.
Vector: Number and activity of bees or other insects that can spread the bacteria during bloom-time.
Billings revised system (BRS) is a modification of Billings original system (BOS, see model 7), which was intended to make it more simple, logical, and precise. BRS is intended to offer guidance to pathologists and growers with knowledge of the essentials of fire blight epidemiology and of local orchard disease incidence. This model uses weather data to assess the risk of fire blight infection and estimate the disease development rate. It also uses intensive scouting and systematic recording of host growth stages and disease incidence to determine the risk of new disease.
The model has the following components:
|Max. Temp. (°C)||Minimum Temperature (°C)|
|PD on Day of Rain or on Day Before||WIR Score When Daily Rainfall (mm) >=|
Note that in Table 2 a trace amount of rain is equivalent to less than 0.1 mm, mist, heavy dew, or irrigation. When the PD score is higher than 11.0, the WIR score is 2 when there is no rain during bloom only.
The model provides information on when to look for signs of new disease and when to apply sprays during bloom. From bud-break, search for signs of new disease when D-periods end, particularly D-periods which began on a day with the WIR score >=4. In low risk years, BRS can reduce the time spent searching for signs of disease.
Spray guidelines include the following:
The WIR score alone is not a reliable guide to infection risk because it does not in itself take into account inoculum level or host susceptibility. A low score may still be associated with high risk, or a high score associated with low risk, depending on the level of inoculum present, as estimated by the D-period length.
BOS, and subsequently BRS, was developed and tested over a period of 23 years (1968-1990) using data from England. The model has also been tested using weather and disease data from the USA. The model seems to be useful for diverse climatic areas.
Information not given.
This model is intended to be used by people with previous experience and knowledge of the epidemiology of fire blight. Other limitations of BRS, according to E. Billing, are mainly due to a lack of knowledge of the disease and the quality of the data used as input into the model, problems that are also true for other disease models.
Schouten, H.J. 1987. A Revision of Billing's Potential Doubling Table for Fire Blight Prediction. Netherlands Journal of Plant Pathology 93:55-60.Top of page
Model 7 of 10
Billing, E. 1984. Principles and Applications of Fire Blight Risk Assessment Systems. Acta Horticulturae 151:15-22.
Billing, E. 1980a. Fire Blight in Kent, England in Relation to Weather (1955-1976). Annals of Applied Biology 95:341-464.
Billing, E. 1980b. Fire Blight (Erwinia amylovora) and Weather: a Comparison of Warning Systems. Annals of Applied Biology 95:365-377.
Regionally based weather stations.
Environmental: Daily rainfall and daily maximum and minimum temperature.
Pathogen: Daily potential doublings of bacteria (PD) (based on temperature).
This model assesses the potential for fire blight of pears and apples based on daily minimum and maximum temperatures and rainfall, and can be used to help determine the most effective times to apply chemicals.
The model uses daily potential doublings (PD) of the bacteria plus rain scores to estimate potential infection days (PIF), theoretical incubation period ( I ), and potential for fire blight activity (PFA).
|Maximum Daily Temperature (C)||Minimum Daily Temperature (C)||Potential Doublings per Day (PD)|
|Max Temp (C)||PD Score||Rain (mm) / Rain Score|
|0 / 0||<2.5 / 0.5||>=2.5 / 1.0|
Table 2 can be used to assess infection risk during spring bloom time. Initiate sprays during the first medium or high risk day, or during a low risk day if high inoculum levels or future favorable weather are suspected. Continue sprays according to observed inoculum levels, rate of new blossoms opening, dates of completion of I periods, occurrence of medium to high risk infection days, and likely insect activity. Spray immediately after any damaging storms.
This model has been tested for a range of conditions. Adjustments may be needed for specific locations, particularly dry climates.
Dinesen, I., Friis, E., Oleson, J.E., 1983. Climate and Fire blight: Billing's "System 1" Tested Under Danish Conditions and Computerized for Operational Use. Acta Horticulturae 151: 79-83.
Extensively implemented in Europe. It has been replaced by Billings revised system (BRS).
Usefulness of PD tables should be tested for each region. Suggestions offered by the model should not be taken as final solutions but as a working basis for further tests, used in conjunction with local knowledge and experience and adjusted if necessary to suit local conditions.
Billing, E. 1979. Fire Blight: the Development of a Predictive System. In Plant Pathogens pp. 51-59, edited by D.W. Lovelock. Academic Press, London.Top of page
Model 8 of 10
Jacquart-Romon, C. and Paulin, J.P. 1991. A Computerized Warning System for Fire Blight Control. Agronomie 11: 511-519.
Within orchard using an automatic weather station.
Environmental: Daily minimum and maximum temperature, rainfall, and occurrence of storms (strong winds and/or heavy rain and hail).
Calculated: Daily rain above or below a 2.5 mm threshold, forecast of minimum and maximum temperature and rainfall for the next day.
Host: Stage of development (D, E2 or G, according to Fleckinger, 1945), presence of succulent shoots and occurrence of secondary blossoms.
Pathogen: Nature of symptoms in time and space (old or recent lesions in the orchard to be protected or in adjacent orchards).
This IBM-PC computer based model was developed as a warning system for fire blight of pears and apples. The model is intended for use between February 1st and the end of July, and uses climatic and inoculum potential values and weather forecasts to evaluate fire blight risk.
Potential doublings of bacterial populations and the end of incubation periods are calculated according to Billing (see model 7), and are used to define scales of climatic potential (CP) for specific phenological stages of the plant, as is given in Tables 1 to 3. Inoculum potential (IP) during the pre-bloom and blossom periods (Table 4), and during the post-bloom period (Table 5) is estimated based on records of disease in the field under protection and nearby fields. Fire blight risks and recommended actions during the pre-bloom and blossom periods are defined for each combination of CP and IP.
Table 6 indicates the type of action required for each CP and IP combination, where N suggests no action is required, V suggests a visit to the plot to detect and remove symptoms, and S suggests spraying with a suitable chemical.
|None of the following events||1|
|End of I period, or 2 PD>=9||3|
|End of I period and 2 PD>=9 or storm||4|
|No end of I period||None of the following events is forecast||1|
|PD>= 9, or storm, or other infection conditions||2|
|Storm and: PD>=9 or other infection conditions, or rain >=2.5 mm||3|
|End of I period (J-1, J, J+1)||No other event foreseen||Rank of I period + 2|
|One infection condition foreseen||Rank of I period + 3|
|No shoot growth||No storm||1|
|Shoot growth||No rain >=2.5 mm, or storm||1|
|Rain >=2.5 mm||2|
|Secondary blossom||No rain, storm, or PD >= 9||1|
|Rain >= 2.5 mm, or PD >=9||2|
|No fire blight in the area||1|
|Fire blight introduced in the area and:||Not detected in the orchard the previous year||2|
|Detected in the orchard the previous year||3|
|Fire blight detected this year in the orchard and:||Disease activity observed before bloom||4|
|Disease activity observed in the close vicinity of the plot during bloom||4|
|Disease activity observed in the plot during bloom||4|
|Observations or Calculation||IP|
|Young active symptoms not observed during the 15 days before the day of questioning||CP during bloom < 2 and CP during last 15 days >=2||2|
|CP during bloom < 2 and CP during last 15 days >=2||2|
|CP during bloom >= 2 and CP during last 15 days < 2||2|
|CP during bloom >= 2 and CP during last 15 days > 2||3|
|Young active symptoms observed in the plot during the last 15 days and removed.||3|
|Young active symptoms observed in the close vicinity of the plot during the last 15 days and not removed.||3|
|Young active symptoms observed in the plot during the last 15 days and not removed.||4|
Decisions noted with a superscripted 1 suggest a spray if very active pollinating insects and symptoms are present in the orchard. Decisions noted with a superscripted 2 suggest a visit only if secondary blossoms are present. Decisions noted with a superscripted 3 suggest spraying and/or removing secondary blossoms.
The model has been used under different climatic situations in France over several years. Results are published in:
Jacquart-Romon, C. and J.P. Paulin. 1990. Preliminary Experimentation of a Computerized Warning System for the Control of Fire Blight. Acta Horticulturae 273: 131-137.
Lecomte. P., Jacquart-Romon, C. and Paulin, J.P. 1996. Evaluation and Utilization of FIRESCREENS, a Computer Program for the Control of Fire Blight. Bulletin OEPP/EPPO 26:549-553.
No data available.
CP is based on Billings original system, which was validated under local conditions before implementation. This system should be studied under local climatic conditions before implementation.Top of page
Model 9 of 10
Timmermans, Y. 1990. A Warning System for Fire Blight on Pears in Belgium: Preliminary Model and Practical Prospects. Acta Horticulturae 273: 121-137.
Information not given.
Environmental: Temperature, rain and dew.
Calculated: Degree-hours above 18.3 C.
Host: Presence of flowers, mean number of flowers, shoots and fruit, susceptibility of cultivar, tissue age, host vigor.
Pathogen: Number of primary inoculum sources (cankers) in the orchard.
This simulation model was constructed to adjust existing warning systems of pear fire blight to the conditions prevailing in Belgium. The model consists of three successive stages:
Each day, the simulation expresses the relative number of infected flowers, shoots and fruits and the relative quantities of symptoms resulting from previous infections.
In this model, the assessment of the number and spatial distribution of inoculum sources is considered more important than the evaluation of bacterial populations in the estimation of inoculum potential in an orchard. The model considers primary (INOCS) and secondary (INOCS2) inoculum sources, which are specified as
INOCS = ACS*INOCH
INOC2 = INOC21+INOC22, where
ACS = canker reactivation associated to the accumulation of degree hours > 18.3C. INOCH = number of primary sources of inoculum.
INOC21 = share of total symptoms removed daily from the orchard.
INOC22 = symptoms left unnoticed by the grower.
Inoculum dispersal (DISP) is modeled as a function of maximum temperature (TX), presence of flowers (FL), rain (RR), and storms (ST).
DISP = f(TX, RR, ST, FL)
Infection risk (INFx) is estimated from weather (INFCLIM) and host dependent factors (INFORG):
INFx = INFCLIM*INFORG
INFCLIM is a function of potential doublings (PD) of Erwinia amylovora, calculated according to Schouten (see reference under related work for model 6) and rain (RR).
INFCLIM = f (PD, RR)
INFORG is calculated from the number (Nx) and susceptibility (Sx) of host targets:
INFORG = Nx * Sx, where
x = flowers, shoots , pears per tree.
The total number of infected targets on the day of infection (INFOx) is calculated from the total number of infected trees on the day of infection (INOCO) and INFx.
INFOx = INOCO * INFx
INOCO = (1+DISP) * (INOCS +INOC2)
Use of INOCO assumes that one tree is bearing only one inoculum source.
Incubation period (INCx) is calculated as the sum of internal multiplication time (MULTx) and latency before external visualization of the disease (EXPR):
INCx = MULTx + EXPR
Multiplication of Erwinia amylovora is a function of temperature and moisture (PD and RR) inside infected tissues. The model assumes that multiplication varies beyond a minimal value corresponding to optimal host conditions for each type of infection:
MULTx = ; (PD* (1+y*RR)/z)
After completion of internal multiplication, symptom expression is modeled as a function of temperature:
EXPR = f (TX)
Total symptoms for each infection type (SYMPTx) are equivalent to INFOx. Daily symptoms (SYMPDx) are the proportions of SYMPTx appearing each day, which is a function of INCx:
SYMPDx = f(INCx)
The total daily number of symptoms represents the sum of all the symptoms resulting from previous infection days whose symptom expression period overlap.
No action thresholds were reported.
The model was tested during 1982-1988. Good agreement in the dates of appearance and the quantities of symptoms was observed.
Additional data is needed to validate the model. Assessment of factors was done using working hypothesis on scoring values.Top of page
Model 10 of 10
Mills, W. D. 1955. Fire Blight Development on Apple in Western New York. Plant Disease Rep. 39:206-207.
Environmental: Maximum temperature, rainfall and relative humidity.
This model was developed by W. D. Mills from disease records and weather data from counties bordering Lake Ontario. According to this model, blossom blight of apple only occurs with maximum temperatures during bloom over 65 F, with precipitation or very high humidity.
Sprays should be applied on the first day after the beginning of bloom when the maximum temperature exceeds 65 F and precipitation or high humidity has been forecasted. If these conditions do not arrive before full bloom, streptomycin should be applied and a protection period of up to seven days should be considered to be in effect if no rain occurs. If rain follows a streptomycin spray, the treatment should be repeated after the rain as soon as temperatures over 65 F with precipitation or high humidity occur. If the first spray is applied early in bloom, further applications every 4 to 7 days are indicated if favorable conditions for disease are met. No sprays should be made afterwards if these conditions do not occur.
This model was validated for five years by Luepschen, Parker and Mills (1961). They found that a minimum of two favorable days were required for severe blossom infections.
This model has been used in California, Ohio, Virginia and Pennsylvania. Partial control and some failures have been reported from Michigan and New York.