Home Articles Weather-based Apple fire blight risk map generation using GIS

Weather-based Apple fire blight risk map generation using GIS

Hojjat yazdan panah
Assistant prof.,Geography Dept.,Faculty of literature and humanities,
The university of Isfahan,Isfahan
Mail id: [email protected]

Parviz Ziaeian
Earth science Dept,Shahid beheshti university

Maryam Soleimanitabar
Teaching and training organization of Isfahan province


Fire blight is the most dangerous disease on apple in the north-west of Iran (Azerbaijan province).Weather-based fire blight risk maps of the east Azerbainan province were made by Geographic Information System (GIS) with the use of 20 years data from 10 weather stations using the MARYBLYT model. At first step, the long term meteorological data was assess using STATISTICA software for calculating conditional probability of occurring a day with suitable thermal and humiity conditions which fire Erwinia need for flower infection and outbreak in the province. Then, by using Geographical Information System (GIS) and preparing the digital elevation model (DEM) of province the map of fire blight risk was prepared. The results of this study show that regions of west and north-west of province are of high fire blight risk.

Geographic Information Systems/Science (GIS) seem to be a natural fit for epidemiology, the study of the origin and transmission of disease. Epidemiology attempts to integrate a vast array of risk factors such as temperature, moisture, vegetation type/percent cover, the presence and density of disease vectors (i.e. mosquitoes, ticks, etc.), the presence and density of susceptible hosts, pathogen transmission rates- even the molecular, cellular, reproductive and behavioral biology of the host(s)- in an orderly way. This information is used to identify which factors are necessary for disease, the areas that support disease vectors, and the risk of disease transmission and outbreak. A GIS provides us with the ability to overlay many of the larger-scale risk factors and spatially analyze the data in an attempt to better understand which factors are essential, or how the factors influence each other, to increase or decrease the occurrence of a particular disease. The articles summarized below provide an introduction to some epidemiological studies which have utilized GIS technology Fire blight, caused by the bacterium Erwinia amylovora, affects over 130 plant species in the rose family. In the east Azerbaijan province Fire blight is a destructive bacterial disease of apples and pears that kills blossoms, shoots, limbs, and, sometimes, entire trees. Once the first few open blossoms are colonized by the bacteria, pollinating insects rapidly move the pathogen to other flowers, initiating more blossom blight. These colonized flowers are subject to infection within minutes after any wetting event caused by rain or heavy dew when the average daily temperatures are equal to or greater than 60 F (16 °C) while the flower petals are intact. Although mature shoot and limb tissues are generally resistant to infection by E. amylovora, injuries caused by hail, late frosts of 28 F (-2 °C) or lower, and high winds that damage the foliage can create a trauma blight situation in which the normal defense mechanisms in mature tissues are breached and infections occur (Infected flowers first appear water-soaked, then shrivel, turning brown or black. As the infection progresses, leaves on the same spur turn dark brown or black as thought scorched by fire. The dark, shriveled leaves hang downward and usually cling to blighted twigs. Infected shoots, twigs, and suckers turn brown to black and often bend in a characteristic shepherd’s-crook. Infected immature fruit turns dark, shrivels, mummifies, and rots. Mummified fruit may cling to the tree for several months. A canker is formed when an infection progresses into larger branches

1.1 Climatic factors and fire blight:
A very important aspect of fire blight management involves monitoring of weather for the specific conditions that govern the build-up of inoculum in the orchard, the blossom infection process and the appearance of symptoms. A weather station record of daily minimum and maximum temperatures and rainfall amounts is needed. When 50 percent of the buds show green tissues, begin keeping a daily record of the cumulative degree days (DD) greater than 55’F (12.7 °C) .This information can be used to signal when symptoms are likely to appear in the orchard for blossom blight [103 DD greater than 55 F (57 DD greater than 12.7 °C) after infection] , canker blight [about 300 DD greater than 55 F (167 DD greater than 12.70 °C) after green tip] , and early shoot blight [about 103 DD greater than 55 F (57 DD greater than 12.7 °C) after blossom blight or canker blight symptoms appear]

As the blight bacteria can thrive only about four days on each individual flower, the bacteria must develop to dangerous numbers during the immediate three or four days leading up to blossom wetting. Warmer temperatures allow rapid bacterial growth in flowers. If bacteria numbers exceed a certain minimum while the flower is in good condition, then the flower is lightly wetted, infection is possible.

b.Blossom Wetting:
Blossom wetting alone does not cause fire blight. Rain during cold or cool weather does not lead to infection, or blight would be common everywhere, every year. However, if recent temperatures have allowed rapid bacterial development, blossom wetting is the infection “trigger.” Moisture can be from rain, dew or light irrigation wetting. The water moves the bacteria from the stigma tip into the flower’s nectary. The bacteria may then grow rapidly, attack the fruitlet, and then move into the tree, attacking the living portion between the wood and the bark.

1.2 Fire blight risk assessment models
ClaimsFire blight risk assessment models are briefly discussed below:

a) Maryblyt Model:
The most successful computerised prediction system developed in the United States is the MARYBLYT™ model. MARYBLYT™ has been trialed for over 10 years in the United States and is now being used by American growers and consultants to predict fire blight.

The MARYBLYT™ programme uses the infection criteria to identify infection events and predict the development of symptoms.

These criteria to identify infection events are:

  • Flowers open with stigmas and petals intact
  • The accumulation of at least 110 degree hours when temperatures are greater than 18.3°C after first bloom
  • Wetting caused either by dew or 0.25 mm of rain (or at least 2.5 mm of rain the previous day).
  • An average daily temperature of at least 15.6°C.

Computer printouts give the daily temperatures, wetness and likelihood of infection which can be interpreted to decide whether a streptomycin spray is required

b.Cougar blight model:
During the flowering period and on the basis of degree hours accumulation, this model can forecast the risk of infection of Ervina in host tree.The minimum four-day Celsius degree hour accumulation that appeared to be necessary for infection to occur during a blossom wetting was set at approximately 270 °C (500 F). The degree hours can be calculated with bellow equation:

Ti:hourly temperature
Tb:base temperature
DH:degree hour

Further experience has shown that this 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 “possible, but unlikely, infection” to “probable infection,” “high risk of infection” and “extreme risk of infection.” Stair-step thresholds were found to be an improbable model assumption for two reasons: first, The ultimate size of the colony after a specific amount of time and heat may be greatly influenced by its’ CFU(what is cfu) numbers when it was first placed upon the stigma surface, and second, It is not likely that a single degree hour unit would be sufficient to actually move risk from “moderate” 269 four-day degree hour total to a “high” 270 four-day degree hour total.

Only the days that trees are blooming are counted. Daily degree hours are forecasted for the three days in future using the look-up chart and their most trusted forecasting source. The value for each of the days is added to the sum of the days that precede it. Each morning, the user can adjust the model, updating the previous days forecast to an actual high and low temperature degree day value, sum the current four previous days total and a new three-day forecast. After the third day of blooming, each present day degree hour value is the sum of the past three days’ degree hours, plus the present days forecasted value. The grower can judge the daily fire blight risk from the present four-day degree hour total. As the four-day degree hour total rises to the level that indicates “extreme” risk, growers are advised to apply all available preventative materials on their recommended schedule until the risk drops, or the blossoms are no longer on the trees.

In this study based on climatic conditions of east province we found the high risk areas to fire blight outbreak.

2.Material and methods

2.1studied area
Figure 1 one shows the geographical location of the East Azerbaijan province in Iran. with an area of 45480 Km2 East Azerbaijan is one of the northwest provinces of Iran. Daily mean ,maximum and minimum temperature, daily relative humidity data as well as precipitation information were obtained from the synoptic and climatic stations of the area for a 25 year period (1980-2004) (Table 1).The phonological (flowering date) data of apple and pear also were collected from ministry of Agriculture for the same period.

Fig1-Geographical location of east Azerbaijan in Iran

Tab.(1): List of meteorological station of area

2.2 Information layers preparation in GIS
In this study based on the MARYBLYT model we prepared the temperature and relative humidity maps of area and then by using GIS we found the area of the province that their climatic conditions were suitable for spreading and infecting the orchards.

a) Digital elevation model(DEM):
Digital elevation model(DEM) was prepared using topographic map. For this purpose Arc/Info software and a Digitizer were used to digitize all necessary features presented on the map based on UTM map projection system. By exporting the layer prepared in Arc/Info to IDRISI software the format of the map was converted from vector to raster

b) Temperature Map:
The phonological data of apple and pear shown that the flowering period of these two host in east Azerbaijan is during April and may. so,we first calculated the probability of occurrence of daily temperature between 18.3 and 30.0°C for all meteorological stations available in the area.To do this we used daily temperature data and HYFA software to calculate probabilities of occurrence of daily temperature between 18.3 and 30.0°C in during flowering date of Apple. Then the probability map of occurrence daily temperature between 18.3 and 30.0°C during the flowering period was prepared using IDRISI software.(figure2).

Fig. 2)Probability map of a daily temperature between 18.2 and 30 degree centigrade during the flowering of Apple

c)Probality map of occurring a wet day with suitable temperature for fire blight:
Wetting period is one of the most important factors affecting the epidemy of disease. So, the daily relative humidity and rainfall amount of all meteorological stations available in the province was first analysed.. For each station, the probability of occurrence of daily relative humidity greater than 60% or rainfall amount greater than 0.25 mm was calculated.. To do this, we extracted in each station days which had daily relative humidity greater than 60% or rainfall greater than 0.25 during the flowering date of host tree from the historical data were extracted. Finally the probability map of wetting days(RH>60% or rainfall>0.25) during the flowering date of apple and pear were prepared using Statistica and IDRISI software.(fig. 3)

Fig 3- the probability map of wetting days(RH>60% or rainfall>0.25) during the flowering period

3. Results and Discussions:
For preparing of the fire blight risk map the conditional probability of occurrence of a day with daily temperature between 18.3 and 30 degree centigrade and relative humidity greater than 60%(or rainfall>0.25) should be calculated. To do two probability map of temperature(figure2) and moisture(figure3)were combined using the Analytic Hierarchy Process (AHP).

The result map contain the probability map of suitable condition for infection and outbreak of fire blight during the flowering period. In this map, fire blight risk areas were determined according to the climatic parameters. Finally we reclassified the province to four fire blight risk class: low risk, medium risk, high and very high risl level. (figure4) .

Fig. 4- fire blight risk map of the east Azerbainan province

Our investigation shows that the risk of fire blight epidemy is very high in the west and northwest of Azerbaijan province because of the presence of climatic factors which affect infection and outbreak of fire blight. In this regions, the probability of a wet day with daily temperature between 18.2 and 30.0 degree centigrade during the flowering period is very high. This probability was calculated in this research according to the historical daily temperature, rainfall and relative humidity of meteorological stations available in the province. In the Mountainous area of Azerbaijan province with elevation more than 2500 m have a not unsuitable climatic condition for fire blight. The other areas of province such as Tabriz, Azarshahr and Jolfa have the medium and low risk.


  1. Agrios, George N. 1978. Fire Blight of Pear and Apple, pp. 457-462 In: Plant Pathology, Second Edition. Academic Press, Inc., Orlando, FL. 703 pp
  2. Beer, S.V. 1990. Fire Blight, pp. 61-63 In: Compendium of Apple and Pear Diseases. The American Phytopathological Society, St. Paul, MN. 100 pp.
  3. Jacquart,K,IBM-PC computer based model,
  4. Paul W. Steiner and Gary W. Lightner, MaryBlyt 4.1, A Predictive Program For Forecasting Fire Blight Disease in Apples and Pears, University of Maryland at College Park. 1992
  5. Smith,T,Development and use of Cougar blight 98-c ,
  6. Timothy J.Smith,Cougar Blight 2002 Fire Blight Risk Assessment Model.
  7. Timothy J. Smith, Report on the Development and Use of Cougar Blight 98C – A Situation Specific Fire Blight Risk Assessment Model for Apple and Pear. Washington State University
  8. W. Steiner, T. van der ,Fire blight outbreak in the orchards
    www.caf.wvu.edu/Kearneysville /disease_descriptions/omblight.htm
  9. Yazdanpanah H. ,Fire blight weather-based models ,technical reports,Tehran university,2003 ,110 pp