• The Plant Pathology Journal
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• v.39 no.5
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• pp.504-512
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• 2023

After transitioning from periodic to model-based control policy for fire blight blossom infection, it is crucial to provide the timing of field application with easy and accurate information. To assess the risk of blossom infection, Maryblyt was employed in 31 sites across apple-producing regions nationwide, including areas prone to fire blight outbreaks, from 2021 to 2023. In 2021 and 2023, two and seven sites experienced Blossom Infection Risk-Infection warning occurrences among 31 sites, respectively. However, in 2022, most of the sites observed Blossom Infection Risk-Infection from April 25 to 28, highlighting the need for blossom infection control. For the comparison between the two model-based control approaches, we established treatment 1, which involved control measures according to the Blossom Infection Risk-Infection warning and treatment 2, aimed at maintaining the Epiphytic Infection Potential below 100. The analysis of control values between these treatments revealed that treatment 2 was more effective in reducing Blossom Infection Risk-Infection and the number of days with Epiphytic Infection Potential above 100, with respective averages of 95.6% and 93.0% over the three years. Since 2022, the implementation of the K-Maryblyt system and the deployment of Automated Weather Stations capable of measuring orchard weather conditions, with an average of 10 stations per major apple fire blight county nationwide, have taken place. These advancements will enable the provision of more accurate and timely information for farmers based on fire blight models in the future.

• The Plant Pathology Journal
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• v.37 no.6
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• pp.543-554
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• 2021

To preventively control fire blight in apple trees and determine policies regarding field monitoring, the Maryblyt ver. 7.1 model (MARYBLYT) was evaluated in the cities of Chungju, Jecheon, and Eumseong in Korea from 2015 to 2020. The number of blossom infection alerts was the highest in 2020 and the lowest in 2017 and 2018. And the common feature of MARYBLYT blossom infection risks during the flowering period was that the time of BIR-High or BIR-Infection alerts was the same regardless of location. The flowering periods of the trees required to operate the model varied according to the year and geographic location. The model predicts the risk of "Infection" during the flowering periods, and recommends the appropriate times to control blossom infection. In 2020, when flower blight was severe, the difference between the expected date of blossom blight symptoms presented by MARYBLYT and the date of actual symptom detection was only 1-3 days, implying that MARYBLYT is highly accurate. As the model was originally developed based on data obtained from the eastern region of the United States, which has a climate similar to that of Korea, this model can be used in Korea. To improve field utilization, however, the entire flowering period of multiple apple varieties needs to be considered when the model is applied. MARYBLYT is believed to be a useful tool for determining when to control and monitor apple cultivation areas that suffer from serious fire blight problems.

• Research in Plant Disease
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• v.24 no.3
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• pp.182-192
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• 2018

Since fire blight (Erwinia amylovora) firstly broke out at mid-Korea in 2015, it is necessary to investigate potential spread of the invasive pathogen. To speculate environmental factors of fireblight epidemic based on disease triangle, a fire blight predicting program, MARYBLYT, was run with the measured meteorological data in 2014-2017 and the projecting future data under RCP8.5 scenario for 2020-2100. After calculating blossom period of Singo pear from phenology, MARYBLYT was run for blossom blight during the blossom period. MARYBLYT warned "Infection" blossom blight in 2014-15 at Anseong and Cheonan as well as Pyungtak and Asan. In addition, it warned "Infection" in 2016-17 at Naju. More than 80% of Korean areas were covered "Infection" or "High", therefore Korea was suitable for fire blight recently. Blossom blight for 2020-2100 was predicted to be highly fluctuate depending on the year. For 80 years of the future, 20 years were serious with "Infection" covered more than 50% of areas in Korea, whereas 8 years were not serious covered less than 10%. By comparisons between 50% and 10% of the year, temperature and amount of precipitation were significantly different. The results of this study are informative for policy makers to manage the alien pathogen.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.302-310
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• 2023

In this study, the extent of the impact of rainfall on the outbreak of fire blight during the blossom infection risk period was explored. In the Chungnam province, the outbreak of fire blight disease began in 2015, and changes in the outbreak's scale were most pronounced between 2020 and 2022, significantly escalating from 63 orchards in 2020 to 170 orchards in 2021, before decreasing to 46 orchards in 2022. In 2022, the number of incidence has decreased and the number of canker symptom in branches has also decreased. It was evaluated that the significant decrease of fire blight disease in 2022 was due to the dry weather during the flowering season. In other words, this yearly fluctuation in fire blight outbreaks was correlated with the presence or absence of rainfall and accumulated precipitation during the blossom infection risk period. This trend was observed across all surveyed regions where apples and pears were cultivated. Among the weather conditions influencing the blossom infection risk period, rainfall notably affected the activation of pathogens from over-wintering cankers and flower infections. In particular, precipitation during the initial 3 days of the blossom infection risk warning was confirmed as a decisive factor in determining the outbreak's scale.

• Research in Plant Disease
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• v.21 no.2
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• pp.67-73
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• 2015

Bacterial blossom blight of kiwifruit (Actinidia deliciosa) caused by Pseudomonas syringae pv. syringae is known to be largely affected by weather conditions during the blooming period. While there have been many studies that investigated scientific relations between weather conditions and the epidemics of bacterial blossom blight of kiwifruit, no forecasting models have been developed thus far. In this study, we collected all the relevant information on the epidemiology of the blossom blight in relation to weather variables, and developed the Pss-KBB Risk Model that is based on the Maryblyt model for the fire blight of apple and pear. Subsequent model validation was conducted using 10 years of ground truth data from kiwifruit orchards in Haenam, Korea. As a result, it was shown that the Pss-KBB Risk Model resulted in better performance in estimating the disease severity compared with other two simple models using either temperature or precipitation information only. Overall, we concluded that by utilizing the Pss-KBB Risk Model and weather forecast information, potential infection risk of the bacterial blossom blight of kiwifruit can be accurately predicted, which will eventually lead kiwifruit growers to utilize the best practices related to spraying chemicals at the most effective time.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.347-349
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• 2001

Black blights attacked the blossom and flower buds of wild chrysanthemum (Chrysanthemum boreale) in the experimental field in Hamyang in 1998. The infection rate of the disease on the plant ranged from 4.0 to 91.8%. The pathogen isolated from the infected flower buds produced numerous conidia in pycnidia. The pycnidia, which were immersed into the petals, emerged through the epidermis by short ostiolate neck. Conidia had 0-3 septate (mostly uniseptate) and were 10-27.5 $\times$5-7.5 ㎛ in size. The fungus produced pseudothecia on potato dextrose agar (PDA), and uniseptate ascospores produced in asci were 10$\times$2.7 ㎛ in size. The pathogen also produced pycnidia and pycnidiospores on PDA after 4 weeks in the dark condition. The conidia produced on PDA were smaller than those from infected plants. Based on the examined mycological characteristics, the fungus was identified as Didymella chrsanthemi.

• The Plant Pathology Journal
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• v.40 no.3
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• pp.290-298
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• 2024

K-Maryblyt has been developed for the effective control of secondary fire blight infections on blossoms and the elimination of primary inoculum sources from cankers and newly emerged shoots early in the season for both apple and pear trees. This model facilitates the precise determination of the blossom infection timing and identification of primary inoculum sources, akin to Maryblyt, predicting flower infections and the appearance of symptoms on various plant parts, including cankers, blossoms, and shoots. Nevertheless, K-Maryblyt has undergone significant improvements: Integration of Phenology Models for both apple and pear trees, Adoption of observed or predicted hourly temperatures for Epiphytic Infection Potential (EIP) calculation, incorporation of adjusted equations resulting in reduced mean error with 10.08 degree-hours (DH) for apple and 9.28 DH for pear, introduction of a relative humidity variable for pear EIP calculation, and adaptation of modified degree-day calculation methods for expected symptoms. Since the transition to a model-based control policy in 2022, the system has disseminated 158,440 messages related to blossom control and symptom prediction to farmers and professional managers in its inaugural year. Furthermore, the system has been refined to include control messages that account for the mechanism of action of pesticides distributed to farmers in specific counties, considering flower opening conditions and weather suitability for spraying. Operating as a pivotal module within the Fire Blight Forecasting Information System (FBcastS), K-Maryblyt plays a crucial role in providing essential fire blight information to farmers, professional managers, and policymakers.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.305-317
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• 2022

To investigate the blossom infection risk of fire blight on pears, the program Maryblyt has been executed from 2018 to 2022 based on meteorological data from central-Korean cities where fire blight has occurred as well as from southern Korean cities where the disease has not yet occurred. In the past five years, years with the highest risk of pear blossom blight were 2022 and 2019. To identify the optimal time for spraying, we studied the spray mode according to the Maryblyt model and recommend spraying streptomycin on the day after a "High" warning and then one day before forecasted precipitation during the blossom period. Maryblyt also recommends to initiate surgical controls from mid-May for canker blight symptoms on pear trees owing to over-wintering canker in Korea. Web-cam pictures from pear orchards at Cheonan, Icheon, Sangju, and Naju during the flowering period of pear trees were used for comparing real data and constructing a phenological model. The actual starting dates of flowering at southern cities such as Sangju and Naju were consistently earlier than those calculated by the model. It is thus necessary to improve the forecasting model to include field risks by recording the actual flowering period and the first day of the fire blight symptoms, according to the farmers, as well as mist or dew-fall, which are not easily identifiable from meteorological records.

• Research in Plant Disease
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• v.10 no.4
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• pp.290-296
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• 2004

Bacterial blossom blight is one of the most important diseases of kiwifruit (Actinidia deliciosa). The disease occurs during flowering in the late May and disease outbreaks associated with rainfall during the flowering season have resulted in a severe reduction in kiwifruit production. The causal organism isolated from diseased blossoms of kiwifruits was identified as Pseudomonas syringae pv, syringae based on the physiological and biochemical characteristics and pathogenicity test. Dead fruit stalks, dead pruned twigs, fallen leaves and soils mainly provided R syringae pv. syringae with overwintering places in the kiwifruit orchards, and the inocula also overwintered on buds, trunks, branches, and twigs on the kiwifruit trees. Among the overwintering places, the incula were detected in the highest frequencies from dead fruit stalks. The population density of P. syringae pv. syringae was speculated to be over $1{\times}10^4$cfu/ml for the bacterial infection, and the optimum temperature for the bacterial growth ranged 20 to $25^{\circ}C$. The highest population density of P. syringae pv. syringae on the overwintering places was detected in May and June when the daily average temperature coincided with the optimum temperature for bacterial growth of P. syringae pv. syringae.

• The Korean Journal of Mycology
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• v.50 no.4
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• pp.331-341
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• 2022

This study was conducted to identify yeast species isolated from domestic pear blossom through gene sequencing and analysis of morphological characteristics, and to confirm specific yeast species inhibitory effects toward fire blight in immature apples, pears, and crab apple blossoms. Yeast morphological characteristics were consistent with the known characteristics of Aureobasidium pullulans. Nucleotide sequencing of the D1/D2 region of large-subunit (LSU) 26S ribosomal DNA and the internal transcribed spacer (ITS) region confirmed its identity as A. pullulans (MHAU2101). Inoculation of immature fruits with A. pullulans MHAU2101 before exposure to Erwinia amylovora prevented fire blight symptoms in apples and pears. A. pullulans MHAU2101 treated crab apple blossoms had a significantly lower flower infection rate than untreated blossoms, revealing 64% of the potency of streptomycin. The A. pullulans MHAU2101 treated group also displayed lower E. amylovora density in both pistil and hypanthium compared to the untreated group, especially in the hypanthium. This study confirms that A. pullulans MHAU2101 isolated from domestic pear blossom can effectively suppress the onset of fire blight.