• 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.

• 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.

• 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.

• 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.