• 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.

• 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.

• 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.

• 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.25 no.3
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• pp.208-217
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• 2023

Accurate estimation of dormancy breaking and first bloom dates is crucial for effective fire blight control by disease model such as Maryblyt in apple orchards. The duration from dormancy breaking to first bloom in apple trees was influenced by daily minimum temperatures during the dormant period. The purpose of this study is to investigate the relationship between minimum temperatures during this period and the time taken for flowering to commence. Webcam data from eight apple orchards, equipped by the National Institute of Horticultural and Herbal Science, were observed from 2019 to 2023 to determine the dates of starting bloom (B1). Additionally, the dormancy breaking dates for these eight sites were estimated using an apple chill day model, with a value of -100.5 DD, based on collected weather data. Two regressions were performed to analyze the relationships: the first regression between the number of days under 0℃ (X₁) and the time from calculated dormancy breaking to observed first bloom (Y), resulting in Y = 0.87 × X₁ + 40.76 with R² = 0.84. The second regression examined the starting date of breaking dormancy (X₂) and the duration from dormancy breaking to observed first bloom (Y), resulting in Y = -1.07 × X₂ + 143.62 with R² = 0.92. These findings suggest that apple anti-chill days are significantly affected by minimum temperatures during the period from dormancy breaking to flowering, indicating their importance in fire blight control measures.