{"title":"Improvement of Fire Blight Blossom Infection Control Using Maryblyt in Korean Apple Orchards.","authors":"Kyung-Bong Namkung, Sung Chul Yun","doi":"10.5423/PPJ.OA.08.2023.0110","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":20173,"journal":{"name":"Plant Pathology Journal","volume":"39 5","pages":"504-512"},"PeriodicalIF":1.8000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9c/47/ppj-oa-08-2023-0110.PMC10580054.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plant Pathology Journal","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.5423/PPJ.OA.08.2023.0110","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.