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2015 - 2020年期间,Maryblyt模型在忠州、堤川和阴城苹果树火疫病感染中的应用。

Application of the Maryblyt Model for the Infection of Fire Blight on Apple Trees at Chungju, Jecheon, and Eumsung during 2015-2020.

作者信息

Ahn Mun-Il, Yun Sung Chul

机构信息

EPINET Co., Ltd., Anyang 14056, Korea.

Department of Pharmaceutical Engineering & Biotechnology, Sunmoon University, Asan 31460, Korea.

出版信息

Plant Pathol J. 2021 Dec;37(6):543-554. doi: 10.5423/PPJ.OA.07.2021.0120. Epub 2021 Dec 1.

DOI:10.5423/PPJ.OA.07.2021.0120
PMID:34897247
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8666245/
Abstract

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.

摘要

为预防性控制苹果树的火疫病并确定田间监测政策,2015年至2020年期间在韩国忠州市、堤川市和阴城郡对Maryblyt ver. 7.1模型(MARYBLYT)进行了评估。2020年开花期感染警报数量最多,2017年和2018年最少。花期MARYBLYT开花期感染风险的共同特征是,无论位置如何,BIR-高或BIR-感染警报时间相同。运行该模型所需的树木花期因年份和地理位置而异。该模型预测花期“感染”风险,并推荐控制开花期感染的合适时间。2020年,火疫病严重时,MARYBLYT预测的花期疫病症状预期日期与实际症状检测日期之间的差异仅为1至3天,这意味着MARYBLYT非常准确。由于该模型最初是基于从美国东部地区获得的数据开发的,该地区气候与韩国相似,因此该模型可在韩国使用。然而,为提高田间利用率,应用该模型时需要考虑多个苹果品种的整个花期。MARYBLYT被认为是确定何时对遭受严重火疫病问题的苹果种植区进行防治和监测的有用工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/9ba8f07152b8/ppj-oa-07-2021-0120f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/1e8d3471b508/ppj-oa-07-2021-0120f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/b2d256d8e9be/ppj-oa-07-2021-0120f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/4d5851be5441/ppj-oa-07-2021-0120f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/8a5c0e27da87/ppj-oa-07-2021-0120f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/86738b169033/ppj-oa-07-2021-0120f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/9ba8f07152b8/ppj-oa-07-2021-0120f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/1e8d3471b508/ppj-oa-07-2021-0120f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/b2d256d8e9be/ppj-oa-07-2021-0120f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/4d5851be5441/ppj-oa-07-2021-0120f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/8a5c0e27da87/ppj-oa-07-2021-0120f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/86738b169033/ppj-oa-07-2021-0120f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30e7/8666245/9ba8f07152b8/ppj-oa-07-2021-0120f6.jpg

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本文引用的文献

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The use and role of predictive systems in disease management.预测系统在疾病管理中的使用和作用。
Annu Rev Phytopathol. 2013;51:267-89. doi: 10.1146/annurev-phyto-082712-102356. Epub 2013 May 13.
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