Development of K-Maryblyt for Fire Blight Control in Apple and Pear Trees in Korea

M. Ahn, Hyeon-Ji Yang, Sung-Chul Yun
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Abstract

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.
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开发用于控制韩国苹果和梨树火疫病的 K-Maryblyt
开发 K-Maryblyt 的目的是为了有效控制苹果和梨树花朵上的次生火疫病感染,并消除苹果和梨树在花季初期从干茎和新萌发枝条上产生的主要接种源。这种模式有助于精确确定花朵感染的时间和确定主要接种源,类似于 Maryblyt,可预测花朵感染情况和植物各部位(包括茎干、花朵和嫩枝)出现的症状。尽管如此,K-Maryblyt 仍有很大改进:整合了苹果树和梨树的物候学模型,采用观测到的或预测的小时温度计算附生植物感染潜势 (EIP),采用调整后的方程,从而减少了平均误差,苹果树为 10.08 度小时 (DH),梨树为 9.28 度小时 (DH),在梨树 EIP 计算中引入了相对湿度变量,并针对预期症状调整了修改后的度日计算方法。自 2022 年过渡到基于模型的控制政策以来,该系统在启用的第一年就向果农和专业管理人员发布了 158440 条与花期控制和症状预测有关的信息。此外,考虑到花朵开放条件和喷洒农药的天气适宜性,该系统还改进了防治信息,将农药的作用机理考虑到分发给特定县农民的防治信息中。作为火疫病预测信息系统(FBcastS)中的一个关键模块,K-Maryblyt 在为农民、专业管理人员和政策制定者提供重要的火疫病信息方面发挥着至关重要的作用。
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