Maryblyt 기반 참다래 꽃썩음병 예측모형 개발 = Development of a Maryblyt-based Forecasting Model for Kiwifruit Bacterial Blossom Blight

다국어 초록 (Multilingual Abstract)

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.

국문 초록 (Abstract)

P. syringae pv. syringae에 의해 발생하는 참다래 꽃썩음병은개화기 전후의 기상조건에 영향을 크게 받는다. 지금까지 기상조건과 꽃썩음병 발생의 상관관계를 밝힌 연구들은 많았지만, 이를 활용해 꽃썩음병의 감염 위험도를 나타낼 수 있는 예측모형은 개발되지 않았다. 본 연구에서는 기존 정보를 조사하고꽃썩음병의 병원생태와 유사한 화상병 예측모형인 Maryblyt 모형을 기반으로 참다래 꽃썩음병 예측모형인 Pss-KBB Risk Model을 개발하였다. 비교평가를 통한 검증 결과, Pss-KBB Risk Model은 각각 온도와 강수 정보만을 이용하는 개화전 평균온도 모형과 강우일수 모형에 비해 실제 과수원의 병해 발생정도를 더 잘 모의하는 것으로 나타났다. 따라서 Pss-KBB Risk Model 과 기상예보자료를 활용해 꽃썩음병의 발병 위험도를 예측하여 꽃썩음병에 대한 적기적량 방제가 가능할 것으로 판단된다.

참고문헌 (Reference)

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