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우리나라 남부지방에서의 2014년 벼 이삭도열병 대발생
강위수,서명철,홍성준,이경재,이용환 한국식물병리학회 2019 식물병연구 Vol.25 No.4
Rice panicle blast occurred severely in southern provinces of Korea in 2014. The proportion of panicle blast incidence area to cultivated area of rice were 11.0% and 14.6% in Jeollanam-do and Gyeongsangnam- do, respectively. To identify the causal factors of the outbreak, we investigated weather conditions in August, amount of cultivated area of mainly grown cultivars, and nitrogen contents in plants with different disease incidences in 2014. ‘Saenuri,’ ‘Ilmibyeo,’ ‘Unkwang,’ ‘Dongjin 1 ho,’ ‘Nampyeongbyeo,’ and ‘Hwangkeumnuri’ were mainly grown cultivars. Monthly average of daily air temperature in August 2014 was 3.2°C and 3.1°C less than 2018 in Haenam and Miryang, respectively. Rainfall in August 2014 was 70.0% and 42.0% greater than 2018 in Haenam and Miryang, respectively. The numbers of blast warning days in August calculated nationwide using a forecast model for blast infection were higher in 2014 than in 2018, and they were in high level throughout the country in 2014. Nitrogen contents in plant samples from high-incidence plots were significantly higher than those from low-incidence plots. Consequently, excessive use of nitrogen fertilizers was the main factor for the disease outbreak at the level of specific farms, in addition to the collective cultivation of susceptible cultivar, low temperatures and frequent rainfalls in August.
강위수,홍순성,한용규,김규랑,김성기,박은우 한국식물병리학회 2010 Plant Pathology Journal Vol.26 No.1
This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of 240 m×240 m based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast,sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.