The Plant Pathology Journal

The Korean Society of Plant Pathology (한국식물병리학회)
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Application of Numerical Weather Prediction Data to Estimate Infection Risk of Bacterial Grain Rot of Rice in Korea

  • Kim, Hyo-suk (Department of Agricultural Biotechnology, Seoul National University) ;
  • Do, Ki Seok (National Center for Agrometeorology) ;
  • Park, Joo Hyeon (EPINET Corporation) ;
  • Kang, Wee Soo (Department of Agro-food Safety and Crop Protection, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Lee, Yong Hwan (Department of Agro-food Safety and Crop Protection, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Park, Eun Woo (Department of Agricultural Biotechnology, Seoul National University)
  • Received : 2019.11.26
  • Accepted : 2019.11.28
  • Published : 2020.02.01
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Abstract

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (Ci) and the 20-day and 7-day moving averages of Ci for the inoculum build-up phase (Cinc) prior to the panicle emergence of rice plants and the infection phase (Cinf) during the heading stage of rice plants, respectively. Based on Cinc and Cinf, we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

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References

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