The Plant Pathology Journal

The Korean Society of Plant Pathology (한국식물병리학회)
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BGRcast: A Disease Forecast Model to Support Decision-making for Chemical Sprays to Control Bacterial Grain Rot of Rice

  • Lee, Yong Hwan (Disaster Management Division, Rural Development Administration) ;
  • Ko, Sug-Ju (Jeonnam Agricultural Research and Extension Services) ;
  • Cha, Kwang-Hong (Jeonnam Agricultural Research and Extension Services) ;
  • Park, Eun Woo (Department of Agricultural Biotechnology, Seoul National University)
  • Received : 2015.07.13
  • Accepted : 2015.08.22
  • Published : 2015.12.01
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Abstract

A disease forecast model for bacterial grain rot (BGR) of rice, which is caused by Burkholderia glumae, was developed in this study. The model, which was named 'BGRcast', determined daily conduciveness of weather conditions to epidemic development of BGR and forecasted risk of BGR development. All data that were used to develop and validate the BGRcast model were collected from field observations on disease incidence at Naju, Korea during 1998-2004 and 2010. In this study, we have proposed the environmental conduciveness as a measure of conduciveness of weather conditions for population growth of B. glumae and panicle infection in the field. The BGRcast calculated daily environmental conduciveness, $C_i$, based on daily minimum temperature and daily average relative humidity. With regard to the developmental stages of rice plants, the epidemic development of BGR was divided into three phases, i.e., lag, inoculum build-up and infection phases. Daily average of $C_i$ was calculated for the inoculum build-up phase ($C_{inf}$) and the infection phase ($C_{inc}$). The $C_{inc}$ and $C_{inf}$ were considered environmental conduciveness for the periods of inoculum build-up in association with rice plants and panicle infection during the heading stage, respectively. The BGRcast model was able to forecast actual occurrence of BGR at the probability of 71.4% and its false alarm ratio was 47.6%. With the thresholds of $C_{inc}=0.3$ and $C_{inf}=0.5$, the model was able to provide advisories that could be used to make decisions on whether to spray bactericide at the preand post-heading stage.

Keywords

References

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