• Plant Disease and Agriculture
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• v.5 no.2
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• pp.111-114
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• 1999

A bacterial disease of rice plant that rotted the sheath to brown was found in rice plants at Tanbuk Uisong Kyungbuk in June 1999, When the bacterial isolates from the diseased rice plants were inoculated to health plant by the artificial needle prick method the same symptoms were examined. According to its characteristics and pathogenicity on the his plant the causal bacterium was identified as Burkholderia glumae which is known as the pathogen of bacterial grain rot of rice.

• Plant Disease and Agriculture
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• v.5 no.1
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• pp.1-7
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• 1999

The grain rot of caused by Bukholderia glumae was fist reported in japan in 1955 and then reported in other countries as well as in Korea in 1986. The pathogen causes both seedling and grain rot of rice but it cannot attack any other parts of adult rice plant. Bacterial colonies grow slowly, and are circular and greyish white. The causal bacterium is Gram-negative and rod shape with 1-3 polar flagella, and produce a diffusible yellow-greenish nonfluorescent pigment on King's medium B. Biochemical characteristics such as negative in arginine dehydrolase, oxidase reaction and nitrate reduction and positive in lecithinase, and the utilization of L-arginine and inositol are useful in differentiation of this from other nonfluorescent bacteria pathogenic to rice. This pathogenic bacterium had belonged to the genus of Pseudomonas but recently was transferred to the new genus Burkholderia on the basis of physiological characteristics and DNA-DNA hybridization data. However, other characteristics such as colony heterogenicity or colonial variation after subcultures, phytotoxin, secreting antibiotics, and relationship between yellow greenish pigment production and pathogenicity need to be clarified more. To develop an effective control strategy for this disease, understanding of detailed life cycle of the disease and critical environmental factors affecting disease development is prerequisite. Although 5,435 ha of rice paddy in Korea was infested during 1998, there is no exact estimation of yield losses and distribution of the pathogen. The review will focus on recent progress on the understanding of the bacteriological and ecological characteristics of the causal bacterium and control means of the disease.

• Plant Resources
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• v.7 no.1
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• pp.36-38
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• 2004

Bacterial grain rot of rice caused by Burkholderia glumae was examined between weather condition and disease incidence. From 1998 to 2000, average disease incidence was 3.0 ％ without difference in survey regions. However, it was related closely to amount of rainfall that disease incidence higher in 1998 and 2000 to 3.0 ％ and 3.6 ％ respectively than 2.3 in 1999 relatively small volum of rainfall season.

• The Plant Pathology Journal
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• v.26 no.3
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• pp.271-272
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• 2010

Burkholderia glumae causes bacterial rice grain rot and bacterial wilt on many field crops. We developed a simple diagnostic streak method for the isolation of B. glumae from diseased plant material. The geographical distribution of 178 Korean isolates shows that B. glumae is widely spread in South Korea.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.24-24
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• 2003

Since Rice bacterial grain rot (RGBR) was reported at 1986 in Korea, it has been severely occurred in 1994, 1995, 1998, and especially around 16,609 ha in 2000, and became a major disease in rice cultivation field. This study was focused on investigation of ecology of RGBR, weather conditions that affect development of epidemics, and development of an effective RGBR forecast system based on weather conditions during the rice heading period.(중략)

• Research in Plant Disease
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• v.7 no.3
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• pp.150-154
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• 2001

The relationships between the weather conditions and the occurrence of rice bacterial grain rot caused by Burkholderia glumae during the rice heading period, were examined by analying the data accumulated from 1992 through 2000 and by conducting greenhouse and field experiment to develop a model far forecasting the disease. The disease severely occurred in 1994, 1995, 1998, and 2000, when it was high in temperature and rainfall during the heading period of middle-late rice varieties. While it occurred weakly in 1993 was high in rainfall and low temperature and it was reversely in 1997. When treated under wetting condition (above the 24-hour)after inoculation at heading period, the infection rate increased as the inculum concentration increased, it was 86.1% at 10$^{8}$ cfu/ml. When under drying condition, the disease of 12.5% occurred only at $^{8}$ cfu/ml. On the other hand, 1,000 grain weight and the percentage of ripened grains remarkably decreased as the infection rate increased.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.373.3-373
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• 2001

• Proceedings of the Microbiological Society of Korea Conference
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• 2003.05a
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• pp.7-8
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• 2003

• The Plant Pathology Journal
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• v.36 no.1
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• pp.54-66
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• 2020

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 (C_i) and the 20-day and 7-day moving averages of C_i for the inoculum build-up phase (C_inc) prior to the panicle emergence of rice plants and the infection phase (C_inf) during the heading stage of rice plants, respectively. Based on C_inc and C_inf, 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.

• The Plant Pathology Journal
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• v.31 no.4
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• pp.350-362
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• 2015

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.