• The Plant Pathology Journal
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• 제36권1호
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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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• 제24권1호
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• pp.46-51
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• 2008

With the goal of achieving better integrated pest management for hot pepper, a disease-forecasting system was compared to a conventional disease-control method. Experimental field plots were established at Asan, Chungnam, in 2005 to 2006, and hourly temperature and leaf wetness were measured and used as model inputs. One treatment group received applications of a protective fungicide, dithianon, every 7 days, whereas another received a curative fungicide, dimethomorph, when the model-determined infection risk (IR) exceeded a value of 3. In the unsprayed plot, fruits showed 18.9% (2005) and 14.0% (2006) anthracnose infection. Fruits sprayed with dithianon at 7-day intervals had 4.7% (2005) and 15.4% (2006) infection. The receiving model-advised sprays of dimethomorph had 9.4% (2005) and 10.9% (2006) anthracnose infection. Differences in the anthracnose levels between the conventional and model-advised treatments were not statistically significant. The efficacy of 10 (2005) and 8 (2006) applications of calendar-based sprays was same as that of three (2005 and 2006) sprays based on the disease-forecast system. In addition, we found much higher the IRs with the leaf wetness sensor from the field plots comparing without leaf wetness sensor from the weather station at Asan within 10km away. Since the wetness-periods were critical to forecast anthracnose in the model, the measurement of wetness-period in commercial fields must be refined to improve the anthracnose-forecast model.

• The Plant Pathology Journal
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• 제31권4호
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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.

• The Plant Pathology Journal
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• 제28권2호
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• pp.172-184
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• 2012

An infection risk model for Phytophthora blight on chili pepper was developed to estimate the first date of disease occurrence in the field. The model consisted of three parts including estimation of zoosporangium formation, soil water content, and amount of active inoculum in soil. Daily weather data on air temperature, relative humidity and rainfall, and the soil texture data of local areas were used to estimate infection risk level that was quantified as the accumulated amount of active inoculum during the prior three days. Based on the analysis on 190 sets of weather and disease data, it was found that the threshold infection risk of 224 could be an appropriate criterion for determining the primary infection date. The 95% confidence interval for the difference between the estimated date of primary infection and the observed date of first disease occurrence was $8{\pm}3$ days. In the model validation tests, the observed dates of first disease occurrence were within the 95% confidence intervals of the estimated dates in the five out of six cases. The sensitivity analyses suggested that the model was more responsive to temperature and soil texture than relative humidity, rainfall, and transplanting date. The infection risk model could be implemented in practice to control Phytophthora blight in chili pepper fields.

• 식물병연구
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• 제22권3호
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• pp.168-177
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• 2016

한국에서 발생하는 참다래 잎에서의 궤양병의 감염위험도를 예측하는 모형을 Magarey 등(2005)의 일반 감염 모형식을 이용하여 개발하였다. 이 연구를 통해 개발한 모형과 뉴질랜드에서 개발된 KVH PSA-V 모형을 2015년 서귀포시 남원읍의 녹색참다래 헤이워드 품종 재배 과원과 표선면과 성산읍 신산리의 황색 참다래 Hort16A 품종 재배 과원들에서 수집된 기상 조사 자료와 병조사 자료를 사용하여 분할표 분석을 통해 평가하였다. 자체 개발한 모형과 뉴질랜드에서 개발한 KVH PSA-V 모형, 감염 판단기준을 31로 조정한 KVH PSA-V 모형들은 실제 병이 일어났을 경우에 감염이 일어났다고 경고하는 비율인 probability of detection값이 모두 80% 이상으로 한국의 참다래 궤양병 방제 의사 결정지원용으로 사용하기에는 충분하였다. 모형이 일어나는 현상을 정확히 예측하는 지표인 proportion of correct는 이 연구를 통해 개발된 감염 위험 예측 모형이 가장 높은 51.1%를 나타내고 실제병이 일어났을 경우에 감염이 일어났다고 경고하는 비율인 probability of detection과 모형의 경고에 따라 방제를 결정하였을 때에 효율성 지표인 critical success index도 각각 가장 높은 수치인 90.9%와 47.6%를 나타내어 한국에서 발생하는 참다래 궤양에 대해서는 KVH PSA-V 모형보다 더 우수한 모형으로 판단되었다. 이 연구를 통해 새로 개발된 모형은 한국의 참다래 재배자들의 궤양병 방제를 위한 의사결정에 도움을 주어 궤양병으로 인한 피해를 줄이는 데에 도움이 될 것이다.

• The Plant Pathology Journal
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• 제35권6호
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• pp.585-597
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• 2019

A disease forecast model for Marssonina blotch of apple was developed based on field observations on airborne spore catches, weather conditions, and disease incidence in 2013 and 2015. The model consisted of the airborne spore model (ASM) and the daily infection rate model (IRM). It was found that more than 80% of airborne spore catches for the experiment period was made during the spore liberation period (SLP), which is the period of days of a rain event plus the following 2 days. Of 13 rain-related weather variables, number of rainy days with rainfall ≥ 0.5 mm per day (L_day), maximum hourly rainfall (P_max) and average daily maximum wind speed (W_avg) during a rain event were most appropriate in describing variations in airborne spore catches during SLP (S_i) in 2013. The ASM, Ŝ_i = 30.280+5.860×L_day×P_max-2.123×L_day×P_max×W_avg was statistically significant and capable of predicting the amount of airborne spore catches during SLP in 2015. Assuming that airborne conidia liberated during SLP cause leaf infections resulting in symptom appearance after 21 days of incubation period, there was highly significant correlation between the estimated amount of airborne spore catches (Ŝ_i) and the daily infection rate (R_i). The IRM, ${\hat{R}}_i$ = 0.039+0.041×Ŝ_i, was statistically significant but was not able to predict the daily infection rate in 2015. No weather variables showed statistical significance in explaining variations of the daily infection rate in 2013.

• The Plant Pathology Journal
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• 제36권5호
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• pp.406-417
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• 2020

Early warning services for crop diseases are valuable when they provide timely forecasts that farmers can utilize to inform their disease management decisions. In South Korea, collaborative disease controls that utilize unmanned aerial vehicles are commonly performed for most rice paddies. However, such controls could benefit from seasonal disease early warnings with a lead time of a few months. As a first step to establish a seasonal disease early warning service using seasonal climate forecasts, we developed the EPIRICE Daily Risk Model for rice blast by extracting and modifying the core infection algorithms of the EPIRICE model. The daily risk scores generated by the EPIRICE Daily Risk Model were successfully converted into a realistic and measurable disease value through statistical analyses with 13 rice blast incidence datasets, and subsequently validated using the data from another rice blast experiment conducted in Icheon, South Korea, from 1974 to 2000. The sensitivity of the model to air temperature, relative humidity, and precipitation input variables was examined, and the relative humidity resulted in the most sensitive response from the model. Overall, our results indicate that the EPIRICE Daily Risk Model can be used to produce potential disease risk predictions for the seasonal disease early warning service.

• 한국국방경영분석학회지
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• 제29권2호
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• pp.26-44
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• 2003

This study Is to forecast the damage of smallpox as a biological weapon and to measure the effect of potential responses (quarantine, vaccination and cure) to the spread of smallpox infection when a smallpox bioterrorism attack occurs. We designed the smallpox spreading simulation model through the literature study on a basis of some existing infectious disease models such as SIR, SEIR model by using Vensim program. In order to evaluate the performance of responses to smallpox, we measure the total infection population, infection sustaining duration, average infection rate and the infection spreading behavior of the smallpox. This study can help those who are related to the bioterrorism forecast the present and possible demage, and take more effective actions for minimizing the damage by smallpox bioterrorism.

• 식물병연구
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• 제26권4호
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• pp.229-238
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• 2020

고추 역병의 감염위험도 예측모델인 PBcast 포장검증 연구를 2012-2013년 동안 수행하였다. 그리고 2014-2017년 동안 우리나라 26개 지점에서 PBcast 모델을 이용하여 발병환경을 평가하였다. PBcast 모델은 기상과 토성자료를 이용하여 Phytophthora capsici의 일일 감염위험도를 추정한다. 시험포장에서 7일 간격으로 살균제를 살포하는 정기방제(RTN7) 처리, 예측된 감염위험도가 200 이상(IR200), 224 이상(IR224)일 때 살포하는 예찰방제 처리, 무방제(CTRL) 처리를 발병주율과 살균제 살포횟수로 비교하였다. 2012년에 감염위험도가 200 이상이 2회였지만, 224 이상인 경우는 없었다. 2013년은 200이상 3회, 224 이상 1회였다. RTN7 처리구는 2012년과 2013년에 17회, 18회 살포하였다. 우리나라의 기상조건은 고추 역병 발생에 유리하였고 방제의사결정에 PBcast 예측 정보를 활용할 경우 살포횟수를 3-4회 감소시킬 수 있다. 결과적으로 PBcast 모델은 고추 역병으로부터 보호를 위해 병방제 효과의 감소없이 살균제 살포횟수를 줄일 수 있을 것으로 생각된다.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2017년도 추계 학술발표초록집
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• pp.59-60
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• 2017