• Research in Plant Disease
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• v.26 no.4
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• pp.229-238
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• 2020

Field validation of PBcast, an infection risk model for Phytophthora blight of pepper, was conducted through a designed field experiment in 2012 and 2013. Conduciveness of weather conditions at 26 locations in Korea in 2014-2017 was also evaluated using PBcast. The PBcast estimated daily infection risk (IR) of Phytophthora capsici based on weather and soil texture data. In the designed filed experiment, four treatments including routine sprays at 7-day intervals (RTN7), forecast-based sprays when IR reached 200 (IR200) and 224 (IR224), and no spray (CTRL) were compared in terms of disease incidence and number of sprays recommended for disease control. In 2012, IR had reached over 200 twice, but never reached 224. In 2013, IR had reached over 200 three times and once higher than 224. The RTN7 plots were sprayed 17 and 18 times in 2012 and 2013, respectively. Weather conditions throughout the country were generally conducive for Phytophthora blight and 3-4 times of fungicide sprays would have been reduced if the PBcast forecast information was adopted in the decision-making for fungicide sprays. In conclusion, the PBcast forecast would be useful to reduce fungicide applications without losing the disease control efficacy to protect pepper crop from Phytophthora blight.

• Journal of the military operations research society of Korea
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• v.29 no.2
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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.

• The Plant Pathology Journal
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• v.35 no.6
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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.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2018.06a
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• pp.131-131
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• 2018

• The Plant Pathology Journal
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• v.36 no.5
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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.

• Journal of Ecology and Environment
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• v.44 no.2
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• pp.98-105
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• 2020

Background: The purpose of this study, mosquito forecast system implemented by Seoul Metropolitan city, was to obtain the mosquito prediction formula by using the mosquito population data and the environmental data of the past. Results: For this study, the mosquito population data from April 1, 2015, to October 31, 2017, were collected. The mosquito population data were collected from the 50 smart mosquito traps (DMSs), two of which were installed in each district (Korean, gu) in Seoul Metropolitan city since 2015. Environmental factors were collected from the Automatic Weather System (AWS) by the Korea Meteorological Administration. The data of the nearest AWS devices from each DMS were used for the prediction formula analysis. We found out that the environmental factors affecting the mosquito population in Seoul Metropolitan city were the mean temperature and rainfall. We predicted the following equations by the generalized linear model analysis: ln(Mosquito population) = 2.519 + 0.08 × mean temperature + 0.001 × rainfall. Conclusions: We expect that the mosquito forecast system would be used for predicting the mosquito population and to prevent the spread of disease through mosquitoes.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2017.11a
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• pp.59-60
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• 2017

• The Plant Pathology Journal
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• v.21 no.2
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• pp.111-118
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• 2005

Weather data for disease forecasts are usually derived from automated weather stations (AWS) that may be dispersed across a region in an irregular pattern. We have developed an alternative method to simulate local scale, high-resolution weather and plant disease in a grid pattern. The system incorporates a simplified mesoscale boundary layer model, LAWSS, for estimating local conditions such as air temperature and relative humidity. It also integrates special models for estimating of surface wetness duration and disease forecasts, such as the grapevine downy mildew forecast model, DMCast. The system can recreate weather forecasts utilizing the NCEP/NCAR reanalysis database, which contains over 57 years of archived and corrected global upper air conditions. The highest horizontal resolution of 0.150 km was achieved by running 5-step nested child grids inside coarse mother grids. Over the Finger Lakes and Chautauqua Lake regions of New York State, the system simulated three growing seasons for estimating the risk of grape downy mildew with 1 km resolution. Outputs were represented as regional maps or as site-specific graphs. The highest resolutions were achieved over North America, but the system is functional for any global location. The system is expected to be a powerful tool for site selection and reanalysis of historical plant disease epidemics.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2012.07a
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• pp.114-118
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• 2012

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2018.06a
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• pp.115-116
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• 2018