Development of a Daily Epidemiological Model of Rice Blast Tailored for Seasonal Disease Early Warning in South Korea

Comparison of projected rice blast epidemics in the Korean Peninsula between the CMIP5 and CMIP6 scenarios

“…4 ) revealed that temperature, with combinatory effects of precipitation and relative humidity, was the prominent driver that influenced the projected epidemic trends and the differences between North and South Korea during the near future period (2041–2070). Although precipitation and relative humidity did not dramatically change compared to the historical period, greater epidemic risks of rice blast were observed when the temperature reaches a predefined optimum range and both the rainfall and relative humidity increase, similar with our previous study (Kim and Jung, 2020 ). Moreover, during our experiments, the EPIRICE-LB outputs were highly dependent on the interannual variability in weather parameters in the scenarios generated by the GCM models, although these variabilities and the model outputs were offset by averaging them for a 30-year period.…”

Sustainable Management of Diseases in Horticulture: Conventional and New Options

“…Some examples of the basic studies that link crop and pathogen epidemiology to the selection of biocontrol agents and fine-tune the spread of active ingredients for disease control are as follows [40][41][42][43][44][45][46]. Knowledge of the pathogen cycle of diseases is also the basis for the development and implementation of disease forecasting [47,48]. Moreover, the sustainability of modern pathogen control should be considered in addition to crop productivity, the ecological function of the crop, and the social acceptance of the strategy [49].…”

“…Similarly, in Florida, the Strawberry Advisory System (SAS) based on local weather data allows growers to reduce the use of chemical sprays for controlling anthracnose, caused by Colletotrichum spp., and grey mould, caused by Botrytis cinerea [63], by 50%. In South Korea, the EPIRICE model was developed to assess the daily risk for the occurrence of rice blast caused by Magnaporthe oryzae [48]. The model utilises some climatic data linked to fungus multiplication, such as air relative humidity, temperature, and precipitation, and can be used to predict the risk of disease at an early stage [48].…”

“…In South Korea, the EPIRICE model was developed to assess the daily risk for the occurrence of rice blast caused by Magnaporthe oryzae [48]. The model utilises some climatic data linked to fungus multiplication, such as air relative humidity, temperature, and precipitation, and can be used to predict the risk of disease at an early stage [48].…”

Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

“…In Korea, unmanned aerial vehicles are commonly utilized in most rice paddies for collaborative disease control (Kim and Jung, 2020). Disease early warnings that use seasonal climate forecasts (SCFs) with a lead time of a few months support collaborative disease controls requiring at least a month before the decision-making of scheduling and preparing the control activities.…”