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 20day 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.

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 ± 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.

We developed a model, termed D-PSA-K, to estimate the accumulated potential damage on kiwifruit canes caused by bacterial canker during the growing and overwintering seasons. The model consisted of three parts including estimation of the amount of necrotic lesion in a non-frozen environment, the rate of necrosis increase in a freezing environment during the overwintering season, and the amount of necrotic lesion on kiwifruit canes caused by bacterial canker during the overwintering and growing seasons. We evaluated the model’s accuracy by comparing the observed maximum disease incidence on kiwifruit canes against the damage estimated using weather and disease data collected at Wando during 1994–1997 and at Seogwipo during 2014–2015. For the Hayward cultivar, D-PSA-K estimated the accumulated damage as approximately nine times the observed maximum disease incidence. For the Hort16A cultivar, the accumulated damage estimated by D-PSA-K was high when the observed disease incidence was high. D-PSA-K could assist kiwifruit growers in selecting optimal sites for kiwifruit cultivation and establishing improved production plans by predicting the loss in kiwifruit production due to bacterial canker, using past weather or future climate change data.

We estimated the averaged maximum incidences of bacterial canker at suitable sites for kiwifruit cultivation in 2020s and 2050s using D-PSA-K model with RCP4.5 and RCP8.5 climate change scenarios. Though there was a little difference between the estimation using RCP4.5 and that using RCP8.5, the estimated maximum disease incidences were more than 75% at all the suitable sites in Korea except for some southern coastal areas and Jeju island under the assumption that there are a plenty of infections to cause the symptoms. We also analyzed the intermediate and final outputs of D-PSA-K model to find out the trends on the change in disease incidence affected by climate change. Whereas increase of damage to kiwifruit canes in a non-frozen environment caused by bacterial canker was estimated at almost all the suitable sites in both the climate change scenarios, rate of necrosis increase caused by the bacterial canker pathogen in a frozen environment during the last overwintering season was predicted to be reduced at almost all the suitable sites in both the climate change scenarios. Directions of change in estimated maximum incidence varied with sites and scenarios. Whereas the maximum disease incidence at 3.14% of suitable sites for kiwifruit cultivation in 2020s under RCP4.5 scenario was estimated to increase by 10% or more in 2050s, the maximum disease incidence at 25.41% of the suitable sites under RCP8.5 scenario was estimated so.

No abstract