Application of Numerical Weather Prediction Data to Estimate Infection Risk of Bacterial Grain Rot of Rice in Korea.

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.

MBCAST: A Forecast Model for Marssonina Blotch of Apple in Korea.

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 air-borne spore catches during SLP (S i ) in 2013. The ASM, S 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 (S i ) and the daily infection rate (R i ). The IRM, R̂ i = 0.039+0.041×S 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.

Predicting temporal shifts in the spring occurrence of overwintered Scotinophara lurida (Hemiptera: Pentatomidae) and rice phenology in Korea with climate change.

Climate change could shift the phenology of insects and plants and alter their linkage in space and time. We examined the synchrony of rice and its insect pest, Scotinophara lurida (Burmeister), under the representative concentration pathways (RCP) 8.5 climate change scenario by comparing the mean spring immigration time of overwintered S. lurida with the mean rice transplanting times in Korea. The immigration time of S. lurida was estimated using an overwintered adult flight model. The rice transplanting time of three cultivars (early, medium, and medium-late maturing) was estimated by forecasting the optimal cultivation period using leaf appearance and final leaf number models. A temperature increase significantly advanced the 99% immigration time of S. lurida from Julian day 192.1 in the 2000s to 178.4 in the 2050s and 163.1 in the 2090s. In contrast, rice transplanting time was significantly delayed in the early-maturing cultivar from day 141.2 in the 2000s to 166.7 in the 2050s and 190.6 in the 2090s, in the medium-maturing cultivar from day 130.6 in the 2000s to 156.6 in the 2050s and 184.7 in the 2090s, and in the medium-late maturing cultivar from day 128.5 in 2000s to 152.9 in the 2050s and 182.3 in the 2090s. These simulation results predict a significant future phenological asynchrony between S. lurida and rice in Korea.

Development of a Model to Predict the Primary Infection Date of Bacterial Spot (Xanthomonas campestris pv. vesicatoria) on Hot Pepper.

A population model of bacterial spot caused by Xanthomonas campestris pv. vesicatoria on hot pepper was developed to predict the primary disease infection date. The model estimated the pathogen population on the surface and within the leaf of the host based on the wetness period and temperature. For successful infection, at least 5,000 cells/ml of the bacterial population were required. Also, wind and rain were necessary according to regression analyses of the monitored data. Bacterial spot on the model is initiated when the pathogen population exceeds 10(15) cells/g within the leaf. The developed model was validated using 94 assessed samples from 2000 to 2007 obtained from monitored fields. Based on the validation study, the predicted initial infection dates varied based on the year rather than the location. Differences in initial infection dates between the model predictions and the monitored data in the field were minimal. For example, predicted infection dates for 7 locations were within the same month as the actual infection dates, 11 locations were within 1 month of the actual infection, and only 3 locations were more than 2 months apart from the actual infection. The predicted infection dates were mapped from 2009 to 2012; 2011 was the most severe year. Although the model was not sensitive enough to predict disease severity of less than 0.1% in the field, our model predicted bacterial spot severity of 1% or more. Therefore, this model can be applied in the field to determine when bacterial spot control is required.