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

Abstract

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.

Web-based epidemiological surveillance systems and applications to coffee rust disease

“…html). Esta visión comercial ha limitado el enfoque SVE regional original y restringiendo el sistema epidemiológico al componente clima como variables predictoras, una tendencia operativa práctica a nivel parcelario, pero limitado en una visión productiva sustentable (Kim et al, 2020;Firanj Sremac et al, 2018).…”

“…), almond (Prunus dulcis), apple (Malus domestica), pear (Pyrus communis), and peaches (Prunus persica) (https:// www.ambientweather.com/dadi6571.html). This commercial vision has limited the original regional ESS approach and biased the epidemiological system to the climate component as predictive variables, a pragmatically operational trend feasible at the field-plot level but limited to the sustainable plant health approach (Kim et al, 2020;Firanj Sremac et al, 2018).…”

“…During this century, advances in digital web and mobile telephony technology have promoted regional ES and strengthened classic predictive strategies (Coria-Contreras et al, 2019;Mora-Aguilera et al, 2015a). However, the adoption of systemic epidemiological approaches remains limited (Carbajal-Yepez et al, 2019;Parnell et al, 2017), privileging, for example, automatic and massive generation variables such as climatic ones (Kim et al, 2020;Firanj Sremac et al, 2018;Newlands, 2018), or relying heavily on classical theoretical modeling pathogenoriented (Parnell et al, 2017). The evolution of knowledge and technological developments in ES has been limited mainly to fulfill the principles of infection and protection at the field-plot level.…”

“…Revista Mexicana de FITOPATOLOGÍA Mexican Journal of Phytopathology Mora-Aguilera et al, 2015a). Sin embargo, la adopción de enfoques epidemiológicos sistémicos sigue siendo limitada (Carbajal-Yepez et al, 2019;Parnell et al, 2017), privilegiándose por ejemplo variables de generación automática y masiva como las climáticas (Kim et al, 2020;Firanj Sremac et al, 2018;Newlands, 2018), o enfatizando modelos teóricos clásicos orientados al patógeno (Parnell et al, 2017). La evolución del conocimiento y desarrollos tecnológicos en VE se han circunscrito principalmente a operar los principios de infección y protección a nivel parcelario.…”

“…Another limitation to overcome is the tendency to use a rational, analytical framework generally restricted or limited to a subsystem of the epidemiology system as a predictive context of changes in pest dynamics (Figure 1). For example, a trend in the last 20 years has been the emphasis on variables of the climate subsystem, such as temperature, wind, relative humidity, etc., favored by the availability of digital weather stations and public access to regional databases (e.g., CONAGUA in Mexico) and global (NOAA) (Magarey and Isard, 2017), for the development of regional phytosanitary risk alerts (Kim et al, 2020;Moschini et al, 2004). This subsystem has shown viability in extensive crops with linear thermal gradients (Tsukahara et al, 2008;Moschini et al, 2004), but in general, its application is not feasible in heterogeneous agroecosystems due to the exclusion of other subsystems associated with rational biological processes of pests (Mora-Aguilera et al, 2016a, b;Mora-Aguilera et al, 2014d) (Figure 1).…”