- Volume 36 Issue 1
DOI QR Code
Application of Numerical Weather Prediction Data to Estimate Infection Risk of Bacterial Grain Rot of Rice in Korea
- Kim, Hyo-suk (Department of Agricultural Biotechnology, Seoul National University) ;
- Do, Ki Seok (National Center for Agrometeorology) ;
- Park, Joo Hyeon (EPINET Corporation) ;
- Kang, Wee Soo (Department of Agro-food Safety and Crop Protection, National Institute of Agricultural Sciences, Rural Development Administration) ;
- Lee, Yong Hwan (Department of Agro-food Safety and Crop Protection, National Institute of Agricultural Sciences, Rural Development Administration) ;
- Park, Eun Woo (Department of Agricultural Biotechnology, Seoul National University)
- Received : 2019.11.26
- Accepted : 2019.11.28
- Published : 2020.02.01
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 (Ci) and the 20-day and 7-day moving averages of Ci for the inoculum build-up phase (Cinc) prior to the panicle emergence of rice plants and the infection phase (Cinf) during the heading stage of rice plants, respectively. Based on Cinc and Cinf, 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.
Supported by : Korea Meteorological Administration Research
- Ashfaq, M., Mubashar, U., Haider, M. S., Ali, M., Ali, A. and Sajjad, M. 2017. Grain discoloration: an emerging threat to rice crop in Pakistan. J. Anim. Plant Sci. 27:696-707.
- Beresford, R. M. and Manktelow, D. W. L. 1994. Economics of reducing fungicide use by weather-based disease forecasts for control of Venturia inaequalis in apples. N. Z. J. Crop Hortic. Sci. 22:113-120.
- Bourke, P. M. A. 1970. Use of weather information in the prediction of plant disease epiphytotics. Annu. Rev. Phytopathol. 8:345-370.
- Branislava, L., Mihailovic, D. T., Radovanovic, S., Balaz, J. and Cirisan, A. 2007. Input data representativeness problem in plant disease forecasting models. Q. J. Hung. Meteorol. Serv. 111:199-208.
- Bregaglio, S., Donatelli, M., Confalonieri, R., Acutis, M. and Orlandini, S. 2011. Multi metric evaluation of leaf wetness models for large-area application of plant disease models. Agric. For. Meteorol. 151:1163-1172.
- Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J. and Shelly, A. 2012. Unified modeling and prediction of weather and climate: a 25-year journey. Bull. Am. Meteorol. Soc. 93:1865-1877.
- Chakraborty, S., Ghosh, R., Ghosh, M., Fernandes, C. D., Charchar, M. J. and Kelemu, S. 2004. Weather-based prediction of anthracnose severity using artificial neural network models. Plant Pathol. 53:375-386.
- Collins, S. N., James, R. S., Ray, P., Chen, K., Lassman, A. and Brownlee, J. 2013. Grids in numerical weather and climate models. In: Climate change and regional/local responses, eds. by Y. Zhang and P. Ray, pp. 111-128. Intech, Rijeka, Croatia.
- Cullen, M. J. P. and Davies, T. 1991. A conservative split-explicit integration scheme with fourth-order horizontal advection. Q. J. R. Meteorol. Soc. 117:993-1002.
- Darolt, J. C., Rocha Neto, A. C. and Di Piero, R. M. 2016. Effects of the protective, curative, and eradicative applications of chitosan against Penicillium expansum in apples. Braz. J. Microbiol. 47:1014-1019.
- De Wolf, E. D. and Isard, S. A. 2007. Disease cycle approach to plant disease prediction. Annu. Rev. Phytopathol. 45:203-220.
- Do, K. S., Kang, W. S. and Park, E. W. 2012. A forecast model for the first occurrence of Phytophthora blight on chili pepper after overwintering. Plant Pathol. J. 28:172-184.
- Duthie, J. A. 1997. Models of the response of foliar parasites to the combined effects of temperature and duration of wetness. Phytopathology 87:1088-1095.
- Fernandes, J. M. C., Pavan, W. and Sanhueza, R. M. 2014. SISALERT: a generic web-based plant disease forecasting system. In: Proceedings of the 5th International Conference on Information and Communication Technologies for Sustainable Agri-production and Environment (HAICTA 2011), eds. by M. Salampasis and A. Matopoulos, pp. 225-233. CEURWS, Aachen, Germany.
- Firanj Sremac, A., Lalic, B., Marcic, M. and Dekic, L. 2018. Toward a weather-based forecasting system for fire blight and downy mildew. Atmosphere 9:484.
- Gleason, M. L., Duttweiler, K. B., Batzer, J. C., Taylor, S. E., Sentelhas, P. C., Monteiro, J. E. B. A. and Gillespie, T. J. 2008. Obtaining weather data for input to crop disease-warning systems: leaf wetness duration as a case study. Sci. Agric. 65:76-87.
- Gonzalez-Dominguez, E., Armengol, J. and Rossi, V. 2014. Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae. PLoS ONE 9:e107547.
- Ham, J. H., Melanson, R. A. and Rush, M. C. 2011. Burkholderia glumae: next major pathogen of rice? Mol. Plant Pathol. 12:329-339.
- Hirschi, M., Spirig, C., Weigel, A. P., Calanca, P., Samietz, J. and Rotach, M. W. 2012. Monthly weather forecasts in a pest forecasting context: downscaling, recalibration, and skill improvement. J. Appl. Meteorol. Climatol. 51:1633-1638.
- Hollomon, D. W. 2015. Fungicide resistance: facing the challenge. Plant Prot. Sci. 51:170-176.
- Horsfield, A., Wicks, T., Davies, K., Wilson, D. and Paton, S. 2010. Effect of fungicide use strategies on the control of early blight (Alternaria solani) and potato yield. Australas. Plant Pathol. 39:368-375.
- Huber, L. and Gillespie, T. J. 1992. Modeling leaf wetness in relation to plant disease epidemiology. Annu. Rev. Phytopathol. 30:553-577.
- Jeong, Y., Kim, J., Kim, S., Kang, Y., Nagamatsu, T. and Hwang, I. 2003. Toxoflavin produced by Burkholderia glumae causing rice grain rot is responsible for inducing bacterial wilt in many field crops. Plant Dis. 87:890-895.
- Kang, W. S., Hong, S. S., Han, Y. K., Kim, K. R., Kim, S. G. and Park, E. W. 2010. A web-based information system for plant disease forecast based on weather data at high spatial resolution. Plant Pathol. J. 26:37-48.
- Kim, J., Kang, Y., Kim, J.-G., Choi, O. and Hwang, I. 2010. Occurrence of Burkholderia glumae on rice and field crops in Korea. Plant Pathol. J. 26:271-272.
- Kim, S., Kim, H. M., Kay, J. K. and Lee, S.-W. 2015. Development and evaluation of the high resolution limited area ensemble prediction system in the Korea Meteorological Administration. Atmosphere 25:67-83 (in Korean).
- Kurita, T. 1967. On the pathogenic bacterium of bacterial grain rot of rice. Ann. Phytopathol. Soc. Jpn. 33:111 (in Japanese).
- Lalic, B., Francia, M., Eitzinger, J., Podrascanin, Z. and Arsenic, I. 2016. Effectiveness of short-term numerical weather prediction in predicting growing degree days and meteorological conditions for apple scab appearance. Meteorol. Appl. 23:50-56.
- Lee, D.-B. and Chun, H.-Y. 2015. Development of the Korean Peninsula-Korean Aviation Turbulence Guidance (KP-KTG) system using the Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration (KMA). Atmosphere 25:367-374 (in Korean).
- Lee, Y. H., Ko, S.-J., Cha, K.-H. and Park, E. W. 2015. BGRcast: a disease forecast model to support decision-making for chemical sprays to control bacterial grain rot of rice. Plant Pathol. J. 31:350-362.
- Magarey, R. D. and Isard, S. A. 2017. A troubleshooting guide for mechanistic plant pest forecast models. J. Integr. Pest Manag. 8:3.
- Magarey, R. D., Seem, R. C., Russo, J. M., Zack, J. W., Waight, K. T., Travis, J. W. and Oudemans, P. V. 2001. Site-specific weather information without on-site sensors. Plant Dis. 85:1216-1226.
- Magarey, R. D., Sutton, T. B. and Thayer, C. L. 2005. A simple generic infection model for foliar fungal plant pathogens. Phytopathology 95:92-100.
- Mesinger, F. 1981. Horizontal advection schemes of a staggered grid: an enstrophy and energy-conserving model. Mon. Weather Rev. 109:467-478.
- Mihailovic, D. T., Koci, I., Lalic, B., Arsenic, I., Radlovic, D. and Balaz, J. 2001. The main features of BAHUS - biometeorological system for messages on the occurrence of diseases in fruits and vines. Environ. Model. Softw. 16:691-696.
- Nandakumar, R., Shahjahan, A. K. M., Yuan, X. L., Dickstein, E. R., Groth, D. E., Clark, C. A., Cartwright, R. D. and Rush, M. C. 2009. Burkholderia glumae and B. gladioli cause bacterial panicle blight in rice in the southern United States. Plant Dis. 93:896-905.
- Olatinwo, R. and Hoogenboom, G. 2014. Weather-based pest forecasting for efficient crop protection. In: Integrated pest management: current concepts and ecological perspective, ed. by D. P. Abrol, pp. 59-78. Academic Press, Amsterdam, Netherlands.
- Orlandini, S., Magarey, R. D., Park, E. W., Sporleder, M. and Kroschel, J. 2017. Methods of agroclimatology: modeling approaches for pests and diseases. In: Agronomy monograph, No. 60. Agroclimatology: linking agriculture to climate, eds. by J. L. Hatfield, M. V. K. Sivakumar and J. H. Prueger, pp. 1-36. American Society of Agronomy, Madison, WI, USA.
- Park, E. W., Seem, R. C., Gadoury, D. M. and Pearson, R. C. 1997. DMCAST: a prediction model for grape downy mildew development. Vitic. Enol. Sci. 52:182-189.
- Russo, J. M. 2000. Weather forecasting for IPM. In: Emerging technologies for integrated pest management: concepts, research, and implementation, eds. by G. G. Kennedy and T. B. Sutton, pp. 453-473. American Phytopathological Society, APS Press, St. Paul, MN, USA.
- Sokal, R. R. and Rohlf, F. J. 1973. Introduction to biostatistics. W. H. Freeman, San Francisco, CA, USA. 368 pp.
- Staniforth, A., Melvin, T. and Wood, N. 2014. Gungho! a new dynamical core for the unified model. In: Proceeding of the ECMWF seminar on recent developments in numerical methods for atmosphere and ocean modelling, pp. 15-29. European Centre for Medium-Range Weather Forecasts, Reading, UK.
- Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K. and Zerroukat, M. 2019. The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geosci. Model Dev. 12:1909-1963.
- Webster, R. K. and Gunnell, P. S. 1992. Compendium of rice diseases. American Phytopathological Society, St. Paul, MN, USA. 62 pp.