참고문헌
- 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. https://doi.org/10.1080/01140671.1994.9513814
- Bourke, P. M. A. 1970. Use of weather information in the prediction of plant disease epiphytotics. Annu. Rev. Phytopathol. 8:345-370. https://doi.org/10.1146/annurev.py.08.090170.002021
- 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. https://doi.org/10.1002/qj.49711146709
- 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. https://doi.org/10.1016/j.agrformet.2011.04.003
- 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. https://doi.org/10.1175/BAMS-D-12-00018.1
- 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. https://doi.org/10.1111/j.1365-3059.2004.01044.x
- 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. https://doi.org/10.1002/qj.49711750106
- 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. https://doi.org/10.1016/j.bjm.2016.07.007
- De Wolf, E. D. and Isard, S. A. 2007. Disease cycle approach to plant disease prediction. Annu. Rev. Phytopathol. 45:203-220. https://doi.org/10.1146/annurev.phyto.44.070505.143329
- 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. https://doi.org/10.5423/PPJ.2012.28.2.172
- 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. https://doi.org/10.1094/PHYTO.1997.87.11.1088
- 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. https://doi.org/10.3390/atmos9120484
- 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. https://doi.org/10.1590/S0103-90162008000700013
- 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. https://doi.org/10.1371/journal.pone.0107547
- Ham, J. H., Melanson, R. A. and Rush, M. C. 2011. Burkholderia glumae: next major pathogen of rice? Mol. Plant Pathol. 12:329-339. https://doi.org/10.1111/j.1364-3703.2010.00676.x
- 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. https://doi.org/10.1175/JAMC-D-12-082.1
- Hollomon, D. W. 2015. Fungicide resistance: facing the challenge. Plant Prot. Sci. 51:170-176. https://doi.org/10.17221/42/2015-PPS
- 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. https://doi.org/10.1146/annurev.py.30.090192.003005
- 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. https://doi.org/10.1094/PDIS.2003.87.8.890
- 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. https://doi.org/10.5423/PPJ.2010.26.1.037
- 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. https://doi.org/10.5423/PPJ.2010.26.3.271
- 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). https://doi.org/10.14191/Atmos.2015.25.1.067
- 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. https://doi.org/10.1002/met.1521
- 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). https://doi.org/10.14191/Atmos.2015.25.2.367
- 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. https://doi.org/10.5423/PPJ.OA.07.2015.0136
- 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. https://doi.org/10.1094/PDIS.2001.85.12.1216
- 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. https://doi.org/10.1094/PHYTO-95-0092
- Mesinger, F. 1981. Horizontal advection schemes of a staggered grid: an enstrophy and energy-conserving model. Mon. Weather Rev. 109:467-478. https://doi.org/10.1175/1520-0493(1981)109<0467:HASOAS>2.0.CO;2
- 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. https://doi.org/10.1016/S1364-8152(01)00032-9
- 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. https://doi.org/10.1094/PDIS-93-9-0896
- 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. https://doi.org/10.5194/gmd-12-1909-2019
- Webster, R. K. and Gunnell, P. S. 1992. Compendium of rice diseases. American Phytopathological Society, St. Paul, MN, USA. 62 pp.