Forecasting Brown Planthopper Infestation in Korea using Statistical Models based on Climatic tele-connections

기후 원격상관 기반 통계모형을 활용한 국내 벼멸구 발생 예측

  • Kim, Kwang-Hyung (Climate Research Department, APEC Climate Center) ;
  • Cho, Jeapil (Climate Research Department, APEC Climate Center) ;
  • Lee, Yong-Hwan (Disaster Management Division, Rural Development Administration)
  • Received : 2016.04.04
  • Accepted : 2016.05.12
  • Published : 2016.06.01


A seasonal outlook for crop insect pests is most valuable when it provides accurate information for timely management decisions. In this study, we investigated probable tele-connections between climatic phenomena and pest infestations in Korea using a statistical method. A rice insect pest, brown planthopper (BPH), was selected because of its migration characteristics, which fits well with the concept of our statistical modelling - utilizing a long-term, multi-regional influence of selected climatic phenomena to predict a dominant biological event at certain time and place. Variables of the seasonal climate forecast from 10 climate models were used as a predictor, and annual infestation area for BPH as a predictand in the statistical analyses. The Moving Window Regression model showed high correlation between the national infestation trends of BPH in South Korea and selected tempo-spatial climatic variables along with its sequential migration path. Overall, the statistical models developed in this study showed a promising predictability for BPH infestation in Korea, although the dynamical relationships between the infestation and selected climatic phenomena need to be further elucidated.


Seasonal outlook;Brown planthopper;Moving window regression;Statistical model


Supported by : APEC 기후센터


  1. Choi, G.M., 1998. Occurrence and control methods for brown planthopper and white backed planthopper. Rural Development Administration, Korea, 193pp. (In Korean)
  2. Chang, C.-P., Zhang, Y., Li, T., 1999. Interannual and interdecadal variations of the East Asian summer monsoon and tropical pacific SSTs. Part II: Meridional structure of the monsoon. J. Climate 13, 4326-4340.
  3. Cho, J., 2014. Development of integrated methods for long-term water quantity and quality prediction using seasonal climate prediction. APCC Research Report 2015-13.
  4. Choi, K.-S., Moon, I.-J. 2013. Two climate factors in May that affect Korean rainfall in September. Acta Oceanol. Sin. 32, 32-47.
  5. Hu, G., Lu, F., Zhai, B.-P., Lu, M.-H., Liu, W.-C., Zhu, F., et al., 2014. Outbreaks of the Brown Planthopper Nilaparvata lugens (Stal) in the Yangtze River Delta: Immigration or Local Reproduction? PLoS ONE 9, e88973.
  6. Hyun, J.S., 1982. Meteorological condition and pest management. Korean J. Crop Sci. 27, 361-370.
  7. Kang, H., Park, C.-K., Hameed, S. N., Ashok, K., 2009. Statistical downscaling of precipitation in Korea using multimodel output variables as predictors. Mon. Weather Rev. 137, 1928-1938.
  8. Kang, S., Hur, J., Ahn, J. B., 2014. Statistical downscaling methods based on APCC multi-model ensemble for seasonal prediction over South Korea. International J. Climatol. 34, 3801-3810.
  9. Kim, M.-K., Kim, Y.-H., Lee, W.-S., 2007. Seasonal prediction of Korean regional climate from preceding large-scale climate indices. Int. J. Climatol. 27, 925-934.
  10. Kim, M.-K., Kim, Y.-H., 2010. Seasonal prediction of monthly precipitation in China using large-scale climate indices. Adv. Atmos. Sci. 27, 47-59.
  11. Kim, Y.-H., Kim, M.-K., Lee, W.-S., 2008. An Investigation of Large-Scale Climate Indices with the influence on Temperature and Precipitation Variation in Korea. Atmosphere 18, 85-97.
  12. Kisimoto, R., Sogawa, K., 1995. Migration of the brown planthopper, Nilaparvata lugensand the white-backed planthopper Sogatella furcifera in East Asia: the role of weatherand climate, pp. 67-91. In V. A. Drake and A. G. Gatehouse (eds.), Insect migration: tracking resources through space and time. Cambridge University Press, Cambridge, UK.
  13. Lee, S.W., 2012. Moving simulation of migratory insects and surveillance system of their occurrence. Rural Development Administration, Korea, 73pp. (In Korean)
  14. Matsumura, M., Takeuchi, H., Satoh, M., Sanada-Morimura, S., Otuka, A., Watanabe, T., Van Thanh, D., 2009. Current status of insecticide resistance in rice planthoppers in Asia, In Heong KL, Hardy B, editors. 2009. Planthoppers: new threats to the sustainability of intensive rice production systems in Asia. Los Banos (Philippines): International Rice Research Institute. pp 233-244.
  15. Otuka, A., 2013. Migration of rice planthoppers and their vectored re-emerging and novel rice viruses in East Asia. Front. Microbiol. 4, Article 309.
  16. Xiaoqing, X., Baoping, Z., Xiaoxi, Z., Xianian, C., Jianqiang, W., 2007. Teleconnection between the early immigration of brown planthopper (Nilaparvata lugens Stal) and ENSO indices: implication for its medium-and long-term forecast. Acta Ecol. Sin. 27, 3144-3154.
  17. Park, C.G., Hyun, J.S., 1983. Effects of temperatures and relative humidities on the development of brown planthopper, Nilaparvata lugens (Stal). Kor. J. Pl. Prot. 22, 262-270.
  18. Park, J.S., Park, K.T., Choi, K.R., Paik, J.C., 1975. Studies on the investigating method on migratory insects. Ann. Rept. Inst. Agric. Sci. 2, 85-91.
  19. Peng, Z., Wang, Q.J., Bennett, J.C., Pokhrel, P., Wang, Z., 2014. Seasonal precipitation forecasts over China using monthly largescale oceanic-atmospheric indices. J. Hydrol. 519, 792-802.
  20. RDA, 2010. Crop Diseases and Pests Monitoring Management Report (1979-2010). Rural Development Administration, Korea.
  21. Schepen, A., Wang, Q.J., Robertson, D., 2012. Evidence for using lagged climate indices to forecast Australian seasonal rainfall. J. Climate 25, 1230-1246.
  22. Selvin, S., 2004. Statistical analysis of epidemiologic data (No. Ed. 3). Oxford University Press.
  23. Sidhu, G.S., Khush, G.S., 1978. Genetice analysis of brown planthopper resistance in twenty varieties of rice, Oryza sativa L. Theor. Appl. Genet. 53, 1999-2003.
  24. Sogawa, K., 1997. Overseas immigration of rice planthoppers into Japan and associatedmeteorological systems. pp. 13-35 in China National Rice Research Institute (Ed.) Proceedings of China-Japan Joint Workshop on "Migration and Management of Insect Pest of Rice in Monsoon Asia", November 27-29, 1997, Hangzhou, P.R. China.
  25. Song, Y., Lee, J.H., 2007. Studies on the prediction models for the outbreaks of the long range migratory planthoppers on rice. Rural Development Administration, Korea, 90pp.
  26. Stigter, J., 2012. Climate-smart agriculture can diminish planthopper outbreaks, but a number of bad habits are counterproductive. (accessed on 23 March, 2016).
  27. Trenberth, K.E., 1997. The definition of el nino. B. Am. Meteorol. Soc. 78, 2771-2777.<2771:TDOENO>2.0.CO;2
  28. Wu, Z., Wang, B., Li, J., Jin, F.-F., 2009. An empirical seasonal prediction model of the East Asian summer monsoon using ENSO and NAO. J. Geophys. Res. 114, D18120.