Chinese Journal of Agrometeorology ›› 2016, Vol. 37 ›› Issue (01): 98-110.doi: 10.3969/j.issn.1000-6362.2016.01.013

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Medium and Long-term Forecasting Models of Nilaparvata lugens (st?l)’s Immigration Amount in Jiangsu Province

BAO Yun-xuan, XUE Zhou-hua, LIU Yao, JIANG Rong, XIE Xiao-jin, YANG Rong-ming, ZHU Feng   

  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;2.Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044;3.Meteorological Bureau of Fu’an City in Fujian Province, Fu’an 355000;4.Jiangsu Province Plant Protection Station, Nanjing 210013
  • Received:2015-05-04 Online:2016-02-20 Published:2016-02-24

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Abstract:

In order to predict the annual total immigration heads of brown planthoppers (BPH), Nilaparvata lugens (st?l), at a station in a rice-growing region by using the atmospheric background fields in the earlier stages and provide a basis for the early warning of BPH’s catastrophic immigrations and their effective prevention and controlling, the BPH’s lighting catches of all plant protection stations in Jiangsu Province during the period from 1983 to 2010 and the reanalyzed meteorological data from the National Center of Environmental Predicting (NCEP) in USA during the period from 1982 to 2010 were collected to analyze the teleconnections between the BPH’s annual total immigration heads of Gaoyou, Tongzhou and Yixing as the representative plant protection stations in the different rice-growing regions of Jiangsu Province and the sea surface temperature anomalies (SSTA) on the Pacific Ocean from January of the preceding year to June of the present year, the temperature field of Indo-China Peninsula (T-INP) from December of the preceding year to June of the present year and the atmospheric circulation characteristic variables (ACCV) from July of the preceding year to June of the present year. A stepwise regression method was used to establish a series of the forecasting models for the annual total immigration heads of BPH at the three stations. The results showed as follows: (1) there were the correlations of different extents between the BPH’s immigration heads of Gaoyou, Tongzhou and Yixing and the SSTA on the Pacific Ocean, the T-INP and the ACCV on the Northern Hemisphere in the different temporal and spatial thresholds. The significant negative correlations exist between the logarithms of BPH’s annual total immigration heads at the three stations and the SSTA in the preceding year. Among them, the remarkable correlative regions between the logarithms of BPH’s annual total immigration heads at Tongzhou and Yixing and the SSTA on the Pacific Ocean mainly distributed on the northern and middle Pacific Ocean and the obvious correlative regions at Gaoyou situated in the southern Pacific. There were the positive correlations between the logarithms of BPH’s annual total immigration heads at Gaoyou and the T-INP in December of the preceding year and April of the present year, the negative correlations between the logarithms of BPH’s annual total immigration heads at Tongzhou and the T-INP in December of the preceding year, February and March of the present year and the positive correlations between the logarithms of BPH’s annual total immigration heads at Yixing and the T-INP in January and March of the present year, but the negative correlations between the logarithms of BPH’s annual total immigration heads at Yixing and the T-INP in April of the present year. The logarithms of BPH’s immigration heads at the three stations had the significant relationships with the characteristic indices of all subtropical highs, the characteristic indices of all polar vortexes, the atmospheric circulation types on the Atlantic Ocean and Europe, Zonal circulation indices on Asia, the strength of East Asian trough, the strength of cold air in East Asia, the strength of serial number typhoons on West Pacific and the indices of Southern Oscillation during from July of the preceding year to June of the present year. (2)Some significant factors (P<0.05) screened from the above factors were used as the key predictors to establish the forecast models for the BPH’s annual total immigration heads of the three stations and 17 forecast equations with the historical fitting accordance of more than 70% and the pre-examination accuracy rate of more than 66.7% were selected from the established models. The results of applicability evaluation on these equations displayed that the fitting values of these models were identical to the observed values on the whole and the models were feasible in the predicting practice of BPH’s annual total immigration amount at a station in a rice-growing region.

Key words: Immigration heads of Nilaparvata lugens (St?l), SSTA (sea surface temperature anomaly), T-INP temperature field of Indo-China Peninsula), ACCV (atmospheric circulation characteristic variable), Correlation analysis