中国农业气象

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奇异交叉谱分析方法在中国南方稻纵卷叶螟发生预测中的应用

高文婷,陈心怡,包云轩,王琳,谢晓金,陆明红   

  1. 1.南京信息工程大学气象灾害预报和评估协同创新中心/江苏省农业气象重点实验室,南京 210044;2.农业部全国农业技术推广与服务中心,北京 100125
  • 收稿日期:2017-01-10 出版日期:2017-09-20 发布日期:2017-09-14
  • 作者简介:高文婷(1991-),硕士生,研究方向为农业气象、农业病虫测报学。E-mail:285223808@qq.com
  • 基金资助:
    国家公益性行业(气象)科研专项(GYHY201306053);国家自然科学基金面上项目(41475106;31601221);江苏省高校优势学科建设工程

Application of Singular Cross-Spectrum Analysis in the Prediction of Cnaphalocrocis medinalis’ Occurrence in Southern China

GAO Wen-ting,CHEN Xin-yi, BAO Yun-xuan, WANG Lin, XIE Xiao-jin, LU Ming-hong   

  1. 1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2.National Agricultural Technology Extension and Service Center, Ministry of Agricultural, Beijing 100125
  • Received:2017-01-10 Online:2017-09-20 Published:2017-09-14

摘要: 利用奇异交叉谱分析方法(SCSA),对中国南方水稻主产区中广西全州、重庆秀山、湖南湘阴和江苏张家港4个代表站点1994-2014年稻纵卷叶螟田间逐日赶蛾量资料和与4站虫量相关性最显著的环流因子进行耦合周期分析,通过重建耦合分量序列(RCCS)对分量序列的时间变化特征进行探讨,并采用SCSA-AR方法对稻纵卷叶螟发生量进行外推预测。结果表明:预测结果与实际情况基本吻合。对预测平均误差(ME)、平均绝对误差(MAE)、均方根误差(RMSE)及预测值与实况值的符号相关率(RSC)进行计算,4站综合平均的ME为-0.071、MAE为0.349、RMSE为0.446、RSC为0.75,表明预测误差较小,预测序列较稳定,预报精度较高,可将此方法推广应用于中国南方稻区稻纵卷叶螟的发生趋势预测。

关键词: 大气环流因子, 耦合振荡, 重建耦合分量序列, 自回归分析, 虫量发生预测

Abstract: In this paper, the daily moth catches data of Cnaphalocrocis medialis(Guenee)in the paddy field at the four representative plant protection stations of southern China during the period from 1994 to 2014 was collected. The four representative stations are Quanzhou in the Guangxi Zhuang Autonomous Region, Xiushan in Chongqing City, Xiangyin in Hunan Province and Zhangjiagang in Jiangsu Province and they represented the rice-growing region of the south China, the rice-growing region of the southwestern China, the rice-growing region between the Nanling Mountains and the Yantze River Valley and the rice-growing region between the Yantze River Valley and the Huaihe River Valley in China respectively. The most significant atmospheric circulation factors related to the C. Medinalis’ moth catches of the four stations in the early or same periods were screened out. Based on the above works, a method of singular cross-spectrum analysis (SCSA) was used to the analysis of the coupling cycles between the moth catches of C. Medinalis of the four stations and the selected circulation factors and the time-varying characteristics of the component sequences was discussed by the reconstruction of coupled component sequence(RCCS). Consequently, the singular cross-spectrum analysis combined with the autoregressive function was used to the extrapolating prediction of C. Medinalis’ occurrence amount. The results were showed that the predicted results were in good agreement with the actual occurred situations. The mean correlation error (ME), average absolute error (MAE), root mean square error (RMSE) and the rate of sign correlation between the predicted values and the actual moth catches amount were found that the ME was -0.071, MAE was 0.349, RMSE was 0.446 and RS was 0.75. They indicated that the prediction error was small, the prediction sequence was stable and the prediction accuracy was high. This method can be applied to the prediction of the occurrence trend of C. Medinalis in southern China.

Key words: Atmospheric circulation factor, Coupled oscillations, Reconstructed coupled component sequence, Autoregressive, Prediction of insect occurrence