北京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (5): 946-954.DOI: 10.13209/j.0479-8023.2015.103

• 北京大学学报 • 上一篇    下一篇

基于LMDI 和EMD 模型的中国玉米产量变化及其波动性研究

李艳梅1,2;陈秧分3;刘玉1,4;高秉博1,4   

  1. 1. 北京市农林科学院, 北京 100097; 
    2. 北京市农林科学院植物营养与资源研究所, 北京 100097; 
    3. 中国农业科学院农业经济与发展研究所, 北京 100081; 
    4. 北京农业信息技术研究中心, 北京 100097;
  • 出版日期:2015-09-20 发布日期:2015-09-20
  • 通讯作者: 刘玉  liuyu@nercita.org.cn
  • 基金资助:
    国家自然科学基金(41471115, 41401193)和北京市农林科学院科技创新能力建设项目(KJCX201204002)资助

Evolutive Trend of China’s Corn Output and Its Fluctuation Characteristics Based on LMDI Model and EMD Model

LI Yanmei1,2;CHEN Yangfen3;LIU Yu1,4;GAO Bingbo1,4   

  1. 1. Beijing Academy of Agricultural and Forest Science, Beijing 100097; 
    2. Institute of Plant Nutrition and Resource, Beijing Academy of Agricultural and Forest Science, Beijing 100097; 
    3. Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081; 
    4. Beijing Research Center for Information Technology in Agriculture, Beijing 100097;
  • Online:2015-09-20 Published:2015-09-20
  • Contact: LIU Yu  liuyu@nercita.org.cn

摘要: 采用对数平均迪氏分解模型(LMDI)、经验模态分解模型(EMD)和方差分解模型(VDM), 系统分析 1978—2012 年中国玉米产量的变化趋势和波动特征。结果表明, 35 年间玉米产量增加1.50×108 t, 东北区和黄淮海区的玉米生产优势进一步凸显; 玉米播种面积效应和玉米单产效应分别为0.79×108 和0.71×108 t, 播种面积是中国玉米产量快速增加的主要因素。玉米产量以趋势增长为主, 且存在3 年左右的准周期波动, 玉米单产波动是准3 年周期波动的主要影响因素。从八大粮食产区看, 黄淮海区玉米产量的波动量最大, 其次为东北区和黄土高原区; 黄淮海区、东北区和黄土高原区自身的产量波动及其相互间的正向联动作用是中国玉米产量波动的主要因素; 黄淮海区和东北区的玉米产量波动量大且变化剧烈, 应重点关注这两个区的玉米生产。

关键词: 玉米产量, 变化趋势, 波动特征, LMDI 模型, 经验模态分解模型

Abstract: Based on logarithmic mean weigh division index method (LMDI), empirical mode decomposition method (EMD) and variance decomposition model (VDM), the evolutive trend and fluctuation characteristics of corn production in China during 1978–2012 was analyzed. The contribution difference to corn output fluctuation during eight grain production regions was revealed. The following results were obtained. Corn output increased by 1.50×108 ton from 1978 to 2012, and the corn production advantage in Huang-Huai-Hai region and Northeast Region in China was further highlighted. It was estimated that the accumulated contribution values of corn sowing area and corn yield per hectare at national scale were 0.79×108 ton and 0.71×108 ton respectively, and corn sowing area was the major contributor to the increment of corn output. The residual trend of corn output showed a trend of gradual increase and the grain output has 3-year periodic oscillation. During the eight grain production regions in China, Huang-Huai-Hai Region played the prominent role in the total fluctuation, following by the Northeast Region and Loess Plateau Region. The self-fluctuations in the three regions and their positively mutual affect were the main factors of China’s corn output fluctuation, including Huang-Huai-Hai Region, Northeast Region and Loess Plateau Region in China. For the high contribution ratio and drastic change, more attention should be paid to Huang-Huai-Hai Region and Northeast Region in China.

Key words: corn output, evolutive trend, fluctuation characteristics, logarithmic mean weigh division index method, empirical mode decomposition