Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2017, Vol. 53 ›› Issue (3): 421-428.DOI: 10.13209/j.0479-8023.2017.006

• Orginal Article • Previous Articles     Next Articles

Spatial Differentiation and Its Driving Factors of Agricultural Mechanization Level: A Case Study of Hebei Province

Linnan TANG1,2,3,4, Yanpeng WU4, Yu LIU1,2,3,4(), Xiumei TANG1,2,3,4   

  1. 1. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097
    2. Beijing Research Center
    for Information Technology in Agriculture, Beijing 100097
    3. Key Laboratory of Agri-informatics, Ministry of Agriculture,
    Beijing 100097
    4. Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097
  • Received:2015-12-01 Revised:2016-02-20 Online:2017-05-16 Published:2017-05-20


唐林楠1,2,3,4, 吴彦澎4, 刘玉1,2,3,4(), 唐秀美1,2,3,4   

  1. 1. 国家农业信息化工程技术研究中心, 北京 100097
    2. 北京农业信息技术研究中心, 北京 100097
    3. 农业部农业信息技术重点实验室, 北京 100097
    4. 北京市农林科学院, 北京 100097
  • 基金资助:
    国家自然科学基金(41401193, 41401203)资助


This paper made a comprehensive discussion about the spatial differentiation, evolution trend, correlation and driving factors of the regional agricultural mechanization level by using TOPSIS method, trend surface analysis, ESDA and GWR model. The results show that Hebei county’s agricultural mechanization level presents obvious spatial directivity and topographical distribution differences in 2013. The agricultural mechanization level develops better in central southeast plain, followed by the northwest plateau, and hilly region relatively worse. There exists a significant spatial autocorrelation characteristic and regional convergence phenomenon. The southern area of Hebei major in HH type, and northern area major in LL type. GWR shows great superiority in explaining the spatial non-stationary of elements, and reveals both positive and negative correlations between farmland scale and plant structure (expect for terrain), which is different from OLS result that all the factors are positive. In the future the government can consider such measures as enhancing the cultivated land scale and proportion of planting structure in the plateau area, considering other factors in the plain area to promote county’s agricultural mechanization level.

Key words: agricultural mechanization level, spatial differentiation pattern, driving factor, GWR, Hebei Province


借助TOPSIS模型、空间探索性数据分析和地理加权回归模型等方法, 系统分析河北省县域农业机械化水平的空间特征、关联水平及其影响因素, 以期为因地制宜地推进农业机械化发展提供支撑。结果表明: 1) 2013年河北省县域农业机械化水平在空间上呈现明显的方向性和地形分布差异: 中南部偏东平原地区的农业机械化水平较高, 北部偏西地区次之, 西部丘陵区发展滞后; 2) 县域农业机械化发展水平存在较强的空间自相关特性, 区域趋同性比较明显, 河北南部以高高集聚(HH)类型居多, 北部以低低集聚(LL)类型居多; 3) 与OLS模型相比, GWR模型在揭示农业机械化水平空间非平稳性方面具有优势。在研究所选的自变量中, 地形因子对县域农业机械化的影响较大且均为正向, 耕地经营规模和种植结构对农业机械化水平的影响有正有负, 但整体上为正。建议在高原县域采取适度增加耕地经营规模, 调整作物种植比例等措施提升农业机械化发展水平, 在平原县域通过改变其他经济因素来提高发展水平。

关键词: 农业机械化水平, 空间分异格局, 影响因素, 地理加权回归模型, 河北省