资源科学 ›› 2019, Vol. 41 ›› Issue (1): 75-86.doi: 10.18402/resci.2019.01.08

• 水资源 • 上一篇    下一篇

中国农业水贫困评价及时空特征分析

张华(), 王礼力()   

  1. 西北农林科技大学经济管理学院,杨凌 712100
  • 收稿日期:2018-08-16 出版日期:2019-01-25 发布日期:2019-01-25
  • 作者简介:

    作者简介:张华,女,内蒙古呼伦贝尔人,博士生,主要从事农业经济与水资源管理方面的研究。E-mail: huazhang0129@163.com

  • 基金资助:
    国家社会科学重点项目(16AJY009)

Evaluation and spatio-temporal analysis for agricultural water poverty in China

Hua ZHANG(), Lili WANG()   

  1. College of Economics and Management, Northwest Agriculture and Forestry University, Yangling 712100, China
  • Received:2018-08-16 Online:2019-01-25 Published:2019-01-25

摘要:

本文以提高农业用水有效性为指导思想,提出“农业水贫困”概念,基于农业水贫困概念界定和评价指标体系构建的基础上,利用POME-两级模糊模式识别模型测度2000—2016年中国30个省(市)农业水贫困指数,借助探索性时空数据分析(ESTDA),从时空耦合视角分析其空间格局动态性。研究发现:中国虽然存在较严重的农业水贫困问题,但是大部分省(市)的农业水贫困程度呈缓解趋势;各省(市)大部分年份的农业水贫困具有显著的空间负相关性,空间差异呈逐渐扩大趋势;相对于西南地区,华南地区和华东地区具有更加动态的局部空间结构;东北地区和西北地区的时空依赖效应较弱;农业水贫困空间格局具有较强的空间整合性,协同高增长的省(市)主要分布在华东地区和华南地区,协同低增长的省(市)主要分布在北部地区;各省(市)农业水贫困的局部空间联动性较弱,空间集聚性存在相对较高的路径锁定特征。此外,提出了降低农业水贫困的对策建议,为中国农业水资源管理和可持续发展提供借鉴。

关键词: 农业水贫困, POME-两级模糊模式识别模型, 时空动态性, 时空跃迁, 中国

Abstract:

In this paper, the concept of "agricultural water poverty" is put forward under the guided by the idea of improving the efficiency of water use in agriculture. Based on the conceptual definition and evaluation index system of agricultural water poverty, the POME-two-level fuzzy pattern recognition model was used to evaluate the agricultural water poverty index of 30 provinces (cities) in China from 2000 to 2016 under the common constraints of existing water resource endowment, water supply facilities, water resource use, ecological environment and social economy, education and people's life. Then calculated by Exploratory Time-space Data Analysis (ESTDA) to analysis its dynamic spatial and temporal pattern from the perspective of time and space coupling. The results show that: Although China has a serious problem of agricultural water poverty, the degree of agricultural water poverty in most provinces (cities) is alleviating. The agricultural water poverty of provinces (cities) in most years has a significant spatial negative correlation, and the spatial difference is gradually expanding. Compared with the southwest region, south China and east China have more dynamic local spatial structure. The spatial and temporal dependence is weak in northeast and northwest China. The spatial pattern of agricultural water poverty has strong spatial integration, the provinces (cities) that showed the same trend increased are mainly distributed in East China and South China, and the provinces (cities) that showed the same trend decreased are mainly distributed in the northern regions. The local spatial linkage of agricultural water poverty in provinces (cities) is relatively weak, and the spatial clustering has relatively high path-locking characteristics. In addition, the countermeasures and suggestions for reducing agricultural water poverty are put forward to provide reference for China's agricultural water resources management and sustainable development.

Key words: agricultural water poverty, POME-two-level fuzzy pattern recognition model, space-time dynamics, space-time transitions, China