Resources Science ›› 2019, Vol. 41 ›› Issue (1): 75-86.doi: 10.18402/resci.2019.01.08

• Orginal Article • Previous Articles     Next Articles

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


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