Status of Empowerment of Villagers in Improving Water Resources Management

Subject Areas : Rural and nomadic studies

masoumeh  pazoki ¹ (department of geography and rural planning. PN university, Tehran - Iran)
davood  sheikhi ² (Department of Geography and Rural Planning, Payame Noor University, Tehran, Iran)

Received: 2019-02-12 Accepted : 2020-03-16 Published : 2021-01-20

Keywords: Cluster analysis, empowerment, water resources management, Rural Districts, Pakdasht Township,

Abstract :

The indicators of the state of water resources and consumption in the agricultural sector indicate the imbalance between supply and demand for water. Through empowerment, Villagers gain more control over their activities and with high power, control over water resources, capacity building and active participation; they are able to properly guide their activities. The present study was conducted to study the position of villager’s empowerment in water resources management and identifying effective indicators in empowerment for sustainable water resources management and grouping of Pakdasht countryside based on water management capability indicators. The statistical population of the study is the villagers of Pakdasht Township. The sample size was 381 and distributed randomly. Data were collected BY using a questionnaire. The result of the review of the average rank of variables showed that the mean of all rankings is higher than the average, which indicates the desired status of research indicators among respondents. The results of one sample t-test also confirmed the mean of the variables. In order to rank components, the coefficient of variation was used that index of decrease of immigration ranked first and production efficiency increase in on farms, gardens and greenhouses ranked the last. In order to group the effect of empowerment on the indices, the fracture technique was used by using coefficient of variation were used and finally, villages were grouped according to the degree of homogeneity in water resource management via cluster analysis.

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