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Psr Framework Of The Arable Land Intensive Use Of The Evaluation Index System

Posted on:2011-07-26Degree:MasterType:Thesis
Country:ChinaCandidate:J H ZhouFull Text:PDF
GTID:2199360308982554Subject:Human Geography
Abstract/Summary:PDF Full Text Request
For such a large population of China, cultivated land is related to food security, social stability and even the rise and fall of an important strategic element of the regime. But with the continuous development of our economy, urbanization and industrialization process, step by step, the decrease of cultivated land caused by building occupancy, the agricultural structure adjustment, disaster damage is an inevitable trend. In this trend, the thinking method should be changed, not only in the potential of the existing construction land, but also of efforts to promote the intensive use of cultivated land. Therefore, intensive utilization of land is imperative.Basing on the understanding of conception, analysing and comparing of envaluation index system and envaluation methods about intensive cultivated land use, the PSR model was selected as a building evaluation index system in this paper. Focusing on the basic principles and application status of PSR model, the evaluation index system of intensive cultivated land was established based on PSR framework with 24 specific indicators.Using this index system and principal component analysis in SPSS13.0 software, the level of Yuxi City's intensive cultivated land use is evaluated with the actual data from 2002 to 2007. Evaluation results show that: From time to develop point of view, the level of Yuxi City's intensive arable land use was gradually improving; from the spatial distribution, the level of intensive arable land use of Hongta, Jiangchuan, Tonghai, Chengjiang County which economic development is rapid was slightly higher than the counties which economic development is slower. The evaluation index system was indicated definitely more reasonable.
Keywords/Search Tags:cultivated land intensive use, index system, PSR, principal component analysis
PDF Full Text Request
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