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山东省耕地资源态势分析及可持续利用研究
引用本文:孔伟,欧名豪.山东省耕地资源态势分析及可持续利用研究[J].中国人口.资源与环境,2006,16(4):44-48.
作者姓名:孔伟  欧名豪
作者单位:南京农业大学公共管理学院,江苏,南京,210095
摘    要:利用山东省建国以来的耕地序列数据以及近10多年来的相关社会经济资料,宏观分析了山东省耕地资源的时空动态变化规律。用相关分析和主成分分析相结合,定量地研究耕地变化的驱动因子,揭示耕地变化的驱动机制;研究表明,社会经济发展、农业科技进步和人口压力是造成山东省耕地资源持续藏少的三类主要动因。在此基础上,运用多元线性回归分析构建耕地变化回归模型,同时通过人均粮食需求量预测法,分别预测出2020年的山东省的耕地数量和耕地最低需求量.结果表明山东省未来的耕地资源态势不容乐观,人地矛盾十分尖锐。最后提出了实现耕地资源可持续利用的几点建议。

关 键 词:山东省  耕地变化  驱动因子  趋势预测
文章编号:1002-2104(2006)04-0044-05
收稿时间:2005-09-29
修稿时间:2005年9月29日

Study on the State of Cultivated Land and Its Sustainable Use in Shandong Provinces
KONG Wei,OU Ming-hao.Study on the State of Cultivated Land and Its Sustainable Use in Shandong Provinces[J].China Polulation.Resources and Environment,2006,16(4):44-48.
Authors:KONG Wei  OU Ming-hao
Institution:College of Public Administration, Nanjing Agricultural University, Nanjing Jiangsu 210095, China
Abstract:According to the statistical and survey data at provincial and county levels,the trend of cultivated land change,the spatial difference,and the driving forces of cultivated land change in Shandong province in recent years are discussed in this paper.The total area of arable land and per capital availability is reducing.The trend of decrease will continue with the development of economy and increase of population.The change has relatively regional differences.As a result of the principal component analysis and correlation analysis,the eight driving force variables affecting arable land changes are analyzed,which could be classified into three types: the dynamic development of economy,progress of science and technology in agriculture and pressure of the population system.By using regressive multianalysis equation and per demand for foodstuff,along with population growth and economic development,the prediction and the minimum demand for cultivated land in Shandong province are done till the year 2020.Finally,the article advances some advice for the sastainable use of the cultivated land.
Keywords:Shandong Province  cultivated land change  driving forces  trend prediction
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