广西耕地压力时空差异分析及驱动力探究

【目的】分析研究广西耕地压力水平时空演变特征及其驱动力,为广西有关部门制定保护耕地政策提供科学依据。【方法】以1980~2014年相关统计数据为基础研究广西耕地压力水平变化规律,以各市级行政区划为评价单元,通过建立耕地压力指数模型分析耕地压力水平在时间和空间上的变化趋势,采用主成分分析法对耕地压力驱动力进行探究。【结果】1980~2014年的35年中,广西耕地压力有12个年份处于一级水平,19个年份处于二级水平,4个年份处于三级水平;以各市2005~2014年耕地压力平均值作为该市耕地压力的平均水平,其中崇左、百色、来宾、柳州、防城港、南宁、河池、贵港市的耕地压力值均低于同期广西全区平均水平。影响广西耕地压力指数变化的驱动因素主要包括城镇化率、农村居民家庭恩格尔系数、人均GDP、总人口数、农民人均纯收入、粮食单产水平和人均耕地面积等。【建议】提高农民收入水平,降低农村居民家庭恩格尔系数,提高人均GDP;增加科技投入,提高粮食单产水平;严格把控耕地数量,保证人均耕地面积。

Analysis on temporal and spatial differences of cultivated land pressure and driving factors in Guangxi

Objective]The present study was conducted to analyze time-space evolution of cultivated land pressure and its driving forces in Guangxi in order to provide references for related authorities in Guangxi to formulate cultivated land protection policies. [Method]Cultivated land pressure evolution in Guangxi was studied based on related statistics during 1980-2014. With municipal administrative division as evaluation unit, temporal and spatial variations of cultivated land pressure were researched by establishing cultivated land pressure index model, and driving forces of cultivated land pressure were studied through principal component analysis. [Result]Among thirty-five years from 1980 to 2014, cultivated land pressure indexes in Guangxi were at grade one in twelve years, at grade two in nineteen years and at grade three in four years. Taking the average value of cultivated land pressure during 2005-2014 in city as the average municipal culti-vated land pressure, the average cultivated land pressure of Chongzuo, Baise, Laibin, Liuzhou, Nanning, Hechi, Fangcheng-gang and Guigang were lower than Guangxi average level during the same period. Driving forces affecting cultivated land pressure index include urbanization rate, Engel’s coefficient of rural households, GDP per capita, total population, per capita net income of rural residents, grain yield level and per capita cultivated land. [Suggestion]The following sugges-tions are proposed: increasing income of rural residents, decreasing Engel’s coefficient of rural households to raise per capita GDP; boosting science and technology input to increase grain yield level; strictly controling the cultivated land area to ensure certain coverage of per capita cultivated land.