基于能值分析的农业土地利用强度

随着城市扩张和人口的剧增，区域土地利用格局变化对农业系统产生影响，需要以一个共同基准对农业土地利用强度进行量化。该研究以北京远郊区县为例，将各种形式的社会经济数据用转换因子（能值转换率）转换到太阳能值，通过主成分分析（PCA）和聚类方法来识别研究区的农业土地利用强度的特征和分类。提取了4个农业投入强度分量和6个产出强度分量，并分别综合成投入强度指数和产出强度指数。通过K-means聚类将农业强度分成6类，其中低投入中产出、低投入低产出类型分别占研究区的34%和27%。研究结果表明，PCA为区域农业土地利用强度的总体评价提供了有效的指标，并且能值方法可以将分析数据统一到一个共同的标准，使得本研究的结果更具有可解释性。

Agricultural land use intensity based on emergy analysis

Since the change of regional land use pattern influences agricultural system with the urban expansion and population growth, an approach to quantify agricultural land use intensity on a common basis is very necessary. Taking Beijing suburban districts and counties as a case, agricultural land use intensity through principal component analysis (PCA) and cluster analysis was investigated to identify its characteristics and types with translating each form of socio-economic data into its solar emergy by way of a conversion factor (transformity). In the study areas four indices of agricultural input intensity and six indices of output intensity were identified, then those components were combined to input intensity index and output intensity index, respectively. Six types of agricultural intensity were identified through K-means cluster analysis using intensity indices, and 34% of the experimental towns belong to low input and moderate output intensity, while 27% of the experimental towns belong to low input and low output intensity. The results showed PCA could provide available indices of land use intensity for an overall assessment of regional agricultural land use intensity, and the emergy method could make the results more solubility.