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天津滨海新区不同土壤的生物学性状及土壤质量评价 被引量:3

Evaluation of Soil Biological Quality and Biological Characteristics on Agricultural Soils in the New Coastal Region of Tianjin
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摘要 以天津滨海新区农田、菜地、果园、湿地4种利用类型土壤为研究对象。对土壤微生物量、微生物群落数、微生物呼吸、土壤酶活性等11种生物学指标进行测定,运用主成分和聚类分析方法,对不同利用类型土壤的生物质量进行综合评价。结果表明,滨海新区不同利用类型土壤生物学性质差异明显。经主成分分析得到3个主成分,涵盖了原有变量86.21%的信息,其中微生物生物量和土壤酶活性在主成分上的载荷较高,可以作为滨海新区土壤的特征生物学指标。滨海新区土壤生物质量顺序为善门口果园、杨北农田、头道沟农田>新城菜地、四道桥农田、刘庄子农田、新城果园、新城农田>临港湿地。聚类分析结果与主成分综合得分评价结果相似率达到89%,可以作为不同土壤生物质量评价的科学依据。 Soil biological indicators are important parameters of soil quality.In this study,soil samples were collected from different landuse types,including farmlands,gardens,orchards and wetlands,in New Coastal Region of Tianjin.Eleven kinds of soil biological indexes,including microbial biomass,soil microflora,respiration,enzyme activity,and so on,were measured.Principal components and cluster analysis were used to evaluate the soil biological quality of different landuse types.Results indicated that there were significant differences of soil biological properties in different landuse types in New Coastal Region of Tianjin.Three principal components which accounted for 86.21% of cumulative variance extracted from the original data.For the high load on the principal components,microbial biomass and soil enzyme activity can be used as biological indicators of soil characteristics in New Coastal Region of Tianjin.The order of soil biological quality is as follows,Shanmenkou orchard,Yangbei farmland and Toudaogou farmland Xincheng vegetable field,Sidaoqiao farmland,Liuzhuangzi farmland,Xincheng orchard and Xincheng farmland Lingang wetland.The results of cluster analysis and principal components analysis were similar to the rate of 89%,which indicated that they can provide scientific evidence for soil biological quality assessment of different landuse types.
出处 《西北农业学报》 CAS CSCD 北大核心 2011年第4期200-206,共7页 Acta Agriculturae Boreali-occidentalis Sinica
基金 国家自然科学基金项目(40971158)
关键词 生物学指标 土壤生物质量 主成分分析 聚类分析 Biological indexes Soil biological quality Principal components analysis Cluster analysis
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