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南京城市林业土壤可蚀性及影响因素 被引量:12

Erodibility of urban forest soil in Nanjing
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摘要 以南京市城市林业土壤为研究对象,研究了土壤的可蚀性特征及影响因素。结果表明:南京市城市林业土壤属于高易蚀土,土壤抗水蚀能力较弱,容易发生水土流失;不同功能区间,土壤的可蚀性K值和水稳性指数没有显著差异,而渗透性由高到低依次为城郊天然林、公园、道路绿化带、裸地、大学校园。回归分析表明,土壤的可蚀性K值与土壤的粉粒含量呈正相关(r=0.499 7),与黏粒含量呈负相关(r=-0.978 3);土壤的渗透性与容重(r=-0.519 6)和粉粒含量(r=-0.306 3)呈负相关,与有机质(r=0.359 6)、总孔隙度(r=0.367 5)和砂粒含量(r=0.380 1)呈正相关;土壤的体积质量和机械组成是反映城市林业土壤可蚀性的重要指标;城市绿地不同的植被配置方式对防治土壤的流失效果不同,表现为乔、灌、草混交林优于草坪,而裸地效果最差;城市客土和原土的抗蚀性差异不明显,但均存在较高的水土流失风险。 Erodibility and erosion factors of urban forest soil in Nanjing were studied. The result revealed that urban forest soil had week erosion-resistant capacity and was easy to erode and lose. Soil erodibility K value and water stable index had no significant difference between different functional areas, while soil permeability was in the order of suburb natural, i. e. , forest 〉 park 〉 road green belt 〉 bare land 〉 university land. Soil erodibility K value was positively correlated with soil silt content(r =0. 499 7), but it was negatively correlated with soil clay content( r = -0. 978 3 ). Soil permeability is negatively correlated with soil bulk density( r = -0. 519 6) and silt content( r = -0. 306 3 ), but it was positively correlated with organic matter( r = 0. 359 6 ) , total porosity( r = 0. 367 5 ) and sand content( r = 0. 380 1 ). Soil bulk density and texture were important indicators for reflecting the erodibility of urban forest soil. Vegetation types had great effect on urban forest soil erosion. The capacity of vegetation controlling soil loss was in the order of forest mixed with tree, shrub and grass 〉 lawn 〉 bare land. There was no significant difference in soil erosion-resistant capacity between anthrosol and autochthonous soil, but both of them had high risk of soil and water loss.
出处 《南京林业大学学报(自然科学版)》 CAS CSCD 北大核心 2008年第2期47-50,共4页 Journal of Nanjing Forestry University:Natural Sciences Edition
基金 江苏省社会发展项目(BS2007064) 国家林业局"948"项目(2005-4-17) 江苏省自然科学基金资助项目(BK2001208)
关键词 城市林业土壤 可蚀性 水土保持 逐步回归 Urban forest soil Erodibility Soil and water conservation Stepwise regression analysis
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