文献类型: 外文期刊
作者: Li Yong 1 ; Zhang Qingwen 2 ; Wan Guojiang 2 ; Huang Ronggui 2 ; Piao Hechun 3 ; Bai Lingyu; Li Lu;
作者机构: 1.Chinese Acad Sci, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Peoples R China
2.Chinese Acad Sci, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Peoples R China; Chinese Acad Agr Sci, Inst Agroenvironm & Sustainable Dev, Beijing 100081, Peoples R China; Chinese Acad Sci, Inst Geochem, Guiyang 550002, Peoples R China
3.Chinese Acad Sci, Inst So
关键词: plant roots;soil weathering and leaching rates;soil environment field;principal components analysis (PCA);Chinese Loess Plateau
期刊名称:SCIENCE IN CHINA SERIES D-EARTH SCIENCES ( 影响因子:1.588; 五年影响因子:1.42 )
ISSN: 1006-9313
年卷期: 2006 年 49 卷 9 期
页码:
收录情况: SCI
摘要: Plant roots have potential impacts on soil mineral weathering and leaching. Our objective is to understand the physical mechanisms of plant roots affecting weathering and leaching of loess soil. Root densities were measured through the method of a large-size dug profile, and transport fluxes of soil elements were determined using an undisturbed monolith soil infiltration device on the hilly and gully regions of the Chinese Loess Plateau. The results show that the improvement effects of soil environment by plant roots are mainly controlled by the density and weight of the fibrous roots with the diameters less than 1 mm. Plant roots have the stronger effects on soil physical properties than chemical properties. The principal components analysis (PCA) indicates that soil physical properties by plant roots account for 56.7% of variations in soil environment whereas soil chemical properties and pH contribute about 24.2% of the soil variations. The roles of plant roots in controlling soil weathering and leaching increased in the following order: infiltration enhancement > increase of bioactive substance > stabilization of soil structure. The effects of plant roots on soil mineral weathering and leaching can be quantified using the multiple regression models with the high prediction accuracies developed in this study.
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