李世华, 牛铮, 路鹏, 王长耀, 冯小燕. 基于主成分分析红壤有效含水量估算模型[J]. 农业工程学报, 2007, 23(5): 92-94.
    引用本文: 李世华, 牛铮, 路鹏, 王长耀, 冯小燕. 基于主成分分析红壤有效含水量估算模型[J]. 农业工程学报, 2007, 23(5): 92-94.
    Li Shihua, Niu Zheng, Lu Peng, Wang Changyao, Feng Xiaoyan. Red soil available water capacity statistical model based on principal component analysis[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2007, 23(5): 92-94.
    Citation: Li Shihua, Niu Zheng, Lu Peng, Wang Changyao, Feng Xiaoyan. Red soil available water capacity statistical model based on principal component analysis[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2007, 23(5): 92-94.

    基于主成分分析红壤有效含水量估算模型

    Red soil available water capacity statistical model based on principal component analysis

    • 摘要: 季节性干旱是南方红壤地区农业可持续发展面临的关键科学问题,土壤有效含水量是评价土壤对植物给水能力的重要因子之一。该文以红壤为研究对象,在江西省采集了34个红壤样品,测定了土壤田间持水量、永久萎蔫系数、有机质含量、土壤容重、土粒密度和土壤质地组成(砂粒,粉砂粒和黏粒)的百分含量等土壤物理参数,并对这些因子进行主成分分析,建立经验回归模型,相关系数为0.87。结果表明:区域红壤有效含水量可以通过土壤物理参数估算,通过主成分分析等统计方法对于大面积估算土壤有效含水量是可行的。

       

      Abstract: Seasonal drought is the key scientific problem which faced by agricultural sustainable development in the red soil hilly region of South China. Soil available water capacity is one of the key factors which evaluate the water supply capability of soil to plant. Thirty-four red soil samples were collected in Jiangxi Province. Soil field capacity, permanent wilting point, soil organic matter, soil bulk density, soil grain density, and soil texture(sand, silt and clay) percentage component were measured in the laboratory according to experimental criterion. These soil physical parameters were analyzed with principal component analysis. The statistical model between soil available water capacity and principal components was constructed, and the correlation coefficient is 0.87. Results show that regional red soil available water capacity can be calculated from soil physical. And it is feasible that large areas soil available water capacity can be estimated using principal component analysis and other statistical methods.

       

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