• ISSN 1008-505X
  • CN 11-3996/S
徐新朋, 赵士诚, 张云贵, 何萍, 高强. 吉林省玉米种植区土壤养分空间变异特征研究[J]. 植物营养与肥料学报, 2011, 17(6): 1342-1350. DOI: 10.11674/zwyf.2011.1044
引用本文: 徐新朋, 赵士诚, 张云贵, 何萍, 高强. 吉林省玉米种植区土壤养分空间变异特征研究[J]. 植物营养与肥料学报, 2011, 17(6): 1342-1350. DOI: 10.11674/zwyf.2011.1044
XU Xin-peng, ZHAO Shi-cheng, ZHANG Yun-gui, HE Ping, GAO Qiang. Spatial variations of soil nutrients in maize production areas of Jilin province[J]. Journal of Plant Nutrition and Fertilizers, 2011, 17(6): 1342-1350. DOI: 10.11674/zwyf.2011.1044
Citation: XU Xin-peng, ZHAO Shi-cheng, ZHANG Yun-gui, HE Ping, GAO Qiang. Spatial variations of soil nutrients in maize production areas of Jilin province[J]. Journal of Plant Nutrition and Fertilizers, 2011, 17(6): 1342-1350. DOI: 10.11674/zwyf.2011.1044

吉林省玉米种植区土壤养分空间变异特征研究

Spatial variations of soil nutrients in maize production areas of Jilin province

  • 摘要: 本文以吉林省中部玉米种植区9县市为研究区域,采用传统统计学和地统计学相结合的研究方法,探讨研究区域表层土壤(020cm)有机质、全氮、碱解氮、有效磷、速效钾和pH值的空间变异特征。结果表明:各养分的变异系数在10%~100%之间,属中等变异;有机质、碱解氮、有效磷和速效钾的块金值(C0)与基台值(C0+C)比值介于25%~75%之间,表现出中等强度的空间自相关,而全氮和pH值表现强烈的空间自相关;根据决定系数(R2)和残差平方和(RSS)进行模型选择,几种养分均用指数模型拟合的效果较好;依据GIS所做的养分空间变异图可以很好地反应该地区的土壤养分空间分布状况,可以为该地区土壤养分的综合评价和施肥管理决策提供依据。

     

    Abstract: Statistics and geostatistics methods were adopted to study spatial variations of soil nutrients including total nitrogen (N), available N, available phosphorus (P), available potassium (K), organic matter and pH in top soils from nine counties/cities in main production areas of Jilin province. The results show that the variation coefficients of the soil nutrients are from 10 % to 100 %, which indicate that the variations are at their medium levels. The spatial structures (nugget/sill) of available N, available P available K and organic matter are from 25% and 75%, which indicate moderate spatial self-correlations, while pH and total N are strong spatial self-correlations. According to the decisive coefficients and the least residual sums of squares( RSS) for the model selections, all nutrients are fitted to exponential model well. The GIS-based nutrient spatial variability figures can reflect spatial distributions of the soil nutrients and provide theoretical bases for soil nutrient management and decision-making in the study area.

     

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