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土壤速效养分空间变异研究
引用本文:王宏庭,金继运,王斌,胡全才,卢朝东,米保明.土壤速效养分空间变异研究[J].植物营养与肥料学报,2004,10(4):349-354.
作者姓名:王宏庭  金继运  王斌  胡全才  卢朝东  米保明
作者单位:1.山西省农业科学院土壤肥料研究所 山西太原030031;
基金项目:山西省科技攻关项目,PPIC北京办事处资助项目
摘    要:选择山西省一年两作和一年一作两种种植制度下的两个自然村作为试点,通过网格方法采取土样,应用土壤养分系统研究法进行了养分分析,并应用传统统计学和半方差函数对土壤养分变异进行了研究。结果表明,不同种植制度下,土壤养分性质均存在着空间变异,受农业传统施肥措施影响较大的N、P和OM,变异相对较大;而Ca、Mg、K、Cu、Fe、Mn等养分,传统施肥投入较少,土壤变异相对较小;土壤养分性质存在半方差结构,拟合模型包括球型、线型和指数型,但同一养分拟合半方差模型不尽相同。即使模型一致,最大相关距离也不一样,而且空间变异性强弱程度也不尽相同。Ca、N、Mn显示有较强的空间相关性,K、P、S显示具有中等的空间相关性。

关 键 词:土壤养分变异    半方差结构    模型
文章编号:1008-505X(2004)04-0349-06
修稿时间:2003年9月29日

Study on variability of soil available nutrients in Shanxi province
WANG Hong-ting,JIN Ji-yun,WANG Bin,HU Quan-cai,LU Chao-dong,MI Bao-ming.Study on variability of soil available nutrients in Shanxi province[J].Plant Nutrition and Fertilizer Science,2004,10(4):349-354.
Authors:WANG Hong-ting  JIN Ji-yun  WANG Bin  HU Quan-cai  LU Chao-dong  MI Bao-ming
Institution:1.Inst. of Soil and Fertilizer;Shanxi AAS;Taiyuan 030031;China;
Abstract:Two villages were selected as experimental sites under different croping systems in Shanxi province. Soil samples were collected with grid method, and determined with soil nutrient systematic approach. Soil variability was studied with traditional statistics method and semi-variance function. The results showed that there existed spatial variability of soil nutrient under different plantation system. N, P and OM had a greater variability, which were mainly affected by agricultural fertilization measures. On the contrary, Ca, Mg, K, Cu, Fe and Mn have a smaller spatial variability; Soil nutrient had semi-variance structure, and model best fit included spherical, linear and exponential. Semi-variance model were not always consistent for one nutrient, and though some nutrient had same model, there still existed different in the limit distance of spatial correlation and the extent of soil spatial variability. There existed stronger spatial correlation in Ca, N and Mn and K, P, and S had medium spatial correlation.
Keywords:soil nutrient variability  semi-variance structure  model
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