| 浙西北丘陵区农田土壤微量元素空间变异特征及影响因子 |
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| 引用本文： | 刘永红,倪中应,谢国雄,徐立军,钟林炳,马立强.浙西北丘陵区农田土壤微量元素空间变异特征及影响因子[J].植物营养与肥料学报,2016,22(6):1710-1718. |
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| 作者姓名： | 刘永红 倪中应 谢国雄 徐立军 钟林炳 马立强 |
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| 作者单位： | 1.桐庐县农业和林业技术推广中心, 浙江桐庐 311500 |
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| 基金项目： | 2009～2012年农业部测土配方施肥补贴项目；桐庐县农业“两区”土壤污染防治项目资助。 |
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| 摘 要： | 【目的】采用地统计学和GIS技术相结合的方法能较好地综合评价土壤微量元素空间数据的结构性、空间格局变异产生原因及影响因子。本论文以生态型城市桐庐县为研究区，研究了土壤微量元素的空间变异特征及其影响因素，为长期耕作的浙西北丘陵区农田土壤高效施肥提供依据。【方法】在满足空间分析要求的基础上，根据研究区主要土壤类型、土地利用类型、地形地貌和交通条件等因素布置采样点数目与密度，在作物收获后，选取水田区、旱地区、茶园区、果园区和桑园区等有代表性的田块采集分析土样386个。利用GS+7.0和ArcGIS 10.1进行半方差分析和Kriging插值，运用逐步回归分析比较各因子对微量元素含量的影响程度。【结果】铁、锰、铜、锌4种微量元素变异系数在58.37%~90.22%之间，块金效应值在10.9%~12.5%之间。4种微量元素的空间分布结构相似程度较小，呈斑块状特点。不同土壤类型对有效Fe和有效Zn的含量有显著影响。不同土地利用方式间4种微量元素含量差异显著。有效Fe与有机质和速效钾呈极显著负相关，与有效Cu呈极显著正相关。有效Cu与有效磷呈极显著正相关，与速效钾呈显著负相关，与有效Mn呈显著正相关。有效Zn与碱解氮呈极显著负相关，与有效磷和速效钾呈极显著正相关，与有效Mn呈显著正相关，与海拔呈显著负相关。土壤类型、土地利用方式、有机质、pH、碱解氮、有效磷、速效钾和海拔等8个因子合计分别能够解释4种微量元素变异空间变异的19.1%、2.2%、12.2%和12.1%，8个因子中土地利用方式能够独立解释空间变异的3.1%~13.5%。【结论】研究区内铁、锰、铜、锌4种微量元素总体处于丰富水平，呈中等变异。不同元素的主导影响因素不同，除土壤类型、土地利用方式、有机质、pH、碱解氮、有效磷、速效钾和海拔等8个因子外还有众多其它结构性影响因子，土地利用方式有重要影响但非主导因子。
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| 关 键 词： | 土壤微量元素 空间变异 地统计学 影响因子 |
| 收稿时间： | 2015-08-01 |
Spatial variability and impacting factors of trace elements in hilly region of cropland in northwestern Zhejiang Province |
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| LIU Yong-hong,NI Zhong-ying,XIE Guo-xiong,XU Li-jun,ZHONG Lin-bing,MA Li-qiang.Spatial variability and impacting factors of trace elements in hilly region of cropland in northwestern Zhejiang Province[J].Plant Nutrition and Fertilizer Science,2016,22(6):1710-1718. |
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| Authors: | LIU Yong-hong NI Zhong-ying XIE Guo-xiong XU Li-jun ZHONG Lin-bing MA Li-qiang |
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| Institution: | 1.Agricultural and Forestry Technology Promotion Center of Tonglu County, Tonglu, Zhejiang 311500, China |
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| Abstract: | Objectives]Geostatistics combined with geographical information system (GIS) technique is thought well for evaluation of the spatial structure of data and the impacting factors causing the variation. In this paper, this method was used to analyze the spatial variability and main impacting factors of available soil Fe, Mn, Cu and Zn in the permanently cultivated cropland in Tonglu County, in order to provide base for the efficient application of micronutrient fertilizer.Methods]Soil types, land-use types, organic matter, pH, available N, available P, available K and altitude were chosen as impacting factors in the research. There were totall 386 soil samples collected from paddy fields, dry lands, tea plantations, and orchard fields after crop harvest. Semi-variance analysis and Kriging interpolation were performed by GS + 7.0 and ArcGIS 10.1.Results]The variation coefficient of four elements was between 58.37%and 90.22%. The nugget effect value was between 10.9% and 12.5%. The spatial distribution of Fe, Mn, Cu and Zn contents was patchy and their space structure had little similarity. The contents of Fe and Zn were significantly different among different soil types, and the availabale Fe, Mn, Cu and Zn contents were significantly different among the land-use types. Fe content has extremely significant and negative correlation with soil organic matter content and available K content, and had extremely significant positive correlation with Cu content; Cu content had extremely significant positive correlation with available P, and had significant negative correlation with available K and significant positive correlation with Mn; Zn content had highly significant negative correlation with available N, highly significant positive correlation with available P and K, had significant positive correlation with Mn and had significant negative correlation with altitude. Stepwise regression analysis indicated that soil type, land-use types, organic matter content, pH, available N, available P, available K and elevation together could explain 19.1%, 2.2%, 12.2%and 12.1% of the variability of soil available Fe, Mn, Cu and Zn in the study area. The land-use types alone could explain 3.1%–13.5% of the variability.Conclusions]The available Fe, Mn, Cu and Zn contents were generally at the rich level, and belong to medium spatial variability in the studied area. Structural factors played a dominating role and human activity factors played a secondary role. The contributions of soil type, land-use type, organic matter content, pH, available N, available P, available K and altitude were significant but only accounted for a small proportion of the variation. Among these factors land-use types play a major role, although not the dominating factor. |
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| Keywords: | soil trace element spatial variability geostatistics affecting factor |
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