长江流域资源与环境 >> 2018, Vol. 27 >> Issue (05): 1159-.doi: 10.11870/cjlyzyyhj201805023

• 农业发展 • 上一篇    下一篇

江西省耕地土壤有效态微量元素含量空间变异特征及其影响因素

 
江叶枫1,2,钟珊1,2,饶磊1,2,孙凯1,2,郭熙1,2*
  

  1. (1.江西农业大学国土资源与环境学院, 江西 南昌 330045;
    2. 江西省鄱阳湖流域农业资源与生态重点实验室, 江西 南昌 330045)
  • 出版日期:2018-05-20

 
Spatial Variability of Soil Available Microelement Contents of  Cultivated Land in Jiangxi Province and Their Influencing Factors

 
JIANG Yefeng1,2, ZHONG Shan1,2, RAO Lei1,2, SUN Kai1,2, GUO Xi1,2
  

  1.  
    (1. Academy of Land Resource and Environment, Jiangxi Agricultural University, Nanchang 330045, China;
    2. Key laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, Nanchang 330045, China)
  • Online:2018-05-20

摘要:

 采用Pearson相关性分析、Spearman相关性分析、偏相关性分析、方差分析和地统计学方法,对江西省耕地表层(0~20 cm)土壤有效态B、Cu、Fe、Mn和Zn等5种微量元素含量空间变异特征及其影响因素进行研究。结果表明:研究区土壤有效态B、Cu、Fe、Mn和Zn总体处于中等以上水平,B表现为强变异性,其他有效态元素均呈中等变异性。空间结构方面,B、Cu、Fe、Mn和Zn的块金效应值依次为15.68%、12.09%、87.63%、77.98%和25.27%,表明B和Cu具有强烈的空间相关性,Zn呈中等空间相关性,而Fe和Mn空间相关性较弱。空间分布方面,土壤有效B含量表现出“两边低、中间高”的空间分布趋势,Fe、Cu、Zn呈现出较为平滑的空间分布格局,Mn在空间分布上并无明显规律。Pearson相关性分析和偏相关性分析表明5种微量元素之间存在一定的耦合关系但并无直接相关性。Spearman相关性分析和方差分析结果表明,高程、坡度、pH、有机质、全氮、成土母质、地貌类型、土壤类型、灌溉能力、秸秆还田方式和常年耕作制度对5种微量元素含量的空间变异影响显著(P<0.05)。结果可为江西省测土配方施肥卡制定与耕地资源利用与管理及优质高产种植提供可靠参考依据。
关键词: 江西省;微量元素;空间变异;相关性分析;偏相关性分析;影响因素

Abstract:

Soil available microelement contents are essential mineral nutrients for plant growth, and important parts of organic nutrient and soil fertility, they play important roles in growth and development of plants, community composition of the vegetation and structure and function of the ecosystem as well. The study on spatial variation characteristics of five soil available microelement including B, Fe, Cu, Zn and Mn as well as their influencing factors in the terrestrial ecosystem at a provincial scale is the fundamental basis of the study on regulate the circulation of trace elements, improve the yield and quality of plants, and formulate the formula card for accurate fertilization. Based on the data of the 16 582 soil samples collected in the topsoil layer (0~20 cm) of the farmlands during implementation of the project of soiltestbased formulated fertilization in Jiangxi Province in 2012, using pearson correlation analysis, spearman correlation analysis, partial correlation analysis, combined with oneway variance analysis and geostatistics, analysing the spatial variation characteristics of soil available microelement contents and their influencing factors. Results show as follows: ① Soil available B, Fe, Cu, Zn and Mn were reached the above moderate level, B showed strong variability, other soil available microelement showed moderate variability. ② In respect of the spatial structure, soil available B, Fe, Cu, Zn and Mn of the nuggest/still ratio were 15.68%, 87.63%, 12.09%, 25.27% and 77.98%, which show B and Cu have strong spatial correlation, Zn showed moderate spatial correlation and Fe and Mn indicated weak spatial correlation. In respect of the spatial distribution, soil available B content showed a spatial distribution tendency with lower in two sides and higher in middle, Cu, Zn and Mn displayed a similar distribution tendency seen smooth and there is no discernible distribution rule of Mn. ③ Pearson correlation analysis and partial correlation analysis showed that there is a definite coupling relationship between five soil available microelements but there is no direct contact. Spearman correlation analysis and oneway variance analysis results showed that topographic factors, soil chemical factors(including pH, organic matter and total nitrogen), soilforming factor and stochastic factors had significant impacts on the spatial variability of five soil available microelements(P<0.05). The results can provide a reliable reference for the formulation of soil testing formula fertilizer card, the utilization and management of cultivated land resources and the planting of good quality and high yield in Jiangxi Province.
Key words:Jiangxi Province; soil available microelement; spatial variability; correlation analysis; partial correlation analysis; influencing factors

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