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应用生态学报 ›› 2019, Vol. 30 ›› Issue (3): 913-922.doi: 10.13287/j.1001-9332.201903.011

• 研究论文 • 上一篇    下一篇

福建省耕地土壤潜性酸动态变化及其驱动因素

周洁莹1, 张黎明1,2, 杨文浩1,2, 周碧青1,2, 邢世和1,2*   

  1. 1福建农林大学资源与环境学院, 福州 350002;
    2土壤生态系统健康与调控福建省高校重点实验室, 福州 350002
  • 收稿日期:2018-08-13 出版日期:2019-03-20 发布日期:2019-03-20
  • 通讯作者: E-mail: fafuxsh@126.com
  • 作者简介:周洁莹,女,1994年生,硕士研究生.主要从事土壤资源评价与持续利用研究. E-mail: jyz20169@126.com
  • 基金资助:
    本文由国家农业农村部耕地质量提升项目(20170011)资助

Dynamic and its driving factors of soil potential acid in croplands of Fujian Province, China

ZHOU Jie-ying1, ZHANG Li-ming1,2, YANG Wen-hao1,2, ZHOU Bi-qing1,2, XING Shi-he1,2*   

  1. 1College of Resources and Environment, Fujian Agricultural and Forestry University, Fuzhou 350002, China;
    2University Key Lab of Soil Ecosystem Health and Regulation in Fujian, Fuzhou 350002, China
  • Received:2018-08-13 Online:2019-03-20 Published:2019-03-20
  • Supported by:
    This work was supported by the Project of Cropland Quality Improvement, Ministry of Agriculture and Rural Affairs of People’s Republic of China (20170011).

摘要: 土壤潜性酸是植物生长的潜在限制因子,是土壤酸性调控的重要依据.按比例抽取并测定福建省耕地表层土壤代表性样点的潜性酸量和pH值,拟合潜性酸(PA)与活性酸(pH)的最优关系模型,利用全省1982年36777个、2008年236445个和2016年21269个耕地表层土壤调查样点pH等属性数据,建立3期1∶5万耕地土壤潜性酸量数据库,借助GIS技术和灰色关联分析模型探讨福建省耕地土壤潜性酸动态变化规律及其驱动因素.结果表明: 1982—2016年,全省耕地土壤潜性酸量整体呈上升趋势,2008和2016年潜性酸量分别比1982年上升1.30和1.49 cmol·kg-1,1982—2008年的潜性酸上升速率比2008—2016年高0.03 cmol·kg-1·a-1.1982—2016年,全省耕地土壤潜性酸变化量空间差异明显,龙岩市耕地土壤潜性酸变化量最大,比最小的三明市高4倍以上;不同利用类型耕地土壤潜性酸变化量大小依次为水田>水浇地>旱地;咸酸水稻土、潜育水稻土和淹育水稻土亚类的潜性酸变化量最大,是全省潜性酸变化量均值的1倍以上;赤红壤和盐渍水稻土亚类变化量最小,分别为全省均值的25.7%和28.4%.福建省耕地土壤潜性酸动态变化的主要驱动因素包括氮、磷肥施用量、阳离子交换量(CEC)、黏粒含量、pH和粉粒含量,灰色关联系数绝对值>0.92.科学优化施肥结构、合理施用碱性调理剂改酸是减缓福建省耕地土壤潜性酸增加的重要途径.

关键词: 耕地, 灰色关联分析, 潜性酸, GIS

Abstract: Soil potential acid is one of the potential factors limiting plant growth, and also an important base in soil acidity regulation. The potential acid and pH value of surface soil samples of cultivated lands in Fujian Province were proportionally selected and measured, and then the optimized relational model between soil pH and potential acid value was fitted. The 1:50 000 databases of cropland soil potential acid in 1982, 2008 and 2016 were established by using the topsoil pH data of 36777, 236445 and 21269 sampling sites collected in 1982, 2008 and 2016 respectively. The dynamics of cropland soil potential acid in Fujian Province and its driving factors were explored by the integrative method of GIS and grey correlation analysis. The results showed that the quantities of soil potential acid in cropland generally increased in Fujian Province from 1982 to 2016. Compared with 1982, the averages of soil potential acid in 2008 and 2016 increased 1.30 and 1.49 cmol·kg-1, respectively. The increase rate of soil potential acid from 1982 to 2008 was 0.03 cmol·kg-1·a-1 higher than that from 2008 to 2016. Meanwhile, the changes of cropland soil potential acid showed significant spatial difference. The change of cropland soil potential acid in Sanming was minimum, and the change in Longyan was maximum, being four times higher than that in Sanming. The change of soil potential acid in different use types of cropland was following the order: paddy field > irrigated land > dry land. The changes of soil potential acid in acid sulfate paddy soils, gleyed paddy soils and submergenic paddy soils were maximum, which were one time higher than the mean change across the whole Province, while the changes in latosolic red earths and salinized paddy soils were minimum, which were 25.7% and 28.4% of the mean change in the Province, respectively. The driving factors for the dynamics of cropland soil potential acid in Fujian Province included the application rates of nitrogen and phosphorus fertilizer, cation exchange capacity (CEC), clay content, pH and silt content, with grey correlation coefficient (absolute value) being higher than 0.92. Accordingly, it would be an effective approach to slow down the increase of cropland soil potential acid in Fujian Province to optimize fertilization management and apply alkaline ameliorant scientifically.

Key words: cropland, grey relational analysis, potential acid, GIS