摘要
为研究新疆典型绿洲土地盐分特征及分布特征,以新疆第二师31团为例,采用GPS对研究区域进行定点采样(N=66),采样深度为100 cm,采样层数为5层,对土壤盐分离子进行化验检测,运用SPSS软件对数据进行描述性、离子间相关性及离子主成分分析。结果表明:不同土壤层中各离子处于中等以上变异程度,各土壤层的pH=8.11,各土壤层中Cl^-、K^++Na+与SO4^2-离子含量大于HCO3^-、Ca^2+与Mg^2+离子;Cl^-、K^++Na^+、SO4^2-、Ca^2+、Mg^2+之间的相关系数较高,呈现极显著相关性。各土壤层的第一主成分主要包含Cl^-、SO4^2-、Ca^2+、Mg^2+、K^++Na^+离子信息,第二主成分与第三主成分主要包含HCO3-离子信息。以第一主成分、第二主成分及第三主成分所表征不同信息为基准,分析了研究区不同位置不同土壤层的盐渍化程度及碱度,该研究区域受盐碱影响较大。
To study the salinity characteristics and distribution of typical oasis land in Xinjiang,we took the land of the 31 stregiment of the Second Division of Xinjiang Production and Construction Corps as an example,used GPS to conduct the fixed-point sampling(N=66),the sampling depth was 100 cm of 5 layers.The soil salt ion was tested,and SPSS software was used to perform descriptive,inter-ion correlation and ion principal component analysis.The results showed that the ions in different soil layers were at a medium or higher variation level with pH 8.11,and the content of Cl^-,K^++Na+and SO4^2-in each soil layer was greater than that of HCO^3-,Ca^2+and Mg^2+;the correlation coefficient of Cl^-,K^++Na+,SO4^2-,Ca^2+and Mg^2+was relatively high,showing a very significant correlation.The first main component of each soil layer mainly contained Cl^-,SO4^2-,Ca^2+,Mg^2+,K^++Na^+ion information,and the second and third main components mainly contained HCO3-ion information.Based on the different information represented by the first,the second and the third principal component,the salinization degree and alkalinity of different soil layers in different locations in the study area were analyzed.The study area was greatly affected by salt and alkali.
作者
郑明
白云岗
张江辉
丁邦新
肖军
Zheng Ming;Bai Yungang;Zhang Jianghui;Ding Bangxin;Xiao Jun(Xinjiang Institute of Water Conservancy and Hydropower Sciences,Urumqi 830049)
出处
《中国农学通报》
2020年第27期81-87,共7页
Chinese Agricultural Science Bulletin
基金
国家“十三五”重点研发计划“农田节水减排控盐技术及应用”子课题“节水减排控盐技术集成与示范”(2017YFC0403305)。
关键词
土壤层
盐分离子
主成分
相关性
pH值
盐碱化
空间变异
soil layer
salt ion
principal component
correlation
pH
salinization
spatial variability