Cokriging of Soil Cation Exchange Capacity Using the First Principal Component Derived from Soil Physico-Chemical Properties

Kai-hua LIAO , Shao-hui XU , Ji-chun WU , Shu-hua JI , Qing LIN
{"title":"Cokriging of Soil Cation Exchange Capacity Using the First Principal Component Derived from Soil Physico-Chemical Properties","authors":"Kai-hua LIAO ,&nbsp;Shao-hui XU ,&nbsp;Ji-chun WU ,&nbsp;Shu-hua JI ,&nbsp;Qing LIN","doi":"10.1016/S1671-2927(11)60116-8","DOIUrl":null,"url":null,"abstract":"<div><p>As soil cation exchange capacity (CEC) is a vital indicator of soil quality and pollutant sequestration capacity, a study was conducted to evaluate cokriging of CEC with the principal components derived from soil physico-chemical properties. In Qingdao, China, 107 soil samples were collected. Soil CEC was estimated by using 86 soil samples for prediction and 21 soil samples for test. The first two principal components (PC1 and PC2) together explained 60.2% of the total variance of soil physico-chemical properties. The PC1 was highly correlated with CEC <em>(r</em>=0.76, <em>P</em>&lt;0.01), whereas there was no significant correlation between CEC and PC2 (<em>r</em>=0.03). The PC1 was then used as an auxiliary variable for the prediction of soil CEC. Mean error (ME) and root mean square error (RMSE) of kriging for the test dataset were −1.76 and 3.67 cmol<sub>c</sub> kg<sup>−1</sup>, and ME and RMSE of cokriging for the test dataset were −1.47 and 2.95 cmol<sub>c</sub> kg<sup>−1</sup>, respectively. The cross-validation <em>R</em><sup>2</sup> for the prediction dataset was 0.24 for kriging and 0.39 for cokriging. The results show that cokriging with PC1 is more reliable than kriging for spatial interpolation. In addition, principal components have the highest potential for cokriging predictions when the principal components have good correlations with the primary variables.</p></div>","PeriodicalId":7475,"journal":{"name":"Agricultural Sciences in China","volume":"10 8","pages":"Pages 1246-1253"},"PeriodicalIF":0.0000,"publicationDate":"2011-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1671-2927(11)60116-8","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural Sciences in China","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1671292711601168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

Abstract

As soil cation exchange capacity (CEC) is a vital indicator of soil quality and pollutant sequestration capacity, a study was conducted to evaluate cokriging of CEC with the principal components derived from soil physico-chemical properties. In Qingdao, China, 107 soil samples were collected. Soil CEC was estimated by using 86 soil samples for prediction and 21 soil samples for test. The first two principal components (PC1 and PC2) together explained 60.2% of the total variance of soil physico-chemical properties. The PC1 was highly correlated with CEC (r=0.76, P<0.01), whereas there was no significant correlation between CEC and PC2 (r=0.03). The PC1 was then used as an auxiliary variable for the prediction of soil CEC. Mean error (ME) and root mean square error (RMSE) of kriging for the test dataset were −1.76 and 3.67 cmolc kg−1, and ME and RMSE of cokriging for the test dataset were −1.47 and 2.95 cmolc kg−1, respectively. The cross-validation R2 for the prediction dataset was 0.24 for kriging and 0.39 for cokriging. The results show that cokriging with PC1 is more reliable than kriging for spatial interpolation. In addition, principal components have the highest potential for cokriging predictions when the principal components have good correlations with the primary variables.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用由土壤理化性质导出的第一主成分计算土壤阳离子交换容量
土壤阳离子交换容量(CEC)是土壤质量和污染物固存能力的重要指标,本文研究了土壤阳离子交换容量与土壤理化性质衍生的主成分之间的关系。在中国青岛,收集了107份土壤样品。采用86个预测土样和21个试验土样估算土壤CEC。前两个主成分(PC1和PC2)共同解释了土壤理化性质总方差的60.2%。PC1与CEC高度相关(r=0.76, P<0.01),而PC2与CEC无显著相关(r=0.03)。将PC1作为预测土壤CEC的辅助变量。测试数据集的克里格均值误差(ME)和均方根误差(RMSE)分别为- 1.76和3.67 cmolc kg - 1,测试数据集的共克里格均值误差(ME)和RMSE分别为- 1.47和2.95 cmolc kg - 1。预测数据集的交叉验证R2为kriging为0.24,cokriging为0.39。结果表明,用PC1进行空间插值比用克里格法进行空间插值更可靠。此外,当主成分与主变量具有良好的相关性时,主成分具有最高的共克里格预测潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Agricultural Sciences in China
Agricultural Sciences in China AGRICULTURE, MULTIDISCIPLINARY-
自引率
0.00%
发文量
0
审稿时长
3.2 months
期刊最新文献
Synthesis of Cationized Magnetoferritin for Ultra-fast Magnetization of Cells. Fine Mapping and Cloning of the Grain Number Per-Panicle Gene (Gnp4) on Chromosome 4 in Rice (Oryza sativa L.) Cloning and Characterization of a Novel Gene GmMF1 in Soybean (Glycine max L. Merr.) Optimization of Two-Dimensional Gel Electrophoresis for Kenaf Leaf Proteins Cloning of a Calcium-Dependent Protein Kinase Gene NtCDPK12, and Its Induced Expression by High-Salt and Drought in Nicotiana tabacum
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1