Spatial Interpolation of Soil Texture Using Compositional Kriging and Regression Kriging with Consideration of the Characteristics of Compositional Data and Environment Variables

• Published in: Journal of Integrative Agriculture Vol. 12; no. 9; pp. 1673 - 1683
• Main Authors: ZHANG, Shi-wen, SHEN, Chong-yang, CHEN, Xiao-yang, YE, Hui-chun, HUANG, Yuan-fang, LAI, Shuang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2013; Science Press; China Agricultural University/Key Laboratory of Arable Land Conservation North China, Minstry of Agriculture/Key Laboratory of Agricultural Land Quality Monitoring, Ministry of Land and Resources, Beijing 100193, P.R.China%School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, P.R.China%Afforestation Management 0ffice, Sichuan Forestry Department, Chengdu 610081, P.R.China; KeAi Communications Co., Ltd
• Subjects: auxiliary variables; compositional kriging; compositional kriging auxiliary variables regression kriging symmetry logratio transform; kriging; prediction; regression kriging; soil heterogeneity; soil organic matter; soil texture; symmetry logratio transform; 回归; 土壤质地; 均方根误差; 成分数据; 普通克里格法; 环境变量; 空间插值; 预测精度; compositional kriging; auxiliary variables; symmetry logratio transform; regression kriging
• ISSN: 2095-3119; 2352-3425
• DOI: 10.1016/S2095-3119(13)60395-0

The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they （especially the categorical variables） are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging （CK） approaches. The root mean squared error （RMSE）, the relative improvement value of RMSE and Aitchison＇s distance （DA） were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.