Pedotransfer Functions for Estimating Soil Bulk Density: A Case Study in the Three-River Headwater Region of Qinghai Province, China

文献类型： 外文期刊

第一作者： Yi Xiangsheng

作者： Yi Xiangsheng;Yi Xiangsheng;Li Guosheng;Yin Yanyu

作者机构：

关键词： alpine soil;artificial neural network;multiple linear regression;organic carbon;soil depth;soil texture

期刊名称：PEDOSPHERE （ 影响因子：3.911； 五年影响因子：4.814 ）

ISSN： 1002-0160

年卷期： 2016 年 26 卷 3 期

页码：

收录情况： SCI

摘要： Bulk density (BD) is an important soil physical property and has significant effect on soil water conservation function. Indirect methods, which are called pedotransfer functions (PTFs), have replaced direct measurement and can acquire the missing data of BD during routine soil surveys. In this study, multiple linear regression (MLR) and artificial neuron network (ANN) methods were used to develop PTFs for predicting BD from soil organic carbon (00), texture and depth in the Three-River Headwater region of Qinghai Province, China. The performances of the developed PTFs were compared with 14 published PTFs using four indexes, the mean error (ME), standard deviation error (SDE), root mean squared error (RMSE) and coefficient of determination (R-2). Results showed that the performances of published PTFs developed using exponential regression were better than those developed using linear regression from OC. Alexander (1980)-B, Alexander (1980)-A and Manrique and Jones (1991)-B PTFs, which had good predictions, could be applied for the soils in the study area. The PTFs developed using MLR (MLR-PTFs) and ANN (ANN-PTFs) had better soil BD predictions than most of published PTFs. The ANN-PTFs had better performances than the MLR-PTFs and their performances could be improved when soil texture and depth were added as predictor variables. The idea of developing PTFs for predicting soil BD in the study area could provide reference for other areas and the results could lay foundation for the estimation of soil water retention and carbon pool.

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