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

作　　者：YI Xiangsheng LI Guosheng YIN Yanyu 

机构地区：[1]Agriculture Resource Monitoring Station,Chinese Academy of Agricultural Engineering [2]Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences [3]Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

出　　处：《Pedosphere》2016年第3期362-373,共12页土壤圈（英文版）

基　　金：supported by the National Key Technology R&D Program of China(No.2009BAC61B01);the National Basic Research Program(973Program) of China(No.2012CB95570002);the Innovative Team(Investigation and Management for Agricultural Land Resource) of Predominant Science and Technology in Chinese Academy of Agricultural Engineering

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(OC), 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.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（OC）, 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.

关 键 词：alpine soil artificial neural network multiple linear regression organic carbon soil depth soil texture 

分 类 号：S151.9[农业科学—土壤学]