Prediction of Bulk Density of Soils in the Loess Plateau Region of China
Wang, YQ; Shao, MA; Liu, ZP; Zhang, CC
2014
Source PublicationSURVEYS IN GEOPHYSICS
ISSN0169-3298
Volume35Issue:2Pages:395-413
AbstractSoil bulk density (BD) is a key soil physical property that may affect the transport of water and solutes and is essential to estimate soil carbon/nutrients reserves. However, BD data are often lacking in soil databases due to the challenge of directly measuring BD, which is considered to be labor intensive, time consuming, and expensive especially for the lower layers of deep soils such as those of the Chinese Loess Plateau region. We determined the factors that were closely correlated with BD at the regional scale and developed a robust pedotransfer function (PTF) for BD by measuring BD and potentially related soil and environmental factors at 748 selected sites across the Loess Plateau of China (620,000 km(2)) at which we collected undisturbed and disturbed soil samples from two soil layers (0-5 and 20-25 cm). Regional BD values were normally distributed and demonstrated weak spatial variation (CV = 12 %). Pearson's correlation and stepwise multiple linear regression analyses identified silt content, slope gradient (SG), soil organic carbon content (SOC), clay content, slope aspect (SA), and altitude as the factors that were closely correlated with BD and that explained 25.8, 6.3, 5.8, 1.4, 0.3, and 0.3 % of the BD variation, respectively. Based on these closely correlated variables, a reasonably robust PTF was developed for BD using multiple linear regression, which performed equally with the artificial neural network method in the current study. The inclusion of topographic factors significantly improved the predictive capability of the BD PTF and in which SG was an important input variable that could be used in place of SA and altitude without compromising its capability for predicting BD. Thus, the developed PTF with only four input variables (clay, silt, SOC, SG), including their common transformations and interactive terms, predicted BD with reasonable accuracy and is thus useful for most applications on the Loess Plateau of China. More attention should be given to the role of topography when developing PTFs for BD prediction. Testing of the developed PTF for use in other loess regions in the world is required.
SubtypeJournal
KeywordPedotransfer functions Loessial soil Artificial neural network Multiple regression Topography
Subject AreaGeochemistry & Geophysics
WOS Subject ExtendedGeochemistry & Geophysics
WOS KeywordSATURATED HYDRAULIC CONDUCTIVITY ; ARTIFICIAL NEURAL-NETWORKS ; PEDOTRANSFER FUNCTIONS ; WATER RETENTION ; QUALITY ; CONSERVATION ; ATTRIBUTES ; PARAMETERS ; REGRESSION ; TEXTURE
Indexed BySCI
Language英语
WOS IDWOS:000330348900004
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/44575
Collection生态系统网络观测与模拟院重点实验室_生态网络实验室
Recommended Citation
GB/T 7714
Wang, YQ,Shao, MA,Liu, ZP,et al. Prediction of Bulk Density of Soils in the Loess Plateau Region of China[J]. SURVEYS IN GEOPHYSICS,2014,35(2):395-413.
APA Wang, YQ,Shao, MA,Liu, ZP,&Zhang, CC.(2014).Prediction of Bulk Density of Soils in the Loess Plateau Region of China.SURVEYS IN GEOPHYSICS,35(2),395-413.
MLA Wang, YQ,et al."Prediction of Bulk Density of Soils in the Loess Plateau Region of China".SURVEYS IN GEOPHYSICS 35.2(2014):395-413.
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