Journal of Soils and Sediments

, Volume 15, Issue 4, pp 902–916 | Cite as

Evaluation of pedotransfer functions for estimating saturated hydraulic conductivity in coastal salt-affected mud farmland

  • Rong-Jiang Yao
  • Jing-Song YangEmail author
  • Dan-Hua Wu
  • Fu-Rong Li
  • Peng Gao
  • Xiang-Ping Wang
Soils, Sec 2 • Global Change, Environ Risk Assess, Sustainable Land Use • Research Article



Pedotransfer functions (PTFs) have gained wide development in recent years as approaches to establishing the relationship between easily measurable or readily available soil characteristics found in soil surveys and more complicated model input parameters. However, PTFs developed from databases with limited types of soil conditions might not be directly applicable to other soils whose conditions are different from those used to establish PTFs. Our primary objectives were to determine the influencing factors of saturated hydraulic conductivity (Ks) in the coastal salt-affected farming area, to identify the most appropriate one from the widely used PTFs, and to develop new PTFs with higher accuracy and suitability according to the influencing factors of Ks in our experimental sites.

Materials and methods

A total of 16 soil attributes including 9 physical properties and 7 chemical properties, which were collected in typical coastal newly reclaimed farmlands of north Jiangsu Province, China, were used as input soil data of the PTFs. Factor analysis was employed to group soil basic properties into influencing factors of Ks. The appropriate PTFs were identified according to the prediction criteria, and new PTFs were established using multiple linear regression, modified Vereecken PTF, and artificial neural network methods.

Results and discussion

Results indicated that Ks in the soil profile was classified as low permeability and 20–40-cm layer (Ap2 horizon) had the lowest Ks and highest bulk density values. With 91.05 % of variance explained, the 16 soil basic properties were classified into five factors, i.e., soil porosity component, water retention component, organic matter component, soil salinity component, and unavailable water component. Among all the selected PTFs, Ahuja PTF was identified as the most convenient method which only needed effective porosity. Vereecken PTF was suitable for a wider range of soil textural classes. Using SA, CL, Bd, SOM, and ECe as input soil data, the modified Vereecken (MV) PTF and artificial neural network (ANN) PTF had better prediction performance than the published PTFs.


We concluded that soil salinity played an important role in the estimation of Ks and should be considered as input soil data. The established ANN-based PTF using the suggested input soil data was recommended as the best approach for estimation of soil Ks in the coastal salt-affected farming area.


Coastal Influencing factor Pedotransfer function Reclaimed farmland Saturated hydraulic conductivity 



Content of sand particles (%)


Content of silt particles (%)


Content of clay particles (%)


Bulk density (g cm−3)


Total porosity


Effective porosity


Soil saturated water content (cm3 cm−3)


Field capacity (cm3 cm−3)


Wilting point (cm3 cm−3)


Electrical conductivity of saturated soil paste extract (dS m−1)


Sodium adsorption ratio


Soil organic matter (g kg−1)


Soil total nitrogen (g kg−1)


Available nitrogen (mg kg−1)


Available phosphate (mg kg−1)


Available potassium (mg kg−1)


Saturated soil hydraulic conductivity (cm day−1)


Pedotransfer functions



This study was funded by the financial support of the National Natural Science Foundation of China (41101199; 41171181; 51109204), the Natural Science Foundation of Jiangsu Province (BK20141266), and the Key Technology R&D Program of Jiangsu Province (BE2014678). The authors wish to express cordial thanks to the staff of Jinhai Farm and Huanghai Raw Seed Growing Farm for assistant in soil sampling and field measurement. We also acknowledge the valuable comments of the anonymous reviewers.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Rong-Jiang Yao
    • 1
    • 2
  • Jing-Song Yang
    • 1
    • 2
    Email author
  • Dan-Hua Wu
    • 2
  • Fu-Rong Li
    • 1
  • Peng Gao
    • 3
  • Xiang-Ping Wang
    • 1
  1. 1.State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil ScienceChinese Academy of Sciences (CAS)NanjingChina
  2. 2.Dongtai Institute of Tidal Flat ResearchNanjing Branch of the Chinese Academy of SciencesDongtaiChina
  3. 3.Department of GeographyUniversity of South CarolinaColumbiaUSA

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