Journal of Soils and Sediments

, Volume 19, Issue 1, pp 366–372 | Cite as

Development of pedotransfer functions for predicting the bulk density in the critical zone on the Loess Plateau, China

  • Jiangbo Qiao
  • Yuanjun ZhuEmail author
  • Xiaoxu Jia
  • Laiming Huang
  • Ming’an Shao
Soils, Sec 5 • Soil and Landscape Ecology • Research Article



The bulk density (BD) is an important physical property of soil, which is used to estimate soil carbon/nutrient reserves, and it is an important parameter in various predictive and descriptive models. However, BD data are lacking due to the difficulty of measuring it directly. Pedotransfer function (PTF) may provide an alternative method for estimating BD indirectly based on easily measured soil properties. The Loess Plateau in China (620,000 km2) has deep loess deposits (50–200 m), which makes it difficult to obtain BD values for the deep soil layer, and thus, a PTF is needed for estimating BD.

Materials and methods

In this study, multiple linear regression (MLR) and artificial neural network (ANN) methods were used to develop BD PTFs for the deep layer of the Loess Plateau based on the soil organic carbon, texture, and depth. In total, 534 undisturbed soil cores were obtained by soil core drilling from five typical sites, ranging from the top of the soil profile to the bedrock.

Results and discussion

The BD values all exhibited low variation (CV < 10%). Pearson’s correlation coefficient analysis showed that BD had significant correlations with the sand, silt, clay, soil organic carbon (SOC), and depth (P < 0.01). The performance of MLR was similar to that of the ANN method. The soil depth and clay were also important input variables for the BD PTF. The PTF developed in this study performed better than existing BD PTFs.


In this study, we developed the first BD PTF for the deep layer (50–200 m) of the Loess Plateau.


Artificial neuron network Bulk density Multiple linear regression Pedotransfer function 



The authors thank the editor and reviewers for their valuable comments and suggestions.

Funding information

This study was supported by the National Natural Science Foundation of China for a major international cooperation program between China and England (41571130081), the National Natural Science Foundation of China (41371242 and 41530854), and the National Key Research and Development Program of China (2016YFC0501706-03).


  1. Abuhamdeh NH (2003) Compaction and subsoiling effects on corn growth and soil bulk density. Soil Sci Soc Am J 67.
  2. Balland V, Pollacco JAP, Arp PA (2008) Modeling soil hydraulic properties for a wide range of soil conditions. Ecol Model 219:300–316CrossRefGoogle Scholar
  3. Benites VM, Machado PLOA, Fidalgo ECC, Coelho MR, Madari BE (2007) Pedotransfer functions for estimating soil bulk density from existing soil survey reports in Brazil. Geoderma 139:90–97CrossRefGoogle Scholar
  4. Brahim N, Bernoux M, Gallali T (2012) Pedotransfer functions to estimate soil bulk density for Northern Africa: Tunisia case. J Arid Environ 81:77–83CrossRefGoogle Scholar
  5. Dexter AR (2004) Soil physical quality : part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma 120:201–214CrossRefGoogle Scholar
  6. Heuscher SA, Brandt CC, Jardine PM (2005) Using soil physical and chemical properties to estimate bulk density. Soil Sci Soc Am J 69:51–56Google Scholar
  7. Hollis JM, Hannam J, Bellamy PH (2012) Empirically-derived pedotransfer functions for predicting bulk density in European soils. Eur J Soil Sci 63:96–109CrossRefGoogle Scholar
  8. Huang C, Shao M, Tan W (2011) Soil shrinkage and hydrostructural characteristics of three swelling soils in Shaanxi. China J Soils Sediments 11:474–481CrossRefGoogle Scholar
  9. Kaur R, Kumar S, Gurung HP (2002) A pedo-transfer function (PTF) for estimating soil bulk density from basic soil data and its comparison with existing PTFs. Aust J Soil Res 40:847–858CrossRefGoogle Scholar
  10. Li Y, Chen D, White RE, Zhu A, Zhang J (2007) Estimating soil hydraulic properties of Fengqiu County soils in the North China Plain using pedo-transfer functions. Geoderma 138:261–271CrossRefGoogle Scholar
  11. Lin H (2010) Earth’s critical zone and hydropedology: concepts, characteristics, and advances. Hydrol Earth Syst Sci 6:3417–3481CrossRefGoogle Scholar
  12. Liu YP, Tong J, Li XN (2005) Analysing the silt particles with the Malvern Mastersizer 2000. Water Conserv Sci Tech Econ 11:329–331Google Scholar
  13. Merdun H, Çınar Ö, Meral R, Apan M (2006) Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil Till Res 90:108–116CrossRefGoogle Scholar
  14. Motaghian HR, Mohammadi J (2011) Spatial estimation of saturated hydraulic conductivity from terrain attributes using regression, kriging, and artificial neural networks. Pedosphere 21:170–177CrossRefGoogle Scholar
  15. Nanko K, Ugawa S, Hashimoto S, Imaya A, Kobayashi M, Sakai H, Ishizuka S, Miura S, Tanaka N, Takahashi M (2014) A pedotransfer function for estimating bulk density of forest soil in Japan affected by volcanic ash. Geoderma 213:36–45CrossRefGoogle Scholar
  16. National Research Council (2001) Basic research opportunities in earth science. National Academy Press, Washington DCGoogle Scholar
  17. Nelson DW, Sommers LE, Sparks DL, Page AL, Helmke PA, Loeppert RH, Soltanpour PN, Tabatabai MA, Johnston CT, Sumner ME (1982) Total carbon, organic carbon, and organic matter. Methods of soil analysis part—chemical methods, pp 961–1010Google Scholar
  18. Nielsen DR, Bouma J (1985) Soil spatial variability : proceedings of a workshop of the ISSS and the SSSA, Las Vegas, USA, 30 November - 1 December, 1984. PudocGoogle Scholar
  19. Santra P, Das BS (2008) Pedotransfer functions for soil hydraulic properties developed from a hilly watershed of Eastern India. Geoderma 146:439–448CrossRefGoogle Scholar
  20. Shi H, Shao M (2000) Soil and water loss from the Loess Plateau in China. J Arid Environ 45:9–20CrossRefGoogle Scholar
  21. Suuster E, Ritz C, Roostalu H, Reintam E, Kõlli R, Astover A (2011) Soil bulk density pedotransfer functions of the humus horizon in arable soils. Geoderma 163:74–82CrossRefGoogle Scholar
  22. Tranter G, Minasny B, Mcbratney AB, Murphy B, Mckenzie NJ, Grundy M, Brough D (2007) Building and testing conceptual and empirical models for predicting soil bulk density. Soil Use Manag 23:437–443CrossRefGoogle Scholar
  23. Vos BD, Meirvenne MV, Quataert P, Deckers J, Muys B (2005) Predictive quality of pedotransfer functions for estimating bulk density of forest soils. Soil Sci Soc Am J 69:500–510CrossRefGoogle Scholar
  24. Wang L, Wang Q, Wei S, Shao MA, Li Y (2008) Soil desiccation for Loess soils on natural and regrown areas. For Ecol Manag 255:2467–2477CrossRefGoogle Scholar
  25. Wang Y, Shao MA, Liu Z, Horton R (2013) Regional-scale variation and distribution patterns of soil saturated hydraulic conductivities in surface and subsurface layers in the loessial soils of China. J Hydrol 487:13–23CrossRefGoogle Scholar
  26. Wang Y, Shao MA, Liu Z, Zhang C (2014) Prediction of bulk density of soils in the Loess Plateau region of China. Surv Geophys 35:395–413CrossRefGoogle Scholar
  27. Xiong Y, Wallach R, Furman A (2011) Modeling multidimensional flow in wettable and water-repellent soils using artificial neural networks. J Hydrol 410:92–104CrossRefGoogle Scholar
  28. Yao RJ, Yang JS, Wu DH, Li FR, Gao P, Wang XP (2015) Evaluation of pedotransfer functions for estimating saturated hydraulic conductivity in coastal salt-affected mud farmland. J Soils Sediments 15:902–916CrossRefGoogle Scholar
  29. Yi X, Li G, Yin Y (2016) Pedotransfer functions for estimating soil bulk density:a case study in the three-river headwater region of Qinghai Province, China. Pedosphere 26:362–373CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jiangbo Qiao
    • 1
  • Yuanjun Zhu
    • 2
    Email author
  • Xiaoxu Jia
    • 3
  • Laiming Huang
    • 3
  • Ming’an Shao
    • 2
    • 3
  1. 1.College of Resources and EnvironmentNorthwest A&F UniversityYanglingChina
  2. 2.State Key Laboratory of Soil Erosion and Dryland Agriculture on the Loess PlateauNorthwest A&F UniversityYanglingChina
  3. 3.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

Personalised recommendations