SOILS, SEC 5 • SOIL AND LANDSCAPE ECOLOGY • RESEARCH ARTICLE
Development of pedotransfer functions for predicting the bulk density
in the critical zone on the Loess Plateau, China
Jiangbo Qiao
1
&
Yuanjun Zhu
2
&
Xiaoxu Jia
3
&
Laiming Huang
3
&
Ming’an Shao
2,3
Received: 22 March 2018 /Accepted: 21 May 2018
#
Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
Purpose 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 km
2
) 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.
Conclusions In this study, we developed the first BD PTF for the deep layer (50–200 m) of the Loess Plateau.
Keywords Artificial neuron network
.
Bulk density
.
Multiple linear regression
.
Pedotransfer function
1 Introduction
The Earth’s critical zone (CZ) is an intersection site where
matter migration and energy exchange occur in the
pedosphere, atmosphere, hydrosphere, biosphere, and litho-
sphere of terrestrial ecosystems (National Research Council
2001), and thus, it is the key area for sustaining ecosystem
functioning and human survival (Lin 2010). The bulk density
(BD) of soil is an important basic physical property in the CZ,
where it can indicate the soil quality and site productivity
(Suuster et al. 2011), soil compaction and limitations on root
growth (Abuhamdeh 2003;Dexter2004), and soil water
movement and retention (Huang et al. 2011). In addition, the
BD is an important parameter for calculating the carbon and
nutrient masses in soil layers (Nanko et al. 2014). However,
obtaining BD measurements directly is considered labor-in-
tensive, time-consuming, tedious, and expensive (Kaur et al.
2002; Vos et al. 2005; Suuster et al. 2011). Therefore, an
efficient pedotransfer function (PTF) is necessary for estimat-
ing the BD indirectly from easily measured soil properties.
In recent decades, many BD PTFs have been developed at
different scales using various methods (Balland et al. 2008;
Suuster et al. 2011;Hollisetal.2012;Yietal.2016). For
example, Benites et al. (2007) used a forward stepwise multiple
regression model to predict the BD based on 17 soil properties
obtained from the soil archives of Embrapa Solos, Rio de
Janeiro, Brazil. In addition, Wang et al. (2014) developed
PTFs for the BD in the Loess Plateau region of China by using
Responsible editor: Lu Zhang
* Yuanjun Zh u
zhuyuanjun@foxmail.com
1
College of Resources and Environment, Northwest A&F University,
Yangling 712100, China
2
State Key Laboratory of Soil Erosion and Dryland Agriculture on the
Loess Plateau, Northwest A&F University, Yangling 712100, China
3
Key Laboratory of Ecosystem Network Observation and Modeling,
Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Journal of Soils and Sediments
https://doi.org/10.1007/s11368-018-2040-1
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