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Modelling of soil texture and its verification with related soil properties.

Introduction

A model represents a natural phenomenon or reality in a numerical form, and is usually used for investigation of the properties of the system and, in some cases, prediction of future outcomes. According to Murthy (2002), 'a model is a schematic representation of the conception of a system or an act of mimicry or a set of equations, which represents the behaviour of a system that facilitates understanding, explaining and improving performance of a system'. Shein (2015) stated that any advanced discipline of soil science is highly related to mathematical simulation--currently used for examining hypotheses, analysis of processes occurring in soil, polyvariant computation for optimum solutions and prediction of soil construction and many other processes occurring in nature. He also opined that modelling and its application must be quite logical and understandable. The numerical approaches of soil modelling are very useful in describing the intrinsic heterogeneity of soil environments and the temporal and spatial variability of boundary conditions of soils. Moreover, the relationship of the nonlinearity of involved processes and various soil constituents cannot be expressed without numerical approaches (Vereecken et al. 2016).

Soil texture is an important soil property that regulates soil fertility. Relationships between soil texture and environmental factors are necessary to understand and explain at fine scales in site-specific planning for proper management (Greve et al. 2012). Many other soil properties and processes greatly depend on soil texture, including water holding capacity, aeration, drainage, soil organic matter content, compactibility, susceptibility to erosion, pH, buffering capacity, cation exchange capacity (CEC) (Brady and Weil 2002) and electrical conductivity (EC) (Franzen 2003; Domsch and Giebel 2004; Sonmez et al. 2008). Silva et al. (2012) stated that soil texture is the main factor controlling plant root development, soil biodiversity and biogeochemical cycling. Usually soil quality as affected by land use change and agricultural practices depends on soil texture (Khan et al. 2012; van Capelle et al. 2012; Cotching et al. 2013). The dependency or relationship between soil properties and soil texture is governed by how a soil is disturbed and how much time it has for resilience. A relatively undisturbed and resilient soil is more dependent on soil texture.

Historically, a model for soil texture obtained from relative amounts of sand, silt and clay was developed by Whitney (1911). That model was modified by Davis and Bennett in 1927 (Davis and Bennett 1927). The final modified form, the 'USDA Equilateral Soil Textural Triangle' (Soil Survey Staff 1951), has been used extensively for determining soil textural classes. The main drawbacks of this equilateral triangle are that there are a fixed number of textural classes and it does not provide a texture for each soil. In the real world, it is likely that variation exists in soils within each textural class and spatial changes of soil fineness do not occur abruptly, thereby underpinning the defining of a textural index that is numerical, continuous and can yield a flexible number of textural classes. The numerical index might be appropriate for studying the spatial variability of soil at small or large scales. Most soils belong to a broad single USDA textural class, and in such cases the textural class can be subdivided into more narrow textural classes using the index. The numerical index might also help provide a quick and appropriate understanding of relative fineness of soils. Therefore, the present study was undertaken to develop and verily a numerical model for simulating soil texture.

Materials and methods

Defining soil textural indices

The index Txi can be expressed as 'the natural logarithmic value of the ratio of the sum of coarse fractions and that of fine fractions of soil', mathematically expressed as follows:

Txi = ln (%Sand + %Silt)/(%Silt + %Clay)

The second index (Txw) is a modified form of Txi as follows:

Txw = ln [(%Sand x 5 + %Silt)/(%Silt + %Clay x 10)]

The hypothetical value of Txi ranges approximately between -5 to +5 for soils. The coarseness of soil increases with increasing Txi value and vice versa, and soil of Txi equal or very close to zero is medium textured (Fig. 1).

The Txi can be modified and extended as Txj:

Txj = ln [(%Sand + %Silt) (%Sand)/(%Silt + %Clay x 1.45)(%Clay)]

In all cases of indices Txi, Txw and Txj, both numerator and denominator in the equations must be greater than zero. Logically, Txw and Txj would be appropriate and better than Txi because different weights are given to different fractions of soil in order to contribute to fineness of soil. However, Txj would be more suitable when further classifications of a particular soil texture are necessary, because it would give a higher numerical value than Txi and Txw.

Datasets

There were 150 surface soil (0-20 cm) samples collected from the south-west coastal saline zone of Bangladesh covering the districts of Khulna, Satkhira and Bhola with the assistance of a GPS device to ensure well-distributed sampling from the study areas. Hypothetical data with a maximum range and higher variations of percent sand, silt and clay covering the USDA textural classes (Fig. 2) and data obtained from the collected soil samples were used to justify, simulate and validate various soil properties with the indices. The USDA textural classes were numerically coded 1-12 in accordance with ascending order of coarseness of soil for use in making correlations and graphical representations of USDA soil textural classes.

Laboratory analysis of collected soil samples

All soil samples were air-dried, ground with a vibrating sample mill (TI-200, HEIKO, HEIKO sample mill, Tokyo-Japan, TI-200) and sieved using 2-ram mesh before analysis. Particle size analysis of the air-dried soil samples was done using the hydrometer method (Bouyoucos 1962). The CEC of soil samples was determined by 1N ammonium acetate extraction (Jackson 1973). Walkley and Black wet digestion method (Nelson and Sommers 1982) was followed to determine organic carbon (OC).

The soil-water ratio method of Sonmez et al. (2008) was slightly modified for EC determination. To make a suspension, 75 mL of distilled water was added to 15 g of soil sample. The container receiving the suspension was then shaken in a mechanical shaker for 1 h and the filtrate collected by passing the suspension through Whatmann no. 42 filter paper under the action of gravity. The EC of a 1 : 5 soil: water extract (E[C.sub.1:5]) was measured using a conductivity cell. Corrections were made on EC reading taking account of cell constant of the conductivity meter and temperature of respective extracts at 25[degrees]C.

All laboratory analyses were done using air-dried soil samples, but calculation was on an oven-dry basis to eliminate variations arising due to initial moisture contents in soil samples.

Statistical analyses

Statistical analyses were carried out by SPSS version 16 and spreadsheet. Linear or polynomial regression models were used to show relationships. Coefficient of determination ([R.sup.2]) and significance of regression (P) were considered to validate overall regression equations with a view to describing variation in dependent variables as a function of independent variables (Kleinbaum et al. 1988).

Geostatistical analysis

A spatial interpolated map of soil texture was produced by ordinary kriging using ArcGIS 10.2.

Results and discussion

Rationale for the indices to simulate soil texture

The frequency histograms of the indices derived from hypothetical datasets are shown in Fig. 3. The derived values for indices Txi, Txw and Txj ranged from -5.30 to +5.30, -6.50 to +5.20 and -11.65 to +11.08 respectively. The indices followed a normal distribution (Fig. 3). The range for each fraction of soil in the hypothetical data was 0.25-99.50% (Fig- 2).

Inboonchuay et al. (2016) found that sand, silt and clay contents were in the ranges of 0.42-90.40, 1.92-67.50 and 5.59-70.00% respectively in paddy soils of the Khorat Basin in Thailand. Corresponding values were 94.43-95.66, 3.71-4.56 and 0.64-1.01% in mobile dunes (0-40 cm) in the desert of Ordos Plateau of Mongolia in China (Jin et al. 2013). Basin and paddy soils are generally very heavy soils but desert sand dunes are very sandy. These previous findings are consistent with our hypothetical dataset. Therefore, the ranges for soil fractions as well as for computed indices values in the hypothetical dataset could be fitted for all soils.

If an index shows good association with soil separates, it is logical that the index can indicate soil texture. The scatter diagram demonstrated the association of Txi with soil fractions (sand, silt and clay) using hypothetical datasets. The Txi increased gradually with increasing sand content but decreased gradually with increasing clay contents of soil (Fig. 4). Results from Fig. 4 also revealed that soil tended to become moderate in texture when silt content was higher. When silt content was lower, soil could be either coarse or fine in texture depending on the relative amounts of sand and clay. The trend of results for Txw and Txj were similar in the scatter diagrams. It should also be noted that Txi, Txw and Txj might show variations in their usefulness for the location-spccific soil variability.

The trend of relationship of soil textural indices with USDA textural class codes

A similar trend of relationship was observed between Txw and USDA textural class when using the hypothetical dataset (Fig. 5). Relationships similar to that for Txw were also observed for Txi and Txj (data not shown). Thus, the indices were comparable with the USDA textural class.

Simulation of soil texture by the defined model

The indices were easy to compute and gave quick and appropriate perceptions on relative particle size of soil. Based on the values of the indices, a generalised classification of soil texture is presented in Table 1. A customised or flexible classification of texture would be possible depending on the nature of soils under investigation. Any of the developed indices can be used to classify or study soils based on the relationships of the corresponding index with soil properties.

Relationship between developed model and soil properties

The correlations of the indices, individual soil separates and EC were obtained based on the datasets of our study samples (Tables 2 and 3). Percentages of sand, silt and clay were in the ranges of 7.36-76.92, 8.82-69.08 and 6.26-45.04% respectively, indicating a wide range of all separates; the corresponding coefficients of variations (CVs) were 49.52, 45.81 and 50.60%.

Data presented in Table 3 shows the correlations of the indices with USDA textural class codes and soil properties. There were significant correlations (r=0.79-0.90) between the indices and USDA textural class codes. Similarly, significant (r= 0.78-0.96) correlations were observed between the indices and individual amounts of soil separates. However, significant negative correlations (r= -0.48 to -0.55) were found between EC and the indices. Likewise, OC was also significantly negatively correlated with the indices (r=-0.34 to -0.53). The OC and EC were significantly correlated with soil separates (r=0.39-0.53).

Sultan (2006) found moderate correlations between EC and silt and clay contents ([r.sub.silt] = 0.41 and [r.sub.clay] = 0.46) which supported our findings in terms of the relationship of EC with soil texture. The EC value increased with increasing silt and clay contents but decreased with increasing sand content. This might be due to increasing adsorption and decreasing the leaching loss of dissociating cations of salts in the fine-textured soil. Plante et al. (2006) observed a significant relationship ([R.sup.2] = 0.46) of silt + clay contents with OC in Saskatchewan soils, which agreed with our findings regarding the correlations of soil separates with OC.

The nonlinear relationship between CEC and soil texture is presented in Fig. 6. A second-order polynomial regression model successfully described the relationship of CEC with Txw ([R.sup.2] = 0.732, P< 0.001; Fig. 6), meaning that CEC decreased gradually with increasing Txw. The associations of other indices (Txi and Txj) with CEC were also significant ([R.sup.2.sub.Txi] = 0.699, P<0.001; and [R.sup.2.sub.Txj] = 0.672, Pc0.001). In comparison, the relationships of individual soil separates with CEC were [R.sup.2.sub.sand] = 0.530, [R.sup.2.sub.silt] = 0.310 and [R.sup.2.sub.clay] = 0.716 (all P< 0.001). Ersahin et al. (2006) also found significant relationships between CEC and soil separates ([R.sup.2.sub.sand] = 0.220, [R.sup.2.sub.silt] = 0.311 and [R.sup.2.sub.clay] = 0.480) which supported our findings. Therefore, the indices representing the soil textural components as a whole could reflect soil texture and its relationship with soil properties.

Use of the developed indices within a single USDA textural class of soil

The textural indices can quantify the association with soil properties like CEC, EC and OC along with showing variations of soils in terms of texture even within a single textural class as presented in Fig. 7.

About 53 and 42% of the variations in CEC ([R.sup.2] = 0.528 at P<0.002; [R.sup.2] = 0.422 at P<0.001 respectively) were found within silty clay loam and silt loam following second-degree polynomial regression and linear regression models respectively due to differences in soil fractions as denoted by Txi (Fig. lb, c).

The relationship of Txi with EC within the silty clay loam textural class was moderate ([R.sup.2] = 0.289 at P < 0.06) which was fitted with a second-order polynomial regression (Fig. lb). A strong linear relationship ([R.sup.2] = 0.630, P< 0.001) was observed between Txi and OC within the silt loam textural class (Fig. 7c).

Use of the index for making customised textural groups

An association of CEC with texture is not valid if there are few data points. We found some groups of small numbers of data points in our soils under the different textural classes: sandy clay loam (n = 1), clay loam (n = 1), silty clay (n = 3) and loam (n = 2). To address the insufficiency in data points, we categorised the entire soil samples (n = 150) into two equal groups (group I--fine textured and group II--coarse textured) based on fineness of Txw so that the small samples within the above mentioned textural classes were merged into one group. Consequently, the relationship of CEC and the index was established in two separate groups (Fig. 8).

A strong relationship ([R.sup.2] = 0.759, P< 0.001) was observed between CEC and Txw within group 1 (Fig. 8a) but a weak relationship ([R.sup.2] = 0.115, P < 0.02) within group 11 (Fig. 8b) following a second-degree polynomial regression. The overall relationship of CEC with soil texture was attributed mainly to the finer fractions of soil. In the same way (data not shown), Txw showed a higher association with EC and OC within group I ([R.sup.2.sub.EC] = 0.171 and [R.sup.2.sub.OC] = 0.209, both P < 0.001) than group II ([R.sup.2.sub.EC] = 0.001 at P < 0.80, [R.sup.2.sub.OC] = 0.051 at P < 0.20).

Use of soil textural index in soil mapping

The numerical value of the soil textural index could also be effectively used in digital mapping of soil to represent soil texture. For example, a soil textural map of Khulna district is shown in Fig. 9. Moderately fine-textured soil dominated the regions under study in Khulna and there were few areas with fine- and medium-textured soils.

Conclusions

The developed model numerically represents soil texture and shows relationships with soil properties. Soil textural indices of the model fitted well in developing relationships of soil texture with other soil properties such as EC, OC and CEC. The indices were validated and shown to well represent soil texture using hypothetical data. The indices could be used in making digital soil maps of soil texture and might help with advanced modelling of soil properties, processes and functions.

Conflicts of interest

The authors declare no conflicts of interest.

Supplementary material

Hypothetical dataset of particle size distribution of soil (Table SI) simulated the USDA soil textural triangle. The data were used to justify and validate the developed indices.

https://doi.org/10.1071/SR17252

Acknowledgements

We are thankful to the Ministry of Science and Technology of the Government of the People's Republic of Bangladesh and Department of Soil Science of Bangabandhu Sheikh Mujibur Rahman Agricultural University for providing funds and facilities to carry out the research as a part of a dissertation. We are also grateful to the anonymous reviewers for their comments and suggestions that helped to make the manuscript more scientific and understandable.

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M. Shahadat Hossain (A), C. K. M. Mustafizur Rahman (B,D), M. Saiful Alam (B), M. Mizanur Rahman (B), A. R. M. Solaiman (B), and M. A. Baset Mia (C)

(A) Department of Soil Science, Sylhet Agricultural University, Sylhet, Bangladesh.

(B) Department of Soil Science, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh.

(C) Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh.

(D) Corresponding author. Email: mustafiz@bsmrau.edu.bd

Received 23 September 2017, accepted 25 January 2018, published online 19 April 2018

Caption: Fig. 1. Approximate scale of soil textural index Txi, denoting the fineness and coarseness of soils.

Caption: Fig. 2. Soil textural triangle showing hypothetical data points of particle size distribution.

Caption: Fig. 3. Histogram showing distribution of indices Txi, Txw and Txj derived from hypothetical dataset.

Caption: Fig. 4. Scatter diagram showing association of soil textural index Txi with individual soil mineral fractions based on hypothetical dataset.

Caption: Fig. 5. Line chart showing trend of relationship between soil textural index Txw and USDA soil textural class code.

Caption: Fig. 6. Relationship of CEC with soil texture (soil textural index, Txw).

Caption: Fig. 7. Relationship of soil properties (CEC, EC and OC) with soil texture within single USDA textural class: (a) USDA textural class code, (b) Txi within Silty clay loam and (c) Txi within Silt loam.

Caption: Fig. 8. Relationships of CEC with Txw in textural groups I and II.

Caption: Fig. 9. Soil textural classes based on soil textural index Txw at selected unions in Khulna district.
Table 1. Textural classes based on tcxtural indices along
with USDA textural class of soil

Txi, Txw and Txj values in a class are not equivalent to each other

Soil textural index       Txi value         Txw value
class (Txi, Txw and      (approximate     (approximate
Txj)                        range)           range)

Extremely coarse        +2.00 to +5.00   + 1.80 to +5.00
Very coarse             +1.50 to +2.00   + 1.00 to +1.80
Coarse                  +0.30 to +1.50   +0.20 to +1.00
Slightly coarse         +0.10 to +0.30   +0.10 to +0.20
Very slightly coarse    +0.05 to +0.10   +0.05 to +0.10
Moderate                +0.05 to -0.05   +0.05 to -0.05
Very slightly fine      -0.05 to -0.10   -0.05 to -0.20
Slightly fine           -0.10 to -0.30   -0.20 to -0.50
Fine                    -0.30 to -1.50   -0.50 to -2.00
Very fine               -1.50 to -2.00   -2.00 to -3.00
Extremely fine          -2.00 to -5.00    3.00 to -6.00

Soil textural index        Txj value      USDA textural class
class (Txi, Txw and      (approximate     (Soil Survey Staff 1951)
Txj)                        range)

Extremely coarse        +6.00 to +11.00   Sands
Very coarse             +3.00 to +6.00    Loamy sands
Coarse                  +1.50 to +3.00    Sandy loam
Slightly coarse         +0.50 to +1.50    Loam
Very slightly coarse    +0.10 to +0.50    Silt loam
Moderate                +0.10 to -0.10    Silt
Very slightly fine      -0.10 to -0.50    Sandy clay loam
Slightly fine            -0.50 to 1.50    Silty clay loam
Fine                    -1.50 to -3.00    Clay loam
Very fine               -3.00 to -6.00    Sandy clay
Extremely fine          -6.00 to -11.00   Silty clay Clay

Table 2. Descriptive statistics of soil separates of soil samples
CV, Coefficient of variation

        Mean (%)   Median (%)   Std. deviation   Kurtosis
                                     (%)

Sand     46.55       56.72          23.05         -1.36
Silt     36.00       32.41          16.49         -1.13
Clay     17.45       14.22           8.83          0.25

        Skewness   Min. (%)   Max. (%)   CV (%)

Sand     -0.56       7.36      76.92     49.52
Silt      0.39       8.82      69.08     45.81
Clay      1.05       6.26      45.04     50.60

Table 3. Pearson's correlations of textural indices, soil separates
and electrical conductivity

** Correlation significant at PcO.Ol (two-tailed); Txi, Txw and Txj are
textural indices; Code: USDA soil textural class code; EC, electrical
conductivity; OC, organic carbon

        Sand       Silt       Clay       Txi

Sand    1
Silt    -0.95 **   1
Clay    -0.83 **   0.62 **    1
Txi     0.90 **    -0.85 **   -0.78 **   1
Txw     0.96 **    -0.84 **   -0.94 **   0.87 **
Txj     0.94 **    -0.84 **   -0.89 **   0.87 **
Code    0.83 **    -0.67 **   -0.92 **   0.79 **
EC      -0.53 **   0.46 **    0.53 **    -0.48 **
OC      -0.48 **   0.39 **    0.52 **    -0.34 **

        Txw        Txj        Code       EC        OC

Sand
Silt
Clay
Txi
Txw     1
Txj     0.96 **    1
Code    0.89 **    0.90 **    1
EC      -0.55 **   -0.54 **   -0.54 **   1
OC      -0.53 **   -0.52 **   -0.47 **   0.26 **   1
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Author:Hossain, M. Shahadat; Rahman, C.K.M. Mustafizur; Alam, M. Saiful; Rahman, M. Mizanur; Solaiman, A.R.
Publication:Soil Research
Article Type:Report
Geographic Code:9BANG
Date:Jul 1, 2018
Words:4359
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