Soil particle density as affected by soil texture and soil organic matter: 2. Predicting the effect of the mineral composition of particle-size fractions
Introduction
The particle density of soil (ρS) represents one of the basic physical properties of soil and is defined as the mass per unit volume of solid soil components, i.e., excluding voids and water. This parameter depends on the composition of both the mineral and the organic soil components. Therefore, ρS varies for different soils, e.g., within a group of mineral soils, ρS usually ranges from 2.4 to 2.9 Mg m−3 soil; in organic soils, ρS can decrease to approximately 1.5 Mg m−3 soil. Because ρS is not part of common routine soil inventories in most countries, pedotransfer functions (PTFs) were developed to calculate ρS. The basic approach of Adams (1973) comprises a mixture ratio between the soil mineral substance (SMS) and soil organic matter (SOM), both on a mass basis, and the corresponding mean particle densities of SMS () and SOM (). This approach was the starting point of the stepwise development of ρS predicting PTFs that still persists today, as described by Ruehlmann (2020). In that contribution, a complex PTF to calculate ρS was introduced, separating the soil mineral substance (SMS) into clay, silt and sand fractions as well as separating the soil organic matter (SOM) into heavy-density and low-density fractions (Eq. (1)):where clay, silt, and sand are mass proportions of SMS given in kg kg−1 minerals and , and are the corresponding particle densities given in Mg m−3 fraction. High density and low density are mass proportions of SOM and given in kg kg−1 SOM; and are the corresponding particle densities given in Mg m−3 SOM. The calculations of and are explained in detail by Ruehlmann (2020).
This ρS predicting model has a mechanistic base and a hierarchical structure to consider the soil’s main and subcomponents. The mass proportions of the main and subcomponents are strictly related to the corresponding particle densities. The model was applied to a dataset from locations worldwide, covering the full range of possible soil organic matter contents, diverse textures, and soil parent materials; thus, the model was able to predict the mean particle densities of the five subcomponents, as mentioned in Eq. (1), with , , , and , always given in Mg m−3 fraction (Ruehlmann, 2020).
Different rocks, as basic materials of SMS, differ in particle density according to their mineralogy and according to their chemical composition and crystal structure. The particle density of single minerals can significantly differ from the abovementioned mean particle densities of the clay, silt and sand-size fractions. The ranges of selected clay minerals as reported by Deer et al. (1966) include smectites 2.0–2.6, illites 2.6–2.9, kaolinites 2.61–2.68, chlorites 2.6–3.3 and muscovites 2.77–2.88 Mg m−3 mineral. Totten et al. (2002) summarised that the published mineral densities span a rather wide range — from 2.0 (low end for smectites) to 3.3 (high end for chlorites). In heavy minerals, the particle density varies between 2.9 and 4.0 Mg m−3 mineral, respectively (Schachtschabel et al. 1992). These values are directly affected by the mineral type, e.g., biotite and haematite have values of 2.80–3.20 and 4.80–5.30 Mg m−3 mineral, respectively (Skopp, 2012). If one mineral type is dominant in a single soil particle size class, the particle density of this particle size class might be similar to that of the dominant mineral. McBride et al. (2012) calculated the linear regressions ρS = 2.560 + 0.024 (%clay) and ρS = 2.800–0.024 (%sand + %silt) and discussed the differences in the y-intercepts, 2.560 Mg m−3 for the lighter sand + silt fraction and 2.800 Mg m−3 for the heavier clay fraction, against the background of the soil’s mineral composition. Schjønning et al., 2017a, Schjønning et al., 2017b reported similar findings. Χ-ray diffraction analysis, as an appropriate experimental method to analyse the soil’s mineral composition, was applied to soils of 17 long-term experiments located in Germany (Rühlmann et al., 2006). They used the experimentally estimated proportions of different minerals to predict the particle density of the bulk soil’s mineral substances. However, to our knowledge, no findings are available on the effect of the mineral inventories occurring in different particle size fractions on ρS. Therefore, the aim of this paper is to analyse the mineral inventories of soil particle size fractions and to generate an appropriate structured ρS predicting PTF that allows us to consider the particle size fraction-dependent effect of mineral compositions on ρS.
Section snippets
Generating the PTF
To generate an appropriate structured ρS predicting PTF that considers the specific composition of the particle-size fractions, we wanted strictly to follow the principle of the Adams (1973) approach to relate the mass proportions of soil components to the corresponding particle densities. Therefore, we extended the SMS term in Eq. (1) by an additional hierarchical level – now the fractions of clay, silt, and sand are divided in each case into the proportions of the different mineral types
Results and discussion
The densities of the 8 mineral types (excluding calcite, with a constant density) and of the amorphous phase that were identified by Χ-ray diffraction analysis and calculated by the model calibration are shown in comparison to the minimum and maximum mineral densities given in the literature (Table 3).
Regarding the amorphous material, we set its density range according to Varajao et al. (2002) between the relatively low values of the aluminosilicate allophane (1.9 Mg m−3 mineral) and poor
Conclusions and perspectives
A hierarchically structured model approach that has been previously used to analyse the effects of clay, silt and sand fractions (as well as of SOM) on ρS (Ruehlmann, 2020) was successfully extended by a further hierarchical level – the mineral compositions of the clay, silt and sand fractions. Each parameter fitted as mentioned above represents the particle density of the particular fraction. The model performed very well, although all the fitted mineral densities were restricted by their
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank Joachim Luckert (Landesamt für Geowissenschaften und Rohstoffe Brandenburg) for implementing the Χ-ray diffraction analyses and interpretation of their results. Furthermore, we thank the two anonymous reviewers of a former version of the manuscript for their helpful comments and suggestions.
This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.
References (19)
- et al.
A new approach to calculate the particle density of soils considering properties of the soil organic matter and the mineral matrix
Geoderma
(2006) - et al.
Predicting soil particle density from clay and soil organic matter contents
Geoderma
(2017) - et al.
Corrigendum to ‘Predicting soil particle density from clay and soil organic matter contents’ [Geoderma 286 (2017) 83–87]
Geoderma
(2017) The effect of organic matter on the bulk and true densities of some uncultivated podzolic soils
J. Soil Sci.
(1973)- Deer, D.A., Howie, R.A., Zussman, J., 1966. An introduction to the rock forming minerals, Longman Scientific &...
- et al.
Relationship between particle density and soil bulk chemical composition
J. Soils Sediments.
(2016) - DIN ISO 10694:1995-03. Soil quality - Determination of organic and total carbon after dry combustion (elementary...
- DIN ISO 11277:2002-08. Soil quality - Determination of particle size distribution in mineral soil material - Method by...
- DIN EN ISO 11508:2018-04. Soil quality - Determination of particle density (ISO 11508:2017); German version EN ISO...