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Article

Soil Compressibility and Resilience Based on Uniaxial Compression Loading Test in Response to Soil Water Suction and Soil Organic Matter Content in Northeast China

by
Zhiqiu Xiao
1,2,3,
Na Yu
1,2,3,*,
Jing An
1,2,3,
Hongtao Zou
1,2,3 and
Yulong Zhang
1,2,3,*
1
College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
2
Key Laboratory of Arable Land Conservation (Northeast China), Ministry of Agriculture, Shenyang 110866, China
3
National Engineering Research Center for Efficient Utilization of Soil and Fertilizer Resources, Shenyang 110866, China
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(5), 2620; https://doi.org/10.3390/su14052620
Submission received: 12 January 2022 / Revised: 20 February 2022 / Accepted: 21 February 2022 / Published: 24 February 2022
Browse Figures
Versions Notes

Abstract

:
Due to the widespread use of heavy machinery, improper soil tillage practices, and insufficient soil organic materials input, soil compaction has become a major issue affecting soil function in modern agriculture and the sustainability of the environment. The aim of the present study was to evaluate the responses of soil mechanical parameters to soil water content and soil organic matter content (SOM), and to investigate the physical properties of nine disturbed soils in a black soil region in Northeast China. The soil samples were capillary saturated and subjected to 6, 10, 100, 600, and 800 kPa soil water suction (SWS), and pre-compression stress (σp), compression index (Cc), and decompression index (Dc) were measured. SWS and SOM, and their interaction, significantly influenced the mechanical parameters. σp increased with an increase in SWS until 600 kPa, while Dc exhibited an opposite trend with an increase in SWS. Cc had a peak value at SWS of 100 kPa. All mechanical parameter values were higher under high SOM than under low SOM. σp, Cc, and Dc were influenced variably by different soil physicochemical factors. Structural equation modeling results revealed that soil mechanical parameters were directly and indirectly influenced by soil texture and mean weight diameter of aggregates, in addition to SOM and SWS. According to the results of the present study, based on soil mechanical and physical properties, increasing SOM and ensuring suitable soil water content during tillage could be applied as management strategies to minimize further soil compaction and improve soil resilience, and thus promote the sustainable development of agriculture in Northeast China.

1. Introduction

The black soil in Northeast China is characterized by a dark surface and rich organic matter [1,2]. However, long-term agricultural activities in the region have degraded the physical and hydraulic properties of the soil, leading to numerous issues, such as thinning of the tillage layer [3], decline in soil organic carbon content [4,5], and, especially, increasing soil compaction [6], in turn disturbing the balance of the ecosystem and resulting in a decline in soil functionality, productivity, and yield [7,8,9].
Soil organic matter (SOM) availability influences soil susceptibility to compaction. Interactions among SOM, soil organisms, and soil minerals drive the formation of soil aggregates [10], directly increasing soil porosity and decreasing bulk density [11]. Soil compaction is evaluated based on soil mechanical parameters, including soil pre-compression stress (σp), compression index (Cc), and decompression index (Dc), which are calculated from soil compression curves and resilience curves that typically describe the relationship between void ratio and the logarithm of applied mechanical stress, and represent load-bearing capacity and resilience capacity following compaction [12,13,14,15]. In a study by Reichert et al. [16] involving four types of soil in Brazil, the authors observed that SOM was positively correlated with Cc, while there was a negative correlation between SOM and σp. An increase in SOM with lower initial bulk density could reduce soil compactability by increasing resistance to deformation and elasticity [17]. In addition, Kuan et al. [18] observed that SOM content was correlated strongly with resilience following the elimination of physical stress. Such elastic rebound following stress release was relatively high in two Oxisols and was attributed to high SOM concentrations under a no-till condition [19].
The effect of SOM on reducing soil compressibility may be influenced by soil moisture content at the time of load application [20,21]. For example, Mosaddeghi et al. [22] observed that manure application had a high suppressive effect on soil compactability under high moisture levels and high loads. The increase in soil moisture tended to reduce the impacts of compaction on inter-pore and intra-pore spaces, while high compaction altered pore size distribution considerably [23]. σp significantly increased with an increase in soil water suction (SWS) over the 10–3000 kPa range [24], while SWS over the 6–100 kPa range significantly influenced Cc and Dc [16]. On the contrary, according to Imhoff et al. [20], SOM and soil water content do not affect Cc over the range of 10–100 kPa suction. Meanwhile, according to Pöhlitz et al. [25], σp of the aggregates was not influenced at suction between 6 kPa and 1000 kPa. Therefore, uncertainty persists regarding the effect of soil moisture on susceptibility to compaction.
To date, the research on soil compressibility and elasticity of black soil has mainly focused on the changes in the field soil physicochemical properties after over-compaction. Few studies have explored the influence of SOM content on soil mechanical parameters, and most of the different SOM contents have been obtained via indoor incubation and artificial amendment of organic materials [26,27,28]. Soil compressibility and elasticity could vary due to differences in SOM content, composition, morphology, origins, distribution, and the degree of combination of SOM and mineral particles [29]. Therefore, soil incubated by the addition of organic materials may not reliably reflect compaction dynamics under natural soil weathering and decomposition conditions.
Soil bulk density was the most significant factor affecting compressibility [19,26]. For sandy soils, mechanical parameters were more dependent on initial soil bulk density [30]. The differences in soil bulk density at different sampling sites were the most important factor affecting σp and Cc [16]. Soil mechanical parameters are affected by several soil attributes, e.g., soil type, texture, initial bulk density, organic matter content, and especially the interaction of initial bulk density and soil properties. In this sense, the influences caused by initial bulk density may interfere with soil compressibility, which in turn must be underestimated or overestimated by the influences caused by soil properties. Thus, we hypothesized that SWS and SOM have great influence on soil compaction sensitivity and resilience under the same soil bulk density of 1.33 g cm−3. Therefore, the objective of this study was to evaluate the response of mechanical parameters, soil pre-compression stress, soil compression index, and soil decompression index to soil properties dominated by SWS and SOM in disturbed black soils with uniform bulk density.

2. Materials and Methods

2.1. Sampling Sites

Soil samples were collected from nine agricultural fields over a 40–47° latitude range across three provinces in Northeast China, as illustrated in Figure 1. The samples were obtained from sites in Hailun city (126°55′46″ E, 47°27′13″ N) and Harbin city (126°40′48″ E, 45°46′12″ N) in Heilongjiang Province; Gongzhuling city (124°48′14″ E, 43°30′38″ N) in Jilin Province; and Changtu county (123°46′43″ E, 42°36′18″ N), Shenyang city (123°25′31″ E, 41°48′12″ N), and Haicheng city (122°40′31″ E, 40°51′43″ N) in Liaoning Province. Soil background physicochemical property data are listed in Table 1. SOM concentration decreased with a decrease in latitude from north to south in the study region. The sampling sites were temperate continental, temperate monsoon, and temperate continental monsoon climate from north to south. The annual accumulated temperature and precipitation were 2300 °C and 550 mm in Hailun city, 2700 °C and 569 mm in Harbin city, 3522 °C and 595 mm in Gongzhuling city, 3600 °C and 400 mm in Changtu county, 3900 °C and 650 mm in Shenyang city, and 4019 °C and 715 mm in Haicheng city, respectively [31].

2.2. Experimental Design and Soil Analysis

Disturbed soil was used in the treatments based on a randomized design, with three replicates for each treatment. Soils under the following water suction treatments were included: 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. The nine disturbed soils represented soils with different SOM contents. Soils were collected at each site at a 0–20 cm depth. Visible stone and plant debris were manually removed. Soil samples were air-dried, and then passed through a 2 mm sieve. Those steps were prepared for the analysis of soil properties and compression tests in subsequent analysis.
To eliminate the influence of soil bulk density, the bulk density of nine soils was set at 1.33 g cm−3. The particle densities of nine soils were around 2.65. From the equation of void ratio e0 = ρs/ρd − 1 [32], the initial void ratios of nine soils were the same. By destroying the original soil structure (remolded samples), it is possible to effectively reduce the differences of soils or treatments [33]. Soils with preserved structure should also be tested in future studies, which can eliminate the effect of the interaction of soil bulk density (or state of compaction) and moisture.
The soils were passed through a 2 mm sieve and then poured and knocked slightly into cylinders with a height of 20 mm and a diameter of 61.8 mm using a manual press in order to achieve the uniform initial dry bulk density of 1.33 g·cm−3 [26,34,35]. The remolded samples were capillary saturated and then equilibrated at SWS levels of 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. SWS levels of 6 kPa and 10 kPa were equilibrated using constant water-head pressures, and 100 kPa, 600 kPa, and 800 kPa were equilibrated on ceramic plates inside a pressure membrane apparatus (Soil Moisture Equipment Corp, Santa Barbara, CA, USA) according to Klute’s method [36]. Subsequently, the relationship between soil volumetric water content (VWC) and soil water suction was measured by the method described by Klute [36].
The samples were subjected to uniaxial compression tests using a model GZQ-1 Full Automatic Pneumatic Consolidation Test Apparatus (Nanjing soil instrument factory co. Ltd.) after equilibrium at different SWS levels. Sequential static loads were applied for 10 min and the displacement (accuracy ± 0.01 mm) was read at the end of each loading interval [15]. The void ratio was calculated from the vertical displacements. The applied pressures versus deformation data were used to generate the soil compression, resilience, and recompression curves, which were used to calculate soil mechanical parameters (σp, Cc, and Dc, Figure 2).
(1)
Compression curve: the soil cores were subjected to pressures (12.5, 25, 50, 75, 100, 200, 300, 400, 500, 600, 800, 1000, 1200, and 1600 kPa) and the displacement at each applied pressure was recorded.
(2)
Resilience curve: after the loading step of 200 kPa, the soil was unloaded to the starting load of 8 kPa, with sequential unloadings of 200, 150, 100, 50, 25, 12.5, 10, and 8 kPa.
(3)
Recompression curve: after unloading to 8 kPa, loads of 12.5, 50, 100, 150, and 200 kPa were applied sequentially.
SOM was determined using an Element Analyzer (Vario EL III, Elementar, Langenselbold, Germany). Soil pH was measured using a 1:2.5 soil:distilled water mixture with an Orion Star A211 pH meter (Thermo Fisher Scientific, Waltham, MA, USA). Soil particle size distribution was measured using the sieve-pipette method (sand: 2–0.05 mm; silt: 0.05–0.002 mm; clay: <0.002) according to the Soil Taxonomy of the USDA Classification [37]. Soil aggregate distribution was measured using the wet sieving method [38] through a series of four sieves (2 mm, 1 mm, 0.25 mm, 0.1 mm). The liquid limit and plastic limit were determined using the drop-cone penetrometer test (80 g cone plus shaft, cone angle 30°) (CYS-2 photoelectric liquid-plastic limit tester, Nanjing Soil Instrument Factory Co. Ltd., Nanjing, China), and plasticity index was calculated based on the liquid limit minus plastic limit [39].

2.3. Calculation of Compression Parameters

The void ratio (e) was calculated from the vertical displacement (d) as follows:
e = ρ s ρ d × H − d H − 1
where ρs is soil particle density (Mg m−3), ρd is soil initial bulk density (Mg m−3), and H is the initial height of the soil core (m).
The capacity of the compression curve to describe soil compression characteristics was evaluated, following fitting according to the Gompertz model (Equation (2)) based on non-linear regression analysis [13,40].
e = a + c   exp { − exp [ b ( log P − m ) ] }
where e is void ratio, P is applying load, log is the logarithm to base 10. a, b, c, and m are empirical parameters. Compression index (Cc) was estimated using Equation (3), where parameters b and c are from Equation (2).
C c = b c exp ( 1 )
The Newton method was used to determine the maximum curvature point in the Gompertz model, and the value of σp was calculated using the classic Casagrande method [13].
The resilience and recompression curves were fitted using polynomial functions. The slopes of the intersection line of the resilience and recompression curves were defined to the decompression index (Dc) (Figure 2).
The mean weight diameter of soil aggregates was calculated using the following Equation (4):
MWD = ∑ i = 1 n X ¯ i W i ∑ i = 1 n W i × 100
where MWD is the mean weight diameter (mm), X ¯ i is the mean diameter of each size fraction (mm), and Wi is the weight of the analytical samples.

2.4. Statistical Analysis

One-way analysis of variance (ANOVA) was performed to evaluate the effects of the different SOM and SWS on soil mechanical parameters. Afterward, two-way ANOVA was further used to analyze the effects of different SOM, SWS, and their interactions (SOM × SWS) on soil mechanical parameters (σp, Cc, and Dc). A partial eta-squared value (ƞ2) was used to estimate the effect size. Duncan’s post-hoc test was used to compare differences among the treatments at p = 0.05 and p = 0.01 levels. The functions fitting of compression, resilience, and decompression curves were performed using the least-squares curve fitting function lsqcurvefit (MATLAB 2016), and the maximum curvature and σp were calculated using Python v3.7.3 (Python Software Foundation 2017). Pearson correlation coefficients were calculated and used to analyze the relationships between soil mechanical parameters and physicochemical characteristics (p < 0.05) using the vegan package in R v3.5.2 (R Core Development Team 2018).
To eliminate the influence of multi-collinearity, factor analysis (FA) of principal component analysis (PCA) was performed to reduce dimension by varimax rotation to identify latent factors [41] using IBM SPSS Statistics 21 (IBM Corp., Armonk, NY, USA). Bartlett’s test of sphericity reveals whether the correlation matrix is an identity matrix, which indicates that the variables are unrelated. If the significance level is <0.05, this implies strong relationships among variables in the test [42]. The first and second PCA components were used to establish latent variables for SWC (soil water content) and ST (soil texture), respectively. The component matrix of PCA is listed in Table S1, and the criterion of selection was a factor loading absolute value ≥0.60. Volumetric water content (VWC), liquid limit (LL), and plastic limit (PL) were extracted as the first component, and named SWC. Clay content and sand content were extracted as the second component, and named ST. A hypothetical model that contained all plausible interaction paths between the soil physicochemical properties and mechanical parameters was built based on current knowledge, which was further identified using a maximum likelihood parameter estimation method via structural equation modeling (SEM). Soil physicochemical properties (SOM, MWD, SWC, and ST) and mechanical parameters (σp, Cc, and Dc) were included in the hypothetical model. Only significant effects are plotted (p < 0.05). Criteria for evaluating SEM fitness, such as the ratio of chi-square values ( χ 2 ) to the degrees of freedom (df), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), and root mean square error of approximation (RMSEA) were adopted according to previous studies [41,43]. The standardized path coefficients (ranging from 0 to 1) were used to estimate the relative weight of independent variables’ influence on the dependent variables. The high path coefficients indicated that the independent variable has a greater influence on the dependent variable in the relationship. The SEM was conducted using AMOS 20.0 (Amos, Development Corporation, Meadville, PA, USA). Plots were generated using Origin Pro v9.3 (OriginLab Corp., Northampton, MA, USA).

3. Results

3.1. Soil Pre-Compression Stress (σp)

The σp increased with an increase in SWS; however, σp declined sharply at SWS of 800 kPa in the HHL, HHR, JGA, LCA, LCB, and LSY samples (Figure 3). The σp of HHL soil was significantly higher than that of other soils at SWS values of 6 kPa, 10 kPa, 100 kPa, and 600 kPa. The soils sampled at sites in the northern part of the study area, such as HHR, JGA, JGB, and JGC, exhibited increasing σp trends with an increase in latitude under SWS values of 6 kPa, 10 kPa, 100 kPa, and 800 kPa; however, σp exhibited a decreasing trend under an SWS of 600 kPa. The soils sampled from the southern sites, such as LCA, LCB, LSY, and LAH, had no significant differences in σp at SWS conditions of 6 kPa and 10 kPa. σp increased first and then decreased under SWS conditions of 100 kPa, 600 kPa, and 800 kPa.
According to the results of two-way ANOVA, SWS and SOM, in addition to their interaction, significantly influenced soil σp (p < 0.01). According to the partial eta-squared values, SWS influenced σp the most (ŋ2 = 0.833), followed by SOM (ŋ2 = 0.812) and the SWS–SOM interaction effect (ŋ2 = 0.725).

3.2. Soil Compression Index (Cc)

Soil Cc values at low SWS conditions were significantly lower than those of Cc values at high SWS conditions (p < 0.05) (Figure 4). There were no significant differences in soil Cc between the 6 kPa and 10 kPa SWS conditions (p > 0.05). Cc increased firstly and then decreased with an increase in SWS, with the peak Cc values observed under the 100 kPa condition. Although Cc values sharply declined after 100 kPa, the Cc values under 800 kPa SWS were higher than those under 6 kPa and 10 kPa SWS.
The HHL, HHR, JGA, JGB, and JGC soils did not reveal any discernible Cc trends from north to south under 6 kPa and 10 kPa SWS conditions. However, under 100 kPa, 600 kPa, and 800 kPa SWS treatments, Cc values decreased first and then increased. In addition, the Cc value in LAH was significantly higher than those in LCA, LCB, and LSY under different SWS conditions.
Two-way ANOVA results indicated that SWS, SOM, and their interaction significantly influenced soil Cc (p < 0.01), and the effects were in the order of SWS (ŋ2 = 0.916) > SOM (ŋ2 = 0.738) > SWS × SOM (ŋ2 = 0.621) based on partial eta-squared values.

3.3. Soil Decompression Index (Dc)

Soil Dc value ranged from 0.016 to 0.053 under different SWS treatments (Figure 5). Generally, soil Dc decreased with an increase in SWS up to 600 kPa. Dc at the HHL, HHR, JGA, JGB, LCA, LCB, and LSY sites increased under 800 kPa SWS. Furthermore, Dc values under low SWS conditions (6 kPa and 10 kPa) were significantly higher than those under high SWS conditions (100 kPa to 800 kPa).
The Dc of HHL was significantly higher than that of the other soils, which became more discernible with a decrease in SWS. In HHR, JGA, JGB, and JGC samples, Dc increased first and then decreased at 6 kPa and 10 kPa SWS, while decreasing trends were observed at 100 kPa and 800 kPa SWS. Conversely, in LCA, LCB, LSY, and LAH samples, Dc decreased first and then increased under different SWS conditions.
According to the two-way ANOVA results, SWS, SOM, and their interaction significantly influenced soil Dc (p < 0.01). According to the partial eta-squared values, the effect size of SWS (0.963) was the greatest, followed by SOM (ŋ2 = 0.920) and the SWS–SOM interaction effect (ŋ2 = 0.811).
Overall, increasing SOM contents and regulating SWS in the range of 100–600 kPa could be appropriate measures to maintain soil productivity and mitigate soil degradation of compaction.

3.4. Relationship between Soil Physiochemical and Mechanical Parameters

Correlation analysis among soil physicochemical properties and soil mechanical parameters is illustrated in Figure 6. Among the soil mechanical parameters, only Cc and Dc exhibited significantly positive correlations (p = 0.037). σp was significantly positively correlated with SOM, VWC, and PL; Cc was significantly positively correlated with LL, PL, and MWD; and Dc was significantly positively correlated with SOM, VWC, LL, PL, and CLAY. Furthermore, SOM exhibited positive correlations with VWC, LL, and PL (p = 0.001, p = 0.003 and p = 0.001). There were significant negative correlations between soil sand and clay contents.
The SEM analysis elucidated the path of soil properties affected on soil mechanical parameters. Influential factors in the models explained 68.5%, 67.4%, and 93.2% of the variations in mechanical parameters σp, Cc, and Dc, respectively (Figure 7). Standardized direct, indirect, and total effects are listed in Table S2 for the physicochemical properties in the model. SEM demonstrated that SOM had an exclusive and the greatest direct effect (path coefficient = 0.827) on σp, which was the same with the result of multiple linear regression. Cc was significantly positively influenced by both SWC and MWD directly, and the path coefficients were 0.559 and 0.493, respectively. SWC and ST directly influenced Dc positively and negatively, respectively. SWC positively influenced Dc via Cc indirectly, and Cc directly influenced Dc. An indirect effect of SOM on Dc was mediated through an effect on ST. MWD indirectly affected Dc via influence on Cc, with path coefficients of 0.087. There was a significant correlation between SOM and SWC (p < 0.01), and the correlation coefficient was 0.940.

4. Discussion

4.1. Variations in Soil Mechanical Parameters with Soil Properties

4.1.1. Soil Pre-Compression Stress (σp)

Soil organic matter significantly affects soil compressibility parameters, as shown in our study results (Figure 3). However, the magnitude and type of effect on these parameters depend on soil texture and its impact on water retention, soil cohesion, and bulk density [44]. Soil σp sampled in the northern section of the study region (Heilongjiang, Jilin Province), which had higher SOM concentrations, was generally higher than those of soils from the southern section of the study region (Liaoning Province), which had lower SOMs, and which illustrated a positive correlation with SOM. The finding is supported by studies of Défossez et al. [45], Cavalcanti et al. [46], and Pue et al. [47]. SOM represents an extremely variable mixture of complex chemicals with high degrees of elasticity and expansibility [48]. As external stress is applied, soil organic particles participate in stress buffering and directly influence soil capacity to resist compaction [49]. SEM results also revealed that σp was directly affected by SOM (Figure 7).
The significant positive linear relationship between SWS and σp has been reported in several studies [16,50,51,52,53,54]. However, in the present study, σp increased with an increase in SWS until 600 kPa under various SOM conditions and decreased when SWS exceeded 600 kPa (Figure 3). The concave–parabolic response of σp to changes in SWS was also observed by Silva et al. [55], although they expressed moisture changes as different saturation degrees. The concave–parabolic changes may be explained by the water film surrounding soil particles and pores acting as a lubricant under low SWS conditions [56]. As SWS increases, the soil becomes dry, so that the lubrication and buffering effects decline, the capacity to resist forces is enhanced, and σp increases. With a continuous increase in SWS, individual clay particles no longer slide against each other up to a certain moisture level without the role of lubricant, and mechanical energy inputs cause the stable micro-aggregates to roll over each other, which is the onset of cracking. Due to the high soil aggregate water stability and high resistance to deformation, σp of JGB, JGC, and LAH did not decline with SWS conditions greater than 600 kPa, which was also confirmed by the high MWD values of these three samples (Table 1).
We found that only SOM had a significant relationship with σp in the SEM results (Figure 7), although σp value changes with both SOM content and SWS levels. As Pereira et al. [56] concluded, soil susceptibility to compaction depended firstly on soil porosity and secondly on soil carbon content. The unified bulk density of soil samples with the same initial porosity could explain our results. Arthur et al. [11] also found that there was a correlation between σp and water content, but there was no further improvement of introducing soil water content in the regression model.

4.1.2. Soil Compression Index (Cc)

In the present study, we observed a non-linear relationship between Cc and SWS within the 6–800 kPa range, with the highest values observed at 100 kPa (Figure 4), similar to the finding of Lima et al. [19], who modeled the relationship between Cc and SWS by polynomial function and observed peaks at around 25–50 kPa in undisturbed soils. Cc values in the present study were much higher than those reported in the previous study, which could be explained by the soil water retention capacity causing variations in soil resistance to external load due to differences in soil bulk density and soil structure between disturbed and undisturbed soils. Cc values in the present study did not show discernible trends from north to south in the study region. However, Cc values differed among soils; soil Cc values of silty clay loam were higher than of silty loam, and the lowest Cc values were silty loam soils of Liaoning province. The results indicate that soil MWD values of silty clay loam were obviously higher than of silty loam (Table 1).
Reichert et al. [16] and Pesch et al. [32] reported that soil aggregate stability exhibited a negative correlation with the Cc value. Conversely, we observed a positive correlation and that MWD had direct effects on the Cc value (Figure 6 and Figure 7); similar results have been reported by Blanco-Canqui and Lal [49]. The positive relationship of this study may be due to the higher proportion of macro-aggregates (particle size 2–0.25 mm) and high MWD value (Table 1). The soil macro-porosity among the aggregate particles was higher, the aggregates were more stable, and it was not easy to collapse when bearing a load, resulting in higher soil compressibility. The aggregation process of agricultural soil is a complex system, in which the relationship of the aggregate–water–organic matter is very important for the quantitative description of the degree of soil compressibility [57].

4.1.3. Soil Decompression Index (Dc)

In the present study, the Dc value decreased with increases in SWS and SOM, which is consistent with the findings of several other studies [16,18,58,59]. Dc was directly and indirectly affected by SWC, SOM, ST, and MWD. The explanations of the variations were much higher than σp and Cc, and the influencing factors that explain the variations in Dc were also more than σp and Cc based on the results of SEM.
Soil water content significantly influences soil compressibility and resilience. Under wet conditions, most of the soil pores are filled with water, and some of the water-filled pores resist external loads and protect the soil structure from damage [60]. In the process of soil water absorption (wet soil), greater moisture makes it difficult to expel air from soil pores, increasing the confinement of air bubbles, resulting in an increase in soil elasticity. This effect is more pronounced when the interval between soil unloading and reloading is short, as in the case of this study (10 min), or even more intense in field conditions, during wheeling or animal trampling, in which the residence time of the applied load is very short [61]. In addition, soil water acts as a lubricant within aggregates, which makes the soil sensitive to forces. Under dry soil conditions, forces act directly on soil particles, which destroy soil structure and lead to a decline in resilience [62].
SOM could indirectly affect soil Dc via its influence on ST. In the present study, latent effects of ST showed a negative correlation with clay content and a positive correlation with sand content (Table S1 and Figure 7), which were the reasons for negative path coefficients between SOM and ST, as well as ST and Dc (Figure 7). SOM could also affect Dc via its influence on ST. SOM, especially the humic material and mucilage, which are coated on mineral soil particles in the form of films, improves soil structure through effects on pore networks [60,63,64]. According to Bonetti et al. [65], clayey soils are more resilient due to the existing strong connections between clay particles. Therefore, the interaction of SOM and clay particles influences soil resilience due to the presence of iron oxides in the clay fraction [66]. In the present study, the Dc values in HHR were lower than those in the other black soils; however, SOM content was higher in HHR. The above trends could be explained by the high sand and low clay proportions.

4.2. Relationship among Mechanical Parameters Dominated by SOM

The Dc trends under different SWS values were opposite the σp trends (Figure 3 and Figure 5). There were significant positive correlations between Cc and Dc values (Figure 6). In addition, SEM results revealed that Cc directly affected Dc (Figure 7), which further demonstrated that compression deformation was closely associated with soil resilience, and both Cc and Dc were influenced by soil physicochemical properties. The findings are consistent with the findings of Pesch et al. [32].
Inconsistent relationships have been reported among soil mechanical parameters in previous studies. Different SOM mineralization and humification processes may take place in soils from different study sites, leading to various SOM composition and distribution patterns [67]. In the present study, the interaction between water content and SOM had impacts on soil texture, soil porosity, and permeability. In general, SOM content can directly affect soil physical properties. A decrease in SOM content reduces aggregate stability and strength, increases soil’s susceptibility to excessive compaction, and reduces macro-porosity, hydraulic conductivity, and water retention [68]. Soil physical properties can be improved through providing organic binders by SOM, inducing slight water repellency, reducing soil bulk density, and improving the elasticity and resilience of the whole soil [69].
Our study revealed that mechanical parameters varied under different SWS levels, and there were no significant differences in soil physicochemical properties under different soil water conditions following equilibrium (data not shown). However, the field soils were much more complex than conditions in laboratory experiments. The physicochemical properties observed under different SWS conditions should be further verified under field conditions. Meanwhile, a quantitative analysis of the soil compression parameters and physicochemical properties offers the possibility for predicting soil mechanical parameters based on soil properties in the future.

5. Conclusions

The mechanical parameters of pre-compression stress (σp), compression index (Cc), and decompression index (Dc) were significantly influenced by both soil water suction (SWS) and soil organic matter (SOM) of black soil. In addition, the mechanical parameters in high SOM soils were higher than those in low SOM soils. There was an inflection point of 600 kPa in mechanical parameters of σp and Dc with the change in SWS, while the inflection point of 100 kPa of Cc changed with the change in SWS. σp and Cc increased with an increase in SWS and decreased after the inflection point. However, the Dc trend with the change in SWS was inversed with σp and Cc. Soil mechanical parameters (σp, Cc, and Dc) were influenced by different soil physicochemical factors. Soil mechanical parameters were most influenced by SOM and water content. Notably, factors such as aggregate stability and soil particles jointly influence the role of SOM and SWS in soil mechanical parameters. Increasing SOM contents and tillage during SWS of 100–600 kPa are potential strategies recommended to mitigate the soil degradation associated with compaction. The results of the study can be used to improve black soil cultivability and promote the sustainable development of agriculture in Northeast China.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14052620/s1, Table S1: Component matrix of observed variables in PCA; Table S2: Standardized total, direct and indirect effects of influencing factors on soil mechanical parameters calculated by structural equation modeling.

Author Contributions

Conceptualization, Z.X. and Y.Z.; Data curation, Z.X.; Formal analysis, Z.X.; Funding acquisition, N.Y. and Y.Z.; Investigation, J.A., H.Z. and Y.Z.; Methodology, Z.X. and N.Y.; Project administration, Y.Z.; Resources, N.Y., H.Z. and Y.Z.; Software, Z.X. and N.Y.; Supervision, Y.Z.; Validation, Z.X. and N.Y.; Visualization, Z.X. and J.A.; Writing—original draft, Z.X.; Writing—review & editing, N.Y., J.A., H.Z. and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Technology R&D Program of China, grant number 2016YFD0300807, and the National Basic Research Program of China (973 Program), grant number 2011CB100502.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Sampling site locations in Northeast China. HHL and HHR sites are in Heilongjiang Province; JGA, JGB, and JGC sites are in Jilin Province; LCA, LCB, LSY, and LAH sites are in Liaoning Province.
Figure 1. Sampling site locations in Northeast China. HHL and HHR sites are in Heilongjiang Province; JGA, JGB, and JGC sites are in Jilin Province; LCA, LCB, LSY, and LAH sites are in Liaoning Province.
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Figure 2. Compression, resilience, and recompression curves plotted based on void ratio(e) as a function of logarithm of applied stress (logP(kPa)). Cc: soil compression index; σp: soil pre-compression stress; Dc: soil decompression index.
Figure 2. Compression, resilience, and recompression curves plotted based on void ratio(e) as a function of logarithm of applied stress (logP(kPa)). Cc: soil compression index; σp: soil pre-compression stress; Dc: soil decompression index.
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Figure 3. Soil pre-compression stress (σp) in nine studied soils equilibrated at SWS of 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. Mean values and standard error are presented. Capital letters represent differences among soil water suction (SWS) in the same soil sample at the p < 0.05 significance level. Lower letters express differences among soil samples under the same SWS at the p < 0.05 significance level.
Figure 3. Soil pre-compression stress (σp) in nine studied soils equilibrated at SWS of 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. Mean values and standard error are presented. Capital letters represent differences among soil water suction (SWS) in the same soil sample at the p < 0.05 significance level. Lower letters express differences among soil samples under the same SWS at the p < 0.05 significance level.
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Figure 4. Soil compression index (Cc) of nine studied soils equilibrated at SWS of 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. Mean values and standard error are presented. Capital letters represent differences among SWS in the same soil sample at the p < 0.05 significance level. Lower letters denote differences among soil samples under the same soil water suction (SWS) at the p < 0.05 significance level.
Figure 4. Soil compression index (Cc) of nine studied soils equilibrated at SWS of 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. Mean values and standard error are presented. Capital letters represent differences among SWS in the same soil sample at the p < 0.05 significance level. Lower letters denote differences among soil samples under the same soil water suction (SWS) at the p < 0.05 significance level.
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Figure 5. Soil decompression index (Dc) of nine studied soils equilibrated at SWS of 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. Mean values and standard error are presented. Capital letters represent differences among different soil water suction (SWS) treatments in the same soil sample at the p < 0.05 significance level. Lower letters express differences among soil samples in the same SWS at the p < 0.05 significance level.
Figure 5. Soil decompression index (Dc) of nine studied soils equilibrated at SWS of 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. Mean values and standard error are presented. Capital letters represent differences among different soil water suction (SWS) treatments in the same soil sample at the p < 0.05 significance level. Lower letters express differences among soil samples in the same SWS at the p < 0.05 significance level.
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Figure 6. Correlation analysis of soil mechanical parameters and soil physicochemical properties. Pearson’s correlation coefficient r was used to express linear correlation. The color blue indicates positive correlation, red indicates negative correlation; ** denotes p < 0.01, * denotes p < 0.05, and no asterisk denotes p > 0.05. Soil pre-compression stress (σp, kPa), soil compression index (Cc), soil decompression index (Dc), soil organic matter content (SOM, g kg−1), soil volumetric water content (VWC, cm3 cm−3 ), liquid limit (LL, %), plastic limit (PL, %), sand content (SAND, %), silt content (SILT, %), clay content (CLAY, %), mean weight diameter (MWD, mm).
Figure 6. Correlation analysis of soil mechanical parameters and soil physicochemical properties. Pearson’s correlation coefficient r was used to express linear correlation. The color blue indicates positive correlation, red indicates negative correlation; ** denotes p < 0.01, * denotes p < 0.05, and no asterisk denotes p > 0.05. Soil pre-compression stress (σp, kPa), soil compression index (Cc), soil decompression index (Dc), soil organic matter content (SOM, g kg−1), soil volumetric water content (VWC, cm3 cm−3 ), liquid limit (LL, %), plastic limit (PL, %), sand content (SAND, %), silt content (SILT, %), clay content (CLAY, %), mean weight diameter (MWD, mm).
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Figure 7. Structural equation model of soil physicochemical properties and soil mechanical parameters. Rectangles represent observed variables, ovals represent latent variables, a single arrow indicates the direct effect of a variable assumed to be a cause on another variable assumed to be an effect, and arc double-headed arrows denote a correlation between two variables. Only significant effects are plotted (p < 0.05). Blue arrows denote positive relationships, while red arrows denote negative relationships. Numbers on arrows are standardized path coefficients (*** p < 0.001; * p < 0.05). R2 in brackets indicates the percentage of variance explained by the model. Error variables for the unexplained variance in all endogenous variables are not included in the figure. ꭓ2: chi-square value; df: degree of freedom; GFI: goodness-of-fit index; AGFI: adjusted GFI; RMSEA: root mean square error of 11 approximation; SOM: soil organic matter; SWC: soil water content (including PL, LL, and VWC); VWC: soil volumetric water content; LL: liquid limit; PL: plastic limit; ST: soil texture (including CLAY, and SAND); SAND: sand content; CLAY: clay content; MWD: mean weight diameter; σp: soil pre-compression stress; Cc: soil compression index; Dc: soil decompression index.
Figure 7. Structural equation model of soil physicochemical properties and soil mechanical parameters. Rectangles represent observed variables, ovals represent latent variables, a single arrow indicates the direct effect of a variable assumed to be a cause on another variable assumed to be an effect, and arc double-headed arrows denote a correlation between two variables. Only significant effects are plotted (p < 0.05). Blue arrows denote positive relationships, while red arrows denote negative relationships. Numbers on arrows are standardized path coefficients (*** p < 0.001; * p < 0.05). R2 in brackets indicates the percentage of variance explained by the model. Error variables for the unexplained variance in all endogenous variables are not included in the figure. ꭓ2: chi-square value; df: degree of freedom; GFI: goodness-of-fit index; AGFI: adjusted GFI; RMSEA: root mean square error of 11 approximation; SOM: soil organic matter; SWC: soil water content (including PL, LL, and VWC); VWC: soil volumetric water content; LL: liquid limit; PL: plastic limit; ST: soil texture (including CLAY, and SAND); SAND: sand content; CLAY: clay content; MWD: mean weight diameter; σp: soil pre-compression stress; Cc: soil compression index; Dc: soil decompression index.
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Table
Table 1. Basic soil physicochemical properties.
Table 1. Basic soil physicochemical properties.
Soil
Samples		LocationsSOM
(g kg−1)		Particle Size Distribution
(%)		TextureMechanical Physical Property (%)Soil Aggregates Composition (%) (mm)MWD
SandSiltClayLLPLPI2–11–0.250.25–0.1<0.1
HHLHailun 55.2 a11.13 e50.46 g38.46 abSilty clay loam50.4834.8415.643.16 27.3220.79 48.73 0.279
HHRHarbin 34.0 b23.93 b53.61 f22.48 dSilt loam42.2927.8814.414.4326.9127.0441.620.301
JGAGongzhuling 30.0 c5.47 h55.24 e39.15 aSilty clay loam41.3624.5416.821.3624.3218.9955.330.239
JGB28.1 d13.22 d50.54 g36.17 bSilty clay loam43.0525.8017.254.6526.8026.8241.730.299
JGC27.0 e30.37 a64.37 c5.25 gSilt loam44.3927.6416.759.6825.3625.2839.680.363
LCAChangtu 20.3 f21.76 c57.01 d21.34 deSilt loam41.2524.3616.890.569.6017.7972.050.145
LCB17.8 h10.69 f69.99 a19.22 eSilt loam35.3421.3813.963.9510.3918.8866.790.203
LSYShenyang 13.5 i30.35 a57.14 d12.22 fSilty clay loam34.8224.1910.634.1314.1019.7961.980.216
LAHHaicheng 18.8 g6.74 g66.34 b26.92 cSilty clay loam42.8426.1116.738.6824.7626.0140.550.341
LL: liquid limit; PL: plastic limit; PI: plasticity index, PI = LL − PL; MWD: mean weight diameter. Different letters indicate significant differences between soil samples based on Duncan’s Multiple Range test at p < 0.05.
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Xiao, Z.; Yu, N.; An, J.; Zou, H.; Zhang, Y. Soil Compressibility and Resilience Based on Uniaxial Compression Loading Test in Response to Soil Water Suction and Soil Organic Matter Content in Northeast China. Sustainability 2022, 14, 2620. https://doi.org/10.3390/su14052620

AMA Style

Xiao Z, Yu N, An J, Zou H, Zhang Y. Soil Compressibility and Resilience Based on Uniaxial Compression Loading Test in Response to Soil Water Suction and Soil Organic Matter Content in Northeast China. Sustainability. 2022; 14(5):2620. https://doi.org/10.3390/su14052620

Chicago/Turabian Style

Xiao, Zhiqiu, Na Yu, Jing An, Hongtao Zou, and Yulong Zhang. 2022. "Soil Compressibility and Resilience Based on Uniaxial Compression Loading Test in Response to Soil Water Suction and Soil Organic Matter Content in Northeast China" Sustainability 14, no. 5: 2620. https://doi.org/10.3390/su14052620

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