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Citation: 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

Academic Editor: Teodor Rusu

Received: 12 January 2022

Accepted: 21 February 2022

Published: 24 February 2022

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sustainability

Article

Soil Compressibility and Resilience Based on UniaxialCompression Loading Test in Response to Soil Water Suctionand Soil Organic Matter Content in Northeast ChinaZhiqiu 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;[email protected] (Z.X.); [email protected] (J.A.); [email protected] (H.Z.)

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

* Correspondence: [email protected] (N.Y.); [email protected] (Y.Z.)

Abstract: Due to the widespread use of heavy machinery, improper soil tillage practices, and insuffi-cient soil organic materials input, soil compaction has become a major issue affecting soil function inmodern agriculture and the sustainability of the environment. The aim of the present study was toevaluate the responses of soil mechanical parameters to soil water content and soil organic mattercontent (SOM), and to investigate the physical properties of nine disturbed soils in a black soilregion 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, significantlyinfluenced the mechanical parameters. σp increased with an increase in SWS until 600 kPa, whileDc 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, andDc were influenced variably by different soil physicochemical factors. Structural equation modelingresults revealed that soil mechanical parameters were directly and indirectly influenced by soil textureand mean weight diameter of aggregates, in addition to SOM and SWS. According to the results ofthe present study, based on soil mechanical and physical properties, increasing SOM and ensuringsuitable soil water content during tillage could be applied as management strategies to minimizefurther soil compaction and improve soil resilience, and thus promote the sustainable developmentof agriculture in Northeast China.

Keywords: soil degradation; soil pre-compression stress; soil compression index; soil decompressionindex; soil organic matter; soil water suction

1. Introduction

The black soil in Northeast China is characterized by a dark surface and rich organicmatter [1,2]. However, long-term agricultural activities in the region have degraded thephysical and hydraulic properties of the soil, leading to numerous issues, such as thinningof the tillage layer [3], decline in soil organic carbon content [4,5], and, especially, increasingsoil compaction [6], in turn disturbing the balance of the ecosystem and resulting in adecline in soil functionality, productivity, and yield [7–9].

Soil organic matter (SOM) availability influences soil susceptibility to compaction.Interactions among SOM, soil organisms, and soil minerals drive the formation of soilaggregates [10], directly increasing soil porosity and decreasing bulk density [11]. Soil com-paction is evaluated based on soil mechanical parameters, including soil pre-compressionstress (σp), compression index (Cc), and decompression index (Dc), which are calculated

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from soil compression curves and resilience curves that typically describe the relation-ship between void ratio and the logarithm of applied mechanical stress, and representload-bearing capacity and resilience capacity following compaction [12–15]. In a study byReichert et al. [16] involving four types of soil in Brazil, the authors observed that SOMwas positively correlated with Cc, while there was a negative correlation between SOM andσp. An increase in SOM with lower initial bulk density could reduce soil compactabilityby increasing resistance to deformation and elasticity [17]. In addition, Kuan et al. [18] ob-served that SOM content was correlated strongly with resilience following the eliminationof physical stress. Such elastic rebound following stress release was relatively high in twoOxisols 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 moisturecontent at the time of load application [20,21]. For example, Mosaddeghi et al. [22] observedthat manure application had a high suppressive effect on soil compactability under highmoisture levels and high loads. The increase in soil moisture tended to reduce the impactsof compaction on inter-pore and intra-pore spaces, while high compaction altered poresize distribution considerably [23]. σp significantly increased with an increase in soilwater suction (SWS) over the 10–3000 kPa range [24], while SWS over the 6–100 kPa rangesignificantly influenced Cc and Dc [16]. On the contrary, according to Imhoff et al. [20], SOMand 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 between6 kPa and 1000 kPa. Therefore, uncertainty persists regarding the effect of soil moisture onsusceptibility to compaction.

To date, the research on soil compressibility and elasticity of black soil has mainlyfocused 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 andartificial amendment of organic materials [26–28]. Soil compressibility and elasticity couldvary due to differences in SOM content, composition, morphology, origins, distribution,and the degree of combination of SOM and mineral particles [29]. Therefore, soil incubatedby the addition of organic materials may not reliably reflect compaction dynamics undernatural soil weathering and decomposition conditions.

Soil bulk density was the most significant factor affecting compressibility [19,26]. Forsandy 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 importantfactor affecting σp and Cc [16]. Soil mechanical parameters are affected by several soilattributes, e.g., soil type, texture, initial bulk density, organic matter content, and especiallythe interaction of initial bulk density and soil properties. In this sense, the influencescaused by initial bulk density may interfere with soil compressibility, which in turn mustbe underestimated or overestimated by the influences caused by soil properties. Thus, wehypothesized that SWS and SOM have great influence on soil compaction sensitivity andresilience under the same soil bulk density of 1.33 g cm−3. Therefore, the objective of thisstudy was to evaluate the response of mechanical parameters, soil pre-compression stress,soil compression index, and soil decompression index to soil properties dominated by SWSand SOM in disturbed black soils with uniform bulk density.

2. Materials and Methods2.1. Sampling Sites

Soil samples were collected from nine agricultural fields over a 40–47◦ latitude rangeacross three provinces in Northeast China, as illustrated in Figure 1. The samples wereobtained 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 JilinProvince; 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. Soilbackground 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 samplingsites were temperate continental, temperate monsoon, and temperate continental monsoonclimate from north to south. The annual accumulated temperature and precipitation were2300 ◦C and 550 mm in Hailun city, 2700 ◦C and 569 mm in Harbin city, 3522 ◦C and595 mm in Gongzhuling city, 3600 ◦C and 400 mm in Changtu county, 3900 ◦C and 650 mmin Shenyang city, and 4019 ◦C and 715 mm in Haicheng city, respectively [31].

Sustainability 2022, 14, x FOR PEER REVIEW 3 of 16

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].

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.

Table 1. Basic soil physicochemical properties.

Soil Samples Locations SOM (g kg−1)

Particle Size Distribution (%) Texture

Sand Silt Clay HHL Hailun 55.2 a 11.13 e 50.46 g 38.46 ab Silty clay loam HHR Harbin 34.0 b 23.93 b 53.61 f 22.48 d Silt loam JGA

Gongzhuling 30.0 c 5.47 h 55.24 e 39.15 a Silty clay loam

JGB 28.1 d 13.22 d 50.54 g 36.17 b Silty clay loam JGC 27.0 e 30.37 a 64.37 c 5.25 g Silt loam LCA

Changtu 20.3 f 21.76 c 57.01 d 21.34 de Silt loam

LCB 17.8 h 10.69 f 69.99 a 19.22 e Silt loam LSY Shenyang 13.5 i 30.35 a 57.14 d 12.22 f Silty clay loam LAH Haicheng 18.8 g 6.74 g 66.34 b 26.92 c Silty clay loam

Soil samples

Mechanical Physical Property (%)

Soil Aggregates Composition (%) (mm) MWD

LL PL PI 2–1 1–0.25 0.25–0.1 <0.1

Figure 1. Sampling site locations in Northeast China. HHL and HHR sites are in HeilongjiangProvince; JGA, JGB, and JGC sites are in Jilin Province; LCA, LCB, LSY, and LAH sites are inLiaoning Province.

Table 1. Basic soil physicochemical properties.

SoilSamples Locations

SOM(g kg−1)

Particle Size Distribution(%) Texture

Mechanical PhysicalProperty (%)

Soil Aggregates Composition (%)(mm) MWD

Sand Silt Clay LL PL PI 2–1 1–0.25 0.25–0.1 <0.1

HHL Hailun 55.2 a 11.13 e 50.46 g 38.46ab

Siltyclayloam

50.48 34.84 15.64 3.16 27.32 20.79 48.73 0.279

HHR Harbin 34.0 b 23.93 b 53.61 f 22.48 d Siltloam 42.29 27.88 14.41 4.43 26.91 27.04 41.62 0.301

JGAGongzhuling

30.0 c 5.47 h 55.24 e 39.15 aSiltyclayloam

41.36 24.54 16.82 1.36 24.32 18.99 55.33 0.239

JGB 28.1 d 13.22 d 50.54 g 36.17 bSiltyclayloam

43.05 25.80 17.25 4.65 26.80 26.82 41.73 0.299

JGC 27.0 e 30.37 a 64.37 c 5.25 g Siltloam 44.39 27.64 16.75 9.68 25.36 25.28 39.68 0.363

LCA Changtu 20.3 f 21.76 c 57.01 d 21.34de

Siltloam 41.25 24.36 16.89 0.56 9.60 17.79 72.05 0.145

LCB 17.8 h 10.69 f 69.99 a 19.22 e Siltloam 35.34 21.38 13.96 3.95 10.39 18.88 66.79 0.203

LSY Shenyang 13.5 i 30.35 a 57.14 d 12.22 fSiltyclayloam

34.82 24.19 10.63 4.13 14.10 19.79 61.98 0.216

LAH Haicheng 18.8 g 6.74 g 66.34 b 26.92 cSiltyclayloam

42.84 26.11 16.73 8.68 24.76 26.01 40.55 0.341

LL: liquid limit; PL: plastic limit; PI: plasticity index, PI = LL − PL; MWD: mean weight diameter. Different lettersindicate significant differences between soil samples based on Duncan’s Multiple Range test at p < 0.05.


2.2. Experimental Design and Soil Analysis

Disturbed soil was used in the treatments based on a randomized design, with threereplicates for each treatment. Soils under the following water suction treatments wereincluded: 6 kPa, 10 kPa, 100 kPa, 600 kPa, and 800 kPa. The nine disturbed soils representedsoils 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, andthen passed through a 2 mm sieve. Those steps were prepared for the analysis of soilproperties and compression tests in subsequent analysis.

To eliminate the influence of soil bulk density, the bulk density of nine soils was setat 1.33 g cm−3. The particle densities of nine soils were around 2.65. From the equationof void ratio e0 = ρs/ρd − 1 [32], the initial void ratios of nine soils were the same. Bydestroying the original soil structure (remolded samples), it is possible to effectively reducethe differences of soils or treatments [33]. Soils with preserved structure should also betested 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 slightlyinto cylinders with a height of 20 mm and a diameter of 61.8 mm using a manual pressin order to achieve the uniform initial dry bulk density of 1.33 g·cm−3 [26,34,35]. Theremolded 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 equilibratedusing constant water-head pressures, and 100 kPa, 600 kPa, and 800 kPa were equilibratedon ceramic plates inside a pressure membrane apparatus (Soil Moisture Equipment Corp,Santa Barbara, CA, USA) according to Klute’s method [36]. Subsequently, the relationshipbetween soil volumetric water content (VWC) and soil water suction was measured by themethod described by Klute [36].

The samples were subjected to uniaxial compression tests using a model GZQ-1Full Automatic Pneumatic Consolidation Test Apparatus (Nanjing soil instrument fac-tory co. Ltd.) after equilibrium at different SWS levels. Sequential static loads were appliedfor 10 min and the displacement (accuracy ± 0.01 mm) was read at the end of each loadinginterval [15]. The void ratio was calculated from the vertical displacements. The appliedpressures 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 eachapplied pressure was recorded.

(2) Resilience curve: after the loading step of 200 kPa, the soil was unloaded to thestarting load of 8 kPa, with sequential unloadings of 200, 150, 100, 50, 25, 12.5, 10, and8 kPa.

(3) Recompression curve: after unloading to 8 kPa, loads of 12.5, 50, 100, 150, and 200 kPawere applied sequentially.

SOM was determined using an Element Analyzer (Vario EL III, Elementar, Langensel-bold, Germany). Soil pH was measured using a 1:2.5 soil:distilled water mixture withan Orion Star A211 pH meter (Thermo Fisher Scientific, Waltham, MA, USA). Soil par-ticle 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 Classifi-cation [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 plasticlimit were determined using the drop-cone penetrometer test (80 g cone plus shaft, coneangle 30◦) (CYS-2 photoelectric liquid-plastic limit tester, Nanjing Soil Instrument FactoryCo. Ltd., Nanjing, China), and plasticity index was calculated based on the liquid limitminus plastic limit [39].



(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].

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.

2.3. Calculation of Compression Parameters The void ratio (e) was calculated from the vertical displacement (d) as follows:

(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].

(2)

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).

( )[ ]{ }mlogPbexpcexpae −−+=

1H

dHρρe

d

s −−×=

Figure 2. Compression, resilience, and recompression curves plotted based on void ratio(e) as a functionof logarithm of applied stress (logP(kPa)). Cc: soil compression index; σp: soil pre-compression stress;Dc: soil decompression index.

2.3. Calculation of Compression Parameters

The void ratio (e) was calculated from the vertical displacement (d) as follows:

e =ρsρd× H− d

H− 1 (1)

where ρs is soil particle density (Mg m−3), ρd is soil initial bulk density (Mg m−3), and H isthe initial height of the soil core (m).

The capacity of the compression curve to describe soil compression characteristicswas evaluated, following fitting according to the Gompertz model (Equation (2)) based onnon-linear regression analysis [13,40].

e = a + c exp{− exp[b(log P−m)]} (2)

where e is void ratio, P is applying load, log is the logarithm to base 10. a, b, c, and m areempirical parameters. Compression index (Cc) was estimated using Equation (3), whereparameters b and c are from Equation (2).

Cc =bc

exp(1)(3)

The Newton method was used to determine the maximum curvature point in the Gom-pertz model, and the value of σp was calculated using the classic Casagrande method [13].

The resilience and recompression curves were fitted using polynomial functions. Theslopes of the intersection line of the resilience and recompression curves were defined tothe decompression index (Dc) (Figure 2).

The mean weight diameter of soil aggregates was calculated using the followingEquation (4):

MWD =

n∑

i=1XiWi

n∑

i=1Wi

× 100 (4)

where MWD is the mean weight diameter (mm), Xi is the mean diameter of each sizefraction (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 thedifferent SOM and SWS on soil mechanical parameters. Afterward, two-way ANOVAwas 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(η2) was used to estimate the effect size. Duncan’s post-hoc test was used to comparedifferences among the treatments at p = 0.05 and p = 0.01 levels. The functions fitting ofcompression, resilience, and decompression curves were performed using the least-squarescurve fitting function lsqcurvefit (MATLAB 2016), and the maximum curvature and σp werecalculated using Python v3.7.3 (Python Software Foundation 2017). Pearson correlationcoefficients were calculated and used to analyze the relationships between soil mechanicalparameters and physicochemical characteristics (p < 0.05) using the vegan package in Rv3.5.2 (R Core Development Team 2018).

To eliminate the influence of multi-collinearity, factor analysis (FA) of principal com-ponent analysis (PCA) was performed to reduce dimension by varimax rotation to identifylatent factors [41] using IBM SPSS Statistics 21 (IBM Corp., Armonk, NY, USA). Bartlett’stest of sphericity reveals whether the correlation matrix is an identity matrix, which indi-cates that the variables are unrelated. If the significance level is <0.05, this implies strongrelationships among variables in the test [42]. The first and second PCA components wereused to establish latent variables for SWC (soil water content) and ST (soil texture), respec-tively. The component matrix of PCA is listed in Table S1, and the criterion of selection wasa 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 contentand sand content were extracted as the second component, and named ST. A hypotheticalmodel that contained all plausible interaction paths between the soil physicochemicalproperties and mechanical parameters was built based on current knowledge, which wasfurther identified using a maximum likelihood parameter estimation method via structuralequation modeling (SEM). Soil physicochemical properties (SOM, MWD, SWC, and ST) andmechanical parameters (σp, Cc, and Dc) were included in the hypothetical model. Only sig-nificant effects are plotted (p < 0.05). Criteria for evaluating SEM fitness, such as the ratio ofchi-square values (χ2) to the degrees of freedom (df), goodness-of-fit index (GFI), adjustedgoodness-of-fit index (AGFI), and root mean square error of approximation (RMSEA) wereadopted according to previous studies [41,43]. The standardized path coefficients (rangingfrom 0 to 1) were used to estimate the relative weight of independent variables’ influenceon the dependent variables. The high path coefficients indicated that the independentvariable has a greater influence on the dependent variable in the relationship. The SEMwas 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. Results3.1. Soil Pre-Compression Stress (σp)

The σp increased with an increase in SWS; however, σp declined sharply at SWS of800 kPa in the HHL, HHR, JGA, LCA, LCB, and LSY samples (Figure 3). The σp of HHLsoil 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 SWSvalues of 6 kPa, 10 kPa, 100 kPa, and 800 kPa; however, σp exhibited a decreasing trendunder 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. σpincreased first and then decreased under SWS conditions of 100 kPa, 600 kPa, and 800 kPa.



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).

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.

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

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2 = 0.833), followed by SOM (









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cDbcD bcB

bC

bcD bcC bcB bcD

aA

bC bCDbcB

bC

bcCDbcC

cB

bcC

aA

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bcA bcB

dBC

bcdB

bA

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aA

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deB cdeA

bA

bcA

eBbB

cC

bB

aAaA

cB

bB

cB

bA


50

100

150

200

250

300


Soil

pre-

com

pres

sion

stres

s σ p

(kPa

)

Soil Samples

2 = 0.812) and theSWS–SOM interaction effect (









aB

cDbcD bcB

bC

bcD bcC bcB bcD

aA

bC bCDbcB

bC

bcCDbcC

cB

bcC

aA

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bcA bcB

dBC

bcdB

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aA

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deB cdeA

bA

bcA

eBbB

cC

bB

aAaA

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50

100

150

200

250

300


Soil

pre-

com

pres

sion

stres

s σ p

(kPa

)

Soil Samples

2 = 0.725).

3.2. Soil Compression Index (Cc)

Soil Cc values at low SWS conditions were significantly lower than those of Cc valuesat high SWS conditions (p < 0.05) (Figure 4). There were no significant differences in soilCc between the 6 kPa and 10 kPa SWS conditions (p > 0.05). Cc increased firstly and thendecreased with an increase in SWS, with the peak Cc values observed under the 100 kPacondition. Although Cc values sharply declined after 100 kPa, the Cc values under 800 kPaSWS 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 fromnorth 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, theCc value in LAH was significantly higher than those in LCA, LCB, and LSY under differentSWS conditions.

Two-way ANOVA results indicated that SWS, SOM, and their interaction significantlyinfluenced soil Cc (p < 0.01), and the effects were in the order of SWS (









aB

cDbcD bcB

bC

bcD bcC bcB bcD

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bC

bcCDbcC

cB

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aA

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bcA bcB

dBC

bcdB

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aA

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bA

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cC

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aAaA

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50

100

150

200

250

300


Soil

pre-

com

pres

sion

stres

s σ p

(kPa

)

Soil Samples

2 = 0.916) > SOM(









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bC

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aA

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bC

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bcA bcB

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bA

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cC

bB

aAaA

cB

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50

100

150

200

250

300


Soil

pre-

com

pres

sion

stres

s σ p

(kPa

)

Soil Samples

2 = 0.738) > SWS × SOM (









aB

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bC

bcD bcC bcB bcD

aA

bC bCDbcB

bC

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cB

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bcA bcB

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aAaA

cB

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bA


50

100

150

200

250

300


Soil

pre-

com

pres

sion

stres

s σ p

(kPa

)

Soil Samples

2 = 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 valuesunder low SWS conditions (6 kPa and 10 kPa) were significantly higher than those underhigh SWS conditions (100 kPa to 800 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.

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.

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).

aD

bC bB bB bC bCD

cC cB

bC

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HHL HHR JGA JGB JGC LCA LCB LSY LAH0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65


Soil

com

pres

sion

inde

x C C

Soil Samples

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 lettersrepresent differences among SWS in the same soil sample at the p < 0.05 significance level. Lowerletters denote differences among soil samples under the same soil water suction (SWS) at the p < 0.05significance 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.

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.

aA

cdA

bA bA

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HHL HHR JGA JGB JGC LCA LCB LSY LAH0.010

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Soil

deco

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Soil Samples

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 representdifferences among different soil water suction (SWS) treatments in the same soil sample at the p < 0.05significance level. Lower letters express differences among soil samples in the same SWS at thep < 0.05 significance level.

The Dc of HHL was significantly higher than that of the other soils, which becamemore discernible with a decrease in SWS. In HHR, JGA, JGB, and JGC samples, Dc increasedfirst and then decreased at 6 kPa and 10 kPa SWS, while decreasing trends were observed at100 kPa and 800 kPa SWS. Conversely, in LCA, LCB, LSY, and LAH samples, Dc decreasedfirst and then increased under different SWS conditions.

According to the two-way ANOVA results, SWS, SOM, and their interaction signifi-cantly influenced soil Dc (p < 0.01). According to the partial eta-squared values, the effectsize of SWS (0.963) was the greatest, followed by SOM (









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50

100

150

200

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300


Soil

pre-

com

pres

sion

stres

s σ p

(kPa

)

Soil Samples

2 = 0.920) and the SWS–SOMinteraction effect (









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50

100

150

200

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Soil

pre-

com

pres

sion

stres

s σ p

(kPa

)

Soil Samples

2 = 0.811).


Overall, increasing SOM contents and regulating SWS in the range of 100–600 kPacould be appropriate measures to maintain soil productivity and mitigate soil degradationof compaction.

3.4. Relationship between Soil Physiochemical and Mechanical Parameters

Correlation analysis among soil physicochemical properties and soil mechanical pa-rameters is illustrated in Figure 6. Among the soil mechanical parameters, only Cc andDc exhibited significantly positive correlations (p = 0.037). σp was significantly positivelycorrelated 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, andCLAY. 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 andclay contents.


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).

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.

σp

Cc

Dc

SOM

VWC

LL

PL

SAND

SILT

CLAY

MWD

0.52

0.63 0.70*

0.85** 0.65 0.87**

0.67* 0.40 0.73* 0.90**

0.65 0.87** 0.94** 0.85** 0.66

0.80* 0.80* 0.86* 0.90** 0.76* 0.89**

–0.21 –0.07 –0.34 –0.23 –0.14 –0.21 –0.01

–0.37 –0.21 –0.64 –0.60 –0.60 –0.46 –0.53 –0.03

0.40 0.19 0.67* 0.56 0.47 0.46 0.33 –0.81** –0.56

0.21 0.77* 0.35 0.29 –0.01 0.51 0.46 0.01 0.06 –0.04-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

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 indicatespositive correlation, red indicates negative correlation; ** denotes p < 0.01, * denotes p < 0.05, andno asterisk denotes p > 0.05. Soil pre-compression stress (σp, kPa), soil compression index (Cc), soildecompression 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).

The SEM analysis elucidated the path of soil properties affected on soil mechanicalparameters. Influential factors in the models explained 68.5%, 67.4%, and 93.2% of thevariations in mechanical parameters σp, Cc, and Dc, respectively (Figure 7). Standardizeddirect, indirect, and total effects are listed in Table S2 for the physicochemical propertiesin 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 linearregression. Cc was significantly positively influenced by both SWC and MWD directly, andthe path coefficients were 0.559 and 0.493, respectively. SWC and ST directly influencedDc 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 aneffect 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 correlationcoefficient was 0.940.



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).

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.

σp

Cc

Dc

SOM

VWC

LL

PL

SAND

SILT

CLAY

MWD

0.52

0.63 0.70*

0.85** 0.65 0.87**

0.67* 0.40 0.73* 0.90**

0.65 0.87** 0.94** 0.85** 0.66

0.80* 0.80* 0.86* 0.90** 0.76* 0.89**

–0.21 –0.07 –0.34 –0.23 –0.14 –0.21 –0.01

–0.37 –0.21 –0.64 –0.60 –0.60 –0.46 –0.53 –0.03

0.40 0.19 0.67* 0.56 0.47 0.46 0.33 –0.81** –0.56

0.21 0.77* 0.35 0.29 –0.01 0.51 0.46 0.01 0.06 –0.04-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Figure 7. Structural equation model of soil physicochemical properties and soil mechanical parame-ters. Rectangles represent observed variables, ovals represent latent variables, a single arrow indicatesthe 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 effectsare plotted (p < 0.05). Blue arrows denote positive relationships, while red arrows denote negativerelationships. Numbers on arrows are standardized path coefficients (*** p < 0.001; * p < 0.05). R2 inbrackets indicates the percentage of variance explained by the model. Error variables for the unex-plained variance in all endogenous variables are not included in the figure. χ2: chi-square value; df:degree of freedom; GFI: goodness-of-fit index; AGFI: adjusted GFI; RMSEA: root mean square errorof 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 (includingCLAY, and SAND); SAND: sand content; CLAY: clay content; MWD: mean weight diameter; σp: soilpre-compression stress; Cc: soil compression index; Dc: soil decompression index.

4. Discussion4.1. Variations in Soil Mechanical Parameters with Soil Properties4.1.1. Soil Pre-Compression Stress (σp)

Soil organic matter significantly affects soil compressibility parameters, as shownin our study results (Figure 3). However, the magnitude and type of effect on theseparameters depend on soil texture and its impact on water retention, soil cohesion, andbulk 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 ofsoils from the southern section of the study region (Liaoning Province), which had lowerSOMs, and which illustrated a positive correlation with SOM. The finding is supportedby studies of Défossez et al. [45], Cavalcanti et al. [46], and Pue et al. [47]. SOM representsan extremely variable mixture of complex chemicals with high degrees of elasticity andexpansibility [48]. As external stress is applied, soil organic particles participate in stressbuffering and directly influence soil capacity to resist compaction [49]. SEM results alsorevealed that σp was directly affected by SOM (Figure 7).

The significant positive linear relationship between SWS and σp has been reported inseveral studies [16,50–54]. However, in the present study, σp increased with an increasein SWS until 600 kPa under various SOM conditions and decreased when SWS exceeded600 kPa (Figure 3). The concave–parabolic response of σp to changes in SWS was alsoobserved by Silva et al. [55], although they expressed moisture changes as different sat-uration degrees. The concave–parabolic changes may be explained by the water filmsurrounding 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 inSWS, individual clay particles no longer slide against each other up to a certain moisturelevel 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 soilaggregate water stability and high resistance to deformation, σp of JGB, JGC, and LAH didnot decline with SWS conditions greater than 600 kPa, which was also confirmed by thehigh MWD values of these three samples (Table 1).

We found that only SOM had a significant relationship with σp in the SEM re-sults (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 soilporosity and secondly on soil carbon content. The unified bulk density of soil samples withthe same initial porosity could explain our results. Arthur et al. [11] also found that therewas a correlation between σp and water content, but there was no further improvement ofintroducing 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 SWSwithin the 6–800 kPa range, with the highest values observed at 100 kPa (Figure 4), similarto the finding of Lima et al. [19], who modeled the relationship between Cc and SWSby 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 soilresistance to external load due to differences in soil bulk density and soil structure betweendisturbed and undisturbed soils. Cc values in the present study did not show discernibletrends 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 valueswere silty loam soils of Liaoning province. The results indicate that soil MWD values ofsilty 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 anegative correlation with the Cc value. Conversely, we observed a positive correlation andthat MWD had direct effects on the Cc value (Figures 6 and 7); similar results have beenreported by Blanco-Canqui and Lal [49]. The positive relationship of this study may be dueto the higher proportion of macro-aggregates (particle size 2–0.25 mm) and high MWDvalue (Table 1). The soil macro-porosity among the aggregate particles was higher, theaggregates were more stable, and it was not easy to collapse when bearing a load, resultingin higher soil compressibility. The aggregation process of agricultural soil is a complexsystem, in which the relationship of the aggregate–water–organic matter is very importantfor 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, whichis consistent with the findings of several other studies [16,18,58,59]. Dc was directly andindirectly affected by SWC, SOM, ST, and MWD. The explanations of the variations weremuch higher than σp and Cc, and the influencing factors that explain the variations in Dcwere also more than σp and Cc based on the results of SEM.

Soil water content significantly influences soil compressibility and resilience. Underwet conditions, most of the soil pores are filled with water, and some of the water-filledpores resist external loads and protect the soil structure from damage [60]. In the process ofsoil water absorption (wet soil), greater moisture makes it difficult to expel air from soilpores, 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 isshort, as in the case of this study (10 min), or even more intense in field conditions, duringwheeling or animal trampling, in which the residence time of the applied load is veryshort [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, whichdestroy 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, latenteffects of ST showed a negative correlation with clay content and a positive correlation withsand content (Table S1 and Figure 7), which were the reasons for negative path coefficientsbetween SOM and ST, as well as ST and Dc (Figure 7). SOM could also affect Dc via itsinfluence on ST. SOM, especially the humic material and mucilage, which are coated onmineral soil particles in the form of films, improves soil structure through effects on porenetworks [60,63,64]. According to Bonetti et al. [65], clayey soils are more resilient due tothe existing strong connections between clay particles. Therefore, the interaction of SOMand clay particles influences soil resilience due to the presence of iron oxides in the clayfraction [66]. In the present study, the Dc values in HHR were lower than those in theother black soils; however, SOM content was higher in HHR. The above trends could beexplained 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 (Figures 3 and 5).There were significant positive correlations between Cc and Dc values (Figure 6). In addi-tion, SEM results revealed that Cc directly affected Dc (Figure 7), which further demon-strated that compression deformation was closely associated with soil resilience, and bothCc and Dc were influenced by soil physicochemical properties. The findings are consistentwith the findings of Pesch et al. [32].

Inconsistent relationships have been reported among soil mechanical parameters inprevious studies. Different SOM mineralization and humification processes may take placein soils from different study sites, leading to various SOM composition and distributionpatterns [67]. In the present study, the interaction between water content and SOM hadimpacts on soil texture, soil porosity, and permeability. In general, SOM content can directlyaffect soil physical properties. A decrease in SOM content reduces aggregate stability andstrength, increases soil’s susceptibility to excessive compaction, and reduces macro-porosity,hydraulic conductivity, and water retention [68]. Soil physical properties can be improvedthrough providing organic binders by SOM, inducing slight water repellency, reducing soilbulk 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 differentsoil water conditions following equilibrium (data not shown). However, the field soilswere much more complex than conditions in laboratory experiments. The physicochemicalproperties observed under different SWS conditions should be further verified under fieldconditions. Meanwhile, a quantitative analysis of the soil compression parameters andphysicochemical properties offers the possibility for predicting soil mechanical parametersbased 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 parametersin high SOM soils were higher than those in low SOM soils. There was an inflection pointof 600 kPa in mechanical parameters of σp and Dc with the change in SWS, while theinflection point of 100 kPa of Cc changed with the change in SWS. σp and Cc increased withan increase in SWS and decreased after the inflection point. However, the Dc trend with thechange 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 weremost influenced by SOM and water content. Notably, factors such as aggregate stabilityand 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 resultsof the study can be used to improve black soil cultivability and promote the sustainabledevelopment 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 vari-ables in PCA; Table S2: Standardized total, direct and indirect effects of influencing factors on soilmechanical 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 ofthe manuscript.

Funding: This research was funded by the National Key Technology R&D Program of China, grantnumber 2016YFD0300807, and the National Basic Research Program of China (973 Program), grantnumber 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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