Research on the application of a cement and soil aggregate for the ecological restoration of vegetation in artificial soil

Experimental design

To investigate the effects of cement and soil aggregate on seedling survival and growth, an orthogonal experimental design with two factors was adopted in this study. A PO-32.5 cement (Yaobai, Shaanxi, China) was applied in the experiment with cement content tested at 0%, 5%, 10%, 15%, and 20%, according to the methods outlined by Chen et al. (2013). These levels of cement were combined with different levels of soil aggregate (Lvmeng, Fujian, China), according to the manufacture’s recommendations, with 0.00%, 0.03%, 0.06%, and 0.09% soil aggregate tested. This study used a 5 × 4 orthogonal experiment design; there were 20 groups of treatments with three replicates per treatment for a total of 60 pots in this experiment.

Elymus nutans, a perennial herb (Mao et al., 2019), is an ideal pioneer plant for ecological restoration in alpine grassland areas of China (Gu et al., 2009; Tan et al., 2020). Mature Elymus nutans seeds were collected from the Qinghai-Tibet Plateau in China (36°57′N, 105°51′E, 3140 m asl) in 2020. There were 60 seeds per pot (pot size: 32.5 cm depth and 25.5 cm diameter), sown by hand to a depth of two cm. The 32.5 cm pot depth was suitable for root growth because the root systems of herbaceous plants are densely distributed in the top 30 cm soil layer (Burylo et al., 2012). The substrate used in this study was a combination of loose, loamy soil (Table 1) and organic matter (3:1). Each pot was filled with a different combination of cement and soil aggregate with the same weight of mixed substrate added, according to the experiment design. The pots were each watered to saturation and then maintained at 60% of the maximum field water holding capacity by weighing the pots daily and replenishing the water to the desired weight. All cells under the experimental treatment were isolated from each other to prevent the competition effect. To minimize the effect of the pot position, the pots were randomly rotated every three days.

After six months of growth (Fig. 1), the plant samples with a stubble height of two cm were cutting by hand during a vigorous growth period. The soil samples were collected, ground, and mixed well; 20% of the soil samples were tested for soil indexes. The dry plant biomass of the plant samples was measured after oven drying (65 °C, 24 h). After removing stones and plant and animal debris, the air-dried soil samples were ground and then passed through a 100 mesh (0.15 mm) nylon screen (Chen et al., 2015). The samples were then stored in a dark place at a low temperature for chemical analysis.

Sample analysis and experimental methods

The treated samples were preserved and analyzed for the physical-chemical index analyses. The soil organic carbon (SOC) was determined using the potassium dichromate oxidation method. Soil/water suspension (1:2.5 ratio) pH was tested using the potentiometric method. Matric suction and unconfined compressive strength were tested using a densiometric (2100F) and strain-controlled triaxial (TSZ-3) testing apparatus (Zhang et al., 2022). Alkali-hydrolyzed nitrogen (AN) was tested using the alkali solution diffusion method; available phosphorus (AP) was measured using the spectrophotometric colorimetry method (Olsen, 1954); available potassium (AK) was measured using flame Xphotometry; microwave digestion and the Kjeldahl method were used to measure total nitrogen (TN); the sodium hydroxide melt-molybdenum antimony colorimetric method was used to measure total phosphorus (TP); total potassium (TK) was measured using the sodium hydroxide melt-flame photometry method (Zheng et al., 2020); and the concentration of microbial biomass carbon (MBC) was tested using the chloroform fumigation-extraction method (Vance, Brookes & Jenkinson, 1987). Dry soil samples had distilled water added for the microbial activity culture (Wollum, 1983). Soil samples were then divided into fumigated and unfumigated groups with the pretreatment process based on previously published methods (Khan et al., 2010). The microbial biomass carbon (MBC) content was measured using an automated Total Organic Carbon analyzer (Shimazu, TOC-5000, Japan). MBC = 2.22 (C_fumigated −C_{non fumigated}), in which C was extracted from the fumigated and non-fumigated soil samples, respectively (Khan et al., 2010).

Evaluation of soil quality indexes

The SQI can assess soil nutrition quality using minimum data sets (MDS; Andrews et al., 2003), which allows investigators to select representative soil indicators to improve the validity of the evaluation data (Doran & Parkin, 1994). Calculating the SQI requires three steps (Andrews et al., 2003; Brejda et al., 2000): (1) selecting the minimum data set (MDS), (2) scoring the MDS indicators, and (3) calculating the integrated SQI values. A principal component analysis (PCA) and a Pearson’s correlation analysis are used to identify the appropriate indicators for selecting the MDS (Doran & Parkin, 1994). According to previous studies (Andrews et al., 2003; Andrews, Karlen & Cambardella, 2004), only principal components (PCs) with eigenvalues ≥1 that explain at least 5% of the total variation can be considered for the MDS (Andrews et al., 2003). Within each PC, the indicators with absolute loading values in the highest 10% (Bastida et al., 2006) or indicators that were well correlated (r ≥ 0.6) were selected as vital indicators (Andrews, Karlen & Cambardella, 2004). After selecting the indicators of the MDS, a non-linear scoring function was used to transform the soil indicators into scores that ranged from 0 to 1. The sigmoidal function (Eq. (1)) was used as follows (Andrews, Karlen & Cambardella, 2004):

In this study, it was assumed that different soil parameters played various roles in maintaining soil quality, so the soil quality of different treatments was determined using the SQI, which was calculated using the selected soil factor membership values and their weights, as presented below (Andrews et al., 2003; Lin et al., 2017; Masto et al., 2008): (1)${\displaystyle SQI=\sum _{i=1}^n{W}_i\times Q\left(x_i\right)}$ where W_i denotes the weight of soil quality factor i (soil property), Q (x_i) represents the membership value of each soil quality factor, and n signifies the number of the selected soil quality factors. The Q (x_i) values were assessed with different variation functions (Zheng et al., 2005). The pH value was a decreasing function, where the higher the pH value, the higher the alkalization degree; an ascending function was applied for SOC, AN, AP, AK, TN, TP, TK, and MBC (Xu et al., 2009), where higher values indicated a greater contribution to the SQI. The ascending and descending functions were calculated using the following formulas:

(2)${\displaystyle Q\left(x_i\right)=\left(x_{ij}-x_{i\text{min}}\right)/\left(x_{i\text{max}}-x_{i\text{min}}\right)}$ (3)${\displaystyle Q\left(x_1\right)=\left(x_{i\text{max}}-x_{ij}\right)/\left(x_{i\text{max}}-x_{i\text{min}}\right)}$ where x_ij denotes the value of the selected fundamental parameters for the SQI calculation; x_imax and x_imin represents the maximum and minimum values of the soil property i under various combinations of cement and soil aggregate.

In this study, a PCA analysis was employed to determine the component capacity score coefficient, and the weight of the soil quality factor (W_i) was calculated according to the score coefficient, as presented below (Xu et al., 2009): (4)${\displaystyle W_i=\frac{C_i}{\sum _{i=1}^n\left(C_i\right)}}$ where C_i represents the score coefficient of soil quality factor i while n denotes the number of selected soil quality factors.

Data analysis

Analysis of variance was used to compare the soil properties under various treatments. Fisher’s least substantial difference (LSD) was used for the mean separation at 0.05 or 0.01, Spearman’s test was used to determine correlations among soil properties, and linear regression was used to calculate the PSD fractal dimensions. All analyses were conducted using the SPSS software (19.0; SPSS, Inc., Chicago, IL, USA).

The statistical analysis was conducted using SPSS Ver. 19. An analysis of variance was applied to compare the soil properties under different treatments, Fisher’s least substantial difference was used for the mean separation at a significance level of P < 0.05 or P < 0.01, a linear regression analysis was used to measure the physical and chemical soil properties, a Pearson’s test determined the correlations between selected soil parameters, and a PCA analysis was used for factor extraction. Three replicates were separated for independent testing in the laboratory and for statistical analysis.

Biomass and soil weight

The results indicated that cement had a more dramatic impact on biomass and soil weight than the control (CK), as shown in Fig. 2. Increasing cement content levels in the soil first increased the biomass of the pots, but then the biomass descended gradually. The maximum biomass (26.50–32.77 g DW/pot) was obtained under 10% cement treatment, which was a statistically higher biomass (P < 0.01) compared with other cement percentages. Soil weight also increased as cement percentage increased, with soil weight differing significantly (P < 0.01) under different cement percentages. The analysis of variance further indicated that the addition of cement significantly impacted (P < 0.01) both biomass and soil weight.

The soil aggregate additions had a varied impact on biomass and soil weight. With the same percentage of cement added, the influence of soil aggregates on biomass was more substantial at low concentrations (0.03% and 0.06%); the biomass after 0.09% soil aggregate addition was lower than CK. In contrast, soil weight was higher with the addition of lower concentrations of soil aggregate compared to CK, but the difference was not statistically significant.

Soil physical properties

Soil physical properties were influenced by both cement and soil aggregate addition (Fig. 3). Matric suction gradually increased with cement addition, and there were significant differences between different levels of added cement (P < 0.01). The highest matric suction was observed with 20% cement addition, which was 29%–57% higher than CK. The same trend was found in unconfined compressive strength, which increased with cement addition. There was no significant difference between 5% cement addition and CK, but unconfined compressive strength was significantly higher with a 10% cement addition than with a 5% cement addition and significantly lower than a 15%–20% cement addition, and there was no significant difference between 15% and 20% cement addition. Conversely, the level of soil bulk density decreased with cement addition. The soil bulk density level after a 10%–20% cement addition was significantly lower than in CK, but there was no significant difference in soil bulk density between 10% and 20% cement additions.

The influence of soil aggregate on different soil physical properties varied (Fig. 3). Without cement addition, the addition of soil aggregate made the matric suction stronger than in CK. Matric suction value was higher in the 0.03% soil aggregate pot than in both the 0.06% and 0.09%. However, with more cement addition, soil aggregate addition made the matric suction stronger than in both the 10% and 15% cement addition pots, but weaker than the 5% and 20% cement addition pots. The analysis further revealed that statistical significance varied among the treatments. The influence of soil aggregate on unconfined compressive strength was more consistent: unconfined strength increased with more cement addition. Soil bulk density was less influenced by soil aggregates: without cement addition, soil bulk density decreased significantly; with more cement addition, the soil bulk density value was relatively stable, but increased significantly with 0.09% soil aggregate (3.75%–14.22% higher than in CK).

Soil Ph, SOC, and BMC

The influence of cement and soil aggregate on soil pH, SOC, and BMC is shown in Fig. 4. These results revealed that soil pH increased with cement addition and varied between 8.05 and 8.64. Both 5% and 10% cement addition had less influence on soil Ph; 15% and 20% cement addition increased Ph levels, but not significantly. In contrast, the SOC content with 10% cement addition was higher compared with other treatments, whereas the impact of soil aggregate on the SOC content was more significant. The SOC content with 0.09% soil aggregate addition under 0–15% cement was significantly higher compared with other treatments.

The impacts of various soil aggregate treatments on MBC were not significantly different, but the MBC content was higher under 5% (0.56–0.79 mg/kg) and 10% (0.66–0.89 mg/kg) cement addition treatments compared with CK (0.51–0.56 mg/kg). The results indicate that cement and soil aggregates might have an interaction effect on MBC, which was remarkable with a low cement addition and higher soil aggregate addition.

Plant element

Cement and soil aggregate addition had different influences on plant elements (N, P, and K, as presented in Fig. 5), with the most significant differences found in N and P. Specifically, N content increased with increased cement and soil aggregate content, with N content reaching the highest level with 10%–20% cement addition and 0.06% soil aggregate addition. P content increased with cement addition, but decreased with soil aggregate addition in CK, with impacts to P content varying under other treatments. Similarly, K content was influenced by both cement and soil aggregates, increasing with 5% and 10% cement addition but decreasing with 15% and 20% cement addition. K content in CK increased with 5% cement addition, maintained with 10% cement addition, and decreased with 15% and 20% cement addition.

Soil element

Soil element contents were influenced by both cement and soil aggregate addition (Fig. 6). The N level decreased with increased cement addition, with N levels significantly lower with 15%–20% cement addition compared with CK. Soil aggregates had no substantial influence on N levels without cement addition, but this interaction of cement and soil aggregate varied under different cement additions. The N level increased with increased soil aggregate content, especially with low cement addition. The influence of cement and soil aggregates on P level was roughly consistent. P level decreased with the addition of cement and plummeted with 15%–20% cement addition, but was slightly affected by different soil aggregate additions.

K level trends were more consistent. The K level decreased significantly with increased cement, but there were no changes to K levels observed under different soil aggregate additions, indicating that cement had more influence on K content than soil aggregates. Total soil silicon (Si) content increased significantly with cement addition (P < 0.01), but did not significantly differ under different soil aggregate additions.

Available soil nutrients

Cement and soil aggregates both impacted available nutrients in the soil (Fig. 7). AN content decreased with cement addition (5%–20%), with decreases significant with low cement levels but not significant with high cement levels. In contrast, the AN level increased with soil aggregate addition, with significant increases observed with less cement addition (5% and 10%), but insignificant increases with higher cement addition (15% and 20%).

Soil AP content increased with cement addition, with varied increases under different treatments. The highest level of AP was found with 5% cement addition (with 0 and 0.03% soil aggregate) and 15% cement addition (with 0.06% and 0.09% soil aggregate). However, soil aggregates had a different influence on AP, which was more significant with less soil aggregate addition and 5% cement addition as well as with moderate soil aggregate addition with 15% cement addition. These results might indicate that there is an interaction effect of cement and soil aggregate addition on AP.

AK was significantly impacted by both cement and soil aggregates. Compared with the control group, the AK content was lower with 5%–10% cement addition but higher with 15%–20% cement addition, being lowest with 10% cement addition and highest with 20% cement addition. With increased soil aggregates, AK level was higher with 5% and 10% cement addition, lower with 15% cement addition, and stable with 20% cement addition. The variance analysis indicated that the AK level with 15% and 20% cement addition was substantially higher (P < 0.01) than with 5% and 10% cement addition.

The influence on the SQI of artificial soils

SQI was introduced to explore the effect of the combination of cement and soil aggregates on artificial soils. The first step was to obtain the constant weight of the soil parameters. After a Varimax rotation, a PCA analysis produced four principal components (PCs; Table 2). PC1-4 explained 80.54% of the variability in the measured soil parameters, most representing the original parameters. Therefore, the weight of each parameter was calculated through PC1-4 in this study (Table 2). After determining the weight of each soil index, the membership value of each soil index [Q (x_i)] was determined using Eqs. (2) and (3). Finally, Eq. (1) was applied to calculate the SQI (Table 3). The results indicated that SQI value decreased with cement addition but increased with moderate soil aggregate addition, with the highest value in CK (0.53–0.58) and the lowest with 20% cement addition (0.19–0.34).

To further distinguish the relationship among the different soil physicochemical properties, Pearson’s correlation coefficients were applied in the analysis (Table 4). The results showed that Si content was positively correlated with pH (P < 0.01), AP, and AK, but negatively correlated with TN, TP, TK, and AN. Conversely, TN was positively correlated with TP, TK, and AN; TP was positively correlated with TK and AN; and TK was positively correlated with AN. In summary, TN, TP, TK, and AN were positively correlated with each other, but negatively correlated with Si. Cement addition, the primary source of Si and an accelerant for soil solidification, restricts TN, TP, TK, and AN levels, explaining the relationship between Si and other soil physiochemical properties. In addition, soil pH was negatively correlated with the TN, TP, TK, and AN of artificial soil (Table 4), while TN content was positively correlated with TP, TK, and AN. Soil pH was also positively correlated with SMC, which was related to cement addition, as shown in Table 4. In conclusion, the Pearson correlation indicated that there was a positive correlation between soil physical and chemical parameters.