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Article

Soil Quality Evaluation and Analysis of Driving Factors of Pinus tabuliformis in Loess Hilly Areas

by
Junzhe Li
1,
Fangfang Qiang
1,
Ning Ai
1,*,
Changhai Liu
1,*,
Guangquan Liu
2,
Menghuan Zou
1,
Qianwen Ren
1 and
Minglu Liu
1
1
College of Life Science, Yan’an University/Key Laboratory of Applied Ecology of Loess Plateau, Shaanxi Province (Yan’an University), Yan’an 716000, China
2
China Institute of Water Resources and Hydropower Research, Beijing 100038, China
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(9), 1603; https://doi.org/10.3390/f15091603
Submission received: 31 July 2024 / Revised: 8 September 2024 / Accepted: 9 September 2024 / Published: 11 September 2024
(This article belongs to the Special Issue Soil Organic Carbon and Nutrient Cycling in the Forest Ecosystems)
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Abstract

:
The selection of suitable tree species and the reasonable allocation of planting areas are important measures for improving soil quality. To evaluate the soil quality (SQ) and its driving factors of Pinus tabuliformis forests in loess hilly areas where forestry ecological projects, such as returning farmland to forest (grass), have been implemented, this study selected P. tabuliformis forests with different restoration years (1a, 6a, 11a, 18a, and 22a) in Wuqi County and used grassland before afforestation (PRG) and abandoned grassland (AG) with 22 years as controls. In this study, soil physicochemical indices, soil fauna indices, and herbaceous plant indices obtained via principal component analysis were used to establish a soil quality evaluation model via the fuzzy comprehensive evaluation method to comprehensively evaluate SQ. Structural equation modeling (SEM) was used to identify the key factors affecting the SQ of P. tabuliformis forests. The goal was to create a model that could effectively evaluate the SQ while considering all relevant factors. The findings of the study showed that: (1) by performing a principal component analysis on the 27 indicator factors, the first six principal components had eigenvalues > 1, and the cumulative contribution rate was 90.028%, effectively encompassing the information of the original variables. (2) The highest soil quality index (SQI) was 0.592 (p < 0.05) in the restored 6a P. tabuliformis forest, whereas the lowest SQI was 0.323 in the restored 1a P. tabuliformis forest. As the number of years of restoration increased, the SQ of the P. tabuliformis plantation forest progressively approached that of the long-term abandoned grassland, with only a 1.8% difference after 22 years of restoration. The SQI of the P. tabuliformis woodland in restored 6a was 83% higher than that of 1a, and following 6a of restoration, the SQI showed a decreasing trend with increasing restoration years. Nevertheless, the SQI increased by >52% compared with the early stage of restoration (1a) and by 31% compared with the grassland before afforestation (PRG). (3) SEM revealed that the SQ of P. tabuliformis forest land was mainly driven by soil physical and herbaceous plant indicators, and soil fauna indicators and restoration years had a negative effect on the evolution of SQ in P. tabuliformis forests. The driving factors of P. tabuliformis forests of different restoration years were different, and with the increase in restoration years, the effects of soil fauna and herbaceous plant indicators on the SQ of P. tabuliformis plantation forests showed an overall upward trend.

1. Introduction

The overall fragile ecological environment of the loess hilly region is characterized by serious soil erosion and land degradation problems [1]. In recent years, the Chinese government has implemented a series of ecological restoration and management projects to improve the ecological environment of the region and achieve the goals of regional sustainable development and ecological civilization construction [2], resulting in significant improvement in the coverage of forest and grass vegetation and the ecological environment of the region [3]. P. tabuliformis, is a unique tree species in China, which has the ecological functions of drought resistance, barren resistance, strong adaptability, and soil and water conservation. It can also be cultivated in timber forests for timber production and has been widely used in forestry ecological projects, such as vegetation restoration in the loess hilly area [4,5]. Studies have shown that vegetation restoration not only increases vegetation cover, effectively reduces soil erosion, slows land desertification, and prevents the loss of water resources, which is conducive to improving the ecological environment and restoring biodiversity [6,7,8,9,10,11], but also promotes structural improvement and nutrient accumulation in the soil, which is conducive to improving soil fertility and retaining moisture, thereby improving SQ [12]. Therefore, plant growth can alter the physical structure, chemical properties, and composition of biological communities in the soil. Changes in soil physical structure, composition, chemical properties, and biological communities can, in turn, affect plant growth [13,14].
Soil not only provides water and nutrient support for plant growth and development but also directly affects vegetation productivity, environmental quality, wildlife habitat, and human health [15,16,17]. According to the Sustainable Development Goals (SDGs) proposed by the United Nations, SQ has become an important constraint for sustainable development [18]. SQ not only maintains biological productivity but also environmental quality, promotes plant and animal health, and serves as an extremely important indicator of soil capacity within the ecosystem [19,20]. Studies have shown that numerous factors affect changes in SQ [21,22,23,24,25,26,27,28], which can be comprehensively assessed using physical, chemical, and biological indicators of the soil. Ting Xiang [28] studied loess hill and gully areas to explore the evaluation of SQ of typical vegetation and its response to precipitation. Raiesi, F [27] evaluated SQ and its restoration after a fire in semi-arid mountainous areas of central Iran by constructing a minimum data set. Yida An [21] assessed the SQ of different land use types in the sandy area of the northern loess hills using the TOPSIS method.
Xining Zhao [29] used microbial bacterial indicators to assess the SQ of various amendments in mountain orchards located in the hilly areas of the Loess Plateau. Rui G [30] developed a SQ evaluation system that encompassed both physical and chemical indicators of the loess area of northern Shaanxi, as well as chemometric characterization indices. Most existing research on SQ evaluation processes has focused on soil physical and chemical indicators. These indicators are typically used to assess microbial and enzyme activity in response to environmental conditions [31,32,33,34,35]. However, soil fauna and herbaceous plant indicators as relevant evaluation indices for SQ assessment have rarely been reported. The soil fauna and herbaceous plants are crucial components of the ecosystem. They play significant roles in the maintenance of nutrient cycling and soil fertility [36,37]. These organisms are highly sensitive to soil changes and are involved in most soil functions and processes [38,39]. These roles are important for ecosystem reconstruction [40]. The characteristics of soil faunal communities are closely related to the SQ [41,42,43]. Meanwhile, during vegetation growth, woodlands enhance the food supply and provide shelter and breeding sites for soil fauna. In addition, increasing canopy density creates a favorable soil environment for the survival and reproduction of the soil fauna [44]. Therefore, soil fauna have a crucial impact on SQ through their sensitivity to environmental changes, adaptability, and modification. Soil fauna also serve as a potential indicator for evaluating SQ. In this study, we selected P. tabuliformis plantation forests with different restoration years in a loess hilly area as our research focus. Additionally, we selected grassland before afforestation and abandoned grassland as controls, which were accompanied by a 22-year restoration of P. tabuliformis forests. This study aimed to comprehensively evaluate SQ in the study area by incorporating soil fauna and herbaceous plant indices. To achieve this, we used a structural equation model (SEM) to identify the key driving factors affecting the SQ of P. tabuliformis forests. Our findings offer a more comprehensive understanding of the SQ conditions after the implementation of forestry ecological projects in the study area. Moreover, our research provides a more systematic, accurate, and scientific evaluation system and model for the quantitative assessment of SQ and evaluation of the benefits of forestry ecological projects in the study area in the future.

2. Materials and Methods

2.1. Study Area

The study area is situated in Wuqi County, Shaanxi Province (107°38′57″–108°32′49″ E, 36°33′33″–37°24′27″ N), which is part of the Loess Plateau hilly gully region and features windy springs, hot and rainy summers, cool and wet falls, and cold and dry winters. With an altitude ranging from 1233 to 1809 m, the region experiences a semi-arid temperate continental monsoon climate characterized as dry. The average annual rainfall is 483.4 mm, the average annual temperature is 7.8 °C, and the average annual frost-free period is 146 days. Most of the surface is covered by loess and most of the soil is loessal soil. The main tree species were P. tabuliformis, Populus × hopeiensis, Robinia pseudoacacia, Populus simonii, and Prunus sibirica. The main shrub was Hippophae rhamnoides and the main herbs were Artemisia giraldi, Lespedeza davurica, and Agropyron cristatum. (Figure 1).

2.2. Layout of Sample Plots

Since 1998, Wuqi County has been at the forefront of China to fully implement the project of closing the mountains to grazing and returning farmland to forest (grass). The county has established a vast area of P. tabuliformis plantation forests as part of its ecological forestry projects. We selected P. tabuliformis plantation forests that had been restored for 1, 6, 11, 18, and 22 years as research sample plots. Fallow grassland before afforestation (PRG) was selected, and the abandoned grassland (AG) with 22 years as the control. Fixed sample squares of 20 m × 20 m were established in each sample plot for the sampling and analysis of soil, vegetation, soil fauna, and other indicators. Subsequently, samples were collected. The vegetation, soil fauna, and other indicators were sampled and measured. Detailed information on the sample plots in the study area is provided in Table 1.

2.3. Collection and Determination of Soil Samples

In each standard sample plot, three 15 cm deep soil profiles were established, and soil samples were assembled using the ring knife method vertically from the surface into three layers: i.e., 0–5 cm, 5–10 cm, and 10–15 cm. Three replicates were obtained from each layer of the same profile to decide the physical properties of the soil. Concurrently, soil samples collected from each layer of the profile were placed in self-sealing bags and transported back to the laboratory for natural air drying. Subsequently, the samples were sieved to determine their physical and chemical properties. Soil indicators were determined via the soil physical properties determination method [45] as well as soil agrochemical analysis [46]. Details are shown in Table 2 and Table 3.

2.4. Collection and Identification of Soil Fauna

Within a standard sample plot, three sample squares were randomly arranged in a triangular shape and three sampling points were randomly selected within each sample square for sampling. The sampling area for macrosoil fauna at each sampling point was 25 cm × 25 cm, and the depth was 15 cm [47]. Macrofauna at depths of 0 to 5 cm, 5 to 10 cm, and 10 to 15 cm were captured via hand sorting. Macrosoil fauna from each sample were preserved in 75% alcohol and transported back to the laboratory for identification. Small and medium soil faunal communities were collected from the soil sample using the Tullgren and Baermann methods. The species were separated under 60 W incandescent lamps for 48 h, and the separated soil fauna was stored in a 75% ethanol solution [48]. Finally, data from the three soil faunal samples collected from the same sample area were combined into one sample. The collected soil fauna was identified using an SMZ 1270 stereo microscope, and the number of species and individuals in each sample square was counted. The division of soil fauna was mainly based on references such as “Subtropical Soil Animals of China” [49] and “Pictorial Keys to Soil Animal of China” [50]. Most adults were identified as families and larvae as orders in accordance with the classification system and the soil animal diversity index was calculated. Details are shown in Table 3.

2.5. Investigation of Understory Herbaceous Vegetation

In each standard sample plot, five herbaceous sample plots were set up in an “S” shape to investigate the understory herbaceous plants. The aboveground biomass (AGB) and belowground biomass (BGB) of understory herbaceous plants were measured using the complete harvest method. Canopy density (CD) was measured using the cover method. Additionally, the herbaceous diversity index (HD), herbaceous richness index (HR), and herbaceous evenness index (HE) were calculated based on herbaceous cover, herbaceous height, and number of herbaceous species. Details are shown in Table 3.

2.6. Soil Quality Evaluation Methods

The SQ in the study area was evaluated using the soil quality index (SQI) method [51]. SQI is a metric that can be used to quantify the overall health and fertility of soil, and is widely used by many scholars to evaluate SQ [52,53,54]. The evaluation methods were as follows:
(1)
Selection of soil quality evaluation indicators
Based on previous research results [17], 27 indicators in four categories of soil physics, chemistry, soil fauna community diversity, and herbaceous plant community diversity were selected as the SQ evaluation indices for P. tabuliformis forests with different restoration years.
(2)
Evaluation of index weights
To avoid errors caused by subjectivity, this study used principal component analysis (PCA) to calculate the weight value of each evaluation index ( W i ). The common factor variance obtained via the PCA reflects the degree of contribution of an indicator to the overall variance. The larger the common factor variance, the greater the contribution to the overall variance. The common factor variance of each evaluation indicator was divided by the sum of the common factor variances of all the indicators to determine the weight of each evaluation indicator [55].
(3)
Calculation of the membership degree of each evaluation indicator
In fuzzy comprehensive evaluation, the degree of affiliation is determined by the affiliation function associated with the evaluation index. The affiliation function usually includes ascending and descending affiliation functions.
The formula for calculating the ascending order affiliation function is as follows:
F ( x ) = 1.0 0.9 ( x − a ) / ( b − a ) + 0.1 0.1 ( x ≥ b ) a < x < b x ≤ a
The descending order affiliation function was calculated as follows:
F x = 1.0 0.9 b − x / b − a + 0.1 0.1 x ≤ a a < x < b x ≥ b
where F ( x ) is the degree of affiliation of each evaluation indicator, x is the measured mean value of each indicator, a is the lowest value of the indicator and b is the highest value of the indicator [56].
(4)
Calculation of SQI
After obtaining the weights and affiliations of the indicators, a weighted evaluation method was used to calculate the SQI. The higher the SQI value, the better the SQ at each sample site. The formula used was as follows:
S Q I = ∑ i = 1 n W i × F x i
where SQI is the soil quality index, n is the number of evaluation indicators, W i is the value of the weight of each indicator, and F ( x i ) is the value of the affiliation of each indicator. Where BD uses a descending order affiliation function and pH is an ascending order function when the soil is acidic. In contrast, when the soil is alkaline, pH uses a descending order function, and the rest of the indicators use an ascending order affiliation function [57,58].

2.7. Statistical Analysis

In this study, the data were preprocessed using Excel 2023 software. Indicator affiliation, weight, and SQ were calculated for the soil indicators at a later stage. Principal component analysis was conducted using SPSS 27.0, and plotted using Origin2022. Based on the relationship between soil fauna and environmental factors such as vegetation characteristics and physical and chemical properties of soil, a SEM was constructed to examine the impact of restoration ages on soil fauna, SEM reveals the influence mechanism of restoration years on soil physicochemical properties, vegetation characteristics and soil fauna structure and function. SEM was performed using R4.3.2. R packages mainly use PLS-PM packages [59,60,61,62,63].

3. Results

3.1. Construction of Soil Quality Evaluation System Based on Fuzzy Comprehensive Evaluation

3.1.1. Membership Characteristics of Soil Indicators

As shown in Figure 2, there was a significant difference (p < 0.05) in the affiliation values of the study area across the same locations. In the PRG, the CWHC, CP, EC, SFD, and SFE indicators showed higher affiliation, whereas most other indicators were limiting factors. The indicators EC, SFD, SFE, and HE showed high affinity in the restored 1a P. tabuliformis forest, whereas most other indicators were identified as limiting factors. The restoration of 6a P. tabuliformis forests showed high affiliations for CWHC, CP, TCP, pH, OM, SFR, HD, CD, and HR, whereas EC, AP, BGB, and C/N were identified as limiting factors. In P. tabuliformis forests restored for 11a, pH, OM, SFD_2, CD, and C/N metrics showed high affiliation, whereas TN, TP, SFE, HD, HR, and N/P metrics were identified as limiting factors. In P. tabuliformis forests with 18a of restoration, most of the indicators were limiting factors, except for the physical indicators of SWC, which all had affiliations >0.5, and the chemical indicator OM, which also had a high affiliation with the soil faunal indicators SFD_2 and CD. Restoration of 22a P. tabuliformis forests showed a high affiliation with physical indicators, except CP. Chemical indicators, on the other hand, exhibited lower overall affiliation, whereas biological indicators showed higher overall affiliation, except for HR and BGB. In the AG, all indicators had affiliations >0.5, except for the CP, EC, AN, CD, HR, C/N, C/P, and N/P indicators.

3.1.2. Weighted Values of Soil Indicators

PCA was performed on the 27 soil indicators and the results are presented in Table 4. The eigenvalues of the first six principal components were >1 and the cumulative contribution rate reached 90.028%, which fully explained the information of the original variables. The variance of the common factors among the 27 indicators ranged from 0.674 to 0.985, all of which were >0.5. Except for the SWC, AP, and HE indicators, the variance in the common factors for the indicators exceeded 0.8. This suggests that the extracted common factors effectively expressed the information contained in the SQ evaluation indicators.

3.1.3. Analysis of the SQI

As illustrated in Figure 3, the SQ of P. tabuliformis forests at different restoration years indicated that the restoration of 6a P. tabuliformis forests (0.592) was superior to the restoration of 11a P. tabuliformis forests (0.530), 18a P. tabuliformis forests (0.500), and 22a P. tabuliformis forests (0.492), as well as AG (0.483), PRG (0.452), and restoration of 1a P. tabuliformis forests (0.323). The SQ of P. tabuliformis forests first increased and then decreased with the increasing number of restoration years. However, all the SQI increased by >52% compared with 1a, reaching their peak values after 6a. The SQI of P. tabuliformis forest after 6a increased by 83% compared to that of the P. tabuliformis forest after 1a and by 31% compared to the grassland before afforestation (PRG). The SQI of restored 6a P. tabuliformis forests was significantly higher than that of PRG and AG. In addition, the SQI of the restored 11a P. tabuliformis forests was higher than that of AG and PGR. Both AG and PGR had significantly higher SQ than restored 1a P. tabuliformis-forested land. The SQ of the P. tabuliformis plantation forest became more similar to that of the abandoned grassland as the restoration years increased, with a difference of only 1.8% after 22a.

3.2. Analysis of Soil Quality Drivers in P. tabuliformis in Different Restoration Years

The relationship between SQ and soil physical and chemical biotic factors in P. tabuliformis forests of different restoration years was analyzed using SEM, and the results are shown in Figure 4. The overall SQ of the P. tabuliformis forest was influenced by restoration years, physical indicators, chemical indicators, soil fauna, and herbaceous plant indicators. Among them, the positive influence of physical indicators and herbaceous plant indicators was most significant, with path coefficients of 0.93 and 0.74, respectively. The path coefficients of the restoration years, chemical indicators, and soil fauna indicators were −0.45, −0.15, and −0.51, respectively. This suggests that soil fauna indicators and restoration years had a negative effect on the SQ of the P. tabuliformis forest. The interactions between different restoration years, physical indicators, chemical indicators, soil fauna indicators, and herbaceous plant indicators collectively affected the SQ of the P. tabuliformis forest.
Further analysis revealed that physical indicators had a positive effect on SQI in GBR and AG. In addition, SFR had the most significant negative effect on SQI in the GBR, with a path coefficient of −0.62. TP had a greater negative effect on SQI in AG, with a path coefficient of −0.41. Among the different restoration years of P. tabuliformis forests, physical indicators had significant positive effects on the restoration of 1a and 11a P. tabuliformis forests with path coefficients of 0.99 and 0.79, respectively. However, they had direct negative effects on the restoration of 6a, 18a, and 22a P. tabuliformis forests. TN, AN, and EC had positive effects on the SQI, whereas TP negatively affected the SQI of the restored 18a P. tabuliformis forests, consequently affecting the SQI of the restored 22a P. tabuliformis forest. The SQI had a significant positive effect, with a path coefficient of 0.84. The herbaceous plant indicators HD, HE, and HR had a positive effect on restoring the SQ of the 6a P. tabuliformis forest, 18a P. tabuliformis forest, and 22a P. tabuliformis forest. However, HD had a negative effect on the restoration of the SQ of the 11a P. tabuliformis forest. The soil fauna indicators SFD and SFD_2 had some effects on the SQ of restoring 1a, 6a, 11a, and 22a P. tabuliformis forests. However, the intensity of these effects was small. They had a significant negative effect on the restoration of 18a P. tabuliformis forest SQ, with a path coefficient of −0.95.

4. Discussion

4.1. Analysis of Soil Quality in P. tabuliformis in Different Restoration Years

In this study, we analyzed the SQ of P. tabuliformis forests in different years of restoration using PCA combined with weighted and fuzzy comprehensive evaluation methods. The results indicated that the SQ in the study area followed a pattern of “improving first, then decreasing” with the increase in the restoration years. The SQ of the P. tabuliformis forest in the first year of restoration was significantly lower than that of grassland before afforestation. It reached its peak value in the sixth year of restoration. The SQ of the P. tabuliformis forest in the first year of restoration improved by 83%, which was consistent with the findings of Qianwen Ren [64]. This improvement can be attributed to significant anthropogenic disturbance caused by the afforestation process to the understory herbaceous and soil environment in the study area during the early stages of P. tabuliformis forest establishment. With increasing restoration years, the SQ showed significant improvement after stabilization of the plant community. SQ in P. tabuliformis forests tended to decrease from 6 to 18 years of restoration, with a total decrease of 16% compared with the highest value at 6 years of restoration. However, it remained higher than that of the grassland left fallow for 22 years. Yang yang’s [65] evaluation of the impact of forest age on the soil fertility quality of P. tabuliformis plantation forests in the loess area of West Jin found that the SQ of P. tabuliformis forests with 20 to 45 years of restoration showed a consistent increase. This finding contrasts with the results of our study, possibly because of the high density of P. tabuliformis plantation forests in the study area. Severe drought and low rainfall in the region have led to a significant soil moisture deficit, with moisture being the primary limiting factor for vegetation restoration in loess hilly areas [66,67]. Therefore, as the average annual rainfall in the loess area of Shanxi has been above 570 mm for many years [65], the rainfall in the study area was approximately 450 mm [55]. It was also affected by rainfall and afforestation density, leading to a gradual aggravation of the soil moisture deficit in the forest land with an increase in forest land restoration time. The contradiction between the supply and demand of forest water has resulted in a decline in the SQ. It is also possible that, compared with previous research results, this study included herbaceous plants and soil fauna indicators, making the analysis more comprehensive and systematic, resulting in differences from other researchers. In this study, the maximum number of years of P. tabuliformis forest restoration was 22. Our group will continue to conduct research on the evolution of SQ after the restoration of P. tabuliformis plantation forests over an extended period. In this study, the SQ of the long-term abandoned grassland was significantly higher than that of the grassland before afforestation and lower than that of P. tabuliformis forests restored for 6 and 11 years. This suggests that the SQ of the grassland improved with prolonged abandonment, albeit at a slow pace with a modest increase of 7%. In contrast, afforestation led to a rapid improvement in SQ. Therefore, to implement the project of returning farmland to the forest (grass) and establishing a healthy ecological environment in the loess hilly area, artificial vegetation restoration of abandoned grasslands can rapidly enhance SQ. Additionally, the effective artificial management of P. tabuliformis forests in loess regions is essential. It is crucial to establish an appropriate afforestation density during the initial stages to ensure the healthy and sustainable development of forest stands and the continuous improvement of SQ. For the early afforestation of P. tabuliformis forests, the SQ of forests with excessively high densities should be improved. For high-density P. tabuliformis forests, artificial interventions should be implemented 6 years after planting. Measures such as thinning or construction should be implemented to enhance the stand conditions. This will help maintain and improve the SQ of artificial P. tabuliformis forests, thereby promoting the healthy development of P. tabuliformis plantation forests.

4.2. Analysis of Drivers of Soil Quality Change

The drivers that affect SQ vary significantly under different environmental conditions [68]. Studies have shown variations in the factors affecting SQ in P. tabuliformis forests during different years of restoration. In this study, SEM was used to analyze the correlation between the SQI and path coefficients. The results indicated that the P. tabuliformis forest was affected as a whole by the number of restoration years, with a significant effect on the physical, chemical, soil fauna, and herbaceous plant indices. The physical and herbaceous plant indices interacted with the overall SQI of the P. tabuliformis forest as the most critical factor. Liu Junting [69] found that P. tabuliformis forests had the most significant effect on enhancing soil physical properties as the number of years of vegetation restoration increased. This finding is consistent with the results of the present study. This analysis suggests that as the number of years of vegetation restoration increases, the root system of the vegetation grows deeper into the soil layer, leading to a significant interpretation effect. This process reduces the soil bulk density, increases porosity, and subsequently affects SQ [70]. The herbaceous plant indicators HD, HE, and HR had significant effects on SQ in restored 6a, 18a, and 22a P. tabuliformis forests. This is likely because the growth of herbaceous plants can change the physical structure of the soil, thereby affecting the chemical properties and composition of the biotic community. These changes, in turn, affect overall SQ [71]. However, no herbaceous plant indicator affected the SQ of restored 1-year P. tabuliformis. This was because the forestland, after afforestation, was nearly devoid of herbaceous plants in the understory, with only sporadic growth of Lysimachia and Artemisia. Soil fauna indicators had a significant negative impact on P. tabuliformis plantation forests overall. This can be attributed to the effect of small soil fauna such as protozoa and nematodes, which can change the composition of the soil microbial community through predation and other activities. Consequently, this affects the decomposition processes and biogeochemical cycles [72,73,74]. This may also be due to common large soil fauna, such as earthworms and termites, which can affect SQ by destroying the soil structure through non-feeding activities, such as weaving and burrowing [75,76]. The soil fauna indicator SFD did not affect the SQ of the restored 1-year-old and 6-year-old P. tabuliformis forests. This lack of impact may be attributed to the low soil fauna community resulting from the tilling of the 1-year-old restored P. tabuliformis forest. In contrast, restoration of the 6-year-old P. tabuliformis forest with low HD and phenolic acid substances led to the development of the largest herbaceous community in the understory. The decomposition of herbaceous apomictic material can provide nutrients and create habitats for the soil fauna community, thereby enhancing the stability and diversity of the soil fauna community through a form of dynamic stabilization [77,78,79]. In contrast, SFD_2 played a negative role in restoring SQ in 18-year and restoring 22-year P. tabuliformis forests. This negative effect is likely due to soil fauna altering the community structure of other biological components in the soil food web through predation or non-predation, which subsequently affects plant growth and soil health [80]. Soil fertility is one of the main indicators of SQ and is expressed via soil nutrients, i.e., soil chemical indicators [81]. Many studies have shown that the trend of soil physicochemical properties with an increase in restoration years is related to vegetation type [82,83]. In this study, various chemical indicators affected the SQ of P. tabuliformis forests with different restoration years. Therefore, it can be inferred that understory herbs also change with different restoration years, disrupting the balance between vegetation growth requirements and soil nutrient availability. This process involves the input of root secretions and plant residues into the soil, which fundamentally affects SQ [84].

5. Conclusions

The results of this study showed that soil fauna and herbaceous plant indices are important components of SQ evaluation. Based on 27 soil physicochemical and biochemical indices, we analyzed and calculated the SQI of P. tabuliformis plantation forests and control grasslands. This study found that afforestation significantly improved the SQ in loess areas. The SQI of the study area was ranked as follows: restoring 6a P. tabuliformis forest > restoring 11a P. tabuliformis forest > restoring 18a P. tabuliformis forest > restoring 22a P. tabuliformis forest > abandoned grassland > grassland before afforestation > restoring 1a P. tabuliformis forest. Compared with the other sample plots the 6a P. tabuliformis forest restoration had the highest SQI (p < 0.05), whereas the 1a P. tabuliformis forest restoration had the lowest SQI. SEM indicated that the overall SQ of P. tabuliformis forests was jointly influenced by the number of years of restoration, physical indicators, chemical indicators, soil fauna, and herbaceous plant indicators. Among them, physical and herbaceous plant indicators had the most significant positive effects, whereas restoration year and soil fauna indicators had the most negative effects on the overall SQI of the P. tabuliformis forests. The drivers of the SQI in P. tabuliformis forests varied across the restoration years. The importance of the weights of soil fauna and herbaceous plant indicators in determining the SQ of P. tabuliformis plantation forests tended to increase with increasing restoration years, showing greater significance over time. Therefore, compared with previous studies, this study breaks through traditional SQ evaluations using only soil-related indicators. It introduces soil fauna and herbaceous plant indicators for a comprehensive evaluation of SQ in the study area. This enriches the construction of a SQ evaluation index system and develops SQ evaluation methods, and the results of the study more comprehensively reflect the status of SQ in the study area after the construction of the forestry ecological project. The results of this study more comprehensively reflect the SQ conditions of the study area after the construction of a forestry ecological project. It provides a more accurate, systematic, and scientific evaluation system and model for the future quantitative assessment of SQ in the study area, as well as the evaluation of the benefits of forestry ecological projects.

Author Contributions

Conceptualization, J.L., N.A. and F.Q.; methodology, J.L., N.A. and C.L.; software, J.L., Q.R. and M.Z.; validation, J.L., N.A. and G.L.; formal analysis, J.L.; investigation, J.L. and N.A.; data curation, J.L.; writing—original draft preparation, J.L.; writing—review and editing, J.L. and N.A.; visualization, J.L. and N.A.; supervision, F.Q., N.A., G.L., C.L., Q.R., M.Z. and M.L.; project administration, N.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Plan of China (2023YFF130510401); the National Natural Science Foundation of China (32060297); and the Research Project of Yan’an University (2021ZCQ009, 2023JBZR-20).

Data Availability Statement

Data available on request from the corresponding authors.

Acknowledgments

The authors greatly appreciate the assistance of Caixia Yuan, Zeyang Wu, Jie Wang, Jiahao Shi, Zhengzheng Nan, Kaixuan Zang, and Ting Xiang.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Sample plot map.
Figure 1. Sample plot map.
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Figure 2. Radar chart of soil indicator membership degree for P. tabulaeformis forests with different planting years.
Figure 2. Radar chart of soil indicator membership degree for P. tabulaeformis forests with different planting years.
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Figure 3. SQI for different planting years, different letters indicate significant differences (p ≤ 0.05). Different horizontal coordinates represent different research areas. PT: P. tabuliformis.
Figure 3. SQI for different planting years, different letters indicate significant differences (p ≤ 0.05). Different horizontal coordinates represent different research areas. PT: P. tabuliformis.
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Figure 4. Effects of different main control factors on the SQI. (A) represents abandoned grassland, (G) represents pre reforestation grassland, (B–F) represents P. tabulaeformis planted for 1, 6, 11, 18, and 22 years, and (H) represents the overall P. tabulaeformis. Red arrows indicate negative effects and green arrows represent positive effects. Numbers adjacent to arrows are path coefficients (p-values) indicating the effect size of the relationship. *** represents a significant correlation at the 0.001 level, ** represents a significant correlation at the 0.01 level, and * represents a significant correlation at the 0.05 level. Note: physical indicators: PI; chemical indicators: CI; soil biological indicators: SBI; herbal biological indicators: HBI; and restoration years: RY.
Figure 4. Effects of different main control factors on the SQI. (A) represents abandoned grassland, (G) represents pre reforestation grassland, (B–F) represents P. tabulaeformis planted for 1, 6, 11, 18, and 22 years, and (H) represents the overall P. tabulaeformis. Red arrows indicate negative effects and green arrows represent positive effects. Numbers adjacent to arrows are path coefficients (p-values) indicating the effect size of the relationship. *** represents a significant correlation at the 0.001 level, ** represents a significant correlation at the 0.01 level, and * represents a significant correlation at the 0.05 level. Note: physical indicators: PI; chemical indicators: CI; soil biological indicators: SBI; herbal biological indicators: HBI; and restoration years: RY.
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Table 1. Basic conditions of study plots.
Table 1. Basic conditions of study plots.
Sample Plot AbbreviationVegetationPlanting YearsSlopeAspectAltitude (m)Latitude and Longitude
PRGAbandoned grassland028°Sunny slope140636°54′31″ N,
108°10′35″ E
1aPinus tabuliformis117°Sunny slope138636°54′32″ N,
108°10′34″ E
6aPinus tabuliformis642°Shady slope1370.936°49′54″ N,
108°9′30″ E
11aPinus tabuliformis1120°Half-shady slope1510.936°53′33″ N,
108◦13′10″ E
18aPinus tabuliformis1830°Shady slope1383.736°54′38″ N,
108°12′92″ E
22aPinus tabuliformis2220°Half-shady slope131436°50′52″ N,
108°9′37″ E
AGAbandoned grassland2230°Shady slope1400.336°49′54″ N,
108°12′28″ E
Table 2. Soil indicator measurement.
Table 2. Soil indicator measurement.
Soil IndicatorsMeasurement Methods
Saturated water content (SWC)Drying method
Soil bulk density (BD), maximum water holding capacity (MWHC), capillary water holding capacity (CWHC), non-capillary porosity (NCP), capillary porosity (CP), total porosity (TCP)Ring-knife-soaking method
pHpHS-320 high precision intelligent acidity meter
Electrical conductivity (EC)DDS-608 multifunctional conductivity meter
Alkaline nitrogen (AN)Alkali dissolved diffusion method
Available phosphorus (AP)Sodium bicarbonate method
Total phosphorus (TP)Sodium hydroxide-molybdenum-antimony colorimetry method
Total nitrogen (TN)Sulfuric acid decoction-sodium salicylate method
Soil organic matter (OM)Potassium dichromate volumetric method
Soil fauna Shannon diversity index (SFD), soil fauna Simpson diversity index (SFD_2), soil fauna richness index (SFR), soil fauna evenness index (SFE)Calculate
Aboveground biomass (AGB), belowground biomass (BGB)Complete harvest method
Canopy density (CD)Cover method
Herbaceous diversity index (HD), herbaceous richness index (HR), herbaceous evenness index (HE)Calculate
Table 3. Soil quality indices of Pinus tabulaeformis in different restoration years.
Table 3. Soil quality indices of Pinus tabulaeformis in different restoration years.
Sample Plot AbbreviationPRG1a6a11a18a22aAG
SWC (%)6.67 ± 0.667.24 ± 1.469.97 ± 1.6211.31 ± 2.596.46 ± 1.2414.82 ± 0.2216.95 ± 3.52
BD (g/cm3)1.19 ± 0.041.45 ± 0.041.20 ± 0.031.19 ± 0.051.03 ± 0.041.16 ± 0.011.15 ± 0.04
MWHC (%)43.71 ± 2.4127.44 ± 2.8545.25 ± 2.4445.41 ± 3.3757.17 ± 2.4046.39 ± 0.3644.04 ± 2.95
CWHC (%)42.20 ± 2.2726.87 ± 2.6939.55 ± 2.2236.61 ± 3.4042.46 ± 1.8034.23 ± 0.3934.93 ± 2.96
NCP (%)1.80 ± 0.300.83 ± 0.236.81 ± 0.1010.45 ± 2.8414.97 ± 1.0414.08 ± 0.1910.28 ± 0.55
CP (%)50.27 ± 0.9739.00 ± 2.8247.49 ± 1.7743.20 ± 2.3843.52 ± 0.5939.59 ± 0.3039.82 ± 2.80
TCP (%)52.07 ± 1.1039.83 ± 3.0454.30 ± 1.8853.65 ± 1.9058.49 ± 1.6253.67 ± 0.3350.11 ± 2.31
pH8.42 ± 0.038.24 ± 0.037.88 ± 0.077.79 ± 0.038.08 ± 0.128.21 ± 0.028.10 ± 0.07
EC (µs/cm)164.55 ± 6.25141.65 ± 10.7595.53 ± 10.11135.27 ± 27.5094.97 ± 5.4096.68 ± 3.5696.75 ± 11.58
AN (mg/kg)7.40 ± 4.093.21 ± 0.2012.65 ± 2.7713.86 ± 5.4311.87 ± 2.833.51 ± 1.303.59 ± 1.26
TN (g/kg)0.11 ± 0.030.10 ± 0.020.40 ± 0.220.04 ± 0.010.08 ± 0.030.33 ± 0.090.41 ± 0.19
AP (mg/kg)2.00 ± 1.002.07 ± 0.932.98 ± 0.915.87 ± 0.264.32 ± 1.318.48 ± 5.647.78 ± 1.30
TP (g/kg)0.06 ± 00.06 ± 00.17 ± 0.060.05 ± 0.030.06 ± 0.020.10 ± 0.040.25 ± 0.09
OM (g/kg)13.10 ± 4.929.37 ± 1.6523.37 ± 3.0621.89 ± 1.5620.50 ± 1.3217.52 ± 0.6216.73 ± 0.32
SFR4.35 ± 0.104.32 ± 0.154.75 ± 0.323.92 ± 0.164.02 ± 0.123.78 ± 0.263.88 ± 0.04
SFD2.56 ± 0.142.50 ± 0.152.29 ± 0.061.67 ± 0.141.72 ± 0.241.75 ± 0.122.02 ± 0.19
SFE0.89 ± 0.050.86 ± 0.040.73 ± 0.030.56 ± 0.040.56 ± 0.070.59 ± 0.040.67 ± 0.05
SFD-20.10 ± 0.030.11 ± 0.020.16 ± 00.31 ± 0.030.29 ± 0.070.27 ± 0.020.21 ± 0.04
AGB (g)151.84 ± 23.5234.91 ± 7.67173.30 ± 55.4150.07 ± 30.83120.26 ± 39.34177.57 ± 14.81266.63 ± 30.39
BGB (g)282.36 ± 54.838.95 ± 1.5286.1 ± 21.40238.71 ± 56.81203.40 ± 37.08192.66 ± 5.91799.44 ± 112.88
HD1.28 ± 0.161.45 ± 02.10 ± 0.111.14 ± 0.041.05 ± 0.201.77 ± 0.241.34 ± 0.15
CD (%)0 ± 040.00 ± 095.67 ± 1.5392.67 ± 5.8670.67 ± 5.0361.00 ± 3.610 ± 0
HR1.17 ± 0.081.36 ± 02.77 ± 0.570.82 ± 0.301.00 ± 0.061.47 ± 0.251.12 ± 0.23
HE0.80 ± 0.100.90 ± 00.81 ± 0.080.70 ± 0.120.61 ± 0.100.89 ± 0.110.72 ± 0.02
C/N121.88 ± 18.3598.39 ± 1.8075.15 ± 40.77513.60 ± 96.43300.77 ± 127.5955.31 ± 14.8152.53 ± 35.71
C/P218.33 ± 82.00156.08 ± 27.42146.75 ± 47.60578.74 ± 251.33374.60 ± 120.00204.92 ± 101.5576.30 ± 34.05
N/P1.75 ± 0.421.58 ± 0.252.57 ± 1.701.22 ± 0.761.54 ± 1.184.34 ± 3.541.60 ± 0.36
Table 4. Principal component analysis and weight of soil indicator.
Table 4. Principal component analysis and weight of soil indicator.
IndicatorPC1PC2PC3PC4PC5PC6Common Factor VarianceWeight
SWC0.3330.784−0.19−0.108−0.060.1490.7990.033
BD−0.84−0.046−0.164−0.436−0.2110.0430.9720.040
MWHC0.853−0.0970.2360.3540.235−0.1140.9850.041
CWHC0.514−0.3110.440.6320.142−0.0580.9770.040
NCP0.8820.221−0.078−0.1420.218−0.0920.9090.037
CP0.002−0.4510.5640.578−0.006−0.0140.8560.035
TCP0.826−0.1370.3550.3070.199−0.0970.9690.040
pH−0.5710.088−0.2890.450.538−0.2490.9710.040
EC−0.551−0.541−0.2180.227−0.0190.3560.8220.034
AN0.488−0.5980.441−0.007−0.2890.2130.920.038
TN0.0310.7770.487−0.0190.0470.210.8880.037
AP0.5310.527−0.31−0.0910.0350.2160.7130.029
TP0.0590.7840.20.137−0.48−0.1260.9230.038
OM0.762−0.0580.412−0.158−0.256−0.0720.8490.035
SFR−0.505−0.2670.7350.03−0.097−0.0170.8770.036
SFD−0.889−0.070.2630.254−0.1250.1280.9620.040
SFE−0.92−0.0710.1250.272−0.0890.0730.9530.039
SFD−20.884−0.007−0.229−0.2770.117−0.1140.9380.039
AGB0.4380.6490.1250.404−0.2270.1780.8740.036
BGB0.2840.6−0.3010.536−0.2570.2050.9260.038
HD−0.2390.420.726−0.3730.133−0.0710.9220.038
CD0.469−0.3590.404−0.672−0.071−0.0340.970.040
HR−0.1960.2180.878−0.21−0.09−0.0320.910.037
HE−0.6010.2270.131−0.3650.316−0.1040.6740.028
C/N0.521−0.637−0.312−0.173−0.234−0.0060.860.035
C/P0.497−0.671−0.116−0.1470.0670.4620.950.039
N/P0.0120.2780.387−0.1850.6490.5070.940.039
Eigenvalues9.1435.3514.1523.0021.6581.002
Variance contribution rate/%33.86419.81715.37811.1186.1393.712
Cumulative contribution rate/%33.86453.68169.05980.17786.31690.028
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Li, J.; Qiang, F.; Ai, N.; Liu, C.; Liu, G.; Zou, M.; Ren, Q.; Liu, M. Soil Quality Evaluation and Analysis of Driving Factors of Pinus tabuliformis in Loess Hilly Areas. Forests 2024, 15, 1603. https://doi.org/10.3390/f15091603

AMA Style

Li J, Qiang F, Ai N, Liu C, Liu G, Zou M, Ren Q, Liu M. Soil Quality Evaluation and Analysis of Driving Factors of Pinus tabuliformis in Loess Hilly Areas. Forests. 2024; 15(9):1603. https://doi.org/10.3390/f15091603

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Li, Junzhe, Fangfang Qiang, Ning Ai, Changhai Liu, Guangquan Liu, Menghuan Zou, Qianwen Ren, and Minglu Liu. 2024. "Soil Quality Evaluation and Analysis of Driving Factors of Pinus tabuliformis in Loess Hilly Areas" Forests 15, no. 9: 1603. https://doi.org/10.3390/f15091603

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