Next Article in Journal
The Influence of Logging Equipment on the Content, Stock and Stratification Coefficient of Elements of the Mineral Nutrition of Plants in the Soils of the Taiga Zone of Karelia
Next Article in Special Issue
Afforestation Alters the Molecular Composition of Soil Organic Matter in the Central Loess Plateau of China
Previous Article in Journal
A Three-Day Forest-Bathing Retreat Enhances Positive Affect, Vitality, Optimism, and Gratitude: An Option for Green-Care Tourism in Italy?
Previous Article in Special Issue
Soil Quality Evaluation and Dominant Factor Analysis of Economic Forest in Loess Area of Northern Shaanxi
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 7
10.3390/f14071425
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Soil Quality Evaluation and Driving Factor Analysis of Hippophae rhamnoides Plantations in Coal Mine Reclamation Areas Based on Different Restoration Durations

by
Qianwen Ren
1,
Guangquan Liu
2,
Changhai Liu
1,
Fangfang Qiang
1,* and
Ning Ai
1,2,*
1
College of Life Science, 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 2023, 14(7), 1425; https://doi.org/10.3390/f14071425
Submission received: 23 May 2023 / Revised: 5 July 2023 / Accepted: 10 July 2023 / Published: 12 July 2023
(This article belongs to the Special Issue Effects of Natural Disturbances and Human Activities on Forest Soils)
Browse Figures
Versions Notes

Abstract

:
The driving factors affecting soil quality were identified to evaluate the effect of vegetation on soil quality in coal mine reclamation areas with various restoration durations. This study used Hippophae rhamnoides subsp.sinensis Rousi with different reclamation durations (3, 4, 5, 6, and 7 years) in the abandoned land area of the Juxinlong coal mine in Ordos as the research subject. Artificial and abandoned grasslands were selected as the study’s controls. A soil quality evaluation model was constructed to assess the soil quality in the reclamation area. A structural equation model was used to thoroughly analyze the driving factors affecting soil quality in the study area. The findings show that: (1) Reclamation duration significantly affected the physicochemical characteristics of the soil. As the reclamation duration increased, soil nutrients such as organic carbon accumulated while the bulk density index (BD) decreased. (2) The soil quality index of Hippophae rhamnoides forest land in China was the highest after 6 years of reclamation. The Hippophae rhamnoides forest land with the lowest soil quality index after 4 years of reclamation differed significantly from that after 6 years (p < 0.05). The soil quality index (SQI) of 6a (years) significantly increased by 67.44% compared to 4a. (3) By constructing a structural equation model, it was found that physical indicators (saturated water content and silt) and reclamation durations were the main drivers of soil quality. SQI had a strong interaction with organic matter (OM) and different restoration durations. The findings of this study will serve as important guidelines for future quantitative evaluation of soil quality following land reclamation and management during the ecological restoration process.

1. Introduction

Open-pit coal mining is the primary source of mineral resources in China, which supports economic development and provides a significant number of basic materials. Resource extraction significantly negatively impacts the ecological environment and land resources and accelerates economic growth [1]. Ecological restoration is more challenging because most open-pit mines are located in ecologically fragile areas in arid and semi-arid regions [2]. Therefore, the most important challenge in the current ecological construction of mining areas is the restoration of mining areas [3]. To achieve sustainable development, land reclamation in coal mining areas is an important step in ecological restoration [4]. Vegetation restoration, water conservation, and soil erosion reduction can prevent the loss of soil nutrients in coal mining subsidence areas [5]. Artificial vegetation reconstruction is one of the primary methods for restoring vegetation and improving soil quality in reclaimed open-pit mine areas [6]. In addition to improving species diversity and promoting soil development and restoration in damaged areas, vegetation restoration and reconstruction can significantly speed up the ecosystem restoration process in mining areas. Plant roots effectively promote the nutrient cycle between plants and soil during the restoration process; however, seasonal litter also has a significant impact on soil fertility changes [7,8,9], in which in turn affect ecosystem function and soil quality [10].
Ecological restoration depends on soil restoration [11]. Soil quality and vegetation establishment are interdependent components and both contribute to the sustainable function of an ecosystem [12]. Plant growth is carried out by soil, which also serves as the building block for its development. Changes in soil quality directly impact how plants grow and develop, the amount of water, heat, and gas required, and how vegetation grows in succession. Simultaneously, changes in vegetation facilitate the improvement and stabilization of soil structure [13]. Recently, research has focused on using vegetation to improve and enhance the soil quality of degraded land. Several researchers have mainly focused on the effects of different vegetation types on heavy soil metals [14,15], physical soil conditions [16], soil nutrients [17,18], etc., as well as the effects of vegetation reclamation on soil remediation in mining areas and another degraded land areas. According to research, using native shrubs as restoration plants can improve the environmental conditions and initiate vegetation succession in mining areas [19]. Hippophae rhamnoides is an excellent economic tree species for vegetation reconstruction and restoration in the North and land reclamation in mining areas because it has the following characteristics: windbreak and sand fixation properties, drought and cold resistance, rapid growth, root sprouting, strong adaptability, nitrogen fixation, and soil cultivation. It is the principal ecological and economical tree species for land reclamation and ecological restoration in mining areas in arid and semi-arid areas [20,21]. The roots of Hippophae rhamnoides penetrate the soil deeply and provide food for microorganisms in a large soil volume. In semi-arid areas, it may thus take several years for this plant species to improve soil fertility [22]. The effectiveness of reclamation has been significantly improved by artificial vegetation restoration, and the timing of vegetation planting is a key determinant of that improvement. The improvement of soil quality is more significant the longer the vegetation restoration durations [23]. The contents of soil OM, AN, available phosphorus (AP), and available potassium (AK) will increase as the reclamation duration increases [24].
The status of vegetation restoration is directly correlated with the physiochemical characteristics of the soil. Therefore, land reclamation and ecological restoration can be better understood by comprehending the response mechanisms of soil and vegetation restoration [25]. However, time rather than space methods are used for most research on the vegetation and soil in coal mine reclamation areas. Therefore, the changes in soil quality and its primary driving factors following Hippophae rhamnoides forest land reclamation in continuous time series are rarely reported. Therefore, this study uses the research target of the Juxinlong Coal Mine reclamation area in Ordos. A Hippophae rhamnoides forest land with a continuous reclamation period of 3a–7a, an artificial grassland of 7a, and an abandoned grassland of 7a were assessed. This study aims to provide a scientific basis and data support for the quantitative evaluation of soil quality and land management in the process of land reclamation and ecological restoration in this area and other coal mine reclamation areas in the future by analyzing the soil quality of Hippophae rhamnoides forest land with different reclamation durations and the main factors driving the change in soil quality.

2. Materials and Methods

2.1. Overview of the Study Area

The Juxinlong coal mine is at 110°4′ E, 39°54′ N in the eastern part of Dongsheng District, Ordos, Inner Mongolia Autonomous Region. It is located in the sand-covered loess hilly area on the northeastern margin of the Mu Us Desert, with serious water and wind erosion. It has a temperate continental climate; the summers are hot, and the winters are cold. Hippophae rhamnoides plantation was used for the preliminary land restoration of coal mine wasteland due to drought, decreased rain, low soil moisture, and a low nutrient content. The study area includes Hippophae rhamnoides, Hippophae rhamnoides Linn. Cv., and Achnatherum splendens. Due to recent vegetation restoration, Hippophae rhamnoides dominates the vegetation [26] (Figure 1).

2.2. Layout of Sample Plots

The Hippophae rhamnoides forest planted in the reclamation area of the study area between 2011 and 2015 was selected by the research group through field research. The reclamation period was 3–7 years. We set up a 20 m × 20 m standard sample plot to allow sampling and investigate the adjacent artificial and abandoned (control) grasslands [26] (Table 1).

2.3. Sample Collection and Soil Index Determination

Three 1 m deep soil profiles were set up in each standard plot, and soil samples were collected using the ring knife method. The soil samples were divided into five layers starting from the surface: 0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm. Three replicates from each layer of the same profile were collected to determine the physical characteristics of the soil. Simultaneously, the soil samples collected from each profile layer were packaged into self-sealing bags and returned to the laboratory for natural air drying. Physical indicators are measured as follows [27]: The soil water content was measured using the drying method. The soil bulk density (BD) was sampled by a ring knife (100 cm3). The soil moisture content (SMC), capillary water-holding capacity (CWHC), and total capillary porosity (TCP) were measured using the ring knife immersion method. The soil chemistry indicators refer to soil analysis in agricultural chemistry. The measurement methods for the soil chemical indicators are as follows: The soil particle size was determined using a BT-9300S laser particle size analyzer, the soil pH was determined using a Rex brand pH (PHS-3C) meter, the soil conductivity was determined using Fangzhou Technology, i.e., a multifunctional conductivity meter (DDS-608), the soil available nitrogen (ALN) was determined using the alkaline hydrolysis diffusion antimony method, available phosphorus (AP) was determined using the sodium bicarbonate method, and available potassium (AK) was determined using the NH4OAc extraction flame photometer. First, the insoluble silicates in the soil samples were decomposed into soluble compounds using NaOH melting. Finally, a flame photometer was used for the determination. The soil organic matter was determined using the potassium dichromate volumetric method [28]. The carbon in the soil organic matter was first oxidized with an excess of K2Cr2O7-H2SO4 solution under heated conditions and then calibrated with FeSO4 calibration solution, and finally the organic matter content was calculated. Total phosphorus (TP) was determined using the sodium hydroxide molybdenum antimony colorimetric method.

2.4. Soil Quality Evaluation Methods

2.4.1. Establishment of the Soil Quality Scoring Model

The measured soil index values were converted into acceptable scores between 0 and 1 by a linear evaluation model. The equations selected for this study were “the more the better” and “the less the better”. The model is as follows:
S L = X − L H − L
S L = 1 − X − L H − L
where S L represents the linear score (0–1), X represents the measured value of the index, L represents the lowest value of the index, and H represents the highest value of the index. Equation (1) represents “the more the better” index scoring function, and Equation (2) represents “the less the better” index scoring function [29].

2.4.2. Evaluation Index Weights

The more the contribution of an index to the overall variance, the more it can reflect the common factor variance determined using principal component analysis. Therefore, principal component analysis was used in this study to calculate the weight value of each index. The weight was determined by dividing the common factor variance of each index by the sum of the common factor variance of all indicators [29].
The soil quality index was determined once the score and weight of each index were obtained using Equations (1) and (2):
S Q I = ∑ i = 1 n W i S i
Si represents the index score, n represents the number of indicators, and Wi represents the index weight value. The higher the soil quality index (SQI) value, the better the soil quality.

2.5. Data Analysis

In this study, Excel 2016 was used for data processing and calculation, and the Statistical Package for Social Sciences 24.0 was used to perform correlation analysis of the data. One-way analysis of variance was used to analyze the significant differences between treatments. Origin 2021 was used for drawing, and R 4.2.3 was used to analyze the structural equation model.

3. Results

3.1. Analysis of the Soil Index Characteristics of the Hippophae rhamnoides Forest across Different Reclamation Durations

Table 2 shows that regarding the different soil indexes in the study area, there were significant differences in AK, CWHC, TCP, OM, TP, and C/N (p < 0.05). Soil BD, total nitrogen (TN), C/P, and N/P did not differ significantly (p > 0.05). The average contents of AK, AP, OM, and TP increased until they reached the maximum in the seventh year of reclamation with the increase in reclamation duration. The average BD decreased as the reclamation duration increased, and the average TP, OM, AK, and AP contents and C/P were significantly higher in the late stage of Hippophae rhamnoides restoration compared with the early stage. The coefficient of variation of pH (4.21%), TCP (5.78%), CWHC (9.49%), and other indicators was low, indicating low variation, as per the standard of coefficient of variation [30]. The coefficient of variation of C/P (197.44%), C/N (123.47%), and OM (66.55%) was high, indicating moderate and strong variation. Soil chemical indexes had a higher variance than physical indexes in the study area following vegetation restoration after coal mine reclamation. This is because they were more sensitive to the soil under different reclamation durations. Vegetation restoration significantly affected the soil SOC, TN, and TP contents and the N:P and C:P ratios as the reclamation duration increased. The C:N, C:P, and N:P values showed an upward trend with increased reclamation duration.

3.2. Construction of the Soil Quality Evaluation System Based on the Linear Model

3.2.1. Soil Factor Variance and Weight Analysis of Soil Quality Evaluation

It can be seen from Figure 2 that the highest weight value of all physical and chemical indicators was sand, and the lowest was OM. Sand had the largest contribution rate to the soil quality index of Hippophae rhamnoides reclamation under different durations in the study area, followed by silt.

3.2.2. Analysis of the Soil Quality Index

As shown in Figure 3, the soil quality of Hippophae rhamnoides forest land with different reclamation durations was as follows: Hippophae rhamnoides for 6 years (0.653) > Hippophae rhamnoides for 7 years (0.623) > Hippophae rhamnoides for 5 years (0.611) > abandoned grassland (0.511) > artificial grassland (0.423) > Hippophae rhamnoides forest for 3 years (0.42) > Hippophae rhamnoides forest for 4 years (0.39). The soil quality of Hippophae rhamnoides forests in China increased significantly and reached the maximum in the sixth year of reclamation with an increase in reclamation duration. Compared to artificial grassland and Hippophae rhamnoides forest land, abandoned grassland had a significantly higher soil quality index after 4 years of reclamation.

3.3. Analysis of the Driving Factors of the Soil Quality of Hippophae rhamnoides Forest in Different Years

To describe the relationship between the soil quality and soil physicochemical factors of Hippophae rhamnoides forest land with different reclamation durations, a structural equation model (SEM) was constructed (Figure 4). Different reclamation durations, AP, OM, and physical indexes (SMC, silt) were the main influential factors that led to changes in soil quality. In SEM, the path coefficients of SQI with different reclamation durations, AP, OM, and physical indexes were 0.64, −0.17, 0.19, and 0.56, respectively, indicating that the positive change in SQI with different reclamation durations was the most significant. The path coefficient between different recovery durations and AP and OM was >0.8, indicating a strong interaction between different recovery durations and SQI and OM. The interaction of different restoration durations, AP, OM, and physical indicators affected the temporal variation of soil quality.

4. Discussion

4.1. Difference Analysis of Soil Physicochemical Indexes in Different Reclamation Years

The nutrients and biogeochemical cycles associated with new soils are the main factors limiting soil restoration in mining areas [31]. Among the different soil physical and chemical indicators, BD, SOC, TP, and TN are easily influenced by human activities, especially in the 0–10 cm soil layer [32]. According to studies, TN, alkali-hydrolyzable nitrogen, available phosphorus, and soil organic matter increase with restoration duration [33]. This is consistent with the findings of this study. With increasing reclamation duration, the OM and AP levels in soil increased. Time has an impact on the accumulation of soil nutrients during the process of land reclamation [34]. Studies have shown that carbon, nitrogen, and phosphorus, which are the basic elements of organisms, can reflect how well plants absorb nutrients [35]. A key indicator for evaluating the soil nutrient supply capacity, quality, and function is the stoichiometric ratio of soil nutrients [36]. TN and TP in the soil are the main determinants and indicators of soil fertility and quality and are strongly related to soil productivity. Decreased TN and TP levels will decrease the soil nutrient supply, fertility, porosity, and permeability, reducing soil productivity [37]. The rate of soil OM decomposition is inversely proportional to C:N, a sensitive index of soil quality, and soil with lower N values mineralizes quickly [38]. From Table 2, it can be inferred that the Hippophae rhamnoides forest land reclaimed for 6–7 years had a lower C:N ratio than the land that had been reclaimed in the early stage, indicating that the 6–7-year reclamation in the study area was more conducive to the transformation of soil organic matter than the early stage of reclamation. The amount of SOC and TN in the soil varies significantly with the reclamation duration, whereas the amount of TP in the soil barely changes [39]. This difference may be due to the fact that while TP is primarily affected by the parent material of the soil, SOC and TN are more strongly influenced by litter decomposition and plant absorption and utilization [40]. N:P is an important index to determine the limitations of phosphorus and nitrogen as a nutrient index to assess production limitations. In this study, the Hippophae rhamnoides woodland lacked phosphorus, and phosphorus limited Hippophae rhamnoides growth in different years.

4.2. Soil Quality Analysis of Different Reclamation Durations

The reclamation duration affects the distribution of soil nutrients by changing the ecosystem’s composition and plant growth environment. A longer reclamation duration changed the dominant tree species and understory vegetation, disrupting the balance between the soil nutrient supply and vegetation growth, development, and physiological metabolism. Additionally, the physical binding effect of soil on plant roots and the chemical secretion binding effect of root exudates significantly improve soil characteristics [41,42]. Forest growth influences the decomposition of litter to form humus, direct root penetration, soil microbial quantity, and community diversity, altering the hydrothermal conditions in the forest. This successfully improves the physical and chemical properties of the soil [43]. According to Li Pengfei, the soil quality of the mining area significantly increased as the reclamation duration increased, with the soil quality index under shrub planting for 20 years came closest to the soil quality level under the restoration of natural vegetation [29,44,45]. The results of Wang Jijie et al. were corroborated by the comprehensive evaluation of the soil quality of the Hippophae rhamnoides plantation, which revealed that the soil quality in the study area showed a trend of ‘decrease–increase–decrease’ with the change in reclamation duration [46]. The afforestation process causes some disruption to the soil environment and nutrient loss in the early stages of reclamation. After the growth of the vegetation community is stabilized, the soil quality begins to improve significantly. The plant growth rate is fast, and nutrient uptake increases significantly. As a result, the soil nutrient supply is lower than the growth consumption, resulting in a decline in soil quality.
The amount of surface litter increased, and the amount of nutrient regression accumulate through the humification process increased with the reclamation duration, increasing the soil nutrient content. In the study area, land reclaimed by Chinese thorn forest for 7 years had a lower soil quality index than land reclaimed by Hippophae rhamnoides for 6 years. Hippophae rhamnoides will decline after 5–6 years of growth [47]. Although the Hippophae rhamnoides forest has declined, it has helped to foster the renewal of undergrowth vegetation. According to the survey, it was found that Hippophae rhamnoides regeneration seedlings and plant species were significantly higher in the plots reclaimed for 7 years than in other woodlands. This may have resulted in a significant consumption of soil nutrients and an uncoordinated relationship between soil and vegetation growth, which has been associated with a decline in soil quality. Aboveground biomass and species diversity also increased with the reclamation duration. Aboveground biomass was closely correlated with soil characteristics [46]. The fact that abandoned grassland in the study had better soil quality than artificial grassland may be related to the unreasonable plant configuration of artificial grassland, excessive density, and species, which caused the vegetation to absorb many nutrients from the soil to maintain high biomass. The vegetation of artificial grassland grew rapidly, while aboveground biomass continued to accumulate. The cumulative rate was higher than the rate at which soil nutrients were returned. Therefore, improving the management of Hippophae rhamnoides woodland in the reclamation area is crucial to maintain soil productivity and create good environmental ecology.

4.3. The Dominant Factor Analysis of Soil Quality Change

C, N, and P abundance and deficiency in soil impact the nutritional balance of the system, which might indicate the status of plants at different growth stages and their adaptability to the environment [47]. This is consistent with the results of this study. The interaction between different recovery durations and SQI and OM is the most critical (Figure 4). This may be because Hippophae rhamnoides is a deciduous shrub that grows rapidly. In the forest, the branches and leaves grow densely. Every year, there will be a lot of understory litter. The shallow layer is where the root system is mainly dense. Inorganic acids, amino acids, and phenols are secreted into the soil through plant litter and roots during the humification process, which can decompose nutrient elements like insoluble P and K in the parent soil material and increase the content of available components [42,48,49]. However, the restored vegetation in the mining area is still in the primary stages of succession. More plants lacking phosphorus are growing as the reclamation duration increases and the soil quality index increases [50]. SMC and silt (physical indicators) significantly impact soil quality. Silt is a significant component of the mechanical composition of the soil. The gradation and content of its grain size components directly affect the soil’s physical and chemical properties [51]. The mechanical components of the soil Hippophae rhamnoides plantation increased with the Hippophae rhamnoides reclamation duration; the sand content decreased while soil clay and silt increased. This shows that the Hippophae rhamnoides plantation has a positive impact on improving the mechanical composition of the soil in the mining area. This is similar to the research results of Li Yuqiang and Guo Yujia [52,53]. According to the findings of this study, different restoration durations had an indirect impact on soil OM, which had an impact on soil quality. The richness of the understory herbaceous or vegetation diversity increases with reclamation duration, resulting in a continuous accumulation of understory litter, which increases soil organic matter and the soil quality index.

5. Conclusions

The findings of this study showed that the OM and TP contents increased with the increase in reclamation duration, but the BD decreased with the increase in reclamation duration. The SQI was calculated using 15 soil physicochemical characteristics. The artificial vegetation restoration significantly improved the soil quality of the mining area. The following was the soil quality index ranking: Hippophae rhamnoides for 6 years > Hippophae rhamnoides for 7 years > Hippophae rhamnoides for 5 years > abandoned grassland > artificial grassland > Hippophae rhamnoides forest for 3 years > Hippophae rhamnoides forest for 4 years. In the 6 years of reclamation, the soil quality index of the Hippophae rhamnoides forest land was the highest. The structural equation model showed that the soil quality of the Hippophae rhamnoides woodland was directly affected by the reclamation duration and indirectly affected the soil quality by changing different physicochemical characteristics. Therefore, it is suggested that in the future, suitable technical means and measures can be proposed for the recovery period to carry out precise construction and management while carrying out land remediation and management in the coal mine reclamation area and forest tending in the study area.

Author Contributions

N.A. designed the study; F.Q., G.L., C.L. and F.Q. assisted in the experimental design; F.Q., N.A. and Q.R. performed the experimental preparation, performed fieldwork, and entered data; Q.R. analyzed field data; Q.R. wrote and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (U2243601), China Institute of Water Resources and Hydropower Research (IWHR) R&D Support Programs (SC0145C022023 and SC0202A012018), and Ordos Key Water Conservancy Science and Technology Project (SC110199A0012023). The authors greatly appreciate the assistance of Ting Xiang, Zhiyong Zhang, Rui Gao, and Kaixuan Zang.

Data Availability Statement

Not applicable.

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.

References

  1. Wang, J.; Wang, P.; Qin, Q.; Wang, H. The effects of land subsidence and rehabilitation on soil hydraulic properties in a mining area in the Loess Plateau of China. Catena 2017, 159, 51–59. [Google Scholar] [CrossRef]
  2. Cao, Y.; Bai, Z.; Sun, Q.; Zhou, W. Rural settlement changes in compound land use areas: Characteristics and reasons of changes in a mixed mining-rural-settlement area in Shanxi province, China. Habitat Int. 2017, 61, 9–21. [Google Scholar] [CrossRef]
  3. Peng, J.; Jiang, Y.; Wu, J.; Liu, S. The ecological environment effect of mining and the typical technology of land reclamation in China. Adv. Geogr. Sci. 2005, 24, 38–48. [Google Scholar]
  4. Wang, H.; Xie, M.; Li, H.; Feng, Q.; Zhang, C.; Bai, Z. Monitoring ecosystem restoration of multiple surface coal mine sites in China via landsat images using the google earth engine. Land Degrad. Dev. 2021, 32, 2936–2950. [Google Scholar] [CrossRef]
  5. Sun, J.; Guo, E.; Yang, X.; Kong, Y.; Yang, L.; Liu, H.; Lin, X. Seasonal and spatial variations in soil biochemical properties in areas with different degrees of mining subsidence in central China. Catena 2023, 224, 106984. [Google Scholar] [CrossRef]
  6. Wang, X.; Li, J.; Yue, J.; Zhou, X.; Guo, C.; Lu, N.; Wang, Y.; Yang, S. Comparison of soil enzyme activity and fertility under different artificial vegetation restoration in reclaimed land of antaibao open-pit mine. Environ. Sci. 2013, 34, 3601–3606. [Google Scholar]
  7. Cao, L.; Yi, W.; Liu, H.; Yang, H.; Lian, Y.; Guo, F. Characteristics of leaf and litter nutrient contents of main forest types in Sejila mountain, southeastern Tibet. Acta Ecol. Sin. 2019, 39, 4029–4038. [Google Scholar]
  8. Yang, C. The decrease of quantity and quality of soil organic matter is the key factor restricting the growth of forest trees. For. Guard-Sci. 2016, 52, 1–12. [Google Scholar]
  9. Yang, X.; Li, Y.; An, S.; Zeng, Q. Decomposition characteristics of typical plant roots and their effects on soil nutrients in mountainous areas of southern Ningxia. Acta Ecol. Sin. 2019, 39, 2741–2751. [Google Scholar]
  10. Lucas-Borja, M.E.; Delgado-Baquerizo, M. Plant diversity and soil stoichiometry regulates the changes in multifunctionality during pine temperate forest secondary succession. Sci. Total Environ. 2019, 697, 134204. [Google Scholar] [CrossRef]
  11. Liu, F.; Lu, L. Research progress of ecological restoration in coal mining subsidence area. J. Nat. Resour. 2009, 24, 612–620. [Google Scholar]
  12. Soliveres, S.; Maestre, F.T. Plant–plant interactions, environmental gradients and plant diversity: A global synthesis of community-level studies. Perspect. Plant Ecol. Evol. Syst. 2014, 16, 154–163. [Google Scholar] [CrossRef]
  13. Bi, Y.; Guo, C.; Wang, K. Research progress on biological improvement of reclaimed soil in coal mine area. Coal Sci. Technol. 2020, 48, 52–59. [Google Scholar]
  14. Fan, W.; Bai, Z.; Li, H.; Qiao, J.; Xu, J.; Li, X. Potential ecological risk assessment of heavy metal pollution in reclaimed soil. Agric. Eng. 2011, 27, 348–354. [Google Scholar]
  15. Ma, J.; Zhang, S.; Yao, H.; Wang, J. Time accumulation effect of heavy metals and heavy metals in reclaimed soil. J. Arid Land Resour. Environ. 2012, 26, 69–74. [Google Scholar]
  16. Wang, L.; Han, Y.; Zhang, C.; Pei, Z. The variation characteristics of reclaimed soil properties and weathered coal gangue under different vegetation restoration modes. Acta Ecol. Sin. 2011, 31, 6429–6441. [Google Scholar]
  17. Ding, Q.; Wang, Q.; Wei, Z.; Han, C.; Wang, H.; Wang, X. Study on the characteristics of soil nutrients and organic carbon in different reclamation years in fushun mining area. Chin. J. Soil Sci. 2007, 38, 262–267. [Google Scholar]
  18. Ren, X.; Cai, T.; Wang, X. Effects of different vegetation restoration models on soil nutrients in abandoned mining areas. Beijing For. Univ. J. 2010, 32, 151–154. [Google Scholar]
  19. Torroba-Balmori, P.; Zaldívar, P.; Alday, J.G.; Fernández-Santos, B.; Martínez-Ruiz, C. Recovering quercus species on reclaimed coal wastes using native shrubs as restoration nurse plants. Ecol. Eng. 2015, 77, 146–153. [Google Scholar] [CrossRef]
  20. Liu, J.; Ding, X.; Zhang, W. Effects of artificial seabuckthorn forest on soil fertility of dump slope in Huolinhe south open-pit coal mine. J. Arid Land Resour. Environ. 2017, 31, 150–154. [Google Scholar]
  21. Wang, X.; Wang, L.; Zhang, X.; Ma, J.; Zheng, J.; Zhang, X. The effect of different vegetation on the 16-year recovery degree of soil nutrients in the dump of Shanxi-Shaanxi-inner Mongolia mining area. Agric. Eng. 2016, 32, 198–203. [Google Scholar]
  22. Mao, R.; Zeng, D.-H.; Ai, G.-Y.; Yang, D.; Li, L.-J.; Liu, Y.-X. Soil microbiological and chemical effects of a nitrogen-fixing shrub in poplar plantations in semi-arid region of northeast China. Eur. J. Soil Biol. 2010, 46, 325–329. [Google Scholar] [CrossRef]
  23. Liu, W.; Zhao, B.; Bai, Z.; Shangguan, T.; Duan, Y.; Zhang, J.; Guo, D. Correlation analysis of soil nutrients and plant community in ecological reclamation of open-pit mine in semi-arid area. Chin. J. Ecol. 2014, 33, 2369–2375. [Google Scholar]
  24. Wang, X.; Li, Y.; Wei, Y.; Meng, H.; Cao, Y.; Lead, J.R.; Hong, J. Effects of fertilization and reclamation time on soil bacterial communities in coal mining subsidence areas. Sci. Total Environ. 2020, 739, 139882. [Google Scholar] [CrossRef] [PubMed]
  25. Domínguez-Haydar, Y.; Velásquez, E.; Carmona, J.; Lavelle, P.; Chavez, L.F.; Jiménez, J.J. Evaluation of reclamation success in an open-pit coal mine using integrated soil physical, chemical and biological quality indicators. Ecol. Indic. 2019, 103, 182–193. [Google Scholar] [CrossRef]
  26. Qiang, D.; Ai, I.; Liu, C.; Liu, G.; Li, Y.; Qiang, F.; Shao, Y. Study on spatial and temporal distribution characteristics of soil moisture in seabuckthorn plantation in coal mine reclamation area. J. Irrig. Drain. 2019, 38, 82–87. [Google Scholar]
  27. Zhang, Z.; Ai, N.; Liu, G.; Liu, C.; Qiang, F. Soil quality evaluation of various microtopography types at different restoration modes in the loess area of northern Shaanxi. Catena 2021, 207, 105633. [Google Scholar] [CrossRef]
  28. Walkley, A.; Black, I.A. An examination of the degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 1934, 37, 29–38. [Google Scholar] [CrossRef]
  29. Li, P.; Zhang, X.; Hao, M.; Zhang, Y.; Cui, Y.; Zhu, S. Evaluation of reclaimed soil quality in mining area of Loess Plateau based on minimum data set. Agric. Eng. 2019, 35, 265–273. [Google Scholar]
  30. Wu, D.; Chen, Y.; Zhao, X.; Li, Y.; Xiong, M.; Yao, X.; Li, M.; Zou, M.; Li, Z. Spatial variation analysis of available molybdenum in tobacco planting soil in Qiannan mountainous area. J. Yunnan Agric. Univ. 2012, 27, 851–857. [Google Scholar]
  31. Sun, Y.; Zhang, N.; Yan, J.; Zhang, S. Effects of soft rock and biochar applications on millet (Setaria italica l.) crop performance in sandy soil. Agronomy 2020, 10, 669. [Google Scholar] [CrossRef]
  32. Zhang, W.; Weindorf, D.C.; Zhu, Y.; Haggard, B.J.; Bakr, N. Soil series and land use impacts on major soil properties: A quantitative comparison. Soil Res. 2012, 50, 390–396. [Google Scholar] [CrossRef]
  33. Zhang, N.; Huang, S.; Lei, H.; Lei, X.; Liu, P.; Yan, J. Changes in soil quality over time focusing on organic acid content in restoration areas following coal mining. Catena 2022, 218, 106567. [Google Scholar] [CrossRef]
  34. Kumar, S.; Maiti, S.K.; Chaudhuri, S. Soil development in 2–21 years old coalmine reclaimed spoil with trees: A case study from Sonepur-Bazari opencast project, Raniganj coalfield, India. Ecol. Eng. 2015, 84, 311–324. [Google Scholar] [CrossRef]
  35. Jager, M.M.; Richardson, S.J.; Bellingham, P.J.; Clearwater, M.J.; Laughlin, D.C. Soil fertility induces coordinated responses of multiple independent functional traits. J. Ecol. 2015, 103, 374–385. [Google Scholar] [CrossRef]
  36. Zhao, W.; Huang, L.-M. Changes in soil nutrients and stoichiometric ratios reveal increasing phosphorus deficiency along a tropical soil chronosequence. Catena 2023, 222, 106893. [Google Scholar] [CrossRef]
  37. Wang, Y.; Zhang, X.; Huang, C. Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China. Geoderma 2009, 150, 141–149. [Google Scholar] [CrossRef]
  38. Zhu, Q.; Xing, X.; Zhang, H.; An, S. Soil ecological stoichiometry characteristics of different vegetation areas in loess hilly and gully region. Acta Ecol. Sin. 2013, 33, 4674–4682. [Google Scholar]
  39. Zhao, X.; Zeng, Q.; Zeng, S.; Fang, Y.; Ma, R. Ecological stoichiometric characteristics of soil and plant roots in grasslands with different enclosure years on the Loess Plateau. J. Soil Sci. 2016, 53, 1541–1551. [Google Scholar]
  40. Liu, X.; Zhou, G.; Zhang, D.; Liu, S.; Chu, G.; Yan, J. Stoichiometric characteristics of n and p in plants and soils at different successional stages of south subtropical forests. Acta Phytoecol. Sin. 2010, 34, 64–71. [Google Scholar]
  41. Guo, M.; Wang, W.; Kang, H.; Yang, B. Effects of vegetation natural restoration years on soil anti-scourability in gully region of Loess Plateau. Agric. Eng. 2018, 34, 138–146. [Google Scholar]
  42. Leul, Y.; Assen, M.; Damene, S.; Legass, A. Effects of land use types on soil quality dynamics in a tropical sub-humid ecosystem, western Ethiopia. Ecol. Indic. 2023, 147, 110024. [Google Scholar] [CrossRef]
  43. Zhang, Z.-Y.; Qiang, F.-F.; Liu, G.-Q.; Liu, C.-H.; Ai, N. Distribution characteristics of soil microbial communities and their responses to environmental factors in the sea buckthorn forest in the water-wind erosion crisscross region. Front. Microbiol. 2023, 13, 1098952. [Google Scholar] [CrossRef] [PubMed]
  44. Chukwu, E.D.; Udoh, B.T.; Afangide, A.I.; Osisi, A.F. Evaluation of soil quality under oil palm cultivation in a coastal plain sands area of Akwa Ibom State Nigeria. Soil Secur. 2023, 10, 100087. [Google Scholar] [CrossRef]
  45. Yang, E.; Zhao, X.; Qin, W.; Jiao, J.; Han, J.; Zhang, M. Temporal impacts of dryland-to-paddy conversion on soil quality in the typical black soil region of China: Establishing the minimum data set. Catena 2023, 231, 107303. [Google Scholar] [CrossRef]
  46. Wang, J.; Wang, B.; Li, B.; Yu, Y. Changes of soil nutrients in eucalyptus grandis × e.Urophylla plantations at different ages. J. For. Environ. 2016, 36, 8–14. [Google Scholar]
  47. Ning, A.; Qiang, D.; Liu, G.; Tu, X.; Liu, C. Effects of forest age and leaf position on leaf water content of sea buckthorn in reclamation area of coal mine. J. Northwest For. Univ. 2020, 35, 68–72. [Google Scholar]
  48. Fang, Y.; Ma, R.; An, S.; Zhao, J.; Xiao, L. Study on the enzymatic activity and physicochemical properties of soil reclaimed by different vegetation in haidaigou opencast coal mine drainage site. Environ. Sci. 2016, 37, 1121–1127. [Google Scholar]
  49. Wan, R.; Luo, D.; Liu, J.; Zhang, Y.; Xiang, Y.; Yan, W.; Xie, Y.; Mi, J.; Zhang, F.; Wan, X.; et al. Superior improvement on soil quality by pennisetum sinese vegetation restoration in the dry-hot valley region, sw China. Sci. Total Environ. 2023, 878, 163185. [Google Scholar] [CrossRef]
  50. Chen, R.; Han, L.; Zhao, Y.; Liu, Z.; Fan, Y.; Li, R.; Xia, L. Response of plant element traits to soil arsenic stress and its implications for vegetation restoration in a post-mining area. Ecol. Indic. 2023, 146, 109931. [Google Scholar] [CrossRef]
  51. Su, Z.; Liu, R.; Liang, A.; Ma, Y.; Wang, G.; Gao, J.; Ha, S. Preliminary study on soil mechanical composition and organic matter of desertified land in northwest Shanxi. Res. Soil Water Conserv. 2018, 25, 61–67. [Google Scholar]
  52. Guo, Y.; Niu, Q.; Cun, Y.; Fan, H.; Peng, B.; Gu, J.; Lu, G. Soil improvement benefits of Pinus sylvestris var. Mongolica forest with different forest ages in Bashang area. J. Northeast For. Univ. 2019, 47, 2741–2751. [Google Scholar]
  53. Li, Y.; Zhao, X.; Liu, X.; Shang, W.; Feng, J.; Su, N. Response of soil carbon sequestration and soil respiration to dry and wet changes in Pinus sylvestris var. Mongolica sand-fixing forest. Chin. Desert 2011, 31, 282–287. [Google Scholar]
Forests 14 01425 g001 550
Figure 1. Sample plot map. Note: (A) mine site before restoration measures; (B) mine site after restoration measures.
Figure 1. Sample plot map. Note: (A) mine site before restoration measures; (B) mine site after restoration measures.
Forests 14 01425 g001
Forests 14 01425 g002 550
Figure 2. Weight of soil indicators.
Figure 2. Weight of soil indicators.
Forests 14 01425 g002
Forests 14 01425 g003 550
Figure 3. SQI after restoration; different letters indicate significant differences (p ≤ 0.05).
Figure 3. SQI after restoration; different letters indicate significant differences (p ≤ 0.05).
Forests 14 01425 g003
Forests 14 01425 g004 550
Figure 4. Structural equation model (SEM) analysis of the effects of physical indicators, OM, and restoration duration on the soil quality index (SQI). Red arrows indicate negative effects and purple 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.
Figure 4. Structural equation model (SEM) analysis of the effects of physical indicators, OM, and restoration duration on the soil quality index (SQI). Red arrows indicate negative effects and purple 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.
Forests 14 01425 g004
Table
Table 1. Basic information of sampled land.
Table 1. Basic information of sampled land.
Sample
Number		Plot TypeRestore Year (yr)Altitude (m)Plant Spacing (m) × Row Spacing (m)Base Diameter (cm)Height (cm)
1Hippophae rhamnoides713602 m × 2 m3.0 ± 0.11157 ± 3.37
2Hippophae rhamnoides613602 m × 2 m3.5 ± 0.11166 ± 4.64
3Hippophae rhamnoides513702 m × 2 m3.7 ± 0.13183 ± 4.04
4Hippophae rhamnoides413702 m × 2 m3.2 ± 0.1172 ± 2.80
5Hippophae rhamnoides313402 m × 2 m3.2 ± 0.13138 ± 2.62
6Abandoned land71390///
7Artificial grassland71390///
Table
Table 2. Soil physical and chemical properties.
Table 2. Soil physical and chemical properties.
Indicator3a4a5a6a7aRCLCCoefficient of Variation%
pH8.067 ± 0.117 b7.67 ± 0.246 cd7.913 ± 0.006 bc8.423 ± 0.015 a8.137 ± 0.176 b7.527 ± 0.259 d7.563 ± 0.091 d4.21%
EC/(mS/cm)146.847 ± 6.076 a99.99 ± 16.675 b127.037 ± 27.563 ab124.267 ± 8.458 ab121.637 ± 5.605 ab123.633 ± 22.546 ab58.267 ± 8.358 c24.71%
ALN/(mg/kg)8.99 ± 0.248 b9.813 ± 0.484 ab8.877 ± 0.589 b11.033 ± 1.559 a10.777 ± 0.751 a3.578 ± 0.486 c3.578 ± 0.819 c39.40%
Clay/%2.783 ± 0.389 b2.99 ± 0.699 ab3.847 ± 0.474 a3.747 ± 0.621 a2.987 ± 0.346 ab3.333 ± 0.212 ab3.7 ± 0.221 a12.87%
Silt/%58.507 ± 3.396 abc49.257 ± 7.997 c58.743 ± 10.455 abc63.643 ± 6.143 ab54.993 ± 1.808 bc66.483 ± 3.343 a68.44 ± 1.698 a11.20%
Sand/%38.707 ± 3.78 abc47.75 ± 8.268 a37.403 ± 10.935 abc32.6 ± 6.768 bc42.02 ± 2.16 ab30.18 ± 3.394 bc27.854 ± 1.523 c19.05%
AK/(mg/kg)98.443 ± 32.693 b86.22 ± 3.563 b93.67 ± 3 b169.11 ± 17.011 a177.223 ± 7.065 a41.556 ± 3.834 c42.889 ± 6.466 c53.52%
AP/(mg/kg)11.117 ± 5.197 d16.43 ± 2.015 cd26.887 ± 5.485 ab21.08 ± 2.774 abc29.047 ± 1.797 a14.768 ± 0.762 cd19.084 ± 10.461 bcd32.64%
SMC/(g/kg)36.937 ± 2.555 b31.31 ± 2.856 b37.92 ± 2.764 b44.323 ± 5.18 a33.233 ± 1.785 b37.764 ± 3.5 b34.951 ± 4.311 b11.41%
CWHC29.64 ± 1.207 a22.87 ± 1.892 c29.297 ± 0.93 a29.343 ± 0.673 a27.027 ± 0.8 ab25.004 ± 2.954 bc28.736 ± 2.353 a9.49%
BD/(g/cm3)1.213 ± 0.095 b1.37 ± 0.082 a1.183 ± 0.117 b1.19 ± 0.05 b1.313 ± 0.055 ab1.167 ± 0.082 b1.196 ± 0.072 b6.25%
TCP44.883 ± 0.89 ab39.99 ± 1.751 c44.693 ± 0.714 ab47.78 ± 1.984 a44.29 ± 0.745 b43.358 ± 1.41 bc41.433 ± 3.575 bc5.78%
OM/(g/kg)1.617 ± 0.23 c1.697 ± 0.733 c4.11 ± 0.926 c4.087 ± 0.74 c5.55 ± 0.521 bc9.218 ± 5.066 ab10.722 ± 3.338 a66.55%
TN/(g/kg)0.275 ± 0.092 a0.215 ± 0.185 a0.365 ± 0.327 a0.533 ± 0.334 a0.495 ± 0.359 a0.143 ± 0.021 a0.24 ± 0.121 a45.24%
TP/(g/kg)0.46 ± 0.017 bc0.47 ± 0.036 bc0.567 ± 0.085 bc0.583 ± 0.101 ab0.693 ± 0.05 a0.444 ± 0.083 c0.472 ± 0.051 bc17.32%
C/N10.781 ± 2.89 c26.665 ± 26.195 bc31.821 ± 21.281 bc25.104 ± 28.433 bc27.043 ± 16.835 bc106.678 ± 46.822 a98.768 ± 78.022 ab123.47%
C/P6.083 ± 1.051 b6.139 ± 2.428 b12.457 ± 1.682 b12.335 ± 3.007 b13.893 ± 2.185 b38.819 ± 26.094 a40.097 ± 15.379 a197.44%
N/P0.602 ± 0.218 a0.478 ± 0.447 a0.605 ± 0.493 a0.931 ± 0.623 a0.709 ± 0.508 a0.335 ± 0.102 a0.514 ± 0.285 a49.40%
Note: The single factor variance LSD method was used to analyze the difference between the same index in the same soil layer (p < 0.05), and different letters indicate significant differences. Bulk density: BD; Soil moisture content: SMC; Conductivity: EC; Organic matter: OM; Available phosphorus: AP; Total phosphorus: TP; Available potassium: AK; Available nitrogen: ALN. 3a: Reclamation of Hippophae rhamnoides for 3 years; 4a: Reclamation of Hippophae rhamnoides for 4 years; 5a: Reclamation of Hippophae rhamnoides for 5 years; 6a: Reclamation of Hippophae rhamnoides for 6 years; 7a: Reclamation of Hippophae rhamnoides for 7 years; RC: artificial grassland; LC: abandoned grassland.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ren, Q.; Liu, G.; Liu, C.; Qiang, F.; Ai, N. Soil Quality Evaluation and Driving Factor Analysis of Hippophae rhamnoides Plantations in Coal Mine Reclamation Areas Based on Different Restoration Durations. Forests 2023, 14, 1425. https://doi.org/10.3390/f14071425

AMA Style

Ren Q, Liu G, Liu C, Qiang F, Ai N. Soil Quality Evaluation and Driving Factor Analysis of Hippophae rhamnoides Plantations in Coal Mine Reclamation Areas Based on Different Restoration Durations. Forests. 2023; 14(7):1425. https://doi.org/10.3390/f14071425

Chicago/Turabian Style

Ren, Qianwen, Guangquan Liu, Changhai Liu, Fangfang Qiang, and Ning Ai. 2023. "Soil Quality Evaluation and Driving Factor Analysis of Hippophae rhamnoides Plantations in Coal Mine Reclamation Areas Based on Different Restoration Durations" Forests 14, no. 7: 1425. https://doi.org/10.3390/f14071425

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop