Next Article in Journal
Revolutionizing the Garment Industry 5.0: Embracing Closed-Loop Design, E-Libraries, and Digital Twins
Next Article in Special Issue
Analysis of the Degradation Characteristics of Chlorpyrifos in an Electrochemically Constructed Wetland Coupled System under Different Pesticide Exposure Conditions and Microbial Community Analysis
Previous Article in Journal
Psychophysiological Data Harmonization for the Sustainability of Outdoor Activities
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 22
10.3390/su152215837
sustainability-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

Plant-Soil Carbon Storage in Dynamic Succession of Ecological Restoration in National Grassland Natural Park

by
Junfang Wang
1,*,
Guodong Han
1,*,
Zhaoming Wang
2,
Jinfeng Yun
1,
Zhongwu Wang
1,
Zhiguo Li
1,
Shijie Lv
1 and
Jie Qin
1
1
College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010030, China
2
Mengcao Ecological Environment (Group) Co., Hohhot 010030, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(22), 15837; https://doi.org/10.3390/su152215837
Submission received: 14 August 2023 / Revised: 17 October 2023 / Accepted: 25 October 2023 / Published: 10 November 2023
(This article belongs to the Special Issue Applied Research on Phytoremediation of Soil and Water: New Insights and Approaches)
Browse Figures
Versions Notes

Abstract

:
Ecological restoration has a positive impact on global climate change. How plant-soil stores carbon in degraded grassland ecological restoration requires long-term monitoring and support. To reveal the dynamics of plant-soil carbon storage in the succession process of ecological restoration, compare the effects of artificial interference and natural restoration, and determine the impact of climate change and biodiversity on vegetation soil carbon storage, we conducted a study in National Grassland Natural Park, which is located on the southern foot of the Yinshan Mountains in Hohhot, Inner Mongolia, China. Based on long restoration chronosequences (2012–2022), using a space-for-time substitution approach and one-way ANOVA tests, Pearson correlation and structural equation modeling were used to investigate the interactions among these various factors. The results indicated that the carbon storage of aboveground vegetation first increased, and then, decreased with time. The underground root carbon storage and soil carbon storage at 0–10 cm and 20–30 cm first increased, then decreased, and finally, stabilized. The highest soil carbon storage (0–30 cm) was 102.11 t/ha in 2013, which accounted for 96.61% of the total organic carbon storage. The Shannon–Wiener index, individual number of species, and surface root carbon storage (0–10 cm) significantly increased the carbon storage of surface soil (0–10 cm) (p < 0.05). Compared to natural restoration, artificial restoration over seven years decreased soil carbon storage at 0–30 cm and underground root carbon storage at 0–10 cm (p < 0.05). Consequently, combining artificial restoration with natural restoration can help in establishing a more stable ecosystem faster and in increasing the carbon storage of the ecosystem. It is an effective management measure to promote grassland restoration in arid areas. Also, climate (MAT, MAP) change was closely correlated with plant-soil carbon storage.

1. Introduction

Degraded land restoration will not only greatly improve the sustainability of the surrounding environment, increase species diversity, improve soil structure, increase soil fertility, increase productivity, and promote local agricultural production and socio-economic sustainable development, but also help mitigate the pressure of the carbon cycle due to global change [1,2]. Some studies have indicated that more than two billion hectares of degraded land and landscapes in the world have restoration potential, and the cost of the loss of species and ecosystem services accounts for about 10% of the global gross product per year [3]. The carbon cycle is an important part of the chemical cycle in terrestrial ecosystems [4,5]. It plays a crucial role in maintaining balance in the ecosystem [6]. It is a unique indicator that can be used to evaluate ecological processes and ecological restoration. The main objectives of damaged ecosystem restoration are to repair the carbon pool system, establish a new carbon cycle system in a region, circulate and flow the carbon pool, restore the carbon sequestration value of a site, and fulfill a site’s ecological service value and function [7]. Ecological restoration involves the restoration of the original ecological service value functions of natural ecosystems. Natural ecosystems provide services to humans that are essential for life. For example, they provide water, food, and air, which are necessary for survival, as well as playing a role in water conservation, soil conservation, windbreak and sand fixation services, air purification, the provision of biological habitats and recreational functions, and the reinforcement of socio-cultural attributes [8]. The impact of socioeconomic activities on the natural environment has led to its degradation to varying degrees [9].
Many researchers have investigated the global soil organic carbon pool [10]. Soil is the largest carbon pool in terrestrial ecosystems and stores two-thirds of the carbon of the entire terrestrial ecosystem, which is about thrice the plant carbon pool and twice the atmospheric carbon pool. Several researchers have investigated soil organic carbon dynamics, carbon accumulation, carbon storage, and the effects on the soil carbon pool from different ecological restoration modes [11,12,13,14,15], land use modes [16,17,18,19], disturbance modes [20], restoration years and phases [21,22,23], and management modes [24,25], for comparing the carbon storage and sequestration of different types of plant communities and vegetation carbon pool construction [26]. Several studies have elucidated the mechanism of the ecological restoration of abandoned cropland succession and degraded land on the Loess Plateau, investigated the dynamic characteristics of carbon sequestration and limiting factors [7], and estimated the potential of soil carbon sequestration in heavily degraded grasslands after complete restoration [27].
Grasslands are the largest organic carbon pool in terrestrial ecosystems, and the soil carbon pool in grassland ecosystems accounts for about 90% of the total carbon stock [13]. Ecological restoration is a dynamic and balanced process [28,29], where the main aim is the restoration of the function and state of the damaged system to its original condition. In this context, understanding the carbon cycle in the restoration process of damaged grasslands can be quite helpful. The change in the carbon stock during ecological recovery is an important ecological indicator and is closely associated with ecological service function after restoration [30]. Therefore, studying the aboveground, belowground, and soil carbon stock in the process of the recovery of damaged ecosystems is important. There is very little knowledge about the long-term vegetation development in grassland restoration. Long-term studies and chronosequence analyses would be necessary to evaluate the development of the vegetation, as well as ecosystem services and functions, on restored grassland [31]. We conducted a dynamic study for 11 years on carbon stock during the restoration process of the damaged areas of the Chilechuan Grassland. The purpose of this was to study the long-term succession effect of carbon storage after the ecological restoration of damaged ecosystems, compare the effect of natural restoration and artificial restoration, determine what factors affect soil carbon storage, and propose suggestions for improving carbon storage and ecological restoration models of degraded grasslands.

2. Materials and Methods

2.1. General Overview of the Study Area

The study area was located in the southern foothills of the Yinshan Mountains, Cilechuan Grassland, Nomatu Village, New District of Hohhot (40°55′08″ N, 111°52′12″ E), Inner Mongolia, China (Figure 1). It was rated as a national grassland natural park in 2020. This area lies in the middle temperate zone, has an arid and semi-arid continental monsoon climate, and is located at an altitude of 1135–1180 m. The area has an average annual temperature of 3.5–8 °C and an average annual precipitation of 337–418 mm. The study site was located in an alluvial fan area on the southern foothills of the Daqing Mountains, covering an area of 2000 ha. The area is sandy and gravelly, and the site is a sand and gravel quarry and abandoned wasteland. The main plants growing in the region include Leymus chinensis (Trin.) Tzvel. and Artemisia scoparia Waldst. et Kit., and native plants, such as Stipa capillata Linn., Pennisetum centrasiaticum, and other plants. The study was started in July 2012 with a trial area of 66.67 ha, which was gradually expanded to 2000 ha. The artificial recovery test area was maintained via fertilization, irrigation, mowing, and replanting. NPK compound fertilizer (140 kg/ha) was used after the grassland turned green. Irrigation was carried out according to natural precipitation conditions from May to August. There were no measures used in the natural recovery test area [32].
The plants that were seeded included Leymus chinensis (Trin.) Tzvel., Agropyron mongolicum Keng, Elymus dahuricus Turcz., Elymus sibiricus Linn., Leguminosae, Melilotus suaveolens Ledeb., and Lolium multiflorum Lam.
The water samples had a pH of 7.66 with a total salt content of 296 mg/L. The soil samples contained 12 g/kg soil organic matter [33], 51 mg/kg hydrolyzable nitrogen [34], 9.3 mg/kg Olsen-P [35], 480 mg/kg Olsen-K [36], and 1.19 g/kg total water-soluble salts [37], and had a pH of 7.97 [38]. The annual mean precipitation is 417.77 mm, and the annual mean temperature is 8.65 °C [39]. Details are shown in Figure 2.

2.2. Study Methods

2.2.1. Experimental Design

The experiment was started in May 2012 and ended in October 2022. Samples were monitored from 2012 to 2022. Three undisturbed areas (original sample plots), sustained artificially disturbed areas (1000 acres in extent, sustained disturbance after seeding), and artificially disturbed naturally restored areas (undisturbed after seeding) were selected at the study site in the 7th (2018) and 11th (2022) years of ecological restoration, respectively. Three points were selected in the west, north, and south of the Chilechuan grassland, and a sample line of 300 m was set up in the four directions of each point, with sample squares set up at 100 m intervals to conduct aboveground vegetation surveys, which were carried out in August of each year. The sample area was 1 m2, and plant height, cover, density, and biomass were measured (Table 1).

2.2.2. Sample Collection and Processing

(1)
Aboveground biomass measurement method
The height, density, coverage, and species number of plants were measured in a 1 m × 1 m quadrat. Herbs were cut flush with the ground, weighed, and dried to obtain the dry weight. Then, the samples were crushed using a laboratory mill 3100 (machine type: Perten), passed through a 0.149 mm sieve, and stored until further use.
(2)
Root determination
In each sample plot, underground root samples (0–30 cm) were collected using a root drill with an inner diameter of 7 cm. The samples were washed with groundwater and air-dried in the shade. Then, the samples were crushed using a laboratory mill 3100 (machine type: Perten), passed through a 0.149 mm sieve, and stored until further use.
(3)
Soil survey and sampling
The root auger method was used to collect underground soil samples (0–30 cm) using a root auger (7 cm internal diameter) in each sample plot. The soil samples were brought to the laboratory, dried, and stored for use. The soil bulk weight was determined using the cutting ring method.
At each sample site (100 cm × 100 cm), random sampling was carried out at 3 points using a 7 cm diameter soil drill. Three soil layers were sampled at intervals of 0–10 cm, 10–20 cm, and 20–30 cm. The bagged soil samples were transported to the laboratory, where plant roots, gravel, and sundries were subsequently removed. The samples were then air-dried in the shade, powdered, and stored in a sealed bag after sieving through a 0.149 mm sieve [1,40,41].

2.2.3. Determination of Carbon (C) Content

The C content of the plants was determined by multiplying the respective biomass by its C content. The total stock of soil was calculated based on its C content, sample depth, and bulk density [42]. C concentrations of both plant and soil samples were determined using the elemental analyzer vario MACRO cube (Figure 3).

2.2.4. Data Processing

Data organization and calculation were performed in Microsoft Excel 2010. All analyses were performed using SPSS ver. 17.0 for Windows. Means and standard error (mean ± SD) were calculated for all data. One-way ANOVA tests were performed to compare the mean values of C storage in aboveground plants, underground roots, and soil among different years and soil layers. The significance level was set at p < 0.01 or 0.05 for all statistical analyses and indicates a significant difference among different treatments. A Pearson correlation analysis was performed. Structural equation modeling was conducted using the lavaan (version 06-16) and semplot (version 1.1.6) packages of R software (version 4.3.1), and was used to investigate the interactions among these various factors.
The calculation methods were as follows:
Important value: Pi = (Relative cover + Relative density + Relative plant height)/3
Species Richness Index: M = S/ln(N)
Shannon - Wiener   Index :   H ′ = − ∑ i = 1 S p i ln ( p i )
Simpson   Index :   D = ∑ i = 1 S p i 2
Pielou   Index :   J ′ = H ′ ln ( S )
SC = BD × Cc/100 × D
CT = CAC + CRC + CS
Note: S is the number of species, Pi is the important value of species in the sample, and N is the number of individuals of all species in a plant community [1,43]. BD is the soil bulk density (g/cm3), Cc is the soil C concentration (%), and D is the soil sampling depth (cm). The ecosystem carbon storage was calculated by summing the total of each component (aboveground, roots, and soil) [42].

3. Results and Analyses

3.1. Carbon Stock Dynamics of Aboveground Vegetation in Different Restoration Years

The aboveground vegetation carbon stock first increased, and then, decreased with the duration of ecological restoration; we recorded a minimum of 0.11 t/ha in 2012 and a peak of 2.15 t/ha in 2013 (Figure 1). The results of LSD multiple variance comparisons showed a significant difference between the aboveground vegetation carbon stock in 2012 and 2013 (p < 0.01). The difference was not significant between 2013 and 2018, but a significant difference was found between 2018 and 2022 (p < 0.01). Details are shown in Figure 4.

3.2. Changes in Carbon Stocks in Belowground Root Systems in Different Restoration Years (0–30 cm)

The underground root carbon stock is an important component of the whole carbon pool. From 2012 to 2022, the carbon stock in the 0–10 cm underground root system before and after ecological restoration first increased (p < 0.05), and then, stabilized (Figure 5). The 0–20 cm underground root system reached a maximum of 1.10 t/ha in 2014, which was significantly different (p < 0.05) from the other years. A significant difference (p < 0.05) was recorded in the 0–30 cm underground root system between pre-ecological restoration (2012) and post-ecological restoration (2013–2022).

3.3. Changes in Soil Carbon Stocks in Different Years of Restoration (0–30 cm)

3.3.1. Soil Carbon Stocks (0–30 cm)

The 0–30 cm soil carbon stock first increased, then decreased, and finally, stabilized (Figure 6). Carbon stock in the 0–10 cm soil surface layer first increased, then decreased, and finally, stabilized. The difference between 2012–2014 and 2016–2022 was significant (p < 0.05). Carbon stock in the 0–20 cm soil layer differed significantly across 2012, 2013, and 2014. It also differed significantly across 2014, 2016, and 2018 (p < 0.05). Carbon stock in the 20–30 cm soil layer first increased, then decreased, and finally, stabilized; the highest carbon stock was recorded in 2013.

3.3.2. Plant Roots and Soil Carbon Stocks (0–100 cm)

The soil carbon stock in the 0–100 cm soil layer decreased with the increase in soil depth, and the soil carbon stock in the 0–10 cm, 10–20 cm, and 20–30 cm soil layers and aboveground plant parts and underground root system were positively correlated (Figure 7). The soil carbon stock increased with the increase in plant biomass and root biomass, and it decreased with the decrease in plant biomass and root biomass. In the pre-restoration period (2012 and 2013), the soil surface carbon stocks in the 0–10 cm soil layer were lower than that in the 10–20 cm and 20–30 cm layers. In 2014, with the accumulation of organic matter and soil carbon, the 0–10 cm soil carbon stocks were higher than the 10–20 cm and 20–30 cm soil carbon stocks.
The underground root biomass in the 0–100 cm layer decreased with the increase in soil depth. The maximum carbon stock was 0.33 t/ha in the 0–10 cm soil layer, and the carbon stock of the underground root system in the 0–10 cm layer was significantly different (p < 0.05) from that recorded in the other soil layers. The underground root carbon stock in the top 0–30 cm of soil was 0.55 t/ha, accounting for 60.33% of the root carbon stock in the 0–100 cm soil layer [44].

3.4. Total Carbon Stock with Different Recovery Years

The total carbon stock in the restoration area first increased, and then, decreased from 2012 to 2022, reaching a maximum of 105.69 t/ha in 2013 and a minimum of 54.62 t/ha in 2018 (Table 2). Soil carbon stock was the highest in 2013 (102.11 t/ha), accounting for 96.61% of the total organic carbon stock. The aboveground vegetation carbon stock was the highest in 2013 (2.15 t/ha), accounting for 2.03% of the total organic carbon stock. The underground root organic carbon stock was the highest in 2014 (3.72 t/ha), accounting for 4.98% of the total organic carbon stock.
Soil organic carbon stocks accounted for more than 92.92% of the total organic carbon stocks from 2012 to 2022, which indicated that the soil organic carbon stock was the largest carbon reservoir during ecological restoration. The aboveground plant carbon stocks and belowground root carbon stocks accounted for 3.80% and 4.98% of the total organic carbon stocks.

3.5. Carbon Stocks in Aboveground Vegetation with Different Restoration Methods

Under different restoration methods, the carbon stock of aboveground vegetation was not significantly different from that of native grassland in seven years of artificial restoration and natural restoration (Figure 8). The difference between the carbon stock of aboveground vegetation in 11 years of artificial restoration and native grassland was significant (p < 0.05).
Under different restoration methods, the carbon stock in the underground root system in the 0–10 cm soil layer was significantly different between seven years of artificial restoration and seven years of natural restoration, and between 11 years of artificial restoration and 11 years of natural restoration (p < 0.05) (Figure 9). The differences were not significant for the 10–20 cm and 20–30 cm soil layers.
Soil carbon storage in the 0–10 cm layer, for 7 and 11 years of artificial restoration, and for 11 years of natural restoration were significantly different from that recorded in native grasslands (p < 0.05) (Figure 10). We also found that 10–20 cm and 20–30 cm of artificial restoration for seven years was significantly different from native grassland, natural restoration for seven years, and artificial restoration for 11 years (p < 0.05).
The Shannon–Wiener index (H’), Simpson index, and Pielou evenness index were significantly negatively correlated with aboveground vegetation carbon stock (p < 0.01) (Table 3). These indices were negatively correlated with the 20–30 cm belowground root carbon stock (p < 0.01). The aboveground vegetation carbon stock was significantly correlated with the 20–30 cm underground root carbon stock (p < 0.05). The 0–10 cm soil carbon stock was significantly correlated with the Shannon–Wiener index (H’) (p < 0.05), the number of plants (p < 0.01), and the 0–10 cm plant root system (p < 0.05). The 0–10 cm soil carbon content was significantly correlated with the Shannon–Wiener index (H’) (p < 0.05), number of plants (p < 0.05), and 0–10 cm plant root system (p < 0.01).
The results of this study showed that the aboveground vegetation carbon stock and belowground root carbon stock were negatively correlated with diversity, which matched the results of a study by Yang Lucun [45]. The aboveground vegetation carbon stock and belowground root carbon stock were closely related. Plant diversity, the number of individuals of a species, and carbon stock in the surface root system (0–10 cm) significantly increased the carbon stock in the surface soil (0–10 cm) (p < 0.05).

4. Discussion

4.1. Community Succession and Changes in Carbon Stock

Ecological restoration succession is closely related to carbon stock [46]. Our findings suggested that overall ecological restoration was divided into two phases. The first phase was the artificial intervention phase (2012–2013), in which carbon stocks increased in the short term through artificial intervention measures. The aboveground vegetation carbon peaked in 2013 when grassland carbon stocks increased through artificial seeding and planting. The second stage was the natural succession stage after artificial intervention (2013–2022), during which the emergence of intraspecific competition, the self-thinning of plants, biomass growth, and continuous community succession occurred, and the carbon stock of aboveground vegetation first increased, and then, decreased with restoration time. As plant diversity increased, the soil carbon stock stabilized. Ou Yansheng et al. [21] investigated the soil carbon content of artificial grasslands across different restoration years in the hilly and gully areas of the Loess Plateau, and found that the soil organic carbon content of artificial grasslands increased with the increase in restoration years. They also found that the organic carbon content remained stable after eight years of restoration, which was similar to the results of this study.
Carbon stocks in the surface soil (0–10 cm) were significantly correlated (p < 0.05) with plant density, belowground root carbon stocks (0–10 cm), and diversity. The carbon stock of the underground root system (20–30 cm) was correlated with the carbon stock of aboveground plants. When the plant density was higher, the underground biomass was greater. Interspecies competition and community succession resulted in a decrease in the density of plants, and the underground biomass decreased accordingly. As the restoration time increased, the underground root system first increased, then decreased, and finally, stabilized. The underground root system carbon stock reached a peak value in 2014, which was one year later than that of the aboveground vegetation carbon stock in 2013 [47]; the underground root system carbon stock and the accumulation of biomass in the underground root system were correlated.

4.2. Impact of Artificial and Natural Restoration on Carbon Stocks

In the short term (1–3 years), the carbon sequestration rate of the ecosystem promoted by artificial restoration is significantly faster than that of natural restoration [48], and soil organic carbon increases significantly after artificial vegetation restoration and organic matter addition [49]. The direction and speed of artificial restoration are determined by the artificially constructed soil, sown plants and artificial ecological environment. The important role played by artificial interference is to create better conditions and environments for plants, animals and microorganisms to enable them to enter the natural succession state more quickly [50].
In the long term (7th and 11th years), the 0–30 cm soil carbon stock and 0–10 cm underground root carbon stock were significantly lower after seven years of artificial restoration than after seven years of natural restoration (p < 0.05) (Figure 9 and Figure 10). After 11 years of artificial restoration, aboveground carbon storage was lower than that of natural restoration (p < 0.05) (Figure 8). Therefore, over a long period of ecological restoration, the carbon storage of artificial restoration is lower than or equal to that of natural restoration. The succession of natural vegetation shows more and more ecological adaptability and stability [51].
A good ecological restoration policy will not only have a good ecological effect, but also reduce the huge investment in ecological restoration funds. China’s “mountains and rivers” ecological engineering uses both natural restoration and artificial restoration, and makes efforts to find the best solution for ecological protection and restoration according to local conditions [52]. Natural restoration generally refers to achieving the goal of ecological restoration without or with minimal artificial intervention [53]. Continuous artificial disturbance reduces carbon storage, is not conducive to the stability of plant communities, and at the same time generates more capital investment. In the process of ecological restoration practice, short-term artificial restoration should be combined with long-term natural succession, which has a long-term effect on grassland ecological restoration [44].

4.3. Effects of Ecological Restoration on Soil Carbon Storage

Soil carbon stock accounted for more than 92.92% of the total organic carbon stock, which indicated that soil carbon stock is the largest carbon reservoir (Table 2). Thus, improving soil carbon stock is extremely necessary for ecological restoration. Plant diversity strongly affected the grassland productivity and soil C in [54]. The soil carbon stock in the 0–10 cm soil layer was significantly and positively correlated with the Shannon–Wiener index (H’) (p < 0.05); greater biodiversity was associated with higher soil carbon stock in the top layer of soil (0–10 cm). Therefore, combined with the results of the previous study, we can increase soil carbon storage through two measures. Firstly, the introduction and selection of plants in the preliminary stage can greatly influence the accumulation of soil carbon stocks in degraded ecosystems. Herbaceous plants with large biomass provide more carbon stock value. Thus, during the pre-plant selection process, annual plants with large biomass can be utilized more. Specifically, annual plants, plants with high carbon sequestration capacity, and plants with high carbon content become the targets of selection. After the completion of the growth cycle, the apomictic and aboveground parts of annual plants are retained and enter into the soil through decomposition and the underground root system. The underground root system is retained in the soil and preserved in the soil carbon pool through the decomposition of microorganisms, which contributes to the accumulation of the carbon pool during the restoration of vegetation and increases the size of the soil carbon pool. This carbon pool becomes the most basic nutrient reserve in the early stage of ecological restoration and provides more nutrients for the survival of animals and microorganisms in the soil, which can help to rapidly increase the soil carbon pool in infertile soil and ensure the initiation of the natural restoration process. Secondly, the carbon sequestration effect of soil can be increased by increasing species diversity through natural succession, and grassland vegetation can be naturally renewed and restored, which can effectively increase the carbon sequestration of vegetation, restore the original ecosystem, and further exert the ecological functions of the grassland ecosystem.
The difference between the soil surface carbon stock (0–10 cm) and native grassland carbon stock in seven years of artificial restoration, 11 years of artificial restoration, and 11 years of natural restoration was substantial (Figure 10), which showed that soil needed a longer period of restoration to approach the natural state.

4.4. Effects of Plant Diversity and Climate on Soil Carbon Stocks

In the long-term recovery process, both artificial and natural recovery will be affected by climate (Figure 11). The results of our study showed that from 2012 to 2022, the regional average annual temperature increased by 3.3 °C. An extreme drought occurred in 2022, and the average annual precipitation was only 254.3 mm, which was 311.9 mm lesser than that recorded in 2012 (Figure 2). Climatic factors directly affect the accumulation of carbon stocks, and short-term recovery can increase carbon stocks. Long-term carbon stocks are determined by a combination of multiple factors, such as climatic conditions (temperature and precipitation). The greenhouse effect exacerbates the increase in soil temperature, which can promote microbial activity and the accelerated decomposition of organic carbon. However, changes in the quantity of water available as the temperature increases can lead to different outcomes. Thus, it is a complex and multifaceted system. Some studies have shown that carbon density significantly decreases with an increase in temperature and increases with an increase in precipitation. Xin Xiaoping investigated grassland carbon stock and found that climatic factors influence carbon stock more [55].
Above all, the long-term monitoring of ecosystem restoration succession should be strengthened in the process of damaged ecosystem restoration practice. Short-term artificial intervention and long-term natural restoration should be combined to increase biodiversity and improve ecosystem stability to increase carbon storage.

5. Conclusions

(1)
The carbon stock changed over time during the ecological restoration of damaged sites. Overall, it first increased, then decreased, and finally, stabilized.
(2)
Soil carbon stocks accounted for more than 92.92% of the total organic carbon stocks, making it the largest carbon pool. The surface soil (0–10 cm) carbon stock was significantly correlated with the density of plants, underground root carbon stock (0–10 cm), and diversity.
(3)
Artificially restored plant communities contributed more to plant-soil carbon stock in the early stage of restoration and prepared for natural succession. In the later stage of ecological restoration, the natural restoration stage was more adaptable to the environment. The combination of artificial intervention and natural restoration can help establish a stable ecosystem faster and more effectively, and is an economic and effective measure of ecological restoration.

Author Contributions

Writing—original draft, J.W.; Writing—review & editing, G.H., Z.W. (Zhaoming Wang), J.Y., Z.W. (Zhongwu Wang), Z.L., S.L. and J.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Key Laboratory of Grassland Resources, Ministry of Education; Key Laboratory of Grassland Management and Utilization in Inner Mongolia Autonomous Region, China; Supported by the Innovation Team of the Ministry of Education—Research on Sustainable Utilization of Grassland Resources (IRT-17R59); Science and Technology Plan Project, Hohhot (2022-Social-4).

Data Availability Statement

Data will be made available on request.

Acknowledgments

We would like to express our gratitude to the editors and the reviewers for their comments and suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Li, T.; Kamran, M.; Chang, S.; Peng, Z.; Wang, Z.; Ran, L.; Jiang, W.Q.; Jin, Y.; Zhang, X.; You, Y.; et al. Climate-soil interactions improve the stability of grassland ecosystem by driving alpine plant diversity. Ecol. Indic. 2022, 141, 109002. [Google Scholar] [CrossRef]
  2. Lyons, K.G.; Török, P.; Hermann, J.-M.; Kiehl, K.; Kirmer, A.; Kollmann, J.; Overbeck, G.E.; Tischew, S.; Allen, E.B.; Bakker, J.D.; et al. Challenges and opportunities for grassland restoration: A global perspective of best practices in the era of climate change. Glob. Ecol. Conserv. 2023, 46, e02612. [Google Scholar] [CrossRef]
  3. United Nations Environment Agency. Resolution 73/284: United Nations Decade on Ecosystem Restoration (2021–2030). Available online: https://documents-dds-ny.un.org/doc/UNDOC/GEN/N19/060/16/PDF/N1906016.pdf?OpenElement (accessed on 13 August 2023).
  4. Liu, L.H.; Xing, S.H.; Gao, C.F.; Cui, J.C.; Wei, D.L.; Hu, Z.H.; Lu, C. The Research Progress, Problems and Prospects of Soil Carbon Storage at Home and Aboard. Chin. J. Sci. 2009, 40, 697–701. [Google Scholar]
  5. Li, X.; Fan, R.; Liu, X. Advance in Studies on Carbon Storage and Carbon Process in Grassland Ecostystem of China. Ecol. Environ. Sci. 2014, 23, 1845–1851. [Google Scholar]
  6. Li, L.; Cheng, Z. Carbon cycle of grassland ecosystem and response to global change. Chin. Bull. Bot. 1998, 15, 14–22. [Google Scholar]
  7. Jing, Z. Mechanism for Restoration and Regulatory of Degraded Grassland Ecosystem in Yunwu Mountain; Northwest A&F University: Xianyang, China, 2014. [Google Scholar]
  8. Tuo, D.; Lu, Q.; Que, X.; Cheng, L.; Yang, Y.; Gao, P.; Cui, G. Assessment of grassland ecosystem services in northern China. Acta Ecol. Sin. 2024, 44, 1–8. [Google Scholar] [CrossRef]
  9. Hu, H. Research situation of grassland degradation and restoration: Based on the evolution of research themes from 1930 to 2022. Sci. Found. China 2023, 37, 613–622. [Google Scholar] [CrossRef]
  10. Lin, Z.Q.; Dai, Y.J.; Umakant, M.; Wang, G.C.; Shangguan, W.; Zhang, W.; Qin, Z.C. Global and regional soil organic carbon estimates: Magnitude and uncertainties. Pedosphere 2023. [Google Scholar] [CrossRef]
  11. Wang, L. Effects of Vegetation Restoration of Different Tree Species on Soil Carbon in a Post-Mining Land; Taiyuan University of Technology: Taiyuan, China, 2018. [Google Scholar]
  12. Guan, B.; Liu, J.; Guo, D.; Li, H. Dynamics of soil organic carbon in mining area under different vegetation restoration modes. In Proceedings of the Abstracts of the 20th Meeting of Soil Environment Committee of Chinese Soil Society and Symposium on Farmland Soil Pollution and Remediation, Hefei, China, 5–7 August 2018. [Google Scholar]
  13. Pu, Q. Study on Soil Organic C, N and Enzyme Activity Characteristics with Different Ecological Restoration Patterns in Desert Land of Northwestern Sichuan, China; Sichuan Agricultural University: Ya’an, China, 2017. [Google Scholar]
  14. Ren, H.; Wu, C.; Li, S.; Yao, X. Research progress of soil organic carbon pool dynamics and its influencing factors in vegetation restoration process. In Proceedings of the Annual Meeting of China Association for Science and Technology, Symposium on Ecological Forestry and People’s Livelihood Forestry and Scientific and Technological Innovation in Western China, Guiyang, China, 25–27 May 2013. [Google Scholar]
  15. Pu, Q. Study on Changes of Organic Carbon, Nitrogen and Enzyme Activities in Sandy Soil under Different Ecological Restoration Models in Northwest River; Sichuan Agricultural University: Ya’an, China, 2017; p. 62. [Google Scholar]
  16. Liu, Y.; Zhu, G.; Deng, L.; Chen, L.; Shangguan, Z. Effects of natural vegetation restoration and afforestation on soil carbon and nitrogen storage in the Loess Plateau, China. Chin. J. Appl. Ecol. 2018, 29, 2163–2172. [Google Scholar]
  17. Li, X.; Zhang, X.; Sun, N.; Zhang, C.; Xu, M.; Feng, L. Impact of land uses on the ratio of soil organic and inorganic carbon. Plant Nutr. Fertil. Sci. 2018, 24, 1508–1519. [Google Scholar]
  18. Jiao, K.; Duan, J.; Wang, X.; Yi, X.; Chen, Z.; Bu, T. Analysis of soil carbon and nitrogen content and storage under different land use patterns. J. Mt. Agric. Biol. 2018, 37, 17–26. [Google Scholar]
  19. Li, L.; Qing, F.; Jiang, L.; Yaiao, X.; Wang, X. Effects of Land Use Type and Terrain on Soil Organic Carbon (SOC) Content in Semi-arid Region. Soils 2019, 51, 406–412. [Google Scholar]
  20. Yang, C.; Xie, Y.; Chu, X.; Cheng, G.; Yuan, F.; Yin, H.; Shan, G. Effects of Different Disturbing Approaches on Biomass and Carbon Storage of Sub-alpine Meadow in Northwest Yunnan Province. Chin. J. Grassland 2018, 40, 62–67. [Google Scholar]
  21. Ou, Y.; Wang, X.; Li, J.; Jia, H.; Zhao, Y.; Huang, Z.; Hong, M. Soil carbon, nitrogen and phosphorus contents and their ecostoichiometric characteristics of artificial grassland with different restoration years. Chin. J. Appl. Ecol. 2019, 25, 38–45. [Google Scholar]
  22. Wang, B.; Duan, Y.; Wang, W.; Li, X.; Liu, Z. Carbon and nitrogen storage and distribution patterns of desert ecosystems at different vegetation restoration stages in the eastern Hobq Desert. Acta Ecol. Sin. 2019, 39, 2470–2480. [Google Scholar]
  23. Li, J. Restoration Succession and Ecological Mechanism of Old-Fields in Subalpine Meadow; Lanzhou University: Lanzhou, China, 2010. [Google Scholar]
  24. Jiang, L.; Yang, X.; Yang, J.; He, X.; Lv, G. Effects of different management strategies on soil organic carbon and nitrogen pools in arid areas and their influencing factors. Acta Pratacult. Sin. 2018, 27, 22–33. [Google Scholar]
  25. Wang, W.; Wu, J.; Han, X. Estimation of soil carbon sequestration potential in typical steppe of Inner Mongolia and associated uncertainty. Chin. J. Appl. Ecol. 2012, 23, 29–37. [Google Scholar]
  26. Su, D. A Study on Vegetation Carbon Storage in Ecological Restoration Area of Abandoned Mines. Bull. Soil Water Conserv. 2018, 38, 234–237. [Google Scholar]
  27. Xu, D.; Xu, X.; Wang, G.; Tao, L. Variations in soil organic carbon content and distribution during natural restoration succession on the desert steppe in Ningxia. Acta Pratacult. Sin. 2017, 26, 35–42. [Google Scholar]
  28. Xie, J. Changes of Carbon Reserve, Energy and Nutrient Cycle of Extremen Degrade Ecosystem in the Process of Ecological Restoration; Fujian Agriculture and Forestry University: Fuzhou, China, 2002. [Google Scholar]
  29. Li, D.; Chen, H.; Xiao, K.; Zhang, W.; Wang, K. Nitrogen biogeochemical cycling and its effects on carbon sequestration in karst ecosystems, southwest China. Res. Agric. Mod. 2018, 39, 916–921. [Google Scholar]
  30. Li, L.; Huang, X.; Yang, H. A new framework for identifying ecological conservation and restoration areas to enhance carbon storage. Ecol. Indic. 2023, 154, 110523. [Google Scholar] [CrossRef]
  31. Chen, L.; Sun, J.; Taogetao, B. Changes in soil organic carbon and nitrogen stocks following revegetation in a semi-arid grassland of North China. J. Environ. Manag. 2023, 346, 118995. [Google Scholar] [CrossRef] [PubMed]
  32. SL 334-2005; Technical Specification on Irrigation and Drainage for Grassland in Pasture Areas. Standards Press of China: Beijing, China, 2005.
  33. LY/T 1237-1999; Determination of Organic Matter in Forest Soil and Calculation Carbon-Nitrogen Ratio. Standards Press of China: Beijing, China, 1999.
  34. LY/T 1228-2015; Nitrogen Determination Methods of Forest Soils. Standards Press of China: Beijing, China, 2015.
  35. LY/T 1232-2015; Phosphorus Determination Methods of Forest Soils. Standards Press of China: Beijing, China, 2015.
  36. LY/T 1234-2015; Potassium Determination Methods of Forest Soils. Standards Press of China: Beijing, China, 2015.
  37. LY/T 1251-1999; Analysis Methods of Water Soluble Salts of Forest Soil. Standards Press of China: Beijing, China, 1999.
  38. LY/T 1239-1999; Determination of pH Value in Forest Soil. Standards Press of China: Beijing, China, 1999.
  39. Wang, S.; Zhang, S.; Lin, X.; Li, X.; Li, R.; Zhao, X.; Liu, M. Response of soil water and carbon storage to short-term grazing prohibition in arid and semi-arid grasslands of China. J. Arid. Environ. 2022, 202, 104754. [Google Scholar] [CrossRef]
  40. Harbo, L.S.; Olesen, J.E.; Liang, Z.; Christensen, B.T.; Elsgaard, L. Estimating organic carbon stocks of mineral soils in Denmark: Impact of bulk density and content of rock fragments. Geoderma Reg. 2022, 30, e00560. [Google Scholar] [CrossRef]
  41. Ma, T.; Deng, X.; Chen, L.; Xiang, W. The soil properties and their effects on plant diversity in different degrees of rocky desertification. Sci. Total Environ. 2020, 736, 139667. [Google Scholar] [CrossRef] [PubMed]
  42. Guo, L.B.; Gifford, R.M. Soil carbon stocks and land use change: A meta analysis. Glob. Chang. Biol. 2002, 8, 345–360. [Google Scholar] [CrossRef]
  43. He, K.; Huang, Y.; Qi, Y.; Sheng, Z.; Chen, H. Effects of nitrogen addition on vegetation and soil and its linkages to plant diversity and productivity in a semi-arid steppe. Sci. Total Environ. 2021, 778, 146299. [Google Scholar] [CrossRef]
  44. Xu, T.; Zhang, M.; Ding, S.; Liu, B.; Chang, Q.; Zhao, X.; Wang, Y.; Wang, J.; Wang, L. Grassland degradation with saline-alkaline reduces more soil inorganic carbon than soil organic carbon storage. Ecol. Indic. 2021, 131, 108194. [Google Scholar] [CrossRef]
  45. Yang, L.; Zhao, Y.; Xu, W.; Zhou, G. Species diversity, biomass and relationship of alpine shrub in Qinghai province. J. Ecol. 2018, 38, 309–315. [Google Scholar] [CrossRef]
  46. Yu, Z.; Zhang, W.; Liu, Y.; Cao, L.; Bai, Y.; Xin, X.; Zhou, Y.; Xie, Z.; Hu, T.; Yang, P. Dynamics of SOC density and driving factors during the restoration of artificial grassland and abandoned farmland in Mu Us Desert, China. CATENA 2023, 224, 106991. [Google Scholar] [CrossRef]
  47. Peng, Y.; Chang, J.; Zhao, X.; Shi, Y.; Bai, Y.X.; Li, Q.L.; Yao, S.T.; Ma, W.H.; Fang, J.Y.; Yang, Y.H.; et al. Carbon sequestration capacity of grassland ecosystem and its promotion approaches in China. China Sci. Found. 2023, 37, 587–602+1–3. [Google Scholar]
  48. Yan, M.; Wang, L.; Hao, C.; Liu, Y.; Cui, F.; Ren, H.; Zhang, J. Effects of ecological restoration on soil organic carbon in post-mining lands. Acta Ecol. Sin. 2019, 39, 1838–1845. [Google Scholar]
  49. Jia, X.; Li, X.; Li, Y. Soil organic carbon and nitrogen dynamics during the re-vegetation process in the arid desert region. J. Plant Ecol. 2007, 31, 66–74. (In Chinese) [Google Scholar]
  50. Xu, C.; Xie, J.-S.; Zeng, H.-D.; Fu, L.C.; Chen, T.; Yang, Y.S. Effects of Natural Restoration and Artificial Rehabilitation on Carbon Storage in Degraded Forest Ecosystems. J. Subtrop. Resour. Environ. 2014, 9, 53–60. [Google Scholar]
  51. He, S. Soil Carbon Dynamics During Natural Grass Restoration on Abandoned Sloping Cropland in The Hilly Area of Loess Plateau; Northwest A&F University: Xianyang, China, 2015. [Google Scholar]
  52. Food and Agriculture Organization of the United Nations. Global Assessment of Soil Carbon in Grasslands; Food and Agriculture Organization of the United Nations: Rome, Italy, 2023. [Google Scholar]
  53. Commentary Department of This Newspaper. Properly Handle the Relationship between Natural Restoration and Artificial Restoration. The People’s Daily, 11 August 2023. [Google Scholar]
  54. Du, M.; Fan, J.; Liu, M.; Niu, X.; Wang, S. Mowing and phosphorus affect plant diversity and soil carbon and nitrogen storage under nitrogen enrichment in the semi-arid alpine steppe. CATENA 2022, 217, 106458. [Google Scholar] [CrossRef]
  55. Xin, X.; Jin, D.; Ge, Y.; Wang, J.; Chen, J.; Qi, J.; Chu, H.; Shao, C.; Murray, P.; Zhao, R.; et al. Climate change dominated long-term soil carbon losses of Inner Mongolian grasslands. Glob. Biogeochem. Cycles 2020, 34, e2020GB006559. [Google Scholar] [CrossRef]
Sustainability 15 15837 g001
Figure 1. Study area. Note: NG—native grassland; AR—artificial recovery; NR—natural recovery. (A) Inner Mongolia, China; (B) Location and environment of Sampling point, Hohhot (DEM); (C) Sample plots.
Figure 1. Study area. Note: NG—native grassland; AR—artificial recovery; NR—natural recovery. (A) Inner Mongolia, China; (B) Location and environment of Sampling point, Hohhot (DEM); (C) Sample plots.
Sustainability 15 15837 g001
Sustainability 15 15837 g002
Figure 2. Mean annual temperature and precipitation.
Figure 2. Mean annual temperature and precipitation.
Sustainability 15 15837 g002
Sustainability 15 15837 g003
Figure 3. Experimental procedures.
Figure 3. Experimental procedures.
Sustainability 15 15837 g003
Sustainability 15 15837 g004
Figure 4. Aboveground biomass from 2012 to 2022. Note: Different lowercase letters (a, b, and c) indicate significant differences in different years at the 0.05 level.
Figure 4. Aboveground biomass from 2012 to 2022. Note: Different lowercase letters (a, b, and c) indicate significant differences in different years at the 0.05 level.
Sustainability 15 15837 g004
Sustainability 15 15837 g005
Figure 5. Underground root carbon storage from 2012 to 2022. Note: Different lowercase letters (a and b) indicate significant differences in different years at the 0.05 level.
Figure 5. Underground root carbon storage from 2012 to 2022. Note: Different lowercase letters (a and b) indicate significant differences in different years at the 0.05 level.
Sustainability 15 15837 g005
Sustainability 15 15837 g006
Figure 6. Carbon storage in the 0–30 cm soil layer from 2012 to 2022. Note: Different lowercase letters (a, b, and c) indicate significant differences in different years at the 0.05 level.
Figure 6. Carbon storage in the 0–30 cm soil layer from 2012 to 2022. Note: Different lowercase letters (a, b, and c) indicate significant differences in different years at the 0.05 level.
Sustainability 15 15837 g006
Sustainability 15 15837 g007
Figure 7. Carbon storage in the 0–100 cm underground roots and soil from 2012 to 2022 (kg/hm2).
Figure 7. Carbon storage in the 0–100 cm underground roots and soil from 2012 to 2022 (kg/hm2).
Sustainability 15 15837 g007
Sustainability 15 15837 g008
Figure 8. Carbon storage in aboveground vegetation in different restoration years. Note: NG—native grassland; AR-7—artificial recovery for 7 years; NR-7—natural recovery for 7 years; AR-11—artificial recovery for 11 years; NR-11—Natural recovery for 11 years. Different lowercase letters (a and b) indicate significant differences in different years at the 0.05 level.
Figure 8. Carbon storage in aboveground vegetation in different restoration years. Note: NG—native grassland; AR-7—artificial recovery for 7 years; NR-7—natural recovery for 7 years; AR-11—artificial recovery for 11 years; NR-11—Natural recovery for 11 years. Different lowercase letters (a and b) indicate significant differences in different years at the 0.05 level.
Sustainability 15 15837 g008
Sustainability 15 15837 g009
Figure 9. Carbon storage in roots in different restoration years. Note: NG—native grassland; AR-7—artificial recovery for 7 years; NR-7—natural recovery for 7 years; AR-11—artificial recovery for 11 years; NR-11—natural recovery for 11 years. Different lowercase letters (a and b) indicate significant differences in different years at the 0.05 level.
Figure 9. Carbon storage in roots in different restoration years. Note: NG—native grassland; AR-7—artificial recovery for 7 years; NR-7—natural recovery for 7 years; AR-11—artificial recovery for 11 years; NR-11—natural recovery for 11 years. Different lowercase letters (a and b) indicate significant differences in different years at the 0.05 level.
Sustainability 15 15837 g009
Sustainability 15 15837 g010
Figure 10. Carbon storage in soil in different restoration years. Note: NG—native grassland; AR-7—artificial recovery for 7 years; NR-7—natural recovery for 7 years; AR-11—artificial recovery for 11 years; NR-11—natural recovery for 11 years. Different lowercase letters (a and b) indicate significant differences in different years at the 0.05 level.
Figure 10. Carbon storage in soil in different restoration years. Note: NG—native grassland; AR-7—artificial recovery for 7 years; NR-7—natural recovery for 7 years; AR-11—artificial recovery for 11 years; NR-11—natural recovery for 11 years. Different lowercase letters (a and b) indicate significant differences in different years at the 0.05 level.
Sustainability 15 15837 g010
Sustainability 15 15837 g011
Figure 11. Structural equation modeling [39]. Chisq = 76.879, p = 0.000, gfi = 0.851, cfi = 0.945, rmr = 0.089, srmr = 0.090, rmsea = 0.115. Note: MAT—annual mean temperature; MAP—annual mean precipitation; SC—soil carbon stock; RC—underground root carbon stock; AC—aboveground vegetation carbon; SW—Shannon–Wiener’s index; SP—Simpson’s index; N—number of individuals; Y—aboveground vegetation carbon stock; R1—0–10 cm underground root carbon stock; R2—10–20 cm underground root carbon stock; R3—20–30 cm underground root carbon stock; T1—0–10 cm soil carbon stock; T2—10–20 cm soil carbon stock; T3—20–30 cm soil carbon stock; C1—0–10 cm soil layer carbon content; C2—10–20 cm soil carbon content; C3—20–30 cm soil carbon content; Solid yellow lines and solid red lines indicate the significant positive and negative effects, respectively. Dotted yellow lines indicate the nonsignificant effects.
Figure 11. Structural equation modeling [39]. Chisq = 76.879, p = 0.000, gfi = 0.851, cfi = 0.945, rmr = 0.089, srmr = 0.090, rmsea = 0.115. Note: MAT—annual mean temperature; MAP—annual mean precipitation; SC—soil carbon stock; RC—underground root carbon stock; AC—aboveground vegetation carbon; SW—Shannon–Wiener’s index; SP—Simpson’s index; N—number of individuals; Y—aboveground vegetation carbon stock; R1—0–10 cm underground root carbon stock; R2—10–20 cm underground root carbon stock; R3—20–30 cm underground root carbon stock; T1—0–10 cm soil carbon stock; T2—10–20 cm soil carbon stock; T3—20–30 cm soil carbon stock; C1—0–10 cm soil layer carbon content; C2—10–20 cm soil carbon content; C3—20–30 cm soil carbon content; Solid yellow lines and solid red lines indicate the significant positive and negative effects, respectively. Dotted yellow lines indicate the nonsignificant effects.
Sustainability 15 15837 g011
Table 1. Experimental design.
Table 1. Experimental design.
Experimental DesignYear/
Recovery Method		SoilPlantMeasurement Frequency
LayerMeasurement ContentLayerMeasurement Content
Temporal succession
experiment		20120–10 cm
10–20 cm
20–30 cm		T1, T2, T3
C1, C2, C3		Aboveground biomass
Underground roots		M, SW, PI, SP, Y, N
R1, R2, R3		Once
every year
2013
2014
2016
2018
2022
Recovery method
experiment		Native grasslandEcological restoration in the 7th and 11th years
Artificial recovery
Natural recovery
Table 2. Carbon storage in the 0–30 cm soil layer from 2012 to 2022 (t/ha).
Table 2. Carbon storage in the 0–30 cm soil layer from 2012 to 2022 (t/ha).
YearSoil Carbon StorageProportionAboveground Carbon Storage of VegetationProportionCarbon Storage in Underground Roots of VegetationProportionTotal
201278.9899.23%0.110.14%0.500.63%79.60
2013102.1196.61%2.152.03%1.441.36%105.69
201469.3492.92%1.572.10%3.724.98%74.63
201652.5194.76%2.103.80%0.801.44%55.41
201853.5393.85%1.412.48%2.093.67%57.04
202255.9294.57%0.681.15%2.534.28%59.13
Table 3. Correlation of plant-soil carbon stocks with plant diversity and number of individuals.
Table 3. Correlation of plant-soil carbon stocks with plant diversity and number of individuals.
MSWSPPINYR1R2R3T1T2T3C1C2C3
M100.0020.0600.6920.3130.6650.380.2470.1510.1770.5580.2860.968
SW0.650 **10000.0410.5360.1280.0060.0130.9150.4320.0250.1330.024
SP0.587 **0.991 **1000.0290.4370.120.0050.0510.9440.3870.0660.2030.054
PI0.3730.932 **0.950 **10.0230.0020.4590.0740.0010.1360.9140.510.1210.2630.061
N0.991 **0.538 **0.485 **0.291 *10.690.440.5870.6050.0050.9720.5260.0480.1750.147
Y−0.09−0.271 *−0.289 *−0.406 **0.05410.5840.4280.0320.8710.1110.2570.7880.370.321
R10.22−0.084−0.105−0.1−0.104−0.07510.0010.1740.0480.1330.210.0060.3020.926
R2−0.096−0.204−0.209−0.239−0.0740.1080.439 **100.380.1680.4670.0530.1370.933
R3−0.197−0.363 **−0.368 **−0.423 **−0.0710.289 *0.1840.662 **10.970.1350.0230.6770.4560.865
T10.2520.323 *0.2580.1980.367 **0.0220.263 *0.1190.00510.0190.476000
T2−0.3090.0140.0090.0140.0050.213−0.2010.1850.2020.307 *10000
T3−0.292−0.105−0.116−0.088−0.0850.153−0.1680.0980.304 *0.0950.595 **10.1910.0010
C10.1290.295 *0.2430.2060.261 *0.0360.363 **0.2570.0570.853 **0.496 **0.174100
C20.2380.2030.1730.1520.1840.1230.1420.2030.1040.659 **0.928 **0.447 **0.796 **10
C30.010.315 *0.2710.2640.2060.143−0.0140.0120.0250.774 **0.778 **0.948 **0.750 **0.850 **1
Note: Upper triangle is the p-value at a significant level, lower triangle is the correlation coefficient; M—Margalef’s richness index; SW—Shannon–Wiener’s index; PI—Pielou’s evenness index; SP—Simpson’s index; N—number of individuals; Y—aboveground vegetation carbon stock; R1—0–10 cm underground root carbon stock; R2—10–20 cm underground root carbon stock; R3—20–30 cm underground root carbon stock; T1—0–10 cm soil carbon stock; T2—10–20 cm soil carbon stock; T3—20–30 cm soil carbon stock; C1—0–10 cm soil layer carbon content; C2—10–20 cm soil carbon content; C3—20–30 cm soil carbon content; **—0.001–0.01 significant correlation; *—0.01–0.05 significant correlation.
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

Wang, J.; Han, G.; Wang, Z.; Yun, J.; Wang, Z.; Li, Z.; Lv, S.; Qin, J. Plant-Soil Carbon Storage in Dynamic Succession of Ecological Restoration in National Grassland Natural Park. Sustainability 2023, 15, 15837. https://doi.org/10.3390/su152215837

AMA Style

Wang J, Han G, Wang Z, Yun J, Wang Z, Li Z, Lv S, Qin J. Plant-Soil Carbon Storage in Dynamic Succession of Ecological Restoration in National Grassland Natural Park. Sustainability. 2023; 15(22):15837. https://doi.org/10.3390/su152215837

Chicago/Turabian Style

Wang, Junfang, Guodong Han, Zhaoming Wang, Jinfeng Yun, Zhongwu Wang, Zhiguo Li, Shijie Lv, and Jie Qin. 2023. "Plant-Soil Carbon Storage in Dynamic Succession of Ecological Restoration in National Grassland Natural Park" Sustainability 15, no. 22: 15837. https://doi.org/10.3390/su152215837

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