Impact of Various Vegetation Restoration Strategies on Soil Characteristics

Kangning Xiong^*
*Correspondence: Kangning Xiong, Department of Management Science, Institute of Guizhou Normal University, Guiyang 550001, Guizhou, China, Email:

Keywords

Restoration strategies • Soil characteristics • Vegetation

Introduction

The majority of these opencast coal mines are situated in northwest China's fragile regions, such as Shanxi Province, Inner Mongolia, and Shaanxi Province. Extensive mining sites are developing in these places as a result of the incentives provided by rapid economic development. Northwest China's loess region is home to the Pingshuo Antaibao opencast coal mine, which opened in 1985. Due to long-term and extensive mining disruptions, the eco-environment has undergone significant alterations. In this mining area3, the annual average area of land destroyed was almost 6.6 104 hm². The mining operations significantly devastated the vegetation, altered the soil's physicochemical qualities, and created a sizable mountain [1].

Vegetation restoration may fully utilise the function of the soil-plant composite system, improve the local environment, and promote a regional ecological balance as one of the crucial steps in the governance of an ecological environment6. Vegetation, soil, and topography variables are tightly related at various scales. Understanding how topographic and soil factors affect vegetation restoration is crucial for both land reclamation and ecological restoration in opencast coal mining areas. The process behind the induction of some soil modifications that favour an increase in plant growth in mined areas8 involves functional changes in vegetation. At various scales, vegetation restoration influences soil qualities, and vice versa, topographic features affect vegetation development9. Previous research has shown that soil organic matter, total nitrogen, available phosphorus, and available potassium are all strongly positively linked with vegetation growth. Restoring vegetation can also considerably increase soil water retention, bulk density, and porosity. In contrast, under conditions of vegetation growth and development, the soil organic matter has a noticeable impact on the vegetation and also has some impacts on the other soil nutrients [2].

Additionally, vegetation managers and ecologists have long sought to comprehend the impact of topographic gradients on vegetation restoration. According to a study done to examine the early stages of the relationship between vegetation and topographic factors, landscape variations in vegetation are strongly correlated with these factors, and in Nevada, USA, shrub land vegetation patterns frequently exhibit a strong correlation with the slope position. In the remaining moist Afromontane forest of Wondo Genet, which is located in south central Ethiopia19, the tree density is adversely correlated with elevation, slope, and slope aspect. Previous research in this area has shown that the slope position and altitude are mostly related to the correlation between species diversity [3].

Description

Traditional multivariate analysis has traditionally been used in previous studies on the interactions between vegetation, soil, and topographic factors, but the results may be subjective as a result of the high number of environmental variables and the significant topographic variability. The drawback of data conversion and multivariate analysis, such as Canonical Correspondence Analysis (CCA) and Redundancy Analysis (RDA), can be avoided using analysis based on CANOCO. The effects of topographic features and soil characteristics on vegetation growth have been documented in areas unaffected by opencast coal mining; however, studies in opencast coal mining areas are uncommon, particularly when using the CCA or RDA method. Therefore, the goal of this study was to examine how topographic and soil parameters affected vegetation restoration utilising CCA and RDA [4].

With vegetation-environment correlations of 0.636 on the first axis and 0.492 on the second axis, there was a significant relationship between vegetation and environmental parameters (soil and topography). The first four axes of the RDA explained a total of 37.1% of the variance in the data on vegetation presence. In other words, the first and second axes together explained 96.3% of the relationship between the vegetation and the environment. The cumulative percentage variance of the vegetation-environment relationship on the first axis was 89.7%, while that on the second axis was 6.6%. This result showed a strong correlation between the variables under study and the vegetation and environment axes. The Monte Carlo permutation test revealed a correlation between the tested environmental factors and vegetation restoration [3].

The relationship between soil variables and topography parameters discovered by the RDA, the available P had a -0.213 correlation with slope. The slope position (0.379) and slope aspect had good correlations with the total N. (0.251). The slope and soil water content have a favourable relationship. The slope and slope position had a positive correlation with the rock content. The silt concentration had a statistically significant negative connection with the slope (0.210), according to the study of the soil texture factors. In contrast, the slope and slope position were significantly positively correlated with the sand content. The topographic parameters and the clay and sand contents did not clearly correlate [5].

As the evaluation aim for the current study, we calculated the ference between the soil quality of the paired treated and untreated sites. We assessed soil quality metrics related with various vegetation restoration types as well as those of comparable adjacent unrestored croplands. Since the soil parent material, climate, and topographic circumstances are the same at every pair of nearby restored and unrestored sites, our evaluation object may exclude the effects of those environmental elements and only represent the benefits of vegetation restoration. As a result, our findings will enable more accurate assessment of the various effects of different vegetation types distributed across various sites on soil quality.

Conclusion

The means and standard errors of all the data are displayed. The differences in the soil physicochemical and SQI values among various vegetation restoration types and various soil layers were evaluated at the P 0.05 level using one-way analyses of variation, followed by the Tukey pairwise multiple comparison test. The differences between the restored project and the nearby unrestored crops were assessed using a paired sample t test. The indicators for the soil were chosen and weighted using PCA and Pearson's correlation analysis. The variation in SQI that was explained by each factor was calculated using a boosting regression tree model (BRT), which was carried out in R (R 3.50), using the gbm. step function from the dismo package.

Acknowledgement

None.

Conflict of Interest

None.

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