Effects of Compost Application of Green Waste on Soil Properties: A Meta-Analysis

Abstract

Objective: With the accelerating urbanization process, the garden area is gradually expanding, and the production of green waste is also increasing. Composting green waste can not only reduce environmental pollution caused by incineration and landfill and improve the utilization efficiency of resources but also improve the soil and increase soil productivity. The study aims to investigate the comprehensive impact of green waste compost (GWC) application on soil nutrient conditions. Through comprehensive data analysis, the impact of compost application of green waste on soil properties was revealed as a reference for compost application and fertilizer reduction. Methods: Based on meta-analysis, we quantitatively investigated the response of soil properties to the application of green waste, collected published experimental data, and integrated 25 domestic and international literature to analyze the effects of different soil properties on soil nutrients. Literature was used to analyze the impact of different application rates of GWC on the physicochemical properties of soils with varying pH levels. The results were compared to control conditions with no GWC application. Results: The application of significantly improved soil quality by reducing soil bulk density and increasing the levels of soil organic matter, organic carbon, total nitrogen, available phosphorus, available potassium, and dehydrogenase activity. The increases in available potassium and soil organic carbon were consistently significant across all subgroups. However, the effects on available phosphorus and organic matter varied in significance depending on initial soil pH. Soil bulk density was influenced by the GWC content, while dehydrogenase activity showed significant effects only when initial soil pH was ≥8. Total nitrogen levels were significantly impacted by GWC application in soil with an initial pH of <8 and at GWC contents of ≥50%. Conclusion: The application of green waste compost demonstrates a positive effect on soil improvement. This study provides a comprehensive database that supports the use of GWC in enhancing soil quality and reducing the need for chemical fertilizers.

1. Introduction

Green waste (GW) primarily consists of natural fall residues from garden plants or artificially pruned plant materials, including leaves, grass clippings, and shrub cuttings [1]. It is also referred to as “garden waste” or “green waste” [2]. With the ongoing promotion of green urban areas, the national greening coverage is expanding, leading to a gradual increase in the volume of green waste. Moreover, green waste contains a substantial amount of lignin, cellulose, hemicellulose, and other organic nutrients [3], which are considered promising renewable biomass resources [4]. Traditional disposal methods are incineration and landfill, but incineration will pollute the atmosphere. Landfill is not only a waste of land, but its leachate may contaminate the soil and groundwater. Therefore, the simple treatment of green waste cannot adapt to the requirements of sustainable development and harmlessness. Resource-based treatment is the inevitable tendency of composting, which is not only an effective means of dealing with garden waste but also one of the treatment methods with great potential for development [5], but it realizes the recycling of resources by transforming waste into valuable fertilizers, cultivation substrates, soil conditioners, and so on [6], and further forms a closed loop of ecological circular economy. In addition, the operation technology of compost production is easy to grasp and promote, with great feasibility; at the same time, a wide range of sources produce large quantities, which guarantees the sustainability of compost production, as people’s requirements for the quality and safety of agricultural products continue to improve, as well as the awareness of environmental protection, the demand for green fertilizers is also increasing. Feng Xiaojie et al. [7] used green waste compost as organic fertilizer to fertilize forest soils and found that soil nitrogen, phosphorus, and potassium contents were the highest in the late stage of compost application alone or under the fertilizer composting scheme. Xu Dong et al. [8] concluded from a potting experiment that the reduction of chemical fertilizers and the application of green waste compost can effectively reduce the soil bulk density and pH and increase the porosity, water content, EC, and organic matter content. These studies further suggest that the application of green waste compost has a certain positive impact on the promotion of soil health.

Soil health is fundamental to the well-being of the planet [9]. It encompasses the physical, chemical, and biological aspects of soil that are critical for sustaining crop yields, environmental quality, and the health of both animals and humans [10,11]. However, soil pollution in China has garnered significant attention both at home and abroad. As early as 2014, the Ministry of Environmental Protection and the Ministry of Land and Resources of the People’s Republic of China jointly released a report on the status of soil pollution in China [12]. The report highlighted that the overall soil environmental condition in the country was concerning, with significant soil pollution in some areas, deteriorating soil environmental quality in arable land, and prominent soil environmental issues in industrial and mining waste sites [13]. To address soil health challenges, the use of organic materials for soil improvement is a crucial strategy to enhance soil productivity and advance eco-agriculture [14].

Meta-analysis, also known as integrative analysis or comprehensive analysis [15], is a statistical method used to synthesize and compare the results of multiple independent experiments addressing the same issue [16]. The goal is to derive more generalizable conclusions. Unlike traditional review articles, meta-analysis covers a broader range of studies and provides more detailed data, thereby offering a more robust evaluation of research ecology [14]. The application of meta-analysis to ecology began with Jarvinen [17], who introduced the method to the field, laying the groundwork for its ecological applications. In 1998, Peng Shaolin and Tang Xiaoyan [18] first introduced meta-analysis in China, and the method has been applied to soil ecology [16]. However, from the published articles, there are few that link meta-analysis with the effect of fertilization of green waste on soil properties. In the study, we hope to summarize and analyze the results of these studies by applying meta-analysis in order to draw more comprehensive and reliable conclusions so as to overcome the limitations of small sample sizes and inconsistent results of individual studies and to improve the scientific validity and credibility of the study. The article may have covered a wider geographical area through meta-analysis than previous studies more focused on specific regions or under specific conditions, thus providing a global or broad perspective understanding of the effectiveness of green waste compost application. This expanded perspective could help to identify commonalities and differences in the effectiveness of composting of green waste in different regions and soil conditions and inform the development of more generalized soil management strategies.

2. Materials and Methods

2.1. Data Sources and Screening

We searched for articles about the effect of green waste compost (GWC) on soil properties through China Knowledge (http://www.cnki.net, Date 23 September 2023) and the Web of Science (https://www.webofscience.com/, Date 23 September 2023). The search terms used in this study included “garden waste” OR “green waste” AND “composting” OR “compost” AND “soil properties” OR ”soil nutritions”, which is searched on China Knowledge by searching the corresponding Chinese language. As of 23 September 2023, a total of 6792 articles were retrieved, with 190 articles from China Knowledge and 6602 articles from Web of Science.

The retrieved articles were initially screened based on their titles and abstracts, of which those that did not meet the criteria were categorized as follows: (1) Meta-analysis of 23 articles; (2) review articles: 146 articles; (3) example report: 2 articles; (4) non-English and non-Chinese: 26 articles; (5) conference abstracts: 6 articles; (6) articles where the study object did not meet the criteria: 833 articles; (7) articles where the study purpose did not meet the criteria: 165 articles; (8) articles where the study object and purpose did not meet the criteria: 5531 articles.

After the initial screening, 60 articles were screened for further review. These 60 articles are thoroughly examined and re-screened based on the following conditions:

(1)

The trial data must contain the mean and standard deviation;

(2)

The trial design must contain both the GWC treatment and the control conditions;

(3)

The trial sites must have latitude and longitude or can be searched by the relevant software;

(4)

The trial has an obvious number of replicates and soil property indicators.

Based on the strict screening of the above criteria, 25 articles were deemed eligible for inclusion, of which 18 were in Chinese and 7 were in English. The specific flow chart is shown in Figure 1, And the coordinates of the specific test sites are presented in Figure 2.

2.2. Data Extraction

Based on experimental data, the indicators were grouped into the following categories: soil physical indicators, soil chemical indicators, soil biological indicators, and heavy metal indicators, as detailed in

Table 1. Study data and subgroups.

Indicator	Name
Soil physical indicators	Bulk density, pH, Electrical Conductivity (EC)
Soil chemical indicators	Organic carbon, total nitrogen, available phosphorus, available potassium, organic matter
Soil biological indicators	Urease, Alkaline Phosphatase, Dehydrogenase
Heavy metal indicators	Cadmium (Cd) content

. Soil indicators were selected if they were covered in at least three articles or more. Meta-subgroup analysis was conducted to examine the effect of GWC on these soil properties.

2.3. Meta-Analysis Methods

Meta-analysis statistical methods include the fixed effect model and random effects model. The fixed effect model assumes that all independent studies are drawn from a single overall sample, where the effect estimate of each study represents a single realization of the overall parameter. Differences between studies are attributed solely to sampling errors, with minimal variability between studies. In contrast, the random effects model means that each study is drawn from a different overall population, resulting in substantial variability both within and between studies. This model accounts for both within-study and between-study variability, with each study contributing its own overall effect estimate. The combined effect estimate of the meta-analysis is a weighted average of several different overall parameters [19]. For this study, the primary literature data extracted for this study were means (m), standard deviations (SD), and sample sizes (n). The mean effect estimate was calculated using the random effects model, with confidence intervals (95% Cl) computed under the assumption that individual study sites are independent.

In meta-analysis, the effect estimate is a key summary statistic in quantitative meta-analysis, primarily relying on the data extracted from the original literature [20]. Each independent experiment’s results are typically expressed as an effect indicator, and then, these effect estimates are merged into different studies to summarize the findings [21], where the diamond shape of the forest plot indicates the combined effect estimate and its confidence interval. Each study’s effect estimate and confidence interval are represented by the short horizontal lines following the study’s entry. The center of the plot features a line indicating no effect (SMD = 0). If the confidence interval or horizontal line intersects the no-effect line, it indicates that the combined effect is not statistically significant.

2.3.1. Effect Values and Heterogeneity Tests

Review Manager was employed to calculate effect estimates and combined effect estimates, which required the input of the mean, standard deviation, and sample size of the treatment and control groups of the relevant indicators. The confidence interval (CI) was set to 95% for the statistical analysis, and the standardized mean difference (SMD) was chosen for the effect statistics, which was calculated as follows [16]:

$$SMD={\displaystyle \frac{{\overline{X}}_E-{\overline{X}}_C}{S}}$$

(1)

$$S=\sqrt{{\displaystyle \frac{(N_E-1)S{D}_E^2+(N_C-1)S{D}_C^2}{N_E+N_C-2}}}$$

(2)

where ${\overline{X}}_E$, ${\overline{X}}_C$ are the mean values of the test and control groups in each independent study; SD_E, SD_C are the standrd deviations of the test and control groups, respectively; N_E, N_C are the sample sizes of the test and control groups, respectively; S is the total standard deviation.

Heterogeneity is usually tested using the chi-square Q test and the I² statistic before combining the effect values [16], which is calculated using the following formula [22]:

$$Q={\displaystyle \sum _{i=1}^{\mathrm{n}}{w}_i{(E_{si}^2-{\overline{E}}_s)}^2}$$

(3)

$$I^2={\displaystyle \raisebox{1ex}{$\left[Q-(n-1)\right]$}\!\left/ \!\raisebox{-1ex}{$Q$}\right.}$$

(4)

where w_i is the data weight of the ith group, n is the number of effect sizes, E_si is the effect estimate of the study, and ${\overline{E}}_s$ is the mean of the effect sizes of all studies. Heterogeneity was considered to exist among the included studies when p < 0.05 or I² > 50%, and no heterogeneity was considered to exist among the studies when p > 0.05 or I² < 50%. It is generally accepted that a random effects model is chosen when I² > 50% [16].

2.3.2. Subgroup Analysis

In subgroup analysis, only one combined effect model can be selected. When the choice of effect model for each subgroup is contradictory, a random effect model is usually selected for combining because the random effect model takes into account not only within-group heterogeneity but also between-group heterogeneity, and the results obtained are more reliable. Since heterogeneity is generally large when using the random effects model, subgroups need to be set up to analyze sources of consistency [23]. In this study, the GWC content was divided into two subgroups based on the GWC content of more than 50% and less than 50%. The necessity of using GWC contents as a grouping criterion is reflected in the fact that there may be differences in the effects of different contents of GWC on soil properties. Higher levels of GWC usually contain more organic matter and nutrients, which can directly improve soil structure, nutrient availability, and microbial activity. However, in practice, the content of GWC applied often varies widely due to resource constraints or agricultural needs. Therefore, subgroup analyses of GWC content can reveal the relationship between its content and soil improvement effects and help policymakers and farmers apply GWC more rationally for sustainable soil management. Additionally, since 24 of the 25 articles included in this study had a soil pH between 7 and 9, and only 1 article had a soil pH less than 7, in order to explore in depth the effect of the application of green waste compost on soil properties after application source of heterogeneity of its effect, it was categorized into two subgroups based on soil acidity and alkalinity, soil pH < 8 and soil pH ≥ 8. The main reason for using a pH of 8 as a threshold for grouping is that alkaline soils (pH ≥ 8) differ significantly from neutral and slightly alkaline soils (pH < 8) in terms of their chemical properties. For example, alkaline soils have higher concentrations of certain metal ions such as calcium, magnesium, and potassium, while the effectiveness of trace elements (e.g., zinc, iron, manganese, etc.) may be limited, which may result in a different chemical reaction between the applied green waste compost and the soil. In addition, pH 8 is often the critical point for plant nutrient uptake, and changes in soil pH after green waste compost application may directly affect crop growth results. This pH grouping allows for clearer identification of differences in the performance of green waste compost application in soils with different acidity and alkalinity and helps to reveal the sources of heterogeneity in their application effects. This process will help us to better understand the role of soil acidity and alkalinity in soil improvement and crop production, which in turn will provide a scientific basis for the rational use of green waste compost.

2.4. Data Processing

Excel 2021 was used for data summarization and organization; R 4.2.2 for point plot analysis of study data; Review Manager 5.4.1 for meta-analysis and subgroup analysis; and Origin Pro 2018C for plotting.

3. Results

3.1. Effect of GWC Application on Soil Physicochemical Properties

As shown in

Table 2. General characteristics of soil properties after application of compost from green waste.

Soil Properties	Forest Plot	Effect Estimate (95% CI)	p
Cd content		−2.63 (−7.87, 2.61)	0.33
Dehydrogenase		22.86 (3.20, 42.51)	0.02 *
Alkaline phosphatase		1.27 (−1.67, 4.21)	0.40
Urease		2.09 (−0.06, 4.25)	0.60
Organic matter		17.97 (12.51, 23.43)	<0.001 ***
Available potassium		27.60 (19.10, 36.11)	<0.001 ***
Available phosphorus		7.65 (3.27, 12.04)	0.0006 **
Bulk density		−5.55 (−8.88, −2.23)	<0.001 ***
Organocarbon		11.02 (8.18, 13.85)	<0.001 ***
Total nitrogen		19.40 (10.66, 28.15)	<0.001 ***
EC		2.29 (−0.11, 4.70)	0.06
pH value		0.06 (−1.30, 1.43)	0.93

Note: The dashed line in the forest plot represents the null line, i.e., SMD = 0. The black circle represents the combined effect value of the indicator, and the short black horizontal line represents the 95% confidence interval; * represents p < 0.05, ** represents p < 0.01, and *** represents p < 0.001.

, the synthesis of the included studies showed that the application of GWC into the soil had a significant effect (p < 0.05) on several soil properties. Specifically, GWC application significantly impacted soil dehydrogenase, soil organic matter, available potassium, available phosphorus, bulk density, organic carbon, and total nitrogen. The most pronounced effects were observed in soil-available potassium and dehydrogenase activity, with increased 27.60% and 22.86%, respectively. Soil total nitrogen, soil organic matter, and soil organic carbon content also showed notable improvements, with increases ranging from 10% to 20%, specifically 19.40%, 17.97%, and 11.02%, respectively. Additionally, soil bulk density showed a decrease of 5.55%, while the combined effect values of soil Cd content, alkaline phosphatase, urease, EC, and pH intersected with the null line, indicating that the results were not statistically significant, and at the same time, the p values were all greater than 0.5 and the effects were not significant.

3.2. Results of Subgroup Analysis

The results of these two subgroup analyses showed that for the 12 indicators included in this study, the source classes of heterogeneity after GWC application were categorized into three types: (1) The amount of GWC applied, (2) the pH of the initial soil, and (3) the amount of GWC applied and the pH of the initial soil.

3.2.1. Effect of Applied GWC Content on Soil Properties

As shown in Figure 3, after the results of subgroup analysis, the effects of soil bulk density and Cd content were mainly from the applied GWC content, while both subgroups of initial soil pH showed no statistical significance or missing data. It can be seen that the applied content of ≥50% GWC reduced soil bulk density and soil Cd content with effect estimate(95% CI) of −5.55 (−8.88, −2.23) and −2.90 (4.10, −1.70), respectively (based on the content of the included literature, the soils in which Cd content in the soil was determined after the application of GWC were Cd-contaminated soils, and since the number of other heavy metal element indicators in the screening process is less than three valid articles, they are not discussed in this paper).

3.2.2. Effect of Initial Soil pH on Soil Properties

As illustrated in Figure 4, the analysis of two subgroups based on soil pH revealed that the effects of GWC on urease, alkaline phosphatase, and dehydrogenase activity were not statistically significant when considering different levels of GWC application. However, when examining the effects based solely on the initial pH of the soil had their effect estimate and confidence intervals that did not intersect the dashed line with SMD = 0 and were statistically significant. Specifically, for soil with an initial pH ≥ 8, GWC application can decrease soil pH with an effect estimate (95% CI) of −1.61 (−2.98, −0.24). Conversely, in soil with an initial pH < 8, GWC application can increase soil pH with an effect estimate (95% CI) of 3.19 (0.79, 5.58). Additionally, in soil with an initial pH < 8, GWC application increased urease content with an effect estimate (95% CI) of 3.12 (0.42, 4.68). For the initial soil pH was ≥8, both alkaline phosphatase and dehydrogenase showed statistical significance with an effect estimate (95% CI) of −2.79 (−3.97, 1.62) and 68.36 (47.19, 89.53).

3.2.3. Effect of Applied GWC Content and Initial Soil pH on Soil Properties

All Four Subgroups Included in the Two Subgroup Classifications Are Affected

Figure 5 shows the forest plot of the subgroup analysis of available potassium and soil organic carbon. This plot indicates that both subgroups categorized by initial soil pH and those categorized by the amount of GWC applied exhibited statistically significant effects, and their combined effect values were statistically significant. The effect estimate (95% CI) for available potassium and soil organic carbon was 8.79 (0.57, 17.01) and 11.79 (8.06, 15.53), respectively. When the initial soil pH was ≥8, the effect estimate (95% CI) for available potassium and soil organic carbon was 62.76 (37.92) and 10.78 (6.49, 15.07); the effect estimate (95% CI) for available potassium and soil organic carbon was 14.26 (5.66, 22.86) and 11.79 (8.06, 15.53), respectively. Conversely, when GWC was applied ≥50%, and when GWC was applied <50%, the effect estimate (95% CI) for available potassium and soil organic carbon were 54.50 (29.63, 79.37) and 10.78 (6.49, 15.07), respectively.

The Four Subgroups Included in the Two Subgroup Classifications Are Partially Affected

Based on the results of subgroup analysis of EC and soil total nitrogen (Figure 6), both showed no statistical significance when the initial soil pH was ≥8, and both were statistically significant when the soil pH was <8, with 5.34 (0.93, 9.75) and 16.34 (5.60, 26.98), respectively. Whereas, based on the results of the two subgroups of the analysis of the applied GWC content, it can be seen that when the content ≥ 50%, soil total nitrogen was statistically significant with an effect estimate (95% CI) of 17.36 (6.59, 28.14), and EC was statistically significant with an effect estimate (95% CI) of 4.40 (1.38, 7.42) when the content was <50%.

According to the results of the subgroup analysis of available phosphorus and soil organic matter (Figure 7), different contents of applied GWC had statistically significant effects on both indicators. Notably, the highest increase in available phosphorus and soil organic matter was observed when GWC content was <50%, with an effect estimate (95% CI) reaching 358.55 (275.80, 441.31) and 48.23 (39.43, 57.03), respectively. Meanwhile, when the contents of GWC were ≥ 50%, the increase in available phosphorus and organic matter, although still present, was less pronounced compared to the <50% content subgroup, with an effect estimate (95% CI) of 5.29 (2.06, 8.52) and 10.13 (5.78, 14, 47), respectively. Additionally, when the initial soil pH was <8, available phosphorus did not show a statistically significant effect, whereas soil organic matter increased significantly, with an effect estimate (95% CI) of 28.47 (16.78, 40.15). When the soil pH was ≥8, soil organic matter did not show a statistically significant change, while available phosphorus showed a significant increase, with an effect estimate (95% CI) of 22.10 (6.10, 38.11).

4. Discussion

4.1. Effect of GWC on Soil Bulk Density

After composting the green waste, the organic matter is transformed from an unstable state into a stable humus. Compared to sludge and other municipal organic solid waste, the content of heavy metals is lower, and the compost product is cleaner and safer [6]. In addition, it is rich in essential macro- and micronutrients required by plants and soil [24]. Compost serves as a significant source of soil organic matter [25] and can be effectively utilized as a soil conditioner or fertilizer. The findings of this study demonstrate that the application of different levels of GWC has different effects on soil properties and specific indicators and that the application of GWC not only returns nutrients to the soil and reduces the use of chemical fertilizers but also increases soil porosity [5]. The application of GWC can not only return nutrients to the soil and reduce the use of chemical fertilizers but also increase the soil porosity and reduce the soil bulk density [26]. This is consistent with the results of the subgroup analysis of soil density in this study, and also with the results of the experiment of Ismail Celik et al. [27]. Furthermore, the results of AMLINGER et al. [28] also showed that the application of GWC could improve the soil structure, reduce the soil density, and increase the stability of aggregates in both sandy and clay soils [29]. This improvement is likely due to the low-density, porous nature of GWC [25], which enhances soil porosity. Additionally, the nutrient-rich nature of GWC boosts soil microbial activity, promoting the formation of humus, further improving soil structure, and reducing bulk density [14]. Soil bulk density reflects the soil’s porosity, tightness, and properties of water storage, water permeability, and aeration [30]. Higher soil bulk density results in a more compact, less structured, less aerated, and less permeable soil, which has an impact on plant growth and development. Therefore, a moderate reduction in soil bulk density provides more space for microorganisms in the soil, which is conducive to the growth and expansion of the crop root system.

4.2. Effect of GWC on Soil Organic Matter and Organic Carbon

From the results of the meta-analysis of the literature included in this study, the application of GWC significantly increased the content of soil organic matter and organic carbon. Furthermore, the increase in soil organic matter, regardless of the amount of applied GWC content or the initial pH of the soil, showed significance, which is in line with the results of the study by Seong Eun Lee et al. [31]. In addition, the study by Zheng Lijin [32] and Juan Li [33] also showed that the application of GWC can increase the soil organic matter content. The underlying reason may be attributed to the rich organic matter content of GWC itself, which can effectively increase soil organic matter when applied to the soil. Moreover, the results of the subgroup analyses showed that both subgroups of the applied GWC content increased organic matter and organic carbon in the soil, i.e., the positive effects on soil organic carbon and organic matter exhibited by GWC application were not affected by the amount of application. As shown in Figure 8, an effect estimate of 0 is a null line, and both organic matter content and organic carbon content showed positive effects after the application of different GWC contents. However, when the initial soil pH is ≥8, the effect of increasing organic matter is not significant. This may be due to the impact of soil acidity and alkalinity on the turnover and accumulation of soil organic matter [34]. Research by Dai Wanhong et al. [35] found that there was a highly significant negative correlation between organic matter content and soil pH in all six geographic regions.

4.3. Effect of GWC on Soil Total Nitrogen, Available Phosphorus, and Available Potassium

Soil total nitrogen, available phosphorus, and available potassium are the key factors for examining soil fertility because nitrogen, phosphorus, and potassium are all essential elements in the growth and development of plants [36]. From the results of the meta-analysis of the literature included in this study, the application of GWC significantly increased the content of total nitrogen, available phosphorus, and available potassium in the soil. This is likely because GWC already contains sufficient nutrients [37], which is consistent with the findings of Xiong Yinjun [36] and Li Qiao [6]. As shown in Figure 9, both subgroups of GWC application resulted in significant increases in soil-available phosphorus and available potassium, i.e., the positive effects of GWC application on soil-available phosphorus and available potassium were not affected by the amount of GWC applied, whereas the content of total nitrogen was significant at ≥50% of GWC, which has been shown to have a certain inhibitory effect on the growth and development of plants when too much GWC is applied to the soil [38], and Zou Yuzhu et al. [39] also proved that compost applied rated exceeding 40% to 50% can also inhibit plant growth. Therefore, the increase in total nitrogen content in soil with high GWC application rates might be attributed to the inhibitory effect on plant growth, which reduces nitrogen uptake and results in higher soil nitrogen content.

4.4. Effect of GWC on Soil Dehydrogenases

Soil is like a living organism, and the essential indicator of its biological activity is soil enzyme activity, so soil enzyme activities can reflect the performance of soil microorganisms [40], which is a kind of biological catalyst participating in various biochemical processes in soil, which not only reflects the level of biological activity, but also can characterize the speed of soil nutrient transformation, and to a certain extent can reflect the soil fertility status [41]. There were three soil enzyme indicators included in this study: urease, alkaline phosphatase, and dehydrogenase. Among them, urease activity characterizes the nitrogen supply capacity of the soil, and alkaline phosphatase activity is related to soil phosphorus conversion [42]. However, the meta-analysis results showed statistical significance only for dehydrogenase, which can characterize the level of soil microbial activity and can be used for simple toxicity testing and as an indicator for pollution monitoring [40]. The results of the meta-analysis of dehydrogenase by Teng Ying et al. [43] and Ma Qixue [44] indicate that dehydrogenase activity has a certain negative correlation with the degree of soil heavy metal pollution. Since the included study involved heavy metal-contaminated soils, the increase in dehydrogenase activity following GWC application indirectly suggests a reduction in the degree of heavy metal pollution. The effect was significant when the initial soil pH was ≥8, probably due to the alkaline pH of heavy metal-polluted soils, which changed more significantly after the application of GWC in the heavy metal-contaminated soil changed more significantly.

5. Conclusions

The meta-analysis in this study confirmed the significant effects of GWC in improving soil properties and enhancing soil nutrient and organic matter content and also highlighted its importance as a resourceful way to utilize organic solid waste in the context of the “dual carbon” goals [45]. Therefore, the promotion of GWC application not only helps to improve soil quality and promote sustainable agricultural development but also effectively reduces the application of chemical fertilizers. Future research should further explore the optimal application strategies of GWC under different soil types and climatic conditions to maximize its ecological and environmental benefits.