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Article

Effects of Litter Removal and Biochar Application on Soil Properties in Urban Forests of Southern China

by
Tianyi Yan
1,2,†,
Xin Liu
1,†,
Wende Yan
1,3,4,
Junjie Lei
1,
Yuanying Peng
5,
Jun Wang
1,3,4,
Xiang Zhang
1,3,4,* and
Xiaoyong Chen
6,*
1
College of Life Science and Technology, Central South University of Forestry and Technology, Changsha 410004, China
2
College of Information Technology, Hunan Biological and Electromechanical Polytechnic, Changsha 410127, China
3
National Engineering Laboratory for Applied Forest Ecological Technology in Southern China, Changsha 410004, China
4
Key Laboratory of Urban Forest Ecology of Hunan Province, Changsha 410004, China
5
College of Arts and Sciences, Lewis University, Romeoville, IL 60446, USA
6
College of Arts and Sciences, Governors State University, University Park, IL 60484, USA
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2024, 15(10), 1745; https://doi.org/10.3390/f15101745
Submission received: 31 August 2024 / Revised: 27 September 2024 / Accepted: 30 September 2024 / Published: 2 October 2024
(This article belongs to the Section Forest Soil)
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Abstract

:
Urban forests are crucial components of cities, serving as vital ‘green lungs’ that embody urban civilization and sustainability. Despite their significance in maintaining the urban environment and ecological functions, management practices for urban forests can be unreasonable at times. This study investigated the impact of two common practices, litter removal and biochar application, on soil properties in an urban forest in Changsha city, China. The aim was to understand how these practices affect soil carbon, nutrients, and microbial activity in urban settings. The results showed that soil water content (SWC), pH, available phosphorus (AP), and soil microbial biomass carbon (SMBC) were significantly reduced in areas where litter was removed compared to areas where litter was retained. Conversely, biochar application led to a significant increase in SWC, pH, AP, and SMBC. The treatment alone had no significant effects on total nitrogen (TN), soil organic carbon (SOC), and soluble soil organic carbon (SSOC) in the examined urban forests. However, the SOC and SSOC contents significantly increased over time with biochar application. Our results demonstrated that the influences of litter removal and biochar application on soil property were attributed to the regulation of AP and SMBC in the studied urban forests. This study provides a scientific basis and reference for understanding the sustainable management of urban environments and guiding future conservation efforts in urban greening spaces.

1. Introduction

Urban forests are an essential component of urban ecosystems, playing a vital role in city construction, sustainability, and development by providing numerous benefits to both the environment and human well-being [1,2]. Specifically, urban forests serve as critical green infrastructure for improving urban environmental quality, reducing the urban heat island effect, mitigating stormwater flooding and soil erosion, enhancing property values and quality of life, facilitating carbon (C) sequestration, and mitigating the adverse effects of urbanization and global climate change [3]. Therefore, appropriate management practices are necessary to guide urban forest policy and ensure the sustainable management of urban forests [4,5].
Litter is a key component in the process of returning C and nutrients from plants to the soil [6]. Forest litter plays a crucial role in modifying microenvironmental conditions on the forest floor [7], improving soil fertility and quality through decomposition, release, and leaching processes [8], maintaining nutrient biogeochemical cycles by consistently adding organic matter [9], and reducing peak flow during large flood runoff events in forest ecosystems [10]. Within urban areas, forest litter forms a significant C pool that has the potential to reduce greenhouse gas emissions [11]. Additionally, urban forest litter contributes to soil biodiversity conservation [12], nutrient transport and budget in urban watersheds [13], water infiltration, runoff processes, and stormwater regulation [14]. Despite the importance of litter in the structure and function of urban forests and green spaces, litter management practices are often inappropriate in urban areas. Under urban forest management practices in many cities, litter is routinely swept and removed from forests to maintain aesthetic appeal, reduce fire hazards, and minimize potential pest infestations [15,16]. The litter removal (NL) may have negative effects on C and nutrient cycling, microbial composition and activity, soil biodiversity, and ecological processes in urban areas [15]. However, the influence of NL on soil physical, chemical, and biological properties in urban forests is less documented. Less information is available about the effect of NL on soil C and nutrient status in urban settings.
Soil is a fundamental component of urban forest ecosystems, serving as a critical medium for nutrient cycling, water retention, and supporting plant growth. Soil properties such as pH, nutrient availability, and microbial activity play a crucial role in the health and functioning of urban forests [17]. However, urbanization has a lasting influence on soil C and nitrogen (N) dynamics, even in old urban parks and greenspaces compared to those in natural forests [18].
Biochar is a stable, carbon-rich material produced from the pyrolysis of organic biomass such as wood, crop residues, and manure. This process involves thermal decomposition in the absence of oxygen at temperatures ranging from 300 °C to 700 °C [19]. The production of biochar can be accomplished through various pyrolysis techniques. Biochar is noted for its high carbon content, porous structure, and extensive surface area, which enhance its capacity to retain water and nutrients and improve soil structure [20]. Its typically alkaline pH and high cation exchange capacity make it effective in neutralizing acidic soils and reducing nutrient leaching [19]. Notably, biochar is highly stable in the soil, offering long-term benefits for soil fertility and carbon sequestration, underscoring its potential as a sustainable tool for environmental management [21]. The application of biochar to soil has been demonstrated to enhance soil fertility, improve water retention, and significantly contribute to carbon sequestration [19]. Additionally, biochar application (BC) has emerged as a potential soil amendment strategy to improve soil fertility and enhance ecosystem resilience in urban forests [22]. However, there is a lack of comprehensive understanding regarding the impacts of biochar management practices on soils specifically in urban forests, particularly in the context of Southern China [23]. Biochar application (BC) and litter management are two promising strategies in urban forest management, each known for their potential to influence soil properties and ecosystem dynamics [24]. Meanwhile, NL can alter nutrient cycling, microbial communities, and organic matter decomposition rates in forest soils [25]. Despite their individual benefits, the interactive effects of BC and NL, as well as the temporal dynamics following their addition, remain poorly understood in urban forest ecosystems.
Among the diverse species found in urban forests, Camphor (Cinnamomum camphora) stands out as a dominant evergreen broad-leaved tree species, particularly in regions with subtropical or tropical climates [26]. Originating from East Asia, Camphor has been widely planted in urban areas worldwide due to its adaptability, aesthetic appeal, and the various ecosystem services it offers [27]. Camphor-dominated urban forests play a vital role in mitigating urban environmental issues such as air pollution, noise reduction, temperature regulation, and stormwater management [28]. The dense foliage and extensive root systems of Camphor trees contribute to improving air quality by capturing airborne pollutants and particulate matter [29]. Additionally, the shade provided by these trees helps to reduce urban heat island effects, thereby cooling urban environments and enhancing human comfort [27]. However, the effects of NL and organic matter addition on soil properties in Camphor forests remain largely unexplored. Understanding the effects of NL and BC on soil physiochemical properties is essential for predicting ecosystem responses and informing sustainable management strategies in urban forests [30].
This study aimed to address the existing knowledge gap by investigating the effects of litter removal (NL) and biochar application (BC) on soil physical, chemical, and biological properties in urban forests of Southern China. We hypothesized that (1) NL would decrease the soil organic carbon (SOC) and other nutrient contents due to the removal of organic matter from the litter inputs to the soil, and (2) BC would increase the accumulation of carbon and nutrient storage in the soil by providing more organic matter and stimulating microbial activities. The specific objectives of the research were (1) to quantify changes in the SOC, soluble soil organic carbon (SSOC), soil total nitrogen (TN), and available phosphorus (AP) under NL and BC treatments compared to litter-maintaining (L) treatments; (2) to evaluate the effects of L, NL, and BC treatments on soil properties in an urban evergreen broad-leaved forest; (3) to assess changes in the soil water content (SWC) and soil pH under these treatments; and (4) to evaluate the response of soil microbial activity to NL and BC treatments in urban forests. Our study provides a scientific reference and useful guidance for developing appropriate operations and management practices in urban forests and urban greening infrastructure.

2. Materials and Methods

2.1. Study Site

The experimental site is located at the Urban Forest Ecological Station of Central South University of Forestry and Technology in Changsha (Latitude: 28°13′ N, Longitude: 113°00′ E). The region experiences a typical subtropical humid monsoon climate characterized by short springs and autumns, long summers, and extended winters. The annual average temperature is 17.2 °C, with an annual average precipitation of 1361.6 mm and an annual relative humidity reaching 80%. Precipitation is mainly concentrated in late spring and early summer, with lower amounts during severe cold winters. The soil type in this experimental area is red loam, which is acidic, with relatively flat terrain. The soil is a typical red earth type developed from slate parent rock, equivalent to AllitiUdic Ferrosols according to the World Reference Base for Soil Resources. The soil texture ranges from clay loam to sandy loam, with a depth of about 1 m. The soil is acidic, with a pH ranging from 4.5 to 5.5, and the bulk density varies between 1.18 and 1.32 g/cm3. The vegetation in the study area predominantly consists of evergreen broad-leaved species, with a diverse mix of trees including Cinnamomum camphora (L.) Presl, Quercus variabilis L., Ilex championii Loes., Ligustrum lucidum W. T. Aiton, Elaeocarpus sylvestris (Lour.) Poir., Eucommia ulmoides Oliver, Choerospondias axillaris (Roxb.) B. L. Burtt & A. W. Hill, and Michelia macclurei Dandy. Three 20 m × 20 m plots were set up in the station. All trees (≥4 cm diameter at breast height, DBH) within each plot were identified by species, and their DBH and tree height were recorded. The average canopy width was determined by direct measurement using a tape measure to determine the horizontal distance across the widest part of the tree canopy. The average canopy width was determined by direct measurement using a measuring tape to determine the horizontal distance across the widest part of the tree canopy. The characteristics of the studied stands are presented in Table 1.

2.2. Experimental Design

The three 20 m × 20 m plots were selected using a randomized complete block design (RCBD) with split-plot arrangements to ensure each plot represented a distinct experimental block. This design accounted for spatial variability within the Camphor-dominated evergreen broad-leaved urban forest study area, thereby preventing observed differences between treatments from being attributed solely to site characteristics. Within each block, three main treatments were applied: (1) NL treatment, where litter was manually removed from the soil surface at the study’s onset; (2) BC treatment, where natural litter was removed followed by biochar addition; and (3) L treatment, where litter was maintained and undisturbed on the forest floor. Thus, the L treatment was designed as a control in the current study. The blocks were spaced at least 20 m apart to ensure spatial independence and minimize cross-contamination between treatments. Each treatment was applied to 1 m × 1 m subplots, replicated three times within the three larger plots (blocks), totaling 27 subplots. Subplots within blocks were spaced approximately 5 m apart to minimize cross-treatment effects. The NL treatment involved collecting litter from the litter-collection device three to four times per month. For the BC treatment, biochar was uniformly applied at 1500 kg/ha in March 2022, selected based on established effects [31]. Before BC treatment, litter was removed to maintain consistency with the NL treatment. Biochar was distributed evenly by hand to minimize soil disturbance and integrate into the topsoil. In the L treatment, litter was maintained on the forest floor and undergo natural decomposition processes and thus this treatment was a control. Experimental plots were carefully designed with 5 m spacing between subplots and 20 m spacing between blocks to prevent treatment contamination and ensure distinct observation of treatment effects. The experiment was initiated in March 2022 at the study site. Biochar was applied with careful attention to ensure even distribution without disturbing the soil structure, aiming to maximize its beneficial effects on soil properties such as water retention, nutrient availability, and microbial activity. Soil samples were then collected from the treated plots to assess changes in soil characteristics over time, providing valuable insights into the impact of biochar on soil health and fertility. These measures were crucial for maintaining consistent experimental conditions and preserving soil structure, enabling a clear assessment of the effects of NL and BC on soil properties. Soil sampling collection.

2.3. Soil Sample Collection and Processing

Soil samples were collected from the subplots in June, October, and December 2022, and March 2023, using the X-shaped five-point sampling method to ensure representative coverage of each subplot. A soil auger with an inner diameter of 4.0 cm was employed to obtain samples from the 0–15 cm surface layer, a depth that captures the most biologically active portion of the soil where significant root activity and nutrient cycling occur. The chosen sampling times, June, October, December, and March, were strategically selected to capture seasonal variations in soil properties, representing summer growth, post-growing season in autumn, winter dormancy, and early spring, respectively. To avoid cross-contamination, the sampling tool was thoroughly cleaned between each collection, and care was taken to sample away from tree trunks and other potential sources of bias. The soil samples from each subplot were then pooled to create a composite sample, ensuring a comprehensive representation of the subplot’s soil characteristics. Subsequently, the samples were transported back to the laboratory for detailed analysis. In the laboratory, the soil samples underwent standard processing protocols, including air-drying, sieving, and homogenization, before being subjected to analysis for various soil properties.

2.4. Measurements of Soil Properties

The soil pH was measured using a 1:2.5 soil-to-water ratio with a pH meter (Leici PHS-3C, Shanghai, China). The soil water content (SWC) was determined by drying samples in an oven at 105 °C until a constant weight was achieved. The soil organic carbon (SOC) content was quantified using the Walkley–Black procedure, which involves oxidation with potassium dichromate (K2Cr2O7) and heating. The soil total nitrogen (TN) concentration was determined using the Kjeldahl semi-micro method. The available phosphorus (AP) was measured using the double acid leaching spectrophotometric colorimetric method, where a 0.03 M ammonium fluoride and 0.025 M hydrochloric acid solution was used for extraction, and the absorbance was read at 882 nm. The soil microbial biomass carbon (SMBC) was determined by the chloroform fumigation–extraction method, followed by extraction with 0.5 M potassium sulfate (K2SO4), with a conversion factor of 0.45 applied to the carbon difference to estimate the microbial biomass. The soluble soil organic carbon (SSOC) was determined by extraction with 0.5 M potassium sulfate. Three parallel measurements were conducted for each soil sample to ensure accuracy and minimize experimental errors.

2.5. Statistical Analysis

Statistical tests were conducted to evaluate the effects of NL, BC, and L treatments, both individually and interactively, on soil properties. A two-way analysis of variance (ANOVA) was employed to examine the effects of the treatments, seasonal variations, and their interactions on soil properties over time. To account for repeated sampling across different seasons (June, October, and December 2022, and March 2023), repeated measures ANOVA was utilized. This approach allowed for the assessment of temporal variations and treatment effects concurrently, ensuring that variations within the same plots over time were properly accounted for. Prior to analysis, data were evaluated for normality and homogeneity of variance using the Shapiro–Wilk and Levene’s tests, respectively. Soil property data that did not meet these assumptions, such as SOC and MBC, which exhibited skewed distributions in their raw forms, were log-transformed to approximate a normal distribution and stabilize variances. Sample means and standard errors (SE) of soil properties were calculated for each treatment group: NL, BC, and L (litter maintained). To identify significant differences among the treatments, post-hoc Tukey–Kramer tests were performed following ANOVA. These tests compared the means of the NL, BC, and L treatments within each sampling plot to determine the specific treatment effects and interactions. All statistical analyses were conducted using the SAS statistical package (SAS Institute, Inc., Cary, NC, USA, versions 1999–2001) and the R statistical programming language (R Core Development Team, 2004), ensuring rigorous data analysis and result interpretation.

3. Results

The treatments had a significant effect on SWC, pH, AP, and MBC (p < 0.05), but not on TN, SOC, and SSOC in the examined forests (Table 2). However, the interaction of the two factors (treatment and time) had a significant influence on AP, SOC, SSOC, and MBC in the studied site (p < 0.05).
Across the three treatments, SWC exhibited significant variation over time (Figure 1). At the first sampling time (June 2022), no significant differences in SWC were observed among the treatments (p > 0.05). By the second sampling time (October 2022), SWC was significantly higher in the BC treatment compared to both the NL and L treatments (p < 0.05), while there was no significant difference between the NL and L treatments (p > 0.05). During the third sampling time (December 2022), no significant differences in SWC were detected among the treatments (p > 0.05). Conversely, in the fourth sampling time (March 2023), the L treatment exhibited a significantly higher SWC than both NL and BC treatments (p < 0.05), with no significant difference between the NL and BC treatments (p > 0.05) (Figure 1). Soil pH exhibited distinct patterns across all treatments throughout the study period, showing a consistent ranking order of BC > L > NL (Figure 2). The soil pH was significantly higher in BC compared to the control and in NL as well (p < 0.05). No significant differences in soil pH were found between the NL and L treatments (p > 0.05).
No significant differences in soil TN were found among the three treatments in each sampling time (p < 0.05) (Figure 3). The soil TN gradually increased in all treated plots with time. On average, the soil TN was in the order of BC > L > NL in the study site. The averaged soil AP was significantly higher in the BC treatment (18.39 ± 2.98 mg/kg) when compared to the control (4.71 ± 0.74 mg/kg) and the NL (4.52 ± 0.64 mg/kg) (p < 0.05) across all seasons (Figure 4). There were no significant differences in soil AP between the NL and L treatments, except the third and fourth sampling times.
Under the NL treatment, the average SOC was 18.04 ± 0.74 g/kg, exhibiting a seasonal pattern of initial decrease followed by an increase (Figure 5). Specifically, the SOC content was highest in June 2022 at 24.67 ± 0.36 g/kg and lowest in December at 14.27 ± 0.73 g/kg. SOC content differed significantly between the control treatment and the NL and BC treatments during the last three sampling periods. For the NL treatment, the average SOC was 17.86 ± 0.64 g/kg, showing a gradual decrease. The highest SOC content occurred in June 2022 at 23.26 ± 0.65 g/kg, while the lowest was recorded in March 2023 at 13.67 ± 0.83 g/kg. Under the BC treatment, the average SOC was 18.68 ± 0.83 g/kg. The highest SOC content was observed in June 2022 at 24.46 ± 0.46 g/kg, and the lowest was in October at 12.1 ± 0.54 g/kg. The SOC content was significantly lower under the BC treatment compared to the NL treatment in the second sampling period but significantly higher in the third and fourth sampling periods (p < 0.05) (Figure 5).
The seasonal pattern of SSOC content was significantly lower in the BC and NL treatments compared to the control treatment only during the second sampling period in October (p < 0.05) (Figure 6). Under the L treatment, the highest SSOC content occurred in December at 119.4 ± 3.42 mg/kg, while the lowest was recorded in October at 80.65 ± 3.5 mg/kg. Similarly, under the NL treatment, the highest SSOC content was 117.58 ± 7.34 mg/kg in December 2022 and 2023, while the lowest was in March at 95.96 ± 1.83 mg/kg. Under the BC treatment, the highest SSOC content was 140.37 ± 12.0 mg/kg in December, and the lowest was in October at 87.59 ± 3.63 mg/kg.
The average SMBC content under the NL treatment was 111.94 ± 6.36 mg/kg, with the highest value recorded in December 2022 at 231.21 ± 9.19 mg/kg and the lowest in June at 43.25 ± 6.48 mg/kg (Figure 7). There were no significant differences among the treatments during the first sampling period (p > 0.05). However, during the subsequent observations, SMBC was significantly higher in the BC treatment, followed by the L treatment, and then the NL treatment (p < 0.05, Figure 7). Under the L treatment, the average SMBC content was 215.68 ± 11.21 mg/kg, with the maximum value observed in December 2022 at 334.32 ± 9.90 mg/kg and the minimum in June at 86.46 ± 9.75 mg/kg. SMBC under the NL treatment was significantly lower than under the L treatment throughout the year, with both L and NL significantly lower than under the BC treatment (p < 0.05). Under the BC treatment, the average SMBC content was 250.26 ± 14.84 mg/kg, reaching a peak in October 2022 at 398.26 ± 8.51 mg/kg and dropping to a minimum in June at 91.18 ± 9.99 mg/kg. SMBC was significantly higher in the BC treatment than in the NL and L treatments during most sampling periods (p < 0.05) (Figure 7).

4. Discussion

The trends in SWC across different treatments provide valuable insights into the effects of NL and BC on soil moisture dynamics within urban forest ecosystems. SWC fluctuated throughout the study due to variations in management practices, precipitation, evaporation rates, and vegetation dynamics [32]. Litter was particularly effective in retaining moisture in early spring, while BC consistently exhibited a higher SWC compared to NL and L treatments, demonstrating its water-retention properties [33,34]. Biochar’s porous structure likely enhanced water infiltration and retention, reducing evaporation losses and promoting moisture availability for plants [35,36]. This is consistent with studies that show biochar’s positive effects on soil structure and water-holding capacity [37,38,39]. The higher SWC observed in the BC and NL treatments in October 2022 emphasizes their role in enhancing soil moisture toward the end of the growing season [31,40]. The NL treatment likely reduced competition for water between decomposing litter and soil, further improving moisture retention [41]. The superior SWC in BC during December 2022 highlights biochar’s ability to mitigate water deficits during dormancy [35,42]. However, by March 2023, the L treatment exhibited a significantly higher SWC, attributed to litter’s insulating effects, which reduce evaporation [43]. This indicates that while biochar improves soil moisture retention, litter may be more effective under certain seasonal conditions [44].
Changes in soil pH across treatments are key indicators of soil health in urban forest ecosystems. Our study consistently found the highest pH in the BC treatment, followed by L, and then NL, confirming biochar’s alkalizing effect due to its liming properties and high cation exchange capacity [31,38]. BC’s ability to maintain higher pH levels is linked to its alkalinity and buffering capacity against pH fluctuations [45,46]. These results align with research showing that soil pH tends to decline over time due to microbial activity and organic matter decomposition [32]. The interaction effects across sampling times suggest that the benefits of biochar, particularly its pH-stabilizing properties, persist over time, supporting its use for sustainable soil management [47]. NL, on the other hand, may cause soil acidification due to reduced organic matter inputs and increased nitrification rates [33].
The analysis of soil TN provides insights into the nutrient dynamics and cycling in terrestrial ecosystems. NL may reduce N availability by slowing litter decomposition and microbial activity, leading to decreased N mineralization [48]. In contrast, BC can increase N availability through N retention and enhanced microbial activity [34]. Our results show that the TN content in the BC and NL treatments initially increased, then stabilized, indicating that both biochar and litter retention may enhance N retention and availability through increased microbial activity and organic matter decomposition [49]. These findings align with studies highlighting the positive effects of biochar and litter retention on soil N dynamics [37]. In the control, TN initially rose but then declined, likely due to the lack of litter inputs, reducing N contributions from decomposition and microbial activity [50]. The third sampling period showed a significantly higher TN under the BC treatment compared to NL and the control, illustrating biochar’s positive impact on soil N. However, no significant differences were observed in later periods, suggesting that the N enrichment effect of biochar may diminish over time as N dynamics stabilize [37].
In this study, the highest soil AP recorded in March 2023 indicates sustained phosphorus enrichment from biochar. The BC treatment had a consistently higher soil AP compared to the NL and control treatments, reflecting biochar’s effectiveness in enhancing phosphorus availability through its phosphorus content and retention capabilities [19]. Conversely, NL may lower phosphorus availability by reducing litter decomposition and microbial activity [51]. The peak soil AP content in June coincides with the maximum litter decomposition, while the lowest levels in March suggest phosphorus depletion. The L treatment showed an initial depletion of phosphorus followed by enrichment and a subsequent decline, emphasizing the role of litter in maintaining phosphorus levels [26]. BC enhances phosphorus availability through mechanisms like desorption and microbial solubilization [52].
Under the NL treatment, the average SOC content displayed a seasonal pattern characterized by an initial decrease followed by an increase. reflecting the influence of seasonal changes on SOC dynamics, driven by temperature, precipitation, and vegetation growth [30]. The peak SOC content in June 2022 suggests increased organic matter inputs during high plant productivity, while the December decline likely results from reduced microbial activity and slower decomposition in colder conditions. Significant SOC differences were observed between the L treatment and the NL and BC treatments in later sampling periods, indicating that management practices, such as NL and biochar addition, impact SOC dynamics over time. The NL treatment showed a gradual SOC decrease, possibly due to reduced litter inputs and microbial decomposition. In contrast, BC treatment exhibited fluctuating SOC content, suggesting biochar’s complex effects on soil C dynamics [19]. These findings highlight how management practices affect SOC dynamics, with NL potentially reducing SOC and biochar possibly mitigating these losses or enhancing SOC accumulation under certain conditions. Treatment type alone did not significantly affect TN, SOC, and SSOC in the urban forests, consistent with other studies indicating that short-term management interventions might not immediately alter these soil properties [53,54,55]. However, SOC and SSOC significantly increased over time with biochar application, underscoring biochar’s role in long-term soil improvement. The gradual increase in SOC and SSOC with biochar is attributed to its integration into the soil, enhancing microbial processes, soil structure, and nutrient retention [56,57]. This time-dependent effect highlights biochar’s potential for long-term soil carbon sequestration and quality improvement, supporting its use as a sustainable soil management practice in urban forest ecosystems [58].
The observed trend of an initial increase followed by a decrease in SMBC under all treatments reflects the dynamic nature of microbial populations in response to changing environmental conditions and resource availability [30]. Soil microbial biomass is sensitive to variations in organic C inputs and environmental factors like temperature and moisture [59]. Under the NL treatment, the relatively low SMBC content indicates limited organic C inputs from litter decomposition, consistent with studies showing that NL re-duces microbial biomass due to decreased substrate availability [16]. Conversely, BC significantly increased SMBC compared to NL and L treatments, particularly in later sampling periods. Biochar provides a stable C source and habitat for microorganisms, enhancing microbial growth and activity [60]. The higher SMBC under BC and the peak observed in December across all treatments correspond to favorable conditions for microbial growth, such as increased soil moisture and organic matter availability [61].

5. Conclusions

This study investigated the impacts of forest management practices, specifically NL and BC, on soil properties in urban forests. These properties included SWC, soil pH, TN, AP, SOC, SSOC, and SMBC. The BC treatment consistently resulted in a higher SWC and pH compared to NL and L treatments, highlighting its potential to improve soil water retention and increase soil alkalinity. Moreover, biochar-treated soils exhibited higher levels of AP and SMBC, indicating enhanced nutrient availability and a more robust soil microbial community. While SOC and SSOC levels did not significantly differ among treatments, these two variables significantly increased over time with the interaction of BC application in the examined urban forests. Overall, these findings demonstrate the importance of retaining litter on the forest floor and underscore the potential of biochar as a sustainable soil management strategy to enhance soil fertility, nutrient cycling, and microbial activity in urban forests. Further research is warranted to explore the long-term effects of litter retention and biochar application across diverse urban greening spaces and their implications for soil health and ecosystem functioning in urban environments.

Author Contributions

Conceptualization, W.Y., X.Z. and X.C.; methodology, T.Y., X.L., X.Z. and X.C.; data curation, T.Y. and X.L.; investigation, T.Y. and X.L.; validation, J.L., Y.P. and J.W.; formal analysis, X.Z., J.L. and J.W.; visualization, J.L. and Y.P.; resources, X.Z. and X.C.; writing—original draft preparation, T.Y., X.L. and Y.P.; writing—review and editing, T.Y., X.L., W.Y., J.L., Y.P., J.W., X.Z. and X.C.; supervision, W.Y., X.Z. and X.C.; project administration, W.Y. and X.Z.; funding acquisition, W.Y., X.Z. and X.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Joint Funds of the National Natural Science Foundation of China (U21A20187), the Creative Research Groups of Provincial National Science Foundation of Hunan (2024JJ1016), and a ‘Shu Ren Scholar’ plan of Central South University of Forestry and Technology.

Data Availability Statement

The data presented in this study are available upon request from the corresponding authors.

Acknowledgments

We would like to thank Can Mao and Xiaoxin Feng for their assistance in fieldwork and lab analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

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Forests 15 01745 g001
Figure 1. Changes in soil water content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
Figure 1. Changes in soil water content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
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Figure 2. Changes in soil pH values under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
Figure 2. Changes in soil pH values under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
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Figure 3. Changes in soil total nitrogen (TN) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
Figure 3. Changes in soil total nitrogen (TN) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
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Figure 4. Changes in soil available phosphorus (AP) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
Figure 4. Changes in soil available phosphorus (AP) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
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Figure 5. Changes in soil organic carbon (SOC) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
Figure 5. Changes in soil organic carbon (SOC) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
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Figure 6. Changes in soluble soil organic carbon (SSOC) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
Figure 6. Changes in soluble soil organic carbon (SSOC) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
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Figure 7. Changes in soil microbial biomass carbon (SMBC) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
Figure 7. Changes in soil microbial biomass carbon (SMBC) content under litter removal (NL), litter retention (L), and biochar application (BC) treatments during the study period. Bars with different capital letters indicate significant differences among the three treatments at the same sampling time, while bars with different lowercase letters indicate significant differences among the different sampling times within the same treatment (p < 0.05).
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Table 1. Characteristics of the examined forests in the study site *.
Table 1. Characteristics of the examined forests in the study site *.
Forest TypeStand Density (Tree ha−1)Mean DBH (cm)Mean Tree Height (m)Canopy Width (m)
Evergreen Broad-leaved Forests *1025 ± 32522.2 ± 12.312.5 ± 4.34.9 × 5.1
* Values: mean ± S.E. The composition of the studied forests included Cinnamomum camphora (61%), Elaeocarpus sylvestris (22%), Quercus variabilis (7%), Cyclobalanopsis axillaris (5%), and other tree species (5%).
Table 2. Results from two factor analysis of variance for various soil properties under different treatments in the study site.
Table 2. Results from two factor analysis of variance for various soil properties under different treatments in the study site.
Soil Property.FactordfFp
SWC (%)Time316.51***
Treat25.79**
Time × Treat61.13
pHTime392.29***
Treat223.20***
Time × Treat61.81
TN (g/kg)Time310.75***
Treat20.62
Time × Treat60.43
AP (mg/kg)Time333.08***
Treat2190.05***
Time × Treat620.39***
SOC (g/kg)Time399.10***
Treat21.53
Time × Treat614.78***
SSOC (mg/kg)Time319.24***
Treat20.35
Time × Treat63.35*
MBC (mg/kg)Time3269.67***
Treat2127.95***
Time × Treat619.25***
* < 0.05, ** < 0.01, *** < 0.001.
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Yan, T.; Liu, X.; Yan, W.; Lei, J.; Peng, Y.; Wang, J.; Zhang, X.; Chen, X. Effects of Litter Removal and Biochar Application on Soil Properties in Urban Forests of Southern China. Forests 2024, 15, 1745. https://doi.org/10.3390/f15101745

AMA Style

Yan T, Liu X, Yan W, Lei J, Peng Y, Wang J, Zhang X, Chen X. Effects of Litter Removal and Biochar Application on Soil Properties in Urban Forests of Southern China. Forests. 2024; 15(10):1745. https://doi.org/10.3390/f15101745

Chicago/Turabian Style

Yan, Tianyi, Xin Liu, Wende Yan, Junjie Lei, Yuanying Peng, Jun Wang, Xiang Zhang, and Xiaoyong Chen. 2024. "Effects of Litter Removal and Biochar Application on Soil Properties in Urban Forests of Southern China" Forests 15, no. 10: 1745. https://doi.org/10.3390/f15101745

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