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Article

Effects of Bamboo-Sourced Organic Fertilizer on the Soil Microbial Necromass Carbon and Its Contribution to Soil Organic Carbon in Moso Bamboo (Phyllostachys edulis) Forest

by
Zhiyuan Huang
1,2,3,
Qiaoling Li
1,
Fangyuan Bian
1,3,
Zheke Zhong
1,3 and
Xiaoping Zhang
1,3,4,*
1
China National Bamboo Research Center, Key Laboratory of State Forestry and Grassland Administration on Bamboo Forest Ecology and Resource Utilization, Hangzhou 310012, China
2
Bamboo Research Institute, Nanjing Forestry University, Nanjing 210037, China
3
Long-Term Observation and Research Station for Farmland Shelterbelt Ecosystem in Hangzhou-Jiaxing-Huzhou Plain, Hangzhou 310012, China
4
Engineering Research Center of Biochar of Zhejiang Province, Hangzhou 310021, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(3), 553; https://doi.org/10.3390/f16030553
Submission received: 17 February 2025 / Revised: 11 March 2025 / Accepted: 19 March 2025 / Published: 20 March 2025
(This article belongs to the Special Issue Ecological Research in Bamboo Forests: 2nd Edition)
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Abstract

:
Microbial necromass carbon (MNC) is crucial for soil carbon sequestration in bamboo (Phyllostachys edulis) forests. However, the response of MNC to bamboo-sourced organic fertilizers (BSOF) prepared by composting bamboo plant growth-promoting microorganisms and bamboo residues remains unclear. This study examined MNC and its contribution to soil organic carbon (SOC) in Moso bamboo plantations under four BSOF treatments: control (CK, 0 t·hm−2), low fertilizer application (LF, 7.5 t·hm−2), medium fertilizer application (MF, 15 t·hm−2), and high fertilizer application (HF, 30 t·hm−2) across 0–20 cm and 20–40 cm soil layers. In these two layers, HF and MF significantly (p < 0.05) increased the total MNC, fungal necromass carbon (FNC), and their contributions to SOC compared to CK, and HF led to higher (p < 0.05) bacterial necromass carbon (BNC) levels and SOC contributions than LF and CK. Soil depth and BSOF treatment were found to interact significantly. A random forest model showed that in the 0–20 cm layer, SOC was the best predictor of total MNC and FNC, whereas available potassium was optimal for BNC. Nitrate-nitrogen (NO3−-N) was the top predictor for total MNC, BNC, and FNC in the 20–40 cm layer. Partial least squares path modeling indicated that available soil nutrients directly influenced BNC and FNC, affecting SOC accumulation. These findings suggest a new method for enhancing soil carbon sequestration in bamboo forests.

1. Introduction

Moso bamboo (Phyllostachys edulis) forests are among the highly significant forest types in subtropical regions, particularly China. As of 2021, China has 5,277,600 ha of Moso bamboo forest stands, accounting for 69.78% of the total bamboo forest area [1]. These forests demonstrate remarkable productivity and environmental adaptability and contribute substantially to ecological sustainability and economic development [2,3]. Moso bamboo is characterized by rapid biomass accumulation, making it an ideal subject for studying ecosystem carbon dynamics [4,5]. Yen and Lee found that the average aboveground C sequestration of Moso bamboo was 8.13 ± 2.15 Mg C ha−1 yr−1, which was much higher than that of Chinese fir (3.35 ± 2.02 Mg C ha−1 yr−1) [6]. Li et al. showed that the carbon stock in Chinese Moso bamboo forests is approximately 611.15 ± 142.31 Tg C, of which 75% is in 0–60 cm soil [7]. Intensive management practices, particularly heavy fertilization, are crucial for maintaining high productivity in bamboo forests [8,9]. However, long-term intensive management has led to serious degradation of the ecological functions of Moso bamboo forests, reduction in soil organic carbon, and other problems that seriously restrict the healthy development of the bamboo industry [10,11,12]. Therefore, the manner in which bamboo forests are cultivated must be changed to realize the green and healthy development of bamboo resources in the future.
Soil organic carbon (SOC) is one of the highly significant indicators of soil quality, affecting soil fertility and structure, and is closely related to the carbon sink capacity of the soil and environmental impacts [13,14,15,16]. Increased exogenous C input is an important method for increasing SOC content [17]. Common organic materials, such as straw, livestock manure, and biochar, can deliver large amounts of organic matter to the soil, thereby increasing SOC and its active C fraction [4,18,19]. Organic fertilizer input has shown superior benefits in enhancing microbial activity, improving soil quality, and promoting sustainable C sequestration in forest ecosystems [20,21]. Studies have shown that the effect of exogenous organic materials on SOC is related to the application method, the type of organic material, and its properties [22,23]. Bamboo processing produces a large amount of waste that is rich in nutrients, and irrational treatment causes environmental pollution as well as a waste of resources. Fermentation is an effective method of waste resource utilization. Previous studies have shown that pH, conductivity, cation exchange, and nitrogen content are significantly increased; the carbon-to-nitrogen ratio decreases after the fermentation of bamboo chips [24], and application to bamboo forests can improve the structure of soil microbial communities and raise the pH of the soil [25]. Bamboo-sourced organic fertilizer (BSOF), prepared using bamboo forest-promoting bacteria and solid waste from bamboo processing, can be used as organic soil amendments [26]. Currently, BSOF is prepared on-site using bamboo processing waste in bamboo forest production areas, and then it is returned to the bamboo forests, forming a circular management model of raising bamboo with bamboo. However, the effects of the BSOF on the soil ecology of bamboo forests remain unclear.
Microbial necromass carbon (MNC) is a critical component of soil organic matter, accounting for 34%–44% of the SOC in forest soil ecosystems [27]. This significant C pool is formed through the accumulation of dead microbial cells and residual products and plays a crucial role in long-term C sequestration [28,29]. Recent studies have revealed that MNC is more stable than plant-derived organic matter, with residence times ranging from decades to centuries [30,31]. Research across various ecosystems has demonstrated that MNC accumulation is strongly influenced by environmental conditions, substrate availability, and microbial community composition [32,33]. The formation and stabilization of MNC involve complex interactions between microbial metabolism, soil properties, and management practices [34]. Studies have shown that agricultural practices significantly affect MNC dynamics, with organic farming systems generally showing higher MNC accumulation than conventional systems [21]. A global meta-analysis revealed that climate-smart agricultural practices could enhance MNC by 15%–30% compared with traditional management approaches [35]. Furthermore, studies have demonstrated that the quality and quantity of organic inputs directly influence microbial necromass formation, with higher substrate availability leading to increased MNC accumulation [34,36]. The stability of microbial necromasses in soil is attributed to various mechanisms, including the physical protection of soil aggregates and chemical interactions with soil minerals [29,37]. Studies have shown that microbial necromass plays a major role in the stabilization and formation of soil aggregates, creating a positive feedback loop that enhances C sequestration [29]. Recent research has also revealed that different microbial groups contribute differently to necromass formation, with fungal necromasses showing greater stability than bacterial necromasses [38]. The turnover rates and stabilization mechanisms of microbial necromasses vary across ecosystems and soil types, highlighting the importance of understanding these dynamics in specific contexts [32,39]. Zhang et al. [40] showed that forest management alters soil MNC and its contribution to SOC in Moso bamboo forests. Zhu et al. [41] showed that maize stover mulch contributed to SOC by shaping the microbial assemblage to influence the amount of microbial necromass. A long-term experiment showed that manure mulching of crop residues increased soil organic matter but decreased the relative contribution of microbial necromass material in upland Ultisols [42]. However, the effects of BSOF on soil MNC and their contribution to organic carbon in bamboo forests have not yet been reported. The specific biomarkers for measuring the amount of soil organic matter contributed by microbial necromass are amino sugars (ASs) [43,44]. These compounds, particularly glucosamine (GluN) and muramic acid (MurA), provide valuable insights into the relative contributions of bacterial and fungal necromass to SOC pools [45].
In this study, we investigated the effects of BSOF application on MNC formation and C sequestration in Moso bamboo forest soil. Our objectives were to (1) compare the effects of different BSOF application rates and soil depths on soil MNC changes in Moso bamboo forests, (2) characterize the changes in soil MNC contribution to SOC under different BSOF application rates, and (3) assess the contribution of BSOF application to soil C sequestration in Moso bamboo forests. Therefore, we proposed the following hypotheses: (1) the soil MNC content and its contribution to SOC gradually increase with an increase in BSOF application; (2) the effect of BSOF on the 0–20 cm soil layer is higher than that on the 20–40 cm soil layer; and (3) the application of BSOF can effectively improve the C sequestration capacity of Moso bamboo forest soils.

2. Materials and Methods

2.1. Site Description

Xiaofeng Town, Anji County, Zhejiang Province, China (30°38′ N, 119°28′ E) is the study area (Figure S1). The region experiences a mean annual temperature of 17 °C and an average annual rainfall of roughly 1300 mm, indicating a mid-latitude subtropical monsoon climate. The average length of sunshine per year is 1946 h, and there are 230 days without frost. The topography of the study area is mountainous and hilly. The soil type at the study site is ferric Luvisol [46].

2.2. Material Preparation, Experimental Design, and Soil Sampling

In September 2023, at a temperature of 20–30 °C, bamboo processing waste (bamboo scraps) that had been crushed to a length of less than 2 cm was supplemented with 1% of bran, and subsequently, 1.5% of urea dissolved in water was uniformly sprayed on the bamboo scraps to adjust the carbon-to-nitrogen ratio of the raw material to between 40 and 60. Following this, 0.2%–0.4% mixed bacterial solution (Pseudomonas K-22 D, Trichoderma harzianum) was added and mixed thoroughly. Subsequently, the moisture content of the mixed raw materials should be adjusted to 60%–70% by adding water. Finally, a pile of mixed raw materials was allowed to ferment for 14 days; the pile was turned every 5 days during the fermentation period. After the completion of fermentation, the moisture content of the heap is reduced to 30%–40%, and it can be used as an organic fertilizer (Table S1).
In October 2023 (pre-emergence of bamboo shoots), a sample plot of the Moso bamboo forest with consistent and relatively flat terrain was selected in the experimental area, in which four 35 m × 10 m large sample plots spaced 2 m apart were established, and six 5 m × 10 m small sample plots spaced 1 m apart were established in each of the large sample plots. Subsequently, the strip fertilization method was used to apply BSOF at four fertilization rates of high (HF, 30 t·hm−2), medium (MF, 15 t·hm−2), low (LF, 7.5 t·hm−2), and no fertilizer (CK, 0 t·hm−2), respectively, to four small sample plots within the four large plots, and the procedure was repeated six times (Figure S2). In May 2024 (the end of the process of bamboo shoots growing into bamboo), five sampling points were randomly selected according to the shape of “S” in small sample plots, and appropriate amounts of soil from 0 to 20 cm and 20 to 40 cm were collected from each sampling point. Then, each soil layer’s 5 samples were mixed into 1 sample; a total of 48 soil samples were obtained. Fresh soil samples were sieved through a 2 mm sieve and air-dried to determine the soil physicochemical properties and AS content.

2.3. Basic Soil Physicochemical Properties

The soil bulk density (BD) was determined using the cutting ring method. The soil pH was determined using a pH meter at a soil-to-water ratio of 1:2.5 (w/v). Soil organic carbon (SOC) was determined using a TOC analyzer (MultiN/C3100; Analytikjena, Germany). Ammonium-nitrogen (NH4+-N) was determined using the indophenol blue colorimetric method, and nitrate-nitrogen (NO3−-N) was determined using the ultraviolet spectrophotometric method after extraction with 2 M KCl and a water–soil mass ratio of 1:5 [47]. The method described by Bray and Kurtz [48] was used to determine soil available phosphorus (AP). Soil available potassium (AK) content was determined using the flame photometric method after ammonium acetate (CH3 COONH4) leaching [49].

2.4. Soil Microbial Necromass Carbon

The content of three ASs, glucosamine (GluN), muramic acid (MurA), and galactosamine (GalN), in the soil samples was determined based on the method (gas chromatography-flame ionization detection) described by Zhang et al. [50]. In particular, the soil samples (containing about 0.3 mg N) were hydrolyzed with 6 mol-L HCl at 105 °C for 8 h. Ten micrograms of myo-inositol solution was added, shaken, filtered, and evaporated; deionized water was added to dissolve the residue, and the solution pH was adjusted to 6.6–6.8. The supernatant obtained by centrifugation was lyophilized, anhydrous methanol was added, and the solids were dissolved and centrifuged again. The supernatant obtained after centrifugation was blown dry with N2 and freeze-dried by adding 1.0 mL of deionized water for at least 8 h. To the dried sample, 300 μL of derivatization reagent was added, and after mixing, 1.0 mL of acetic anhydride and 1.5 mL of dichloromethane were added and mixed. After removing the excess derivatization reagent, the remaining organic phase was blown dry using N2, and 400 μL of a solvent mixture of ethyl acetate and n-hexane (1:1, v/v) was taken to dissolve the blown-dried product, and the derivatized product was determined using a gas chromatograph Agilent 7890A (Agilent Technologies, Santa Clara, CA, USA). The content of the three amino sugars was calculated using the internal standard method. According to Liang et al. [36], we converted the concentrations of MurA and GluN to bacterial and fungal necromass carbons using the following equations:
B N C = M u r A × 45 ÷ 1000
F N C = G l u N 179.17 − 2 × M u r A 251.23 × 179.17 × 9 ÷ 1000
M N C = B N C + F N C
where BNC represents bacterial necromass. FNC is a fungal necromass. MNC is the total microbial necromass carbon content. The relative molecular masses of MurA and GluN are 251.23 and 179.17, respectively. The MurA conversion factor for conversion to BNC is 45, and 9 is the conversion factor for conversion of GluN to FNC. We assumed here that the molar ratio of MurA to GluN in bacteria is 2:1

2.5. Data Analysis

Version 26.0 of IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) was employed to analyze the data. A two-way analysis of variance (ANOVA) was used to examine the impacts of various fertilizer treatments and soil depths on the physicochemical characteristics of the soil and MNC. A one-way ANOVA was used to assess the significance (p < 0.05) of the variations in the data among the treatments. A t-test was used to test the significance (p < 0.05) of the differences in the data among the different soil layers. Mantel and Pearson correlation analysis was used to examine the relationship between MNC and soil microorganisms and soil physicochemical properties. Redundancy analysis (RDA) of the environmental factors affecting soil MNC was performed using the R package “vagan” [51] in R (version 4.3.0). The explanatory factors for MNC were predicted in R using a random forest model. Spearman’s correlation analysis was used to examine the correlation between MNC and SOC. Finally, we constructed partial least squares path modeling (PLS-PM) using the R package “plspm” [52] to investigate the direct and indirect effects of soil BD, pH, and the available nutrient concentration on MNC and SOC.

3. Results

3.1. Effects of BSOF on Soil Physicochemical Properties of Moso Bamboo Forests

BSOF application significantly altered the soil properties across the depths (Table 1). In the 0–20 cm layer, SOC increased by 25% under HF treatment compared to CK, whereas the 20–40 cm layer showed smaller increases (12%). A significant treatment × depth interaction (p < 0.001) indicated stronger fertilization effects in the 0–20 cm layer. The soil physical properties showed moderate changes, with bulk density decreasing under the HF treatment, particularly in the 20–40 cm layer. The treatment × depth interaction for pH (p < 0.001) revealed different patterns, with MF increasing the pH in the 0–20 cm layer while all the treatments elevated the pH in the 20–40 cm layer compared with CK. The mineral nitrogen content exhibited contrasting patterns. NO3−-N in the 0–20 cm layer increased dramatically under HF (675%) compared to that in CK, whereas NH4+-N decreased by 43%. The 20–40 cm layer showed smaller changes, with significant treatment × depth interactions (p < 0.001) for both forms. Both the AP and AK groups demonstrated strong treatment responses (p < 0.001). AK in the 0–20 cm layer nearly doubled under HF, whereas AP showed complex treatment × depth interactions, with LF and MF treatments having greater effects in the 20–40 cm layer. All parameters, except AP, showed significant stratification between the soil layers (p < 0.001).

3.2. Effects of BSOF on Soil Microbial Necromass C and Its Contribution to SOC of Moso Bamboo Forests

The analysis results showed that in 20–40 cm soil, compared to CK, the HF treatment significantly increased the content of the three ASs in both layers of soil, while the LF and MF treatments did not have a significant effect on the amino sugar content (Table S2). In contrast, in 20–40 cm soil, the contents of all three ASs decreased and then increased with the increase in fertilizer application (Table S2). Meanwhile, there was a significant interaction between the effects of different fertilizer treatments and soil depth on the changes in the GluN contents (p < 0.01).
The results of this study showed that different application rates of BSOF significantly affected the MNC content and its contribution to SOC at different soil depths, and there was a significant treatment effect and soil depth interaction (Figure 1). In the 0–20 cm layer, the HF treatment significantly increased the total MNC by 48% compared to CK, whereas the MF and LF treatments showed modest increases of 5% and 2%, respectively (Figure 1A). The BNC and FNC contents responded similarly to BSOF application; however, the increase in FNC was greater (52% with HF) than that in BNC (38% with HF) (Figure 1B,C). The 20–40 cm layer exhibited different patterns, with the total MNC increasing by 42% under HF and 25% under MF compared to CK (Figure 1A). The contribution of total MNC to SOC varied significantly between treatments and depths, with higher ratios in the 0–20 cm layer (54%–66%) than in the 20–40 cm layer (36%–53%) (Figure 1D). The treatment × depth interactions were significant for all necromass parameters. The FNC/SOC ratio showed distinct patterns, with higher values in the 0–20 cm layer under the HF treatment, indicating an enhanced fungal contribution to microbial necromass formation (Figure 1F). The LF treatment had minimal effects on the MNC components in both soil layers.

3.3. Factors Affecting Soil Microbial Necromass C Under BSOF Application

The RDA and Mantel tests were used to analyze the effects of the soil physicochemical properties on MNC (Figure 2). The results showed that the first and second axes of the RDA explained 99.99% of the variance in MNC in the 0–20 cm soil layer, with soil pH being the most important but not significant factor (Figure 2A). The Mantel test results showed significant correlations among SOC, NO3−-N, AP, AK, BNC, FNC, and total MNC (Figure 2C). The first and second axes of the RDA explained 99.98% of the variance in MNC in the 20–40 cm soil layer, with soil AK and AK having a significant effect (Figure 2B). The results of the Mantel test showed significant correlations between SOC, soil pH, NO3−-N, AP, AK, BNC, FNC, and total MNC (Figure 2D).
Random forest analysis was used to predict the contribution of each soil factor to the variation in the MNC component characteristics (Figure 3). The results showed that in the 0–20 cm soil layer, SOC and NO3−-N were the factors that explained the most variation in total MNC (Figure 3A), while AK and SOC were the factors that explained the most variation in BNC (Figure 3B), and SOC and AK were the factors that explained the most variation in total FNC (Figure 3C). In the 20–40 cm soil layer, NO3−-N and AK were the factors with the highest explanatory power for total MNC variation (Figure 3D), NO3−-N and SOC exhibited the highest explanatory power for BNC variation (Figure 3F), and NO3−-N and AK showed the highest explanatory power for total FNC variation (Figure 3E).
Spearman’s correlation analysis indicated a statistically significant correlation between soil BNC, FNC, and SOC and that SOC content increased with an increase in necromass C content (Figure 4). In addition, the slopes of the straight regression lines of BNC and FNC with SOC showed no important changes in either the 0–20 cm soil layer or in the 20–40 cm soil layer, indicating that the correlation between BNC and FNC with SOC did not change.
PLS-PM showed that changes in the available nutrients as a result of BSOF application directly affected BNC and FNC and, ultimately, the total MNC and SOC contents (Figure 5). In addition, the changes in soil pH directly affected the SOC content in the 0–20 cm soil layer (Figure 5A). In contrast, in the 20–40 cm soil layer, the changes in soil pH did not directly affect SOC content, but BD affected soil pH, and thus soil available nutrients, and ultimately BNC, FNC, total MNC, and SOC (Figure 5B).

4. Discussion

4.1. Effects of BSOF on Soil Microbial Necromass C of Moso Bamboo Forests

Th application of organic fertilizers enhances the diversity and resilience of microbial communities, which can increase the amount of active microbial biomass and carbon residues produced [53]. BSOF application significantly enhanced microbial necromass carbon accumulation, with the most pronounced effects observed under HF treatment. In the 0–20 cm soil layer there was a 48% increase in MNC, which is consistent with prior findings that organic amendments can significantly increase microbial necromass [42]. A global meta-analysis revealed that organic management practices (e.g., nutrient management and conservation tillage) typically enhance microbial necromass by 18.24% compared with conventional approaches [35]. The FNC content was much higher than the BNC content in all the treatments and was consistent with the results of previous studies on various ecosystems [27,40,54]. The different responses between FNC (52% increase) and BNC (38% increase) under HF treatment indicated preferential stimulation of the fungal community, which could be attributed to the fact that BSOF contains a large and complex matrix of organic matter, for which fungi are the main decomposers [27]. Research has shown that when easily degradable organic matter (e.g., sugars and amino acids) is added to the soil, the fungal/bacterial ratio usually decreases because bacteria (especially Gram-negative bacteria) can rapidly utilize these simple substrates, thereby increasing their relative abundance [55]. Conversely, when difficult-to-degrade organic matter (e.g., lignin and cellulose) is added, the relative abundance of fungi increases because fungi are more adept at breaking down complex organic compounds [55]. Thus, BSOF application affected the fungi-to-bacteria ratio in bamboo forest soil, which in consequence influenced the microbial necromass composition. In addition, there was significant depth stratification in the MNC distribution. Studies on carbon dynamics in Moso bamboo forests have shown that management effectiveness typically decreases significantly with depth of soil [56], which is in line with our observed pattern of MNC distribution.

4.2. Factors Affecting Soil Microbial Necromass C Under BSOF Application

Our multivariate analysis revealed the complex control of MNC formation by various soil factors under BSOF application, with available soil nutrients being the main driver. The strong correlations between available nutrients and MNC components reflect fundamental nutrient–microbe relationships documented across forest ecosystems [34,57]. Soil nutrients play a critical role in microbial activity and the subsequent accumulation of microbial necromass. One study found that N addition increased MNC, altering the microbial composition and P availability in a subtropical forest [58]. This indicated that nutrient availability directly influenced microbial biomass and its contribution to SOC pools. A meta-analysis revealed that balanced nutrient availability could increase microbial carbon use efficiency by 11.5% [59], supporting our observation of nutrient-mediated effects on MNC formation. Additionally, these results suggest that soil pH is one of the drivers of soil MNC changes in Moso bamboo forests following BSOF application. This finding is in agreement with continental-scale studies that have shown that soil pH is a key driver of changes in soil microbial communities in forest ecosystems [60]. However, depth-specific patterns of change were observed in the controls. Our results showed that SOC and NO3−-N dominated surface processes, whereas NO3−-N and AK were crucial in deeper soils; this is similar to previous findings in forest soils [37,61].

4.3. Effects of Soil Microbial Necromass C on Soil Carbon Sequestration Under BSOF Application

A significant increase in the contribution of total MNC to SOC occurred under BSOF application, especially in the 0–20 cm soil layer (54%–66%), which greatly exceeded the global average for forest soils (35%) [27]. Previous studies have shown that soil carbon stocks in Moso bamboo forests are significantly influenced by management practices [62], with changes in the contribution of MNC to carbon stocks being particularly prominent [40]. The contribution of FNC to SOC was significantly higher than that of BNC in the case of BSOF application due to the fact that fungal biomass is larger than bacterial biomass, and because fungal cellular compounds decompose slowly and remain in the soil for a longer time, with the potential to stabilize C in the long term [27]. The vertical distribution of MNC contributions to SOC indicated depth-specific processes in carbon stabilization. Recent research has shown that subsurface soil layers can contribute 30%–50% of the total soil carbon storage in bamboo forests [56], with microbial necromass playing a crucial role in deep soil carbon stabilization [63]. Some studies have shown that MNC can significantly enhance the stability of deep soil carbon through their binding to soil minerals and their protective role in soil aggregates [29,64]. Zhang et al. [65] found that mineral fertilizer application was effective in increasing MNC and SOC content as well as SOC stability. This stability is critical for long-term carbon sequestration, especially in the context of climate change and land-use change [54,66]. Ni et al. [34] showed that fertilizer application significantly increased MNC content, and its contribution to recalcitrant C reached 22.3%. A study of forest soil carbon concentrations in response to interventions showed that management practices that promote microbial activity (e.g., the application of organic fertilizers) were effective in contributing to increased soil C stocks [67]. These findings suggest that the application of BSOF in bamboo forest management has great potential for long-term soil C sequestration.
However, this study was limited to investigating the effects of MNC on soil carbon sequestration under short-term applications of BSOF, and the effects of long-term applications could be monitored in the future.

5. Conclusions

This study demonstrated that BSOF application significantly affected MNC dynamics and soil C sequestration in Moso bamboo forests. In comparison to CK, the HF treatment notably enhanced the accumulation of MNC, resulting in an increase of 48% in total MNC content for the 0–20 cm soil layer and a 42% rise for the 20–40 cm layer. On the other hand, the MF and LF treatments showed only minor impact on MNC levels. Under the conditions of BSOF application, the increase in FNC content and its contribution to SOC was greater than that of BNC, indicating that the application of BSOF has a high potential for stabilizing C sequestration and is an effective strategy for promoting long-term C sequestration in Moso bamboo forest ecosystems. In addition, the results of multivariate analysis indicated that available soil nutrients, such as NO3−-N and AK, were the main drivers of MNC formation and SOC accumulation. The relationships between MNC and SOC at different soil depths were consistent, indicating that microbial C treatment efficiency was highly stable. These results suggest that the application of BSOF in Moso bamboo forests is an effective method for establishing an efficient and sustainable management system for bamboo forests.
As our study on the effect of BSOF on C sequestration in Moso bamboo forests is only one aspect of MNC, we will pay further attention to the effect of BSOF on SOC fractions as well as microbial community structure and nutrient limitation in Moso bamboo forests in future studies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16030553/s1, Figure S1. Location map of the experimental site, Figure S2. Schematic diagram of the experiment, Table S1. The chemical and nutrient properties of bamboo-sourced organic fertilizer. Table S2. Soil amino sugar content under different application rates of bamboo-sourced organic fertilizer.

Author Contributions

Conceptualization, Z.H., X.Z. and Z.Z.; funding acquisition, X.Z.; investigation, Z.H., Q.L. and F.B.; writing—original draft, Z.H.; writing—review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Key R&D Program of China (2023YFD2201202) and the Fundamental Research Funds of CAF (CAFYBB2021QB007, CAFYBB2018ZD002).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We are sincerely grateful to the anonymous reviewers and editors for their valuable suggestions to improve the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SOCSoil organic carbon
BSOFBamboo-sourced organic fertilizers
MNCMicrobial necromass carbon
BNCBacterial necromass carbon
FNCFungal necromass carbon
ASAmino sugar
GluNGlucosamine
GalNGalactosamine
MurAMuramic acid
BDBulk density
NH4+-NAmmonium-nitrogen
NO3−-NNitrate-nitrogen
APAvailable phosphorus
AKAvailable potassium
RDARedundancy analysis
PLS-PMPartial least squares path modeling
GOFGoodness of fit
CCarbon
NNitrogen
PPhosphorus

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Figure 1. Soil total microbial necromass C (A,D), bacterial necromass C (B,E), and fungal necromass C (C,F) contents and their contributions to SOC under different fertilizer application treatments. MNC, total microbial necromass C; FNC, fungal necromass C; BNC, bacterial necromass C. Different capital letters indicate significant differences between treatments (p < 0.05); different lowercase letters indicate significant differences between soil depths (p < 0.05). *, p < 0.05; **, p < 0.01; ***, p < 0.001. Data ± SD.
Figure 1. Soil total microbial necromass C (A,D), bacterial necromass C (B,E), and fungal necromass C (C,F) contents and their contributions to SOC under different fertilizer application treatments. MNC, total microbial necromass C; FNC, fungal necromass C; BNC, bacterial necromass C. Different capital letters indicate significant differences between treatments (p < 0.05); different lowercase letters indicate significant differences between soil depths (p < 0.05). *, p < 0.05; **, p < 0.01; ***, p < 0.001. Data ± SD.
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Figure 2. Redundancy analysis (RDA) (A,B) and Mantel test (C,D) of soil total microbial necromass C, bacterial necromass C, and fungal necromass C contents with soil physicochemical properties in the different soil depths. MNC, total microbial necromass C; FNC, fungal necromass C; BNC, bacterial necromass C; BD, bulk density; SOC, soil organic carbon; NO3−-N, nitrate-nitrogen; NH4+-N, ammonium-nitrogen; AK, available potassium; AP, available phosphorus. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 2. Redundancy analysis (RDA) (A,B) and Mantel test (C,D) of soil total microbial necromass C, bacterial necromass C, and fungal necromass C contents with soil physicochemical properties in the different soil depths. MNC, total microbial necromass C; FNC, fungal necromass C; BNC, bacterial necromass C; BD, bulk density; SOC, soil organic carbon; NO3−-N, nitrate-nitrogen; NH4+-N, ammonium-nitrogen; AK, available potassium; AP, available phosphorus. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
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Figure 3. Random forest model predictions of soil physicochemical properties on total microbial necromass C (A,D), bacterial necromass C (B,E), and fungal necromass C (C,F) contents in the different soil depths. MNC, total microbial necromass C; FNC, fungal necromass C; BNC, bacterial necromass C; BD, bulk density; SOC, soil organic carbon; NO3−-N, nitrate-nitrogen; NH4+-N, ammonium-nitrogen; AK, available potassium; AP, available phosphorus. *, p < 0.05; **, p < 0.01.
Figure 3. Random forest model predictions of soil physicochemical properties on total microbial necromass C (A,D), bacterial necromass C (B,E), and fungal necromass C (C,F) contents in the different soil depths. MNC, total microbial necromass C; FNC, fungal necromass C; BNC, bacterial necromass C; BD, bulk density; SOC, soil organic carbon; NO3−-N, nitrate-nitrogen; NH4+-N, ammonium-nitrogen; AK, available potassium; AP, available phosphorus. *, p < 0.05; **, p < 0.01.
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Figure 4. Spearman’s correlation analysis between soil organic C (SOC) and bacterial necromass C (BNC, (A)) and fungal necromass C (FNC, (B)).
Figure 4. Spearman’s correlation analysis between soil organic C (SOC) and bacterial necromass C (BNC, (A)) and fungal necromass C (FNC, (B)).
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Figure 5. Partial least squares path modeling (PLS-PM) of soil physicochemical properties on the effect of microbial necromass C in 0–20 cm (A) and 20–40 cm soil layer (B) under different fertilizer application rates of BSOF. The goodness of fit (GOF) is calculated as the geometric mean of the average communality and the average R2 value, which assess the overall prediction performance of the model. Red and blue arrows indicate positive and negative causality, respectively. Numbers on the arrows indicate standardized path coefficients, and dashed lines indicate that the path coefficient is not significant. *, p < 0.05; **, p < 0.01; ***, p < 0.001. Availability nutrients: nitrate-nitrogen (NO3−-N), ammonium-nitrogen (NH4+-N), available potassium (AK), available phosphorus (AP); BD, bulk density; SOC, soil organic carbon; FNC, fungal necromass C; BNC, bacterial necromass C; MNC, total microbial necromass C.
Figure 5. Partial least squares path modeling (PLS-PM) of soil physicochemical properties on the effect of microbial necromass C in 0–20 cm (A) and 20–40 cm soil layer (B) under different fertilizer application rates of BSOF. The goodness of fit (GOF) is calculated as the geometric mean of the average communality and the average R2 value, which assess the overall prediction performance of the model. Red and blue arrows indicate positive and negative causality, respectively. Numbers on the arrows indicate standardized path coefficients, and dashed lines indicate that the path coefficient is not significant. *, p < 0.05; **, p < 0.01; ***, p < 0.001. Availability nutrients: nitrate-nitrogen (NO3−-N), ammonium-nitrogen (NH4+-N), available potassium (AK), available phosphorus (AP); BD, bulk density; SOC, soil organic carbon; FNC, fungal necromass C; BNC, bacterial necromass C; MNC, total microbial necromass C.
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Table 1. Effects of different fertilizer application rates and soil depths on soil physicochemical properties of Moso bamboo forests.
Table 1. Effects of different fertilizer application rates and soil depths on soil physicochemical properties of Moso bamboo forests.
Soil LayerTreatmentBD
(g·cm−3)		pHSOC
(g·kg−1)		NO3−-N
(g·kg−1)		NH4+-N
(g·kg−1)		AP
(g·kg−1)		AK
(g·kg−1)
0–20 cmCK1.18 ± 0.07 Ab4.48 ± 0.02 Ba22.20 ± 0.85 Ba3.15 ± 0.65 Da6.86 ± 0.59 Aa0.53 ± 0.07 Da46.97 ± 3.50 Ba
LF1.13 ± 0.06 Ab4.49 ± 0.01 Bb20.85 ± 0.29 Ca7.52 ± 0.71 Ca4.62 ± 0.25 Ba1.00 ± 0.19 Bb44.35 ± 4.23 Ba
MF1.16 ± 0.07 Ab4.69 ± 0.05 Aa20.92 ± 0.80 Ca9.80 ± 1.34 Aa4.02 ± 0.11 Ca0.76 ± 0.10 Cb44.68 ± 2.47 Ba
HF1.12 ± 0.08 Ab4.47 ± 0.07 Bb27.76 ± 1.30 Aa24.44 ± 3.28 Ba3.90 ± 0.65 Cb1.33 ± 0.16 Aa88.32 ± 3.32 Aa
20–40 cmCK1.60 ± 0.04 Aa4.67 ± 0.01 Cb13.62 ± 0.79 Bb1.03 ± 0.31 Bb5.65 ± 0.25 Bb0.43 ± 0.04 Cb34.99 ± 1.30 Bb
LF1.53 ± 0.06 ABa4.77 ± 0.02 Aa11.71 ± 1.20 Cb1.28 ± 0.33 Bb5.11 ± 0.32 Ca1.56 ± 0.14 Aa26.65 ± 1.97 Db
MF1.56 ± 0.05 ABa4.73 ± 0.03 Ba14.10 ± 0.62 ABb3.77 ± 0.47 Ab4.57 ± 0.43 Da1.07 ± 0.17 Ba30.74 ± 1.60 Cb
HF1.52 ± 0.09 Ba4.72 ± 0.02 Ba15.22 ± 1.04 Ab3.12 ± 0.46 Ab6.38 ± 0.66 Aa0.48 ± 0.05 Cb42.12 ± 2.41 Ab
Source of variation (F statistic and probability level)Treatment2.63
(p = 0.064) ns		35.37
(p < 0.001)		71.87
(p < 0.001)		173.95
(p < 0.001)		40.06
(p < 0.001)		77.82
(p < 0.001)		297.23
(p < 0.001)
Soil depth204.72
(p < 0.001)		381.55
(p < 0.001)		1232.68
(p < 0.001)		204.72
(p < 0.001)		204.72
(p < 0.001)		0.50
(p = 0.482) ns		204.72
(p < 0.001)
Treatment × Soil depth0.14
(p = 0.933) ns		29.53
(p < 0.001)		20.55
(p < 0.001)		122.37
(p < 0.001)		33.49
(p < 0.001)		69.59
(p < 0.001)		100.49
(p < 0.001)
BD, bulk density; SOC, soil organic carbon; NO3−-N, nitrate-nitrogen; NH4+-N, ammonium-nitrogen; AP, available phosphorus; AK, available potassium. Different capital letters indicate significant differences between treatments (p < 0.05); different lowercase letters indicate significant differences between soil depths (p < 0.05). ns, no significant difference between data (p > 0.05). Data ± SD.
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Huang, Z.; Li, Q.; Bian, F.; Zhong, Z.; Zhang, X. Effects of Bamboo-Sourced Organic Fertilizer on the Soil Microbial Necromass Carbon and Its Contribution to Soil Organic Carbon in Moso Bamboo (Phyllostachys edulis) Forest. Forests 2025, 16, 553. https://doi.org/10.3390/f16030553

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Huang Z, Li Q, Bian F, Zhong Z, Zhang X. Effects of Bamboo-Sourced Organic Fertilizer on the Soil Microbial Necromass Carbon and Its Contribution to Soil Organic Carbon in Moso Bamboo (Phyllostachys edulis) Forest. Forests. 2025; 16(3):553. https://doi.org/10.3390/f16030553

Chicago/Turabian Style

Huang, Zhiyuan, Qiaoling Li, Fangyuan Bian, Zheke Zhong, and Xiaoping Zhang. 2025. "Effects of Bamboo-Sourced Organic Fertilizer on the Soil Microbial Necromass Carbon and Its Contribution to Soil Organic Carbon in Moso Bamboo (Phyllostachys edulis) Forest" Forests 16, no. 3: 553. https://doi.org/10.3390/f16030553

APA Style

Huang, Z., Li, Q., Bian, F., Zhong, Z., & Zhang, X. (2025). Effects of Bamboo-Sourced Organic Fertilizer on the Soil Microbial Necromass Carbon and Its Contribution to Soil Organic Carbon in Moso Bamboo (Phyllostachys edulis) Forest. Forests, 16(3), 553. https://doi.org/10.3390/f16030553

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