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Article

Synergistic Effects of Organic and Chemical Fertilizers on Microbial-Mediated Carbon Stabilization: Insights from Metagenomics and Spectroscopy

by
Wei Wang
1,
Yue Jiang
2,
Shanshan Cai
1,
Yumei Li
1,
Juanjuan Qu
2,* and
Lei Sun
1,*
1
Heilongjiang Academy of Black Soil Conservation & Utilization, Harbin 150030, China
2
College of Resources and Environmental Science, Northeast Agricultural University, Harbin 150030, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(7), 1555; https://doi.org/10.3390/agronomy15071555
Submission received: 29 May 2025 / Revised: 22 June 2025 / Accepted: 24 June 2025 / Published: 26 June 2025
(This article belongs to the Special Issue Microbial Carbon and Its Role in Soil Carbon Sequestration)
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Abstract

Fertilization management constitutes a critical determinant of agroecosystem productivity. Reasonable fertilization can increase the organic matter content in soil; however, the potential mechanism of how different fertilization regimes impact soil carbon sequestration is unclear. We hypothesized that the combined application of biochar and organic fertilizer would enhance soil carbon sequestration by improving soil physicochemical conditions, increasing microbial activity, and promoting the accumulation of stable forms of carbon. This study systematically investigated different regimes, including the application of chemical fertilizer alone (SCN), chemical fertilizer with biochar (SCB), chemical fertilizer with organic fertilizer (SCO), and chemical fertilizer with both biochar and organic fertilizer (SCBO), on soil physiochemical properties, enzyme activities, labile organic carbon fractions, microbial carbon fixation gene expression, and community composition. The results demonstrated that (1) the application of organic materials significantly enhanced soil nutrient levels and enzyme activities, with the best performance from SCBO; (2) the organic materials increased the labile soil organic carbon (SOC) content and the carbon pool management index, with SCO showing the highest at 69.82%; (3) SCB and SCBO improved the stability of soil carbon components by increasing the proportion of Aromatic C; and (4) the carbon fixation genes ACAT and sdhA exhibited the highest abundance in SCBO. In parallel, the relative abundance of Actinomycetota increased with the application of organic materials, reaching its peak in SCBO. Mantel testing revealed a strong correlation between microbial community composition and SOC, emphasizing the importance of SOC in microbial growth and metabolism. Moreover, the strong correlation between carbon fixation genes and aromatic carbon suggested that specific carbon forms, particularly aromatic structures, played a critical role in driving microbial carbon fixation processes.

1. Introduction

Soil organic carbon (SOC) is a fundamental component of terrestrial ecosystems, crucial for maintaining soil fertility and sustaining microbial activity and playing a central role in the global carbon cycle [1]. The primary pathway for carbon fixation in agricultural soils is through crop photosynthesis, which captures atmospheric CO2 and converts it into organic matter utilized by plants [2]. This organic matter subsequently enters the soil organic carbon pool via crop residues and root exudates, completing the carbon fixation process [3]. However, approximately 90% of the carbon entering the crop ecosystem is eventually returned to the atmosphere through various transformation pathways, leading to substantial carbon loss [4]. Therefore, reducing carbon losses and enhancing the sequestration of organic carbon in soil has become a key focus of current research.
Chemical fertilizers have played a vital role in increasing crop productivity, but their long-term use has been associated with negative effects on soil health [5]. Recent studies have shown that organic materials, such as biochar and organic fertilizer, can mitigate the negative effects of chemical fertilizers by promoting the retention of SOC and stimulating beneficial microbial activity [6]. Biochar, a stable carbon-rich material produced via pyrolysis of organic matter, has been demonstrated to improve soil structure and enhance carbon sequestration in soils [7]. Organic fertilizers contribute to the formation of a more stable soil organic carbon pool, enhance carbon sequestration potential, improve nutrient use efficiency, and promote soil enzyme activity [8]. Through long-term positioning experiments, Xu et al. [9] demonstrated that the combination of chemical and organic fertilizer significantly enhances the labile organic carbon components in soil. These findings suggest that organic materials, together with root inputs, increase SOC content, enhance its stability, and promote carbon activity in soil [10]. Additionally, analyzing changes in SOC components alone is insufficient to fully capture the carbon fixation process; hence, further investigation into the structural and functional changes in SOC is necessary [11]. Currently, structure analysis techniques such as Fourier transform infrared spectroscopy (FTIR) and solid-state 13C-NMR spectroscopy (13C-NMR) have provided valuable insights into the molecular structure of soil organic matter. Research has shown that with increasing straw return rates and longer periods of straw application, the aliphatic C-H stretching of soil organic carbon intensifies, promoting increased hydrophobicity of organic carbon and enhancing its accumulation [12].
Microbial communities also play a crucial role in soil carbon cycling. Carbon-fixing microorganisms convert atmospheric CO2 into SOC through their metabolic activities, thereby enhancing long-term carbon sequestration in soils and offering significant potential for carbon storage [13]. Previous studies have demonstrated that biochar application can enhance the diversity of soil microbial communities and improve carbon sequestration efficiency [14]. Autotrophic carbon fixation is a crucial process for sustaining life on Earth, and seven pathways for carbon fixation have been described in existing research [15]. Metagenomic sequencing has emerged as a powerful tool for unraveling the complex interactions between soil microbial communities and carbon sequestration processes. Hu et al. [16] used metagenomic analysis to examine the effects of long-term application of chemical and organic fertilizers on carbon and nitrogen cycling functions in black soil regions. Organic fertilizer application increased the abundance of genes related to methane oxidation. In addition, organic fertilizer application promotes the proliferation of Pseudomonadota and Actinomycetota, ultimately contributing to the development of a more complex bacterial symbiotic network [17].
In summary, the application of organic materials not only increases SOC content and improves crop carbon use efficiency, but also influences soil carbon fixation by altering the structure of the soil microbial community. However, the mechanism by which combined fertilization affects carbon fixation and microbial metabolic activity remains unclear. In this study, farmland soils subjected to long-term fertilization regimes, including the application of chemical fertilizer alone, chemical fertilizer combined with biochar, chemical fertilizer combined with organic fertilizer, and chemical fertilizer combined with both biochar and organic fertilizer, were used as research materials. FTIR and 13C-NMR spectroscopy were employed to investigate the thermal stability and molecular characteristics of SOC under different fertilization treatments. Metagenomics was used to assess the influence of different fertilization regimes on microbial carbon fixation genes at both community and functional levels. Therefore, this study aims to provide a scientific basis for the rational application of fertilizers to improve soil quality and enhance soil organic carbon sequestration in agricultural production. This study provides a scientific basis for the rational application of fertilizers to improve soil quality and enhance soil organic carbon sequestration in agricultural production. It also offers a theoretical foundation for maintaining farmland ecological health and promoting the sustainable development of agricultural ecosystems.

2. Materials and Methods

2.1. Overview of the Experimental Site

The field experiment was conducted in the Heilongjiang Modern Agriculture Demonstration Zone, located in Daowai District, Harbin (126°51′ E, 45°50′ N), with a treated plot area of 39 m2. The site is situated on the second terrace of the Songhua River, characterized by flat terrain and an elevation of 151 m, and lies within a temperate climate zone. The region has an average annual temperature of 3.5 °C and receives approximately 533 mm of precipitation per year, with most rainfall occurring between June and August. The frost-free period typically ranges from 130 to 140 days. The soil at the site is classified as chernozem, with the parent material predominantly composed of alluvial loess-like clay. The physicochemical properties of the topsoil in the experimental area are presented in Table S1.

2.2. Experimental Design

Four fertilization treatments were established in the experiment: (1) chemical fertilizer only (SCN); (2) chemical fertilizer combined with biochar (SCB); (3) chemical fertilizer combined with organic fertilizer (SCO); and (4) chemical fertilizer combined with both biochar and organic fertilizer (SCBO). Each treatment was replicated three times.
Soybeans (Hei Nong 68) were sown on 23 April 2024 and harvested on 26 September 2024. The sowing density was 260,000 plants per hectare. Fertilizers were applied once at the time of sowing. The chemical fertilizers, purchased from Shandong Hualu Hengsheng Chemical Co., Ltd. (Dezhou, China), included urea (46% N), diammonium phosphate (18% N and 46% P2O5), and potassium sulfate (50% K2O). The organic fertilizer was obtained from Heilongjiang Longqi Co., Ltd. (Harbin, China), with an organic matter content of no less than 30% and a total nutrient content of at least 9%, at a nutrient ratio of 2.0:1.1:0.6. Its basic physicochemical properties are listed in Table S2. Biochar was sourced from Henan Xingnuo Co., Ltd. (Zhengzhou, China) and produced by pyrolysis of corn straw at 500 °C. The composition and characteristics of the biochar are provided in Table S3. The corresponding fertilization methods are described in detail in Table S4.

2.3. Soil Sampling and Analysis Determination

At the end of the 2024 harvest season, a random five-point sampling method was used to collect soil samples. A soil drill with a diameter of 5 cm was employed to sample the 0–20 cm soil layer. Each treatment was replicated three times. The soil samples were thoroughly mixed, sealed, and transported to the laboratory for analysis. According to the requirements for different analyses, the following sample treatments were applied: soil samples for physical and chemical property analysis, labile organic carbon components, soil thermal stability, and molecular structure analysis were air-dried under natural conditions; soil samples for enzyme activity analysis were stored at 4 °C; and soil samples for metagenomic analysis were stored at −80 °C.
Soil pH was measured using a pH meter (PB-10, Sartorius, Goettingen, Germany) at a soil-to-water ratio of 1:2.5. Organic matter (OM) was determined by the potassium dichromate oxidation external heating method [18]. Alkaline nitrogen (AN) in the soil was determined using the 1 mol/L NaOH alkaline diffusion method, while total nitrogen (TN) was extracted with a 1 mol/L ammonium acetate solution and quantified by a Kjeldahl Nitrogen Determination Instrument (K-360, BUCHI, Flawil, Switzerland) [19]. Available phosphorus (AP) in the soil was measured by the sodium bicarbonate extraction method, and total phosphorus (TP) was determined by the sodium carbonate melting method; both phosphorus forms were determined using an ultraviolet–visible spectrophotometer (SPECTRONIC 200, Thermo Fisher Scientific, Waltham, MA, USA) [20]. For enzyme activity analysis, air-dried soil samples, obtained from a cool place, were sieved through a 0.25 mm mesh. Soil urease (UE), sucrose (SC), catalase (CAT), and β-glucosidase (β-GC) activities were determined using reagent kits (Suzhou Greasy Biotechnology Co., Ltd., Suzhou China). After incubation, the supernatant was transferred to a 96-well plate, and the absorbance was measured using a multimode microplate reader (INFINITE M200 PRO, TECAN, Männedorf, Switzerland).
Easily oxidizable organic carbon (EOC) was determined using the 333 mmol/L potassium permanganate oxidation method, while microbial biomass carbon (MBC) was measured by the chloroform fumigation–extraction method. Dissolved organic carbon (DOC) was extracted by shaking 10 g of soil with 50 mL distilled water (1:5 ratio) for 1 h, followed by centrifugation and filtration, and then quantified using a total organic carbon analyzer (Vario TOC, Elementer, Langenselbold, Germany). Particulate organic carbon (POC) was obtained by mixing 20 g of air-dried soil with 60 mL of chemical dispersant, shaking for 18 h, and filtering through a 0.053 mm sieve. All measurements were conducted in accordance with the methods outlined by prior research [21]. The carbon pool management index (CPMI) was calculated based on these data [22].

2.4. Determination of Soil Molecular Structure

Air-dried soil samples were finely ground and passed through a 0.053 mm sieve. The sieved soil was then mixed with KBr thoroughly and pressed into pellets. Infrared spectral characterization was performed using a Fourier transform micro-infrared Raman spectrometer (FTIR-8900, Shimadzu, Kyoto, Japan), with a scanning wavenumber range of 4000–400 cm−1. To prepare the samples, 5 g of sieved soil was placed into a 50 mL centrifuge tube and treated with 40 mL of 10% HF solution. The mixture was shaken for 1 h and centrifuged at 3500 rpm for 10 min. The supernatant was discarded, and the HF treatment was repeated ten times. After treatment, the remaining soil was rinsed thoroughly with distilled water and dried in an oven at 40 °C. The processed samples were analyzed using a Bruker AVANCE III WB nuclear magnetic resonance spectrometer, applying the solid-state 13C cross-polarization magic angle spinning (CPMAS) technique.

2.5. Extraction and Sequencing of DNA

Total DNA was extracted from 0.5 g of soil using the FastDNA® SPIN Kit for Soil (MPBiomedicals, Irvine, CA, USA). The concentration and purity of the extracted DNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) to ensure sample quality. Metagenomic sequencing was conducted by Shanghai Meiji Biological Technology Co., Ltd. (Shanghai, China), and data analysis was performed using the free online Majorbio Cloud Platform (www.majorbio.com, accessed on 10 January 2025). Paired-end Illumina reads were processed using fastp (https://github.com/OpenGene/fastp, accessed on 10 January 2025, version 0.20.0) to remove adapter sequences and low-quality reads, including those shorter than 50 bp, with a quality score below 20, or containing ambiguous bases (N). Reads with lengths ≥300 bp after quality filtering were retained for subsequent assembly and used for gene prediction and functional annotation.

2.6. Gene Prediction and Classification

Open reading frames (ORFs) from the assembled metagenomic sequences were predicted using MetaGene (https://metagene.nig.ac.jp/, accessed on 25 June 2025). ORFs longer than 100 bp were extracted and translated into amino acid sequences using the National Center for Biotechnology Information (NCBI) Biotechnology Information Translation Table (http://www.ncbi.nlm.nih.gov, accessed on 10 January 2025). To construct a non-redundant gene catalog, CD-HIT (http://www.bioinformatics.org/CD-HIT/, accessed on 10 January 2025, version 4.6.1) was employed with a sequence identity threshold of 90%. Subsequently, SOAPaligner (http://soap.genomics.org.cn/, accessed on 10 January 2025) was used to align high-quality reads from each sample to the non-redundant gene set, and gene abundance information for each sample was obtained. Potential amino acid sequences were functionally classified by comparing them to the NCBI non-redundant (NR) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using DIAMOND (http://www.diamondsearch.org/index.php, accessed on 10 January 2025, version 0.8.35) with BLAST (BLAST version 2.3.0; E-value ≤ 1 × 10−5). The abundance of each functional category was calculated by summing the abundances of genes corresponding to KEGG Orthology (KO). Species identification was performed using the taxonomic information linked to the NR database. To ensure consistency with current microbial taxonomy, phylum-level nomenclature was updated according to the International Committee on Systematics of Prokaryotes (ICSP) [23], and species abundance was determined based on the total gene abundance. In this study, 86 microbial carbon fixation genes related to soil carbon cycling were identified.

2.7. Statistical Analysis

Experimental data were organized using Microsoft Excel 2016. Pearson correlation analysis and one-way analysis of variance (ANOVA) based on Duncan’s multiple range test were performed using SPSS 26.0 (IBM, USA). Graphs were created using Origin 2021. Principal coordinate analysis (PCoA), Mantel tests, clustering heatmaps, random forest, and LEfSe analysis (inear discriminant analysis effect size) were conducted using the R packages (version 4.4.0) “vegan”, “ggplot2”, “corrplot”, “Dplyr”, “devtools”, “grid”, “linkE”, “randomForest”, “rfPermute”, “magrittr”, “limma”, “dplyr”, and “tidyverse”. Redundancy analysis (RDA) was visualized using Canoco 5.0. Partial least-squares path modeling (PLS-PM) was conducted using the “plspm” package (version 4.4.1) in R.

3. Results

3.1. Effects of Different Fertilization Regimes on Soil Physicochemical Properties and Enzyme Activities

3.1.1. Effects of Different Fertilization Regimes on Soil Physicochemical Properties

As shown in Table 1, soil pH gradually decreased with the addition of organic materials, with the greatest reduction observed in SCO (0.40 units). OM contents increased significantly under all treatments, reaching a maximum of 41.82% in SCB. AN contents increased by 2.82% to 54.59%, with the highest value in SCO (149.80 mg/kg). Compared to SCN, TN contents increased in all treatments, with the most notable increase in SCBO (31.25%, p < 0.05). For AP, compared to SCN, SCB decreased by 12.81%, whereas SCO and SCBO increased by 94.60% and 78.68%, respectively. Regarding TP, SCB and SCO decreased by 16.18% and 2.94%, respectively, while SCBO increased by 8.82% compared to SCN (0.68 g/kg).

3.1.2. Effects of Different Fertilization Regimes on Soil Enzyme Activities

As shown in Figure 1, different fertilization regimes affect soil enzyme activity, with enzyme activities generally increasing following the input of organic materials. As shown in Figure 1a, UE exhibited the most significant change among the four enzymes. Compared with SCN, UE activity increased most in SCBO (369.89%). Figure 1b shows that SC activity significantly increased in all amended treatments; however, no significant differences were observed among these three treatments. As shown in Figure 1c, CAT activity increased most in SCBO (14.54%). In Figure 1d, compared with SCN, β-GC activity increased markedly, by 96.77%, 130.44%, and 144.96% under SCB, SCO, and SCBO, respectively.

3.2. Effects of Different Fertilization Regimes on Soil Labile Organic Carbon Pool

As shown in Figure S1a, the application of organic materials significantly increased the SOC content. The SCB treatment exhibited the highest increase of 41.80%.
Overall, compared with SCN, the addition of organic materials led to varying degrees of increase in soil labile organic carbon fractions. For EOC (Figure S1b), no significant increase was observed in SCO, while SCB showed the largest increase (73.15%). In terms of MBC (Figure S1c), all three co-application treatments showed significant increases compared to SCN, with SCO exhibiting the highest increase (287.98%). DOC contents showed no significant difference between SCB and SCN, whereas SCO and SCBO significantly increased DOC by 11.24% and 21.45%, respectively (Figure S1d). POC contents were significantly higher in all three treatments compared to SCN, with SCO showing the greatest increase (34.44%, Figure S1e).
In Table S5, the effects of different treatments on the soil carbon pool are presented, using SCN as the reference. The results indicate that the addition of organic materials improved the CPMI, with the SCO treatment showing the greatest increase of 76.30%.

3.3. Effects of Different Fertilization Regimes on the Molecular Structure of Organic Carbon

As shown in Figure 2a, the FTIR spectra of soils under different fertilization treatments revealed similar absorption peak positions and spectral band distributions, indicating comparable functional group compositions. The absorption band at 1033 cm−1 (C-O in polysaccharides) was weaker in all treatments compared to SCN. SCB showed the highest peak intensity at 1590 cm−1 (Aromatic C=C) among all treatments. Additionally, both SCB and SCBO exhibited stronger peaks at 2810 cm−1 (aliphatic methyl and methylene C-H) and at 3390 cm−1 (phenolic and alcoholic -OH) compared to SCN. The relative intensity of a characteristic peak—defined as the percentage of its area to the total peak area—was used to indicate the relative abundance of the corresponding organic carbon functional group and to reflect compositional changes. Figure 2b presents a semi-quantitative analysis of the infrared spectra. Overall, phenolic and alcoholic compounds accounted for the largest proportion. Compared with SCN, the relative content of O-alkyl C decreased in all treatments, with SCBO showing the greatest reduction (35.12%). The relative content of Aromatic C significantly increased in SCB and SCBO by 44.69% and 16.55%, respectively. These results suggest that the addition of organic materials, particularly in SCB and SCBO, enhanced the chemical stability of SOC.
As shown in Figure S2, the 13C-NMR spectra of SOC under different fertilization treatments reveal the chemical structure distribution across four main functional group regions: Alkyl C (0–45 ppm), O-alkyl C (45–110 ppm), Aromatic C (110–160 ppm), and Carboxyl C (160–220 ppm).
The relative abundances of the main functional groups in each chemical shift region under different fertilization treatments are shown in Figure 2c. For Alkyl C, compared with SCN, no change was observed in SCBO, a 12.00% decrease occurred in SCB, and a 16.00% increase was observed in SCO. Regarding O-alkyl C, there was no significant difference between SCO and SCN, while SCB and SCBO showed varying degrees of reduction, with SCB showing the largest decrease (25.93%). The variation in Aromatic C was opposite to that of O-alkyl C. Both SCB and SCBO exhibited notable increases compared to SCN, of 41.94% and 38.71%, respectively. Carboxyl C contents declined under all fertilization treatments relative to SCN, with the greatest reduction observed in SCBO (58.82%). As shown in Figure 2d, the ratios of Alkyl C/O-alkyl C, Aromatic C/Aliphatic C, and hydrophobic C/hydrophilic C are indicative of the degree of alkylation, aromaticity, and hydrophobicity of humic substances, respectively. Overall, these three ratios increased to varying extents in SCB and SCBO, compared with SCN. Specifically, SCB showed the greatest increases in both the Alkyl C/O-alkyl C ratio (15.05%) and the Aromatic C/Aliphatic C ratio (75.00%). The hydrophobic C/hydrophilic C ratio increased most notably in SCBO, with a 62.20% rise compared to SCN.

3.4. Effects of Different Fertilization Regimes on Soil Microbial Genes

Effects of Different Fertilization Regimes on Soil Carbon Fixation Genes Based on KEGG Database Annotation

Based on KEGG database annotation, a total of 86 KO functional categories related to carbon fixation were identified. Figure 3a illustrates the relative abundances of genes (greater than 1%) involved in carbon fixation in soils under the different fertilization treatments, while genes with relative abundances below 1% were grouped as “others”. Compared to SCN, most of the top 10 abundant genes showed the highest abundances in SCB, including ppdK, E5.4.99.2A, korA, and accC. Genes such as E2.2.1.1 and sucC had the highest abundances in SCO, while ACO, sdhA, and GAPDH were most abundant in SCBO. As shown in Figure 3b, the Arnon–Buchanan cycle exhibited the highest relative abundance across all treatments. Furthermore, all combined fertilization treatments led to an increase in the abundance of these pathways compared to SCN, with the most significant increase observed in SCB. Figure 3c presents the results of LEfSe analysis using LDA (Linear Discriminant Analysis) scores to evaluate the significance of carbon fixation functional gene differences among treatments. SCBO contained the largest number of enriched carbon fixation genes, while SCB had only korB as a significantly enriched gene.
PCoA based on Bray–Curtis distance was used to evaluate the differences in soil functional genes under different fertilization treatments (Figure 4a). ANOSIM results (R = 0.432 and p = 0.002) indicated significant differences among treatments. Samples from treatments combining chemical fertilizer with organic materials were more widely dispersed than those from SCN, suggesting greater variation in functional gene profiles. Notably, the SCO and SCBO treatments partially overlapped, indicating some similarities in gene composition, though distinct differences remained. RDA was performed to explore the relationships between soil labile organic carbon fractions and the top ten most abundant carbon fixation genes across treatments. As shown in Figure 4b, treatment groups were relatively dispersed, reflecting substantial variation. Among the carbon fractions, DOC, SOC, and MBC exerted the greatest influence. MBC was positively correlated with key carbon fixation genes, such as GAPDH, E5.4.99.2A, ACO, sucC, and sdhA, while EOC was negatively correlated. SCB and SCBO were positioned in the same direction as most carbon fixation genes, indicating strong positive associations, particularly with MBC and DOC.

3.5. Effects of Different Fertilization Regimes on Soil Microbial Community Structure

3.5.1. Changes in Microbial Community Structure

Figure S3a shows the dominant microbial phyla (relative abundance > 1%) under different fertilization treatments. The abundance of Actinomycetota increased progressively with the addition of organic materials, reaching the highest level (41.88%) in SCBO. In contrast, Pseudomonadota showed the opposite trend, with the highest abundance (32.34%) in SCN and the lowest (25.97%) in SCB. Acidobacteriota abundance was significantly higher in SCB (12.47%) compared to SCN, but decreased markedly in SCBO, reaching only 6.40%. The genus-level analysis of dominant genera (relative abundance > 1%) under different fertilization treatments is shown in Figure S3b. The abundance of most unclassified genera showed an increasing trend with the addition of organic materials. However, Bradyrhizobium exhibited the highest relative abundance in SCN (10.39%) among all dominant genera. Notably, unclassified_p_Acidobacteriota showed the highest relative abundance in SCB (9.84%).

3.5.2. Analysis of Species Diversity

To further explore the underlying causes of microbial community changes, LEfSe analysis was performed on significantly altered microbial taxa at the genus level under different fertilization treatments (Figure 5a). The results showed that the SCO and SCBO treatments harbored the highest number of significantly enriched genera, while SCN had the lowest. At the phylum level, Pseudomonadota, Gemmatimonadota, and Actinomycetota were the dominant taxa. Enrichment significance analysis of the annotated taxa (Figure 5b–d; p < 0.001) revealed that, compared with SCN, most significantly enriched genera in the three experimental treatments belonged to the phyla Pseudomonadota, Bacteroidota, and Actinomycetota. Among the treatments, SCB had the highest number of significantly enriched genera (eight in total).
PCoA was conducted based on the microbial community composition at the genus level under different fertilization treatments. As shown in Figure S4a, PC1 explained 67.27% of the total variation and PC2 explained 20.60%, indicating a reliable ordination result. According to the ANOSIM analysis (R = 0.367, p = 0.001), the effects of different fertilization treatments on genus-level microbial community composition were significant. Similar to the PCoA results for functional genes, SCO and SCBO showed clear separation from SCN, while some overlap was observed between SCO and SCBO, suggesting differences between groups but similar community structures. As shown in Figure S4b, RDA was performed to explore the relationships between labile organic carbon fractions and the top ten most abundant microbial genera. Overall, the genus-level microbial community in SCN was distinct from that in the other three treatments. Most genera were positively correlated with MBC, whereas EOC showed a negative correlation. In addition, SCO and SCBO clustered in the same direction as most microbial genera, indicating that SOC, DOC, MBC, and POC were positively correlated with the microbial communities in these treatments.

3.5.3. Correlation Between Microbial Community and Soil Factors

To further elucidate the mechanisms underlying microbial variation under different fertilization treatments, a Mantel test was conducted to assess the correlations among environmental factors, labile organic carbon fractions, microbial communities, and carbon fixation genes (Figure 6a). The results showed that OM was positively correlated with SOC, EOC, MBC, POC, and DOC, indicating that increases in OM were generally accompanied by increases in these labile carbon fractions, suggesting a tightly coupled carbon sequestration relationship. Moreover, microbial communities exhibited strong correlations with enzyme activities and SOC, highlighting the crucial role of microbial activity in SOC accumulation. Notably, carbon fixation genes showed a strong association with Aromatic C, implying that the chemical structure of soil organic carbon may influence functional gene composition. The random forest model was further applied to identify key drivers affecting the CPMI (Figure 6b). The analysis identified soil OM, microbial community composition, AN, gene abundance, UE, CAT, pH, and Aromatic C as the most influential variables. This suggests that both biological and physicochemical factors contribute significantly to CPMI regulation. Furthermore, PLS-PM confirmed that soil enzyme activity, organic carbon molecular composition, microbial community structure, and carbon fixation genes are all critical factors regulating CPMI (Figure 6c). Among these, enzyme activity exhibited a significant negative effect on CPMI, whereas the other three factors had positive effects. Collectively, these variables explained 97% of the total variance in CPMI, indicating a robust and integrated influence of biochemical and microbial processes on soil carbon management.

4. Discussion

4.1. Effects of Different Fertilization Regimes on Soil Properties

The results of this study indicated that fertilization treatments significantly affected soil pH and nutrient content, with different combinations of organic materials exhibiting varying effectiveness in improving soil quality. Soil pH varied significantly among treatments, and the application of chemical fertilizer combined with organic fertilizer (SCO) significantly reduced soil pH to 6.31. This may be attributed to the release of organic acids during the decomposition of organic fertilizers, which promotes acidification and subsequently affects soil pH [24]. Furthermore, significant differences in OM contents were observed across the different fertilization treatments. The OM contents in SCO and SCBO were higher than that of SCN, which suggests that the application of organic fertilizers effectively increases OM contents, thereby improving the soil’s physical structure and water retention capacity [24]. AN and TN also showed significant increases under the combined treatments. AN was highest in SCO, followed by SCBO, likely due to enhanced nitrogen mineralization driven by microbial activity stimulated by organic materials [25]. The AP contents under the SCO and SCBO treatments were nearly twice that of SCN. This may be due to organic acids and biochar promoting phosphorus desorption from soil particles, enhancing its availability [26]. TP also increased slightly but consistently with the addition of organic matter and biochar. Overall, SCBO showed a balanced improvement in most soil properties.
Compared with SCN, enzyme activities were elevated in the other three treatments. Among the four enzymes measured, UE showed the greatest variation, particularly in SCBO. This may be due to its key role in nitrogen transformation, where it catalyzes the hydrolysis of urea to ammonium, a process highly responsive to both chemical and organic nitrogen inputs. The addition of organic fertilizer provides easily decomposable nitrogen-rich substrates, while biochar enhances microbial habitat conditions, jointly stimulating the growth and activity of urease-producing microorganisms [27]. As a carbon-rich organic material, biochar stabilizes organic carbon and enhances sequestration through adsorption and by promoting microbial activity, with microorganisms being the primary producers of soil enzymes [28]. Organic fertilizer further promotes microbial and enzymatic activities by supplying easily decomposable substrates, which support microbial metabolism and facilitate carbon transformation and stabilization [29]. In SCBO, the synergistic effect of biochar and organic fertilizer not only increased microbial community diversity but also improved the long-term carbon sequestration. These findings align with a previous study showing that their combined application significantly enhances soil carbon sequestration rates [30]. Enhanced enzyme activity may accelerate organic matter decomposition, promote carbon transformation, and improve energy availability for microbes [31]. Therefore, SCBO improved both carbon availability and microbial decomposition capacity, demonstrating superior effectiveness in promoting soil carbon sequestration.

4.2. Effects of Different Fertilization Regimes on the Soil Organic Carbon Pool and the Molecular Structure of Soil

This study found that the co-application of chemical fertilizers with organic materials had a more pronounced impact on the labile fractions of SOC than chemical fertilizers alone. SOC and EOC increased most in SCB, while SCO showed the highest MBC and POC levels. DOC exhibited a notable rise in SCBO. Although both biochar and organic fertilizer positively influence soil properties, their mechanisms of action differ [32]. Biochar stabilizes organic carbon and provides microbial habitats through its porous structure, while organic fertilizer supplies readily decomposable substrates that stimulate microbial activity [33]. When applied together, their combined effect may be limited due to potential competition for nutrient adsorption, microbial shifts, or differences in nutrient release rates, thereby reducing synergistic benefits [34]. CPMI analysis showed that SCO performed best across all indicators, suggesting enhanced carbon turnover and microbial activity, likely driven by higher organic carbon inputs and improved microbial dynamics [35].
The relative content of O-alkyl C in this study was lower in all treatments compared to SCN, likely due to enhanced microbial activity and the abundant organic matter input accelerating its transformation into other forms of organic matter or mineralizing it into CO2 [36]. Compared to SCN, SCB showed decreasing trends in both Alkyl C and O-alkyl C, while Aromatic C reached the highest level among all treatments, which is primarily attributable to the inherent abundance of aromatic structures in biochar and its high resistance to microbial degradation [37]. Additionally, biochar promotes the protection and accumulation of aromatic organic compounds in soil. This reflects the combined effect of the long-term stability of pyrogenic carbon and the selective decomposition of labile carbon fractions by soil microorganisms [38]. SCBO also exhibited a relatively high Aromatic C content and the highest hydrophobic C/hydrophilic C ratio. This may be attributed to enhanced microbial activity stimulated by the combined application of biochar and organic fertilizer [39], which promotes the conversion of hydrophilic carbon into more stable hydrophobic forms [40,41].

4.3. Effects of Different Fertilization Regimes on Soil Carbon Sequestration Genes

Excessive application of chemical fertilizers may lead to nutrient accumulation and suppress beneficial microbes, particularly those involved in carbon fixation [42]. In contrast, organic fertilizers supply abundant carbon sources that enhance microbial diversity and stimulate the expression of carbon fixation-related genes [43]. The stable structure of biochar enables it to adsorb and preserve organic carbon, reducing losses and supporting long-term sequestration [44]. Their combined application (SCBO) thus enhances both the carbon fixation potential and the stability of soil organic carbon [45]. RDA results further showed that SCBO was closely associated with MBC and labile carbon fractions, indicating strong microbial activity and functional gene expression related to carbon fixation. This finding is consistent with previous studies; for instance, Zhou et al. reported that organic material inputs significantly increased MBC and the abundance of carbon cycle functional genes such as GH48 and cbbL [46]. In contrast, SCN was distant from SOC and MBC and closely associated with genes such as ppdK, korA, and ACAT, which are related to carbon metabolism. This suggests that although certain carbon metabolic pathways may be activated in the absence of organic amendments, the establishment and stability of carbon-fixing microbial communities may be constrained due to the lack of organic inputs [47]. In summary, SCBO treatment not only enhances the content of labile SOC but also strengthens the functional capacity of carbon-fixing microbial communities, thereby providing scientific support for improving the carbon sequestration potential of agricultural soils.

4.4. Effects of Different Fertilization Regimes on Soil Carbon Sequestration Microorganisms

Actinomycetota are key microbial taxa involved in the decomposition of complex organic materials such as cellulose and lignin. Organic materials can supply abundant organic carbon sources—especially more recalcitrant forms of carbon, which create favorable conditions for the growth of Actinomycetota [31,48]. In contrast, Pseudomonadota include many fast-growing microorganisms that thrive in nitrogen-rich environments. When chemical fertilizers are applied alone, the soil typically contains higher levels of available nitrogen, which promotes the proliferation of Pseudomonadota. Consequently, SCN exhibited the highest relative abundance of Pseudomonadota (32.34%). However, in SCBO, the nitrogen composition may have been altered, or the biochar may have modified the microbial community structure, leading to a reduction in the relative abundance of Pseudomonadota [49,50]. Acidobacteriota are microbial taxa well-adapted to more acidic and nutrient-poor soil environments. In this study, the application of biochar improved soil pH and OM contents, thereby creating more favorable conditions for the growth of Acidobacteriota. The RDA results showed that SCN was located farther from SOC, MBC, and POC, indicating a lower association with these soil carbon fractions. Instead, they were more closely associated with microbial taxa such as Bradyrhizobium, a nitrogen-fixing genus capable of surviving in soil without legume hosts [51], suggesting that chemical fertilization alone may promote a distinct microbial community structure less conducive to carbon accumulation. However, SCBO showed strong positive correlations with carbon fractions such as SOC, DOC, MBC, and POC. These treatments also enriched carbon cycle-associated microbial taxa, including Nocardioides, unclassified_p__Gemmatimonadota, unclassified_p__Chloroflexota, and unclassified_c__Actinomycetia. These groups are known for their ability to degrade complex organic matter and participate in carbon fixation and stabilization processes, acting as key drivers of soil carbon cycling [52].
Mantel test results (Figure 6a) indicated a relatively weak correlation between Aromatic C and MBC, likely due to the inherent chemical stability of aromatic compounds, which are less susceptible to microbial degradation [53]. However, OM showed strong correlations with carbon-related indices, underscoring its role as a primary microbial substrate. Random forest and PLS-PM analyses further identified microbial communities and carbon fixation genes as key drivers of CPMI, highlighting the importance of improved soil conditions in promoting microbial activity, diversity, and carbon sequestration [54]. Therefore, this study emphasizes the limitations of sole chemical fertilizer application in promoting the establishment of soil carbon-fixing microbial communities. Adding organic materials offers a more balanced nutrient supply and ecological niche structure, thereby generating synergistic benefits for microbial diversity and carbon cycling functions [55]. Overall, these findings highlight the limitations of sole chemical fertilizer use in supporting carbon-fixing microbial communities, while co-application with organic materials creates a more favorable environment for microbial diversity and soil carbon cycling.

5. Conclusions

This study demonstrated that co-application of organic materials significantly enhanced soil nutrient status, enzyme activity, and labile organic carbon fractions, with SCBO showing the most comprehensive improvements. SCB and SCBO increased the proportion of Aromatic C, thereby enhancing carbon stability, as revealed by FTIR and 13C-NMR analysis. Metagenomics results further indicated that SCBO promoted the enrichment of carbon-fixing microbes and key functional genes. These findings highlight the synergistic role of biochar and organic fertilizer in improving soil carbon sequestration and provide a scientific basis for optimizing fertilization strategies in agricultural ecosystems.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15071555/s1: Table S1: Basic physicochemical properties of the cultivated soil layer; Table S2: Basic physicochemical properties of organic fertilizers; Table S3: Biochar composition; Table S4: Fertilization amount for soybean; Table S5: Effect of different fertilization regimes on soil organic carbon pool; Figure S1: Effect of different fertilization regimes on SOC and its labile components; Figure S2: 13C-NMR spectra of soil under different fertilization regimes; Figure S3: Microbial community composition under different fertilization regimes at (a) the phylum level and (b) the genus level; Figure S4: (a) PCoA of microbial communities at the genus level under different fertilization regimes; (b) RDA showing the relationship between labile organic carbon fractions and the top 10 most abundant microbial genera.

Author Contributions

W.W.: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing—Original Draft; Y.J.: Data Curation, Methodology, Software, Investigation, Writing—Original Draft; S.C.: Visualization, Software, Investigation; Y.L.: Visualization, Investigation; J.Q. (Corresponding Author): Conceptualization, Funding Acquisition, Resources, Supervision, Writing—Review and Editing; L.S. (Corresponding Author): Conceptualization, Funding Acquisition, Resources, Supervision, Writing—Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

The research reported here was performed with funding from the National Key Research and Development Program of China (2024YFD1501602). This article was supported by the earmarked fund for CARS04.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

This work was supported by Xuesheng Liu, whose guidance in experimental design and technical assistance during the experiments are gratefully acknowledged.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Effects of different fertilization regimes on soil enzyme activity: (a) UE; (b) SC; (c) CAT; (d) β-GC. Note: The data in the figures are mean values ± standard errors; different lowercase letters indicate different treatments reaching a significant difference level of 5%.
Figure 1. Effects of different fertilization regimes on soil enzyme activity: (a) UE; (b) SC; (c) CAT; (d) β-GC. Note: The data in the figures are mean values ± standard errors; different lowercase letters indicate different treatments reaching a significant difference level of 5%.
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Figure 2. FTIR of soil under different fertilization regimes (a); relative contents of SOC functional groups (b); relative proportions of main functional groups in 13C-NMR of soil organic carbon (c,d).
Figure 2. FTIR of soil under different fertilization regimes (a); relative contents of SOC functional groups (b); relative proportions of main functional groups in 13C-NMR of soil organic carbon (c,d).
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Figure 3. Carbon fixation functions under different fertilization regimes: (a) functional categories of carbon fixation; (b) relative gene abundances; (c) LEfSe analysis of differentially enriched carbon fixation genes.
Figure 3. Carbon fixation functions under different fertilization regimes: (a) functional categories of carbon fixation; (b) relative gene abundances; (c) LEfSe analysis of differentially enriched carbon fixation genes.
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Figure 4. PCoA analysis of genes under different fertilization regimes (a); RDA (b) of labile organic carbon components and carbon fixation functional genes.
Figure 4. PCoA analysis of genes under different fertilization regimes (a); RDA (b) of labile organic carbon components and carbon fixation functional genes.
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Figure 5. (a) LEfSe analysis of significantly different microbial genera under different fertilization regimes; (b–d) significance analysis of enriched microbial taxa at the genus level compared with SCN.
Figure 5. (a) LEfSe analysis of significantly different microbial genera under different fertilization regimes; (b–d) significance analysis of enriched microbial taxa at the genus level compared with SCN.
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Figure 6. (a) Mantel test showing the correlations between environmental factors, labile organic carbon fractions, microbial community structure, and carbon fixation genes. (b) Variable importance in the prediction of the CPMI based on soil physicochemical properties, enzyme activities, microbial communities, and carbon fixation genes using the random forest model. (c) PLS-PM illustrating the effects of soil environmental factors, enzyme activity, molecular structure, microbial community, and carbon fixation genes on the CPMI. *, **, and *** represented p < 0.05, p < 0.01, and p < 0.001, respectively.
Figure 6. (a) Mantel test showing the correlations between environmental factors, labile organic carbon fractions, microbial community structure, and carbon fixation genes. (b) Variable importance in the prediction of the CPMI based on soil physicochemical properties, enzyme activities, microbial communities, and carbon fixation genes using the random forest model. (c) PLS-PM illustrating the effects of soil environmental factors, enzyme activity, molecular structure, microbial community, and carbon fixation genes on the CPMI. *, **, and *** represented p < 0.05, p < 0.01, and p < 0.001, respectively.
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Table 1. Effects of different fertilization methods on soil physicochemical properties.
Table 1. Effects of different fertilization methods on soil physicochemical properties.
TreatmentpHOM
g/kg		AN
mg/kg		TN
g/kg		AP
mg/kg		TP
g/kg
SCN6.71 ± 0.04 a27.57 ± 0.46 c96.90 ± 0.90 b0.16 ± 0.01 b13.51 ± 1.40 b0.68 ± 0.01 a
SCB6.58 ± 0.01 b39.10 ± 0.55 a99.63 ± 0.52 b0.17 ± 0.01 ab11.78 ± 0.12 b0.57 ± 0.03 b
SCO6.31 ± 0.04 c36.43 ± 0.71 b149.80 ± 4.60 a0.20 ± 0.02 ab26.29 ± 0.81 a0.66 ± 0.02 ab
SCBO6.65 ± 0.05 ab38.60 ± 0.32 a143.23 ± 1.84 a0.21 ± 0.02 a24.14 ± 0.71 a0.74 ± 0.03 a
Note: The data in the table are mean values ± standard errors; different lowercase letters indicate different treatments reaching a significant difference level of 5%.
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Wang, W.; Jiang, Y.; Cai, S.; Li, Y.; Qu, J.; Sun, L. Synergistic Effects of Organic and Chemical Fertilizers on Microbial-Mediated Carbon Stabilization: Insights from Metagenomics and Spectroscopy. Agronomy 2025, 15, 1555. https://doi.org/10.3390/agronomy15071555

AMA Style

Wang W, Jiang Y, Cai S, Li Y, Qu J, Sun L. Synergistic Effects of Organic and Chemical Fertilizers on Microbial-Mediated Carbon Stabilization: Insights from Metagenomics and Spectroscopy. Agronomy. 2025; 15(7):1555. https://doi.org/10.3390/agronomy15071555

Chicago/Turabian Style

Wang, Wei, Yue Jiang, Shanshan Cai, Yumei Li, Juanjuan Qu, and Lei Sun. 2025. "Synergistic Effects of Organic and Chemical Fertilizers on Microbial-Mediated Carbon Stabilization: Insights from Metagenomics and Spectroscopy" Agronomy 15, no. 7: 1555. https://doi.org/10.3390/agronomy15071555

APA Style

Wang, W., Jiang, Y., Cai, S., Li, Y., Qu, J., & Sun, L. (2025). Synergistic Effects of Organic and Chemical Fertilizers on Microbial-Mediated Carbon Stabilization: Insights from Metagenomics and Spectroscopy. Agronomy, 15(7), 1555. https://doi.org/10.3390/agronomy15071555

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