Improvement of soil organic carbon turnover and microbial community niche differentiation with the addition of commercial organic fertilizer in wheat–green manure systems

Improvement of soil organic carbon turnover and microbial community niche differentiation with the addition of commercial organic fertilizer in wheat–green manure systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improvement of soil organic carbon turnover and microbial community niche differentiation with the addition of commercial organic fertilizer in wheat–green manure systems Liyang Cheng, Hao He, Tao Min, Tong Luo, Junhua Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4593466/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Plant and Soil → Version 1 posted 6 You are reading this latest preprint version Abstract Background and Aims Green manure and commercial organic fertilizer are widely used in agricultural production to improve farmland soil carbon reserves; however, their combined impact on soil organic carbon (SOC) turnover is not yet fully understood. Methods We designed a potted wheat-soybean green manure system to investigate the impact of different fertilization treatments on SOC content and structure, extracellular enzyme activity community characteristics of fungi and bacteria after wheat harvest in 2021 and 2022. Results The results indicated that compared to chemical fertilization (CF), following the addition of soybean green manure, organic fertilization (OF) led to a 12.5% increase in SOC content, 19.3% increase in the highly active organic carbon (HAOC) fraction and 10.2% increase in the recalcitrant organic carbon (ROC) fraction. Additionally, there was a 16.1% increase in the alkyl-C to O-alkyl-C ratio and a 63.4% decrease in aliphatic C to aromatic C ratio. Significant increases were observed in the contents of extracellular enzyme, soil total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, microbial carbon and microbial nitrogen. The abundance of observed species of fungi and bacteria significantly decreased in OF compared with that in CF, with the symbiotic network indicating a higher level of positive interaction between fungi and bacteria in OF. Conclusion OF primarily altered soil enzyme activity by influencing soil nutrient contents, resulting in the decomposition of labile organic carbon and an increase in microbial residue biomass, without affecting ROC formation or humification degree. These findings can maximise SOC content in organic agriculture through land use and fertilization techniques. Organic substitution Acid hydrolyzable organic carbon Solid-state nuclear magnetic resonance Enzyme activity Symbiotic network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Soil organic carbon (SOC) is an important indicator of soil quality and fertility and plays a vital role in mitigating climate change (Lal 2010 ; Lehmann and Kleber, 2015 ). Agricultural practices such as tillage, irrigation and fertilization can impact the sequestration of SOC (Qaswar et al., 2020 ; Mayer et al., 2022 ). The application of synthetic fertilizers increases the SOC content because of increased crop yields and residual carbon (Yan et al., 2011 ; Minasny et al., 2012 ; Abbas et al., 2020 ). However, excessive use of fertilizers can lead to environmental issues such as soil acidification, increased greenhouse gas emissions and water pollution (Guo et al., 2010 ; Fan et al., 2012 ). Hence, to overcome these issues, China introduced policies such as ‘zero growth in the use of synthetic fertilizers’ in 2015 and ‘action plans for reduced fertilizer use by 2025’, thereby encouraging farmers to reduce the negative impacts of excessive fertilizer use and achieve carbon sequestration goals using organic fertilizers, green manure planting and straw returning (Zhang et al., 2020; Zhou and Ding, 2023 ). Therefore, to achieve durable SOC storage, it is essential to understand the influence of carbon management measures on the pathways that stabilise the soil carbon pool (Chen et al., 2023 ). Livestock and poultry farming is an important pillar industry in agricultural production, and its products can meet human needs; however, the use of untreated animal waste as fertilizer exacerbates environmental pollution (He et al., 2020 ; Gu et al., 2020 ; Goldfarb et al., 2022 ). As an organic carbon source, manure has low nutrient content and slow-release processes, resulting in decreased crop yields (Liu et al., 2022 ). Research has indicated that composting and maturing of livestock and poultry manure (Tortosa et al., 2012 ) or direct pyrolysis (Fakayode et al., 2020 ; Su et al., 2022 ) can be used for harmless treatment. Granulation and production of commercially viable organic fertilizers with stable nutrient content and efficacy have facilitated easy transportation, storage, sale and application (Moeller and Schultheiss, 2014). To further achieve large-scale agricultural production, some studies have proposed the use of organic fertilizers as a substitute for chemical fertilizers (Cen et al., 2020 ; Lv et al., 2020 ; Lu et al., 2021 ). Several studies have revealed that partial substitution of chemical fertilizers with commercial organic fertilizers can achieve stable yields (Sacco et al., 2015 ; He et al., 2022 ) and result in fewer greenhouse gas emissions (Fang et al., 2021 ) while increasing the SOC content (He et al., 2023 ) and altering the soil microbial community structure (Li et al., 2022b ). However, the impact of adding commercial organic fertilizers on the SOC structure and enzyme activity has not yet been reported. The bare exposure of farmland soil leads to issues such as soil erosion and nutrient loss, ultimately reducing the quality of arable land (Holman et al., 2018 ). Green manure, a cover crop, suppresses soil moisture evaporation by increasing vegetation cover. Its growth process also involves absorbing excess mineral nutrients from the soil to reduce pollution (Irmak et al., 2018 ; Blanco-Canqui et al., 2021). Subsequently, it can be turned over into the soil as a source of plant-derived organic carbon to decompose and release nutrients (Dabney et al., 2001 ; Mbuthia et al., 2015 ). The increased quantity of easily decomposable organic matter gradually stimulates the activity of hydrolytic enzymes (Xu et al., 2021 ; Zhou et al., 2021a ), affecting the community structure of bacteria (Zhou et al., 2021b ) and fungi (Zhou et al., 2021c ), altering the composition and turnover of SOC (Gao et al., 2018 ; Li et al., 2024 ), achieving SOC sequestration (Zhang et al., 2019 ; Yue et al., 2023 ) and ultimately improving soil structure (Blesh, 2018 ; Thapa et al., 2022 ). Although many studies have clarified the changes in SOC turnover and microbial community under the green manure system, there are several knowledge gaps regarding how these microbial communities interact and the relationships between different communities and SOC turnover from a macro-ecological perspective (Bastida et al., 2021 ). The impact of land use and agricultural management techniques on the soil organic carbon pool may result in increased contributions of plant-derived lipids (cutin, suberin and lignin) and microbial and animal-derived lipids (Lorenz et al., 2007 ). The addition of animal-derived carbon is beneficial in improving agricultural environmental quality and enhancing crop productivity (Bhunia et al., 2021 ), while also increasing the content of soil organic carbon and its components, promoting organic carbon mineralization, activating rare microorganisms (low but existent in unfertilized soil) (Semenov et al., 2021 ), ultimately increasing community diversity (Kopittke et al., 2019 ; Li et al., 2021 ), but may also lead to decreased aggregate stability (Li et al., 2022a ). The addition of plant-derived carbon may result in more active soil nutrient turnover and ecosystem processes (Faust and Raes, 2012 ; Coyte et al., 2015 ), increasing the content of macroaggregates in soil and the stability of aggregates of various sizes (Li et al., 2022a ). Due to low nutrient efficiency, microbial mining of nutrients has reduced the carbon retention from organic matter input (Koishi et al., 2020 ), while also enhancing the stability of the microbial community (Yu et al., 2024 ). Studies also suggest that in organic carbon management without reserves, the addition of plant-derived carbon can increase and stabilize animal manure organic carbon, ultimately promoting carbon turnover and sequestration (Koishi et al., 2020 ). However, there is limited research on how different sources of organic carbon impact soil carbon turnover in cropping systems based on the application of commercial organic fertilizers in the main crop and the cultivation of green manure during fallow periods. Our field research over the past 3 years (He et al., 2022 ) has indicated that the application of commercial organic fertilizers to two typical soils can enhance wheat yield and soil fertility. However, the changes in SOC content and structure following the introduction of soybean green manure and the mechanism underlying SOC turnover under the influence of enzymes and microbial carbon pumps have not yet been fully explored. Therefore, this study aimed to conduct experiments based on the substitution of chemical fertilizers with commercial organic fertilizers and the subsequent re-sowing of soybean green manure and field plowing. Our main objectives were to 1) quantify the changes in SOC content, composition and structure as well as extracellular enzyme activity and microbial ecological networks under different fertilization treatments combined with soybean green manure; 2) elucidate the relationship between SOC and soil microbial characteristics and 3) explore the mechanism underlying SOC turnover under the combined action of chicken manure organic fertilizers and soybean green manure. The findings may contribute to a better understanding of the mechanisms by which microorganisms control SOC turnover in the wheat–soybean green manure system, further promoting the widespread use of commercial organic fertilizers and green manure in organic agriculture. 2. Materials and methods 2.1 Study area The in situ pot experiment was conducted from March 2021 to July 2022 at the experimental station of the School of Agriculture, Shihezi University, Shihezi, Xinjiang, China (44°18′N, 86°3′E). The study site is located in the middle section of the northern foot of the Tianshan Mountains and has a temperate continental climate, with an average annual temperature of 7.5°C–8.2°C, annual precipitation of 180–270 mm and annual evaporation of 1,500–2,000 mm. The soil used in the experiment was collected from a wheat field (0–20 cm plow layer) of a continuous 3-year organic substitution trial (Lu et al., 2021 ), and the soil type was Calcareous Fluvisol. The basic physical and chemical properties of the soil are listed in Table S1 . 2.2 Experiment design Based on the cropping model of spring wheat ( Triticum aestivum L. cv Xinchun 38) and soybean ( Glycine max (L.) Merr. cv Henong 70) green manure, the in situ pot experiment was conducted from March 2021 to July 2022. Specifically, the entire soil experiment encompassed a growth cycle of spring wheat–soybean green manure–spring wheat (lasting 272 days). In this experiment, high-density polyethylene open-top rectangular boxes were used, each measuring 46.5 cm in length, 35.0 cm in width and 22.0 cm in height and containing 40.0 kg of air-dried undisturbed soil. To simulate field soil temperature, the containers were placed in soil pits to ensure that they were level with the soil surface. The experiment involved three fertilization treatments, each repeated three times: no fertilization (CK), application of chemical fertilizers (chemical fertilization [CF]) and application of commercial organic fertilizers (organic fertilization [OF]) as a substitute for 24% of the chemical fertilizers. The chemical fertilizers included urea and ammonium phosphate, whereas the commercial organic fertilizer was organic chicken manure from ZeShang Biotechnology Co., Ltd. (Shihezi City, Xinjiang, China). Organic chicken manure was applied to the soil before sowing wheat. The fertilization strategy and quantities for all treatments are described in Table S2 . After harvesting wheat, soybean green manure was sown and no fertilization was applied during the soybean growth period. After 60 days of growth, the soybean green manure was incorporated back into the soil in the pots. The biomass and nutrient contents of soybean green manure for each treatment are detailed in Table S3. To maintain consistent return levels across treatments, the biomass for soybean incorporation was set at 350.0 g/pot, using the treatment with the lowest biomass as the standard for all treatments. 2.3 Soil sample collection and determination In July 2021 and 2022, following the wheat harvest, soil samples were collected from each pot at 6 points within the 0-20cm soil layer using a 3cm diameter soil auger. The collected samples were mixed to form composite samples and promptly transported to the laboratory. After removal of stones, plant residues, and organic debris, the soil samples were divided into three parts: one part of fresh soil samples, sieved through a 2mm mesh, were preserved at -80°C for analysis of soil microbial communities, another part of fresh soil samples, sieved through a 2mm mesh, were kept in a refrigerator at 4°C, for the determination of soil microbial carbon (SMBC), nitrogen (SMBN), and extracellular enzyme activity, and a third part of the samples were air-dried at 25°C, sieved through 0.149mm and 0.075mm mesh, for the determination of SOC, SOC fractions, solid-state 13 C nuclear magnetic resonance spectra, and attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). 2.3.1 SOC, SOC fractions, SMBC and SMBN The improved acid hydrolysis method was used to measure the components of soil organic carbon (Rovira and Vallejo, 2000 ; Rovira and Vallejo, 2007). Specifically, the portion extracted with 2.5 mol/L H 2 SO 4 was referred to as high active organic carbon fraction (HAOC), while the portion extracted with 13 mol/L H 2 SO 4 was referred to as low active organic carbon fraction (LAOC). The residual product after the two-step extraction was considered as resistant organic carbon (ROC). The content of various organic carbons was determined using the potassium dichromate (K 2 Cr 2 O 7 ) and sulfuric acid (H 2 SO 4 ) oxidation method (Walkley and Black, 1934), and the units are g/kg. Chloroform (CHCl 3 ) fumigation followed by leaching with 0.5 mol/L potassium sulfate (K 2 SO 4 ) solution was used, and the leachate was used to determine SMBC (Ocio et al., 1990) and SMBN (Brookes et al., 1985 ). The content of SMBC and SMBN was determined using the methods of Walkley and Black (1934) and the Kjeldahl nitrogen determination method (Jackson, 1973), respectively. 2.3.2 ATR-FTIR and solid-state 13 C NMR characterization Soil samples were collected after the wheat harvest in 2021 and 2022, and mixed to create three separate composite samples, resulting in a total of six composite samples. The soil samples were analyzed using ATR-FTIR spectroscopy (Thermo Scientific Nicolet iN10, USA). The characterization was performed using the KBr-tablet method. In a dry environment, 2 mg of soil sample was weighed, and then 200.0 mg of dry KBr powder was added to an agate mortar and ground thoroughly to create a uniform mixture, which was then pressed into transparent thin sheets using a mold. The wavenumber range was set from 4000 to 400 cm − 1 , with 64 scans and a resolution of 4 cm − 1 . Prior to testing, scans of the background (atmospheric and KBr) were performed, and the background spectrum was automatically subtracted during the scanning process to obtain the infrared spectra. This study specifically calculated the organic absorption peaks at 3400 cm − 1 , 3620 cm − 1 , 2920 cm − 1 , 2850 cm − 1 , 1630 cm − 1 , 1420 cm − 1 , 1030 cm − 1 , and 798 cm − 1 . The functional group references for each absorption peak are based on Bernier et al. ( 2013 ) and Nguyen et al. ( 1991 ). The composite samples from the three treatments of wheat harvested in 2022 were subjected to solid-state 13 C nuclear magnetic resonance (NMR) characterization. Prior to the analysis, the soil samples were treated with 10% hydrofluoric acid (HF) to remove paramagnetic compounds and concentrate organic carbon (Skjemstad et al., 1994 ). The specific pre-treatment process involved weighing 5.0 g of air-dried soil, passing through a 0.149 mm sieve, into a centrifuge tube, adding 50 ml of 10% HF solution, shaking for 1 hour at 3000 rpm, centrifuging for 10 minutes, removing the supernatant, and repeating the HF treatment for a total of 8 times with shaking durations of 4 times × 1 h, 3 times × 12 h, and 1 time × 24 h. After the HF treatment, the samples were washed with 20 ml of distilled water to neutrality (5–6 times), freeze-dried, and ground using agate mortar and pestle, passing through a 0.075mm sieve for analysis. This treatment may lead to Soil Organic Matter (SOM) loss through dissolution, but no significant changes in SOM chemical structure were found (Skjemstad et al., 1994 ; Fang et al., 2010 ). The samples were analyzed on a Bruker Avance NEO 400WB solid-state NMR spectrometer (Germany), with 100 mg of soil sample passing through a 0.075 mm sieve, calibrated using the chemical shift of the CH peak in adamantane ( 13 C, δ = 38.5 ppm) before testing (Morcombe and Zilm, 2003 ). Cross-polarization/total sideband suppression (CP/TOSS) technique was used, with a 4.0mm H/X MAS DVT double-resonance probe. The magic angle spinning rate was 8000 Hz, contact time was 2 ms, recycle delay was 1.2 s, and the resonance frequency was 100.6 MHz, to obtain qualitative structural information of functional groups. All 13 C NMR spectra were assigned to different carbon functional groups according to literature, by dividing the spectra into 5 different chemical shift regions: 160–220 ppm, 110–160 ppm, 60–110 ppm, 45–60 ppm, and 0–45 ppm, which were assigned to carboxyl C, aromatic C, O-alkyl C, methoxyl/N-alkyl C, and alkyl C, respectively, for quantitative analysis (Schmidt et al., 1999 ; Kiem et al., 2000 ; Mao et al., 2017 ). The stable, less degradable aliphatic carbon composition was contrasted with the more unstable and easily decomposable alkoxyl carbon composition, with their ratio being the humification index (Xue et al., 2020 ; Sun et al., 2022 ). A higher ratio indicates a higher degree of decomposition or humification, and hence, a higher resistance to rapid carbon loss (Huang et al., 2021 ). Additionally, the aliphatic C/aromatic C (AL/AR) ratio can be used to predict the complexity of SOM chemical composition, with higher values indicating fewer aromatic structures, lower condensation, and simpler molecular structures (Zhao et al., 2012 ; Xue et al., 2020 ). 2.3.3 Soil extracellular enzyme activity According to the method by Paz-Ferreiro et al. ( 2012 ), the enzymatic activity of α-glucosidase (AG), β-glucosidase (BG), β-cellobiosidase (CL), β-xylosidase (XYL), N-acetyl-glucosaminidase (NAG), L-leucine aminopeptidase (LAP), and alkaline phosphomonoesterase (APT) were determined and slightly modified. Specifically, the soil was cultivated with substrates containing some o-nitrophenol, and then the release of o-nitrophenol during the enzymatic hydrolysis process was measured using a spectrophotometer at a wavelength of 400 nm to determine the enzyme activity. CL activity was determined following the method described by Miller ( 1959 ). The measurement of soil enzyme activity was conducted in 96-well EIA/RIA plates (Costar, REF 3590) and analyzed using an enzyme analyzer (Thermo Scientific, Multiskan SkyHigh, Singapore). The activity of all enzymes was expressed as substrate nanomoles released per gram of soil per hour (nmol g − 1 h − 1 ). 2.3.4 Extraction and sequencing of 16S rDNA and ITS rDNA After the harvest of wheat in 2022, DNA extraction was performed on nine soil samples obtained from three different treatments using the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). Following extraction, the DNA concentration and quality were assessed by 1% (wt/vol) agarose gel electrophoresis and Nanodrop 2000 spectrophotometer (Thermo Scientific, Wilmington, NC, USA). The obtained DNA was then utilized as a template to amplify the V3-V4 variable region of the bacterial 16S rRNA gene using primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) carrying barcode sequences (Liu et al., 2016 ). For fungal DNA, the ITS1 region was amplified using primers with barcode sequences, ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3'), as described by Adams et al. ( 2013 ). The amplification products were sequenced using the Illumina Miseq PE300/NovaSeq PE250 platform (Shanghai Majorbio Bio-pharm Technology Co., Ltd.), and the sequencing data were deposited in the Sequence Read Archive (SRA) of the NCBI BioProject PRJNA1085476. The raw sequencing reads underwent quality control using Fastp (Chen et al., 2018 ) ( https://github.com/OpenGene/fastp , version 0.19.6), followed by merging using Flash (Magoc et al., 2011) ( http://www.cbcb.umd.edu/software/flash , version 1.2.11). After this, Uparse (Edgar, 2013 ) software ( http://drive5.com/uparse/ , version 11) was used to cluster and remove chimeric sequences, generating Operational Taxonomic Units (OTUs) at a threshold of 97% sequence similarity. Subsequently, taxonomic annotation of the bacterial and fungal OTUs was carried out using the RDP classifier (Wang et al., 2007 ) ( http://rdp.cme.msu.edu/ , version 2.13) against the SILVA 16S rRNA gene database ( https://www.arb-silva.de/ , version 138) and the UNITE fungal ITS database ( https://unite.ut.ee/ , version 8.0), with a confidence threshold of 70%. To minimize the potential impact of sequencing depth on subsequent observations of species data analysis, the sequence numbers for all samples were rarified to the minimum sequence count, resulting in 30489 for bacteria and 46165 for fungi. Despite this, the average sequence coverage for each sample remained at 97.7% (for bacteria) and 99.8% (for fungi). 2.3.5 Construction of bacteria-fungus co-occurrence network According to Ma et al. ( 2016 ), we constructed three treatment bacterial-fungal cross-domain networks. To reduce rare OTUs from the dataset, OTUs with relative abundance lower than 0.01% of the total bacterial and fungal sequences were removed. The co-occurrence networks were implemented in the ggClusterNet package (Wen et al., 2022 ). In essence, the package utilises the cor() and Pvalue() functions from the WGCNA package, the sparccboot() function from the SpiecEasi package, and the corr.test() function from the psych package to compute correlations, with reference to layout algorithms used in ggraph and sna, along with functions for in-depth exploration such as average.path.length() and centralized.closeness(). When constructing the networks, we initially calculated Spearman correlation and Kullback Leibler divergence (KLD) measures between all OTU pairs. Then, we set the dissimilarity threshold to the maximum value of the KLD matrix and the Spearman correlation threshold to 0.6. We resampled 100 times, and the resulting distribution was used to generate p-values for the observed association measures. We merged the p-values and used the Benjamini-Hochberg method for multiple testing correction (Benjamini and Hochberg, 1995 ). Finally, only edges that were supported by both measures and had adjusted p-values below 0.05 were considered statistically significant in the network. The top 100 OTUs by relative abundance for both fungi and bacteria were selected as nodes in this network, with edges connecting these nodes representing correlations between OTUs. We used Gephi ( http://gephi.github.io/ ) to generate network visualisations (Bastian et al., 2009 ). 2.4 Statistical analysis We conducted statistical analyses using SPSS 19.0 (SPSS Inc., Chicago, IL, USA) software and performed differential testing between different treatment groups using the Least Significant Difference (LSD) method (P < 0.05). We employed a two-way analysis of variance (ANOVA) to analyse the impact of fertilization treatment (T), year (Y), and their interaction on soil nutrients and organic carbon component indicators. All results are expressed as means. The error bars in the results represent standard deviations (n = 3). ATR-FTIR data were transformed by transmittance and absorbance using OMNIC Specta software (Version 8.2) and subjected to image smoothing and baseline correction. Solid-state nuclear magnetic resonance spectroscopy images were processed using MestReNova software (Version 14.0.0). The Circos sample-to-species relationship diagram was analysed using Circos-0.67-7 ( http://circos.ca/ ). The db-RDA analysis was conducted using the R language (version 3.3.1) vegan (2.4.3) package. Pearson correlation coefficients for all indicators were computed using the "corrplot" package in R software (version 3.5.2). A path analysis model between soil nutrients, enzyme activity, organic carbon components, organic carbon structural composition, and microbial features was built using the "plspm" package in R (4.3.3). Plots were generated using Origin 2019 (Origin Lab Co., Northampton, MA, USA). 3. Results 3.1 SOC and its fractions Our results indicated significant differences in soil nutrient contents between soil samples treated with OF and CF before and after the addition of soybean green manure (Table S4). OF treatment significantly increased the contents of total nitrogen (TN), soil available nitrogen, soil available phosphorus, soil available potassium (SAK), soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN) while lowering the pH. In addition, a significant variation was observed in the SOC content, which was notably higher in OF treatment than in CK and CF treatments in both 2021 and 2022 (12.81 and 13.26 g/kg, respectively) (Fig. 1 a). Following the addition of soybean green manure, the SOC content increased by 10.6% in CK treatment, 14.4% in CF treatment and 3.6% in OF treatment. To gain further insight into the changes in SOC composition, we determined the content of acid-extractable organic carbon. The comparative results of the two years revealed that in all treatments, the HAOC content and recalcitrant organic carbon (ROC) fraction were significantly higher in OF treatment than in CK or CF treatments, whereas there was no significant difference in LAOC content. Compared with 2021, in 2022, the HAOC content in CK, CF and OF treatments increased by 34.7%, 42.1% and 5.9% (Fig. 1 b), ROC fraction increased by 3.4%, 0.2% and 1.0% and LAOC content increased by − 8.0%, 2.4% and 6.6%, respectively (Fig. 1 c and 1 d). Overall, the addition of green manure increased soil nutrient contents, reduced the pH and significantly increased the SOC content, with the most significant increase observed in HAOC content, followed by ROC fraction; however, LAOC content showed a decreasing trend with no significant change. 3.2 ATR-FTIR and CP/TOSS 13 C NMR The results of ATR-FTIR reflected changes in soil SOC composition (Fig. 2 a), identifying six characteristic peaks, representing -COOH (3400 + 3620 cm − 1 ), -CH (2920 + 2850 cm − 1 ), C = C, C = O (1630 cm − 1 ), C-OH (1420 cm − 1 ), C-O (1030 cm − 1 ), and Minerals (798 cm − 1 ), with their respective proportions outlined in Table S5. In comparison with 2021, the relative absorption intensity of CK, CF, and OF in 2022 has increased. Polysaccharide-C and Minerals proportions have decreased, while the proportions of other characteristic peaks have increased. Furthermore, the OF absorption intensity was higher compared to CK and CF. In 2022, compared to different treatments, the proportion of Aromatic-C in OF increased by 2.51% and 4.42% compared to CK and CF treatments. The proportion of Aliphatic-C1 increased by 1.38% and 2.11% compared to CK and CF treatments, respectively, while the proportion of Polysaccharide-C decreased by 11.5% relative to CK and increased by 7.16% relative to CF. The CP/TOSS 13 C NMR spectra (Fig. 2 b) with integration results (Table S6) further confirmed the changes in SOC components in 2022. Across all treatments, the functional group proportions ranked from highest to lowest were Alkyl-C, Aromatic-C, Methoxyl/N-alkyl-C, Carbonyl-C, and O-alkyl-C. In comparison to CK and CF, the proportions of Alkyl-C and Aromatic-C increased in OF, while the proportions of Methoxyl/N-alkyl-C, Carbonyl-C, and O-alkyl-C decreased. Additionally, the aliphatic/alcohol ratio (AL/OA) in the OF treatment was higher by 22.0% and 16.1% compared to CK and CF treatments, respectively. The aliphatic/aromatic ratio (AL/AR) in the CF treatment was the highest, with proportions 21.1% and 63.4% higher than CK and OF treatments, respectively. Overall, both methods demonstrate that under the conditions of returning soybean green manure, the OF treatment altered the SOC composition, increasing the degree of humification and the sequestration of Aromatic-C. 3.3 Soil extracellular enzyme activity To elucidate the response of soil extracellular enzyme activity to amendments, we measured the enzyme activity involved in C, N, and P acquisition (Fig. 3 a-g). The results indicated significant differences in enzyme activity among different fertilization treatments, with CL showing OF > CK > CF (Fig. 3 c), while LAP showed no significant difference between treatments (Fig. 3 f), and other enzyme activities showed OF > CF > CK ( P < 0.05). Soil enzyme activity all showed a significant increase after green manure returning. In 2022, compared to 2021, the activity of AG, BG, CL, XYL, NAG, LAP, and APT in the OF treatment increased by 64.7%, 156.0%, 100.4%, 41.1%, 37.8%, 41.1%, and 100.2% respectively. The activity of the 7 extracellular enzymes in the CF treatment increased by 47.8%, 132.7%, 121.6%, 10.4%, 9.6%, 44.3%, and 86.8%. The activity of the 7 extracellular enzymes in the CK treatment increased by 14.3%, 102.2%, 59.2%, 118.0%, 42.5%, 24.6%, and 35.8%, respectively. 3.4 Microbial abundance and community composition The addition of soybean green manure significantly influenced the changes in soil microbial communities. Based on this, the soil microbial community composition of different treatments in 2022 was determined. In the different treatments, the species number of bacteria and fungi showed similar trends: CK > CF > OF (Fig. 4 a). As shown by the Venn diagram (Fig. S1 ), there were a total of 2474 bacterial OTUs and 396 fungal OTUs across all soils, with each treatment showing a similar trend in the number of specific bacterial and fungal OTUs: CK > CF > OF. Further exploration of the changes in the taxonomic composition was conducted by comparing the relative abundance differences of the main phyla and genera in different treatments (Fig. 4 b-e). Looking at the bacterial community, Actinobacteriota , Proteobacteria , Chloroflexi , and Acidobacteriota were the top four dominant phyla (> 75%) in all treatments (Fig. 4 d). Among these, the relative abundance for CK of the top four dominant phyla was 24%, 20%, 14%, 19%, for CF it was 27%, 23%, 14%, 11%, and for OF it was 27%, 19%, 16%, 14%. At the bacterial genus level, the relative abundance of the top 10 genera was only 29.1% − 32.8% (Fig. 4 b), with Bacillus being the most abundant genus in all treatments, showing the trend OF > CF > CK. Regarding the fungal community, Ascomycota , Mortierellomycota , and Basidiomycota were the top three dominant phyla (> 96%) in all treatments (Fig. 4 e). The relative abundance for CK of these dominant phyla was 85%, 11%, 2%, for CF it was 84%, 10%, 4%, and for OF it was 84%, 8%, 4%. At the fungal genus level, the structure of the fungal community varied more significantly between different treatments (Fig. 4 c). Across all treatments, Chaetomium , Retroconis , Mortierella , Gibberella , and Talaromyces were the top five dominant genera (> 75%), with the abundance of Chaetomium , Retroconis , and Mortierella being higher in the OF treatment than in CF and CK, while the abundance of Gibberella was higher in CF than in OF and CK, and the abundance of Talaromyces was higher in CK than in CF and OF. The distance-based redundancy analysis was used to study the relationship between soil abiotic properties and microbial structure (Fig. S1 c-d). Soil physicochemical properties and changes in SOC structure explained 43.9% and 62.7% of the variation in bacterial and fungal communities, respectively. The changes in bacterial and fungal communities caused by the OF were associated with soil nutrients, SOC content, and the degree of humification of organic matter. 3.5 Symbiotic networks of bacteria and fungi and their topological characteristics The data for constructing the soil microbial abundance table used in the bacterial–fungal co-occurrence network comes from three treatments (n = 3) (Fig. 5 ). After filtering out low relative abundance OTUs (0.01%), 3887 and 954 OTUs were detected in the bacterial and fungal communities, respectively. The top 100 OTUs in abundance were retained to generate Fig. 5 a. CK, CF, and OF each obtained 3 modules, with edge numbers of 3003, 3467, and 3215, respectively. The positive interactions between bacteria and fungi were 51.75%, 54.2%, and 46.69%, while the negative interactions were 48.25%, 45.8%, and 53.31% in CK, CF, and OF. The dominant fungal phyla in the three modules of CK, CF, and OF are Ascomycota and Mortierellomycota , and the dominant bacterial phyla are Actinobacteriota , Chloroflexi , and Gemmatimonadota (Table S7). Using the aforementioned co-occurrence networks, we studied whether the OTUs under different fertilization treatments with soybean green manure returning exhibited unique node-level topological features. The results indicated that the average degree in the CF treatment was higher than that in OF and CK, while the average path length, centralization degree, and centralization betweenness in all three treatments showed a consistent trend, with CK > CF > OF. 3.6 Correlations between soil nutrients, SOC fractions, enzyme activity, and microbial characteristics. Using Partial Least Squares Path Modelling (PLS-PM) to reveal the influence of different soil factors on SOC content in each treatment (Fig. 6 a), where "Fertilization" represents the optimization process of fertilization strategies, corresponding to the treatment codes CK, CF, and OF. Factor loading analysis (Fig. 6 c) indicates that, except for AD and LAP, all other indicators can serve as powerful indicators of soil nutrients, enzyme activity, and microbial characteristics. Pearson correlation analysis (Fig. 6 b) shows that SOC and SOC fractions are significantly positively correlated with soil nutrient indicators, extracellular enzyme activity, and microbial biomass ( P < 0.05), but negatively correlated or uncorrelated with the observed species numbers and co-occurrence network characteristics of fungi and bacteria. Standardized total effect analysis (Fig. 6 d) reveals that SOC stability (ROC and AL/OA) directly and positively influences SOC content, while soil nutrients, enzyme activity, and microbial characteristics indirectly and positively affect SOC content. 4. Discussion 4.1 SOC contents This study revealed that prior to the addition of soybean green manure, OF treatment increased the SOC content and modified the SOC structure to some extent (Fig. 1 a and Fig. 2 a-b). This indicates that substituting some of the chemical fertilizer with commercial organic fertilizer can improve soil nutrient contents (Table S4) regulate SOC turnover, achieving carbon sequestration. This is in line with previous research outcomes (Lan et al., 2022 ; He et al., 2023 ; Liu et al., 2023 ; Xu et al., 2023 ). SOC content is influenced by external carbon input, such as plant residues, root exudates, organic amendments and the regulation of organic carbon mineralisation. The increase in SOC content was related to consecutive organic fertilizer applications, and the decomposition rate of organic carbon in soil treated with organic fertilizers was lower than that in unfertilised soil and soil treated with chemical fertilizers, leading to greater organic carbon accumulation (Xu et al., 2024 ). Based on the changes in SOC fractions, it is evident that OF treatment primarily increased the HAOC and ROC fractions and decreased the LAOC content, resulting in an increase in the total SOC content (Fig. 1 b-d). According to Rovira and Vallejo (2007), the least stable component (HAOC) consists mainly of carbon compounds such as starch, hemicellulose and soluble sugars, the moderately stable component (LAOC) consists primarily of cellulosic carbon compounds and the recalcitrant component (ROC) consists of aromatic rings and lignin, which are difficult to biodegrade. The reduction in LAOC content may be related to the increased diversity of external carbon input. This is because the introduction of external organic carbon into the soil, mixed with plant residues, root exudates and other inputs, increases the diversity of organic waste and inhibits the loss of quality associated with the least stable component, although it does not affect the decomposition of the moderately stable component (Grossman et al., 2020 ; Wang et al., 2022 ). After adding soybean green manure, all treatments showed an increase in SOC content and its components; however, there was no significant change in LAOC content. Compared with fertilization treatments (CF and OF), no fertilizer treatment and addition of green manure reduced the LAOC content, which may be related to the decomposition of SOC. Our results showed that OF treatment significantly increased the SMBC (Table S4) content, which may represent a series of microbial activities involved in the decomposition of unstable carbon because cellulose is a readily available source of carbon and energy for decomposing agents (Talbot and Treseder, 2012 ). In general, no reduction in total SOC content was noted after organic correction, mainly because green manure residues, as easily decomposed carbon sources, can effectively change the turnover of organic carbon, resulting in an increase in the levels of unstable carbon and refractory carbon (Chaudhary et al., 2017 ). 4.2 Characterization of SOC structure The ATR–FTIR and CP/TOSS 13 C NMR spectra provided important findings, which are crucial for understanding the persistence and transformation of SOC. Both characterisation methods yielded similar results, indicating that compared to CK, OF and CF treatments led to an increase in aromatic C content and a decrease in alkyl-C content. Remarkably, NMR revealed more detailed phenomena, indicating that the alkyl-C content was higher in OF treatment than in CF or CK treatment, whereas the contents of methoxyl/N-alkyl-C and O-alkyl-C were lower in OF treatment than in CF and CK treatments (Table S6). This finding is consistent with that of Huang et al. ( 2022 ), who found that the decomposition of Chinese milk vetch in the soil increased the relative alkyl-C content and decreased the O-alkyl-C content, possibly due to the presence of unstable forms of SOC in Chinese milk vetch that are initially utilised by microorganisms, leading to further degradation and alkyl-C production (Kopittke et al., 2020 ; Lan et al., 2022 ). Additionally, Guigue et al. (2021) evaluated the trends of molecular changes in straw using 13 C CP–MAS–NMR results and revealed a slight increase in the alkyl/O-N-alkyl ratio. This may represent the processes that include a reduction in unstable components such as carbohydrates and peptides in SOC (Pedersen et al., 2011 ; Rodriguez et al., 2021 ) and an increase in the contents of lipids, amino acids and cutin (Prietzel et al., 2018 ). Similar phenomena were observed in the decomposition of spruce litter, as reported by Helfrich et al. ( 2006 ), who suggested that the loss of easily decomposable carbohydrates and the selective preservation of more recalcitrant alkyl-C were responsible for such findings. These results are consistent with the proposed residual forms of natural organic residues suggested by Baldock et al. ( 1997 ), indicating that decomposition is almost always accompanied with an increase in alkyl-C content and a decrease in O-alkyl-C content and that the ratio of alkyl to O-alkyl carbon (aromatic C/O-alkyl-C) may serve as a sensitive indicator of decomposition extent. Further calculation of the ratio of the absorption intensities of two bands revealed the humification degree (decomposition extent). We revealed that the humification degree increased with the combined action of organic and green manure, which may be one of the reasons for the increase in the proportion of aromatic C (Fig. 6 a, Table S6). This is consistent with the findings reported by Golchin et al. ( 1995 ), who observed an accumulation of aromatic C structures in particulate organic matter components. We speculate that the increase in aromatic carbon content in OF treatment is due to mineral protection, indicating that the turnover rate of SOC in OF treatment was higher than that in CF and CK treatments, and the increase in alkyl and aromatic carbon content may be due to the relatively high decomposition rate of carbon compounds. When SOC rich in carbon compounds is enveloped by mineral particles, its rapid decomposition is hindered, resulting in the accumulation of recalcitrant components. This supports the aggregate formation and stabilisation model proposed by Golchin et al. ( 1997 ), where decomposable carbon compounds are encapsulated in aggregates, eventually being incorporated into microbial biomass and metabolites, which become associated with clay minerals. In terms of the complexity of SOC structure, our results indicate a decreasing trend of AL/AR, implying a more complex molecular structure due to the addition of chemical and organic fertilizers. 13 C NMR data by Xue et al. ( 2020 ) demonstrated that the incorporation of straw reduced the fatty carbon/aromatic carbon ratio (organic carbon complexity index) in the soil. Similarly, Gao et al. ( 2018 ) characterised the composition of soil dissolved organic matter long-term application of milk vetch and rape green manure resulting in greater complexity. Purakayastha et al. ( 2020 ) reported the highest humification and aromatisation in soils with a combination of organic fertilizer and green manure ( Sesbania acculeata and Leucaena leucocephala ). They provided a more detailed explanation: the amount of polyaromatic compounds was lower in the isomers formed under organic combination treatment, and the semiquinone free radical content was lower, resulting in the formation of quinones with higher organic carbon stability. In conclusion, a 272-day soil experiment under the background of fertilization treatments can provide preliminary indications regarding the comprehensive effects of organic amendments and combined green manure on SOC dynamics. However, it may not precisely indicate long-term trends. Future studies to quantify the SOC mineralisation and the mechanisms underlying mineral protection in this process will help further integrate short- and long-term field data to investigate long-term carbon turnover dynamics. 4.3 Change of enzyme activity Extracellular enzymes play a crucial biochemical role in the decomposition of organic matter in soil systems, and their activity is related to the characteristics of exogenous organic matter (Zimmerman and Ahn 2010 ; Cenini et al., 2016 ; Liu et al., 2017 ). Our results revealed significant differences in extracellular enzyme activity among different treatments (Fig. 3 ). The addition of soybean green manure led to a significant increase in enzyme activity. This could be due to the low carbon-to-nitrogen ratio of legume green manure, which can substantially activate soil microbes (Tejada et al., 2008 ), thereby promoting the mineralisation of native soil organic matter through a priming effect (Kuzyakov et al., 2000 ). These changes are in line with the theoretical concept that substantial inputs of unstable carbon can promote the growth rate of soil microbes while also shortening their lifespan, thus regulating the dynamic changes of carbon components after organic matter decomposition (Cui et al., 2020 ; Rousk et al., 2016 ). Furthermore, our results indicated a significant increase in SMBC content after the addition of green manure (Table S4). Previous studies have suggested a positive correlation between SOC content and carbon-transforming enzyme activity (Qi et al., 2016 ; Dai et al., 2019 ). In the current study, SOC, HAOC and ROC fractions showed positive correlations with most enzyme activities (except for LAP), whereas LAOC content did not show a significant correlation with enzyme activity (Fig. 6 b). This may be related to the increase in humification after organic amendments, whereas the LAOC content remains relatively constant. Notably, our results showed a different trend for cellulase (OF > CK > CF), which may be related to the surplus of N and P fertilizer. Tie et al. ( 2020 ) revealed that the addition of N, P and S reduced the activity of soil cellulase. Moreover, Gao et al. ( 2019 ) reported that the addition of P reduced cellulase activity. Changes in substrate composition and effectiveness can cause variations in the structure and activity of the microbial community, subsequently influencing differences in soil carbon cycling. Considering the ‘Microbial Stoichiometry Theory’, the addition of leguminous green manure, which is rich in nitrogen, increases the effectiveness of the substrate to some extent by promoting microbial utilisation of nitrogen and drives the synergistic mineralisation of green manure residues for carbon and nitrogen (Zhu et al., 2018 ). Moreover, we revealed that adding green manure increased the labile organic carbon content as well as accelerated ROC formation, consequently increasing the activity of various carbon-transforming enzymes. However, compared to CK with only green manure addition, CF and OF treatments did not show a significant increase in the proportion of observed species (Fig. 2 ). This may be because treatments with chemical and organic fertilizers have been in place for 3 years, leading to the formation of specific dominant bacterial groups; thus, the addition of green manure only increased the microbial community diversity of the control soil. 4.4 Composition of soil microbial community In terms of bacterial communities, the abundance of Acidobacteria in the soil was higher in CF and OF treatments than in CK treatment. This may be due to the fact that Acidobacteria belongs to the K-strategy oligotrophic phylum, which prefers nutrient-limited environments (Fierer et al., 2007 ; Naether et al., 2012 ). The increase in the relative abundance of Acidobacteria may be due to the decrease in TN and SAK contents in the control (Table S4). At the bacterial genus level, the relative abundance of Bacillus was the highest in all treatments, in the order of OF > CF > CK. Fan et al. ( 2014 ) reported that Bacillus and other microorganisms could directly fix nitrogen during the decomposition of corn residues; moreover, there is evidence that organic manure significantly increases the abundance of Bacillus (Gao et al., 2023 ). Notably, the abundance of Chloroflexi was higher in OF treatment than in CK and CF treatments, and Ye et al. ( 2021 ) also revealed that the application of organic manure increased the abundance of Chloroflexi . Additionally, we found that the abundance of Actinobacteria was higher in CF and OF treatments than in CK treatment. One possible explanation for this finding is that as the decomposition of green manure and organic manure progresses, from the early stage of abundant resources (due to rapid degradation of unstable substrates) to the later stage of resource depletion, nutritionally poor bacterial communities gradually shift to dominant communities (Bastian et al., 2009 ; Sun et al., 2013 ). In terms of fungal communities, the relative abundance of Ascomycota and Mortierellomycota showed a trend of OF > CF > CK, which may be related to the growth of soybean green manure. Espana et al. ( 2011 ) revealed that Mortierellomycota in soybean residues is considered a fast-growing fungal decomposer. The abundance of the filamentous fungus Talaromyces was higher in CK treatment than in CF and OF treatments, possibly due to the exclusive use of soybean green manure in CK treatment. Asghar and Kataoka ( 2023 ) reported that the abundance of Talaromyces in the soil where leguminous plants are grown is related to the promotion of plant growth and inhibition of pathogen growth by volatile organic compounds. The relative abundance of Gibberella was higher in CF treatment than in OF or CK treatment, which may be related to potential pathogenicity. Ding et al. ( 2017 ) also revealed that the abundance of Gibberella was higher in chemical fertilizer treatments than in the combination treatment of organic and chemical fertilizers, whereas Gao et al. ( 2023 ) showed that the addition of earthworms and organic manure from cattle reduced the abundance of Gibberella . Overall, OF treatment enriched the effectiveness of enzyme substrates and stimulated the enzyme activity, and the altered bacterial and fungal communities preferred specific carbon components, thereby promoting the diversification of SOC structure (Sun et al., 2020 ). 4.5 Symbiotic network The evolution of bacterial or fungal community composition may not be directly related to the increase in SOC content. Therefore, we employed a comprehensive dataset of bacterial and fungal operational taxonomic units (OTUs) to conduct co-occurrence network analysis to illustrate and quantify the topological differences between different treatments. The results indicated that the network topological characteristics of CK, CF and OF treatments differed, with the positive influence of bacteria being greater than the negative influence in CK and CF treatments, and the opposite effect was true for OF treatment. CF exhibited the highest average degree, whereas OF had the lowest average path length, centralisation degree and centralisation betweenness (Fig. 5 ). The topological structure of the network can reflect interactions between microorganisms, and key species may be associated with many other species and ecological functions, indicating the topological patterns within the network (Berry and Widder, 2014 ). The degree value in OF treatment tended to be higher, whereas the network centrality values tended to be lower. Differences in soil mineral nutrient or SOC content resulted in significant ecological niche differentiation, with weak niche differentiation potentially leading to stronger interactions between soil microorganisms (Faust and Raes, 2012 ). This finding may also imply that the interactions between soil bacteria and fungi in OF treatment were not as strong as those in CK and CF treatments. In our study, key species included dominant phyla of bacteria and fungi, with Ascomycota and Mortierellomycota as dominant fungi and Actinobacteriota , Chloroflexi and Gemmatimonadota as dominant bacteria (Table S6). Dominant taxa within the phyla Ascomycota , Actinobacteriota , Chloroflexi and Gemmatimonadota varied among the different treatments. This may be attributed to their diverse lifestyles and involvement in a wide range of ecological processes (Farag et al., 2017 ; Ma et al., 2020 ). 4.6 Relationship between SOC and soil enzyme activity, microbial community composition and topological characteristics In terms of the source of SOC, following the addition of commercial chicken manure organic fertilizer in the fourth year, the significant increase in soil nutrient contents may have shaped a stable microbial community with strong ecological niche differentiation. This led to a consistent distribution of different active organic carbon sources and a significant increase in the SOC content (Fig. 1 ). Subsequently, the introduction of carbon from soybean green manure into the soil had no significant impact on the proportion of SOC components, but the significant increase in SOC content indicates a rapid turnover and sequestration of SOC (Fig. 1 d). Furthermore, characterisation of the SOC structure revealed changes in humification degree and molecular structure (Fig. 2 , Table S6), demonstrating the impact of increased plant-derived carbon content on the stable quantity and structure of animal-derived SOC. This is consistent with the findings of Koishi et al. ( 2020 ). PLS-PM analysis (Fig. 6 a) showed that OF treatment mainly altered the quantity of enzyme activity substrates by affecting soil nutrient contents, thereby influencing extracellular enzyme activity. With the increase in enzyme activity, some labile organic carbon was decomposed (as reflected by the decrease in LAOC content) and the microbial residue biomass was increased (as indicated by the increase in SMBC and SMBN contents), but it did not affect ROC formation or humification degree. This ultimately led to a stable microbial community and an increase in SOC content. 5. Conclusion The 2-year in situ pot experiment of wheat–soybean green manure systems showed that the addition of commercial chicken manure organic fertilizer led to greater improvements in soil nutrient contents, organic carbon components, extracellular enzyme activity, microbial residue biomass and microbial network structure compared with chemical fertilizers and the control. The increase in the SOC content was attributed to the stable exogenous carbon supply in this planting pattern, leading to the distribution of stable organic carbon components. The addition of green manure effectively promoted the turnover and sequestration of organic carbon in the short term while also resulting in a more complex molecular structure of SOC. The increase in soil nutrient contents affected substrate quantity, influencing enzyme activity, and shaped stronger ecological niche differentiation and weaker interactions between bacteria and fungi. These research findings provide experimental and conceptual evidence regarding the impact of the combination of commercial chicken manure organic fertilizer and soybean green manure on soil carbon sequestration, offering theoretical guidance for achieving agricultural carbon sequestration and sustainable development. Further research is warranted to quantify the effects of these treatment measures on SOC mineralisation and potential loss pathways as well as consider its long-term impact. Declarations The authors declare no conflict of interest. Authors’ contributions Liyang Cheng: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Hao He: Writing – review & editing, Methodology. Tao Min: Writing – review & editing, Methodology, Conceptualization. Tong Luo: Writing – review & editing, Methodology. Junhua Li: Writing – review & editing, Conceptualization, Project administration, Funding acquisition. Acknowledgements This work was supported by the Earmarked fund for XJARS, Major science and technology projects of Xinjiang Uygur Autonomous Region "Research on Obstacle Reduction and Production Capacity Enhancement Technology for Medium and Low Yield Fields" (2022A02007) and National Key Research and Development Program of China (2021YFD1900802). References Abbas, F., Hammad, H.M., Ishaq, W., Farooque, A.A., Bakhat, H.F., Zia, Z., Fahad, S., Farhad, W., Cerda, A., 2020. A review of soil carbon dynamics resulting from agricultural practices. J. Environ. 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Using milk vetch ( Astragalus sinicus L.) to promote rice straw decomposition by regulating enzyme activity and bacterial community. Bioresour. Technol. 319, 124215. https://doi.org/10.1016/j.biortech.2020.124215. Zhou, G., Gao, S., Chang, D., Shimizu, K., Cao, W., 2021c. Succession of fungal community and enzyme activity during the co-decomposition process of rice ( Oryza sativa L.) straw and milk vetch ( Astragalus sinicus L.). Waste Manage. 134, 1-10. https://doi.org/10.1016/j.wasman.2021.08.002. Zhou, W., Ding, W.C., 2023. Strategic researches of reducing fertilizer use and increasing use efficiency in China in the new era. J. Plant Nutr. Fertil. 29, 1-7 (in Chinese). https://doi.org/10.11674/zwyf.2022647. Zhu, Z., Ge, T., Luo, Y., Liu, S., Xu, X., Tong, C., Shibistova, O., Guggenberger, G., Wu, J., 2018. Microbial stoichiometric flexibility regulates rice straw mineralization and its priming effect in paddy soil. Soil Biol. Biochem. 121, 67-76. https://doi.org/10.1016/j.soilbio.2018.03.003. Zimmerman, A.R., Ahn, M.Y., 2010. Organo-mineral–enzyme interaction and soil enzyme activity. In: Shukla G, Varma A (eds) Soil enzymology, Soil biology, vol 22. Springer, Berlin, Heidelberg, pp 217–292. Supplementary Files AppendixA.Supplementarydata.docx GraphicalAbstract.docx Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Plant and Soil → Version 1 posted Editorial decision: Major revisions 28 Nov, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviewers invited by journal 25 Jun, 2024 Editor invited by journal 18 Jun, 2024 First submitted to journal 17 Jun, 2024 Editor assigned by journal 17 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4593466","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318818241,"identity":"ba86b332-89a9-4290-97e8-e0cd7ca9b303","order_by":0,"name":"Liyang Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIie3RsQrCMBCA4SuB1CHa9USxrxDpC/gocepUcCpdpXBZfAZfQnBO6Rp8AZf6Bo7dNJtjz00w33w/IXcAUfSLUhCPZ/PayNQ6ZiJAFuhFsVDesBPAOYn9GXeaV2St0npJsiQEA2NznU6wV3o43FRFq6NLTv4+nWgRXsEaK1o7IxJiJqikLiUa/U1CxvAT7GUdluy2FJbcsf6S2fYSTuny3NpuGBtGAjD7nMNx5oN0YA5GURT9rTc9wjaQvINwswAAAABJRU5ErkJggg==","orcid":"","institution":"Shihezi University College of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Liyang","middleName":"","lastName":"Cheng","suffix":""},{"id":318818242,"identity":"67ee51d9-1301-4eeb-8154-c09ec4151a66","order_by":1,"name":"Hao He","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"He","suffix":""},{"id":318818243,"identity":"a3ecfd84-dfdb-45b8-b23c-69224834a6fa","order_by":2,"name":"Tao Min","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Min","suffix":""},{"id":318818244,"identity":"a10c946b-bcba-49a4-9d40-b90f5003f65b","order_by":3,"name":"Tong Luo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Luo","suffix":""},{"id":318818245,"identity":"8627c0d3-587a-48a2-a78e-31d226358932","order_by":4,"name":"Junhua Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Junhua","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-17 10:18:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4593466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4593466/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-025-07280-2","type":"published","date":"2025-02-20T15:57:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60496123,"identity":"3f0b13be-5412-4c93-a82e-b2960e1c8cdf","added_by":"auto","created_at":"2024-07-17 11:44:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75230,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different treatments on soil organic carbon (a), highly active organic carbon fractions (b), low active organic carbon fractions (c), refractory organic carbon fractions (d). Different letters above the columns indicate that the significance between the treatments reaches \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, where uppercase letters represent the comparison between 2021 and 2022, and lowercase letters represent the comparison between different fertilization treatments.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/107912e95867ae7365e4fae4.jpg"},{"id":60497972,"identity":"9878d33a-127f-4601-a9bb-0310716400fe","added_by":"auto","created_at":"2024-07-17 12:00:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62195,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different treatments on total reflection Fourier transform infrared spectroscopy (a) after wheat harvest in 2021 and 2022. CP/TOSS \u003csup\u003e13\u003c/sup\u003eC-NMR characterization (b) of soil in 2022.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/02c3b0af4951aa12e6a45b40.jpg"},{"id":60496122,"identity":"e46737a7-2877-43e8-9123-a1027bfe3e01","added_by":"auto","created_at":"2024-07-17 11:44:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85270,"visible":true,"origin":"","legend":"\u003cp\u003eThe changes in soil extracellular enzyme activity after wheat harvest in 2021 and 2022 under different treatments (a - g, n = 3). Different lowercase letters represent significant differences between different fertilization treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Different uppercase letters indicate significant differences between years (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/c0fe2ec1206787275d569c40.jpg"},{"id":60497310,"identity":"fdaf9d1e-c927-4938-8160-6ee0673d4a77","added_by":"auto","created_at":"2024-07-17 11:52:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143854,"visible":true,"origin":"","legend":"\u003cp\u003eThe changes in soil microbial communities under different treatments. The observed species number of soil microbial fungi and bacteria (a) after wheat harvest in 2022 shows the differences in species composition at the level of genus (b-c) and phylum (d-e) for different treatments (n = 3). Different lowercase letters represent significant differences between different treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/bcb352e6e99a85dee1a5305a.jpg"},{"id":60497312,"identity":"995239de-f5df-489d-9116-58b11c9500a8","added_by":"auto","created_at":"2024-07-17 11:52:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":134658,"visible":true,"origin":"","legend":"\u003cp\u003eThe interaction of soil bacteria and fungi co-occurrence networks under different fertilization treatments with soybean green manure returning (a) and comparison of key topological parameters (b). In (a), the different points represent the OTUs in the network, and the size of the points is proportional to the degree of each node. The red and blue lines represent strong positive and negative interactions (Spearman r \u0026lt; 0.6 or r \u0026gt; -0.6) and significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) correlation.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/8253edf1e2ccff35c1caa7e7.jpg"},{"id":60496125,"identity":"5d4017c0-372d-45d9-9734-6dd6eaf0770a","added_by":"auto","created_at":"2024-07-17 11:44:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135460,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Partial Least Squares Path Modelling of the influence of various factors under different fertilization treatments with soybean green manure returning on soil organic carbon (SOC) content. Boxes represent exogenous variables (observed variables), ellipses represent endogenous variables (latent variables), path coefficients are indicated by the width of the arrows, where red indicates a positive effect, blue indicates a negative effect, and the numbers on the dashed and solid lines represent non-significant and significant standardized path coefficients respectively. The R\u003csup\u003e2\u003c/sup\u003e within the variables represents the multiple correlation squared. GOF values indicate the goodness-of-fit of the model. *, **, and *** indicate significant correlations at the P \u0026lt; 0.05, P \u0026lt; 0.01, and P \u0026lt; 0.001 levels respectively. (b) Pearson correlation heat map between all observed variables, where red indicates positive correlation, blue indicates negative correlation, and * indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. (c) Crossloadings of all defined endogenous variables on their corresponding exogenous variables. The total standardized effects of these factors on organic carbon content, with the numbers on the horizontal axis representing the standardized coefficients in the model.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/6e965695fe0cc691735bff84.jpg"},{"id":77053557,"identity":"f909b17a-0262-4327-901a-1ff59deb78f2","added_by":"auto","created_at":"2025-02-24 16:29:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1758001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/92655951-d8ea-47f9-953c-9f3f6ee6363d.pdf"},{"id":60496127,"identity":"089ba217-cd8f-4ef0-a328-8ed42c7cf542","added_by":"auto","created_at":"2024-07-17 11:44:51","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":239310,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/460692cbfab60c11ca92c47f.docx"},{"id":60496129,"identity":"8f7175fc-08aa-49a6-9e9c-5f4979e6e72d","added_by":"auto","created_at":"2024-07-17 11:44:52","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":291718,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-4593466/v1/cd36832faf514ead16cb351f.docx"}],"financialInterests":"","formattedTitle":"Improvement of soil organic carbon turnover and microbial community niche differentiation with the addition of commercial organic fertilizer in wheat–green manure systems","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoil organic carbon (SOC) is an important indicator of soil quality and fertility and plays a vital role in mitigating climate change (Lal \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lehmann and Kleber, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Agricultural practices such as tillage, irrigation and fertilization can impact the sequestration of SOC (Qaswar et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mayer et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The application of synthetic fertilizers increases the SOC content because of increased crop yields and residual carbon (Yan et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Minasny et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, excessive use of fertilizers can lead to environmental issues such as soil acidification, increased greenhouse gas emissions and water pollution (Guo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Hence, to overcome these issues, China introduced policies such as \u0026lsquo;zero growth in the use of synthetic fertilizers\u0026rsquo; in 2015 and \u0026lsquo;action plans for reduced fertilizer use by 2025\u0026rsquo;, thereby encouraging farmers to reduce the negative impacts of excessive fertilizer use and achieve carbon sequestration goals using organic fertilizers, green manure planting and straw returning (Zhang et al., 2020; Zhou and Ding, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, to achieve durable SOC storage, it is essential to understand the influence of carbon management measures on the pathways that stabilise the soil carbon pool (Chen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLivestock and poultry farming is an important pillar industry in agricultural production, and its products can meet human needs; however, the use of untreated animal waste as fertilizer exacerbates environmental pollution (He et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Goldfarb et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As an organic carbon source, manure has low nutrient content and slow-release processes, resulting in decreased crop yields (Liu et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Research has indicated that composting and maturing of livestock and poultry manure (Tortosa et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) or direct pyrolysis (Fakayode et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) can be used for harmless treatment. Granulation and production of commercially viable organic fertilizers with stable nutrient content and efficacy have facilitated easy transportation, storage, sale and application (Moeller and Schultheiss, 2014). To further achieve large-scale agricultural production, some studies have proposed the use of organic fertilizers as a substitute for chemical fertilizers (Cen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Several studies have revealed that partial substitution of chemical fertilizers with commercial organic fertilizers can achieve stable yields (Sacco et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; He et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and result in fewer greenhouse gas emissions (Fang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) while increasing the SOC content (He et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and altering the soil microbial community structure (Li et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). However, the impact of adding commercial organic fertilizers on the SOC structure and enzyme activity has not yet been reported.\u003c/p\u003e \u003cp\u003eThe bare exposure of farmland soil leads to issues such as soil erosion and nutrient loss, ultimately reducing the quality of arable land (Holman et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Green manure, a cover crop, suppresses soil moisture evaporation by increasing vegetation cover. Its growth process also involves absorbing excess mineral nutrients from the soil to reduce pollution (Irmak et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Blanco-Canqui et al., 2021). Subsequently, it can be turned over into the soil as a source of plant-derived organic carbon to decompose and release nutrients (Dabney et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Mbuthia et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The increased quantity of easily decomposable organic matter gradually stimulates the activity of hydrolytic enzymes (Xu et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), affecting the community structure of bacteria (Zhou et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) and fungi (Zhou et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e), altering the composition and turnover of SOC (Gao et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), achieving SOC sequestration (Zhang et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yue et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and ultimately improving soil structure (Blesh, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Thapa et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although many studies have clarified the changes in SOC turnover and microbial community under the green manure system, there are several knowledge gaps regarding how these microbial communities interact and the relationships between different communities and SOC turnover from a macro-ecological perspective (Bastida et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe impact of land use and agricultural management techniques on the soil organic carbon pool may result in increased contributions of plant-derived lipids (cutin, suberin and lignin) and microbial and animal-derived lipids (Lorenz et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The addition of animal-derived carbon is beneficial in improving agricultural environmental quality and enhancing crop productivity (Bhunia et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while also increasing the content of soil organic carbon and its components, promoting organic carbon mineralization, activating rare microorganisms (low but existent in unfertilized soil) (Semenov et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), ultimately increasing community diversity (Kopittke et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but may also lead to decreased aggregate stability (Li et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The addition of plant-derived carbon may result in more active soil nutrient turnover and ecosystem processes (Faust and Raes, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Coyte et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), increasing the content of macroaggregates in soil and the stability of aggregates of various sizes (Li et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Due to low nutrient efficiency, microbial mining of nutrients has reduced the carbon retention from organic matter input (Koishi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while also enhancing the stability of the microbial community (Yu et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies also suggest that in organic carbon management without reserves, the addition of plant-derived carbon can increase and stabilize animal manure organic carbon, ultimately promoting carbon turnover and sequestration (Koishi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, there is limited research on how different sources of organic carbon impact soil carbon turnover in cropping systems based on the application of commercial organic fertilizers in the main crop and the cultivation of green manure during fallow periods.\u003c/p\u003e \u003cp\u003eOur field research over the past 3 years (He et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) has indicated that the application of commercial organic fertilizers to two typical soils can enhance wheat yield and soil fertility. However, the changes in SOC content and structure following the introduction of soybean green manure and the mechanism underlying SOC turnover under the influence of enzymes and microbial carbon pumps have not yet been fully explored. Therefore, this study aimed to conduct experiments based on the substitution of chemical fertilizers with commercial organic fertilizers and the subsequent re-sowing of soybean green manure and field plowing. Our main objectives were to 1) quantify the changes in SOC content, composition and structure as well as extracellular enzyme activity and microbial ecological networks under different fertilization treatments combined with soybean green manure; 2) elucidate the relationship between SOC and soil microbial characteristics and 3) explore the mechanism underlying SOC turnover under the combined action of chicken manure organic fertilizers and soybean green manure. The findings may contribute to a better understanding of the mechanisms by which microorganisms control SOC turnover in the wheat\u0026ndash;soybean green manure system, further promoting the widespread use of commercial organic fertilizers and green manure in organic agriculture.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003ein situ\u003c/em\u003e pot experiment was conducted from March 2021 to July 2022 at the experimental station of the School of Agriculture, Shihezi University, Shihezi, Xinjiang, China (44\u0026deg;18\u0026prime;N, 86\u0026deg;3\u0026prime;E). The study site is located in the middle section of the northern foot of the Tianshan Mountains and has a temperate continental climate, with an average annual temperature of 7.5\u0026deg;C\u0026ndash;8.2\u0026deg;C, annual precipitation of 180\u0026ndash;270 mm and annual evaporation of 1,500\u0026ndash;2,000 mm. The soil used in the experiment was collected from a wheat field (0\u0026ndash;20 cm plow layer) of a continuous 3-year organic substitution trial (Lu et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the soil type was Calcareous Fluvisol. The basic physical and chemical properties of the soil are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experiment design\u003c/h2\u003e \u003cp\u003eBased on the cropping model of spring wheat (\u003cem\u003eTriticum aestivum L. cv\u003c/em\u003e Xinchun 38) and soybean (\u003cem\u003eGlycine max (L.) Merr. cv\u003c/em\u003e Henong 70) green manure, the in situ pot experiment was conducted from March 2021 to July 2022. Specifically, the entire soil experiment encompassed a growth cycle of spring wheat\u0026ndash;soybean green manure\u0026ndash;spring wheat (lasting 272 days). In this experiment, high-density polyethylene open-top rectangular boxes were used, each measuring 46.5 cm in length, 35.0 cm in width and 22.0 cm in height and containing 40.0 kg of air-dried undisturbed soil. To simulate field soil temperature, the containers were placed in soil pits to ensure that they were level with the soil surface. The experiment involved three fertilization treatments, each repeated three times: no fertilization (CK), application of chemical fertilizers (chemical fertilization [CF]) and application of commercial organic fertilizers (organic fertilization [OF]) as a substitute for 24% of the chemical fertilizers. The chemical fertilizers included urea and ammonium phosphate, whereas the commercial organic fertilizer was organic chicken manure from ZeShang Biotechnology Co., Ltd. (Shihezi City, Xinjiang, China). Organic chicken manure was applied to the soil before sowing wheat. The fertilization strategy and quantities for all treatments are described in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. After harvesting wheat, soybean green manure was sown and no fertilization was applied during the soybean growth period. After 60 days of growth, the soybean green manure was incorporated back into the soil in the pots. The biomass and nutrient contents of soybean green manure for each treatment are detailed in Table S3. To maintain consistent return levels across treatments, the biomass for soybean incorporation was set at 350.0 g/pot, using the treatment with the lowest biomass as the standard for all treatments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Soil sample collection and determination\u003c/h2\u003e \u003cp\u003eIn July 2021 and 2022, following the wheat harvest, soil samples were collected from each pot at 6 points within the 0-20cm soil layer using a 3cm diameter soil auger. The collected samples were mixed to form composite samples and promptly transported to the laboratory. After removal of stones, plant residues, and organic debris, the soil samples were divided into three parts: one part of fresh soil samples, sieved through a 2mm mesh, were preserved at -80\u0026deg;C for analysis of soil microbial communities, another part of fresh soil samples, sieved through a 2mm mesh, were kept in a refrigerator at 4\u0026deg;C, for the determination of soil microbial carbon (SMBC), nitrogen (SMBN), and extracellular enzyme activity, and a third part of the samples were air-dried at 25\u0026deg;C, sieved through 0.149mm and 0.075mm mesh, for the determination of SOC, SOC fractions, solid-state \u003csup\u003e13\u003c/sup\u003eC nuclear magnetic resonance spectra, and attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 SOC, SOC fractions, SMBC and SMBN\u003c/h2\u003e \u003cp\u003eThe improved acid hydrolysis method was used to measure the components of soil organic carbon (Rovira and Vallejo, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Rovira and Vallejo, 2007). Specifically, the portion extracted with 2.5 mol/L H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e was referred to as high active organic carbon fraction (HAOC), while the portion extracted with 13 mol/L H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e was referred to as low active organic carbon fraction (LAOC). The residual product after the two-step extraction was considered as resistant organic carbon (ROC). The content of various organic carbons was determined using the potassium dichromate (K\u003csub\u003e2\u003c/sub\u003eCr\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e7\u003c/sub\u003e) and sulfuric acid (H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) oxidation method (Walkley and Black, 1934), and the units are g/kg. Chloroform (CHCl\u003csub\u003e3\u003c/sub\u003e) fumigation followed by leaching with 0.5 mol/L potassium sulfate (K\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e) solution was used, and the leachate was used to determine SMBC (Ocio et al., 1990) and SMBN (Brookes et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The content of SMBC and SMBN was determined using the methods of Walkley and Black (1934) and the Kjeldahl nitrogen determination method (Jackson, 1973), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 ATR-FTIR and solid-state \u003csup\u003e13\u003c/sup\u003eC NMR characterization\u003c/h2\u003e \u003cp\u003eSoil samples were collected after the wheat harvest in 2021 and 2022, and mixed to create three separate composite samples, resulting in a total of six composite samples. The soil samples were analyzed using ATR-FTIR spectroscopy (Thermo Scientific Nicolet iN10, USA). The characterization was performed using the KBr-tablet method. In a dry environment, 2 mg of soil sample was weighed, and then 200.0 mg of dry KBr powder was added to an agate mortar and ground thoroughly to create a uniform mixture, which was then pressed into transparent thin sheets using a mold. The wavenumber range was set from 4000 to 400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with 64 scans and a resolution of 4 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Prior to testing, scans of the background (atmospheric and KBr) were performed, and the background spectrum was automatically subtracted during the scanning process to obtain the infrared spectra. This study specifically calculated the organic absorption peaks at 3400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 3620 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 2920 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 2850 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1630 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1420 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1030 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 798 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The functional group references for each absorption peak are based on Bernier et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Nguyen et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe composite samples from the three treatments of wheat harvested in 2022 were subjected to solid-state \u003csup\u003e13\u003c/sup\u003eC nuclear magnetic resonance (NMR) characterization. Prior to the analysis, the soil samples were treated with 10% hydrofluoric acid (HF) to remove paramagnetic compounds and concentrate organic carbon (Skjemstad et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The specific pre-treatment process involved weighing 5.0 g of air-dried soil, passing through a 0.149 mm sieve, into a centrifuge tube, adding 50 ml of 10% HF solution, shaking for 1 hour at 3000 rpm, centrifuging for 10 minutes, removing the supernatant, and repeating the HF treatment for a total of 8 times with shaking durations of 4 times \u0026times; 1 h, 3 times \u0026times; 12 h, and 1 time \u0026times; 24 h. After the HF treatment, the samples were washed with 20 ml of distilled water to neutrality (5\u0026ndash;6 times), freeze-dried, and ground using agate mortar and pestle, passing through a 0.075mm sieve for analysis. This treatment may lead to Soil Organic Matter (SOM) loss through dissolution, but no significant changes in SOM chemical structure were found (Skjemstad et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Fang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The samples were analyzed on a Bruker Avance NEO 400WB solid-state NMR spectrometer (Germany), with 100 mg of soil sample passing through a 0.075 mm sieve, calibrated using the chemical shift of the CH peak in adamantane (\u003csup\u003e13\u003c/sup\u003eC, δ\u0026thinsp;=\u0026thinsp;38.5 ppm) before testing (Morcombe and Zilm, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Cross-polarization/total sideband suppression (CP/TOSS) technique was used, with a 4.0mm H/X MAS DVT double-resonance probe. The magic angle spinning rate was 8000 Hz, contact time was 2 ms, recycle delay was 1.2 s, and the resonance frequency was 100.6 MHz, to obtain qualitative structural information of functional groups. All \u003csup\u003e13\u003c/sup\u003eC NMR spectra were assigned to different carbon functional groups according to literature, by dividing the spectra into 5 different chemical shift regions: 160\u0026ndash;220 ppm, 110\u0026ndash;160 ppm, 60\u0026ndash;110 ppm, 45\u0026ndash;60 ppm, and 0\u0026ndash;45 ppm, which were assigned to carboxyl C, aromatic C, O-alkyl C, methoxyl/N-alkyl C, and alkyl C, respectively, for quantitative analysis (Schmidt et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Kiem et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Mao et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The stable, less degradable aliphatic carbon composition was contrasted with the more unstable and easily decomposable alkoxyl carbon composition, with their ratio being the humification index (Xue et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A higher ratio indicates a higher degree of decomposition or humification, and hence, a higher resistance to rapid carbon loss (Huang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the aliphatic C/aromatic C (AL/AR) ratio can be used to predict the complexity of SOM chemical composition, with higher values indicating fewer aromatic structures, lower condensation, and simpler molecular structures (Zhao et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Xue et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Soil extracellular enzyme activity\u003c/h2\u003e \u003cp\u003eAccording to the method by Paz-Ferreiro et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the enzymatic activity of α-glucosidase (AG), β-glucosidase (BG), β-cellobiosidase (CL), β-xylosidase (XYL), N-acetyl-glucosaminidase (NAG), L-leucine aminopeptidase (LAP), and alkaline phosphomonoesterase (APT) were determined and slightly modified. Specifically, the soil was cultivated with substrates containing some o-nitrophenol, and then the release of o-nitrophenol during the enzymatic hydrolysis process was measured using a spectrophotometer at a wavelength of 400 nm to determine the enzyme activity. CL activity was determined following the method described by Miller (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1959\u003c/span\u003e). The measurement of soil enzyme activity was conducted in 96-well EIA/RIA plates (Costar, REF 3590) and analyzed using an enzyme analyzer (Thermo Scientific, Multiskan SkyHigh, Singapore). The activity of all enzymes was expressed as substrate nanomoles released per gram of soil per hour (nmol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Extraction and sequencing of 16S rDNA and ITS rDNA\u003c/h2\u003e \u003cp\u003eAfter the harvest of wheat in 2022, DNA extraction was performed on nine soil samples obtained from three different treatments using the E.Z.N.A.\u0026reg; Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). Following extraction, the DNA concentration and quality were assessed by 1% (wt/vol) agarose gel electrophoresis and Nanodrop 2000 spectrophotometer (Thermo Scientific, Wilmington, NC, USA). The obtained DNA was then utilized as a template to amplify the V3-V4 variable region of the bacterial 16S rRNA gene using primers 338F (5\u0026prime;-ACTCCTACGGGAGGCAGCAG-3\u0026prime;) and 806R (5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;) carrying barcode sequences (Liu et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For fungal DNA, the ITS1 region was amplified using primers with barcode sequences, ITS1F (5'-CTTGGTCATTTAGAGGAAGTAA-3') and ITS2R (5'-GCTGCGTTCTTCATCGATGC-3'), as described by Adams et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The amplification products were sequenced using the Illumina Miseq PE300/NovaSeq PE250 platform (Shanghai Majorbio Bio-pharm Technology Co., Ltd.), and the sequencing data were deposited in the Sequence Read Archive (SRA) of the NCBI BioProject PRJNA1085476. The raw sequencing reads underwent quality control using Fastp (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 0.19.6), followed by merging using Flash (Magoc et al., 2011) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbcb.umd.edu/software/flash\u003c/span\u003e\u003cspan address=\"http://www.cbcb.umd.edu/software/flash\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 1.2.11). After this, Uparse (Edgar, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://drive5.com/uparse/\u003c/span\u003e\u003cspan address=\"http://drive5.com/uparse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 11) was used to cluster and remove chimeric sequences, generating Operational Taxonomic Units (OTUs) at a threshold of 97% sequence similarity. Subsequently, taxonomic annotation of the bacterial and fungal OTUs was carried out using the RDP classifier (Wang et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/\u003c/span\u003e\u003cspan address=\"http://rdp.cme.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 2.13) against the SILVA 16S rRNA gene database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arb-silva.de/\u003c/span\u003e\u003cspan address=\"https://www.arb-silva.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 138) and the UNITE fungal ITS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unite.ut.ee/\u003c/span\u003e\u003cspan address=\"https://unite.ut.ee/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 8.0), with a confidence threshold of 70%. To minimize the potential impact of sequencing depth on subsequent observations of species data analysis, the sequence numbers for all samples were rarified to the minimum sequence count, resulting in 30489 for bacteria and 46165 for fungi. Despite this, the average sequence coverage for each sample remained at 97.7% (for bacteria) and 99.8% (for fungi).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Construction of bacteria-fungus co-occurrence network\u003c/h2\u003e \u003cp\u003eAccording to Ma et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we constructed three treatment bacterial-fungal cross-domain networks. To reduce rare OTUs from the dataset, OTUs with relative abundance lower than 0.01% of the total bacterial and fungal sequences were removed. The co-occurrence networks were implemented in the ggClusterNet package (Wen et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In essence, the package utilises the cor() and Pvalue() functions from the WGCNA package, the sparccboot() function from the SpiecEasi package, and the corr.test() function from the psych package to compute correlations, with reference to layout algorithms used in ggraph and sna, along with functions for in-depth exploration such as average.path.length() and centralized.closeness(). When constructing the networks, we initially calculated Spearman correlation and Kullback Leibler divergence (KLD) measures between all OTU pairs. Then, we set the dissimilarity threshold to the maximum value of the KLD matrix and the Spearman correlation threshold to 0.6. We resampled 100 times, and the resulting distribution was used to generate p-values for the observed association measures. We merged the p-values and used the Benjamini-Hochberg method for multiple testing correction (Benjamini and Hochberg, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Finally, only edges that were supported by both measures and had adjusted p-values below 0.05 were considered statistically significant in the network. The top 100 OTUs by relative abundance for both fungi and bacteria were selected as nodes in this network, with edges connecting these nodes representing correlations between OTUs. We used Gephi (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gephi.github.io/\u003c/span\u003e\u003cspan address=\"http://gephi.github.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to generate network visualisations (Bastian et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe conducted statistical analyses using SPSS 19.0 (SPSS Inc., Chicago, IL, USA) software and performed differential testing between different treatment groups using the Least Significant Difference (LSD) method (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We employed a two-way analysis of variance (ANOVA) to analyse the impact of fertilization treatment (T), year (Y), and their interaction on soil nutrients and organic carbon component indicators. All results are expressed as means. The error bars in the results represent standard deviations (n\u0026thinsp;=\u0026thinsp;3). ATR-FTIR data were transformed by transmittance and absorbance using OMNIC Specta software (Version 8.2) and subjected to image smoothing and baseline correction. Solid-state nuclear magnetic resonance spectroscopy images were processed using MestReNova software (Version 14.0.0). The Circos sample-to-species relationship diagram was analysed using Circos-0.67-7 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://circos.ca/\u003c/span\u003e\u003cspan address=\"http://circos.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The db-RDA analysis was conducted using the R language (version 3.3.1) vegan (2.4.3) package. Pearson correlation coefficients for all indicators were computed using the \"corrplot\" package in R software (version 3.5.2). A path analysis model between soil nutrients, enzyme activity, organic carbon components, organic carbon structural composition, and microbial features was built using the \"plspm\" package in R (4.3.3). Plots were generated using Origin 2019 (Origin Lab Co., Northampton, MA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 SOC and its fractions\u003c/h2\u003e \u003cp\u003eOur results indicated significant differences in soil nutrient contents between soil samples treated with OF and CF before and after the addition of soybean green manure (Table S4). OF treatment significantly increased the contents of total nitrogen (TN), soil available nitrogen, soil available phosphorus, soil available potassium (SAK), soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN) while lowering the pH. In addition, a significant variation was observed in the SOC content, which was notably higher in OF treatment than in CK and CF treatments in both 2021 and 2022 (12.81 and 13.26 g/kg, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Following the addition of soybean green manure, the SOC content increased by 10.6% in CK treatment, 14.4% in CF treatment and 3.6% in OF treatment. To gain further insight into the changes in SOC composition, we determined the content of acid-extractable organic carbon. The comparative results of the two years revealed that in all treatments, the HAOC content and recalcitrant organic carbon (ROC) fraction were significantly higher in OF treatment than in CK or CF treatments, whereas there was no significant difference in LAOC content. Compared with 2021, in 2022, the HAOC content in CK, CF and OF treatments increased by 34.7%, 42.1% and 5.9% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), ROC fraction increased by 3.4%, 0.2% and 1.0% and LAOC content increased by \u0026minus;\u0026thinsp;8.0%, 2.4% and 6.6%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Overall, the addition of green manure increased soil nutrient contents, reduced the pH and significantly increased the SOC content, with the most significant increase observed in HAOC content, followed by ROC fraction; however, LAOC content showed a decreasing trend with no significant change.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ATR-FTIR and CP/TOSS \u003csup\u003e13\u003c/sup\u003eC NMR\u003c/h2\u003e \u003cp\u003eThe results of ATR-FTIR reflected changes in soil SOC composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), identifying six characteristic peaks, representing -COOH (3400\u0026thinsp;+\u0026thinsp;3620 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), -CH (2920\u0026thinsp;+\u0026thinsp;2850 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), C\u0026thinsp;=\u0026thinsp;C, C\u0026thinsp;=\u0026thinsp;O (1630 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), C-OH (1420 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), C-O (1030 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and Minerals (798 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), with their respective proportions outlined in Table S5. In comparison with 2021, the relative absorption intensity of CK, CF, and OF in 2022 has increased. Polysaccharide-C and Minerals proportions have decreased, while the proportions of other characteristic peaks have increased. Furthermore, the OF absorption intensity was higher compared to CK and CF. In 2022, compared to different treatments, the proportion of Aromatic-C in OF increased by 2.51% and 4.42% compared to CK and CF treatments. The proportion of Aliphatic-C1 increased by 1.38% and 2.11% compared to CK and CF treatments, respectively, while the proportion of Polysaccharide-C decreased by 11.5% relative to CK and increased by 7.16% relative to CF.\u003c/p\u003e \u003cp\u003eThe CP/TOSS \u003csup\u003e13\u003c/sup\u003eC NMR spectra (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) with integration results (Table S6) further confirmed the changes in SOC components in 2022. Across all treatments, the functional group proportions ranked from highest to lowest were Alkyl-C, Aromatic-C, Methoxyl/N-alkyl-C, Carbonyl-C, and O-alkyl-C. In comparison to CK and CF, the proportions of Alkyl-C and Aromatic-C increased in OF, while the proportions of Methoxyl/N-alkyl-C, Carbonyl-C, and O-alkyl-C decreased. Additionally, the aliphatic/alcohol ratio (AL/OA) in the OF treatment was higher by 22.0% and 16.1% compared to CK and CF treatments, respectively. The aliphatic/aromatic ratio (AL/AR) in the CF treatment was the highest, with proportions 21.1% and 63.4% higher than CK and OF treatments, respectively. Overall, both methods demonstrate that under the conditions of returning soybean green manure, the OF treatment altered the SOC composition, increasing the degree of humification and the sequestration of Aromatic-C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Soil extracellular enzyme activity\u003c/h2\u003e \u003cp\u003eTo elucidate the response of soil extracellular enzyme activity to amendments, we measured the enzyme activity involved in C, N, and P acquisition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-g). The results indicated significant differences in enzyme activity among different fertilization treatments, with CL showing OF\u0026thinsp;\u0026gt;\u0026thinsp;CK\u0026thinsp;\u0026gt;\u0026thinsp;CF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), while LAP showed no significant difference between treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef), and other enzyme activities showed OF\u0026thinsp;\u0026gt;\u0026thinsp;CF\u0026thinsp;\u0026gt;\u0026thinsp;CK (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Soil enzyme activity all showed a significant increase after green manure returning. In 2022, compared to 2021, the activity of AG, BG, CL, XYL, NAG, LAP, and APT in the OF treatment increased by 64.7%, 156.0%, 100.4%, 41.1%, 37.8%, 41.1%, and 100.2% respectively. The activity of the 7 extracellular enzymes in the CF treatment increased by 47.8%, 132.7%, 121.6%, 10.4%, 9.6%, 44.3%, and 86.8%. The activity of the 7 extracellular enzymes in the CK treatment increased by 14.3%, 102.2%, 59.2%, 118.0%, 42.5%, 24.6%, and 35.8%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Microbial abundance and community composition\u003c/h2\u003e \u003cp\u003eThe addition of soybean green manure significantly influenced the changes in soil microbial communities. Based on this, the soil microbial community composition of different treatments in 2022 was determined. In the different treatments, the species number of bacteria and fungi showed similar trends: CK\u0026thinsp;\u0026gt;\u0026thinsp;CF\u0026thinsp;\u0026gt;\u0026thinsp;OF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). As shown by the Venn diagram (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), there were a total of 2474 bacterial OTUs and 396 fungal OTUs across all soils, with each treatment showing a similar trend in the number of specific bacterial and fungal OTUs: CK\u0026thinsp;\u0026gt;\u0026thinsp;CF\u0026thinsp;\u0026gt;\u0026thinsp;OF.\u003c/p\u003e \u003cp\u003eFurther exploration of the changes in the taxonomic composition was conducted by comparing the relative abundance differences of the main phyla and genera in different treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-e). Looking at the bacterial community, \u003cem\u003eActinobacteriota\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003e were the top four dominant phyla (\u0026gt;\u0026thinsp;75%) in all treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Among these, the relative abundance for CK of the top four dominant phyla was 24%, 20%, 14%, 19%, for CF it was 27%, 23%, 14%, 11%, and for OF it was 27%, 19%, 16%, 14%. At the bacterial genus level, the relative abundance of the top 10 genera was only 29.1% \u0026minus;\u0026thinsp;32.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), with \u003cem\u003eBacillus\u003c/em\u003e being the most abundant genus in all treatments, showing the trend OF\u0026thinsp;\u0026gt;\u0026thinsp;CF\u0026thinsp;\u0026gt;\u0026thinsp;CK.\u003c/p\u003e \u003cp\u003eRegarding the fungal community, \u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eMortierellomycota\u003c/em\u003e, and \u003cem\u003eBasidiomycota\u003c/em\u003e were the top three dominant phyla (\u0026gt;\u0026thinsp;96%) in all treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). The relative abundance for CK of these dominant phyla was 85%, 11%, 2%, for CF it was 84%, 10%, 4%, and for OF it was 84%, 8%, 4%. At the fungal genus level, the structure of the fungal community varied more significantly between different treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Across all treatments, \u003cem\u003eChaetomium\u003c/em\u003e, \u003cem\u003eRetroconis\u003c/em\u003e, \u003cem\u003eMortierella\u003c/em\u003e, \u003cem\u003eGibberella\u003c/em\u003e, and \u003cem\u003eTalaromyces\u003c/em\u003e were the top five dominant genera (\u0026gt;\u0026thinsp;75%), with the abundance of \u003cem\u003eChaetomium\u003c/em\u003e, \u003cem\u003eRetroconis\u003c/em\u003e, and \u003cem\u003eMortierella\u003c/em\u003e being higher in the OF treatment than in CF and CK, while the abundance of \u003cem\u003eGibberella\u003c/em\u003e was higher in CF than in OF and CK, and the abundance of \u003cem\u003eTalaromyces\u003c/em\u003e was higher in CK than in CF and OF.\u003c/p\u003e \u003cp\u003eThe distance-based redundancy analysis was used to study the relationship between soil abiotic properties and microbial structure (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec-d). Soil physicochemical properties and changes in SOC structure explained 43.9% and 62.7% of the variation in bacterial and fungal communities, respectively. The changes in bacterial and fungal communities caused by the OF were associated with soil nutrients, SOC content, and the degree of humification of organic matter.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Symbiotic networks of bacteria and fungi and their topological characteristics\u003c/h2\u003e \u003cp\u003eThe data for constructing the soil microbial abundance table used in the bacterial\u0026ndash;fungal co-occurrence network comes from three treatments (n\u0026thinsp;=\u0026thinsp;3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). After filtering out low relative abundance OTUs (0.01%), 3887 and 954 OTUs were detected in the bacterial and fungal communities, respectively. The top 100 OTUs in abundance were retained to generate Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea. CK, CF, and OF each obtained 3 modules, with edge numbers of 3003, 3467, and 3215, respectively. The positive interactions between bacteria and fungi were 51.75%, 54.2%, and 46.69%, while the negative interactions were 48.25%, 45.8%, and 53.31% in CK, CF, and OF. The dominant fungal phyla in the three modules of CK, CF, and OF are \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eMortierellomycota\u003c/em\u003e, and the dominant bacterial phyla are \u003cem\u003eActinobacteriota\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e, and \u003cem\u003eGemmatimonadota\u003c/em\u003e (Table S7).\u003c/p\u003e \u003cp\u003eUsing the aforementioned co-occurrence networks, we studied whether the OTUs under different fertilization treatments with soybean green manure returning exhibited unique node-level topological features. The results indicated that the average degree in the CF treatment was higher than that in OF and CK, while the average path length, centralization degree, and centralization betweenness in all three treatments showed a consistent trend, with CK\u0026thinsp;\u0026gt;\u0026thinsp;CF\u0026thinsp;\u0026gt;\u0026thinsp;OF.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlations between soil nutrients, SOC fractions, enzyme activity, and microbial characteristics.\u003c/h2\u003e \u003cp\u003eUsing Partial Least Squares Path Modelling (PLS-PM) to reveal the influence of different soil factors on SOC content in each treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), where \"Fertilization\" represents the optimization process of fertilization strategies, corresponding to the treatment codes CK, CF, and OF. Factor loading analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec) indicates that, except for AD and LAP, all other indicators can serve as powerful indicators of soil nutrients, enzyme activity, and microbial characteristics. Pearson correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) shows that SOC and SOC fractions are significantly positively correlated with soil nutrient indicators, extracellular enzyme activity, and microbial biomass (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but negatively correlated or uncorrelated with the observed species numbers and co-occurrence network characteristics of fungi and bacteria. Standardized total effect analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) reveals that SOC stability (ROC and AL/OA) directly and positively influences SOC content, while soil nutrients, enzyme activity, and microbial characteristics indirectly and positively affect SOC content.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 SOC contents\u003c/h2\u003e \u003cp\u003eThis study revealed that prior to the addition of soybean green manure, OF treatment increased the SOC content and modified the SOC structure to some extent (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). This indicates that substituting some of the chemical fertilizer with commercial organic fertilizer can improve soil nutrient contents (Table S4) regulate SOC turnover, achieving carbon sequestration. This is in line with previous research outcomes (Lan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). SOC content is influenced by external carbon input, such as plant residues, root exudates, organic amendments and the regulation of organic carbon mineralisation. The increase in SOC content was related to consecutive organic fertilizer applications, and the decomposition rate of organic carbon in soil treated with organic fertilizers was lower than that in unfertilised soil and soil treated with chemical fertilizers, leading to greater organic carbon accumulation (Xu et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on the changes in SOC fractions, it is evident that OF treatment primarily increased the HAOC and ROC fractions and decreased the LAOC content, resulting in an increase in the total SOC content (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-d). According to Rovira and Vallejo (2007), the least stable component (HAOC) consists mainly of carbon compounds such as starch, hemicellulose and soluble sugars, the moderately stable component (LAOC) consists primarily of cellulosic carbon compounds and the recalcitrant component (ROC) consists of aromatic rings and lignin, which are difficult to biodegrade. The reduction in LAOC content may be related to the increased diversity of external carbon input. This is because the introduction of external organic carbon into the soil, mixed with plant residues, root exudates and other inputs, increases the diversity of organic waste and inhibits the loss of quality associated with the least stable component, although it does not affect the decomposition of the moderately stable component (Grossman et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). After adding soybean green manure, all treatments showed an increase in SOC content and its components; however, there was no significant change in LAOC content. Compared with fertilization treatments (CF and OF), no fertilizer treatment and addition of green manure reduced the LAOC content, which may be related to the decomposition of SOC. Our results showed that OF treatment significantly increased the SMBC (Table S4) content, which may represent a series of microbial activities involved in the decomposition of unstable carbon because cellulose is a readily available source of carbon and energy for decomposing agents (Talbot and Treseder, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In general, no reduction in total SOC content was noted after organic correction, mainly because green manure residues, as easily decomposed carbon sources, can effectively change the turnover of organic carbon, resulting in an increase in the levels of unstable carbon and refractory carbon (Chaudhary et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Characterization of SOC structure\u003c/h2\u003e \u003cp\u003eThe ATR\u0026ndash;FTIR and CP/TOSS \u003csup\u003e13\u003c/sup\u003eC NMR spectra provided important findings, which are crucial for understanding the persistence and transformation of SOC. Both characterisation methods yielded similar results, indicating that compared to CK, OF and CF treatments led to an increase in aromatic C content and a decrease in alkyl-C content. Remarkably, NMR revealed more detailed phenomena, indicating that the alkyl-C content was higher in OF treatment than in CF or CK treatment, whereas the contents of methoxyl/N-alkyl-C and O-alkyl-C were lower in OF treatment than in CF and CK treatments (Table S6). This finding is consistent with that of Huang et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found that the decomposition of Chinese milk vetch in the soil increased the relative alkyl-C content and decreased the O-alkyl-C content, possibly due to the presence of unstable forms of SOC in Chinese milk vetch that are initially utilised by microorganisms, leading to further degradation and alkyl-C production (Kopittke et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, Guigue et al. (2021) evaluated the trends of molecular changes in straw using \u003csup\u003e13\u003c/sup\u003eC CP\u0026ndash;MAS\u0026ndash;NMR results and revealed a slight increase in the alkyl/O-N-alkyl ratio. This may represent the processes that include a reduction in unstable components such as carbohydrates and peptides in SOC (Pedersen et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rodriguez et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and an increase in the contents of lipids, amino acids and cutin (Prietzel et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similar phenomena were observed in the decomposition of spruce litter, as reported by Helfrich et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), who suggested that the loss of easily decomposable carbohydrates and the selective preservation of more recalcitrant alkyl-C were responsible for such findings. These results are consistent with the proposed residual forms of natural organic residues suggested by Baldock et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), indicating that decomposition is almost always accompanied with an increase in alkyl-C content and a decrease in O-alkyl-C content and that the ratio of alkyl to O-alkyl carbon (aromatic C/O-alkyl-C) may serve as a sensitive indicator of decomposition extent.\u003c/p\u003e \u003cp\u003eFurther calculation of the ratio of the absorption intensities of two bands revealed the humification degree (decomposition extent). We revealed that the humification degree increased with the combined action of organic and green manure, which may be one of the reasons for the increase in the proportion of aromatic C (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Table S6). This is consistent with the findings reported by Golchin et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), who observed an accumulation of aromatic C structures in particulate organic matter components. We speculate that the increase in aromatic carbon content in OF treatment is due to mineral protection, indicating that the turnover rate of SOC in OF treatment was higher than that in CF and CK treatments, and the increase in alkyl and aromatic carbon content may be due to the relatively high decomposition rate of carbon compounds. When SOC rich in carbon compounds is enveloped by mineral particles, its rapid decomposition is hindered, resulting in the accumulation of recalcitrant components. This supports the aggregate formation and stabilisation model proposed by Golchin et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), where decomposable carbon compounds are encapsulated in aggregates, eventually being incorporated into microbial biomass and metabolites, which become associated with clay minerals.\u003c/p\u003e \u003cp\u003eIn terms of the complexity of SOC structure, our results indicate a decreasing trend of AL/AR, implying a more complex molecular structure due to the addition of chemical and organic fertilizers. \u003csup\u003e13\u003c/sup\u003eC NMR data by Xue et al. (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that the incorporation of straw reduced the fatty carbon/aromatic carbon ratio (organic carbon complexity index) in the soil. Similarly, Gao et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) characterised the composition of soil dissolved organic matter long-term application of milk vetch and rape green manure resulting in greater complexity. Purakayastha et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported the highest humification and aromatisation in soils with a combination of organic fertilizer and green manure (\u003cem\u003eSesbania acculeata\u003c/em\u003e and \u003cem\u003eLeucaena leucocephala\u003c/em\u003e). They provided a more detailed explanation: the amount of polyaromatic compounds was lower in the isomers formed under organic combination treatment, and the semiquinone free radical content was lower, resulting in the formation of quinones with higher organic carbon stability. In conclusion, a 272-day soil experiment under the background of fertilization treatments can provide preliminary indications regarding the comprehensive effects of organic amendments and combined green manure on SOC dynamics. However, it may not precisely indicate long-term trends. Future studies to quantify the SOC mineralisation and the mechanisms underlying mineral protection in this process will help further integrate short- and long-term field data to investigate long-term carbon turnover dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Change of enzyme activity\u003c/h2\u003e \u003cp\u003eExtracellular enzymes play a crucial biochemical role in the decomposition of organic matter in soil systems, and their activity is related to the characteristics of exogenous organic matter (Zimmerman and Ahn \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cenini et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results revealed significant differences in extracellular enzyme activity among different treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The addition of soybean green manure led to a significant increase in enzyme activity. This could be due to the low carbon-to-nitrogen ratio of legume green manure, which can substantially activate soil microbes (Tejada et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), thereby promoting the mineralisation of native soil organic matter through a priming effect (Kuzyakov et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These changes are in line with the theoretical concept that substantial inputs of unstable carbon can promote the growth rate of soil microbes while also shortening their lifespan, thus regulating the dynamic changes of carbon components after organic matter decomposition (Cui et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rousk et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, our results indicated a significant increase in SMBC content after the addition of green manure (Table S4). Previous studies have suggested a positive correlation between SOC content and carbon-transforming enzyme activity (Qi et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the current study, SOC, HAOC and ROC fractions showed positive correlations with most enzyme activities (except for LAP), whereas LAOC content did not show a significant correlation with enzyme activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). This may be related to the increase in humification after organic amendments, whereas the LAOC content remains relatively constant. Notably, our results showed a different trend for cellulase (OF\u0026thinsp;\u0026gt;\u0026thinsp;CK\u0026thinsp;\u0026gt;\u0026thinsp;CF), which may be related to the surplus of N and P fertilizer. Tie et al. (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) revealed that the addition of N, P and S reduced the activity of soil cellulase. Moreover, Gao et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that the addition of P reduced cellulase activity. Changes in substrate composition and effectiveness can cause variations in the structure and activity of the microbial community, subsequently influencing differences in soil carbon cycling. Considering the \u0026lsquo;Microbial Stoichiometry Theory\u0026rsquo;, the addition of leguminous green manure, which is rich in nitrogen, increases the effectiveness of the substrate to some extent by promoting microbial utilisation of nitrogen and drives the synergistic mineralisation of green manure residues for carbon and nitrogen (Zhu et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, we revealed that adding green manure increased the labile organic carbon content as well as accelerated ROC formation, consequently increasing the activity of various carbon-transforming enzymes. However, compared to CK with only green manure addition, CF and OF treatments did not show a significant increase in the proportion of observed species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This may be because treatments with chemical and organic fertilizers have been in place for 3 years, leading to the formation of specific dominant bacterial groups; thus, the addition of green manure only increased the microbial community diversity of the control soil.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Composition of soil microbial community\u003c/h2\u003e \u003cp\u003eIn terms of bacterial communities, the abundance of \u003cem\u003eAcidobacteria\u003c/em\u003e in the soil was higher in CF and OF treatments than in CK treatment. This may be due to the fact that \u003cem\u003eAcidobacteria\u003c/em\u003e belongs to the K-strategy oligotrophic phylum, which prefers nutrient-limited environments (Fierer et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Naether et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The increase in the relative abundance of \u003cem\u003eAcidobacteria\u003c/em\u003e may be due to the decrease in TN and SAK contents in the control (Table S4). At the bacterial genus level, the relative abundance of \u003cem\u003eBacillus\u003c/em\u003e was the highest in all treatments, in the order of OF\u0026thinsp;\u0026gt;\u0026thinsp;CF\u0026thinsp;\u0026gt;\u0026thinsp;CK. Fan et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported that \u003cem\u003eBacillus\u003c/em\u003e and other microorganisms could directly fix nitrogen during the decomposition of corn residues; moreover, there is evidence that organic manure significantly increases the abundance of \u003cem\u003eBacillus\u003c/em\u003e (Gao et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, the abundance of \u003cem\u003eChloroflexi\u003c/em\u003e was higher in OF treatment than in CK and CF treatments, and Ye et al. (\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also revealed that the application of organic manure increased the abundance of \u003cem\u003eChloroflexi\u003c/em\u003e. Additionally, we found that the abundance of \u003cem\u003eActinobacteria\u003c/em\u003e was higher in CF and OF treatments than in CK treatment. One possible explanation for this finding is that as the decomposition of green manure and organic manure progresses, from the early stage of abundant resources (due to rapid degradation of unstable substrates) to the later stage of resource depletion, nutritionally poor bacterial communities gradually shift to dominant communities (Bastian et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of fungal communities, the relative abundance of Ascomycota and \u003cem\u003eMortierellomycota\u003c/em\u003e showed a trend of OF\u0026thinsp;\u0026gt;\u0026thinsp;CF\u0026thinsp;\u0026gt;\u0026thinsp;CK, which may be related to the growth of soybean green manure. Espana et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) revealed that \u003cem\u003eMortierellomycota\u003c/em\u003e in soybean residues is considered a fast-growing fungal decomposer. The abundance of the filamentous fungus \u003cem\u003eTalaromyces\u003c/em\u003e was higher in CK treatment than in CF and OF treatments, possibly due to the exclusive use of soybean green manure in CK treatment. Asghar and Kataoka (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that the abundance of \u003cem\u003eTalaromyces\u003c/em\u003e in the soil where leguminous plants are grown is related to the promotion of plant growth and inhibition of pathogen growth by volatile organic compounds. The relative abundance of \u003cem\u003eGibberella\u003c/em\u003e was higher in CF treatment than in OF or CK treatment, which may be related to potential pathogenicity. Ding et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also revealed that the abundance of \u003cem\u003eGibberella\u003c/em\u003e was higher in chemical fertilizer treatments than in the combination treatment of organic and chemical fertilizers, whereas Gao et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) showed that the addition of earthworms and organic manure from cattle reduced the abundance of \u003cem\u003eGibberella\u003c/em\u003e. Overall, OF treatment enriched the effectiveness of enzyme substrates and stimulated the enzyme activity, and the altered bacterial and fungal communities preferred specific carbon components, thereby promoting the diversification of SOC structure (Sun et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Symbiotic network\u003c/h2\u003e \u003cp\u003eThe evolution of bacterial or fungal community composition may not be directly related to the increase in SOC content. Therefore, we employed a comprehensive dataset of bacterial and fungal operational taxonomic units (OTUs) to conduct co-occurrence network analysis to illustrate and quantify the topological differences between different treatments. The results indicated that the network topological characteristics of CK, CF and OF treatments differed, with the positive influence of bacteria being greater than the negative influence in CK and CF treatments, and the opposite effect was true for OF treatment. CF exhibited the highest average degree, whereas OF had the lowest average path length, centralisation degree and centralisation betweenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The topological structure of the network can reflect interactions between microorganisms, and key species may be associated with many other species and ecological functions, indicating the topological patterns within the network (Berry and Widder, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The degree value in OF treatment tended to be higher, whereas the network centrality values tended to be lower. Differences in soil mineral nutrient or SOC content resulted in significant ecological niche differentiation, with weak niche differentiation potentially leading to stronger interactions between soil microorganisms (Faust and Raes, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This finding may also imply that the interactions between soil bacteria and fungi in OF treatment were not as strong as those in CK and CF treatments. In our study, key species included dominant phyla of bacteria and fungi, with \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eMortierellomycota\u003c/em\u003e as dominant fungi and \u003cem\u003eActinobacteriota\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e and \u003cem\u003eGemmatimonadota\u003c/em\u003e as dominant bacteria (Table S6). Dominant taxa within the phyla \u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eActinobacteriota\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e and \u003cem\u003eGemmatimonadota\u003c/em\u003e varied among the different treatments. This may be attributed to their diverse lifestyles and involvement in a wide range of ecological processes (Farag et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Relationship between SOC and soil enzyme activity, microbial community composition and topological characteristics\u003c/h2\u003e \u003cp\u003eIn terms of the source of SOC, following the addition of commercial chicken manure organic fertilizer in the fourth year, the significant increase in soil nutrient contents may have shaped a stable microbial community with strong ecological niche differentiation. This led to a consistent distribution of different active organic carbon sources and a significant increase in the SOC content (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequently, the introduction of carbon from soybean green manure into the soil had no significant impact on the proportion of SOC components, but the significant increase in SOC content indicates a rapid turnover and sequestration of SOC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Furthermore, characterisation of the SOC structure revealed changes in humification degree and molecular structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S6), demonstrating the impact of increased plant-derived carbon content on the stable quantity and structure of animal-derived SOC. This is consistent with the findings of Koishi et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). PLS-PM analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) showed that OF treatment mainly altered the quantity of enzyme activity substrates by affecting soil nutrient contents, thereby influencing extracellular enzyme activity. With the increase in enzyme activity, some labile organic carbon was decomposed (as reflected by the decrease in LAOC content) and the microbial residue biomass was increased (as indicated by the increase in SMBC and SMBN contents), but it did not affect ROC formation or humification degree. This ultimately led to a stable microbial community and an increase in SOC content.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe 2-year \u003cem\u003ein situ\u003c/em\u003e pot experiment of wheat\u0026ndash;soybean green manure systems showed that the addition of commercial chicken manure organic fertilizer led to greater improvements in soil nutrient contents, organic carbon components, extracellular enzyme activity, microbial residue biomass and microbial network structure compared with chemical fertilizers and the control. The increase in the SOC content was attributed to the stable exogenous carbon supply in this planting pattern, leading to the distribution of stable organic carbon components. The addition of green manure effectively promoted the turnover and sequestration of organic carbon in the short term while also resulting in a more complex molecular structure of SOC. The increase in soil nutrient contents affected substrate quantity, influencing enzyme activity, and shaped stronger ecological niche differentiation and weaker interactions between bacteria and fungi. These research findings provide experimental and conceptual evidence regarding the impact of the combination of commercial chicken manure organic fertilizer and soybean green manure on soil carbon sequestration, offering theoretical guidance for achieving agricultural carbon sequestration and sustainable development. Further research is warranted to quantify the effects of these treatment measures on SOC mineralisation and potential loss pathways as well as consider its long-term impact.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eLiyang Cheng: Writing \u0026ndash; original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Hao He: Writing \u0026ndash; review \u0026amp; editing, Methodology. Tao Min: Writing \u0026ndash; review \u0026amp; editing, Methodology, Conceptualization. Tong Luo: Writing \u0026ndash; review \u0026amp; editing, Methodology. Junhua Li: Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Project administration, Funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported by the Earmarked fund for XJARS, Major science and technology projects of Xinjiang Uygur Autonomous Region \"Research on Obstacle Reduction and Production Capacity Enhancement Technology for Medium and Low Yield Fields\" (2022A02007) and National Key Research and Development Program of China (2021YFD1900802).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas, F., Hammad, H.M., Ishaq, W., Farooque, A.A., Bakhat, H.F., Zia, Z., Fahad, S., Farhad, W., Cerda, A., 2020. A review of soil carbon dynamics resulting from agricultural practices. J. Environ. Manage. 268, 110319. https://doi.org/10.1016/j.jenvman.2020.110319.\u003c/li\u003e\n\u003cli\u003eAdams, R.I., Miletto, M., Taylor, J.W., Bruns, T.D., 2013. 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Springer, Berlin, Heidelberg, pp 217\u0026ndash;292.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Organic substitution, Acid hydrolyzable organic carbon, Solid-state nuclear magnetic resonance, Enzyme activity, Symbiotic network","lastPublishedDoi":"10.21203/rs.3.rs-4593466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4593466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e \u003cp\u003eGreen manure and commercial organic fertilizer are widely used in agricultural production to improve farmland soil carbon reserves; however, their combined impact on soil organic carbon (SOC) turnover is not yet fully understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe designed a potted wheat-soybean green manure system to investigate the impact of different fertilization treatments on SOC content and structure, extracellular enzyme activity community characteristics of fungi and bacteria after wheat harvest in 2021 and 2022.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results indicated that compared to chemical fertilization (CF), following the addition of soybean green manure, organic fertilization (OF) led to a 12.5% increase in SOC content, 19.3% increase in the highly active organic carbon (HAOC) fraction and 10.2% increase in the recalcitrant organic carbon (ROC) fraction. Additionally, there was a 16.1% increase in the alkyl-C to O-alkyl-C ratio and a 63.4% decrease in aliphatic C to aromatic C ratio. Significant increases were observed in the contents of extracellular enzyme, soil total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, microbial carbon and microbial nitrogen. The abundance of observed species of fungi and bacteria significantly decreased in OF compared with that in CF, with the symbiotic network indicating a higher level of positive interaction between fungi and bacteria in OF.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOF primarily altered soil enzyme activity by influencing soil nutrient contents, resulting in the decomposition of labile organic carbon and an increase in microbial residue biomass, without affecting ROC formation or humification degree. These findings can maximise SOC content in organic agriculture through land use and fertilization techniques.\u003c/p\u003e","manuscriptTitle":"Improvement of soil organic carbon turnover and microbial community niche differentiation with the addition of commercial organic fertilizer in wheat–green manure systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 11:44:46","doi":"10.21203/rs.3.rs-4593466/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-11-28T10:28:14+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-14T01:53:41+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-25T11:59:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2024-06-18T20:34:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2024-06-17T23:07:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-17T13:19:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"543c5830-2ea3-4a8d-8d04-4f0b06e6f026","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T16:22:55+00:00","versionOfRecord":{"articleIdentity":"rs-4593466","link":"https://doi.org/10.1007/s11104-025-07280-2","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2025-02-20 15:57:56","publishedOnDateReadable":"February 20th, 2025"},"versionCreatedAt":"2024-07-17 11:44:46","video":"","vorDoi":"10.1007/s11104-025-07280-2","vorDoiUrl":"https://doi.org/10.1007/s11104-025-07280-2","workflowStages":[]},"version":"v1","identity":"rs-4593466","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4593466","identity":"rs-4593466","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}