Effect of Wheat Straw Addition on Organic Carbon Mineralisation and Bacterial Community in Orchard Soil

Crop straw returning can stimulate organic carbon mineralisation and C sequestration simultaneously, which affects soil fertility. However, the effects of crop straw on organic carbon mineralisation and soil bacterial community in orchards are not fully understood. A 90-day incubation experiment was performed to investigate the effects of wheat straw (0, 1, 4, 6, 8, and 10 t·ha−1) on organic carbon mineralisation and bacterial community in orchard soil. Wheat straw addition enhanced the CO2 efflux rate and cumulative organic carbon mineralisation (Cmin), especially high level. The trend of CO2 efflux rate was increased sharply, especially during the early incubation stage (the first 13 days), and then decreased in the later phase. Furthermore, soil bacterial community structure displayed distinct changes in response to straw addition. Available nitrogen, potassium, organic carbon, β-glucosidase, and pH were the key factors driving soil bacterial community changes. The bacterial taxa in networks were significantly related to Cmin. The Proteobacteria, Actinobacteria, and Chloroflexi were positively related to Cmin; while Planctomycetes, Patescibacteria, and Gemmatimonadetes showed a negative relationship with Cmin by correlation and redundancy analyses. Co-occurrence network analysis showed a discrete bacterial network in 10 t·ha−1 of straw, while cohesive networks in others. Straw addition promoted organic carbon mineralisation by improving the soil biochemical properties, including enzymes activities, and nutrient contents, and regulating bacterial community composition. On the whole, 4 t·ha−1 of straw could be considered an economical level for improving soil organic carbon and bacterial community stability in orchards.


Introduction
Henan province in China is an important agricultural-producing area that has typical saline-alkali soil induced by the Yellow River. Soil with a relatively high pH inhibits plant growth directly, disturbs soil properties, and limits sustainable agricultural development (Setia et al. 2013).
Consequently, improvement of alkaline soils to enhance plant qualities and yields are essential for meeting food demand and sustainable ecosystem development goals in China (Zhang et al. 2017). Recently, research has been conducted on ameliorating alkaline soil (Yan et al. 2019). Crop straw is a recyclable organic resource, which when returned to the soil can simultaneously improve soil quality and mitigate air pollution resulting from straw burning (López-Valdez et al. 2010;Liang et al. 2022). Recently, research on the use of organic residues has been widespread implementation in orchards aiming to enhance soil fertility as well as improve fruit quality and yield, particularly in saline-alkaline soil conditions Zhang et al. 2021;Shi et al. 2023).
Soil organic carbon (SOC) is a major component of soil organic matter (SOM), which is involved in soil biogeochemical cycling (Trivedi et al. 2018). Previous studies have indicated that rice straw increases SOC content (Yan et al. 2019;Zhao et al. 2020) and promotes organic carbon (C) sequestration in saline-alkali soil (Wu et al. 2021). However, adding crop residue also stimulates organic C mineralisation and induces organic C loss in the form of CO 2 (Blagodatskaya and Kuzyakov 2008). Mineralisation is a key process for C balance and nutrient cycling in soils ). The addition of exogenous organic matter can significantly impact the intensity of C mineralisation, which increases as the level of crop straw addition rises . Furthermore, the increased CO 2 emissions resulting from the application of organic matter in saline conditions have adverse effects on the stability of the organic C pool and the efficient utilisation of resources (Zheng et al. 2022). However, the characteristics of organic C mineralisation in orchards under different levels of wheat straw application remain poorly understood.
Soil microorganisms and the enzymes derived from them regulate organic matter decomposition (Kallenbach et al. 2016). Organic matter returns not only changes the function and community structure of bacteria in soils but also positively affects bacterial abundance and diversity (Shi et al. 2019). Soil bacteria more actively respond to saline-alkali stress than fungi and more sensitive to soil managements in the short term . Otherwise, organic C mineralisation is regulated by soil bacterial communities, and some species play an important functional role (Banerjee et al. 2016). Actinobacteria and Proteobacteria of bacteria were mostly involved in organic C mineralisation after straw addition (Xiao et al. 2022). Moreover, soil bacterial networks reveal the ecological interactions among key bacterial taxa and organic C dynamics in response to exogenous organic amendments ). Co-occurrence networks have been used to explore the co-occurrence pattern of the keystone taxa and other co-existing microbes involved in C cycling, providing insights into understanding of organic C mineralisation (Xiao et al. 2022). Understanding these taxa and the correlations among them is essential for revealing the underlying microbial mechanism of organic C mineralisation. Our current understanding of the relationship between the bacterial community, co-occurrence networks, and the process of organic C mineralisation in orchard soils in response to crop straw addition remains limited.
Therefore, this study aimed to examine the effects of different levels of wheat straw on organic C mineralisation and the bacterial community in orchard soil. The following hypotheses were tested: (1) organic C mineralisation is linear with increasing levels of wheat straw; (2) soil bacterial community structure and co-occurrence networks are changed by wheat straw addition; and (3) high levels of wheat straw content are not optimal for net organic C sequestration and bacterial community stability in soil. This study aimed to offer insight into the underlying effect of exogenous straw on organic C mineralisation and the bacterial community in orchard soil.

Study Site and Experimental Materials
Soil samples were collected from persimmon orchards in the Xinxiang city, Henan province, China (35°17′59″ N, 113°56′37″ E; altitude, 70 m). The area is characterised by a warm temperate continental monsoon climate, with four seasons. The average yearly temperature is 14 °C, and the annual precipitation is 500-600 mm. No fertiliser is applied in this orchard, and 15 plots in the inter-row of trees were selected randomly according to "S". Soil samples were collected at a depth of 0-40 cm in each plot in September 2019. Debris, roots, fresh litter, and other substances were removed from soil samples by hand. The soil samples were then air dried, homogenised, and sieved through a 2-mm sieve, before use in the laboratory experiment. The straw residues used in the present study were obtained from wheat. The residues were air dried and then crushed into 0.5-1 cm fragments. The basic properties of soil and wheat straw were noted in Table S1.

Laboratory Incubation Experiment
The incubation experiment was conducted in April 2020 at the Henan Institute of Science and Technology. The 100 g homogenised soil samples were each placed in a plastic dish. Prior to the incubation experiment, the soil samples were kept in the dark at 25 °C for 7 days to stimulate soil microbial activity. Subsequently, soil was fully mixed with the crushed wheat straw: 1 (S1), 4 (S2), 6 (S3), 8 (S4), and 10 (S5) t·ha −1 . Soil free of wheat straw was considered the control (CK). Each treatment was performed in triplicate. Each plastic dish was placed in a mason jar containing a beaker with of 5 mL NaOH (1 M) to absorb CO 2 respired during the subsequent incubation according to Chen et al. (2021) with some modifications. The mason jars were sealed and incubated in an incubator at 25 °C in the dark for 90 days. Simultaneously, three microcosms with no soil or wheat straw were placed under the same conditions and used to obtain the blank values for the measurement of CO 2 -C content at each sampling point during the 90-day incubation period. The soil samples were weighed every 3 days and were maintained at 50% water content with distilled water during the experiment.
After 90 days of incubation, the straw residues were picked out and soil samples were harvested. Some of the soil samples were stored at 4 °C for subsequent labile C fraction and enzyme activity analyses, and aliquots of the samples were stored at − 80 °C for soil bacterial community structure determination. The remaining soil samples 1 3 were air dried and used for the analysis of SOC, nutrient content, and other properties.

CO 2 Efflux Rate and Cumulative Organic C Mineralisation
The CO 2 efflux rate was measured as described by Chen et al. (2021). The CO 2 was captured with NaOH solution and precipitated by adding 1 M CaCl 2 solution. The mixture was then titrated with 0.2 M HCl using a phenolphthalein indicator. The CO 2 traps were exchanged on days 3, 7, 13, 21, 34, 59, and 90. The kinetic models were evaluated using the "deSolve" and "basicTrendline" packages in "R" software version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria) to select the most appropriate model to assess the dynamic of organic C mineralisation in soils. Based on model fitting criteria, i.e. P value and highest determination coefficient (R 2 ), an exponential model (Eq. 1 was selected) expressed as where the C t is the cumulative CO 2 -C emission via mineralisation (mg·kg −1 ) and t is the incubation time (days), C 0 and C 1 are the sizes of potentially mineralisable CO 2 -C and easily mineralised CO 2 -C (mg·kg −1 ), respectively. k is the turnover rates of organic C pools (days −1 ), C 0 , C 1 , and k could be obtained by regression. The mean residence time (MRT) of the organic C was calculated as 1/k. The half-lives of mineralisable organic C (T 1/2 ) = ln(2/k).

Contents of SOC and Labile Fractions
The SOC content was measured using traditional H 2 SO 4 -potassium dichromate oxidation with an additional heat method (Walkley and Black 1934). Microbial biomass carbon (MBC) content was measured by CHCl 3 fumigation and extracted using K 2 SO 4 solution (Vance et al. 1987). The readily oxidisable C (ROC) content was oxidated by 0.02 M KMnO 4 and measured at absorbance of 565 nm (Blair et al. 1995). Approximately 5.0 g of fresh soil was extracted by 0.5 M K 2 SO 4 solution and filtered through a 0.22-μm membrane, and the extracts were used for the dissolved organic C (DOC) content measurement (Jones and Willett 2006). Particulate organic C (POC) and light fraction organic C (LOC) were extracted by 30 mL of sodium hexametaphosphate (5 g·L −1 ) and sodium iodide (density 1.8 g·cm −3 ), respectively (Cambardella and Elliott 1992). POC and LOC contents were determined using the same method as for SOC.

Soil Enzyme Activities
Soil β-glucosidase activity was measured according to Geng et al. (2012). Soil sucrase and cellulase activities (1) C t = C 0 × 1 − e -kt + C 1 were determined using a substrate of sucrose and carboxymethylcellulose, respectively. It was subsequently, estimated using the 3,5-dinitrosalicylic acid method (Schinner and von Mersi 1990). The geometric mean of the total enzyme activity (GMea) is an integrative approach to combine a large number of enzyme activities related to different soil functions and nutrients. The value of GMea was calculated using Eq. (2) (García-Ruiz et al. 2009).

Soil Chemical Analysis
Traditional methods were employed to measure available phosphorus (AP) and available nitrogen (AN) in the soil. Furthermore, available potassium (AK) and exchangeable Na + contents were measured using an atomic absorption spectrophotometer (ZEEnit700P, Analytik Jena Co. Ltd., Germany) method (Massadeh et al. 2016). Cl − content was measured according to Lashari et al (2015). Soil pH was extracted with water (1:5 w/v) and detected using digital electrodes (FE28, Mettler-Toledo Co. Ltd. Shanghai, China). Soil electrical conductivity (EC; 1:5 w/v) was measured using a conductivity meter (DDS-307, Shanghai Precision & Scientific Instrument Co. Ltd., China). Soil cation exchange capacity (CEC) was determined according to Mandal et al. (2007).

Soil Bacterial Community Analysis
Frozen soil samples (5.0 g) from each treatment were used to analyse the soil bacterial community in triplicate. The soil DNA was extracted and amplified with universal primers (16S rRNA; 341F: CCT ACG GGNGGC WGC AG, and 806R: GGA CTA CHVGGG TAT CTAAT) in the V3-V4 region (Guo et al. 2017). The PCR reaction mixtures consisted of 1 μL of template DNA, 1 μL forward and reverse primers, 10 μL of 2 × PCR Ex Taq (Takara, Japan), and 7 μL of double distilled water. The PCR programme started with an initial denaturation at 95 °C for 5 min; followed by 28 cycles at 95 °C for 30 s, annealing at 50 °C for 28 s; and 72 °C for 30 s and extension at 72 °C for 10 min. Sequencing was performed using the Illumina MiSeq System by Gene Denovo Biotechnology Co. Ltd. (Guangzhou, China).
The quality of the raw sequencing data was assessed and filtered using Quantitative Insights Into Microbial Ecology (QIIME). Operational taxonomic units (OTUs) with 97% similarity were clustered and annotated using the SILVA database (http:// www. arb-silva. de).
All of the raw sequencing data in this study were deposited in the National Centre for Biotechnology Information (NCBI) database, accession number PRJNA871251.

Statistical Analyses
Values in the figures and tables represent the mean ± standard deviation (n = 3). A one-way analysis of variance (ANOVA) was used to analyse the effect of different levels of wheat straw in soil on organic C contents, organic C mineralisation, enzyme activities, bacterial community abundance, and diversity. Differences between means were tested using Tukey's test (P < 0.05). All statistical analyses were performed using PASW Statistics 18 (SPSS, Chicago, IL, USA). Bacterial α-diversities including ACE, Chao1, and Shannon indexes were estimated. Nonmetric multidimensional scaling (NMDS) was used to analyse the structural changes of bacterial communities in response to straw levels. NMDS was calculated with the "vegan" package (version 2.5-6) (Dixon 2003). Redundancy analysis (RDA) was performed using the R software to illustrate the relationship between C mineralisation and bacterial community. A heatmap analysis of soil bacterial phyla was constructed using the "pheatmap" package in R software. The co-occurrence network was generated to indicate the interactions among bacterial taxa. An integrated OTU table of bacterial OTUs was used to construct networks, the topological properties of networks were analysed on the molecular ecological network analysis pipeline platform (MENA, http:// ieg2. ou. edu/ MENA/; Deng et al. 2012), and then visualised by the Fruchterman-Reingold layout in Gephi version 0.9.1 (Bastian et al. 2009). The correlation analysis among soil properties, soil bacterial diversity, and community was conducted using R software version 3.6.3 using the "corrplot" and "vegan" packages. The other figures were created with Origin 8.5 (Originlab Corporation, Northampton, MA, USA).

CO 2 Efflux Rate and Cumulative Organic C Mineralisation
Over the 90-day incubation, the application of wheat straw increased the CO 2 efflux rate and cumulative organic C mineralisation (C min ) content ( Fig. 1). Generally, the CO 2 efflux rate increased with increasing wheat straw levels in the following order: S5 > S4 > S3 > S2 > S1 > CK. The rates of CO 2 efflux in wheat straw treatments increased sharply, especially during the early incubation stage (the first 13 days) and decreased in the later incubation phase, irrespective of straw levels (Fig. 1a). In contrast, the C min was generally increased along with incubation times (Fig. 1b). During the incubation, the C min in response to the addition of wheat straw occurred in stages where 53.23-58.07% of the total C min response took place in the first 34 days; and then decreased during the subsequent two stages (days 34-59, and 59-90; Fig. 1c). Straw addition significantly increased the C min and showed the following descending order: S5 > S4 > S3 > S2 > S1 > CK. The best-fit parameters for the one-exponential first-order kinetics model were obtained as shown in Table S2 (R 2 = 0.98-0.99). The C 0 of the parameters of the kinetics of organic C mineralisation was similar to C min , that in S5 was 93.59%, 62.31%, 38.39%, and 14.80% higher than that in S1, S2, S3, and S4, respectively. Ratios of C 0 to SOC under S1 and S2 were similar to those of CK, which were markedly lower than S3, S4, and S5. The K value, which represents turnover rates of organic C pools, was increased by the S1 and S2 treatments, while it showed no marked effect in other treatments. Furthermore, the MRT and T 1/2 values in S1 and S2 treatments were markedly lower than those recorded in the S4 treatment (Table S2).

Contents of Labile Organic C Fractions and Activities of Soil Enzymes
Exogenous wheat straw increased the contents of MBC, DOC, ROC, and POC, while no significant effects on LOC contents were noted when the samples were compared with CK ( Fig. S1). The contents of MBC and ROC under S3, S4, and S5 treatments were similar and notably higher than those observed for the S1 and S2 treatments. There was no significant difference in DOC contents among the five straw levels and POC contents in the S3, S4, and S5 treatments. ROC contents in S3, S4, and S5 treatments were higher than that in S1. Wheat straw addition not affected the ratio of MBC to SOC, as compared with CK, except the markedly increased in S3 (Table S3). The DOC comprised 2.64-3.42% of the SOC, that in S4 and S5 treatments were markedly lower than in CK and S1 treatments. The ratios of ROC to SOC and POC to SOC were significantly higher in the S3 treatment than those recorded in the CK and S1, while there was no notable difference with the other treatments (Table S3). Compared to the CK, wheat straw amendments increased soil β-glucosidase activity in all treatments except that no significant difference was observed for treatment S1 (Fig. 2a). There was no marked difference in soil β-glucosidase activity among the S3, S4, and S5 treatments, while the activity was 46.47%, 54.44%, and 57.83% higher, respectively, than that noted in the S1 treatment. The sucrase activity under the S3 and S5 treatments was similar, higher than the S1 treatment (Fig. 2b). The cellulase activities in treatment S1 and S4 were higher than that in S2, S3, and S5 treatments (Fig. 2c). Wheat straw addition increased the GMea value, which in the S2 treatment was 16.94%, 23.64%, and 16.21% lower than that observed in the S3, S4, and S5 treatments, and no marked difference with S3 (Fig. 2d).

Soil Chemical Properties
The addition of wheat straw increased SOC content, that in the S5 treatment was 49.33% higher than the value observed in the CK, and 23.65%, 15.19%, 16.69%, and 9.80% higher than those observed in the S1, S2, S3, and S4 treatments, respectively (Table 1). However, there was no significant difference in SOC content among the S1, S2, S3, and S4 treatments. And the net SOC increment among straw-treated soil samples was similar (Fig. S2). When considering AN, AP, and AK contents, they were higher under straw treatments than that observed under the CK, with the exception of AN in S1 and AP in S1 and S5. Furthermore, AN and AP contents in the S2, S3, and S4 treatments were significantly higher than that noted in S1, while AK content under the S4 and S5 treatments was markedly higher than those observed in S1, S2, and S3. Soil pH was reduced by wheat straw treatments, but no significant differences were observed among straw levels. Straw application increased EC and Cl − values as well as the exchangeable Na + contents in soil. The CEC in the S5 treatment was 13.18% higher than that in CK, but no marked difference was noted among other treatments.

Soil Bacterial Diversity
The α diversity, including OTUs, ACE, and Chao1 values, was not affected by wheat straw treatments. The Shannon index under the S1 treatment was notably lower than that observed in CK, but no such difference was seen in the other treatments (Table S4). The NMDS was used to visualise the β-diversity of the bacterial community present at different wheat straw levels (Fig. 3a). The results showed that bacterial communities were divided into different groups on the basis of straw levels. This result suggests that wheat straw addition impact soil bacterial community composition. the same time range that measured via one-way ANOVA with Tukey testing at a P < 0.05. CK is the control without wheat straw; S1, S2, S3, S4, and S5 are the soil amended with 1 t, 4 t, 6 t, 8 t, and 10 t wheat straw·ha −1 soil, respectively

Soil Bacterial Community Composition
The main bacterial phyla were similar between soils with and without wheat straw amendments, with relatively different abundances (Fig. 3b). The relatively highest abundance was Proteobacteria (25.74-34.92%), followed by Acidobacteria (17.03-20.82%), Gemmatimonadetes (12.20-17.22%), Planctomycetes (8.28-13.59%), Actinobacteria (5.53-8.45%), Chloroflexi (4.41-6.34%), and Bacteroidetes (3.51-5.35%). The addition of straw increased the relative abundances of Proteobacteria and Actinobacteria when compared with the CK. The highest abundance of these two taxa was found in the S5 treatment, which was notably higher than the other treatments. In response to the addition of straw, the relative abundances of Nitrospirae were increased, and this was lower in treatment S2 than other straw levels. In treatments S2, S3, and S4, the relative abundances of Acidobacteria increased, while it decreased in S5. Chloroflexi increased under treatments S3, S4, and S5, resulting in higher levels than those observed under treatments S1 and S2. Straw addition reduced the relative abundances of Gemmatimonadetes, Firmicutes, Patescibacteria, and Verrucomicrobia, compared with the CK. The relative abundances of Gemmatimonadetes and Firmicutes under S1 were higher than those recorded for other straw levels, while the relative abundances of Patescibacteria and Verrucomicrobia under S2 were higher than those observed for the other straw levels. The relative abundance of Planctomycetes was enhanced by the S2 treatment, whereas it was reduced by high levels of straw addition (S3, S4, and S5), compared with CK. The lowest abundance of Bacteroidetes and Rokubacteria was observed in the S2 treatment among all treatments (Fig. 3c).

Soil Bacterial Co-occurrence Networks
Network analysis revealed that the soil bacterial communities varied between the CK and different levels of straw application (Fig. 4). The dominant identifiable OTUs in the CK, S1, and S2 treatments belonged to Proteobacteria, Acidobacteria, Planctomycetes, and Gemmatimonadetes. However, the Proteobacteria, Acidobacteria, Gemmatimonadetes, and Chloroflexi were the dominant taxa in the S3 treatment, while Proteobacteria, Acidobacteria, Gemmatimonadetes, and Actinobacteria were the dominant taxa in the S4 and S5 treatments. The modularity in empirical networks was higher than that in random networks, indicating "smallworld" and modularity characteristics (Table 2). Therefore, the networks of bacteria in this study can be used to reveal the response of bacterial co-occurrence to different levels of wheat straw treatments. Straw application decreased the modularity of soil bacterial networks (Table 2). However, the avgK was increased by wheat straw, except for the 63.16% reduction in the S5 treatment, when compared with the CK. The avgK in the S2 treatment was 92.11%, 77.33%, 8.96%, and 30.36% higher than the CK in the S1, S3, and S4 treatments, respectively. The numbers of nodes, negative, and total links in straw treatments were higher than those in the CK, especially the S4 and S5 treatments. Furthermore, straw addition also increased the positive links up to a certain level. In the S2, S3, and S4 treatments, the positive links were higher than those noted in the S1 and S5 treatments. The highest ratio of negative/positive (N/P) was found in the S5 treatment, which was 14.81%, 13.41%, 30.99%, 29.17%, and 20.78% higher than that under CK, S1, S2, S3, and S4, respectively. Moreover, the N/P under S2, S3, and S4 treatments was 12.35%, 11.11%, and 4.94%, respectively, lower than that in the CK.

Effect of Organic Matter Amendments on Organic C Mineralisation and Soil Organic C Components
Organic C mineralisation is an inevitable biochemical process subsequent to the addition of external organic substrates (Yang et al. 2020). In our study, the CO 2 efflux rate peaked on day 7 and decreased during the ensuing 90 days of incubation, irrespective of straw levels. This change was primarily because of the disintegration of easily decomposed Table 1 Effect of wheat straw on the chemical properties of soil in orchard Data were presented as means with standard deviation (n = 3); different letters in the same column indicate the significant difference measured via one-way ANOVA with Tukey testing at P < 0.05. SOC is soil organic carbon; TN is total nitrogen; AN is available nitrogen; AP is available phosphorus; AK is available potassium; CK is the control without wheat straw; and S1, S2, S3, S4, and S5 are the soil amended with 1 t, 4 t, 6 t, 8 t, and 10 t wheat straw·ha −1 soil, respectively components in wheat straw in the initial stage, which provided labile substrates for soil microorganisms (Shar et al. 2021). Following this, the generally decreased CO 2 efflux rates are related to the substances such as lignin and xylan in straw that are difficult to decompose and require a relatively long time to be consumed by soil microorganisms (Yang et al. 2020). In our study wheat straw application markedly promoted the CO 2 emission rate and C min , and these increased as more straw was added. These results corroborated those reported by Shar et al. (2021) and supported our previous hypothesis (1). The generally increased C min by wheat straw indicated that the organic C flows out of the soil via decomposition (Kéraval et al. 2016). Because of increase in unstable C during straw decomposition induce more organic C mineralisation (Shahbaz et al. 2017;, that is consistent with the promoted potentially and easily mineralisable C contents by the first-order exponential model in our study (Table S2). In addition, the activities of soil C-cycle enzymes that are synthesised by soil microbes are involved in decomposing organic matter (Li et al. 2016).
The increased activities of β-glucosidase, invertase, and cellulase under straw treatments contributed to the cycling of organic C. Similar results have been reported by Guo et al. (2019) when studying organic C mineralisation by adding wheat residue to soil. Sufficient C input may increase substrate C allocation for microbial respiration and secretion of C-acquisition enzymes and lead to greater organic C mineralisation and lower C use efficiency ). Node colours indicate the bacterial genus. Node size represents the cluster coefficient. Blue and red edges indicate negative and positive relationships between nodes, respectively 1 3 Therefore, addition of wheat straw accelerated the mineralisation of organic C, especially at high levels.
A previous study reported that more straw-C was lost in the form of CO 2 than retained as SOC . The net sequestration of organic C is determined by the balance of the amount of newly formed SOC and mineralisation of organic C (Anikwe 2010). Both the increasing organic C inputs and reducing organic C mineralisation can increase net organic C sequestration (Luo et al. 2003). Wheat straw application significantly increased SOC and active C fraction contents in this study, which likely resulted from straw-derived C entering the soil C pool. Xu et al. (2019) found that SOC content was significantly linearly correlated with straw-C input level; however, such a finding was not observed in the present study. This could be because the nutrient stoichiometry in soil may exceed the suitable requirements of soil microorganisms in high organic matter applications (Alberti et al. 2015). However, the net SOC Table 2 Topological properties of the empirical phylogenetic molecular ecological networks across the different wheat straw levels in comparison to the random networks CK is the control without wheat straw; S1, S2, S3, S4, and S5 are the soil amended with 1 t, 4 t, 6 t, 8 t, and 10 t wheat straw·ha −1 soil, respectively 0.15 ± 0.0061 0.31 ± 0.011 0.12 ± 0.0049 0.12 ± 0.0049 0.12 ± 0.0048 0.12 ± 0.0047

Fig. 5
Correlations (a) and redundancy analysis (b) illustrating the relationships between the bacterial community and soil properties. AN is available nitrogen, AP is available phosphorus, AK is available potassium, β-Glu is β-glucosidase, Suc is sucrase, Cel is cellulase, SOC is soil organic carbon, DOC is dissolved organic carbon, POC is particulate organic carbon, ROC is readily oxidisable C, MBC is microbial biomass carbon, C min is cumulative organic carbon mineralisation, and GMea is geometric mean of the total enzyme activity increments among five straw treatments were not significantly different in our study (Fig. S2), which suggested that high wheat straw has no additive promotion for net SOC sequestration. Furthermore, GMea is an index that imitates soil microbial metabolic capacity (Paz-Ferreiro et al. 2012;Zhao et al. 2023) and negatively affects SOC stability . The relative lowest GMea value in the S2 treatment in this study suggested the highest use efficiency of microbial substrate, which efficiently transformed straw-C to the organic C pool of soil ). Therefore, the high level of straw supply induced more organic C mineralisation rather than sequestration. Taken together, these results supported our previous hypothesis that excess wheat straw addition may be not optimal for organic C stability in soil.

Effect of Organic Matter Amendments on Soil Bacterial Community Composition and Co-occurrence Network
Exogenous organic amendment affects soil bacterial diversity and community structure (Wichern et al. 2020;Liu et al. 2020). However, the bacterial community structure is significantly regulated by wheat straw supply, rather than bacterial α diversity in this study. Yan et al. (2019) also found that rice straw addition did not affect soil microbial diversity. These inconsistent findings are probably a result of different experimental conditions, such as nutrients availability, application levels, and treatment times. In this study, the relative abundance of bacterial taxa in the top 10 phyla and some bacterial taxa selected as representative biomarkers (specific abundant taxa) was analysed (Figs. S3 and S4). These results supported our initial hypothesis that the soil bacterial community compositions were changed by wheat straw addition. Proteobacteria and Actinobacteria are involved in consuming easily degradable substances in straw . Chloroflexi is one of the major acid-producing bacteria; it degrades high-molecular-weight organic polymers into low-molecular-weight compounds (Yu et al. 2021). Therefore, the promoted relative abundance of these three taxa by wheat straw accelerated the decomposition of organic matter, especially at high rates (S3, S4, and S5) in this study. Acidobacteria is one of the major bacterial phyla with low mineralisation rates ; the higher abundance of this phylum in S2, S3, and S4 treatments suggested the potentially greater enhancement of SOC accumulation than in S5 treatment. Otherwise, Acidobacteria as "oligotrophs" are involved in recalcitrant organic C degradation (Jenkins et al. 2010); the decreased abundance of Acidobacteria suggested a slow growth rates in soils with high nutrients under high straw addition (S5 treatment). Gemmatimonas and Firmicutes are involved in facilitating cellulose degradation (Tang et al. 2021); their lower relative abundances resulting from straw application may be attributed to the functional redundancy of bacterial taxa. Nitrospira is a nitrite oxidiser that is critical for the N cycle (Attard et al. 2010), and the increased abundance of this bacteria might be due to the higher AN in soil under straw returning, which accelerates N loss by soil nitrification. Furthermore, the lowest relative abundance of Nitrospira in the S2 treatment indicated a minimum N loss compared to the other treatments. These various responses of the soil bacterial community to straw addition have some degree of connection to bacterial taxa and relationship with soil properties . Soil pH is one of the key factors that regulated bacterial community by correlation analysis in our study. The narrow pH range for optimal bacterial growth may be the critical factor affecting bacterial community composition (Lauber et al. 2009;Muneer et al. 2022). Wheat straw addition decreased soil pH from 7.82 to 7.43, which benefited Actinobacteria growth because its suitable pH is near neutral in a broad range of ecosystems (Lauber et al. 2009). Bacteroidetes also prefer living in nearneutral pH conditions (Lauber et al. 2009), while its abundance was reduced with decreasing soil pH in the S2, S3, and S4 treatments in this study. These inconsistent results may be attributable to the fact that some bacterial taxa show a site-specific response to native soil pH and soil environments (Waldrop and Zak 2006;Marcos et al. 2019).
Soil bacterial networks reveal the ecological interactions among key taxa (Guseva et al. 2022). The modularity is over 0.4, suggesting that the bacterial networks had a modular structure (Ling et al. 2016). Unexpectedly, the modularity was suppressed by the application of wheat straw (Table 2, Fig. S5). Fewer modules but more nodes in networks illustrated that straw addition suppressed bacterial functional diversity, but intensified the complexity of community structure and resulted in less uniform and larger functional assemblages . Otherwise, the increased avgK in S1-S4 treatments indicated the more complex connections among bacterial taxa . Greater complexity in the microbial network suggested more intense activity, greater community stability, and higher resistance to disturbance and perturbation of the environment (Karimi et al. 2017). In contrast, the lowest avgK in the S5 network indicated the reduction of coupling among the dominant nodes of the bacterial network and lower resistance to distribution and perturbation of the soil environment ). According to ecological theory, positive or negative interactions reflect cooperative or competitive relationships among microbial communities (de Vries et al. 2018). The numbers of positive links of soil bacterial networks in all treatments were much higher than the negative links (Fig. 4,  Table 2), indicating that commensalism or mutualism dominated the interactions between bacterial taxa (Siles et al. 2021) and provide more functional redundancy (Mougi and Kondoh 2012). The positive correlations among bacteria were predominantly in co-occurrence networks in the S2, S3, and S4 treatments, which indicated more synergistic action of specialised groups . In contrast, the highest ratio of negative to positive links was observed in the S5 treatment, which suggested the enhanced proportion of negative relationships, such as parasitism, antagonism, predation, or competition for resources among taxa (Faust and Raes 2012). Taken together, our results demonstrated more cohesive and complex co-occurrence networks were present in the S2, S3, and S4 treatments, which enhanced the sensitivity of the bacterial functions to the external environment and created a more stable bacterial community in soil. However, an increased mutual possible selective competitive pressure and a more discrete bacterial network in the S5 treatment, which adverse to soil bacterial community stability. These results further supported our hypothesis (3) that high level of wheat straw application may be not optimal for soil bacterial community stability.

Key Factors Regulate Organic C Mineralisation
In this study, the relationship between C min , soil properties, and bacterial communities was analysed. Soil pH has a significant effect on organic C mineralisation (Yang et al. 2020). Rao and Pathak (1996) found that CO 2 emission was 18% lower at pH 8.7 than at 7.0 because high pH has a negative effect on SOC availability and trapping of evolved CO 2 within soil (Oren and Steinberger 2008). A negative relationship between soil pH and C min was also found in our study. The reduction of pH was attributed to the humification and formation of organic acids during straw decomposition (Xu et al. 2017), and nitrification of the N after organic matter was added (Pocknee and Sumner 1997). Otherwise, our results showed that straw addition increased Cl − and exchangeable Na + in soil. This increase may be attributed to part of crop biomass especially wheat straw, which generally contains several inorganic elements (e.g. Cl, S, Si), which during biomass conversion will be released into the soil . Otherwise, the Na + content increase may be due to exchange with other cations. This result was similar to that of a previous study (Mehdizadeh et al. 2020). Although these alkali ions were promoted by straw application, they are still in the safe range and no additive damage for soil quality (Miller et al. 2005). In general, the improved soil qualities including nutrients and enzymes activities, which also contributed to soil microbe growth and more straw-C loss by microbial respiration , further induced greater C min .
The decomposition of organic matter is drive by soil microbes (Su et al. 2020). Liu et al. (2020) reported a significant relationship between organic C mineralisation and soil bacterial communities. The present study also showed that C min was strongly associated with bacterial community by the correlation and RDA analysis. Exogenous straw addition improved soil qualities and stimulate the growth of microorganisms involved in degrading the straw residue, especially the r-strategists (Zhao et al. 2017). Proteobacteria and Actinobacteria are r-strategists and dominated straw and SOM decomposition (Xiao et al. 2022). Therefore, the positive relationship between Proteobacteria, Actinobacteria, and C min suggested that the promoted relative abundances of these two taxa accelerated organic C mineralisation under straw addition in our study. Furthermore, the bacterial taxa with function trends in the predicted genes involved in the degradation of cellulose, hemicellulose, and cellooligosaccharides based on PICRUSt (Fig. S6) suggested the promotion of organic C degradation in response to wheat straw addition. In contrast, Gemmatimonadetes, Planctomycetes, Patescibacteria, and Verrucomicrobia demonstrated a negative correlation with C min . Additionally, the reduction of the relative abundance of these bacteria taxa under straw treatment suggested that wheat straw promotes organic C mineralisation by inhibiting the activity of these taxa. These results suggested that soil chemical properties and bacterial communities significantly regulate organic C mineralisation (Fu et al. 2022).

Conclusion
The present study showed that returning wheat straw promoted organic C mineralisation, which increased with increasing straw levels. The intensified interactions with keystone taxa (Proteobacteria, Actinobacteria, and Chloroflexi) of the bacterial network were the dominant control on stimulating organic C mineralisation. Soil bacterial community composition and relative abundance were affected by wheat straw and exhibited pronounced variability across different levels. Soil pH value, nutrient content, enzyme activity, and organic C components were important factors affecting the soil bacterial community composition. In addition, co-occurrence network analysis showed a discrete bacterial network at high straw addition (10 t·ha −1 ), while cohesive and complex co-occurrence networks were observed at the other levels of wheat straw addition. Furthermore, wheat straw addition increased the SOC contents, while the net SOC increment among the five straw addition levels showed no significant difference. These results provided empirical evidence that high wheat straw additions facilitate C mineralisation, not conducive to soil net organic C sequestration and soil bacterial community stability. Accordingly, the addition of 4 t·ha −1 of wheat straw could be considered the most economical and efficient method for reducing organic C mineralisation and improving the resistance of soil bacterial communities against disturbances and alterations of environment.
Funding This study was funded by the National Natural Science Foundation of China (No. 32202419) and Science and Technology Project of Henan Province (No. 202102110054/202102110051).

Conflict of Interest
The authors declare no competing interests.
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