Effects of Mulching Practices on Soil Soluble Nitrogen in Orchards in Red Soil Hilly Areas

Effects of Mulching Practices on Soil Soluble Nitrogen in Orchards in Red Soil Hilly Areas | 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 Effects of Mulching Practices on Soil Soluble Nitrogen in Orchards in Red Soil Hilly Areas Zuopin Zhuo, Heming Li, Zumei Wang, Lei Wang, Fangshi Jiang, Jinshi Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7959888/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims Mulching practices significantly contribute to soil and water conservation and soil ecological regulation in red soil hilly regions. However, it remains unclear how grass cover and plastic mulching affect the composition of soil soluble nitrogen by modifying microenvironments and functional microbial communities. Methods We examined the effects of grass cover and plastic mulching on nitrogen-related microbial communities and soluble nitrogen components in the 0–60 cm soil layer of citrus orchards in a red soil hilly area. Results Both grass cover and plastic mulching significantly increased soluble nitrogen content in the 0–20 cm soil layer. Compared to the control, grass cover increased ammonium nitrogen, nitrate nitrogen, and soluble organic nitrogen by 20.2%, 18.3%, and 56.7%, respectively, while plastic mulching raised them by 3.3%, 11.5%, and 38.9%. Grass cover not only induced greater increases in soil nitrogen fractions than plastic mulching but also promoted a higher relative abundance of key nitrogen-cycling microorganisms such as Proteobacteria, Actinobacteriota, Verrucomicrobiota, and Acidobacteriota. Structural equation modeling revealed that soil microorganisms (path coefficient = 0.68) had the strongest influence on total soluble nitrogen, exceeding the effects of physical (0.33) and chemical (0.49) properties. Among chemical factors, total nitrogen had the strongest direct effect on ammonium and dissolved organic nitrogen, while the relative abundance of Proteobacteria most strongly promoted nitrate nitrogen. Conclusion Grass cover is more effective than plastic mulching or no treatment in enhancing soil soluble nitrogen components and enriching nitrogen-related microbial communities in red soil hilly regions. Cover measures Orchard Microbial community Soluble organic nitrogen Structural equation model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights Effects of different cover measures on TSN in citrus orchard were investigated. Grass cover was optimal for improving soil nitrogen availability. Cover measures enriched nitrogen-cycling microbes, especially Proteobacteria. SON correlated with soil TN, while NO₃⁻-N was linked to Proteobacteria. SEM linked soil properties and microbes to TSN under different cover measures. Introduction Total soluble nitrogen (TSN), which serves as a critical source of nitrogen for crops and provides a sustained supply of nitrogen for plant growth (Murphy et al., 2007 ), includes soluble organic nitrogen (SON), ammonium nitrogen (NH 4 + -N), nitrate nitrogen (NO 3 − -N), and other inorganic nitrogen forms directly absorbed by plants (Chen and Xu, 2008 ). Lu et al. ( 2008 ) reported that TSN accounts for approximately 8.33% of total nitrogen (TN) on average in waterlogged farmland soils of the Loess Plateau. Despite its low proportion of TN, TSN represents the most active fraction of soil nitrogen, acting as a “hub” in nitrogen transformation and transport (Chen et al., 2005 ). The “active” nature of TSN is reflected in two aspects. First, TSN is directly available to plants, which shortens the nitrogen cycle on land and improves nitrogen use efficiency. Second, its mobility allows it to be transported via runoff or leaching, potentially leading to water pollution (Ma et al., 2020 ; Shi et al., 2022 ; Ye et al., 2022 ). Therefore, understanding the dynamics of TSN and its interaction with environmental factors under different agricultural practices is key to optimizing nitrogen use efficiency. Soil physical properties (Jiang et al., 2016 ), chemical properties (Filep and Rékási, 2011 ), and microbial biomass (Schmidt et al., 2011 ) collectively influence the content and composition of TSN in soil. Bargmann et al. ( 2014 ) reported that optimal temperature and moisture conditions accelerate the release of TSN in the surface soils of Australian forests. Similarly, Xing et al. ( 2019 ) reported that proteases in subtropical mountain ecosystems catalyze the conversion of organic nitrogen to TSN and that organic matter increases bacterial biomass and organic nitrogen content. Yang et al. ( 2021 ) demonstrated that bacteria decompose organic matter to directly or indirectly increase the TSN content in paddy soils. Zhu and Carreiro ( 2004 ) reported that microbial enzymatic degradation of humic nitrogen increases TSN in deciduous forest soils. Furthermore, Zhang et al. ( 2024 ) revealed in a study on the ecological restoration of mining areas that microbes release soil nitrogen metabolism-related enzymes, which significantly increase the TSN content. Bacterial groups play a pivotal role in the dynamics of TSN and contribute to regional and ecosystem-specific differences in the factors driving TSN variation. The functional microbial communities involved in nitrogen transformation typically include taxa capable of nitrogen mineralization and nitrification, and their composition is influenced by climate, soil type, and vegetation. In northern arid saline–alkaline soils, for instance, these functional communities are predominantly composed of Proteobacteria and Bacteroidetes (Nan et al., 2022 ). While a study on the loess plateau suggesting that the bacterial communities in soils are determined primarily by environmental characteristics, with Proteobacteria, Actinobacteria, Chlorobi, and Nitrospirae being the dominant phyla in N-fixing (Xu et al., 2019 ). Existing studies have delineated the dynamics of the soil total soluble nitrogen pool and highlighted the key synergistic regulatory role of soil properties. However, within specific agroecosystems, particularly in the subtropical red soil hilly region characterized by high temperature and abundant rainfall, the precise mechanisms by which functional microbial communities drive nitrogen transformations remain poorly understood. Clarifying the relative contributions of these driving factors and the underlying microbial pathways is essential for accurate prediction and effective management of nitrogen cycling in agricultural ecosystems. In tropical and subtropical lateritic hilly regions, orchards serve as critical agricultural foundations, sustaining farmer livelihoods and regional economies. The convenience of weed management has driven the widespread adoption of clear tillage (Xie et al., 2022; Chen et al., 2022); however, decades of evidence reveal its hidden costs: prolonged implementation triggers soil nutrient depletion, structural collapse, and erosion of microbial diversity, ultimately undermining land sustainability (Fu et al., 2023 ; Li et al., 2024). This degradation imperative has inspired interest in alternative ground management practices. Grass cover emerges as a regenerative solution, enhancing soil structure and microbial vitality across ecosystems (Xiao et al., 2022 ), notably increasing nitrogen stocks and microbial efficiency in citrus orchards through alfalfa integration (Wu et al., 2021 ). However, its nitrogen-enhancing capacity falters under nutrient-scarce conditions, where intensified plant‒crop competition constrains nitrogen uptake (Crusciol et al., 2021 ). Plastic mulching, widely used in orchards as an effective weed control measure, simultaneously creates a dualistic soil microenvironment: it suppresses nitrification and nitrate formation through oxygen limitation (Qian et al., 2022 ) but simultaneously conserves moisture in arid zones, stimulating microbial proliferation (Liao et al., 2021 ). Although both grass cover and plastic mulching have been proven to enhance soil fertility, they create fundamentally different microenvironments in the red soil hilly region. Grass cover improves soil aeration and organic matter input through plant root activity, whereas plastic mulching tends to induce oxygen-deficient conditions and relies mainly on physical isolation. Nevertheless, existing research has largely concentrated on assessing the effects of cover measures on soil nitrogen dynamics, whereas the mechanisms through which these two cover measures shape nitrogen cycling via the key linkage between microenvironmental conditions and functional microorganisms remain insufficiently understood, particularly in the red soil hilly region. To bridge these knowledge gaps, this study focused on citrus orchards in the red soil hilly region of southern China, aiming to clarify how different ground cover practices (grass cover, plastic mulching, and clean tillage) regulate soluble nitrogen pool dynamics (NH₄⁺-N, NO₃⁻-N, and SON) and reshape nitrogen-related microbial communities. We specifically examined the interactions among cover measures, total soluble nitrogen (TSN) composition, and microbial mechanisms of nitrogen transformation, with the following hypotheses: (1) Compared with plastic mulching and clean tillage, grass cover increases soluble nitrogen concentrations in surface soil by providing more abundant organic inputs and promoting better soil aeration; (2) compared with grass cover and clean tillage, plastic mulching suppresses strictly aerobic nitrifying microorganisms, leading to the relative accumulation of ammonium nitrogen and a decline in nitrate nitrogen proportion; and (3) variations in the structure and function of soil microbial communities exert greater explanatory power than physical or chemical properties alone, indicating that microorganisms serve as the core link between cover measures and nitrogen cycling responses. Our findings have implications for preventing nitrogen loss, optimizing the soil nitrogen supply, and advancing our understanding of the nitrogen cycle in citrus orchards in red soil hilly regions. Materials and Methods Study Area The experiments were conducted in citrus orchard (25°68′51.41″N and 119°33′57.02″E), which was located in Fuzhou city, Fujian Province, China. Fuzhou experiences a typical subtropical monsoon climate, with an annual average temperature ranging from 19 to 21°C. Seasonal temperature variations are significant, with summer highs reaching 35°C and winter lows reaching approximately 5°C. The annual precipitation is approximately 1,350 mm, which is predominantly concentrated in the spring and early summer (March to June). The terrain in this region is characterized by low mountains and hilly landscapes, and the experimental area primarily consists of terraced fields at relatively low elevations. According to the World Reference Base for Soil Resources (WRB, 2015), the predominant soil type in this region is red soil. Fuzhou’s agricultural land mostly comprises sloped orchards, and the dominant fruit trees are citrus, longan and lychee. The basic properties of the soil were showed in Table 1 and Table 2 , and they were already thoroughly analyzed and discussed by Li et al. (2024). The study was conducted in a citrus orchard, which began planting in 2017. The experimental site was situated on a southeast-facing slope with an average gradient of approximately 15° and an elevation of 120 m. The fertilizer management in the orchard followed an annual standardized practice. This regimen consisted of a basal fertilization applied in winter (December), using a combination of NPK compound fertilizer (N + P₂O₅+K₂O = 15%) and organic fertilizer containing 50% organic matter at a rate of 1200 kg·hm⁻². In the subsequent spring and summer (March, May, and July), a total of 960 kg·hm⁻² of mixed fertilizer was applied in three split doses to support tree growth. The orchard was designed in a terraced layout to reduce runoff, enhance soil moisture conservation, and ensure a more uniform nutrient distribution. The planting density was approximately 925 trees per hectare, with a tree spacing of 3 m and row spacing of 3.5 m. Table 1 Basic chemical properties of orchard soil. Depth /(cm) Treatment SOC /(g·kg − 1 ) TN /(g·kg − 1 ) TP /(g·kg − 1 ) TK /(g·kg − 1 ) pH C/N N/P GC 26.62 ± 3.87Aa 1.64 ± 0.037Aa 1.75 ± 0.60Aa 27.98 ± 0.98Aa 5.31 ± 0.024Ba 16.28 ± 2.41Aa 1 ± 0.31Aa 0–10 PM 16.88 ± 1.43Ba 1.33 ± 0.10Ba 1.69 ± 0.07Ba 21.54 ± 0.50Ba 5.49 ± 0.037Ab 12.73 ± 0.42Bb 0.79 ± 0.091Ba CK 15.69 ± 0.19Ba 1.22 ± 0.038Ba 1.47 ± 0.097Ca 17.23 ± 1.23Ca 5.19 ± 0.041Cc 12.91 ± 0.54Ba 0.83 ± 0.030Ba GC 23.39 ± 0.43Ab 1.36 ± 0.17Ab 1.54 ± 0.20Ab 23.6 ± 0.57Ab 4.98 ± 0.004Cb 17.24 ± 0.51Aa 0.89 ± 0.11Ab 10–20 PM 15.84 ± 1.11Bb 1.06 ± 0.018Bb 1.49 ± 0.13Bb 18.6 ± 0.57Bb 5.38 ± 0.002Bb 15.01 ± 1.23Ba 0.71 ± 0.056Ba CK 13.15 ± 0.43Cb 0.96 ± 0.023Cb 1.26 ± 0.002Cb 14.23 ± 0.62Cb 5.48 ± 0.002Aa 13.70 ± 0.64Ca 0.76 ± 0.032Bb GC 19.96 ± 3.77Ac 1.09 ± 0.015Ac 1.22 ± 0.092Ac 19.78 ± 1.11Ac 4.94 ± 0.02Cb 18.32 ± 3.27Aa 0.89 ± 0.054Ab 20–40 PM 10.35 ± 1.35Bc 0.79 ± 0.014Bc 1.19 ± 0.072Bc 12.96 ± 0.17Bc 5.07 ± 0.020Bd 13.12 ± 1.49Bb 0.67 ± 0.029Bb CK 9.44 ± 0.66Cc 0.75 ± 0.016Cc 1.05 ± 0.011Cc 11.38 ± 0.39Cc 5.26 ± 0.021Ab 12.54 ± 0.85Ba 0.71 ± 0.0083Bb GC 10.47 ± 6.67Ad 0.89 ± 0.14Ad 1.02 ± 0.018Bd 16.47 ± 1.13Ad 5 ± 0.030Cc 11.72 ± 7.29Ab 0.87 ± 0.022Ab 40–60 PM 7.91 ± 2.33Bd 0.67 ± 0.011Bd 1.07 ± 0.010Ad 12.96 ± 0.98Bd 5.61 ± 0.010Aa 11.78 ± 3.65Ac 0.63 ± 0.064Bb CK 7.3 ± 0.62Cd 0.58 ± 0.013Cd 0.88 ± 0.014Cd 10.76 ± 1.15Cd 5.47 ± 0.020Ba 12.57 ± 1.33Aa 0.66 ± 0.02Bb Note: Data are presented as mean value ± standard deviation. SOC, TN, TP, TK, pH, C/N and N/P represent soil organic carbon, total nitrogen, total phosphorus, total potassium, ratio of SOC and TN and ratio of TN and TP. CK means that the orchard adopts clean tillage, GC means that the orchard adopts grass cover and PM means that the orchard adopts plastic mulch. Capital letters indicate significant differences among treatments within the same soil layer (P < 0.05), small letters indicate significant differences among soil layers within the same treatment (P < 0.05). Table 2 Basic physical properties of orchard soil. Depth (cm) Treatment BD (g·cm − ³) Soil texture (%) Sand (0.05-2 mm) Silt (0.05 − 0.002 mm) Clay (< 0.002 mm) GC 1.05 ± 0.022Cc 21.15 ± 0.87Aa 67.75 ± 0.19Ab 11.1 ± 0.87Ab 0–10 PM 1.12 ± 0.025Bc 23.99 ± 0.86Ba 65.07 ± 0.61Bc 10.95 ± 0.28Ab CK 1.17 ± 0.047Ac 27.51 ± 0.59Ca 63.34 ± 0.71Cd 9.16 ± 0.74Bb GC 1.09 ± 0.003Cc 20.09 ± 1.02Aa 68.71 ± 1.09Aab 11.21 ± 0.07Ab 10–20 PM 1.18 ± 0.004Bb 22.87 ± 0.20Bab 66.04 ± 0.57Bbc 11.09 ± 0.37Aab CK 1.24 ± 0.003Ab 25.79 ± 0.68Cb 64.98 ± 0.60Bc 9.23 ± 0.19Bb GC 1.18 ± 0.010Cb 19.59 ± 0.72Aa 69.04 ± 0.57Aab 11.37 ± 0.19Ab 20–40 PM 1.22 ± 0.020Bb 21.77 ± 0.58Bb 67.02 ± 0.47Bb 11.21 ± 0.13Aab CK 1.29 ± 0.030Ab 24.44 ± 0.4Cc 66.07 ± 0.21Bb 9.49 ± 0.19Bb GC 1.40 ± 0.040Aa 16.59 ± 0.86Ab 70.47 ± 1.07Aa 12.94 ± 0.21Aa 40–60 PM 1.41 ± 0.014Aa 18.99 ± 1.39Bc 68.96 ± 0.80ABa 12.04 ± 0.61Ba CK 1.42 ± 0.010Aa 21.97 ± 0.11Cd 67.26 ± 0.02Ba 10.77 ± 0.09Ca Note: Data are presented as mean value ± standard deviation. BD and soil texture represent soil bulk density and the relative proportions of sand, silt, and clay, respectively. CK means that the orchard adopts clean tillage, GC means that the orchard adopts grass cover and PM means that the orchard adopts plastic mulch. Capital letters indicate significant differences among treatments within the same soil layer (P < 0.05), small letters indicate significant differences among soil layers within the same treatment (P < 0.05). Experimental Design This study investigated three common soil management practices in orchards of the southern red soil hilly areas: clean tillage (CK; Fig. 1 b), grass cover (GC; Fig. 1 c), and plastic mulching (PM; Fig. 1 d). A randomized block design was adopted to minimize the impact of terrain variation. The experiment was conducted on a hillside with uniform slope aspect, gradient, and elevation, where three blocks were established, each serving as one replication. Within each block, the three treatments were randomly assigned to individual experimental blocks. This design resulted in a total of 9 plots (3 treatments × 3 blocks), with each plot covering 100 m 2 and containing 15 fruit trees. In the CK treatment, manual weeding was performed monthly to remove weeds while minimizing soil disturbance, thereby maintaining bare ground. In the GC treatment, natural herbaceous vegetation was allowed to grow within the canopy projection area, with Portulaca oleracea L. (purslane) and Nepeta cataria L. (catnip) being the dominant species, accounting for approximately 50% and 20% of the plant coverage, respectively, while maintaining a target ground cover ≥ 70%. In the PM treatment, the area extending from the tree trunk base to the drip line was cleared and then fully covered with black polyethylene film, ensuring effective moisture retention and weed suppression around the root zone, and the covered area extended from the trunk base to 30 cm beyond the drip line. The orchard had been established for seven years prior to the experiment and was managed under consistent agronomic conditions. Soil Sampling and Measurement Methods Orchard soil samples were collected in March 2023 prior to the spring fertilization event, thus characterizing the soil's baseline state before nutrient amendment. Analyses of microbial community structure and physicochemical properties therefore reflect a stable condition unaffected by the immediate perturbations of fertilizer application. Three representative citrus trees were randomly selected within each treatment plot. At each tree, sampling points were established approximately 35 cm inside and outside the canopy drip line. After surface litter was removed, a soil auger was used to collect soil samples from four depths: 0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm. Samples from the same depth for each tree were combined to form a composite sample. Each treatment had three replicates, resulting in a total of 36 composite samples. Following transport to the laboratory, the soil samples were cleared of stones and visible plant or animal debris. A portion of the fresh samples was stored at 4°C to determine soil SON, NH 4 + -N, and NO 3 − -N. The remaining samples were air-dried and passed through 2 mm and 0.15 mm sieves for analysis of soil physicochemical properties. Total nitrogen and organic carbon were measured using an elemental analyzer (Elementar VARIO EL III) (Chen et al., 2005 ). Soil pH was measured in a 1:2.5 soil-to-water suspension. Bulk density (BD) was determined using the core method, with oven-dried weights used for calculations. Total phosphorus (TP) was measured using the NaOH fusion–molybdenum antimony blue colorimetric method. Total potassium (TK) was analyzed using the NaOH fusion–flame photometry method. Total soluble nitrogen (TSN) was determined by potassium persulfate oxidation followed by ultraviolet spectrophotometry (Shimadzu TOC-L CPH, Japan). Ammonium-N (NH₄⁺-N) and nitrate-N (NO₃⁻-N) were quantified in the same extracts with a continuous flow injection analyzer (Flowsys, Systea, Italy). Soluble organic nitrogen (SON) in each sample was obtained by subtracting the summed concentrations of NH₄⁺-N and NO₃⁻-N from the corresponding TSN value for that sample. All the results were converted to a dry-soil basis using the extract volume and oven-dried soil mass. Quality assurance measures included reagent blanks, calibration verification standards, and duplicate analyses, with relative standard deviations maintained below 5%. DNA extraction and metagenomic sequencing All amplicon sequencing was performed by Personal Biotechnology Co., Ltd. (Shanghai, China). Total genomic DNA was extracted from soil samples using the PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA, USA), with three technical replicates per sample. The V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified via PCR with the primers F (5′-ACTCCTACGGGAGGCAGCA-3′) and R (5′-GGACTACHVGGGTWTCTAAT-3′) (Wang et al., 2024 ). The PCR was performed in a 25 µL reaction system containing 0.25 µL of Q5 High-Fidelity DNA Polymerase, 5 µL of template DNA, 1 µL each of the forward and reverse primers, and 8.75 µL of deionized water. The thermal cycling protocol began with a preheating step at 98°C for 30 s, followed by amplification cycles (denaturation at 98°C for 10 s, annealing at 55°C for 30 s, and extension at 72°C for 45 s), and a final extension at 72°C for 5 min. The amplification products were checked by 2% agarose gel electrophoresis, and the target bands were purified using a gel extraction kit. The resulting amplicon libraries, constructed in triplicate, were then subjected to paired-end sequencing (2×300 bp) on an Illumina MiSeq platform. Raw sequencing data were processed within the QIIME2 platform. Using the DADA2 plugin, sequences underwent quality filtering, denoising, dereplication, and chimera removal to generate amplicon sequence variants (ASVs), which represent exact biological sequences. These ASVs were then taxonomically classified using the classify-sklearn algorithm against the Greengenes database (Release 13.8). Relative abundances at phylum to genus levels were calculated as the percentage of sequences assigned to each taxon relative to the total high-quality sequences per sample. All microbial information was analysed on the Genescloud platform ( www.genescloud.cn ). To identify nitrogen-cycling microorganisms, we traced the contribution of individual ASVs to the abundance of key nitrogen-cycling genes predicted by PICRUSt2, which generated a profile of predicted gene abundances based on KEGG Orthology (KO). To identify nitrogen-cycling microorganisms, we first extracted the predicted abundances of KOs associated with key nitrogen cycle processes (e.g., nitrogen fixation, nitrification, and denitrification) from the KEGG pathway database. We then identified the individual ASVs contributing to these nitrogen-cycling KOs. These ASVs were then taxonomically traced to their corresponding phylum and genus-level classifications. Only microbial taxa with a mean relative abundance > 1% across all samples were retained for subsequent analysis. Statistical Analysis The data were processed using Excel 2010. Statistical analyses were performed using R version 4.3.1, and graphs were constructed using Origin 2022. The relative abundance of nitrogen-related microbial communities was determined through Illumina high-throughput sequencing, which generated microbial community structure data for the soil samples. One-way analysis of variance (ANOVA) was applied to test the effects of the different treatments, and the threshold for statistical significance was P < 0.05. Tukey’s HSD test was performed at P = 0.05 for multiple comparisons between treatments. Pearson correlation analysis was conducted to assess the relationships between the relative abundance of the microbial community and soil physicochemical properties. Redundancy analysis (RDA) was used to examine the relationships between soil physicochemical properties and microbial community structure. Structural equation modeling (SEM) was performed using the R package ‘lavaan’ to evaluate the relationships between soil physicochemical properties, microbial communities, and TSN components. Data analysis was performed, and plots were constructed using the R packages “vegan” and “ggplot2” to ensure the accuracy and clarity of the results. Results Soil TSN Components Across the 0–60 cm soil profile, the NH₄⁺-N concentrations under both the GC and PM treatments decreased gradually with increasing depth. In contrast, the CK treatment exhibited a distinct trend, with NH₄⁺-N levels increasing initially and reaching a peak at 10–20 cm before decreasing at greater depths (Fig. 2 a). In the 0–10 cm layer, the NH₄⁺-N concentrations under GC and PM were 20.2% and 3.3% higher, respectively, than those observed in CK, although the differences were not statistically significant. At the 10–20 cm depth, compared with the CK treatment, GC resulted in a marked increase of 25.5%, whereas PM resulted in a slight decrease of 0.3%. In the deeper layer (20–60 cm), the average NH₄⁺-N content decreased to 3.74 mg·kg⁻¹ under GC and 3.98 mg·kg⁻¹ under PM, corresponding to decreases of 15.2% and 9.8%, respectively, compared with those in CK. These results collectively suggest that both GC and PM promote surface accumulation of NH₄⁺-N, likely because of enhanced nitrogen retention and microbial ammonification near the soil surface, while simultaneously limiting its downward movement or transformation in deeper layers. The NO 3 − -N content tended to decrease with increasing soil depth across all the treatments, with the most pronounced reduction occurring in the 20–40 cm layer under the GC and PM treatments. In the 0–20 cm layer, NO 3 − -N levels in GC and PM were consistently higher than those in the CK (Fig. 2 b). Specifically, the average NO 3 − -N concentration in this layer reached 42.43 mg·kg⁻¹ in GC and 39.96 mg·kg⁻¹ in PM, representing increases of 18.3% and 11.5%, respectively, compared with the CK value of 35.86 mg·kg⁻¹. In contrast, in the 20–60 cm layer, the average NO 3 − -N concentration decreased to 19.90 mg·kg⁻¹ in GC and 26.23 mg·kg⁻¹ in PM, corresponding to reductions of 36.9% and 16.7%, respectively, relative to those in CK. These findings indicate that both the GC and PM treatments increased NO 3 − -N accumulation in the surface layer (0–20 cm) but reduced its presence in deeper soil, particularly in the 20–40 cm layer, where the loss was most substantial. The SON content in both the GC and PM treatments clearly decreased with increasing soil depth, with the most substantial reduction occurring in the 20–40 cm layer. In contrast, the CK treatment showed a different pattern, with SON levels increasing initially and peaking at 10–20 cm before declining at greater depths. Across all soil layers, the SON concentrations were significantly higher under GC and PM compared to CK (Fig. 2 c). Averaged over the 0–60 cm profile, the SON content reached 24.76 mg·kg − 1 under GC and 23.85 mg·kg − 1 under PM, representing increases of 24.3% and 19.7%, respectively, relative to that of CK. In the 0–20 cm layer, compared with CK, both cover treatments significantly increased the SON concentration, with the concentrations in GC and PM increasing by 56.7% and 38.9%, respectively. Conversely, in the 20–60 cm layer, the SON content under GC and PM was significantly lower than that in CK, with reductions of 45.3% and 13.3%, respectively. These findings collectively indicate that while GC and PM promote SON accumulation in surface soils, they concurrently reduce its retention in deeper layers. The TSN content in the 0–20 cm layer significantly increased under both the GC and PM treatments compared with that in CK, whereas the opposite trend was observed in the 20–60 cm layer (Fig. 2 d). In the 0–20 cm layer, the average TSN concentrations reached 88.75 mg·kg − 1 under GC and 80.34 mg·kg − 1 under PM, representing increases of 33.80% and 21.13%, respectively, relative to those under CK. In the deeper layer (20–60 cm), the TSN concentration decreased to 37.70 mg·kg − 1 in GC and 41.11 mg·kg − 1 in PM, corresponding to reductions of 29.13% and 22.73%, respectively, compared with those in CK. Notably, in the surface 0–10 cm layer, the TSN content was 68.70% higher under GC and 49.91% higher under PM than under CK. Across the 0–60 cm profile, the TSN consistently decreased with increasing soil depth, with the most pronounced declines observed in the 20–40 cm layer under GC and PM. These results indicate that cover measures promote TSN accumulation in the upper soil layers while reducing its concentration in deeper horizons. Overall, the contents of all nitrogen forms were significantly greater in the 0–20 cm soil layer in the GC and PM treatments than in the CK treatment, and this pattern was especially pronounced for SON and TSN; the nitrogen content was lower in the 20–60 cm layer in the GC and PM treatments than in the CK treatment. This suggests that GC and PM promote the accumulation of nitrogen in surface soil but may inhibit its migration to deeper layers. Changes in Soil Nitrogen-Related Microbial Communities On the basis of the methods of Jiao et al. ( 2019 ) and Wang et al. ( 2024 ), the dominant nitrogen-related microbial communities (with relative abundances > 1%) at the phylum and genus levels were identified for all the treatments (Fig. 3 ). The dominant phyla were Proteobacteria, Actinobacteriota, Verrucomicrobiota, and Acidobacteriota. In the GC and PM treatments, the relative abundance of Proteobacteria was greatest, followed by that of Actinobacteriota, Acidobacteriota, and Verrucomicrobiota, and this pattern was consistent across all the treatments. Compared with that in the CK treatment, the relative abundance of Proteobacteria increased by 154.3% in the GC treatment and by 67.5% in the PM treatment. The relative abundance of Actinobacteriota was 4.3% greater in the GC treatment and 8.2% greater in the PM treatment than in the CK. Both the GC and the PM treatments led to notable increases in the relative abundance of key nitrogen-associated microbial taxa. Specifically, the abundance of Acidobacteriota increased by 30.2% under GC and 49.8% under PM compared with that under CK, whereas the abundance of Verrucomicrobiota slightly increased by 2.1% under GC but substantially increased by 61.8% under PM. At the genus level, Burkholderia–Caballeronia–Paraburkholderia , Massilia , and Rhodanobacter were identified as the dominant taxa across all the treatments, all of which belong to Proteobacteria (Fig. 4 ). The relative abundance of Burkholderia–Caballeronia–Paraburkholderia increased by 49.93% in GC and by 16.77% in PM, whereas that of Massilia increased even more strongly, with increases of 175.55% and 40.53% in GC and PM, respectively. In contrast, compared with that in the CK treatment, the abundance of Rhodanobacter decreased by 21.98% in the GC treatment but increased by 12.94% in the PM treatment. Along the 0–60 cm soil profile, both the GC and the PM treatments increased the abundance of nitrogen-related microbes at both the phylum and genus levels, particularly in the surface layers, with a declining trend observed with increasing soil depth. In comparison, the CK treatment exhibited a nonlinear depth-dependent pattern, where the microbial abundance first increased but then decreased, peaking in the 10–20 cm layer. Collectively, these results indicate that both GC and PM effectively promoted nitrogen-related microbial communities throughout the soil profile, although the response was most pronounced in the topsoil. In contrast, the vertical distribution of the soil in the CK treatment was distinct and was likely influenced by limited organic input and microbial activity. Correlations between Soil Physicochemical Properties and Nitrogen-Related Microbial Communities Significant correlations were observed between microbial communities and soil physicochemical properties (Fig. 5 ). TN and SOC were highly significantly positively correlated with Proteobacteria, Actinobacteriota, and Acidobacteriota. Soil pH was significantly negatively correlated with Verrucomicrobiota. BD was negatively correlated with Proteobacteria and Acidobacteriota. Total potassium (TK) was not significantly correlated with most microbial communities. Nutrient indicators, such as total phosphorus (TP), C/N, N/P, and C/P, were positively correlated with Proteobacteria and Acidobacteriota. NH 4 + -N, NO 3 − -N, and SON were highly significantly positively correlated with Proteobacteria and Acidobacteriota. The clay content was highly significantly positively correlated with the abundance of Proteobacteria and Actinobacteriota. Silt and sand were highly significantly negatively correlated with both Proteobacteria and Actinobacteriota. These results indicate that soil physicochemical properties, particularly TN, SOC, and nutrient ratios, play crucial roles in shaping nitrogen-related microbial communities. Soil texture also significantly influences the distribution of these microbial groups and highlights the complex interactions between soil properties and microbial ecology. Redundancy Analysis of Soil Physicochemical Properties and TSN Components To determine the effects of soil environmental factors and microbial communities on different TSN components, RDA was conducted at the phylum level, with a focus on the relationships between soil bacterial community structure and environmental factors (Fig. 6 ). RDA1 and RDA2 explained 61.34% and 18.9% of the total variation, respectively. The relative abundances of Proteobacteria and Acidobacteriota were significantly positively correlated with NH₄⁺-N, NO₃⁻-N, and SON. The relative abundances of Actinobacteriota and Verrucomicrobiota were significantly positively correlated with nutrient ratios, such as C/N, C/P, and N/P. BD and the sand content were significantly negatively correlated with microbial communities and all TSN components. The TN, TP, SOC, silt, and clay contents were strongly positively correlated with all TSN components. Soil pH was moderately positively correlated with TSN components. These findings suggest that Proteobacteria and Acidobacteriota play dominant roles in influencing TSN components, particularly NH₄⁺-N, NO₃⁻-N, and SON. Soil texture, nutrient content, and environmental conditions, such as pH and BD, also significantly shape the dynamics of TSN components and microbial community structure. Structural equation modeling (SEM) revealed the path coefficients of soil physical properties, chemical properties, and microbial communities to different components of TSN (Fig. 7 ). The direct path coefficients of soil physical properties, chemical properties, and microbial communities to TSN were 0.33, 0.49, and 0.68, respectively. The contributions of soil physical properties, chemical properties, and microbial communities to NH 4 + -N were 40.5%, 43.04%, and 16.46%, respectively; the contributions to NO 3 − -N were 65%, 4.7%, and 30.3%, respectively; and the contributions to SON were 47.47%, 36.71%, and 15.82%, respectively. Analysis of the path coefficients for individual soil indicators indicated that TN was the primary factor promoting the NH 4 + -N and SON contents, accounting for 11.70% and 9.71% of their effects, respectively. Proteobacteria was the dominant factor promoting increases in the NO 3 − -N content, accounting for 12.93% of its effect. These findings underscore the significant role of soil chemical properties, particularly TN, in enhancing the NH 4 + -N and SON contents, and that microbial communities, specifically Proteobacteria, play a crucial role in regulating NO 3 − -N dynamics. The interplay between soil properties and microbial communities is critical in shaping the distribution and transformation of TSN components. Discussion Effects of Cover Measures on Soil TSN in Orchards Both GC and PM significantly increased the TSN content in the 0–20 cm soil layer of citrus orchards in the red soil hilly region and reduced it in the 20–60 cm soil layer, and the effect of GC was more pronounced. The TSN content decreased with soil depth across all treatments, primarily because of two factors: (1) the lower organic matter content in deeper soils, which reduced the nitrogen pool, and (2) anaerobic conditions in deeper layers, which promoted denitrification and reduced the TSN content in the soil solution (Jahangir et al., 2012 ). In the GC treatment, the dominant herbaceous plants ( Portulaca oleracea L. and Nepeta cataria L.) contributed to the nitrogen pool through root exudates, decayed root residues, and aboveground litter, which provided abundant carbon and nitrogen sources for microbial activity. Microbial decomposition of organic matter includes ammonification, wherein extracellular enzymes (e.g., protease and urease) breakdown complex organic nitrogen compounds (e.g., proteins, nucleic acids, and urea) into simpler, highly SON compounds (e.g., amino acids and peptides) (Jones et al., 2004 ). These compounds accumulate in the soil solution, which increases the SON content. Small organic nitrogen molecules subsequently undergo deamination to form ammonia (NH₃), which reacts with water to form ammonium ions (NH₄⁺), thereby increasing the NH₄⁺-N content. GC treatment increases organic matter input, which provides substrates that stimulate microbial proliferation and enzyme production, thereby increasing the efficiency of ammonification (Zhang et al., 2021 ). As NH₄⁺-N levels increase, nitrifying bacteria within Proteobacteria oxidize NH₄⁺-N to nitrite (NO₂⁻) and subsequently to nitrate (NO₃⁻). Additionally, P. oleracea and N. cataria possess shallow root systems concentrated in the 0–20 cm layer, with deeper taproots reaching up to 40 cm, which improves soil structure and enhances water retention in surface layers (Song et al., 2008 ). This reduces nitrogen leaching into deeper layers. In the PM treatment, the use of black plastic mulch reduced soil moisture evaporation, which maintained high and stable soil humidity and temperature. This microenvironment favors microbial growth and enzymatic activity, which promotes organic matter decomposition and ammonification and thus increases NH₄⁺-N production (Liu et al., 2019 ). However, long-term mulching may create hypoxic conditions that inhibit nitrifying bacteria, which slows the conversion of NH₄⁺-N to NO₃⁻-N and reduces NO₃⁻-N production (Gao et al., 2023 ). The moist environment under mulch may also increase the activity of denitrifying bacteria, which convert NO₃⁻-N to gaseous nitrogen (e.g., N₂ and N₂O), further decreasing the NO₃⁻-N content. Compared with CK, PM creates stable environmental conditions that promote the microbial decomposition of organic matter and increase SON release (Liu et al., 2017 ). Mulching also prevents nutrient loss because of rainwater runoff and reduces water infiltration into deeper soil layers. In the CK treatment, limited organic matter input restricted microbial activity, which reduced the ammonification and nitrification rates and significantly decreased the NH₄⁺-N and NO₃⁻-N contents (Burger and Jackson, 2003 ). Additionally, the exposed soil in the CK treatment was more prone to nitrogen leaching during rainfall, resulting in nitrogen loss. Overall, the GC and PM treatments effectively increased the TSN content in the surface soil while limiting nitrogen migration to deeper layers, thereby increasing nutrient retention and soil fertility. Effects of Cover Measures on Soil Nitrogen-Related Microbial Communities in Orchards Both GC and PM significantly increased the relative abundances of Proteobacteria, Actinobacteriota, and Acidobacteriota in the 0–60 cm soil layer of citrus orchards in the red soil hilly region. During the growth of Portulaca oleracea and Nepeta cataria under GC, easily decomposable carbon sources such as root exudates, low-molecular-weight organic acids, and soluble sugars are released into the soil (Sokolova, 2020 ). These labile compounds serve as rich sources of energy and nutrients for Proteobacteria, which are predominantly copiotrophic and capable of rapidly assimilating such substrates to support their proliferation (Dai et al., 2018 ). The enriched organic matter also nourished nitrogen-transforming functional bacteria, such as Burkholderia-Caballeronia-Paraburkholderia , Massilia , and Rhodanobacter , thereby promoting their proliferation (Fig. 4 ). Actinobacteriota are known for their ability to decompose complex organic materials, such as cellulose, lignin, and chitin (Xu et al., 2015 ). They are well adapted to the acidic soils of southern China, where increased plant residues provide abundant substrates for functional genera, such as Actinospoica and Phenylobacterium , which support their growth and activity. Conversely, Acidobacteriota and Verrucomicrobiota are predominantly oligotrophic microorganisms adept at utilizing recalcitrant organic matter (Nie et al., 2018 ). The plant residues in the GC treatment, which were rich in cellulose, lignin, and pectin, served as viable substrates for Candidatus Koribacter and Granulicella , which are functional genera within Acidobacteriota. PM significantly increased the relative abundances of Actinobacteriota and Acidobacteriota (Fig. 3 ) by maintaining stable soil humidity and temperature, which is conducive to the metabolic activity of Actinobacteriota (Zhao et al., 2017 ). Bhatti et al. ( 2017 ) reported that stable environmental conditions facilitate the formation of actinobacterial hyphal networks by increasing their ability to decompose soil organic matter. Plastic mulching promotes the accumulation of recalcitrant organic compounds, such as fulvic and humic acids, which can be utilized by Acidobacteriota as alternative carbon and energy sources, thereby facilitating their proliferation (Liu et al., 2017 ). Moreover, the mulching layer restricts gas exchange between the soil and atmosphere, resulting in low-oxygen microenvironments. These hypoxic conditions suppress the activity of aerobic microorganisms and shift the microbial community composition in favor of Acidobacteriota and Actinobacteriota, which are better adapted to oligotrophic and microaerophilic environments (Li et al., 2021 ). In contrast, CK disrupted microbial habitats through frequent soil tillage, reducing the abundance and diversity of microbial communities. In conclusion, the GC and PM treatments significantly altered the soil microbial community structure, especially by increasing the relative abundances of Proteobacteria, Actinobacteriota and Acidobacteriota. These findings highlight the potential for these management practices to improve soil microbial health and nitrogen-related processes in orchards. Relationships Between Nitrogen-Related Microbial Communities and TSN Components In this study, there was a significant positive correlation between the abundance of nitrogen-related microbial communities and soil TSN content in the red soil hilly region. Pearson correlation analysis revealed that the relative abundances of Proteobacteria and Acidobacteriota were strongly positively correlated with NH₄⁺-N and NO₃⁻-N. Proteobacteria are involved in both ammonification, which produces NH₄⁺-N through the microbial decomposition of organic nitrogen compounds, and nitrification, which generates NO₃⁻-N via the oxidation of ammonium. These microbes thus play a critical and multifaceted role in nitrogen transformation processes within the soil, especially under conditions that favor active microbial metabolism. Acidobacteriota decompose recalcitrant and complex organic matter, releasing low-molecular-weight compounds such as amino acids, ammonia, and other nitrogen-containing substances as byproducts of microbial catabolism, thereby contributing to the pool of readily available nitrogen in the soil. These byproducts serve as substrates for other nitrogen-cycling microbes, which collectively increase organic matter decomposition and nitrogen cycling (Liu et al., 2020 ). RDA further demonstrated that Proteobacteria and Acidobacteriota were most positively correlated with NH₄⁺-N, NO₃⁻-N, and SON, highlighting their significant roles in shaping soil TSN composition. In line with these findings, studies in other ecosystems have explored the variability of microbe–nitrogen associations. For example, Yang et al. ( 2021 ) reported that Chloroflexi, Proteobacteria, and Bacteroidetes were most strongly positively correlated with TSN in agricultural systems. Similarly, Che et al. ( 2020 ) reported that root exudates and plant residues provide additional carbon and nitrogen sources in grassland ecosystems, stimulating the growth of nitrogen-related functional microbes and promoting nitrogen mineralization and nitrification, thereby increasing the TSN content. Actinobacteriota and Verrucomicrobiota were significantly positively correlated with nutrient ratios, such as C/N, C/P, and N/P. This suggests that they play a role in maintaining a balance in nutrient supply, indirectly affecting TSN components. Shen et al. ( 2019 ) observed that The Actinobacteriota accelerated organic matter decomposition and nutrient release, altering soil stoichiometry. Similarly, Shen et al. ( 2017 ) reported that Verrucomicrobiota utilize diverse carbon sources to facilitate nitrogen and phosphorus cycling in montane ecosystems. The structural equation model indicated that soil physical properties, soil chemical properties, and microbial communities had direct path coefficients of 0.33, 0.49, and 0.68, respectively, to TSN, with the highest coefficient for microbial communities. These findings indicate that the activity and abundance of nitrogen-related microbial communities are the most critical factors influencing TSN dynamics. In conclusion, by regulating nitrogen-related microbial communities and increasing soil TSN content, grass cover (GC) was the most effective measure for optimizing nitrogen cycling in orchard soils. These findings highlight the potential of GC as a sustainable management practice for orchard ecosystems. Conclusion In this study, TSN components and nitrogen-related microbial communities were investigated under different cover measures (GC, PM, and CK) in a citrus orchard. The findings revealed that both GC and PM significantly increased the content of TSN components in the soil, and the most pronounced effects were observed in the 0–20 cm surface soil layer. GC was superior to PM. In comparison, the TSN increase under GC and PM was dominated by higher SON contents in the 0–20 cm layer, with NH₄⁺-N showing modest surface increases and NO₃⁻-N decreasing in the 20–60 cm layer relative to those in the CK. Additionally, GC and PM increased the abundance of nitrogen-related microbial taxa, particularly Proteobacteria, Actinobacteriota, and Acidobacteriota. RDA indicated that Proteobacteria and Acidobacteriota were most strongly correlated with TSN components. SEM demonstrated that soil physical, chemical, and biological properties directly influenced the TSN components. Soil TN was identified as the primary factor influencing NH₄⁺-N and SON, and Proteobacteria was the most critical factor affecting NO₃⁻-N. These results provide new insights into the management of orchards in red soil hilly regions and indicate that GC and PM are effective measures for improving soil nitrogen availability and nitrogen use efficiency in orchards; thus, GC was the most effective approach. Future research should explore the long-term impacts of different cover measures on soil nitrogen cycling and microbial functionality, especially under varying climatic and soil conditions. Declarations Acknowledgments This study was supported financially by Water Conservancy Science and Technology Project of Fujian Province, Grant Number: MSK202429 and KJG21009A, and The Significant Science and Technology Projects of the Ministry of Water Resources, Grant Number: SKS-2022073. 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Effect of thermo-tolerant actinomycetes inoculation on cellulose degradation and the formation of humic substances during composting. Waste Management. 68, 64-73. https://doi.org/10.1016/j.wasman.2017.06.022. Zhu, W.X., Carreiro, M.M., 2004. Variations of soluble organic nitrogen and microbial nitrogen in deciduous forest soils along an urban–rural gradient. Soil Biology and Biochemistry. 36(2), 279-288. https://doi.org/10.1016/j.soilbio.2003.09.011. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7959888","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539705579,"identity":"bc2138e3-bd72-440b-b9c4-7f7f6d92a606","order_by":0,"name":"Zuopin Zhuo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zuopin","middleName":"","lastName":"Zhuo","suffix":""},{"id":539705580,"identity":"35e0487f-3dac-4e6d-b618-3235419ebc98","order_by":1,"name":"Heming 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17:50:04","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179663,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/3b7280aa3677018ce0942c09.html"},{"id":95860530,"identity":"38a2c329-5340-49da-b555-bf66f93fc5ef","added_by":"auto","created_at":"2025-11-13 17:50:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":723284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of different mulching measures in the orchard\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: b. clean tillage (CK) in the study area, c. grass cover (GC) in the study area, d. plastic mulch (PM) in the study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/98dc334a11dbe77b4385245e.png"},{"id":96241663,"identity":"be170fb9-04b0-4ce0-bb4f-401534462da5","added_by":"auto","created_at":"2025-11-19 07:11:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe content of each TSN fraction in orchard soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N, SON and TSN represent ammonium nitrogen, nitrate nitrogen, soluble organic nitrogen and total soluble nitrogen. CK means that the orchard adopts clean tillage, GC means that the orchard adopts grass cover and PM means that the orchard adopts plastic mulch. Capital letters indicate significant differences among treatments within the same soil layer (P \u0026lt; 0.05), small letters indicate significant differences among soil layers within the same treatment (P \u0026lt; 0.05). a-d. Representing NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N content, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e-N content, SON content, and TSN content in the different cover measure and soil layer, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/e900945bf5a413ec17d4f77f.png"},{"id":95860531,"identity":"27210a22-eafe-40ef-b771-8fc11202aad4","added_by":"auto","created_at":"2025-11-13 17:50:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance of microbial communities in orchard soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Different color shows the relative abundance of microbial communities orchard at the phylum level. CK means that the orchard adopts clean tillage, GC means that the orchard adopts grass cover and PM means that the orchard adopts plastic mulch.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/560d37ba0243fc97440095e8.png"},{"id":96240754,"identity":"5034ed43-3180-4989-9f46-87ccb761b28c","added_by":"auto","created_at":"2025-11-19 07:09:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance of bacteria at the genus level identified in orchard soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Different color shows the relative abundance of microbial communities orchard at the genus level. BCP in denote means \u003cem\u003eBurkholderia-Caballeronia-Paraburkholderia\u003c/em\u003e. CK means that the orchard adopts clean tillage, GC means that the orchard adopts grass cover and PM means that the orchard adopts plastic mulch.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/3edfcfe28da0b7577cbee9bc.png"},{"id":96241796,"identity":"0283e15a-deb3-4fb5-b433-485820b09271","added_by":"auto","created_at":"2025-11-19 07:11:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":394522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of basic soil properties and microbial communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: * means significant differences among treatments (P \u0026lt; 0.05) and ** means highly significant differences among treatments (P \u0026lt; 0.01). SOC, soil organic carbon; TN, total nitrogen; BD, bulk density; TP, total phosphorus; TK, total potassium; TSN, total soluble nitrogen., C/N, ratio of SOC and TN, N/P, ratio of TN and TP, C/P, ratio of SOC and TP, SON, soluble organic nitrogen, SMN, inorganic nitrogen.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/f22f095535eed9c9e40cb8ca.png"},{"id":96241405,"identity":"ee179cb9-2f83-41ed-a7fd-2af43e4bd6c0","added_by":"auto","created_at":"2025-11-19 07:10:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":136140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRDA of soil bacterial community structure and environmental factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: SOC, soil organic carbon; TN, total nitrogen; BD, bulk density; TP, total phosphorus; TK, total potassium; C/N, ratio of SOC and TN, N/P, ratio of TN and TP, C/P, ratio of SOC and TP, SON, soluble organic nitrogen.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/cbee8fa8dea0dacd36550b6d.png"},{"id":96241870,"identity":"172304be-de8d-43f5-8ac7-03c1d767b14c","added_by":"auto","created_at":"2025-11-19 07:11:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":223485,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe structural equation model reveals the standardized path coefficients of soil physicochemical properties and biological characteristics to different TSN fractions under different cover treatments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The path coefficients from multiple regression are indicated by numbers next to unidirectional arrows (*P \u0026lt; 0.05, **P \u0026lt; 0.01); red and blue arrows represent positive and negative effects, respectively. CK means that the orchard adopts clean tillage, GC means that the orchard adopts grass cover and PM means that the orchard adopts plastic mulch. SOC, soil organic carbon; TN, total nitrogen; BD, bulk density; TP, total phosphorus; TK, total potassium; TSN, total soluble nitrogen., C/N, ratio of SOC and TN, N/P, ratio of TN and TP, C/P, ratio of SOC and TP, SON, soluble organic nitrogen, SMN, inorganic nitrogen.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/9e89f364fe0e5273366c85f4.png"},{"id":97671519,"identity":"0be6b1b8-7769-45aa-a16f-5d0f7c8d4525","added_by":"auto","created_at":"2025-12-08 09:32:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2835101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7959888/v1/69d0d6a7-08b5-4e01-a85b-8544bb82e186.pdf"}],"financialInterests":"","formattedTitle":"Effects of Mulching Practices on Soil Soluble Nitrogen in Orchards in Red Soil Hilly Areas","fulltext":[{"header":"Highlights","content":"\u003cp\u003eEffects of different cover measures on TSN in citrus orchard were investigated.\u003c/p\u003e\n\u003cp\u003eGrass cover was optimal for improving soil nitrogen availability.\u003c/p\u003e\n\u003cp\u003eCover measures enriched nitrogen-cycling microbes, especially Proteobacteria.\u003c/p\u003e\n\u003cp\u003eSON correlated with soil TN, while NO₃⁻-N was linked to Proteobacteria.\u003c/p\u003e\n\u003cp\u003eSEM linked soil properties and microbes to TSN under different cover measures.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eTotal soluble nitrogen (TSN), which serves as a critical source of nitrogen for crops and provides a sustained supply of nitrogen for plant growth (Murphy et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), includes soluble organic nitrogen (SON), ammonium nitrogen (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N), nitrate nitrogen (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N), and other inorganic nitrogen forms directly absorbed by plants (Chen and Xu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Lu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported that TSN accounts for approximately 8.33% of total nitrogen (TN) on average in waterlogged farmland soils of the Loess Plateau. Despite its low proportion of TN, TSN represents the most active fraction of soil nitrogen, acting as a \u0026ldquo;hub\u0026rdquo; in nitrogen transformation and transport (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The \u0026ldquo;active\u0026rdquo; nature of TSN is reflected in two aspects. First, TSN is directly available to plants, which shortens the nitrogen cycle on land and improves nitrogen use efficiency. Second, its mobility allows it to be transported via runoff or leaching, potentially leading to water pollution (Ma et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ye et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, understanding the dynamics of TSN and its interaction with environmental factors under different agricultural practices is key to optimizing nitrogen use efficiency.\u003c/p\u003e\u003cp\u003eSoil physical properties (Jiang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), chemical properties (Filep and R\u0026eacute;k\u0026aacute;si, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and microbial biomass (Schmidt et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) collectively influence the content and composition of TSN in soil. Bargmann et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported that optimal temperature and moisture conditions accelerate the release of TSN in the surface soils of Australian forests. Similarly, Xing et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that proteases in subtropical mountain ecosystems catalyze the conversion of organic nitrogen to TSN and that organic matter increases bacterial biomass and organic nitrogen content. Yang et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that bacteria decompose organic matter to directly or indirectly increase the TSN content in paddy soils. Zhu and Carreiro (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) reported that microbial enzymatic degradation of humic nitrogen increases TSN in deciduous forest soils. Furthermore, Zhang et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) revealed in a study on the ecological restoration of mining areas that microbes release soil nitrogen metabolism-related enzymes, which significantly increase the TSN content. Bacterial groups play a pivotal role in the dynamics of TSN and contribute to regional and ecosystem-specific differences in the factors driving TSN variation. The functional microbial communities involved in nitrogen transformation typically include taxa capable of nitrogen mineralization and nitrification, and their composition is influenced by climate, soil type, and vegetation. In northern arid saline\u0026ndash;alkaline soils, for instance, these functional communities are predominantly composed of Proteobacteria and Bacteroidetes (Nan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While a study on the loess plateau suggesting that the bacterial communities in soils are determined primarily by environmental characteristics, with Proteobacteria, Actinobacteria, Chlorobi, and Nitrospirae being the dominant phyla in N-fixing (Xu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Existing studies have delineated the dynamics of the soil total soluble nitrogen pool and highlighted the key synergistic regulatory role of soil properties. However, within specific agroecosystems, particularly in the subtropical red soil hilly region characterized by high temperature and abundant rainfall, the precise mechanisms by which functional microbial communities drive nitrogen transformations remain poorly understood. Clarifying the relative contributions of these driving factors and the underlying microbial pathways is essential for accurate prediction and effective management of nitrogen cycling in agricultural ecosystems.\u003c/p\u003e\u003cp\u003eIn tropical and subtropical lateritic hilly regions, orchards serve as critical agricultural foundations, sustaining farmer livelihoods and regional economies. The convenience of weed management has driven the widespread adoption of clear tillage (Xie et al., 2022; Chen et al., 2022); however, decades of evidence reveal its hidden costs: prolonged implementation triggers soil nutrient depletion, structural collapse, and erosion of microbial diversity, ultimately undermining land sustainability (Fu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., 2024). This degradation imperative has inspired interest in alternative ground management practices. Grass cover emerges as a regenerative solution, enhancing soil structure and microbial vitality across ecosystems (Xiao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), notably increasing nitrogen stocks and microbial efficiency in citrus orchards through alfalfa integration (Wu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, its nitrogen-enhancing capacity falters under nutrient-scarce conditions, where intensified plant‒crop competition constrains nitrogen uptake (Crusciol et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Plastic mulching, widely used in orchards as an effective weed control measure, simultaneously creates a dualistic soil microenvironment: it suppresses nitrification and nitrate formation through oxygen limitation (Qian et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) but simultaneously conserves moisture in arid zones, stimulating microbial proliferation (Liao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although both grass cover and plastic mulching have been proven to enhance soil fertility, they create fundamentally different microenvironments in the red soil hilly region. Grass cover improves soil aeration and organic matter input through plant root activity, whereas plastic mulching tends to induce oxygen-deficient conditions and relies mainly on physical isolation. Nevertheless, existing research has largely concentrated on assessing the effects of cover measures on soil nitrogen dynamics, whereas the mechanisms through which these two cover measures shape nitrogen cycling via the key linkage between microenvironmental conditions and functional microorganisms remain insufficiently understood, particularly in the red soil hilly region.\u003c/p\u003e\u003cp\u003eTo bridge these knowledge gaps, this study focused on citrus orchards in the red soil hilly region of southern China, aiming to clarify how different ground cover practices (grass cover, plastic mulching, and clean tillage) regulate soluble nitrogen pool dynamics (NH₄⁺-N, NO₃⁻-N, and SON) and reshape nitrogen-related microbial communities. We specifically examined the interactions among cover measures, total soluble nitrogen (TSN) composition, and microbial mechanisms of nitrogen transformation, with the following hypotheses: (1) Compared with plastic mulching and clean tillage, grass cover increases soluble nitrogen concentrations in surface soil by providing more abundant organic inputs and promoting better soil aeration; (2) compared with grass cover and clean tillage, plastic mulching suppresses strictly aerobic nitrifying microorganisms, leading to the relative accumulation of ammonium nitrogen and a decline in nitrate nitrogen proportion; and (3) variations in the structure and function of soil microbial communities exert greater explanatory power than physical or chemical properties alone, indicating that microorganisms serve as the core link between cover measures and nitrogen cycling responses. Our findings have implications for preventing nitrogen loss, optimizing the soil nitrogen supply, and advancing our understanding of the nitrogen cycle in citrus orchards in red soil hilly regions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area\u003c/h2\u003e\u003cp\u003eThe experiments were conducted in citrus orchard (25\u0026deg;68\u0026prime;51.41\u0026Prime;N and 119\u0026deg;33\u0026prime;57.02\u0026Prime;E), which was located in Fuzhou city, Fujian Province, China. Fuzhou experiences a typical subtropical monsoon climate, with an annual average temperature ranging from 19 to 21\u0026deg;C. Seasonal temperature variations are significant, with summer highs reaching 35\u0026deg;C and winter lows reaching approximately 5\u0026deg;C. The annual precipitation is approximately 1,350 mm, which is predominantly concentrated in the spring and early summer (March to June). The terrain in this region is characterized by low mountains and hilly landscapes, and the experimental area primarily consists of terraced fields at relatively low elevations. According to the World Reference Base for Soil Resources (WRB, 2015), the predominant soil type in this region is red soil. Fuzhou\u0026rsquo;s agricultural land mostly comprises sloped orchards, and the dominant fruit trees are citrus, longan and lychee. The basic properties of the soil were showed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and they were already thoroughly analyzed and discussed by Li et al. (2024). The study was conducted in a citrus orchard, which began planting in 2017. The experimental site was situated on a southeast-facing slope with an average gradient of approximately 15\u0026deg; and an elevation of 120 m. The fertilizer management in the orchard followed an annual standardized practice. This regimen consisted of a basal fertilization applied in winter (December), using a combination of NPK compound fertilizer (N\u0026thinsp;+\u0026thinsp;P₂O₅+K₂O\u0026thinsp;=\u0026thinsp;15%) and organic fertilizer containing 50% organic matter at a rate of 1200 kg\u0026middot;hm⁻\u0026sup2;. In the subsequent spring and summer (March, May, and July), a total of 960 kg\u0026middot;hm⁻\u0026sup2; of mixed fertilizer was applied in three split doses to support tree growth. The orchard was designed in a terraced layout to reduce runoff, enhance soil moisture conservation, and ensure a more uniform nutrient distribution. The planting density was approximately 925 trees per hectare, with a tree spacing of 3 m and row spacing of 3.5 m.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic chemical properties of orchard soil.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepth\u003c/p\u003e\u003cp\u003e/(cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSOC\u003c/p\u003e\u003cp\u003e/(g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003cp\u003e/(g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTP\u003c/p\u003e\u003cp\u003e/(g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTK\u003c/p\u003e\u003cp\u003e/(g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eC/N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eN/P\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31Aa\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030Ba\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004Cb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11Ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056Ba\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43Cb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023Cb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002Cb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62Cb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64Ca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032Bb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77Ac\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015Ac\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.092Ac\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11Ac\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02Cb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.054Ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35Bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014Bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.072Bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17Bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020Bd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029Bb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66Cc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016Cc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011Cc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39Cc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0083Bb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.67Ad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14Ad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018Bd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13Ad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030Cc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022Ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33Bd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011Bd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010Ad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98Bd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.78\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65Ac\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.064Bb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62Cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013Cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014Cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15Cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02Bb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Data are presented as mean value\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. SOC, TN, TP, TK, pH, C/N and N/P represent soil organic carbon, total nitrogen, total phosphorus, total potassium, ratio of SOC and TN and ratio of TN and TP. CK means that the orchard adopts clean tillage, GC means that the orchard adopts grass cover and PM means that the orchard adopts plastic mulch. Capital letters indicate significant differences among treatments within the same soil layer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), small letters indicate significant differences among soil layers within the same treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic physical properties of orchard soil.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDepth\u003c/p\u003e\u003cp\u003e(cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003cp\u003e(g\u0026middot;cm\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup3;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eSoil texture (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003cp\u003e(0.05-2 mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSilt\u003c/p\u003e\u003cp\u003e(0.05\u0026thinsp;\u0026minus;\u0026thinsp;0.002 mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClay\u003c/p\u003e\u003cp\u003e(\u0026lt;\u0026thinsp;0.002 mm)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022Cc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87Ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025Bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61Bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28Ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.047Ac\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59Ca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71Cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74Bb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003Cc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09Aab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07Ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20Bab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57Bbc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37Aab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68Cb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60Bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19Bb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010Cb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57Aab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19Ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13Aab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4Cc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21Bb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19Bb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.040Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21Aa\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39Bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80ABa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61Ba\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010Aa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11Cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02Ba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09Ca\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Data are presented as mean value\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. BD and soil texture represent soil bulk density and the relative proportions of sand, silt, and clay, respectively. CK means that the orchard adopts clean tillage, GC means that the orchard adopts grass cover and PM means that the orchard adopts plastic mulch. Capital letters indicate significant differences among treatments within the same soil layer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), small letters indicate significant differences among soil layers within the same treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExperimental Design\u003c/h3\u003e\n\u003cp\u003eThis study investigated three common soil management practices in orchards of the southern red soil hilly areas: clean tillage (CK; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), grass cover (GC; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), and plastic mulching (PM; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). A randomized block design was adopted to minimize the impact of terrain variation. The experiment was conducted on a hillside with uniform slope aspect, gradient, and elevation, where three blocks were established, each serving as one replication. Within each block, the three treatments were randomly assigned to individual experimental blocks. This design resulted in a total of 9 plots (3 treatments \u0026times; 3 blocks), with each plot covering 100 m\u003csup\u003e2\u003c/sup\u003e and containing 15 fruit trees. In the CK treatment, manual weeding was performed monthly to remove weeds while minimizing soil disturbance, thereby maintaining bare ground. In the GC treatment, natural herbaceous vegetation was allowed to grow within the canopy projection area, with \u003cem\u003ePortulaca oleracea\u003c/em\u003e L. (purslane) and \u003cem\u003eNepeta cataria\u003c/em\u003e L. (catnip) being the dominant species, accounting for approximately 50% and 20% of the plant coverage, respectively, while maintaining a target ground cover\u0026thinsp;\u0026ge;\u0026thinsp;70%. In the PM treatment, the area extending from the tree trunk base to the drip line was cleared and then fully covered with black polyethylene film, ensuring effective moisture retention and weed suppression around the root zone, and the covered area extended from the trunk base to 30 cm beyond the drip line. The orchard had been established for seven years prior to the experiment and was managed under consistent agronomic conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSoil Sampling and Measurement Methods\u003c/h3\u003e\n\u003cp\u003eOrchard soil samples were collected in March 2023 prior to the spring fertilization event, thus characterizing the soil's baseline state before nutrient amendment. Analyses of microbial community structure and physicochemical properties therefore reflect a stable condition unaffected by the immediate perturbations of fertilizer application. Three representative citrus trees were randomly selected within each treatment plot. At each tree, sampling points were established approximately 35 cm inside and outside the canopy drip line. After surface litter was removed, a soil auger was used to collect soil samples from four depths: 0\u0026ndash;10 cm, 10\u0026ndash;20 cm, 20\u0026ndash;40 cm, and 40\u0026ndash;60 cm. Samples from the same depth for each tree were combined to form a composite sample. Each treatment had three replicates, resulting in a total of 36 composite samples. Following transport to the laboratory, the soil samples were cleared of stones and visible plant or animal debris. A portion of the fresh samples was stored at 4\u0026deg;C to determine soil SON, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N. The remaining samples were air-dried and passed through 2 mm and 0.15 mm sieves for analysis of soil physicochemical properties.\u003c/p\u003e\u003cp\u003eTotal nitrogen and organic carbon were measured using an elemental analyzer (Elementar VARIO EL III) (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Soil pH was measured in a 1:2.5 soil-to-water suspension. Bulk density (BD) was determined using the core method, with oven-dried weights used for calculations. Total phosphorus (TP) was measured using the NaOH fusion\u0026ndash;molybdenum antimony blue colorimetric method. Total potassium (TK) was analyzed using the NaOH fusion\u0026ndash;flame photometry method. Total soluble nitrogen (TSN) was determined by potassium persulfate oxidation followed by ultraviolet spectrophotometry (Shimadzu TOC-L CPH, Japan). Ammonium-N (NH₄⁺-N) and nitrate-N (NO₃⁻-N) were quantified in the same extracts with a continuous flow injection analyzer (Flowsys, Systea, Italy). Soluble organic nitrogen (SON) in each sample was obtained by subtracting the summed concentrations of NH₄⁺-N and NO₃⁻-N from the corresponding TSN value for that sample. All the results were converted to a dry-soil basis using the extract volume and oven-dried soil mass. Quality assurance measures included reagent blanks, calibration verification standards, and duplicate analyses, with relative standard deviations maintained below 5%.\u003c/p\u003e\n\u003ch3\u003eDNA extraction and metagenomic sequencing\u003c/h3\u003e\n\u003cp\u003eAll amplicon sequencing was performed by Personal Biotechnology Co., Ltd. (Shanghai, China). Total genomic DNA was extracted from soil samples using the PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA, USA), with three technical replicates per sample. The V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified via PCR with the primers F (5\u0026prime;-ACTCCTACGGGAGGCAGCA-3\u0026prime;) and R (5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;) (Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The PCR was performed in a 25 \u0026micro;L reaction system containing 0.25 \u0026micro;L of Q5 High-Fidelity DNA Polymerase, 5 \u0026micro;L of template DNA, 1 \u0026micro;L each of the forward and reverse primers, and 8.75 \u0026micro;L of deionized water. The thermal cycling protocol began with a preheating step at 98\u0026deg;C for 30 s, followed by amplification cycles (denaturation at 98\u0026deg;C for 10 s, annealing at 55\u0026deg;C for 30 s, and extension at 72\u0026deg;C for 45 s), and a final extension at 72\u0026deg;C for 5 min. The amplification products were checked by 2% agarose gel electrophoresis, and the target bands were purified using a gel extraction kit. The resulting amplicon libraries, constructed in triplicate, were then subjected to paired-end sequencing (2\u0026times;300 bp) on an Illumina MiSeq platform.\u003c/p\u003e\u003cp\u003eRaw sequencing data were processed within the QIIME2 platform. Using the DADA2 plugin, sequences underwent quality filtering, denoising, dereplication, and chimera removal to generate amplicon sequence variants (ASVs), which represent exact biological sequences. These ASVs were then taxonomically classified using the classify-sklearn algorithm against the Greengenes database (Release 13.8). Relative abundances at phylum to genus levels were calculated as the percentage of sequences assigned to each taxon relative to the total high-quality sequences per sample.\u003c/p\u003e\u003cp\u003eAll microbial information was analysed on the Genescloud platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.genescloud.cn\" target=\"_blank\"\u003ewww.genescloud.cn\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.genescloud.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To identify nitrogen-cycling microorganisms, we traced the contribution of individual ASVs to the abundance of key nitrogen-cycling genes predicted by PICRUSt2, which generated a profile of predicted gene abundances based on KEGG Orthology (KO). To identify nitrogen-cycling microorganisms, we first extracted the predicted abundances of KOs associated with key nitrogen cycle processes (e.g., nitrogen fixation, nitrification, and denitrification) from the KEGG pathway database. We then identified the individual ASVs contributing to these nitrogen-cycling KOs. These ASVs were then taxonomically traced to their corresponding phylum and genus-level classifications. Only microbial taxa with a mean relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;1% across all samples were retained for subsequent analysis.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe data were processed using Excel 2010. Statistical analyses were performed using R version 4.3.1, and graphs were constructed using Origin 2022. The relative abundance of nitrogen-related microbial communities was determined through Illumina high-throughput sequencing, which generated microbial community structure data for the soil samples.\u003c/p\u003e\u003cp\u003eOne-way analysis of variance (ANOVA) was applied to test the effects of the different treatments, and the threshold for statistical significance was P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Tukey\u0026rsquo;s HSD test was performed at P\u0026thinsp;=\u0026thinsp;0.05 for multiple comparisons between treatments. Pearson correlation analysis was conducted to assess the relationships between the relative abundance of the microbial community and soil physicochemical properties. Redundancy analysis (RDA) was used to examine the relationships between soil physicochemical properties and microbial community structure. Structural equation modeling (SEM) was performed using the R package \u0026lsquo;lavaan\u0026rsquo; to evaluate the relationships between soil physicochemical properties, microbial communities, and TSN components. Data analysis was performed, and plots were constructed using the R packages \u0026ldquo;vegan\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; to ensure the accuracy and clarity of the results.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eSoil TSN Components\u003c/h2\u003e\u003cp\u003eAcross the 0\u0026ndash;60 cm soil profile, the NH₄⁺-N concentrations under both the GC and PM treatments decreased gradually with increasing depth. In contrast, the CK treatment exhibited a distinct trend, with NH₄⁺-N levels increasing initially and reaching a peak at 10\u0026ndash;20 cm before decreasing at greater depths (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In the 0\u0026ndash;10 cm layer, the NH₄⁺-N concentrations under GC and PM were 20.2% and 3.3% higher, respectively, than those observed in CK, although the differences were not statistically significant. At the 10\u0026ndash;20 cm depth, compared with the CK treatment, GC resulted in a marked increase of 25.5%, whereas PM resulted in a slight decrease of 0.3%. In the deeper layer (20\u0026ndash;60 cm), the average NH₄⁺-N content decreased to 3.74 mg\u0026middot;kg⁻\u0026sup1; under GC and 3.98 mg\u0026middot;kg⁻\u0026sup1; under PM, corresponding to decreases of 15.2% and 9.8%, respectively, compared with those in CK. These results collectively suggest that both GC and PM promote surface accumulation of NH₄⁺-N, likely because of enhanced nitrogen retention and microbial ammonification near the soil surface, while simultaneously limiting its downward movement or transformation in deeper layers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N content tended to decrease with increasing soil depth across all the treatments, with the most pronounced reduction occurring in the 20\u0026ndash;40 cm layer under the GC and PM treatments. In the 0\u0026ndash;20 cm layer, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N levels in GC and PM were consistently higher than those in the CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Specifically, the average NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N concentration in this layer reached 42.43 mg\u0026middot;kg⁻\u0026sup1; in GC and 39.96 mg\u0026middot;kg⁻\u0026sup1; in PM, representing increases of 18.3% and 11.5%, respectively, compared with the CK value of 35.86 mg\u0026middot;kg⁻\u0026sup1;. In contrast, in the 20\u0026ndash;60 cm layer, the average NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N concentration decreased to 19.90 mg\u0026middot;kg⁻\u0026sup1; in GC and 26.23 mg\u0026middot;kg⁻\u0026sup1; in PM, corresponding to reductions of 36.9% and 16.7%, respectively, relative to those in CK. These findings indicate that both the GC and PM treatments increased NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N accumulation in the surface layer (0\u0026ndash;20 cm) but reduced its presence in deeper soil, particularly in the 20\u0026ndash;40 cm layer, where the loss was most substantial.\u003c/p\u003e\u003cp\u003eThe SON content in both the GC and PM treatments clearly decreased with increasing soil depth, with the most substantial reduction occurring in the 20\u0026ndash;40 cm layer. In contrast, the CK treatment showed a different pattern, with SON levels increasing initially and peaking at 10\u0026ndash;20 cm before declining at greater depths. Across all soil layers, the SON concentrations were significantly higher under GC and PM compared to CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Averaged over the 0\u0026ndash;60 cm profile, the SON content reached 24.76 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under GC and 23.85 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under PM, representing increases of 24.3% and 19.7%, respectively, relative to that of CK. In the 0\u0026ndash;20 cm layer, compared with CK, both cover treatments significantly increased the SON concentration, with the concentrations in GC and PM increasing by 56.7% and 38.9%, respectively. Conversely, in the 20\u0026ndash;60 cm layer, the SON content under GC and PM was significantly lower than that in CK, with reductions of 45.3% and 13.3%, respectively. These findings collectively indicate that while GC and PM promote SON accumulation in surface soils, they concurrently reduce its retention in deeper layers.\u003c/p\u003e\u003cp\u003eThe TSN content in the 0\u0026ndash;20 cm layer significantly increased under both the GC and PM treatments compared with that in CK, whereas the opposite trend was observed in the 20\u0026ndash;60 cm layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). In the 0\u0026ndash;20 cm layer, the average TSN concentrations reached 88.75 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under GC and 80.34 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e under PM, representing increases of 33.80% and 21.13%, respectively, relative to those under CK. In the deeper layer (20\u0026ndash;60 cm), the TSN concentration decreased to 37.70 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in GC and 41.11 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in PM, corresponding to reductions of 29.13% and 22.73%, respectively, compared with those in CK. Notably, in the surface 0\u0026ndash;10 cm layer, the TSN content was 68.70% higher under GC and 49.91% higher under PM than under CK. Across the 0\u0026ndash;60 cm profile, the TSN consistently decreased with increasing soil depth, with the most pronounced declines observed in the 20\u0026ndash;40 cm layer under GC and PM. These results indicate that cover measures promote TSN accumulation in the upper soil layers while reducing its concentration in deeper horizons.\u003c/p\u003e\u003cp\u003eOverall, the contents of all nitrogen forms were significantly greater in the 0\u0026ndash;20 cm soil layer in the GC and PM treatments than in the CK treatment, and this pattern was especially pronounced for SON and TSN; the nitrogen content was lower in the 20\u0026ndash;60 cm layer in the GC and PM treatments than in the CK treatment. This suggests that GC and PM promote the accumulation of nitrogen in surface soil but may inhibit its migration to deeper layers.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eChanges in Soil Nitrogen-Related Microbial Communities\u003c/h3\u003e\n\u003cp\u003eOn the basis of the methods of Jiao et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Wang et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the dominant nitrogen-related microbial communities (with relative abundances\u0026thinsp;\u0026gt;\u0026thinsp;1%) at the phylum and genus levels were identified for all the treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The dominant phyla were Proteobacteria, Actinobacteriota, Verrucomicrobiota, and Acidobacteriota. In the GC and PM treatments, the relative abundance of Proteobacteria was greatest, followed by that of Actinobacteriota, Acidobacteriota, and Verrucomicrobiota, and this pattern was consistent across all the treatments. Compared with that in the CK treatment, the relative abundance of Proteobacteria increased by 154.3% in the GC treatment and by 67.5% in the PM treatment. The relative abundance of Actinobacteriota was 4.3% greater in the GC treatment and 8.2% greater in the PM treatment than in the CK. Both the GC and the PM treatments led to notable increases in the relative abundance of key nitrogen-associated microbial taxa. Specifically, the abundance of Acidobacteriota increased by 30.2% under GC and 49.8% under PM compared with that under CK, whereas the abundance of Verrucomicrobiota slightly increased by 2.1% under GC but substantially increased by 61.8% under PM. At the genus level, \u003cem\u003eBurkholderia\u0026ndash;Caballeronia\u0026ndash;Paraburkholderia\u003c/em\u003e, \u003cem\u003eMassilia\u003c/em\u003e, and \u003cem\u003eRhodanobacter\u003c/em\u003e were identified as the dominant taxa across all the treatments, all of which belong to Proteobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The relative abundance of \u003cem\u003eBurkholderia\u0026ndash;Caballeronia\u0026ndash;Paraburkholderia\u003c/em\u003e increased by 49.93% in GC and by 16.77% in PM, whereas that of Massilia increased even more strongly, with increases of 175.55% and 40.53% in GC and PM, respectively. In contrast, compared with that in the CK treatment, the abundance of \u003cem\u003eRhodanobacter\u003c/em\u003e decreased by 21.98% in the GC treatment but increased by 12.94% in the PM treatment. Along the 0\u0026ndash;60 cm soil profile, both the GC and the PM treatments increased the abundance of nitrogen-related microbes at both the phylum and genus levels, particularly in the surface layers, with a declining trend observed with increasing soil depth. In comparison, the CK treatment exhibited a nonlinear depth-dependent pattern, where the microbial abundance first increased but then decreased, peaking in the 10\u0026ndash;20 cm layer. Collectively, these results indicate that both GC and PM effectively promoted nitrogen-related microbial communities throughout the soil profile, although the response was most pronounced in the topsoil. In contrast, the vertical distribution of the soil in the CK treatment was distinct and was likely influenced by limited organic input and microbial activity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCorrelations between Soil Physicochemical Properties and Nitrogen-Related Microbial Communities\u003c/h2\u003e\u003cp\u003eSignificant correlations were observed between microbial communities and soil physicochemical properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). TN and SOC were highly significantly positively correlated with Proteobacteria, Actinobacteriota, and Acidobacteriota. Soil pH was significantly negatively correlated with Verrucomicrobiota. BD was negatively correlated with Proteobacteria and Acidobacteriota. Total potassium (TK) was not significantly correlated with most microbial communities. Nutrient indicators, such as total phosphorus (TP), C/N, N/P, and C/P, were positively correlated with Proteobacteria and Acidobacteriota. NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, and SON were highly significantly positively correlated with Proteobacteria and Acidobacteriota. The clay content was highly significantly positively correlated with the abundance of Proteobacteria and Actinobacteriota. Silt and sand were highly significantly negatively correlated with both Proteobacteria and Actinobacteriota. These results indicate that soil physicochemical properties, particularly TN, SOC, and nutrient ratios, play crucial roles in shaping nitrogen-related microbial communities. Soil texture also significantly influences the distribution of these microbial groups and highlights the complex interactions between soil properties and microbial ecology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRedundancy Analysis of Soil Physicochemical Properties and TSN Components\u003c/h2\u003e\u003cp\u003eTo determine the effects of soil environmental factors and microbial communities on different TSN components, RDA was conducted at the phylum level, with a focus on the relationships between soil bacterial community structure and environmental factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). RDA1 and RDA2 explained 61.34% and 18.9% of the total variation, respectively. The relative abundances of Proteobacteria and Acidobacteriota were significantly positively correlated with NH₄⁺-N, NO₃⁻-N, and SON. The relative abundances of Actinobacteriota and Verrucomicrobiota were significantly positively correlated with nutrient ratios, such as C/N, C/P, and N/P. BD and the sand content were significantly negatively correlated with microbial communities and all TSN components. The TN, TP, SOC, silt, and clay contents were strongly positively correlated with all TSN components. Soil pH was moderately positively correlated with TSN components. These findings suggest that Proteobacteria and Acidobacteriota play dominant roles in influencing TSN components, particularly NH₄⁺-N, NO₃⁻-N, and SON. Soil texture, nutrient content, and environmental conditions, such as pH and BD, also significantly shape the dynamics of TSN components and microbial community structure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eStructural equation modeling (SEM) revealed the path coefficients of soil physical properties, chemical properties, and microbial communities to different components of TSN (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The direct path coefficients of soil physical properties, chemical properties, and microbial communities to TSN were 0.33, 0.49, and 0.68, respectively. The contributions of soil physical properties, chemical properties, and microbial communities to NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N were 40.5%, 43.04%, and 16.46%, respectively; the contributions to NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N were 65%, 4.7%, and 30.3%, respectively; and the contributions to SON were 47.47%, 36.71%, and 15.82%, respectively. Analysis of the path coefficients for individual soil indicators indicated that TN was the primary factor promoting the NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and SON contents, accounting for 11.70% and 9.71% of their effects, respectively. Proteobacteria was the dominant factor promoting increases in the NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N content, accounting for 12.93% of its effect. These findings underscore the significant role of soil chemical properties, particularly TN, in enhancing the NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and SON contents, and that microbial communities, specifically Proteobacteria, play a crucial role in regulating NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N dynamics. The interplay between soil properties and microbial communities is critical in shaping the distribution and transformation of TSN components.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEffects of Cover Measures on Soil TSN in Orchards\u003c/h2\u003e\u003cp\u003eBoth GC and PM significantly increased the TSN content in the 0\u0026ndash;20 cm soil layer of citrus orchards in the red soil hilly region and reduced it in the 20\u0026ndash;60 cm soil layer, and the effect of GC was more pronounced. The TSN content decreased with soil depth across all treatments, primarily because of two factors: (1) the lower organic matter content in deeper soils, which reduced the nitrogen pool, and (2) anaerobic conditions in deeper layers, which promoted denitrification and reduced the TSN content in the soil solution (Jahangir et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the GC treatment, the dominant herbaceous plants (\u003cem\u003ePortulaca oleracea\u003c/em\u003e L. and \u003cem\u003eNepeta cataria\u003c/em\u003e L.) contributed to the nitrogen pool through root exudates, decayed root residues, and aboveground litter, which provided abundant carbon and nitrogen sources for microbial activity. Microbial decomposition of organic matter includes ammonification, wherein extracellular enzymes (e.g., protease and urease) breakdown complex organic nitrogen compounds (e.g., proteins, nucleic acids, and urea) into simpler, highly SON compounds (e.g., amino acids and peptides) (Jones et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These compounds accumulate in the soil solution, which increases the SON content. Small organic nitrogen molecules subsequently undergo deamination to form ammonia (NH₃), which reacts with water to form ammonium ions (NH₄⁺), thereby increasing the NH₄⁺-N content. GC treatment increases organic matter input, which provides substrates that stimulate microbial proliferation and enzyme production, thereby increasing the efficiency of ammonification (Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As NH₄⁺-N levels increase, nitrifying bacteria within Proteobacteria oxidize NH₄⁺-N to nitrite (NO₂⁻) and subsequently to nitrate (NO₃⁻). Additionally, \u003cem\u003eP. oleracea\u003c/em\u003e and \u003cem\u003eN. cataria\u003c/em\u003e possess shallow root systems concentrated in the 0\u0026ndash;20 cm layer, with deeper taproots reaching up to 40 cm, which improves soil structure and enhances water retention in surface layers (Song et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This reduces nitrogen leaching into deeper layers. In the PM treatment, the use of black plastic mulch reduced soil moisture evaporation, which maintained high and stable soil humidity and temperature. This microenvironment favors microbial growth and enzymatic activity, which promotes organic matter decomposition and ammonification and thus increases NH₄⁺-N production (Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, long-term mulching may create hypoxic conditions that inhibit nitrifying bacteria, which slows the conversion of NH₄⁺-N to NO₃⁻-N and reduces NO₃⁻-N production (Gao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The moist environment under mulch may also increase the activity of denitrifying bacteria, which convert NO₃⁻-N to gaseous nitrogen (e.g., N₂ and N₂O), further decreasing the NO₃⁻-N content. Compared with CK, PM creates stable environmental conditions that promote the microbial decomposition of organic matter and increase SON release (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Mulching also prevents nutrient loss because of rainwater runoff and reduces water infiltration into deeper soil layers. In the CK treatment, limited organic matter input restricted microbial activity, which reduced the ammonification and nitrification rates and significantly decreased the NH₄⁺-N and NO₃⁻-N contents (Burger and Jackson, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Additionally, the exposed soil in the CK treatment was more prone to nitrogen leaching during rainfall, resulting in nitrogen loss. Overall, the GC and PM treatments effectively increased the TSN content in the surface soil while limiting nitrogen migration to deeper layers, thereby increasing nutrient retention and soil fertility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEffects of Cover Measures on Soil Nitrogen-Related Microbial Communities in Orchards\u003c/h2\u003e\u003cp\u003eBoth GC and PM significantly increased the relative abundances of Proteobacteria, Actinobacteriota, and Acidobacteriota in the 0\u0026ndash;60 cm soil layer of citrus orchards in the red soil hilly region. During the growth of \u003cem\u003ePortulaca oleracea\u003c/em\u003e and \u003cem\u003eNepeta cataria\u003c/em\u003e under GC, easily decomposable carbon sources such as root exudates, low-molecular-weight organic acids, and soluble sugars are released into the soil (Sokolova, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These labile compounds serve as rich sources of energy and nutrients for Proteobacteria, which are predominantly copiotrophic and capable of rapidly assimilating such substrates to support their proliferation (Dai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The enriched organic matter also nourished nitrogen-transforming functional bacteria, such as \u003cem\u003eBurkholderia-Caballeronia-Paraburkholderia\u003c/em\u003e, \u003cem\u003eMassilia\u003c/em\u003e, and \u003cem\u003eRhodanobacter\u003c/em\u003e, thereby promoting their proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Actinobacteriota are known for their ability to decompose complex organic materials, such as cellulose, lignin, and chitin (Xu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). They are well adapted to the acidic soils of southern China, where increased plant residues provide abundant substrates for functional genera, such as \u003cem\u003eActinospoica\u003c/em\u003e and \u003cem\u003ePhenylobacterium\u003c/em\u003e, which support their growth and activity. Conversely, Acidobacteriota and Verrucomicrobiota are predominantly oligotrophic microorganisms adept at utilizing recalcitrant organic matter (Nie et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The plant residues in the GC treatment, which were rich in cellulose, lignin, and pectin, served as viable substrates for \u003cem\u003eCandidatus Koribacter\u003c/em\u003e and \u003cem\u003eGranulicella\u003c/em\u003e, which are functional genera within Acidobacteriota. PM significantly increased the relative abundances of Actinobacteriota and Acidobacteriota (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) by maintaining stable soil humidity and temperature, which is conducive to the metabolic activity of Actinobacteriota (Zhao et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Bhatti et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that stable environmental conditions facilitate the formation of actinobacterial hyphal networks by increasing their ability to decompose soil organic matter. Plastic mulching promotes the accumulation of recalcitrant organic compounds, such as fulvic and humic acids, which can be utilized by Acidobacteriota as alternative carbon and energy sources, thereby facilitating their proliferation (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, the mulching layer restricts gas exchange between the soil and atmosphere, resulting in low-oxygen microenvironments. These hypoxic conditions suppress the activity of aerobic microorganisms and shift the microbial community composition in favor of Acidobacteriota and Actinobacteriota, which are better adapted to oligotrophic and microaerophilic environments (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, CK disrupted microbial habitats through frequent soil tillage, reducing the abundance and diversity of microbial communities. In conclusion, the GC and PM treatments significantly altered the soil microbial community structure, especially by increasing the relative abundances of Proteobacteria, Actinobacteriota and Acidobacteriota. These findings highlight the potential for these management practices to improve soil microbial health and nitrogen-related processes in orchards.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eRelationships Between Nitrogen-Related Microbial Communities and TSN Components\u003c/h2\u003e\u003cp\u003eIn this study, there was a significant positive correlation between the abundance of nitrogen-related microbial communities and soil TSN content in the red soil hilly region. Pearson correlation analysis revealed that the relative abundances of Proteobacteria and Acidobacteriota were strongly positively correlated with NH₄⁺-N and NO₃⁻-N. Proteobacteria are involved in both ammonification, which produces NH₄⁺-N through the microbial decomposition of organic nitrogen compounds, and nitrification, which generates NO₃⁻-N via the oxidation of ammonium. These microbes thus play a critical and multifaceted role in nitrogen transformation processes within the soil, especially under conditions that favor active microbial metabolism. Acidobacteriota decompose recalcitrant and complex organic matter, releasing low-molecular-weight compounds such as amino acids, ammonia, and other nitrogen-containing substances as byproducts of microbial catabolism, thereby contributing to the pool of readily available nitrogen in the soil. These byproducts serve as substrates for other nitrogen-cycling microbes, which collectively increase organic matter decomposition and nitrogen cycling (Liu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). RDA further demonstrated that Proteobacteria and Acidobacteriota were most positively correlated with NH₄⁺-N, NO₃⁻-N, and SON, highlighting their significant roles in shaping soil TSN composition. In line with these findings, studies in other ecosystems have explored the variability of microbe\u0026ndash;nitrogen associations. For example, Yang et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that Chloroflexi, Proteobacteria, and Bacteroidetes were most strongly positively correlated with TSN in agricultural systems. Similarly, Che et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that root exudates and plant residues provide additional carbon and nitrogen sources in grassland ecosystems, stimulating the growth of nitrogen-related functional microbes and promoting nitrogen mineralization and nitrification, thereby increasing the TSN content. Actinobacteriota and Verrucomicrobiota were significantly positively correlated with nutrient ratios, such as C/N, C/P, and N/P. This suggests that they play a role in maintaining a balance in nutrient supply, indirectly affecting TSN components. Shen et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observed that The Actinobacteriota accelerated organic matter decomposition and nutrient release, altering soil stoichiometry. Similarly, Shen et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that Verrucomicrobiota utilize diverse carbon sources to facilitate nitrogen and phosphorus cycling in montane ecosystems. The structural equation model indicated that soil physical properties, soil chemical properties, and microbial communities had direct path coefficients of 0.33, 0.49, and 0.68, respectively, to TSN, with the highest coefficient for microbial communities. These findings indicate that the activity and abundance of nitrogen-related microbial communities are the most critical factors influencing TSN dynamics. In conclusion, by regulating nitrogen-related microbial communities and increasing soil TSN content, grass cover (GC) was the most effective measure for optimizing nitrogen cycling in orchard soils. These findings highlight the potential of GC as a sustainable management practice for orchard ecosystems.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, TSN components and nitrogen-related microbial communities were investigated under different cover measures (GC, PM, and CK) in a citrus orchard. The findings revealed that both GC and PM significantly increased the content of TSN components in the soil, and the most pronounced effects were observed in the 0\u0026ndash;20 cm surface soil layer. GC was superior to PM. In comparison, the TSN increase under GC and PM was dominated by higher SON contents in the 0\u0026ndash;20 cm layer, with NH₄⁺-N showing modest surface increases and NO₃⁻-N decreasing in the 20\u0026ndash;60 cm layer relative to those in the CK. Additionally, GC and PM increased the abundance of nitrogen-related microbial taxa, particularly Proteobacteria, Actinobacteriota, and Acidobacteriota. RDA indicated that Proteobacteria and Acidobacteriota were most strongly correlated with TSN components. SEM demonstrated that soil physical, chemical, and biological properties directly influenced the TSN components. Soil TN was identified as the primary factor influencing NH₄⁺-N and SON, and Proteobacteria was the most critical factor affecting NO₃⁻-N. These results provide new insights into the management of orchards in red soil hilly regions and indicate that GC and PM are effective measures for improving soil nitrogen availability and nitrogen use efficiency in orchards; thus, GC was the most effective approach. Future research should explore the long-term impacts of different cover measures on soil nitrogen cycling and microbial functionality, especially under varying climatic and soil conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported financially by Water Conservancy Science and Technology Project of Fujian Province, Grant Number: MSK202429 and KJG21009A, and The Significant Science and Technology Projects of the Ministry of Water Resources, Grant Number: SKS-2022073. We would like to thank Hongli Ge, Bangning Zhou, Linting Zhong and Yue He for their helps in experimental works.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBargmann, I., Rillig, M.C., Kruse, A., Greef, J.M., K\u0026uuml;cke, M., 2014. Effects of hydrochar application on the dynamics of soluble nitrogen in soils and on plant availability. Plant and Soil. 177(1), 48-58. https://doi.org/10.1007/s11104-005-7530-4.\u003c/li\u003e\n\u003cli\u003eBhatti, A.A., Haq, S., Bhat, R.A., 2017. Actinomycetes benefaction role in soil and plant health. Microbial Pathogenesis. 111, 458-467. https://doi.org/10.1016/j.micpath.2017.09.036.\u003c/li\u003e\n\u003cli\u003eBingham, A.H., Cotrufo, M.F., 2016. Organic nitrogen storage in mineral soil: implications for policy and management. Science of The Total Environment. 551, 116-126. https://doi.org/10.1016/j.scitotenv.2016.02.020.\u003c/li\u003e\n\u003cli\u003eBurger, M., Jackson, L.E., 2003. 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Soil Biology and Biochemistry. 36(2), 279-288. https://doi.org/10.1016/j.soilbio.2003.09.011.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cover measures, Orchard, Microbial community, Soluble organic nitrogen, Structural equation model","lastPublishedDoi":"10.21203/rs.3.rs-7959888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7959888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e\u003cp\u003eMulching practices significantly contribute to soil and water conservation and soil ecological regulation in red soil hilly regions. However, it remains unclear how grass cover and plastic mulching affect the composition of soil soluble nitrogen by modifying microenvironments and functional microbial communities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe examined the effects of grass cover and plastic mulching on nitrogen-related microbial communities and soluble nitrogen components in the 0\u0026ndash;60 cm soil layer of citrus orchards in a red soil hilly area.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBoth grass cover and plastic mulching significantly increased soluble nitrogen content in the 0\u0026ndash;20 cm soil layer. Compared to the control, grass cover increased ammonium nitrogen, nitrate nitrogen, and soluble organic nitrogen by 20.2%, 18.3%, and 56.7%, respectively, while plastic mulching raised them by 3.3%, 11.5%, and 38.9%. Grass cover not only induced greater increases in soil nitrogen fractions than plastic mulching but also promoted a higher relative abundance of key nitrogen-cycling microorganisms such as Proteobacteria, Actinobacteriota, Verrucomicrobiota, and Acidobacteriota. Structural equation modeling revealed that soil microorganisms (path coefficient\u0026thinsp;=\u0026thinsp;0.68) had the strongest influence on total soluble nitrogen, exceeding the effects of physical (0.33) and chemical (0.49) properties. Among chemical factors, total nitrogen had the strongest direct effect on ammonium and dissolved organic nitrogen, while the relative abundance of Proteobacteria most strongly promoted nitrate nitrogen.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eGrass cover is more effective than plastic mulching or no treatment in enhancing soil soluble nitrogen components and enriching nitrogen-related microbial communities in red soil hilly regions.\u003c/p\u003e","manuscriptTitle":"Effects of Mulching Practices on Soil Soluble Nitrogen in Orchards in Red Soil Hilly Areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 17:49:59","doi":"10.21203/rs.3.rs-7959888/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a17ef0b0-f411-44fc-baf4-40a63868d3dc","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-06T09:49:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 17:49:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7959888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7959888","identity":"rs-7959888","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}