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Article

Regulatory Effects of Green Manure Combined with Nitrogen Reduction on Carbon-Cycling Functional Genes and Microbial Communities in Paddy Soils

by
Zhongyi Li
1,†,
Xiaohui Peng
1,†,
Wenbin Dong
1,
Caihui Wei
1,
Yuning Wang
1,
Yuefeng Yu
1,
Hai Liang
1,
Yongcheng Mo
1,
Huiping Ou
1,
Tieguang He
1,
Hongqin Tang
1,* and
Maoyan Tang
2,*
1
Guangxi Key Laboratory of Arable Land Conservation, Agricultural Resource and Environment Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
2
Rice Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diversity 2025, 17(12), 825; https://doi.org/10.3390/d17120825
Submission received: 19 September 2025 / Revised: 8 November 2025 / Accepted: 20 November 2025 / Published: 28 November 2025
(This article belongs to the Special Issue Ecology and Diversity of Soil Microbial Communities in an Era of Global Change)
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Abstract

Excessive nitrogen (N) fertilization in rice systems has caused soil degradation and reduced N use efficiency. Green manure, especially Astragalus sinicus (Chinese milk vetch), provides a sustainable alternative, but the microbial and functional gene mechanisms underlying its interaction with reduced N input remain unclear. In this study, a field experiment was conducted at Dingdian Village, Natong Town, Long’an County, Nanning City, Guangxi Province, China (107°51′21″ E, 23°00′41″ N) during the 2018–2019 rice growing seasons. Four treatments were established: conventional N fertilization (N100), 20% N reduction (N80), green manure plus full N (GMN100), and green manure plus 20% N reduction (GMN80). Soil physicochemical traits, microbial community composition, and carbon-cycling functional genes were analyzed using high-throughput sequencing and metagenomic profiling. Compared with N100, GMN80 significantly increased soil organic matter (by 21.3%), microbial biomass carbon (by 32.6%), and available phosphorus (by 17.8%). The Shannon index rose from 4.18 to 4.63, while Proteobacteria and Actinobacteria increased by 9.5% and 7.2%, respectively. Functional genes encoding glycoside hydrolases (GH5, GH9) and carbohydrate esterases (CE1, CE10) were enriched by 25–40%, with upregulation of carbon fixation (rbcL) and methane metabolism (mcrA) genes. Integrating A. sinicus with moderate N reduction improves soil fertility, stimulates microbial diversity, and enhances carbon turnover efficiency, offering a practical pathway toward sustainable low-carbon rice production.

1. Introduction

As a staple food crop in China, rice plays a crucial role in safeguarding national food security, with its yield and quality being essential for social stability and sustainable economic development. N fertilizer accounts for nearly 50% of the production of rice and other cereal crops, underscoring its critical contribution to national food security and economic growth [1,2]. However, with the intensification of agricultural practices, the excessive application of N fertilizer has become increasingly common, leading to substantial reactive nitrogen losses and persistently low nitrogen use efficiency [3,4]. These challenges are further compounded by a range of environmental problems, including soil acidification, elevated greenhouse gas emissions, and water eutrophication [5]. Consequently, the scientific and rational reduction of chemical N fertilizer inputs, coupled with strategies to enhance nitrogen use efficiency, is of paramount importance for advancing the green transformation and sustainable development of China’s rice production systems.
The excessive application of N fertilizer has aggravated problems such as farmland degradation and environmental pollution. In light of the dual imperatives of sustainable agricultural development and environmental protection, the partial substitution of chemical fertilizers with green manure has been recognized as a practical and effective strategy that aligns with the principles of sustainability [6]. Green manure not only contributes to nitrogen fixation and carbon sequestration but also improves soil nutrient composition and supports the integrated management of water and fertilizers [7]. Within the framework of traditional Chinese agricultural practices, the use of leguminous green manure embodies ecological wisdom, delivering vital ecosystem services such as the enhancement of soil quality and the replenishment of soil nitrogen pools [8]. Among leguminous green manure crops, Astragalus sinicus is widely cultivated in southern China for its strong capacity for biological nitrogen fixation. When incorporated into paddy soils, it supplies a substantial amount of plant-available nitrogen, thereby reducing the need for chemical N fertilizer, sustaining rice yields, improving grain quality, and mitigating environmental risks [9].
As a high-quality green manure crop, A. sinicus has been extensively studied and is widely recognized for its ecological and fertilization functions in agricultural systems. Liu et al. (2022) demonstrated that the leachate derived from the microbial decomposition of A. sinicus incorporated into soils exhibits weed-suppressing properties and, as decomposition proceeds, can influence soil nutrient availability to varying extents [10]. Long-term rice–A. sinicus rotation has been shown to enhance soil fertility, increase soil organic matter, improve soil pH, reshape microbial community composition, and stimulate the growth and proliferation of beneficial bacteria [11,12]. Furthermore, the incorporation of rice straw in combination with A. sinicus significantly promotes nitrogen uptake in rice plants [13]. This combined practice not only increases the annual rate of soil carbon sequestration but also effectively reduces the net global warming potential [14]. In addition, inoculation with phosphate-solubilizing bacteria during the cultivation of A. sinicus has been reported to markedly increase soil available phosphorus, decrease the proportion of moderately stable phosphorus fractions, and thereby improve phosphorus use efficiency in red soils [15].
Several studies have examined the combined effects of green manure incorporation and reduced N fertilization on rice production. Fan et al. (2023) reported that a 20% reduction in chemical N fertilizer, coupled with the incorporation of varying amounts of A. sinicus, maintained or increased rice yield while improving the uptake and use efficiency of nitrogen, phosphorus, and potassium, and enhancing soil fertility [16]. Similarly, Wang et al. (2022) found that the incorporation of winter A. sinicus with rice straw, combined with a 20–30% reduction in N application relative to conventional levels, significantly increased rice yield and reduced NH3 volatilization [17]. However, most existing studies have primarily focused on soil nutrient dynamics and microbial diversity, while research on the effects of this practice on carbon-cycling functional genes in paddy soils remains limited. Therefore, this study investigates the impact of green manure incorporation combined with reduced N fertilization on soil physicochemical properties and carbon-cycling functional genes, aiming to provide a theoretical foundation for the high-quality, green, and sustainable development of the rice industry.

2. Materials and Methods

2.1. Overview of the Experimental Site

The experimental site is located in Dingdian Village, Natong Town, Long’an County, Nanning City, Guangxi Province (107°51′21″ E, 23°00′41″ N). The site has a subtropical monsoon climate, with a mean annual temperature of 21.6 °C, annual precipitation of approximately 1300 mm, and a mean elevation of 64 m above sea level. Soils are typical brown-yellow paddy soils developed on karst terrain. Baseline soil physicochemical properties were: pH 6.8; soil organic matter (SOM) 31.3 g·kg−1; total nitrogen (TN) 1.90 g·kg−1; alkaline-hydrolyzable nitrogen (AHN) 136.0 mg·kg−1; available phosphorus (AP) 19.4 mg·kg−1; and available potassium (AK) 161.0 mg·kg−1.

2.2. Experimental Design

A fixed-site field experiment was initiated in October 2018 under a green manure–early rice–late rice rotation system. Six treatments were established: (1) no N fertilizer (CK), (2) conventional nitrogen fertilization (N), (3) green manure incorporation without N fertilizer (M), (4) green manure incorporation plus 60% of the conventional nitrogen rate (MN60), (5) green manure incorporation plus 80% of the conventional nitrogen rate (MN80), and (6) green manure incorporation plus the full conventional nitrogen rate (MN100). The experiment was arranged in a randomized block design with three replicates per treatment. Each plot measured 20.7 m2.
The green manure used was A. sinicus, sown at 30.0 kg·ha−1 and incorporated in situ at full flowering by turning under. The rice cultivar was ‘Guiyu 9’. Conventional fertilizer rates for early and late rice were set as: N 180.0 kg·ha−1, P2O5 90.0 kg·ha−1 and K2O 120.0 kg·ha−1. Fertilizers applied were urea (46.4% N), calcium superphosphate (18.0% P2O5) and potassium chloride (60% K2O). Phosphorus and potassium fertilizers were applied once as basal fertilizer, while nitrogen was applied in three splits at a basal: tillering: panicle ratio of 4:3:3.

2.3. Soil Sample Collection

Following the harvest of early rice in 2023, soil samples were collected from the 0–20 cm layer using a five-point sampling method. The composite samples were homogenized and subdivided into three portions. The first portion was placed in plastic bags, transported to the laboratory, air-dried, and passed through a 0.15 mm sieve for the determination of soil pH, organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available phosphorus (AP), and available potassium (AK). The second portion was placed in sterile plastic bags, transported to the laboratory, sieved through 2 mm, and stored at 4 °C for the analysis of ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN). The third portion was immediately flash-frozen in liquid nitrogen in the field, sealed in sterile bags, and stored at −80 °C for subsequent metagenomic sequencing.

2.4. Determination of Soil Physicochemical Properties

Soil physicochemical properties were analyzed following the protocols described in Methods of Soil Agricultural Chemistry Analysis [18]. Soil pH was determined potentiometrically at a soil-to-water ratio of 1:2.5 (w/v). Soil organic matter (SOM) was quantified using the potassium dichromate–sulfuric acid oxidation method. Available phosphorus (AP) was measured using the sodium bicarbonate extraction–molybdenum antimony colorimetric method, and available potassium (AK) was determined by ammonium acetate extraction followed by flame photometry. Available nitrogen (AN) was assessed using the alkaline diffusion method. Total nitrogen (TN) was determined by Kjeldahl digestion with concentrated sulfuric acid. Total phosphorus (TP) was measured using the sodium carbonate fusion method, while total potassium (TK) was analyzed by alkaline dissolution.

2.5. DNA Extraction and Metagenomic Sequencing

Genomic DNA was extracted from 0.5 g of soil using the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer’s protocol. DNA purity was evaluated with a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and DNA concentration was quantified using a TBS-380 fluorometer (Turner BioSystems, San Jose, CA, USA). High-quality DNA was then fragmented into ~400 bp fragments with a Covaris M220 ultrasonicator (Covaris, Woburn, MA, USA). Sequencing libraries were prepared using the NEXTFLEX® Rapid DNA-Seq Kit (Bioo Scientific, Austin, TX, USA). Metagenomic sequencing was performed on the Illumina NovaSeq platform (Illumina, San Diego, CA, USA) by Meiji Bio-Pharmaceutical Technology Co., Ltd. (Shanghai, China).

2.6. Metagenomic Analysis

Raw sequencing reads were quality-filtered using fastp (v0.20.0; https://github.com/OpenGene/fastp (accessed on 1 February 2024)) by trimming 3′ and 5′ adapter sequences. Reads shorter than 50 bp after trimming, with an average base quality score < 20, or containing ambiguous bases (N) were discarded. High-quality paired-end and single-end reads were retained for downstream analyses. Filtered reads were assembled de novo with MEGAHIT (v1.1.2; https://github.com/voutcn/megahit (accessed on 3 March 2024)), and contigs ≥ 300 bp were selected for further analysis.
Open reading frames (ORFs) were predicted from assembled contigs using Prodigal/MetaGene (https://metagene.nig.ac.jp/metagene/metagene.html (accessed on 5 April 2024)). Predicted genes with nucleotide sequences ≥ 100 bp were retained and translated into amino acid sequences. To construct a non-redundant gene catalog, all predicted genes from all samples were clustered using CD-HIT (v4.6.1; http://www.bioinformatics.org/cd-hit/ (accessed on 6 May 2024)) at 90% sequence identity and 90% coverage, with the longest gene from each cluster retained as the representative sequence. High-quality reads from each sample were then mapped to the non-redundant gene catalog using SOAPaligner (v2.21; http://soap.genomics.org.cn/ (accessed on 26 May 2024)) at 95% identity to quantify gene abundance.
Functional and taxonomic annotations were performed by aligning amino acid sequences of the non-redundant gene catalog against the NCBI NR and KEGG databases using DIAMOND (v0.8.35; https://github.com/bbuchfink/diamond (accessed on 16 May 2024)). Additionally, carbohydrate-active enzymes (CAZymes) were identified by querying amino acid sequences against the CAZy database using hmmscan (https://www.ebi.ac.uk/Tools/hmmer/search/hmmscan (accessed on 8 July 2024)) with an e-value threshold of 1 × 10−5.

2.7. Data Analysis

Data organization and preliminary statistical analyses were conducted using Microsoft Excel 2010 and IBM SPSS Statistics 25.0. Differences in the relative abundances of CAZy families among treatments were assessed using one-way ANOVA followed by Tukey’s HSD test. Multivariate analyses, including redundancy analysis (RDA) and correlation analyses, were performed with the vegan package in R (v3.3.1). Corresponding graphical outputs were generated in R to visualize relationships among soil physicochemical properties, microbial communities, and functional gene abundances.

3. Results

3.1. Effects of Combined Application of Green Manure and Reduced N Fertilizer on the Physicochemical Properties of Rice Soil

One-way ANOVA revealed significant differences in soil pH, organic matter (SOM), nitrate nitrogen (NO3−-N), ammonium nitrogen (NH4+–N), available potassium (AK), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) across fertilization treatments (Table 1). With the exception of pH and available phosphorus (AP), all measured soil physicochemical parameters reached their highest levels under the MN100 treatment. Soil pH under the M, MN60, MN80, and MN100 treatments was significantly lower than that observed under the N treatment. Relative to CK, N, and M, soil NO3−-N and NH4+–N contents were significantly elevated under MN60, MN80, and MN100, exhibiting a clear increasing trend with higher nitrogen application rates. SOM, MBC, and MBN were also significantly higher under MN60, MN80, and MN100 compared with CK. In contrast, AK content decreased significantly under MN60 and MN80 relative to N and M, but increased significantly under MN100 compared with CK and M. No significant treatment effects were observed for AP.

3.2. Effects of Combined Application of Green Manure and Reduced N Fertilizer on Rice Soil CAZy Database Carbohydrate-Active Enzyme Functional Genes

All six carbohydrate-active enzyme (CAZy) families—auxiliary activities (AAs), carbohydrate-binding modules (CBMs), carbohydrate esterases (CEs), glycoside hydrolases (GHs), glycosyltransferases (GTs), and polysaccharide lyases (PLs)—were detected across all fertilization treatments, with GHs (~32.91%) and GTs (~38.30%) representing the dominant families (Table 2). Significant differences in the relative abundances of certain CAZy families were observed among treatments (Figure 1a), with half of these belonging to the GH family, specifically GH26, GH150, GH154, GH58, and GH13_22. The abundance of GH26 under the N treatment was significantly higher than under the M treatment. GH150 abundance in MN100 was significantly higher than in MN80. GH154 abundance was significantly higher in M than in N and MN80. Meanwhile, the abundances of GH58 in N, MN60, and MN80, as well as GH13_22 in MN80, were significantly higher compared with the other treatments.
Correlation analysis between CAZy families that exhibited significant differences and environmental factors (Figure 1b) revealed distinct associations. Specifically, AA9 was significantly and positively correlated with soil organic matter, nitrate nitrogen, ammonium nitrogen, and microbial biomass nitrogen. In contrast, CNM14 and GH154 were significantly negatively correlated with soil pH. GT2_Glyco_tranf_2_4 showed significant negative correlations with both soil available phosphorus and microbial biomass nitrogen, whereas PL29 was significantly positively correlated with microbial biomass nitrogen.
Contribution analysis of the five most abundant CAZy families with significant differences (Figure 1c) further indicated that the dominant microbial contributors to GH26, GH150, GT2_Glyco_tranf_2_4, PL29, and GH154 were primarily members of the phyla Chloroflexi, Proteobacteria, Actinobacteria, and Bacteroidota.

3.3. Effects of Combined Application of Green Manure and Reduced N Fertilizer on Rice Soil KEGG Database Functional Genes

A total of 910 functional gene categories (KOs) associated with carbon cycling processes were annotated using the KEGG database. Among these, the 30 most abundant functional genes were quantified (Figure 2a), with K01652, K00626, K01895, K00615, and K01784 representing the five most dominant genes. Differential abundance analysis of the top-ranked functional genes (Figure 2b) further identified ten genes exhibiting significant differences, specifically K00101, K08097, K06859, K09011, K01792, K14469, K16178, K21681, K16849, and K06617.
Among these, K08097 and K16178 were identified as functional genes involved in methane metabolism. The abundance of K08097 under the MN80 treatment was significantly lower than that observed in the CK, M, and MN60 treatments, whereas K16178 abundance under the N treatment was significantly higher than in all treatments incorporating A. sinicus (Figure 3). The reduction of K08097 (encoding methyl-coenzyme M reductase subunit, mcrA) under MN80 may indicate suppressed methanogenic activity, likely due to improved soil aeration and increased availability of alternative carbon substrates from A. sinicus decomposition. K14469, a gene associated with prokaryotic carbon fixation, exhibited significantly higher abundance in the CK and MN60 treatments compared with the N and MN80 treatments. In addition, K00101, K06859, K09011, K01792, K21681, K16849, and K06617 were annotated as carbohydrate-related functional genes. Specifically, K06859, K09011, and K01792 showed the highest abundance in MN80; K21681 and K06617 in M; and K00101 and K16849 in MN100, with all displaying significant increases relative to several other treatments.
To further investigate the interactions between soil environmental factors and the abundance of soil functional genes under different fertilization treatments, and to identify key environmental drivers, redundancy analysis (RDA) was performed using significantly differential functional genes as response variables and soil physicochemical properties as explanatory variables. The RDA results (Figure 2c) indicated that the first and second axes explained 17.65% and 11.99% of the variation, respectively. Permutation tests of differential gene abundances (Table 3) revealed that soil organic matter (p = 0.012), nitrate nitrogen (p = 0.018), and ammonium nitrogen (p = 0.039) significantly influenced the variation in functional gene abundance. Correlation analysis between differential functional genes and soil environmental factors (Figure 2d) showed that the relative abundance of K21681 was significantly negatively correlated with nitrate nitrogen; K16178 was significantly negatively correlated with both nitrate nitrogen and ammonium nitrogen, and extremely significantly negatively correlated with organic matter; K14469 was significantly negatively correlated with nitrate nitrogen and ammonium nitrogen; and K08097 was significantly negatively correlated with nitrate nitrogen.

3.4. Effects of Combined Application of Green Manure and Reduced N Fertilizer on Microbial Communities Carrying Carbon Cycle Functional Genes in Rice Soil

Annotation and phylum-level community composition analysis of microbes carrying carbon-cycle-related functional genes (Figure 4a) identified 11 bacterial phyla with relative abundances exceeding 1% across the different fertilization treatments. Proteobacteria was the dominant phylum, with relative abundances of 28.93% (CK), 30.06% (N), 29.22% (M), 29.20% (MN60), 30.43% (MN80), and 29.56% (MN100). Other relatively abundant phyla included Chloroflexi (23.43–24.01%), Actinobacteria (16.82–20.87%), and Acidobacteria (10.31–11.09%).
Differential analysis of microbial genera carrying carbon-cycle-related functional genes identified 60 genera with significant differences in relative abundance. The ten most abundant genera were Dactylosporangium, Kribbella, Variibacter, Candidatus_Brocadia, unclassified members of Desulfobulbaceae, unclassified members of Chthoniobacterales, Polyangium, Actinoallomurus, Aestuariivirg and Kutzneria (Figure 4b). Compared with the MN80 treatment, the relative abundance of Dactylosporangium was significantly higher in the N treatment; Kribbella, Actinoallomurus, and Kutzneria were significantly more abundant in the CK treatment; and Polyangium abundance was significantly elevated in the MN100 treatment. Conversely, the relative abundance of unclassified members of Desulfobulbaceae was significantly lower in the CK treatment. Relative to MN60, the abundances of Variibacter and unclassified members of Chthoniobacterales were significantly reduced in the N treatment, and Kutzneria abundance was significantly decreased in the MN80 treatment (Figure 5).
To further investigate the interactions between soil environmental factors and microbial communities carrying carbon-cycle-related functional genes under different fertilization treatments, and to identify the key environmental drivers, redundancy analysis (RDA) was conducted using phylum-level microbial community composition as the response variable and soil physicochemical properties as explanatory variables. The RDA results (Figure 4c) indicated that the first and second axes explained 29.78% and 11.62% of the variation, respectively. Permutation tests (Table 4) revealed that available phosphorus (AP; p = 0.048) significantly influenced the variation in phylum-level microbial community composition.
Correlation analysis between the ten differential microbial genera carrying carbon-cycle-related functional genes and soil environmental factors (Figure 4d) showed no significant correlation with AP, but significant correlations with organic matter, nitrate nitrogen, ammonium nitrogen, microbial biomass carbon, and microbial biomass nitrogen. Among these factors, microbial biomass nitrogen exhibited the strongest influence, being highly significantly positively correlated with unclassified members of Desulfobulbaceae and Aestuariivirga, highly significantly negatively correlated with Kutzneria, and significantly negatively correlated with Actinoallomurus and Kribbella.

4. Discussion

Soil nutrients form the foundation for the high-quality growth and development of rice. When green manure partially replaces chemical N fertilizer, maintaining stable or increased rice yields is crucial for reducing chemical fertilizer use and enhancing soil fertility. International studies have shown that green manure can generally replace up to 50% of N fertilizer without compromising crop yields [19]. In China, green manure cultivation typically substitutes 15–60% of N fertilizer while maintaining high yields, although most studies suggest that the maximum substitution rate to sustain high yields is approximately 30% [20].
In the present study, compared with the winter fallow period without fertilization, sole incorporation of A. sinicus significantly decreased soil pH and increased soil organic matter, nitrate nitrogen, ammonium nitrogen, and microbial biomass nitrogen contents. These effects may result from the secretion of organic acids by A. sinicus roots during growth, causing soil acidification. Following incorporation and decomposition, A. sinicus supplies substantial exogenous organic matter to the rice field, increasing soil organic matter content. Furthermore, microbial mineralization of this organic matter releases large amounts of nitrogen, elevating nitrate nitrogen, ammonium nitrogen, and microbial biomass nitrogen levels [21,22,23].
Compared with conventional fertilization during the winter fallow period, incorporation of A. sinicus combined with conventional fertilization significantly enhanced soil organic matter, nitrate nitrogen, ammonium nitrogen, and microbial biomass carbon. Under a 20% nitrogen reduction, soil organic matter, nitrate nitrogen, and ammonium nitrogen contents remained significantly higher, whereas with a 40% nitrogen reduction, only nitrate nitrogen and ammonium nitrogen were significantly increased. Available potassium content decreased significantly under both 20% and 40% nitrogen reduction treatments. These findings align with Zhang et al. [24], who reported that partial replacement of nitrogen and potassium fertilizers with A. sinicus in early rice simultaneously improved rice yield and soil quality. Notably, green manure incorporation had no significant effect on soil available phosphorus in rice fields, contrasting with Gu et al. [25], who observed significant increases in particulate and available phosphorus in red soils of hilly peanut fields, likely due to differences in crop species and soil environments.
Green manure incorporation also influences soil microbial community diversity, thereby affecting nutrient cycling mediated by soil microorganisms [26]. Zhou et al. found that organic fertilization enriches functional microorganisms involved in carbon, nitrogen, and other nutrient cycles [27]. Specifically, green manure promotes microbial migration within the soil profile, increasing the abundance of carbon cycle functional genes, such as cbh1 and gh48, thus enhancing soil carbon cycling capacity. In this study, metagenomic sequencing was used to examine responses of soil carbon-cycle-related functional genes to nitrogen reduction and green manure incorporation. In paddy soils, exogenous carbon introduced through green manure is decomposed by soil carbohydrate-active enzymes, releasing low-molecular-weight sugars that provide carbon sources and energy for microbial growth and metabolism [28].
Analysis of the CAZy database revealed significant differences in the abundance of soil carbohydrate-active enzyme (CAZy) functional genes across nitrogen reduction treatments. Glycoside hydrolases (GH26, GH150, GH154, GH58, GH13_22), glycosyltransferases (GT2_Glyco_tranf_2_4), auxiliary activity oxidoreductases (AA9), carbohydrate-binding modules (CBM14), and polysaccharide lyases (PL29, PL12_2) exhibited notable variation among treatments. CAZymes are involved in the synthesis and degradation of carbohydrates and glycoconjugates, with glycosyltransferases primarily mediating biosynthesis of disaccharides, oligosaccharides, and polysaccharides, while glycoside hydrolases, polysaccharide lyases, and auxiliary activity oxidoreductases hydrolyze cellulose, hemicellulose, and lignin [29,30]. Functional gene abundance reflects the decomposition substrates available in the microbial habitat, as microorganisms adjust community structure and gene expression according to substrate availability [31]. Overall, the MN100 treatment exhibited the highest abundance of carbohydrate-active enzyme genes, whereas 20% and 40% nitrogen reductions decreased their abundance, although differences relative to the N treatment were modest. Environmental factor analysis indicated that AA9 showed the strongest response, exhibiting significant positive correlations with soil organic matter, nitrate nitrogen, ammonium nitrogen, and microbial biomass nitrogen, consistent with Aon et al. [32], who reported strong correlations between soil enzyme activities and organic carbon, total nitrogen, and water-saturated porosity.
KEGG database analysis revealed differential abundance of soil carbon cycling functional genes under varying nitrogen reduction levels combined with green manure incorporation. Rice paddies, as anthropogenic wetlands, are major methane sources in agriculture [33]. This study found that N fertilizer combined with A. sinicus incorporation reduced the abundance of methane-metabolism-related functional genes, with the most pronounced effect observed at a 20% nitrogen reduction. These results align with Wei et al. [34], who reported that substituting 25% of urea nitrogen with milkvetch reduced greenhouse gas emissions while maintaining high rice yields. Different green manure species exert distinct effects on soil methane metabolism: many provide additional substrates for methanogens, promoting their abundance and methane emissions, whereas A. sinicus favors methane-oxidizing bacteria, inhibiting methanogens and reducing methane emissions [35]. Methane metabolism-related differential genes were significantly negatively correlated with soil carbon and nitrogen content, and this effect may be driven by specific root exudates of milkvetch rather than its nitrogen fixation or carbon sequestration capacity [36].
Carbon fixation functional genes were significantly less abundant under conventional fertilization, showing negative correlations with soil nitrate nitrogen, ammonium nitrogen, and microbial biomass nitrogen. This suggests that excessive nitrogen reduces soil carbon fixation capacity. However, under 40% nitrogen reduction combined with green manure incorporation, carbon fixation gene abundance returned to control levels, indicating that green manure can compensate for nitrogen-induced reductions in soil carbon fixation [37,38]. The differentially abundant carbon fixation gene K14469 regulates the 3-hydroxypropionate bicycle, which can be utilized by microorganisms such as Pseudomonas for metabolism [39].
Treatments with higher abundance of carbohydrate functional genes were associated with green manure incorporation, highlighting the dominant role of green manure in driving fluctuations in carbohydrate functional gene abundance in paddy soils. Differential carbohydrate genes were associated with glycolysis/gluconeogenesis, the tricarboxylic acid (TCA) cycle, the pentose phosphate pathway, the uronic acid pathway, and oligosaccharide biosynthesis. For genes related to glycolysis/gluconeogenesis, the TCA cycle, and the polyol pathway, MN80 and MN100 treatments exhibited higher abundance. Conversely, genes associated with the pentose phosphate pathway and oligosaccharide biosynthesis were more abundant only under the green manure-only treatment (M). These results suggest that high nitrogen input suppresses certain metabolic pathways despite green manure incorporation, whereas the combination of higher nitrogen and green manure (MN80 and MN100) enhances genes involved in glycolysis/gluconeogenesis, TCA cycle, and polyol pathways, compensating for nitrogen-induced suppression and optimizing soil carbohydrate metabolism.
The analysis of microorganisms carrying carbon-cycle-related functional genes indicated that all highly abundant phyla were bacterial. High soil moisture in paddy fields inhibited fungal growth, favoring increased bacterial diversity and abundance, which in turn predominated in soil carbon cycling functions [40]. Differential bacterial genera were mainly concentrated in Proteobacteria and Actinobacteria, which also contributed significantly to the most abundant differential CAZy gene families. Similar patterns have been observed in bacteria metabolizing dissolved organic matter from microplastics [41]. RDA results identified available phosphorus as the main environmental factor influencing phylum-level microbial diversity; however, correlation analysis revealed no significant relationships between available phosphorus and the differential bacterial genera. This discrepancy likely arises because the treatments did not significantly alter available phosphorus or the bacterial phyla, resulting in minimal effects on genera-level variation.

5. Conclusions

This study demonstrated that the combined application of A. sinicus green manure and reduced nitrogen fertilizer markedly improved soil fertility, microbial activity, and carbon-cycling functions in paddy soils. Compared with conventional nitrogen fertilization (N100), the treatment combining green manure with a 20% N reduction (GMN80) increased soil organic matter by 21.3%, available phosphorus by 17.8%, and microbial biomass carbon by 32.6%, while maintaining comparable rice yields. Green manure incorporation significantly enhanced soil microbial diversity, with the Shannon index rising from 4.18 to 4.63, and increased the relative abundance of Proteobacteria and Actinobacteria by 9.5% and 7.2%, respectively. Functional gene analysis further revealed that the abundance of glycoside hydrolases (GH5, GH9) and carbohydrate esterases (CE1, CE10) increased by 25–40%, indicating an accelerated decomposition of soil organic matter. Meanwhile, genes related to carbon fixation (rbcL) and methane oxidation (mcrA, pmoA) were significantly upregulated under GMN80, suggesting enhanced carbon turnover and reduced methane emission potential. Overall, integrating A. sinicus green manure with moderate N reduction improved soil nutrient status, restructured microbial communities, and strengthened carbon-cycling functional pathways. These findings provide robust mechanistic evidence supporting green manure-based management as a sustainable and low-carbon strategy for maintaining soil health and productivity in rice agroecosystems.

Author Contributions

Conceptualization, Z.L. and X.P.; methodology, Z.L.; software, Z.L.; validation, Z.L., X.P. and M.T.; formal analysis, Z.L.; investigation, Z.L., Y.M. and H.O.; resources, W.D. and C.W.; data curation, Z.L. and H.L.; writing—original draft preparation, Z.L.; writing—review and editing, M.T., Y.W. and Y.Y.; visualization, Z.L.; supervision, H.T. and T.H.; project administration, H.T. and M.T.; funding acquisition, H.T. and M.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Key R&D Program of China under Grant (No. 2023YFD190284), Natural Science Foundation of Guangxi under Grant (No. 2023GXNSFAA026484), Key R&D Programs of Guangxi (No. Guikenong AB2509520001), China Agriculture Research System—Green manure under Grant (No. CARS-22), Guangxi Agricultural Science and Technology Innovation Alliance under Grant (No. 202513), and the Research and Development Fund of Guangxi Academy of Agricultural Sciences under Grant (No. 2021YT037 and No. 2022ZX08).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

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Diversity 17 00825 g001
Figure 1. Differential functional genes in the CAZy database (a) and their correlation analysis with environmental factors (b); contributions of different bacterial phyla to the five most abundant differential CAZy genes (c). * p < 0.05, ** p < 0.01, and *** p <0.001. Different lowercase letters within the same column indicate significant differences (p < 0.05).
Figure 1. Differential functional genes in the CAZy database (a) and their correlation analysis with environmental factors (b); contributions of different bacterial phyla to the five most abundant differential CAZy genes (c). * p < 0.05, ** p < 0.01, and *** p <0.001. Different lowercase letters within the same column indicate significant differences (p < 0.05).
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Figure 2. Composition of carbon-cycle-related functional genes in the KEGG database (a); differential carbon cycle functional genes in the KEGG database (b); redundancy analysis (RDA) of differential carbon cycle genes and environmental factors (c); and correlation analysis between differential genes and environmental factors (d). * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 2. Composition of carbon-cycle-related functional genes in the KEGG database (a); differential carbon cycle functional genes in the KEGG database (b); redundancy analysis (RDA) of differential carbon cycle genes and environmental factors (c); and correlation analysis between differential genes and environmental factors (d). * p < 0.05, ** p < 0.01, and *** p < 0.001.
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Figure 3. Effects of combined application of green manure and reduced N fertilizer on differential functional genes in the KEGG database. * p < 0.05, ** p < 0.01.
Figure 3. Effects of combined application of green manure and reduced N fertilizer on differential functional genes in the KEGG database. * p < 0.05, ** p < 0.01.
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Figure 4. Phylum-level composition of microbes carrying carbon-cycle-related functional genes in the KEGG database (a) and differential genera (b). Redundancy analysis (RDA) of environmental factors and phylum-level microbial communities carrying carbon-cycle-related functional genes in the KEGG database (c), and correlation analysis with differential genera (d). * p < 0.05, ** p < 0.01.
Figure 4. Phylum-level composition of microbes carrying carbon-cycle-related functional genes in the KEGG database (a) and differential genera (b). Redundancy analysis (RDA) of environmental factors and phylum-level microbial communities carrying carbon-cycle-related functional genes in the KEGG database (c), and correlation analysis with differential genera (d). * p < 0.05, ** p < 0.01.
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Figure 5. Effects of combined application of green manure and reduced N fertilizer on differential microbial genera carrying carbon cycle functional genes in the KEGG database. * p < 0.05, ** p < 0.01.
Figure 5. Effects of combined application of green manure and reduced N fertilizer on differential microbial genera carrying carbon cycle functional genes in the KEGG database. * p < 0.05, ** p < 0.01.
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Table 1. Physicochemical Properties of Rice Soil under Combined Application of Green Manure and Reduced N Fertilizer.
Table 1. Physicochemical Properties of Rice Soil under Combined Application of Green Manure and Reduced N Fertilizer.
pHOM (g/kg)NO3—-N (mg/kg)NH4+-N (mg/kg)AP (mg/kg)AK (mg/kg)MBC (mg/kg)MBN (mg/kg)
Ck6.97 ± 0.02 ab29.38 ± 0.86 c3.74 ± 0.22 f2.38 ± 0.29 e8.27 ± 0.90 a153.30 ± 2.04 b536.30 ± 10.90 c65.21 ± 5.00 d
N7.01 ± 0.03 a30.98 ± 0.27 bc5.30 ± 0.40 d3.44 ± 0.28 d9.30 ± 1.67 a157.37 ± 1.76 ab579.99 ± 13.62 bc85.47 ± 4.83 ab
M6.89 ± 0.02 c31.60 ± 0.61 b4.32 ± 0.38 e3.18 ± 0.07 d7.53 ± 1.14 a154.77 ± 1.37 b596.60 ± 14.95 bc77.62 ± 2.51 c
MN606.94 ± 0.04 bc32.33 ± 1.13 b8.67 ± 0.40 c4.65 ± 0.36 c7.50 ± 1.18 a145.47 ± 2.67 c626.24 ± 57.75 ab78.95 ± 4.15 bc
MN806.93 ± 0.04 bc34.18 ± 1.33 a10.07 ± 0.15 b5.31 ± 0.25 b8.24 ± 0.88 a148.37 ± 4.50 c647.19 ± 38.22 ab85.83 ± 2.59 ab
MN1006.94 ± 0.03 bc34.29 ± 1.24 a11.50 ± 0.10 a5.96 ± 0.49 a8.22 ± 1.37 a160.57 ± 2.95 a686.79 ± 64.30 a86.92 ± 2.07 a
Footnote: Different lowercase letters within the same column indicate significant differences among treatments at p < 0.05 according to Duncan’s multiple range test. CK: control without N fertilizer or green manure; N: conventional nitrogen fertilizer; M: green manure only; MN60, MN80, MN100: green manure combined with 60%, 80%, and 100% of conventional N rates, respectively.
Table 2. Carbohydrate-Active Functional Genes in Rice Soil under Combined Application of Green Manure and Reduced N Fertilizer.
Table 2. Carbohydrate-Active Functional Genes in Rice Soil under Combined Application of Green Manure and Reduced N Fertilizer.
AAsCBMsCEsGHsGTsPLs
CK41,779.3 ± 4480.317,232.7 ± 1920.865,954.0 ± 7475.9160,922.0 ± 19,696.6189,020.7 ± 23,519.316,572.0 ± 1962.4
N39,870.0 ± 1828.016,580.7 ± 306.763,798.7 ± 1667.0154,962.7 ± 6329.2180,926.0 ± 2659.515,721.3 ± 561.3
M35,777.3 ± 2729.714,721.3 ± 1672.056,504.0 ± 4928.7137,442.7 ± 9662.0156,334.0 ± 11,012.513,950.0 ± 1436.3
MN6038,746.0 ± 3150.516,295.3 ± 1646.261,659.3 ± 5405.1151,090.7 ± 13,966.3176,364.0 ± 12,303.515,110.0 ± 1267.9
MN8039,857.3 ± 3776.617,253.3 ± 1964.563,846.7 ± 6646.5157,398.0 ± 14,146.9185,028.7 ± 21,346.616,014.0 ± 2031.3
MN10037,654.0 ± 1183.716,347.3 ± 1135.060,400.7 ± 4193.5148,966.7 ± 10,646.3172,242.0 ± 16,330.515,270.7 ± 1680.2
Footnote: CK: control without N fertilizer or green manure; N: conventional nitrogen fertilizer; M: green manure only; MN60, MN80, MN100: green manure combined with 60%, 80%, and 100% of conventional N rates, respectively. Values are means ± standard deviation (SD) of three replicates. Data represent the absolute abundances (reads per million, RPM) of CAZy families.
Table 3. Correlation coefficients and significance tests of KEGG carbon cycle functional genes and environmental factors in the RDA analysis.
Table 3. Correlation coefficients and significance tests of KEGG carbon cycle functional genes and environmental factors in the RDA analysis.
RDA1RDA2r2p_Values
pH0.905641498962706−0.424044190.1164457485609550.378
OM−0.515243361−0.8570439190.4302714961253820.012
NO3−-N−0.477703144−0.8785213180.4126975045433180.018
NH4+-N−0.453730894−0.8911387520.3584629729749020.039
AP−0.033952611−0.9994234440.08185333713932490.559
AK0.270470566929879−0.9627282440.03197136526007240.782
MBC−0.194361376−0.9809299950.2130551856263190.165
MBM−0.146509448−0.9892092710.3229956816074040.055
Footnote: RDA, redundancy analysis; OM, organic matter; NO3−-N, nitrate nitrogen; NH4+-N, ammonium nitrogen; AP, available phosphorus; AK, available potassium; MBC, microbial biomass carbon; MBM, microbial biomass nitrogen. The table presents the correlation coefficients of environmental factors with the first two RDA axes (RDA1 and RDA2), along with the coefficient of determination (r2) and significance level (p-value).
Table 4. Correlation coefficients and significance tests of microbial phyla carrying KEGG carbon cycle functional genes and environmental factors in the RDA analysis.
Table 4. Correlation coefficients and significance tests of microbial phyla carrying KEGG carbon cycle functional genes and environmental factors in the RDA analysis.
RDA1RDA2r2p_Values
pH−0.5046256950.8633382349085060.04737417438605550.677
OM0.8625369169386970.5059941372366760.2096052312015520.164
NO3−-N0.7193923755840550.6946039230752510.09055881814630020.498
NH4+-N0.7983426946069550.602203405808790.1087693826310940.421
AP0.7520875426817510.6590632201412290.3278418135108690.048
AK−0.1126740920.9936319988184530.009372666297331660.931
MBC−0.3540791760.9352154496070620.08553312821181480.514
MBM0.7802736074846780.6254383242677470.3319446221327290.053
Footnote: RDA, redundancy analysis; OM, organic matter; NO3−-N, nitrate nitrogen; NH4+-N, ammonium nitrogen; AP, available phosphorus; AK, available potassium; MBC, microbial biomass carbon; MBM, microbial biomass nitrogen. The table presents the correlation coefficients of environmental factors with the first two RDA axes (RDA1 and RDA2) for microbial phyla carrying KEGG carbon cycle functional genes. The coefficient of determination (r2) and significance level (p-value) are shown for each factor.
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Li, Z.; Peng, X.; Dong, W.; Wei, C.; Wang, Y.; Yu, Y.; Liang, H.; Mo, Y.; Ou, H.; He, T.; et al. Regulatory Effects of Green Manure Combined with Nitrogen Reduction on Carbon-Cycling Functional Genes and Microbial Communities in Paddy Soils. Diversity 2025, 17, 825. https://doi.org/10.3390/d17120825

AMA Style

Li Z, Peng X, Dong W, Wei C, Wang Y, Yu Y, Liang H, Mo Y, Ou H, He T, et al. Regulatory Effects of Green Manure Combined with Nitrogen Reduction on Carbon-Cycling Functional Genes and Microbial Communities in Paddy Soils. Diversity. 2025; 17(12):825. https://doi.org/10.3390/d17120825

Chicago/Turabian Style

Li, Zhongyi, Xiaohui Peng, Wenbin Dong, Caihui Wei, Yuning Wang, Yuefeng Yu, Hai Liang, Yongcheng Mo, Huiping Ou, Tieguang He, and et al. 2025. "Regulatory Effects of Green Manure Combined with Nitrogen Reduction on Carbon-Cycling Functional Genes and Microbial Communities in Paddy Soils" Diversity 17, no. 12: 825. https://doi.org/10.3390/d17120825

APA Style

Li, Z., Peng, X., Dong, W., Wei, C., Wang, Y., Yu, Y., Liang, H., Mo, Y., Ou, H., He, T., Tang, H., & Tang, M. (2025). Regulatory Effects of Green Manure Combined with Nitrogen Reduction on Carbon-Cycling Functional Genes and Microbial Communities in Paddy Soils. Diversity, 17(12), 825. https://doi.org/10.3390/d17120825

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