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12 January 2026

Effects of Different Mixing Ratios of Glycyrrhiza pallidiflora Maxim. and Leymus chinensis on Silage Fermentation Characteristics and Bacterial Community Structure

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1
Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
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Liaoning Institute of Saline-Alkali Land Utilization, Shenyang Academy of Agricultural Sciences, Panjin 124010, China
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Institute of Economic Crops, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
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Authors to whom correspondence should be addressed.
Agriculture2026, 16(2), 189;https://doi.org/10.3390/agriculture16020189
This article belongs to the Special Issue Assessment of Nutritional Value of Animal Feed Resources
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Abstract

Glycyrrhiza pallidiflora Maxim., a perennial legume with high biomass yield and good nutritional value, has potential as a forage resource. This study examined how mixing G. pallidiflora (C) with Leymus chinensis (Y) at varying ratios (C10Y0, C9Y1, C8Y2, C7Y3, C6Y4) affects silage fermentation, chemical composition, and microbial community structure. All treatments were inoculated with Lactiplantibacillus plantarum (1 × 106 CFU/g fresh weight) and ensiled for 120 days. The results indicated that mixed silages markedly improved overall fermentation quality compared to the sole C silage (C10Y0). These mixed silages exhibited superior lactic acid (LA) concentrations, lower pH. Bacterial community profiling revealed that the addition of Y shifted the microbiota from a diverse community to one dominated by Lactobacillus. Although the C6Y4 and C7Y3 groups exhibited lower pH, they showed significantly elevated NH3-N contents, while their crude protein contents and the relative abundances of Lactobacillus were both lower than those of the C9Y1 and C8Y2 groups. Considering the core requirements of comprehensive quality, the mixing ratios of 9:1 (C9Y1) and 8:2 (C8Y2) demonstrated the optimal effects: at these ratios, the silage maintained a CP content of 12.84–14.48% DM, with NDF and ADF contents stabilized at 47.55–51.09% DM and 33.67–34.14% DM, respectively, and DM content of 28.85–31.32%; in terms of fermentation quality, the pH value decreased from 4.85 in the sole C silage (C10Y0) to 4.04–4.11, the LA content increased from 13.91 g/kg DM to 28.86–30.87 g/kg DM, the LA/AA ratio rose from 1.31 to 3.37–3.97, and the NH3-N content was reduced by 0.56–0.96% TN compared to the C10Y0 (decreasing to 4.16–4.45% TN), effectively inhibiting protein degradation; at the microbial level, the LAB count reached 9.03–9.05 log10 CFU/g FM, an increase of 2.12–2.14 compared to the C10Y0, with a relative abundance exceeding 80%, successfully suppressing the proliferation of undesirable bacteria such as Raoultella and Weissella and ensuring fermentation stability. This provides technical support for utilizing this plant as a viable alternative forage resource.

1. Introduction

In northern China, harsh winters and short growing seasons often lead to a severe shortage of high-quality forage, limiting the sustainable development of local animal husbandry [1]. Such forage is essential for ruminant health, providing key nutrients including energy, protein, and minerals [2]. Therefore, developing novel resources that can fill this seasonal gap has become increasingly important.
Glycyrrhiza pallidiflora Maxim. (C) is a perennial legume widely used in traditional Chinese medicine due to bioactive compounds, such as flavonoids and glycyrrhizin [3]. Its well-developed root system endows it strong drought and saline-alkali tolerance, making it valuable for ecological restoration, particularly in soil and water conservation [4,5]. With the expanding cultivation of C across northern China, the aboveground biomass—stems and leaves—has not been effectively utilized, resulting in both resource waste and potential environmental concerns. Studies have shown that these aerial parts are rich in protein, carbohydrates, vitamins, and essential amino acids, and that replacing corn stover and grains with fresh C stems and leaves can modulate the gut microbiota in livestock [6,7]. Thus, tapping into its potential as a functional feed could support more sustainable forage-livestock systems.
Ensiling offers an economical and reliable way to preserve forage. It relies on lactic acid bacteria (LAB) to metabolize water-soluble carbohydrates (WSC) into organic acids (predominantly lactic acid), which lowers the pH value and inhibits the activity of undesirable microorganisms, thereby achieving long-term preservation [8]. However, similar to other legumes like alfalfa, C has low WSC content and a high buffering capacity (BC), making it difficult to ensile on its own [9]. Leymus chinensis (Y), a dominant perennial grass species in northern grasslands, is characterized by tender stems, abundant leaves, and high feeding value [10,11]. Mixed ensiling of legumes and grasses is widely recognized as an effective strategy to improve silage quality [12,13]. This approach not only addresses the challenges of ensiling legumes alone but also enhances the protein content of grass silage, achieving a more balanced nutritional profile. In addition to the mixing ratio, LAB play a critical role in determining the quality of silage fermentation. Studies by Muck et al. [14,15] have shown that the addition of Lactiplantibacillus plantarum. (LP) can effectively improve the fermentation quality of forage silage. Although similar approaches have been extensively studied in alfalfa-grass mixtures [9,16,17,18], research on the ensiling potential of C aerial parts—which possess both ecological and medicinal value—remains limited.
To address this gap, this study conducted mixed ensiling trials using C and Y at different ratios with the addition of LP as an inoculant. By analyzing fermentation quality, nutritional composition, and microbial community dynamics, we aimed to identify the optimal mixing ratio that realizes the synergistic optimization of nutrient composition, fermentation quality, and microbial safety. The findings are expected to provide theoretical and technical support for developing C as a promising feed resource.

2. Materials and Methods

2.1. Silage Preparation

C and Y were harvested on 26 June 2025, from the experimental base of the Heilongjiang Academy of Agricultural Sciences (45.842731° N, 126.844513° E). C was at the early flowering stage with a stubble height of 10 cm, while Y was at the milk stage with a stubble height of 5 cm. After harvesting, all materials were immediately transported to the laboratory and chopped into 1–2 cm lengths using a forage chopper. The additive (LP, viability ≥ 10 billion CFU/g) was supplied by Zhenjiang Tianyi Biotechnology Co., Ltd., Zhenjiang, China. The experiment adopted a completely randomized design. The chopped C and Y were mixed on a fresh weight (FW) basis into five treatment groups: 10:0 (C10Y0), 9:1 (C9Y1), 8:2 (C8Y2), 7:3 (C7Y3), and 6:4 (C6Y4), with three replicates per group. Due to the low epiphytic lactic acid bacteria (LAB) counts on the raw materials, all treatments were inoculated with LP at 1 × 106 CFU/g FW. The prepared mixed raw materials from each treatment group were loaded into polyethylene vacuum bags (20 cm × 30 cm × 1.5 cm), with 300 g per bag. They were then vacuum-sealed using a commercial vacuum sealer (Model 380B, ANSHENGKE, Quanzhou, China) to ensure a packing density of 0.33 g FM/cm3, resulting in a total of 15 bags. After vacuum-sealed, the bags were stored at room temperature (23–25 °C) in a dry condition, with the humidity controlled at 30–40%. The silages were sampled for analysis after 120 days of ensiling.

2.2. Analysis of Chemical Composition and Fermentation Quality

All silage samples were oven-dried at 65 °C for 72 h to determine dry matter (DM) content using an electric blast drying oven (Model DHG-9145A, HENGYI, Shanghai, China). Dried samples were then ground through a 1 mm screen using a Wiley mill (Thomas Scientific, Swedesboro, NJ, USA) for chemical composition analysis. WSC content was measured using the anthrone-sulfuric acid method [19]. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents were determined using an ANKOM Model A2000i Fully Automatic Fiber Meter (Ankom Technology, Fairport, NY, USA) [20]. Crude protein (CP) content was measured using the Kjeldahl method [21]. The calculation method of relative feed value (RFV) was adopted from Sun [22]. For fermentation analysis, 20 g of silage sample was homogenized with 180 mL of sterile water and refrigerated at 4 °C for 12 h. The homogenate was then filtered through a 0.22-μm membrane to obtain the extract for fermentation parameter determination. The pH was measured using a pH meter (Model PHSJ-5; LEICI, Shanghai, China). The concentrations of organic acids, including lactic acid (LA), acetic acid (AA), propionic acid (PA), and butyric acid (BA), were analyzed using high-performance liquid chromatography (Shimadzu Corporation, Kyoto, Japan) following the method described in [23]. Ammonia nitrogen (NH3-N) concentration was determined using the phenol-sodium hypochlorite colorimetric method [24].

2.3. Measurement of Microbial Number

10 g of each silage sample was mixed with 90 mL of sterile physiological saline (0.90% NaCl) and shaken at 180 rpm for 1 h, followed by serial dilution. Microbial populations were enumerated using the plate count method: LAB were counted after anaerobic incubation on De Man, Rogosa and Sharpe (MRS) agar at 37 °C for 48 h; Total bacteria (TB) were counted on Luria–Bertani (LB) agar after incubation at 37 °C for 24 h [25]; Fungi were counted on Potato Dextrose Agar (PDA) after incubation at 28 °C for 72 h [26]. All culture media were supplied by Beijing Aobaosi Biotechnology Co., Ltd., Beijing, China. Microbial counts are expressed as log10 colony-forming units per gram of fresh matter (FM) [log10 (CFU/g FM)].

2.4. Bacterial Community Analysis

Total microbial DNA was extracted from the silage samples using the E.Z.N.A.® soil DNA kit (Omega Bio-Tek, Norcross, GA, USA). The purity and concentration of the extracted DNA were assessed using the NanoDrop2000 system, and DNA integrity was verified by 1% agarose gel electrophoresis. The V3-V4 region of the bacterial 16S rDNA was subsequently amplified using primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The amplification products were separated by 2% agarose gel electrophoresis and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). Purified products were quantified using the QuantiFluor™-ST blue fluorescence quantification system (Promega, Madison, WI, USA). A blank control (PCR reaction system without sample template) was set up during the experiment. Sequencing results showed that no valid microbial sequences were detected in the negative control, indicating no exogenous contamination throughout the experiment and reliable sequencing data.
Sequencing libraries were prepared following Illumina’s genomic DNA library preparation protocol. Purified DNA fragments were used to construct Illumina paired-end libraries, which were then sequenced on the Illumina PE300 platform. The sequencing depth of each sample in this study was ≥100,000 reads/sample. Raw data were processed using FASTP (v0.18.0). FLASH (v1.2.11) merged clean reads into tags using minimum overlap of 10 bp and maximum mismatch rate 2%. Usearch (v11.0.667) clustered clean tags into OTUs (97% similarity) via UPARSE. Chimeras were removed using UCHIME. Effective tags were used for OTU abundance statistics. Representative sequences of each OTU were selected using QIIME (v1.9.1) and taxonomically classified via the uclust method against the SILVA rRNA database (v138.2). The rarefaction method was used to normalize the sequence count of all samples to the minimum sequencing depth, eliminating the impact of sequencing depth differences on subsequent analyses.

2.5. Statistic Analysis

After preliminary data were processed in Excel 2020, all data were presented as the mean values of biological replicates. Statistical analysis was conducted using one-way analysis of variance (ANOVA) in SPSS 25.0 (IBM, Chicago, IL, USA), followed by Duncan’s multiple range test for comparisons. Alpha diversity, Principal coordinate analysis (PCoA), PERMANOVA and Spearman’s correlation analysis were conducted in R software (v3.6.2). Linear discriminant analysis effect size (LEfSe) was applied to identify differentially abundant taxa, with significance determined by an LDA score > 4.0 and p < 0.05.

3. Results

3.1. Chemical Composition and Microbial Counts of the Raw Materials

The chemical composition and microbial population in C and Y showed considerable differences, as presented in Table 1. Compared with C, Y demonstrates elevated levels of DM, WSC and fiber (NDF and ADF) content, alongside higher microbial counts. In contrast, the CP content of Y is lower than that of C.
Table 1. Chemical composition and microbial population in fresh raw materials.

3.2. Chemical Composition of Silage

The chemical composition after 120 days of ensiling is presented in Table 2. The mixing ratios of silage affected all measured chemical components (p < 0.05). DM content increased with higher Y proportion (p < 0.05), ranging from 24.54% in C10Y0 to 37.25% in C6Y4, with significant differences among all treatments (p < 0.05). The content of CP was influenced by mixing ratios (p < 0.05). C10Y0 had the highest CP content (16.23%), and with increasing proportion of Y, CP content decreased significantly (p < 0.05), the lowest CP content (9.37%) was observed in the C6Y4, C9Y1 and C8Y2 showed no significant difference in CP content (p > 0.05). WSC content increased with increasing proportion of Y (p < 0.05), with C10Y0 showing the lowest value (1.16%) and C7Y3 and C6Y4 the highest (1.57% and 1.56%, respectively). Both NDF and ADF contents were significantly affected by mixing ratios (p < 0.05), with the highest values detected in C6Y4 (57.38% and 36.67%, respectively) and the lowest in C10Y0 (40.94% and 31.65%, respectively). For NDF, C10Y0 was significantly lower than C6Y4 (p < 0.05), while C9Y1, C8Y2, and C7Y3 showed no significant differences (p > 0.05); for ADF, only C10Y0 was significantly lower than C6Y4 (p < 0.05), and no significant differences were found among other treatments (p > 0.05). RFV decreased significantly as the proportion of Y increased (p < 0.01), with C10Y0 exhibiting the highest value (147.11), C9Y1 showed no significant difference from C8Y2/C7Y3 (p > 0.05), and C6Y4 the lowest (97.82).
Table 2. Chemical composition after ensiling.

3.3. Fermentation Quality of Silage

The fermentation quality after 120 days of ensiling is shown in Table 3. The pH decreased significantly with increasing Y proportion, from 4.85 in C10Y0 to 3.93 in C6Y4 (p < 0.01). In contrast, LA content increased markedly with increasing Y proportion (p < 0.01), it was lowest in C10Y0 (13.91 g/kg DM), and C9Y1-C6Y4 showed no significant differences (p > 0.05), with the highest value (33.76 g/kg DM) in C6Y4. AA content was highest in C10Y0 (10.61 g/kg DM) and lowest in C7Y3 (7.56 g/kg DM), showing a general decreasing trend with increasing Y proportion, and C9Y1 had no significant difference from either C10Y0 or C8Y2/C7Y3 (p > 0.05), although C6Y4 displayed a slight rebound (8.13 g/kg DM, b), it was still significantly lower than C10Y0 (p = 0.014). PA content was highest in C10Y0 (0.57 g/kg DM) and trended to decrease in all mixed silages, but there was no significant difference among all treatments (p = 0.056). The LA/AA ratio, rose significantly from 1.31 in C10Y0 to a maximum of 4.32 in C7Y3, with C6Y4 (4.22) showing a similarly high ratio (p < 0.01), and C9Y1–C6Y4 showed no significant differences (p > 0.05). Additionally, NH3-N level was highest in C6Y4 (5.40% TN) and lowest in C9Y1 (4.16% TN), which had no significant difference from C8Y2 (p > 0.05). C10Y0 had no significant difference from C7Y3 (p > 0.05), and no significant difference was observed between C8Y2 and C7Y3 (p > 0.05). BA was not detected in any treatment.
Table 3. Fermentation quality after ensiling.

3.4. Dynamics of Microbial Population After Ensiling

The microbial population after 120 days of ensiling was significantly influenced by mixing ratio (Table 4). The LAB number increased with higher Y proportion, peaking in C8Y2 (9.03) and C7Y3 (9.05), while C10Y0 had the lowest value (6.91). TB number was highest in C10Y0 (3.41) and declined with higher Y proportion, reaching <2.00 found in C8Y2 and C7Y3. Similarly, fungal number was highest in C10Y0 (2.91) and fell below the detection limit (<2.00) in all mixed silages, with no significant differences among them.
Table 4. Microbial population after ensiling.

3.5. Bacterial Community of Silage

3.5.1. Alpha Diversity Analysis of Mixed Silage with Different Ratios

The alpha diversity and Coverage of bacterial communities in silage was analyzed (Table 5). C10Y0 had the highest Shannon (1.50) and Simpson (0.33) indices, significantly surpassing the values of mixed silage groups except C6Y4 (p < 0.05). Consistently, C10Y0 also had the highest community richness, as reflected by its Chao 1 (197.79) and ACE (199.39) indices. Meanwhile, the mixed-silage treatments (from C9Y1 to C6Y4), displayed a consistent upward trend in all diversity indices: the Shannon index increased gradually from 0.27 in C9Y1 to 1.18 in C6Y4, while the Simpson index rose from 0.05 to 0.27. Similimarly, both the Chao 1 and ACE indices showed a steady increase from 104.65 and 119.52 in C9Y1 to 181.58 and 185.41 in C6Y4, respectively.
Table 5. Microbial alpha diversity indexes of Mixing Silage with Different Ratios.

3.5.2. Beta Diversity Analysis of Mixed Silage with Different Ratios

Principal coordinate analysis (PCoA) illustrating differences in bacterial community diversity among the five silage groups is presented in Figure 1. PCo1 and PCo2 explained 61.02% and 19.48% of the observed variations, respectively, accounting for 80.50% cumulatively and effectively capturing the major patterns of community structure. Through this analysis, the bacterial communities were classified into three distinct clusters. Specifically, C10Y0 formed an independent cluster with no overlap with the mixed-silage groups, indicating substantial divergence in its bacterial community. In contrast, C9Y1 and C8Y2 clustered closely together, reflecting high compositional similarity. Also, C7Y3 and C6Y4 formed distinct clusters, with C7Y3 samples aggregating in one region and C6Y4 samples in another, suggesting distinct bacterial community differentiation as the proportion of Y increased. To further quantify the significance of these community differences, PERMANOVA was performed: the results indicated that the bacterial community structure differed extremely significantly among the five silage groups (R2 = 0.806, p < 0.001), which strongly supported the distinct clustering patterns observed in the PCoA plot.
Figure 1. Principal coordinate analysis (PCoA) in silage. G. pallidiflora and L. chinensis were mixed at proportions of 10:0 (C10Y0), 9:1 (C9Y1), 8:2 (C8Y2), 7:3 (C7Y3) and 6:4 (C6Y4).

3.5.3. Phylum-Level Distribution of Bacteria in Mixed Silage with Different Ratios

At the phylum level (Figure 2), Firmicutes and Proteobacteria dominated the bacterial community in C10Y0. When a small proportion of Y was added (C9Y1), the relative abundance of Firmicutes increased sharply from approximately 60% to 98%. However, with further increase in Y proportion (C8Y2 to C6Y4), Firmicutes displayed a gradual decreasing trend, although it remained the prominent phylum across all mixed-silage treatments (C9Y1 to C6Y4). Conversely, the relative abundance of Proteobacteria increased progressively with the rising proportion of Y.
Figure 2. Relative abundance of bacteria at the phylum level. G. pallidiflora and L. chinensis were mixed at proportions of 10:0 (C10Y0), 9:1 (C9Y1), 8:2 (C8Y2), 7:3 (C7Y3) and 6:4 (C6Y4).

3.5.4. Genus-Level Distribution of Bacteria in Mixed Silage with Different Ratios

At the genus level (Figure 3), within the C10Y0 group, Lactobacillus exhibited the highest relative abundance in C10Y0, while other genera, such as Raoultella, Escherichia–Shigella, and Weissella, were present at lower but notable proportions. In the mixed-silage groups, Lactobacillus became overwhelmingly dominant, with its relative abundance exceeding 80% in the majority of mixed-silage treatments. However, a marked shift in community composition was observed in C7Y3 and C6Y4, where the relative abundance of Lactobacillus decreased substantially, accompanied by a pronounced increase in Raoultella, which emerged as one of the dominant genera in C6Y4. Other genera, such as Escherichia–Shigella, Weissella, and Bacillus, were consistently detected at low abundances and showed minor variations among different groups.
Figure 3. Relative abundance of bacteria at the genus level. G. pallidiflora and L. chinensis were mixed at proportions of 10:0 (C10Y0), 9:1 (C9Y1), 8:2 (C8Y2), 7:3 (C7Y3) and 6:4 (C6Y4).

3.5.5. Comparison of Bacterial Variations in Mixed Silage of Different Ratios

The linear discriminant analysis (LDA) effect size (LEfSe) method was used to identify bacterial taxa that differed significantly among different silage groups (LDA score > 4.0; Figure 4). In C9Y1, Lactobacillus and its affiliated taxa, including Lactobacillaceae, Lactobacillales, Bacilli, and Firmicutes, exhibited significantly high LDA scores, demonstrating their strong enrichment in this treatment. Neisseriaceae, Caulobacteraceae, and Caulobacterales were the characteristic taxa in C7Y3. In C6Y4, taxa such as Raoultella, Enterobacteriaceae, Enterobacterales, Gammaproteobacteria, and Proteobacteria were prominently enriched. In contrast, Escherichia–Shigella was the distinct taxon in C10Y0. No taxa in C8Y2 exhibited an LDA score exceeding the threshold of 4.0.
Figure 4. Comparison of bacterial variations in mixed silage using the LEfSe online tool. G. pallidiflora and L. chinensis were mixed at proportions of 10:0 (C10Y0), 9:1 (C9Y1), 7:3 (C7Y3) and 6:4 (C6Y4).

3.5.6. Spearman Correlation Analysis of Bacterial Community with Fermentation Characteristics

Spearman correlation analysis was performed to assess the relationships between fermentation parameters and bacterial communities (Figure 5). pH was significantly positively correlated with Lactobacillus (p < 0.01) and significantly negatively correlated with Raoultella (p < 0.01), as well as Weissella, Serratia, Bacillus, and Pantoea (p < 0.05). LA showed a significant negative correlation with Lactobacillus (p < 0.05), while significant positive correlations were observed with Raoultella, Escherichia–Shigella, and Weissella (p < 0.05). PA was significantly negatively correlated with Lactobacillus (p < 0.05) and extremely significantly positively correlated with Escherichia–Shigella (p < 0.01). NH3-N exhibited an extremely significant negative correlation with Lactobacillus (p < 0.01) and significant positive correlations with Raoultella, Escherichia–Shigella, Weissella, Serratia, and Pantoea (p < 0.05). Other genera, such as Pseudomonas, Streptococcus, and Methylobacterium–Methylorubrum, showed relatively weak correlations with the fermentation indicators, with limited statistical significance.
Figure 5. Spearman correlation analysis of bacterial community with fermentation characteristics at the genus level. A positive correlation was indicated by red color, and negative correlation was indicated by blue color. * and ** represent p < 0.05 and p < 0.01, respectively. LA, lactic acid; AA, acetic acid; PA, propionic acid; NH3-N, Ammonia nitrogen.

4. Discussion

4.1. Effects of Mixed Silage with Different Ratios of G. pallidiflora and L. chinensis on Chemical Composition

The DM content of raw materials prior to ensiling is a key determinant of silage quality. Studies have shown that silage made from raw materials with DM content ranging from 30% to 35% is more likely to ferment successfully [27]. This is because high DM can reduce palatability and impede the rapid establishment of anaerobic conditions, whereas low DM may lead to effluent losses of WSC [28]. In the present study, the DM content increased linearly (from 28.85% to 37.25%) with rising proportion of Y, consistent with its inherently higher DM content compared to C. This trend aligns with findings reported by Carpici [29], who reported that mixed ensiling with maize can effectively offset the high moisture content of soybean which poses challenges for ensiling it alone. Leguminous forages are notoriously difficult to ensile due to their relatively high CP and low WSC contents, leading to poor fermentation quality and inadequate nutrient preservation [30]. To address this issue, mixing leguminous forages with WSC-rich gramineous crops during ensiling can supply additional fermentable substrates for LAB, thereby enhancing fermentation efficiency [31]. The WSC content above 5% is crucial for achieving high fermentation quality [32]. In the current study, compared with the single C silage group inoculated with LP, the inclusion of Y effectively increased the WSC content of the mixed silage, providing ample fermentative substrates for LAB proliferation. This enhancement accelerated LA production and effectively lowered pH, consistent with results from mixed silage of alfalfa and whole-plant corn [12]. CP, NDF, and ADF are key indicators of forage feeding value, with high-quality forage characterized by high CP content and appropriate fiber levels. In this experiment, the CP content in the silage showed a linear decrease with the increasing proportion of Y, which is likely attributed to the inherently lower protein content of Y itself. The generally rising trends in NDF and ADF with higher Y inclusion correspond to its greater fiber content compared to C. Interestingly, C7Y3 exhibited the lowest NDF and ADF contents, potentially attributed to its high LA accumulation. LAB may produce acidic hydrolytic/fibrinolytic enzymes that degrade plant cell walls [33], thereby reducing the fiber content. RFV is used to classify forage quality: premium (>151), prime (125–151), good (103–124), fair (87–102), poor (75–86), and very poor (<75) [34]. In this study, RFV decreased markedly from 147.11 in C10Y0 to 97.82 in C6Y4, suggesting that a high proportion of Y may negatively impact potential intake and digestibility compared to C or moderately mixed silage. Therefore, future studies can further evaluate the effects of mixed silage of C and Y at different ratios on ruminant growth performance, nutrient digestibility, and rumen fermentation parameters, so as to provide more comprehensive technical support for the practical promotion and application of the forage utilization of C. Notably, among all the mixing ratios tested, the 9:1 and 8:2 combinations achieved the optimal balance between nutritional quality and fermentation performance: they retained relatively high CP content and RFV while ensuring sufficient WSC supply for desirable lactic acid fermentation and pH reduction. In contrast, higher Y proportions (7:3 and 6:4) further improved fermentation traits (e.g., pH/LA), but caused excessive CP loss and RFV decline, whereas lower Y proportions (10:0) failed to provide adequate fermentable substrates for stable silage formation. Collectively, these findings highlight that the 9:1 and 8:2 ratios are the ideal mixing strategies for practical production of C-Y mixed silage.

4.2. Effects of Mixed Silage with Different Ratios of G. pallidiflora and L. chinensis on Fermentation Quality

High-quality silage is characterized by high LA content, low BA and NH3-N levels, and an appropriate pH value [8]. Additionally, the presence of AA, PA, and BA is generally undesirable [35,36]. For high-moisture (70–75%) material, a pH below 4.5 is recommended for well-fermented silage [37]. In this study, C10Y0 exhibited typical characteristics of failed fermentation, including an elevated pH (4.85) exceeding the recommended threshold (pH < 4.2) [38] and a low LA concentration (13.91 g/kg DM). This combination of high pH and low LA indicates weak LAB fermentation in C10Y0, where LAB failed to compete effectively against undesirable microorganisms. This may be attributed to the high BC and insufficient WSC content of C, which hindered rapid growth and LA production by the inoculated LAB, even with external inoculation. As the proportion of Y increased, fermentation quality improved significantly. First, pH decreased in a stepwise manner (from 4.85 in C10Y0 to 3.93 in C6Y4), indicating a rapid and effective acidification of the silage, accompanied by sharp increases in LA production. LA doubled from C10Y0 to C9Y1 (from 13.91 to 28.86 g/kg DM) and reached its highest levels in C7Y3 and C6Y4 (32.56 and 33.76 g/kg DM, respectively). Similar enhancements have been reported in mixed silage of Licorice stems and leaves and corn [39]. This improvement can be attributed to: first, increased DM content, which reduces effluent losses and creates a more favorable physical environment for microbial fermentation; secondly, higher and more diverse WSC from Y, promoting homolactic fermentation [12,40]. This shift is strongly supported by the significant increase in the LA/AA ratio, which rose from 1.31 in C10Y0 to 4.32 in C7Y3. A higher LA/AA ratio generally indicates more efficient homolactic fermentation, which helps better preserve nutrients. Non-protein nitrogen in silage, including NH3-N, results mainly from proteolysis during ensiling, driven by plant proteases and undesirable microbial activity [41]. Higher NH3-N content indicates greater protein degradation and loss. The high NH3-N content in C10Y0 aligns with its high pH, as proteolytic enzymes—both plant and microbial—remain highly active under less acidic conditions. This trend is consistent with the findings of Khan et al. [42], who reported that rapid pH reduction helps suppress proteolytic enzyme activity, thereby reducing protein degradation and better preserving the nutritional value of silage. The inclusion of Y, even in small amounts (C9Y1), immediately and significantly lowered NH3-N content. This reduction is directly attributed to pH drop resulting from LAB-dominated rapid acidification, which effectively inhibited proteolytic enzyme activity [43]. Interestingly, C6Y4 showed elevated AA and NH3-N levels compared to C7Y3 despite having the lowest pH and highest LA. This could be attributed to the higher DM content in C6Y4 delaying the establishment of complete anaerobic conditions, allowing some proteolysis to persist. Additionally, Legume surfaces harbor acetic acid bacteria capable of metabolizing fructose and glucose via the pentose phosphate pathway, producing AA from acetaldehyde as an intermediate before anaerobic conditions are fully established [44]. Another possibility is that certain acid-tolerant microorganisms may convert LA into AA once the pH drops below 4.0 [45].

4.3. Effects of Different Mixing Ratios on the Microbial Community of Silage

Ensiling is a complex microbiologically driven process in which microbial community structure significantly influences the nutritional composition and fermentation quality of the silage [46]. After the ensiling period, C10Y0 exhibited the highest total bacterial and fungal counts, along with the highest Shannon and Simpson indices. This indicates that LAB failed to establish dominance, allowing various aerobic spoilage microorganisms to proliferate [47]. These findings are fully consistent with the poor fermentation quality observed in C10Y0 (high pH, high NH3-N). As the proportion of Y increased, LAB counts increased significantly, peaking in C7Y3 (9.05), while total bacteria and fungi decreased to <2.00. Concurrently, all alpha diversity indices in the mixed silage groups were significantly lower than those in C10Y0. Xu et al. [48] reported that alpha diversity typically decreases when LAB become dominant. This shift from a heterogenous to a LAB-dominated community is a key indicator of successful ensiling and stabilized fermentation [49]. Beta diversity (PCoA) reflects a clear segregation of C10Y0 from all mixed groups. Li et al. [50] observed a similar pattern in the beta diversity analysis of mixed silage of corn stover and cassava, indicating that the addition of Y significantly influenced the bacterial community in the silage. Firmicutes, which include many beneficial bacteria, can degrade compounds such as starch and fiber, thereby promoting silage fermentation. In contrast, Proteobacteria encompass various pathogenic bacteria that compete with LAB for WSC, reducing silage quality [51]. In this experiment, Firmicutes and Proteobacteria coexisted in C10Y0, whereas Firmicutes established absolute dominance in the mixed silage groups, with a relative abundance exceeding 80%. This result aligns with the findings of Si et al. [52], suggesting that the addition of Y provides sufficient fermentable substrates for LAB, enabling their rapid proliferation and establishment of dominance. During ensiling, bacteria such as Lactobacillus and Pediococcus are the primary contributors to LA production [47]. In this study, Lactobacillus absolutely dominated in C9Y1 and C8Y2, corresponding to their excellent fermentation quality. Consistent with the present study, Xu et al. [53] found that in L. chinensis silage inoculated with LAB, the absolutely dominant Lactobacillus could effectively inhibit Enterobacter, while decreasing pH, increasing lactic acid content, reducing ammonia nitrogen levels, and ultimately improving silage fermentation quality. However, in groups with the highest Y proportions (C7Y3 and C6Y4), the abundance of Lactobacillus decreased, while that of Raoultella increased. Raoultella is a harmful bacterium capable of competing with LAB for fermentable substrates and tolerating low pH conditions [54,55]. Its increase may be related to the higher DM content associated with high Y proportion, which could delay the establishment of strict anaerobic conditions and provide an ecological niche for acid-tolerant, facultative anaerobic bacteria like Raoultella. LEfSe analysis further identified group-specific biomarkers, such as Lactobacillus in C9Y1 and Raoultella in C6Y4. These shifts in key taxa largely account for the observed differences in community structure. Correlation analysis between the microbial community and fermentation parameters ultimately links structure to function. Lactobacillus showed a significant negative correlation with NH3-N, confirming its vital role as a beneficial functional bacterium. In contrast, Raoultella, Weissella, and others correlated positively with NH3-N and negatively with LA, confirming their negative role in participating in undesirable fermentation and promoting protein degradation. It is noteworthy that while a positive correlation between Lactobacillus abundance and LA concentration is commonly accepted, the relative abundance of Lactobacillus was found to be negatively correlated with LA concentration in the present study. This could be attributed to the “substitutional effect” of other acid-producing genera (e.g., Raoultella, Weissella): these taxa also contributed to LA synthesis, weakening Lactobacillus’ exclusive association with LA accumulation. A similar finding was also reported by Wang et al. in alfalfa silage [56], suggesting this pattern may be common in mixed silage systems with complex microbial communities. The balance between beneficial taxa (e.g., Lactobacillus) and undesirable genera (e.g., Raoultella) directly shapes key fermentation indicators (LA, NH3-N), while the relative contribution of different acid-producing bacteria modulates the association between Lactobacillus abundance and lactic acid accumulation.

5. Conclusions

This study demonstrates that under the condition of inoculation of LP, co-ensiling C with Y can significantly enhances the chemical composition and fermentation quality of silage. Although the C6Y4 and C7Y3 groups exhibited lower pH, they showed significantly elevated NH3-N contents, while their crude protein contents and the relative abundances of Lactobacillus were both lower than those of the C9Y1 and C8Y2 groups. Considering the core requirements of comprehensive quality, the mixing ratios of 9:1 (C9Y1) and 8:2 (C8Y2) demonstrated the optimal effects. at these ratios, the silage maintained a CP content of 12.84–14.48% DM, with NDF and ADF contents stabilized at 47.55–51.09% DM and 33.67–34.14% DM, respectively, and DM content of 28.85–31.32%; in terms of fermentation quality, the pH value decreased from 4.85 in the sole C silage (C10Y0) to 4.04–4.11, the LA content increased from 13.91 g/kg DM to 28.86–30.87 g/kg DM, the LA/AA ratio rose from 1.31 to 3.37–3.97, and the NH3-N content was reduced by 0.56–0.96% TN compared to the C10Y0 (decreasing to 4.16–4.45% TN), effectively inhibiting protein degradation; at the microbial level, the LAB count reached 9.03–9.05 log10 CFU/g FM, an increase of 2.12–2.14 compared to the C10Y0, with a relative abundance exceeding 80%, successfully suppressing the proliferation of undesirable bacteria such as Raoultella and Weissella and ensuring fermentation stability.

Author Contributions

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

Funding

This research was supported by the Agricultural Science and Technology Innovation Leap Project of Heilongjiang Province (CX23GG06) and the Project of Laboratory of Advanced Agricultural Sciences, Heilongjiang Province (ZY04JD05-004).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. Sequencing data were submitted to the NCBI Sequence Read Archive database (accession number: PRJNA1397025).

Conflicts of Interest

The authors declare no conflicts of interest.

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