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Article

Effects of Different Additives on the Chemical Composition, Fermentation Quality, Bacterial Community and Gene Function Prediction of Caragana korshinskii Kom. Silage

by
Yuxiang Wang
1,
Manlin Wei
1,*,
Fuyu Yang
2,
Haiying Zheng
3,
Junjie Gao
3,
Wen Peng
1,
Ming Xiao
1,
Runze Zhang
1 and
Yongjie Zheng
1
1
College of Animal Science and Technology, Inner Mongolia Minzu University, Tongliao 028000, China
2
College of Grassland Science and Technology, China Agricultural University, Beijing 100091, China
3
Tongliao Institute of Agriculture and Animal Husbandry, Tongliao 028000, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(10), 2386; https://doi.org/10.3390/agronomy14102386
Submission received: 29 August 2024 / Revised: 13 October 2024 / Accepted: 14 October 2024 / Published: 15 October 2024
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
Browse Figures
Figure 1
<p>Venn diagram of the bacterial species. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p> "> Figure 2
<p>PCoA of the bacterial species diversity in <span class="html-italic">C. korshinskii</span> Kom. silage at 15 days (<b>A</b>) and 60 days (<b>B</b>). CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p> "> Figure 3
<p>Distribution of bacterial communities at the phylum (<b>A</b>) and genus (<b>B</b>) levels at days 15 and 60 in <span class="html-italic">C. korshinskii</span> Kom. silage. Small populations with abundances less than 0.01 were combined as others. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p> "> Figure 4
<p>Species differences in bacterial genera (LDA = 3) between 15 days (<b>A</b>) and 60 days (<b>B</b>) of ensiling. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p> "> Figure 4 Cont.
<p>Species differences in bacterial genera (LDA = 3) between 15 days (<b>A</b>) and 60 days (<b>B</b>) of ensiling. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p> "> Figure 5
<p>Heatmap of the Spearman correlation coefficients of chemical composition, fermentation quality and bacterial genera of <span class="html-italic">C. korshinskii</span> Kom. silage at 15 (<b>A</b>) and 60 (<b>B</b>) days. The colour of the heatmap indicates the Spearman correlation coefficient “R” (−1 to 1). R &gt; 0 indicates a positive correlation, and R &lt; 0 indicates a negative correlation. *, 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05; **, 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p> "> Figure 6
<p>Predicted pathways of the bacterial community in <span class="html-italic">C. korshinskii</span> Kom. at 15 days and 60 days of ensiling. (<b>A</b>) the first metabolic pathway at 15 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>B</b>) the first metabolic pathway at 60 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>C</b>) the second metabolic pathway at 15 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>D</b>) the second metabolic pathway at 60 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>E</b>) carbohydrate metabolism of the third pathway level at 15 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. (<b>F</b>) carbohydrate metabolism of the third pathway level at 60 days of <span class="html-italic">C. korshinskii</span> Kom. Silage. CK, control; LP, <span class="html-italic">Lentilactobacillus plantarum</span>; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.</p> ">
Versions Notes

Abstract

:
The aim of this study was to investigate the effects of Lentilactobacillus plantarum (LP), cellulase (CE), and xylanase (XE) supplementation on the fermentation quality, chemical composition, and bacterial community of Caragana korshinskii Kom. silage. Four groups were designed for the study. No additives were used in the control group (CK), and LP (1 × 106 cfu/g), CE (1 × 104 IU/g) and XE (2 × 105 IU/g) were added to the experimental groups on a fresh matter basis, with three replicates per group. To promote fermentation, 5% molasses was added to all of the groups. On days 15 and 60, fermentation quality, chemical composition and the bacterial community were analysed. The pH of groups CE and XE was lower than that of the CK group at 60 days. During ensiling, the lactic acid (LA) content in the experimental groups and the acetic acid (AA) content in the CK and LP groups increased. At 60 days, the dominant genera in the CK and LP groups was Weissella and the dominant genera in the CE and XE groups was Lentilactobacillus. At different times during silage, nucleotide metabolism was enhanced, whereas the metabolism of carbohydrate, amino acids, energy, cofactors and vitamins was inhibited in the LP group. However, the metabolism of amino acids, energy, cofactors and vitamins in the CE and XE groups was increased, whereas the metabolism of nucleotides was inhibited. In conclusion, LP, CE and XE could exert a positive effect on the fermentation quality of C. korshinskii Kom. silage by shifting the bacterial community composition.

1. Introduction

In recent years, with the development of animal husbandry, the insufficient supply of fodder, especially roughage, has become a bottleneck factor. The increasing cost of fodder reduces the economic benefits of animal production; therefore, developing cheaper feedstuffs and new roughages has become particularly important [1]. Compared with conventional roughage, Caragana korshinskii Kom. not only has ecological functions such as windbreaks, sand fixation and water retention but is also rich in crude protein, amino acids and trace elements, which makes it an excellent fodder for ruminants [2,3]. In northern China, the area of C. korshinskii Kom. plantations are increasing by more than 10,000 hm2 per year, and the total area of the fifty-four C. korshinskii Kom. plantations has reached more than 4 million ha in Inner Mongolia. The annual biological yield is estimated to be more than 6 million tons on the basis of an annual dry weight of 1.5 t/ha [4]. It can meet the forage needs of approximately 1.2 million cattle on the basis of the estimated annual consumption of 1 ton of dry matter forage per cattle, which indicates that C. korshinskii Kom. has great application potential.. However, with increasing lignification during the ripening process of C. korshinskii Kom., its palatability, feed intake and digestibility decrease [5]. Therefore, we attempted to improve the quality of C. korshinskii Kom. silage by adding silage agents such as microbial inoculants or enzyme preparations.
As a microbial anaerobic fermentation technology, ensiling can soften the spines on the branches of C. korshinskii Kom. and produce aromatic odours, thus improving the palatability of C. korshinskii Kom. silage [6]. The anaerobic microorganisms in silage, especially lactic acid bacteria (LAB), decompose water-soluble carbohydrate (WSC) to produce lactic acid (LA), which helps reduce the pH and inhibits undesirable microorganisms [7]. The quality of silage is usually affected by many factors, such as the water content of the feedstuff and the content of WSC [8]. Studies have shown that the use of biological additives can improve the fermentation quality and aerobic stability of silage [9]. Lentilactobacillus plantarum (LP), a commonly used bacterial inoculant in silage [10], can promote the acidification of silage, adapt well to low-pH environments, and maintain a dominant state for a long period of time [11]. In addition, cellulase (CE) and xylanase (XE) are often used to promote fibre degradation and increase the content of WSC to provide substrates for LAB fermentation [12,13] and improve the quality of silage [14]. Moreover, the degradation of the plant cell wall also helps to promote digestion and improve feed conversion efficiency in ruminants [15]. In addition, molasses is usually added to silage to compensate for the lack of WSC, increase the fermentation substrate for LAB activities [16] and improve the fermentation quality of silage [17,18]. This phenomenon is particularly important for C. korshinskii Kom. silage because fresh C. korshinskii Kom. has a high crude fibre (CF) content [19] and low WSC content [20]. In addition, the high crude protein (CP) content of approximately 20.2% [20] leads to a high pH buffering capacity1 [21], which makes it difficult to ensile alone. The addition of 3–6% molasses can effectively promote the fermentation of LAB [22].
It is hypothesized that LP, CE, and XE additives can alter the chemical composition, fermentation quality, gene function, and bacterial community of C. korshinskii Kom. silage. Therefore, the objective of this study was to evaluate the effects of these additives and offer references for C. korshinskii Kom. silage production.

2. Materials and Methods

2.1. Silage Additives

LP was obtained from Beijing Biobw Biotechnology Co., Ltd. (Beijing, China). CE (enzyme activity ≥ 10,000 IU/g) and XE (enzyme activity ≥ 200,000 IU/g) were obtained from Ningxia Sheng Enzyme Biotechnology Co., Ltd (Ningxia, China). Molasses (sugar content 42–50%, brix ≥ 60) was obtained from Weifang Fengguan Biotechnology Co., Ltd. (Weifang, China).

2.2. Ensiling with Additives

C. korshinskii Kom. was harvested in an experimental field at Inner Mongolia Minzu University in the Horqin District of Tongliao city, Inner Mongolia Autonomous Region, in September 2023 (43°37′21″ N 122°15′21″ E). After wilting for 5 h to achieve the desired dry matter (DM) content of approximately 44%, the plant material was chopped to 10 to 20 mm in length with a grass chopper and was sampled to determine the chemical composition and pH (Table 1). LP (1 × 106 cfu/g), CE (40 mg/kg) and XE (40 mg/kg) were supplemented in the LP, CE and XE groups, respectively, on a fresh matter (FM) basis. An equal amount of sterile water was added to the control group (CK). Molasses (5% on a FM basis) was added to all of the groups to promote fermentation. Approximately 300 g of C. korshinskii Kom. from each group was vacuum-sealed into polyethylene plastic silage bags (25 cm × 30 cm, 0.24 mm in thickness) with a sealer (Type 360, Ouxin, Jinhua, China) as a replicate, with 3 replicates per group. The C. korshinskii Kom. silage was stored at room temperature (21–25 °C) away from light and was sampled on days 15 and 60 for further analysis.

2.3. Analysis of Chemical Composition and Fermentation Quality

C. korshinskii Kom. silage was sampled and dried at 65 °C via a drying oven (DHG-9240A, Shanghai, China) for approximately 48 h to a constant weight to determine the DM content [23]. The dried samples were subsequently ground and passed through a 1.0 mm sieve for chemical composition analysis. The crude protein (CP) content was determined via the Kjeldahl method [24]. The neutral detergent fibre (NDF) and acid detergent fibre (ADF) contents were determined according to the methods of Van Soest et al. [25]. WSC content was determined via the anthrone colorimetric method [26]. The ether extract (EE) content was determined according to the methods of Firestone et al. [27].
A 20 g sample of silage was added to 180 mL of distilled water, stirred with a blender (MJ-WBL2521H, Midea, Foshan, China) for 1 min and filtered with 4 layers of gauze and qualitative filter paper. The filtrate was centrifuged at 12,000× g at 4 °C for 15 min, and the supernatant was collected for further determination of fermentation quality [28]. The pH was immediately measured with a portable pH meter (Testo 205, Beijing, China). The ammonia nitrogen content (NH3-N) was measured via phenol–hypochlorous acid colorimetry [29]. The acetic acid (AA), propanoic acid (PA) and butyric acid (BA) contents were determined via gas chromatography (GC-6800, Beifentianpu, Beijing, China). The chromatographic conditions were as follows: Φ6 mm × 2 m quartz glass-filled column (Stationary phase 15% FFAP, support 80–100 mesh Chromosorb), 150 °C column temperature, 220 °C injection port temperature, 280 °C FID temperature, and 1 μL injection volume. High-purity N2 carrier gas was used, the flow rate was 30 mL/min, and the pressure was 200 kPa. The gas was H2, and the flow rate was 30 mL/min. The auxiliary gas was air, and the flow rate was 300 mL/min. The lactic acid (LA) content was determined via high-performance liquid chromatography (1260 Infinity II, Agilent, Santa Clara County, USA). The chromatographic conditions were as follows: TUP-AQ C18 column of 5 μm particle size, 250 × 4.6 mm model, 0.1% phosphoric mobile phase, acid:acetonitrile = 97.5:2.5, 1 mL/min flow rate, 35 °C column temperature, 20 μL injection volume, SPD-20A UV detector and 210 nm detection wavelength.

2.4. Analysis of the Bacterial Community

Approximately 5 g of silage sample from each group was transferred to a 50 mL sterile tube supplemented with 25 mL of 0.1 M potassium phosphate buffer (pH = 8.0), followed by ultrasonic oscillation for 1 min and vortex oscillation for 10 s. This step was repeated twice. The mixtures were subsequently filtered through a double layer of sterile gauze. The filtrate was centrifuged (13,000 r/min, 4 °C, 10 min) with a centrifuge (Eppendorf 5424R, Hamburg, Germany), and the precipitate was stored at −80 °C for bacterial community analysis.
The metagenomic sequencing, including DNA extraction and polymerase chain reaction amplification, followed by Illumina MiSeq sequencing and final sequencing data processing, was performed at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). Using UPARSE (version 11) software, OTU clustering of sequences was performed at 97% similarity, and single sequences and chimaeras were eliminated in the process of clustering. Each sequence was annotated by the RDp classifier and compared with the Silva database (SSU123), and the alignment threshold was set at 70%. Tax4Fun functional prediction is achieved using the nearest neighbour method based on minimum 16S rRNA sequence similarity: (1) Extracted whole genome 16S rRNA gene sequences of prokaryotes from the KEGG database and compared them to the SILVA SSU Ref NR database (BLAST bitscore > 1500) with the BLASTN algorithm to establish a correlation matrix; (2) The whole genome functional information of prokaryotes in the KEGG database was annotated by UProC and PAUDA, corresponding to the SILVA database to achieve functional annotation of the SILVA database; and (3) Prediction: Sequencing samples were clustered out of the OTU, and SILVA database sequences were compared as reference sequences to obtain functional annotation information.

2.5. Statistical Analysis

The data in this study were analysed via two-way ANOVA with the SPSS 26.0 program (SPSS Inc., Chicago, IL, USA), and the statistical model was as follows:
Yijk = μ + αi + βj + αβij + εijk
where Yijk is an observation, μ is the overall mean, αi is the effect of the additives (i = CK, LP, CE, XE), βj is the number of ensiling days (j = 15, 60), αβij is the additives × number of ensiling days interaction, and εijk is the error.
Duncan’s tests were used to separate significant differences and was considered statistically significant when the probability was less than the 5% level.

3. Results

3.1. Chemical Composition

The composition and pH of Caragana korshinskii Kom. before ensiling are shown in Table 1.
Table 2 shows the effects of ensiling days, treatments, and their interactions on the nutritional components of C. korshinskii Kom. silage. The DM content of C. korshinskii Kom. silage was significantly affected by the treatments (p < 0.05). Compared with the DM content in the LP group on day 15, the DM content in the LP group on day 60 was significantly decreased (p < 0.05). At day 60, the DM content was similar among the groups, but at day 15, the DM content in the LP group was significantly higher than that in the CK group (p < 0.05). The CP content of C. korshinskii Kom. silage was significantly affected by ensiling days, treatments, and their interactions (p < 0.05). Compared with the CP content in the LP group on day 15, the CP content in the LP group on day 60 was significantly increased (p < 0.05). On day 15, CP content in LP group was significantly lower than that in the CK, CE and XE groups (p < 0.05), but on day 60, the CP content in the CK group was significantly higher than that in the LP, CE and XE groups (p < 0.05). The EE content of C. korshinskii Kom. silage was significantly affected by ensiling days, treatments, and their interactions (p < 0.05). Compared with the EE content of all groups on day 15, the EE content of all groups on day 60 was significantly increased (p < 0.05). On day 15, the EE content in the CE and XE groups was significantly lower than that in the CK and LP groups (p < 0.05), but on day 60, the EE content in the CK group was significantly higher than that in the LP, CE and XE groups (p < 0.05). The WSC content of C. korshinskii Kom. silage was significantly affected by ensiling days and treatments (p < 0.05). The WSC content of each group on day 15 was similar to that on day 60. On day 15, the WSC content in the LP group was significantly lower than that in the CK group (p < 0.05), but on day 60, the WSC content in the LP group was significantly lower than that in the CK and CE groups (p < 0.05). The NDF and ADF contents of C. korshinskii Kom. silage was not affected by ensiling days, treatments, or their interactions (p > 0.05).

3.2. Fermentation Quality of C. korshinskii Silage

Table 3 shows the effects of ensiling days, treatments, and their interactions on fermentation quality of C. korshinskii Kom. silage. The pH of C. korshinskii Kom. silage was significantly affected by ensiling days, treatments, and their interactions (p < 0.05). Compared with the pH value of all groups on day 15, the pH value of all groups on day 60 was significantly decreased (p < 0.05). On day 15, the pH value of the XE group was significantly higher than that of the LP, CE and CK groups, but on day 60, the pH value of the CK and LP groups was significantly higher than that of the CE and XE groups (p < 0.05). The NH3-N content of C. korshinskii Kom. silage was significantly affected by ensiling days and treatments (p < 0.05). Compared with the NH3-N content of all groups on day 15, the NH3-N content of all groups on day 60 was significantly increased (p < 0.05). On day 60, the NH3-N content of all groups was similar, but on day 15, the NH3-N content of the CK group was significantly lower than that of the LP, CE and XE groups (p < 0.05). The LA content of C. korshinskii Kom. silage was significantly affected by ensiling days (p < 0.05). Compared with the LA content in the LP, CE and XE groups on day 15, the LA content in the LP, CE and XE groups on day 60 was significantly increased (p < 0.05). The LA content of all groups was similar on day 15, but the LA content of the CE group was significantly higher than that of the CK group on day 60. The AA content of C. korshinskii Kom. silage was significantly affected by ensiling days and treatments (p < 0.05). Compared with the AA content in the CK and LP groups on day 15, the AA content in the CK and LP groups on day 60 was significantly increased (p < 0.05). the AA content in all groups was similar on day 60, but the AA content in the XE group was significantly higher than that in the CK group on day 15 (p < 0.05). PA and BA were not detected in all groups.

3.3. Bacterial Community of C. korshinskii Kom. Silage

3.3.1. Effects of Different Additives on the Alpha Diversity of Bacterial Communities

In this study, a total of 875,616 valid sequences were obtained from 24 samples, and the average number of valid sequences per sample was 36,484. OTU cluster analysis was performed on the nonrepetitive sequences (excluding single sequences) of each bacterial group according to 97% similarity. A total of 422 OTUs were obtained for 11 phyla, 17 classes, 56 orders, 94 families, 158 genera and 253 species.
As shown in Figure 1, the number of common OTUs in all the groups was 47. At 15 days, the numbers of unique OTUs in the CK, LP, CE and XE groups were 12, 6, 12 and 10, respectively. At 60 days, the numbers of unique OTUs in the CK, LP, CE, and XE groups were 18, 4, 128, and 18, respectively. The number of unique OTUs in the CE group was the highest (128), and that in the LP group was the lowest (4). Moreover, the number of unique OTUs decreased only in the LP group and increased in the other groups with ensiling time.
According to the results shown in Table 4, the species coverage of all of the tested samples was greater than 99%, indicating that the sequencing results could accurately reflect the characteristics of the bacterial community.
The Shannon index was significantly affected by the treatments (p < 0.05). The Shannon index of the CE group was significantly higher than that of the CK and LP groups on day 15 (p < 0.05), but the Shannon index of the LP group was significantly lower than that of the CK, CE and XE groups on day 60 (p < 0.05). The Simpson index was significantly affected by the treatments (p < 0.05). The Simpson index of the LP group was significantly higher than that of the CK, CE and XE groups on day 15 (p < 0.05), but the Simpson index of the LP group was still significantly higher than that of the CK, CE and XE groups on day 60 (p < 0.05). The ACE index and Chao1 index were not affected by ensiling days, treatments, or their interactions.
Principal component analysis (PCoA) was used to compare the species diversity among different bacterial communities to reveal the similarities or differences in community composition among the different groups. The results at 15 days are shown in Figure 2A, and there were significant differences in the bacterial communities among the groups.
The results at 60 days are shown in Figure 2B; there was no significant difference between groups CE and XE, but there was a significant difference among the other groups.
The changes in bacterial communities at the phylum level among the groups are shown in Figure 3A, where Firmicutes was the dominant bacterial phylum in all of the groups at both 15 and 60 days, followed by Proteobacteria. The relative abundance of Firmicutes increased, whereas that of Proteobacteria decreased with ensiling time.
At the genus level, the bacterial genera associated with C. korshinskii Kom. silage after 15 days and 60 days are shown in Figure 3B. At 15 days, the relative abundance of Lentilactobacillus was greater in the CK and XE groups (63.23% and 59.04%, respectively), but the relative abundance of Weissella was greater in the LP and CE groups (81.42% and 62.13%, respectively). The relative abundances of Enterobacter in the CK, LP, CE, and XE groups were quite low (3.42%, 0.80%, 2.21%, and 1.44%, respectively).
At 60 days, the relative abundance of Lentilactobacillus in the CE and XE groups increased to 57.31% and 59.54%, respectively, and the relative abundance of Weissella in the CK and LP groups increased to 63.67% and 85.22%, respectively. The percentage of Enterobacter in the CK, LP, CE and XE groups decreased to 0.30%, 0.17%, 0.80% and 0.62%, respectively. These results revealed that with increasing fermentation time, the dominant genus in the CK group changed from Lentilactobacillus to Weissella, whereas the dominant genus in the CE group changed from Weissella to Lentilactobacillus. However, the dominant genera in groups LP and XE did not change.
The differences in bacterial genera in C. korshinskii Kom. were analysed using the multistage species discriminant analysis (LEfSe) (Linear Discriminant Analysis [LDA] = 3). At 15 days, as shown in Figure 4A, Lentilactobacillus and Enterobacter were significantly enriched in the CK group; Weissella was significantly enriched in the LP group; and Sphingomonas was significantly enriched in the XE group. However, the bacterial community structure of the CE group was not significantly different from that of the other groups.
At 60 days, as shown in Figure 4B, Weissella was significantly enriched in the LP group; Enterobacter was significantly enriched in the CE group; and Lentilactobacillus was significantly enriched in the XE group. However, there was no significant difference in bacterial community abundance between the CK group and the other groups.

3.3.2. Correlation between Chemical Composition, Fermentation Quality and Bacterial Genera of C. korshinskii Kom. Silage

The results for the silage at 60 days are shown in Figure 5A. DM was positively correlated with Weissella (p < 0.05) and negatively correlated with Lentilactobacillus, Pediococcus, and Enterobacter (p < 0.05) at 15 days. pH, NH3-N, WSC and AA were negatively correlated with Enterobacter (p < 0.05), while CP was positively correlated with Enterobacter (p < 0.01), Lentilactobacillus and Klebsiella (p < 0.05). The results for the silage at 60 days are shown in Figure 5B, where LA was positively correlated with Enterobacter (p < 0.05); CP was negatively correlated with unclassified_o__Lactobacillales (p < 0.01); EE was negatively correlated with Enterobacter (p < 0.05); and pH was positively correlated with Weissella (p < 0.001) but negatively correlated with Lentilactobacillus (p < 0.001), Enterobacter (p < 0.01), Romboutsia, Methylobacterium-Methylorubrum and Sphingomonas (p < 0.05).

3.3.3. KEGG Metabolic Pathway Analysis of Bacterial Communities in Different Groups

The results of the 16S rRNA gene-predicted functions at the first, second, and third pathway levels for the different groups are shown in Figure 6 and Table 5.
Metabolism was significantly affected by treatments (p < 0.05). Compared with the Metabolism of the CE and XE groups on day 15, the Metabolism of the CE and XE groups on day 60 was significantly increased (p < 0.05). Metabolism in the CK group was significantly higher than that in the LP, CE and XE groups on day 15 (p < 0.05), but Metabolism in the LP group was significantly lower than that in the CK, CE and XE groups on day 60 (p < 0.05).
Carbohydrate metabolism was significantly affected by treatments and the interaction between treatments and ensiling days (p < 0.05). Carbohydrate metabolism in the CE group at day 60 was significantly increased compared with that in the CE group at day 15 (p < 0.05). Carbohydrate metabolism in the CK group was significantly higher than that in the LP, CE and XE groups on day 15 (p < 0.05), but at day 60, Carbohydrate metabolism in the LP group was significantly lower than that in the CK, CE and XE groups (p < 0.05).
Metabolism of cofactors and vitamins was significantly affected by treatments (p < 0.05). Compared with the Metabolism of cofactors and vitamins in the CE and XE groups on day 15, the Metabolism of cofactors and vitamins in the CE and XE groups on day 60 was significantly increased (p < 0.05). On day 15, the Metabolism of cofactors and vitamins in the CK and XE groups was significantly higher than that in the LP and CE groups (p < 0.05). However, the Metabolism of cofactors and vitamins in the LP group was significantly lower than that in the CK, CE and XE groups on day 60 (p < 0.05).
Energy metabolism was significantly affected by treatments and the interaction between treatments and ensiling days (p < 0.05). Compared with the Energy metabolism of the CE and XE groups on day 15, Energy metabolism of the CE and XE groups on day 60 was significantly increased (p < 0.05). Energy metabolism in the CK group was significantly higher than that in the LP, CE and XE groups on day 15 (p < 0.05), but Energy metabolism in the LP group was significantly lower than that in the CK, CE and XE groups on day 60 (p < 0.05).
Nucleotide metabolism was significantly affected by treatments and the interaction between treatments and ensiling days (p < 0.05). Nucleotide metabolism in the CE and XE groups was significantly decreased on day 60 compared with that on day 15 (p < 0.05). Nucleotide metabolism in the LP group was significantly higher than that in the CK, CE and XE groups on day 15 (p < 0.05), but it was still significantly higher than that in the CK, CE and XE groups on day 60 (p < 0.05).
Amino acid metabolism was significantly affected by treatments (p < 0.05). Compared with the Amino acid metabolism in the CE and XE groups on day 15, the Amino acid metabolism in the CE and XE groups on day 60 was significantly increased (p < 0.05). The Amino acid metabolism in the CK group was significantly higher than that in the LP, CE and XE groups on day 15 (p < 0.05), but the Amino acid metabolism in the LP group was significantly lower than that in the CK, CE and XE groups on day 60 (p < 0.05).
Glycolysis/Gluconeogenesis was significantly affected by treatments and the interaction between treatments and ensiling days (p < 0.05). Compared with Glycolysis/Gluconeogenesis in the CE group on day 15, Glycolysis/Gluconeogenesis in the CE group on day 60 was significantly increased (p < 0.05). On day 15, Glycolysis/Gluconeogenesis in the CK group was significantly higher than that in the LP, CE and XE groups (p < 0.05). However, on day 60, Glycolysis/Gluconeogenesis in the LP group was significantly lower than that in the CK, CE and XE groups (p < 0.05).
The Citrate cycle (TCA cycle) was significantly affected by treatments and the interaction between treatments and ensiling days (p < 0.05). Compared with the Citrate cycle (TCA cycle) of the CE group on day 15, the Citrate cycle (TCA cycle) of the CE group on day 60 was significantly increased (p < 0.05). The Citrate cycle (TCA cycle) of the CK group was significantly higher than that of the LP and CE groups on day 15 (p < 0.05), but the Citrate cycle (TCA cycle) of the LP group was significantly lower than that of the CK, CE and XE groups on day 60 (p < 0.05).

4. Discussion

4.1. Effects of Different Additives on the Chemical Composition and Fermentation Quality of C. korshinskii Kom. Silage

DM is an important indicator used to measure the fermentation quality of silage. The respiration of plant cells and the activities of microorganisms usually cause the decomposition of nutrients, resulting in a reduction in the silage DM content [30]. In this study, the DM content in the LP group was greater than that in the CK group because of the greater abundance of Weissella, which inhibits the proliferation and activity of putrefication microbes (such as Clostridia) at 15 days [31]. The inhibition of harmful microbial activity improved the DM content of C. korshinskii Kom. silage. Enterobacter can utilise glucose and LA to produce AA and ethanol, as well as degrade proteins to ammonia [32]. However, ethanol is considered to be a poor product of forage preservation because it causes greater DM and energy losses [33]. In this study, there was no significant difference in DM content among the groups at 60 days because molasses provided a high degradable DM content [34,35] and sufficient WSC for the growth of LAB, which rapidly formed a low-pH environment (<4.2) and inhibited the proliferation of undesirable microorganisms such as Enterobacter and Clostridia in all of the groups, resulting in improved nutrient preservation [9]. However, the DM content of the LP group tended to decrease with ensiling time, which was related to the decomposition of nutrients caused by heterofermentation by Weissella [36]. In addition, it is expected that the Lentilactobacillus population increased with LP addition under normal circumstances, but the addition of molasses, as in this study, may lead to the rapid proliferation of Lentilactobacillus in the early stages of fermentation, followed by a rapid drop in pH and WSC content, which weakened the competitive advantage of Lentilactobacillus over bacteria like Weissella. This was confirmed by the fact that Weissella emerged as the dominant strain at 15 days of fermentation. However, the slow release of soluble carbohydrates due to the cell wall degradation by adding cellulase and xylanase significantly increased the abundance of Lentilactobacillus, confirming the original hypothesis of this study.
In this study, the CP content of all of the groups was greater than that of the fresh C. korshinskii Kom. At 15 days, the CP content of the LP group was lower than that of the CK group, which may be due to its high DM content and protein degradation by some microorganisms involved in the fermentation process of silage [36]. At 60 days, the CP content of the experimental groups was lower than that of the CK group, possibly because the CK group more fully utilised part of the nitrogen in molasses [31]. In addition, the CP content in the LP group increased with increasing ensilage time, which may be related to its decreased DM content. With the increase of silage time, the EE content increased from 63.53 g/kg DM to 114.67 g/kg DM, which is related to the decrease of the nitrogen free extract content (NFE), including WSC. WSC is the main fermentation substrate during ensiling and is one of the common carbon sources utilised by microorganisms, which can indirectly reflect the fermentation potential of ensiling. High-quality silage requires sufficient WSC to ensure the proliferation of LAB [37]. The addition of molasses relieved the competition between LAB and other microorganisms caused by the WSC deficiency in C. korshinskii Kom. itself and assisted LA in creating an acidic environment [38]. The greater abundance of Weissella in the LP group resulted in a significantly lower WSC content and final pH than those in the CK group due to the full utilisation of WSC. On the other hand, the NFE content in C. korshinskii Kom. was reported to be as high as about 35% [39], some of which can also be used by lactic acid bacteria. Since crude fibre was not measured in this experiment, the nitrogen free extract content could not be estimated accurately. In addition, the addition of molasses during ensiling could also increase the respiration and fermentation of plant fibres [40], which could further improve the degradation of NDF and ADF. However, in this study, no significant changes in fibre composition were observed even when fibre decomposition enzymes were added. Whether the rapid decrease in acidity caused by the addition of high amounts of molasses weakened the effect of the fibre decomposition enzymes or directly degraded the enzymes needs further investigation by changing the amount of molasses added.
Generally, a pH of 4.2 is considered good fermentation [36]. In this study, the pH of all of the groups after 15 days was less than 4.2, indicating that the activity of LAB was quite fast, which was related to the addition of sufficient molasses. After 60 days, the changes in pH in all of the groups were consistent with the trend of LA content, the fermentation products of WSC that ensure fermentation quality [8,41]. On the other hand, heterofermentative LAB can produce AA, which is conducive to improving the aerobic stability of silage [6]. In this study, the AA content of the XE group was significantly greater than that of the CK group at 15 days, possibly because the addition of XE led to the degradation of some xylan to xylose and promoted the activity of heterofermentative LAB to produce more AA [42]. The AA content gradually increased in all of the experimental groups with ensiling time, but there was no significant difference among the groups, indicating that heterofermentation was fully carried out in all of the groups. The production of PA and BA can usually lead to the loss of feed energy and a decrease in silage quality [43]. In this study, neither PA nor BA were detected, indicating that the proliferation of the undesired microorganisms was effectively inhibited [44,45].
Studies have shown that the concentration of NH3-N can reflect the degree of proteolysis during silage [10]. At 15 days, the NH3-N concentration in the experimental groups was greater than that in the CK group, possibly because the lower pH value in the CK group inhibited protease activity and microbial decomposition [46]. At 60 days, the decrease in pH in all of the groups inhibited the growth of undesirable microorganisms and slowed the production of NH3-N, resulting in no significant difference in NH3-N concentration among all of the groups [9]. In addition, the concentration of NH3-N in each group increased with ensiling time, which was related to excessive microbial fermentation and proteolysis of the feed protein [47].

4.2. Effects of Different Additives on the Bacterial Community of C. korshinskii Kom.

Ensiling is a very complex fermentation process, and the composition of bacterial communities determines the quality of silage [45]. In this study, Firmicutes was the most dominant phylum in all of the groups, and Lentilactobacillus and Weissella were the most dominant genera in the ensiling process. In contrast, Proteobacteria was gradually replaced by Firmicutes during ensiling in all of the groups. The main microorganisms involved in lactic acid fermentation are Firmicutes, which adapt well to acidic environments. However, the permeability of the outer membrane of Proteobacteria, a type of Gram-negative bacteria, is strongly inhibited by low pH [48,49].
An abundance of fermentable substrate can greatly promote the fermentation of LAB. The increased abundance of Lentilactobacillus in groups XE and CE indicated that adding CE and XE increased the fermentable substrates for Lentilactobacillus [50], which resulted in a lower pH at 60 days due to the proliferation of LAB [48]. Weissella, an early settler of silage, is a heterofermentative LAB that produces a mixture of LA and AA by metabolising WSC [51]. As shown in the present study, Weissella maintained a high abundance throughout fermentation in the LP group, which was inconsistent with the results of Li et al. [52]. This may be attributed to the heterofermentation of epiphytic Weissella caused by the addition of large amounts of molasses. On the other hand, Enterobacter, which causes undesired fermentation, were inhibited in all of the groups due to the rapid decrease in pH [53].
Alpha diversity quantifies the diversity of features in a single sample and enables comparisons between sample groups [54]. It also accounts for species richness, evenness, and diversity in bacterial communities. Studies have shown that differences in silage quality are associated with changes in bacterial communities [45,55]. In this study, at different time points, the Shannon index of the LP group was lower than that of the CK group, indicating a decrease in bacterial diversity because the addition of exogenous LP inhibited the growth of other microorganisms in the LAB [44]. On the other hand, beta diversity can reflect the differences in bacterial communities between groups. The additives used in silage can usually affect the bacterial community or the quality of silage [38,53], which was also observed in the PCoA results in this study. Spearman correlation analysis revealed that Lentilactobacillus was negatively correlated with the pH at 60 days, which is a common situation in silage [7,56,57]. However, Weissella was positively correlated with pH, which can be explained by the weak acid tolerance of Weissella [58]. In addition, Enterobacter, a facultative anaerobe, competes with LAB for fermentation substrates and produces NH3-H and other metabolites, thereby reducing the quality of silage [59]. Inconsistent with previous reports [60], Enterobacter in the experimental groups was inhibited in the early stage of fermentation in this study, showing a negative correlation with NH3-H and pH, which was considered a result of the influence of Weissella on reducing the pH.

4.3. Predictive Function Analysis

Predicting functional changes in bacterial communities can help in assessing the activities of microbes in silage. With respect to the primary pathways, metabolism was the dominant pathway, suggesting that fermentation is a process by which multiple microorganisms metabolise nutrients. With respect to the secondary pathway, carbohydrate metabolism, cofactor and vitamin metabolism, energy metabolism, nucleotide metabolism, and amino acid metabolic pathways are relatively abundant. This is related to the nutrient composition of the silage. Bai et al. [61] reported that these metabolic pathways are closely related to the fermentation quality of silage. The lower metabolism in the LP group than in the CK group indicated that less diverse microbes reduced the overall metabolism of the substrate nutrients but enhanced the nucleotide metabolism caused by the proliferation of LAB because nucleotides are the substrates of DNA synthesis and major energy donors for cellular processes [62]. In contrast, the relative abundance of genes involved in the metabolism of cofactors and vitamins, energy metabolism, and amino acid metabolism in the CE and XE groups increased with fermentation, and carbohydrate metabolism (including glycolysis/gluconeogenesis and the citrate cycle [63]) in the CE group also increased. These findings were related to the greater abundance of Lentilactobacillus in the CE and XE groups, which has a high metabolic intensity during the degradation of WSC.

5. Conclusions

The results showed that LP, CE and XE, in combination with 5% molasses, improved the quality of Caragana korshinskii Kom. silage and inhibited the activities of undesirable microorganisms. Lentilactobacillus was dominant in the CE and XE groups, and Weissella was dominant in the CK and LP groups. This study reveals the potential changes of C. korshinskii Kom. silage and provide reference for the utilisation of C. korshinskii Kom. In future studies, more addition levels and more combined additions are needed to further improve silage quality.

Author Contributions

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

Funding

This research was funded by the Inner Mongolia Science and Technology Support Project (No. 2022YFXZ0015), Xing’an League science and technology project “Development and demonstration of comprehensive supporting technology for efficient and safe utilisation of forage and healthy and high yield for dairy cows”, Inner Mongolia Natural Science Foundation (No. 2022MS03072) and Tongliao City science and technology project” Tongliao beef cattle new varieties breeding and related technology demonstration and promotion”.

Data Availability Statement

The 16S rDNA sequencing data presented in this study are available at the NCBI Sequence Read Archive (SRA) (http://www.ncbi.nlm.nih.gov/sra) (accessed on 21 August 2024) under accession number PRJNA1150369.

Acknowledgments

We would like to thank the staff at our laboratory for their ongoing assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Venn diagram of the bacterial species. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
Figure 1. Venn diagram of the bacterial species. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
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Figure 2. PCoA of the bacterial species diversity in C. korshinskii Kom. silage at 15 days (A) and 60 days (B). CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
Figure 2. PCoA of the bacterial species diversity in C. korshinskii Kom. silage at 15 days (A) and 60 days (B). CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
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Figure 3. Distribution of bacterial communities at the phylum (A) and genus (B) levels at days 15 and 60 in C. korshinskii Kom. silage. Small populations with abundances less than 0.01 were combined as others. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
Figure 3. Distribution of bacterial communities at the phylum (A) and genus (B) levels at days 15 and 60 in C. korshinskii Kom. silage. Small populations with abundances less than 0.01 were combined as others. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
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Figure 4. Species differences in bacterial genera (LDA = 3) between 15 days (A) and 60 days (B) of ensiling. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
Figure 4. Species differences in bacterial genera (LDA = 3) between 15 days (A) and 60 days (B) of ensiling. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
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Figure 5. Heatmap of the Spearman correlation coefficients of chemical composition, fermentation quality and bacterial genera of C. korshinskii Kom. silage at 15 (A) and 60 (B) days. The colour of the heatmap indicates the Spearman correlation coefficient “R” (−1 to 1). R > 0 indicates a positive correlation, and R < 0 indicates a negative correlation. *, 0.01 < p < 0.05; **, 0.001 < p < 0.01; ***, p < 0.001. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
Figure 5. Heatmap of the Spearman correlation coefficients of chemical composition, fermentation quality and bacterial genera of C. korshinskii Kom. silage at 15 (A) and 60 (B) days. The colour of the heatmap indicates the Spearman correlation coefficient “R” (−1 to 1). R > 0 indicates a positive correlation, and R < 0 indicates a negative correlation. *, 0.01 < p < 0.05; **, 0.001 < p < 0.01; ***, p < 0.001. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
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Figure 6. Predicted pathways of the bacterial community in C. korshinskii Kom. at 15 days and 60 days of ensiling. (A) the first metabolic pathway at 15 days of C. korshinskii Kom. Silage. (B) the first metabolic pathway at 60 days of C. korshinskii Kom. Silage. (C) the second metabolic pathway at 15 days of C. korshinskii Kom. Silage. (D) the second metabolic pathway at 60 days of C. korshinskii Kom. Silage. (E) carbohydrate metabolism of the third pathway level at 15 days of C. korshinskii Kom. Silage. (F) carbohydrate metabolism of the third pathway level at 60 days of C. korshinskii Kom. Silage. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
Figure 6. Predicted pathways of the bacterial community in C. korshinskii Kom. at 15 days and 60 days of ensiling. (A) the first metabolic pathway at 15 days of C. korshinskii Kom. Silage. (B) the first metabolic pathway at 60 days of C. korshinskii Kom. Silage. (C) the second metabolic pathway at 15 days of C. korshinskii Kom. Silage. (D) the second metabolic pathway at 60 days of C. korshinskii Kom. Silage. (E) carbohydrate metabolism of the third pathway level at 15 days of C. korshinskii Kom. Silage. (F) carbohydrate metabolism of the third pathway level at 60 days of C. korshinskii Kom. Silage. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. The numbers behind CK, LP, CE, and XE represent the days of ensiling.
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Table 1. Chemical composition and pH of fresh C. korshinskii Kom. (g/kg DM).
Table 1. Chemical composition and pH of fresh C. korshinskii Kom. (g/kg DM).
ItemsDM (g/kg FM)CPEEWSCNDFADFpH
Content437.82 ± 9.00174.29 ± 0.7163.53 ± 0.6739.28 ± 1.03462.30 ± 1.67433.24 ± 2.295.85 ± 0.01
Note: FM, fresh material; DM, dry matter; CP, crude protein; EE, ether extract; WSC, water soluble carbohydrate; NDF, neutral detergent fibre; ADF, acid detergent fibre.
Table 2. Chemical composition of C. korshinskii Kom. at 15 days and 60 days of ensiling (g/kg DM).
Table 2. Chemical composition of C. korshinskii Kom. at 15 days and 60 days of ensiling (g/kg DM).
ItemsEnsiling
Days		CKLPCEXESEMp Value
MDD × M
DM (g/kg FM)15411.78 b426.26 Aa420.14 ab421.24 ab1.4830.0220.7120.320
60411.65417.20 B423.27423.56
CP15189.05 a179.40 Bb188.35 a186.06 a0.695<0.0010.0310.003
60189.74 a186.82 Ab186.32 b186.98 b
EE1582.32 Ba81.00 Ba73.02 Bb70.35 Bb3.106<0.001<0.001<0.001
60114.67 Aa103.27 Ab95.46 Ac101.27 Ab
WSC1528.77 a26.84 b27.54 ab27.27 ab0.2710.0050.0030.400
6027.13 a24.93 b27.34 a25.90 ab
NDF15416.29417.26431.61435.913.9060.3420.1370.557
60396.24427.28422.76425.75
ADF15398.58397.72410.48416.513.5470.1210.2480.727
60380.05399.85396.14402.30
Note: CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. Different capital letters indicate significant differences among different ensiling days under the same treatment (p < 0.05). Different lowercase letters indicate significant differences among different treatments on the same ensiling days (p < 0.05); the same letter indicates no significant difference (p > 0.05). SEM, standard error of the mean; M, treatments; D, ensiling days; M × D, interaction between treatments and ensiling days.
Table 3. Effects of different additives on fermentation quality of C. korshinskii Kom. silage at 15 days and 60 days of ensiling (g/kg DM).
Table 3. Effects of different additives on fermentation quality of C. korshinskii Kom. silage at 15 days and 60 days of ensiling (g/kg DM).
ItemsEnsiling DaysCKLPCEXESEMp Value
MDM × D
pH154.06 Ac4.11 Ab4.06 Ac4.15 Aa0.016<0.001<0.001<0.001
604.01 Ba4.03 Ba3.94 Bb3.91 Bc
NH3-N150.35 Bb0.45 Ba0.46 Ba0.51 Ba0.0220.003<0.0010.080
600.60 A0.62 A0.63 A0.63 A
LA1535.0334.02 B34.15 B36.59 B1.4540.104<0.0010.073
6039.67 b45.7 Aab51.29 Aa47.05 Aab
AA152.53 Bb2.83 Bab2.85 ab3.16 a0.1050.021<0.0010.418
603.51 A3.68 A3.283.95
PA15NDNDNDNDNDNDNDND
60NDNDNDND
BA15NDNDNDNDNDNDNDND
60NDNDNDND
Note: LA, lactic acid; AA, acetic acid; PA, propanoic acid; BA, butyric acid; NH3-N, ammonia nitrogen. ND: not detected. CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. Different capital letters indicate significant differences among different ensiling days under the same treatment (p < 0.05). Different lowercase letters indicate significant differences among different treatments on the same ensiling days (p < 0.05); the same letter indicates no significant difference (p > 0.05). SEM, standard error of the mean; M, treatments; D, ensiling days; M × D, interaction between treatments and ensiling days.
Table 4. Effects of different additives on the alpha diversity of bacteria in the C. korshinskii Kom. silage at 15 days and 60 days of ensiling.
Table 4. Effects of different additives on the alpha diversity of bacteria in the C. korshinskii Kom. silage at 15 days and 60 days of ensiling.
ItemsEnsiling
Days		CKLPCEXESEMp Value
DMM × D
Shannon index151.43 b0.77 c1.61 a1.47 ab0.078<0.0010.2620.927
601.24 a0.68 b1.59 a1.37 a
Simpson index150.40 b0.64 a0.28 Ac0.37 b0.033<0.0010.1870.898
600.47 b0.70 a0.32 Ab0.37 b
ACE index15115.22 a63.80 b119.52 a102.63 ab10.0780.0580.3250.721
60110.2570.75171.83123.08
Chao1 index15112.57 a62.1 b115.28 a102.32 ab9.8760.0600.4420.738
60108.1563.39163.76114.51
Coverage (%)1599.9699.9799.9399.950.0070.0780.3130.947
6099.9599.9799.9199.94
Note: CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. Different capital letters indicate significant differences among different ensiling days under the same treatment (p < 0.05). Different lowercase letters indicate significant differences among different treatments on the same ensiling days (p < 0.05); the same letter indicates no significant difference (p > 0.05). SEM, standard error of the mean; M, treatments; D, ensiling days; M × D, interaction between treatments and ensiling days.
Table 5. Predicted pathways of the bacterial community in C. korshinskii Kom. at 15 days and 60 days of ensiling.
Table 5. Predicted pathways of the bacterial community in C. korshinskii Kom. at 15 days and 60 days of ensiling.
ItemsEnsiling
Days		CKLPCEXESEMp Value
MDM × D
Metabolism1559.40 a57.28 d58.59 Bc59.20 Bb0.197<0.0010.7560.175
6058.78 a57.21 b59.26 Aa59.47 Aa
Carbohydrate metabolism1516.36 a15.49 d15.90 Bc16.26 b0.075<0.0010.5780.007
6015.93 a15.44 b16.21 Aa16.27 a
Metabolism of cofactors and vitamins155.47 a4.99 c5.33 Bb5.44 Ba0.048<0.0010.5730.503
605.39 a4.97 b5.47 Aa5.52 Aa
Energy metabolism155.02 a4.41 d4.74 Bc4.96 Bb0.055<0.0010.9610.031
604.77 a4.38 b4.96 Aa5.01 Aa
Nucleotide metabolism157.94 d8.88 a8.41 Ab8.03 Ac0.084<0.0010.8410.013
608.36 b8.93 a8.04 Bb7.98 Bb
Amino acid metabolism158.92 a7.26 d8.26 Bc8.76 Bb0.154<0.0010.7550.153
608.42 a7.20 b8.81 Aa8.97 Aa
Glycolysis/Gluconeogenesis151.83 a1.71 d1.76 Bc1.82 b0.011<0.0010.3650.006
601.77 a1.70 b1.81 Aa1.81 a
Citrate cycle (TCA cycle)150.37 a0.32 c0.34 Bb0.37 a0.005<0.0011.0000.007
600.35 a0.32 b0.37 Aa0.37 a
Note: CK, control; LP, Lentilactobacillus plantarum; CE, cellulase; XE, xylanase. Different capital letters indicate significant differences among different ensiling days under the same treatment (p < 0.05). Different lowercase letters indicate significant differences among different treatments on the same ensiling days (p < 0.05); the same letter indicates no significant difference (p > 0.05). SEM, standard error of the mean; M, treatments; D, ensiling days; M × D, interaction between treatments and ensiling days.
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Wang, Y.; Wei, M.; Yang, F.; Zheng, H.; Gao, J.; Peng, W.; Xiao, M.; Zhang, R.; Zheng, Y. Effects of Different Additives on the Chemical Composition, Fermentation Quality, Bacterial Community and Gene Function Prediction of Caragana korshinskii Kom. Silage. Agronomy 2024, 14, 2386. https://doi.org/10.3390/agronomy14102386

AMA Style

Wang Y, Wei M, Yang F, Zheng H, Gao J, Peng W, Xiao M, Zhang R, Zheng Y. Effects of Different Additives on the Chemical Composition, Fermentation Quality, Bacterial Community and Gene Function Prediction of Caragana korshinskii Kom. Silage. Agronomy. 2024; 14(10):2386. https://doi.org/10.3390/agronomy14102386

Chicago/Turabian Style

Wang, Yuxiang, Manlin Wei, Fuyu Yang, Haiying Zheng, Junjie Gao, Wen Peng, Ming Xiao, Runze Zhang, and Yongjie Zheng. 2024. "Effects of Different Additives on the Chemical Composition, Fermentation Quality, Bacterial Community and Gene Function Prediction of Caragana korshinskii Kom. Silage" Agronomy 14, no. 10: 2386. https://doi.org/10.3390/agronomy14102386

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