Effect of Lentilactobacillus buchneri on Chemical and Microbial Compositions of Herba Leonuri (Leonurus japonicus Houtt.)-Contained Alfalfa Silage

Abstract

Lactic acid bacteria (LAB) inoculants are commonly used in silage production, yet their effects on silage containing antimicrobial components, such as those found in Leonurus japonicus, remain less explored. Herein, the harvested alfalfa were thoroughly mixed with dried Leonurus japonicus Houtt. (LJH) at a ratio of 9:1 on a fresh weight basis and treated without (CK) or with a lactic acid bacterial inoculant (L; Lentilactobacillus buchneri). The mixtures were stored under anaerobic conditions in vacuum-sealed polyethylene bags for 30 days at ambient temperature. The L-treated silage exhibited high levels of water-soluble carbohydrates (4.98% dry matter (DM)) and acid detergent fiber (27.88% DM). Compared to that of treatment CK, treatment with L increased the acetic acid content of the silage, as a result of increased (p < 0.05) bacterial dominance and decreased (p < 0.05) bacterial richness indices (e.g., Pielou’s E, Shannon, and Simpson) in the pre-storage period. However, these changes gradually reduced as the storage length increased. Treatment L reshaped the bacterial community structure of silage, by increasing the prevalence of Lactobacillus and reducing relative abundances of Enterococcus and Weissella. However, the principal coordinate and Bray–Curtis index analyses illustrated that samples from the L-treated silages exhibited similarities to the CK samples post-fermentation. Overall, the effect of LJH on LAB was only observed in the later stages of fermentation, which did not sufficiently change the silage quality. Hence, using LJH in silage is vital for clean livestock production without compromising the function of LAB when mixed with alfalfa silage.

1. Introduction

Livestock play a critical role in global food production and a significant role in human society in terms of food, income, employment, nutrition, and risk insurance, as well as contributing to the local landscapes and ecosystems [1]. In addition, livestock are a significant contributor to economic growth and poverty reduction in low- and middle-income countries [2]. Following rapid global population growth and significant quality of life improvements of the population since 1980, the global demand for livestock products has more than doubled [3]. This demand has resulted in the continued expansion of the livestock industry. Although livestock farming has generated huge dividends to human society, it also poses serious threats to water, air, and soil, as well as public health.

The successful clinical use of penicillin in 1940 has increased the use of antibiotics in livestock vaccinations and disease treatments [4]. However, antibiotics are not entirely absorbed and are excreted in urine and feces, thus contributing to antibiotic contamination of the soil [5]. In particular, introducing antibiotics to water bodies can cause irreversible damage to aquatic ecosystems, and this increased antibiotic pollution, due to livestock breeding, is considered a significant potential risk to environmental ecology and human health [6]. Reducing ecological antibiotic contamination and pursuing stable livestock production are conflicting goals. Therefore, health foods are added to animal feed to enhance animal health and reduce antibiotic contamination in animal husbandry.

Leonurus japonicus Houtt. (LJH) is from the family Labiatae and is commonly known as Yi Mu Cao in China. LJH is an herbaceous flowering plant native to several parts of Asia, including China, Korea, Japan, and Cambodia. It has escaped cultivation and become naturalized in other parts of the world, including South and North America, Europe, and Africa [7]. LJH contains alkaloids, flavonoids, cyclic bodies, cyclic peptides, and anti-inflammatory labdane diterpenoids [7,8], which exhibit significant effects on promoting blood circulation, resolving blood stasis, and relieving pain, as well as acting as diuretics and possessing detoxifying properties [9]. Therefore, these plants may be beneficial to livestock health when mixed silage with other forages. However, material limitations, such as the high buffering capacity of alfalfa, have resulted in adding lactic acid bacterial inoculants to silage production processes [10]. Lactic acid bacteria (LAB) produce lactic acid in silage to rapidly reduce the pH and inhibit the undesired depletion of forage nutrients, thus preserving the forage. Silage fermentation quality is closely linked to LAB and can influence the diversity, quantity, and activity of the LAB in silage [11]. Therefore, LAB additives are employed to improve the fermentation quality of the silage. Several recent studies have shown that antimicrobial compounds affect LAB, which may result in the variability in silage quality. However, little is known about the effects of LJH (a material containing antimicrobial components) on LAB in silage. It is evident that when considering the utilization of LJH as a health food in silage blends with other forages, the impact of LJH on LAB should be duly considered.

The aim of this study was to evaluate the effects of LJH as an additive on the chemical composition, fermentation characteristics, and bacterial communities of silage inoculated with LAB. We hypothesized that the bacterial community in the silage that contained LJH and was inoculated with LAB would be altered. However, this change was not sufficient to cause a shift in silage quality and fermentation characteristics.

2. Materials and Methods

2.1. Sample Preparation

LJH and alfalfa were harvested in Pingba District, Anshun City, Guizhou Province, China (26°40′ N, 106°25′ E). Harvested LJH were dried naturally. Alfalfa plants were cut into 1–3 cm lengths and mixed with previously dried LJH in a 9:1 weight ratio, and then randomly divided into two blocks with 12 replicates for each treatment. Subsequently, they were treated with Lentilactobacillus buchneri (L; 10⁵ CFU/g fresh materials (FM), Xi’an Jushengyuan Biotechnology Co., Xi’an, China) or no additive (CK). Approximately 1.0 kg of treated forage was collected and homogeneously mixed with each inoculant in individual polythene bags and finally vacuum sealed. A total of 24 bags (2 treatments × 4 storage periods × 3 replicates) were ensiled at ambient temperature (>15 °C) and 3 bags from each treatment were sampled at 3, 7, 15, and 30 days for analysis.

2.2. Physical and Chemical Analysis

Briefly, each stored sample (20 g) was mixed uniformly with 180 mL of sterile water in a laboratory juicer for 1 min and filtered through four layers of gauze. Approximately 20 mL of filtrate for centrifugation (4500× g, 15 min, 4 °C). Supernatants were analyzed for lactic, acetic, propionic, and butyric acid by high performance liquid chromatography [12]. The ammoniacal nitrogen concentration was determined by the method described by Broderick and Kang (1980). A pH meter (APERA pH700, Shanghai San-Xin Instrumentation Co., Shanghai, China) was used to measure the pH of the sample solutions.

Approximately 300 g of silage was dried at 65 °C to constant weight to determine the dry matter (DM) content. Dried samples were ground using a 0.2 mm mesh sieve for further analysis. The AOAC [13] method was used to determine crude protein (CP). The methods of Van Soest et al. [14] were used to determine neutral detergent fiber (NDF) and acid detergent fiber (ADF). The water-soluble carbohydrate (WSC) content was determined using the method of McDonald et al. [15].

2.3. Bacterial Community Analysis

We used the CTAB method to extract total genomic DNA from each stored sample to determine the identity of the species present. Purified DNA was diluted to 1 ng/μL with sterile water. Barcoded primers (1514R and 27F) were used to amplify the full-length 16S ribosomal RNA (rRNA) gene [16]. PCR products were purified using a Qiagen Gel Extraction Kit (Qiagen, Hilden, Germany). For 16S rRNA gene amplicon sequencing, the purified DNA samples were sent to Novogene Company (Beijing, China). Paired-end (PE) sequencing was performed on an Illumina MiSeq PE250 platform (Novogene Bioinformatics Technology Co., Ltd., Beijing, China). Raw sequence data were submitted to National Center for Biotechnology Information under accession number PRJNA978969. The microbial communities were analyzed for alpha diversity and principal coordinate analysis (PCoA) using the NovoMagic platform (Beijing Novozymes Biotechnology Co., Ltd., Beijing, China).

2.4. Statistical Analysis

ANOVA analyses of variations in chemical composition, fermentation characteristics, and bacterial community indices during storage were performed using SPSS (version 26.0; IBM Corp., Armonk, NY, USA) and Duncan’s multiple comparisons were performed on some samples. When the probability level was less than 0.05 (p < 0.05), differences were considered statistically significant. The main effects were not discussed if an interaction occurred between the treatment and storage period.

3. Results

3.1. Change in Chemical Composition of the Samples during Fermentation

Changes in chemical composition during fermentation are shown in

Table 1. Changes in chemical composition of the samples during fermentation.

Items		DM	WSC	NDF	ADF	CP
		% FM	% DM
Treatment (T) × Storage period (S)
CK	3 days	38.59	7.61 a	42.35	27.83 a	18.84
	7 days	38.73	4.94 bc	38.89	26.69 ab	18.31
	15 days	37.2	4.73 c	41.31	28.35 a	18.22
	30 days	36.94	4.60 c	36.51	22.28 b	18.78
L	3 days	33.88	6.05 b	40.36	26.57 ab	19.76
	7 days	37.37	5.15 bc	42.73	28.27 a	17.71
	15 days	35.91	5.13 bc	37.44	24.73 ab	19.44
	30 days	35.89	4.98 b	39.42	27.88 a	18.31
SEM		0.308	0.132	0.589	0.509	0.156
Treatment (T)
CK		37.87 a	5.47	39.77	26.29	18.54
L		35.76	5.33	39.99	26.86	18.81
Storage period (S)
3 days		36.24	6.83	41.36	27.2	19.30
7 days		38.05	5.04	40.81	27.48	18.01
15 days		36.56	4.93	39.38	26.54	18.83
30 days		36.42	4.79	37.97	25.08	18.55
Significance (p-value)
T		0.004	0.595	0.850	0.582	0.402
S		0.182	<0.001	0.212	0.374	0.061
T × S		0.154	0.049	0.093	0.031	0.122

CK, without additives; L, inoculation with Lentilactobacillus buchneri; SEM, standard error of the mean; WSC, water-soluble carbohydrates; DM, dry matter; FM, fresh materials; CP, crude protein; NDF, neutral detergent fiber; and ADF, acid detergent fiber. Means with different superscripts in the same column (a–c) are significantly different from each other (p < 0.05).

. For the NDF and CP, no interaction occurred between the treatment and fermentation days for the DM; however, DM content was higher in treatment L than in treatment CK (p < 0.05). In addition, WSCs were lower in treatment L than in treatment CK after 3 days of fermentation. Following 30 days of fermentation, treatment L exhibited a higher WSC content than that for treatment CK (p < 0.05).

3.2. Fermentation Characteristics of Silage

Changes in fermentation characteristics during silage fermentation are shown in

Table 2. Changes in fermentative characteristics of the samples during fermentation.

Items		pH	Lactic Acid	Acetic Acid	Propionic Acid	Butyric Acid	Ammonia-N
			%DM				%TN
Treatment (T) × Storage period (S)
CK	3 days	5.29	2.49	3.30	0.85	0.00	3.65
	7 days	5.21	4.44	3.92	1.28	0.13	3.89
	15 days	5.00	4.97	3.27	1.36	0.12	4.47
	30 days	4.94	5.97	3.77	1.96	0.17	4.85
L	3 days	5.20	2.18	3.55	0.68	0.03	4.07
	7 days	5.32	3.51	4.95	1.29	0.11	4.57
	15 days	4.95	4.28	4.91	1.73	0.09	4.14
	30 days	4.95	6.11	4.48	2.11	0.21	5.60
SEM		0.019	0.095	0.134	0.039	0.007	0.096
Treatment (T)
CK		5.11	4.47 a	3.57	1.36	0.11	4.22
L		5.11	4.02	4.47 a	1.45	0.11	4.59
Storage period (S)
3 days		5.25 a	2.34 d	3.43	0.77 d	0.02 c	3.86 b
7 days		5.27 a	3.98 c	4.44	1.29 c	0.12 b	4.23 b
15 days		4.98 b	4.63 b	4.09	1.55 b	0.11 b	4.30 b
30 days		4.95 b	6.04 a	4.13	2.04 a	0.19 a	5.23 a
Significance (p-value)
T		0.862	0.031	0.001	0.275	0.693	0.067
S		<0.001	<0.001	0.094	<0.001	<0.001	0.001
T × S		0.306	0.241	0.345	0.130	0.338	0.212

CK, without additives; L, inoculation with Lentilactobacillus buchneri; SEM, standard error of the mean; DM, dry matter; and TN, total N. Means with different superscripts in the same column (a–d) are significantly different from each other (p < 0.05).

. Regarding fermentation composition, no interaction between the treatment and fermentation time was observed. The pH gradually decreased with increasing silage time, which is consistent with silage fermentation. Moreover, the organic acid and ammoniacal nitrogen contents gradually increased. Acetic acid was higher in treatment L than in treatment CK, whereas lactic acid was higher in treatment CK than in treatment L.

3.3. Bacterial α-Diversity of Silage

The bacterial α-diversities of the silage samples are shown in

Table 3. Bacterial α-diversity of samples during fermentation.

Items		Chao1	Dominance	Observed Otus	Pielou’s E	Shannon	Simpson	Goods Coverage
Treatment (T) × Storage period (S)
CK	3 days	231.28	0.08 b	231.00	0.62 ab	4.76 a	0.92 s	0.999
	7 days	264.99	0.09 b	264.33	0.63 ab	4.99 a	0.91 a	0.999
	15 days	328.47	0.07 b	328.00	0.66 a	5.41 a	0.93 a	0.999
	30 days	315.02	0.05 b	314.33	0.67 a	5.31 a	0.95 a	0.999
L	3 days	180.20	0.48 a	179.00	0.34 c	2.52 b	0.52 b	0.999
	7 days	442.13	0.38 a	441.67	0.41 c	3.51 b	0.62 b	0.999
	15 days	120.33	0.44 a	120.00	0.36 c	2.49 b	0.56 b	0.999
	30 days	506.81	0.15 b	506.67	0.56 b	4.93 a	0.85 a	0.999
SEM		38.192	0.014	38.205	0.010	0.124	0.014	-
Treatment (T)
CK		284.94	0.07	284.42	0.65	5.12	0.93	0.999
L		312.37	0.36	311.84	0.42	3.36	0.64	0.999
Storage period (S)
3 days		205.74	0.28	205.00	0.48	3.64	0.72	0.999
7 days		353.56	0.24	353.00	0.52	4.25	0.77	0.999
15 days		224.40	0.26	224.00	0.51	3.95	0.75	0.999
30 days		410.92	0.10	410.50	0.62	5.12	0.90	0.999
Significance (p-value)
T		0.724	<0.001	0.724	<0.001	<0.001	<0.001	-
S		0.207	0.002	0.207	0.002	0.004	0.002	-
T × S		0.232	0.006	0.231	0.020	0.014	0.006	-

CK, without additives; L, inoculation with Lentilactobacillus buchneri; and SEM, standard error of the mean. Means with different superscripts in the same column (a–c) are significantly different from each other (p < 0.05).

. All samples exhibited a good coverage index (>0.999), indicating that the majority of the bacteria were captured using this sequencing method. Among the bacterial diversity indices measured for silage, only four indices, Dominance, Pielou’s E, Shannon, and Simpson, indicated an interaction between the treatment and fermentation days (p < 0.05). The analysis revealed that treatment L showed different results from that for treatment CK in the early stages of fermentation for all the indices. As the fermentation progressed, the L treatment values for Dominance and Pielou’s E became progressively more dominant, whereas the Shannon and Simpson disadvantages decreased.

3.4. Bacterial Composition of Silage

The relative abundances of the top ten bacteria in the silage at the phylum and genus levels are shown in Figure 1a and Figure 1b, respectively. The dominant phylum in the FM was Cyanobacteria, followed by Proteobacteria. Cyanobacteria were gradually replaced by Firmicutes after fermentation, and the addition of LAB increased the abundance of Firmicutes in the early stages. At the genus level, Lactobacillus was the dominant genus in the fermented samples, with treatment L resulting in an increase in the abundance of Lactobacillus and a decrease in the abundance of Enterococcus relative to that for treatment CK. However, the trend toward an increase in Lactobacillus became progressively weaker in the later stages of fermentation. In addition, Lactococcus and Weissella were also gradually reduced in treatment CK, but to a lesser extent than in treatment L. Treatment L showed a different abundance of Lactococcus and Weissella than that for treatment CK; however, the Weissella abundance gradually equaled that of treatment CK in the later stages.

3.5. Beta Diversity of Silage

The PCoA of the bacterial community structure of the silage is shown in Figure 2a. The PCoA plot shows the inconsistency of the bacterial community between the L and CK treatments. For example, the L- and CK-treated samples were in separate locations, and the same treatments tended to cluster together in the early stages. Furthermore, the Bray–Curtis index was chosen to determine the variability of the bacterial community in both treatments (Figure 2b). The Bray–Curtis analysis showed similar results as the PCoA analysis, whereby the bacterial communities of the two treatments were altered in the early stages of fermentation (p < 0.05). However, the bacterial communities of the two treatments gradually became similar as the fermentation progressed.

3.6. MetaStat Analysis of Silage

We carried out MetaStat analysis during various stages of fermentation to determine the effect of LJH on LAB (Figure 3). We found a decreasing trend in the Lactobacillus variability between the two treatments at 3, 7, and 15 d of fermentation until 30 d when there was no difference between the two treatments.

4. Discussion

Silage processes are accompanied by the loss of WSC, which results from the consumption of soluble carbohydrates by microorganisms in the silage system, thus producing organic acids [17]. As expected, the WSC gradually decreased with prolonged fermentation time. LAB metabolize WSC to produce organic acids in silage to lower the pH for preservation purposes [10], and inclusion with exogenous LAB inevitably results in a reduction in WSC. Herein, the WSC content data confirmed this phenomenon following treatment L for 3 days. However, the WSC of treatment L was higher than that of treatment C after 30 days. This result may be due to the presence of antimicrobial substances (flavonoids and alkaloids) in LJH, thus affecting the activity of the LAB and resulting in an increased WSC content. Flavonoids have antibacterial activity against LAB by causing membrane damage and disrupting the bacterial surface and internal ultrastructure, which results in the leakage of reducing sugars and proteins [18]. This phenomenon likely contributed to the high levels of WSC in the L-treated silage at 30 days. With respect to ADF, the DM content in treatment L was lower than that in treatment CK. The addition of LAB likely increased the degradation of certain less-fermentable fractions, such as cellulose and lignin [19], which resulted in a decrease in DM content and an increase in ADF content.

Changes in fermentation composition are indicative of preserving silage nutrients. Among the indicators of fermentation, pH is a direct indicator of silage quality, with good-quality silage exhibiting a pH < 4.2 [15]. However, in the present study, although the pH decreased over time, the final pH was greater than 4.2 and did not differ significantly between treatments. Several factors may cause higher-than-normal pH values in the silage. For example, an unusually high buffering capacity (e.g., in legume silages with very high protein and ash contents) or limited fermentation (e.g., cold climatic conditions) may be responsible for higher-than-expected pH values [20]. Furthermore, some studies have demonstrated that antimicrobial analogs cause changes in pH by affecting microorganisms [21]. Therefore, we believe that the high buffering energy of alfalfa (crude protein up to 18% DM), coupled with the antimicrobial activity of LJH, contributed to the difficulty in achieving the desired quality of silage, even with the addition of the LAB. Nevertheless, the addition of LAB was not completely ineffective, as the high acetic acid content in treatment L, and the low lactic acid content in treatment CK, were due to the inoculations of Lactobacillus heterotrophicus and Lentilactobacillus buchneri, which produce acetic acid by breaking down lactic acid [12]. This variation demonstrates that the inoculation of LAB has an effect on alfalfa that contains antimicrobial ingredients from LJH. Although this variation did not result in a difference in silage quality, it provides significant insight in the pursuit of antimicrobial substances in the production of feeds with LAB additives.

The chemical composition of silage is primarily caused by changes in microorganisms. Therefore, bacterial diversity analysis of the samples revealed that adding Lactobacillus (treatment L) produced significantly different results from those of treatment CK regarding the Dominance, Pielou’s E, Shannon, and Simpson indices. The addition of LAB increased the dominance of Lactobacillus, which is consistent with the results in a previous study, where LAB increased the dominance of certain bacteria [22]. The change in Pielou’s E indicates that the addition of LAB inhibited the activity of certain microorganisms, thus resulting in altered evenness between species and reducing Pielou’s E. The addition of microorganisms disrupts the original bacterial community, resulting in variability in the microbial community [23], which may account for the variation in the Pielou’s E index in this study. More recently, Bai et al. [24] demonstrated that inoculation with LAB suppressed the Shannon and Simpson indices of the bacteria in the alfalfa bacterial community, which explains the changes in the L-treated samples’ Shannon and Simpson indices in this study. Notably, all the above changes occurred in the early stages of fermentation, with the variability between the two treatments diminishing at later stages. Microbiological changes during silage fermentation are primarily achieved by a reduction in pH caused by LAB [10]. Based on the lack of significant variability in pH between the two treatments during the later stages of fermentation (Table 2; p > 0.05), we believe that the antimicrobial compounds in LJH contributed to microbial changes during the later stages of fermentation. During the silage process, some antimicrobial compounds (alkaloids and flavonoids) do not disappear with fermentation time and are present in the final silage [25,26]. Therefore, the altered microbial α-diversity was likely caused by the presence of antimicrobial compounds during the later stages of fermentation. However, such indicators were not measured in this study, and such work should be conducted to demonstrate the effects of antimicrobial compounds on silage bacterial α-diversity in subsequent studies.

In general, fermented samples show a large increase in the abundance of Firmicutes, owing to the proliferation of LAB belonging to Firmicutes under anaerobic conditions [27]. In addition, certain LAB additives increase the relative abundance of Firmicutes. Previous studies have shown that LAB are the dominant genera during fermentation [28]. Inoculation of LAB at the time of silage production promotes the rapid growth of LAB to inhibit the depletion of nutritional quality by undesirable bacteria, thereby preserving the nutritional quality of the raw material [17]. In this study, adding Lentilactobacillus buchneri (treatment L) increased the relative abundance of Lactobacillus and decreased the relative abundance of Enterococcus. This result may explain why treatment L possessed a higher acetic acid content than that for treatment CK.

Previous reports have suggested that Weissella, Pediococcus, and Lactococcus are present only in the pre-silage stage and are then replaced by bacteria that are more tolerant to a low pH [29]. A similar phenomenon was observed in the present study, where Lactococcus and Weissella decreased in abundance; however, the pH was higher (4.95 in the late phase), compared to the low pH described earlier. In addition, treatment CK also showed a decreasing trend in the relative abundance of Lactococcus and Weissella. Therefore, we conclude that factors other than the inhibitory effect of Lactobacillus influenced the relative abundances of Lactococcus and Weissella in the silage. These factors likely include the antibacterial compounds in LJH. The LJH were found to possess inhibitory effects in microorganisms in the clinical treatment of gynecological diseases [30]. Notably, the relative abundance of Weissella gradually equalized in both treatments in the later stages, which indicates that the antimicrobial component of LJH functions gradually in the later stages; however, it has little effect on LAB in the early stages of fermentation. The timely feeding of livestock on silage that contains added antimicrobial components may have unexpected effects on animal inflammation. However, these findings require further investigation.

Silage samples with similar bacterial community structures tend to cluster together [31], and changes in the bacterial community structure indicate differences in silage quality [32]. The PCoA plot and Bray–Curtis index analysis showed that the bacterial communities in the two treatments were not the same. Treatment L induced alterations in the bacterial community of the alfalfa that harbored the antimicrobial constituent, LJH. Remarkably, with the progression of fermentation time, the bacterial profiles in both treatments converged, a phenomenon that can be attributed to the antimicrobial properties of LJH. These properties appear to restrain the growth of lactic acid bacteria (LAB) during the later stages of fermentation, without exerting a similar effect in the pre-fermentation phase.

Increasing evidence indicates that an interaction exists between Lactobacillus and silage fermentation indicators [33,34]. Through MetaStat variability analysis, we determined the differential variation in Lactobacillus between two treatments. In silage inoculated with LAB and containing LJH, LJH contained antimicrobial compounds that could have influenced Lactobacillus, indirectly causing the differences in Lactobacillus diversity between the two treatments. Even though we did not evaluate the effect of antimicrobial compounds on LAB, comparing the fermentation indicators of the two treatments in silage dynamics showed that they were no significantly different and we believe that the effects observed in lactic acid bacteria were due to the antimicrobial compounds of LJH. This is substantiated by a study carried out by Gan et al. [35] who also confirmed this by extracting substances from LJH with antibacterial activity (Leonurine).

While this study provides initial insights into the role of LJH in shaping LAB populations during alfalfa fermentation, further investigations are warranted to elucidate the complex mechanisms at play. Future research should focus on identifying the specific biochemical pathways through which LJH influences LAB, as well as exploring strain-specific responses to better understand the genetic determinants involved. Moreover, optimizing the application of LJH to maximize antimicrobial benefits and studying the effects of environmental factors on its performance will be crucial for practical applications in the food and agricultural industries.

5. Conclusions

LJH appears to coexist with LAB in silage as an additive, which has significant therapeutic potential. Although LAB were inhibited, this effect did not result in changes to the silage quality. In the silage containing LJH, certain antimicrobial components affected the later stages of silage production. In summary, our findings demonstrate the effectiveness of LJH in modulating the bacterial ecology in alfalfa-based feeds during fermentation, providing a safer, more sustainable alternative to conventional practices. This novel approach not only enhances feed quality but also offers substantial economic benefits to farmers by potentially decreasing reliance on costly chemicals and antibiotics. Moreover, the use of LJH aligns with growing consumer demands for environmentally friendly and antibiotic-free products, presenting a promising avenue for the livestock industry to adopt greener practices without compromising productivity. Further research is warranted to fully explore the long-term effects on livestock health and environmental sustainability.