Investigation and Analysis of Microbial Diversity in Rice Husk-Based Fermentation Bed Material

Abstract

The rapid expansion of the meat duck industry in China has intensified environmental challenges, particularly those related to managing high-moisture duck manure. Fermentation bed systems, utilizing rice husks as a primary substrate, offer a sustainable solution by promoting waste decomposition and improving animal welfare. This study investigated microbial diversity in rice husk-based fermentation bed materials across different usage durations to assess their ecological feasibility. Samples were collected from a duck farm in Linyi, China, after one, three, five and seven batches of duck rearing (21 days per batch). Microbial communities were analyzed using polymerase chain reaction–denaturing gradient gel electrophoresis (PCR-DGGE), followed by cluster analysis, principal component analysis (PCA) and sequencing of recovered DGGE bands. The results revealed significant shifts in microbial composition, with low similarity (18% overall) and distinct abundance patterns among groups. Bacteroidetes abundance increased with prolonged usage, while Staphylococcus aureus was only detected in the first batch. A total of 32 sequenced bands identified dominant phyla, including Actinobacteria, Proteobacteria, Firmicutes and Bacteroidetes. Group 4 (seven batches) exhibited the highest microbial diversity and richness (Shannon index: 2.68; mean abundance: 16.33 bands), which was attributed to organic matter accumulation and nutrient release during fermentation. These findings demonstrate that rice husk-based fermentation beds maintain robust microbial diversity over time, effectively supporting waste degradation and duck health. We conclude that rice husks are a viable, eco-friendly substrate for waterfowl fermentation bed systems, with periodic microbial supplementation recommended to enhance long-term efficacy. This work provides critical insights for optimizing sustainable livestock farming practices.

1. Introduction

Meat-type duck breeding is a characteristic Chinese animal husbandry industry, with an average annual growth rate of 5–8% and the highest-ranked stock worldwide [1,2]. Compared with other poultry, meat ducks have the advantages of fast growth and strong stress resistance, and a strong ability to adapt to hot, cold and humid environments and other harsh conditions [3]. In recent years, with the increasing demand for duck products, the scale of meat duck breeding has expanded, and the resultant environmental problems have worsened; in particular, the high water content of duck feces increases the difficulty and cost of manure treatment. Feeding meat ducks using a cushion fermentation bed can greatly reduce breeding waste discharge, avoid duck foot inflammation and greatly improve animal welfare and breeding production efficiency [4,5], and as such has become commonplace [6,7]. Fermentation beds are typically made from rice husks, straw, saw bran, coconut bran and other agricultural by-products mixed with fermentation bacteria; they are also known as deep-litter systems [8,9].

China’s rice production is also ranked first in the world, with rice husks being the main by-products, which account for about 15–25% of rice quality [10,11]. Because rice husks are not perishable and are rich in silicon, lignin, organic compounds and other ingredients [12], it is difficult to use them in aquaculture and planting, and their long-term accumulation will therefore not only occupy a lot of land, but also be prone to fire and other safety risks [13,14]. Therefore, using rice husks in fermentation beds to develop livestock breeding can not only keep the area clean and reduce the occurrence of disease, but also provide a new method for recycling biomass such as rice husks. The establishment of a model where ducks are raised in a fermentation bed is based around controlling the excretion and pollution caused by meat duck feces and urine. Raising ducks on a fermentation bed ensures that feces is directly discharged onto the bed, eliminates the excretion of feces and urine, and reduces the concentration of harmful gases in the building [15]. During the fermentation bed culture process, there is no need to scour the duck house, and sewage discharge is therefore low. Raising ducks in fermentation beds can not only protect poultry farms from the pressure of sewage treatment, but also solidify urine to facilitate the production of organic fertilizer in the later stages, making this an environmentally friendly, efficient, energy-saving and pollution-free ecological breeding model. Microorganisms play an important role in the fermentation bed culture process, so it is of great significance to explore the structure and change trends of microflora in the bedding material used. In this study, denaturing gradient gel electrophoresis (DGGE) was used to analyze and investigate microbial diversity in a duck farm’s rice husk fermentation bed.

DGGE, which was first proposed by Fischer and Lerman in 1979, is mainly used to detect DNA mutations. It has a higher resolution than ordinary agarose electrophoresis and polyacrylamide gel electrophoresis, and can detect differences in nucleic acid levels [16]. The main advantage of this technique is that electrophoretic bands can be recovered from the gel, and subsequently the phylogenetic subordination of the flora can be analyzed with clone sequencing, and the existence of specific bacterial populations can be detected. In this experiment, we used duck fermentation bed material as the research object to explore the effect of different use times on the microbial species and quantity of the material, characterize microbial shifts across batches, correlate diversity with waste degradation efficiency and assess pathogen dynamics, in addition to providing data support and a scientific basis for popularizing the method and applying it to raising ducks in rice husk fermentation beds.

2. Materials and Methods

2.1. Sample Collection

From September 2019 to March 2020, we analyzed the microorganisms present in the rice husk fermentation beds of a white feather duck farm in Linyi, China. The fermentation beds comprised rice husks mixed with fermentation bacteria (Bacillus, lactic acid bacteria, yeast and filamentous fungi; the total amount of effective living bacteria was more than 1 billion per gram). Each 20 m² duck house was paved with rice husks per cubic meter, was 23 m long and 12 m wide, and was equipped with six fans at the end, six ventilation windows on the side walls and wet curtains at the front. Fermentation beds were located in the center of each house, with a length, width and height of 20 m, 10 m and 60 cm, respectively. Overall, 1600 meat ducks were raised in each building, and the feeding density was 8/m². The meat duck feed was purchased from the Shandong Liuhe Group (Linyi City, Shandong Province, China). A duck house was randomly selected, and after 21 days of raising each batch of meat ducks, one, three, five and seven batches of duck padding were sampled using a five-point sampling method (Figure 1). First, the surface 5 cm was removed, and sterile bedding samples were collected and placed in aseptic bags. Next, these batches were mixed as samples and divided into Group 1 (one batch), Group 2 (three batches), Group 3 (five batches) and Group 4 (seven batches).

2.2. Main Reagents and Instruments

The PCR instrument used was the T-gradient produced by the Biometra Company (Göttingen, Germany) and the gel imager was the Gel Doc 2000 gel imaging system produced by the Bio-Rad Company (Hercules, CA, USA). The PCR product gel recovery kit, DNA Gel Extraction Kit, DGGE band recovery kit Poly-Gel DNA Extraction Kit, dNTP Mix, Taq enzyme and buffer were purchased from the OMEGA Company, Biel/Bienne, Switzerland.

2.3. DNA Extraction and PCR Amplification of Bacterial 16S rDNA Fragments

Genomic DNA was extracted from samples using CTAB hand-held DNA [17]. Using sample genomic DNA as a template, the 16S rDNA hypervariable region sequence of the sample was amplified using the bacterial universal primers GC-338F (CGCCCGGGGCGCGCCCCGGGGCGGGGCGGGGGCGCGGGGGGCCTACGGGAGGCAGCAG) and 518R (ATTACCGCGGCTGCTGG) [18].

The genomic DNA extracted from the sample was amplified by PCR in a 50 μL volume containing 5 μL of 10× PCR buffer, 1 μL of primer, 3.2 μL of dNTP, 0.4 μL of rTaq and a variable volume of template DNA (equal to 50 ng).

The PCR amplification protocol was as follows: 94 °C for 5 min of pre-denaturation; 94 °C for 1 min of denaturation, 55 °C for 45 s of annealing and 72 °C for 1 min of extension, repeated for 30 cycles. The final 72 °C extension step lasted 10 min.

The amplified products were separated using 1.2% agarose gel electrophoresis, and the separated bands were purified and recovered using a DNA purification kit and stored in a refrigerator at −20 °C.

2.4. Denaturing Gradient Gel Electrophoresis

PCR products were analyzed using DGGE under a denaturant concentration of 40–70% [19]. After electrophoresis, the gel was analyzed and photographed using a gel imaging system. The visible electrophoretic bands were cut and recovered, and the gel was amplified by PCR with 338F/518R as primers and electrophoretic bands after sol as templates. After gel cutting and purification, the reamplified products were ligated to the vector pMD18-T and transferred to DH5 α-competent cells. Positive clones were screened and sequenced [6].

2.5. Principal Component Analysis and Calculation of the Shannon Index, Abundance and Evenness Index

CANOCO software (Version 4.0) was used for principal component analysis (PCA). According to the intensity (gray scale) of each band in the electrophoretic map and the number of sample bands, the bacterial richness (S), aroma concentration index (H) and evenness (E) in the samples were analyzed [20]. The strip density of the DGGE pattern of each sample was calculated using Quantity one software (Version 4.6.6). Using digital analysis, the number of electrophoretic bands, Shannon index, abundance and evenness index were used to compare the microbial diversity of different samples [21].

The algorithm is as follows:

$${\mathrm{H}}^{\prime }=-{\sum }_{i=1}^{S}pi\ln pi=-{\sum }_{i=1}^S(Ni/N)\ln (Ni/N),$$

E = H/Hmax = H/lnS,

where S is the sum of all bands in the sample, pi is the ratio of the strength of a single band to the total strength of all bands in the sample, Ni is the abundance of band i and N is the abundance of all bands in a single lane of the DGGE map.

3. Results

3.1. PCR Amplification of 16S rDNA of Bacteria

Amplification of 16S rDNA with 518R and GC-338F as primers produced the target DNA fragments, with Group 1, Group 2, Group 3 and Group 4 shown from left to right. The results showed that the fragment sizes of the four groups were all between 200 and 300 bp, as shown in Figure 2.

3.2. DGGE of PCR Products

DGGE analysis results of the 16S rDNA PCR products from meat duck farm padding samples are shown in Figure 3. The number, intensity and mobility of DGGE bands differed among the four samples, reflecting the differences in the species and number of bacteria in the different batches of fermentation beds. The first batch contained 13 stripes, the third batch contained 12 stripes, the fifth batch contained 12 stripes and the seventh batch contained 16 stripes. The seventh batch of meat duck farm samples contained the most stripes, indicating that the microbial diversity was high. In our quantitative analysis of the gel map, it was found that there were differences in bacterial composition and diversity among the four samples.

3.3. Construction of the Cluster Diagram

Clustering was formed according to the Deiss similarity coefficient of each group. Microbial cluster analysis results for the different fermentation mattress materials showed the four batches were grouped into two categories, where the similarity between the four groups was 18%. Specifically, the similarity between Group 1 and Group 3 was 39% and that between Group 2 and Group 4 was 45%. The results indicate that there is little similarity among the four groups, as shown in Figure 4.

3.4. Analysis of Microbial Diversity Index

The diversity index analysis showed that there were differences in the Shannon diversity index, evenness and abundance among the four groups. The diversity index and richness of Group 4 were higher than those of the other groups. There was a significant difference between Group 2 and Group 3 (p < 0.05), while the uniformity of Group 2 and Group 4 was significantly higher than that of Group 1 and Group 3. The results showed that the species, quantity and evenness of microorganisms in fermentation mattress materials were significantly affected by different use times. See

Table 1. Analysis of bacterial diversity in different bedding materials.

Group	Aroma Concentration Index	Evenness	Abundance
1	2.53 ± 0.01 ab	0.96 ± 0.00 b	13.15 ± 0.03 ab
2	2.44 ± 0.01 b	0.99 ± 0.01 a	12.22 ± 0.02 b
3	2.40 ± 0.01 b	0.97 ± 0.00 b	12.67 ± 0.03 b
4	2.68 ± 0.02 a	0.98 ± 0.01 a	16.33 ± 0.58 a

Note: Different lowercase letters in the same column of data indicate a significant difference (p < 0.05), whereas no letters or the same lowercase letters indicate no significant difference (p > 0.05).

for details.

3.5. PCA

PCA showed that Group 1, Group 2, Group 3 and Group 4 were widely dispersed. As shown in Figure 5, the similarity of the microorganisms in each group of cushions was small.

3.6. Determination of the Electrophoretic Band Sequence

After DGGE gel strips were recovered, PCR amplification with 338F/518R as a primer was performed and approximately 200 bp DNA fragments were obtained. The PCR product was purified and ligated to the pMD18-T vector, transformed into DH5 α-competent cells and screened, and the positive clones were sequenced. Three clones were selected for each recovery band and sequenced. Electrophoresis bands at the same level were recovered using gel cutting; a total of 32 bands were recovered and sequenced successfully. The sequencing results were compared with sequences in GenBank, and the bacterial types represented by the bands were obtained (

Table 2. DGGE gel band recovery sequence results.

Band Number	Similar Strain	Accession Number	Similarity	Classification
1	Pedobacter ginsenosidimutans	NR_108685	92	Bacteroidetes; Sphingobacteriia Pedobacter
2	Actinomadura sputi	NR_116929	98	Actinobacteria; Streptosporangiales Actinomadura
3	Bacillus oleronius	NR_119157	94	Firmicutes; Bacilli; Bacillus
4	Uncultured Desulfomicrobium sp.	LC001580	99	Proteobacteria; Deltaproteobacteria Desulfomicrobium
5	Glycomyces arizonensis	NR_025791	98	Actinobacteria; Glycomycetales; Glycomyces
6	Pedobacter kyungheensis	NR_132668	92	Bacteroidetes; Sphingobacteriia; Pedobacter
7	Xylanimonas cellulosilytica	NR_074544	95	Actinobacteria; Micrococcales
8	Pedobacter antarcticus	NR_104917	92	Bacteroidetes; Sphingobacteriia; Pedobacter
9	Pedobacter antarcticus	NR_113717	91	Bacteroidetes; Sphingobacteriia Pedobacter
10	Uncultured Sphingobacteriaceae bacterium	KF508174	98	Bacteroidetes; Sphingobacteriia; environmental samples
11	Conexibacter arvalis	NR_113264	92	Actinobacteria; Thermoleophilia Conexibacter
12	Uncultured bacterium	KJ454285	97	Bacteria; environmental samples
13	Uncultured bacterium	JF417963	99	Bacteria; environmental samples
14	Corynebacterium casei	NR_122062	99	Actinobacteria; Corynebacteriales Corynebacterium
15	Silicibacter lacuscaerulensis	NR_118852	96	Proteobacteria; Alphaproteobacteria Ruegeria
16	Staphylococcus sciuri	NR_025520	97	Firmicutes; Bacilli; Staphylococcus
17	Serinibacter tropicus	NR_134805	97	Actinobacteria; Micrococcales Serinibacter
18	Streptomyces spectabilis	NR_115463	99	Actinobacteria; Streptomycetales Streptomyces
19	Bdellovibrio bacteriovorus str. Tiberius	NR_102470	93	Proteobacteria; Deltaproteobacteria Bdellovibrio
20	Gracilibacillus halotolerans	NR_024876	98	Firmicutes; Bacilli; Gracilibacillus
21	Virgibacillus zhanjiangensis	NR_116658	98	Firmicutes; Bacilli; Virgibacillus
22	Oceanobacillus indicireducens	NR_113330	99	Firmicutes; Bacilli; Oceanobacillus
23	Jeotgalicoccus nanhaiensis	NR_116596	96	Firmicutes; Bacilli; Jeotgalicoccus
24	Brachybacterium faecium	NR_027599	97	Actinobacteria; Micrococcales; Brachybacterium
25	Uncultured bacterium	AB518162	99	Bacteria; environmental samples
26	Fodinibius salinus	NR_117802	97	Bacteroidetes; Sphingobacteriia; Fodinibius
27	Thermomonas koreensis	NR_113982	97	Proteobacteria; Gammaproteobacteria; Thermomonas
28	Shewanella amazonensis	NR_074842	94	Proteobacteria; Gammaproteobacteria; Shewanella
29	Brevibacterium avium	NR_026485	99	Actinobacteria; Micrococcales; Brevibacterium
30	Herbaspirillum rubrisubalbicans	NR_114141	89	Proteobacteria; Betaproteobacteria; Herbaspirillum
31	Brevibacterium linens	NR_026166	98	Actinobacteria; Micrococcales; Brevibacterium
32	Brevibacterium marinum	NR_042586	97	Actinobacteria; Micrococcales; Brevibacterium

).

The results showed that the similarity between the 32 sequences and the corresponding prokaryotic 16S rRNA sequences in the database reached 89–100%. Most of these sequences were from actinomycetes (11 sequences), followed by Bacteroides (6 sequences), Proteus (6 sequences), Bacillus (5 sequences), Staphylococci (1 sequence) and unculturable unknown bacteria (3 sequences). The matching microbial sources in the gene bank were mainly soil environments, lake sediments and forests, which is consistent with the sources of the microorganisms used in the experiment.

Among the groups, Group 1 mainly contained four kinds of bacteria: actinomycetes, Proteus, Bacillus and Bacteroides, the proportions of which were 46.15%, 23.08%, 15.38% and 7.69%, respectively. Group 2 mainly contained bacteria of five genera: Bacillus, actinomycetes, Proteus, Bacteroides, Staphylococci and uncultured bacteria, the proportions of which were 33.33%, 16.67%, 16.67%, 8.33%, 8.33% and 16.67%, respectively. Group 3 mainly contained actinomycetes, Bacteroides, Proteus, Staphylococci, Bacillus and uncultured bacteria, the proportions of which were 33.33%, 16.67%, 16.67%, 8.33%, 8.33% and 16.67%, respectively. Group 4 mainly contained bacteria of four genera: actinomycetes, Bacteroides, Proteus, Bacillus and uncultured bacteria, the proportions of which were 37.50%, 18.75%, 18.75%, 12.50% and 12.50%, respectively.

4. Discussion

Fermentation bed culture technology is a comprehensive pollution control technique that combines reduced discharge, resource utilization and harmless manure utilization. It can significantly improve the welfare of livestock and poultry. Research by Tam et al. demonstrates that using sawdust inoculated with beneficial bacteria as bedding material significantly enhances manure degradation [22]. Morrison et al. utilized rice husks as bedding to create fermentation beds for pig farming, achieving good results [8]. Karlen et al. found that fermentation beds are highly beneficial for raising pregnant sows, comprehensively protecting their warmth and insulation during pregnancy, reducing self-inflicted injuries from normal pig activity, and improving piglet immunity and survival rates [9]. Furthermore, microorganisms have been shown to play a vital role [23,24], including Bacillus, actinomycetes and lactic acid bacteria [25]. The diversity of bacteria is not just affected by livestock and poultry manure, but also the sources of the bacteria. Fermentation beds can be used as the main area for livestock to live and rest, and as such the choice of bedding material is important; the comfort provided by a fermentation bed directly affects the behavior and health of livestock and plays an important role in growth performance [26].

After fermentation, rice husks demonstrate strong water retention and permeability; using them in duck raising in the form of a fermentation bed can effectively solidify duck manure. The water content of duck manure can be reduced by the water absorption and fermentation heat of the cushion material used, which makes the cleaning, transportation and resource utilization of duck manure more convenient. Therefore, it is necessary to evaluate the microbial quality of rice husk-based fermentation beds for ducks, subsequently popularizing the applications of fermentation bed culture technology.

In this experiment, a nonculture method was used to identify and analyze the diversity of bacteria in rice husk-based fermentation mattress material using the PCR-DGGE technique. This method can avoid the limitations of traditional detection techniques and has the advantages of good repeatability, low cost and rapidity. We found that different use times had certain effects on the microbial diversity of the cushion material. Staphylococcus aureus was found in Group 1, but not in Groups 2, 3 or 4. Staphylococcus aureus is a common Gram-positive bacterium that is prevalent in human and animal populations. It usually causes superficial skin, soft tissue and other organ infections, and in severe cases, it can even lead to fatal blood infections and lung infections. S. aureus can affect all avian species, causing synovitis, osteomyelitis, gangrenous dermatitis, bumble foot, omphalitis, chondronecrosis and septicemia [27]. Due to the ability of S. aureus to colonize the skin and body of animals, it is expected several meat foods such as chicken, pork and beef are very suitable contamination reservoirs for the growth and colonization of S. aureus [28,29]. Worryingly, Staphylococcus aureus shows strong resistance to the latest antibiotics [30,31]; however, this study showed that it gradually disappeared as the fermentation process progressed. It may be that the beneficial actinomycetes, Bacteroides, cocci and other bacteria in the cushion antagonized and inhibited the growth of S. aureus, which could reduce the incidence of infection in ducks to some extent.

The microbial diversity index analysis of the rice husk fermentation mattresses showed that there were significant differences in the aroma index and evenness between the four groups, indicating that the species of microorganisms changed significantly from the first batch to the seventh batch. The results of the abundance analysis showed that the number of microorganisms in Group 4 was significantly higher than it was in Groups 1, 2 and 3, indicating that the number of microorganisms increased in the later stage of rice husk cushion usage. The possible reasons for this are as follows. Firstly, because ducks continued to excrete feces and urine into the rice husk cushion, the content of organic matter in the cushion increased, which was rich in negatively charged functional groups. These functional groups likely absorbed positively charged antibiotics and disinfectants, thus providing a beneficial environment for the growth of bacteria. Second, microbial fermentation promoted the full release of crude fiber, lignin, N, P, K and other nutrients in rice husks, which could be more easily used by microorganisms and provide sufficient nutrients for the growth of microorganisms. Therefore, the number and species of microorganisms increased. Third, during and after microbial fermentation, the adsorption and permeability of the rice husks increased, as did the temperature of the cushion, which provided a favorable environmental basis for the growth of microorganisms. Therefore, the number of microorganisms in the later stages increased.

5. Conclusions

This study demonstrates the ecological viability of rice husk-based fermentation beds in duck farming, emphasizing their capacity to sustain microbial diversity and functionality over extended use periods. PCR-DGGE analysis revealed dynamic shifts in microbial communities, with Bacteroidetes abundance increasing significantly across successive batches, while Staphylococcus aureus was eliminated after initial use. The highest microbial richness and diversity (Shannon index: 2.68; abundance: 16.33) were observed in the seventh batch, driven by organic matter enrichment, nutrient release and favorable fermentation conditions. These microbial dynamics enhance waste degradation efficiency, reduce pathogenic risks and improve bedding stability. The findings confirm that rice husks, as a renewable substrate, effectively support long-term fermentation processes while mitigating environmental impacts associated with duck manure. To optimize system performance, the periodic supplementation of beneficial microbial consortia is recommended to counteract potential diversity loss during recycling. This research underscores the practical value of rice husk fermentation beds in promoting sustainable poultry farming, offering a cost-effective and eco-friendly alternative to conventional waste management practices. Future studies should explore tailored microbial inoculants and operational protocols to further enhance system resilience and scalability.