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Article

Assessment of Effects of Storage Time on Fermentation Profile, Chemical Composition, Bacterial Community Structure, Co-Occurrence Network, and Pathogenic Risk in Corn Stover Silage

by
Zhumei Du
1,
Ying Meng
1,
Yifan Chen
1,
Shaojuan Cui
1,
Siran Wang
2,* and
Xuebing Yan
1,*
1
College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
2
Institute of Animal Science, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
*
Authors to whom correspondence should be addressed.
Fermentation 2025, 11(8), 425; https://doi.org/10.3390/fermentation11080425
Submission received: 17 June 2025 / Revised: 14 July 2025 / Accepted: 16 July 2025 / Published: 23 July 2025
(This article belongs to the Special Issue Application of Fermentation Technology in Animal Nutrition: 2nd Edition)
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Abstract

In order to achieve the efficient utilization of agricultural by-products and overcome the bottleneck of animal feed shortages in dry seasons, this study utilized corn stover (CS; Zea mays L.) as a material to systematically investigate the dynamic changes in the fermentation quality, bacterial community structure, and pathogenic risk of silage under different fermentation times (0, 3, 7, 15, and 30 days). CS has high nutritive value, including crude protein and sugar, and can serve as a carbon source and a nitrogen source for silage fermentation. After ensiling, CS silage (CSTS) exhibited excellent fermentation quality, characterized by relatively high lactic acid content, low pH, and ammonia nitrogen content within an acceptable range. In addition, neither propionic acid nor butyric acid was detected in any of the silages. CS exhibited high α-diversity, with Serratia marcescens being the dominant bacterial species. After ensiling, the α-diversity significantly (p < 0.05) decreased, and Lactiplantibacillus plantarum was the dominant species during the fermentation process. With the extension of fermentation days, the relative abundance of Lactiplantibacillus plantarum significantly (p < 0.05) increased, reaching a peak and stabilizing between 15 and 30 days. Ultimately, lactic acid bacteria dominated and constructed a microbial symbiotic network system. In the bacterial community of CSTS, the abundance of “potential pathogens” was significantly (p < 0.01) lower than that of CS. These results provide data support for establishing a microbial regulation theory for silage fermentation, thereby improving the basic research system for the biological conversion of agricultural by-products and alleviating feed shortages in dry seasons.

1. Introduction

In tropical developing countries, the primary roughage sources for ruminants are local native grasses and agricultural by-products [1]. Insufficient forage quality and quantity, particularly during the dry season, have become key constraints to the development of livestock production in this region [2]. Forage crops and grass typically grow well in the summer and autumn when water sources are abundant. Their dry matter (DM) yield and nutritive value are at high levels, resulting in a surplus supply and adequate nutrition for cattle and sheep. However, during dry seasons such as winter and spring, a severe shortage of feed supply can cause cattle and sheep to lose weight or even die [3]. Therefore, it is particularly important to develop efficient technologies to optimize the utilization of grasses and crop by-products, thereby effectively ensuring the nutritional requirements for animal growth [4].
Corn (Zea mays L.) as an important food and cash crop, is widely cultivated due to its high yield potential and broad adaptability, growing from tropical to temperate regions, making it one of the most widely cultivated crops globally (second only to wheat and rice) [5]. During mechanized corn harvesting, after the kernels are separated from the cobs by the threshing mechanism, the stover is left in the field due to the lack of efficient methods for its utilization [6]. Therefore, converting by-products into usable resources has become a key strategy to alleviate resource constraints and improve the ecological environment. Corn stover (CS) has a high fiber content, and direct feeding with this by-product poses issues such as poor palatability and low digestibility. Using technology for the production and storage of silage is the most effective means to enhance the crude protein (CP) digestibility of straw and reduce DM loss rates [7]. Therefore, developing efficient technologies to optimize the utilization of grasses and crop by-products, thereby effectively ensuring the nutritional requirements for animal growth, is of particular importance [4].
Traditional microbial plate culture methods struggle to fully analyze the dynamic changes in microbial communities within complex silage systems. Although next-generation high-throughput sequencing technology has been widely applied in studies of microbial population structure, its resolution is limited to the genus level. PacBio’s single-molecule real-time (SMRT) sequencing technology has the advantage of longer read lengths and enables microbial classification at the species level, significantly enhancing detection sensitivity and accuracy [8]. This study employed a combination of multi-omics technologies and conventional physicochemical analyses to systematically elucidate the interactive mechanisms underlying bacterial community succession, nutrient transformation, and the evolution of physicochemical properties during CS fermentation. A quantitative assessment model linking fermentation duration to silage quality was constructed to provide precise fermentation cycle regulation strategies for the efficient utilization of CS.

2. Materials and Methods

2.1. CS Silage (CSTS) Sample Preparation

Corn (cv. Futian 301) harvesting was carried out at the Fresh Corn Introduction Trial Base (Yangzhou, Jiangsu, China) in 20 October 2023. After harvesting corn grain-bearing ears with a harvester (HF441G, Iseki Co., Ltd., Matsuyama, Tokyo, Japan), the remaining CS left in the field was harvested manually with a sickle. Subsequently, a forage chopper (CX-201, Yamamoto Co., Ltd., Tendo, Japan) was employed to chop the CS residue into lengths of 1–2 cm. After thorough mixing, samples of 1 kg were weighed out in triplicate. The samples were then immediately placed in a 4 °C freezer and transported to the laboratory for an analysis of chemical composition and bacterial community.
This study utilized four CSTS treatments: CSTS-3 (3 days of fermentation), CSTS-7 (7 days), CSTS-15 (15 days), and CSTS-30 (30 days). Polyethylene plastic bags of consistent specifications (FB2025, 350 mm × 500 mm, Fisher Scientific, Hampton, NY, USA) were selected to ensure uniformity of the experimental conditions. By thoroughly mixing the CS, four independent replicates were set up for each of the four CSTS treatments, with each replicate corresponding to one silage bag, resulting in a total of 16 silage bags (4 treatments × 4 replicates). Approximately 1 kg of CS was placed in each silage bag, and then the bags were vacuum-sealed for 1 min using a vacuum-packing sealer machine (AliceV952S, ASONE, Osaka, Japan). After sealing, the bags were placed in a room-temperature environment of 19–28 °C and fermented for 3, 7, 15, and 30 days, respectively.
After fermentation was complete, three samples were randomly selected from each treatment at the different fermentation periods for opening and sampling. Subsequently, after thorough mixing and homogenization, each treatment sample was divided into 3 portions: the first portion (approximately 50 g) was stored in a −80 °C freezer for use in PacBio SMRT sequencing technology; the second portion (approximately 200 g) was dried for chemical composition analysis; and the remaining portion (approximately 10 g) was used to prepare liquid filtrates for the analysis of microbial population and fermentation quality.

2.2. Chemical Composition, Fermentation Quality, and Microbial Population

The CS and CSTS were dried in a 65 °C constant-temperature oven for 48 h to achieve a constant weight state. Subsequently, the dried samples in each bag were thoroughly mixed, and then ground using a mill (T1-200; CMT Co., Ltd., Tokyo, Japan) until all of the samples could pass through a 1 mm sieve for subsequent experimental analysis. The DM, organic matter (OM), CP, ether extract (EE), neutral detergent fiber (NDF), and acid detergent fiber (ADF) were determined by the AOAC method [9]. Water-soluble carbohydrate (WSC) was determined by High-Performance Liquid Chromatography (HPLC, LC-2000 plus, JASCO Corporation, Tokyo, Japan) [10].
Cold-water extraction was used to determine fermentation indicators, including pH, ammonia nitrogen (NH3-N), and organic acids, in the silages [10]. Briefly, 10 g of sample was homogenized with 90 mL of deionized water and refrigerated at 4 °C for 24 h to prepare the extraction solution. The pH was directly determined by a pH meter (S-90, Mettler Toledo, Zurich, Switzerland). The phenol-sodium hypochlorite colorimetric method was used to measure the NH3-N content [10]. Organic acids were analyzed via HPLC (Agilent 1260 Infinity II, Santa Clara, CA, USA), with the machine equipped with a Phenomenex ReZex ROA column (7.8 mm × 300 mm; Phenomenex, Torrance, CA, USA). The chromatographic conditions included an oven temperature of 60 °C, UV detection of 450 nm, and isocratic elution with 3 mmol/L HClO4 of 1.0 mL/min.
Samples of 10 g were blended with 90 mL of 0.85% sterilized saline solution. Then, the mixture was shaken for 5 min using a Vortex-Genie Blender (Scientific Industries, Bohemia, New York, NY, USA), and the supernatant was subjected to serial dilutions ranging from 10−1 to 10−8. The lactic acid bacteria (LAB) were determined using MRS medium (Difco Laboratories, Detroit, MI, USA); aerobic bacteria using nutrient agar medium (Nissui-Seiyaku Co., Ltd., Tokyo, Japan); coliform bacteria using blue-light broth agar medium (Nissui-Seiyaku Co., Ltd.); and yeast and mold using potato dextrose agar medium (Nissui-Seiyaku Co., Ltd.) [11]. The microbial count was performed using the spread plate method, with three replicates set for each dilution gradient. The results are expressed as lg colony-forming units (cfu)/g of fresh matter (FM).

2.3. Measurement of Bacterial Community Structure by SMRT

Sample preparation: A 10 g sample (CS or CSTS) was mixed with 90 mL of 0.85% sterilized saline solution and shaken at 120 rpm for 2 h, with 3 replicates per sample. The mixture was filtered through four layers of pre-sterilized gauze, and the filtrate was centrifuged at 10,000 rpm for 10 min at 4 °C. The total genomic DNA was extracted using the DNA kit (DP302-02, Tiangen kit, Beijing, China) according to the method described by Yan et al. [12].
Library construction: Specific primers containing barcodes were synthesized based on the full-length primer sequence, and PCR technology was used to amplify the extracted DNA [12]. After purification, quantification, and homogenization, the samples were used to construct a SMRTbell sequencing library. Following quality control of the library, it was sequenced using the PacBio Sequel II platform (Pacific Biosciences of California, Inc., Menlo Park, CA, USA).
Bioinformatic analysis: The raw data was stored in BAM format and converted to circular consensus sequence (CCS) files using SMRT Link 8.0 software from Pacific Biosciences [13]. The samples were separated by barcodes, and FASTQ format data was generated for subsequent analysis. Based on the SILVA reference database (version 138.1), operational taxonomic unit (OTU) classification and annotation were conducted by employing the naive Bayes classifier integrated into QIIME2. Venn diagrams were constructed using the R package (version 1.2) to describe the shared and unique microorganisms in all of the samples [14]. Using the QIIME software (version 2020.6.0), the alpha diversity indices, including abundance-based coverage estimator (ACE), Chao1, Shannon, and Simpson, were computed [15]. The relative abundance of the microbial community at the genus and species level were analyzed using the Excel Statistical Package for Windows (version 2003). Microbial network analysis was conducted and hierarchical clustering heatmaps were constructed using PyNAST v1.2.2 and the pvclust package of R v3.0.2 [16,17]. Bugbase phenotypic prediction analysis was performed using the Bugbase tool (version 0.1.0) [18].

2.4. Statistical Analysis

One-way analysis of variance (ANOVA) with the general linear model (GLM) procedure in SPSS (Version 19.0, SPSS Inc., Chicago, IL, USA) was employed to assess variations in chemical composition and fermentation quality across treatments [19]. All data were analyzed using orthogonal contrasts to evaluate linear and quadratic responses to fermentation duration, followed by Tukey’s multiple comparison test to assess statistical significance (p < 0.05).

3. Results

Table 1 presents the chemical composition and microbial population of CS. DM and OM accounted for over 31% of FM and 91% of DM, respectively. The CP and EE contents in CS were below 7.0% and 2.1% of DM, respectively. The NDF, ADF, and WSC contents exceeded 54%, 28%, and 6.6% of DM, respectively. The microbial counts of CS showed that LAB, coliform bacteria, and yeast exceeded 5.0 lg cfu/g FM; aerobic bacteria exceeded 7.0 lg cfu/g FM; and mold was below 4.0 lg cfu/g FM.
The chemical composition, fermentation quality, and microbial population for CSTS fermented for 3, 7, 15, and 30 days are shown in Table 2. In terms of chemical composition, with the extension of fermentation duration, CP, EE, NDF, and ADF contents were linearly (p < 0.05) decreased, while DM and OM contents were consistently maintained at a stable level, with no significant (p > 0.05) differences observed. For fermentation quality, with the extension of fermentation duration, pH value and NH3-N content showed linear (p < 0.05) and quadratic (p < 0.05) decreases, whereas lactic acid content showed a linear (p < 0.05) and quadratic (p < 0.05) increase. Specifically, the lowest pH and NH3-N content, along with the highest lactic acid content, were observed in the CSTS-15 treatment. With the extension of fermentation duration, acetic acid showed a linear (p < 0.05) increase, while propionic acid and butyric acid were not detected during the entire period. For the microbial population, with the extension of fermentation duration, LAB count was linearly (p < 0.05) and quadratic (p < 0.05) increased, whereas the counts of aerobic bacteria and yeast were linearly (p < 0.05) or quadratic (p < 0.05) decreased. The highest LAB count and the lowest counts of aerobic bacteria and yeast were found in the CSTS-15 treatment. No coliform bacteria or mold (<2.0 lg cfu/g FM) was enumerated.
The number of OTUs and their overlapping relationships for CS and CSTS treated with different fermentation durations are shown in Figure 1. The number of OTUs in CS was significantly (p < 0.05) higher than that in all of the silages (Figure 1A). In Figure 1B, the number of shared OTUs between CS and CSTS treatments was 148, while the number of unique OTUs in CS was significantly (p < 0.05) higher than that in the CSTS treatment. Figure 1C shows that the four treatments, CSTS-3, CSTS-7, CSTS-15, and CSTS-30, shared 67 OTUs. As the fermentation duration extended, the number of unique OTUs in each treatment generally increased, but the CSTS-15 treatment exhibited the lowest number of unique OTUs.
Figure 2 shows the changes in the α-diversity indices of CS before and after ensiling. Compared with the CS treatment, the ACE indices of the CSTS-3, CSTS-7, and CSTS-30 treatments significantly (p < 0.05) increased, while there were no marked (p > 0.05) differences with the CSTS-15 and CSTS-30 treatments (Figure 2A). There were no significant (p > 0.05) differences in the Chao 1 indices between CS and all of the CSTSs (Figure 2B). Compared with the CS, the Simpson and Shannon indices were significantly (p < 0.05) lower in all of the silages, with the lowest indices observed in the CSTS-15 treatment (Figure 2C,D).
The relative abundance of the bacterial community at the genus and species levels in the CS and CSTS treatments is shown in Figure 3A and Figure 3B, respectively. Before ensiling, the dominant bacterial species was Serratia marcescens (genus Serratia). After ensiling, Lactiplantibacillus plantarum (genus Lactiplantibacillus) became the dominant species in the CSTS-3, CSTS-7, CSTS-15, and CSTS-30 treatments. As the ensiling days increased, the relative abundance of Lactiplantibacillus plantarum in CSTS rose steadily from day 3 to day 15 (CSTS-3, CSTS-7, and CSTS-15), and then stabilized at later stages (CSTS-15 and CSTS-30).
Figure 4A shows the correlation analyses of the network of microbes related to CSTS at the species level. The key species in CSTS was Lactobacillus spicheri, which was positively correlated with Levilactobacillus hammesii. Lentilactobacillus kefiri was negatively correlated with Clostridium beijerinckii. Figure 4B displays the correlation heatmap and hierarchical cluster analysis of the bacterial community and terminal fermentation products in CSTS. Lactic acid and LAB were strongly and positively correlated with Lactiplantibacillus plantarum, and pH was negatively correlated with Weissella paramesenteroides.
Figure 5 shows the results of the Bugbase phenotypic prediction analysis before and after the ensiling of CS. Compared with the CS, the proportion of Gram-positive bacteria in the CSTS treatment significantly (p < 0.05) increased, while the proportions of Gram-negative bacteria and potential pathogenic bacteria significantly (p < 0.05) decreased.

4. Discussion

4.1. Biochemical Composition of CS

CS, as the residual part of crops after harvesting the seeds, is a renewable agricultural by-product with a huge yield and rich in a variety of micronutrients [20]. The traditional way of utilizing this by-product is incineration, but the incineration process releases large amounts of CO2, CO, SO2, N2O and other greenhouse gases [21]. The utilization of CSTS can not only effectively reduce the amount of agricultural waste generated, but also achieve the recycling of resources, providing strong support for the sustainable development of green agriculture [7]. Crop by-products are usually dried naturally in the field after harvesting and preserved as hay; however, nutrient losses and degradation can occur. Also, drying artificially is energetically expensive [22]. Therefore, silage fermentation is considered to be the ideal technology for feed harvest and storage. Theoretically, the ideal conditions for silage fermentation include a moisture of 60–70%, WSC content greater than 5% of DM, and a LAB count exceeding 105 cfu/g of FM [23]. It was evident from the results of this study that CS had a low moisture content as well as a high WSC content and LAB count, indicating relatively high nutritional value. Therefore, preserving and utilizing the nutrient content of these agricultural by-products with the help of silage fermentation is important to enhance feed quality and livestock productivity.

4.2. Fermentation Quality of Silage

During the fermentation process, LAB ferment sugars to lactic acid, thereby lowering the pH, inhibiting the growth of harmful bacteria, and ultimately producing high-quality silage [24]. During the ensiling of CS in this study, oxygen in the silage environment was continuously consumed, leading to the death of aerobic microorganisms and promoting the growth of LAB. This resulted in a continuous decrease in pH and the aerobic bacteria count, while the lactic acid content and LAB count increased, indicating good fermentation quality. Additionally, after 15 days of fermentation, the silage environment stabilized, resulting in peak fermentation quality.

4.3. Microbial Alpha Diversity and Community Dynamics

The dynamic evolution of bacterial community structure during silage production is characterized by significant changes in microbial abundance and α-diversity. This process is affected by a number of factors, including material composition, environmental parameters, and microbial interactions, directly impacting the fermentation quality of silage [25]. In the field of bacterial community research, traditional plate culture techniques and modern high-throughput sequencing technologies exhibit significant technical differences and functional complementarity. Traditional plate culture techniques are limited by the cultivability of microorganisms, typically only enabling genus-level identification. For some groups, species-level classification requires additional physiological and biochemical tests, making it difficult to fully reflect the true composition of the community [26]. In contrast, modern high-throughput sequencing technologies based on NGS and SMRT have overcome the cultivability bottleneck, enabling the analysis of the entire community structure, including uncultivable microorganisms, at the genus and species levels, with resolution reaching the species or even strain level. Additionally, these technologies can perform multidimensional analyses of community diversity, abundance distribution, and functional gene potential [27].
The number of OTUs combined with the ACE and Chao1 indices is commonly used to assess the species richness of bacterial communities, while the Shannon and Simpson indices are used to measure species diversity [28]. The results of this study showed that the moisture of the CS was approximately 78%, which falls within the high moisture range, providing an ideal environment for the proliferation of aerobic microorganisms (Figure 1 and Figure 2). Under adequate oxygen supply, the bacterial community on the surface of CS exhibited high species richness and diversity. As the silage process progressed, oxygen in the system was rapidly depleted, while acidic substances produced during fermentation continued to accumulate. Under the dual environmental stresses of anaerobic conditions and low pH, Gram-negative bacteria struggle to adapt to environmental changes and undergo significant mortality. By the end of fermentation, acid-tolerant and anaerobic Gram-positive LAB emerge as dominant, establishing a dominant position through vigorous lactic acid fermentation. This dynamic succession of microbial community was characterized by LAB gradually replacing other microbial groups in the CS, becoming the dominant population, ultimately leading to a significant reduction in microbial abundance and diversity in CSTS.
Analyzing the community structure composition and dynamic succession at the genus and species levels is crucial for precisely regulating fermentation quality and optimizing process parameters. Figure 3 of this study shows that Serratia marcescens was the initial dominant bacterial species in CS. As a Gram-negative opportunistic pathogen belonging to the Enterobacteriaceae family, it is widely distributed in soil, water bodies, and the surfaces of plants and animals, and commonly causes hospital-acquired infections in critically ill patients or immunocompromised individuals [29,30]. As silage fermentation progressed, Lactiplantibacillus plantarum became the dominant bacterial community in CSTS. Additionally, its relative abundance continued to increase with fermentation time. This phenomenon was attributed to the strong acid tolerance of Lactiplantibacillus plantarum, which enables it to rapidly respond to the dual stresses of anaerobic and acidic conditions within the silage system. [12]. As fermentation time extended, the pH within the silage environment continued to decrease. Lactiplantibacillus plantarum further intensified the acidic environment through vigorous lactic acid fermentation, forming a positive feedback loop of acid production–bacterial inhibition. This not only effectively suppressed the proliferation of harmful bacteria such as Serratia marcescens, but also enhanced the nutrient retention and storage stability of silage by maintaining a low-pH steady state [31].

4.4. Correlation Analysis Between Microbial Network and Fermentation Product

Silage fermentation is a dynamic process regulated by the synergistic interaction between microbial communities and metabolic products [32]. During ensiling, various microorganisms utilize distinct metabolic pathways to convert substrates into a wide range of metabolic products, directly determining the fermentation quality of silage. Concurrently, these metabolic products act as environmental regulatory factors, reshaping the structure of microbial communities through mechanisms such as pH changes and antibacterial effects [23]. This bidirectional interaction between microbial metabolism and environmental feedback constitutes the core mechanism of silage fermentation, ultimately determining key quality indicators such as the nutritive value, shelf life, and palatability of silage [28].
Lactobacillus spicheri, as a facultative anaerobic homofermentative LAB, rapidly accumulated lactic acid to decrease the pH of silage, thereby safeguarding feed nutrients. This bacterium often establishes a symbiotic relationship with the heterofermentative LAB Levilactobacillus hammesii through a lactic acid–acetic acid feedback metabolic mechanism, thereby optimizing the composition of fermentation products [33,34]. Lentilactobacillus kefiri, a Gram-positive heterofermentative LAB, exhibits inhibitory activity against pathogenic bacteria like Bacillus cereus and Staphylococcus aureus. This is achieved through the production of bioactive components during fermentation, thereby enhancing the safety and quality attributes of fermented food products [35]. During ensiling, the proliferation of Clostridium beijerinckii can trigger butyric acid-driven spoilage, a process that depletes nutrients and generates undesirable odors [36]. Consequentially, a negative associative pattern was observed between Lentilactobacillus kefiri and Clostridium beijerinckii, underscoring their opposing roles in shaping silage fermentation outcomes. Lactiplantibacillus plantarum in silage can accelerate the rapid fermentation of LAB, produce lactic acid, and lower the pH, thereby inhibiting the reproduction of plant enzymes and harmful bacteria and preserving the nutrients associated with ensiling [37]. Weissella paramesenteroides is a heterofermentative LAB, and the acetic acid accumulated during ensiling can effectively improve the aerobic stability of silage. Homo- and hetero-fermented LAB synergistically improve the silage quality [38].

4.5. Bugbase Phenotypes Predicted in the Bacterial Community

By leveraging microbial genome sequence information such as gene functional annotation and metabolic pathways, Bugbase can accurately predict the phenotypic characteristics of unknown strains from multiple perspectives, including cellular structure, metabolic function, ecology, and pathogenicity. This tool has demonstrated its application value in multiple fields. In silage research, its core functions focus on fermentative bacterial community structure analysis, metabolic function prediction, and safety assessment [18].
In this study, damage to CS during harvesting and transportation released juices containing carbon sources, mainly including glucose and amino acids. These substances promoted the proliferation of Gram-negative bacteria inherent in the plant surface. At the same time, due to the presence of pathogenic bacteria in the bacterial community, the CS posed a biosafety risk [39]. However, since the silage system is mainly dominated by obligate anaerobic bacteria and facultative anaerobic bacteria, these bacterial communities efficiently accumulate lactic acid through glycolysis, causing the pH of the system to decrease and forming an acidic microenvironment, thereby inhibiting the growth of acid-sensitive Gram-negative bacteria [40]. This process ultimately promoted Gram-positive bacteria to become the dominant bacterial community and stabilized colonization, thereby improving the fermentation quality and reducing the potential pathogenic risk of CSTS.

5. Conclusions

This study indicated that CS contained carbon and nitrogen sources suitable for natural silage fermentation, with the primary microbial community in the CS being Serratia marcescens, which shifted to Lactiplantibacillus plantarum as the fermentation progressed. Also, the optimal coupling period for microbial metabolism and nutrient transformation occurred between 15 and 30 days. This study provided a theoretical foundation for improving the research framework for the biological conversion of agricultural by-products.

Author Contributions

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

Funding

This work was supported by the Project of the National Natural Science Foundation of China (grant numbers 32401483) and High-level Talents Program of “Lv-Yang-Jin-Feng of Yangzhou” (grant numbers 137012846/5019).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

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Fermentation 11 00425 g001
Figure 1. Intergroup features and overlapping relationships of OTU numbers in CS before and after ensiling. (A) Feature number of CS, CSTS-3, CSTS-7, CSTS-15, and CSTS-30; (B) OTU of CS and CSTS; (C) OTU of all CSTS treatments. CS, corn stover; CSTS, corn stover silage; CSTS-3, corn stover fermented for 3 days; CSTS-7, corn stover fermented for 7 days; CSTS-15, corn stover fermented for 15 days; CSTS-30, corn stover fermented for 30 days.
Figure 1. Intergroup features and overlapping relationships of OTU numbers in CS before and after ensiling. (A) Feature number of CS, CSTS-3, CSTS-7, CSTS-15, and CSTS-30; (B) OTU of CS and CSTS; (C) OTU of all CSTS treatments. CS, corn stover; CSTS, corn stover silage; CSTS-3, corn stover fermented for 3 days; CSTS-7, corn stover fermented for 7 days; CSTS-15, corn stover fermented for 15 days; CSTS-30, corn stover fermented for 30 days.
Fermentation 11 00425 g001
Fermentation 11 00425 g002
Figure 2. General information on alpha diversity analysis of CS before and after ensiling. (A) ACE index of alpha diversity; (B) Chao 1 index of alpha diversity; (C) Simpson index of alpha diversity; (D) Shannon index of alpha diversity. ACE, abundance-based coverage estimator; CS, corn stover; CSTS, corn stover silage; CSTS-3, corn stover fermented for 3 days; CSTS-7, corn stover fermented for 7 days; CSTS-15, corn stover fermented for 15 days; CSTS-30, corn stover fermented for 30 days. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 2. General information on alpha diversity analysis of CS before and after ensiling. (A) ACE index of alpha diversity; (B) Chao 1 index of alpha diversity; (C) Simpson index of alpha diversity; (D) Shannon index of alpha diversity. ACE, abundance-based coverage estimator; CS, corn stover; CSTS, corn stover silage; CSTS-3, corn stover fermented for 3 days; CSTS-7, corn stover fermented for 7 days; CSTS-15, corn stover fermented for 15 days; CSTS-30, corn stover fermented for 30 days. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
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Fermentation 11 00425 g003
Figure 3. Relative abundance of the top 30 bacterial communities associated with CS at the genus (A) and species (B) levels before and after ensiling. CS, corn stover; CSTS, corn stover silage; CSTS-3, corn stover fermented for 3 days; CSTS-7, corn stover fermented for 7 days; CSTS-15, corn stover fermented for 15 days; CSTS-30, corn stover fermented for 30 days.
Figure 3. Relative abundance of the top 30 bacterial communities associated with CS at the genus (A) and species (B) levels before and after ensiling. CS, corn stover; CSTS, corn stover silage; CSTS-3, corn stover fermented for 3 days; CSTS-7, corn stover fermented for 7 days; CSTS-15, corn stover fermented for 15 days; CSTS-30, corn stover fermented for 30 days.
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Fermentation 11 00425 g004
Figure 4. Species-level correlation network (A) and correlation analysis (B) between bacterial community and terminal fermentation product. LAB, lactic acid bacteria. *, p < 0.05, ***, p < 0.0001.
Figure 4. Species-level correlation network (A) and correlation analysis (B) between bacterial community and terminal fermentation product. LAB, lactic acid bacteria. *, p < 0.05, ***, p < 0.0001.
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Fermentation 11 00425 g005
Figure 5. Bugbase phenotype prediction analysis of CS before and after ensiling. CS, corn stover; CSTS, corn stover silage.
Figure 5. Bugbase phenotype prediction analysis of CS before and after ensiling. CS, corn stover; CSTS, corn stover silage.
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Table 1. Chemical composition and microbial population of CS.
Table 1. Chemical composition and microbial population of CS.
ItemsCS
Chemical composition
 DM (%)31.21 ± 0.22
 OM (% DM)91.38 ± 1.14
 CP (% DM)7.01 ± 0.04
 EE (% DM)2.08 ± 0.03
 NDF (% DM)54.95 ± 1.23
 ADF (% DM)28.74 ± 0.67
 WSC (% DM)6.65 ± 0.30
Microbial population (lg cfu/g FM)
 Lactic acid bacteria5.84 ± 0.10
 Aerobic bacteria7.54 ± 0.12
 Coliform bacteria5.31 ± 0.19
 Yeast5.05 ± 0.06
 Mold4.22 ± 0.11
Data are expressed as mean ± standard deviation, and statistics are based on three independent replicate samples. ADF, acid detergent fiber; cfu, colony-forming units; CS, corn stover; CP, crude protein; DM, dry matter; EE, ether extract; FM, fresh matter; NDF, neutral detergent fiber; OM, organic matter; WSC, water-soluble carbohydrate.
Table 2. Chemical composition, fermentation quality, and microbial population of CSTS for different periods of time.
Table 2. Chemical composition, fermentation quality, and microbial population of CSTS for different periods of time.
ItemsTreatmentsSEMp Value for Contrasts
CSTS-3CSTS-7CSTS-15CSTS-30P-LP-Q
Chemical composition
DM (%)31.8231.2231.9731.490.310.860.86
OM (% DM)91.0590.9390.8991.000.230.860.63
CP (% DM)6.17 a5.76 ab5.66 ab5.28 b0.18<0.010.96
EE (% DM)2.02 a1.94 ab1.78 bc1.65 c0.05<0.010.69
NDF (% DM)53.54 a52.76 ab52.27 b52.10 b0.24<0.010.24
ADF (% DM)28.26 a27.94 ab27.63 bc27.31 c0.12<0.010.99
Fermentation quality
pH3.88 a3.80 a3.53 c3.64 b0.02<0.01<0.01
Lactic acid (% DM)0.62 b1.84 a2.03 a1.90 a0.07<0.01<0.01
Acetic acid (% DM)0.32 b0.57 a0.56 a0.59 a0.05<0.010.41
Propionic acid (% DM)NDNDNDND------
Butyric acid (% DM)NDNDNDND------
NH3-N (% DM)0.83 a0.77 ab0.66 c0.72 bc0.02<0.010.02
Microbial population (lg cfu/g FM)
Lactic acid bacteria 5.35 c6.16 b6.51 a6.45 a0.05<0.01<0.01
Aerobic bacteria 6.19 a5.68 b5.10 c5.13 c0.05<0.01<0.01
Coliform bacteriaNDNDNDND------
Yeast5.525.305.245.360.070.130.04
MoldNDNDNDND------
Data are the average of three samples. a–c Values with different superscripts in the same row indicate statistically significant differences among different fermentation days (p < 0.05). P-L and P-Q represent linear and quadratic effects of fermentation day. CSTS, corn stover silage; CSTS-3, corn stover fermented for 3 days; CSTS-7, corn stover fermented for 7 days; CSTS-15, corn stover fermented for 15 days; CSTS-30, corn stover fermented for 30 days. --, value is zero; ADF, acid detergent fiber; cfu, colony-forming units; CP, crude protein; DM, dry matter; EE, ether extract; FM, fresh matter; ND, not detected; NDF, neutral detergent fiber; NH3-N, ammonia nitrogen; OM, organic matter; SEM, standard error of the mean.
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Du, Z.; Meng, Y.; Chen, Y.; Cui, S.; Wang, S.; Yan, X. Assessment of Effects of Storage Time on Fermentation Profile, Chemical Composition, Bacterial Community Structure, Co-Occurrence Network, and Pathogenic Risk in Corn Stover Silage. Fermentation 2025, 11, 425. https://doi.org/10.3390/fermentation11080425

AMA Style

Du Z, Meng Y, Chen Y, Cui S, Wang S, Yan X. Assessment of Effects of Storage Time on Fermentation Profile, Chemical Composition, Bacterial Community Structure, Co-Occurrence Network, and Pathogenic Risk in Corn Stover Silage. Fermentation. 2025; 11(8):425. https://doi.org/10.3390/fermentation11080425

Chicago/Turabian Style

Du, Zhumei, Ying Meng, Yifan Chen, Shaojuan Cui, Siran Wang, and Xuebing Yan. 2025. "Assessment of Effects of Storage Time on Fermentation Profile, Chemical Composition, Bacterial Community Structure, Co-Occurrence Network, and Pathogenic Risk in Corn Stover Silage" Fermentation 11, no. 8: 425. https://doi.org/10.3390/fermentation11080425

APA Style

Du, Z., Meng, Y., Chen, Y., Cui, S., Wang, S., & Yan, X. (2025). Assessment of Effects of Storage Time on Fermentation Profile, Chemical Composition, Bacterial Community Structure, Co-Occurrence Network, and Pathogenic Risk in Corn Stover Silage. Fermentation, 11(8), 425. https://doi.org/10.3390/fermentation11080425

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