Silage review: Using molecular approaches to define the microbial ecology of silage
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Ensiling of forages was recognized as a microbial-driven process as early as the late 1800s, when it was associated with the production of "sweet" or "sour" silage. Classical microbiological plating techniques defined the epiphytic microbial populations associated with fresh forage, the pivotal role of lactic acid-producing bacteria in the ensiling process, and the contribution of clostridia, bacilli, yeast, and molds to the spoilage of silage. Many of these classical studies focused on the enumeration and characterization of a limited number of microbial species that could be readily isolated on selective media. Evidence suggested that many of the members of these microbial populations were viable but unculturable, resulting in classical studies underestimating the true microbial diversity associated with ensiling. Polymerase chain reaction-based techniques, including length heterogeneity PCR, terminal RFLP, denaturing gradient gel electrophoresis, and automated ribosomal intergenic spacer analysis, were the first molecular methods used to study silage microbial communities. Further advancements in whole comparative genomic, metagenomic, and metatranscriptomic sequencing have or are in the process of superseding these methods, enabling microbial communities during ensiling to be defined with a degree of detail that is impossible using classical microbiology. These methods have identified new microbial species in silage, as well as characterized shifts in microbial communities with forage type and composition, ensiling method, and in response to aerobic exposure. Strain- and species-specific primers have been used to track the persistence and contribution of silage inoculants to the ensiling process and the role of specific species of yeast and fungi in silage spoilage. Sampling and the methods used to isolate genetic materials for further molecular analysis can have a profound effect on results. Primer selection for PCR amplification and the presence of inhibitors can also lead to biases in the interpretation of sequence data. Bioinformatic analyses are reliant on the integrity and presence of sequence data within established databases and can be subject to low taxonomic resolution. Despite these limitations, advancements in molecular biology are poised to revolutionize our current understanding of the microbial ecology of silage.
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Emerging tools in metatranscriptomics and metaproteomics may have the potential to measure and quantify these responses to enable selection of bacteria with desirable properties and result in more consistent and predictable improvements in silage quality.
“Emerging tools in the fields of metatranscriptomics and metaproteomics may have the potential to measure and quantify these responses to enable selection of bacteria with these desirable properties and result in more consistent and predictable improvements in silage quality (McAllister et al., 2018).”
From Silage review: Recent advances and future uses of silage additives (2018) · cites this paper as McAllister et al., 2018
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A Study of the Microorganisms from Grass Silage
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Metagenomic Analysis of Silage
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Silage review: Recent advances and future uses of silage additives
Muck, R. E., Nadeau, E. M. G., McAllister, T. A., Contreras-Govea, F. E., Santos, M. C., & Kung, L. (2018) · Journal of Dairy Science · doi:10.3168/jds.2017-13839 · Cited by 1,038 · see citing works
Self-citationYang, L., Yuan, X., Li, J., Dong, Z., & Shao, T. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2018.12.067 · Cited by 268 · see citing works
Bacterial Dynamics of Wheat Silage
Keshri, J., Chen, Y., Pinto, R., Kroupitski, Y., Weinberg, Z. G., & Sela Saldinger, S. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01532 · Cited by 82 · see citing works
Guan, H., Ran, Q., Li, H., & Zhang, X. (2021) · Fermentation · doi:10.3390/fermentation7040258 · Cited by 30 · see citing works
Wang, Z., Tan, Z., Wu, G., Wang, L., Qin, G., Wang, Y., & Pang, H. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1004495 · Cited by 19 · see citing works
Self-citation
- Authors
- McAllister, T. A., Dunière, L., Drouin, P., Xu, S., Wang, Y., Munns, K., & Zaheer, R.
- Affiliations
- Agriculture and Agri-Food Canada (CA)
- Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
- Lallemand (France) (FR)
- Corresponding author
- McAllister, T.A.
- Venue
- Journal of Dairy Science
- Publisher
- American Dairy Science Association
- Published
- 2018-05
- Volume
- 101
- Issue
- 5
- Pages
- 4060-4074
- ISSN
- 0022-0302 (ISSN-L)
- OpenAlex
- W2800069651
- Cited by
- 162
- References
- 102
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 24 · 2023: 27 · 2022: 33 · 2021: 17 · 2020: 22 · 2019: 10 · 2018: 3
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Dairy Science (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Ruminant Nutrition and Digestive Physiology
- Physical Sciences › Engineering › Building and Construction › Anaerobic Digestion and Biogas Production
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Mycotoxins in Agriculture and Food
- Keywords
- silage fermentation, forage silage, lactic acid bacteria, silage spoilage, Clostridia, metagenomics, metatranscriptomics, comparative genomics, silage inoculants, aerobic exposure, sampling bias
- MeSH terms
- Animal Feed, Bacteria, Fermentation, Food Contamination, Fungi, Molecular Biology, Silage, Metagenomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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